Publication

Usingrainfallthresholdsandensembleprecipitationforecaststo issueandimproveurbaninundationalerts


Publication Date : 2016-11-30
Author : Yang, T. H.Gong-Do, H.Chin-Cheng, T.Jui-Yi, H.
Countries :
Disaster Management Theme :
Disaster Type : Flood
Document Type : Research Paper
Languange : en
Link : https://www.hydrol-earth-syst-sci.net/20/4731/2016/hess-20-4731-2016.pdf

Abstact :

Urban inundation forecasting with extended lead times is useful in saving lives and property. This study proposestheintegrationofrainfallthresholdsandensembleprecipitation forecasts to provide probabilistic urban inundationforecasts.Utilizationofensembleprecipitationforecasts can extend forecast lead times to 72h, predicting peak ?ows and to allow response agencies to take necessary preparatory measures. However, ensemble precipitation forecasting is time- and resource-intensive. Using rainfall thresholds to estimate urban areas’ inundation risk can decrease this complexity and save computation time. This study evaluated the performance of this system using 352 townships in Taiwan and seven typhoons during the period 2013–2015. The levels of forecast probability needed to issue inundation alerts were addressed because ensemble forecasts are probability based. This study applied six levels of forecast probability andevaluatedtheirperformanceusing?vemeasures.Theresults showed that this forecasting system performed better before a typhoon made landfall. Geography had a strong impactatthestartofthenumericalweathermodeling,resulting intheunderestimationofrainfallforecasts.Regardlessofthis ?nding,theinundationforecastperformancewashighlycontingent on the rainfall forecast skill. This study then tested a hybrid approach of on-site observations and rainfall forecasts to decrease the in?uence of numerical weather predictions and improve the forecast performance. The results of this combined system showed that forecasts with a 24h lead time improved signi?cantly. These ?ndings and the hybrid approach can be applied to other hydrometeorological early warning systems to improve hazard-related forecasts.