We increasingly rely on complex models to understand the functioning and fate of our planet. But what hydrological theory underlies these models and thus the conclusions we draw from them? How do old and rapidly increasing new observations help to advance both theory and models? In this contribution, we discuss the importance of functional relationships for (large-scale) hydrology. We define functional relationships as relationships between two or more variables that characterize the functioning of hydrological systems, such as relationships between forcing and response variables (e.g. precipitation and runoff). Functional relationships are not only a central part of hydrological theory, but they also inform how we contextualize and make measurements, and they help us to build, constrain, and evaluate models. To illustrate their value, we first provide an overview of some relationships in large-scale hydrology. We then show how such relationships can be used to evaluate global water models. We conclude by discussing what our current state of knowledge can tell us about what we should explore next, in particular the need for mechanistic explanations of empirical relationships and the potential of linking multiple hydrological fluxes within a unified framework.

How to cite: Gnann, S. and Wagener, T.: The value of functional relationships for large scale hydrology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3968, https://doi.org/10.5194/egusphere-egu24-3968, 2024.

Contemporary initiatives such as the Digital Twins and paradigms like the Hyper-resolution Modeling are pushing the boundaries of high-performance computing, bringing resolutions of large-scale hydrological models (HM) down to sub-kilometer, in attempts of making "Locally Relevant Hydrological Models Everywhere" a reality. As global water models converge towards this hyper-resolution, we notice the insufficient attention given to model scalability i.e., consistent simulations across different resolutions using the same set of model parameters. Besides, the way distributed HMs have been resolving the stream network with grids requires high resolution model runs to offset the errors, especially at locations with smaller catchment area, hence the calling of the hyper-resolution modeling.

We equip the mesoscale hydrological model (mHM, https://mhm-ufz.org) with a novel stream network upscaling scheme called subgrid catchment conservation or SCC. We hypothesize the conservation of the subgrid catchment area to have threefold effect in distributed HMs: 1) improvement in the consistency of model performance across modeling resolutions, 2) streamflow simulations become both plausible everywhere and locally relevant, and 3) the long-standing conundrum of streamflow estimation at multiple gauging stations within a grid cell will be solved.

The experimental setup is a single modeling domain encompassing 187 GRDC streamflow stations in the Rhine river basin. The wide range of catchment sizes at the gauges (1 km² to more than 150,000 km²), notable presence of proximate clusters of gauges, and good data availability (average availability of 30 years) makes Rhine an apt case for the hypotheses testing. We compare streamflow simulated by the SCC with the D8 stream network upscaling scheme, both with default parameter set. SCC shows remarkable streamflow scalability with nine out of 10 stations exceeding the mean flow benchmark across 1 km to 100 km model resolutions. In comparison, D8 shows poor scalability where the percentage of stations exceeding the benchmark reduces drastically from ≈80 % at 1 km to 50 % at 12 km to <5 % at 100 km. SCC performs significantly better than D8 at smaller catchments (<100 km²) e.g., KGE of 0.34 (±0.02) at Rappengraben (1 km²), at all model resolutions. This demonstrates, for the first time, the ability of a distributed HM to produce locally relevant streamflow everywhere, irrespective of model resolution. The Rhine's 25 km configuration encompasses grid cells featuring as many as 10 gauges within a single grid. The mean annual streamflow at these stations unrealistically exhibit identical values with the D8, whereas the SCC simulations yield values close to the observations at each station.

SCC requires the locations of interest (e.g., gauges) in the model configuration i.e., the catchment area conservation can not be achieved post-process. Still, SCC remains a significant advancement over the older (EAM, DMM) as well as the state-of-the-art (FLOW, IHU) stream network upscaling schemes. SCC finds practical application in switchable systems that require consistent simulations across resolutions demanded by end-users. The opportunity to improve hydrological forecasts using SCC also remains to be explored. But most importantly, the outcome of this research reminds us about "the overlooked hallmark of model reliability i.e., scalability".

How to cite: Shrestha, P. K., Samaniego, L., Kumar, R., and Thober, S.: Everywhere and Locally Relevant Streamflow Simulations in Hydrological Modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8839, https://doi.org/10.5194/egusphere-egu24-8839, 2024.

How to cite: Dorigo, W., Stradiotti, P., Preimesberger, W., Gruber, A., Formanek, M., Frederikse, T., Van der Schalie, R., Rodriguez-Fernandez, N., and Hirschi, M.: 45 years of global satellite soil moisture for hydrological and climate applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19999, https://doi.org/10.5194/egusphere-egu24-19999, 2024.

The GEMStat database, under the auspices of the UN Environment Programme and hosted by the GEMS/Water Data Centre at the International Centre for Water Resources and Global Change, currently holds over 29 million measurements for over 600 water quality parameters, from more than 18,000 stations in 91 countries and covering the time period from 1906 to 2023. The data is available via an online platform. Furthermore, the measurements under the GEMStat „open“ data policy are currently being compiled to be made available in an online repository.

The corresponding publication focusses on long-term timeseries, with continuous data records of at least 10 years, for the five parameter groups that can be included in submissions for the UN SDG 6.3.2 Level 1 indicator (acidification, nitrogen, oxygen, phosphorus, and salinity). The incentive behind this course of action is to support the use of modelling approaches for future data submission on the SDG 6.3.2 indicator.

Overall, 1,082 timeseries stations were identified providing data for at least one parameter group, with 2,904,185 data points in total. At most stations (405), data for four of the selected parameter groups are available, followed by stations where data on five parameter groups are available (285 stations). River stations make up the largest part of the stations (978 or 90%). In addition, lake stations (84 or 7.8%), groundwater stations (17 or 1.6%) and reservoir stations (3 or 0.3%) contribute to the total number of stations.

Timeseries length ranges from 10 to 114 years with a mean duration between 23.7 (Oxygen) and 30.5 years (Salinity).

The number of measurements per year ranges from 3 to 233, with a mean frequency between 9.4 (oxygen) and 14.1 (pH), indicating an overall tendency of monthly measurements for many timeseries.

Timeseries were also analyzed for significant trends in the data using prewhitened nonlinear trend analysis.

In total, 4,050 of 7,019 timeseries (57.70%) showed significant trends (p<0.05). The fraction of significant timeseries stations within a parameter group was highest for nitrogen (1,299 of 2,094 stations or 62.03%), followed by phosphorus (587 of 978 stations or 60.02%), acidification (546 of 914 stations or 59.74%), salinity (1,158 of 1,995 stations or 58.05%), and oxygen (460 of 1,038 stations or 44.32%).

