HS2.5.1

Humans have fundamentally altered global river flow by constructing reservoirs and building water-diversion schemes for irrigation. Reservoir operation and the regulation of river flow is important for estimating global water fluxes and water availability. Reservoirs and dam management are however generally not represented in Earth System Models. Recently, efforts are made to incorporate human water management in Land Surface Models by improving the irrigation representation and including high-resolution river networks.

Here, we present the integration of a reservoir routine in the vector-based river routing model mizuRoute, to be coupled with the Community Terrestrial Systems Model (CTSM). We use the Hydrologic Derivatives for Modeling and Applications (HDMA) vector-based river network, which is intersected with lake and reservoir polygons from the HydroLAKES and GRanD databases to model both natural lakes and reservoirs. We implement reservoir management based on the parametrization of Hanasaki et al. (2006) and develop an irrigation topology to determine the irrigation water demand for every individual reservoir based on gridded water demands modeled by CTSM.

We then evaluate our reservoir implementation both in a local setup, driven by observed inflow for 26 reservoirs, and in a global-scale setup, driven by gridded runoff from CTSM and using the Hydrologic Derivatives for Modeling and Applications (HDMA) river network. The local simulations show that accounting for reservoirs improves the skill compared to resolving reservoirs with a natural lake parametrization and not accounting for lakes/reservoirs. In the global-scale simulation, the reservoir management and natural lake parametrizations show however similar results, which could be attributed to biases in modeled reservoir inflow. These biases originate from biases in runoff simulated by CTSM and/or unresolved reservoirs on the river network.

This study overall underlines the need to further develop and test water management parametrizations for improving the representation of anthropogenic interference with the terrestrial water cycle in Earth system models.

References:
Hanasaki, N., Kanae, S., & Oki, T. (2006). A reservoir operation scheme for global river routing models. Journal of Hydrology, 327(1–2), 22–41

Mizukami, N., Clark, M. P., Gharari, S., Kluzek, E., Pan, M., Lin, P., Beck, H. E., & Yamazaki, D. (2021). A Vector-Based River Routing Model for Earth System Models: Parallelization and Global Applications. Journal of Advances in Modeling Earth Systems, 13(6).

How to cite: Vanderkelen, I., Gharari, S., Mizukami, N., Lawrence, D. M., Swenson, S., Clark, M., van Griensven, A., Pokhrel, Y., Hanasaki, N., and Thiery, W.: Evaluating a reservoir parametrisation in a vector-based global routing model for Earth System Model coupling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-834, https://doi.org/10.5194/egusphere-egu22-834, 2022.

Topography influences how water is precipitated on, evaporated from, stored in, and routed through the landscape, often because of long-term evolutionary processes. How well do we know the links between topography and hydrology at large scales? Do we use this knowledge in, for example, large scale modelling efforts, or does topographic data contain information that is currently underused? To shed light on the role of topography in the global hydrological cycle, we explore three key themes.

First, topography leads to gradients and contrasts in climatic/weather forcing. Well-known examples are orographic precipitation, rain shadowing, or the presence of snow and ice at high elevations.

Second, topography is strongly related to different landforms, such as mountains and plains. These generic landforms provide a first broad classification, but there are further properties that vary along topographic gradients in a more nuanced way, such as sediment size or the depth of the critical zone.

Third, topographically induced differences in potential energy drive water movement. This can result in surface and subsurface flow across large (horizontal) distances, providing water to distant areas, and thus decoupling local hydrology to some extent from local climate.

The three themes (often in concert) describe partial controls on large scale hydrological processes and patterns. We derive several hypotheses based on these three themes, which would improve our understanding of large-scale hydrological processes, and help us in evaluating, constraining, and building hydrological models.

How to cite: Gnann, S. and Wagener, T.: The role of topography in the global hydrological cycle, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2955, https://doi.org/10.5194/egusphere-egu22-2955, 2022.

Global hydrological models simulate water storages and fluxes of the water cycle, motivated to assess water problems such as water scarcity, high flows and more generally the impact of anthropogenic change on the global water system. However, the models include many uncertainties due to the model inputs (e.g. climate forcing data), model parameters, and model structure which can lead to disagreements when simulation results are compared to observations. To reduce and quantify these uncertainties, some of the models are 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 to directly improve the realism of the models.

