Displays

Availability of historical hydroclimatic data for different climate regions is necessary for hydrological change modeling and analysis. Nowadays, many global products are available that provide hydrological and meteorological datasets based on direct measurements, remote sensing observations, re-analysis outputs, and model simulations. However, differences in spatial and temporal resolutions, and inconsistencies seen between observed hydrological patterns and different model results and datasets makes it difficult to choose an appropriate combination of data products for hydrological studies. This study provides a new combined historical database of five key hydroclimatic variables at monthly and daily scales, obtained from different observational and re-analysis global datasets, including runoff (R; from GSIM), precipitation (P; from GPCC-V7 and ERA5), evapotranspiration (ET; from GLEAM 3.3 and ERA5), soil moisture (SM; from ESACCI-v04.5, GLEAM 3.3 and ERA5), and temperature (T; GHCN-CAMS, ERA5). The new database combines these variables for each of 6,400 catchments of different scales around the world. In order to select the catchments, the existing nearly 35,000 streamflow time series in the GSIM database was analyzed and 8,400 catchments were selected based on the criterion of having at least 25 years of monthly runoff data available from 1980 to 2010. After further quality controls on the accuracy of catchment polygons, and reported catchment areas and stream flows, and consistency of the range, average values, and variations of variables time series, the 6,400 catchments were selected for the final development of the new catchment-related database in this study. The other hydroclimatic variables, besides runoff, are also spatially aggregated for each individual catchment and corresponding catchment-average time series are produced from 1980 to 2019. The final database thus provides a collection of long-term multi-climate and multi-catchment time series of the five key hydroclimate variables, aggregated over each of the 6,400 hydrological catchments around the world. In choosing the data sources for each variable, first priority was given to direct observational datasets (available for all variables except for the ET), and further to re-analysis outputs that many researchers regard as being close to directly observed data. The database developed in this study can be used for different types of studies on hydrology, water resources, and their changes under shifting climate and land use conditions in different parts of the world. The standardized format of this database ensures easy applicability with possibility of expansion to include more and other types of data, e.g., on land use/cover types and their changes, and on other climatic, geomorphologic, and anthropogenic conditions.

How to cite: Ghajarnia, N., Kalantari, Z., and Destouni, G.: A historical database of key hydroclimatic variables in and across 6400 catchments around the world, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7691, https://doi.org/10.5194/egusphere-egu2020-7691, 2020.

Vegetation acts as an interface between the earth's surface and the atmosphere, modulating exchanges of water, carbon and energy and responding to environmental stressors. Improved understanding of water transport through the soil-vegetation-atmosphere continuum is essential to understand the role of vegetation at a catchment and a global scale. The sensitivity of radar remote sensing observations to the water content of soil and vegetation makes it well-suited to monitoring spatio-temporal dynamics of processes in the soil-vegetation-atmosphere continuum.

Here, we present the latest results from studies using ground-based and spaceborne radar demonstrating the potential of radar to monitor vegetation water dynamics at scales from meters to tens of kilometers. Field data will be used to demonstrate the sensitivity of radar observations to surface and internal vegetation water content. These results illustrate the potential value of radar for monitoring rapid plant water dynamics, and the impact of water-limited conditions on land-atmosphere exchanges. Satellite data will be used to illustrate the degree to which current spaceborne radar systems can already be used to monitor these processes and the limitations posed by revisit time and resolution.

We will conclude with an outline of future opportunities and challenges. The next generation of spaceborne radar sensors offers  unprecedented monitoring capability. To avail of this opportunity, we need improved alignment between the treatment of vegetation in hydrological and radiative transfer models. This is essential to ensure meaningful relationships between new radar data products and hydrological states of interest, and to facilitate the assimilation of radar observations to constrain vegetation processes in hydrological models.

How to cite: Steele-Dunne, S., Vermunt, P., Khabbazan, S., Petchiappan, A., Judge, J., Vreugdenhil, M., Hahn, S., and Wagner, W.: Advances in using radar to observe vegetation water dynamics, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11348, https://doi.org/10.5194/egusphere-egu2020-11348, 2020.

The last century of hydrological research has led to significant improvements in representing different hydrological processes in rainfall-runoff models. With widely available streamflow data, such models are typically calibrated against this reference time series, which can limit their predictive power. One option to improve the realism of rainfall-runoff models is by incorporating environmental tracers such as stable isotopes of water, water temperature and electrical conductivity within the modeling setup. Conventionally, stable water isotopes have been used to learn more about the dominant hydrological processes that occur within a given catchment, which generally helps improve the hydrologic model structure, but often at the cost of increased model complexity to simulate the tracer concentration along with streamflow.

In this study, we develop a framework to incorporate stable water isotopes in continuous hydrological modeling, without significantly increasing model complexity. In the first step, stable water isotopes are used along with streamflow recession analysis to initialize the model state variables. After that, a Bayesian mixing model is used to infer the proportion of slow vs fast subsurface flow, and the results are used as additional constraints during the model calibration. This framework is extensively tested in a snow-dominated experimental catchment called Vallon de Nant, located in the Southwestern Swiss Alps (1189-3051 m. a.s.l.). During the presentation, we will discuss the advantages and limitations of such a modeling approach and how it can be extended to other experimental catchments.

How to cite: Beria, H., Benoit, L., Ceperley, N., Michelon, A., Larsen, J. R., Mariéthoz, G., and Schaefli, B.: Improving hydrologic model realism by using stable water isotopes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19769, https://doi.org/10.5194/egusphere-egu2020-19769, 2020.

Stable isotope tracers in water (e.g., ²H and ¹⁸O) have recently been widely used in soil-plant-atmosphere-continuum studies to quantify storage-flux-age interactions, mixing processes and the partitioning of precipitation into evaporation and plant transpiration, as well as groundwater recharge and runoff generation. Tracer-aided ecohydrological modeling can explicitly capture the role of vegetation dynamics in these processes, and constraining models using tracers can provide more realistic representation of water flow paths and ages. Such constraints are of particular importance in the context of catchment nutrient modeling, which integrates conservative hydrological mixing and reactive ecological and biogeochemical processes. Therefore, coupled tracer-aided modeling of ecohydrology and water quality has the potential to improve our understanding of catchment functioning and provide an evidence base for managing environmental trends under changing anthropogenic pressures. Moreover, in the domain of process-based modeling, fully distributed models have been shown to be advantageous in terms of efficiently capturing the high heterogeneity of natural and anthropogenic controls, and linking the modeling efforts with multiple data sources at different scales.

In this project, we apply advanced isotope-based modeling concepts to the intensively monitored TERENO - Bode catchment (ca. 3300 km²), which exhibits high gradients of hydroclimate, geology and landscape characteristics, and has associated anthropogenic impact gradients. We firstly focused on a well-studied, agricultural sub-catchment (Schäfertal, 1.44 km²). Rich data sets of long-term, high-frequency hydrometeorological conditions, vegetation dynamics, isotopes and agricultural management practices were integrated into the new tracer-aided ecohydrological model EcH₂O-iso, which here is further coupled with the nitrate turnover and transport routines from the new mHM-Nitrate model. The flexible, fully distributed structure of the coupled model allows in-deep, extensive investigation of flow, tracer and nitrate dynamics across scales. Measurements at different spatial scales and under contrasting flow conditions (from lysimeter plots to the catchment monitoring network) were integrated for multi-criteria calibration in order to test and improve the model. The initial modeling in the small headwater catchment opens new opportunities for future upscaling investigations based on the hierarchical monitoring settings in the Bode catchment (from plots to headwaters, and to nested catchments (from ca. 100 to 3000 km²)).

How to cite: Yang, X., Tetzlaff, D., Soulsby, C., and Borchardt, D.: Cross-scale insights into flow and nutrient dynamics through coupled tracer-aided ecohydrological and biogeochemical modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-333, https://doi.org/10.5194/egusphere-egu2020-333, 2020.

Complex models suffer from a multiplicity of parameters, allowing many combinations of values to yield apparently acceptable results and thus entailing a risk of obtaining “right answers for wrong reasons”. Aiming to compute key components of the water and energy balances from readily available meteorological observations while reducing the need for free parameters, we propose new formulations to extend the SPLASH model of Davis et al. (2017, Geoscientific Model Development) to deal with complex topography. SPLASH is a parsimonious, multi-purpose set of algorithms designed principally for ecological and ecohydrological applications. Wherever possible we based model construction on first principles, attempting to balance realism with robustness. By adopting analytical rather than numerical solutions for many processes, we have been able to apply the model at high spatial resolution without unreasonably inflating computational demands – allowing us to include terrain effects directly in the calculations of water and energy fluxes. Slope and aspect were included in the analytical integrals originally used to compute accumulated energy fluxes through the day. Upslope area, the terrain-induced hydraulic gradient, and an analytical solution for the soil column transmittance were included in the calculations of subsurface water flow, following TOPMODEL ideas. Whenever empirical calculations were used (pedotransfer functions, albedo-snow cover functions), they were recalibrated using a combination of remote sensing data and globally distributed observational datasets. Simulations of soil water content, evapotranspiration and snow-water equivalent were compared against in situ measurements using diverse and combined data sources (including FLUXNET and SNOTEL). The statistical performance of the model was tested with pooled measurements from multiple stations. Global simulations were run at 5 km resolution and compared with remote-sensing retrievals and state-of-the-art land surface models.

How to cite: Sandoval, D. and Prentice, I. C.: Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes in complex terrain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5073, https://doi.org/10.5194/egusphere-egu2020-5073, 2020.

Central Yakutian Plain (Russia) is situated in Eastern Siberia in the Lena River basin and is characterized by severe continental climate, continuous permafrost and flat relief. The combination of semi-arid climate, gentle topography and ice-rich permafrost provides favorable conditions for the development of thermokarst lakes. Poorly developed river drainage system and the distribution of thermokarst lakes within the river basins form the areas with internal drainage which contribute runoff to river network only in wet conditions. The results of such environment are the special hydrological regime of the region which is characterized by extreme seasonal and annual variability of streamflow.

In this project we study the hydrological processes in four rivers of Central Yakutia with the basin area from 1270 to 8290 km² and available long-term streamflow data. Thermokarst lakes take up to 5-10 % of the area of those basins. Annual precipitation of this area is about 240 mm, while average annual streamflow varies from 1 to 15 mm depending on the river basin. Due to climate warming the number and area of thermokarst lakes in Central Yakutia is increasing (Kravsova, Tarasenko, 2011). The aim of the project is to investigate the impact of thermokarst lakes on hydrological regime and provide some reasonable projections of its changes in the future. Previous study (Lebedeva, 2018) has shown that the results of streamflow simulations in this region based on standard hydrological modeling approach were not satisfactory.

We used remote sensing data (Landsat images) to assess the seasonal and annual variation of thermokarst lakes area and their contributing area and combined that data with hydrological modelling of runoff formation processes. The hydrological model Hydrograph (Vinogradov et al., 2011) was applied in this study. The model contains the algorithms of heat and moisture dynamics in the upper part of soil profile which allow its use in the permafrost conditions. New part of the model algorithm was developed which considers the variations of thermokarst area depending on meteorological conditions, evaporation from open water areas and the dynamic of surface runoff retention depth. These model improvements allowed for the satisfactory results in streamflow simulations for historical period and future projections. In general, with the future development of thermokarst lakes in Central Yakutia one may expect the decrease of annual streamflow and its higher variation from one year to another.

Th results of the study will be presented. The study was funded by RFBR, project number 19-35-50030.

How to cite: Makarieva, O., Nesterova, N., Fedorov, A., and Shikhov, A.: The impact of thermokarst lakes on streamflow generation in Central Yakutia (Russia): data assessment and modelling , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1095, https://doi.org/10.5194/egusphere-egu2020-1095, 2020.

Measurements and models constitute the core modes of understanding environmental processes, where a major paradigm of doing science involves confronting hypotheses (represented by models) with data from measurements. Of course, both models and measurements involve uncertainties which can make reasoning about the validity of our hypotheses difficult. This difficulty is exemplified in the study of turbulent heat fluxes where measurements made by eddy-covariance towers often have energy balance gaps and simple regression models often outperform the most sophisticated physically-based models. Our study addresses these issues by identifying the conditions in which either or both models and measurements break down as well as identify potential reasons for these breakdowns.

We use the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to develop an ensemble of models representing multiple hypotheses about how turbulent heat fluxes are generated and compare them against measurements from FluxNet towers at a number of hydro-climatically diverse sites. We evaluate the models against the measurements using both traditional error measures as well as with a general framework based on information theory and conditional probabilities. Extending this base analysis, we compute conditional mutual information of the modeled and observed relationships between turbulent heat fluxes and other meteorological variables (such as shortwave radiation, air temperature, and humidity). This allows us to go further than traditional error measures to explore how well the modeled relationships match the observed, providing a proxy for process correctness. We perform this analysis for a variety of conditions. We first analyze how much information the meteorological variables provide to the observed heat fluxes to estimate the robustness of the measurements. We then compare this with the amount of information that the meteorological variables provide to the simulations to determine whether there are significant deviations between the shared information from the simulations to the observations. This analysis is used to provide recommendations for post processing observations as well as identifying possible process deficiencies in our models.

How to cite: Bennett, A. and Nijssen, B.: Hard to measure, hard to model: Using information theory to understand turbulent heat fluxes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5957, https://doi.org/10.5194/egusphere-egu2020-5957, 2020.

Fill-and-spill hydrology, where landscape storage features such as bogs, lakes, prairie sloughs, or surface depressions impound and then dynamically release water after a deficit is filled, has received increased attention in recent years. In systems dominated by fill-and-spill, the contributing runoff area is a function of both local storage deficit and the degree and nature of connectivity between storage features. Here, a closed-form analytical upscaled probabilistic event model of runoff response from thousands of bog cascades in a wetland complex is developed and demonstrated. The efficient mathematical model represents the individual wetland contributing area, runoff coefficient, and pre-event deficit of each bog as probability distributions that may be estimated via a combination of spatial analysis and field observation.

The model is here used to explore the impacts of cascade depth, network branching ratio, local contributing area, and deficit distribution on runoff response. The upscaling results provide insight into the critical runoff characteristics and emergent behaviour of watersheds typified by fill-and-spill hydrology and clarify the role of ‘gatekeeper’ storage features at large scales and for systems with shallow cascade depth. The mathematical solution is found to be a generalization of the well-known PDM (Probability Distributed Model) and Xinanxiang probabilistic runoff models for the specific case where network depth is one and contributing area of each storage feature is zero, and therefore can be readily generalized to support simulation of classical rainfall-runoff responses in heterogeneous landscapes. The results of the model enable exploration of scaling and distribution effects upon catchment runoff in basins influenced by fill-and-spill hydrology.

How to cite: Craig, J., Taheri, M., and Ranjram, M.: Analytical upscaling of fill-and-spill hydrology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11347, https://doi.org/10.5194/egusphere-egu2020-11347, 2020.

Microtopography is recognised as one of the morphological features which controls runoff generation, surface hydrodynamics, and surface runoff hydrological response. The spatial scales of microtopography are orders of magnitude smaller than typical hydrological domains such as hillslopes or catchments. The hydrodynamic response in the presence of microtopography  is complex and its impact on hydrological behaviour is inherently a multiscale problem, influenced by a number of processes and features. In particular, the geometrical properties of microtopography, and the ponded volume in relation to rainfall volume play a role at the micro and meso scales, while the hydrological response at a larger “macro” scale depends on how large such spatial macroscale is: at sufficiently small scales, the hydrological response is ill-defined; at very large scales, microtopography may not be relevant. Yet, at some intermediate scales, the hydrological dynamics can be strongly dominated by microtopography.

In this work, a state-of-the-art, high-performance shallow water solver is used to simulate rainfall-runoff processes on an idealised catchment, at a spatial resolution which explicitly and completely resolves microtopography.  For simplicity, microtopography is modelled as a 2D sine wave, which is superimposed on a planar hillslope. A four-dimensional parameter space is explored, defined by different slopes, different amplitudes and wavelengths for the microtopography, and different rainfall events. The large parameter space, together with the high resolution and the inherent cost of the solver result in a very large computational cost. In consequence, we implement SERGHEI, a parallelised, high-performance shallow water equations solver based on the Kokkos programming framework. SERGHEI enables computations on heterogeneous systems and multiple graphics processor units (GPU), which allows to address very large computational studies such as this one.

Rainfall-runoff-infiltration partitioning is evaluated in terms of runoff, infiltration and ponding volumes, as well as in terms of a contingency table of flooded surfaces for a reference smooth surface and a set of rough surfaces with microtopography. The results are compared both globally (for the entire domain) and in a spatially-distributed manner in order to assess at which spatial scales the hydrodynamic heterogeneity manifests itself as an emergent hydrological behaviour. The preliminary results show a non-linear response of hydrological signatures to the different parameters, and a complex dependency across scales.