The length of these timeseries together with the high sampling frequency predestines their application for model forcing in support of the SDG 6.3.2 indicator. Furthermore, this study looked at one fairly specific application of GEMStat timeseries data and more timeseries are available for other water quality parameters and could advance model development with a range of other foci. The additional trend analysis indicated potential effects of global change in more than 50% of the timeseries, highlighting regional hotspots for further in-depth analysis.

How to cite: Heinle, M., Saile, P., and Lisniak, D.: Long-term timeseries in the GEMStat Water Quality Database - temporal trends and potential for model forcing with a focus on the UN SDG 6.3.2 indicator., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16817, https://doi.org/10.5194/egusphere-egu24-16817, 2024.

The accurate estimation of bankfull discharge (Q_BF) plays a central role in multiple disciplines including geomorphology, hydrology, and ecology. For example, bankfull discharge is an essential input in many large-scale flood models which are widely used in understanding flood risk across large scales. However, in the context of extremely limited bankfull discharge observations, these Global Flood Models (GFMs) typically assume that bankfull discharge has a spatially uniform recurrence interval, with a value of 1-2 years widely adopted. In reality, many studies have found that the recurrence of bankfull discharge is highly variable. Therefore, more reliable estimates of bankfull discharge that account for river variability across different regions and climate zones are vital. Here, we train a random forest model to estimate bankfull discharge from global datasets encompassing river catchment characteristics, river geometry, topography, reservoir capacity, hydrological and climate indicators, alongside a newly compiled bankfull discharge database with over two thousand observations. The trained machine learning model is then used to develop the first estimate of bankfull discharge for 22 million km of rivers globally, using a newly developed, high-resolution, multi-threaded river network, Global River Topology (GRIT, Wortmann et al., 2023). Independent testing against observed values of Q_BF shows that the random forest model has good performance (R²=0.79), and the estimated Q_BF has better accuracy compared to the use of uniform recurrence-interval flows. This is the first study to estimate bankfull discharge for rivers at the global scale. Our dataset aims to improve bankfull representation in large-scale flood modelling, and to support river and water resources research more generally.

Wortmann, M., Slater, L., Hawker, L., Liu, Y., & Neal, J. (2023). Global River Topology (GRIT) (0.4) [Data set]. Zenodo. 10.5281/zenodo.7629907

How to cite: Liu, Y., Wortmann, M., Slater, L., Hawker, L., Neal, J., Yin, J., Boothroyd, R., Gebrechorkos, S., Leyland, J., Darby, S., Parsons, D., Griffith, H., Cloke, H., Vahidi, E., Nicholas, A., Delorme, P., and McLelland, S.: First global estimation of bankfull river discharge, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19997, https://doi.org/10.5194/egusphere-egu24-19997, 2024.

Over recent years, considerable advances have been made in Land Surface Models (LSM) to enhance the representation of small-scale heterogeneity while maintaining reasonable computational efficiency. Such is the case of HydroBlocks, which employs fine-scale clustering to define Hydrologic Response Units (HRUs) or tiles as its core modeling element. These innovations have facilitated a better representation of water and energy balances over large-scale domains by capturing local dynamics and their signature over continental processes.

While the benefits of these advances are substantial, there is still a growing need to understand surface and subsurface dynamics under these novel approaches. In our current study, we propose a novel multi-scale scheme designed to capture subsurface interactions within an LSM. This approach builds upon the abstraction introduced in HydroBlocks and addresses the lateral contributions of soil columns for local, intermediate, and regional subsurface flows. Importantly, this is achieved without compromising the computational efficiency of the already efficient HydroBlocks model. Our approach enables us to capture complex fine-scale interactions between surface and subsurface hydrological processes over continental extents, potentially providing insights that traditional models cannot achieve.

To implement this, we decompose the domain into regional units and compute the subsurface flux exchange, efficiently updating the one-dimensional vertical solution of Richard’s equation within the LSM. A convergence analysis is performed by comparing the efficiency of our framework to that of the quasi-fully distributed solution. The methodology has been tested within a 1.0°x1.0° domain in the United States to evaluate its performance. The inclusion of intermediate and regional groundwater representation led to significant shifts in soil moisture redistribution and streamflow patterns. Notably, we uncover regional water flow patterns from ridges to valleys, often underrepresented in the traditional model. Additionally, we explore the impact of spatial scale on water redistribution, offering profound insights into the uncertainties associated with groundwater structure and its influence on surface fluxes.

Our findings reveal that the multi-scale scheme converges towards a quasi-fully distributed solution for the LSM HydroBlocks emphasizing the efficacy of our method in achieving a comprehensive representation of subsurface dynamics while maintaining computational cost.

How to cite: Guyumus, D., Torres-Rojas, L., Bacelar, L., and Chaney, N.: A Multi-Scale Scheme for Simulating Subsurface Dynamics in Land Surface Models using HydroBlocks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12547, https://doi.org/10.5194/egusphere-egu24-12547, 2024.

Global hydrological models (GHMs) play a crucial role in understanding Earth's water resources. To evaluate the strengths and limitations of these models, and how their performance changes with parameter modification or calibration, model outputs are compared against observational data. Traditionally, hydrological models have been evaluated against in-situ streamflow observations. However, this can lead to incomplete assessments of models because models might simulate streamflow well while failing in other aspects, such as overall terrestrial water storage anomaly (TWSA) or the dynamics of specific storage compartments. Nowadays, geodetic and remote sensing data (time series) have become available and are suitable for model evaluation in addition to streamflow. This study takes a comprehensive look at the GHM WaterGAP2.2e by extending the evaluation beyond streamflow observations by integrating different GRAC TWSA products, snow cover fraction, and the dynamics of lake and reservoir surface areas, and the corresponding storage anomalies. The multi-variable evaluation approach is particularly valuable in identifying areas where the model might need improvement. As an example, by comparing the model against GRACE TWSA and streamflow observation, we can test the effect of increasing water storage capacity in soils or decreasing the groundwater discharge coefficient. These parameters govern the flow from groundwater to surface water bodies, offering viable options to address, for example, underestimation or overestimation of the temporal variability of GRACE TWSA when using models like WaterGAP. Evaluating the snow cover fraction model output against observed data improves the model’s ability to simulate snowpack dynamics, a crucial element for estimating seasonal water supply in areas that depend on snowfall. Furthermore, comparing the model output with observed surface areas and storage anomalies of lakes and artificial reservoirs helps to improve, for example, the estimation of surface water use and the simulation of reservoir management. Ultimately, the multi-variable evaluation approach could pave the way for creating models better suited to address the complex questions in global water research.

