HS2.2.1 | Advancing process representation for hydrological modelling across spatio-temporal scales
EDI
Advancing process representation for hydrological modelling across spatio-temporal scales
Convener: Simon Stisen | Co-conveners: Björn Guse, Luis Samaniego, Sina Khatami, Elham R. Freund
Orals
| Fri, 28 Apr, 08:30–12:25 (CEST)
 
Room 2.44
Posters on site
| Attendance Fri, 28 Apr, 14:00–15:45 (CEST)
 
Hall A
Posters virtual
| Attendance Fri, 28 Apr, 14:00–15:45 (CEST)
 
vHall HS
Orals |
Fri, 08:30
Fri, 14:00
Fri, 14:00
Understanding and representing hydrological processes is the basis for developing and improving hydrological and Earth system models. Relevant hydrological data are becoming globally available at an unprecedented rate, opening new avenues for modelling (model parametrization, evaluation, and application) and process representation. As a result, a variety of models are developed and trained by new quantitative and qualitative data at various temporal and spatial scales.
In this session, we welcome contributions on novel frameworks for model development, evaluation and parametrization across spatio-temporal scales.

Potential contributions could (but are not limited to):
(1) introduce new global and regional data products into the modeling process;
(2) upscale experimental knowledge from smaller to larger scale for better usage in catchment models;
(3) advance seamless modeling of spatial patterns in hydrology and land models using distributed earth observations;
(4) improve model structure by representing often neglected processes in hydrological models such as human impacts, river regulations, irrigation, as well as vegetation dynamics;
(5) provide novel concepts for improving the characterization of internal and external model fluxes and their spatio-temporal dynamics;
(6) introduce new approaches for model calibration and evaluation, especially to improve process representation, and/or to improve model predictions under changing conditions;
(7) develop novel approaches and performance metrics for evaluating and constraining models in space and time

This session is organized as part of the grass-root modelling initiative on "Improving the Theoretical Underpinnings of Hydrologic Models" launched in 2016.

Orals: Fri, 28 Apr | Room 2.44

Chairpersons: Simon Stisen, Elham R. Freund
08:30–08:35
Land surface models
08:35–08:55
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EGU23-7472
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solicited
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Highlight
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On-site presentation
Matthias Cuntz

What is the difference between a hydrologic model and a land surface model (LSM)? While hydrologic models concentrate on water fluxes and stores, LSMs describe the coupled energy, water and carbon cycles. There are also little conceptual LSMs so that they can best be compared to so-called process-based hydrologic models. Quite a few of the LSMs were developed as part of Earth System Models. Their primary output variables are hence the exchange fluxes with the atmosphere and they are often operated on continental to global scale, which implies very coarse spatial resolutions compared to hydrologic models.

Here I will describe how state-of-the-art LSMs describe the water fluxes and how the fluxes are evaluated. I will outline current developments in the LSM community, focusing on the developments related to the hydrologic cycle. I will discuss current trends amongst developers of LSMs and problems that originate from these trends. I will also point to the challenges that come from ever increasing model resolutions. I will discuss in this context the scaling issue of, for example, soil parameters and how specific choices lead to problems in other parts of the LSM.

How to cite: Cuntz, M.: Recent progress in land surface models related to the hydrological cycle, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7472, https://doi.org/10.5194/egusphere-egu23-7472, 2023.

08:55–09:05
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EGU23-11061
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On-site presentation
Nathaniel Chaney and Laura Torres-Rojas

Space-time patterns of surface fluxes and states have direct implications for boundary layer growth, cloud development, phenology, and runoff generation, among other processes. Emerging field-scale resolving land surface models (the terrestrial component of Earth system models), such as HydroBlocks, aim to represent this complexity by modeling the water, energy, and biogeochemical cycles at meter-km spatial scales over continental extents. Although there have been significant advances in the representation of heterogeneity in land surface modeling over the past decade, there has yet to be a concerted effort to evaluate the realism of the simulated time-evolving field-scale spatial patterns; this, in part, is due to the challenge of how to interpret the space-time fields. Empirical space-time covariance presents a unique solution; it can robustly summarize the space-time structure of a given flux or state for a given area (e.g., watershed) via a simple 2D surface (e.g., Figure 1). In this presentation, we will demonstrate how space-time covariance provides an effective and efficient approach to facilitate evaluation of the simulated spatiotemporal patterns.

            As a proof of concept, the simulated spatiotemporal patterns of land surface temperature (LST) of a HydroBlocks model simulation over the central United States are evaluated using observations from satellite remote sensing (GOES-16/17). First, for each 0.25 arcdegree grid cell over the study domain, the empirical spatiotemporal covariance functions (ESTCFs) are assembled for HydroBlocks (simulation) on one side and GOES (observation) on another. For this case, each ESTCF is calculated from hourly data for clear-sky pixels during the summers of 2017-2022. The ESTCFs are then initially compared via simple metrics (e.g., RMSE). To ease understanding, a space-time parametric covariance function is then fit to each ESTCF; the comparison of the parameters (e.g., spatial correlation length) provides a richer understanding of the strengths and weaknesses of the model. The resulting analysis illustrates how space-time covariance can efficiently summarize the complex simulated spatiotemporal patterns and thus serve as a useful metric to both evaluate and inform model development to improve process representation.

How to cite: Chaney, N. and Torres-Rojas, L.: Space-time covariance: An effective tool to evaluate the simulated spatiotemporal patterns in land surface models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11061, https://doi.org/10.5194/egusphere-egu23-11061, 2023.

09:05–09:15
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EGU23-9526
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ECS
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On-site presentation
Stephan Thober, Robert Schweppe, Matthias Kelbling, Sebastian Müller, Juliane Mai, Christel Prudhomme, Gianpaolo Balsamo, and Luis Samaniego

Accurately and efficiently estimating parameters for spatially distributed environmental models is impossible without proper regularization of the parameter space. The Multiscale Parameter Regionalization (MPR, Samaniego et al. 2010) makes use of high-resolution physiographic data (i.e., physiographic data such as soil maps and land cover information) to translate local land surface properties into model parameters. MPR consists of two steps: first, the high-resolution model parameters are derived from physiographic data via transfer functions at the native resolution. Second, the model parameters are upscaled to the target resolution used by the environmental modelling application. MPR has been already successfully applied for the mesoscale hydrologic model (mHM, Samaniego et al. 2010, Kumar et al. 2013). The model agnostic, stand-alone version  implementation of MPR (Schweppe et al., 2022) allows applying this technique to any land-surface model or hydrological model.

In this study, we apply the MPR to optimize parameters for the land-surface model ECLand (Boussetta et al. 2021) of the ECMWF Integrated Forecasting System. Calibrating ECLand parameters at individual river basins leads to an improved representation of river discharge, i.e., an improved Kling-Gupta efficiency. In an ongoing effort, we explore model parameters optimization on multiple basins simultaneously to provide an improved representation of river discharge at a global scale. The calibration locations are chosen to cover different climates, soil, and land characteristics among other features.

References:

Samaniego L., Kumar, R., and Attinger, S.: “Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale”, Water Resour. Res., 46, 2010.

Kumar, R., Samaniego, L., and Attinger, S.: “Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations”, Water Resources Res, 2013

Schweppe, R., Thober, S., Müller, S., Kelbling, M., Kumar, R., Attinger, S., and Samaniego, L.: MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models, Geosci. Model Dev., 15, 859–882, https://doi.org/10.5194/gmd-15-859-2022, 2022

Boussetta S, Balsamo G, Arduini G, Dutra E, McNorton J, Choulga M, Agustí-Panareda A, Beljaars A, Wedi N, Munõz-Sabater J, de Rosnay P, Sandu I, Hadade I, Carver G, Mazzetti C, Prudhomme C, Yamazaki D, Zsoter E. ECLand: The ECMWF Land Surface Modelling System. Atmosphere. 2021; 12(6):723. https://doi.org/10.3390/atmos12060723

How to cite: Thober, S., Schweppe, R., Kelbling, M., Müller, S., Mai, J., Prudhomme, C., Balsamo, G., and Samaniego, L.: Multi-basin calibration of the ECMWF land-surface model ECLand, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9526, https://doi.org/10.5194/egusphere-egu23-9526, 2023.

09:15–09:25
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EGU23-14099
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ECS
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On-site presentation
Pedro Felipe Arboleda Obando, Agnès Ducharne, Luiza Vargas-Heinz, Zun Yin, and Philippe Ciais

Irrigation activities play a key role in food production and consume 90% of freshwater withdrawal worldwide. These activities have a strong impact on water and energy budgets and associated biogeochemical cycles, and can have effects on local and regional climate. Furthermore, irrigation activities are projected to increase due to population growth and climate change. This context has encouraged the inclusion of irrigation in land surface models (LSMs), which simulate the continental branch in earth system models.

Here we present an irrigation scheme within the ORCHIDEE LSM that replicates flood and drip techniques. Water demand is calculated as the soil moisture deficit with respect to a target value. This deficit is estimated in the root zone of the crop and grass fraction (which contains both irrigated and rainfed crops), but the demand is limited by the fraction equipped for irrigation and by the water supply, i.e. water available in rivers and aquifers reduced to preserve a minimum volume in each water store for ecosystems. In addition, the scheme prioritizes water abstraction by source (surface or groundwater) according to the Siebert et al. map (2010). Hence, in a gridcell with little groundwater pumping infrastructure, most of the water will be extracted from the river, even if the water demand is not fully supplied. The water finally withdrawn for irrigation is allocated on the surface of the soil column for infiltration, and a maximum irrigation rate is set to prevent runoff production. The user-defined parameters that drive the scheme's response are the root zone depth and soil moisture target, the minimum volume left for ecosystems, and the maximum irrigation rate.

