Long-term variations in catchment evapotranspiration control water availability for human societies and freshwater ecosystems, with potential negative impacts particularly during low-flow conditions. Previous studies reported increases in water balance-derived evapotranspiration for parts of Central Europe, mostly between 1980s and 2010s. However, knowledge gaps still remain around (i) the extent of these increases in space and time, and (ii) uncertainties from the catchment water balance. Here we analyse trends in water balance-derived evapotranspiration for 461 German near-natural catchments, over multiple time windows in the last six decades. We constrain uncertainties through estimates of storage changes derived from recession analysis and the use of multiple precipitation products. Results show wide-spread, significant increases in catchment evapotranspiration during 1970s–2000s (for example, average regional trends of 3.2 mm year^-2 with an uncertainty from precipitation of ±1 mm year^-2 for the period 1970–2002). Yet, catchment evapotranspiration shows no significant changes or rather a tendency to the decrease after 2000s (-3.6±1.4 mm year^-2 for Pre-Alpine catchments over 2000–2019). The directions of these variations are robust to the considered uncertainties and consistent with sparse in-situ data. We further discuss implications of these variations with respect to low-flow conditions.  This study offers a comprehensive synthesis on past variations in catchment evapotranspiration and their uncertainties, which is critical for a proper understanding of recent hydrological changes.   

How to cite: Bruno, G. and Duethmann, D.: Inter-decadal variations in water balance-derived catchment evapotranspiration in Central Europe and their uncertainties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1145, https://doi.org/10.5194/egusphere-egu24-1145, 2024.

Empirically derived sensitivities of streamflow to precipitation are often assumed to be temporally unchanging. This assumption may be unrealistic because changes in climate and storage are known to alter this relationship. We present a non-stationary regional regression approach which is functionally similar to typical elasticity estimation approaches. This is applied to 2967 catchments in the United States to estimate variability in interannual, and trends in long-term, streamflow elasticity to precipitation over a 39-year period. We show that interannual elasticity is highly variable in water-limited catchments, indicating that these are especially sensitive to year-to-year climate variability, as compared to other regions. Interannual elasticity is more often correlated with the one-year lagged standardized precipitation index than with temperature or in-phase standardized precipitation index, suggesting that antecedent soil moisture, groundwater storage, and precipitation seasonality influence streamflow sensitivity. Finally, statistically significant long-term trends in elasticity exist in some regions, but trend magnitude is generally small. These findings suggest that an assumption of stationarity in long-term average elasticity may still be appropriate at the regional scale, however, year-to-year variation in streamflow responsiveness to precipitation is often substantial.    

How to cite: Anderson, B., Slater, L., Rapson, J., Brunner, M., Dadson, S., Yin, J., and Buechel, M.: Inter-annual and long-term variability in streamflow elasticity to precipitation reveal bias in estimates of hydrological sensitivity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11244, https://doi.org/10.5194/egusphere-egu24-11244, 2024.

How to cite: Gründemann, G., Zorzetto, E., van de Giesen, N., and van der Ent, R.: Historical changes in the seasonality and timing of extreme precipitation events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14039, https://doi.org/10.5194/egusphere-egu24-14039, 2024.

Understanding precipitation variability on subseasonal-to-decadal timescales is important because of its influence on regional water resources and hydrological extremes. The response of precipitation to global warming can be understood in terms of a superposition of thermodynamic and dynamic effects. The former has been studied on a range of timescales, including ENSO variability and precipitation extremes, and is strongly constrained by Clausius-Clapeyron scaling. Changes in dynamics, however, modulate the overall change significantly and represent an important source of uncertainty in projected changes of hydrological cycle variability.

