Water is a key resource for agricultural production and sustainable water resources management, particularly in Mediterranean regions where water availability is highly variable. Improving irrigation management is, therefore, essential to enhance water-use efficiency. In this context, land surface models provide a valuable tool to simulate irrigation practices and assess their impacts at regional scale. This study presents a comparison of irrigation scenarios simulated with the SASER modelling chain over the agricultural irrigated areas located within the Ebro basin (northeastern Spain). SASER is a physically based and distributed hydrological modelling chain that couples SAFRAN meteorological forcing with the SURFEX modelling platform, which includes an irrigation scheme. Drainage and runoff outputs are then provided to the RAPID scheme via the Eaudyssée platform to estimate streamflow. Three irrigation scenarios were defined: default, optimal, and realistic. The default scenario uses the standard irrigation parameters of the SURFEX irrigation scheme. The optimal and realistic scenarios share irrigation parameters derived from a farmer survey conducted in the Algerri-Balaguer region (eastern part of the Ebro basin). The main difference between both lies in the irrigation threshold: the optimal scenario considers the FAO-recommended threshold, while the realistic scenario is derived from in-situ data from the survey region, reflecting local conditions and more realistic irrigation behaviour. Overall, comparing the optimal and realistic scenarios, results show an average difference of about 20% in irrigation amounts, while differences in evaporation remain below 5%, and drainage differences range between 20% and 30%. Flood irrigation zones located along the Ebro riverbed and in the delta exhibit smaller differences between scenarios. In contrast, drip irrigation areas at the confluence of the Cinca and Segre rivers show the largest discrepancies. Overall, the study demonstrates how scenario-based modelling can support water management strategies and promote sustainable irrigation in the region.

The Surface Water and Ocean Topography (SWOT) mission provides the first global measurements of river water surface elevation at reach scale, captured through the vector-based SWORD database. While these observations offer unprecedented spatial detail, most large-scale hydrological models represent rivers on gridded routing networks, creating a structural mismatch that limits the direct use of SWOT data in global analyses. A robust and scalable translation between SWORD reaches and model grid cells is therefore essential for enabling SWOT-based hydrology. Here we present a global, confidence-oriented strategy for aligning SWORD reaches with the 1/12° river network of the CTRIP routing model. The method evaluates candidate associations using several hydrologically meaningful criteria, including geographic proximity, upstream area and basin delineation coherence inherited from MERIT-Hydro, reach morphology, and alignment with D8 flow directions. Each pixel receives a confidence category that distinguishes unambiguous single-reach matches from robust or uncertain multi-reach configurations. This classification provides transparent information on mapping quality and identifies locations where model–observation alignment is intrinsically ambiguous. We demonstrate the performance of the method through a global application at 1/12° resolution. The resulting reach-to-grid associations produce spatially coherent river corridors, consistent basin topology, and near-complete coverage across observable rivers. Diagnostics across continents show that the framework performs reliably in challenging systems such as deltas, braided rivers, and multi-thread channels where simpler geometric approaches commonly fail. The final outputs include confidence-tier maps, reach–pixel match tables, and gridded river masks that translate SWOT’s vector observations into hydrologically meaningful model space. These products provide the community with a ready-to-use, reproducible translation layer that supports a wide range of SWOT-based research activities, including large-scale river characterization, network comparison, uncertainty assessment, and future assimilation experiments. The approach enables consistent use of SWOT observations in global hydrology and opens new avenues for connecting reach-scale satellite measurements with continental-scale hydrological understanding.

