We are in a state of simultaneous exuberance and starvation of Earth system data. Model ensembles of increasing complexity provide petabytes of output, while remote sensing products offer terabytes of new data every day. On the other hand, we lack data on processes that are more challenging to observe, like groundwater recharge, or have data heavily impacted by un-quantified anthropogenic change. These problems leave us with highly imbalanced datasets.

Our ability to produce and collect mountains of data contrasts with our progress in improving scientific process understanding. How can we harness simulated and observed data alike to enhance our knowledge and test scientific hypotheses about process relationships given poorly known uncertainties? Our contribution discusses methods to approach this problem while being agnostic to the data source (model or observation). We introduce a new strategy that allows us to interrogate given datasets to identify correlational and possibly causal relationships between the variables included. We test the method on an ensemble of complex global hydrological models and observations to demonstrate its usefulness and limitations, i.e., from the ISIMIP experiments. We show that our approach can provide powerful insights into dominant process controls while scaling with large amounts of data.

How to cite: Reinecke, R., Pianosi, F., and Wagener, T.: Towards better identification of dominant controls in Earth system data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11095, https://doi.org/10.5194/egusphere-egu23-11095, 2023.

Global water models are widely used for policy-making and in scientific studies, but substantial inter-model differences highlight the need for additional evaluation. Here we evaluate global water models by assessing so-called functional relationships between system forcing and response variables. The more widely used comparisons between observed and simulated fluxes provide insight into model behavior for the representative area of an observation, and can therefore potentially improve the model for that area. Functional relationships, by contrast, aim to capture how system forcing and response variables co-vary across large scales, and thus offer the potential for model improvement over large areas. Using 30-year annual averages from 8 global water models, we quantify such functional relationships by calculating correlations between key forcing variables (precipitation, net radiation) and water fluxes (actual evapotranspiration, groundwater recharge, total runoff). We find strong disagreement for groundwater recharge, some disagreement for total runoff, and the best agreement for evapotranspiration. Observation- and theory-derived functional relationships show varying agreements with models, indicating where model representations and our process understanding are particularly uncertain. Overall, our results suggest that model improvement is most important for the representation of energy balance processes, recharge processes, and generally for model behavior in dry and cold regions. We argue that advancing our ability to simulate global hydrology requires a better perceptual understanding of the global water cycle. To evaluate if our models match that understanding, we should explore alternative evaluation strategies, such as the use of functional relationships.

How to cite: Gnann, S., Reinecke, R., Stein, L., Wada, Y., Thiery, W., Müller Schmid, H., Satoh, Y., Pokhrel, Y., Ostberg, S., Koutroulis, A., Hanasaki, N., Grillakis, M., Gosling, S. N., Burek, P., Bierkens, M. F. P., and Wagener, T.: Functional relationships reveal differences in the water cycle representation of global water models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2001, https://doi.org/10.5194/egusphere-egu23-2001, 2023.

In 1982, Jim Dooge[1] stated that ``the parameterization of hydrological processes to the grid scale of GCMs is a problem that has not been tacked, let alone solved''. Almost a decade later, Eric Wood (1990) reported that we have not performed the right experiments to address Dooge's Problem nor to solve the scale problem in hydrology. Decades later, reviews of the state of Land Surface Models (LSM) revealed that the source codes of LSMs tend to have up to hundreds of ``hidden'' parameters, many of them exhibiting large sensitivity to key fluxes and state variables like evapotranspiration, streamflow and soil moisture [3, 4]. Most of these parameters have a physical meaning, but often they are calibrated or provided to the models as inputs from look-up tables. These practices have undesirable implications such as overparameterization, lack of transferability across time, space or resolution, artifact generation, and biased predictions [5].

LSMs are currently used for high or hyper-resolution hydrological simulations that are the core of global monitoring or seasonal forecasting systems or providing boundary conditions (state variables) and land surface fluxes to GCMs. In a few years, they will become one of the main modules of existing efforts towards Digital Twins of the Earth's water cycle. Consequently, it is time to find better solutions for the old Dooge's Problem.

The parameterization of a LSM is an ill-posed problem leading to equifinal solutions [6]. Brute force calibration using only streamflow leads to non-transferable solutions [7]. An alternative approach is to use regularisation techniques (e.g., transfer functions) to reduce the degrees of freedom together with scaling operators to estimate effective parameters at the target resolution of the LSM. Multiscale Paramater Regionalization (MPR) [8] is one possible solution following this approach. Recent research have determined that the equifinality of transfer-functions and the corresponding parameters is very large [9].

