Mesoscale Hydrologic Model Research Articles

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

An increase in the spatial extent of European floods over the last 70 years

Abstract. Floods regularly cause substantial damage worldwide. Changing flood characteristics, e.g., due to climate change, pose challenges to flood risk management. The spatial extent of floods is an important indicator of potential impacts, as consequences of widespread floods are particularly difficult to mitigate. The highly uneven station distribution in space and time, however, limits the ability to quantify flood characteristics and, in particular, changes in flood extents over large regions. Here, we use observation-driven routed runoff simulations over the last 70 years in Europe from a state-of-the-art hydrological model (the mesoscale Hydrologic Model – mHM) to identify large spatiotemporally connected flood events. Our identified spatiotemporal flood events compare well against an independent flood impact database. We find that flood extents increase by 11.3 % on average across Europe. This increase occurs over most of Europe, except for parts of eastern and southwestern Europe. Over northern Europe, the increase in flood extent is mainly driven by the overall increase in flood magnitude caused by increasing precipitation and snowmelt. In contrast, the increasing trend in flood extent over central Europe can be attributed to an increase in the spatial extent of heavy precipitation. Overall, our study illustrates the opportunities to combine long-term consistent regional runoff simulations with a spatiotemporal flood detection algorithm to identify large-scale trends in key flood characteristics and their drivers. The detected change in flood extent should be considered in risk assessments as it may challenge flood control and water resource management.

Evaluating the skill of the mesoscale Hydrologic Model (mHM) for discharge simulation in sparsely-gauged basins in Nigeria

Evaluating the skill of the mesoscale Hydrologic Model (mHM) for discharge simulation in sparsely-gauged basins in Nigeria

Toward Improved Simulations of Disruptive Reservoirs in Global Hydrological Modeling

AbstractAccurate simulation of reservoirs has been a challenge for global hydrological models due to highly discontinuous water management and uncertainties in reservoir shape representation. In addition, at a global scale, it is crucial to consider those reservoirs that disrupt the downstream flow regime. We augment the mesoscale Hydrological Model with a newly developed lake module (LM) that incorporates an existing reservoir regulation scheme with non‐consumptive demand predictions from random forest. We also evaluate the sensitivity of reservoir shape on streamflow and evaporation for three shape approximations of varying complexities. We tested the LM across 31 non‐consumptive reservoirs covering an extensive range of hydroclimatic characteristics and demonstrate the applicability by using freely available global reservoir information. Streamflow simulations with reservoirs and model calibration show a median Kling‐Gupta Efficiency improvement of +0.94 (calibration) and +0.77 (validation) when compared against model simulations without reservoirs and default parameter set. We find reservoir evaporation highly sensitive to reservoir shape with half‐pyramid approximation consistently resulting in best fit at reservoirs with surveyed bathymetry. In contrast, the linear approximation (rectangular prism) produced a median bias of +114% relative to half‐pyramid, for estimating evaporation, across all the reservoirs. Streamflow simulations were insensitive to the reservoir shape. Our analysis shows that 30% of the non‐consumptive hydropower reservoirs of the GRanD data set are non‐disruptive and can be excluded without loss to model realism. Further work is necessary for testing the regulation approach in reservoirs with consumptive water usage.

Incorporating Spatio-Temporal Changes of Well Irrigation into a Distributed Hydrologic Model to Improve Groundwater Anomaly Estimations for Basins with Expanding Agricultural Lands