A chained hydrologic-hydraulic model is implemented using predicted runoff from a large-scale hydrologic model (namely ISBA-CTRIP) as inputs to local hydrodynamic models (TELEMAC-2D) to issue forecasts of water level and flood extent. The uncertainties in the hydrological forcing and in friction parameters are reduced by an Ensemble Kalman Filter that jointly assimilates in-situ water levels and flood extent maps derived from remote sensing observations. The data assimilation framework is cycled in a real-time forecasting configuration. A cycle consists of a reanalysis and a forecast phase. Over the analysis, observations up to the present are assimilated. An ensemble is then initialized from the last analyzed states and issued forecasts for next 36 hr. Three strategies of forcing data for this forecast are investigated: (i) using CTRIP runoff for reanalysis and forecast, (ii) using observed discharge for analysis, then CTRIP runoff for forecast and (iii) using observed discharge for reanalysis and keep a persistent discharge value for forecast. It was shown that the data assimilation strategy provides a reliable reanalysis in hindcast mode. The combination of observed discharge and CTRIP runoff provides the most accurate results. For all strategies, the quality of the forecast decreases as the lead time increases. When the errors in CTRIP forcing are non-stationary, the forecast capability may be reduced. This work demonstrates that the forcing provided by a hydrologic model, while imperfect, can be efficiently used as input to a hydraulic model to issue reanalysis and forecasts, thanks to the assimilation of in-situ and remote sensing observations.

Multi-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability.Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response.

The integration of satellite-based observations into hydrological models offers transformation potential for improving discharge predictions globally, especially in regions lacking in situ measurements. This study presents CTRIP-HyDAS, a global-scale hydrological data assimilation framework that merges SWOT-derived discharge observations with the CTRIP river routing model at 1/12 degrees spatial resolution. The framework was applied at the global scale and evaluated using Observing System Simulation Experiments under controlled discharge observation uncertainty scenarios (10%, 20%, and 40%). Performance metrics computed globally show widespread improvements, with Assimilation Index (AI) values exceeding 0.7 in most regions and relative errors reduced to within 5%-10% under low-error conditions. To illustrate the framework's adaptability, six representative river basins, that is, Amazon, Congo, Ganges, Indus, Mississippi, and Reka, were selected to showcase HyDAS performance under diverse hydrological regimes. A physics-based localization method enabled efficient propagation of corrections beyond the observed swath. These findings confirm the scalability and robustness of CTRIP-HyDAS for global SWOT-based assimilation and underline its potential to enhance discharge prediction and water management in data-scarce regions.

A large transient multi-scenario and multi-model ensemble of future streamflow and groundwater projections in France developed in a national project named Explore2 was recently made available. The main objective of Explore2 is to provide rich and spatially-consistent information for the future evolution of hydrological (surface and groundwater) resources and extremes in France to support adaptation strategies. The Explore2 dataset was obtained using a nested multi-scenario multi-model approach to estimate future uncertainty and to assess local climate at the catchment scale: three greenhouse gas (GHG) emission scenarios, a set of 17 combinations of Global Climate Models and Regional Climate Models (GCM/RCM), and two bias correction methods provide the meteorological forcing for nine surface hydrology models and four groundwater hydrology models (one to simulate groundwater recharge and three to simulate groundwater level). In this paper, we present the methodology underlying the dataset, the evaluation of the hydrological models against daily observations of streamflow and groundwater level, and the key messages on the impact of climate change on both mean river flows and groundwater recharge. This large set of hydrological projections shows a high model agreement on the decrease in seasonal flows in the South of France under the RCP8.5 high-emission scenario, confirming its hotspot status. The surface hydrological models agree on the decrease in summer flows across France under the RCP8.5 scenario, with the exception of northern part France. This area may indeed benefit from more active winter recharge that may counterbalance decrease in summer precipitation and increase in evapotranspiration. In addition to northern France, annual groundwater recharge is projected to increase slightly in the north-east while remaining unchanged elsewhere by the end of the century, according to the RCP8.5 scenario. In the mountainous areas, winter flows will increase as a result of higher air temperature and the high degree of agreement between the models holds regardless of the RCP considered. Unsurprisingly, the higher the GHG emission scenario, the higher the median changes. Most of these changes are organised in France along a north-south gradient, regardless of the RCP considered.

