Predicting groundwater level fluctuations under climate change scenarios for Tasuj plain, Iran

Abstract

Coastal aquifers world-wide are being confronted with several major challenges, such as overextraction of groundwater, climate change impacts, contamination by wastewater, and saltwater intrusion into water resources. Climate change induced alteration of the hydrological cycle is one of the main threats to future accessibility of water resources. Effective prediction of possible impacts of climate change on groundwater reserves, a crucial water resource, could be of great importance for sustainable water management. In a comparative study, artificial neural network (ANN), least square support vector machine (LSSVM), and nonlinear autoregressive network with exogenous inputs (NARX) models was applied to evaluate possible impacts of three representative concentration pathways (RCP) climate change scenarios (RCP2.6, RCP4.5, RCP8.5) on groundwater levels in Tasuj Plain, Iran. Four general circulation models (GCM) was used to predict temperature and precipitation values for the future period 2022–2050 and found that future temperature increased, while the amount of precipitation decreased. To improve the accuracy of three models in groundwater level prediction, db4 wavelet transform was applied. The results indicated that the Wavelet-NARX approach gave the best accuracy in forecasting groundwater level in the study area. In all cases, prediction indicated that groundwater level in all representative wells would decline in future.