Water security is defined as a combination of water for achieving our goals as a society, and an acceptable level of water-related risks. Hydrologic modeling can be used to predict streamflow and aid in the decision-making process with the goal of attaining water security. Developed countries usually have their own hydrologic models; however, developing countries often lack hydrologic models due to factors such as the maintenance, computational costs, and technical capacity needed to run models. A global streamflow prediction system (GSPS) would help decrease vulnerabilities in developing countries and fill gaps in areas where no local models exist by providing extensive results that can be filtered for specific locations. The development of a GSPS has been deemed a grand challenge of the hydrologic community. To this end, many scientists and engineers have started to develop large-scale systems to an acceptable degree of success. Renowned models like the Global Flood Awareness System (GloFAS), the US National Water Model (NWM), and NASA's Land Assimilation System (LDAS) are proof that our ability to model large areas has improved remarkably. Even so, during this evolution the hydrologic community has started to realize that having a large-scale forecasting system does not make it immediately useful. New hydroinformatic challenges have surfaced that prevent these models from reaching their full potential. I have divided these challenges in four main categories: big data, data communication, adoption, and validation. I present a description of the background leading to the development of a GSPS including existing models, and the components needed to create an operational system. A case study with the NWM is also presented where I address the big data and data communication challenges by developing cyberinfrastructure and accessibility tools such as web applications and services. Finally, I used the GloFAS-RAPID model to create a forecasting system covering Africa, North America, South America, and South Asia using a service-oriented approach that includes the development of web applications, and services for providing improved data accessibility, and helping address adoption and validation challenges. I have developed customized services in collaboration with countries that include Argentina, Bangladesh, Colombia, Peru, Nepal, and the Dominican Republic. I also conducted validation tests to ensure that results are acceptable. Overall, a model-agnostic approach to operationalize a GSPS and provide meaningful results at the local level is provided with the potential to allow decision makers to focus on solving some of the most pressing water-related issues we face as a society.

Cypress Creek is an under researched sub-watershed of the larger Cache River system located in Southern Illinois and is managed by the Cypress Creek National Wildlife Refuge (CCNWR). In 1996, the Cypress Creek Watershed and its encompassing wetlands were listed under the United Nations Education, Scientific, and Cultural Organization (UNESCO) and Ramsar as a “Wetland of International Importance”. These wetland habitats house unique aquatic woody species, such as the Bald Cypress and Water Tupelo, and have diminished in size due to agricultural priorities and changes in hydrology (Demissie et al. 1990; Illinois Department of Natural Resources 1997). Heitmeyer and Mangan (2012) conducted by the U.S. Fish and Wildlife Service (USFWS), assessed the Cypress Creek Watershed using historical references of pre-settlement topography and geomorphology and present-day soils to determine habitat potential. These variables alone do not address the needs of the aquatic woody species who require flood inundation to survive. This hydrologic and hydraulic assessment examines the present-day hydrologic conditions within the boundary limits of CCNWR by collecting channel geometry and stream discharges, building flow frequencies, and constructing a hydraulic model of Cypress Creek to simulate water surface elevations (WSELs) for the bankfull, 2-year, 5-year, 10-year, 20-year, and 25-year exceedance probabilities. Flood inundations were generated from simulated WSELs and local topography. The calculated potential habitat for Bald Cypress and Water Tupelo is 289 hectares and is located in the northwest part of the study area, south of Cypress Creek Road and above Hickory Bottoms Bridge on CCNWR land. Potential habitat overlap found between Heitmeyer and Mangan (2012) and this assessment is roughly 19 hectares.