Earth-system and weather forecasting models are moving to km-scale resolutions to provide more pertinent information to society on extreme events or the impacts of climate change. As some parametrized processes can be represented explicitly, increasing spatial resolution is expected to be beneficial for the atmosphere and oceanic components. It is not obvious that the same benefits will be achieved for land-surface models (LSMs), as landscape organizing processes start to play a role. To evaluate the consequences of increasing resolution, six LSMs driven by 3-km resolution forcings are compared with their reference simulation at 50-km resolution. These high-resolution atmospheric forcing data are developed over a region covering all catchments flowing off the Pyrenees. It is shown that these forcings capture the contrasts in atmospheric conditions between mountainous areas and valleys absent at coarser resolutions. At finer resolution, the LSMs display reduced evaporation over semi-arid catchments, which cannot be explained by differences in the atmospheric forcings. The cause has to be sought in the lack of spatial redistribution of water within the catchments. The observed diurnal amplitude of land-surface temperature shows that the models do not reproduce the local minima along rivers and in irrigated areas caused by increased evaporation. We conclude that, at km-scale resolution, lateral transfers of water that organize landscapes play an important role in predicting evaporation correctly. At resolutions of a few deca-kilometres, the contribution of grid-cell lateral flows to evaporation can be neglected. However, at higher resolutions, groundwater, riparian recharge, and human water management for irrigation need to be simulated to represent realistic spatial contrasts in the surface fluxes that drive the atmosphere. We call upon the community to invest in the development of representation of these processes in LSMs, so that they are ready for higher resolution applications.

Abstract. Groundwater is a key resource for human activities, and anticipating its evolutions months in advance is a major challenge for stakeholders. Hydrological model for subsurface flows can be used for groundwater level forecasts. However, due to uncertainties in the model's forcings and parameters, forecast initial state estimation may be inaccurate. We propose the implementation of a sequential data assimilation (DA) scheme within the Aqui-FR modelling platform, aiming at improving groundwater state estimation over a regional scale for future seasonal forecasting system. We assimilated in situ groundwater level observations into a regional hydrogeological model, using a Localized Ensemble Kalman Filter (LEnKF). Two localization methods are assessed to evaluate the best way to propagate data assimilation increment from observation sites into the model space. A distance based method is compared to a correlation method, based on a variogram analysis. Both method show good performances to improve groundwater head simulations, with a root-mean-square error (RMSE) reduction of 90% compared to a reference simulation without DA [open loop (OL) run]. Experiments with validation observations sites show that the correlation method lead to a more robust DA analysis, with less degradation of the simulation compared to OL run and measurements. Hindcast experiments using reanalysis of atmospheric forcing suggest that state assimilation, in context of inertial aquifers, can help improve forecast within a six-month range. The persistence of DA correction varies within the model space domain and may be due by an initial calibration that could be improved. After a three months lead time, 75% of assimilated observation sites still show an improvement of RMSE compared to OL.

A global land data assimilation system (LDAS-Monde) forced by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis is used to simulate land surface variables (LSVs) over China from 1979 to 2019 at a spatial resolution of 0.25 degrees. LDAS-Monde is coupled with the CNRM version of the Total Runoff Integrating Pathways (CTRIP) to convert runoff into streamflow simulations. Four experiments are conducted, with and without assimilating satellite derived leaf area index (LAI) observations, with and without gauge-corrected ERA5 precipitation. Four independent reference datasets are used to assess the impact of different model setups over contrasting climate zones and land cover types. LAI assimilation tends to reduce simulated LAI, evapotranspiration (ET) and gross primary production (GPP), and increase soil moisture (SM) and streamflow. Over semi-arid areas, the corrected precipitation is generally larger than the original ERA5, leading to increased ET, SM and streamflow. Meanwhile, the overestimation of precipitation in relatively humid regions is significantly reduced, leading to a decrease in ET, SM and streamflow. Overall, LAI assimilation alone shows a general improvement for all LSVs, including GPP and ET fluxes, over regions with dense vegetation cover, but degrades streamflow. Precipitation correction shows a general improvement for all LSVs, especially for water-related LSVs (SM and river discharge), but shows little improvement for ET. The impact of LAI assimilation and precipitation correction is more pronounced over agricultural areas in southeastern China, where a wet bias of ERA5 is observed. Except for ET, the combination of LAI assimilation and precipitation correction performs best among all experiments.