This paper seeks to address the deficiency of utilizing satellite-based GRACE observations and model-based GLDAS water budget components in estimating the changes in the groundwater storage in Konya Endorheic Basin (KEB), a basin experiencing considerable land use land cover (LULC) change, primarily agricultural expansion. Cereal cultivation in the basin has a slight decreasing trend, however, the cultivation of crops with high water consumption, such as maize and sunflower, is increasing substantially. And total agricultural areas are increasing. GRACE-GLDAS approach does not accurately give the long-term groundwater decline in the basin, mainly because the land surface models employed in GLDAS cannot realistically simulate variations in water budget components as they do not consider the changes in LULC and do not possess an elaborated irrigation scheme. Here, we used a fully-distributed mesoscale hydrologic model, mHM, that can handle multiple LULC maps from different years. The model was modified to incorporate the spatio-temporal changes of agricultural fields in KEB and an explicit irrigation scheme since we hypothesized that the groundwater depletion is mainly caused by well irrigation. mHM was calibrated against streamflow observations for the period 2004–2019. The simulations show that the use of mHM with the incorporated features gives groundwater storage changes that are more consistent with the well-based observations than those obtained from the GRACE-GLDAS approach. On the other hand, the mHM simulation with a static LULC map, as in GLDAS models but with a better representation of irrigated fields, provides groundwater anomaly changes that are more consistent with the GRACE-GLDAS results, a further justification of insufficiency of the GLDAS-based approach in estimating groundwater variations for basins with considerable landscape change.

Impacts of extreme river discharge on coastal dynamics and environment: Insights from high-resolution modeling in the German Bight

The intricate relationship between extreme river discharge and its consequential effects on coastal basins’ environment and dynamics remains a subject of profound significance. The impact of extreme river discharge on coastal basin dynamics and environment is a complex phenomenon, particularly relevant in the context of the German Bight. The inflow of freshwater from rivers into the German Bight plays a crucial role in driving the complex thermohaline circulation. Addressing the challenges posed by local hydro-meteorological extremes, compounded by strong wind waves, currents and tides is vital for comprehensive impact assessment. Utilizing the Geesthacht Coupled cOAstal model SysTem (GCOAST) with high-resolution configuration, we investigate the sensitivity of the thermohaline properties to the river forcings. By incorporating river discharge as lateral land forcing in a coupled hydrodynamic and wave model, we conducted five sensitivity experiments. Our findings reveal that the simulated temperatures closely match measurements in all experiments. The salinity, however, is remarkably sensitive to the variation of freshwater from the rivers Elbe and Ems in the German Bight, causing the haline stratification. The statistical evaluation, as demonstrated by the Taylor diagram at the Marnet DB station, underscores the skill of the Mesoscale Hydrologic Model (mHM) in generating the freshwater discharge that drives the thermohaline characteristics of the German Bight, especially during events like the June 2013 flooding. Significantly, the use of climatological runoff proves to be ineffective in simulating stratification during extreme flooding events. In essence, this investigation enhances our understanding of the pivotal role played by high-frequency river freshwater buoyancy. It emerges as a driving force behind salinity fluctuations during extreme floods, providing valuable insights into coastal dynamics within the German Bight.

Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins

AbstractOptimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

Water accounting under climate change in the transboundary Volta River Basin with a spatially calibrated hydrological model

Sustainable water management requires evidence-based information on the current and future states of water resources. This study presents a comprehensive modelling framework that integrates the fully distributed mesoscale Hydrologic Model (mHM) and climate change scenarios with the Water Accounting Plus (WA+) tool to anticipate future water resource challenges and provide mitigation measures in the transboundary Volta River basin (VRB) in West Africa. The mHM model is forced with a large ensemble of climate change projection data from CORDEX-Africa. Outputs from mHM are used as inputs to the WA+ framework to report on water flows and consumption over the historical baseline period 1991–2020 and the near-term future 2021–2050 at the basin scale, and also across spatial domains including four climatic zones, four sub-basins and six riparian countries.The long-term multi-model ensemble mean of the net inflow to the basin is found to be 419 km3/year with an inter-annual variability of 11% and is projected to slightly increase in the near-term future (2021–2050). However, evaporation consumes most of the net inflow, with only 8% remaining as runoff. About 4 km3/year of water is currently used for man-made activities. Only 45% of the available water is beneficially consumed, with the agricultural sector representing 34% of the beneficial water consumption. Water availability is projected to increase in the future due to the increase in rainfall, along with higher inter-model and inter-annual variabilities, thereby highlighting the need for adaptation strategies. These findings and the proposed climate-resilient land and water management strategies can help optimize the water-energy-food-ecosystem nexus and support evidence-based decisions and policy-making for sustainable water management in the VRB.

