An-Najah Staff

Artificial neural networks have proven to be an attractive mathematical tool to represent complex relationships in many branches of hydrology. Due to this attractive feature, neural networks are increasingly being applied in subsurface modeling where intricate physical processes and lack of detailed field data prevail. In this paper, a methodology using modular neural networks (MNN) is proposed to simulate the nitrate concentrations in an agriculture-dominated aquifer. The methodology relies on geographic information system (GIS) tools in the preparation and processing of the MNN input–output data. The basic premise followed in developing the MNN input–output response patterns is to designate the optimal radius of a specified circular-buffered zone centered by the nitrate receptor so that the input parameters at the upgradient areas correlate with nitrate concentrations in ground water. A three-step approach that integrates the on-ground nitrogen loadings, soil nitrogen dynamics, and fate and transport in ground water is described and the critical parameters to predict nitrate concentration using MNN are selected. The sensitivity of MNN performance to different MNN architecture is assessed. The applicability of MNN is considered for the Sumas-Blaine aquifer of Washington State using two scenarios corresponding to current land use practices and a proposed protection alternative. The results of MNN are further analyzed and compared to those obtained from a physically-based fate and transport model to evaluate the overall applicability of MNN.

We present an integrated methodology for the optimal management of nitrate contamination of ground water combining environmental assessment and economic cost evaluation through multi-criteria decision analysis. The proposed methodology incorporates an integrated physical modeling framework accounting for on-ground nitrogen loading and losses, soil nitrogen dynamics, and fate and transport of nitrate in ground water to compute the sustainable on-ground nitrogen loading such that the maximum contaminant level is not violated. A number of protection alternatives to stipulate the predicted sustainable on-ground nitrogen loading are evaluated using the decision analysis that employs the importance order of criteria approach for ranking and selection of the protection alternatives. The methodology was successfully demonstrated for the Sumas–Blaine aquifer in Washington State. The results showed the importance of using this integrated approach which predicts the sustainable on-ground nitrogen loadings and provides an insight into the economic consequences generated in satisfying the environmental constraints. The results also show that the proposed decision analysis framework, within certain limitations, is effective when selecting alternatives with competing demands.

This paper presents and implements a framework for modeling the impact of land use practices and protection alternatives on nitrate pollution of groundwater in agricultural watersheds. The framework utilizes the national land cover database (NLCD) of the United State Geological Survey (USGS) grid and a geographic information system (GIS) to account for the spatial distribution of on-ground nitrogen sources and corresponding loadings. The framework employs a soil nitrogen dynamic model to estimate nitrate leaching to groundwater. These estimates were used in developing a groundwater nitrate fate and transport model. The framework considers both point and non-point sources of nitrogen across different land use classes. The methodology was applied for the Sumas–Blaine aquifer of Washington State, US, where heavy dairy industry and berry plantations are concentrated. Simulations were carried out using the developed framework to evaluate the overall impacts of current land use practices and the efficiency of proposed protection alternatives on nitrate pollution in the aquifer.