Daily precipitation performances of regression-based statistical downscaling models in a basin with mountain and semi-arid climates.

ABSTRACT: The impacts of climate change on current and future water resources are important to study local scale. This study aims to investigate the prediction performances of daily precipitation using five regression-based statistical downscaling models (RBSDMs), for the first time, and the ERA-5 reanalysis dataset in the Susurluk Basin with mountain and semi-arid climates for 1979-2018. In addition, comparisons were also performed with an artificial neural network (ANN). Before achieving the aim, the effects of atmospheric variables, grid resolution, and long-distance grid on precipitation prediction were holistically investigated for the first time. Kling-Gupta efficiency was modified and used for holistic evaluation of statistical moments parameters at precipitation prediction comparison. The st

SUBMITTER: San M 

PROVIDER: S-EPMC9734427 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature