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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2014)

Floods are among the most devastating natural disasters which affect millions of people worldwide. Forecasting floods to provide warnings to the public in a timely manner is crucially important, however, this is a very challenging task. HiResFlood-UCI was developed by coupling the National Weather Service's (NWS) distributed hydrologic model (HL-RDHM) with the hydraulic model BreZo (developed by Sanders and Begnudelli) in order to estimate localized flood depths and velocities. A semi-automated technique of efficient unstructured mesh generation for BreZo was developed. HiResFlood-UCI was implemented for the ELDO2 catchment in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10m versus 30m), and computation mesh resolution (10m+ versus 30m+). Simulations show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution, and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using six measured precipitation events as model input for validation of the streamflow at the outlet and an interior point. Validation builds confidence in model predictions of river discharge, flood extent and localized velocities, which are fundamental to reliable flood warning.

HiResFlood-UCI was implemented for flood forecasting in the Cedar River Basin using real-time remote sensing precipitation PERSIANN-CCS data. The model was evaluated for the historical 2008 Iowa flood. The results show HiResFlood-UCI with real-time PERSIANN-CCS was able to capture the observed hydrographs and reasonably match the USDA's AWiFS 56m resolution flood imagery over the most impacted area in the extended Cedar Rapids region. This is promising for a global high resolution flood warning system pairing HiResFlood-UCI with PERSIANN-CCS in the near future.

    Cover page: Developing a high resolution coupled hydrologic-hydraulic model (HiResFlood-UCI) for flood modeling
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2019)
    Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.
      Cover page: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River ClimatologyCreative Commons 'BY' version 4.0 license
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