In this study, we will report new attempts to find constraints in the functional space of the transfer functions and parameters that lead to physically plausible parameter fields for the mHM and HTESSEL models, both of which are used operationally across Europe and are part of the ULYSSES project (C3S) [10]. We will start by creating a catalog of existing pedo-transfer functions (PTF) for typical physical soil parameters such as soil porosity, hydrological conductivity, field capacity among others. Using the MPR stand-alone, model agnostic tool [11] we will perform a simplified sensitivity analysis to determine limiting ranges for the parameters of existing PTFs. The Soil Grids product [12] will be used as a reference to benchmark for the different PTFs.

References

[1] Dooge, J. in Eagleson, P., Cambridge University Press, new York, N.Y., 243–288, 1982
[2] Wood, E. (Ed.): Land Surface, atmosphere interactions for climate modelling: observations. models, and analysis, Kluwer, 1990.
[3] Mendoza et al.  2014.  https://doi.org/10.1002/2014WR015820
[4] Cuntz et al.  2016. https://doi.org/10.1002/2016JD025097
[5] Samaniego et al. 2017.  https://doi.org/10.5194/hess-21-4323-2017
[6] Beven, K., 1993. https://doi.org/10.1016/0309-1708(93)90028-E
[7] Rakovec et al. 2016. https://doi.org/10.1175/JHM-D-15-0054.1
[8] Samaniego et al. WRR 2010a. doi.org/10.1029/2008WR007327
[9] Feigl et al. WRR 2021. https://doi.org/10.1029/2020WR027385
[10] www.ufz.de/ulysses
[11] Schweppe et al. GMD 2021. https://doi.org/10.5194/gmd-2021-103
[12] soilgrids.org

How to cite: Samaniego, L., Mai, J., Schweppe, R., and Thober, S.: On advances and opportunities in estimating effective parameters for  land surface models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7147, https://doi.org/10.5194/egusphere-egu23-7147, 2023.

High-resolution continental scale hydrological modelling often requires distributed computing to solve the model equations. Developing and maintaining these models is an enormous challenge as current approaches require knowledge about parallel and distributed computing. A solution to the problem followed here is the development of a modelling framework that deals with parallelization of model equations under the hood. Using such a framework allows hydrologists who are not familiar with low level software development and high-performance computing to develop models.

Our modelling framework, called LUE (de Jong et al. 2022, 2021), allows models that are large in terms of data set size and the number of calculations used, to use all hardware available to them efficiently. LUE models can be written in Python or C++, and can be executed unchanged on laptops and computer clusters.

In the implementation of LUE model operations we use the asynchronous many-tasks (AMT) approach, as implemented in the HPX C++ library. This makes it possible for model developers to express their models using simple algebraic expressions, while all details related to scheduling computations on parallel and distributed hardware are hidden from view. Executing LUE models results in a relative large collection of tasks that are ready to be scheduled for execution on the available hardware. This, in turn, results in models that are finished sooner and that can use additional hardware efficiently, automatically.

We are currently busy porting the PyCatch modelling suite (Lana-Renault and Karssenberg 2013), which is an integrated set of process-based hydrological and soil-vegetation models, to LUE. PyCatch is implemented in terms of generic modelling operations inspired by map algebra (local, focal, zonal, global operations) and flow routing operations like flow accumulation and the kinematic wave.

In our presentation we will further explain the LUE modelling framework, including the operations that are specifically targeted at hydrological modelling, and show results of applying the PyCatch model to Africa at 3 arc-second resolution (~90 m at the equator) using the MERIT Hydro high resolution raster data set.

References
de Jong, K., D. Panja, D. Karssenberg, and M. van Kreveld. 2022. “Scalability and Composability of Flow Accumulation Algorithms Based on Asynchronous
Many-Tasks.” Computers & Geosciences. https://doi.org/10.1016/j.cageo.2022.105083.
de Jong, K., D. Panja, M. van Kreveld, and D. Karssenberg. 2021. “An Environmental Modelling Framework Based on Asynchronous Many-Tasks: Scalability and Usability.” Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2021.104998.
Lana-Renault, N., and D. Karssenberg. 2013. “PyCatch: Component Based Hydrological Catchment Modelling.” Cuadernos de Investigación Geográfa. https://doi.org/10.18172/cig.1993.

How to cite: de Jong, K. and Karssenberg, D.: The LUE software framework: develop scalable global hydrological models without having to think about high-performance computing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7717, https://doi.org/10.5194/egusphere-egu23-7717, 2023.

Large-domain hydrological modelling is vital to understand and predict water resources under a changing climate. Here we summarize our efforts to develop a model configuration workflow for the Hydrological Predictions for the Environment (HYPE) model as a proof-of-concept of a “bottom-up” approach to community large-scale hydrological modelling. The initiative of Community Workflows to Advance Reproducibility in Hydrologic Modeling (CWARHM, Knoben et al. 2022) provides a blueprint of a hydrological modelling workflow, separating the model-agnostic and model-specific pre-processing tasks. We extend the CWARHM blueprint to establish an open-source and automated HYPE workflow by adding processing codes to generate geospatial fabric, climate forcing, and parametrization.

