Estimation of Catchment Rainfall Uncertainty and its Influence on Runoff Prediction
Estimation of Catchment Rainfall Uncertainty and its Influence on Runoff Prediction
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流域降雨不确定性估计及其对径流预测的影响
DOI：
10.2166/nh.1988.0006
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发表时间：
1988
影响因子：
2.7
通讯作者：
J. Refsgaard
中科院分区：
文献类型：
--
作者：
B. Storm;K. Jensen;J. Refsgaard
空间变化点降水深度的内插在估计的平均面雨量(MAP)中引入了不确定性。本文描述了一种实时计算日地图的地质统计学方法--克立格法。该方法提供具有最小估计方差的线性无偏估计。通过将时变半方差函数模型与样本方差联系起来，实现了随机降水场结构分析的自动化。丹麦IHD集水区的数据说明了这一点。
将概念性降雨径流模型NAM引入卡尔曼滤波算法，研究了MAP中的不确定性对径流预报的影响。测量和加工误差不包括在调查范围内。
Interpolation of spatially varying point precipitation depths introduces uncertainties in the estimated mean areal precipitation (MAP). This paper describes a geostatistical approach – the Kriging method – to calculate the daily MAP on real-time basis. The procedure provides a linear unbiased estimate with minimum estimation variance. The structural analysis of the random precipitation field is automatized by relating the time-varying semivariogram model to the sample variance. This is illustrated on data from a Danish IHD catchment.
The conceptual rainfall-runoff model NAM incorporated into a Kalman-filter algortithm is applied to investigate the effects of uncertainties in MAP on the runoff predictions. Measurement and processing errors are not included in the investigation.