How to cite: Morales-Hernandez, M., Özgen-Xian, I., and Caviedes-Voullième, D.: Effects of microtopography across spatial scales: studying hydrological response through high-resolution shallow-water modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10655, https://doi.org/10.5194/egusphere-egu2020-10655, 2020.

Irrigation modernization, here defined as the replacement of traditional flood irrigation systems by pressurized drip-irrigation technology, has been widely promoted with the aim to move towards a more sustainable use of freshwater resources in irrigated agriculture. However, the scale sensitivity of irrigation efficiency challenged the predominantly positive value attributed to irrigation modernization and asked for an integrated evaluation of the technological change at various scales. The aim of this study is therefore to contribute to an improved understanding of the hydrological functioning in a landscape under irrigation modernization. We used local field observations to propose a regional scale modeling approach that allowed to specifically simulate the difference in water balance as a function of irrigation method and crop type. The approach focused on the modification of the spatial input data and had therefore the benefit of being relatively independent of the final choice of the hydrological model. We applied the proposed approach to the semi-arid agricultural area of Valencia (Spain), where regional information about the use of irrigation technologies and irrigation volumes at farm level were available. The distributed hydrological model Tetis was chosen to simulate the daily water balance from 1994 to 2015 for an area of 913 km² at a spatial resolution of 200 m. Model simulations were based on a random selection of parameter values that were subsequently evaluated in a multi-objective calibration framework. Multiple process scales were addressed within the framework by considering the annual evaporative index, monthly groundwater level dynamics, and daily soil moisture dynamics for evaluation. Simulation results were finally analyzed with a focus on groundwater recharge, which is of particular interest for environmental challenges faced within the study area. Simulation results of groundwater recharge for the entire agricultural area indicated a considerable variability in annual recharge (values from 112 mm up to 337 mm), whereby recharge was strongly controlled by annual rainfall volumes. Annual recharge in flood-irrigated areas tended to exceed annual recharge in drip irrigated-areas except for years with above average rainfall volumes. The observed rainfall dependency could be explained by the fact that recharge in drip-irrigated areas almost exclusively occurred during rainy days, whereby a few heavy rainfall events could produce the majority of annual recharge. Our results indicated interesting differences but also commonalities in groundwater recharge for flood and drip irrigation, and therefore emphasized the importance of explicitly considering irrigation technology when modelling irrigated agricultural areas.

How to cite: Pool, S., Francés, F., Garcia-Prats, A., Puertes, C., Pulido-Velázquez, M., Sanchis-Ibor, C., Schirmer, M., Yang, H., and Jiménez-Martínez, J.: Modeling the effect of flood and drip irrigation on groundwater recharge, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9514, https://doi.org/10.5194/egusphere-egu2020-9514, 2020.

Recently, there has been renewed interest in the utilisation of traditional small-scale storage interventions (check dams, field bunds and tanks) across India for the improvement of local water security. The Central Groundwater Board of India is encouraging the construction of interventions, such as check dams, field bunds and tanks, as the primary policy for the alleviation of water scarcity. It is of critical importance to understand the hydrological effect of these interventions at the small- and large-scale to maximise their impact and effectiveness. The quantification of small- scale interventions in hydrological modelling is often neglected, especially in large- scale modelling exercises. Although individually small, cumulatively these interventions may have a large effect on basin hydrology.  A bespoke version of the Global Water AVailability Assessment (GWAVA) model was developed to incorporate the impact of interventions on the hydrology. Interventions were conceptualised within the model structure using local knowledge, observed data and adaptations of existing reservoir representations. The effect of interventions on the water balance of the Cauvery Basin (81 000 km²), Peninsula India, and various small sub-catchments (each approximately 3500 km²) was studied. To quantify the impact of small interventions, two model runs were generated. An initial simulation was performed including a representation of the check dams, field bunds and tanks thought to be within the catchments, and compared with a “reference” simulation where no interventions were included but instead were replaced by grassland. The percentage difference for each component of the water balance was determined as an indicator of the impact of the interventions. The inclusion of interventions increases the total annual evaporation across the basin and reduces the annual streamflow. Although the interventions are constructed to provide increased surface and groundwater storage within the agricultural and urban areas, the implementation resulted in a significant decrease in total annual water storage within the sub- catchments. The aquifer levels rise minimally in the eastern sub-catchments and exhibit no change in the western sub- catchments. The aquifer levels in the mid- basin remained unchanged with the implantation of interventions.  Although the implementation of interventions are thought to increase the availability of groundwater at a local scale by upwards of two meters, the investigation using GWAVA suggest that aquifer levels are minimally affected. Based on the current understanding of interventions and the catchment hydrology, the wider effects of interventions on the water balance could be more detrimental to surface water security than anticipated and, thus, may not alleviate water poverty. The uncertainty related to the input data on interventions in the Cauvery may have affected the findings and thus further studies in regions with sufficient data availability and varying climate conditions may provide additional insight into the small- and large-scale effects of interventions.

How to cite: Horan, R., Wable, P., Srinivasan, V., Baron, H., Keller, V., Rees, G., Houghton- Carr, H., and Mujumdar, P.: Representing Small- Scale Storage Interventions Across the Cauvery Catchment Using a Macro- Scale Gridded Water Resource Model and Quantifying Their Effect on Catchment Hydrology., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9710, https://doi.org/10.5194/egusphere-egu2020-9710, 2020.

The hydrological regimes of European catchments have been considerably modified by anthropogenic features such as dams, weirs and water abstractions, with nearly every major river fragmented. The negative impacts of such physical modifications on freshwater ecosystems are being increasingly recognised. Currently, European dam removal initiatives are being driven by factors such as the EU Habitats Directive, and the costs associated with maintaining redundant infrastructure. Climate change and the rewilding agenda may encourage further hydrological renaturalisation initiatives. In the English Lake District, several reservoirs are being actively considered for decommissioning within this decade. To understand how such catchments would respond to lake renaturalisation, robust catchment hydrology models are needed that can represent the effects of changes in physical infrastructure on the hydrological regime. However, many models tend to neglect such human impacts.

We present a new tool that incorporates reservoirs, including impounding structures, river regulations and abstractions. The method involved development of an enhanced version of the freely-available catchment modelling software, SHETRAN. A new ‘reservoir’ module was developed which includes the effects of hydraulic structures and sluice operations on lake stage and river flow. Results for the Crummock Water catchment and reservoir show that the reservoir model generates notably fitter simulations, particularly during dry periods where reservoir operations cause a distinct deviation from the regime expected in natural lake-river systems. Further simulations demonstrate quantitatively how lake renaturalisation might affect future hydrological regimes compared with the baseline scenario. Finally, we discuss the implications of this model for decision-making in the Crummock Water catchment, and the utility of the software for other anthropologically-modified catchments.

How to cite: Hughes, D., Parkin, G., and Birkinshaw, S.: A new physically-based catchment modelling tool for reservoir re-engineering and renaturalisation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5807, https://doi.org/10.5194/egusphere-egu2020-5807, 2020.

Fluvial flooding induced by intense or prolonged rainfall poses a regular threat to people’s lives and properties in almost every part of the world. Modelling provides an essential tool for simulating and predicting the hydrological processes from rainfall-runoff to flooding driven by rainfall. Prediction of seasonal or longer-term fluvial processes over large catchments has traditionally been carried out using lumped/distributed hydrological models. However, these traditional hydrological models do not consider strict momentum conservation and they are not suited for accurate simulation of highly transient and dynamic rainfall-runoff and flooding process. On the other hand, sophisticated hydraulic/ hydrodynamic models have been widely used for modelling of flood inundation including those violent flash floods from intense rainfall. But due to their inhibitive computational cost and incapability in representing certain hydrological processes, no attempt has been reported to use a fully 2D hydrodynamic model to simulate long-term fluvial processes to provide more detailed information for the analysis of flood dynamics and subsequent impact on the environment.

Therefore, this work aims to further develop and test a hydrodynamic model to simulate seasonal fluvial processes in a large catchment. The proposed long-term fluvial processes modelling system is based on the High-Performance Integrated hydrodynamic Modelling System (HiPIMS). HiPIMS solves the full 2D nonlinear shallow water equations using a finite volume shock-capturing numerical method, which is further accelerated by modern GPUs for large-scale and long-term simulations. Surface storage, overland flow and flow dynamics are automatically captured by running simulations on high-resolution topographic data. New model components are developed and coupled to HiPIMS to account for infiltration and evaporation. For infiltration, the Green-Ampt method and curve number method are implemented and compared. The enhanced HiPIMS is applied to reproduce, at 20m resolution, the seasonal fluvial processes including flooding and recovery periods in the 2500km² Eden Catchment, England for three months.

The simulation results are compared with gauge measurements of water level and discharge across the catchment to demonstrate the model’s capability in supporting long-term simulations. More simulations have been also carried out to investigate the model sensitivity to key model parameters, e.g. grid resolution, friction, infiltration and evaporation parameters.

How to cite: Tong, X., Liang, Q., and Wang, G.: Hydrodynamic Simulation of Seasonal Fluvial Process over a Large Catchment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6541, https://doi.org/10.5194/egusphere-egu2020-6541, 2020.

Rivers are a key component of the land hydrological cycle and are crucial in many societal activities and natural hazards. Historically, hydrological modeling has not been tightly associated with numerical weather prediction (NWP) due to the different communities involved, requirements and underlying processes. The increased skill of NWP has led to the uptake of weather forecasts in hydrological models, in particular for flood forecasting. At the same time, developments of Earth System Models (ESM), mainly driven by the climate community have lead to a tight integration of the land hydrological cycle. River discharge is a key quality indicator of the integrated water budget, and its use as a forecast skill metric of NWP has a large potential. Freshwater input to the ocean is also important for the ocean circulation, which becomes increasingly relevant with the current atmosphere-ocean coupling in NWP. Considering all these points, the representation of rivers and floodplains dynamics and their associated impact on inland water evolution is of interest for a wide range of applications currently addressed by global NWP. 

In this study we present the key technical developments to achieve a 1-way and 2-way coupling between the global hydrodynamic CaMa-Flood model and the land surface component of the European Center for Medium-Range Weather Forecasts (ECMWF) HTESSEL. The models coupling followed a single executable strategy, i.e. avoiding external couplers. A coupling interface was developed for CaMa-Flood that is independent from the driving model, while keeping the stand-alone configuration. The coupling is flexible, allowing both models to run at different spatial resolutions. The implementation allows for a flexible integration of the models and independent development, and can be applied to other models.  

The current representation of inland water bodies in HTESSEL (lakes) was driven by their impact in NWP, but without the representation of rivers it was not possible to have a consistent water budget. The coupling of CaMa-Flood allows for an integrated earth system model approach. Several options for the 2-way interaction between CaMa-Flood flooded areas in HTESSEL inland water bodies were investigated. Despite the consistent results, several challenges are identified in the representation of inland water bodies, their variability and impact on water cycle.

How to cite: Dutra, E., Yamazaki, D., and Mazzetti, C.: Coupling of the global hydrodynamic CaMa-Flood model with the ECMWF land surface model HTESSEL., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6788, https://doi.org/10.5194/egusphere-egu2020-6788, 2020.

How important is information about distributed precipitation when we do rainfall-runoff modeling on the catchments scale?

The latter is surely one of the more frequently asked research questions in hydrological modeling. Most studies tackling the issue seem thereby to agree that distributed precipitation becomes more important if the ratio of catchment size against storm size decreases or if the spatial gradients of the rainfall field increase. Furthermore, is it often highlighted that catchments are surprisingly effective in smoothing out the spatial variability of the meteorological forcing, at least, if the focus is simulation integral fluxes and average states.

However, despite these agreements there is no straightforward guidance in the hydrological literature when these thresholds have been reached and when the spatial distribution of the precipitation starts dominating. This is because the answer to the above drawn question depends on the spatial variability of system characteristics, on the system state variables as well as on the strength of the rainfall forcing and its space-time variability. As all three controls vary greatly in space and time it is challenging to identify generally valid rules when information about the distribution of rainfall becomes important for predictive modelling.

The present study aims to overcome this limitation by developing a model framework to identify periods where the spatial gradients in rainfall intensity are larger than the ability of the landscape to internally dissipate those. This newly developed spatially adaptive modeling approach, uses the spatial information content of the precipitation to control the spatial distribution of our model. The main underlying idea of this approach is to use distributed models only when they are actually needed resulting in 1) a drastic decrease in computational times as well as 2) in a more appropriate representation of a hydrological system. Our results highlight that only during a few periods throughout a hydrological year do distributed precipitation data actually matter. However, they also show that these periods are often highly relevant with respect to certain extremes and that the successful simulation of these extremes require distributed information about the forcing and state of a given system.

How to cite: Loritz, R., Ehret, U., Neuper, M., and Zehe, E.: The case of distributed rainfall and spatially adaptive modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6789, https://doi.org/10.5194/egusphere-egu2020-6789, 2020.

Nowadays, a plethora of modelling software on rainfall-runoff and groundwater dynamics are available. Considering the complexity and heterogeneity of natural processes governing the water cycle, many of those models involve physically-based formulations. Inevitably, a large amount of data is also required. However, the available data are often insufficient, while their quality questionable. At the same time, an increasing model complexity also gives rise to high computational requirements. In order to mitigate some of the aforementioned issues, during the past years a simple and flexible top-down approach for distributed rainfall-runoff modelling has been developed (Tran et al., 2018). Essentially, the distributed rainfall-runoff model is built starting from a simple lumped model, whose parameters are then spatially disaggregated. Disaggregation is carried out using conceptual links between model parameters and natural catchment characteristics.

We now test an extended version of this methodology involving disaggregation relationships for more model parameters. Moreover, we evaluate modelling performance for 2 different configurations. The first starts from the parameters of a lumped conceptual model and is essentially the original approach. The second one starts from the parameters of a uniform distributed conceptual model. The motivation behind the new approach is that it allows a better-integrated routing scheme with less model parameters. In turn, this can further reduce equifinality (denoting the “phenomenon” that largely different parameter-sets can often result to largely similar model outcomes). The two approaches are inter-compared and evaluated against flow observations.

With the disaggregated models as basis, we also experiment on the potential of simple methods for modelling groundwater levels. We approach this challenge by trying to identify links between a) the variations and b) the reference levels of the modelled groundwater storages and observed groundwater levels. For example, we hypothesize that modelled storages can be scaled to the actual level variations via the specific yield, which expresses the amount of interconnected pores in the soil. The modelling methodology is evaluated against groundwater level measurements.

Tran, Q.Q., De Niel, J., Willems, P., 2018. Spatially Distributed Conceptual Hydrological Model Building: A Generic Top-Down Approach Starting From Lumped Models. Water Resour. Res. 54, 8064–8085.

How to cite: Moustakas, S. and Willems, P.: Experimenting on simple and flexible top-down approaches for hydrological modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5478, https://doi.org/10.5194/egusphere-egu2020-5478, 2020.

In the field of hydrological modeling, many alternative mathematical representations of natural processes exist. To choose specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build a process-based hydrological model with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the coupled framework to identify the most adequate model structure. It turned out that identifiability of model structure varies in space and time, driven by the meteorological and hydrological characteristics of the study area. Moreover, the most accurate numerical solver is often not the best performing solution. This is possibly influenced by correlation and compensation effects among process representation, numerical solver, and parametrization. Overall, the proposed coupled framework proved to be applicable for the identification of adequate process-based model structures and is therefore a useful diagnostic tool for model building and hypotheses testing.

How to cite: Bronstert, A., Pilz, T., Francke, T., and Baroni, G.: How to Tailor my Process-based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20280, https://doi.org/10.5194/egusphere-egu2020-20280, 2020.

All hydrological models need to be calibrated to obtain satisfactory streamflow simulations. Here we present a novel parameter regionalization approach that involves the optimization of transfer equations linking model parameters to climate and landscape characteristics. The optimization was performed in a fully spatially distributed fashion at high resolution (0.05°), instead of at lumped catchment scale, using an unprecedented database of daily observed streamflow from 4229 headwater catchments (<5000 km²) worldwide. The optimized equations were subsequently applied globally to produce parameter maps for the entire land surface including ungauged regions. The approach was implemented using a bounded version of the Kling-Gupta Efficiency metric (KGE_B) and a gridded version of the HBV hydrological model. Ten-fold cross-validation was used to evaluate the generalizability of the approach and to obtain an ensemble of parameter maps. For the 4229 independent validation catchments, the regionalized parameters yielded a median daily KGE_B of 0.30 (equivalent to a conventional KGE of 0.46). The median KGE_B improvement (relative to uncalibrated parameters) was 0.21, with improvements obtained for 88 % of the independent validation catchments. These scores compare favourably to those from previous large catchment sample studies. The degree of performance improvement due to the regionalized parameters did not depend on climate or topography. Substantial improvements were obtained even for independent validation catchments located far from the catchments used for optimization, underscoring the value of the derived parameters for poorly gauged regions. The regionalized parameters — available via www.gloh2o.org/hbv — should be useful for numerous hydrological applications requiring accurate streamflow simulations.

