基于土壤地形指数和下垫面水文分区的流域模型参数率定

针对流域水文响应会受到地质、地形、地貌、植被、土壤等自然地理因素以及土地利用等下垫面因素综合作用的特点,根据流域下垫面的地理特征进行水文响应分区。借助遥感资料对流域下垫面进行分类并获取土地覆盖/土地利用信息,结合流域土壤类型数据与地形指数推求流域的土壤地形指数。进一步地,利用遥感分类信息和土壤地形指数进行流域的水文分区,率定各个水文分区SCS模型的参数CN值并进行水文模拟,结果表明,屯溪流域21次和东湾流域32次洪水径流模拟相对误差均比传统方法的小。

基于土壤地形指数的流域水文敏感区空间分布研究

以丹江口水库上游堵河流域为研究区,借助SAGA GIS和R平台,以ASTER GDEMV2为数据源,在获取流域土壤地形指数及其阈值设定的基础上,利用Python获取3种情景下(STI≥9、STI≥10和STI≥11)水文敏感区内不同土地利用类型及其面积和空间分布。结果表明:利用土壤地形指数识别,提供了更为详细表征的水文敏感区,可合理预测径流产生的空间分异性;3种情景下,林地均是水文敏感区内最主要的土地利用类型,约50%水体均位于水文敏感区;不同情景下,识别的水文敏感区内农业景观和开发景观应禁止开展高强度的土地开发,并优先布置植被缓冲带等保护措施以保护水质安全。

结合土壤地形指数的流域下垫面水文分区方法

以湿润的屯溪流域、半湿润的东湾流域为研究区,对比分析了经验方法、数值平均法、频率平均法三种方法,先修正得到两流域的CN值查找表,再结合下垫面分类信息和土壤地形指数建立水文分区表,用屯溪流域18次和东湾流域16次降水的水文数据进行了模拟验证。结果表明:采用频率平均法水文分区得到的合格率为屯溪94.44%、东湾87.50%,数值平均法水文分区得到的合格率为屯溪88.89%、东湾81.25%,经验方法水文分区得到的合格率为屯溪88.89%、东湾75.00%;就流域而言,土壤地形指数累计频率高的流域模拟效果更好。

基于土壤地形指数和遥感分类的SCS模型参数率定

以RS为主要手段，结合GIS技术，基于屯溪流域遥感影像，研究得到了屯溪流域两期遥感影像的土地利用分类结果。在此基础上，利用屯溪流域DEM数据得到TOPMODEL模型中的土壤地形指数，并与SCS水文模型和遥感信息相结合进行水文分区，率定SCS模型的径流曲线数参数（CN值），然后利用1982~2002年间屯溪流域的水文数据对结果进行精度验证。实验结果表明，该方法取得了较好的效果，模拟径流与实测径流间的平均误差仅为14.1%。

土壤地形指数与枯水期流域退水特征关系分析——以韩江流域为例

流域退水指数反映流域地形、地质特性对枯水期径流调蓄作用。该文选取地形指数变体即土壤地形指数进行研究,分析韩江流域9个子流域土壤地形指数与流域退水指数关系,研究土壤地形指数对枯水期流域退水特征的影响。两组流域土壤地形指数标准差与流域退水指数拟合,R;分别为0.96和0.84;地形指数和土壤地形指数均值与流域退水指数拟合取第二组流域拟合成果,R;分别为0.56和0.60,土壤地形指数与流域退水指数拟合效果更好。研究结果表明:相较于地形指数,土壤地形指数既考虑地形因素,又考虑土壤下渗率及土层厚度,具有较好的物理基础;韩江流域下游地区退水过程更稳定,退水速率更慢;土壤地形指数标准差及均值与流域退水指数呈正相关关系。研究结果对估算水文地质参数、了解流量特性变化机理有着重要作用,可为无(缺)资料流域水文模拟奠定基础。

基于关键污染源区识别的乡村低影响开发措施选址方法研究——以成都市温江区寿安镇为例

近年来中国乡村面源污染问题日益突出,逐渐成为乡村水环境整治的瓶颈。从风景园林、环境工程、水文学等多学科视角出发,将低影响开发(LID)理念引入乡村面源污染治理中。运用ArcGIS、SWAT等软件工具,构建了一种从面源污染风险性与水文敏感性结合出发判定关键污染源区的乡村LID措施选址方法。以具有成都平原典型乡村特征的寿安镇为案例,对方法进行验证。结果表明,通过该方法识别出的寿安镇LID选址区域面积占比适宜且涵盖多种用地类型,对于具有分散式、小规模、源头控制特征的LID措施较为适用。通过优先在选址区域内进行LID设计改造,可在有限的土地资源占用下获得较高的面源污染去除效益。研究结果有利于指导寿安镇及其他类似成都平原乡村地区的未来面源污染防治以及生态发展,同时丰富了LID措施在中国乡村地区选址方面的量化研究。

Spatial and temporal variation of drought index in a typical steep alpine terrain in Hengduan Mountains

This study describes the spatial and temporal variation of a drought index and makes inferences regarding the environmental factors that influence this variability in the Hengduan Mountains. A drought index is typically used to determine the moisture conditions and the magnitude of water deficiency in a given area. Based on data from 26 meteorological stations over the period 1960-2012, the spatial and temporal variations of the drought index were analyzed using a thin plate smoothing splines method that considered elevation as a covariate. The drought index was estimated based on the potential evapotranspiration(E0) as defined by the Penman Monteith model modified by FAO(1998). The results of the reported analysis showed that the drought index in the Hengduan Mountains has been decreasing since 1960 at a rate of-0.008/a. This represented a progressive shift from the "sub-humid" class, which typified the wider area in the Hengduan Mountains, toward the "humid" class, which appeared in the Hengduan Mountains areas. The drought index was relatively high in the north and low in the south and the variation of the drought index varied with seasons. The drought index showed increasing trends in summer and autumn and it is greater in autumn than in summer, while it showed a decreasing trend in spring and winter. Drought index is inversely proportional to the soil relative humidity and Normalized Difference Vegetation Index(NDVI).

Estimating Soil Salinity in the Yellow River Delta, Eastern China——An Integrated Approach Using Spectral and Terrain Indices with the Generalized Additive Model

Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model(DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper(TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model(GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike's information criterion(AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover.Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Visible red and near-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation.