Matching Items (3)
Description
This doctoral thesis investigates the predictability characteristics of floods and flash floods by coupling high resolution precipitation products to a distributed hydrologic model. The research hypotheses are tested at multiple watersheds in the Colorado Front Range (CFR) undergoing warm-season precipitation. Rainfall error structures are expected to propagate into hydrologic simulations with added uncertainties by model parameters and initial conditions. Specifically, the following science questions are addressed: (1) What is the utility of Quantitative Precipitation Estimates (QPE) for high resolution hydrologic forecasts in mountain watersheds of the CFR?, (2) How does the rainfall-reflectivity relation determine the magnitude of errors when radar observations are used for flood forecasts?, and (3) What are the spatiotemporal limits of flood forecasting in mountain basins when radar nowcasts are used into a distributed hydrological model?. The methodology consists of QPE evaluations at the site (i.e., rain gauge location), basin-average and regional scales, and Quantitative Precipitation Forecasts (QPF) assessment through regional grid-to-grid verification techniques and ensemble basin-averaged time series. The corresponding hydrologic responses that include outlet discharges, distributed runoff maps, and streamflow time series at internal channel locations, are used in light of observed and/or reference data to diagnose the suitability of fusing precipitation forecasts into a distributed model operating at multiple catchments. Results reveal that radar and multisensor QPEs lead to an improved hydrologic performance compared to simulations driven with rain gauge data only. In addition, hydrologic performances attained by satellite products preserve the fundamental properties of basin responses, including a simple scaling relation between the relative spatial variability of runoff and its magnitude. Overall, the spatial variations contained in gridded QPEs add value for warm-season flood forecasting in mountain basins, with sparse data even if those products contain some biases. These results are encouraging and open new avenues for forecasting in regions with limited access and sparse observations. Regional comparisons of different reflectivity -rainfall (Z-R) relations during three summer seasons, illustrated significant rainfall variability across the region. Consistently, hydrologic errors introduced by the distinct Z-R relations, are significant and proportional (in the log-log space) to errors in precipitation estimations and stream flow magnitude. The use of operational Z-R relations without prior calibration may lead to wrong estimation of precipitation, runoff magnitude and increased flood forecasting errors. This suggests that site-specific Z-R relations, prior to forecasting procedures, are desirable in complex terrain regions. Nowcasting experiments show the limits of flood forecasting and its dependence functions of lead time and basin scale. Across the majority of the basins, flood forecasting skill decays with lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Both precipitation and flood forecasting skills are noticeably reduced for lead times greater than 30 minutes. Scale dependence of hydrologic forecasting errors demonstrates reduced predictability at intermediate-size basins, the typical scale of convective storm systems. Overall, the fusion of high resolution radar nowcasts and the convenient parallel capabilities of the distributed hydrologic model provide an efficient framework for generating accurate real-time flood forecasts suitable for operational environments.
ContributorsMoreno Ramirez, Hernan (Author) / Vivoni, Enrique R. (Thesis advisor) / Ruddell, Benjamin L. (Committee member) / Gochis, David J. (Committee member) / Mays, Larry W. (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2012
Description
The National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) will provide the next generation of operational streamflow forecasts at different lead times across United States using the Weather Research and Forecasting (WRF)-Hydro hydrologic system. These forecasts are crucial for flood protection agencies and water utilities, including the Salt River Project (SRP). The main goal of this study is to calibrate WRF-Hydro in the Oak Creek Basin (OCB; ~820 km2), an unregulated mountain sub-watershed of the Salt and Verde River basins in Central Arizona, whose water resources are managed by SRP and crucial for the Phoenix Metropolitan area. As in the NWM, WRF-Hydro was set up at 1-km (250-m) resolution for the computation of the rainfall-runoff (routing) processes. Model forcings were obtained by bias correcting meteorological data from the North American Land Data Assimilation System-2 (NLDAS-2). A manual calibration approach was designed that targets, in sequence, the sets of model parameters controlling four main processes responsible for streamflow and flood generation in the OCB. After a first calibration effort, it was found that WRF-Hydro is able to simulate runoff generated after snowmelt and baseflow, as well as magnitude and timing of flood peaks due to winter storms. However, the model underestimates the magnitude of flood peaks caused by summer thunderstorms, likely because these storms are not captured by NLDAS-2. To circumvent this, a seasonal modification of soil parameters was adopted. When doing so, acceptable model performances were obtained during calibration (2008-2011) and validation (2012-2017) periods (NSE > 0.62 and RMSE = ~2.5 m3/s at the daily time scale).
The process-based calibration strategy utilized in this work provides a new approach to identify areas of structural improvement for WRF-Hydro and the NWM.
The process-based calibration strategy utilized in this work provides a new approach to identify areas of structural improvement for WRF-Hydro and the NWM.
ContributorsHussein, Abdinur Jirow (Author) / Mascaro, Giuseppe (Thesis advisor) / Vivoni, Enrique (Thesis advisor) / Xu, Tianfang (Committee member) / Arizona State University (Publisher)
Created2020
Description
A model is presented for real-time, river-reservoir operation systems. It epitomizes forward-thinking and efficient approaches to reservoir operations during flooding events. The optimization/simulation includes five major components. The components are a mix of hydrologic and hydraulic modeling, short-term rainfall forecasting, and optimization and reservoir operation models. The optimization/simulation model is designed for ultimate accessibility and efficiency. The optimization model uses the meta-heuristic approach, which has the capability to simultaneously search for multiple optimal solutions. The dynamics of the river are simulated by applying an unsteady flow-routing method. The rainfall-runoff simulation uses the National Weather Service NexRad gridded rainfall data, since it provides critical information regarding real storm events. The short-term rainfall-forecasting model utilizes a stochastic method. The reservoir-operation is simulated by a mass-balance approach. The optimization/simulation model offers more possible optimal solutions by using the Genetic Algorithm approach as opposed to traditional gradient methods that can only compute one optimal solution at a time. The optimization/simulation was developed for the 2010 flood event that occurred in the Cumberland River basin in Nashville, Tennessee. It revealed that the reservoir upstream of Nashville was more contained and that an optimal gate release schedule could have significantly decreased the floodwater levels in downtown Nashville. The model is for demonstrative purposes only but is perfectly suitable for real-world application.
ContributorsChe, Daniel C (Author) / Mays, Larry W. (Thesis advisor) / Fox, Peter (Committee member) / Wang, Zhihua (Committee member) / Lansey, Kevin (Committee member) / Wahlin, Brian (Committee member) / Arizona State University (Publisher)
Created2015