How to cite: Beck, H., Pan, M., Lin, P., Seibert, J., van Dijk, A., and Wood, E.: Towards global fully-distributed regionalization of hydrological model parameters, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7884, https://doi.org/10.5194/egusphere-egu2020-7884, 2020.

The estimation of parameters for spatially distributed rainfall runoff models is a long-studied, complex and ill-posed problem. Relating parameters of distributed hydrological models to geophysical properties of catchments could potentially solve some of the major difficulties connected to it.

One way to define this relationship is by the use of explicit equations called parameter transfer functions, which relate geophysical catchment properties to the model parameters. Computing parameter fields using transfer functions would result in spatially consistent parameter fields and the potential to extrapolate to other catchments. A further advantage is that the dimensionality of the parameter space is reduced because the transfer function parameters are applied to all computational units (i.e., grid cells). However, the structure and parameterization of transfer functions is often only implicitly assumed or needs to be derived by a laborious literature guided trial and error process.

For this reason we use Function Space Optimization (FSO), a symbolic regression approach which automatically estimates the structure and parameterization of transfer functions from catchment data. FSO transfers the search of the optimal function to a searchable continuous vector space. To create this space, a text generating neural network with a variational autoencoder (VAE) architecture is used. It is trained to map possible transfer functions and their distributions to a 6-dimensional space. After training, a continuous optimization is applied to search for the optimal transfer function in this function space. FSO was already tested in a virtual experiment using a parsimonious hydrological model, where its ability to solve the problem of transfer function estimation was shown.

Here, we further test FSO by applying it in a real world setting to the mesoscale hydrological model (mHM). mHM is a widely applied distributed hydological model, which uses transfer functions for all its parameters. For this study, we estimate transfer functions for the parameters porosity and field capacity, which both influence a range of hydrologic processes, e.g. infiltration and evapotranspiration. We compare the FSO estimated transfer functions with the already existing mHM transfer functions and examine their influence on the model performance.

In summary, we show the general applicability of FSO for distributed hydrological models and the advantages and capabilities of automatically defining parameter transfer functions.

How to cite: Feigl, M., Thober, S., Herrnegger, M., Samaniego, L., and Schulz, K.: Automatic estimation of parameter transfer functions for distributed hydrological models - a case study with the mHM model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10141, https://doi.org/10.5194/egusphere-egu2020-10141, 2020.

Model simulations of hydrological processes are critical for applications in streamflow forecasting and water security assessments. In this work, we develop a model-agnostic benchmarking framework to evaluate the fidelity of continental-domain model simulations. The benchmarking framework includes (1) synthetic test cases to evaluate the implementation of the model equations; (2) process-based diagnostics in research basins to evaluate model representations of individual processes; and (3) continental-domain benchmarks to evaluate the fidelity of large-domain model simulations. As a test case, we use simulations from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) configured across the North America domain. We rely on existing theory about cold-region hydrologic processes and large-domain observations of these processes to define process-specific evaluation metrics. These process diagnostics provide insights in our current ability to model cold region hydrological processes across the North America domain.

How to cite: Knoben, W., Fayad, A., Vionnet, V., and Clark, M.: Process-based model evaluation of cold region hydrological processes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4184, https://doi.org/10.5194/egusphere-egu2020-4184, 2020.

The hydrology of rural lowland catchments in Northern Germany is characterized by near-surface groundwater tables and extensive tile drainage. Previous research has shown that representing these characteristics with the hydrologic model SWAT (Soil and Water Assessment Tool) required an improvement of groundwater processes, which has been achieved by dividing the shallow aquifer into a fast and a slow shallow aquifer. The latest version of the Soil and Water Assessment Tool (SWAT+) features several improvements compared to previous versions of the model, e.g. the definition of landscape units that allow for a better representation of spatio-temporal dynamics. To evaluate the new model capabilities for lowland catchments, we assess the performance of SWAT+ in comparison to previous SWAT applications in the Kielstau Catchment in Northern Germany. The Kielstau Catchment is about 50 km² large, is dominated by agricultural land use, and has been thoroughly monitored since 2005. In particular, we explore the capabilities of SWAT+ in terms of watershed configuration and simulation of landscape processes by comparing two model setups. The first setup is comparable to previous SWAT models for the catchment, i.e. yields from hydrologic response units are summed up at subbasin level and added directly to the stream. In the second SWAT+ model, subbasins are divided into upland areas and floodplains and runoff is routed across the landscape before it reaches the streams. Model performance is assessed with regard to measured stream flow at the outlet of the catchment. Results from the new SWAT+ model confirm that two groundwater layers are necessary to represent stream flow in the catchment. The representation of routing processes from uplands to floodplains in the model further improved the simulation of stream flow. The outcomes of this study are expected to contribute to a better understanding and model representation of lowland hydrology.

How to cite: Wagner, P. D., Bieger, K., Arnold, J. G., and Fohrer, N.: Modeling lowland catchment hydrology: A comparison of model versions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11390, https://doi.org/10.5194/egusphere-egu2020-11390, 2020.

Rainfall-runoff models produce outputs which differ from observations due to uncertainties in process description, process parametrization, uncertainties in observations and changing spatio-temporal variability of input and state variables. Traditionally, attention has been focused mostly on process parameters to quantify runoff uncertainty using e.g. GLUE.

Here we have focused on the role of precipitation uncertainty relating to discharge. For this purpose, we used an inverse model approach. We generated time series of daily precipitation with high spatial resolution  using a modified version of Random Mixing and the Shannon-Whittaker interpolation to improve simulated runoff using the SHETRAN (physically-based) and HBV (conceptual) models, both spatially distributed for various sub-catchments of the Neckar River in Germany.  HBV was initially calibrated using interpolated precipitation, while SHETRAN uses pre-defined parameters. The modelling goal was to find a spatio-temporal series of precipitation which improved the predicted runoff,  under the constraints that the precipitation values be the same at the measurement locations and share their spatial variability with the observations at a given step. Care was taken to select subsequent days for improvement such that the previously improved step considered the effect of the previous steps.

We asked the questions: i) does improving precipitation inputs for one sub-catchment bring runoff improvement for the others? ii) Can the improved precipitation using SHETRAN be used for HBV and still get runoff improvements as compared to the interpolated precipitation and vice versa?

Results showed that overall runoff errors were reduced by 40 to 50% for all sub-catchments. For the peaks, a reduction of 70 to 90% was observed. As compared with the interpolated fields, new fields showed similar overall distribution but different details at finer spatial scales. Swapping improved precipitations between SHETRAN and HBV showed improvement as compared with the discharge from interpolated precipitation.

How to cite: Bárdossy, A., Kilsby, C., Anwar, F., and Wang, N.: The role of precipitation in hydrological model uncertainty, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8079, https://doi.org/10.5194/egusphere-egu2020-8079, 2020.

Information on the sensitivity of model parameters and model components such as processes are essential for model development, model improvement, and model calibration, amongst others.

In this work we apply the method of the extended Sobol’ Sensitivity analysis (xSSA) which not only considers parametric uncertainty but also fully incorporates structural uncertainties (Mai et al. (2019) WRR; under review). The results of such an analysis yield not only the traditional parameter sensitivities but also sensitivities of model process options (e.g., different snowmelt algorithms) and sensitivities of model processes (e.g., snowmelt, infiltration, baseflow). 

The Raven hydrologic modelling framework (http://raven.uwaterloo.ca) allowing for flexible model structures is employed in this work. We used three options each for infiltration, quickflow, and snow melt as well as two options each for baseflow, and soil evaporation. Rather than considering 108 (3x3x3x2x2) discrete model setups, we used weighted sums of all process options yielding an infinite number of models tested.

The analysis is performed for 5797 basins across Canada (CANOPEX; Tarek et al. (2019) HESSD) and the US (USGS). The lumped basin setups use daily precipitation and  minimum/ maximum daily temperature. The sensitivity analysis is based on 20 years of daily streamflow simulations (1991-2010) after two years of spin-up (1989-1990). No observed streamflow is required for the analysis.

In total more than 450 million model runs were performed to determine sensitivities of parameters, process options and processes (51%, 35%, and 14% of model runs, respectively) across the almost 5800 basins. The computational demand was about 12 core years producing 23 TB of raw model outputs.

The analysis allows for unique, new insights into the importance of hydrologic processes and parameters (practically) independent of the model (structure) used. A few highlight results are: 1) Baseflow and other sub-surface processes are of low importance across North America- especially when time points of high flows are of interest. 2) Percolation, evaporation, and infiltration show very similar patterns with increased importance in South-eastern US and west of the Rocky Mountains. 3) Up to 30% of the overall model variability can be attributed to snow melt in regions that are snow dominated (Northern Canada and Rocky mountains). Potential melt shows a similar gradient as snow melt with sensitivities of above 60% in the Province of Quebec and the Rocky Mountains. 4) Direct runoff (quickflow) is the most sensitive of all hydrologic processes- especially in South-Eastern US it is responsible for more than 80% of the model variability.

How to cite: Mai, J., Craig, J., Tolson, B., and Arsenault, R.: The sensitivity of hydrologic processes across North America considering model structure and parametric uncertainty, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6103, https://doi.org/10.5194/egusphere-egu2020-6103, 2020.

Calibration of eco-hydrological models is difficult to carry on, even more if observed data sets are scarce. It is known that calibration using traditional trial-and-error approach depends strongly of the knowledge and the subjectivity of the hydrologist, and automatic calibration has a strong dependency of the objective-function and the initial values established to initialize the process.

The traditional calibration approach mainly focuses on the temporal variation of the discharge at the catchment outlet point, representing an integrated catchment response and provides thus only limited insight on the lumped behaviour of the catchment. It has been long demonstrated the limited capabilities of such an approach when models are validated at interior points of a river basin. The development of distributed eco-hydrological models and the burst of spatio-temporal data provided by remote sensing appear as key alternative to overcome those limitations. Indeed, remote sensing imagery provides not only temporal information but also valuable information on spatial patterns, which can facilitate a spatial-pattern-oriented model calibration.

However, there is still a lack of how to effectively handle spatio-temporal data when included in model calibration and how to evaluate the accuracy of the simulated spatial patterns. Moreover, it is still unclear whether including spatio-temporal data improves model performance in face to an unavoidable more complex and time-demanding calibration procedure. To elucidate in this sense, we performed three different multiobjective calibration configurations: (1) including only temporal information of discharges at the catchment outlet (2) including both temporal and spatio-temporal information and (3) only including spatio-temporal information. In the three approaches, we calibrated the same distributed eco-hydrological model (TETIS) in the same study area: Carraixet Basin, and used the same multi-objective algorithm: MOSCEM-UA. The spatio-temporal information obtained from satellite has been the surface soil moisture (from SMOS-BEC) and the leaf area index (from MODIS).

Even though the performance of the first calibration approach (only temporal information included) was slightly better than the others, all calibration approaches provided satisfactory and similar results within the calibration period. To put these results into test, we also validated the model performance by using historical data that was not used to calibrate the model (validation period). Within the validation period, the second calibration approach obtained better performance than the others, pointing out the higher reliability of the obtained parameter values when including spatio-temporal data (in this case, in combination with temporal data) in the model calibration. It is also reliable to mention that the approaches considering only spatio-temporal information provided interesting results in terms of discharges, considering that this variable was not used at all for calibration purposes.

How to cite: Francés, F., Echeverría, C., Gonzalez-Sanchis, M., and Rivas, F.: Multi-objective calibration of a distributed eco-hydrological model using several remotely sensed information, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18880, https://doi.org/10.5194/egusphere-egu2020-18880, 2020.

Flow prediction in ungauged catchments is a major unresolved challenge in scientific and engineering hydrology. Meeting this challenge is made difficult by the uncertainty in the “regionalization” model used to transpose hydrological data (e.g., flow indices) from gauged to ungauged basins, and by the uncertainty in the hydrological model used to predict streamflow in the ungauged basin. This study combines recent advances in flow index selection, regionalization via machine learning methods, and a Bayesian inference framework. In addition, it proposes two new statistical metrics, “DistanceTest” and “InfoTest”, to assess the adequacy of a model before estimating its parameters. “DistanceTest” quantifies whether a model (hydrological or regionalization) is likely to reproduce the available hydrological information in a catchment. “InfoTest” is based on Bayes Factors and quantifies the information added by a model (hydrological or regionalization) over prior knowledge about the available hydrological information in a catchment). The proposed adequacy tests can be seen as a prerequisite for a model (hydrological or regionalization) being considered capable of providing meaningful and high quality flow time series predictions in ungauged catchments. If a model is found inadequate a priori and rejected, the modeler is spared the effort in estimating the model parameters, which can be a substantial saving.

The proposed regionalization approach is applied to 92 northern Spain catchments, with 16 catchments treated as ungauged. It is found that (1) a small number of PCs capture approximately 87% of variability in the flow indices, and (2) adequacy tests with respect to regionalized information are indicative of (but do not guarantee) the ability of a hydrological model to predict flow time series. The adequacy tests identify the regionalization of flow index PCs as adequate in 12 of 16 catchments but the hydrological model as adequate in only 1 of 16 catchments. In addition, the case study results suggest that the hydrological model is the main source of uncertainty in comparison to the regionalization model, and hence should receive the main priority in subsequent work at the case study catchments.

How to cite: Prieto, C., Le Vine, N., Kavetski, D., Álvarez, C., and Medina, R.: Model adequacy tests for improving predictions in ungauged basins, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-205, https://doi.org/10.5194/egusphere-egu2020-205, 2020.

Hydrological models are valuable tools for short-term forecasting of river flows, long-term predictions for water resources management and to increase our understanding of the complex interactions of water storage and release processes at the catchment scale. Hydrological models provide relatively robust estimates of streamflow dynamics, as shown by the countless applications in many regions across the world. However, various model structures can lead to similar aggregated outputs, i.e. model equifinality. To provide reliable estimates, it is of critical importance that not only the aggregated response but also the internal behaviors are consistent with their real-world equivalents. In a previous international comparison study (de Boer-Euser et al., 2017), eight research groups followed the same protocol to calibrate their twelve models on streamflow for several catchments within the Meuse basin. In the current study, we hypothesize that these twelve process-based models with similar runoff performance have similar representations of internal states and fluxes. We test our hypothesis by comparing internal states and fluxes between models and we assess their plausibility using remotely-sensed products of actual evaporation, snow cover, soil moisture and total storage anomalies. Our results indicate that models with similar runoff performance represent internal states and fluxes differently. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Using remotely-sensed products, the plausibility of process representation could only be evaluated to some extent as many variables remain unknown, highlighting the need for more experimental research. The study further emphasizes the value of multi-model, multi-parameter studies to reveal to decision-makers the uncertainty inherent to the lack of evaluation data and the heterogeneous hydrological landscape.

References:
de Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue, G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H., Thirel, G., and Willems, P.: Looking beyond general metrics for model comparison – lessons from an international model intercomparison study, Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017, 2017.

How to cite: de Boer-Euser, T., Bouaziz, L., Thirel, G., Melsen, L., Buitink, J., Brauer, C., de Niel, J., Moustakas, S., Willems, P., Grelier, B., Drogues, G., Fenicia, F., Nossent, J., Pereira, F., Savenije, H., Weerts, A., and Hrachowitz, M.: Behind the scenes of runoff performance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7825, https://doi.org/10.5194/egusphere-egu2020-7825, 2020.

Hydrological models are often applied to estimate climate change impacts on hydrology. However, several studies demonstrated that hydrological models do not perform well when applied under changing climate conditions. In order to decide on the way forward for improving hydrological modelling in climate change contexts, it is important to understand the reasons for poor performance in a changing climate, but there are only a few studies on this topic.

Here we revisit a study in Austria that demonstrated the inability of a conceptual model to simulate the discharge response to increases in precipitation and air temperature. We set up hypotheses for the differences between the observed and simulated changes in discharge and test these using simulations with various modifications of the model (including modifications of the input data, model calibration, and model structure).

The baseline model overestimates discharge trends over 1978−2013, on average over all 156 catchments, by 93 ± 50 mm yr⁻¹ per 35 years. Accounting for vegetation dynamics in the calculation of reference evaporation based on a satellite-derived vegetation index, reduces the difference between simulated and observed discharge by 35 ± 9 mm yr⁻¹ per 35 years. Inhomogeneities in the precipitation data, caused by a variable number of stations and, to a lesser degree, climate variability effects on the undercatch error, can explain 44 ± 28 mm yr⁻¹ per 35 years of this difference. Extending the calibration period from 5 to 25 years, varying the objective function by including annually aggregated discharge data, or estimating evaporation with the Penman-Monteith instead of the Blaney-Criddle approach has little influence on the simulated discharge trends. The model structure problem with respect to vegetation dynamics has important implications for studies in a climate change context. Our results furthermore highlight the importance of using precipitation data based on a stationary input station network for studying observed hydrologic changes.

How to cite: Duethmann, D., Blöschl, G., and Parajka, J.: Investigating the reasons for poor model performance in a changing climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-487, https://doi.org/10.5194/egusphere-egu2020-487, 2020.