For validation, we use this scheme inside ORCHIDEE to run global offline simulations without and with irrigation. We use a set of parameter values that tries to fit the irrigation rates reported by AQUASTAT, while reducing the bias of evapotranspiration in irrigated areas with respect to the satellite-based products. We explore the possible reduction of bias in other variables like leaf area index, water storage anomalies and observed discharge. Finally, we correlate the bias reduction with landscape features to gain insights on the shortcomings of the irrigation scheme and ORCHIDEE.

How to cite: Arboleda Obando, P. F., Ducharne, A., Vargas-Heinz, L., Yin, Z., and Ciais, P.: Validation of an irrigation scheme inside ORCHIDEE land surface model at global scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14099, https://doi.org/10.5194/egusphere-egu23-14099, 2023.

09:25–09:35
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EGU23-10854
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On-site presentation
Kolbjørn Engeland, Helene Birkelund Erlandsen, Emiliano Gelati, Shaochun Huang, Devaraju Narayanappa, Norbert Pirk, Olga Silantyeva, Lena Merete Tallaksen, Astrid Vatne, and Yeliz A Yilmaz

The main motivation for this study is to improve knowledge about the actual evapotranspiration in cold environments. Erlandsen et al (2021) summarize evapotranspiration estimates that range from 175 – 500 mm/year, i.e. between 13 and 31% of mean annual precipitation for Norway. The study is part of the LATICE (Land-ATmosphere Interactions in Cold Environments) strategic research initiative at the University of Oslo. Here we have launched a new initiative, LATICE MIP-ET that aims to compare model estimates of evapotranspiration (ET) in a high latitude environment.

In this study, we compare local observations of evapotranspiration with local estimates from two land surface models (CLM and SURFEX) and three hydrological models (SHYFT, HBV and Lisflood). Observations are available at five eddy covariance flux sites with a gradient in climate across Norway, from low altitude forested and grassland sites to high mountain and high latitude sites. To run the models three sets of forcing data will be used.

The presentation will summarize the models’ ability to capture diurnal and seasonal variations in evapotranspiration as compared to the observations. We will also compare how models simulate the relationship between potential and actual evapotranspiration and assess the models’ sensitivity to the choice of vegetation-and soil parameters and forcing data used.

References:

Erlandsen, H.B., Beldring, S., Eisner, S., Hisdal, H., Huang, S., Tallaksen, L.M. (2021) Constraining the HBV model for robust water balance assessments in a cold climate. Hydrology Research 2021; nh2021132. doi: https://doi.org/10.2166/nh.2021.132

How to cite: Engeland, K., Erlandsen, H. B., Gelati, E., Huang, S., Narayanappa, D., Pirk, N., Silantyeva, O., Tallaksen, L. M., Vatne, A., and Yilmaz, Y. A.: Intercomparison of local evapotranspiration estimates at high latitudes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10854, https://doi.org/10.5194/egusphere-egu23-10854, 2023.

Process understanding in models
09:35–09:45
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EGU23-9829
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On-site presentation
Rafael Pimentel, Louise Crochemore, Jafet C.M. Andersson, and Berit Arheimer

Catchment modelling of water balance components is nowadays done at high spatial resolution for continental and global scales, thanks to the increasing computational capacity and the growing trend towards open data. One of these process-based models is the World-Wide HYPE (WW-HYPE; Arheimer et al., 2020), which was set-up by a stepwise calibration strategy to avoid equifinality when using streamflow data for parameter estimation. In this presentation we suggest to further evaluate whether the model is right for the right reason by comparing internal variables against independent Earth Observations (EO). We then assume that the results are robust if the two different sources of data reveal the same results. This approach could become a new standard method today for evaluating continuous process-based global models as there are numerous EO products representing various hydrological variables, most of them covering at least the last decade.

We propose to compare three aspects when evaluating robustness in global hydrological variables: i) long-term means, ii) seasonal variability through monthly means, and iii) equifinality by comparing model-streamflow performance versus internal variable performance.

We applied this method by comparing six hydrological variables (potential and actual evapotranspiration, snow cover, snow water equivalent, soil moisture or changes in water storages) from EO-products (based on MODIS, GlobSnow, ESA-CCI Soil Moisture and GRACE) with WWH variables for the time-period 2000-2014 (Pimentel et al, 2023). We then found that the general patterns in the hydrological cycle show good agreement between catchment modelling and EO at the global scale, although some months in water-storage changes differed. These dissimilarities indicate that hydrological variables above the ground and earlier in the flow path are more robust than the sub-surface downstream processes, such as soil moisture distribution and water-storage changes, which reflect more complex processes that can be challenging to describe both by hydrological models and satellite sensors. Regarding geographical distribution, there is a larger spread in results from regions with extreme characteristics, such as cold regions (Canadian prairies), arid regions (western USA, deserts), highly forested areas (Amazonas), and transition zones (Sahel and Mediterranean Basin). This indicate that the particularity of these regions calls for specific regional modelling and monitoring approaches rather than continental or global approaches.

On the contrary, in temperate regions at mid-latitudes, e.g., eastern USA and central Europe, almost all the hydrological variables were found robust. With respect to equifinality, overall, there were no indication on good discharge performance and bad internal model representation. The exercise shows the potential in using EO products for model evaluation beyond traditional river-discharge observations from gauges, to first assess the robustness of hydrological variables and second to determine which processes should be better represented in model parameterisation, without forgetting that EO products are not a ground truth and are also assigned with uncertainties.

 

References:

Arheimer et al., 2020: Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation, HESS 24, 535–559, https://doi.org/10.5194/hess-24-535-2020

Pimentel et al., 2023: Assessing Robustness in Global Hydrological Modelling through EO Comparisons, HSJ (in review)

How to cite: Pimentel, R., Crochemore, L., Andersson, J. C. M., and Arheimer, B.: Evaluating global hydrological-process modelling beyond river discharge observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9829, https://doi.org/10.5194/egusphere-egu23-9829, 2023.

09:45–09:55
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EGU23-9997
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On-site presentation
Leila Farhadi and Asif Mahmood

Evapotranspiration (ET) and recharge fluxes are fluxes at the land-atmosphere interface. Evaporative flux links the surface and atmospheric systems and the recharge flux links the surface and subsurface systems. These are two critical fluxes in the water cycle that have major impact on agriculture, water supply, climate, biogeochemical cycles and etc. These fluxes are interconnected and depend on the soil moisture content.  In situ measurements of ET and recharge are costly, limited and cannot be readily scaled to regional scales relevant to weather and climate studies. Sequence of land surface state observations of moisture (SM) and temperature (LST), widely available from remote sensing across a range of scales, contain implicit information that can be used for characterization and mapping of evapotranspiration and recharge fluxes.

In this work, A variational data assimilation (VDA) framework is developed to estimate key parameters of ET and recharge flux by assimilating Soil Moisture Active Passive (SMAP) soil moisture and Geostationary Operational Environmental Satellite (GOES) land surface temperature data into a coupled dual-source energy and water balance model. These parameters include neutral bulk heat transfer coefficient (CHN) and evaporative fraction from soil (EFS) and canopy (EFC)) that regulate the partitioning of available energy, and the effective saturated hydraulic conductivity (Ks) and bore size index (B) that regulate the movement of moisture into the soil column. The uncertainties of the retrieved parameters are estimated through the inverse of the hessian of the cost function, obtained using the lagrangian methodology. Analysis of the second-order information provides a tool to identify the optimum parameter estimates and guides towards a well-posed estimation problem.

The proposed framework is implemented over the US Southern great plain (SGP) and Oklahoma Panhandle region (with computational grid size of 0.05 degree) to map evapotranspiration and recharge fluxes across a range of temporal scales. Comparison with in-situ observations from the USCRN and the Mesonet sites show that the proposed assimilation framework can accurately estimate the temporal variability of root zone soil moisture profile. The evapotranspiration estimates show good agreement with the in-situ data from Atmospheric Radiation Measurement (ARM) sites at different locations and the estimated annual recharge flux values are within the range suggested in the literature for this region. Results demonstrate the success of the proposed assimilation framework in estimating key water cycle components and their interrelations across a range of spatial and temporal scales from remotely sensed near surface soil moisture and temperature data.

How to cite: Farhadi, L. and Mahmood, A.: An Integrated Variational Framework for Coupled Estimation of Evapotranspiration and Recharge by Assimilating Land Surface Soil Moisture and Soil Temperature Data , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9997, https://doi.org/10.5194/egusphere-egu23-9997, 2023.

09:55–10:05
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EGU23-7622
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On-site presentation
Axel Bronstert, Daniel Niehoff, and Gerd R. Schiffler

A major aim of physically based distributed hydrological models is an adequate representation of hydrological processes, including runoff generation processes. A significant proportion of runoff is generated through the subsurface, i.e. by groundwater flow or unsaturated subsurface stormflow. However, in the case of high rainfall intensity and/or low soil-surface infiltrability, surface runoff may strongly contribute to total runoff, too, either through saturation excess (“Dunne-type surface runoff”) or infiltration excess (“Hortonian surface runoff”). Both types of surface runoff can be rather important if antecedent wetness is high and parts of the catchment area are saturated (leading to saturation excess), or if the maximum infiltration rate into the soil surface is less than the actual rainfall intensity (resulting in infiltration excess). Even though the latter process can be very important during high-intensity rainstorms, both for flood generation and for matter transport linked with surface runoff, an appropriate consideration of this process in catchment models is still challenging. Actually, budgeting between the actual rainfall intensity and the soil surface infiltration capacity is required. This may appear simple in principle, but there are a number of challenges in the details: First, the ‘real’ rainfall intensity may vary tremendously in time increments much smaller than the time step of the model. The soil surface infiltrability can also be significantly reduced, e.g. by crusting, compaction or rain energy-induced sealing of the soil surface or through hydrophobic effects.