The probable maximum flood (PMF) is used for the xxi assessment of maximum flood potentials in spillway sizing of new dams and in evaluating the adequacy of existing hydrologic structures. Determination of the PMF begins with the estimation of the probable maximum precipitation (PMP) for a particular dam site. Selecting hydrologic methods and assumptions for converting PMP to PMF, which are most appropriate for Utah conditions, is important to meet current inflow design flood (IDF) safety standards. The objectives of this study were (a) to demonstrate a maximization approach to PMF determination, (b) to evaluate effects of basin characteristics and isohyetal or uniform rainfall pattern on PMP and hence PMF, (c) to evaluate uncertainties due to hydrologic procedures through sensitivity studies, (d) to analyze historical events for indications of actual loss rates, (e) to analyze historical snowpack and melt conditions for critical snowmelt conditions, (f) to assess implications of this study for a degree of conservatism index, and (g) to evaluate the implications of the study for the selection of procedures for PMF determinations. A sensitivity study was conducted to evaluate the hydrologic methods and assumptions (e.g., loss rates, unit hydrographs, basin subdivision, temporal storm distribution, flood routing, and antecedent events) which may be used in northern Utah PMF studies. The maximization of local storm PMF produced a higher peak reservoir stage than using basin average PMP only. Evaluation of effects of basin characteristics on PMF showed that local storm PMP is critical for basins up to about 2,000 square miles in northern Utah. Estimation of maximum probable rain-on-snow flood conditions for Porcupine Basin for April, May, and June revealed that there are significant increases in peak flows and flood volumes due to the contribution of snowmelt in the months of May and June. The results from the degree of conservatism assessment performed on local storm PMF estimates for Porcupine Basin showed that the overall effect of assumptions made and procedures followed in a typical PMF determination is almost certainly a conservative estimate.

Much work has been published regarding discharge coefficients for various weir structures. What has not been published to the same extent are the effects of model scale associated with the weirs being studied. If laboratory weirs are too small, scale effects can affect the magnitude of the discharge coefficient. These errors may be significant if the weir serves as a control structure for an emergency spillway. It is imperative that discharge be accurately predicted to enable safe design and operation. Numerical and physical means were employed to analyze the effects of scale associated with Froude Modeling of weirs with sharp and flat crests. An inverse formulation for the ideal flow of water over a weir was developed. The formulation appeared to be sound; however, the numerical method failed because the boundary condition on the free surface had multiple roots, which were almost equal in magnitude and sign. Laboratory data were collected and analyzed to determine the existence of scale effects and the flow conditions under which they were manifested. Results indicate that scale effects are present even with relatively large model sizes (12 inches high with a crest thickness of 24 inches). The scale effects appear to be associated with the size of the weir-wall and the viscosity. Although the viscosity was not altered, the results show a characteristic Reynolds Number for a given crest thickness-to-height ratio where scale effects cease to exist for increasing total head. Several graphs defining the conditions where scale effects exist for a given weir size were developed. Use of the graphs allows one to determine the minimum total head (piezometric plus velocity head) that one may operate a given size of weir or size a weir given the minimum total head to be tested to avoid scale effects. A design curve for discharge coefficients was developed to be used for determining the capacity of prototype weirs. The curve can be used to determine the discharge coefficient for new or existing hydraulic control structures.

The object of current efforts at investigating catchment response is to derive a physically based stochastic model of the watershed. Recent studies have, however, indicated that a limiting factor in deriving such models is the dependence of hydrologic response on initial soil moisture. The dependence affects the distributions and moments of the hydrological processes being investigated. A stochastic model of soil moisture dynamics is developed in the form of a pair of stochastic differential equations (SDE's) of the Ito type. The sources of stochasticity are linked to the random inputs of rainfall and evapotranspiration (ET). One of the SDE's describes the "surplus" case, in which sufficient infiltration always occurs to allow for moisture depletion by the processes of drainage through and ET out of the root zone. The other SDE represents the "deficit" case, in which lack of adequate moisture leads only to an ET-controlled depletion process. Sample functions and moments of moisture evolution are obtained from the SDE's. From the general model of soil moisture, a specific model of initial soil moisture (the moisture at the beginning of a rainstorm event) is developed and its moments are derived. Furthermore, the probability distribution of initial moisture is postulated to permit the assessment of how initial moisture affects the estimation of hydrologic response. The moisture dynamics model reveals that the stochastic properties of moisture ae sensitive to initial conditions in the watershed only for less permeable soils under the "surplus" state but are practically insensitive to such conditions for more permeable soils. The stochastic properties are also less sensitive to initial conditions for all soil types whenever under the "deficit" state. These results suggest that hydrologic processes, such as precipitation excess and infiltration, depend on initial moisture only in regions where the soils are generally less permeable and where the climate tends to sustain a "wet" environment, whereas in arid or semi-arid regions, such processes would not depend on initial moisture. These conclusions imply that, in arid regions, an effective value of initial moisture such as the mean can be used to estimate the properties of the hydrologic processes, whereas in "wet" environments, more accurate values of the properties must be "weighted" based on the probability distribution of initial soil moisture.