In this study, we present our framework for jointly assimilating satellite and in-situ observations into the WaterGAP Global Hydrological Model (WGHM). In addition to GRACE TWSA maps, for the first time here we experimentally jointly assimilate in-situ streamflow observations from gauge stations. This is in preparation for the Surface Water and Ocean Topography (SWOT) satellite, which will be launched this year and is expected to allow the derivation of streamflow observations globally for rivers wider than 50-100m.

GRACE assimilation strongly improves the TWSA simulations in the Mississippi River Basin, e.g. the correlation increases to 91%, with which our results are consistent with previous studies. However, we find in this case that the streamflow simulation deteriorates, for example, correlation reduces from 92% to 61% at the most downstream gauge station. In contrast, jointly assimilating GRACE data and streamflow observations from GRDC gauge stations improves the streamflow observations by up to 33% in terms of e.g. RMSE and correlation while maintaining the good TWSA simulations. In view of the upcoming SWOT mission, our data suggest that the SWOT data will help to further improve the structure and simulations of global hydrological models.

How to cite: Schulze, K., Kusche, J., Gerdener, H., Engels, O., Döll, P., Müller Schmied, H., Ackermann, S., and Shadkam, S.: Joint assimilation of GRACE Total Water Storage Anomalies and In-Situ Streamflow Data into a Global Hydrological Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5321, https://doi.org/10.5194/egusphere-egu22-5321, 2022.

Vegetation modulates Earth’s water, energy and carbon cycles and provides a key link between water stores in the deep soil and the atmosphere. How its functions may change in the future largely depends on how it copes with droughts. There is evidence that in places-times of drought, vegetation shifts water uptake to deeper soil and rock moisture and groundwater. We differentiate and assess plant use of four types of water source: precipitation (P) in current month, past P stored in deeper unsaturated soils/rocks, past P stored in locally recharged groundwater, and groundwater from P fallen on uplands via river-groundwater convergence toward lowlands. We examine global and seasonal patterns and drivers in plant uptake of the four sources using inverse modeling and isotope-based estimates. We find that globally and annually, 70% (std 24%) of plant transpiration relies on current month P, 18% (std 15%) on deep soil moisture, only 1% (std 3%) on locally recharged groundwater, and 10% (std 22%) on groundwater or river water from upland more distant sources; (2) regionally and seasonally, recent P is only 19% in semi-arid, 32% in Mediterranean, and 17% in winter-dry tropics in the driest months; (3) at landscape scales, deep soil moisture, taken up by deep roots in the deep vadose zone, is critical in uplands in dry months, but groundwater and river water from uplands is up to 47% in valleys where riparian forests and desert oases are found. Because the four sources originate from different places-times, move at different spatial-temporal scales, and respond with different sensitivity to climate and anthropogenic forces, understanding space-time origin of plant water source can inform ecosystem management and Earth System Models on the critical hydrologic pathways linking precipitation to vegetation.

How to cite: Miguez Macho, G. and Fan, Y.: A Global Assessment of the Spatial-Temporal Origin of Soil Water Taken up by Vegetation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10789, https://doi.org/10.5194/egusphere-egu22-10789, 2022.

The Budyko framework consists of a curvilinear relationship between the evaporative ratio (i.e., actual evaporation over precipitation) and the aridity index (potential evaporation over precipitation) and defines evaporation’s water and energy limits. A basin’s movement within the Budyko space illustrates its hydroclimatic change and can help identify the main drivers of change. Basins are expected to move along their Budyko curves when only long-term changes in the aridity index drive changes in the evaporative ratio. We hypothesize that the increasing effects of global warming on the hydrological cycle will cause basins to move along their Budyko curves. To test our hypothesis, we quantify the movement in Budyko space of 353 river basins from 1901 to 2100 based on the outputs of nine models from the Coupled Model Intercomparison Project - Phase 5 (CMIP5). We find that significant increases in potential evaporation due to global warming will lead to basins moving primarily horizontally in Budyko space accompanied by minor changes in the evaporative ratio. However, 37% of the basins will still deviate from their Budyko curve trajectories, with less evaporation than expected by the framework. We elaborate on how land-use change, vegetation changes, or shifts in precipitation or snow to rain ratios can explain these deviations.

How to cite: Jaramillo, F., Piemontese, L., Berghuijs, W., WAng-Erlandsson, L., and Greve, P.: Most River Basins will Follow their Budyko Curves under Global Warming, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11224, https://doi.org/10.5194/egusphere-egu22-11224, 2022.