How to cite: Rabanizada, E., Müller Schmied, H., and Döll, P.: Efficient and systematic evaluation of the global hydrological model WaterGAP against multiple types of observation data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5402, https://doi.org/10.5194/egusphere-egu24-5402, 2024.

Wetlands play a crucial role in the Earth's system, interacting with various processes such as the hydrological cycle, energy and water exchange with the atmosphere, and global nitrogen and carbon cycles. However, the historical extent of wetlands has suffered significant losses, primarily driven by human activities, particularly in Europe, North America, China, and Southeast Asia. Because of their remote locations, northern Canada and Siberia remain relatively untouched, while South America and Central Africa face current threats. The future trajectory of wetlands is anticipated to be influenced not only by direct human actions but also by climate change. Here we present our assessment of climate-driven global change in wetland extend, focusing on the main wetland complexes. We used an approach based on the Topographic Hydrological model (TOPMODEL), and soil liquid water content projections from 14 models of the Coupled Model Intercomparison Project phase 6 (CMIP6). Our analysis reveals a consistent decrease in wetland extent in the Mediterranean, Central America, and Northern South America, with a substantial long-term loss of 28% in the western Amazon Basin under high radiative forcing (SSP370). Conversely, Central and Western Africa exhibit an increase in wetland extent, excluding the Congo Basin. Nevertheless, most of the area studied (80%) presents uncertain results, due to conflicting projection of changes between the models. Notably, we show that there is significant uncertainty among CMIP6 models regarding liquid soil water content in high latitudes, due to permafrost representation and its thawing. By narrowing our focus to 10 models that seem to best represent the thawing of permafrost, we find modest decline in the overall global area (< 5%), yet significant spatial diversity, with better model agreement. Beyond 50°N, long-term losses of 13% are noted globally, with specific areas like the Hudson Bay Lowlands experiencing a 21% decrease and the Western Siberian Lowlands a 15% decrease under high radiative forcing.

How to cite: Hardouin, L., Decharme, B., Colin, J., and Delire, C.: Assessing the Impact of Climate Change on Global Wetland Extent using CMIP6 multi-model analysis., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11522, https://doi.org/10.5194/egusphere-egu24-11522, 2024.

Climate Change involves changes in precipitation. The propagation of precipitation changes into the water cycle is complex and dependent on e.g. aridity and land cover which are themselves affected by climate change. As a result, estimating effects on water fluxes such as evaporation and runoff as well as water resources such as soil moisture is not straightforward. This study maps future changes in the seasonal cycle of precipitation across the globe, as projected by Earth system models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Additionally, we analyse the propagation of the detected seasonal precipitation surpluses and deficits into the water cycle, and determine the main underlying controls. For this purpose we determine and compare seasonal changes in precipitation, evapotranspiration, runoff and soil moisture across the next decades. While this yields a first indication of the propagation of increasing or decreasing precipitation, we furthermore calculate correlations between the considered variables in each decade as an independent measure of the relation between precipitation and the water cycle components. In this context we use partial correlations to better separate water cycle couplings from the impact of other meteorological forcings such as radiation. A particular focus of our analysis will be on uncertainties of (i) precipitation trends and (ii) their projected propagation into the water cycle across the CMIP6 model ensemble to distinguish robust patterns from areas with high uncertainties. Our analysis helps to understand changes in future water fluxes and resources and the underlying robustness, which can inform the development of Earth system models as well as water resources management.

How to cite: Stephan, R., Kroll, J., and Orth, R.: How do precipitation trends propagate through the terrestrial water cycle?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12603, https://doi.org/10.5194/egusphere-egu24-12603, 2024.

Climate change, land cover change, water use, and flow regulation are driving river streamflow changes globally, and it is crucial to understand the varying contributions of these drivers to prevent and mitigate harmful impacts caused by streamflow alteration. However, previous, scenario-based approaches on this are notably uncertain and may miss interdependencies between different drivers. Here, to overcome these shortcomings, we use a large sample of observed streamflow data globally to quantify flow regime changes and align those against trends in precipitation, evapotranspiration, water use, and damming. With this study, we achieve unprecedented coverage and detail in analysing how varying streamflow regime changes may be linked to different drivers.

We queried the Global Streamflow Indices and Metadata (GSIM) database to yield 5,220 catchments across all continents (surface area greater than than 1,000 km² and more than ten years of record available). Each catchment was assigned a flow regime change (FRC) class based on linear trends in four streamflow metrics: mean, standard deviation, high flows (95^th percentile) and low flows (5^th percentile). Within FRC classes, we further separated between catchments in which precipitation shows a decreasing or an increasing trend. Finally, within groups formed by FRCs and precipitation trends, we analysed linear trends in total evapotranspiration and water use, and increases in damming (by degree of regulation; DOR).

We find that shift down (mean, low, and high flows decreasing) and shrink (standard deviation and high flows decreasing, low flows increasing) are more common FRCs than shift up (mean, low, and high flows increasing) and expand (standard deviation and high flows increasing, low flows decreasing). Most commonly, precipitation trends are parallel to the FRC – decreasing in the shift down and shrink FRCs and increasing in the shift up and expand FRCs. This is more likely in FRCs describing a shift than in FRCs indicating a change in variability, which suggests that drivers beyond precipitation are more likely to exist in catchments that belong to the shrink and expand FRC classes. Water use trends are comparatively strong between shift down, shrink and expand FRCs but nearly nonexistent in the shift up FRC. The general direction of evapotranspiration trends agrees with precipitation trend direction in all four FRCs. When the FRC class and precipitation trend contradict (e.g. shift down FRC & increasing precipitation trend), we find that changes in water use and damming are often strong. Damming mostly affects streamflow by decreasing and homogenising flow because strongly increasing DOR is also associated with the shrink FRC but changes in DOR are minor within the shift up and expand FRCs 

Our global large-sample statistical insights agree with process-based understanding on how different human drivers affect streamflow, which provides a promising outlook on identifying the dominant drivers of streamflow change at large scales.

How to cite: Virkki, V., Sahu, R. K., Smilovic, M., Láng-Ritter, J., Porkka, M., and Kummu, M.: Statistical analysis of global river streamflow regime changes and their alignment with trends in human drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18299, https://doi.org/10.5194/egusphere-egu24-18299, 2024.