Here, we investigate changes in the variance of vertical velocity in the tropics based on monthly outputs from the Community Earth System Model 2 Large Ensemble. We find a robust decrease in the tropical vertical velocity variance under the SSP3-7.0 scenario, even in periods where the underlying ENSO-related SST variance increases. This reduction in vertical velocity variance can be explained by the deepening of the troposphere, which increases the gross moist stability and thus the energetic demands for vertical motion. Finally, we investigate the influence of reduced vertical velocity variance on precipitation probability distribution and intensity.

How to cite: Xuan, Z., Kroll, C., and Jnglin Wills, R.: Future changes in tropical vertical velocity variance and precipitation variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10963, https://doi.org/10.5194/egusphere-egu24-10963, 2024.

Flood prediction has become more popular worldwide because of the devastating socioeconomic effects of this hazard and the predicted rise in its frequency in the near future. In India, public health, civil engineering, and agriculture are all greatly affected by flooding. Anything can be flooded, with levels ranging from a few inches to many feet. They may appear suddenly or develop gradually. The intensity and frequency of flooding will frequently increase due to human modifications to the environment. More frequent and severe weather occurrences could lead to more violent floods. Utilizing data-driven and machine-learning models to solve flow- and flood-related problems has lately gained traction as a subject of study. ML model shows two key advantages over traditional physically-based models controlled by differential equation systems. Firstly, without requiring a complete a priori understanding of the phenomenon, data-driven models are able to generate reasonably accurate predictions. The quantity, quality, and variety of data that are accessible all affect how accurate the model is. This feature shows that we can avoid the complexity of problems faced by physical-based models caused by the growing number of important components by learning from observational data. Second, data-driven flood models replace numerical integration of differential equations, which is an iterative process, with non-iterative procedures like forward propagation of neural networks.

We chose to study the floods in the Barak River basin (BRB), India, a high-elevation and quickly urbanized river basin that is prone to frequent flooding because of recent evidence of the impacts of regional climate change on the hydrological cycle. Using principal component analysis (PCA), the optimal inputs were found. Decision-makers in the hydrological field of research need accurate information regarding effective predictors. This study looks into the viability of using weather input data (rainfall, humidity, evapotranspiration, temperature) to predict monthly floods using a support vector machine customized with Manta-Ray foraging optimization (SVM-MRFO). The accuracy of SVM-MRFO was assessed by comparing it against SVM tuned by the Firefly algorithm, whale optimization algorithm, Salp swarm algorithm based on mean absolute errors (MAE), root mean square errors (RMSE), determination coefficient (R²), and Nash-Sutcliffe Efficiency (NSE). Implementing the FFA, WOA, SSA, and MRFO algorithms enhances the accuracy of the SVM.

The best performance metrics, NSE of 0.9914, RMSE of 0.0182, MAE of 0.0073, and BIAS of were obtained by the SVM model constructed using the MRFO training procedure, suggesting the model's potential for use in flood forecasting. The flood models in this study are significant since they were created using a mix of different inputs and AI algorithms. In conclusion, this study demonstrated the ability of AI algorithm-based models to forecast floods and produced a number of practical methods that the flood control departments of different states, regions, and nations might employ to estimate the likelihood of floods.

How to cite: Samantaray, S., Sahoo, A., and Satapathy, D. P.: Flood prediction based on weather parameters using advanced machine learning-metaheuristic approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-95, https://doi.org/10.5194/egusphere-egu24-95, 2024.

Assessing long-term changes in groundwater is crucial for understanding the impacts of climate change on aquifers and for managing water resources. However, long-term groundwater level (GWL) records are often scarce, limiting understanding of historical trends and variability. In this study, we present a deep learning approach to reconstruct GWLs up to several decades back in time using recurrent-based neural networks with wavelet pre-processing and climate reanalysis data as inputs. GWLs are reconstructed using two different reanalysis datasets with distinct spatial resolutions (ERA5: 0.25◦ x 0.25◦ & ERA20C: 1◦ x 1◦) and monthly time resolution, and the performance of the simulations was evaluated.  Long term GWL timeseries are now available for northern France, corresponding to extended versions of observational timeseries back to the early 20th century. All three types of piezometric behaviors could be reconstructed reliably and consistently capture the multidecadal variability even at coarser resolutions, which is crucial for understanding long-term hydroclimatic trends and cycles. GWLs’multidecadal variability was consistent with the Atlantic multidecadal oscillation. From a synthetic experiment involving a modified long-term observational time series, we highlighted the need for longer training datasets for some low frequency signals. Nevertheless, our study demonstrated the potential of using DL models together with reanalysis data to extend GWL observations and improve our understanding of groundwater variability and climate interactions. 