Nowadays, a large part of hydrological research is focussed on hydrological modelling, both to improve system understanding and to simulate future systems to support decision making. Although the necessary simplifications in hydrological models such as empirical formulas or spatial and temporal discretisation can result in deviations in model predictions, hydrological models often perform well due to model calibration.  However, fundamental changes in system behaviour can occur that are not represented by the used model structure. These changes can therefore not be simulated and can result in deviating model results.  We refer to this situation as ‘systemic change’. To detect systemic change, one can calibrate the model separately for different time periods, and evaluate whether thus-found parameter values change over time, which is an indication of systemic change (Verstegen et al., 2016). The aim of this study is to use this approach to detect possible systemic changes in the Rhine-Meuse basin when modelled with the PCR-GLOBWB hydrological model.

PCR-GLOBWB is run for Rhine-Meuse basin for 1901-2010 at a daily time step with a 30 arcminute resolution, after which a brute force calibration is performed for five parameters (degree day factor, Manning’s roughness coefficient, soil thickness, saturated hydraulic conductivity and groundwater coefficient) using measured discharge data from the Global Runoff Data Centre (GRDC) at four locations in the catchment. To be able to identify the time stability of these parameters, the model is not only calibrated for the entire 1901-2010 period, but also for 10-year rolling calibration periods (i.e. 1901-1911, 1902-1912, 1903-1913, etc.). This results in a time series with 100 parameter values for each parameter, which is analysed for potential trends at the different calibration locations. First results indicate a decrease in the optimal parameter values for soil thickness and saturated hydraulic conductivity and an increase in the optimal parameter values for degree day factor and Manning’s roughness coefficient through time, especially in the upstream areas such as Basel. If the calibration is performed more downstream, for example at Lobith, the optimal parameter values are less variable through time.

These results are used to determine the effect of potential systemic changes on the uncertainty of hydrological predictions by making three forecasts; one with stable parameter values and a stationary climate, one with time-variant parameter values and one with a future climate scenario. The last forecast enables comparing the magnitude of change caused by the potential time-variant parameters with the change caused by time-variant climatic forcing. This way, the study gives more insight in both the occurrence of systemic change and its potential consequences, which can contribute to a better understanding of the behaviour of hydrological models under changing conditions.

Reference

Verstegen, J. A., Karssenberg, D., van der Hilst, F., & Faaij, A. P. C. (2016). Detecting systemic change in a land use system by Bayesian data assimilation. Environmental Modelling & Software, 75, 424–438. https://doi.org/10.1016/j.envsoft.2015.02.013

How to cite: Ruijsch, J., Sutanudjaja, E., Verstegen, J., and Karssenberg, D.: Systemic Change in Hydrology: Spatio-temporal parameter variability of the PCR-GLOBWB hydrological model in the Rhine-Meuse basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4850, https://doi.org/10.5194/egusphere-egu2020-4850, 2020.

The aim of this study was to explore the additional value of using proxy data besides runoff for calibrating a conceptual hydrological model. The study area was the Hydrological Open Air Laboratory (HOAL), a 66 ha large experimental catchment in Austria. A conceptual, HBV type, spatially lumped hydrological model was calibrated following two approaches. First, the model was calibrated in one step using only runoff data. Second, we proposed a step-by-step approach, where the modules of the model (snow, soil moisture and runoff generation) were calibrated using proxy data besides runoff, such as snow, actual evapotranspiration, soil moisture, overland flow and groundwater level. The two approaches were evaluated on annual, seasonal and daily time scales. Using the proposed step-by-step approach, the runoff volume errors in the calibration and validation periods were 0% and -1%, the monthly Pearson correlation coefficients were 0.92 and 0.82, and the daily logarithmic Nash Sutcliffe efficiencies were 0.59 and 0.18, respectively. The additional benefit of using proxy data besides runoff was the improved overall process consistency compared to the approach when only runoff was used for model calibration. Soil moisture and evapotranspiration observations had the largest influence on simulated runoff, while the calibration of the snow and runoff generation modules had a smaller influence.

How to cite: Széles, B., Parajka, J., Hogan, P., Silasari, R., Pavlin, L., Strauss, P., and Blöschl, G.: The additional value of using proxy data besides runoff for calibrating a conceptual hydrological model in a small agricultural catchment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7147, https://doi.org/10.5194/egusphere-egu2020-7147, 2020.

Many studies in recent years have focused on spatio-temporal variability of soil moisture and its value in hydrology and agriculture. The highly dynamic of soil moisture is controlled by soil properties, topography, landuse, climate conditions, and anthropogenic impacts. However, the understanding of soil moisture dynamics is limited by measurement restrictions. The aim of this study is to analyse spatio-temporal patterns of soil moisture using various soil moisture monitoring techniques and numerical modelling approaches that have been developed for application at differing scales at the Nucice experimental catchment (0.53 km²), which is located just outside of Prague, the Czech Republic.

The experimental catchment is dominated by agricultural activities. To identify spatio-temporal patterns in the catchment, we have implemented shallow soil moisture measurements at point-scale, hillslope-scale, and catchment-scale. We have deployed FDR (frequency domain reflectometry) sensors at different depths for point-scale measurements. The monitoring of hillslope-scale and catchment-scale have been mostly accomplished by field surveys with HydroSense II sensors. Subsequently, we have applied geostatistical analyses (Kriging and inverse distance weighting interpolation) for the measured soil moisture data to discover spatial patterns in soil moisture across the catchment. Besides, numerical models Hydrus (1D and 2D), MIKE-SHE, and SWAT have been set up at this study site. These models have been calibrated with event-based data and soil moisture measurements, which present a better image of the hydrological processes and spatio-temporal dynamics of soil moisture at various scales. The modelling outcomes have not only fit agreeably with the observed discharge and the temporal dynamics of soil moisture but have also identified wet zones along hillslopes.

Further research will intensify the soil moisture monitoring at the catchment-scale by using remote sensing and Comsic-ray soil moisture probes. Also, anthropogenic impacts (e.g. influence of wheel track) should be considered in the modelling approach. Ultimately, we should be able to understand and predict the spatio-temporal dynamics of soil moisture in small scale agricultural catchments under different climate conditions.

This research has been supported by project H2020 No. 773903 SHui, focused on water scarcity in European and Chinese cropping systems.

How to cite: Li, T., Noreika, N., Jeřábek, J., Krasa, J., Zumr, D., and Dostál, T.: Investigating spatio-temporal variability of soil moisture in a small farmland: from point to catchment scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-347, https://doi.org/10.5194/egusphere-egu2020-347, 2020.

Surface runoff is widely recognized as playing an important and unique role in contaminant
transport from agricultural fields to the river system. Its quantification however is still
underdeveloped, especially in flat areas. Because micro-topography (< 10 cm) likely is an
important controlling factor in such landscapes, accurate predictions of the occurrence and
quantity of surface runoff are limited by a lack of high-quality data and/or computational power.
This project will explore the applicability of both conceptual (fill-and-spill) and state-of-the-art
physically based models to estimate surface runoff at the field scale. Laser technology will provide
high resolution surface topography data and direct measurements of surface runoff will aid in
validating the hydrologic models. The goal of this research is to use the results of the field study to
develop an efficient and accurate upscaling scheme, centred around a generic parameterization of
micro-topographic variability. This could support decision and policy making and contribute to
increasing the water quality of river systems.

How to cite: Schaap, P., de Louw, P., and van der Zee, S.: SURFLAT: Measuring and modelling surface runoff in flat landscapes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20476, https://doi.org/10.5194/egusphere-egu2020-20476, 2020.

Traditionally, rainfall runoff models are calibrated on discharge observed at the basin outlet. This may result in accurate flow predictions, but not necessarily correctly represent internal processes in space and time; especially in poorly gauged regions where limited ground observations are available. More and more satellite observations become available which can be valuable for model development and calibration to improve the representation of internal processes in space and time. In this study, satellite based evaporation and total water storage observations were used to improve, in a stepwise analysis, the structure of a hydrological model and the selection of feasible parameter sets. For this purpose, a semi-distributed rainfall runoff model, accounting for sub-grid process heterogeneity, was developed for the poorly-gauged Luangwa River basin in Zambia. As benchmark, this model was calibrated with respect to observed discharge. Then, the model was modified by (1) including upwelling groundwater in low-elevation parts of the landscape close to the river, depending on the water availability in the (un-) saturated zone and (2) adjusting the spatial representation of the groundwater. Next, each model was calibrated to all variables simultaneously with respect to discharge, evaporation and total water storage. In the benchmark case, calibrated on discharge only, the model reproduced the discharge well, but failed to provide an adequate spatiotemporal representation of evaporation and total water storage, especially in wetland dominated areas. Overall model performance improved most when including upwelling groundwater as a function of the saturated zone and when calibrating on all variables (discharge, evaporation and total storage) simultaneously. Hence including satellite based data on evaporation and total water storage improved model structure development and identifying feasible parameter sets.

How to cite: Hulsman, P., Savenije, H., and Hrachowitz, M.: Stepwise improvement of hydrological model concepts using satellite based evaporation and total water storage estimations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9094, https://doi.org/10.5194/egusphere-egu2020-9094, 2020.

It is important yet challenging to predict runoff in data sparse regions or ungauged regions, majority of which belong to headwater catchments that are normally the major water source for middle and lower river reaches. There are numerous studies carried out since the launch of the Predictions in Ungauged Basins (PUB) initiative by the International Association of Hydrological Sciences (IAHS) in 2003. Most runoff prediction studies rely on modelling approaches via two steps. The first step is to calibrate the hydrological model against observed streamflow at the gauged catchments. The second step is regionalization in which the set of calibrated parameter values from a suitable donor catchment is used for predicting runoff in a targeted ungauged catchment. The major challenge of this approach is that when the gauged catchments are sparsely distributed or little available, it is hard to get sensible regionalization results. This study develops a new approach to calibrate a hydrological model purely against remote sensed actual evapotranspiration data obtained from 8-day and 500 m resolution PML-V2 products and the calibrated parameters can be directly used for runoff prediction across global land surface. This approach has been successfully used for predicting daily, monthly and annual runoff in Australia and southeastern Tibetan Plateau. This is an exciting research domain for hydrologists to pursue since remote sensing data is accumulated in a fast-increasing rate, and will provide researchers an unprecedent opportunity.

How to cite: Zhang, Y.: Using remote sensing evapotranspiration solely calibrating hydrology model for predicting runoff time series in ungauged regions , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12227, https://doi.org/10.5194/egusphere-egu2020-12227, 2020.

Drought is a reoccurring worldwide natural hazard that affects not only food production but also economics, health, and infrastructure. It is also known that regional drought condition is sensitive to the fine particulate matters (PM) and has relationships with future changes in fine dust levels and associated health impacts under climate change. This mode is strongly correlated to evapotranspiration and land surface conditions and drought index might be good when the actual evapotranspiration and the land surface characteristics are implicitly included in the formula. The procedure for estimating actual evapotranspiration is complex and scientists often tend to select simple model that does not require intensive field data. As a preliminary study this study checks the possibility of PT-JPL which is relatively simple and requires minimum number of observations for estimating local actual evapotranspiration. The model has no calibration, tuning, or spin-up for local adjustment. The model was set up for five representative stations in East Asia. The satellite-collected normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) were used to describe the land surface characteristics. Meteorological information such as temperature, water vapor, radiation, and actual evapotranspiration was retrieved from AsiaFlux. The results show that the PT-JPL is promising for estimating local actual evapotranspiration. This study will extend to developing a drought index and its relationship to particulate matters (PM) in the near future.

Key words: Actual evapotranspiration, Particulate matters (PM), Drought, PT-JPL

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF-2017-2017001809)

How to cite: Lee, K.-H.: Checking actual evapotranspiration model using remotely collected surface data: Case study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20939, https://doi.org/10.5194/egusphere-egu2020-20939, 2020.

In this study, the Hargreaves monthly correction factor is presented to estimate the reference evapotranspiration. For the analysis, I used daily weather data from 1989 to 2018, at 67 meteorological stations located throughout the Korean peninsula.

A large number of more or less empirical methods have been developed over the last 50 years by numerous scientists and specialists worldwide to estimate evapotranspiration from different climatic variables. The FAO Penman-Monteith method is recommended as the sole ETo method for determining reference evapotranspiration. However, the Penman-Monteith method has the disadvantage of inputting a lot of weather data. In addition, there is a lack of meteorological data when using old historical data or as a test bed for developing countries.

In the case of the Hargreaves method, the reference evapotranspiration can be estimated only if the latitude, maximum and minimum temperatures of the meteorological station are known. However, the accuracy of the results is not as good as that of the Penman-monteith method. Thus, using the genetic algorithm method suggested the monthly correction factor of the Hargreaves method each station. The reference evapotranspiration amount calculated by Penman-Monteith was set as the true value, and the learning period of genetic algorithm was set from 1989 to 2013, and the validation period was set from 2014 to 2018.

In order to verify the model efficiency, the root mean square error decreased and the correlation coefficient increased when the monthly correction coefficient was applied to the reference evapotranspiration calculated by the Hargreaves method.

It is very important to estimate the reference evapotranspiration amount in order to develop the water long-term plan.

With the development of measuring equipment and technological capabilities, it is now possible to simulate the state of nature as if it were real, but many problems arise when using historical data or analyzing developing countries.

If the monthly correction coefficient suggested in this study is applied, it is possible to estimate the standard evaporation amount with a more approximate value.

Acknowledgements

 This research is supported by the Research Program (20200041-001) of Korea Institute of Civil Engineering & Building Technology 

How to cite: Kim, D.: Estimation of Evapotranspiration using the Modified Hargreaves Equation by Genetic Algorithm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8421, https://doi.org/10.5194/egusphere-egu2020-8421, 2020.

How to cite: Akbari, M. and Torabi Haghighi, A.: Satellite Data Application to Cover Lack of In-situ Observations for Mapping Precipitation and Direct Runoff in Semi-arid Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13666, https://doi.org/10.5194/egusphere-egu2020-13666, 2020.

River flows are the result of dynamically changing, interacting and non-linear processes of surface, near subsurface and often deeper groundwater flow from climatic drivers. Conceptual rainfall-runoff models, whilst providing advantages in computational efficiency and more minimal data requirements, often struggle to simulate contributions from groundwaters, resulting in poor model calibration. Improving predictions of river flows in these catchments is, however, critical to water resources planning and management, particularly in the UK where groundwater contributes 30% of public water supply in England. In order to improve model predictions in groundwater-dominated catchments, we conduct a detailed analysis of available observational data to better understand groundwater-surface water interactions and processes on a regional (aquifer) and local (river reach) scale, over geologically variable areas.

National meteorological, hydrological, hydrogeological, geological and artificial influence (characterising abstractions and return flows) datasets are used to develop a conceptualisation of the groundwater processes occurring in 99 subcatchments of the River Thames in the UK. We use these data to characterise the water balance, intercatchment groundwater flows, gaining/losing river reaches and hydrograph dynamics of these subcatchments, and investigate how dominant groundwater processes vary spatially and temporally. The River Thames has been selected as our case study owing to its wealth of data, densely gauged river network and geological variability.

We show that intercatchment groundwater flow is needed to ‘close’ the water balance in many catchments located on aquifer outcrops and find evidence of river-groundwater level flow thresholds. Importantly, we find that seasonality is a key control on the accurate representation of groundwater-surface water interaction processes and that the spatial and temporal variability of those processes varies greatly for different geologies across the Thames basin. We also demonstrate the importance of human influences to understand some of these spatial processes. We then identify the physical processes that existing conceptual rainfall-runoff models are likely missing, and what may be required to enable model calibration improvements in groundwater-dominated catchments.

How to cite: Oldham, L., Freer, J., Coxon, G., Howden, N., Bloomfield, J., and Jackson, C.: Evidence-based conceptual requirements of regional groundwater processes for hydrological simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3018, https://doi.org/10.5194/egusphere-egu2020-3018, 2020.

Benefit from the easy access to gridded hydrological datasets and global Digital Elevation Model (DEM) datasets, DEM-based routing methods have been widely developed and used. The routing methods can be divided into two categories, i.e., Source-to-Sink and Cell-to-Cell. Limited by the computation capabilities, routing methods are often performed at more coarse resolution of calculation cell rather than the resolution of DEM. Both the DEM resolution and calculation cell-size are factors that affect the discharge simulation performance of routing method. Too little work has been devoted to how these two factors affect routing performance jointly. This study aims to compare the effects of DEM resolution and calculation cell-size on discharge simulation performance with two most popular routing methods, including a Cell-to-Cell routing method, i.e., Liner-reservoir-routing method (LRR) and a Source-to-Sink routing method, i.e., the improved aggregated network-response function routing method (I-NRF). They are compared/evaluated in terms of the changes of simulation performance with calculation cell-size ranging from 5 arc-minutes to 60 arc-minutes and DEM resolutions of 90 m×90 m, 250 m×250 m, 500 m×500 m, 1000 m×1000 m. Besides, two hydrological runoff-generation models and two study basins are used to test the generality of the result. The study finding will help the researchers to choose the appropriate DEM resolution, calculation cell-size and routing method in hydrological simulation.