Nearly 80% of the global population faces severe water stress, yet changes in global water availability are not well quantified. Two essential factors for identifying water stress and promoting sustainable development include runoff, a flux variable reflecting water use, and freshwater storage, representing the potentially maximum water resources. Despite their importance, integrated analysis of changes in runoff/storage and their impacts on society remains in its infancy at the global scale. By leveraging the strengths of remote sensing techniques, advanced land surface models, and reanalysis data, here we quantified global changes in total water storage (TWS) and runoff over the past two decades. In addition, we proposed new indices to evaluate the relative vulnerability in water availability normalized at the global scale. The results show that 80% of global areas experienced declines in TWS and/or runoff for the past two decades, and 39% of areas suffered from water loss in both storage and runoff. The joint effects of storage-runoff limitation amplified vulnerability in water availability, reflected by not only a larger spatial domain but also higher severity relative to individual stress caused by either TWS or runoff changes. The most vulnerable regions in water availability were found across the north and central South America, south Asia, and Europe. Specific threats include the loss of solid water storage, groundwater extraction for irrigation, and climate-induced extreme heat and drought over the past two decades. Our findings provide valuable insights not only for understanding hydrologic responses to a changing climate and fast-developing society, but also for developing adaptive strategies for water-stressed hotspots.

How to cite: Li, X. and Peng, J.: Joint effects of storage-runoff limitation amplify global stress in water availability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5652, https://doi.org/10.5194/egusphere-egu24-5652, 2024.

Even in Europe, rivers and streams cease to flow during some time of the year. Little is known about these intermittent waterways at the continental scale because the spatial distribution of streamflow gauges is biased in favor of perennial river reaches. In our study, undertaken in two phases, we initially employed a two-step Random Forest (RF) modeling approach to predict the monthly time series of streamflow intermittence at a high spatial resolution across approximately 1.5 million European river reaches spanning the period from 1981 to 2019. Important predictors were computed from time series of monthly streamflow in 15 arc-sec (high-resolution HR) cells that were derived by downscaling the 0.5° (low-resolution LR) output of the global hydrological model WaterGAP. To set up the RF model, we utilized daily time series data of observed streamflow from 3706 gauging stations as the target variable, and incorporated a comprehensive set of 23 dynamic and static hydro-environmental variables as predictors. We computed that 3.8% of all European reach-months and 17.2% of all reaches were intermittent during 1981-2019.

In the subsequent phase, we implemented the developed RF model to quantify alterations in streamflow intermittence that may occur due to future climate change. This involved utilizing the bias-adjusted output of five Global Climate Models (GCMs) from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) and two Representative Concentration Pathway (RCP)-specific runs (ssp126 and ssp585). The reference period spans from 1985 to 2014, with projections for two future periods (2041-2070, 2071-2100).

We downscaled the LR output of WaterGAP runs driven by the different climate model output and recomputed the predictors that depend on WaterGAP output to generate predictor for the climate change impact assessment. Subsequently, we computed the intermittence status (classified as 0, 1-5, 6-15, 16-29, and 30-31 no-flow days in a month) for each reach-month across Europe by applying the developed RF models.

Finally, we established various indicators, such as changes from intermittent to perennial or vice versa, changes in the average annual number of intermittent months or changes in the in the inter-annual variability. Additionally, we incorporated the uncertainty associated with the utilization of five GCMs into our analysis.

How to cite: Abbasi, M. and Döll, P.: Quantifying the potential impacts of climate change on streamflow intermittence in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3816, https://doi.org/10.5194/egusphere-egu24-3816, 2024.

In the present study, assessment of the abating total water storage (TWS) in the three river basins viz Indus (IRB), Ganga (GRB), and Brahmaputra (BRB) and its associated changes in precipitation across the Indian Himalayan Region (IHR) are examined. Time lead and lag relationship among TWS and other contributory factors viz., precipitation, evaporation, runoff, snow water equivalent (SWE), soil moisture, groundwater, etc., are assessed. In the present study precipitation dataset and TWS available from Global Precipitation Measurement Mission (GPM) and Gravity Recovery and Climate Experiment (GRACE) is used respectively while other variables were extracted from ERA5. Mann-Kendall and Theil Sen estimator test is used for calculating trend of precipitation and TWS during different seasons (winter, pre-monsoon, monsoon, post-monsoon). Our study supports, there is a decreasing trend of TWS over the Indus Ganga Brahmaputra (IGB) basin, though all the basins are drying but slower during monsoon. IRB shows maximum decrease in TWS in postmonsoon whereas over GRB and BRB it is observed in premonsoon. In all seasons, heat flux distributions suggested drying, especially over the higher reaches of the IHR and certain areas of the IRB. The changes in temporal and spatial distribution of TWS over IRB indicate a rapid drop in monsoonal moisture flux. More evaporation and runoff during the monsoon season reduce the TWS process. Present work will be of utmost importance for the policy or planning for state-level governance for societal benefit.

How to cite: Yadav, M., Dimri, A. P., Mal, S., and Maharana, P.: Assessment of total water storage and other variables over the Indus, Ganga, and Brahmaputra River basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19178, https://doi.org/10.5194/egusphere-egu24-19178, 2024.

Hydrological droughts are often not limited to a single river basin but affect several basins simultaneously. The co-occurrence of droughts in different basins is influenced by meteorological processes, catchment characteristics and surface processes such as soil moisture and snow cover, the latter of which is particularly important in mountain regions.
Large-scale hydrological models are a useful tool to study the drivers of large-scale droughts and to understand their evolution in a changing climate. In recent years, these models have moved towards increasingly high spatial resolutions, making them more applicable in regions with complex heterogenous mountain topography. However, large-scale hydrological models often have simplified representations of snow and glacier processes.

Here, we set up the PCR-GLOBWB 2.0 global hydrological model, which represents snow cover by using a temperature index model with a constant degree-day factor and which has no explicit representation of glaciers, at a resolution of 30 arcseconds (approximately 1 km) over the Alps. We adapted the model to make it more suitable for mountain regions. Specifically, we (1)  improved the snow module and compare different implementations of temperature-index models and (2) implemented a new dynamic glacier module. In the new model set-up, we calibrated snow water equivalent and glacier elevation changes against observations and reanalysis products and evaluated the model over the Alps, with specific emphasis on the representation of spatial patterns in hydrological extremes. With this new set-up, we are able to tackle model issues related to excess snow accumulation and improve discharge simulations, particularly in glacierized catchments. We apply this new model set-up to the larger Alpine region to study the drivers of large-scale hydrological droughts.

