This study focuses on developing and evaluating efficient and effective parameter calibration and uncertainty methods for hydrologic modeling. Five single objective optimization algorithms and six multi-objective optimization algorithms were tested for automatic parameter calibration of the SWAT model. A new multi-objective optimization method (Multi-objective Particle Swarm and Optimization & Genetic Algorithms) that combines the strengths of different optimization algorithms was proposed. Based on the evaluation of the performances of different algorithms on three test cases, the new method consistently performed better than or close to the other algorithms. In order to save efforts of running the computationally intensive SWAT model, support vector machine (SVM) was used as a surrogate to approximate the behavior of SWAT. It was illustrated that combining SVM with Particle Swarm and Optimization can save efforts for parameter calibration of SWAT. Further, SVM was used as a surrogate to implement parameter uncertainty analysis fo SWAT. The results show that SVM helped save more than 50% of runs of the computationally intensive SWAT model The effect of model structure on the uncertainty estimation of streamflow simulation was examined through applying SWAT and Neural Network models. The 95% uncertainty intervals estimated by SWAT only include 20% of the observed data, while Neural Networks include more than 70%. This indicates the model structure is an important source of uncertainty of hydrologic modeling and needs to be evaluated carefully. Further exploitation of the effect of different treatments of the uncertainties of model structures on hydrologic modeling was conducted through applying four types of Bayesian Neural Networks. By considering uncertainty associated with model structure, the Bayesian Neural Networks can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation.

Spatial variability of rainfall is a significant factor in hydrologic and water quality modeling. In recent years, characterizing and analyzing the effect of spatial variability of rainfall in hydrologic applications has become vital with the advent of remotely sensed precipitation estimates that have high spatial resolution. In this study, the effect of spatial variability of rainfall in hourly runoff generation was analyzed using the Soil and Water Assessment Tool (SWAT) for Big Sandy Creek and Walnut Creek Watersheds in North Central Texas. The area of the study catchments was 808 km2 and 196 km2 for Big Sandy Creek and Walnut Creek Watersheds respectively. Hourly rainfall measurements obtained from raingauges and weather radars were used to estimate runoff for the years 1999 to 2003. Results from the study indicated that generated runoff from SWAT showed enormous volume bias when compared against observed runoff. The magnitude of bias increased as the area of the watershed increased and the spatial variability of rainfall diminished. Regardless of high spatial variability, rainfall estimates from weather radars resulted in increased volume of simulated runoff. Therefore, weather radar estimates were corrected for various systematic, range-dependent biases using three different interpolation methods: Inverse Distance Weighting (IDW), Spline, and Thiessen polygon. Runoff simulated using these bias adjusted radar rainfall estimates showed less volume bias compared to simulations using uncorrected radar rainfall. In addition to spatial variability of rainfall, SWAT model structures, such as overland flow, groundwater flow routing, and hourly evapotranspiration distribution, played vital roles in the accuracy of simulated runoff.

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on December 21, 2009). Thesis advisor: Dr. Yingkui (Philip) Li. Includes bibliographical references.

Thesis (M.A.S.), Memorial University of Newfoundland, 2000. / Bibliography: leaves 71-73.

<p>Wetland monitoring and preservation efforts have the potential to be enhanced with advanced remote sensing acquisition and digital image analysis approaches. Progress in the development and utilization of Unmanned Aerial Systems (UAS) and Unmanned Aerial Vehicles (UAV) as remote sensing platforms has offered significant spatial and temporal advantages over traditional aerial and orbital remote sensing platforms. Photogrammetric approaches to generate high spatial resolution orthophotos of UAV acquired imagery along with the UAV?s low-cost and temporally flexible characteristics are explored. A comparative analysis of different spectral based land cover maps derived from imagery captured using UAV, satellite, and airplane platforms provide an assessment of the Huntington Beach Wetlands. This research presents a UAS remote sensing methodology encompassing data collection, image processing, and analysis in constructing spectral based land cover maps to augment the efforts of the Huntington Beach Wetlands Conservancy by assessing ecological and temporal changes at the Huntington Beach Wetlands.

