Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 131-141).

Drought is the most costly hazard among all natural disasters. Despite the significant improvements in drought modeling over the last decade, accurate provisions of drought conditions in a timely manner is still one of the major research challenges. In order to improve the current drought monitoring and forecasting skills, this study presents a hybrid system with a combination of remotely sensed data assimilation based on particle filtering and a probabilistic drought forecasting model. Besides the proposed drought monitoring system through land data assimilation, another novel aspect of this dissertation is to seek the use of data assimilation to quantify land initial condition uncertainty rather than relying entirely on the hydrologic model or the land surface model to generate a single deterministic initial condition. Monthly to seasonal drought forecasting products are generated using the updated initial conditions. The computational complexity of the distributed data assimilation system required a modular parallel particle filtering framework which was developed and allowed for a large ensemble size in particle filtering implementation. The application of the proposed system is demonstrated with two case studies at the regional (Columbia River Basin) and the Conterminous United States. Results from both synthetic and real case studies suggest that the land data assimilation system significantly improves drought monitoring and forecasting skills. These results also show how sensitive the seasonal drought forecasting skill is to the initial conditions, which can lead to better facilitation of the state/federal drought preparation and response actions.

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.

<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)

In cold and snowy countries, water resources management and planning require accurate and reliable spring peak flow forecasts which call for adequate snowmelt estimation techniques. Thus, exploring the potential of snowmelt models to improve the spring peak flow prediction has been an active research area. Snow models vary in degree of complexity from simple empirical models to complex physically based models. Whereas majority of studies on snowmelt modeling have focused on comparing the performance of empirical snowmelt estimation techniques with physically based methods, very few studies have investigated empirical methods and conceptual models for hydrological applications. This study investigates the potential of a simple Degree-Day Method (DDM) to effectively and accurately predict peak flows compared to sophisticated SNOW-17 model at La-Grande River Basin (LGRB), Quebec and Upper Assiniboine river at Shellmouth Reservoir (UASR), Manitoba. Moreover, since hydrologic models highly rely on estimated parameter vectors to produce accurate streamflow simulations, accurate and efficient parameter optimization techniques are essential. The study also investigates the benefits of seasonal model calibration versus annual model calibration approach. The study is performed using two hydrological models, namely MAC-HBV (McMaster University Hydrologiska Byrans Vattenbalansavdelning) and SAC-SMA (Sacramento Soil Moisture Accounting) and their model combinations thereof. Results indicate that the simple DDM performed consistently better at both study sites and showed significant improvement in prediction accuracy at UASR. Moreover, seasonal model calibration appears to be an effective and efficient alternative to annually calibrated model especially when extreme events are of particular interest. Furthermore, results suggest that SAC-SMA model outperformed MAC-HBV model, no matter what snowmelt computation method, calibration approach or study basin is used. Conclusively, DDM and seasonal model optimization approach coupled with SAC-SMA hydrologic model appears to be a robust model combination for enhanced spring peak flow prediction. A significant advantage of aforementioned modeling approach for operational hydrology is that it demonstrates computational efficiency, ease of implementation and is less time-consuming. / Thesis / Master of Science (MSc)

Surface mining activities are known to affect the quantity and quality of stormwater runoff. This can create flooding and water quality degradation of receiving streams. The Surface Mining Control and Reclamation Act (SMCRA) of 1977 provides regulations intended to produce environmentally acceptable results from mining operations. The SMCRA requires that extensive pre-mining monitoring be carried out to assist in determining the probable hydrologic consequences (PHC) of mining. The Finite Element Storm Hydrograph Model (FESHM) was used to demonstrate the utility of hydrologic modeling concepts in simulating runoff volumes and peak flows. Guidelines were proposed for using this methodology to simulate selected pre-mining hydrologic conditions. The use of geographic information system (GIS) technology as a tool for improving data management and modeling efficiency was demonstrated. The required input watershed characteristics were digitized, stored, and manipulated using a computerized GIS. Appropriate software was developed to integrate the GIS with FESHM. The ability of FESHM to simulate runoff events in an ungaged context was evaluated using an experimental watershed. First, simulations were conducted using two separate data bases, "lumped" and "detailed", in order to evaluate the effect of limited data availability, as expected in mining regions, on FESHM's predictive ability. The "lumped" data base produced better simulation results, however, more thorough and detailed research is needed to determine the level of data resolution necessary for a given level of simulation accuracy. Next., significant runoff events from 17-years of the historical record were simulated using data from the "lumped" data base. Statistical analyses were used to make judgments on parameter estimation and model usage. Regression methodology was used to assess expected error and model bias. Simulation bias was found to be related to the input rainfall intensity levels. The results suggest that either spatial variability or parameter values were not adequately defined and that some form of calibration is needed. Two additional drainage basins were used to evaluate FESHM's predictive capabilities in situations considered representative of mining regions. The results indicated that more thorough investigations of watershed characteristics must be made, that calibration procedures should be performed for each watershed, and that FESHM does not adequately model the physical processes involved in forest hydrology. / Master of Science

