Land use pattern optimization based on CLUE-S and SWAT models for agricultural non-point source pollution control
نویسندگان
چکیده
Improper land use is one major cause of non-point source pollution. Integrated modeling would support the evaluation and optimization of land use for the non-point source pollution control. In this study, the CLUE-S (the Conversion of Land Use and its Effect at Small regional extent) and SWAT (Soil and Water Assessment Tool) models were coupled to simulate pollution loads under different land use scenarios in the upstreamwatershed of Miyun Reservoir in Beijing, China. The results indicated that changes in land use structure and pattern under different land use scenarios have significantly affected the non-point source pollution load. The increase of orchards and loss of forest cover has led to an increase in the potential pollution loads of nitrogen by 5.27% and phosphorus by 4.03%. However, in the agricultural non-point source pollution control scenario, pollution loads of nitrogen decreased by 13.94% and phosphorus by 9.86%, resulting from the establishment of riparian vegetation buffers and restoring forest on unutilized land and slope arable land. Coupling the hydrological model SWAT and the land use model CLUE-S succeeded in evaluating the land use pattern for agricultural non-point source pollution control. The coupling of two models provides a new approach for land use optimization towards non-point source pollution control. © 2011 Elsevier Ltd. All rights reserved.
منابع مشابه
مدل سازی آلودگی غیرنقطه ای با استفاده از سیستم اطلاعات جغرافیایی (GIS) برای ارائه بهترین شیوه های مدیریت (BMP) در حوضه آبخیز گرگانرود
The most important pollutants that cause water pollution are nitrogen and phosphorus from agricultural runoff called Non-Point Source Pollution (NPS). To solve this problem, management practices known as BMPs or Best Management Practices are applied. One of the common methods for Non-Point Source Pollution prediction is modeling. By modeling, efficiency of many practices can be tested before ap...
متن کاملOptimization of Agricultural BMPs Using a Parallel Computing Based Multi-Objective Optimization Algorithm
Beneficial Management Practices (BMPs) are important measures for reducing agricultural non-point source (NPS) pollution. However, selection of BMPs for placement in a watershed requires optimizing available resources to maximize possible water quality benefits. Due to its iterative nature, the optimization typically takes a long time to achieve the BMP trade-off results which is not desirable ...
متن کاملUsing the soil and water assessment tool to estimate dissolved inorganic nitrogen water pollution abatement cost functions in central portugal.
Coastal aquatic ecosystems are increasingly affected by diffuse source nutrient water pollution from agricultural activities in coastal catchments, even though these ecosystems are important from a social, environmental and economic perspective. To warrant sustainable economic development of coastal regions, we need to balance marginal costs from coastal catchment water pollution abatement and ...
متن کاملPrediction of the Type and Amount of Surface Water Pollutants using Time Series Models (ARIMA) and L-THIA Model (Case Study: Namrood Sub-Basin, Hablehrood Watershed)
Due to the important role of non-point source pollution in water resources management, in this study time series modeling was applied to forecast water quality parameters and L-THIA model (one type of non-point source pollution models) was applied to estimate water pollutants. The purpose of this study was to compare results of L-THIA model and ARIMA models in Namrood sub-basin located in ...
متن کاملQuantifying the Relationships of Impact Factors on Non-Point Source Pollution Using the Boosted Regression Tree Algorithm
Non-point source (NPS) pollution contributes greatly to the contamination of surface water quality and has aroused widespread concerns. NPS pollution is influenced by a multitude of site-related factors whose effects are complicated. We estimated NPS pollution with a soil and water assessment tool (SWAT) model in China’s Fan River watershed. A new method, boosted regression tree (BRT), was prop...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Mathematical and Computer Modelling
دوره 58 شماره
صفحات -
تاریخ انتشار 2013