How to cite: Li, J., Chen, H., Xu, C.-Y., Zhao, H., Li, L., Chen, J., and Guo, S.: Evaluation of the joint effects of DEM resolution and calculation cell size on discharge simulation performance with two routing methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3807, https://doi.org/10.5194/egusphere-egu2020-3807, 2020.

How to cite: Silantyeva, O., Burkhart, J. F., Bhattarai, B. C., Skavhaug, O., and Helset, S.: Operational hydrology in highly steep areas: evaluation of tin-based toolchain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8172, https://doi.org/10.5194/egusphere-egu2020-8172, 2020.

Characterizing soil volumetric water content (VWC) dynamics at different soil depth plays a key role in hydrological modeling and is essential for effective catchment management. However, our understanding of how critical zone structure (topography and soil) and rainfall affect VWC dynamics is limited. Therefore, the objective of this study was to investigate the effects of the hillslope structure and rainfall on VWC dynamics in a steep forested, zero-order catchment. VWC was measured from soil surface to soil-bedrock interface at five soil layers (0-8, 8-40, 40-70, 70-110, and 110-160 cm) for a complete water year, and covering various landscapes such as an ephemeral stream, riparian, and different hillslope positions. A total of 13 environmental indices, including eight DEM-derived terrain attributes and five soil attributes, were used to investigate the relationships between soil-terrain attributes and VWC. An all-possible-subsets regression model was adopted to construct the soil water content prediction model (SWPM). A geophysical method (ground penetrating radar, GPR) was used to investigate the soil depth to assist in the establishment of SWPM. The results demonstrate that the all-possible-subsets regression model performed well for predicting VWC. Additionally, the strength of the relationships between soil-terrain attributes and VWC could be different through time. For instance, the relationships between the topographic wetness index (TWI) and VWC were all significant (P<0.05) from August to October, whereas the correlation between TWI and VWC was not significant (P≥0.05) at approximately 25% of measurement days from November to February. The results also show that the high correlation between terrain-related attributes and VWC usually occurs in the measurement days with high catchment storage state, whereas the high correlation between soil-related attributes and VWC more often occurs in the measurement days with low catchment storage state. Therefore, the control factors of VWC spatial organization vary from humid (controlled by topographic redistribution of water) to arid (controlled by vertical processes such as evapotranspiration) seasons.

How to cite: Han, X. and Liu, J.: Seasonal controls of soil water content spatial pattern in a steep forested catchment: A modeling approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12404, https://doi.org/10.5194/egusphere-egu2020-12404, 2020.

High-resolution data are readily available and used more than ever in hydrological modeling, despite few investigations demonstrating the added value. Nonetheless, a few studies have looked into the benefits of using increased spatial resolution data with the widely-used, semi-distributed, SWAT model. Meanwhile, far too little attention has been paid to the physically-based, semi-distributed, hydrological model HYDROTEL which is widely used for hydrological forecasting and hydroclimatic studies in Quebec, Canada. In a preliminary study, we demonstrated that increasing the spatial resolution of the digital elevation model (DEM) had a significant impact on the discretization of a watershed into hillslopes (i.e., computational units of HYDROTEL), and on their topographic attributes (slope, elevation and area). Accordingly, values of the calibration parameters were also substantially affected; whereas model performance was slightly improved for high- and low-flows only. This is why, we hereby propose the systematic assessment of HYDROTEL with respect to the resolution of the spatiotemporal computational domain for a specific physiographic scale. This investigation was conducted for the 350-km² St. Charles River watershed, Quebec, Canada. The DEM used was derived from LiDAR data and aggregated at 20 m. Due to a lack of accurate precipitation information at time scales less than 24 hr, data from the high resolution deterministic precipitation analysis system, CaPA-HRDPA, were used to generate various time steps (6, 8, 12, and 24 hr) and to control results obtained from observed data. This approach, recently applied to three watersheds in Yukon, proved to be an excellent alternative to calibrate a hydrological model in a region known as a hydometeorological desert (see EGU 2020 presentation of Abbasnezhadi and Rousseau). The number of computational units ranged between 5 to 684 hillslopes, with mean areas ranging from 75 km² to 0.5 km². HYDROTEL was automatically calibrated over the 2013-2018 period using PADDS. We combined the Kling Gupta Efficiency and the log-transformed Nash Sutcliffe Efficiency to ensure good seasonal and annual representations of the hydrographs. The 12 most sensitive calibration parameters were adjusted using 150 optimisation trials with 150 repetitions each. Behavioral parameters were used to assess uncertainty and ensuing equifinality. All scenarios were evaluated using flow duration curves, performance indicators (RMSE, % Bias) and hydrograph analyses. In addition, quantitative analyses were done with respect to physiographic features such as: length of river segments, hillslopes, and sub-watershed boundaries for each resolution. We believe this study provides the needed systematic framework to assess trade-offs between spatiotemporal resolutions and modeling performances that can be achieved with HYDROTEL. Moreover, the use of various numbers of CaPA-HRDPA stations for model calibration has allowed us to determine the number of precipitation stations needed to achieve a given performance threshold.

How to cite: Foulon, E., Rousseau, A. N., Scarpari Spolidorio, E. J., and Abbasnezhadi, K.: High resolution data for semi-distributed hydrological modeling: where should we draw the line?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12045, https://doi.org/10.5194/egusphere-egu2020-12045, 2020.

Large scale or global hydrological models (GHMs) show promise in enabling us to accurately predict floods, droughts, navigation hazards, reservoir operations, and many more water related issues. As opposed to regional hydrological models that have many parameters that need to be calibrated or estimated using local observation data (Sood and Smakhtin 2015). GHMs are able to simulate regions that lack observation data, whilst applying a uniform approach for parameter estimation (Döll, Kaspar, and Lehner 2003; Widén‐Nilsson et al. 2009). Up until recently the GHMs used coarse modelling grids of around 0.5 to 1 degree spatial resolution. However, due to advances in satellite data, climate data, and computational resources, GHMs are modelling on higher resolutions (up to 200 meters) that raise questions about how these models can be adjusted in order to take advantage of the finer modelling grid.

In this study, we carry out an extensive assessment of how changes in spatial resolution affect the simulations of the Wflow SBM model for 8 basins in the Continental United States. This is done by comparing the model states and fluxes at three spatial resolutions, namely 3 km, 1km, and 200m. A hypothesis driven approach is used to investigate why changes in states and fluxes are taking place at different spatial resolutions and how they relate to model performance. The latter is determined by validating river discharge, snow extent, soil moisture, and actual evaporation. In addition, we make use of two sets of parameters that rely on different pedo-transfer functions. Further investigating the role parameterization in conjunction with changes in spatial resolution.

By carrying out this study within the eWaterCycle II framework we showcase our ability to handle large datasets (forcing and validation) whilst always complying to the FAIR principles. Furthermore, this study is setup in such that it is scalable in terms of case study areas and hydrological models.

How to cite: Aerts, J., Weerts, A., van Verseveld, W., Drost, N., Hut, R., and van de Giesen, N.: It's impolite to zoom in on global hydrological models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18628, https://doi.org/10.5194/egusphere-egu2020-18628, 2020.

Precipitation and other meteorological variables are very important input data for distributed hydrological models, which determine the simulation accuracy of the models. It is a normal way to subdivide the large area watershed into numerous subbasins to reflect the spatial variation, and the value is usually unique within each subbasin. In most model application, the values of meteorological variables are interpolated from meteorological station observed data to the centroid point of the subbasin with interpolation method (called one-cell interpolation). Because the centroid point could not represent the whole subbasin, the one-cell interpolation will bring input data uncertainty to the model. In this study, a new method is introduced to analysis this uncertainty, which firstly interpolate the values into numerous cells smaller than the subbasin then sum up to the subbasin (called multi-cells interpolation). The results show that one-cell interpolation way is not always consistent with the results of multi-cells interpolation, and the variance is greater in summer than in winter. The consistency grows with the increase of the number of the cells, which indicates that dozens of the cells could got the stable state. The variance is also influenced by the density of meteorological station, but the minimal cell number is almost the same. Thus, in the interpolation of the meteorological variables in distributed hydrological model, it recommends to interpolate the values to numerous smaller cells then sum up to the subbasins, rather than only interpolate to the centroid point.

How to cite: Liu, J., Zhou, Z., Yan, Z., Jia, Y., and Wang, H.: The impact of interpolation method on the accuracy of meteorological variables in distributed hydrological model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6543, https://doi.org/10.5194/egusphere-egu2020-6543, 2020.

We present a distributed hydrological model with minimal calibration requirements, which represents the rainfall-runoff transformation and the flow routing processes. The generation of surface runoff is based on a modified NRCS-CN scheme. Key novelty is the use of representative CN values, which are initially assigned to model cells on the basis of slope, land cover and permeability maps, and adjusted to antecedent soil moisture conditions. For the propagation of runoff to the basin outlet two flow types are considered, i.e. overland flow across the terrain and channel flow along the river network. These are synthesized by employing a novel velocity-based approach, where the assignment of velocities along the river network is based on macroscopic hydraulic information. It also uses the concept of varying time of concentration, which is considered function of the average runoff intensity across the catchment. This configuration is suitable for event-based flood simulation and requires the specification of only two lumped inputs, which are either manually estimated or inferred through calibration. The model can also run in continuous mode, by employing a soil moisture accounting scheme that produces both the surface (overland) runoff and the interflow through the unsaturated zone. The two model configurations are demonstrated in the representation of observed flows across Nedontas river basin at South Peloponnese, Greece.

How to cite: Risva, K., Nikolopoulos, D., and Efstratiadis, A.: Distributed hydrological modelling using spatiotemporally varying velocities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13402, https://doi.org/10.5194/egusphere-egu2020-13402, 2020.

Precipitation is a key factor in controlling the accuracy of runoff simulation, as well as the performance of flood event simulation. Compared with the in-situ rainfall measurement, satellite-based precipitation products provide critical precipitation sources of higher resolution along with detailed depiction of precipitation variability, especially for data-sparse or ungauged regions. This study aims to investigate the impacts of temporal and spatial resolutions of precipitation on flood simulation over a humid region of Southern China. Three versions of Integrated Multi-satellite Retrievals for GPM (IMERG-E, IMERG-L, and IMERG-F) and a gauge-satellite merged precipitation product released by China Meteorological Administration (CMA) at 0.1° and 1 h resolution are used in the study. The lumped hydrological model HBV and semi-distributed hydrological model SWAT are applied to simulate 12 flood events to investigate the impacts of temporal and spatial variabilities of precipitation on flood event simulation. The results show that the spatial resolution of precipitation data affects its capture of characteristics of precipitation events, specifically in magnitude of precipitation variability and the central location of the precipitation event. Furthermore, SWAT shows no improvement compared with HBV in flood event simulation in this case, which may due to the uncertainty of the precipitation spatial variability. The flood events simulated with SWAT indicate that the biases of flood peaks forcing by IMERG-E and IMERG-L increase with the decreasing of precipitation variability, while that forced by IMERG-F are less affected and perform the best among the three IMERG precipitation estimates. The impact of temporal variability of precipitation is conducted with HBV model and the corresponding results are that the higher temporal resolution ensures the better flood event simulation. Furthermore, the CMA source overperforms the other three satellite-based precipitation estimates, and followed by IMERG-F.

How to cite: Zhu, Q. and Zhou, D.: Impacts of spatio-temporal precipitation variabilities on flood event simulation with satellite-based precipitation estimates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6989, https://doi.org/10.5194/egusphere-egu2020-6989, 2020.

Research catchments allow a unique opportunity of acquiring long and varied datasets. This process takes years and is often performed by multiple generations of researchers with different research focuses. In this way, complex processes might be identified and explained on a variety of spatial and temporal scales. But how could these puzzle pieces be put together to form the complete picture of the catchment and would they even fit? Physically-based integrated surface-subsurface models, such as HydroGeoSphere, give us the possibility to jointly model a wide array of processes informed by measurable parameters. Here we present the ongoing work on conceptual models testing by an integrated model in the Hydrological open air laboratory (HOAL). This is a small headwater agricultural catchment in Lower Austria, where a variety of hydrometeorological and hydrogeochemical parameters are monitored with high spatial and temporal resolution. The model in this study builds on the conceptual models of previous studies in the catchment and incorporates features such as tile drainage system, macropores, variable land use and regional groundwater flow. Groundwater levels and discharge data at the tributaries and the catchment outlet from 2013-2017 were used for calibration. We discuss the preliminary findings and the advantages and disadvantages of this modelling approach.

How to cite: Pavlin, L., Széles, B., Blaschke, A. P., and Blöschl, G.: Integrated modelling: a tool for combining findings from multiple studies in a hydrologic observatory, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13384, https://doi.org/10.5194/egusphere-egu2020-13384, 2020.

One of challenges to hydrologists is to estimate runoff from ungauged watershed. Hydrologic estimation through modelling is a reasonable, economical and useful approach to quantity and quality management of watershed. The model framework has been comprehensive and complex to reproduce natural phenomena more realistically with the development of computer hardware. However, driving a complex model requires a lot of effort and time, and the use of many parameters reduces the accessibility of end users and the applicability to the ungauged watershed. In this study, we developed a distributed hydrologic model based on soil moisture simulation using simple composition and fewer parameters. Instead of minimizing the number of parameters, GIS data were used to reflect the watershed characteristics into the model. The proposed model was applied to the four dam watersheds in Korea to assess its performance. As a result, it is confirmed that reasonable hydrologic components simulation is possible through the simulation of soil moisture, even though it was a simple model with only three input parameters. If spatial data such as satellite data is additionally applied, the performance of the model is expected to improve further.

Acknowledgment: This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Public Technology Program based on Environmental Policy Project, funded by Korea Ministry of Environment(MOE)(2016000200002).

Keywords: Distributed hydrological model; Hydrologic components simulation; Soil moisture; Simple hydrological model.

How to cite: Seo, J., Choi, J., and Kim, S.: Development of simple distributed hydrological model based on soil moisture simulation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18236, https://doi.org/10.5194/egusphere-egu2020-18236, 2020.

A catchment-scale hydrological model encompasses a set of hypotheses that are capable of describing, in a lumped way, the water movement in a hydrological catchment. As the catchment undergoes a heavy urbanization gradient, the catchment’s hydrological behavior changes. A new set of hypotheses is then needed to consider the presence of urban-introduced features in the hydrological cycle. Our objective is to reach a parsimonious model structure that is capable of sufficiently reproducing the rainfall-runoff relationship along a wide range of urbanization levels, including the non-urbanized situation. Given a model that is adequate for non-urbanized catchments, what modifications should one operate on the initial model hypotheses to account for (1) the presence of impervious surfaces within the catchment and (2) the interactions between the pervious and the newly added impervious surfaces? To this aim, a large sample of 268 American and French urbanized catchments was prepared. We have chosen an initial hydrological model, GR4H, whose structure has been tested and improved using large international samples of catchments, but predominately non-urbanized. Analyzing the hydrological behavior of the urbanized catchments has helped us in formulating a set of modifications to be made on the initial model structure. Step by step, the relevance of each modification was assessed using 10 continuous, frequency- and event-based evaluation criteria. As a result, the model performances were significantly improved when (a) the net rainfall production was considered to be controlled not only by the antecedent soil moisture conditions but also by the catchment’s mean imperviousness, mainly during low-intensity rainfall events, and (b) the fast flow branch was more privileged in routing, seeing that the response of the urbanized catchments was faster and highly reactive in comparison with the rural ones’. Unlike the initial model structure, the resulting one can help quantifying the impact of future urbanization schemes on the catchment’s hydrological behavior.

How to cite: Saadi, M., Oudin, L., and Ribstein, P.: How to adapt a nonurban model structure to account for urbanization?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3101, https://doi.org/10.5194/egusphere-egu2020-3101, 2020.

Evapotranspiration (ET) is one of the most important factors for the water budget and physical processes in the tropical region. This variable affects the atmospheric water and it is important for its capacity to control precipitation, including its influence on absorption and reflection of solar and terrestrial radiation. In the tropical context ET is a relevant process, where the condensation of large amounts of water vapor leads to the release of latent heat energy. In order to understand ecohydrological and climatic synergies and interactions in the tropical basins, different models have tried to represent the hydrological processes in time and space. But most of these models depend on variables that should be measured in situ and are rarely available or limited in the tropical countries. This inevitably requires the model to be simple enough and the parameters can be estimated from climate and basin characteristics. In this regard, Zhang et al. (2008) developed a hydrological model Dynamic Water Balance (DWB). DWB is a semi-distributed model supported in the Budyko framework, which uses partition curves to distribute water to a number of components based on water availability and demand concepts. In general, the model assumes the control over the water balance is mostly dominated by the precipitation (P) and potential evapotranspiration. 

