Seasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. However, the distribution of seasonal snow in mountains has long been uncertain, for lack of consistent, high resolution satellite retrievals over mountains. Recent research has shown the potential of the Sentinel-1 radar satellite to map snow depth at sub-kilometer resolution in mountainous regions. In this study we assimilate these new snow depth retrievals into the Noah-Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform to provide simulations of routed river discharge. The results show a reduction in the systematic underestimation of snow depth, going from 38 cm for the open loop (OL) to 11 cm for the data assimilation (DA) experiment. The mean absolute error similarly improves from 44 cm to 37 cm with DA, with an improvement at 59% of the in situ sites. The DA updates in snow depth results in enhanced snow water equivalent and discharge simulations. The systematic negative bias in the OL is mostly resolved, and the median temporal correlation between discharge simulations and measurements increases from 0.61 to 0.73 for the DA. Therefore, our study demonstrates the utility of the S1 snow depth retrievals to improve not only snow depth amounts, but also the snow melt contribution to river discharge, and hydrological modeling in general.

High-resolution water budget estimates benefit from modeling of human water management and satellite data assimilation (DA) in river basins with a large human footprint. Utilizing the Noah-MP land surface model, in combination with an irrigation module, Sentinel-1 backscatter and snow depth observations, we produce a set of 0.7-km$^2$ digital water budget replicas of the Po river basin (Italy) for 2015-2023. The results demonstrate that irrigation modeling consistently improves the seasonal soil moisture variation and summer streamflow at all gauges in the valley after withdrawal of irrigation water from the streamflow (12\% error reduction relative to observed low summer streamflow), even if the basin-wide irrigation amount is underestimated. Sentinel-1 backscatter DA for soil moisture updating strongly interacts with irrigation modeling: when both are activated, the soil moisture updates are limited, and the simulated irrigation amounts are reduced. Backscatter DA systematically reduces soil moisture in the spring, which improves downstream spring streamflow. Assimilating Sentinel-1 snow depth retrievals over the surrounding Alps and Apennines further improves spring streamflow in a complementary way (2\% error reduction relative to observed high spring streamflow). Despite the seasonal improvements, irrigation modeling and Sentinel-1 backscatter DA cannot significantly improve short-term or interannual variations in soil moisture, irrigation results in a systematically prolonged high vegetation productivity, and snow depth DA only impacts the deep snowpacks. These findings help to advance the design and production of digital water budget replicas for river basins.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage anomaly (VCWS) on 26 major world rivers. We find mainstem VCWS climatology amplitude (VCWSCA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.2%, range 0.05-13.8% across world rivers), despite the fact that mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate VCWSCA (within {plus minus}50%) in only 19.2 % and 23.1 % of rivers considered (by model). These findings demonstrate VCWS is a useful measurement for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

Here, we develop and test an artificial intelligence (AI)-based approach to monitor major Brazilian aquifers. The approach combines Gravity Recovery and Climate Experiment (GRACE) data and ground-based hydrogeological measurements from Brazil’s Integrated Groundwater Monitoring Network at hundreds of wells distributed in twelve aquifers across the country. We use a model ensemble composed of four different AI models: Extreme Gradient Boost, Light Gradient Boosting Model, CatBoost and Multilayer Perceptron. The approach is further boosted with wavelet and seasonal decomposition processes applied to GRACE data. To determine the sensitivity of the AI approach to data availability, we propose four experiments combining hydrogeological measurements from different aquifers. Groundwater storage estimates from the Global Land Data Assimilation System (GLDAS) are used as the benchmark. The AI approach successfully reproduces groundwater storage estimates ​​at all Brazilian aquifers. Results show that the proposed approach outperforms GLDAS in all experiments, with an average Nash-Sutcliff efficiency of 0.91 and an average RMSE of 0.43cm for the experiment that covers all monitored wells in Brazil. GLDAS resulted in -1.311 and 5.84cm, respectively. This study demonstrates that combining satellite data and AI can be a cost-effective alternative to monitor poorly equipped aquifers at the continental scale.