The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source and ‘FAIR by Design’ Platform where hydrologists can do computational hydrological research using their own, or other’s, models and data. Using eWaterCycle, computational hydrologist can focus on the hydrological part of their work, without the headache that often comes with the computational part.

In eWaterCycle experiments are separated from models: experiments are build and run in Jupyter notebooks and models can be accessed as objects in these notebooks. The models themselves are ‘hidden’ in (Docker) containers and accessed through an easy interface. This interface and the technology behind it that we’ve build allows computational hydrologists to work with (each others) models written in different programming languages without having to access that code. Currently PCRGlobWB 2.0, Hype, LISFlood, WFLOW and MARMoT are among the models supported by eWaterCycle.

Furthermore, pre-processing of atmospheric forcing data is handled transparently by ESMValTool, which separates the process of selecting and standardising variables from the steps needed to make forcing compatible with a specific model. If a source of forcing data has been made ready for one model in eWaterCycle it is easy to use it for any other. If a model has been used with one forcing data source, it is easy to swap it with another. Currently ERA5 and ERA-Interim are supported in eWaterCycle.

With eWaterCycle use cases such as (but not limited to!) these are now easier to implement:

• Comparing two models for the same region against observation data (GRDC discharge observations are standard supported) to determine which model performs best for a given research question
• Coupling two models (in different programming languages) to exchange information at every timestep, for example making a
• Running a multi-model ensemble (including adding data assimilation of observations)

Previously we have announced eWaterCycle as work in progress. At the 2022 General Assembly we will demonstrate the release of v1.0 of the eWaterCycle platform, giving the computational hydrological community access to a platform that supports fully reproducible, open, and FAIR Hydrological modelling.

This work is currently under open review for publication in GMD: https://gmd.copernicus.org/preprints/gmd-2021-344/ and parts of this work have been presented at the AGU 2021 Fall Meeting.

How to cite: Hut, R., Drost, N., van de Giesen, N., van Werkhoven, B., Aerts, J., Alidoost, F., Kalverla, P., Verhoeven, S., Andela, B., Camphuijsen, J., Dzigan, Y., van den Oord, G., Pelupessy, I., and Vreede, B.: The eWaterCycle platform for open and FAIR computational hydrological research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7274, https://doi.org/10.5194/egusphere-egu22-7274, 2022.

Floods and droughts may become more severe in a warming world, potentially furthering the significant adverse impacts they cause to lives and livelihoods, infrastructure, and economies. To adapt to future changes in water quantity and regimes, we need to detect and attribute emerging trends in hydrological variables such as river flow, and we require updated projections of future flood and drought occurrence.

Numerical simulation models are used to provide such scenarios, but they are complex and highly uncertain. We can use long records of past hydrological observations to better understand and constrain these model-based projections; river flows are especially useful because they integrate climate processes over the areas covered by drainage basins.

There have been many studies of long-term changes in river flows around the world although, at a global scale (as represented by successive IPCC (Intergovernmental Panel on Climate Change) reports), confidence in observed trends remains very low. This is primarily due to the modification of river flows by human activities (e.g., presence of dams, land-cover change, channelisation and the abstraction of water for public water supplies, industry and agriculture). These human disturbances can obscure climate change signals and distort trends in river flows and in some cases lead to a complete reversal of the trends / changes caused by climate change. It is also challenging to integrate the results of various regional- and national-scale studies due to the many different methods used, hampering consistent continental- and global-scale assessments.

Therefore, to detect climate-driven trends we need to analyse river basins that are relatively undisturbed by human impacts. Recognising this, many countries have ‘Reference Hydrometric Networks’ (RHNs) consisting of catchments where river flows are measured, and where human impacts are absent or minimal. Globally however, these are sparse and there is a need for an integrated approach to advance international assessments of hydrological change on a consistent basis, such that they can provide a robust foundation for global and regional assessments such as those undertaken by the IPCC.

Here we introduce the 'Reference Observatory of Basins for INternational hydrological climate change detection' or ROBIN initiative, where we are advancing a worldwide effort to bring together a global RHN. With a growing network of partners from 20 countries spanning a broad range of climates and geographies, over the next two years ROBIN will develop a consistently defined network of near-natural catchments across the world, sharing knowledge from countries with established RHNs to enable other countries to define similar networks. ROBIN will use this network to undertake the first, truly global scale analysis of trends in river flows using minimally disturbed catchments.