Rhine flood stories: Spatio‐temporal analysis of historic and projected flood genesis in the Rhine River basin

AbstractThe genesis of floods in large river basins often is complex. Streamflow originating from precipitation and snowmelt and different tributaries can superimpose and cause high water levels, threatening cities and communities along the riverbanks. For better understanding the mechanisms (origin and composition) of flood events in large and complex basins, we capture and share the story behind major historic and projected streamflow peaks in the Rhine River basin. Our analysis is based on hydrological simulations with the mesoscale Hydrological Model forced with both meteorological observations and an ensemble of climate projections. The spatio‐temporal analysis of the flood events includes the assessment and mapping of antecedent liquid precipitation, snow cover changes, generated and routed runoff, areal extents of events, and the above‐average runoff from major sub‐basins up to 10 days before a streamflow peak. We introduce and assess the analytical setup by presenting the flood genesis of the two well‐known Rhine floods that occurred in January 1995 and May 1999. We share our extensive collection of event‐based Rhine River flood genesis, which can be used in‐ and outside the scientific community to explore the complexity and diversity of historic and projected flood genesis in the Rhine basin. An interactive web‐based viewer provides easy access to all major historic and projected streamflow peaks at four locations along the Rhine. The comparison of peak flow genesis depending on different warming levels elucidates the role of changes in snow cover and precipitation characteristics in the (pre‐)Alps for flood hazards along the entire channel of the Rhine. Furthermore, our results suggest a positive correlation between flood magnitudes and areal extents of an event. Further hydro‐climatological research is required to improve the understanding of the climatic impact on the Rhine and beyond.

Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites

Eddy covariance sites are ideally suited for the study of extreme events on ecosystems as they allow the exchange of trace gases and energy fluxes between ecosystems and the lower atmosphere to be directly measured on a continuous basis. However, standardized definitions of hydroclimatic extremes are needed to render studies of extreme events comparable across sites. This requires longer datasets than are available from on-site measurements in order to capture the full range of climatic variability. We present a dataset of drought indices based on precipitation (Standardized Precipitation Index, SPI), atmospheric water balance (Standardized Precipitation Evapotranspiration Index, SPEI), and soil moisture (Standardized Soil Moisture Index, SSMI) for 101 ecosystem sites from the Integrated Carbon Observation System (ICOS) with daily temporal resolution from 1950 to 2021. Additionally, we provide simulated soil moisture and evapotranspiration for each site from the Mesoscale Hydrological Model (mHM). These could be utilised for gap-filling or long-term research, among other applications. We validate our data set with measurements from ICOS and discuss potential research avenues.

Accounting for operational irrigation options in mesoscale hydrological modelling of dryland environments

Agricultural irrigation practices can be a substantial proportion of the water budget in dryland environments. As the pressure on freshwater resources is growing in such environments, sustainable management of these resources is vital. Key issues need to be addressed: How does withdrawal for irrigation purposes alter a river’s flow regime and water yield? How can publicly available irrigation data help to improve the performance of hydrological models? A novel irrigation module was introduced to improve the performance of a mesoscale hydrological model (WASA-SED) and was tested on the hydrological upper meso-scale, as well as on a smaller subbasin in a semi-arid catchment in the Rio São Francisco basin. We could show that including irrigation practices into the modelling process especially helps to improve the quality of the modelled low flows during the dry season, where we achieved an improvement of the RMSE of up to 19%.

