Scatterometers, such as SeaWinds, can provide wide coverage of ocean surface winds. They estimate near-surface wind vectors by relating measured radar backscatter to a geophysical model function. However, SeaWinds measurements are also sensitive to rain, and conventional wind retrieval degrades in rainy conditions. An algorithm that exploits SeaWinds' sensitivity to both wind and rain has be developed. This algorithm, termed simultaneous wind/rain retrieval, retrieves both wind vectors and rain rates for a given ocean area. Instantaneous results of simultaneous wind/rain retrieval in Hurricane events is analyzed through comparison with the NEXRAD ground-based radar system. This comparison allows validation of retrieved rains. Additionally, conditions that affect the accuracy of SeaWinds wind/rain observations are evaluated. It is shown that, when thresholded, the rains retrieved by SeaWinds give an adequate rain flag. The comparisons of SeaWinds and NEXRAD rain estimates facilitate construction of a model to simulate variability in the SeaWinds rain estimates. The model is used to show that rain estimates are unbiased, though with significant variability. The variability is likely to be primarily driven by the noise inherent to the SeaWinds system.

Forest fires demand personnel and financial resources. GIS can monitor ecological conditions that promote forest fire ignition. Visual representations of fire potential in the state could aid in staging firefighting personnel and equipment. This paper details the creation of a descriptive fire potential model for the Southeastern Fire District of Mississippi. The model includes the variables of fuels, ignition based on road density, and climate. No descriptive model of fire potential exists for Mississippi that includes a climate variable. The main objective of this research was to examine the influence of the dynamic climate variable on the model. Estimates of two water budgets were created to identify areas where evaporation exceeded precipitation and raised the potential for fires to occur. The study supported previous findings of road density as a significant variable for fire potential and validated the use of a climate variable in the model describing fire potential.

A two-dimensional numerical model (RiverFLO-2D) has been enhanced to simulate flooding of urban areas by developing an innovative wet and dry surface algorithm, accounting for variable rainfall, and recoding the model computer program for parallel computing. The model formulation is based on the shallow water equations solved with an explicit time-stepping element-by-element finite element method. The dry-wet surface algorithm is based on a local approximation of the continuity and momentum equations for elements that are completely dry. This algorithm achieves global volume conservation in the finite element, even for flows over complex topographic surfaces. A new module was implemented to account for variable rainfall in space and time using NEXRAD precipitation estimates. The resulting computer code was parallelized using OpenMP Application Program Interface, which allows the model to run up to 5 times faster on multiple core computers. The model was verified with analytical solutions and validated with laboratory and field data. Model application to the Malpasset dam break and Sumacarcel flooding event show that the model accurately predicts flood wave travel times and water depths for these numerically demanding real cases. To illustrate the predictive capability of the enhanced model, an application was made of the city of Sweetwater flooding in Miami-Dade County, FL caused by the Hurricane Irene. The simulation starts with dry bed and rainfall is provided by NEXRAD estimates. Integrating NEXRAD rainfall estimates, developing a novel dry-wet area algorithm and parallelizing RiverFLO-2D code, this dissertation presents a proof of concept to accurately and efficiently predict floods in urban areas, identifying future improvements along this line of research.

Flooding is one of the deadliest natural hazards in the world. Forecasting floods in advance can significantly reduce the socio-economic impacts. An accurate and reliable flood forecasting system is heavily dependent on the input precipitation data. Real-time, spatially, and temporally continuous Radar Quantitative Precipitation Estimates (QPEs) is useful precipitation information source. This research aims to investigate the efficacy of American and Canadian weather radar QPEs on hydrological model calibration and validation for flood forecasting in urban and semi-urban watersheds in Canada. A comprehensive review was conducted on the weather Radar network and its’ hydrological applications, challenges, and potential future research in Canada. First, radar QPEs were evaluated to verify the reliability and accuracy as precipitation input for hydrometeorological models. Then, the radar-gauge merging techniques were assessed to select the best method for urban flood forecasting applications. After that, merged Radar QPEs were used as precipitation input for the hydrological models to assess the impact of radar QPEs on hydrological model calibration and validation. Finally, a framework was developed by integrating hydrological and hydraulic models to produce flood forecasts and inundation maps in urbanized watersheds. Results indicated that dual-polarized radar QPEs could be effectively used as a source of precipitation input to hydrological models. The radar-gauge merging enhances both the accuracy and reliability of Radar QPEs, and therefore, the accuracy of streamflow simulation is also improved. Since flood forecasting agencies usually use hydrological models calibrated and validated using gauge data, it is recommended to use bias-corrected Radar QPEs to run existing hydrological models to simulate streamflow to produce flood extent maps. The hydrological and hydraulic models could be integrated into one framework using bias-corrected Radar QPEs to develop a successful flood forecasting system. / Thesis / Doctor of Science (PhD) / Floods are common and increasing deadly natural hazards in the world. Predicting floods in advance using Flood Early Warning System (FEWS) can facilitate flood mitigation. Radar Quantitative Precipitation Estimates (QPEs) can provide real-time, spatially, and temporally continuous precipitation data. This research focuses on bias-correcting and evaluating radar QPEs for hydrologic forecasting. The corrected QPE are applied into a framework connecting hydrological and hydraulic models for operational flood forecasting in urban watersheds in Canada. The key contributions include: (1) Dual-polarized radar QPEs is a useful precipitation input to calibrate, validate and run hydrological models; (2) Radar-gauge merging enhance accuracy and reliability of radar QPEs; (3) Floods could be more accurately predicted by integrating hydrological and hydraulic models in one framework using bias-corrected Radar QPEs; and (4) Gauge-calibrated hydrological models can be run effectively using the bias-corrected radar QPEs. This research will benefit future applications of real-time radar QPEs in operational FEWS.

As hydrological computer modeling software continues to increase in complexity, the need for further understanding of the value of different model input datasets becomes apparent. Frequently used precipitation model input include rain gauge data and next-generation radar–based (NEXRAD) rainfall data. Rain gauge data are usually interpolated across a model domain using various methods including the Thiessen Polygon methodology, which may be data-sparse in some areas and overly data-dense in others. However, rain gauge data are generally very easy to use in hydrologic model development, often requiring little to no data processing. NEXRAD data have the potential to improve hydrologic runoff estimates due to the increased spatial resolution of the data: but has its own issues regarding accuracy, false precipitation indications, and difficulties due to data processing. Previous studies have investigated the value of NEXRAD input versus traditional rain gauge data inputs for hydrologic studies; however, results are inconclusive as to which precipitation source provides more accurate results. Limited work has been done to compare the value of these datasets at multiple spatial scales, especially in Florida, a study area dominated by low topographic drive and sub-tropical weather. In addition, little to no research has been done regarding the value of NEXRAD versus rain gauge data inputs at different rainfall return frequencies. The proposed research will utilize a hydrological rain-runoff model (HEC-HMS) of the Upper St. Johns River Basin, Florida to compare the performance of the two precipitation data input types at various watershed spatial scales and rainfall return frequencies. Statistical analysis of the hydrological model “goodness-of-fit” results will be utilized to assess the watershed scaling and rainfall frequency requirements to xii which NEXRAD data provide little to no advantage over standard rain gauges using the Thiessen Polygon method for estimating rainfall totals across a model domain.