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Plumbing agricultural landscapes for water quality improvement: Coexistence of intensive agriculture and good water quality.

机译:改善农业景观以改善水质:集约化农业与良好水质并存。

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Years of data collection at the bases of 130 lake watersheds in Iowa have enabled us to study the relationships between nutrient concentrations and land use/land cover, as well as to measure the fluxes of key nutrients through hydrologically-linked agroecosystems within the landscape. These ecosystems are dominated by cropland and intensively managed fields with high nutrient export coefficients. Satellite image-derived land cover maps, digital elevation maps and soil mapping units were interpreted and manipulated using geographic information software. We evaluated the relationships between landscape characteristics and lake water quality in Iowa (USA) by regressing four lake water quality responses on landscape variables that were measured for whole watersheds and for three different buffer distances. The response variables were total nitrogen (TN---the combination of dissolved inorganic and organic nitrogen, particulate organic inorganic nitrogen), total phosphorus (TP---includes all forms, dissolved and particulate reactive as well as dissolved and particulate unreactive phosphorus), chlorophyll-a (Chl-a) and Secchi transparency (SD). We developed regression models with a stepwise protocol selecting significant explanatory variables. We tested hypotheses concerning the influence of watershed composition and configuration on the water quality. The models predicted between 15 and 67 percent of the variability of the response variables. Configuration variables were very important regardless of scale. Spatially explicit data improved the statistical power of the whole watershed models for variables TN, Chl-a and SD, but not for TP.
机译:在爱荷华州130个湖泊流域的基地进行的多年数据收集,使我们能够研究养分浓度与土地利用/土地覆盖之间的关系,并通过景观内与水文联系的农业生态系统测量关键养分的通量。这些生态系统主要由农田和集约化管理的农田组成,这些农田的养分出口系数很高。使用地理信息软件对卫星图像衍生的土地覆盖图,数字高程图和土壤测绘单元进行了解释和处理。我们通过对四个湖泊水质响应的景观变量进行回归分析,评估了爱荷华州(美国)的景观特征与湖泊水质之间的关系,该变量针对整个流域和三个不同的缓冲距离进行了测量。响应变量为总氮(TN ---溶解的无机和有机氮的组合,颗粒有机无机氮的组合),总磷(TP--包括所有形式的,溶解的和颗粒反应性的磷以及溶解的和颗粒未反应的磷) ,叶绿素a(Chl-a)和Secchi透明度(SD)。我们通过选择重要解释变量的逐步协议开发了回归模型。我们检验了有关流域组成和构造对水质影响的假设。该模型预测了响应变量的15%至67%的可变性。无论规模如何,配置变量都非常重要。空间显式数据改善了整个流域模型对变量TN,Chl-a和SD的统计能力,但对TP却没有。

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