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Using precise data sets on farming and pesticide properties to verify a diffuse pollution hydrological model for predicting pesticide concentration

机译:使用有关农业和农药特性的精确数据集来验证用于预测农药浓度的弥散污染水文模型

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Verification of a diffuse pollution model involves comparing results actually observed with those predicted by precise model inputs. Acquisition of precise model inputs is, however, problematic. In particular, when the target catchment is large and substantial estimation uncertainty exists, not only model verification but also prediction is difficult. Therefore, in this study, rice-farming data were collected for all paddy fields from all farmers in a catchment and pesticide adsorption and degradation rates in paddy field soil samples were measured to obtain precise model inputs. The model inputs successfully verified the model's capability to predict pesticide concentrations in river water. Sensitivity analyses of the model inputs elucidated the processes significantly affecting pesticide runoff from rice farms. Pesticide adsorption and degradation rates of the soil did not significantly affect pesticide concentrations, although pesticide discharge to river water accounted for less than 50% of the total quantity of pesticide applied to fields, possibly owing to pesticide adsorption and degradation. The timing of increases in pesticide concentrations in river water was affected mostly by the farming schedule, including the time of pesticide application and irrigation, and secondarily by rainfall events.
机译:扩散污染模型的验证涉及将实际观察到的结果与精确模型输入所预测的结果进行比较。但是,获取精确的模型输入是有问题的。特别地,当目标集水量大且估计不确定性存在时,不仅模型验证而且预测困难。因此,在这项研究中,收集了流域内所有农民的所有稻田的稻田农业数据,并测量了稻田土壤样品中农药的吸附和降解速率,以获得精确的模型输入。该模型的输入成功验证了该模型预测河水中农药浓度的能力。对模型输入的敏感性分析阐明了显着影响稻田农药径流的过程。尽管农药向河水中的排放量不到施于田间的农药总量的50%,但农药对土壤的吸附和降解速率并未显着影响农药的浓度,这可能是由于农药的吸附和降解所致。河流水中农药浓度增加的时间主要受耕作计划的影响,包括农药施用和灌溉的时间,其次受到降雨事件的影响。

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