Effect of Dynamic PET Scaling with LAI and Aspect on the Spatial Performance of a Distributed Hydrologic Model

The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on the soil properties and crop-vegetation dynamics. Mostly coarse-scale meteorological forcing is used to estimate only the water balance at the catchment outlet. The mesoscale Hydrologic Model (mHM) is one of the rare models that incorporate remote sensing data, i.e., leaf area index (LAI) and aspect, to improve the actual evapotranspiration (AET) simulations and water balance together. The user can select either LAI or aspect to scale PET. However, herein we introduce a new weight parameter, “alphax”, that allows the user to incorporate both LAI and aspect together for potential evapotranspiration (PET) scaling. With the mHM code enhancement, the modeler also has the option of using raw PET with no scaling. In this study, streamflow and AET are simulated using the mHM in The Main Basin (Germany) for the period of 2002–2014. The additional value of PET scaling with LAI and aspect for model performance is investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) AET and LAI products. From 69 mHM parameters, 26 parameters are selected for calibration using the Optimization Software Toolkit (OSTRICH). For calibration and evaluation, the KGE metric is used for water balance, and the SPAEF metric is used for evaluating spatial patterns of AET. Our results show that the AET performance of the mHM is highest when using both LAI and aspect indicating that LAI and aspect contain valuable spatial heterogeneity information from topography and canopy (e.g., forests, grasslands, and croplands) that should be preserved during modeling. This is key for agronomic studies like crop yield estimations and irrigation water use. The additional “alphax” parameter makes the model physically more flexible and robust as the model can decide the weights according to the study domain.

Automatic Regionalization of Model Parameters for Hydrological Models.

Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large-scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters "saturated hydraulic conductivity" and "field capacity," which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step toward automatic TF estimation of model parameters for distributed models.

High-resolution drought simulations and comparison to soil moisture observations in Germany

Abstract. Germany's 2018–2020 consecutive drought events resulted in multiple sectors – including agriculture, forestry, water management, energy production, and transport – being impacted. High-resolution information systems are key to preparedness for such extreme drought events. This study evaluates the new setup of the one-kilometer German drought monitor (GDM), which is based on daily soil moisture (SM) simulations from the mesoscale hydrological model (mHM). The simulated SM is compared against a set of diverse observations from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations, and lysimeters at 40 sites in Germany. Our results show that the agreement of simulated and observed SM dynamics in the upper soil (0–25 cm) are especially high in the vegetative active period (0.84 median correlation R) and lower in winter (0.59 median R). The lower agreement in winter results from methodological uncertainties in both simulations and observations. Moderate but significant improvements between the coarser 4 km resolution setup and the ≈ 1.2 km resolution GDM in the agreement to observed SM dynamics is observed in autumn (+0.07 median R) and winter (+0.12 median R). Both model setups display similar correlations to observations in the dry anomaly spectrum, with higher overall agreement of simulations to observations with a larger spatial footprint. The higher resolution of the second GDM version allows for a more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality, observational soil moisture database.

Testing the mHM-MPR Reliability for Parameter Transferability across Locations in North–Central Nigeria

Hydrologic modeling in Nigeria is plagued by non-existent or paucity of hydro-metrological/morphological records, which has detrimental impacts on sustainable water resource management and agricultural production. Nowadays, freely accessible remotely sensed products are used as inputs in hydrologic modeling, especially in regions with deficient observed records. Therefore, it is appropriate to utilize the fine-resolution spatial coverage offered by these products in a parameter regionalization method that supports sub-grid variability. This study assessed the transferability of optimized model parameters from a gauged to an ungauged basin using the mesoscale Hydrologic Model (mHM)—Multiscale Parameter Regionalization (MPR) technique. The ability of the fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis product (ERA5), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Global Precipitation Climatology Centre (GPCC), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) gridded rainfall products to simulate observed discharge in three basins was first assessed. Thereafter, the CHIRPS rainfall product was used in three multi-basin mHM setups. Optimized model parameters were then transferred to independent basins, and the reproduction of observed discharges was assessed. Kling–Gupta Efficiency (KGE) scores showed improvements when mHM runs were performed using optimized parameters in comparison to using default parameters for discharge simulations. Optimized mHM runs performed reasonably (KGE > 0.4) for all basins and rainfall products. However, only one basin showed a satisfactory KGE value (KGE = 0.54) when optimized parameters were transferred to an ungauged basin. This study underscores the utility of the mHM-MPR tool for parameter transferability during discharge simulation in data-scarce regions.