With the support of international organisations, including WMO, UNESCO and IPCC, ROBIN will lay the foundations for an enduring network of catchments, to support global assessments of climate-driven trends and variability in the future.

How to cite: Turner, S., Barker, L., Dixon, H., Hannaford, J., Griffin, A., and Killeen, A. and the ROBIN Network: ROBIN - A Reference Observatory of Basins for INternational hydrological climate change detection , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8551, https://doi.org/10.5194/egusphere-egu22-8551, 2022.

There is general agreement about the water cycle acceleration in the community, although its strength over land has been debated lately. While some common behavior is observed under similar climatic conditions across the globe, at the regional scale the water cycle's response to global warming is specific to its location's unique characteristics. Herein, we quantify the water cycle and characterize its climatology over the Czech Republic, which constitutes an essential headwaters area of the European continent, and in hydrological terms, it can be called the “roof of Europe”. The country's location involves three drainage catchments: the Elbe, Oder, and Danube rivers, which lead to the North Sea, the Baltic Sea, and the Black Sea respectively. Our analysis includes various data sets at  different spatiotemporal scales like: The Twentieth Century Reanalysis (20CR), CPC Merged Analysis of Precipitation (CMAP), CPC Global Unified Gauge-Based Analysis of Daily Precipitation (CPC), Climatic Research Unit gridded Time Series (CRU TS), Global Historical Climatology Network monthly (GHCN), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), Global Precipitation Climatology Centre (GPCC), The Global Precipitation Measurement Integrated Multi-satellite Retrievals (GPM IMERG), Global Runoff Data Centre (GRDC), Global Runoff Reconstruction (GRUN), Moderate Resolution Imaging Spectroradiometer Terra Net Evapotranspiration (MOD16A2), National Centers for Environmental Prediction DOE Reanalysis 2 (NCEP DOE), National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP NCAR), NOAA's Precipitation Reconstruction over Land (PRECL), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TRMM 3B43), and University of Delaware Precipitation (UDEL). To exploit the availability of the various data sets for each component of the water cycle we merged them via simple weighted averages, a multi-source data integration method that has proven to be effective and with low computational requirements. Subsequently, we linked the computed components constraining them by the water budget equation. Thereafter, the time series were analyzed to quantify trends and their statistical significance, as well as their uncertainty derived by the multiple datasets. In addition to the time series analysis and the statistics involved so far, a spatial analysis explored the water cycle climatology and its variability over the whole Czech Republic and then its behavior in subdomains defined by the watersheds within the borders of the country.

How to cite: Vargas Godoy, M. R., Bešťáková, Z., Pradhan, R. K., Součková, M., Hanel, M., Juras, R., Kyselý, J., and Markonis, Y.: Assessment of the Water Cycle Acceleration in the Czech Republic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-310, https://doi.org/10.5194/egusphere-egu22-310, 2022.

Streamflow droughts are generated by a variety of processes including rainfall deficits and anomalous snow availability or evapotranspiration. The importance of different driver sequences may vary with event severity, however, it is yet unclear how. To study the variation of driver importance with event severity, we propose a formal classification scheme for streamflow droughts and apply it to a large sample of catchments in Europe. The scheme assigns events to one of eight drought types – each characterized by a set of compounding drivers - using information about seasonality, precipitation deficits, and snow availability. Our findings show that drought driver importance varies regionally, seasonally, and by event severity. More specifically, we show that rainfall deficit droughts are the dominant drought type in western Europe while northern Europe is most often affected by cold snow season droughts. Second, we show that rainfall deficit and cold snow season droughts are important from autumn to spring, while snowmelt and wet to dry season droughts are important in summer. Last, we demonstrate that moderate droughts are mainly driven by rainfall deficits while severe events are mainly driven by snowmelt deficits in colder climates and by streamflow deficits transitioning from the wet to the dry season in warmer climates. This high importance of snow-influenced and evapotranspiration-influenced droughts for severe events suggests that these potentially high-impact events might undergo the strongest changes in a warming climate because of their close relationship to temperature. The proposed classification scheme provides a template that can be expanded to include other climatic regions and human influences.

How to cite: Brunner, M. I., Van Loon, A., and Stahl, K.: Classification reveals varying drivers of severe and moderate hydrological droughts in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2151, https://doi.org/10.5194/egusphere-egu22-2151, 2022.

