Predicting the impacts of climate change on nonpoint source pollutant loads from agricultural small watershed using artificial neural network.

This study described the development and validation of an artificial neural network (ANN) for the purpose of analyzing the effects of climate change on nonpoint source (NPS) pollutant loads from agricultural small watershed. The runoff discharge was estimated using ANN algorithm. The performance of ANN modelwas examined using observed data from s tudy watershed. The simulationresults agreed well with observed values during calibration and validation periods. NPS pollutant loads were calculated from load-discharge relationship driven by long-term monitoring data. LARS-WG (Long Ashton Research Station-Weather Generator) model was used to generate rainfall data. The calibrated ANN model and load-discharge relationship with the generated data from LARS-WGwere applied to analyze the effects of climate change on NPS pollutant loads from the agricultural small watershed. The results showed that the ANN model provided valuable approach i n estimating future runof f discharge, and the NPS pollutantloads.