Rainfall information with high spatial and temporal resolution are essential in various fields.
Heavy rainfall in a short period can cause problems and disasters that result in loss of life …

This work presents the results of the implementation of a probabilistic system to model the
uncertainty associated to radar rainfall (RR) estimates and the way this uncertainty …

Rainfall data are an essential input for many simulation models. In fact, these latter have a
decisive role in the development and application of rational water policies. Since the …

Abstract Quantitative Precipitation Estimation (QPE) is quite important information for the
hydrology fields and has many advantages for many purposes. Its dense spatial and …

Water resources managers commonly rely on information collected by hydrometric networks
without clear knowledge of their efficiency. Optimal water monitoring networks are still …

This study proposes a stochastic artificial neural network (named ANN_GA-SA_MTF), in
which the parameters of the multiple transfer functions considered are calibrated by the …

This study aims to develop a probabilistic rainfall threshold estimation model for shallow
landslides (PRTE_LS) in order to quantify its reliability while being affected by uncertainties …

Satellite‐based precipitation products are expected to offer an alternative to ground‐based
rainfall estimates in the present and the foreseeable future. In this paper, we evaluate the …

The Tibetan Plateau (TP) is the roof of the world and water towers of Asia. However,
research on hydrological processes is restricted by the sparse gauge network in the TP. The …

This study proposes an evaluation framework to identify the optimal raingauge network in a
watershed using grid-based quantitative precipitation estimation (QPE) with high spatial and …