Our primary contribution is to generalize and automate the HYPE workflow to improve the reproducibility of hydrologic experiments. In this research, numerous global geographic, physiographic, and climatic datasets, covering various spatiotemporal scales are used to develop a geospatial fabric and climate forcing for the HYPE model, using the Bow River watershed in Alberta, Canada as a test case. The geographic and physiographic data are obtained through the “gistool” (https://github.com/kasra-keshavarz/gistool), while climate forcing is obtained using the “datatool” (https://github.com/kasra-keshavarz/datatool). Independent of the data source, these tools provide physiographic attributes and meteorological time series as catchment averaged quantities, enabling semi-distributed hydrological modelling with HYPE. The preliminary analysis shows that the HYPE workflow has successfully separated the model-agnostic and model-specific parts of the model workflow. It substantially reduces manual work in preparing model geospatial fabric and input datasets, saving more time for hydrological analysis. This workflow will support developing probabilistic streamflow using different input datasets and will be upgraded to create a HYPE model instantiation for the entire North American domain.

Reference: Knoben, W. J. M., Clark, M. P., Bales, J., Bennett, A., Gharari, S., Marsh, C. B., et al. (2022). Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating model-agnostic and model-specific configuration steps in applications of large-domain hydrologic models. Water Resources Research, 58, e2021WR031753. https://doi. org/10.1029/2021WR031753

How to cite: Wijayarathne, D., Keshavarz, K., Stadnyk, T., Pietroniro, A., Clark, M., and Knoben, W.: HYPE model workflow – a “bottom-up” approach to community large-domain hydrological modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9545, https://doi.org/10.5194/egusphere-egu23-9545, 2023.

Performing hydrological simulations at ‘hyper-resolution’, that is at or below a spatial resolution of 1 km, was and still is a major challenge in hydrological sciences. However, as computational power and the number of readily available and open datasets at useful spatial resolutions increase, several hyper-resolution modelling efforts have been taken. Here, we present a first continental-scale application of the global hydrological model PCR-GLOBWB over Europe at 1 km spatial resolution. To isolate the effect of resolution refinement, results are compared with runs at the thus far ‘default’ resolutions of 10 km and 50 km, respectively. A range of modelled states and fluxes was evaluated against observations: discharge, evaporation, soil moisture, and terrestrial water storage. Evaluation metrics indicate increased accuracy with finer spatial resolutions for simulated discharge. For the other variables, results are mixed possibly due to the coarse resolution of the validation products: while the used validation products have the advantage of long observational records which helps establishing a robust baseline understanding, their spatial resolution may be too coarse to fully assess the accuracy of models at hyper-resolution. At that scale, more recent satellite products can be of more use but at the cost of only short observation record. We thus additionally validated 1 km model output against additional validation products at finer spatial resolution. Furthermore, 1 km output of PCR-GLOBWB is benchmarked against 1 km output over the UK indicating that additional emphasis needs to be put on model parameterization. Despite these outstanding challenges, our findings shows that large-scale hyper-resolution modelling is now feasible and that further pursuing these efforts can eventually lead to more locally-relevant hydrological information and process understanding.

How to cite: van Beek, R., Hoch, J., Sutanudjaja, E., Wanders, N., and Bierkens, M.: Hyper-resolution hydrological modelling over Europe: results and emerging challenges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5190, https://doi.org/10.5194/egusphere-egu23-5190, 2023.

Glaciers are present in many large river basins and influence runoff variations considerably in many mountain areas. Due to climate change, annual runoff volumes originating from glaciers and the glacial melt seasonality are undergoing considerable changes. These changes can affect water availability in basins with glacier cover. Nevertheless, glaciers have been largely neglected in large-scale hydrological models so far, which is a crucial limitation in global climate impact studies on water resources.

To include glacier runoff in large-scale hydrological studies, we have coupled two open-source and well-documented models: a global glacier model (OGGM, Maussion et al., 2019) and a large-scale hydrological model (CWatM, Burek et al., 2020). The coupling offers an explicit inclusion of glacier runoff in large-scale hydrological modeling, and thanks to the dynamic modelling of glaciers, changes in glacier area and volume are explictly considered.

The coupling has been evaluated for selected large river basins, namely the Rhine, Rhone, Fraser and Gloma basins on 5arcmin resolution (~9km) and globally on 30arcmin (~50km) resolution, and differences in simulation results with and without coupling have been assessed. Simulations were run for the recent past (1990–2019) and for two scenarios (SSP1-2.6, SSP5-8.5) for the 21^st century.

