The accurate monitoring and prediction of land water cycle components are crucial for applications in climate, hydrology, and agriculture. However, the remote sensing of ecohydrological variables, though essential, still faces challenges, especially in estimating non-directly observable factors like evaporation. Utilizing GRACE and GRACE-FO satellite data has the potential to improve global evaporation estimates and therefore to enhance our ability to understand and manage these components. Such advancements in global evaporation estimation can furthermore contribute to addressing future water management challenges, including mitigating the impacts of drought and potential groundwater reductions. To date, several remote sensing assets have been underused within the context of global evaporation estimation, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, active since 2002 and 2018, respectively. These missions can play a key role in representing surface and subsurface processes related to water redistribution, providing estimates of Terrestrial Water Storage (TWS), and enriching our ability to navigate global water resource complexities.

The goal of this research is to improve the estimates of evaporation from the Global Land Evaporation Amsterdam Model (GLEAM) by using GRACE and GRACE-FO observations. GLEAM is a set of algorithms dedicated to estimating terrestrial evaporation based on satellite observations of meteorological drivers of terrestrial evaporation, vegetation characteristics, and soil moisture (Miralles et al. 2011). In this regard, we use GRACE observations in a data assimilation approach, based on Newtonian Nudging with model and observation errors defined by triple collocation, to improve the evaporation estimates of GLEAM. The study period comprises 20 years, between January 2003 and December 2022. Preliminary results indicate that the data assimilation output is closer to reality, for instance for estimating evaporation changes in Brazil and South America.  

How to cite: Haghdoost, S., Koppa, A., Lievens, H., and Miralles, D. G.: Improving global evaporation estimation using GRACE and GRACE-FO satellite data assimilation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17588, https://doi.org/10.5194/egusphere-egu24-17588, 2024.

Evapotranspiration (ET) is a key climate indicator closely related to the water, energy, and carbon cycles. ET datasets are produced using various methods, including satellite-based observations, hydrological models, and reanalysis. However, relying on a single ET product might lead to high uncertainty due to the spatio-temporal inhomogeneity of the dataset, highlighting the crucial need for multiple available options. evapoRe addresses the pressing issue of inhomogeneous ET datasets. This package (available at https://CRAN.R-project.org/package=evapoRe) is a pivotal tool in a landscape where diverse organizations and data providers implement varying criteria, resulting in inconsistent ET datasets. evapoRe facilitates the downloading, exploration, visualization, and analysis of ET data at monthly time step and 0.25 resolution (BESS v2.0, CAMELE, ERA5, ERA5-Land, GLEAM v3.7a, JRA-55, FLDAS, GLDAS-CLSM v2.1, GLDAS-NOAH v2.1, GLDAS-VIC v2.1, TerraClimate, MERRA-2, and ETMonitor). Further, with evapoRe, Potential ET (PET) can be calculated using temperature-based methods. In this way, evapoRe enhances ET and PET analysis by integrating diverse datasets, empowering researchers to understand water cycle change and refine models for predicting droughts and climate impacts, fundamentally advancing hydrological and climate science. 

How to cite: Rahmati Ziveh, A., Rodrigo Vargas Godoy, M., Thakur, V., R. Thomson, J., Hanel, M., and Markonis, Y.: evapoRe: An R-based application for exploratory data analysis of evapotranspiration , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-577, https://doi.org/10.5194/egusphere-egu24-577, 2024.

Evapotranspiration (ET) represents a pivotal process interlinking the water, energy, and carbon cycles within the Earth's environmental systems. In the context of global climate change, the discernible escalation in global ET has been extensively documented since the early 1980s. Nevertheless, considerable uncertainties persist in appraising the trajectory of estimated ET trends, with the magnitude of the global ET trends revealed by individual estimates differing by over an order of magnitude. Here, we present a comprehensive comparison of 11 state-of-the-art global ET products, comparing them with water balance-inferred ET across 69 major global basins, and contrasting them with direct, long-term ET observations from 20 eddy covariance sites. Our findings underscore a generally inadequate performance of existing ET products in replicating global and regional ET trends. A notable revelation is that the majority of ET products falter in correctly identifying the sign of water balance-derived and/or eddy covariance-observed ET trends in over 50% of catchments/flux sites. For catchment/flux sites where the signs of ET trends are accurately identified by the products, there is a prevalent tendency towards underestimating the magnitude of these trends. In addition, we find these ET products generally perform better in estimating ET trends in relatively arid climates and in croplands where the vegetation cover is more uniform. Finally, we elucidate that misclassification of land use types and insufficient representation of human activities, such as irrigation, groundwater extraction, and large-scale water diversion, constitute primary sources of uncertainty in the estimated ET. These insights are poised to advance future data assimilation efforts and foster the development of more reliable ET products at both basin and ecosystem scales, offering decision-makers an informed basis for selecting appropriate ET products.

