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Hydrologic modeling response to NEXRAD and raingage spatial variability and strategic watershed management.

机译:对NEXRAD的水文建模响应,提高了空间变异性和战略性分水岭管理。

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Watershed models are fundamental to water resources management. Natural spatial and temporal precipitation patterns cause uncertainty in model response. The goal of this study was to improve model utility for supporting watershed management decisions. Specific objectives were: (1) to refine procedures for determining local bias adjustments for rainfall estimates from NEXRAD Stage III data; (2) to evaluate the uncertainty in predicted streamflow response to spatial rainfall input resolution; and (3) to demonstrate an approach for targeting areas to maximize water-quality benefits from BMP implementation. The study area was 6,316 km2 Smoky Hill River or Kanopolis Lake Watershed in central Kansas, the study period was from 1992-2002, and the hydrologic model used was SWAT.; Hourly NEXRAD Stage III rainfall depths were accumulated for 24-hr periods to match the observation time of raingage data. NEXRAD overestimated monthly average rainfall depths (45% to 184%) in warm months (April to September) and underestimated depths (33% to 64%) in cold months (December to February); depths in the remaining months were within +/-20%. Bias adjustment factor threshold limits of 0.15 (lower) and 2.0 (upper) resulted in the best agreement between daily predicted versus observed streamflows.; The bias-adjusted NEXRAD data, with original resolution of 4 km x 4 km, was aggregated in space with incrementally coarser resolutions of 8 km x 8 km, 16 km x 16 km, 32 km x 32 km, 64 km x 64 km, 124 km x 124 km, and 256 km x 256 km. There was no consistent and clear relationship between rainfall resolution and model performance, but generally, coarser resolution data gave better performance. The best performance was obtained by rainfall input resolutions of 64 km and coarser in the upstream (drier) half of the watershed, compared to 32 km and coarser in the downstream (wetter) half.; Strategic watershed management involved ranking subwatershed sediment yields and evaluating the impacts on sediment, N, and P yields of implementing reduced tillage, edge-of-field vegetative buffers, and contour-terraced practice either randomly or using targeting. Targeting resulted in greater reductions, both overland and at watershed outlet. The benefits of targeting were greater for the initial increments of BMP adoption and decreased as the proportion of BMP adoption on targeted land areas increased.
机译:流域模型是水资源管理的基础。自然的时空降水模式会引起模型响应的不确定性。这项研究的目的是改善模型效用,以支持流域管理决策。具体目标是:(1)完善程序,以根据NEXRAD第三阶段数据确定降雨估计的局部偏差调整; (2)评估预测的流量对空间降雨输入分辨率的不确定性; (3)展示一种针对区域的方法,以使实施BMP的水质收益最大化。研究区域为堪萨斯州中部的6,316 km2烟山河或Kanopolis湖流域,研究期为1992年至2002年,使用的水文模型为SWAT。 NEXRAD III期每小时的降雨深度累计了24小时,以匹配暴雨数据的观测时间。 NEXRAD在温暖月份(4月至9月)高估了月平均降雨深度(45%至184%),而在寒冷月份(12月至2月)低估了深度平均值(33%至64%);其余月份的深度在+/- 20%以内。偏差调整因子阈值限制为0.15(下)和2.0(上)导致每日预测流量与观测流量之间的最佳一致性。经过偏差调整的NEXRAD数据,原始分辨率为4 km x 4 km,是在空间中聚合的,分辨率逐渐提高,分别为8 km x 8 km,16 km x 16 km,32 km x 32 km,64 km x 64 km, 124公里x 124公里和256公里x 256公里。降雨分辨率与模型性能之间没有一致而明确的关系,但通常,分辨率越高的数据性能越好。流域上游(较干燥)一半的降雨输入分辨率为64 km或更粗,而下游(较湿)一半为32 km或更粗,则降雨表现最佳。战略性分水岭管理涉及对分水岭下的沉积物产量进行排序,并评估实施减少耕种,田间无性植物缓冲和轮廓梯田耕作的沉积物,氮和磷产量的影响,这些方法是随机的或使用目标定位的。针对性地导致陆上和流域出口的减少量更大。 BMP最初采用时,目标定位的好处更大,随着目标土地面积上BMP采用比例的增加,目标定位的收益会降低。

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