How to cite: Chidepudi, S. K. R., Massei, N., Jardani, A., and Henriot, A.: Groundwater level reconstruction using long-term climate reanalysis data and deep neural networks , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2089, https://doi.org/10.5194/egusphere-egu24-2089, 2024.

Ice-induced winter flooding, intensified by sustained low temperatures, holds the potential for severe natural disasters, but is seldom explored probabilistically considering warming climate impacts. This study established both marginal and copula-based joint probability distributions of the upstream (QH) and downstream (QL) ice-induced floods in the Lower Yellow River, a hanging river above the ground, under four parametric scenarios (constant, time as covariates, mean air temperature as covariates, and accumulated negative air temperature as covariates), to compare historical and design flood regimes using six inference methods (UNI, OR, AND, KEN, SKEN, and COND) under air temperature changes. The results show that the Lognormal and Weibull marginal distribution models with accumulated negative air temperature as covariate parameters were optimal for QH and QL, respectively and the positive dependence between QH and QL was best described by the Gumbel-Hougaard copula. Impacts of increasing air temperature on flood downtrends and yearly change-points (1990 for QH and 1985 for QL) reduced both historical QH-QL flood magnitude combinations and projected return periods, thus denoting declining flood severities over time. Due to such flood downtrends, the most probable composition (MPC) values of 100-year design floods varied from the highest (1656 m3/s for QH and 1645 m3/s for QL using the OR method) to the lowest (624 m3/s for QH and 342m3/s for QL using the SKEN method). The average decreasing rates of MPC values before and after the discerned flood change-points were 17.4% for QH and 39.6% for QL. When conditioned on the occurrence of upstream QH having flood magnitudes less than 100-year design floods, large floods downstream exceeding a 50-year return period were inferred as improbable. This study can provide a paradigm of flood projections to meet diverse flood control objectives under changing climate.

How to cite: Li, L. and Xu, C.-Y.: Probabilistic projections of winter floods considering cumulative effect of air temperature changes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8371, https://doi.org/10.5194/egusphere-egu24-8371, 2024.

Every year, natural climate variability leads to droughts and floods which have significant impacts for ecosystems and societies. Water reservoirs like soil moisture, lakes, and groundwater act as natural buffers and balance these fluctuations by providing water supply during dry conditions and by storing water surplus after rain and snow events. Such natural fluctuations unfold over time scales that can reach several decades, making it challenging to assess the extent to which trends in water reservoirs observed over the recent past are caused by anthropogenic modifications. Such modifications can themselves be further partitioned into different terms. For instance, one can contrast the contribution of regional land and water management on the one hand, and the contribution of climate change on the other. Another frequent framework is to causally relate changes in water storage to individual changes in precipitation, evapotranspiration, and runoff.

In this contribution, we review the strengths and weaknesses of recent approaches used to causally attribute observed as well as projected changes in water availability. Ensembles of model simulations and factorial experiments typically represent a powerful way of assessing individual responses to drivers and developing a plausible and mechanistic understanding. However, contradictions also quickly emerge between global hydrological model simulations, which typically represent water reservoirs and water management more thoroughly, and Earth system (climate) model simulations, which include biogeochemical effects, like CO₂ fertilization, that are typically neglected by hydrological models. We will show that these two incomplete modeling worlds can be reconciled with large-scale satellite observations in only a few regions, while very large uncertainties remain in other parts of the world and in particular over tropical areas.

