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Title:Using ensemble precipitation forecasts to improve hydrologic risk assessment at river crossings
Author(s):Matus, Sean Alan
Advisor(s):Kumar, Praveen; Dominguez, Francina
Department / Program:Civil & Environmental Eng
Discipline:Environ Engr in Civil Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
risk assessment
Abstract:Current National Weather Service operations forecast hydrologic conditions probabilistically at most of the United States Geological Survey (USGS) stream gauges across the continental United States. The successful implementation of such an approach, operationally, suggests the hypothesis that a probabilistic approach would reduce uncertainty of hydrologic forecasts in ungauged basins. Forecast improvement in ungauged basins is of great interest to the United States Army due to a combination of the remoteness and hydrologic safety risk associated with low-water crossings (LWXs) commonly used as river infrastructure on military training lands. In this work, two historical deadly flooding events were hindcasted at three LWXs at Fort Hood, Texas. A probabilistic precipitation forcing cascades uncertainty through hydrologic and hydraulic models. Each precipitation ensemble member corresponds to an independent model run, resulting in ensembles of streamflow at a 24-hour lead time. The forecast is expanded to predict river hydraulics, through flow velocity and depth, at specific river LWXs. Analysis of the hindcast of two events indicates that cascading probabilistic precipitation through hydrologic and hydraulic models adds robustness to river forecasts compared to deterministic methods. The approach provides a means to communicate the uncertainty of predictions through model member agreement. Analysis of different methods for conveying hydrologic risk from model output leads to our recommendation that a hydraulic safety threshold, calculated as the multiplication of flow velocity and depth, is the best approach for U.S. Army stakeholders in terms of communicating hydrologic risk, as well as associated model uncertainty in the simplest manner possible.
Issue Date:2018-12-11
Rights Information:Copyright 2018 Sean Matus
Date Available in IDEALS:2019-02-06
Date Deposited:2018-12

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