...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Transactions of the ASABE >Using NEXRAD and rain gauge precipitation data for hydrologic calibration of SWAT in a Northeastern watershed. (Special Issue: Soil and water assessment tool (SWAT) modeling technology: current status.)
【24h】

Using NEXRAD and rain gauge precipitation data for hydrologic calibration of SWAT in a Northeastern watershed. (Special Issue: Soil and water assessment tool (SWAT) modeling technology: current status.)

机译：利用NEXRAD和雨量计降水数据对东北流域的SWAT进行水文校准。 （特刊：水土评估工具（SWAT）建模技术：当前状态。）

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The value of watershed-scale, hydrologic and water quality models to ecosystem management is increasingly evident as more programs adopt these tools to evaluate the effectiveness of different management scenarios and their impact on the environment. Quality of precipitation data is critical for appropriate application of watershed models. In small watersheds, where no dense rain gauge network is available, modelers are faced with a dilemma to choose between different data sets. In this study, we used the German Branch (GB) watershed (~50 km2), which is included in the USDA Conservation Effects Assessment Project (CEAP), to examine the implications of using surface rain gauge and next-generation radar (NEXRAD) precipitation data sets on the performance of the Soil and Water Assessment Tool (SWAT). The GB watershed is located in the Coastal Plain of Maryland on the eastern shore of Chesapeake Bay. Stream flow estimation results using surface rain gauge data seem to indicate the importance of using rain gauges within the same direction as the storm pattern with respect to the watershed. In the absence of a spatially representative network of rain gauges within the watershed, NEXRAD data produced good estimates of stream flow at the outlet of the watershed. Three NEXRAD datasets, including (1) non-corrected (NC), (2) bias-corrected (BC), and (3) inverse distance weighted (IDW) corrected NEXRAD data, were produced. Nash-Sutcliffe efficiency coefficients for daily stream flow simulation using these three NEXRAD data ranged from 0.46 to 0.58 during calibration and from 0.68 to 0.76 during validation. Overall, correcting NEXRAD with rain gauge data is promising to produce better hydrologic modeling results. Given the multiple precipitation datasets and corresponding simulations, we explored the combination of the multiple simulations using Bayesian model averaging. The results show that this Bayesian scheme can produce better deterministic prediction than any single simulation and can provide reasonable uncertainty estimation. The optimal water balance obtained in this study is an essential precursor to acquiring realistic estimates of sediment and nutrient loads in future GB modeling efforts. The results presented in this study are expected to provide insights into selecting precipitation data for watershed modeling in small Coastal Plain catchments.
机译：随着越来越多的计划采用这些工具来评估不同管理方案的有效性及其对环境的影响，分水岭规模，水文和水质模型对生态系统管理的价值越来越明显。降水数据的质量对于分水岭模型的正确应用至关重要。在没有密集雨量计网络的小流域，建模者面临着在不同数据集之间进行选择的难题。在这项研究中，我们使用了USDA保护效果评估项目（CEAP）中包含的德国分公司（GB）分水岭（〜50 km 2 ），研究了使用地表雨量计的含义以及有关土壤和水评估工具（SWAT）性能的下一代雷达（NEXRAD）降水数据集。 GB分水岭位于切萨皮克湾东岸的马里兰州沿海平原。使用地表雨量计数据进行的流量估算结果似乎表明，在与分水岭相同的方向上使用雨量计的重要性。在流域内没有一个具有空间代表性的雨量计网络的情况下，NEXRAD数据对流域出口处的水流产生了良好的估计。生成了三个NEXRAD数据集，包括（1）未校正（NC），（2）偏差校正（BC）和（3）逆距离加权（IDW）校正的NEXRAD数据。使用这三个NEXRAD数据进行日常水流模拟的Nash-Sutcliffe效率系数在校准期间为0.46至0.58，在验证期间为0.68至0.76。总体而言，用雨量计数据校正NEXRAD有望产生更好的水文模拟结果。给定多个降水数据集和相应的模拟，我们使用贝叶斯模型平均探索了多个模拟的组合。结果表明，该贝叶斯方案可以比任何单个模拟产生更好的确定性预测，并且可以提供合理的不确定性估计。这项研究中获得的最佳水平衡是在未来的GB建模工作中获取泥沙和养分负荷的现实估计的必要先决条件。预期本研究中提供的结果将为选择降水数据用于沿海小平原流域的流域建模提供见识。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号