How to cite: Humphrey, V., Egli, M., Wittholm, J., Jensen, L., Sippel, S., Eicker, A., Ghiggi, G., and Knutti, R.: Long-term changes in water resources: the challenge of disentangling water management, climate change, and natural variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19475, https://doi.org/10.5194/egusphere-egu24-19475, 2024.

Climate change can considerably affect catchment-scale root zone storage capacity (S_umax) which may further influence the moisture exchange between land and atmosphere, as well as stream flow and biogeochemical processes in terrestrial hydrological systems. However, direct quantification of the evolution of S_umax over multi-decadal time periods at the catchment scale has so far been rare. As a consequence, it remains unclear how climate change affects S_umax (e.g., precipitation regime, canopy water demand) and how changes in S_umax may control the partitioning of water fluxes as well as the hydrological response at catchment scale. The objectives of this study in the upper Neckar river basin in Germany are therefore to provide an analysis of muti-decadal changes in S_umax that can be observed as a result of changing climatic conditions over the past century and how this has further affected hydrological dynamics. More specifically, we test the hypotheses that (1) S_umax significantly changes over multiple decades reflecting vegetation adaptation to climate variability, (2) changes in S_umax control water availability for evapotranspiration and thus multi-decadal deviations from long-term average positions in the Budyko framework, (3) a time-dynamic implementation of S_umax affects the hydrological response, which in return can improve the performance of a hydrological model.

We found that, indeed, a hydroclimatic condition considerably changed over time in the 1953 to 2022 study period, which was reflected by related fluctuations in the values of S_umax derived directly from observed water balance data These ΔSumax values varied by up to -20% in relatively wet decades to +20% in drier decades, which was very similar to ΔSumax obtained from calibration of a hydrological model (R²=0.95, p<0.05) in individual decades. However, evaporation estimated by the hydrological model using a long-term average S_umax for the study period was almost the same as that reproduced by the model when allowing dynamically changing root-zone storage capacities over multiple decades. In addition, no significant improvement in the reproduction of the hydrological response was observed when implementing a time-variant representation of decadally varying S_umax in the model compared with the implementation of a stationary S_umax irrespective of the hydroclimatic conditions in the individual decades.

Overall, this study provides quantitative evidence that S_umax significantly changes over multiple decades reflecting vegetation adaptation to climate variability. However, these changes are not responsible for deviations from the Budyko curves in different climatic conditions, in other words, the temporal evolution of S_umax in the study region is not a major control on the partitioning of water fluxes into evapotranspiration and drainage and does have therefore no significant effects on fundamental hydrological response characteristics of the upper Neckar catchment. This suggests that model predictions of future stream flows remain rather insensitive to uncertainties introduced by the use of time-invariant long-term average values of Sumax as model parameters.

How to cite: Wang, S., Hrachowitz, M., and Schoups, G.: Multi-decadal changes in root zone water storage capacity through vegetation adaptation to hydro-climatic variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3925, https://doi.org/10.5194/egusphere-egu24-3925, 2024.

Increased frequency of extreme and rare hydroclimatic events leading to substantial disruptions in hydrological patterns worldwide can be attributed to climate variability and change. The stationarity assumption routinely used for hydrologic design and water resources planning is no longer valid under an evolving climate. Conventional notions about hydrological stability are now challenged, considering the intricate connection between climate fluctuations and the rising prevalence of extreme weather events. High spatial and temporal variability of extreme events in tropical and semi-arid climatic regions pose challenges in assessing non-stationarity considering available data and understanding processing contributing to short and long-term changes in regional climate. This study proposes and evaluates a novel approach using nonparametric statistical tests to explore the presence of non-stationarity in hydroclimatic extremes for a tropical river basin. Further, changes in the return levels of hydroclimatic extremes under stationary and non-stationary conditions will be carried out using statistical modelling approaches. Using the proposed approach, the identification of pivotal climatic drivers, such as oceanic oscillations and atmospheric circulation patterns, and their roles in influencing hydroclimatic extremes is possible. Long-term observational data is assessed in this work to discern trends and patterns in frequency, intensity, and spatial distribution of extremes and their links to climate change and variability. The impact of shifting precipitation patterns, temperature extremes, and seasonal variations is evaluated. This research study helps to investigate the implications of climate-induced hydroclimatic extremes under diverse geographical and climatic settings. This research can help understand the impact of climate change in river basins driven by the shifts in precipitation, temperature patterns, and extremes and address water availability and management issues.