The hydrologic structure of DWB consists of two tanks, soil moisture store and groundwater store, and adjust its mathematical relations through the optimization of four parameters. Due to its simplicity and strong concepts, DWB had been implemented successfully in several types of basins around the globe (Rodriguez et al., 2019).

This work presents DWBmodelUN, a hydrological R-package with the implementation of DWB in a regular mesh at a monthly time step. DWBmodelUN contains 12 functions related to data entry pre-processing, mathematical development of DWB, calibration algorithm Dynamical Dimension Search and an interactive graphical  module. In overall terms, DWBmodelUN requires: (i) basin geographic data (defines the spatial resolution of the modelling), (ii) hydro-meteorological entry data (P, Temperatute, Streamflow) in raster format, (iii) initial values for the model parameters and (iv) setup data such as warm up, calibration and validation periods. 

In addition, this package includes a practical example of application in Sogamoso River Basin, located at the Oriental mountain range of Colombia.  Therefore, data sets with hydrological, meteorological and setup information were incorporated within the package.

This tool intents to spread  the DWB model and facilitate its implementation in more basins. In this context, to execute DWBmodelUN users do not need extensive programming skills and the R-package was thought for easily adaptability.

References

Rodríguez, E., Sánchez, I., Duque, N., Arboleda, P., Vega, C., Zamora, D., … Burke, S. (2019). Combined Use of Local and Global Hydro Meteorological Data with Hydrological Models for Water Resources Management in the Magdalena - Cauca Macro Basin – Colombia. Water Resources Management. 

Zhang, L., Potter, N., Hickel, K., Zhang, Y., & Shao, Q. (2008). Water balance modeling over variable time scales based on the Budyko framework – Model development and testing. Journal of Hydrology, 360(1–4), 117–131. 

How to cite: García-Echeverri, C., Duque-Gardeazabal, N., Vega-Viviescas, C., Arboleda-Obando, P., and Zamora, D.: DWBmodelUN: an R-package for the hydrological model Dynamic Water Balance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4222, https://doi.org/10.5194/egusphere-egu2020-4222, 2020.

airGR (Coron et al., 2017, 2019) is an R package that offers the possibility to use the GR rainfall-runoff models developed in the Hydrology Research Group at INRAE (formerly at Irstea), including the daily GR4J model as well as hourly, monthly and annual models. Recent model developments are regularly introduced in airGR.

Recently, an hourly model including an interception store was implemented in airGR. The additional interception store, developed by Ficchi et al. (2019), aims at better representing the impact of vegetation on evaporation fluxes. This improved model showed a better consistency of model fluxes across time and enhanced performance.

In addition, the possibility to run the hourly GR models together with the CemaNeige snow accumulation and melt module was added to airGR.

References:

Coron L., Thirel G., Delaigue O., Perrin C., Andréassian V. (2017). The Suite of Lumped GR Hydrological Models in an R package, Environmental Modelling & Software, 94, 166-171. DOI: 10.1016/j.envsoft.2017.05.002.

Coron, L., Delaigue, O., Thirel, G., Perrin, C. and Michel, C. (2019). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R   package version 1.4.3.30. URL: https://CRAN.R-project.org/package=airGR.

Ficchì, A., Perrin, C., and Andréassian, V., 2019. Hydrological modelling at multiple sub-daily time steps: model improvement via flux-matching, Journal of Hydrology, 575, 1308-1327, https://doi.org/10.1016/j.jhydrol.2019.05.084.

How to cite: Thirel, G., Delaigue, O., and Ficchi, A.: Latest developments of the airGR rainfall-runoff modelling R-package: inclusion of an interception store in the hourly model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15275, https://doi.org/10.5194/egusphere-egu2020-15275, 2020.

Bucket-type conceptual hydrological models are widely popular, because of their relatively low data and computational demands. With the improved computational techniques and advances in computer sciences, web based hydrological modelling tools are becoming available too. Conceptual rainfall-runoff (CRR) models are designed to approximate the general physical mechanisms which govern the hydrologic cycle and found practical by many hydrologists and engineers. In this context, a web based, open-source, platform independent, easily accessible hydrological modelling tool Hidro-Odtu has been designed. Aiming at providing fast and accurate results, Hidro-Odtu utilize lumped and semi-distributed hydrological modelling capabilities. The design of the Hidro-Odtu contains pre-processing using the tools to automatically delineate the river network and basin boundaries, input the forcing data, lumped hydrological modelling with parameter calibration capability, hydrological overland flow routing and dynamic result visualization. Moreover, web-based technologies allow remotely prepare model input files, run model calculation and display model results for rainfall-runoff calculations. Bucket storage lumped, conceptual rainfall-runoff model is selected as core feature for hydrological model and it is enhanced to a semi-distributed model by including the Muskingum-Cunge flow routing method to simulate overland flow. Model results are evaluated by several performance indices such that Nash–Sutcliffe Efficiency Index (NSE), Sum of Square of Error (SSE) or Kling-Gupta Efficiency (KGE).

Hydrological modelling, calibration and routing algorithms have been implemented by using Python programming language for the back-end calculations and Node.js framework, html, JavaScript have been utilized for front-end side to handle data preparation and results visualization.

Hidro-Odtu have been evaluated with numerous data sets with different study areas and found successful to delineate sub basins and river network, to define rainfall-runoff relationship on the basis of the sub-basins. With this tool, it is aimed to obtain practical hydrological modelling results using web technologies.

How to cite: Karaman, Ç. H., Akyürek, Z., and Bolat, K.: Web-based hydrological modelling tool - Hidro-Odtu, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-477, https://doi.org/10.5194/egusphere-egu2020-477, 2020.

In 2008, Buytaert et al. asked: “Why can’t we do better than TOPMODEL?” Their answer based on the development of a new generation of hydrological modelling tools, which should be accessible, portable and, especially, modular. Such modular modelling frameworks have now been developed and are used to test hypotheses of catchment behaviour. Some of these frameworks are limited to lumped models, like FUSE, SuperFLEX and MARRMoT, or allow the construction of semi-distributed models like RAVEN. Lumped and semi-distributed models are, due to their little computational costs, great tools for exploring parametric and structural model uncertainty. However, lumped and semi-distributed models are based on the intrinsic hypothesis that the internal spatial configuration of a catchment is not relevant for the runoff processes in a catchment. This assumption of the model structure cannot be scrutinized inside of these frameworks. Modelling systems with the potential to build distributed models, representing the spatial connectivity of landscape features, are eg. SUMMA and CMF.

Our modular, open access Catchment Modelling Framework (CMF, https://philippkraft.github.io/cmf/) is implemented as a library of water fluxes along the nodes of a hydrological network across spatial and temporal scales. It facilitates building models representing current process understanding. It is written in C++ as a library of the Python programming language and is supported and constantly extended since 2009. Due to the open nature, models build with CMF can be adopted to data structure and qualitative expert knowledge. The CMF library contains classical equations of water flux from the Nash-Box to the Richard’s equation. Often neglected anthropogenic infrastructures and activities like sewage water plants, reservoirs, irrigation and pumping can be represented with user-supplied functions. As a library, the connection to other model domains is possible, e.g. plant growth or soil chemistry models, where CMF acts as a water and solute transport module and other models as dynamic boundary conditions.

We will illustrate the use of the library concept with some applications:

• Plot scale (10⁰ m²): Macropore solute transport
• Field scale (10² m²): Feedback loops between CO2 effect in crops and soil water availability
• Hillslope (10⁴ m²): Integrated nitrogen turnover and transport model
• Riparian zone of a continental stream (10⁷ m²): A distributed groundwater model to predict plant species habitats under climate change
• Catchment (10⁸ m²): Spatial explicit risk assessment of open water bodies to pesticide spray drift
• Catchment (10⁹ m²): Incremental break down of a lumped model

How to cite: Kraft, P. and Breuer, L.: Representing dynamic networks of water flow in space, time and structure using process libraries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17050, https://doi.org/10.5194/egusphere-egu2020-17050, 2020.

Hillslope-scale studies play a vital role in understanding the spatial and temporal dynamics of hydrological fluxes of an ungauged watershed. The linkage between static (i.e. topography, soil properties and landuse) and dynamic (i.e. runoff, soil moisture and temperature) characteristics of a hillslope provides a new insight towards hillslope processes. Thus, two Lesser Himalayan hillslopes of Aglar watershed have been selected in two different landuses (grass-covered and agro-forested) and aspects (south and north). In this study, we analyzed the different hydrological fluxes i.e. rainfall, runoff, soil moisture and soil temperature along with the soil properties to get a holistic understanding of hillslope processes. We used the soil moisture dynamics and soil hydraulic conductivity as the major components to derive the hillslope hydrological connectivity. It was observed that the grassed (GA) hillslope generates less runoff than the agro-forested (AgF) hillslope as the upslope runoff of GA hillslope re-infiltrated in the middle portion due to higher soil hydraulic conductivity and surface resistance. Further, this explains that the runoff contributing areas are located at the lower and upper portions of hillslopes due to the presence of low soil hydraulic conductivity zones.  As both the hillslopes are dominated with Hortonian overland flow, the negative correlation was found between topographic indices (TWI) and soil moisture and positive correlation was noticed between soil hydraulic conductivity. Higher runoff (less infiltration) from AgF hillslope results in a higher negative correlation between TWI and soil moisture in comparison to GA hillslope. This results in a higher rate of change in soil temperature of GA hillslope than the AgF hillslope. After analyzing 40 rainfall events, it was concluded that a temperature drop of more than 2^oC was recorded when the average rainfall intensity and event duration exceeds 7.5mm/hr and 7.5hr, respectively. The understanding of covariance of these hydrological fluxes will be used in the future to develop a hillslope-scale conceptual model.

How to cite: Nanda, A. and Sen, S.: Characterizing Hydrological Fluxes of Lesser Himalayan hillslopes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12985, https://doi.org/10.5194/egusphere-egu2020-12985, 2020.

The hydrography of the prairie basins is complicated by the existence of numerous land depressions, known as prairie potholes, which can retain a substantial amount of surface runoff. Consequently, the runoff production in the prairies follows a fill, spill, and merging mechanism, which results in a dynamic contributing area that makes the streamflow simulation challenging. Existing approaches to represent the potholes’ dynamics, in different hydrological models, use either a lumped or a series of reservoirs that contribute flow after exceeding a certain storage threshold. These approaches are simplified and do not represent the actual dynamics of the potholes nor their spatial water extents. Consequently, these approaches may not be useful in capturing the potholes’ complexities and may not be able to accurately simulate the complex prairie streamflow. This study advances towards more accurate and physically-based streamflow simulation in the prairies by implanting a physically-based runoff generation algorithm (Prairie Region Inundation MApping, PRIMA model) within the MESH land surface model, and is referred to as MESH-PRIMA. PRIMA is a recently developed hydrological routing model that can simulate the lateral movement of water over prairie landscape using topographic data provided via DEMs. In MESH-PRIMA, MESH handles the vertical water balance calculations, whereas PRIMA routes the water and determines the amount of water storage and surface runoff. The streamflow simulations of MESH-PRIMA (using different DEM resolution as a topographic input) and MESH with its existing conceptual pothole dynamics algorithm are tested on a number of pothole-dominated watersheds within Saskatchewan, Canada, and compared against observed flows. MESH-PRIMA provides improved streamflow and peak flow simulation, compared to that of MESH with its conceptual pothole algorithm, based on the metrics evaluated for the simulations. MESH-PRIMA shows potential for simulating the actual pothole water extents when compared against water areas obtained from remote sensing data. The use of different DEM resolution changes the resulting pothole water extent, especially for the small potholes as they are not detected in the coarse DEM. MESH-PRIMA can be considered as a hydraulic-hydrologic model that can be used for better understanding and accurate representation of the complex prairie hydrology.

How to cite: Ahmed, M. I., Elshorbagy, A., and Pietroniro, A.: Improving the representation of the prairie pothole dynamics in land surface models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3713, https://doi.org/10.5194/egusphere-egu2020-3713, 2020.

Despite experimental evidence preferential flow is rarely included in hydrologic catchment scale models. This is, at least partly, due to the challenge of deriving preferential flow parameters. Here, we successfully used the optimization algorithm DREAM to calibrate a 3D physics-based dual-permeability model directly at the catchment scale. We limited the number of parameters to be calibrated to the ones being most influential for the simulation of discharge, and we also calibrated parameters of the matrix domain and the macropore domain with a fixed parameter ratio between soil layers. During calibration, saturated hydraulic conductivities of the macropore domain and of the matrix domain converged to very similar values. The dual-permeability parameter sets also did not outperform a calibrated single-domain reference model scenario. We conclude that the incorporation of vertical preferential flow as represented by the dual-permeability approach was not advantageous for reproducing the hydrometric response reasonably well in the studied catchment.

How to cite: Hopp, L., Glaser, B., Klaus, J., and Schramm, T.: The relevance of preferential flow in catchment scale simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7060, https://doi.org/10.5194/egusphere-egu2020-7060, 2020.

Streamflow estimation from rain events is a delicate exercise. Watersheds are complex natural systems and their response to rainfall events is influenced by many factors. Hydrological rainfall-runoff modelling is traditionally used to understand those factors by predicting discharges from precipitation data. These models are simplified conceptualisations and thus still struggle when facing some particular processes linked to the catchment. Among those processes, the tide influence on river discharges is rarely accounted for in hydrological modelling when estimating streamflow series at river mouth areas. Instead, estimated streamflow series are sometimes corrected by coefficients to account for the tide effect.

In this presentation, we explored a semi-distributed hydrological model by adapting it to account for tidal-influence in the river mouth area. This model uses observed spatio-temporal rainfall and potential evapotranspiration databases to predict streamflow at gauged and ungauged locations within the catchment. The hydrological model is calibrated using streamflow observations and priors on parameter values to calibrate each model parameters of each sub-catchments. A drift procedure in the calibration process is used to ensure continuity in parameter values between upstream and downstream successive sub-catchments.

This novel approach was applied to a tidal-affected catchment: the Adour’s catchment in southern France. Estimated results were compared to simulations without accounting for the tidal influence. Results from the new hydrological model were improved at tidal-affected locations of the catchment. They also show similar estimations in tidal-unaffected part of the catchment.

How to cite: Mansanarez, V., Thirel, G., Delaigue, O., and Liquet, B.: Development of a semi-distributed hydrological model on a tidal-affected river: application to the Adour catchment, France., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13582, https://doi.org/10.5194/egusphere-egu2020-13582, 2020.

The Nakdong River is the longest river in Korea with watersheds throughout the Yeongnam region of Korea, and plays an important role as a water source for agricultural water, water supply and industrial water. There is a growing recognition that baseflow measurements are important for effective water management in these large watersheds. To effectively quantify baseflow, specific conductance (SC) data is used, the most effective parameter collected continuously. The baseflow is effectively measured by using SC data and watershed information such as runoff and precipitation for tank model and soil and water assessment tool (SWAT). Our results show that a management approach that considers surface water as well as subsurface water as a resource is important for the effective management of current and future water resources.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1A2C1003114).

How to cite: Kim, R. and Kim, S.: Baseflow measurement and analysis of Nakdong River in South Korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21338, https://doi.org/10.5194/egusphere-egu2020-21338, 2020.

Modelling of the event-based rainfall-runoff process has considerable importance in Hydrology, especially for assessment of water yield potential of a watershed, planning of soil and water conservation measures, reducing sedimentation, and flooding hazards downstream. Antecedent moisture (M) plays a significant role in governing the rainfall-runoff modelling process. It has been the focal point of research in the last decade for improving the Soil Conservation Service Curve Number (SCS-CN) method (also known as NRCS-CN method) for surface runoff computation. In this study, an innovative procedure is proposed to accommodate M in the basic structure of the SCS-CN methodology which otherwise was incorporated externally; to compute M using rainfall-runoff data and verify its applicability by comparing M with the in-situ soil moisture.

Natural rainfall, runoff, and soil moisture data from 6 small experimental farms with different land-use viz. Maize, Finger Millet, and Fallow land, located at Roorkee, India, are utilized. The M is computed by optimizing two parameters, i.e., absolute maximum potential retention (S_abs) and initial abstraction ratio (λ), and the optimization is accomplished by minimizing the root mean square error (RMSE). Results show that there exists a good correlation between theoretical M and measured in-situ moisture. Also, the optimized value of λ has the less error in computing M than the other standard values of λ (λ = 0.2; λ= 0.03). This study not only improves the SCS-CN method but also widens its application horizon in soil moisture studies.

How to cite: Mishra, S. K., Sharma, I., Pandey, A., and Kumre, S. K.: An approach to accommodate and estimate antecedent moisture in runoff curve number methodology- An experimental study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8346, https://doi.org/10.5194/egusphere-egu2020-8346, 2020.

Time of concentration is one of the key time variables in hydrology and it is essential for hydrograph design and hydrological modelling. Uncertainty in its estimation can cause errors in peak discharge rate and timing of flood events.