How to cite: Ruan, F. and Yang, Y.: Global Evaluation of Terrestrial Evapotranspiration Trend from Diagnostic Products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15706, https://doi.org/10.5194/egusphere-egu24-15706, 2024.

With rising temperatures, we can expect significant changes in the terrestrial water cycle (TWC).  Evapotranspiration (ET) represents a significant component of the TWC, linking the water, energy, and carbon cycle of the land and atmosphere. Although recent reviews showed that ET has been increasing and even accelerating since the 1980s due to an increase in the LAI (Yang et al., 2023), some studies suggest that ET might be declining (Kim et al., 2021). So, is the increase of ET product dependent? Where in the world do we find disagreement?

We processed 13 global ET products derived from reanalysis, remote sensing, synthesis, and land surface models thereby representing a wide variety of available data. For 2000-2019, we analyzed the ET slope per grid (0.25 deg 0.25 deg), and meaningful regions including biomes, land cover classes, Koeppen-Geiger regions, elevation classes, evaporation quantiles, and IPCC reference regions. Using indices for dataset similitude and concurrence, we created probability maps, which allow us to pinpoint hotspots of uncertainty and regions with a high likelihood of change.

We confirm that ET has increased for 37 % of the terrestrial land from 2000-2019. However, the direction of change in ET for 36 % of the global land area was uncertain with various products showing significant (p > 0.05) negative and positive trends. The spatial distribution of uncertainty varies greatly spatially. For example, over 60% of the area of mangroves and tropical/subtropical moist broadleaf forests and over 40 % of the area of tropical/subtropical, flooded, and montane grass- and shrublands resulted in uncertain ET changes. Some IPCC reference regions (NWS, CAF, SAM, and NSA) resulted in over 70 % of the area in uncertain ET changes. This indicates that estimating changes in ET is still product dependent.

By pinpointing the regions in which the ET products disagree on the magnitude and direction of change, we can lay the ground for the further improvement of TWC estimates.  On the other hand, dataset consensus can help to increase the credibility of hydrological and climate model evaluations and attribution studies. Overall, there is an urgent need to further constrain ET.   

Kim, S., Anabalón, A., & Sharma, A. (2021). An Assessment of Concurrency in Evapotranspiration Trends across Multiple Global Datasets. Journal of Hydrometeorology, 22(1), 231–244. https://doi.org/10.1175/JHM-D-20-0059.1

Yang, Y., Roderick, M. L., Guo, H., Miralles, D. G., Zhang, L., Fatichi, S., Luo, X., Zhang, Y., McVicar, T. R., Tu, Z., Keenan, T. F., Fisher, J. B., Gan, R., Zhang, X., Piao, S., Zhang, B., & Yang, D. (2023). Evapotranspiration on a greening Earth. Nature Reviews Earth & Environment, 4(9), Article 9. https://doi.org/10.1038/s43017-023-00464-3

How to cite: Thomson, J. and Markonis, Y.: Multi-source analysis of recent changes in global terrestrial evapotranspiration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-917, https://doi.org/10.5194/egusphere-egu24-917, 2024.

Evapotranspiration is an important process in the water balance. It accounts for water transported from the surface of the Earth to the atmosphere. For practical applications, the estimation of evapotranspiration is limited by the potential evapotranspiration (PET), which is the maximum evapotranspiration that occurs when the availability of water on the surface is unlimited. PET can be estimated based on different climate variables. When assessing climate change projections, different climate models often project different change directions for such variables. Furthermore, the reliability of the climate model projections varies for each variable. Therefore, the uncertainty associated with the estimation of PET can be large. This study assesses this uncertainty by employing ten methods commonly used to estimate PET for ten different locations across Europe, capturing different climate and physical conditions. An ensemble of ten Euro-CORDEX climate models is used to assess projected PET changes through the century under the RCP 8.5 scenario. Different climate model bias correction methods are employed to reduce the biases in the climate model outputs when compared to the reference climate. A pseudo-reality experiment is set where each climate model acts as reference to train the correction methods, which are applied to the climate models that remain in the ensemble. The uncertainty of the projected PET is compared to the reference-climate-PET using evaluation metrics. Results are relevant for decision and policy makers and professionals developing impact studies.

How to cite: Pastén Zapata, E. and Lotsari, E.: Assessing the uncertainty of the estimation of potential evapotranspiration under climate change using a pseudo-reality approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2417, https://doi.org/10.5194/egusphere-egu24-2417, 2024.