Otherwise, soil infiltrability can be strongly enhanced as a consequence of preferential flow paths / macropores caused by e.g. bioturbations or other voids.

Finally, there is high variability of such soil surface features appearing at a rather small spatial scale, below the typical spatial modelling unit.

This contribution presents observational data and model approaches to deal with these challenges. We show results from combined infiltration and infiltration-excess experiments and observations at three different spatial scales. Then, we present a model approach based on a double-porosity soil, thus enabling the combined modelling of high infiltration rates and dampened soil moisture distribution after termination of infiltration, as observable in the field. Furthermore, we present an approach to model the effects of soil surface conditions on actual infiltration capacity and its variation.

We show simulation results where these approaches improved the overall plausibility and explanatory power of the model concerning surface runoff generation and soil moisture dynamics. For instance, model results of infiltration experiments at the plot and hillslope/field scales show that it is possible to simulate high infiltration rates jointly with a relatively slow movement of moisture within the soil matrix, field phenomena often observed in the case of heavy rainfall. Other simulation efforts deal with the non-linear and space-time variable effects of soil surface conditions. This is a rather important feature for flood generation in the case of high rainfall intensity and low soil infiltrability.

How to cite: Bronstert, A., Niehoff, D., and Schiffler, G. R.: Modelling Infiltration and Infiltration Excess: The Importance of Fast and Local Processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7622, https://doi.org/10.5194/egusphere-egu23-7622, 2023.

10:05–10:15
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EGU23-1369
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ECS
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On-site presentation
Francesca Zanetti, Gianluca Botter, and Matteo Camporese

Understanding the spatiotemporal dynamics of runoff generation in headwater streams is crucial for better characterizing catchment functioning under current and projected climatic conditions. In this context, experimental data on the expansion and contraction of the stream network can be especially valuable. These data can be gathered exploiting different tools and techniques, from visual surveys to cameras, from remote sensing to electrical conductivity probes. New available data are often used to study joint variations of active stream length and discharge at the catchment outlet and allowed the scientific community to derive general laws for describing and interpreting such complex behavior. However, field mapping is highly time consuming, e.g. because the instruments deployed required an intense supervision to ensure the reliability of the data collected. Using physically based numerical models to simulate the spatial configuration of the wet channels and the corresponding catchment discharge thus represents a promising application. In this study, we used CATHY (CATchment HYdrology), an integrated surface–subsurface hydrological model, to study event-based dynamics of catchment discharge and active stream network in two synthetic catchments with pre-defined geological characteristics (hydraulic conductivity, porosity, water retention curve, depth to bedrock) and different morphometric properties (shape and slope). We run a set of simulations under time-variant conditions and under steady state conditions for different levels of catchment wetness and we analyzed the emerging relationship between total active length (L) and outlet discharge (Q). The numerical simulations were used to investigate the role of topography, climate and morphology on the dynamics of L and Q, and the ensuing L(Q) relationship. Numerical models can be a valuable tool for investigating the internal dynamics of the soil moisture that eventually drives the joint changes of river network length and discharge in response to precipitation.

How to cite: Zanetti, F., Botter, G., and Camporese, M.: Physics-based hydrological modeling of the joint variations of stream network length and catchment discharge, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1369, https://doi.org/10.5194/egusphere-egu23-1369, 2023.

Coffee break
Chairpersons: Björn Guse, Luis Samaniego
Catchment model evaluation
10:45–11:05
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EGU23-9847
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solicited
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Highlight
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On-site presentation
Pablo A. Mendoza, Nicolás Vásquez, Nicolás Cortés-Salazar, Naoki Mizukami, and Ximena Vargas

Distributed hydrological models are useful tools to explicitly simulate the spatial heterogeneity of water and energy fluxes and storages. Nevertheless, their parameters are typically calibrated using streamflow-based objective functions that integrate information on the spatial variability of physical processes into a single metric. Additionally, these models contain several soil parameters that can be distributed in space, affecting the spatial representation of hydrological variables. Here, we examine the implications of streamflow-based calibration metric selection and spatial heterogeneity in soil parameters on the realism of model simulations, with emphasis on spatial patterns. To this end, we conduct several calibration experiments in six pilot basins with different hydrological regimes (two snowmelt-driven, two mixed-regime, and two rainfall-driven basins), in central-southern Chile, using the Variable Infiltration Capacity (VIC) model coupled with the mizuRoute routing model. In each experiment we assess, for a given calibration objective function, the effects of distributing individual soil parameters using a spatial regularization strategy based on principal component analysis of physiographic and soil characteristics (elevation, slope, clay content, sand content and bulk density), defining the case of spatially constant soil parameters as the benchmark (i.e, only meteorological forcing data and vegetation attributes are spatially distributed). To evaluate simulated spatial patterns, we use satellite remote sensing data of soil moisture from the ESA-CCI product, and fractional snow-covered area, actual evapotranspiration (ET), and land surface temperature from MODIS products. The results show that similar streamflow performance metrics can be achieved with different combinations of regularized soil parameter and calibration metric; however, the simulated spatial patterns can be considerably different, without clear connections with the hydrological regime. Further, a streamflow-based calibration is insufficient to represent the seasonality of other variables, especially in water-limited catchments, where important shifts (e.g., up to five months) in peak ET can be obtained compared to the reference product.

How to cite: Mendoza, P. A., Vásquez, N., Cortés-Salazar, N., Mizukami, N., and Vargas, X.: Impact of calibration metric selection and spatial heterogeneity in soil parameters on the realism of distributed hydrological models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9847, https://doi.org/10.5194/egusphere-egu23-9847, 2023.

11:05–11:15
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EGU23-15920
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On-site presentation
Paulina Rodriguez and Ximena Vargas

Hydrological models are powerful tools, that allows users to create a simplified representation of real-world system and that serve to understand the hydrological processes in a basin, and predict their future behavior, including, for example, the effects of climate change. However, these models are subject to multiple sources of uncertainty, including structural uncertainty, related to the hydrological processes simulated and to the spatial discretization applied (lumped, semi-distributed or distributed models). The effects of this modelling decision could be particularly relevant when the objective is to simulate more than one hydrological process.

The objective of this work is to determine if the use of different model structures (lumped or semi-distributed), and the selection of process to be simulated allows reducing the uncertainty of the estimation of more than one hydrological process. Using Raven, a robust and flexible hydrological modelling framework, that supports a wide variety of modelling options, and sits atop a robust and extendible software architecture, eight model structures have been constructed to simulate the River Colorado en Junta con Palos Basin. This basin located in the central zone of Chile (Lat.-35.25, Lon. 71), has a snow-pluvial regime and an average annual rainfall of 1796 mm for the period 1979-2020.  Additionally, this basin covers an area of 879 km2, with a wide elevation range, from 643 m.a.s.l. to 4074 m.a.s.l.

The results have shown some differences at modelling daily streamflow (KGE from 0.68 to 0.72 in the lumped models, and from 0.68 to 0.8 in semi-distributed models). Furthermore, other important changes have been visualized related to the characterization of snow cover and soil moisture in the first layer of soil. The simulated series have been compared to satellite data (products MODIS10A2 for snow cover and NASA-USDA Enhanced SMAP Global Soil Moisture Data for superficial soil moisture).

In the case of the snow cover, the annual duration of snow cover was evaluated, obtaining Pearson's coefficient values between 0.4 and 0.56 for lumped models, while these values reach 0.65 in the case of semi-distributed models. Regarding soil moisture, the changes were more significant when changing the structure of the model (selection and parameterizations of the processes), rather than its spatial discretization, with a range of KGE values from 0.34 to values close to 0.7, strongly influenced by the methods used to evaluate evapotranspiration and infiltration, as well as by the characteristics of the soil.

Overall, this work demonstrates the potential of a flexible hydrologic modeling framework to assess and reduce the structural uncertainty of hydrologic models, taking advantage of the potential of these tools.

How to cite: Rodriguez, P. and Vargas, X.: Evaluation of the structural uncertainty of hydrological models in the estimation of multiple hydrological processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15920, https://doi.org/10.5194/egusphere-egu23-15920, 2023.

11:15–11:25
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EGU23-9706
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ECS
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On-site presentation
Diana Spieler and Niels Schütze

Discussions calling for more rigorous evaluation practices for hydrologic models have recently increased. In addition to the widely used integral objective functions, hydrologic signatures are becoming common evaluation metrics for proving the suitability of hydrologic models for specific application purposes.

This work calibrates 7488 fixed conceptual model structures using KGE as an objective function. These structures range from a 1 to 3 storage model space previously used for an automatic model structure identification experiment. In this experiment we simultaneously calibrated the model structure (number of stores and flux equations) and its parameter values. Additionally, we calibrated 45 literature-based model structures (MARRMoT Toolbox) to extend the structural diversity in the analyzed models. We select well-performing models based on their KGE value (as is common practice) and analyze their performance using 12 selected hydrological runoff signatures. These signatures represent five aspects of the hydrological regime (magnitude, frequency, duration, rate of change, and timing). The large number of model structures, calibrated to the streamflow of 12 MOPEX catchments, allows general insight into how well common conceptual model structures can represent observed hydrological behavior evaluated by signatures.