Including glaciers explicitly in climate impact modelling of large river basins simulates larger future changes in summer discharge. Therefore, it is especially important to include glaciers in studies focusing on changes in summer water availability and its impacts. For the recent past, the contribution of glaciers to discharge at downstream stations of the selected river basins ranges from 7 to 37% for one month and between 2 and 8% annually. For the period 2070–2099, the projected contribution of glaciers drastically decreases to 2 to 13% for one month and 0.2 to 1.3% annually even under the low-emission scenario.

Issues to tackle during the model coupling include precipitation data correction, different spatial and temporal resolutions in the models,  different snow process representations, and the model calibration.

Here, we give an overview of the benefits, challenges and limitations of coupling a global glacier model with a global hydrological model and focus on future discharge projections in large river basins.

References

Burek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., Zhao, F., and Wada, Y.: Development of the Community Water Model (CWatM v1.04) – a high-resolution hydrological model for global and regional assessment of integrated water resources management, Geosci. Model Dev., 13, 3267–3298, https://doi.org/10.5194/gmd-13-3267-2020, 2020.

Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K. et al..: The Open Global Glacier Model (OGGM) v1.1, Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019, 2019.

How to cite: Hanus, S., Schuster, L., Burek, P., Maussion, F., Seibert, J., Marzeion, B., Wada, Y., and Viviroli, D.: Coupling a global glacier model with a global hydrological model - benefits, challenges and limitations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7421, https://doi.org/10.5194/egusphere-egu23-7421, 2023.

Climate change will impact every component of the climate system and water cycle. It does not spare groundwater which account for approximately one third of the human fresh water withdrawals. The combined effect of climate change and groundwater pumping could lead to water scarcity and food insecurity in some regions. Therefore, it is essential to study the groundwater response to climate change to improve the development of adaptation and mitigation plans in water management.

Here, we analyze the response of groundwater recharge to climate change using an ensemble of simulations runs with 22 fully coupled ocean-atmosphere-land models participating to the CMIP6 exercise. They are run from 1850 to 2100 and follow four of the latest IPCC scenarios of greenhouse gas future evolution. This analysis is supplemented with the assessment of the climate-driven response of groundwater level given by the CNRM global climate models (which are part of the CMIP6 exercise). These models represent the hydrogeological processes involving groundwater, including the two-way water exchanges with rivers and the unsaturated soil, the lateral groundwater fluxes, and the interactions with the atmosphere. Results show that on global average, groundwater recharge is expected to increase with climate change. The changes in groundwater recharge follow those of precipitation and, to a lesser extent, evapotranspiration and thus follow the same regional patterns.

As these CMIP6 models do not represent human groundwater withdrawals, the projected changes in recharge are somewhat optimistic and could be out of step in regions with strong groundwater pumping. To address this limitation, results are put in perspective with projections of water withdrawals following the CMIP6 experiments. This analysis shows the combined effects of climate change and groundwater pumping on groundwater and help to understand the evolution of the future large scale water resource.

How to cite: Costantini, M., Colin, J., and Decharme, B.: CMIP6 multi-model projections of the groundwater response to climate change during the 21st century, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14953, https://doi.org/10.5194/egusphere-egu23-14953, 2023.

Despite the recent advances, the identification of influential hydrologic processes and parameters of the process-based hydrologic model is still challenging. Part of the reason is the uncertain and interacting hydrologic process and the high dimensional parameter space. The motivation for this work is to effectively select an appropriate set of hydrologic processes and parameters for each basin on the globe, which is not necessarily the same everywhere. Here we evaluate the applications of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model to a large number of representative areas on the globe. Our objective is to identify the dominant hydrologic processes and sensitive model parameters for each representative area. First, sensitivity indices of the SUMMA parameters are computed using the VISCOUS global sensitivity analysis method. VISCOUS is the abbreviation of Variance-based Sensitivity Analysis using Copulas. Second, the sensitivity values are summarized per hydrologic process (e.g., snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, baseflow, and runoff) and per simulation statistic (e.g., mean, coefficient of variance, and autoregressive lag 1). The summarized sensitivity indices enable modelers to identify the most dominant hydrologic processes in each representative area. The results of this study will provide a foundation to estimate parameters in large-domain applications of process-based hydrologic models.

How to cite: Liu, H., Clark, M., Tang, G., Knoben, W., Gharari, S., Freer, J., Arnal, L., and Casson, D.: Sensitivity Analysis of the SUMMA Model on the Global Scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3985, https://doi.org/10.5194/egusphere-egu23-3985, 2023.

River routing is a critical component of land surface models (LSMs), as it plays a significant role in the closure of the water balance at global scale, linking the estimation of river discharge to the one of sea level. The estimation of river discharge within LSMs also enables researchers to use widely available discharge observations to evaluate their models, and to study the human impact on discharge. However, river discharge calculation is often simplified in continental to global scale applications, as topography varies at a much smaller scale than the one of LSM grid-scales, which requires sub-grid parameterizations.

