UC Santa Barbara

This study develops an integrative methodological framework that combines forensic hydrology, artificial neural networks (ANNs), and physically-based hydrological modeling to assess the cascading impacts of projected land use/land cover (LULC) changes on future flood dynamics in the upstream watershed of Golestan Dam, Iran. A large-magnitude flood event analogous to the destructive 2019 flood was simulated under evolving LULC scenarios for three future horizons: 2030, 2040, and 2050. The curve number (CN) and lag time (Tlag) were reconstructed using linear and nonlinear interpolation and projected with ANN-based regression modeling. Mann-Kendall trend analysis was applied to assess the temporal consistency of ANN-predicted CN and Tlag series and support the selection of the most reliable synthetic datasets. The simulations revealed highly non-linear growth in peak discharges, with the Qarah Shur sub-basin projected to experience a 163.78% increase in flood intensity by 2050, resulting from a substantial increase in the contribution of barren land to flood intensification (from 22.89% to 78.88%) and a threefold increase in the contribution of sparse vegetation (from 9.04% to 31.13%). These two factors, individually and in combination, were identified as the most critical contributors to flood amplification in this sub-basin. The Galikesh sub-basin followed with a 68.99% increase by 2050, being the only sub-basin where the contribution of road expansion (from 8.88% to 27.41%) emerged as the dominant factor driving flood intensification. The Tamar Gorgan and Tangerah sub-basins demonstrated more moderate increases in discharge, 13.89% and 10.44%, respectively, driven by modest land degradation and road building. This work’s findings underscore growing risks to infrastructure, including embankment instability, bridge scour, and urban inundation, and to groundwater recharge, aquatic ecosystems, and water quality through the mobilization of pollutants and sediments under increasing flood risk. The spatial comparison of sub-basin-level responses captures the heterogeneity of flood vulnerability and the urgency of instituting adaptive land use policies grounded in hydrological sensitivity, riparian corridor protection, and nature-based flood mitigation.