How to cite: Janzing, J., Wanders, N., and Brunner, M.: High and dry: model development to improve simulations of large-scale hydrological droughts in the Alps  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9792, https://doi.org/10.5194/egusphere-egu24-9792, 2024.

Land use and land cover change (LULCC) significantly affects the drainage characteristics of catchments. Consequently, this may alter both the availability of surface water and groundwater and the vulnerability of certain areas to extreme hydrological events (e.g. pluvial flash floods due to the altered catchment hydrological response). These effects of LULCC can vary considerably from place to place, thus site-specific studies are required to investigate their impact in combination with those effects produced by other stressors (Mensah et al., 2022). Nevertheless, conducting such detailed studies will require considerable resources and time. Therefore, it is relevant to identify patterns and hotpots to better inform decision making when it is necessary to focus or save efforts in this regard. Considering this, a strategy has been proposed to be implemented for analysing the impact of LULCC processes on the hydrology of small watersheds in several European countries over the last three decades.

The impact on the drainage characteristics are determined using a hydrological model based on the SCS Curve Number (CN) approach. This approach and methods based on CN are widely used to analyse the impact of LULCC; and indeed, it has been successfully used to analyse the impacts of urbanisation on surface runoff in the contiguous United States (Chen et al., 2017).  Unlike other studies, where the main unit of analysis was administrative units, this research focusses on small watersheds. Consequently, watersheds in which LULCC has occurred can be selected and modelled independently. This approach helps to reduce the masking of effects that may be present when modelling LULCC-affected watersheds together with non-affected watersheds. Although impacts on runoff were analysed, the discussion focusses on the variation of the CN, since the SCS-CN method has limitations and it has shown slightly less accurate results than other alternative and derived methods (Walega & Salata, 2019).

For the analysis of LULCC processes, categories of change processes were defined on the basis of the 44 CORINE’s thematic classes around sub-groups of interest, considering their hydrological characteristics (expected level of water retention). For classes that suppose a more intense anthropogenic intervention (artificial surfaces and agricultural areas), the discretisation of LULCC processes were defined in more detail. Discussions and conclusions were drawn on how processes such as deforestation, regeneration and reforestation/revegetation of burnt areas, urban densification, green urbanisation or changes in crop types have affected the different drainage areas analysed around Europe.

References

Chen, J., Theller, L., Gitau, M. W., Engel, B. A., & Harbor, J. M. (2017). Urbanization impacts on surface runoff of the contiguous United States. Journal of Environmental Management, 187, 470-481, doi: https://doi.org/10.1016/j.jenvman.2016.11.017

Mensah, J. K., Ofosu, E. A., Yidana, S. M., Akpoti, K., & Kabo-bah, A. T. (2022). Integrated modeling of hydrological processes and groundwater recharge based on land use land cover, and climate changes: A systematic review. Environmental Advances, 8, 100224, doi: https://doi.org/10.1016/j.envadv.2022.100224

Walega, A., & Salata, T. (2019). Influence of land cover data sources on estimation of direct runoff according to SCS-CN and modified SME methods. CATENA, 172, 232-242, doi: https://doi.org/10.1016/j.catena.2018.08.032

How to cite: García Montealegre, J. P. and Del Jesus Peñil, M.: Land use and land cover change processes in small watersheds: A strategy for identifying patterns and hotspots at continental scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3549, https://doi.org/10.5194/egusphere-egu24-3549, 2024.

The Lake Chad is an endorheic lake in Sahelian Africa, with an extension to hyper arid areas. The lake basin is not exempt from global environmental changes which significantly affect fresh water resources across the globe. Despite a plethora of research on lake Chad, the daily and seasonal surface water dynamics is not clearly understood. This study probes to reconstruct the daily and seasonal surface water dynamics, change points and trends in lake Chad with a novel global daily surface water time series dataset (2003 – 2022). The key methods involved time series decomposition and filtering, trends analysis with Mann Kendall Tau and Sen’s Slope, and change point detection of abrupt shifts in daily and seasonal surface water time series. The results showed that lake Chad water depicts marked seasonal patterns. The maximum water area in all the pools is registered between the months of December – January and the inter-seasonal surface water area varies between ~1500 km² to ~3800 - 4000 km². On daily time scale, the southern pool shows high water area above 2400 km² at the start and end of each year with the exception of drought years (2006 – 2017). For wet years (2004, 2018, 2019, 2020, 2021), surface water area between day 1 to around day 66, and 301 to 365/6 ranges between 2200km² to about 2400km². With the exception of extreme dry years, the water area between the rest of 67 – 300 days of the year is between 1600km² – 2000km². In contrast, the northern pool’s maximum water area ranges between 1600km² to ~1700km². With the exception of 2004, 2012, 2013, 2015, 2020 and 2021, the northern pool only fluctuates between ≤ 200km² to ~800km², which only stays for few days of the year. While surface water area coverage is quasi-stable across all seasons in the southern pool, the northern pool only has minimal water coverage from April to October yearly. Mean annual water coverage in lake Chad varied from 2953km² to 3114km² between 2004 and 2021 respectively. Meanwhile between 2005 – 2012 and 2016 – 2019, surface water area is below 2500km². While the southern pool remains somewhat stable, the northern pool shows recovery and dwindling phases. Within the monitoring period, two abrupt changes were identified on the cycle of lake Chad, a decreasing trend between 2003 – 2012 (2275km²) and an increasing trend from 2013 to 2022 (2745km²), p = 0.000. In conclusion, the study found that lake Chad is slowly recovering as revealed by statistical trend analysis (Tau = 0.157, Sen’s slope = 0.0782 and p = 0.012), with an annual average increase of 28.543km².

How to cite: Fokeng Meli, R., Bachofer, F., Sogno, P., Klein, I., Üreyen, S., and Künzer, C.: Reconstructing daily and seasonal surface water dynamics in Lake Chad with Global WaterPack Time Series , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19489, https://doi.org/10.5194/egusphere-egu24-19489, 2024.