In this study, we present a revision of the current routing module of the ORCHIDEE LSM (Nguyen-Quang et al., 2018) that aims to improve the accuracy of discharge estimation in an agile way. We propose a simple, parallelized, conservative routing module that is based on principal flow directions at the digital elevation model (DEM) scale and independent of the LSM grid cell, enabling the use of routing maps at various resolutions. The influence of topography is factored in by the topographic index, i.e. a product of slope and pixel length in each routing cell. The routing can be solved directly on DEM native grid using conservative interpolations of run-off and drainage computed from LSM. To reduce the computational cost, we developed an upscaling method by aggregating DEM pixels into irregular hydrological transfer units (HTUs), which respect the basin hierarchy by construction, therefore making the computation of effective topographic index straightforward, and which is constrained by validity of the numerical stability criteria. This upscaling method drastically reduces the computational cost, by a factor depending on the targeted resolution, without compromising the discharge estimation.

We test this new routing module via offline simulations, to evaluate the discharge within 10 of the world's largest river basins, at the outlet and in upstream sub-catchments. To this end we use a 2km version of the MERIT global DEM to derive information on flow direction, slope and pixel length; and the GRDC global river discharge observation dataset to evaluate the simulated river discharge. First, using the routing at the DEM pixel scale, we will tune the lag time of reservoirs to improve the discharge estimation. Then, we will test the stability of performances when upscaling the routing over a range of HTU lengths. This work is pivotal for the use of ORCHIDEE and its routing scale at various spatial scales, either off-line or coupled to the IPSL climate model, especially with its scalable atmospheric dynamical core, which is based on a quasi-uniform icosahedral-hexagonal mesh, and can be used for both global or limited-area simulations (Dubos et al., 2015).

References:

Dubos, T., Dubey, S., Tort, M., Mittal, R., Meurdesoif, Y., and Hourdin, F.: DYNAMICO-1.0, an icosahedral hydrostatic dynamical core designed for consistency and versatility, Geosci. Model Dev., 8, 3131–3150, https://doi.org/10.5194/gmd-8-3131-2015, 2015.

Nguyen-Quang, T., Polcher, J., Ducharne, A., Arsouze, T., Zhou, X., Schneider, A., and Fita, L.: ORCHIDEE-ROUTING: revising the river routing scheme using a high-resolution hydrological database, Geosci. Model Dev., 11(12), 4965-4985, https://doi.org/10.5194/gmd-11-4965-2018, 2018.

How to cite: Kilic, D., Meurdesoif, Y., Ducharne, A., Polcher, J., and Ghattas, J.: Evaluation of a high-resolution high-performance routing scheme for regional to global scale applications within Earth System Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14636, https://doi.org/10.5194/egusphere-egu23-14636, 2023.

Observing basin water storage response due to hydroclimatic fluxes and human water use provides valuable insight into how a basin stores and discharges water.  Quantifying basin storage change attributed to climate and use is critical for water management yet remains a challenge globally.  Observations from the Gravity Recovery and Climate Experiment (GRACE) mission are combined with hydroclimate fluxes of precipitation and evapotranspiration to document the sensitivity of available water storage for global basins.  Our results detect substantial global water storage sensitivity to changes in hydroclimatic fluxes.  Comparison with Budyko-derived metrics substantiate our findings, demonstrating that basin water storage resilience to short-term water deficits is linked to basin partitioning predictability, and uniform seasonality of hydroclimatic fluxes.  Our study demonstrates how small shifts in hydroclimate flux may affect available water storage potentially impacting billions globally.

How to cite: Thomas, B. and Nanteza, J.: Global assessment of the sensitivity of water storage to hydroclimatic variations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3109, https://doi.org/10.5194/egusphere-egu23-3109, 2023.

Hydroclimate extremes have a large societal impact if not appropriately monitored and if site-specific adaptation measures are not developed and modified. The impact of hydroclimate extremes such as floods is projected to increase in the future, demanding site-specific adaptation measures to reduce the impacts. Assessing historical and future changes in local scale hydroclimate extremes and estimating trajectories of change requires higher resolution climate projections than those output from Global Climate Models (GCMs). To improve the coarse resolution and bias of climate data from GCMs, we used a statistical downscaling model. The statistical downscaling model, Bias Correction/Constructed Analogues with Quantile mapping reordering (BCCAQ), provides high-resolution climate data suitable for hydrological extremes. Here, we downscaled seven variables (air pressure, precipitation, air temperature, relative humidity, and maximum and minimum temperature) from 18 CMIP6 GCMs under three SSPs (Shared Socioeconomic Pathways). The downscaled global high-resolution climate data is available at The Centre for Environmental Data Analysis (CEDA, https://catalogue.ceda.ac.uk/uuid/c107618f1db34801bb88a1e927b82317).

