Optimal agricultural production under different climatic scenarios through a coupled hydrologic and economic models in Kern county, California.

Abstract:

Estimating the climate change impacts over different economy sectors has become an important task in recent times. In California, it is expected that increases in temperature would modify the selection of crops types, planting areas, irrigation intensities, and water sources. To understand the interactions between climate, water systems, and agricultural system, an agricultural production model and the California Central Valley Groundwater-Surface Water Simulation Model (C2VSim) were coupled, both for the Kern region. Part of the coupling's success is due to the development of an artificial neural network called groundwater response function, which allows estimating the groundwater level at the end of each water year from 1998 to 2015, considering the volumes of surface and ground water used in agriculture, the artificial aquifers recharge, and some climatic variables. Additionally, we were able to estimate the pumping capacity lost due to hanging wells. The results show that groundwater levels will continue declining in the absence of clear groundwater management strategies.