It is known that representing wetland dynamics in land surface modeling improves models’ capacity to reproduce fluxes and land surface boundary conditions for atmospheric modeling in general circulation models. This study presents the development of the full coupling between the Noah-MP land surface model (LSM) and the HyMAP flood model in the NASA Land Information System and its application over the Inner Niger Delta (IND), a well-known hot-spot of strong land surface-atmosphere interactions in West Africa. Here, we define two experiments at 0.02º spatial resolution over the 2002-2018 period to quantify the impacts of the proposed developments on IND dynamics. One represents the one-way approach for simulating land surface and flooding processes (1-WAY), i.e., Noah-MP neglects surface water availability, and the proposed two-way coupling (2-WAY), where Noah-MP takes surface water availability into account in the vertical water and energy balance. Results show that accounting for two-way interactions between Noah-MP and HyMAP over IND improves all selected hydrological variables. Compared to 1-WAY, evapotranspiration derived from 2-WAY over flooding zones doubles, increased by 0.8mm/day, resulting in an additional water loss rate of ~18,900km3/year, ~40% drop of wetland extent during wet seasons and major improvement in water level variability at multiple locations. Significant soil moisture increase and surface temperature drop were also observed. Wetland outflows decreased by 35%, resulting in a substantial a Nash-Sutcliffe coefficient improvement, from -0.73 to 0.79. It is anticipated that future developments in global water monitoring and water‐related disaster warning systems will considerably benefit from these findings.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage (CWS) change on 26 major world rivers. We find mainstem volumetric CWS climatology amplitude (CA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.78%, range 0.04-12.54% across world rivers), despite mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate CA (within ±50%) in only 11.5 % (CaMa-Flood) and 30.7 % (HyMap) of rivers considered. These findings demonstrate volumetric CWS is a useful quantity for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

Understanding the impacts of climate on surface water hydrology is required to predict consequences and implications on freshwater habitats, ecological assets, and wetland functions. Although the Congo basin is considerably a freshwater-rich region, largely characterised by numerous water resources after the similitude of the Amazon basin, recent accounts of droughts in the basin are indications that even the most humid regions of the world can be affected by droughts and its impacts. Given the scarcity and limited availability of hydrological data in the region, GRACE (Gravity Recovery and Climate Experiment) observations are combined with model and SPEI (standardized precipitation evapotranspiration index) data to investigate the likelihood of such impacts on the Congo basin’s surface water hydrology. By integrating multivariate analysis with support vector machine regression (SVMR), this study provides some highlights on the characteristics (intensity and variability) of drought events and GRACE-derived terrestrial water storage (TWS) and the influence of global climate on the Congo river discharge. The southern section of the basin shows considerable variability in the spatial and temporal patterns of SPEI and extreme droughts over the Congo basin appear to have persisted with more than 40% coverage in 1994. However, there has been a considerable fall in drought intensities since 2007 and coincides with periods of strong positive anomalies in discharge (i.e., 2007-010). GRACE-derived TWS over the Congo basin is driven by annual fluctuations in rainfall (r = 0.81 at three months phase lag) and strong inter-annual variations of river discharge (r = 0.88, α= 0.05). Generally, results show that changes in the surface water variations (from gauge and model output) of the Congo basin is a key component of the GRACE water column. The outputs of the SVMR scheme indicate that global climate through sea surface temperature anomalies of the Atlantic (r = 0.79, α= 0.05), Pacific (r = 0.79, α= 0.05), and Indian (r = 0.74, α= 0.05) oceans are associated with fluctuations in the Congo river discharge, and confirm the importance of climatic influence on surface water hydrology in the Congo basin.

Satellite observations of coastal Louisiana indicate an overall land loss over recent decades, which could be attributed to climate- and human-induced factors, including sea level rise (SLR). Climate-induced hydrological change (CHC) has impacted the way flood control structures are used, altering the spatiotemporal water distribution. Based on “what-if” scenarios, we determine relative impacts of SLR and CHC on increased flood risk over southern Louisiana and examine the role of water management via flood control structures in mitigating flood risk over the region. Our findings show that CHC has increased flood risk over the past 28 years. The number of affected people increases as extreme hydrological events become more exceptional. Water management reduces flood risk to urban areas and croplands, especially during exceptional hydrological events. For example, currently (i.e., 2016-2020 period), CHC-induced flooding puts an additional 73km2 of cropland under flood risk at least half of the time (median flood event) and 65km2 once a year (annual flood event), when compared to a past period (1993-1997). A ten- to twenty-fold increase relative to SLR-induced flooding. CHC also increases population vulnerability in southern Louisiana to flooding; additional 9900 residents currently live under flood risk at least half of the time, and that number increases to 27,400 for annual flood events. Residents vulnerable to SLR-induced flooding is lower (6000 and 3300 residents, respectively). Conclusions are that CHC is a major factor that should be accounted for flood resilience and that water management interventions can mitigate risks to human life and activities.