A data-based regional scale autoregressive rainfall-runoff model: a study from the Odra River
A data-based regional scale autoregressive rainfall-runoff model: a study from the Odra River
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基于数据的区域尺度自回归降雨径流模型：奥得河的研究
DOI：
10.1007/s00477-006-0077-y
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
T. Niedzielski
中科院分区：
文献类型：
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作者：
T. Niedzielski
本文旨在比较应用于区域尺度降雨径流建模的多元自回归（MAR）技术和单变量自回归（AR）方法的性能。我们重点研究波兰西南部奥得河中上游及其主要支流的案例。这些河流流经山脉（苏台德河）和低地（尼齐纳·斯拉斯卡）。该地区容易遭受极端水文和气象事件，特别是降雨和融雪洪水。为了进行分析，选择了四个水文和气象变量，即流量（17 个地点）、降水量（7 个地点）、积雪厚度（7 个地点）和地下水位（1 个地点）。时间段为 1971 年 11 月至 1981 年 12 月，时间序列的时间分辨率为 1 天。相同阶数的 MAR 和 AR 模型都适合不同的数据子集，并随后得出流量预测。为了评估预测，应用逐步过程以使验证独立于随机过程的特定样本路径。结果表明，无论是降雨引起的洪峰还是融雪洪峰，该模型都可以提前 2-4 天预测洪峰流量。此外，如果分析流量、降水、积雪和地下水位的组合数据而不是纯粹的流量多元时间序列，流量预测的准确性就会提高。对于有洪水的年份，基于多变量流量数据的基于 MAR 的流量预测比基于 AR 的单变量预测更准确，但是，在无洪水年份的情况下，这种关系相反。相比之下，无论一年内是否发生洪水，基于流量、降水、积雪和地下水位的基于 MAR 的流量预测比基于 AR 的预测更准确。
This paper aims to compare the performances of multivariate autoregressive (MAR) techniques and univariate autoregressive (AR) methods applied to regional scale rainfall-runoff modelling. We focus on the case study from the upper and middle reaches of the Odra River with its main tributaries in SW Poland. The rivers drain both the mountains (the Sudetes) and the lowland (Nizina Śląska). The region is exposed to extreme hydrologic and meteorological events, especially rain-induced and snow-melt floods. For the analysis, four hydrologic and meteorological variables are chosen, i.e., discharge (17 locations), precipitation (7 locations), thickness of snow cover (7 locations) and groundwater level (1 location). The time period is November 1971–December 1981 and the temporal resolution of the time series is of 1 day. Both MAR and AR models of the same orders are fitted to various subsets of the data and subsequently forecasts of discharge are derived. In order to evaluate the predictions the stepwise procedure is applied to make the validation independent of the specific sample path of the stochastic process. It is shown that the model forecasts peak discharges even 2–4 days in advance in the case of both rain-induced and snow-melt peak flows. Furthermore, the accuracy of discharge predictions increases if one analyses the combined data on discharge, precipitation, snow cover, and groundwater level instead of the pure discharge multivariate time series. MAR-based discharge forecasts based on multivariate data on discharges are more accurate than AR-based univariate predictions for a year with a flood, however, this relation is reverse in the case of the free-of-flooding year. In contrast, independently of the occurrence of floods within a year, MAR-based discharge forecasts based on discharges, precipitation, snow cover, and groundwater level are more precise than AR-based predictions.