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To overcome the computational cost of numerical models, we propose a deep-learning approach inspired by hydraulic models that can simulate the spatio-temporal evolution of floods. We show that the model can rapidly predict dike breach floods over different topographies and breach locations, with limited use of ground-truth data.

From the surface, it is hard to tell where underground cave systems are located. We developed a computer model to create maps of the probable cave network in an area, based on the geologic setting. We then applied our approach in reverse: in a region where an old cave network was mapped, we used modeling to test what the geologic setting might have been like when the caves formed. This is useful because understanding past cave formation can help us predict where unmapped caves are located today.

People induce and modify droughts. However, we do not know exactly how relevant human and natural processes interact nor how to evaluate the co-evolution of people and water. Prospect theory can help us to explain the emergence of drought impacts leading to failed welfare expectations (“prospects”) due to water shortage. Our approach helps to explain socio-hydrological phenomena, such as reservoir effects, and can contribute to integrated drought management considering the local context.

When it rains, water remains in the ground for variable amounts of time before it is taken up by plants or becomes streamflow. Understanding how long water stays in the ground before it is taken up by plants or becomes streamflow helps predict what will happen to the water cycle in future climates. Some studies suggest that plants take up water that has been in the ground for a long time; in contrast, we find that plants take up a significant amount of recent rain.

A spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method is proposed. The calibration model is built by post-processing ECMWF raw forecasts, while the bridging models are built using atmospheric intraseasonal signals as predictors. The calibration model and bridging models are merged through a Bayesian modelling averaging (BMA) method. The results indicate that the newly developed method can generate skilful and reliable sub-seasonal precipitation forecasts.

This study shows a methodological approach using machine learning techniques to disentangle the relationships among the variables in a synthetic dataset to identify suitable variables that control the hydrologic response of the slopes. It has been found that not only is the rainfall responsible for the water accumulation in the slope; the ground conditions (soil water content and aquifer water level) also indicate the activation of natural slope drainage mechanisms.

Predicting flood magnitude and location helps decision-makers to better prepare for flood events. To increase the speed and availability of data during flooding, this study presents a vision-based framework for measuring water levels and detecting floods. The deep learning models use time-lapse images captured by surveillance cameras to detect water extent using semantic segmentation and to transform them into water level values with the help of lidar data.

Anthropogenic global warming accelerates the drought evolution in the water cycle, increasing the unpredictability of drought. The evolution of drought is stealthy and challenging to track. This study proposes a new framework to capture the high-precision spatiotemporal progression of drought events in their evolutionary processes and characterize their feature further. It is crucial for addressing the systemic risks within the hydrological cycle associated with drought mitigation.

We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.

Using a new approach based on a combination of modeling and Earth observation, useful information about the filling of the Grand Ethiopian Renaissance Dam can be obtained with limited data and proper rainfall selection. While the monthly streamflow into Sudan has decreased significantly (1.2 × 10⁹–5 × 10⁹ m³) with respect to the non-dam scenario, the negative impact has been masked due to higher-than-average rainfall. We reveal that the dam will need 3–5 more years to complete filling.

An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.

This research scrutinized predicted changes in root zone soil moisture dynamics across different climate scenarios and different climate regions globally between 2021 and 2100. The Mediterranean and most of South America stood out as regions that will likely experience permanently drier conditions, with greater severity observed in the no-climate-policy scenarios. These findings underscore the impact that possible future climates can have on green water resources.

An efficient regional flood frequency model relates drainage area to discharge, with a major assumption of similar basin conditions. In a landscape with variable glacial deposits and land use, we characterize varying hydrological function using 28 explanatory variables. We demonstrate that (1) a heterogeneous landscape requires objective model selection criteria to optimize the fit of flow data, and (2) incorporating land use as a predictor variable improves the drainage area to discharge model.

Rapid climate warming creates barriers for us to investigate water resource states. Using stable and radioactive isotopes, we identified the seasonality and spatiality of the water cycle in the northeastern Tibetan Plateau. Climate warming/humidification accelerates the water cycle in alpine arid basins. Precipitation and meltwater infiltrate along preferential flow paths to facilitate rapid groundwater recharge. Total water resources may show an initially increasing and then decreasing trend.

Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.

The peatland dissolved organic carbon (DOC) lost through aquatic exports can offset a significant proportion of the ecosystem carbon balance. Hence, we propose a new approach to better estimate the DOC exports based on the specific contribution of a boreal peatland (Canada) during periods of high flow. In addition, we studied the relations between DOC concentrations and stream discharge in order to better understand the DOC export mechanisms under contrasted hydrometeorological conditions.

This study assesses the flood risks concurrently induced by river flooding and coastal storm surge along the coast of the contiguous United States using statistical and numerical models. We reveal a few hotspots of such risks, the critical spatial variabilities within a river basin and over the whole US coast, and the uncertainties of the risk assessment. We highlight the importance of weighing different risk measures to avoid underestimating or exaggerating the compound flood impacts.

Drop size distribution characteristics vary with microphysical characteristics. We choose the Qilian mountains and represent the southern and northern slopes and the interior. To investigate discrepancies, DSD characteristics and Z–R relationships are analyzed based on continuous observations in the rainy season. We obtain the finer precipitation of mountains and refine the accuracy of quantitative precipitation estimation, which would help develop cloud water resources in mountainous areas.

This paper intends to demonstrate the adequacy of a hybrid solution to overcome the difficulties related to the incorporation of human behavior when modeling hydrological processes. Two models were implemented, one to estimate the outflow of a reservoir and the other to simulate the hydrological processes of the watershed. With both models feeding each other, results show that the proposed approach significantly improved the streamflow estimation downstream of the reservoir.

Evapotranspiration (ET) plays a pivotal role in terrestrial water cycling, returning up to 90 % of precipitation to the atmosphere. We studied impacts of soil type and management on an agroecosystem using an automated system with modern modeling approaches. We modeled ET at high spatial and temporal resolution to highlight differences in heterogeneous soils on an hourly basis. Our results show significant differences in yield and smaller differences in ET overall, impacting water use efficiency.