Results show a general behavior of model structures calibrated to KGE to perform well in representing runoff ratio, mean discharge, the 95th streamflow percentile, and the mean half-flow date. However, the analyzed conceptual model structures struggle to represent low flow and frequency signatures. When evaluated only for KGE, we can identify dominating model structures over all catchments. When evaluated for signatures, there are no model preferences over all analyzed catchments but different models seem to have their merits under specific conditions. These results support the need for ensuring model adequacy for a given task.

How to cite: Spieler, D. and Schütze, N.: Hydrological Signature Representation of 7533 KGE Calibrated Conceptual Model Structures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9706, https://doi.org/10.5194/egusphere-egu23-9706, 2023.

11:25–11:35
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EGU23-13397
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ECS
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On-site presentation
Shu-Chen Hsu, Alban de Lavenne, Charles Perrin, and Vazken Andréassian

While hydrological models aim to represent the hydrological behaviour of catchments, many of them have been streamlined on the exclusive basis of streamflow simulation performance, i.e. among the possible parameter sets, the 'optimal' is the one which brings the best simulation of streamflow during the calibration period. However, we sometimes encounter 'optimal' sets which perform well in discharge simulation but yield unrealistic simulations of other fluxes (e.g. actual evaporation fluxes, inter-catchment groundwater fluxes). Previous studies tried to constrain the exploration of parameter space with measurements complementary to river discharge: this application of extra information aims to increase the physical realism of the model compared to discharge-only calibration. In this study, we carry out an original investigation to take advantage of the spatial patterns of the complementary data in order to drive the calibration towards a more spatially consistent solution.

We propose here a feasibility test, to constrain the spatial consistency of fluxes of a semi-distributed GR model (GRSD). Our study area is the Somme catchment (6100 km2 ) with 17 internal gauging stations, each of them having more than 15 years of discharge measurement. As a first step, we use the long-term actual evaporation from Budyko-estimation as an extra constraint, which has been widely used for describing spatial patterns of climate. In the second step, we develop a criterion describing the spatial consistency between the pattern of measured and simulated fluxes. By constraining the model with extra information, the model is expected to yield a more consistent simulation of fluxes in comparison with the classical calibration practice. Moreover, we analysed the impact of this additional constraint on the spatial organisation of IGF over the catchment as both the other components in water balance analysis, actual evaporation and discharge, are constrained.

How to cite: Hsu, S.-C., de Lavenne, A., Perrin, C., and Andréassian, V.: How could we improve the spatial consistency of water fluxes in a semi-distributed hydrological model? A multi-criteria approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13397, https://doi.org/10.5194/egusphere-egu23-13397, 2023.

11:35–11:45
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EGU23-12558
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ECS
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On-site presentation
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue

Accounting for the variability of processes and climate conditions between catchments and within catchments remains a challenge in hydrological modelling. To address this issue, various approaches were developed over the past decades. Among them, multi-model approaches provide a way to quantify and reduce the uncertainty linked to the choice of model structure, and semi-distributed approaches propose a good compromise to account for spatial variability of the processes by dividing the catchment in sub-catchments while maintaining a limited level of complexity. However, these two approaches were barely applied together. The aim of this work is to answer the following question: what are the contributions of multi-model approaches in a variable spatial framework for the simulation of streamflow over a large sample of catchments?

To this end, a large set of 121 uninfluenced catchments in France was assembled, with precipitation, evapotranspiration and streamflow data at an hourly time step over the 1998-2018 period. The semi-distribution set-up was kept simple by considering a single intermediate catchment between a downstream station and one or more upstream catchments. The multi-model approach was implemented with 13 hydrological structures, three calibration options and two spatial frameworks, for a total of 78 distinct modelling options. A simple average method was used to combine the streamflow at the outlet of the catchments and sub-catchments. In this work, the benchmark considered is the most efficient lumped model considered individually on each catchment.

The semi-distributed multi-model approach generates better performance at the time-series scale than the lumped models. The gain is mainly brought by the multi-model aspect while the spatial framework gives a more occasional benefit. This study also highlight the advantages of using a large set of models in a semi-distributed multi-model framework to simulate streamflow in a large sample hydrology context.

How to cite: Thébault, C., Perrin, C., Andréassian, V., Thirel, G., Legrand, S., and Delaigue, O.: Multi-model approach in a variable spatial framework for streamflow simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12558, https://doi.org/10.5194/egusphere-egu23-12558, 2023.

11:45–11:55
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EGU23-4911
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ECS
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On-site presentation
|
Mohammad Reza Eini, Christian Massari, and Mikołaj Piniewski

Satellite-based observations of soil moisture, leaf area index, precipitation, and evapotranspiration facilitate agro-hydrological modeling thanks to the spatially distributed information. In this study, the Climate Change Initiative Soil Moisture dataset (CCI SM, a product of the European Space Agency (ESA)) adjusted based on Soil Water Index (SWI) was used as an additional (in relation to discharge) observed dataset in agro-hydrological modeling over a large-scale transboundary river basin (Odra River Basin) in the Baltic Sea region. This basin is located in Central Europe within Poland, Czech Republic, and Germany and drains into the Baltic Sea. The Soil and Water Assessment Tool+ (SWAT+) model was selected for agro-hydrological modeling, and measured data from 26 river discharge stations and soil moisture from CCI SM (for topsoil and entire soil profile) over 1476 sub-basins were used in model calibration for the period 1997-2019. Kling–Gupta efficiency (KGE) and SPAtial EFficiency (SPAEF) indices were chosen as objective functions for runoff and soil moisture calibration, respectively. Two calibration strategies were compared: one involving only river discharge data (single-objective - SO), and the second one involving river discharge and satellite-based soil moisture (multi-objective – MO). In the SO approach, the average KGE for discharge was above 0.60, whereas in the MO approach, it increased to 0.67.The SPAEF values showed that SWAT+ has acceptable accuracy in soil moisture simulations. Moreover, crop yield assessments showed that MO calibration also increases the crop yield simulation accuracy. The results show that in this transboundary river basin, adding satellite-based soil moisture into the calibration process could improve the accuracy and consistency of agro-hydrological modeling.

How to cite: Eini, M. R., Massari, C., and Piniewski, M.: Satellite-based soil moisture could enhance the reliability of agro-hydrological modeling in large transboundary river basins, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4911, https://doi.org/10.5194/egusphere-egu23-4911, 2023.

Virtual talks
11:55–12:05
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EGU23-8106
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Virtual presentation
Flora Branger, Jérémie Bonneau, Sébastien Pouchoulin, and Eric Sauquet

Climate change increases the risk of water scarcity due to a higher probability of droughts and heat waves, even in temperate countries. A currently controversial adaptation strategy deployed by farmers is the multiplication of small dams to intercept water during the winter months (either from hillslopes or headwater streams), and store it through the summer months to secure irrigation and cattle watering. However, the impact of such practices on catchment water balance and streamflow dynamics is difficult to assess, because of the lack of reliable data but also the lack of models able to represent these devices. In the framework of the J2000 distributed hydrological model, we developed a simple component representing farm dams in mesoscale to regional scale catchments. The model was applied to the Rhône catchment in France (~ 100000 km²), using a database of known locations of farm dams to assess the impact of these dams on catchment water balance components and several streamflow dynamics indicators. Several scenarios were simulated, under present climate, according to various parameterizations such as absence / presence of dams but also varying the density and dimensions of the reservoirs, as well as their infiltration properties and drainage areas. The results show that the impact of such dams is potentially high, but is also highly dependent on the parameterization scenarios, thus confirming the need for more complete land and water uses databases.

How to cite: Branger, F., Bonneau, J., Pouchoulin, S., and Sauquet, E.: Cumulative impact of farm dams on catchment water balance and streamflow dynamics at the regional scale. A numerical experiment using a distributed hydrological model., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8106, https://doi.org/10.5194/egusphere-egu23-8106, 2023.

12:05–12:15
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EGU23-15644
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ECS
|
Virtual presentation
Using a holistic modelling approach to assess drivers of hydrological change and the impact of irrigation in a water/data scarce environment
(withdrawn)
Andrew Watson, Annika Kunne, Jodie Miller, and Sven Kralisch
12:15–12:25
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EGU23-10593
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ECS
|
Virtual presentation
Mohamed Ismaiel Ahmed, Kevin Shook, Alain Pietroniro, Tricia Stadnyk, John W. Pomeroy, Charlotta Pers, and David Gustafsson

Modelling the hydrology of the North American prairie region is complicated by the dominance of cold region processes and by the flat topography, which contains millions of depressions. The depressions contribute to variable contributing areas in prairie basins, due to their varying water storage. The relationships between the depressional storage, and the contributing fraction are hysteretic and strongly influence the basin responses. Most hydrological models do not represent these complex hysteretic relationships, and therefore struggle in simulating the hydrology of the region. In this study, we propose a novel Hysteretic Depressional Storage (HDS) algorithm that is based on the known hysteretic properties of prairie depressions. HDS is implemented into the HYPE modelling framework to improve the simulations of prairie streamflow by accounting for the variable contributing area. The modified HYPE, and the original program are tested on two depression-dominated basins in Saskatchewan, Canada. The modified HYPE model show improved simulation of streamflows compared to the original HYPE model. The HDS algorithm can contribute to improving the streamflow simulation of not only the North American prairie region, but also in the arctic and Siberian regions, which are dominated by the same complex depressional storages. The modified HYPE model should also improve the estimates of surface water storage and the resulting evaporative fluxes in these regions, increasing model fidelity and improving water budget estimates in these complex terrains, especially under changing climates.