A unique recognized definition and methodology for its estimate is lacking and the multiple definitions and estimation procedures available in literature can give numerical prediction which can differ by up to 500% (Grimaldi et al., 2012). This result is not surprising given the high subjectivity of the traditionally used method to directly estimate time of concentration, also used for the calibration of the widely applied empirical formulae.

Given the importance of this time parameter in hydrology and the lack of a recognized and easily reproducible procedure for its estimate, here we propose a practical, objective, robust methodology to directly estimate time of concentration from rainfall and streamflow observations only. It’s a timeseries analysis technique used already in the Economics field (Kristoufek, 2014), that have been adapted to estimate time of concentration.

Compared to the traditionally used method, which is event based and requires hyetograph and hydrograph separation, the proposed methodology is designed to find the time delay from the original continuous timeseries but can also be applied to individual events by creating a timeseries of copies of the same event.

In the first place, the median of time of concentration distribution with the proposed methodology has been evaluated against the one with the traditionally used one in 79 catchments across the UK, showing that in most of the sites estimates coming from the two methods are very similar (correlation value of 0.82). This means that it is possible to avoid the separation of the hydrograph, required by the traditionally used method, which is a highly subjective procedure.

Secondly, we show that, when considering the proposed methodology only, for each catchment the time of concentration estimate using the continuous timeseries has a small discrepancy compared to the median of the time of concentration distribution of the single events estimates (correlation value of 0.94). Therefore, rainfall-streamflow events selection is not necessary and a reliable estimate of time of concentration can be obtained by applying the proposed methodology on the continuous timeseries at once, reducing the computational cost.

The proposed timeseries analysis technique is easy to automate, reproducible and make possible to objectively compare time of concentration estimates in all the catchments where the resolution of rainfall and streamflow timeseries is high enough to capture the runoff process.

How to cite: Giani, G., Rico-Ramirez, M. A., and Woods, R.: A practical, objective, robust technique to directly estimate time of concentration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4924, https://doi.org/10.5194/egusphere-egu2020-4924, 2020.

Non-Parametric Bayesian Networks (NPBNs) are graphical tools for statistical inference when new information become available. They have been widely used for reliability analysis and risk assessment. However, few hydrological applications can be found in the literature. Consequently, we explore the potential of NPBNs for maximum river discharge estimation by investigating a number of catchments with contrasting climate across the United States. Different networks schematizing river discharge generation processes at the catchment scale are built and analysed. Hydro-meteorological forcings and catchment's attributes are retrieved from Catchment Attributes for Large-Sample Studies (CAMELS). We highlight the benefits but also the challenges encountered in the application of NPBNs for river discharge estimation. Finally, we provide insights on how to overcome some of the difficulties met.

How to cite: Ragno, E., Hrachowitz, M., and Morales-Nápoles, O.: Non-Parametric Bayesian Networks for Hydrological Studies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13723, https://doi.org/10.5194/egusphere-egu2020-13723, 2020.

Integrated surface/subsurface hydrological models (ISSHMs) are by now widely used in research and applied hydrology. While most studies have so far focused on water flow alone, ISSHMs that include also solute transport are beginning to get attention (e.g., Scudeler et al., 2016, doi:10.5194/hess-20-4061-2016; Gatel et al., 2019, doi:10.1016/j.envsoft.2018.12.006). Numerous numerical challenges are associated with these "doubly coupled" systems: correct treatment of surface boundary conditions and other mass and flux exchange terms; appropriate time stepping schemes across subsystems that are characterized by different dynamic time scales and often also widely different numerical discretization approaches; performance assessments that can be highly sensitive to the response variables of interest; and so on. We will illustrate some of these challenges via test case simulations of an experimental hillslope using the CATHY (CATchment HYdrology) model (Camporese et al., 2010, doi:10.1029/2008WR007536; Weill et al., 2011, doi:10.1016/j.advwatres.2010.10.001). The boundary condition-based coupling strategy used in this model (Putti and Paniconi, 2004, doi:10.1016/S0167-5648(04)80152-7) has been shown to be mathematically rigorous and mass-conservative for the flow model (Sochala et al., 2009, doi:10.1016/j.cma.2009.02.024). The convergence-based time step adaptation strategy used for the nonlinear flow equation (Paniconi and Putti, 1994, doi:10.1029/94WR02046) is likewise thoroughly tested (e.g., D'Haese et al., 2007, doi:10.1002/fld.1369) and widely used. Nonetheless, these schemes, and analogous approaches used in other ISSHMs, need to be adapted and thoroughly tested for coupled systems that include solute transport.

How to cite: Paniconi, C. and Lauvernet, C.: Numerical behavior of a coupled surface/subsurface, flow/transport hydrological model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12074, https://doi.org/10.5194/egusphere-egu2020-12074, 2020.

It has been shown in various experiments that many conceptual rainfall-runoff models experience difficulties to simulate annual or longer-term variations of the streamflow (e.g. Coron et al., 2014). Whether this problem is inherent to the structure of the model in question or could be solved by a change of the calibration procedure is still a matter of debate: for example, the work of Coron (2013) tended to show that no parameter set able to solve the issue can be found, while Fowler et al. (2018) argued that such parameter sets exist, and should be identifiable by a change of objective function.

The aim of this study is to explore further the existence of such a parameter set in the case of the GR4J model (Perrin et al., 2003). Parameters sets were in particular tested against their ability to provide efficient (i.e. with good performance) and robust (i.e. transposable in time) discharge simulations over three flow ranges (low, mean and high flows). To this purpose, a large number of parameters sets of GR4J were sampled in 545 French and Australian catchments. The obtained performances were confronted to those obtained with automatic calibration with a range of objective functions focusing on diverse streamflow ranges.

Because of our large catchment set, we were able to identify a variety of cases: catchments for which highly robust parameter sets exist, catchments for which relatively robust parameter sets exist, and catchments for which no robust parameter sets can be found. Compared to the best sampled parameters sets, those derived through automatic calibration often yielded poorer performances regarding at the same time efficiency and robustness of the discharge simulations over the three flow ranges. We discuss the link between model failures and catchments characteristics, as well as the ability of the GR4J model to adequately simulate streamflow on different timescales and flow regimes.

How to cite: Royer-Gaspard, P., Andréassian, V., and Thirel, G.: Can a hydrological model be efficient and robust at the same time?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18909, https://doi.org/10.5194/egusphere-egu2020-18909, 2020.

A recently introduced framework for Automatic Model Structure Identification (AMSI) allows to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrologic modelling. By combining the mixed-integer optimization algorithm DDS and the flexible hydrologic modelling framework RAVEN, AMSI is able to test a vast number of model structure and parameter combinations in order to identify the most suitable model structure for representing the rainfall runoff behavior of a catchment. The model structure and all potentially active model parameters are calibrated simultaneously. This causes a certain degree of inefficiency during the calibration process, as variables might be perturbed that are not currently relevant for the tested model structure. In order to avoid this, we propose an adaption of the current DDS algorithm allowing for conditional parameter estimation. Parameters will only be perturbed during the calibration process if they are relevant for the model structure that is currently tested. The conditional parameter estimation setup will be compared to the standard DDS algorithm for multiple AMSI test cases. We will show if and how conditional parameter estimation increases the efficiency of AMSI.

How to cite: Spieler, D., Mai, J., Tolson, B., Craig, J., and Schütze, N.: Towards Conditional Parameter Estimation for Automatic Model Structure Identification: Using Mixed-Integer Calibration for Model Development, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13562, https://doi.org/10.5194/egusphere-egu2020-13562, 2020.

Distributed models are useful tools for the assessment of water resources in a context of global change. However, due to the high spatial heterogeneity of the corresponding catchments, these models end up being quite complex with a high number of parameters. In particular, it is not easy to obtain good performances and physically sounded parameter values at all points in the catchment. In order to complement the traditional evaluation approach based on performance criteria, we developed a diagnostic approach based on hydrological signatures. A set of hydrological signatures based on precipitation and runoff data was defined and applied to a regional model of the Rhône basin (100 000 km²) in France. The comparison of simulated and observed signatures for 45 contrasted sub-basins of various sizes, climates, geologies and land uses, show that performance and ability to reproduce signatures are not always correlated. The analysis of signature results, combined with additional hydrogeology expertise, provided directions to improve the model parameterization, especially in the groundwater compartment. The study also provided feedback on the degree of information contained in the signatures and allows us to make recommendations for future studies.

How to cite: Branger, F., Horner, I., Marçais, J., Caballero, Y., and Braud, I.: Diagnostic of a regional distributed hydrological model through hydrological signatures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7416, https://doi.org/10.5194/egusphere-egu2020-7416, 2020.

The convergence performance of global optimization algorithms determines the reliability of the optimized parameter set of hydrological models, thereby affecting the prediction accuracy. This study applies advanced data analysis and visualization techniques to design a novel framework for characterizing and visualizing the convergence behavior of the optimization algorithms when used for the parameter calibration of hydrological models. First, we utilize violin plots to assess the convergence levels and speeds in individual parameter spaces (ECP-VP). The density distributions of violin plots match the possible properties of fitness landscapes. Then, the parallel coordinates techniques are used to simulate the dynamic convergence behavior and assess the convergence performance in multi-parameter space (ECP-PC). Furthermore, the possible mechanism for the effect of linear or nonlinear relationships between the parameters on the convergence performance is investigated using the maximal information coefficient (MIC) and the Pearson correlation coefficient (Pearson r). Finally, the effect of the parameter sensitivity on the convergence performance is analyzed. The proposed framework is applied in multi-period and multi-basin dynamic conditions as case studies. The results showed that the ECP-VP and ECP-PC techniques were well suited for the evaluation of the convergence performance of global optimization algorithms for hydrological models. The evaluation results provided valuable information on determining the reliability of the final optima, as well as the dominant response modes of hydrological models. It is also demonstrated that the convergence levels and speeds in pairwise parameter spaces depend on the linear correlations but not on the nonlinear correlation between the parameters. Additionally, there is no significant relationship between the sensitivity of the parameters and their convergence performance.

How to cite: Lan, T., Lin, K., Xu, C.-Y., and Chen, X.: A framework for visualizing the convergence performance of global optimization algorithms for hydrological models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1792, https://doi.org/10.5194/egusphere-egu2020-1792, 2020.

Assessment of climate change impact on water resources is often based on hydrologic projections developed using monthly water balance models (MWBMs) forced by climate projections. These models are calibrated against historical data but are expected to provide accurate flow simulations under changing climate conditions. However, an evaluation of these models’ performance is needed to explore their applicability under changing climate conditions, assess uncertainties and eventually indicate model components that should be improved. This should be done in a comprehensive evaluation framework specifically tailored to evaluate applicability of MWBMs in changing climatic conditions.

In this study, we evaluated performance of four MWBMs (abcd, Budyko, GR2M and WASMOD) used for hydrologic simulations in the arid Wimmera River catchment in Australia. This catchment is selected as a challenge for model application because it was affected by the Millennium drought, characterised by a decrease in precipitation and a dramatic drop in runoff. The model evaluation within the proposed framework starts with dividing the complete record period into five non-overlapping sub-periods, calibration and cross-validation (i.e., transfers) of the models. The Kling-Gupta efficiency coefficient is used for the calibration in each sub-period. Consistency in model performance, parameter estimates and simulated water balance components across the sub-periods is analysed. Model performance is quantified with statistical performance measures and errors in hydrological signatures. Because the relatively short monthly hydrologic series can lead to biased numerical performance indicators, the framework also includes subjective assessment of model performance and transferability. 

The results show that model transfer between climatically contrasted sub-periods affect all statistical measures of model performance and some hydrologic signatures: standard deviation of flows, high flow percentile and percentage of zero flows. While some signatures are reproduced well in all transfers (baseflow index, lag 1 and lag 12 autocorrelations), suggesting their low informativeness about MWBM performance, many signatures are consistently poorly reproduced, even in the calibrations (seasonal distribution, most flow percentiles, streamflow elasticity). This means that good model performance in terms of statistical measures does not imply good performance in terms of hydrologic signatures, probably because the models are not conditioned to reproduce them. Generally, the greatest drop in performance of all the models is obtained in transfers to the driest period, although abcd and Budyko slightly outperformed GR2M and WASMOD. Subjective assessment of model performance largely corresponds to the numerical indicators.

Simulated water balance components, especially soil and groundwater storages and baseflow, significantly vary across the simulation periods. These results suggest that the model components and the parameters that control them are sensitive to the calibration period. Therefore, improved model conceptualisations (particularly partitioning of fast and slow runoff components) and enhanced calibration strategies that put more emphasis on parameters related to slow runoff are needed. More robust MWBM structures or calibration strategies should advance transferability of MWBMs, which is a prerequisite for effective water resources management under changing climate conditions.

How to cite: Topalovic, Z., Todorovic, A., and Plavsic, J.: Transferability of monthly water balance models under changing climate conditions in an arid catchment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8552, https://doi.org/10.5194/egusphere-egu2020-8552, 2020.

Extensive research is being carried out in developing new calibration procedures for improving the efficacy of hydrologic models. Considering the simulation period into separate wet and dry periods, and performing discrete calibration on each of them has resulted in improvement in model performance, especially during dry periods. In this procedure, it is envisaged that by splitting the time period into wet and dry, the temporal variability of soil moisture, which play a major role in maintaining the water balance of the catchment, is accounted. The discretely calibrated data is then recombined to form the entire time series. However, while recombining the discretely calibrated time periods, the physics of the hydrological processes, at the time of transition from one period to the other, may show abrupt variations. In addition, the short spells of wetness and dryness within this partitioned period, which influences the soil saturation, may not get effectively simulated. This study proposes division of simulation period into wet and dry spells considering the state of saturation of the watershed. This is achieved by clustering the time series of the data using the antecedent precipitation and the soil moisture conditions. A supervised Gustafson-Kessel clustering technique is employed for the same. Subsequently, a relationship between the precipitation, the daily change in soil moisture and a selected model parameter is established for all the cluster transitions and incorporated into the model structure. The proposed methodology is tested using a grid based model with six parameters, on Riesel watershed, Texas, USA. The results indicate that clusters formed are unique, with no fixed duration and no repetitive patterns across the entire simulation period. For preliminary analysis, only one parameter is dynamically varied depending on the incoming rainfall. The performance of the refined model (NSE = 0.85) over the conventional static parameter model (NSE = 0.83), though not significant, indicate that better process representation can aid in improving model simulations. It is noted that this method eliminates the abrupt variation of soil moisture across the wet and dry periods, as the simulation is continuous.

How to cite: Girija, L. and Kulamullaparambathu, S.: Improving hydrological model performance by incorporating dynamic variability of parameters, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-792, https://doi.org/10.5194/egusphere-egu2020-792, 2020.

As understanding river flow regime dynamics is important for future management and conservation of global water resources, the use of hydrological models in ungauged rivers systems has become increasingly common. As the effectiveness of hydrological models to replicate streamflow is limited by the spatial and temporal density of climate stations, it becomes necessary to understand the climate representation of the model at various timesteps. As climate stations are often most dense near cities at low altitude, the importance of having enough stations at different elevation bands impacts the effectiveness of the hydrological model to replicate the sub-basin flow contribution. The use of multi-objective criteria to understand model performance at gauged sub-basins is important during model parameter transfer to ungauged sections. During this study the distributed J2000 rainfall/runoff model was used to understand the impact that climate station density has on model regionalisation and the simulation of hydrological flow components. Furthermore, a station importance factor was used to identify the models station reliance, the maximum station distance for effective hydrological simulation and the relative importance of flow from different sub-basins at the catchment outlet. The rainfall/runoff model was calibrated and validated using multi-objective criteria namely; Nash-Sutcliffe-Efficiency (E1 and E2), Percent Bias (PBIAS) and Kling-Gupta-Efficiency (KGE) coefficients for two gauges, located on the main stem of the river system, to determine a global model parameter dataset which can be used for the model sub-basins. The approach was applied to the Berg River, an inland catchment (7700 km²) located in the Western Cape province of South Africa. While the Berg River is an important agricultural area which is dominated by irrigation, it is also the source of large-scale inter-basin transfers to the metropolitan city of Cape Town. The Western Cape has recently (2012-2017) been subject to a crippling drought which had devastating impacts on agricultural production, as well as inter-basin transfers to the city of Cape Town. The results from the hydrological model showed that for precipitation spatial representation, a station density of 1/20 km² as well as good mid-altitude (200-300 masl) coverage resulted in good hydrological modelling performance. For the simulation of evaporation, the spatial density of measurements impacted the estimation of potential evaporation, but simulated soil-moisture was the main control and station density did not affect the model results. This study highlights the importance of ensuring that precipitation station coverage is sufficient for effective hydrological simulations from sub-basins, with recommendations of both spatial coverage and elevational representation being provided for semi-arid Southern African conditions. The spatial accounting of micro-climatic variability goes some distance to ensure representative sub-basin flow contributions, improving the ability of hydrological models to replicate river flow regimes in semi-arid heterogenous catchments.

