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Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

机译:利用实时数值天气预报预测降雨触发的浅层滑坡的概率

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摘要

A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48 h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70-90%.
机译:新西兰国家水和大气研究所(NIWA)建立的一个项目旨在开发实时滑坡预测系统的原型。目的是根据数值天气预报模型中的定量天气预报,预测降雨触发的浅层滑坡的滑坡概率随时间的变化。英国气象局(MO)的数值天气预报模型(NWP)的全球天气预报与同化NWP模型的区域数据(新西兰有限区域模型,NZLAM)相结合,可预报大气变量,例如长达48小时的降水和温度整个新西兰都领先。天气预报被输入到水文模型中,以预测土壤湿度和地下水位的发展。然后使用地形指数对预测的土壤水分和土壤饱和度的流域尺度模式进行缩减,以预测局部尺度的土壤水分状况,并应用无限边坡稳定性模型来确定局部尺度的触发土壤水分阈值。该模型使用土壤参数的不确定性来提前48小时预测时空滑坡发生的概率预测。对系统进行了评估,以评估新西兰发生的破坏性滑坡事件。与根据卫星图像估计的滑坡密度进行比较,得出的命中率为70-90%。

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