Author(s): Salvador Navas; Manuel Del Jesus

Linked Author(s): Salvador Navas, Manuel del Jesus

Keywords: Climate Change; Artificial Neural Networks; Reservoir management; Water management

Abstract: As a result of climate change and global warming, precipitation and temperature patterns are expected to be altered in many regions worldwide, leading to changes in the water cycle. These hydrological dynamics' disruption may decrease minimum reservoir inflows during low precipitation periods, significantly affecting reservoir operations and the demands they meet. Hence, conducting climate change studies becomes imperative to understand potential water resource evolution and devise adaptive strategies to mitigate climate change effects on reservoirs. Recent regional climate change studies in Spain underscore the importance of ensuring that all information derived from these studies is easily accessible. Consequently, the SIMPCCe tool (https: //github. com/IHCantabria/SIMPCCe) has been developed and presented in this work as a supplement to the "Methodological Guide for Estimating Minimum Reservoir Inflows in the Context of Climate Change. " This tool implements the methodology outlined in the guide, allowing for straightforward assessment of climate change effects on reservoir water availability.

DOI: https://doi.org/10.3850/iahr-hic2483430201-202

Year: 2024