The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model
The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model
复制标题
雨量计网络密度对水文模型性能的影响
DOI：
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
N. Mani
中科院分区：
文献类型：
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作者：
G. Andiego;M. Waseem;Muhammad Usman;N. Mani
由于短距离内的降水变化以及网络稀疏或不规则，雨量计数据存在空间误差。由于上述不规则稀疏网络，使用插值法进行评估通常不可靠。本研究是在Nette河流域的下萨克森萨克森，以减轻使用仪表数据来衡量插值性能的问题。雷达降水数据提取的位置，53个雨量计站，分布在整个范围内的天气监视雷达（WSR）。由于雷达数据传统上遭受时间误差，它是使用平均场偏差（MFB）方法通过利用雨量计数据进行校正，然后进一步作为参考降水的研究。通过交叉验证对反距离加权（IDW）和普通克里格（OK）插值方法的性能进行了评估。通过比较两种插值方法和相应密度的模拟流量与参考降水数据的模拟流量，评估了测量密度对HBV-IWW水文模型的影响。两种插值方法在冬季的插值性能都明显优于夏季。此外，普通克里金法在两个季节的表现略好于逆距离加权法。在面降水量的情况下，观察到两种插值方法的性能随着测量密度的增加而逐渐改善，但发现逆距离加权在密度更高时更加一致。比较表明，普通克里格优于反距离加权只有高达70%的密度，超过此性能是相同的。水文模拟的结果是类似的面雨量，除了这两种方法，有超过50%计密度的性能没有改善。
Rain gauge data suffers from spatial errors because of precipitation variability within short distances and due to sparse or irregular network. Use of interpolation is often unreliable to evaluate due to the aforementioned irregular sparse networks. This study is carried out in the Nette River catchment of Lower Saxony to alleviate the problem of using gauge data to measure the performance of interpolation. Radar precipitation data was extracted in the positions of 53 rain gauge stations, which are distributed throughout the range of the weather surveillance radar (WSR). Since radar data traditionally suffers from temporal errors, it was corrected using the Mean Field Bias (MFB) method by utilizing the rain gauge data and then further used as the reference precipitation in the study. The performances of Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) interpolation methods by means of cross validation were assessed. Evaluation of the effect of the gauge densities on HBV-IWW hydrological model was achieved by comparing the simulated discharges for the two interpolation methods and corresponding densities against the simulated discharge of the reference precipitation data. Interpolation performance in winter was much better than summer for both interpolation methods. Furthermore, Ordinary Kriging performed marginally better than Inverse Distance Weighting in both seasons. In case of areal precipitation, progressive improvement in performance with increase in gauge density for both interpolation methods was observed, but Inverse Distance Weighting was found more consistent up to higher densities. Comparison showed that Ordinary Kriging outperformed Inverse Distance Weighting only up to 70% density, beyond which the performance is equal. The hydrological modelling results are similar to that of areal precipitation except that for both methods, there was no improvement in performance beyond the 50% gauge density.
影响因子：
6.4
作者：
E. Rabiei;U. Haberlandt
通讯作者：
E. Rabiei;U. Haberlandt
针对高时间分辨率和各种站点密度场景的雨量计和雷达数据的地统计合并
影响因子：
6.4
作者：
Berndt;Rabiei;Haberlandt
通讯作者：
Haberlandt