Effect of data length, spin-up period and spatial model resolution on fully distributed hydrological model calibration in the Moselle basin

ABSTRACT Subjective decisions in hydrologic model calibration can have drastic impacts on our understanding of basin processes and simulated fluxes. Here, we present a multicase calibration approach to determine three pillars of an appropriate hydrological model configuration, i.e. calibration data length, spin-up period, and spatial resolution, using a spatially distributed meso-scale hydrological model (mHM) together with a dynamically dimensioned search (DDS) algorithm and Nash-Sutcliffe efficiency (NSE) for the Moselle basin. The results show that a 10-year calibration data length, 2-year spin-up period, and 4-km model resolution are appropriate for the Moselle basin to reduce the computational burden while simulating streamflow with a decent performance. Although the calibration data length and spatial resolution are related to the extent and quality of the data, and the spin-up period is basin dependent, analysing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in the mHM configuration.

Contrasting changes in hydrological processes of the Volta River basin under global warming

Abstract. A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of 12 general circulation models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) that are dynamically downscaled by five regional climate models (RCMs) from the Coordinated Regional-climate Downscaling Experiment (CORDEX)-Africa is used. In total, 43 RCM–GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The bias-corrected climate projections are then used as input to the mesoscale Hydrologic Model (mHM) for hydrological projections over the 21st century (1991–2100). Results reveal contrasting dynamics in the seasonality of rainfall, depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture, and terrestrial water storage) is only projected under RCP8.5. High- and low-flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. The evolutions of streamflow indicate a future delay in the date of occurrence of low flows up to 11 d under RCP8.5, while high flows could occur 6 d earlier (RCP2.6) or 5 d later (RCP8.5), as compared to the historical period. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water–energy–food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision-makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socioeconomic development.

A comprehensive intercomparison study between a lumped and a fully distributed hydrological model across a set of 50 catchments in the United Kingdom

AbstractThe choice of hydrological model, especially on the catchment scale, is never an easy one. This of course is dependent on the problem under consideration and the end user requirement. The continued development in data acquisition and its availability has taken us in the direction of more complex model development and application. Yet, it remains critical to identify a model that is not only parsimonious in parametric space but also provides good results. With the above motivation the purpose of this study is to compare the performance of a widely used lumped conceptual hydrological model (GR4J) and a well‐established fully distributed mesoscale hydrological model (mHM). Model performance is measured by four goodness of fit indicators (GOFIs) as well as five hydrological signatures that examine the capability of the models applied to reproduce different components of the flow. To undertake this comparison, the two models are applied to a set of fifty catchments that are part of the UK benchmark network (UKBN2), supplemented by additional catchments, not so pristine in nature, to incorporate a wider range of catchments. The results, quantified by the GOFIs and hydrological signatures, show that the lumped model performs as well as the fully distributed model and in multiple cases better than the more complex model. However, model results across the set of fifty catchments, examined here, are highly correlated. Model performance is also examined on seasonal basis and a slight deterioration in the model performance whilst simulating the summer flows is observed. Based on the comprehensive comparison undertaken here, we conclude that the applied lumped model might be the model of choice if the problem at hand is an investigation of rainfall‐runoff at the catchment outlet, without the need to consider the spatiotemporal variation of various hydrological states and fluxes.

MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models

Abstract. Distributed environmental models such as land surface models (LSMs) require model parameters in each spatial modeling unit (e.g., grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimensionality of the parameter space in these models is to use regularization techniques. One such highly efficient technique is the multiscale parameter regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of NetCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model (mHM; https://www.ufz.de/mhm, last access: 16 January 2022). By using this tool for the generation of continental-scale soil hydraulic parameters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 % in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.

Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.