Surface runoff response to climate change based on artificial neural network (ANN) models: a case study with Zagunao catchment in Upper Minjiang River, Southwest China

Climate change and its hydrological consequences are of great concern for water resources managers in the context of global change. This is especially true for Upper Minjiang River (UMR) basin, where surface runoff was reported to decrease following forest harvesting, as this unusual forest-water relationship is perhaps attributed to climate change. To quantify the hydrological impacts of climate change and to better understand the forest-water relationship, an artificial neural network (ANN)based precipitation-runoff model was applied to Zagunao catchment, one of the typical catchments in UMR basin, by a climate scenario-based simulation approach. Two variables, seasonality and CTsm (cumulative temperature for snow melting), were devised to reflect the different flow generation mechanisms of Zagunao catchment in different seasons (rainfall-induced versus snow melting-oriented). It was found that the ANN model simulated precipitation-runoff transformation very well (R-2 = 0.962). Results showed runoff of Zagunao catchment would increase with the increase in precipitation as well as temperature and such a response was season dependent. Zagunao catchment was more sensitive to temperature rise in the non-growing season but more sensitive to precipitation change in the growing season. Snow melting-oriented runoff reduction due to climate change is perhaps responsible for the unusual forest-water relationship in UMR basin.