Predicting the impacts of the changes in crop pattern on water table level and electrical conductivity of an unconfined aquifer using a Bayesian decision network

Subject Areas : Article frome a thesis

Mohammad Riki ¹ , Amir Sadoddin ² , Vahedberdi Sheikh ³ , Choghibayram Komaki ⁴

1 - Graduate M.Sc. of Watershed Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran
2 - Associate Professor, Department of Watershed Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran
3 - Associate Professor, Department of Watershed Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran
4 - Assistant Professor, Department of Arid Zone Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran

Received: 2021-02-05 Accepted : 2022-01-19 Published : 2022-01-21

Keywords: Crop pattern management, unconfined aquifer management, Bayesian Decision Networks (BDN), Netica software, the Ali-Abad region (Golestan Province),

Abstract :

The aim of this research is to predict the effects of cropping pattern changes on the water table level and electrical conductivity of an unconfined aquifer in the Ali-Abad region using a Bayesian Decision Network (BDN) as an integrated approach. A conceptual model framework illustrating the relationship among variables in the system was developed and three management scenarios of crop pattern (current condition, high water demand crops, and low water demand crops) were considered for the study area. Marginal probability, Conditional Probability Tables (CPTs) and total probabilities were characterized using observed data, results of CROPWAT model simulation, and expert knowledge. The Netica software was used to run the Bayesian model and to analyze the sensitivity of the model with two criteria namely reduction of variance and belief variance. The analysis shows that the probability of groundwater depletion occurring for low water requirement crops will be about five per cent less than that in the current condition. The probability of improvement in electrical conductivity for low water requirement crops will be about two per cent greater than that for the current crop pattern. The sensitivity analysis of the Bayesian model shows that the water requirement of crops plays an important role in unconfined aquifer's characteristics. The results demonstrate that the BDNs are capable to help planners for improved management of groundwater resources by predicting and displaying the effects of crop pattern changes on the quantitative and qualitative characteristics of unconfined aquifers in a probabilistic context.

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