Efficient management of water resources is imperative for sustainable development, necessitating a meticulous partitioning of evapotranspiration into blue and green components. Our study focuses on the Upper Cauvery region, examining water utilization from both rainfed and irrigated sources. Modifying the Hoekstra framework in the context of employing geospatial data and machine learning techniques, we partitioned water resources into blue (ETb) and green (ETg) evapotranspiration, unravelling valuable insights. Results from the decade-long analysis (2010-2020) reveal that ETb significantly outweighs ETg in this region. Examining the temporal trends, both ETb and ETg exhibit a consistent upward trajectory over the specified period, illustrating the evolving water consumption dynamics from 2010 to 2020. The implications of our study extend to potential applications in sustainable water resource utilization and management practices, providing a valuable contribution to the scientific community and policymakers alike. The findings will also raise awareness about the importance of using water resources responsibly in this vital geographical area.

How to cite: Vasala, S. and Hassan Rangaswamy, S.: Unveiling the Sustainability Quotient: Blue and Green Evapotranspiration Dynamics Analysis in the Upper Cauvery Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1048, https://doi.org/10.5194/egusphere-egu24-1048, 2024.

Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940-2022) and relatively high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land-ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10,168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as or is more reliable than previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability, such as strong El Niño events. GTWS-MLrec dataset consists of three reconstructions based on JPL, CSR and GSFC mascons, three detrended and de-seasonalized reconstructions, and six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management.

GTWS-MLrec is available on Zenodo through https://zenodo.org/records/10040927.

How to cite: Yin, J., Slater, L., Khouakhi, A., Liu, P., Pokhrel, Y., Liu, D., and Gentine, P.: GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3945, https://doi.org/10.5194/egusphere-egu24-3945, 2024.

Accurate estimation of terrestrial water storage anomalies (TWSA) is essential for the assessment of hydrological extreme events, managing water resources, and evaluating climate change impacts. In this study, a two-step method is applied for the reconstruction of a gridded TWSA product in two study basins: Godavari (GRB), a tropical river basin in India, and the Murray-Darling river basin (MDRB) in Australia. In the first step, the probabilistic dependence structure of the target TWSA and 15 potential predictor variables is developed through Bayesian Network technique to obtain the optimal features which strongly influence the target. In the second step, the potential of Machine Learning (ML) algorithms is utilized to obtain TWSA values, considering the grid-specific features selected in the first step as input. The input set of potential predictors includes monthly TWSA simulations from Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (i.e., CTWSA) and the GLDAS Noah Land Surface Model (i.e., NTWSA) as well as meteorological variables such as precipitation and temperature for a lead time of up to 2 months and large scale climate indices such as Dipole Mode Index, North Atlantic Oscillation index, and Oceanic Niño Index (ONI). For both study basins, CTWSA and ONI are prominent features selected by the Bayesian Network that influence TWSA. After obtaining the optimal features, Machine Learning (ML) algorithms such as Convolutional Neural Network (CNN), Support Vector Regression (SVR), Extra Trees Regressor (ETR), and Stacking Ensemble Regression (SER) are employed to derive TWSA values (henceforth named as BNML_TWSA). The performances of BNML_TWSA, as well as CTWSA and NTWSA, are evaluated against GRACE TWSA for both study basins using performance metrics such as the Correlation Coefficient (R), Nash–Sutcliffe Efficiency (NSE), and Root Mean Square Error (RMSE). At GRB, ETR demonstrates superior performance at most of the grids (74.3%), followed by SVR (21.1%). In contrast, at MDRB, all four ML algorithms show similar performance: CNN, SVR, ETR, and SER, each being selected as the best models at 25.9%, 21.4%, 26.1%, and 26.6% of the grids respectively. When evaluated against GRACE TWSA, the median values of R for NTWSA, CTWSA, and BNML_TWSA across all grids are 0.78, 0.90, and 0.93, respectively, at the GRB. Similarly, for the MDRB, these values are 0.79, 0.85, and 0.87, respectively. At the GRB, the best NSE value is obtained for BNML_TWSA (0.84), while the lowest performance is observed for NTWSA (0.475). At the MDRB also, the least performance is shown by NTWSA with an RMSE value of 57.3 mm/month, and the best performance is achieved by BNML_TWSA with an RMSE of 35.0 mm/month. The proposed two-step method offers dependable estimates of TWSA compared to land surface models and hydrological models. Hence, the reconstructed TWSA (1960-2022) proves valuable during the data gap period between GRACE and GRACE-FO and the pre-GRACE period.