How to cite: Ahmed, M. I., Shook, K., Pietroniro, A., Stadnyk, T., Pomeroy, J. W., Pers, C., and Gustafsson, D.: Incorporating The Variable Contributing Area Concept in The HYPE Modelling Framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10593, https://doi.org/10.5194/egusphere-egu23-10593, 2023.

Posters on site: Fri, 28 Apr, 14:00–15:45 | Hall A

A.1
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EGU23-14263
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ECS
Laurène Bouaziz, Joost Buitink, Willem van Verseveld, Dirk Eilander, Mark Hegnauer, Eric Sprokkereef, Jasper Stam, Niek van der Sleen, and Rita Lammersen

Distributed hydrological models are valuable tools for operational and strategic water management planning. These models include a representation of vertical processes such as interception, transpiration and infiltration and require a lateral component to route the water downstream along the river network. The kinematic wave is a commonly used approach for lateral flow in distributed hydrological models, which assumes that topography mainly controls the water flow. While this applies in steep terrain, the assumptions of the kinematic wave do not apply in flatter landscapes. The Wflow framework is a free and open-source distributed hydrological modeling platform developed by Deltares (van Verseveld et al., 2022). The Wflow framework has been extensively tested in catchments around the world using the SBM vertical concept in combination with the kinematic wave for lateral river, overland flow and subsurface flow routing. Recently, the local inertial approximation, which only neglects the convective acceleration term in the Saint-Venant equations, was implemented in the Wflow framework as an alternative lateral routing concept to accurately represent river routing processes in flatter areas. In addition, the numerical scheme proposed by de Almeida et al. (2012) was implemented for the simulation of 2D overland flow. Using the HydroMT (Hydro Model Tools, https://github.com/Deltares/hydromt) Python package, we set-up wflow_sbm models for the Rhine and the Meuse basins and compare alternative concepts for river (and overland flow) routing, including kinematic wave, local inertial 1D and local inertial 1D2D. The results show significant differences in the shape and magnitude of modeled peak flows and the importance of floodplain storage and floodwave attenuation processes, as the local inertial 1D2D simulations were closest to streamflow observations. With this study, we stress the importance of including relevant routing processes (floodwave attenuation through overbank flow in the floodplains) as opposed to a more extensive calibration which would compensate for these lacking processes.

 

References:

de Almeida, G. A. M., P. Bates, J. E. Freer, and M. Souvignet, 2012, Improving the stability of a simple formulation of the shallow water equations for 2-D flood modeling, Water Resour. Res., 48, W05528, https://doi.org/10.1029/2011WR011570.

van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B.: Wflow_sbm v0.6.1, a spatially distributed hydrologic model: from global data to local applications, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-182, in review, 2022.

How to cite: Bouaziz, L., Buitink, J., van Verseveld, W., Eilander, D., Hegnauer, M., Sprokkereef, E., Stam, J., van der Sleen, N., and Lammersen, R.: Comparing river routing concepts in distributed hydrological modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14263, https://doi.org/10.5194/egusphere-egu23-14263, 2023.

A.2
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EGU23-5579
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ECS
|
Saskia Salwey, Gemma Coxon, Francesca Pianosi, Rosanna Lane, Michael Bliss Singer, and Chris Hutton

To meet growing water demand and to satisfy an increasing population, reservoirs are continually being integrated into river systems across the world. The presence of a reservoir can dictate the downstream flow regime, such that in many locations, understanding reservoir operations can be crucial to understanding the hydrological functioning of an impacted catchment. Consequently, over the last two decades, correctly representing reservoirs, and their operations, in hydrological modelling frameworks has become a key area of research for simulating water availability. Although substantial progress has been made in modelling reservoir operations (which control how water volumes are distributed across space and time), there is still no consensus on the best way to define, calibrate and evaluate operating rules within hydrological models. In most locations, data describing reservoir operating rules are not available, and timeseries of reservoir inflow, outflow and storage are often unpublished. Consequently, modelers must simplify and generalize sets of release rules from very little information, particularly where they are to be applied across large scales (e.g. across hundreds of reservoirs). Generic reservoir operating rules have typically been tested and developed using the Global Reservoir and Dam (GranD) database and thus are biased towards large irrigation reservoirs (which make up the majority of the dataset). Whilst operating rules have also been tested across many hydropower and multipurpose reservoirs, a gap remains for the definition of generic reservoir operating rules designed for smaller water supply reservoirs that can be applied nationally in countries such as Great Britain (GB).

In this study, we integrate a new generic reservoir simulation component into a national-scale hydrological model of Great Britain and compare simulation results from two modelling scenarios (with and without the new reservoir component). The first scenario, where reservoirs are omitted, is used as a benchmark representative of current modelling practices in GB (where none of the national-scale hydrological models include reservoirs), whilst the second uses a set of generic operating rules focused on simulating small, water resource reservoirs. In both scenarios, we use Multiscale Parameter Regionalisation (MPR) for model calibration. To assess the suitability of our operating rules for simulating future conditions and evaluating water availability during hydrological extremes, we test the consistency of model performance across the onset, duration and recovery from droughts. This study will demonstrate the importance of including reservoir representation in hydrological models of Great Britain, and will introduce a set of operating rules suitable for smaller reservoirs with a focus on water supply.

How to cite: Salwey, S., Coxon, G., Pianosi, F., Lane, R., Bliss Singer, M., and Hutton, C.: Developing generic reservoir operating rules for inclusion in the national-scale hydrological modelling of Great Britain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5579, https://doi.org/10.5194/egusphere-egu23-5579, 2023.

A.3
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EGU23-10287
Yongchan Kim and Dongkyun Kim

Physically-based hydrologic models can accurately simulate streamflow in natural environment, but they cannot precisely consider the anthropogenic disturbance caused by the operation of large-scale dams. We tried to overcome this issue by developing a hybrid modeling framework, consisting of physically-based models for simulating upstream natural watersheds and deep-learning-based models for simulating dam operation. The model was developed for the Paldang Dam watershed, a major water source for Seoul metropolitan area, where the importance of stable water supply has increased due to the increase of population and water use per capita. The prediction performance of the hybrid model was compared with that of models built based only on the physically-based hydrologic model, namely the Variable Infiltration Capacity model, with single and cascaded structure. For the validation period, Nash-Sutcliffe Efficiency from the developed hybrid model, the single model, and the cascaded model were 0.6410, -0.1054, and 0.2564, respectively, suggesting that the consideration of dam operation aided by the machine learning algorithm is essential for accurate assessment of streamflow.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

How to cite: Kim, Y. and Kim, D.: A Hybrid Hydrological Modelling Approach Combining Physically-Based and Deep-Learning-Based Models to Consider Dam Operations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10287, https://doi.org/10.5194/egusphere-egu23-10287, 2023.

A.4
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EGU23-2314
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ECS
Xu Zhang, Jinbao Li, Qianjin Dong, and Ross A. Woods

The Budyko framework is an effective and widely used method for describing long-term water balance in large catchments. However, it only considers the limits of water and energy in evaporation (E), and ignores the impacts of climate seasonality and water storage capacity (Sc), resulting in errors for Mediterranean climate and catchments with small Sc. Here we combined the Ponce-Shetty model with Budyko hypothesis, and analytically generalized Budyko framework with physical accounts of climate seasonality and Sc. Precipitation (P), potential evaporation (PE), and Sc are used to represent the limits of water, energy, and space for E, respectively. Our results show that previous Budyko-type equations can be treated as special cases of generalized Budyko-type equations with uniform P and PE and infinite Sc. The new generalized equations capture the observed decrease in E due to asynchronous P and PE and small Sc, and perform better than the Budyko-type equations with varying parameters in the contiguous United States with fewer parameters. Overall, our generalization of Budyko framework improves the robustness and accuracy for estimating mean annual E with the aid of physical interpretation, and will facilitate water balance assessment at regional to global scales.

How to cite: Zhang, X., Li, J., Dong, Q., and Woods, R. A.: An analytical generalization of Budyko framework with physical accounts of climate seasonality and water storage capacity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2314, https://doi.org/10.5194/egusphere-egu23-2314, 2023.

A.5
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EGU23-15217
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ECS
Kan Lei, Diana Spieler, and Niels Schütze

Modular hydrological modeling has been around for some time, with early examples such as the Modular Modeling System (MMS) developed in 1996. In 2011,Fenicia et al. introduced the SUPERFLEX modeling framework, refined by Molin et al. (2021) as the Python package SurperflexPy. A framework with an even larger library of processes is the Raven modeling framework introduced by Craig et al. (2020).

This work introduces a c++ based R package prioritizing convenience while still offering flexibility for semi-distributed hydrological modelling. The EDCHM framework defines five basic layers: atmosphere, snow pack, land, soil, and ground, with the soil and ground layers able to be further divided into sublayers. Each layer has its own characteristics and state variables such as capacity and water volume. EDCHM defines 12 basic processes, including 10 hydrological and 2 meteorological processes such as evapotranspiration and infiltration. Each process has a single flux output, and it can occur within a single layer or between layers. The input requirements are flexible and depend on the specific method used. A process with a specific method is referred to as a module in EDCHM. EDCHM also includes 34 predefined model structures with fixed connections between processes and layers, ranging from 6 to 15 processes. The key feature of EDCHM is the model builder, which allows users to easily generate the model function just by selecting the process methods, the input data list, and the parameter list with ranges will also be created. This makes it fast and efficient for users to build and calibrate models. EDCHM is implemented in c++ and supports vectorization and parallelization through R-Package Rcpp and furrr. Users can easily build new models with their own ideas or ideas from literature.

