We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.

How to cite: Beck, H., van Dijk, A., Larraondo, P., McVicar, T., Pan, M., Dutra, E., and Miralles, D.: MSWX: Global 3-Hourly 0.1° Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2246, https://doi.org/10.5194/egusphere-egu23-2246, 2023.

Hydrological budgeting of mountainous watersheds like the Indian Himalayan Region (IHR) is critical as they supply water for most of the Indian Sub-continent. Estimating evapotranspiration (ET), a major eco-hydrological process that returns a significant amount of terrestrial precipitation to the atmosphere, however, is a challenge in data-scare regions like IHR. This is further complicated by the geographic extent of the Indian Himalayas, which cover 13 states within the Himalayan range that possess a significant climatic and biotic gradient from east to west. This study aims to compare the applicability of various potential ET (PET) methods for the eastern and western Himalayan regions under different hydro-climatic conditions and suggest the best-fit method using easily accessible hydrometeorological data. The data for this study was obtained from 17 stations covering the IHR extensively during two study periods (1972-1982 and 2003-2009) from the Indian Meteorological Department (IMD). Four temperature-based methods (Thornthwaite, Blaney-Criddle, Kharrufa, and Hargreaves method) were tested against the modified Penman-Monteith (PM) method using reanalysis data (PM-PET). The PET estimates for the stations showed similar variations across three different elevation ranges. Further, it was observed that the Western Himalayas experienced lesser months of drier conditions (i.e., higher PET values during 2-3 months) compared to the Eastern Himalayas, which typically experienced 4-5 months of higher ET demand. It was observed that the PET values from the Hargreaves method were close to PM-PET with NSE (Nash-Sutcliffe Efficiency) values ranging from 0.75-0.92 and r² values ranging from 0.72-0.92 (except for Jammu; NSE = 0.5, r² = 0.41) and was found to serve as the best temperature-based method among the four methods for PET estimation in the Western and Central Himalayan region. However, no temperature-based method provided reasonable PET estimates in Eastern Himalayas, as the NSE and r² values were less than 0.3 for all the methods.  Thus, there is a need to explore why temperature-based PET methods may not be applicable to the Eastern Himalayan region and evaluate other PET methods that are more reliable in this region.

How to cite: Reddy, P. K., Bhattarai, N., and Sen, S.: Understanding Evapotranspiration Variability between the Eastern and Western Himalayas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10850, https://doi.org/10.5194/egusphere-egu23-10850, 2023.

The complementary evaporation principle (CEP) could well explain the past changes in global evaporation while avoiding uncertainties in precipitation and soil data. However, it is still difficult to estimate the Priestley-Taylor coefficient (α) for the wet-surface evaporation without actual evaporation data. Here, we proposed an empirical approach that links the CEP and a traditional Budyko equation in order to determine α in locations where no actual evaporation (E) data are available. The CEP–Budyko combined framework enabled us to local climate conditions in α, producing more plausible E estimates in the ungauged locations. We also assessed latitudinal and temporal variations of the E estimates for the past 40 years. This study highlights that the optimal α for CEP is unlikely constant in space, since it needs to be conditioned by local climates.

How to cite: Kim, D. and Chun, J. A.: Changes in global evaporation for the past four decades identified by the complementary evaporation principle and the Budyko framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4642, https://doi.org/10.5194/egusphere-egu23-4642, 2023.

The application of agricultural ponds and small engineered impoundments (often < 0.1 km² area) is growing globally to support livestock, irrigation, and local municipal and industrial demands during dry spells. However, evaporation diminishes the storage efficiency of these popular but often un-inventoried resources. This study provides a reliable framework for estimating global abundance of small reservoirs and associated evaporative hotspots under different climate change scenarios serving as a basis for future water management and planning. To show the applicability of the proposed method and the utility of the insights it provides, we use satellite data to identify spatio-temporal distribution of small reservoirs (~0.001 to 0.1 km² area) in southern Europe (Italy, Spain, and Portugal) where irrigation heavily depends on water storage in agricultural ponds. While current estimates of evaporative water losses from small reservoirs often rely on pan measurements or Penman-type approaches with locally calibrated heat and mass transfer coefficients, we employ a physically-based model [Aminzadeh et al., 2018] that accounts for inherent reservoir characteristics (e.g., depth and light attenuation), and radiative energy storage within the water body to quantify energy balance and evaporation dynamics from small water reservoirs. Our preliminary results indicate that cumulative area of small reservoirs in the study area has increased from 518 km² in 2000 to 614 km² in 2020 (18.5% increase) with cumulative evaporative losses that may exceed 400 Mm³ during warm months (April to September). Although the estimated evaporative water loss looks negligible relative to the annual agricultural water use (< 2%), its significance could be gauged by societal impacts in these regions with chronic water stress problems or the cost of alternate water sources (e.g., desalinated water).