We used the global hydrological model, WBMsed, with the downscaled climate data and future population projections and dam scenarios to assess changes in hydrology. The downscaling model is calibrated at 0.25° resolution during the historical period (1981-2014) using a high-resolution climate dataset (e.g., MSWEP for precipitation) and showed a strong correlation (>0.85) for monthly climatology of the seven downscaled variables. The climate data used to calibrate the downscaling models, particularly for precipitation, is selected after a comprehensive evaluation of multiple precipitation datasets for simulating river discharge globally. Based on data from ~2400 stations, MSWEP was found to outperform other precipitation datasets in most of the stations.  The results, based on the downscaled data and WBMsed model, shows a mixed change in river discharge in the future; an increase in the Middle East, Africa, Central and South-Eastern USA and a decrease in parts of Europe, South-western USA, and Northern South America) in the 2050s and 2080s. The global average annual river discharge will be higher than the reference period (1981-2014) in the periods 2015-2040, 2041-2070 and 2071-2100 by more than 6%, 9%, and 13%, respectively. Sediment flux, on the other hand, shows a high spatial variability dominated by a decrease in larger rivers and an increase in smaller rivers. Overall, this high-resolution global scale impact assessment study will help identify potential and risk areas for different sectors and allow the development of climate change adaptation measures at a local scale to minimize the impacts of future changes.

How to cite: Gebrechorkos, S. H., Leyland, J., and Darby, S.: Global high-resolution climate change projection and its impacts on global hydrology and hydrological extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2455, https://doi.org/10.5194/egusphere-egu23-2455, 2023.

A solid understanding of the global water cycle and how land surface processes respond to both changes in climate and pressure due to water use is essential for society. Although Land Surface Models (LSM) and Global Hydrological Models (GHM) are able to simulate the spatiotemporal variability of the water balance relatively reliably, intercomparison studies have indicated considerable differences between the models. Each LSM and GHM present a unique set of equations, parameters and configurations that contribute to the spread of simulated hydrological responses to meteorological forcings. In order to improve our understanding of modeling uncertainties, we propose a variable importance assessment for 5 LSM/GHM (JULES, HTESSEL, PCR-GLOBWB, SURFEX and ORCHIDEE) from the EartH2Observe (E2O) project. The output of the models and the meteorological forcings were collected from the Water Resources Reanalysis Tier 2 of the E2O project, which consists of a global dataset with spatial resolution of 0.25^ox0.25^o. We used soil texture and land cover datasets that most resemble the inputs used by each LSM/GHM during the E2O project. The models’ outputs were used to estimate 6 hydrological indices for every land cell: Evaporation-Precipitation ratio; Runoff-Precipitation ratio; Surface Runoff-Total Runoff ratio; median Soil Moisture variation caused by a Rainfall event; median Surface Runoff caused by a Rainfall event; and Soil Moisture temporal autocorrelation. Then, we evaluate the input features (meteorological, land cover, and soil texture) importance to the hydrological indices of each model using machine learning. With the analysis we aim to  examine a) How much the models differ and why? b) To what extent are the output differences related to the input features or/and to the models formulation? and c) How significant is each feature to the respective hydrological index?

How to cite: Brêda, J. P., Melsen, L., van der Ploeg, M., Athanasiadis, I., Siqueira, V., Verhoef, A., Zeng, Y., and van Dijk, A.: Input features importance to hydrological indices simulated by Land Surface and Global Hydrological Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2649, https://doi.org/10.5194/egusphere-egu23-2649, 2023.

Air can hold more moisture as temperature increases, leading to more extreme rainfall events. Yet, this does not necessarily result in larger river floods. Here we use model projections to explore differences in the response of the atmosphere and catchments to an increase in global mean temperature. For both extreme rainfall and flow, we compute relative changes per °C, often called change factors (CFs) or scaling factors. Unlike some other studies, the multidecadal temperature mean is used instead of the temperature during the extreme event. This allows us to use CFs to produce maps of future changes for any emission scenario and future period.

We relied on rainfall projected by 4 high-resolution GCMs from CMIP6 HighResMIP post-processed using 3 levels spatial smoothing (low to high smoothing). We also used hydrological simulations from 3 global hydrological models (GHMs) forced by 4 GCMs and produced as part of the ISIMIP2b project. We computed changes in the median of annual maxima based on periods of 31 years on 0.25° (HighResMIP) and 0.5° (ISIMIP2b) global grids. Working with two 12-member ensembles enables us to assess uncertainties in future changes.