Process-based impact models are frequently used for a range of applications and are valuable for simulating fundamental processes in a changing world. Model Intercomparison Projects like the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, www.isimip.org) act as an umbrella for various sectors (e.g. water, agriculture, health) and numerous modelling teams that are following a common modeling protocol that enables model intercomparison and (cross-) sectoral multi-model impact assessments. However, such assessments require reliable model outputs which can be checked from two perspectives.

First, a quality control (QC) check ensures that simulated files follow the standards defined in the modelling protocol and includes plausibility checks. For example, structural inconsistencies and correct metadata entries can be assessed, but also in cases where the range of a specific variable exceeds plausibility limits (e.g. negative precipitation values), such a tool can facilitate error checking which is very helpful especially in the case of high data volume simulation outputs (e.g., errors stemming from an erroneous unit conversion).

Second, a quality assessment (QA) tool compares model output to observation data or benchmark models. This is particularly important for model development and improvement as it can highlight benefits and limitations of models for e.g., specific model configurations, but it also informs the identification of models that are best suited for specific regions and research questions.

Within the EU COST-Action “Process-based models for climate impact attribution across sectors“ (PROCLIAS), the aim is to establish a QC/QA workflow for the ISIMIP models. A QC tool is already developed and in operation which checks the data format and, exemplarily for the global water sector, each variable for plausibility ranges. An operational QA tool does not yet exist within PROCLIAS and ISIMIP but some experiences have been gained with existing evaluation frameworks such as ILAMB and the ESMValTool.

This presentation provides experiences gained with the QC tool and the application of ISIMIP data to existing QA frameworks and outlines the next milestones. It is planned to extend the plausibility ranges to all ISIMIP sectors by a survey within the modelling teams. For the QA tool, specific developments are required to integrate sector-specific evaluation methods (e.g., basin outlines into ILAMB). To use ESMValTool, the model output data needs to be restructured to a CF-compliant format. With the ISIMIP global water sector as a pilot sector, experiences are gained that will then be transferred to other sectors. This activity also calls for an exchange of ideas and experiences from other modeling communities.

How to cite: Müller Schmied, H., Büchner, M., Klar, J., Vega del Valle, I., Koutroulis, A., Gosling, S. N., Dobor, L., Nyenah, E., and Reyer, C.: Improving impact model intercomparison by developing and applying quality control and quality assessment tools – the example of the ISIMIP global water sector, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5368, https://doi.org/10.5194/egusphere-egu22-5368, 2022.

Low flow estimation is a crucial part in water management. Prediction of low flow in ungauged basins is often performed through statistical models. This can be either regionalization approaches, where homogeneous regions are used for modeling, or single model frameworks that range from simple linear models to more complex as random forest, support vector regression or deep learning approaches. Although there are large sample studies for the US (e.g. Tyralis et al. 2021) or Australia (e.g. Worland et al. 2018), we are not aware of a study that combines different large datasets and analyzing the effect on prediction accuracy. We are hypothesing that the heterogeneity of many datasets together can improve prediction accuracy for tree-based models relative to linear models. Hence, we propose to combine several similar datasets and analyze the effect on prediction accuracy for estimating Q95 by a simple random forest model.

Our study uses four large hydrological datasets – CAMELS-GB (Coxon et al. 2020), CAMELS-US (Addor et al. 2017), CAMELS-AUS (Fowler et al. 2021) and LamaH-CE (Klinger et al., 2021). We are applying a random forest model to ensure that interactions and non-linearity can be captured. Prediction accuracy is evaluated by leave one out cross-validation (LOOCV) and several performance metrics, e.g. median absolute error (MDAE), or root mean squared error (RMSE). LOOCV is used for each individual dataset and in one run for the merged dataset. Results indicate that merging datasets can improve prediction accuracy, but models fail to correctly predict low flows around zero.

References

• Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
• Fowler, K. J. A., Acharya, S. C., Addor, N., Chou, C., and Peel, M. C.: CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia, Earth Syst. Sci. Data, 13, 3847–3867, https://doi.org/10.5194/essd-13-3847-2021, 2021.
• Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020.
• Klingler, C., Schulz, K., and Herrnegger, M.: LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, 2021.
• Tyralis, H.; Papacharalampous, G.; Langousis, A.; Papalexiou, S.M. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms. Remote Sens. 2021, 13, 333. https://doi.org/10.3390/rs13030333
• Worland, S. C., Farmer, W. H., and Kiang, J. E.: Improving predictions of hydrological low-flow indices in ungaged basins using machinelearning, Environmental modelling & software, 101, 169–182, https://doi.org/10.1016/j.envsoft.2017.12.021, 2018.