In semi-arid areas, irrigation represents the largest human footprint on the water cycle. Representing irrigation in land surface and hydrological models is a challenging task as irrigation decisions depend on environmental, economic and traditional knowledge factors. The objective of this work is twofold: (1) to evaluate the performance of the new irrigation module integrated into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) model and (2) to assess the future trajectories of agricultural water use considering both climate and land use change. The evaluation of the new irrigation module in the ISBA model is done by: (1) comparing the observed and predicted fluxes by the ISBA model, with and without the activation of the irrigation module, and (2) comparing the irrigation water inputs at the level of irrigated perimeters. The evaluation shows that the integration of the new irrigation scheme in ISBA significantly improves the latent heat flux (LE) predictions for the period 2004-2014, compared to the model without this scheme. Considering several flux stations, the LE flux bias was reduced from -60 W/m 2 for the model without irrigation to −15 W/m 2 . The evaluation at the irrigated perimeter scale highlights the ability of the irrigation model to reproduce the overall magnitude and seasonality of observed irrigation water quantities despite a positive bias. The agricultural water use was then projected considering two climate scenarios and two scenarios of land use change based on the actual trend of conversion to tree crops in response to the large subsidy attributed for the conversion to drip irrigation since 2008. It is shown that (1) irrigation water use could almost double for the more extreme scenarios and (2) that most of this drastic increase is attributed to land use change, including irrigation intensification and expansion. Within this context, the results presented in this study highlight the side effect of conversion to drip irrigation largely documented in the literature and open perspectives for making informed decisions for sustainable water management at the watershed level.

The utilization of water by various socio-economic sectors has made this resource highly sought after, especially in arid to semi-arid zones where water is already scarce and limited. In this context, effective management of this resource proves to be crucial. Our study aims to: evaluate the performance of the new irrigation module in ISBA, quantify the water balance, and assess the impact of climate change and anthropogenic factors on this resource by the horizon of 2041-2060, utilizing high-resolution futuristic forcings from the study (Moucha et al., 2021). To assess the ISBA model with its new irrigation module, we initially compared observed and predicted fluxes with and without activation of the irrigation module. Subsequently, we compared irrigation water inputs at the ORMVAH-defined irrigated perimeters within the Tensift basin. The results of this evaluation showed that the predictions of latent heat flux (LE) considering all available stations in the basin shifted from -60 W/m² for the model without irrigation to -15 W/m². This indicates that the integration of the new irrigation system into ISBA significantly improves the predictions of latent heat flux (LE) over the period 2004-2014 compared to the regular model. Considering the irrigated perimeters, the study results demonstrated that the model with the integration of the irrigation module was capable of replicating the overall magnitude and seasonality of water quantities provided by ORMVAH despite a positive bias. Exploration of the water balance at the Tensift basin level revealed the ISBA model's ability, equipped with its irrigation module, to describe complex relationships among precipitation, irrigation, evapotranspiration, and drainage. Finally, the assessment of the impact of climate change and vegetation cover for the period 2041-2060, utilizing high-resolution SAFRAN forcings projected to the same horizon (Moucha et al., 2021), revealed an increase in irrigation water needs. These results are of paramount importance in the context of sustainable water resource management in arid and semi-arid regions.

At global scale, there is still considerable uncertainty about the spatial and temporal variability of water storage and fluxes at the surface of continents. This is even more critical in the context of global climate change and the increasing human pressure on water resources. Despite this context, the following scientific questions remain difficult to answer, due to the coarse spatio-temporal resolution of current data: what is the global distribution of the heterogeneous change undergone by continental surface waters? What is the impact of anthropogenic pressure on water flows and stocks? What is the impact of these changes on the frequency and intensity of hydrological extremes (high and low waters)? To answer these questions, the Global Climate Observing System (GCOS) has identified river levels/discharges and lake/reservoir levels/volumes as essential climate variables, and recommends daily sampling (GCOS, 2022). Besides, extreme events, such as floods or droughts, cover a wide range of spatio-temporal scales. At present, water volume variations can only be observed by satellite at the coarsest scales (and are therefore of interest only for floods on the scale of the world's largest watersheds). The lack of observation of these events in basins with little or no in situ instrumentation is a major issue to understand, simulate and forecast these events. Observing these events globally, at least on a daily scale, would make it possible to quantify local flooding, thus greatly improving our knowledge of these events.One of the main issue to tackle these questions is the still rather coarse temporal sampling of current satellite missions, particularly altimetry missions. To overcome it, we are proposing the SMall Altimetry Satellites for Hydrology (SMASH) mission. This is a constellation of around 10 compact nadir radar altimeters optimized to provide daily observations of water levels in rivers, lakes and reservoirs along the constellation tracks. The specifications of the SMASH mission are the following: daily temporal sampling, observe water bodies larger than 100 m x 100 m and rivers as narrow as 50 m, with an accuracy on water elevation ~10 cm, and should provide products in near-real time and over the long term (10 years) in open access (open science and FAIR principles).Combining "high temporal frequency/low spatial frequency" measurements from the SMASH mission with "high spatial frequency/low temporal frequency" measurements from swath altimetry missions (current SWOT or futur Sentinel-3 Next Generation Topography missions) would cover unprecedented time and space scales and should open new fields of research.