Reference

Aminzadeh, M., Lehmann, P., Or, D. (2018). Evaporation suppression and energy balance of water reservoirs covered with self-assembling floating elements. Hydrol. Earth Syst. Sci., 22, 4015–4032.

How to cite: Aminzadeh, M., Friedrich, N., Madani, K., and Shokri, N.: Insignificant but overlooked: Evaporative losses from small reservoirs in southern Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3326, https://doi.org/10.5194/egusphere-egu23-3326, 2023.

Reservoir operation modeling is challenging since it directly depends on human decision-making that varies from dam to dam. Large-scale reservoir modeling is even more difficult due to the lack of observed operation data. Therefore, generic reservoir operation models are used to model large-scale reservoir operations focusing on a specific purpose, rather than real operations. One of the well-known generic schemes for reservoir operation is Hannsaki et al. (2006) algorithm which is currently used in several global hydrological models including the Water Global Assessment and Prognosis (WaterGAP) model. This algorithm improves hydrological process modeling compared to natural lake methods; however, its performance still needs to be improved, particularly for storage simulations. In this study, a new approach is implemented in the WaterGAP model to improve Hannsaki’s algorithm by using different one-parameter linear operation rules for different reservoir storage levels i.e., above 70 %, between 40 % and 70 %, and below 40 % of reservoir capacity (in total three equations). As a result, we can model each reservoir individually. In addition, we use storage at each time step for estimating the release coefficient instead of the storage at the beginning of the operational year in Hannsaki’s algorithm. The ResOpsUS dataset (historical reservoir inflow, storage, and outflow of major reservoirs across the US) is used to estimate the best parameters for each reservoir and evaluate the results over the US. The results of the new approach show an improvement compared to Hannsaki’s algorithm e.g., when the inflow has a good quality (the Kling-Gupta Efficiency (KGE) greater than 0.50), the median of KEG for storage (outflow) of the new approach reaches 0.23 (0.49) compared to -0.71 (0.25) for Hannsaki’s algorithm.

How to cite: Hosseini-Moghari, S.-M. and Döll, P.: Using observation data to improve simulation of man-made reservoirs in a global hydrological model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9951, https://doi.org/10.5194/egusphere-egu23-9951, 2023.

Partitioning runoff accurately into baseflow and quickflow is crucial for our understanding of the water cycle and for the management of droughts and floods. However, global datasets of long-term mean runoff partitioning are rare, even more so datasets relying on physically-based methods. Here, we present a new global 0.25° dataset of runoff, baseflow and quickflow using a hybrid approach of Budyko-based methods and machine learning (ML). The parameters in the Budyko curve and Budyko-based baseflow curve (BFC curve) are estimated with ML (boosted regression trees, BRT) as a function of catchment characteristics. The BRT models are trained and tested in 1226 catchments worldwide, and then applied globally at grid scale. The catchment-trained models show good performance during the testing phase with R² equal to 0.96 and 0.87 for runoff and baseflow, respectively. The dataset developed in this study shows that 30.3±26.5% (mean ± standard deviation) of the precipitation is partitioned into runoff of which 20.6±22.1% is baseflow and 9.7±10.3% is quickflow. The global long-term mean baseflow in this study (151±181 mm yr^–1) is lower than that from the Global Streamflow Characteristics Dataset (GSCD, 241±321 mm yr^–1) and higher than that from ERA5-Land (79±145 mm yr^–1). This study provides a unique, physically and observationally constrained global dataset of the long-term runoff partitioning. The large differences among different datasets suggest that global runoff partitioning is highly uncertain and requires further investigation.

How to cite: Cheng, S., Hulsman, P., Koppa, A., Cheng, L., Beck, H. E., and Miralles, D. G.: Global runoff partitioning based on Budyko and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8896, https://doi.org/10.5194/egusphere-egu23-8896, 2023.