This thesis is a combination of two separate studies which examine hydrologic data assimilation techniques: 1) to determine the applicability of assimilation of remotely sensed data in operational models and 2) to compare the effectiveness of assimilation and other calibration techniques. The first study examines the ability of Data Assimilation of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF), is made using a coupled SNOW17 and the Microwave Emission Model for Layered Snowpack model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the Advanced Microwave Scanning Radiometer-Earth Observing System at the 36.5GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. The second study compares the effectiveness of different calibration techniques in hydrologic modeling. Currently, the most commonly used methods for hydrologic model calibration are global optimization techniques. While these techniques have become very efficient and effective in optimizing the complicated parameter space of hydrologic models, the uncertainty with respect to parameters is ignored. This has led to recent research looking into Bayesian Inference through Monte Carlo methods to analyze the ability to calibrate models and represent the uncertainty in relation to the parameters. Research has recently been performed in filtering and Markov Chain Monte Carlo (MCMC) techniques for optimization of hydrologic models. At this point, a comparison of the effectiveness of global optimization, filtering and MCMC techniques has yet to be reported in the hydrologic modeling community. This study compares global optimization, MCMC, the PF, the Particle Smoother, the EnKF and the Ensemble Kalman Smoother for the purpose of parameter estimation in both the HyMod and SAC-SMA hydrologic models.

No description available.

This master’s project was conducted under the direction of the Central Coast Regional Water Quality Control Board (Water Board) to assist with augmenting their Basin Plan. An investigation of the current practices for identifying and managing groundwater recharge areas was conducted, and strategies for protecting water quality by managing land uses in groundwater recharge areas were developed. Data sources included a review of agency documents, websites, and maps; interviews with stakeholders, and literature research. Data from these sources provided the foundation on which to base strategies for maintaining and improving groundwater quality in the Central Coast Hydrologic Region. This project recommends standard language for groundwater recharge areas, minimum criteria for identifying and mapping these areas, and protection strategies. The Water Board must establish minimum standards to protect groundwater recharge areas, and collaborate with local agencies to protect the quality of groundwater throughout their jurisdiction.

No description available.

<p> Satellite data from GOES and LANDSAT where evaluated as a source of information for hydrologic distributed models applied to large watersheds. Three basins within the Llanos area of the Orinoco River basin, Venezuela, were selected as study areas. The specific objectives of the study were; (1) To test the applicability of meteorological satellite data for improving information on the temporal and areal distribution of precipitation,as well as estimates of amount over large areas. (2) To investigate photographic and digital LANDSAT data as a source of land surface information for hydrologic distributed models. The satellite and ground data used in this research were: (1) GOES WEFAX electrostatic facsimiles, (2) LANDSAT photographic and digital data, (3) Reports and maps on soil studies by Desarrollo Industrial Agricola C.A (1958) and Comerma and Luque (1971). </p> <p> The analysis of the data was carried out by visual analysis on the photographic products of GOES and LANDSAT using r·egular photo-interpretation techniques. GOES photographic data allowed the analysis of temporal and areal distribution of precipitation over large areas. Follansbe's (1973) method for estimating precipitation using satellite imagery was found potentially applicable to hydrologic distributed models. Variations to the method are suggested. </p> <p> The visual analysis of a single LANDSAT image allowed the mapping of broad land-cover classes and some soil characteristics in the study area. Analysis of the multidate imagery was found very useful in detecting seasonal and non-seasonal changes. </p> <p> Digital analysis of LANDSAT data was carried out on the Image 100 system at the Canada Centre for Remote Sensing in Ottawa. Contrast stretched images and breakpoint enhancement supervised and unsupervised classifications were produced.The results showed that LANDSAT digital analysis either by unsupervised or supervised classification can be used for the extraction of land-use/land-cover information for application in hydrologic distributed models. </p> / Thesis / Master of Arts (MA)