Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways

Abstract

Climate change is one of the main sources of stress on the aquifers. However, few studies have appraised its impact on groundwater systems, especially in Iran with unprecedented groundwater depletion rates. Not influenced by anthropogenic influences, Pali aquifer, southwest Iran, has climate data scarcity. To determine the response of the aquifer, one approach is to utilize a pre-downscaled data set. To this end, WorldClim data set under shared socioeconomic pathways (SSPs), i.e. SSP245 and SSP585, was considered. Furthermore, nonlinear autoregressive exogenous (NARX) and artificial neural networks (ANNs) along with support vector regression (SVR) combined with the meta-heuristic algorithms of wavelet (WSVR) and innovative gunner (AIG-SVR) were applied, regarding 1970–2000 as the base time span, and 2021–2040 and 2041–2060 as the future time periods. Among observational data (2007–2018), 80 and 20% were considered for the network's training and testing, respectively. An increase in temperature, between +1.93 and + 3.7 °C, would occur in the future with the most increment in summer 2041–2060 under SSP585. Additionally, precipitation fluctuate from −0.43 to +1.17 mm/month with a probable rise in fall and most decline in winter 2041–2060 under SSP585. As regards the groundwater modeling, considering that NARX and ANNs were not able to simulate the aquifer altogether, the meta-heuristic approaches demonstrated high proficiencies. With R = 0.995, R2 = 0.989, mean absolute error (MAE) = 0.249 m and Nash-Sutcliffe efficiency (NSE) = 0.982 for the test data, WSVR illustrated the greatest efficacy, which disclosed that the groundwater level would face the utmost reduction from −5.4 to −10.9 m in observation wells, especially in spring 2041–2060. Indeed, this decline in groundwater level is exclusively induced by climate change, emphasizing on proposing groundwater adaptation strategies to address the groundwater issues, like land subsidence, in Iran. Moreover, it is proposed that different modeling approaches be evaluated, such that the outputs of the best model be considered.