The study and understanding of the water cycle is vital, and especially significant due to challenges like climate change and effective water management, among others. The work herein presents a comparison designed in such a way that it covers different spatial scales (at European, basin, and gauging station levels), as well as providing results that offer a characterization of discharge as complete as possible. For this, standard and well known metrics, like NSE and KGE are given, but also low, medium, and high flows are considered through percentile analysis and specific metrics. Observations used in this study belong to the Global Runoff Data Centre (GRDC), which provides data over the globe, and French and Spanish public databases. An offline simulation of the CTRIP routing model was used to simulate river discharge over the period 1993 to 2019. It was forced with surface and subsurface runoff from the CERRA-Land European regional reanalysis at a spatial resolution of 5.5 km.This study is conducted within the framework of the CERISE project (grant agreement No101082139). The results will enable us to evaluate the hydrological quality of the CERRA-Land reanalysis. Additionally, our contributions aim to enhance future reanalyses, thereby improving the next generation of Copernicus Climate Change Service (C3S) Earth system reanalyses.

Phreatic groundwater hydrology has a well-documented influence on the land water/energy/carbon cycles. To capture the resilience of the biosphere to dry spells in land surface models, it is particularly crucial to incorporate groundwater dynamics. With the ISBA-CTRIP land surface system, it is possible to perform a coupled simulation of the land surface fluxes and groundwater hydrology. Here, we evaluate this model configuration over Belgium, and focus on the quality of the simulated groundwater dynamics, soil moisture and resulting surface fluxes. A network of piezometer and eddy covariance towers is used to validate the model outcomes. Furthermore, the sensitivity of the model parametrization is analyzed (considering different pedotransfer functions), and the impact of groundwater coupling on the surface fluxes is quantified.

Abstract. In the context of increasing water stress and climate change, the assessment of changes in groundwater resources is a major challenge for water decision-makers. As part of the EXPLORE2 project, the aim of this study is to estimate changes in groundwater levels over France during the 21st century. We used the hydrogeological modelling platform AquiFR together with 36 regional climate projections from Eurocordex (CMIP5) from three Representative Concentration Pathways (RCPs), bias-corrected according to a state-of-the-art method: RCP2.6, RCP4.5 and RCP8.5. The future evolution of groundwater is assessed using the standardized piezometric level index, a normalized indicator that provides return periods based on the distribution value over a reference period, here 1976–2004. We found significant scatters between regional climate models and RCPs. Overall, a rise in groundwater levels, affecting most of the study area, is the dominant signal, especially in northern France. This result is in contrast to previous studies in this area. Under RCP8.5 (highest greenhouse gas emissions scenario), the evolution of the occurrence of current 10-year return period events shows a significant increase in the risk of high groundwater levels mostly on the northern part of France, together with an increase in the 10-year low groundwater levels mostly observed in South of France, which highlights a North-South differentiation. The increase in high and low flow events is quite common in surface hydrology, but is less common for groundwater, which has a longer residence time. In order to better reflect the uncertainties, 4 storylines based on the RCP8.5 scenario have been selected to be representative of possible futures that can illustrate the impacts of worst-case scenarios and help decision-makers to adopt sustainable groundwater management policies.

