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.

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.

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.

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.

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.

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.

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 (CH_N) and evaporative fraction from soil (EF_S) and canopy (EF_C)) 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.

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.

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.

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.

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.

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 95^th 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.

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.

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.

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.

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.