Keywords: Non-stationarity, Hydroclimatic extremes, Climatic drivers, Statistical modelling, Tropical River basin.

How to cite: Singh, A., Sharma, P. J., and Teegavarapu, R. S. V.: Navigating Hydroclimatic Extremes: Understanding the Interplay of Climate Change and Variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14414, https://doi.org/10.5194/egusphere-egu24-14414, 2024.

The hydrological system of Rift Valley Lakes in Ethiopia has recently experienced changes since the past two decades. Potential causes for these changes include anthropogenic, hydro-climatic and geological factors. The main objective of this study was to utilize an integrated methodology to gain a comprehensive understanding of the hydrological systems and potential driving factors within a complex and data-scarce region. To this end, we integrated a hydrologic model, change point analysis, indicators of hydrological alteration (IHA), and bathymetry survey to investigate hydrological dynamics and potential causes. A hydrologic model (SWAT+) was parameterized for the gauged watersheds and extended to the ungauged watersheds using multisite regionalization techniques. The SWAT+ model performed very good to satisfactory for daily streamflow in all watersheds with respect to the objective functions, Kling–Gupta efficiency (KGE), the Nash–Sutcliffe efficiency (NSE), Percent bias (PBIAS). The findings reveal notable changes of lake inflows and lake levels over the past two decades. Chamo Lake experienced an increase in area by 11.86 km², in depth by 4.4 m, and in volume by 7.8 x 108 m³. In contrast, Lake Abijata witnessed an extraordinary 68% decrease in area and a depth decrease of 1.6 m. During the impact period, the mean annual rainfall experienced a decrease of 6.5% and 2.7% over the Abijata Lake and the Chamo Lake, respectively. Actual evapotranspiration decreased by 2.9% in Abijata Lake but increased by up to 0.5% in Chamo Lake. Surface inflow to Abijata Lake decreased by 12.5%, while Lake Chamo experienced an 80.5% increase in surface inflow. Sediment depth in Chamo Lake also increased by 0.6 m. The results highlight that the changing hydrological regime in Chamo Lake is driven by increased surface runoff and sediment intrusion associated with anthropogenic influences. The hydrological regime of Abijata Lake is affected by water abstraction from feeding rivers and lakes for industrial and irrigation purposes. This integrated methodology provides a holistic understanding of complex data-scarce hydrological systems and potential driving factors in the Rift Valley Lakes in Ethiopia, which could have global applicability.

How to cite: Ayalew, A. D., Wagner, P. D., Sahlu, D., and Fohrer, N.: Unveiling Hydrological Dynamics in Data-Scarce Regions:A Comprehensive Integrated Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-58, https://doi.org/10.5194/egusphere-egu24-58, 2024.

