Assessment of the impacts of nonstationarity on watershed runoff using artificial neural networks: a case study in Ardebil, Iran

The purpose of this study is to investigate the impacts of climate change on the runoff of Gharehsoo River Basin in the northwest of Iran. In this research, the outputs of monthly precipitation and temperature data of PRECIS (Providing Regional Climates for Impacts Studies) model, a regional climate model with 50 × 50 km spatial resolution on the basis of B2 scenario, is used for the base (1961–1990) and the future (2071–2100) periods. The output results of PRECIS show that the average temperature of the watershed increased up to 2–5 °C, for the period spanning from 2070 to 2100. In addition, compare to the base period, we are expecting to receive more precipitation in future for the months of January, February, March, September and December. The artificial neural network (ANN) was applied to quantify the future discharge. The results show that in the future, the discharge of Gharehsoo River watershed decreases for all months. Moreover, the peak discharge in the future period happens 1 month earlier, due to increasing in the temperature and earlier start of snow melting season. Finally, 1 and 2 °C increase in temperature lead to 0.05–8.2 % and 0.1–13.4 % decrease of average monthly discharge, respectively.