We found that whilst extreme rainfall is projected to increase over 87% of the land area (ensemble median), only 69% of the land area is projected to show an increase in extreme flow magnitude. Importantly, while there is high model agreement (at least ¾ of the models agree) that extreme rainfall will increase over 76% of the land area, high agreement that future flows will increase is only found over 40% of the land area. We show that these discrepancies are caused by changes in soil moisture and snow pack projected by the GHMs, highlighting the importance of river flood drivers other than extreme rainfall.

How to cite: Addor, N., Lord, N., Uhe, P., Quinn, N., Wing, O., and Sampson, C.: Contrasting changes in extreme rainfall and river flow as global mean temperature increases, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14715, https://doi.org/10.5194/egusphere-egu23-14715, 2023.

Groundwater is the world's essential water resource, and groundwater flow is strongly linked with surface water. In some cases, groundwater can even affect precipitation through evapotranspiration. In the current Earth System Model (ESM), a fixed and constant one-dimensional vertical grid is used in the unsaturated zone. The thickness of the unsaturated zone is expected to differ in a given region under a future climate. Therefore, the representation of groundwater flow in the ESM may be insufficient. In particular, on steep slopes such as mountainous areas, it is considered that there are limitations to the runoff process. In this study, we developed a three-dimensional variably saturated flow model that was parameterized for the runoff process and validated in the mountainous area.

In mountainous areas where topographic terrain is severe, calculations at hundreds to tens of meters are necessary to better performance for groundwater flow. However, it is unrealistic to calculate the groundwater dynamics at that scale over global lands. Therefore, it is necessary to calculate on a coarse grid with parameterization. We developed the runoff parameterization using a 1-minute grid with topographic information within the grid. The parameterization validation was performed for the whole of Japan, which has a large elevation distribution. A part of the results of the land surface model, MATSIRO, was passed to the developed model to calculate runoff. The calculated runoff was input into the river routine model, CaMa-Flood, and compared to observed river discharge. As a result, the reproducibility of the river discharge was improved compared to the case without the parameterization and the MATSIRO’s results.

How to cite: Miura, Y. and Yoshimura, K.: Runoff parameterization for global scale hydrology based on a variably saturated flow model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3727, https://doi.org/10.5194/egusphere-egu23-3727, 2023.

HydroSHEDS is a well-established database containing global hydrographic information. Although being widely used, the SRTM-based version 1 of HydroSHEDS has important limitations, in particular in areas above 60° N latitude. The coverage of this region is of low quality because no underpinning SRTM elevation data were available. As most hydrological models require topographic information and hydrographic data in terms of stream networks or catchment boundaries, the increased availability of accurate remote sensing data promotes the development of a second and refined version of the HydroSHEDS database. For this reason, HydroSHEDS v2 is currently created in collaboration between the German Aerospace Center (DLR), McGill University, Confluvio Consulting and the World Wildlife Fund. Foundation of HydroSHEDS v2 is the digital elevation model (DEM) of the TanDEM-X mission (TerraSAR-X add-on for Digital Elevation Measurement). This 0.4 arc-second resolution DEM with global coverage of land surfaces was created in partnership between DLR and Airbus Defence and Space. Enhanced pre-processing techniques are applied to preserve details of the high-resolution DEM in its hydrologically conditioned version. These pre-processing steps include an infill of invalid and unreliable elevation values, an automatic coastline delineation refined with manual corrections, an AI-based water detection algorithm, and a modification of elevation data in urban and vegetated areas for improved evaluation of the flow of water. Additionally, experiences and preliminary results from processing the water body mask at global scale are outlined. The hydrologically pre-conditioned DEM and the water body mask derived from the TanDEM-X dataset are in the subsequent steps further processed with refined hydrological optimization and correction algorithms to derive flow direction and flow accumulation maps. These gridded datasets are the core products of HydroSHEDS v2 and will be complemented with secondary information on river networks, lake shorelines, catchment boundaries, and their hydro-environmental attributes in vector format. The main release of HydroSHEDS v2 is scheduled for 2023 under a free license.

How to cite: Warmedinger, L., Huber, M., Walper, C., Anand, M., Lehner, B., Thieme, M., and Roth, A.: Improved hydrologic conditioning of the TanDEM-X dataset for HydroSHEDS v2, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15182, https://doi.org/10.5194/egusphere-egu23-15182, 2023.