How to cite: Mandal, N., Das, P., and Chanda, K.: Performance of two-step technique for gap-filling and reconstruction of basin-scale Terrestrial Water Storage Anomalies (TWSA) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18755, https://doi.org/10.5194/egusphere-egu24-18755, 2024.

Runoff observations are critical for the monitoring and understanding of droughts and floods. Traditional methods for estimating runoff rely on physically-based hydrological models, which, while detailed, are often complex and computationally intensive. In contrast, recent advancements in deep learning have shown potential for more efficient and accurate runoff modeling. This study explores the efficacy of temporal neural networks for daily catchment-level runoff reconstruction in Switzerland from 1962 to 2023.

Our model, based on the long short-term memory (LSTM) architecture, is optimized on 87 catchments minimally affected by human activities. It is evaluated in an 8-fold cross validation setup and demonstrates similar performance compared to PREVAH, a distributed hydrological model that is used operationally in Switzerland. Notably, our model requires only precipitation and temperature as meteorological inputs, allowing for an extended reconstruction period back to 1962, unlike PREVAH's 1980 limitation due to its dependency on additional atmospheric forcings. In terms of Kling-Gupta efficiency, our model matches PREVAH's performance, despite its reduced data needs. We evaluate the quality of our reconstruction in terms of extreme events and trends based on the available observations and in comparison to the PREVAH simulations on the national level.

A key advantage of our neural network approach is its computational efficiency, enabling the reconstruction of daily runoff for 307 catchments that cover the entirety of Switzerland in under a minute on a high-performance GPU. This would facilitate real-time droughts and floods monitoring and support environmental scenario simulations. The findings underscore the potential of data-driven models in environmental monitoring and point towards future research in refining these models for broader applications in climate change impact assessments.

How to cite: Kraft, B., Aeberhard, W., Schirmer, M., Seneviratne, S. I., Zappa, M., and Gudmundsson, L.: A data-driven reconstruction of spatially contiguous daily small catchment runoff for flood and drought monitoring in Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16194, https://doi.org/10.5194/egusphere-egu24-16194, 2024.

Regional water resource management requires adaptation measures to cope with a changing environment and climate. Large- and regional-scale hydrological models can provide information on water cycle components and their future projections for designing these measures. Here, the plausible and consistent spatial distribution of the simulated water balance components is essential, not only when communicating the results to the relevant stakeholders.

Spatially consistent simulation results depend on the spatially distributed parameters estimated for the hydrological model. This task, however, remains a challenging step in the model set-up, especially for larger modeling domains with varying hydrometeorological conditions like Austria, ranging from high alpine areas with high rainfall sums, snow, and glaciers to low-lying areas, with semi-arid conditions exhibiting negative climatic water balances.

The main objective of this study is to analyze the impact of different objective functions (OFs) on the simulated water balance components of a regional hydrological model. The distributed rainfall-runoff model COSERO is set up for the area of Austria (89 000 km²) with a monthly temporal resolution and a target spatial resolution of 1 x 1 km². Initially estimated spatially distributed model parameters are optimized using different OFs, e.g., Nash-Sutcliffe efficiency (NSE), log-transformed NSE, Kling-Gupta efficiency (KGE), and combinations thereof.

The resulting simulations are analyzed based on the spatial distribution of simulated runoff, evapotranspiration, and groundwater recharge across the study area. Furthermore, time-series analysis of the water cycle components is performed, and selected statistical characteristics are derived.

How to cite: Zeitfogel, H., Herrnegger, M., and Schulz, K.: On the sensitivity and robustness of Austrian-wide water balance components & groundwater recharge: A regional-scale evaluation of objective functions in calibration , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7476, https://doi.org/10.5194/egusphere-egu24-7476, 2024.

Lakes, natural or artificial, are important sources of freshwater and are frequently managed to provide ecosystem services. Consequently, the water balance and water quality in lake ecosystems could be subject to different stressors associated to physical, chemical, or anthropogenic activities. Therefore, this study provides insights into the factors that influence the water balance of selected European lakes and their implications on water quality. An analysis of isotopic, chemical, and land use data using statistical and artificial intelligence models showed that climate, in particular air temperature and precipitation, played a key role in intensifying evaporation losses from lakes. Groundwater table depth and other catchment factors also had an impact on the water balance. The study also highlights that lakes at lower altitudes with shallow depths and catchments dominated by urban or crop cover were more sensitive to water balance changes. These lakes had higher evaporation-to-inflow ratios and increased levels of total nitrogen concentration in the water. However, lakes at higher elevations with deeper depths and a predominantly forested catchment area are less sensitive to changes in the water balance. These lakes, which are often of glacial origin, were characterized by lower evaporation losses and, thus, better water quality in terms of total nitrogen concentration. Overall, understanding the relationship between water balance and water quality is crucial for effective lake management and the preservation of freshwater ecosystems.

How to cite: Mercado, M. C., Rabaneda-Bueno, R., Porcal, P., Kopacek, M., Huneau, F., and Vystavna, Y.: Effects of climate and land use on water balance and water quality in selected European lakes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3402, https://doi.org/10.5194/egusphere-egu24-3402, 2024.

Freshwater resources are fundamental to supporting humanity, and measures of water scarcity have been critical for identifying where human water requirements and water availability are imbalanced. Traditional metrics for water scarcity primarily focus on actual blue water withdrawal, while the contribution of rain-fed water requirements (RWR) and water quality – dimensions with important implications for multiple societal sectors – to overall water scarcity remains unclear. Here we address this gap by explicitly merging the three dimensions of water scarcity into an integrated index (iWSI). Specifically, combining a process-based crop water model with spatially detailed information on water pollution and sector-specific withdrawals, we first develop global gridded (30 arcminute) estimates of iWSI and its individual dimensions (blue water, RWR, and water quality) averaged over the period 2001–2010. We then perform a quantitative comparison of water scarcity indices that consider different combinations of the three water scarcity dimensions, together or in isolation, and estimate their water withdrawals and associated global land area and population under conditions of monthly and annual water scarcity. We find that the global land area and population under water scarcity increases by 126% (119–133%) and 53% (49–57%) using this integrated index relative to assessments focusing only on blue water. These effects are most pronounced for populations in Africa and Asia. Examining seasonal water scarcity, we estimate that 4.4 billion people are exposed to integrated water scarcity at least one month per year – 31% more people than under blue water scarcity alone. Our research highlights that water scarcity challenges are more widespread than previously understood. As such, our findings underscore the need for actions to bring human pressure on freshwater resources into balance with both water quantity and quality, addressing previously overlooked blindspots in global water sustainability.

How to cite: Fu, Z., Liu, W., Bai, Y., van Vliet, M. T. H., Ciais, P., Davis, K. F., and Wada, Y.: Integrated water scarcity index reveals increased exposures of populations and areas to water scarcity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13855, https://doi.org/10.5194/egusphere-egu24-13855, 2024.

Hydrological modelling forms a pivotal component in assessing water balance closure and providing valuable seasonal forecasts for essential climate variables such as soil moisture and streamflow. In the pursuit of enhancing forecasting capabilities, this study employs four land surface and hydrological models (HTESSEL, JULES, mHM, and PCR-GLOBWB) driven by four distinct meteorological forcings (ERA5LAND, EM-EARTH, MSWEP, and WE5E). The investigation spans the reference period from 1993 to 2019, focusing on a comprehensive evaluation of streamflow, latent heat, runoff flux, and terrestrial water storage as integral components of the water balance equation.

The assessment begins by scrutinising the performance of observation datasets globally, aiming to discern areas of robust agreement and potential limitations. Subsequently, the simulations, generated by diverse meteorological forcings, are analysed to gauge the individual skill of each hydrological model and forcing combination.
The study then delves into a variability analysis to determine the impact of forcings on hydrological model performance. Furthermore, exploring the elasticity of runoff and streamflow to changes in precipitation adds an additional layer to understanding system dynamics. This multi-faceted approach seeks to quantify the relative contributions of meteorological forcings and hydrological models, providing insights into the intricacies of their interactions and their collective influence on model performance.

In conclusion, this research offers a  differentiated perspective on the global applicability and performance of these four hydrological models under four meteorological forcings. By systematically assessing the impacts of forcing variability and model structure, the study contributes valuable information for refining hydrological modelling practices and enhancing the accuracy of seasonal forecasts. Observational datasets are inconsistent in certain regions, where no single meteorological forcing stands out as the best performance. These areas are predominantly arid regions such as the Sahara, South Western Australia, and Eastern Brazil, in addition to mountainous regions like the Himalayas, where water balance closure poses a challenge.

How to cite: Modiri, E., Samaniego, L., Schweppe, R., Shrestha, P. K., Rakovec, O., Kelbling, M., Kumar, R., Javier Leal Rojas, J., Martínez-de La Torre, A., Robinson, E. L., Chevuturi, A., Facer-Childs, K., Blyth, E., Sutanudjaja, E., Wanders, N., and Thober, S.: Understanding Hydrological Model Performance through Variability Analysis of Observed Water Balance Components and Meteorological Forcings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17758, https://doi.org/10.5194/egusphere-egu24-17758, 2024.