Soil moisture (SM) is a crucial parameter in the hydrological cycle. SM wet and dry trends help to identify extreme weather events, with a rapid increase in SM suggesting heavy rainfall or flood events and a significant or prolonged decrease in SM representing drought events. SMOS and SMAP remote sensing satellites provide surface SM data globally. The surface SM is highly variable in terms of space and time. In contrast, root zone soil moisture (RZSM) is stable and retains long-term information, making it a better indicator of prolonged drought/wet conditions. In this context, SMOS RZSM is computed from the SMOS surface SM using a simple physical model to integrate surface SM information to a root zone. The study benefits from the availability of long-term series data of the SMOS RZSM on a global scale from 2010 to 2023 (approximately 14 years). Then, the SM index is developed using long-time series data of the SMOS RZSM for a better understanding of the distribution of wet and dry SM and its link to extreme events. The study primarily focuses on Australia and Europe. The results show that the developed SMOS SM index captures heavy rainfall/flood and drought conditions. The analysis determines the occurrence of floods due to La Niña and El Niño effects over Australia and the existence of drought in Europe due to the North Atlantic oscillation. This study can help to understand the interconnected factors that influence extreme climatic conditions, ranging from natural climatic phenomena to human-induced activities.

How to cite: Ojha, N., Kerr, Y., and Mialon, A.: Identification of extreme climatic events using SMOS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6799, https://doi.org/10.5194/egusphere-egu24-6799, 2024.

Droughts' persistent impact and growing use of surface water and groundwater will likely exacerbate hydrological droughts. Variations in precipitation patterns worsen the effects in particular catchment regions as a result to climate change. The end result is less groundwater recharge and multi-year droughts that impact vegetation and rivers.

An essential factor to better understand the recovery in catchments affected by drought is to understand the interaction between water availability and vegetation dynamics. At the same time, the vegetation recovery in terms of growth and productivity can also be assessed with this framework. In this study, we focus on natural catchments of central Chile which have experienced drought and multi-year drought periods with severe impacts on surface water and groundwater.

We collected 250 tree ring samples of 5 species that are susceptible to droughts in central Chile in natural catchments, and used CAMELS-CL for statistical analysis. Cross correlation analysis between surface, groundwater and vegetation dynamics was performed for each catchment to quantify the interaction between these factors. To further determine the influence of drought events on vegetation, the compound NDVI correlation and SPEI at a catchment level were used. Finally, the drought termination framework was applied to understand the recovery response of surface, groundwater and vegetation.

Our analysis identifies the typical time lag between droughts in surface water, groundwater and  their impact on vegetation growth. This is done on an annual time scale as we are looking at multi-year events. We find that the typical response time varies throughout the country, depending on the local natural water availability. These findings highlight that the multi-year drought impact on vegetation and its recovery is not uniform and should be better understand in light of climate change and the global increase in multi-year drought events.

How to cite: Vega Briones, J., De Jong, S., Nijland, W., and Wanders, N.: Surface and groundwater drought impact on natural vegetation growth and drought recovery., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12189, https://doi.org/10.5194/egusphere-egu24-12189, 2024.

Several continental regions on Earth are getting wetter, while others are drying out not only in terms of precipitation but also measured by the increase or decrease in surface water, water stored in the soils, the plant root zone, and in groundwater. Drying and wetting as seen in terrestrial, space-geodetic and remote sensing data are generally ascribed to combined effects of global warming due to greenhouse gas forcing, natural variability, and anthropogenic modification of the water cycle. Existing climate models that account for these effects fail to explain observed patterns of hydrological change sufficiently. Contrary to common beliefs, observations also do not support a simple dry-gets-dryer and wet-gets-wetter logic. Instead, the observed trends, e.g. in precipitation, soil moisture, water storage, or flood discharge, differ considerably from a simplified logic.
The CRC 1502 DETECT, a collaborative research centre of the Universities of Bonn and Göttingen, the Geomar, the Research Centre Jülich and the German National Meteorological Service DWD, has been established by the German Research Foundation DFG with the objective of closing this gap of understanding. To better comprehend the origin of these patterns, DETECT  is developing a regional coupled modeling framework further that explains past observations as realistically as possible, accounts for potential drivers of change that may have been understudied in the past, and that can predict future changes. Our modelling framework is based on the TerrSysMP platform (i.e. the coupling of ICON/COSMO, CLM and ParFlow with/without data assimilation) and it ingests various conventional and new satellite and terrestrial data sets.
By applying this modelling framework to both historical and IPCC-type simulations, DETECT will test the hypothesis that humans – through several decades of land use change, and intensified water use and management – have caused persistent modifications in the coupled land and atmospheric water and energy cycles. It is hypothesized that (1) these human-induced modifications contribute considerably, compared to greenhouse gas (GHG) forcing and natural variability, to the observed trends in water storage at the regional scale, (2) land management and land and water use changes have modified the regional atmospheric circulation and related water transports and (3) these changes in the spatial patterns of the water balance have created and magnified imbalances that lead to excessive drying or wetting in more remote regions.
We test this hypothesis for the Euro-CORDEX region. In later phases, we evaluate the transferability of our approach for regions with different environmental conditions. We will develop evidence-based sustainability criteria for land and water use activities. The presentation will provide an overview on the central hypotheses and objectives of our research programme, the study logic and common approach, as well as anticipated results and contributions to the community. After two years, we highlight some first  findings.

