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Combining Gauge and Radar Derived Observations to Estimate Precipitation Input to a Watershed.

机译：结合量规和雷达派生的观测值来估计流域的降水输入。

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摘要

Watersheds are complicated systems that require sophisticated computer models to support environmental and water resource management decision making. For example, once a watershed model has been built and calibrated for a particular watershed system, it can be used to simulate different hypothetical scenarios such as the impact of increased urbanization on runoff, ground water and streamflow, as well as water quality. One challenge in creating an accurate and useful watershed model is obtaining input datasets for building and calibrating the model, and one of the most important input datasets required is estimates of precipitation volume over the watershed area. Precipitation estimates are measured at gauging stations and by radar instruments. Both approaches, gauges and radar, have benefits and weaknesses in their ability to estimate precipitation. The objective of this thesis was to improve precipitation estimates by combining data from gauge and radar precipitation estimates in an attempt to improve the accuracy of a watershed model. Using the Eno River watershed located in Orange County, NC as the study area, three different precipitation datasets were tested to estimate streamflow for the watershed over the period 2005-2010: (1) estimates based on only precipitation gauging stations, (2) estimates based only on gauged-corrected radar observations, and (3) a new dataset that is the combination of estimates from the gauge and radar data sources selected in a way to better capture observed streamflow at the watershed outlet. The hypothesis is that for different storm events, it is possible that one of the two data sources better captured that storm event, and therefore if precipitation estimates from gauged and radar based sources are combined into a single time series, that combined time series will yield more accurate streamflow estimates. The results from the work show that the combined precipitation significantly improves streamflow estimates (R2 = 0.80; E = 0.74) when compared to the gauged estimates only (R 2 = 0.46; E = 0.31) and the radar based estimates only (R2 = 0.62; E = 0.48). Therefore, the results support the hypothesis that combining precipitation from multiple sources improves watershed model accuracy. The experiment was limited to one study watershed, however, and therefore it did not control for factors such as climate, ecology, and hydrogeology that will likely influence the results of the experiment.
机译：流域是复杂的系统，需要复杂的计算机模型来支持环境和水资源管理决策。例如，一旦针对特定流域系统建立了流域模型并对其进行了校准，则可以将其用于模拟不同的假设情景，例如城市化程度提高对径流，地下水和河流流量以及水质的影响。创建准确和有用的流域模型的一个挑战是获取用于构建和校准模型的输入数据集，而所需的最重要的输入数据集之一是对流域区域内的降水量进行估算。降水量的估算是在测量站和雷达仪器测量的。仪表和雷达这两种方法在估计降水量的能力上都有优缺点。本文的目的是通过结合来自轨距和雷达降水估算的数据来改善降水估算，以提高流域模型的准确性。以位于北卡罗来纳州奥兰治县的伊诺河流域为研究区域，测试了三个不同的降水数据集以估算该流域在2005-2010年期间的流量：（1）仅基于降水监测站的估算，（2）估算仅基于经校正的雷达观测值，以及（3）一个新的数据集，该数据集是来自测距仪和雷达数据源的估计值的组合，以更好地捕获流域出口处的观测流的方式进行选择。假设是，对于不同的暴风雨事件，两个数据源之一可能会更好地捕获该暴风雨事件，因此，如果将来自规范和基于雷达的源的降水估计值合并到一个时间序列中，则合并后的时间序列将产生更准确的流量估算。这项工作的结果表明，与仅采用实测值（R 2 = 0.46； E = 0.31）和仅基于雷达的估算值（R2 = 0.62）相比，合并降水显着改善了径流估算值（R2 = 0.80； E = 0.74）。 ； E = 0.48）。因此，结果支持这样的假说，即合并来自多个来源的降水可提高分水岭模型的准确性。但是，该实验仅限于一个研究分水岭，因此它无法控制可能影响实验结果的气候，生态学和水文地质等因素。

著录项

  • 作者

    Ercan, Mehmet Bulent.;

  • 作者单位

    University of South Carolina.;

  • 授予单位 University of South Carolina.;
  • 学科 Hydrology.;Water Resource Management.;Engineering Civil.
  • 学位 M.S.
  • 年度 2011
  • 页码 83 p.
  • 总页数 83
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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