Changes in the terrestrial hydrologic cycle determine the future availability of water around the world, affecting various aspects of society. Accurate estimation of these changes is essential for effective implementation of water management policies. A major source of uncertainty in such estimates is the data products used for analysis. This study provides a global perspective of changes in water availability in terms of Precipitation minus Evapotranspiration (P-E) using three data sources, namely reanalysis (ERA5-Land), hydrological modelling (TerraClimate), and simulations of eight Global Climate Models (GCMs; CMCC-CM2, CNRM-CM6, EC-Earth3P, ECMWF-IFS, HadGEM3-GC31, IPSL-CM6A, MPI-ESM1, and MRI-AGCM3 ). The period of analysis (1960-2014) is divided into two epochs, and the magnitude of change is analyzed by the percent change in the mean and increasing/decreasing trend over the 55-year period. In general, all three data products successfully capture the climatological mean of P-E with comparatively higher values for the equatorial regions. However, when comparing the intensity of changes the results provided by the three data sources differ significantly, especially for regions at higher latitudes. Projections from various GCMs show significant rise in the precipitation for the higher latitudes, which will also affect the extent of P-E and increase the uncertainty over the changes in water availability. It is critical to find reliable data sources for the historical period to increase confidence in future projections and to successfully implement various bias correction techniques wherever necessary. 

How to cite: Dutta, R. and Markonis, Y.: Global perspective of changes in the terrestrial hydrologic cycle using different data products, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12752, https://doi.org/10.5194/egusphere-egu23-12752, 2023.

Increasing aridity is a growing concern for many regions across the globe, primarily driven by an alteration to the balance of evaporative loss and incoming precipitation. While this is likely to drive declines in the long-term mean of surface water, interannual and decadal variability of precipitation will still produce wet and dry periods. Yet, the potential increase in the occurrence of multiple sequential drought years is of particular concern especially for basins without substantive water shortage infrastructure. In this presentation, we evaluate the enhanced likelihood of multiple consecutive , i.e. years with below 20^th percentile annual runoff, occurring within large basins (25,000-150,000 km­²) across the globe that are projected to experience increasing aridity within an ensemble of 16 CMIP6 general circulation models. We use historical simulations (1950-2014) and future projections (2015-2100) from three emission scenarios to demonstrate how aridification, as measured by increases in the long-term ratio of potential evapotranspiration and precipitation, is expected to change. We examine the ways in which changes in aridity paired with projected changes to the interannual variability of precipitation can conspire to enhance the probability, magnitude, and persistence of multi-year drought.

How to cite: Bjarke, N., Livneh, B., and Barsugli, J.: The role of future aridification in multi-year drought persistence for global hydrologic basins, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10386, https://doi.org/10.5194/egusphere-egu23-10386, 2023.

Our study provides a global assessment of water balance components accounting for the uncertainty in globally available precipitation products. This assessment is carried out consistently using a multiscale modelling framework established over more than 6000 GRDC river basins at various spatial resolutions (daily time step, period 1990-2019). The framework is based on the mesoscale Hydrologic Model (mHM; [1,2,3]) equipped with the multiscale parameter regionalization (MPR) scheme. All basins share the same parameterization and are driven with four different state-of-the-art meteorological products: ERA5 reanalysis [4], MSWEP [5], and deterministic EM-Earth v2 [6]. Additionally, the hydrological simulations are benchmarked against E-OBS [7] over Europe and against locally interpolated 1km gridded rain gauge dataset over Germany.

Our results show that EM-Earth clearly exhibits the best streamflow performance across North and South America and Asia with respect to the other products. The MSWEP is the best product in Africa, where the overall model’s performance is rather poor. In Australia, MSWEP and EM-Earth have comparable skills. In Europe, the differences get narrower, although slightly better performance is seen for EM-Earth, with a median performance of 0.5 of daily KGE (N basins=2000), mainly due to correcting the under-catch error of rain gauges which is not considered, e.g. in E-OBS. Furthermore, when we zoom into a subset consisting of medium-sized 200 German basins, the in-house high-resolution meteorologic product clearly overperformed all global products, mainly due to better captured temporal correlation and smaller biases. On the other hand, ERA5 leads to very strong positive biases over German basins using standard parameterization. Finally, our study contributes to discussions on objective quantification of the optimal spatial resolution of hydrological studies.

[1] https://doi.org/10.5281/zenodo.5119952

[2] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008WR007327 

[3] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR012195

[4] https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803

[5] https://hess.copernicus.org/articles/21/589/2017/hess-21-589-2017.html

[6] https://journals.ametsoc.org/view/journals/bams/103/4/BAMS-D-21-0106.1.xml

[7] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2017JD028200

How to cite: Rakovec, O., Kumar, R., Shrestha, P. K., and Samaniego, L.: Global assessment of hydrological components using a seamless multiscale modelling system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11945, https://doi.org/10.5194/egusphere-egu23-11945, 2023.