How to cite: Siegismund, F. and Kusche, J.: Collaborative Research Centre 1502 DETECT: 'Regional Climate Change: Disentangling the Role of Land Use and Water Management', EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20232, https://doi.org/10.5194/egusphere-egu24-20232, 2024.

The GRACE (Gravity Recovery And Climate Experiment) satellite mission as well as its successor GRACE Follow-On have monitored global and regional variability of total water storage (TWS) for the past two decades. Assimilating observations from these missions into hydrological models helps to improve modeled water storages and fluxes, to overcome deficits arising from simplifications or processes that are not considered in the model (e.g. unmodeled anthropogenic impacts), and to disaggregate GRACE observations temporally and spatially. Determining the optimal approach for assimilating these observations into hydrological models remains an ongoing area of research. The choice often depends on specific applications and the characteristics of the model itself.

In this study, we analyze the water storage dynamics of two versions of the Community Land Model (CLM) - versions 3.5 and 5 - within a GRACE data assimilation framework over a 12.5 km grid covering Europe. The analysis focuses on assessing (i) the skill of both models without data assimilation, (ii) the impact of GRACE data assimilation on the model performance and (iii) the distribution of assimilation increments to different storage compartments. We evaluate water storages and fluxes simulated by both models against independent observations such as discharge from river gauges and satellite derived soil moisture. The results offer valuable insights into the impact of advancements made in biophysical processes and the representation of the carbon cycle in CLM5. Furthermore, we discuss the effectiveness of GRACE data assimilation and its influence on the behavior of CLM3.5 and CLM5, analyzing whether the assimilation helps to address differences between the two model versions - particularly considering the advancements in CLM5 - which would underline the ability of GRACE data assimilation in mitigating model deficits.

How to cite: Ewerdwalbesloh, Y., Springer, A., and Kusche, J.: The Impact of GRACE Data Assimilation on Water Storage Dynamics in CLM3.5 and CLM5, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10340, https://doi.org/10.5194/egusphere-egu24-10340, 2024.

Geodetic observations of the Earth’s gravitational and deformational response to changes in terrestrial water storage (∆TWS) have been essential measurements to identify regions experiencing long-term wetting and drying driven by a combination of climate and anthropogenic forces. The northern Italian Plains, home to a third of the country’s population and contributing more than half of the agricultural output, have experienced a dryer-than-normal two decades. Here, we investigate what impact these dry conditions have on the long-term groundwater storage (GWS) using observations of change in terrestrial water storage (∆TWS) from the Gravity Recovery and Climate Experiment (GRACE) and the second-generation follow-on (GRACE-FO) missions and in-situ groundwater level time series from 820 wells over the period of 2003-2022. We use a wavelet time-frequency analysis to deconstruct each signal into seasonal and long-term components and identify multi-year dry and wet epochs. We find two long periods of declining groundwater storage (2003-2007, 2015-2022), two short periods of groundwater recovery (2008-2009, 2012-2014), and one period of near-zero ∆GWS (2010-2011). We find a net volume loss of 12.0 km³ from 2003-2022. Further, we validate these ∆GWS trends and total volume loss estimates using a combination of in-situ groundwater level variations and vertical land motion observed at nearly 500 Global Navigation Satellite System (GNSS) stations. These stations show poroelastic deformation over aquifers related to groundwater storage changes and elastic loading deformation that is highly correlated with predicted elastic loading displacements from GRACE(-FO) ∆TWS outside of aquifer areas. To calculate groundwater storage from groundwater level, we estimate spatially- and depth-variable aquifer storage coefficients using a combination of lithologic information and co-located well and GNSS observations. By analyzing all three datasets in combination we can evaluate the impacts of multi-year dry- and wet- periods on groundwater resources, providing essential contextual information for future water management.

