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Prospects for flash flood forecasting in mountainous regions - An investigation of Tropical Storm Fay in the Southern Appalachians

机译:山区山洪预报的前景-阿巴拉契亚南部热带风暴Fay调查

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摘要

The sensitivity of Quantitative flash-Flood Estimates (QFEs) and Quantitative flash-Flood Forecasts (QFFs) to Quantitative Precipitation Estimates (QPEs) and Quantitative Precipitation Forecasts (QPFs) was investigated in three headwater catchments with different topographic and hydro-geomorphic characteristics during the passage of Tropical Storm Fay, 2008 over the Southern Appalachian Mountains in North Carolina, USA. QFEs and QFFs were generated by a high-resolution hydrologic model (250×250m~2) with coupled surface-subsurface physics and rainfall forcing from the Next Generation Multi-sensor QPE (Q2) spatial rainfall (1×1km~2) product, and from National Digital Forecast Database (NDFD) operational QPF product (5×5km~2). Optimal QPE products (Q2+) were derived by assimilating rainfall observations from a high density raingauge network through adaptive bias correction. Deterministic QFEs simulated by the hydrologic model agree well with streamgauge observations (15-min intervals) regarding total water volume and peak flow with Nash-Sutcliffe (NS) coefficients 0.8-0.9, thus suggesting that the model without calibration captures well the dominant flash-flood physics. The propagation of uncertainty in storm rainfall to rainfall-runoff response was subsequently evaluated through model simulations forced by Monte Carlo replications of the QPEs to generate QFE distributions. Analysis of the joint QPE-QFE distributions shows that flood response at the catchment scale is highly non-linear, and exhibits strong dependence on basin physiography, initial soil moisture conditions (transient basin storage capacity), the space-time organization of runoff generation and conveyance mechanisms, and in particular interflow dynamics, with respect to the space-time structure of rainfall. QFFs for 6- to 1-h lead times using precipitation composites of Q2 QPE and NDFD QPF to drive the hydrology model in operational mode exhibited ubiquitous lack of skill yielding consistently negative NS scores. An experiment consisting of merging satellite-like observations into operational QPE/QPF showed significant improvement in QFF performance (e.g. 5-50% relative NS increases), especially when the timing of satellite overpass is such that it captures transient episodes of heavy rainfall during the event. Future advances in QFF remain principally constrained by progress in QPE and QPF at the spatial resolution necessary to resolve rainfall-interflow dynamics in mountainous regions.
机译:在调查期间三个具有不同地形和水文地貌特征的水源流域,研究了定量洪水预报(QFEs)和定量洪水预报(QFFs)对定量降水估算(QPEs)和定量降水预报(QPFs)的敏感性。美国北卡罗来纳州南部阿巴拉契亚山脉上空经过的热带风暴“费伊”号2008年。 QFE和QFF是由高分辨率水文模型(250×250m〜2)产生的,该模型具有耦合的表-地下物理和降雨强迫,是由下一代多传感器QPE(Q2)空间降雨(1×1km〜2)产品产生的,以及来自国家数字预报数据库(NDFD)的可运行QPF产品(5×5km〜2)。最佳QPE产品(Q2 +)是通过自适应偏差校正从高密度雨量计网络吸收降雨观测值而得出的。水文模型模拟的确定性QFE与流量规制观测值(以15分钟为间隔)有关总水量和峰值流量(纳什-苏克利夫(NS)系数为0.8-0.9)非常吻合,因此表明,未经校准的模型可以很好地捕获主要的闪洪水物理学。随后,通过由QPE的蒙特卡罗模拟强迫产生的QFE分布的模型模拟,评估了暴雨中的不确定性对降雨-径流响应的传播。对QPE-QFE联合分布的分析表明,流域尺度上的洪水响应是高度非线性的,并且强烈依赖于流域的地貌,初始土壤湿度条件(瞬态流域存储能力),径流产生的时空组织和降雨时空结构的输运机制,尤其是内部流动力学。使用Q2 QPE和NDFD QPF的降水复合物来驱动水文模型在运行模式下的6至1小时提前期的QFF显示出普遍缺乏技能,导致NS分数始终为负。一项将类似卫星的观测结果合并到运行中的QPE / QPF中的实验表明,QFF的性能有了显着改善(例如,相对NS增加了5-50%),尤其是当卫星立交的时机能够捕捉到降雨期间瞬态暴雨的情况时事件。 QFF的未来发展仍主要受到QPE和QPF的进展所限制,而该进展在解决山区降雨-降雨流动力学所必需的空间分辨率上。

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