Streamflow data is highly relevant for flood risk analysis, ecological assessment, and water resources management. However, high-resolution vector-based streamflow data for Indian-Subcontinent river (ISC) basins is critically lacking. The finer-scale streamlines are better represented in a vector-based river network than grid-based network. We generated continuous streamflow data from 1901 to 2100 for ISC river basins. We used observed meteorological data from India Meteorological Department (IMD) for historical and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections for future simulations. We combined the H08 land surface model and the MizuRoute routing scheme to generate the streamflow at 9579 river segments of ISC river basins. We also examined how streamflow variability across the ISC river basins changes under a warming climate. We further investigated the river segments that are more prone to flood and drought in the future. The findings of this study may help in improving local flood and drought awareness and response more effectively than previously possible due to simulations at very fine river segments.

How to cite: Singh Chuphal, D. and Mishra, V.: Streamflow projections for Indian subcontinent river basins, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14505, https://doi.org/10.5194/egusphere-egu23-14505, 2023.

Peru is already facing a number of challenges related to climate change, including retreating glaciers and more severe droughts and floods. Therefore, a countrywide analysis of current and future hydroclimatic conditions is crucial to formulate adaptation strategies in water resource development. This study aims to evaluate the effects of climate change on the distribution of water budget components and streamflow variability across Peru. For that, we bias-adjusted and statistically downscaled CMIP6 climate projections of 10 climate models under two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) to obtain a range of possible future regional climatic conditions. These selected scenarios span a range of possible future options from the sustainable pathway (SSP1-2.6, with 2.6 W/m2 by the year 2100) to fossil-fueled development (SSP5-8.5, with 8.5 W/m2 by the year 2100). The adjusted climate data were fed into the hydrological model, Soil and Water Assessment Tool (SWAT), to simulate and analyze future hydroclimatic conditions. SWAT was calibrated and validated at 72 discharge stations against mean, high and low water flows. Climate projections suggest a warmer climate and diverging changes in precipitation, with a drier response over the lowlands of the Upper Amazon related to a substantial reduction in precipitation during June-November and a wetter response over the tropical Andes due to precipitation increases during September-March. Projected changes in hydrological conditions show lower water availability over lowlands and higher water availability along the Andean basins in the future. The projections for hydrological extremes indicate that Peru might face excessive water exposure during floods along the Andean catchments and water scarcity during droughts over the Amazon lowlands in the future, particularly under the fossil-fueled development (SSP5-8.5) scenario. The results of this study provide information to planners and decision-makers for formulating adaptation strategies for sustainable water management under climate change in Peru.

Keywords: water resources, climate change, CMIP6, hydrological extremes, Peru, Andes, Amazon

How to cite: Fernandez-Palomino, C. A., Hattermann, F. F., Krysanova, V., Vega-Jácome, F., Lavado, W., and Bronstert, A.: Climate change impact on water budget and hydrological extremes across Peru, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17281, https://doi.org/10.5194/egusphere-egu23-17281, 2023.

Estimating karst water resources at a global scale represents a key step towards enhancing groundwater management. Although recharge estimations at global scale exist, the special karst features leading to different recharge processes, in particular the preferential flow path, have not been adequately accounted for. Representing these special recharge processes is crucial for realistically quantifying recharge in karst regions. In this study, we present a new version of a global karst recharge model and the estimation of its model parameters using multiple observations. The updated model includes a new routine to consider eight land cover types for calculating evapotranspiration, interception and infiltration, as well as a new routine to account for snow and glacier melt. To prepare parameter estimation, six karst landscapes are defined based on the properties of karst grids (spatial resolution of 0.25°). For each of these landscapes, model parameters are found separately using a Monte Carlo uncertainty estimation framework and observations from the international soil moisture network (ISMN) and actual evapotranspiration observations from FLUXNET. With the new calibration, the updated model provides more precise estimates of groundwater recharge in athe karst regions of the world. It will therefore serve as a too  to improve water management in karst regions and identify areas potentially suffering from water shortage.

How to cite: Hartmann, A., Liu, Y., and Gomez Ospina, M.: Global groundwater recharge modeling in karst with explicit consideration of land cover and snow and glacier storage, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8424, https://doi.org/10.5194/egusphere-egu23-8424, 2023.

Irrigation is one of the crucial Nature-Human interactions and also an anthropogenic forcing in the Earth system. The literature has shown that artificial water supply to soil for example water exploitation from groundwater for irrigation can alter water and heat budgets at the land surface, resulting in changes in regional climate and hydrological cycles. Due to the expected increase in irrigation to meet growing food demand, the impact of irrigation is likely to increase further in the future. Therefore, it is essential to consider to better understand the irrigation-induced changes in the various components of the Earth system, today and in the future.