How to cite: Carlson, G., Massari, C., Rotiroti, M., Preziosi, E., Bonomi, T., Wilder, A., Werth, S., Whitaker, D., Wang, T., Cowherd, M., and Girotto, M.: Groundwater storage trends in northern Italy as observed by GRACE, well measurements, and vertical land motion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15120, https://doi.org/10.5194/egusphere-egu24-15120, 2024.

This study aimed to explore the global spatiotemporal variability in hydrological drought recovery time (DRT) estimated using terrestrial water storage (TWS) and station-based precipitation data. TWS data were gathered from the Gravity Recovery and Climate Experiment (GRACE) between April 2002 and June 2017 and GRACE Follow-On (GRACE-FO) between June 2018 and September 2023. The GRACE and GRACE-FO mascon (RL06) solution were used. Precipitation data were obtained from the Global Precipitation Climatology Project (GPCP) monthly analysis product. DRT was derived from the following two approaches: (1) TWS data via storage deficit and (2) TWS and precipitation data via absolute required precipitation. Storage deficit was computed as the negative deviation of detrended TWS from climatological values. Absolute required precipitation to fill the storage deficit was estimated from the linear relationship between the cumulative detrended smoothed precipitation anomalies (cdPA) and detrended smoothed TWS anomalies (dTWSA). The end of hydrological drought was assumed as when TWS deviation turned positive for the first methodology and as when observed precipitation exceeded absolute required precipitation for the second one. Mean DRT values across continents were obtained for both the GRACE and GRACE-FO periods, and the temporal variability between these periods was explored across different continents. On average, DRT estimate was 29% higher during the GRACE period (11.2 months) than during the GRACE-FO period (8.6 months). The TWS-based method (11.5 months) yielded 38% higher DRT than did the TWS- and precipitation-based one (8.3 months). Overall, Australia exhibited the highest DRT estimate (averaging 11.3 months) among all continents for both methods, whereas Europe showed the lowest one (averaging 8.6 months), with a global average of 9.9 months. Analysis of the temporal consistency between DRT estimates from both methods revealed that 28% of estimates aligned during the GRACE period, increasing to 49% during the GRACE-FO period. In particular, the highest consistency (61%) was observed over Africa during GRACE-FO period, contrasting with the lowest consistency (17%) over Australia during the GRACE period. Overall, the consistency between the DRT estimates from the two methods increased from the GRACE period to the GRACE-FO period across all the continents by 18% to 40%, except for Europe, where consistency dropped by 3%. These findings provide insights not only into the potential of TWS data in globally estimating DRT with significant consistency but also into understanding the dynamics of global hydrological droughts, thus proving beneficial in devising management strategies for water resources.

How to cite: Çakan, Ç., Yılmaz, M. T., Dobslaw, H., Evrendilek, F., Förste, C., Ince, E. S., and Yağcı, A. L.: Spatiotemporal Variability in Hydrological Drought Recovery Time Estimations from GRACE and GRACE-FO Data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15252, https://doi.org/10.5194/egusphere-egu24-15252, 2024.