High streamflow in rivers can lead to flooding, which may have severe impacts on economy, society and ecosystems. Therefore it is imperative to understand their underlying physical mechanisms. Previous research has illustrated the relevance of several hydrological drivers, such as precipitation, snowmelt and soil moisture. However, the relative importance of these drivers compared with each other is unclear. Moreover, the role of vegetation-related drivers is not well studied. In this study, we focus on high river flows and consider a comprehensive set of potential drivers and analyze their relative importance. This is done with streamflow observations from over 250 near-natural catchments located across Europe during 1984–2007, which are matched with driver data from various observation-based sources. Not surprisingly, we find that precipitation is the most relevant driver of high river flows in most catchments. In addition, and more interestingly, we show that next to precipitation a diversity of other drivers is relevant for high flows, including shallow soil moisture, deep soil moisture, snowmelt, evapotranspiration and leaf area index. These non-precipitation drivers tend to be even more relevant for more extreme high flows. The relative importance of most considered drivers is similar across daily, weekly and monthly time scales. The spatial patterns of the relevance of precipitation, snowmelt and soil moisture for supporting high river flows are controlled by vegetation types and terrain characteristics, while climate and basin area are less important. By analyzing a comprehensive selection of drivers of high river flow in a powerful framework which accounts for co-linearities between drivers, this study advances the understanding of flood generation processes and informs respective model development.

How to cite: Lam'barki, M., Li, W., Oh, S., Zhan, C., and Orth, R.: Beyond precipitation: diversity of drivers of high river flows in European near-natural catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1421, https://doi.org/10.5194/egusphere-egu23-1421, 2023.

Global hydrological models simulate water storages and fluxes of the water cycle, which is important for e.g. drought and flood predictions. However, model simulations are underlying uncertainties due to the inputs (e.g. climate forcing data), parameters, and model assumptions, resulting in disagreements with observations. To reduce these uncertainties, models are often calibrated against in-situ streamflow observations or compared against total water storage anomalies (TWSA) derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. In recent years, TWSA data are integrated into some models via data assimilation.

In this study, we jointly assimilated TWSA and streamflow observations into the WaterGAP Global Hydrology Model (WGHM) applying an Ensemble Kalman Filter. Simultaneously, model parameters are calibrated via state vector augmentation. Our simultaneous calibration and assimilation (CDA) approach was tested within the Mississippi River Basin from 2003 to 2016.

First, we evaluated how the spatial resolution and study set up impact our CDA approach. Our results suggest that applying the CDA approach sequentially to all subbasins works better than applying the approach once to the entire Mississippi River Basin. Second, we compared the results of our CDA approach against uncalibrated model simulations as well as the results of the WGHM standard calibration. The CDA approach led to higher Nash-Sutcliffe efficiency (NSE) and lower root mean square error (RMSE) values (and thus a better agreement with the observations) regarding TWSA and streamflow than the uncalibrated WGHM simulations, which is in line with our expectations. In addition, it also resulted in higher NSE and lower RMSE values than the WGHM standard calibration in most subbasins. This was expected for the metrics regarding TWSA. Our expectations regarding the streamflow results were more complex: On one hand, our findings were surprising since the WGHM standard calibration approach is based on streamflow observations only and takes significantly more streamflow stations into account than the CDA approach. On the other hand, the results reflected that less parameters are calibrated and only the long-term averages of the streamflow observations are considered in the WGHM standard calibration approach.

How to cite: Schulze, K., Gerdener, H., Engels, O., Kusche, J., and Döll, P.: Calibration of a global hydrological model while simultaneously assimilating satellite-derived total water storage anomalies and in-situ streamflow observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11733, https://doi.org/10.5194/egusphere-egu23-11733, 2023.

Africa depends on its water resources for hydroelectricity, inland fisheries, and water supply for domestic, industrial, and agricultural operations. Anthropogenic climate change (CC) has changed the state of these water resources. Land use and land cover has also undergone significant changes due to the need to provide resources to a growing population. Yet, the impact of the Land Use and Land Cover Change (LULCC) in addition to CC on the water resources of Africa is underexplored. Here we investigate how precipitation, evapotranspiration (ET), and river-flow respond to both CC and LULCC scenarios across the entire African continent. We set up a SWAT+ model for Africa and calibrated it using the Hydrological Mass Balance calibration (HMBC) methodology detailed in Chawanda et. al., (2020). The model was subsequently driven by an ensemble of bias-adjusted global climate models to simulate the hydrological cycle under a range of CC and LULCC scenarios. The results indicate that the Zambezi and the Congo River basins are likely to experience reduced river flows under CC by up to 7% decrease, while the Limpopo will likely have higher river flows. The Niger River basin is likely to experience the largest decrease in river flows in all of Africa due to CC. The Congo River basin has the largest difference in river flows between scenarios with (over 18%% increase) and without LULCC (over 20% decrease). The projected changes have implications on agriculture and energy sectors and hence the livelihood of people on the continent. Our results highlight the need to adopt policies to halt global greenhouse gas emissions and to combat the current trend of deforestation to avoid the high combined impact of CC and LULCC on water resources in Africa.

How to cite: Chawanda, C. J., Nkwasa, A., Thiery, W., and van Griensven, A.: Combined impacts of climate and land-use change on future water resources in Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10464, https://doi.org/10.5194/egusphere-egu23-10464, 2023.