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Satellite-based remotely sensed data were used to estimate regional-scale surface energy fluxes and a water deficit index of a semi-arid heterogeneous region in southeast Arizona. Spectral reflectance and radiometric temperature of the surface, derived from the digital counts of TM bands of LANDSAT-5 satellite, were used for this purpose. These reflectance and temperature, along with conventional meteorological information of the region, were used as inputs to numerical models which estimate surface energy fluxes. Point-based meteorological data of the region were spatially extrapolated over a grid of 120 m X 120 m so that it could be used with the spatially continuous remotely sensed data. The water deficit index (WDI) was estimated using surface temperature and a spectral vegetation index, "soil adjusted vegetation index" (SAVI). The surface fluxes were net radiation flux, sensible heat flux, soil heat flux and latent heat flux. Measured values obtained from the meteorological flux measurement (METFLUX) stations in the study area were compared with the modeled fluxes. Latent heat flux (LE) was the most important one to estimate in the scope of this study. The method of spatially extrapolating the point-based meteorological information and combining with the remotely sensed data produced good estimation of LE for the region, with a mean absolute difference (MAD) of 65 W/m² over a range of 67 to 196 W/m² . Also it was found that the numerical models that were previously used to estimate daily LE values from a region using mid-day remotely sensed data (mostly from NOAAAVHRR) can also be used with the mid-morning remotely sensed data (from LANDSAT). Out of the two models tested for this purpose (`Seguin-Itier' and 'Jackson' models), one was found to need some modification so that it could use mid-morning remotely sensed data as inputs. The other was found to be useable as it is, without any modification. Outputs from both models compared well with the measured fluxes from the METFLUX stations. In an effort of estimating the water deficit of the different biomes of the region, WDI of the biomes were estimated. The main goal of this effort was to be able to monitor the surface hydrologic conditions of the region using remotely sensed vegetation and surface information, and minimum ground data. Good estimation of the water deficit condition of the area were obtained by this method. This method was found to be sensitive to a few of the ground information such as wind speed and leaf area index (LAI). It was also found that if the required ground data were correctly estimated, this method could be used as an operational procedure for monitoring the vegetation water stress of the biomes and hence for better management of the region.

<p> Long-term precipitation, temperature, and streamflow records were used to compare changes in precipitation and potential evapotranspiration to changes in runoff within 25 stream basins. Historical changes in the region appear to be more consistent with complex transient shifts in seasonal climatic conditions than with gradual climate change. Annual peak streamflow data were divided into two populations, snowmelt/spring and summer/fall, to test the hypotheses that, because of changes in precipitation regimes, the odds of summer/fall peaks have increased and, because of temperature changes, snowmelt/spring peaks happen earlier. The odds of summer/fall peaks occurring have increased across the study area. In northern portions of the study region, snowmelt/spring peaks are occurring earlier by 8.7 to 14.3 days. Tree-ring chronologies and historical precipitation data in a region around the Souris River Basin, were analyzed to model past long-term variations of precipitation. Results show that precipitation varies on multi-decadal time scales. </p><p> The Red River of the North drains much of eastern North Dakota and northwestern Minnesota and flows north into Manitoba, Canada, ultimately into Lake Winnipeg, so phosphorus transport is an International concern. Phosphorus changes over time were determined and phosphorus concentrations at the International border, when adjusted for variability in streamflow (flow-normalized), have generally increased from 1972-2012; however, most of that increase happened in the 1970s. Flux, the total amount of phosphorus transported, has increased dramatically in recent decades; however, when adjusted for streamflow variability (so that flux is from variation caused by the occurrence of high- or low-flow conditions), the flow-normalized flux has declined in recent years. This indicates that an important reason for increased flux is climatic &ndash; the wet conditions experienced since 1993. </p><p> These changes have implications for water interests, such as potential changes in lead-time for flood forecasting or changes in the operation of flood-control dams or wastewater treatment plants. Results suggest that the recent wet period may be a part of natural variability on a very long time scale and that this not only has implications for flood risk, but for nutrient export to Canada.</p>

Project Completion Report, OWRT Project No. A-040-ARIZ / Agreement No. 14-31-0001-4003 / Project Dates: July, 1972 - June, 1974 / Acknowledgement - The work upon which this report is based was supported by funds provided by the United States Department of the Interior, Office of Water Research and Technology, as authorized under the Water Resources Research Act of 1964. / Hydraulic and physicochemical measurements were made on treated sewage effluent releases at established locations within the channel of an ephemeral stream, the Santa Cruz River of Southern Arizona. Water quality samples were taken in sequence so that incremental flows at different hydrograph stages could be traced as the effluent moved downstream. Hydrographs obtained from two H -L flumes were used to calibrate a modified kinematic wave model. Hydraulic parameters from the kinematic model and physicochemical measurements from water quality samples were combined together into a statistical-empirical kinetic model of nitrogen transformations which may occur in sewage effluent releases. There was fair agreement between the measured data and the nitrogen species values calculated with the model. Measured nitrogen species values indicated that the rate of nitrification in sewage effluent releases is related to flow distance and physical characteristics of the stream.

The basin factor is a hydrologic parameter that represents the overall impedance to flow of a watershed in the PC-Hydro peak discharge model for small ( < 10 mi2) semi-arid watersheds in Pima County, Arizona. To improve peak discharge estimates and provide validation of the basin factor as a physically-based parameter, basin factor values were "back-calculated" for return period and observed events on six undeveloped rangeland watersheds and correlated with Manning's n roughness coefficients, rainfall intensities, and hydraulic radii. Observed basin factor values displayed a positive trend with observed peak discharge. The results suggest that the correlated variables can predict the basin factor on small undeveloped sub-basins in Pima County and the basin factor is measurable as a physical parameter. Empirical models for basin factor prediction are proposed. The study may also be useful for estimating hydraulic roughness in hydrologic methods such as the kinematic wave time of concentration equation.