Prediction of the karstic spring flow rates under climate change by climatic variables based on the artificial neural network: a case study of Iran

Abstract

Few studies have evaluated the impact of climate change on groundwater resources for a region with no pumping well. Indeed, the uncertainty of pumping wells may undesirably influence the results. Therefore, a region without any pumping well was selected to assess the impact of climate change on the karstic spring flow rates. NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset was used to extract the climatic variables for the present (1961–1990) and future (2021–2050) time periods by two Representative Concentration Pathways (RCPs), i.e., RCP4.5 and RCP8.5, in Lali region, southwest Iran. Although this dataset has been already verified, its output was evaluated for Lali region. Then, the impact of climate change on the discharge of Bibitarkhoun karstic spring was examined by the Artificial Neural Network (ANN). In this regard, if considering the daily data, ANN is not trained satisfactorily, because of the spring’s lag time response to the precipitation; if monthly time step is considered, the data would not be adequate. Therefore, the average of some previous days was considered to calculate the variables. The average precipitation is 344, 329, and 324 mm/year and the average temperature is 14.18, 15.98, and 16.3 °C both for the present, future time period under RCP4.5 and future time period under RCP8.5, respectively. The network selected demonstrated no climate change impact on the average of spring discharge. However, the discharge increased by about + 8% in spring and summer and decreased by about − 7% in autumn and winter in the future time period.