Water resources management (WRM) for sustainable development presents many
challenges in areas with sparse in situ monitoring networks. The exponential growth of …

The accurate representation of spatio-temporal patterns of precipitation is an essential input
for numerous environmental applications. However, the estimation of precipitation patterns …

This study proposed a novel double machine learning (DML) approach to merge multiple
satellite-based precipitation products (SPPs) and gauge observations, and tested its …

Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate
hydrological forecasting. To improve the spatial resolution and quality of satellite …

Precipitation data with high resolution and high accuracy are significantly important in
numerous hydrological applications. To enhance the spatial resolution and accuracy of …

Merging gauge observation with a single original satellite-based precipitation product (SPP)
is a common approach to generate spatial precipitation estimates. For the generation of high …

Although many multi-source precipitation products (MSPs) with high spatiotemporal
resolution have been extensively used in water cycle research, they are still subject to …

High-spatial-resolution precipitation data are of great significance in many applications,
such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution …

Publicly available satellite precipitation products (SPPs) are essential inputs for hydrological
models. However, the existing SPPs suffer from coarse resolutions and large uncertainties …

Abstract Study region Ethiopia, upper Blue Nile. Study focus This research mainly aims to
investigate the effectiveness of merging and bias correction of Multi-Sources Weighted …