基于 GIS和USLE/RUSLE的小流域土壤侵蚀量研究

依据实地调查资料,建立了典型小流域地理数据库;应用采样分析数据结果及坡面单元法,确定了定量计算通用土壤流失方程RUSLE因子指标的方法.在地理信息系统ArcGIS支持下,根据USLE/RUSLE土壤侵蚀预测模型对数据库实施运算操作,预测了小流域土壤侵蚀量.结果表明:①流域总体土壤侵蚀为中度,治理难度仍很大;②坡耕地是流...     

伊犁低山丘陵区小流域水土保持措施优化配置研究

在调查分析伊犁低山丘陵区水土流失特点的基础上,研究了该区小流域水土保持措施的空间配置。结果表明：伊犁低山丘陵区水土流失主要表现为以水力侵蚀为主的多种土壤侵蚀类型作用下的坡面侵蚀和沟道侵蚀。针对其水土流失特点,伊犁低山丘陵区小流域水土保持措施优化配置为：以生态修复为主,上游地区封禁保护;中游地区种植水保林和经济林带,建立生态缓冲带;下游水平沟种草。通过综合治理开展水土保持生态环境建设,至2012年底,伊犁低山丘陵区治理程度达到77．33％,林草覆盖率48％,改善了当地生态环境。     

Comparison of a Subjective and a Physical Approach for Identification of Priority Areas for Soil and Water Management in a Watershed – A Case Study of Nagwan Watershed in Hazaribagh District of Jharkhand, India

The present investigation was an attempt to compare the within-watershed prioritization capabilities of a physical model based SDSS with the SYI and RPI model based subjective-SDSS, conventionally devised for between-watershed prioritizations, by All India Soil Survey and Land Use Planning Division of Ministry of Agriculture, Government of India. Application of these two approaches on a test watershed situated in Damodar-Barakar catchment in India, the second most seriously eroded area in the world, demonstrated that the proposed physical model based SDSS was capable of realistically and logically mimicking the sub-watershed-scaled water and soil losses thereby suggesting its immense application potential for priority area identification within the test watershed. In contrast to the proposed physical method, the subjective approaches, which assigned totally reverse priorities to about 67–93% of the test-sub-watersheds, were observed to be incapable of realistically assessing the impact of topography and varied land use and soil types in the test watershed on their sub-watershed scaled run-off and soil loss generating potential. Besides, the physical approach could be used for assessing the annual dynamics of the total water and sediment yields, under prevailing resource management systems in the test watershed with good to moderately good correlation coefficients of 0.83 and 0.65; model efficiency coefficients of 0.54 and 0.70; mean relative errors of –4.28% and –17.97% and root mean square prediction errors of 71.8 mm and 9.63 t/ha, respectively.