EDCHM has been tested on 34 east-german catchments, with over 60 models calibrated in lumped form and 6 catchments calibrated with 3 and 5 sub-catchments or more than 50 HRUs. Our results show that EDCHM is highly effective in the application of hydrological modeling, with a key feature being its efficiency.

 

Craig et al. (2020). https://doi.org/10.1016/j.envsoft.2020.104728

Fenicia et al. (2011). https://doi.org/10.1029/2010WR010174

EDCHM: https://github.com/LuckyKanLei/EDCHM

How to cite: Lei, K., Spieler, D., and Schütze, N.: EDCHM: A c++ based R package for flexible semi-distributed conceptual hydrological modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15217, https://doi.org/10.5194/egusphere-egu23-15217, 2023.

A.6
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EGU23-403
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ECS
qianyu zha, Yi He, and Timothy Osborn

Assessment of climate change impacts on flooding risks has been undertaken by using hydrological models calibrated at a daily time step and driven by daily outputs from the global or regional climate models. However, the daily scale model typically underestimates the magnitude of floods. A model run at a higher temporal resolution can be more capable of capturing flood peaks and hence more representative of the expected future flood magnitude (Beylich et al., 2021; Huang et al., 2019). This study aims to develop an hourly HBV hydrological model for Great Britain. The precipitation observations at an hourly time step for Great Britain [CEH-GEAR1hr] (Lewis et al., 2022) were used to calibrate the hourly HBV model. The model was also calibrated using daily observations from HadUK-Grid dataset (Hollis et al., 2019). The CAMELS-GB catchments in Great Britain (Coxon et al., 2020) were selected as the study area. Hourly time series of flow data were obtained from Environment Agency (EA) for England, Scottish Environment Protection Agency (SEPA) for Scotland and Natural Resources Wales (NRW) for Wales. Daily flow data are from National River Flow Archive (NRFA). The calibrating objective function for the HBV hydrological model at both daily and hourly time steps is Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), and also the modified Kling-Gupta efficiency (KGE), the ratio of the root-mean-square error to the standard deviation (RSR)  and Pearson's correlation coefficient (r) were used to compare. For the daily model, more than 77% and 35% of the CAMELS-GB catchments achieve NSE values over 0.6 and 0.8, respectively. The hourly model performance is comparable with the daily model and the hourly model outperforms the daily model in capturing the peak flows.

References
Beylich, M., Haberlandt, U., Reinstorf, F., 2021. Daily vs. hourly simulation for estimating future flood peaks in mesoscale catchments. Hydrol. Res. 52, 821–833. 
Coxon, G., Addor, N., Bloomfield, J.P., Freer, J., Fry, M., Hannaford, J., Howden, N.J.K., Lane, R., Lewis, M., Robinson, E.L., Wagener, T., Woods, R., 2020. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth Syst. Sci. Data 12, 2459–2483. 
Hollis, D., McCarthy, M., Kendon, M., Legg, T., Simpson, I., 2019. HadUK‐Grid—A new UK dataset of gridded climate observations. Geosci. Data J. 6, 151–159. 
Huang, Y., Bárdossy, A., Zhang, K., 2019. Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data. Hydrol. Earth Syst. Sci. 23, 2647–2663. 
Lewis, E., Quinn, N., Blenkinsop, S., Fowler, H.J., Freer, J., Tanguy, M., Hitt, O., Coxon, G., Bates, P., Woods, R., Fry, M., Chevuturi, A., Swain, O., White, S.M., 2022. Gridded estimates of hourly areal rainfall for Great Britain 1990-2016 [CEH-GEAR1hr] v2.
Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10, 282–290. 

 

How to cite: zha, Q., He, Y., and Osborn, T.: Development of an hourly hydrological model for Great Britain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-403, https://doi.org/10.5194/egusphere-egu23-403, 2023.

A.7
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EGU23-4281
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ECS
Li Han, Björn Guse, Dung Nguyen, Oldrich Rakovec, Xiaoxiang Guan, Sergiy Vorogushyn, Luis Samaniego, and Bruno Merz

The July 2021 flood in Western Germany is one of the most severe flood events in small-scale catchments during the past decades. It has led to life loss and severe damage in the Ahr, Erft, and Rur basins. The BMBF-funded joint project KAHR (https://hochwasser-kahr.de) deals with the effects of this flood and develops scientific knowledge to assist the reconstruction process. To analyze past floods and develop future flood management strategies, there is a need for small-scale flood modeling with finer spatial-temporal resolution in this area. Based on the derived simulated floods, we can investigate the spatiotemporal patterns of extreme weather and associated meteorological and hydrological conditions that could lead to similar or more significant flood events.

This study uses the mesoscale hydrological model mHM at hourly resolution for three small catchments Ahr, Erft, and Rur. This is one of the first applications of mHM forced with hourly meteorological forcing data and should enable more accurate processes and representation of such extreme floods. In a further step, a regional weather generator and a disaggregation procedure are applied to generate 10,000 years of synthetic hourly meteorological data for the Ahr, Erft, and Rur catchments. These data are used to create long time series of discharge with the calibrated mHM model. This enables the investigation of extreme floods and the assessment of flood risk under future climate conditions.

How to cite: Han, L., Guse, B., Nguyen, D., Rakovec, O., Guan, X., Vorogushyn, S., Samaniego, L., and Merz, B.: Hourly model simulation to improve the estimation of extreme floods in small catchments in Western Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4281, https://doi.org/10.5194/egusphere-egu23-4281, 2023.

A.8
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EGU23-13592
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ECS
Doris Duethmann, Martha Anderson, Marco Maneta, and Doerthe Tetzlaff

Considering different types of hydrologic observations for model calibration in addition to streamflow is a suitable strategy to better constrain model parameters and improve process-consistency of hydrologic models. In this regard, land surface temperature (Ts) is an interesting variable as it is at the core of the surface energy and water balance. This study aims at evaluating the benefits of integrating spatial patterns of satellite-derived Ts into calibration of the process-based ecohydrologic model EcH2O. We furthermore explore the value of an increasing number of Ts images in the calibration period. The study is performed in a mixed land cover catchment in NE Germany and makes use of Landsat-derived Ts data. Our results show that satellite-derived Ts is useful for reducing uncertainties of energy-balance related vegetation parameters, which are hardly constrained when the model is calibrated to streamflow only. Good model performance with respect to streamflow does not preclude low performance in terms of Ts and including satellite-derived Ts for model calibration clearly improves simulated spatial patterns of Ts. Spatial patterns in observed Ts are shown to be strongly related to land cover class and a vegetation index, and our results indicate that further model improvements may be possible by better representing observed variations of leaf area index within the ecohydrologic model.

How to cite: Duethmann, D., Anderson, M., Maneta, M., and Tetzlaff, D.: Improved ecohydrologic modelling using spatial patterns of remotely sensed land surface temperature, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13592, https://doi.org/10.5194/egusphere-egu23-13592, 2023.

A.9
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EGU23-6075
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ECS
Marketa Podebradska, Milan Fischer, Jan Balek, and Miroslav Trnka

Soil moisture is a key factor for plant growth and agricultural production. Therefore, it has become a fundamental part of agricultural drought monitoring systems developed for various spatial scales ranging from local to regional and global. Despite the development of large-scale soil moisture monitoring systems, in-situ soil moisture observations still remain inadequate for precise soil moisture monitoring, especially in remote areas, where there is a limited number of monitoring stations. Together with remote sensing technologies soil moisture modeling may provide an alternative to in-situ measurements that delivers spatially continuous estimates over large geographic areas. SoilClim is a semi-empirical water balance model that, together with other outputs (e.g., reference and actual evapotranspiration, soil temperature), estimates daily soil moisture in various depths of soil profile. The model has previously been validated on a total of 20 sites (5 in Central Europe and 15 in central USA) and is now used for global monitoring and prediction of soil moisture and drought intensity in an operational and interactive web platform (www.windy.com). Our study evaluates the SoilClim soil moisture global measurements with 0.1° spatial resolution using two independent sources of information: i) in-situ soil moisture measurements from the International Soil Moisture Network, and ii) the soil moisture derived from the Metop ASCAT sensors on Metop-A and Metop-B satellites. In the conference presentation we will introduce the SoilClim model and present results of the global validation including statistical spatial analysis and triple collocation.

Acknowledgement: This study was conducted with support of SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797).

How to cite: Podebradska, M., Fischer, M., Balek, J., and Trnka, M.: Global validation of the SoilClim soil moisture estimates using in-situ and remote sensing observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6075, https://doi.org/10.5194/egusphere-egu23-6075, 2023.

A.10
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EGU23-13624
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ECS
Artur Guzy, Philip Minderhoud, Bente Lexmond, Claudia Zoccarato, and Pietro Teatini

Globally, land subsidence caused by groundwater pumping is a common phenomenon. Numerous subsurface processes, both natural and anthropogenic, contribute to its occurrence. In coastal regions, severe land subsidence exacerbated by groundwater pumping is particularly detrimental. On top of that, coastal areas affected by natural compaction, river delta consolidation processes, and additional exposure to drainage-induced aquifer-system compaction are especially susceptible to flooding and salinization due to the steadily rising sea level triggered by climate change.

