Coupled Model for Assessing the Present and Future Watershed Vulnerabilities to Climate Change Impacts

Abstract

There is great uncertainty about the future effects of climate change on the global economic, social, environmental, and water sectors. This paper focuses on watershed vulnerabilities to climate change by coupling a distributed hydrological model with artificial neural networks and spatially distributed indicators for the use of a predictive model of such vulnerability. The analyses are complemented by a Monte Carlo evaluation of the uncertainty associated with the projections of the global circulation models, including how such uncertainty impacts the vulnerability forecast. To test the proposal, the paper uses current and future vulnerabilities of the Turbio River watershed, located in the semi-arid zone of Guanajuato (Mexico). The results show that nearly 50% of the watershed currently has medium and high vulnerabilities, and only the natural areas in the watershed show low vulnerabilities. In the future, an increase from medium to high vulnerability is expected to occur in urban and agricultural areas of the basin, with an associated uncertainty of ±15 mm in the projected precipitation.

Projecting Surface Water Area Under Different Climate and Development Scenarios

“…LULC and climate data have been used to model surface water dynamics (Gaines et al, 2022;Mitsova, 2014;Tulbure & Broich, 2019;Vanderhoof et al, 2018). However, studies projecting surface water dynamics tend to focus on changes in climate drivers and often do not include LULC change as a dynamic driver (Duan et al, 2017;Kumar et al, 2014;Martínez et al, 2023;Zhao et al, 2023). Our novel approach of using both climate and LULC drivers to project seasonal pSWA across a range of scenarios provides inter-scenario information about potential changes in pSWA that could be useful to adaptation and mitigation efforts (Martin et al, 2017;Montefiore et al, 2023) in the face of future flood and drought events (L. G. Chen et al, 2019;Prein et al, 2016;Sanchez et al, 2023;G.…”