Flood inundation mapping for gauged and ungauged basins relies on chained hydrologic-hydrodynamic models, combined with multi-source remote sensing (RS) datasets and in-situ gauge measurements when available. In this work, a large-scale hydrologic model provides forcing data to a high-fidelity local hydrodynamic model. The latter acts as an advanced interpolator, bridging the gap in both space and time between the high-frequency yet sparse in-situ measurements and the large-coverage but less frequent satellite data gathered from various Earth Observation (EO) missions. These data are combined with physics-based equations using data assimilation (DA) algorithms. This study presents a novel use of nadir and off-nadir altimetry data from the Sentinel-6 (S6) mission, processed with Fully-focused SAR (FFSAR) algorithms, alongside Sentinel-1 (S1) SAR-derived flood extents, for DA over the Garonne River. Using a dual state-parameter Ensemble Kalman Filter (EnKF), it is shown that assimilating S6 altimetry data brings significant improvements along the riverbed, as well as addressing gaps left by other remote sensing datasets. It was demonstrated that DA allows for the combination of various EO datasets, overcoming the limitations of spatial RS low-revisit frequency and improving the representation of the flood dynamics in the riverbed and the floodplains.

The water in Earth's rivers propagates as waves through space and time across hydrographic networks. A detailed understanding of river dynamics globally is essential for achieving accurate knowledge of surface water storage and fluxes to support water resources management and water‐related disaster forecasting and mitigation. Global in situ information on river flows are crucial to support such an investigation but remain difficult to obtain at adequate spatiotemporal scales, if they even exist. Many expectations are placed on remote sensing techniques as key contributors. Despite a rapid expansion of satellite capabilities, however, it remains unclear what temporal revisit, spatial coverage, footprint size, spatial resolution, observation accuracy, latency time, and variables of interest from satellites are best suited to capture the space‐time propagation of water in rivers. Additionally, the ability of numerical models to compensate for data sparsity through model‐data fusion remains elusive. We review recent efforts to identify the type of remote sensing observations that could enhance understanding and representation of river dynamics. Key priorities include: (a) resolving narrow water bodies (finer than 50–100 m), (b) further analysis of signal accuracy versus hydrologic variability and relevant technologies (optical/SAR imagery, altimetry, microwave radiometry), (c) achieving 1–3 days observation intervals, (d) leveraging data assimilation and multi‐satellite approaches using existing constellations, and (e) new variable measurement for accurate water flux and discharge estimates. We recommend a hydrology‐focused, multi‐mission observing system comprising: (a) a cutting‐edge single or dual‐satellite mission for advanced surface water measurements, and (b) a constellation of cost‐effective satellites targeting dynamic processes.

Predicting and managing water resources at regional scale under different climate and socio-economic scenarios is crucial to support drinking water supply and other sectors. At the same time, protecting rivers and wetlands from pollutions and droughts is essential and must include groundwater given its contribution to surface water. Yet, assessing temporal and spatial variability of groundwater contributions to surface water is constrained due to limited observations. This study aims to quantify the spatio-temporal distribution of groundwater discharge (hereafter called "GW discharge") zones, i.e. the groundwater flow to rivers and wetlands, estimated by calibrated regional groundwater flow models (French AquiFR platform). We compare simulation results with two types of surface observations: (i) the spatial distribution of surface water (BD TOPO, RAMSAR and Natura 2000 database) and (ii) an innovative datasets, the river intermittence observed in headwaters (ONDE network). Results show that simulated GW discharge zones are consistent with the observed location of rivers and wetlands. Time variations in GW discharge are well correlated with the intermittence observed at 396 of the 515 selected stations. Of these, groundwater model continues to feed surface water upstream of the station for similar to 75 % of observed river drying up events, which may be consistent with a small alluvial flow. The groundwater withdrawals are shown to have a strong impact on the GW discharge and thus on the river intermittence.

By simultaneously integrating the measurements from the Surface Water and Ocean Topography (SWOT) satellite and those from other Earth-observing satellites into hydrological modelling systems, we could transform our understanding of the global terrestrial water cycle. This opportunity comes with big challenges for the scientific community.