In recent years, the scientific community has directed significant attention towards understanding river-aquifer interactions due to their pivotal role in hydrological and biogeochemical processes with implications for solving diverse engineering challenges. Despite the growing focus on these interactions, most studies remain confined to local scales, hindering their incorporation into comprehensive continental-scale water resources management. Addressing this gap, our study pioneers the empirical verification of river-aquifer flow directions (characterizing losing or gaining rivers) in a tropical context. We leveraged an extensive database comprising approximately 150 thousand wells spanning the entirety of Brazil, and we developed empirical power equations using data from around 500 river gauge stations to estimate river water levels under low-flow conditions. To ascertain the flow direction of river-aquifer interactions, we compared hydraulic gradients between groundwater levels of wells and their nearest rivers. A river was classified as losing when its water levels were above those of neighboring wells, indicating potential water loss to underlying aquifers. Stringent connectivity criteria were applied, including a maximum distance of 1 km between wells and rivers, well depth not exceeding 100 meters, and exclusion of wells in confined aquifers. Our study conducted systematic robustness checks, exploring the sensitivity of the data to chosen time intervals, variations in river water levels under low-flow conditions, and the inclusion of confined aquifers. Our findings reveal that more than half of Brazilian rivers are prone to losing water to underlying aquifers. The results underscore the significance of our in-situ data-driven methodology, indicating that losing rivers, widespread throughout Brazilian territory, may serve as potential points of groundwater contamination. Particularly crucial in tropical regions with elevated organic matter input into rivers, given the inadequate wastewater treatment. The findings emphasize the critical necessity of analyzing river-aquifer interactions for effective water resource management on both local and continental scales.

How to cite: Sousa Mota Uchôa, J. G., Tarso S. Oliveira, P., S. Ballarin, A., Almagro, A., A. Meira Neto, A., Gastmans, D., Jasechko, S., Fan, Y., and C. Wendland, E.: From Field to Flow: Assessing River-Aquifer Dynamics in Tropical Regions with In-Situ Dataset Insights, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13287, https://doi.org/10.5194/egusphere-egu24-13287, 2024.

Groundwater, a critical component of the Earth's hydrological cycle, plays a pivotal role in sustaining ecosystems, supporting agriculture, and ensuring water security worldwide. Understanding the dynamics of groundwater is crucial for effective water resource management, especially in addressing the interconnected challenges of water scarcity and climate change impacts.

Groundwater monitoring data, including levels, are frequently dispersed among institutions, even within a single country, presenting challenges in accessibility and availability. The process of harmonizing this data is intricate and often demands a substantial investment of time. Moreover, the data quality is dependent on the particular source and may exhibit considerable variability.

The Global Groundwater Monitoring Network (GGMN) Programme, managed by the International Groundwater Resources Assessment Centre (IGRAC), is dedicated to collecting and disseminating updated groundwater level data from national authorities. The mission of GGMN is to facilitate the accessibility of groundwater monitoring data and information, supporting IGRAC’s efforts to provide valuable insights into the global groundwater status and trends.

In 2023, data from GGMN played a pivotal role in assessing the status and trends of groundwater in ten countries, contributing for the first time to a preliminary assessment of groundwater for the World Meteorological Organization's (WMO) "State of Global Water Resources 2022" report. In 2024, these efforts continue with an enhanced methodology to understand trends, encompassing a broader range of countries for a more comprehensive assessment.

The collective effort of collecting data for GGMN and utilizing it for the WMO report aims to produce a robust global groundwater assessment based on in-situ data. This assessment will serve as a valuable benchmark for comparison against global models and other products, such as those derived from satellite data like GRACE. The in-situ data also holds immense utility for the scientific community, providing a means to validate and strengthen existing models, ultimately contributing to a more accurate understanding of global groundwater dynamics.

How to cite: Ruz Vargas, C., Sterckx, A., and Gerges, E.: The role of the Global Groundwater Monitoring Network (GGMN) in advancing a global groundwater assessment based on in-situ data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2016, https://doi.org/10.5194/egusphere-egu24-2016, 2024.

Groundwater is a vital component of the hydrologic cycle and also the largest human and ecosystem accessible freshwater storage, which plays an important role in many hydrological processes. However, in groundwater-dominated catchments where inter-catchment groundwater flow through subsurface flow pathways, most hydrological models lack explicit representations of these transboundary surface-subsurface interactions, resulting poor performance in hydrological predictions. Additional complexity introduced by intense groundwater abstractions and management schemes are also poorly represented in current hydrological models, which hinders accurate hydrological simulations. Therefore, developing integrated modelling frameworks for simulating the interactions between surface water, groundwater and human influences is needed for accurate hydrological predictions in these regions.

DECIPHeR is a flexible hydrological modelling framework, which has demonstrated its good performance across a diverse range of catchments in Great Britain. However, in groundwater-dominated catchments, enhancements are needed in representing surface-subsurface water interactions for better model performance. This study integrates a national-scale groundwater model into DECIPHeR. We will utilize observational hydro-meteorological data to calibrate and validate the coupled model across 475 catchments. Additionally, a large sample of groundwater level data (over 3000 sites) in England will be used to further evaluate the model. Initial tests show that the coupled model outperforms DECIPHeR in Chalk catchments and also performs well (KGE>0.6) in other geology. The coupled models enable the assessment of surface-groundwater impacts, facilitating the potential quantification of human-water interactions, i.e. groundwater abstractions, on hydrological simulations. This analysis aims to support effective water supply and demand management strategies across Great Britain by providing insights into the influence of surface-groundwater interactions on the hydrological system.

How to cite: Zheng, Y., Coxon, G., Rahman, M., Woods, R., Salwey, S., and Wendt, D.: Improving the representation of surface-groundwater and human-water interactions in a coupled surface-subsurface water model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11627, https://doi.org/10.5194/egusphere-egu24-11627, 2024.

Groundwater-dependent ecosystems (GDEs) are ecosystems that rely on subsurface or surface expressions of groundwater. These ecosystems host a wide array of unique flora and fauna and provide **important** ecosystem services. However, GDEs are threatened by the unsustainable extraction of groundwater as well as climate change. Mapping the spatio-temporal dynamics of GDEs and **potential** changes therein under climate change and socio-economic developments is an essential step towards their conservation. To this end, **we have** developed a methodological framework for mapping groundwater-dependent ecosystems using the global groundwater model GLOBGM [1], run at 30 arc sec (~1 km) resolution. The advantage of a physically-based groundwater model over statistical or machine learning methods is that it allows for the reconstruction and projection of impacts of changes in climate, land use, and human water use on GDEs.

We distinguish three categories of GDEs: aquatic (rivers and lakes), (inland) wetland, and terrestrial (phreatophyte) GDEs. For each GDE category, we defined a set of criteria for identifying their distribution and degree of groundwater dependence based on groundwater levels, groundwater discharge, and land surface parameters (e.g., saturated area fraction). After calibrating the groundwater model with groundwater heads, we ran the model in both steady and transient states, applying the set of criteria to the model outputs to map the different types of GDEs, their degree of groundwater dependency, and their spatio-temporal dynamics.

