Watershed management and hydrological modeling require data related to the very
important matter of precipitation, often measured using raingages or weather stations …

Gridded precipitation data sets are frequently used to evaluate climate models or to remove
model output biases. Although precipitation data are error prone due to the high spatio …

Spatial interpolation of precipitation data is of great importance for hydrological modelling.
Geostatistical methods (kriging) are widely applied in spatial interpolation from point …

Accurate rainfall data are of prime importance for many environmental applications. To
provide spatially distributed rainfall data, point measurements are interpolated. However, in …

Gridded precipitation and temperature products are inherently uncertain because of myriad
factors, including interpolation from a sparse observation network, measurement …

The spatial distribution of precipitation is an important aspect of water-related research. The
use of different interpolation schemes in the same catchment may cause large differences …

The Upper Blue Nile basin is one of the most data-scarce regions in developing countries,
and hence the hydrological information required for informed decision making in water …

This study compares two nonparametric rainfall data merging methods—the mean bias
correction and double-kernel smoothing—with two geostatistical methods—kriging with …

Increasing meteorological data availability and quality implies an adaptation of the
interpolation methods for data combination. In this paper, we propose a new method to …

Networks of rain‐gauges provide an accurate but highly localized measure of rainfall, with
limited coverage and resolution, whereas radars provide rain‐rate and accumulation …