The Mekong delta, one of the world's largest deltas, is densely populated and crucial for agricultural production. The delta is low-lying and has a high rate of natural compaction, whereas human activities accelerate land subsidence. A numerical model of groundwater-extraction-induced aquifer-system compaction was developed in 2017 to demonstrate the effects of 25 years of groundwater extraction on land subsidence in the delta. The model encompassed the time range from 1991 to 2016 using geological, hydrogeological, geomechanical, and remote sensing data. In 2020, the model was updated to include a surface water network. Six scenarios were developed to simulate potential future pathways of hydraulic head evolution and aquifer-system compaction in the Mekong delta from 2019 to 2100.

Our research aims to enhance the reliability of the existing numerical model of groundwater extraction-induced aquifer-system compaction in the Mekong delta, given the significance of such scenarios in the development of policies to mitigate the negative effects of groundwater pumping.  Our research focuses on four steps.

First, a novel subsurface model representation.

The 3D subsurface model of the Mekong delta was developed using ten hydrogeological cross-sections derived from 96 geological borehole logs interpolated linearly. This resulted in a subsurface model consisting of 15 layers, including seven aquifers, seven aquitards, and a phreatic top layer. The goal of the current study is to develop a new schematisation of the aquifer system within the Mekong delta based on 522 borehole logs and to investigate the spatial variability of the aquifer system using advanced geostatistical tools.

Second, a hydrogeological schematization enhancement.

In the current schematisation, aquitards are discretized as a single layer, resulting in the inability to simulate delayed groundwater pressure propagation within the aquitard. Several additional models with refined aquitard discretization are constructed and compared to evaluate the effect.

Third, the quantification of the influence of deterministic modelling on compaction.

The hydrogeological model is deterministically parameterized and calibrated using hydraulic head time series. Utilizing stochastic modelling of hydrogeological parameters, the impact of this deterministic modelling approach on simulated compaction is determined.

Fourth, a hydrogeological and geomechanical parameters consistency improvement.

The previous hydrogeological and geomechanical model parameterizations are inconsistent since the groundwater model and the geomechanical module were initially parameterized and calibrated independently. To address this issue, an iterative procedure is used to calibrate storage and compression indexes consistently for each individual model layer. This is accomplished by utilising groundwater head datasets recorded by 358 piezometers and land subsidence datasets retrieved by InSAR from 2006 to 2010 and 2016 to 2019.

How to cite: Guzy, A., Minderhoud, P., Lexmond, B., Zoccarato, C., and Teatini, P.: Enhancing Predictions of Land Subsidence Induced by the Groundwater Withdrawal in the Mekong Delta, Vietnam, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13624, https://doi.org/10.5194/egusphere-egu23-13624, 2023.

A.11
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EGU23-10786
Jeong Eun Lee, Chul-Gyum Kim, Jeongwoo Lee, Il-Moon Chung, and Sun Woo Chang

Accurate estimation of groundwater recharge in Jeju Island, which relies on groundwater for most of its water use, is very important for water resource management and planning. However, Jeju watershed is a volcanic island with high permeability, ephemeral streams and mountain areas, so it is difficult to estimate the hydrological components using existing hydrological models. To overcome these limitations, SWAT-K (Soil and Water Assessment Tool-Korea), which can simulate ephemeral stream and threshold runoff, was used to estimate hydrological components (such as precipitation, evapotranspiration, runoff, and groundwater recharge) of Jeju watershed (~1,828 km2). The overall procedure of SWAT-K modeling (model setup, calibration and validation) was performed from 1991 to 2020. The simulated and observed daily streamflows were compared and showed a good agreement. In particular, a reasonable estimation of groundwater recharge was confirmed through the comparison of simulated groundwater recharge and the observed groundwater level. Finally, the spatio-temporal groudwater recharge characteristics were analyzed using the SWAT-K results.

Acknowledgement : Research for this paper was carried out under the KICT Research Program (project no.20220275-001, Development of coastal groundwater management solution) funded by the Ministry of Science and ICT.

How to cite: Lee, J. E., Kim, C.-G., Lee, J., Chung, I.-M., and Chang, S. W.: Estimation of spatio-temporal groundwater recharge in Jeju Island, Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10786, https://doi.org/10.5194/egusphere-egu23-10786, 2023.

A.12
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EGU23-14378
Jiri Nossent and Ronald Nsubuga

Conceptual hydrological models can play an important role in real-time river forecasting systems due to their limited calculation time and versatility. Nevertheless, their simplified structure, very often based on the water content in multiple storages, and constrained physical background hampers their applicability in seasonally influenced catchments. In particular, these models often show good forecasting performance in one season (e.g. for high discharges in wet seasons), but fail to capture events in other seasons (e.g. due to typical high intensity precipitation during dry periods). To overcome this issue, we propose a seasonal calibration approach for conceptual hydrological models, based on the results of a seasonal sensitivity analysis. The obtained seasonal models however induce an additional challenge within a continuous real-time river forecasting system: the transition from one seasonal model to another. The latter is of particular importance when the volume of the storages in the conceptual model changes between different seasons. An application with the conceptual NAM model for three catchments in Belgium will be used to illustrate the proposed calibration strategy and a number of possible solutions for the transition issue.

How to cite: Nossent, J. and Nsubuga, R.: A seasonal calibration approach of conceptual hydrological models for improved real-time river forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14378, https://doi.org/10.5194/egusphere-egu23-14378, 2023.

A.13
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EGU23-10121
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ECS
Leandro Ávila, Reinaldo Silveira, André Campos, Nathalli Rogiski, Camila Freitas, Cássia Aver, and Fernando Fan

The Electric Energy Company of Parana (COPEL GeT), the Meteorological System of Parana (SIMEPAR) and RHAMA Consulting company are undertaking the research project PD-6491- 0503/2018 for the development of a hydrometeorological seasonal forecasting for Brazilian reservoirs. The project, sponsored by the Brazilian Electricity Regulatory Agency (ANEEL) under its research and development program, aims the forecasting of streamflow, at temporal scales ranging from 1 to 270 days, at hydro power enterprises, which are integrated by the National Power System Operator (ONS) through its Interconnected System (SIN). With the aim of implement a seasonal forecasting system using different hydrological modeling approaches, it is necessary first to validate the use of different hydrological models during the calibration and validation stages. This work evaluates the performance of four conceptual hydrological models to represent daily streamflow regimes at four hydropower plants located in the Teles Pires river basin (Brazil). The adopted models included the GR4J, HYMOD, HBV, and the SMAP. The calibration of the parameters for each hydrological model was performed using the SCE-UA method and a triangular weighting function was adopted for routing the hydrograph between sub-watersheds. The evaluation of each model was elaborated by the comparison of the observed and simulated streamflow time series during the calibration (2010-2016) and the validation period (2016-2019) using deterministic metrics and statistical tests. A post-processing procedure based on the quantile-quantile method was applied in order to correct the simulated data and reduce the bias with respect the observed data. In general, the results show that the SMAP model present a better performance to simulate the daily streamflow regimes at the simulated hydropower plants, with Nash-Sutcliffe coefficient (NSE) greater than 0.65, and NSElog values greater than 0.8. In addition, the bias correct procedure shows a significant improvement in the adjust of the simulated data to represent the periodic streamflow regimes in the selected river basin.

How to cite: Ávila, L., Silveira, R., Campos, A., Rogiski, N., Freitas, C., Aver, C., and Fan, F.: Evaluation of four hydrological models to simulate daily streamflow time series in a tropical river basin of Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10121, https://doi.org/10.5194/egusphere-egu23-10121, 2023.

A.14
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EGU23-7358
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ECS
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers.

Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in 6 European catchments (Croatia, Spain, Finland, France, Hungary, Czech Republic) characterized by different climate, geology and anthropogenic use. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in meso-scaled river networks (between 200 km² and 350 km²). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a Random Forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, water temperature measurements, citizen science applications) are used to build the predictive model. In this study we aim to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativeness) on the performance of the predictive model.

Results show that the hybrid modelling approach developed in this study allows to predict precisely the spatio-temporal patterns of drying in the 6 catchments. This study shows the value of combining different sources of observed flow condition data to reduce the uncertainty in predicting flow intermittence.

How to cite: Mimeau, L., Künne, A., Branger, F., Kralisch, S., Devers, A., and Vidal, J.-P.: Flow intermittence prediction using a hybrid hydrological modelling approach: a guide to reducing uncertainty related to observed intermittence data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7358, https://doi.org/10.5194/egusphere-egu23-7358, 2023.

A.15
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EGU23-12205
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ECS
Kobe Braet, Lara Van Der Veken, Emma Tronquo, Jonas-Frederik Jans, and Niko Verhoest

Hydrological models are key instruments to predict hydrological extremes, such as droughts and floods. These hydrological extremes are becoming more and more frequent in Belgium. Therefore, creating a climate robust water system is becoming a priority. This research aims to provide flow rate predictions for several catchments in the province of East-Flanders based on meteorological, soil, land use and DEM data. These were collected by local monitoring networks and transformed by, among others, local pedotransfer functions and spatial interpolation methods.

