Climatic and soil-landscape variations in relation to modelling pesticide fate in hummocky terrains

农药归趋模型可用于预测农药和雌激素在土壤中的归趋。在大规模使用时，农药归趋模型应用中不确定性的主要来源之一是对农药/雌激素吸附和降解输入参数的空间变异性了解不足。因此，拟议的研究将研究农药/雌激素吸附和降解的空间变异性在两个丘状地形，并在此基础上，利用数字地形模型，开发一种改进的方法，随机农药的命运建模在大规模。大草原坑洼地区（770，000平方公里）拥有数百万与农业用地相关的湿地。例如，这项研究的两个丘陵地形每个都有大约200个湿地。草原湿地的水位对气候变化很敏感，但这一信息尚未纳入湿地水中农药浓度的估计。因此，通过使用四个草原湿地的数字高程模型，拟议的研究将完善现有的水文模型，提供气候变化对草原湿地农药浓度的影响建模的手段。至少有三个原因说明这项研究的重要性：（1）这将是第一项详细确定新出现的污染物命运的研究在土壤景观一级的土壤剖面中的雌激素;（2）该研究将导致大规模监管和环境风险评估的重大改进，（3）新的湿地水模型将提高我们对气候变化对草原湿地水质影响的认识。

Pesticide fate models can be used to predict the fate of pesticides and estrogens in soil. When used at the large scale, one of the main sources of uncertainty surrounding the application of pesticide fate models is insufficient knowledge of the spatial variability of pesticide/estrogen sorption and degradation input parameters. Therefore, the proposed research will examine the spatial variability of pesticide/estrogen sorption and degradation in two hummocky terrains and, based on this knowledge, utilize digital terrain models to develop an improved approach to stochastic pesticide fate modelling at the large scale. The Prairie Pothole Region (770,000 km2) has millions of wetlands associated with agricultural land. For example, the two hummocky terrains central to this research each have about 200 wetlands on site. Water levels in prairie wetlands are sensitive to climate variability, but this information has not been integrated into estimates of pesticide concentrations in wetland water. Therefore, through the use of digital elevation models derived for four prairie wetlands, the proposed research will refine existing hydrological models to provide the means of modelling the impact of climate variability on concentrations of pesticides in prairie wetlands. There are at least three reasons why this research is important: (1) This will be the first study to determine, in detail, the fate of emerging contaminants (estrogens) in soil profiles at the soil-landscape level; (2) The research will lead to significant improvements in regulatory and environmental risk assessments at the large scale, and (3) The new wetland water model will improve our knowledge of the impact of climate variability on prairie wetland water quality.