How to cite: Laimighofer, J. and Laaha, G.: Effect of merging large datasets on prediction accuracy of low flow estimation by random forest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7312, https://doi.org/10.5194/egusphere-egu22-7312, 2022.

Water is a major source for growing crops and to ensure freshwater, thus it is essential to prevent the population from water shortages in agriculture and water supply. To globally observe changes in surface water and vegetation from space, remote-sensing satellites enabled a great opportunity in the last decades. But, especially in semi-arid and arid regions observing subsurface water gains a high importance as well. In-situ data and global hydrological models can provide subsurface information, however, the in-situ data are limited to an irregular temporal and spatial resolution that might not cover each climate regime and models do not yet perfectly represent the reality because of structural and forcing uncertainties. So far, the satellite mission GRACE (Gravity Recovery and Climate Experiment) and its successor GRACE-FO (FollowOn) are the only missions that observe the vertical sum of all water storages and thus observe surface and subsurface water, but they are limited to a coarser spatial resolution of about 300 km and can not distinguish between different water storages. To overcome these limitations, we combine GRACE observations with a global hydrological model (WaterGAP 2.2d) via data assimilation to make the model more realistic while spatially downscaling and vertically disaggregating the GRACE data into the different water compartments.

In a case study for South Africa, we use observation-based surface water, soil moisture and groundwater (via assimilation) together with the remote sensed vegetation indices Leaf Area Index and Actual Evapotranspiration (via Moderate Resolution Imaging Spectroradiometer) to extract signatures and subsignals of the water propagation in the water cycle in the period from 2003 to 2016. The observed (via assimilation and remote-sensing) signatures are then compared to modeled signatures and subsignals. Two main processes are analyzed: First, the precipitation-storage dynamics and second, the storage-vegetation dynamics. Thus, we assess the propagation of water that is beginning as precipitation, recharges water storages and finally contributes to vegetation growth. Our study shows an overestimation of the amount of precipitation in the model that refills the water storages and also an overestimation of the amount of water stored that contributes to vegetation growth. Furthermore, we identify differences in the duration of the precipitation-storage-vegetation process. For example, we find that in general the annual peak of modeled groundwater lags the annual precipitation peak by 3 months, while the observations identify a 4-month lag. We believe that this study highlights the importance of assimilating GRACE into hydrological models and that modelers can use this information in future to improve model structures and relevant model processes.

How to cite: Gerdener, H., Kusche, J., Schulze, K., Ghazaryan, G., and Dubovyk, O.: Modeled water – vegetation dynamics under revision using GRACE-based data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6981, https://doi.org/10.5194/egusphere-egu22-6981, 2022.

Southeast Asia’s electricity supply largely depends on the hydropower resources of the Mekong, Chao Phraya, Irrawaddy, and Salween River Basins. Uncertain precipitation patterns, rising temperature, and other climate-driven changes are exposing these resources to unprecedented risks, prompting decision makers to re-evaluate existing reservoir management strategies through climate change risk assessments. These assessments are important in shaping the operators’ response to hydro-climatic variability and are necessary to ensure energy security in the region. In this study, we developed high-resolution, semi-distributed hydrological models to examine the potential changes of hydropower availability under projected future climate scenarios in the four largest river basins in South East Asia. Specifically, we relied on a novel variant of the Variable Infiltration Capacity (VIC) model that integrates reservoir operations into the routing scheme, warranting a more accurate representation of cascade reservoir systems. Climate change impacts were derived from the outputs of five Global Circulation Models (GCMs) forced by two Shared Socioeconomic Pathways (SSPs 2.6 and 8.5) emission scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that hydropower generation would be altered significantly in all scenarios in terms of temporal variability and magnitude due to the changes in duration and magnitude of the summer monsoon. Our findings further stress the importance of exploring how the impact of climate change on hydropower availability propagates through water-energy systems and call for adaptive reservoir operation strategies.

How to cite: Galelli, S. and Dang, T. D.: Assessing the impact of climate change on Southeast Asia’s hydropower availability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4561, https://doi.org/10.5194/egusphere-egu22-4561, 2022.