A conceptual framework of agricultural land use planning with BMP for integrated watershed management

Land use planning is an important element of the integrated watershed management approach It not only influences the environmental processes such as soil and stream bed erosion sediment and nutrient concentrations in streams quality of surface and ground waters in a watershed but also affects social and economic development in that region Although its importance in achieving sustainable development has long been recognized a land use planning methodology based on a systems approach involving realistic computational modeling and meta-heuristic optimization is still lacking in the current practice of integrated watershed management The present study proposes a new approach which attempts to combine computational modeling of upland watershed processes fluvial processes and modern heuristic optimization techniques to address the water-land use interrelationship in its full complexity The best land use allocation is decided by a multi-objective function that minimizes sediment yields and nutrient concentrations as well as the total operation/implementation cost while the water quality and the production benefits from agricultural exploitation are maximized The proposed optimization strategy considers also the preferences of land owners The runoff model AnnAGNPS (developed by USDA) and the channel network model CCHE1D (developed by NCCHE) are linked together to simulate sediment/pollutant transport process at watershed scale based on any assigned land use combination The greedy randomized adaptive Tabu search heuristic is used to flip the land use options for finding an optimum combination of land use allocations The approach is demonstrated by applying It to a demonstrative case study involving USDA Goodwin Creek experimental watershed located in northern Mississippi The results show the improvement of the tradeoff between benefits and costs for the watershed after implementing the proposed optimal land use planning (C) 2010 Elsevier Ltd All rights reserved