How to cite: Watson, A., Miller, J., Kralisch, S., Künne, A., and Fink, M.: Using a multi objective framework for improved calibration and spatial interpolation in hydrological models of the Berg river catchment, South Africa , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13674, https://doi.org/10.5194/egusphere-egu2020-13674, 2020.

Hydrologic modelling is an indispensable tool for simulation of river basin processes in water resources planning and management. Hydrologic models are used to understand dynamic interactions between climate and river basin hydrology. Model calibration, validation, parameter sensitivity and uncertainty analysis are essential prior to the application of hydrologic models. A large catchment with high spatial variability and heterogeneity can be modeled realistically when calibration is done at multiple locations, for multiple hydrologic variables like streamflow, soil moisture, sediment flow, evapotranspiration, etc. This ensures maximum utilization of field measurements of the hydrological variables, reduces the uncertainty in parameter identification and highlights the areas that need greater calibration effort. In the present study, hydrologic model simulations are run for the Mahanadi river basin in India using SWAT (Soil and Water Assessment Tool) and model calibration, uncertainty analysis, sensitivity analysis and validation are performed using SUFI-2 optimization algorithm in SWAT-CUP (SWAT Calibration and Uncertainty Programs). Entire Mahanadi basin is calibrated for several variables like streamflow, soil moisture, sediment load and evapotranspiration at various locations. The spatial heterogeneity of the catchment is taken into account in model calibration by choosing appropriate ranges of different parameters for each sub basin based on the soil types, slope classes and land use land cover present in the sub basins. When multi-site multi-variable calibration is carried out, serial calibration for individual variables and locations gives different result when compared with the simultaneous calibration for all variables and locations. In this study, a comparison of serial calibration for individual hydrologic variables and calibration sites versus simultaneous calibration for all hydrologic variables and calibration sites is made. Various performance measures like Nash-Sutcliffe efficiency (NSE), percent bias, coefficient of determination, modified NSE, etc. are used to quantify the model fit between the observed and the simulated values of various variables. The choice of performance measure affects the calibration solution, and depends on the calibration variables for which observed data is available. The performances of the fitted parameters are conditional with respect to the calibration variables and the choice of the performance measure. The present study talks about the suitability of the performance measure to different hydrologic variables like streamflow, sediment load, soil moisture, etc. The model simulation results for the Mahanadi river basin are compared with the observed values of hydrologic variables using different performance measures for calibration and validation of the model. The results show that model performance is enhanced when it is calibrated at multiple locations, for multiple variables, by taking the spatial variability of parameters across various sub-basins into account. This study explores the suitability of different performance measures for different hydrologic variables and compares the serial and simultaneous calibration for multiple hydrologic variables at multiple locations.

How to cite: Srivastava, S. and Dasika, N. K.: Multi-site multi-variable hydrologic model development for spatially heterogeneous river basins to achieve realistic basin modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5044, https://doi.org/10.5194/egusphere-egu2020-5044, 2020.

SWAT is perhaps the most widely-used basin-scale hydrological model discussed in modern literature. SWAT is typically used to model large basins (100+ km²) and has even successfully modeled basins at continental scales. Regardless of the typical scale that SWAT is used, SWAT has been shown to adequately model various hydrological processes at smaller scales, but this application is much less common in the literature.  The aim of this study is to utilize SWAT+ in a small (<1 km²) agricultural basin (Nucice) approximately 30 kilometers southeast of Prague, Czechia to determine the effects of various spatial distribution patterns of agricultural conservation practices (no/reduced tillage, crop residues, cover crops, etc.) and their respective impacts on projected runoff, soil water retention, and evapotranspiration.

We were able to successfully calibrate our SWAT+ model for the Nucice experimental catchment from 2014 through part of 2018 using discharge data and estimating ET via remote sensing. After successful calibration, we implemented 4 scenarios to analyze the effects of implementing agricultural conservation practices: 25% continuous in upper 50% of basin, 25% fragmented in upper 50% of basin, 25% continuous in lower 50% of basin, and 25% fragmented in lower 50% of basin.

The adaptation pattern of agricultural conservation practices has significant and disproportionate effects on various hydrological balance parameters. Since it is rare that a single farmer manages an entire basin, this study shows that widespread adaptation of agricultural practices is necessary to maximize water conservation within a landscape. We intend to upscale this study (100+ km² basins) and to compare basins across multiple climates to determine if these effects are universal.

This research has been supported by project H2020 No. 773903 Shui, focused on water scarcity in European and Chinese cropping systems.

How to cite: Noreika, N., Dostal, T., Li, T., Zumr, D., and Krasa, J.: The influence of the spatial distribution of agricultural conservation practices on hydrological balance variables in a small basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-993, https://doi.org/10.5194/egusphere-egu2020-993, 2020.

Hydrological models enable comprehensive examination, understanding and quantification of hydrological processes in catchments under the influence of different characteristics. The Soil and Water Assessment Tool (SWAT) has the ability to predict the impact of land management practices on water, sediment and agricultural chemical yield in such catchments.

The objective of this study is to apply the SWAT model on a small agricultural watershed, calibrate and validate it with measured flow, sediment and crop yield data. The model is set up for the HOAL catchment in Petzenkirchen, Lower Austria. The catchment has an area of 66 hectares. The climate is humid with mean annual temperatures of around 10°C, and annual precipitation of around 800 mm. Soils include Cambisols and Planosols with medium to poor infiltration capacities. Gleysols occur close to the stream. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The agricultural activities mainly involve wheat based crop rotation including winter wheat, winter barley, sweet and silage corn and canola. The catchment is divided into 37 fields and for each field exact information about tillage date and type of implement used, date of planting and harvest, date and amount of fertilization and plant protection are available. This information is incorporated in the model during set up. The procedures of model set up, sensitivity analysis, calibration and validation are outlined. A Sequential Uncertainty Fitting (SUFI-2) procedure within SWAT-CUP is used to auto-calibrate and validate the model. The model calibration (2012-2014) and validation (2015-2017) is based on the observed daily discharge and daily sediment concentration at the watershed outlet. Event based observations of runoff and sediment yield from two sub-watersheds are available as well as measured soil water contents at 30 points and crop yield data from different fields. Stream flow and sediment calibration are performed at the watershed outlet as well as at sub watershed level. Results of the SWAT model capability to predict flow, sediment and crop yield as well as soil water contents in the small watersheds will be presented.

How to cite: Musyoka, F. K., Klik, A., and Strauss, P.: Assessment of the performance of Soil Water Assessment Tool (SWAT) model for a small agricultural catchment in Austria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21490, https://doi.org/10.5194/egusphere-egu2020-21490, 2020.

Human activities and climate affect the hydrology of a basin. The effect of Land Use Land Cover (LULC) change and climate change on streamflow are basin specific. In this study, an attempt has been made to evaluate the effects of LULC and climate change on streamflow in the Netravathi basin, Karnataka, India. The SWAT model, which reasonably simulates the streamflow of a basin, is used for this study. The analysis was done from the year 1990 to 2018. The watershed is delineated by using ALOS PALSAR DEM. Rainfall and temperature obtained from IMD are used as the climate variables. LULC maps were prepared using Landsat images of 1990 and 2018 in order to assess the LULC changes in the basin. The results showed that the spatial extent of the LULC classes of built-up (3.82%–6.51%), water bodies (0.76%–0.99%), and agriculture (11.96%–17.89%) increased, whereas that of forest (66.56%–51.7%), fallow (3.82%–6.13%), and barren land (13.07%–16.76%) decreased from 1990 to 2018. The streamflow increased steadily (5.02%) with changes in LULC from 1990 to 2018. The results indicate that LULC changes in urbanisation and agricultural intensification have contributed to the increase in runoff, in the catchment during this period. Thus, hydrological modelling integrating climate change and LULC can be used as an effective tool in estimating streamflow of the basin.

How to cite: Jose, D. M. and Dwarakish, G. S.: Impacts of LULC and climate change on streamflow in Netravati basin, Karnataka, India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-541, https://doi.org/10.5194/egusphere-egu2020-541, 2020.

The intensification of climate change and human activities can lead to non-stationarity of hydrological model parameters, which in turn affects the correctness of model simulation results. Previous studies mainly focus on impacts of climate change, while catchment hydrological responses to human activities require detailed investigation for sustainable water management. This study evaluates anthropogenic impacts on soil water storage capacity of the upper Yangtze River Basin by representing hydrological parameters as functions of human activity indicators. The Xinanjiang (XAJ) model is used since its parameter WM accounts for soil water storage capacity. In this study, time-variations of WM are identified by the split-sample calibration based on dynamic programming (SSC-DP). The variations are further related to ten indicators of human activities from five aspects: population, gross domestic product, farming, irrigation and reservoir construction. Then, the proposed WM functional form is selected by comparing the performance of a set of parameter functions of the identified human activity indicators during the validation period. The study shows that WM increases in 1976-2000, while a relatively high relationship is detected between WM and some indicators such as agricultural acreage, population and reservoir construction. It is further demonstrated that agricultural population has the greatest impact on soil water storage capacity and its linear functional form for WM is validated to be effective in 2001-2010 with best streamflow simulation, especially for low streamflow. These results can help understand the hydrological response to the increasing human development and contribute to adaptive development strategies for future water resource management.

How to cite: Zhang, X., Liu, P., and Xu, C.-Y.: The hydrological response of soil water storage capacity to human activities: A case study in the upper Yangtze River Basin, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5142, https://doi.org/10.5194/egusphere-egu2020-5142, 2020.

With the global climate change and the rapid expansion of urban land use, urban storms and floods have occurred frequently. The state has gradually attached importance to the unified construction of low-impact development facilities (LID) and underground integrated pipe corridors (GL), which makes sponge city both beautiful and practical. In order to study the urban hydrological response of the combination between LID and underground integrated pip corridors (LID_GL), the Yangmei River Basin, a pilot area of ​​Guangzhou's integrated pipe corridors, was taken as an example to evaluate and compare the hydrological response of traditional development, GL, LID, GL_LID scenarios. The results show that:

• (1) The traditional development scenario is verified by the measured rainfall of “2018.06.08”. The simulation results are consistent with the areas where are liable to waterlogging under the actual circumstance, which proves that the SWMM model is suitable for the hydrological response evaluation of LID_GL scenario in the Yangmei River Basin.
• (2) The SUSTAIN model can realize the optimized layout of LID, but the simulation accuracy needs to be improved. On the contrary, the SWMM model cannot realize the LID optimized layout, but the simulation accuracy of urban hydrological response is high. To Combine their advantages, the LID optimized layout schemes calculated by SUSTAIN model are input into SWMM model for hydrological simulation. The results show that this method can avoid the situation that the evaluation results are irrational due to improper layout of LID.
• (3) The overflow reduction in the LID_GL scenario is best, which can exceed 60% under high-return-period rainfall conditions. Its peak outlet flow is lower than GL scenario and the peak appearance time is also delayed.

The above research results can provide reference and theoretical support for the unified construction of LID and underground integrated pip corridors (LID_GL) in the future.

How to cite: Li, S., Wang, Z., and Liu, Q.: The hydrological response of the combination between LID and underground integrated pip corridors based on SUSTAIN, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3199, https://doi.org/10.5194/egusphere-egu2020-3199, 2020.

We develop a technique for reconstructing floods in small-scale data scarce regions using field interview data and hydro-dynamic modelling. The field interview data consist of flood depths and duration data collected from 300 buildings from a flood event in 2017 in Suleja/Tafa area, Nigeria. The flood event resulted from an overflow of water from five river reaches. The hydrodynamic model utilized, called CAESER LisFLOOD, is an integration of a landscape evolution model (CAESER) and a hydraulic model (LisFLOOD-FP). We employ three steps to reconstruct the 2017 Suleja/Tafa flood event. Firstly, we use a linearly increasing hydrograph to; (a) calibrate Manning’s coefficient and (b) determine optimal peak discharge on each reach. This was carried out by minimizing the Root Mean Square Error (RMSE) between the distributed observed flood depths and the simulated flood depths. Secondly, we use synthetic hydrographs with durations between 6, 12, 18, 20, 24 hours, having peak discharge (extracted from the previous step), to simulate flows on all upstream reaches. Using collected flood duration data, we minimized RMSE between distributed observed flood duration and simulated flood duration to determine optimal flow durations on each upstream reach. In the last step, utilizing peak discharge and flow duration for all upstream reaches, we carried out multiple spatial and temporal iterations to match downstream peak discharge. Thereafter, we use determined upstream hydrographs with their relative catchment response timing to simulate the entire river network. Minimum RMSE computed for the entire river network was between ±15 cm of many current studies that use distributed observed data to calibrate flood models. The method developed in this study is useful for simulating floods in regions where data such as high resolution DEMs, river bathymetry and river discharge are limited. In addition, the study extends current knowledge, on utilizing distributed flood data to determine peak discharge, from a single to multiple river networks.

How to cite: Malgwi, M. B., Ramirez, J. A., Zischg, A., Zimmermann, M., Schürmann, S., and Keiler, M.: Reconstructing floods in small-medium scale data-scarce catchments using field interview data and hydrodynamic modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2065, https://doi.org/10.5194/egusphere-egu2020-2065, 2020.

Fluvial flood events are a major threat to people and infrastructure. To compute flood risk estimates, modelling cascades are often applied. Therein, flood hazard is driven by hydrologic or river routing and floodplain flow processes. As such, model selection within such a cascade can determine how well some of these processes can be simulated. Depending on the selection made, obtained flood maps can vary and, in turn, can have major implications for the analysis of how many people, buildings, economic values and so forth is at risk. Understanding the role of model selection in the flood risk modelling process is thus of great importance.

By means of GLOFRIM 2.0, we coupled the global hydrologic model PCR-GLOBWB with the hydrodynamic models CaMa-Flood and LISFLOOD-FP for the delta region of the Ganges-Brahmaputra basin. Applying the model-coupling framework GLOFRIM facilitates forcing various models with identical boundary conditions and thus transparent and objective inter-comparison of flood models.

While replacing the kinematic wave approximation of the hydrologic model with the local inertia equation of hydrodynamic models does not yield better discharge estimates in the Ganges basin, flood maps obtained with LISFLOOD-FP improved representation of observed flood extent. Compared to downscaled products of PCR-GLOBWB and CaMa-Flood, the critical success index increases by around 50 %.

Combining the obtained flood maps with actual exposure maps gives then a first-order estimate how the selection for one specific model set-ups translates into varying flood risk estimates. The research thus shows how those model selections, deliberately made or not, are an important driver of simulated flood risk. As such, it is detrimental that the various specifics of a model are known to facilitate the optimal model selection for objective-specific modelling requirements.

How to cite: Hoch, J., Eilander, D., and Ikeuchi, H.: How model selection can determine flood risk estimates – a case study in the Ganges basin using the GLOFRIM framework, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11086, https://doi.org/10.5194/egusphere-egu2020-11086, 2020.

Wetlands are ecosystems recognized as one of the most valuable natural resources in the world. Although this importance, several wetlands around the world have lost areas due to anthropic threats. One example of a wetland with international importance is the Taim Wetland. This Ramsar Site number 2298, is a fresh-water wetland with 330 km² located in the Southern part of Brazil, close to the border with Uruguay. The primary threat to this wetland is related to water demand conflicts on its watershed. Extensive rice fields occur around Taim Wetland and large yearly volumes of water from its main tributary Mangueira Lake are withdrawn, leading to changes in the hydrodynamics within the wetland. Thus, by one side, there is the regional economic dependence of rice cultivation and, on the other hand, conditions related to water availability are vital for maintaining the ecosystem as a whole. Different human-made infrastructures also impact local hydrodynamics as road, gates, fauna tunnels, natural effects as backwater and climate factors. Due to its importance, the Taim Wetland has been the object of different studies aiming to evaluate strategies for an integrated water management policy, allowing it to reach both environmental and economic benefits. The local complexity leads to the need for applying hydrological-hydrodynamic models able to represent the behavior accurately. Paz, 2003 and Villanueva, 1997 already applied hydrological and 2D-hydrodynamic modeling in the area; however, in the light of information available at that time and computational constraints, these studies needed to adopt several simplifications. In this study, the 2D HEC-RAS 5.0.7 was used to represent the system based on new terrain information obtained from the combination of different sources such as satellite, drone images and local measurement allowing the acquisition of information such as flooding areas, velocities, and flow patterns. New insights of local features such as internal channels, lakes, dunes, road and vegetation such as emergent macrophytes permitted new understandings of hydrodynamics. Nevertheless, hydraulic structures as a set of gates and fauna tunnels were also included in the representation, allowing the analysis of different operational scenarios during the modeling. These results also provide critical information for the environmental evaluation of habitats and points towards better management policies.

How to cite: Peruzzo Bule, B., Tassi, R., and Allasia Piccilli, D. G.: Long-term hydrological and hydrodynamic modeling of a complex Ramsar site using HEC-RAS 5.0.7 2D – The Taim Wetland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21016, https://doi.org/10.5194/egusphere-egu2020-21016, 2020.