We validated the model in two ways. First, we compared our simulated groundwater depths against observed groundwater depths. Our model was able to represent observed conditions for about 75% of the groundwater depth locations. Second, we validated the simulated occurrence of GDEs based on the steady-state model runs against the GDE atlas available for Australia [2], where we found a hit rate above 80%. For Australia, our transient runs revealed an overall decline in groundwater dependency between the periods 1979-1998 and 1999-2019, as measured by the average number of months that GDEs are fed by groundwater. This is corroborated by an increase in groundwater depth at the observation wells, indicating that Australian GDEs have become increasingly threatened over the past decades.

For the next step, we envision upscaling our approach to the entire globe and projecting the fate of GDEs under different global change scenarios. This, in turn, is a key step towards identifying sustainable groundwater management strategies that contribute to the conservation of GDEs and their unique biodiversity.

References

• Verkaik, J., et al., GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development Discussions, 2022. 2022: p. 1-27.
• Doody, T.M., et al., Continental mapping of groundwater dependent ecosystems: A methodological framework to integrate diverse data and expert opinion. Journal of Hydrology: Regional Studies, 2017. 10: p. 61-81.

How to cite: Otoo, N. G., Sutanudjaja, E. H., van Vliet, M. T. H., Schipper, A. M., and Bierkens, M. F. P.: Mapping the spatio-temporal dynamics of groundwater-dependent ecosystems (GDEs) with a global groundwater model. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5869, https://doi.org/10.5194/egusphere-egu24-5869, 2024.

We revisit the classic problem of determining economically optimal groundwater withdrawal rates for irrigation. The novelty compared to previous mathematical analyses is the inclusion of non-linear groundwater-surface water interaction that allows for incorporating the impact of capture, i.e. the fact that all or part of the pumped groundwater comes out of reduced surface water flow or increased recharge. We additionally included the option to internalize environmental externalities (e.g. streamflow depletion) and maximize social welfare rather than farmer’s profit. This analysis results in a fixed optimal groundwater withdrawal rate q_opt when withdrawal q remains smaller than some critical withdrawal rate (maximum capture) q_crit and provides depletion trajectories, either under competition or optimal control, if q is larger than q_crit. Based on the relative value of q, q_crit and q_opt it also yields four quadrants of distinct withdrawal strategies. Using global hydrogeological and hydroeconomic datasets we map the global occurrence of these four quadrants and provide global estimates of optimal groundwater withdrawal rates and depletion trajectories. For the quadrants with groundwater depletion (q >q_crit) we derive and compare depletion trajectories under competition, optimal control and optimal control including environmental externalities, and assessed globally where the differences between these depletion modes are small, which is known as the Gisser-Sánchez effect. We find that the Gisser-Sánchez effect is globally ubiquitous, but only if environmental externalities are ignored. The inclusion of environmental externalities in optimal control withdrawal result in notably reduced groundwater decline and larger values of social welfare in many of the major depletion areas.

How to cite: Bierkens, M., van Beek, R., and Wanders, N.: Gisser-Sánchez revisited: optimal groundwater withdrawal under irrigation including groundwater-surface water interaction and externalities., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1679, https://doi.org/10.5194/egusphere-egu24-1679, 2024.

The Global Gravity-based Groundwater Product (G3P) has evolved with a new version (V1.12), bringing substantial enhancements to our satellite-based groundwater storage anomaly dataset—a prototype for a future product within the EU Copernicus Climate Change Service. Groundwater as the world's largest distributed freshwater storage, is a vital resource for human, industrial, and agricultural needs. Despite its significance, Copernicus lacks a service delivering operational, observation-based, and globally comprehensive data on changing groundwater resources. G3P could serve as a pivotal extension to the Copernicus portfolio. Leveraging the unique capabilities of GRACE and GRACE-FO satellite gravimetry, G3P monitors subsurface mass variations employing a mass balance approach. This involves subtracting the satellite-based and partly model-based water storage compartments (WSCs) snow water equivalent, root-zone soil moisture, glacier mass and surface water storage from GRACE/GRACE-FO monthly terrestrial water storage anomalies (TWSA). Ensuring a consistent subtraction of individual WSCs from GRACE-TWSA involves filtering them similarly to GRACE-TWSA, using filters whose type and parametrization had to be derived by spatial correlation analyses. The G3P dataset spans more than two decades (from 2002 to 2023) with a monthly resolution and global coverage at 0.5-degree spatial resolution. Notable updates in V1.12 compared to previous versions include an extended data time period until September 2023, modifications of the methodology of several WSCs, and the incorporation of new evaluation results.

This study has received funding from the European Union’s Horizon 2020 research and innovation programme for G3P (Global Gravity-based Groundwater Product) under grant agreement nº 870353.

How to cite: Haas, J., Sharifi, E., Dorigo, W., Jäggi, A., Ruz Vargas, C., Boergens, E., Dahle, C., Dobslaw, H., Dussaillant, I., Flechtner, F., Lictevout, E., Kosmale, M., Luojus, K., Mayer-Gürr, T., Meyer, U., Paul, F., Preimersberger, W., Reißland, S., Zemp, M., and Güntner, A.: G3P v1.12: Advancements of a Global Groundwater Storage Anomaly Dataset from Satellite Gravimetry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17637, https://doi.org/10.5194/egusphere-egu24-17637, 2024.

In the face of climate change and human activities, Groundwater Storage (GWS) in karst aquifers of the Euro-Mediterranean region has experienced a significant decline, particularly across 60-80 percent of its terrain. We undertook a spatiotemporal characterization of karst aquifer dynamics in this region by integrating satellite observations (GRACE) and the process-based model VarKarst that simulates potential groundwater recharge in karst terrain. Utilizing the GLDAS and ERA5-Land datasets, we independently obtained Groundwater Storage Anomalies (GWSA) and Subsurface Water Storage Anomalies (SWSA) by decomposing the terrestrial water storage anomaly (TWSA) detected by GRACE. GWSA focuses explicitly on the saturated part of aquifers, while SWSA considers both the saturated and unsaturated parts. This approach is adopted to better understand the role of soil and epikarst in the groundwater recharge processes in large karst regions. Comparisons of GRACE-derived GWSA and the potential groundwater recharge calculated by the model VarKarst for the period 2002-2019 revealed the steepest GWS declines in the polar(tundra) climate zone of the Alpine region. Recharge trends were mixed, with the strongest decline in polar climates (-3.0mm/year) linked to rising temperatures (evapotranspiration). Incorporating gridded sectoral water withdrawal information is imperative for interpreting the observed spatial patterns of GWSA/SWSA. The temperate climate zones show a strong correlation between SWSA and lagged recharge, which should be due to the flow-regulating role of soil/epikarst. In addition, the current study incorporates spring discharge data from selected karst catchments to evaluate the previous analysis. Spatial scale limitations were identified for small karst catchments, as evidenced by poor correlations with spring discharge, while larger karst catchments show stronger correlations. This research highlights the importance of considering groundwater recharge processes and the epikarst dynamics when using GRACE data to assess regional karst hydrogeology. Such an integrated method will provide a clearer picture of the impacts on GWS under current climatic and anthropogenic stressors.

