Build an Ensemble-based Remote-Sensing Driven Coupled Flash Flood and Landslide Warning System and Its Evaluation Across the United States

Monday, 15 December 2014: 2:20 PM
Ke Zhang1, Yang Hong2, Jonathan J Gourley3, Humberto J Vergara4, Xianwu Xue4, Ning Lu5 and Rick Wooten6, (1)The University of Oklahoma, CIMMS, Norman, OK, United States, (2)University of Oklahoma, Norman, OK, United States, (3)National Severe Storms Lab, Oklahoma City, OK, United States, (4)The University of Oklahoma, Norman, OK, United States, (5)Colorado School Mines, Golden, CO, United States, (6)North Carolina Department of Environment and Natural Resources, Mooresville, NC, United States
Flooding and flash flooding are the most costly weather-related natural hazards in the United States and world. Heavy rainfall-triggered landslides are often associated with flash flood events and cause additional loss of life and property. Therefore, it is important to understand the linkage and interaction between flash flood events and landslides. It is also pertinent to build a robust coupled flash flood and landslide disaster early warning system for disaster preparedness and hazard management. In this study, we built a coupled flash flood and landslide disaster early warning system, which is aimed for operational use by the US National Weather Service, based on an existing ensemble framework by extending the model ensemble and coupling a set of distributed hydrologic models, the Coupled Routing and Excess STorage (CREST) model and the SACramento Soil Moisture Accounting (SAC-SMA) model, with two physically based landslide prediction models, the SLope-Infiltration-Distributed Equilibrium (SLIDE) model and the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model. We tested this prototype warning system by conducting multi-year simulations driven by the Multi-Radar Multi-Sensor (MRMS) rainfall estimates at selected basins across the United States. We then comprehensively evaluated the predictive capabilities of this system against observed and reported flood and landslides events. Our results show that the system is generally capable of making accurate predictions of flash flood and landslide events in terms of their locations and time of occurrence. The recently developed ensemble framework also enables us to quantify the uncertainty of the predictions and the probabilities of anticipated disaster events.