Our research aims to advance the quantitative understanding of the impact of irrigation and groundwater exploitation as anthropogenic drivers of regional climate and environmental changes. We developed an earth system modeling framework coupling the updated Earth system model, MIROC-ES2L (Model for Interdisciplinary Research on Climate, Earth System version 2 for Long-term simulations) and newly implemented hydrological human activity modules. This modeling framework enables to simulate a fully coupled Nature-Human system including water cycle dynamics related to irrigation.

Our preliminary results show notable differences between simulations with and without the irrigation process. Here, we show the hydrological variables affected by irrigation and identify the regions and timings of significant impact. Further, we estimate the individual contribution of groundwater and surface water use to such impacts by irrigation.

How to cite: Satoh, Y., Pokhrel, Y., Kim, H., and Yokohata, T.: An evaluation of the impact of irrigation and groundwater pumping on regional climate using an improved Earth-System model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13105, https://doi.org/10.5194/egusphere-egu23-13105, 2023.

United Nations Educational, Scientific and Cultural Organization (UNESCO) pointed out that about 300 million Africans live in poverty because of water scarcity. However, Africa does not lack natural water resources, but only makes insufficient use of them. We aim to understand the distribution of water storage in Africa and its changes for better management of these water resources. Based on the data from the GRACE gravity satellite and the GLDAS hydrological model, the changes in the total water storage (TWS), the surface water storage (SWS), and the groundwater storage (GWS) of Africa are calculated. On the hydrogeological base service platform OneGroundwater, we comprehensively analyzed the effects of rainfall, land use types, and other human activities on the water reserves. We found that the SWS decreases in the recent 15 years, which suggests that the utilization of surface water in Africa is significant. Meanwhile, the increase in the GWS indicates that the development of groundwater is not enough. Promoting the sustainable extraction of groundwater is helpful to the social development in Arica. Our analysis results show that rainfall is decisive for the changes in the GWS of Africa. The seasonal variation trend of the GWS is consistent with that of rainfall, while there is a certain lag in the yearly variations. The effects of land use types are mainly reflected in recharge and evaporation. The increase in vegetation density strengthens transpiration and reduces the recharge rate of groundwater.

How to cite: Cheng, J., Ding, K., Luo, Y., Yu, Y., Lin, Y., He, L., Zhao, X., Zheng, K., and Wang, Y.: Spatial-temporal characteristics of water storage in Africa based on GRACE and GLDAS data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11481, https://doi.org/10.5194/egusphere-egu23-11481, 2023.

The climate change along with anthropogenic water use have been affecting the (re)distribution of water storage and water flux across the Continental United States (CONUS). Understanding these changes requires tools that provide a big picture of the processes that drive these changes. These processes are implemented in this study by combining remote sensing data with available modeling techniques, where the data contains a sample of hydro-climatological signals and models reflect our understanding of these processes. Time series of Terrestrial Water Storage Changes (TWSC) from the Gravity Recovery and Climate Experiment (GRACE, 2003-2017) and its Follow-on (GRACE-FO, 2018-2021) are integrated into the W3RA water balance model within CONUS, where the model is run at 9-km resolution using ERA5 forcing data. The gap in the GRACE and GRACE-FO TWSC products is filled following https://doi.org/10.3390/rs12101639. To achieve the best possible statistical combination, the Markov Chain Monte Carlo-based Data Assimilation (MCMC-DA) approach (https://doi.org/10.1016/j.scitotenv.2020.143579) is applied to use GRACE and GRACE-FO TWSC as a vertical integration constraint to update W3RA's individual water storage estimates. This approach rigorously accounts for the uncertainties of model and observations.

The outputs of MCMC-DA are evaluated using in-situ USGS groundwater level data and the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product. The results indicate changes in trend and seasonality of water storage variations, for example, in southwestern (California and Nevada), southeastern (including Florida, South and North Carolina, Virginia, and Georgia), and south-central CONUS (including Texas, New Mexico, Colorado, Kansas, and Oklahoma). MCMC-DA improves the estimation of soil water in regions with high forest intensity, where ESA CCI and models reveal difficulties in capturing the soil-vegetation-atmosphere continuum. The representation of El Nino Southern Oscillation (ENSO)-related variability in water storage are found to be considerably improved after integrating GRACE(-FO) into W3RA. This new hybrid approach is found efficient for understanding the linkage between the climate variability and large-scale hydrological processes.

Keywords: CONU; Data Assimilation; Bayesian Method; MCMC; GRACE(-FO); W3RA; groundwater storage; soil water storage; USGS; ESA CCI.

How to cite: mehrnegar, N., Schumacher, M., Jagdhuber, T., and Forootan, E.: Two decades (2003-2021) of storage changes in the soil water and groundwater of CONUS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12394, https://doi.org/10.5194/egusphere-egu23-12394, 2023.