Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales
Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales
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DOI：
10.1016/j.jhydrol.2006.02.013
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发表时间：
2006-09
影响因子：
6.4
通讯作者：
T. M. Carpenter;K. Georgakakos
中科院分区：
文献类型：
--
作者：
T. M. Carpenter;K. Georgakakos
分布式水文模型，能够将各种空间变化的土地特征和降水强迫数据，被认为有很大的潜力，改善水文预报。然而，降水和模式参数的高分辨率估计的不确定性可能会减少通过考虑固有的空间变异性而实现的预测准确性的潜在收益。本文开发了一种概率方法比较集合径流模拟水文模型与高，低空间分辨率下的不确定性，在降水输入和模型参数。该方法产生合奏径流模拟使用校准良好的水文模型，并评估的显着性的合奏从高，低分辨率的模型相同的模拟点。研究流域的规模，业务流量预报发出（订单的几个1000平方公里），和模型采用的业务模型的改编由美国国家气象局使用。高分辨率（即，空间分布的）模型和低分辨率（即，空间集总）模型被用来模拟选定的事件，为两个研究流域位于美国中部平原南部使用操作质量的数据来驱动模型。在蒙特卡洛框架内，使用基于雷达的降水估计和水文土壤模型参数的不确定性模型，生成了Enhanced径流模拟。然后采用Kolmogorov-Smirnov检验来评估在高分辨率和低分辨率模型中观察到的峰值流量时的集合流模拟是否可以以高置信度区分。进一步评估评价了模型在再现观察到的峰值流量大小和时间方面的性能。大多数选定的事件表明，高分辨率和低分辨率模型产生的峰值流量在统计学上不同的流量集合。此外，高分辨率模式集合模拟始终有较高的频率发生在所观察到的峰值流量大小和时间的规定范围内。
Distributed hydrologic models, with the capability to incorporate a variety of spatially-varying land characteristics and precipitation forcing data, are thought to have great potential for improving hydrologic forecasting. However, uncertainty in the high resolution estimates of precipitation and model parameters may diminish potential gains in prediction accuracy achieved by accounting for the inherent spatial variability. This paper develops a probabilistic methodology for comparing ensemble streamflow simulations from hydrologic models with high- and low-spatial resolution under uncertainty in both precipitation input and model parameters. The methodology produces ensemble streamflow simulations using well calibrated hydrologic models, and evaluates the distinctiveness of the ensembles from the high- and low-resolution models for the same simulation point. The study watersheds are of the scale for which operational streamflow forecasts are issued (order of a few 1000km2), and the models employed are adaptations of operational models used by the US National Weather Service. A high-resolution (i.e., spatially distributed) model and a low-resolution (i.e., spatially lumped) model were used to simulate selected events for each of two study watersheds located in the southern Central Plains of the US using operational-quality data to drive the models. Ensemble streamflow simulations were generated within a Monte Carlo framework using models for uncertainty in radar-based precipitation estimates and in the hydrologic soil model parameters. The Kolmogorov–Smirnov test was then employed to assess whether the ensemble flow simulations at the time of observed peak flow from the high- and low-resolution models can be distinguished with high confidence. Further assessment evaluated the model performance in terms of reproducing the observed peak flow magnitude and timing. Most of the selected events showed the high- and low-resolution models produced statistically different flow ensembles for the peak flow. Furthermore, the high-resolution model ensemble simulations consistently had higher frequency of occurrence within specified bounds of the observed peak flow magnitude and timing.