A physically based model (SWAT+) is used to simulate flow rate estimates for (un)gauged catchments and to calculate different scenarios related to land use change or a changing climate. The simulated flow rates are used as training data for a simple conceptual model (PDM). The PDM-model is more suited for real-time modelling and can be used as a basis to take policy decisions. The strength of physically based models is that they require minor calibration, while conceptual models have a more feasible computation time. By combining the strengths of both models, an estimate can be made for the flow rates in different (un)gauged catchments of East-Flanders.

How to cite: Braet, K., Van Der Veken, L., Tronquo, E., Jans, J.-F., and Verhoest, N.: Hydrological simulation of flow rates in (un)gauged catchments of East-Flanders (Belgium) by the SWAT+ and PDM models., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12205, https://doi.org/10.5194/egusphere-egu23-12205, 2023.

A.16
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EGU23-15058
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ECS
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Zihan Yan, Huimin Lei, Dawen Yang, and Haidong Gao

Intensive Soil and Water Conservation (SWC) has taken place in the Yellow River basin (YRB) to control soil erosion and river sediment, it has altered the eco-hydrological processes and particularly led to the runoff reduction. However, the SWC are rarely simulated explicitly in the hydrological models of the YRB. In order to understand its hydrological impacts, this study developed a SWC parameterization scheme in an existing distributed physically-based model (GBEHM). The hillslope SWC was parameterized as additional surface storage capacity and simulated together with hillslope hydrological processes. The check dams along the river networks were parameterized as reservoirs and simulated together with the flow routing. The improved model (GBEHM-SWC) had been calibrated and validated comprehensively using the observed river discharge and remote sensing-based evapotranspiration. The annual precipitation and runoff significantly decreased during 1982-2000 at the rate of -4.3 and -1.0 mm/yr, respectively. In the following period 2001-2019, the precipitation recovered at 3.2 mm/yr with a slight increasing in runoff at 0.2mm/yr. Compared to the previous period, the annual average precipitation and temperature increased by 27.3 mm and 0.85 ℃, whereas the observed runoff decreased by 4.3 mm. Therefore, we applied the GBEHM-SWC to quantify the impacts of climate change and SWC in the YRB, spatially and temporally. The SWC contributed to the annual runoff reduction by 3.8 and 3.7 mm (or 2.84 and 2.74 billion m3), respectively, in which the hillslope SWC measures accounted for 51% of the annual runoff reduction. Without the append SWC measures, the annual runoff would increase by 2.9 mm (or 2.17 billion m3) in the recent period due to the precipitation increase. Hillslope SWC and river-networks SWC have their largest impact on runoff reduction in the Longmen-Sanmenxia section and Toudaoguai-Longmen section, respectively. The parameterization scheme developed for the distributed model is useful for the watershed hydrological simulation and prediction under the intensive SWC implementation.

How to cite: Yan, Z., Lei, H., Yang, D., and Gao, H.: Simulating the hydrological impacts of intensive Soil and Water Conservation Measures in the Yellow River Basin Using a Distributed Physically-based Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15058, https://doi.org/10.5194/egusphere-egu23-15058, 2023.

A.17
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EGU23-13738
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ECS
Prashant Istalkar and Basudev Biswal

An accurate estimate of streamflow has been a challenging task due to the complex and interconnected hydrological processes. A simple, robust and calibration-free runoff generation module is desirable in several water resource management applications. But spatial heterogeneity of, but not limited to, topography, soil and land cover makes it challenging to develop desirable runoff generation module. To address this, several runoff generations theories that apply laws of physics at the grid-scale were proposed and tested.  However, these theories have not shown a significant difference in performance on considering catchment as spatially distributed and a single unit(lumped). A typical runoff generation module follows saturation excess or infiltration excess mechanism for runoff generation. The root zone storage capacity (Smax), which controls the dynamics of water storage and partitioning of available water into different fluxes, is an important free-parameter in the saturation excess mechanism. The value of Smax needs to be estimated using observed streamflow time series. However, recent studies demonstrate that the Smax is controlled by local climate and land cover. So, in the current study, we hypothesized that runoff generation is solely governed by climate input and the amount can be estimated without explicit consideration of Smax. We tested the hypothesis using Dynamic Budyko (DB) framework, which simulates the runoff at a daily time scale using ‘instantaneous dryness index (Φ)’. We proposed a universal decay function to predict Φ using rainfall and potential evapotranspiration. The performance of proposed runoff generation module is compared with HBV and GR4J runoff generation modules for 416 MOPEX study basins. The proposed calibration free runoff generation module shows very similar performance to that of calibrated HBV and GR4J with median NSE as 0.68,0.7 and 0.68, respectively. The proposed framework can be coupled with any routing module to estimate the streamflow at basin outlet. The introduction of proposed framework address several long-term challenges in rainfall-runoff modeling. Our results suggest that more efforts should be considered in developing rainfall-runoff modeling frameworks that exploit information available in meteorological input to address streamflow dynamics.

How to cite: Istalkar, P. and Biswal, B.: A universal decay function based meteorologically-driven and calibration-free runoff generation module, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13738, https://doi.org/10.5194/egusphere-egu23-13738, 2023.

A.18
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EGU23-344
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ECS
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Gowri Reghunath and Pradeep P. Mujumdar

Hydrological responses of a catchment evolve due to the complex interactions between various climate inputs and landscape characteristics. Such interactions and the resulting hydrological processes need to be adequately understood to explicitly describe the catchment’s behaviour and process dynamics. Hydrological modelling serves as a powerful tool to strengthen the understanding of such complex process interactions. Conventional hydrological modelling practices focus on calibrating the model outputs with an aim only to match the observed discharge at stream gauge locations. This procedure might not adequately capture the process interactions and the underlying causalities, especially in catchments exhibiting strong non-linear hydrological process relationships. While getting the streamflow right, there is a chance that the other hydrological processes may be wrongly captured, i.e., getting the right calibration results for the wrong reasons. In this study, information-theoretic measures such as Shannon Entropy, Mutual Information and Transfer Entropy are used to understand the process relationships simulated using a physically based hydrological model. The grid-based Variable Infiltration Capacity (VIC) model is employed at a spatial resolution of 0.25 x 0.25-degree over the Cauvery river basin in peninsular India at a daily time scale. Entropy measures are applied to the major hydrological processes such as rainfall, surface runoff, actual evapotranspiration and baseflow, which are simulated using the model, and their relationships are evaluated using non-linear correlation metrics. The study also proposes an entropy-based calibration framework for improving the model efficiency in simulating the catchment water balance. This work highlights the advantages of using information-theoretic measures over conventional methods in evaluating hydrological process relationships, especially in catchments manifesting strong non-linear hydrological behaviour.

How to cite: Reghunath, G. and Mujumdar, P. P.: An information-theoretic approach for evaluating catchment scale process relationships, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-344, https://doi.org/10.5194/egusphere-egu23-344, 2023.

Posters virtual: Fri, 28 Apr, 14:00–15:45 | vHall HS

vHS.1
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EGU23-8529
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ECS
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Nishant Kumar, D. Nagesh Kumar, and Tirthankar Roy

Detection of nonstationarity in hydrological time series is most commonly done through one or two unit root tests, which usually do not account for all the possible reasons that could induce nonstationarity in a time series. To overcome this, we carried out five different unit root tests, i.e., Augmented Dickey-Fuller (ADF) test, Kwiatkowski Phillips Schmidt Shin (KPSS) test, Phillips Perron (PP) test, Variance Ratio (V ratio) test, and Leybourne McCabe (LMC) test, along with the line spectrum analysis in the frequency domain. These tests were conducted on daily rainfall data at forty contiguous grid points around the Malaprabha basin in India using data from the Indian Meteorological Department. The main goal was to find different nonstationary time series and investigate the spatiotemporal patterns of the nonstationarity and use that information to further develop nonstationary time series models through three different modeling approaches, i.e., Seasonal Autoregressive Integrated Moving Average (SARIMA), Exponential Smoothing (ES), and Long Short-Term Memory (LSTM). The performance of these models was evaluated on the basis of Nash Sutcliffe Efficiency (NSE) and R2 value. 

How to cite: Kumar, N., Kumar, D. N., and Roy, T.: Spatiotemporal analysis and modeling of nonstationarity in hydrological time series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8529, https://doi.org/10.5194/egusphere-egu23-8529, 2023.

vHS.2
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EGU23-10748
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ECS
Byeong-Hee Kim and Jonghun Kam

The Geum River basin, located in the west-central part of the Korean Peninsula, is the third largest and minimally human-disturbed river basin in South Korea. Streamflow and available water resources from this basin is critical for water supply for agriculture. Due to the increased population, industrialization, and climate change, changes in streamflow and available water resources for the Geum River are expected. However, there are limitations in analyzing water resource changes in the Geum River Basin with discontinuous and relatively short observational streamflow records.

In this study, we propose to dynamically downscale daily surface and base runoff data from the 10-km ERA5 reanalysis product via VIC-River Routing model. The VIC-River Routing model was ran at the 90-meter spatial resolution with geographical information to reconstruct long-term naturalized streamflow data over 1950-2021. In the VIC-River Routing model, the flow direction, stream order, and slope estimated from the 90-meter digital elevation model (DEM) over Geum River basin as the topographical parameters. This downscaled natural streamflow data will provide an opportunity to investigate hydroclimatic changes of the hydrologic regime of Geum River.

How to cite: Kim, B.-H. and Kam, J.: Dynamical downscaling of ERA5-based high-resolution streamflow dataset over the Geum River basin, South Korea via VIC-river routing model (1950-2021), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10748, https://doi.org/10.5194/egusphere-egu23-10748, 2023.