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The impact of initial conditions on convection-permitting simulations of a flood event over complex mountainous terrain

机译:初始条件对复杂山区地形洪水事件对流扫描允许模拟的影响

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Western Norway suffered major flooding after 4d of intense rainfall during the last week of October 2014. While events like this are expected to become more frequent and severe under a warming climate, convection-permitting scale models are showing their skill with respect to capturing their dynamics. Nevertheless, several sources of uncertainty need to be taken into account, including the impact of initial conditions on the precipitation pattern and discharge, especially over complex, mountainous terrain. In this paper, the Weather Research and Forecasting Model Hydrological modelling system (WRF-Hydro) is applied at a convection-permitting scale, and its performance is assessed in western Norway for the aforementioned flood event. The model is calibrated and evaluated using observations and benchmarks obtained from the Hydrologiska Byr?ns Vattenbalansavdelning (HBV) model. The calibrated WRF-Hydro model performs better than the simpler conceptual HBV model, especially in areas with complex terrain and poor observational coverage. The sensitivity of the precipitation pattern and discharge to poorly constrained elements such as spin-up time and snow conditions is then examined. The results show the following: (1)?the convection-permitting WRF-Hydro simulation generally captures the precipitation pattern/amount, the peak flow volume and the timing of the flood event; (2)?precipitation is not overly sensitive to spin-up time, whereas discharge is slightly more sensitive due to the influence of soil moisture, especially during the pre-peak phase; and (3)?the idealized snow depth experiments show that a maximum of 0.5m of snow is converted to runoff irrespective of the initial snow depth and that this snowmelt contributes to discharge mostly during the rainy and the peak flow periods. Although further targeted experiments are needed, this study suggests that snow cover intensifies the extreme discharge instead of acting as a sponge, which implies that future rain-on-snow events may contribute to a higher flood risk.
机译:在2014年10月的最后一周4D剧烈降雨之后,挪威遭遇了重大洪水。虽然这样的事件,但在温暖的气候下,这些事件将变得更加频繁,对流允许的规模模型正在展示他们对捕获动态的技能。然而,需要考虑几种不确定性来源,包括初始条件对降水模式和放电的影响,特别是在复杂的山地地形上。在本文中,天气研究和预测模型水文建模系统(WRF-Hydro)以对流允许的规模应用,其性能在挪威进行了评估,以获得上述洪水事件。使用从Hydrogisciska Byr获得的观测和基准进行校准和评估模型,并评估来自Hydromiska Byr的观测和基准.NS Vattenbalansavdeling(HBV)模型。校准的WRF-Hydro模型比更简单的概念HBV型号更好,特别是在具有复杂地形和观察覆盖率差的区域。然后检查沉淀图案和放电对受旋转时间和散雪条件的受损元素的敏感性。结果显示如下:(1)?对流允许WRF-Hydro模拟通常捕获降水模式/数量,峰值流量和洪水事件的时机; (2)?沉淀对旋转时间没有过度敏感,而放电由于土壤水分的影响略微敏感,特别是在预峰期期间; (3)?理想化的雪深度实验表明,无论初始雪深度如何,最多0.5米的雪被转换为径流,并且这种雪光在多雨期间主要有助于放电。虽然需要进一步的目标实验,但本研究表明,雪覆盖加剧了极端排放而不是作为海绵起作用,这意味着未来的雨雪事件可能有助于更高的洪水风险。

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