Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

In this paper, we do a critical review of statistical methods for landslide susceptibility
modelling and associated terrain zonations. Landslide susceptibility is the likelihood of a …

New precipitation (P) datasets are released regularly, following innovations in weather
forecasting models, satellite retrieval methods, and multi-source merging techniques. Using …

Precipitation is a key source of freshwater; therefore, observing global patterns of
precipitation and its intensity is important for science, society, and understanding our planet …

Current global precipitation (P) datasets do not take full advantage of the complementary
nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble …

In the context of global warming, precipitation information over the Tibetan Plateau (TP),
known as the water tower of Asia, is crucial for both atmospheric and hydrological cycle …

A new retrospective satellite-based precipitation dataset is constructed as a climate data
record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed …

Precipitation affects many aspects of our everyday life. It is the primary source of freshwater
and has significant socioeconomic impacts resulting from natural hazards such as …

Accuracy assessment of precipitation retrievals is a pre-requisite for many hydrological
studies as it helps to understand the source and the magnitude of the uncertainty in …

Measuring precipitation intensity is not straightforward; and over many areas, ground
observations are lacking and satellite observations are used to fill this gap. The most …