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.

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.

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.

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.

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.

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.