How to cite: Orazulike, C., Hartmann, A., Xanke, J., and Chen, Z.: Exploring the utility of GRACE measurements for characterizing large regional karst systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16541, https://doi.org/10.5194/egusphere-egu24-16541, 2024.

The quantification of present and future groundwater resources at regional scale is necessary for the implementation of national climate change adaptation plans. We present a method to compute the potential groundwater recharge (PGR) by precipitation applied specifically to the scale of France.

A simple water balance approach taking into account the maximum soil water content capacity and the land use is first applied to derive the effective rainfall estimation from the SAFRAN national meteorological reanalysis. The BaseFlow Index (BFI) computed over 611 French river basins with minor human influence on discharge is then used to assess the effective rainfall infiltration ratio for watershed with homogeneous geological lithologies. This infiltration ratio is finally applied to convert effective rainfall into potential recharge at the scale of each groundwater body in France.

A sensitivity analysis of BFI (to the automated BF separation method, the length of discharge time series, etc.) was performed. The low annual variability and uncertainty on BFI estimates allow us to consider, as an initial approximation, that the infiltration ratio remains constant over time.

To validate this global approach, in the framework of the Explore2 project, we compared computed effective rainfall and potential recharge with alternative potential recharge estimates simulated by a set of hydrological models under current condition (1976-2005). Previous computed variables have been compared with SURFEX physical surface model solving energy balance over the entire re-analysis (1958-2020). Additionally, we used Euro-Cordex climatic projections as input of our model to evaluate the future potential groundwater recharge (2021-2100). Future evolution of potential recharge shows contrasting situations between the North and the South of France which were not highlighted by previous assessments.

How to cite: Robelin, O., Lanini, S., Caballero, Y., and Sauquet, É.: Potential Groundwater Recharge at the Scale of France:  Characterization and Future Trends in the Context of Climate Change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15217, https://doi.org/10.5194/egusphere-egu24-15217, 2024.

The obscurity in the distribution of arsenic safe aquifers poses critical challenges in devising mitigation strategies. This study attempts to bridge this knowledge gap by providing insights on the distribution of redox distinct sediments, hydrogeochemical characteristics and the plausible controls that govern As distribution in the safe and unsafe aquifers. In this study, a total of 75 drillings have been conducted in 3 study sites (2 in West Bengal and 1 in Bihar, India) across ~25 km² of area in each of the sites. The results revealed that in the case of North 24 Parganas (NP) in West Bengal, a continuous layer of brown-colored sand was observed at a depth between 40 and 60 m overlain by a grey-colored sand layer, whereas in the case of Nadia (ND) in West Bengal and Bhagalpur (BHG) in Bihar, the grey-colored sand layer was prominent. The brown-colored/brownish-grey-colored sand layer was fragmentary in both ND and BHG, with small lenses found in some parts of the study area. In the case of NP, the brown sand layer was protected by an aquitard layer. Groundwater chemical analysis revealed that the majority of the grey sand aquifers in both NP and ND yielded water with a high As concentration, while >80% of the wells installed in the brown sand layers exhibited a low As concentration (<10 µg/L). However, in the case of BHG, only 56% of the wells installed in brown or brownish-grey sand were As safe. Besides, 32% of the water samples from grey sand aquifers exhibited As safe concentrations in BHG, among which most of them had high Cl/Br, SO₄, and NO₃ concentrations. This states that the ingression of surficial contaminants may have suppressed the release of As concentrations due to the availability of other potential terminal electron acceptors at the BHG study site. Overall, the continuous brown sand layer observed in NP study site can be utilized as a suitable drinking water source however the intermittent layers as observed in ND and BHG study site may not serve as a potent source in terms of safe drinking water supply.

How to cite: Bhowmik, T., Bose, O., Kumar, K., Dutta, A. D., Jha, M., Bose, N., Ghosh, A., Singh, C. K., Sengupta, P., and Mukherjee, A.: An elucidation on the distribution of arsenic safe aquifers: Introspection from the Gangetic basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1501, https://doi.org/10.5194/egusphere-egu24-1501, 2024.

Groundwater accounts for almost 99% of the available liquid freshwater present on Earth and it is the main source of drinking water. It is recharged not only by rainwater (including losing streams) and snowmelt, but also by infiltration of water used for irrigation. In this study, CATHY (CATchment Hydrology), an integrated surface–subsurface hydrological model (ISSHM), is used to quantify current and future recharge fluxes in the Venetian high plain between the Brenta and Piave Rivers. This area, with a size of around 900 km², represents an important source of drinking water supply for the Veneto region, Northeast Italy. In compliance with European directive indications, to decrease water withdrawals from the Piave River and preserve its ecological flow, the irrigation management must be reviewed. First, we calibrated CATHY through a combination of FePEST and the Shuffled Complex Evolution algorithm, whereby both the bottom of the unconfined aquifer and the hydraulic conductivity field were estimated. After validation, the model was used to simulate a scenario in which the flood irrigation method, currently the most widespread in the study area, is fully replaced by sprinkler irrigation. The results show that in response to a 50% decrease in water abstraction from the Piave River, the total recharge decreases by about 10%, with a local decrease in the groundwater level, mainly limited to wells located in areas directly affected by the change in irrigation technique and where hydraulic conductivity is higher. Overall, this work demonstrates that ISSHMs are capable of reproducing groundwater dynamics and its drivers at high resolution and regional scales, representing useful tools to investigate possible responses of hydrosystems to future land use and climate change.

How to cite: Camporese, M., Gatto, B., Furlanetto, D., Trentin, T., and Salandin, P.: Integrated surface–subsurface hydrological modeling for the assessment of groundwater recharge in the Venetian high plain, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13154, https://doi.org/10.5194/egusphere-egu24-13154, 2024.