Finally, the newly integrated model was firstly applied to analyze the relationship of land use and hydrologic regimes based on the land use maps in 1980 and 2000. Then the newly integrated model was applied to simulate the potential impacts of land use change on hydrologic regimes in the East River Basin under a series of hypothetical scenarios. The results show that ET has a positive relationship with Leaf Area Index (LAI) while runoff has a negative relationship with LAI in the same climatic zone, which can be elaborated by surface energy balance and water balance equation. Specifically, on an annual basis, ET of forest scenarios is larger than that of grassland or cropland scenarios. On the contrary, runoff of forest scenarios is less than that of grassland or cropland scenarios. On a monthly basis, for most of the scenarios, particularly the grassland and cropland scenarios, the most significant changes occurred in the rainy season. The results indicate that deforestation would cause increase of runoff and decrease of ET on an annual basis in the East River Basin. On a monthly basis, deforestation would cause significant decrease of ET and increase of runoff in the rainy season in the East River Basin. / In order to effectively simulate hydrologic impact of LUCC, an integrated model of ESEBS and distributed monthly water balance model has been developed in this study. The model is capable of considering basin terrain and the spatial distribution of precipitation and soil moisture. Particularly, the model is unique in accounting for spatial and temporal variations of vegetation cover and ET, which provides a powerful tool for studying the hydrologic impacts of LUCC. The model was applied to simulate the monthly runoff for the period of 1980-1994 for model calibration and for the period of 1995-2000 for validation. The calibration and validation results show that the newly integrated model is suitable for simulating monthly runoff and studying hydrologic impacts ofLUCC in the East River Basin. / Land use/cover change (LUCC) has significantly altered the hydrologic system in the East River (Dongjiang) Basin. Quantitative modeling of hydrologic impacts of LUCC is of great importance for water supply, drought monitoring and integrated water resources management. An integrated eco-hydrologic modeling system of Distributed Monthly Water Balance Model (DMWBM), Surface Energy Balance System (SEBS) was developed with aid of GIS/RS to quantify LUCC, to conduct physically-based ET (evapotranspiration) mapping and to predict hydrologic impacts of LUCC. / The physically-remote-sensing-based Surface Energy Balance System (SEBS) was employed to estimate areal actual ET for a large area rather than traditional point measurements . The SEBS was enhanced for application in complex vegetated area. Then the inter-comparison with complimentary ET model and distributed monthly water balance model was made to validate the enhanced SEBS (ESEBS). The application and test of ESEBS show that it has a good accuracy both monthly and annually and can be effectively applied in the East River Basin. The results of ET mapping based on ESEBS demonstrate that actual ET in the East River Basin decreases significantly in the last two decades, which is probably caused by decrease of sunshine duration. / These results are not definitive statements as to what will happen to runoff, ET and soil moisture regimes in the East River Basin, but rather offer an insight into the plausible changes in basin hydrology due to land use change. The integrated model developed in this study and these results have significant implications for integrated water resources management and sustainable development in the East River Basin. / To begin with, in order to evaluate LUCC, understand implications of LUCC and provide boundary condition for the integrated eco-hydrologic modeling, firstly the long-term vegetation dynamics was investigated based on Normalized Difference Vegetation Index (NDVI) data, and then LUCC was analyzed with post-classification methods and finally LUCC prediction was conducted based on Markov chain model. The results demonstrate that the vegetation activities decreased significantly in summer over the years. Moreover, there were significant changes in land use/cover over the past two decades. Particularly there was a sharp increase of urban and built-up area and a significant decrease of grassland and cropland. All these indicate that human activities are intensive in the East River Basin and provide valuable information for constructing scenarios for studying hydrologic impacts of LUCC. / Wang, Kai. / "December 2010." / Adviser: Yongqin Chen. / Source: Dissertation Abstracts International, Volume: 73-04, Section: A, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 203-227). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Submitted in fulfillment of the requirements for the degree of Master of Engineering, Department of Civil Engineering and Surveying, Durban University of Technology, Durban, South Africa, 2016. / Due to the water scarcity in South Africa, new strategies in management planning are needed in order to sustain water resources. The increase of population and economic growth in South Africa has a negative effect on the water resources. Therefore, it should be well managed. The main concerns of the sustainability of water resources are hydropower, irrigation for agriculture, domestic and industries. Hence, the use of integrated water resources management in a single system which is built up by a river basin will help in water resources. This study was focused on water management issues: some of the principal causes of water shortages in UMdloti River are discussed. The current situation of water supply and demand at present is discussed. It also addressed some essential elements of reasonable, cooperative and sustainable water resources management solutions. Many developing countries are characterized as there is limited data availability, water scarcity and decrease of water levels in the dams. The eThekwini municipality is also having similar problems. Water resources have been modelled under this limited data using the hydrological modelling techniques by assessing the streamflow and observed data. The aim of the study was to address the issue of water management how water supply sources can be sustained to be manageable to meet the population growth demand considering the capacity of Hazelmere Dam demand downstream of the dam. Hydrological models, simulation, and decision making support systems were used to achieve all the research objectives. Hazelmere Dam has been modelled so that it can be used efficiently for the benefit of all users downstream of the dam for their economic and ecological benefits. Monthly reservoir inflow data for Hazelmere Dam was obtained from the Department of Water Affairs, South Africa. The nature of data is streamflow volume in mega liter (Ml) recorded for every month of the year. This was converted to mega cubic meter (Mm3) for use in the analysis herein. A period spanning 19 years of data (1994 – 2013) was used for the analysis. Six parametric probability distribution models were developed for estimating the monthly streamflow at Hazelmere Dam. These probability distribution functions include; Normal, Log-Normal (LN), Pearson III, Log-Pearson type III (LP3), Gumbel extreme value type1 (EVI) and Log-Gumbel (LG). It was observed that UMdloti River is smaller when compared with other rivers within the KwaZulu-Natal Province which could make it difficult to implement integrated water resources management. The hydro-meteorological data collected also has some limitations. The meteorological stations are far away to one another and this would make it difficult to attach their readings with the corresponding water basin. The comparison between the observed and simulated streamflow indicated that there was a good agreement between the observed and simulated discharge. Even though, the performance of the model was satisfactory, yet, it should not be generalized equally for all purposes. The erosion on the study area must be addressed by the stakeholders. It must be minimized in order to sustain the water resources of the UMdloti River. Erosion has a bad impact on the environment because it causes environmental degradation as well. Further investigations are recommended that account for the geological characteristics and the source of the base flow to make sure the rate of groundwater is sufficient for any future developments. Harnessing more energy from existing water sources within the frontier of the country is important in capacitating the South African Government’s commitment to reduction of the country’s greenhouse gas emissions and transition to a low-carbon economy while meeting a national target of 3,725 megawatts by 2030. This study also aimed to determine the amount of energy that can be generated from Hazelmere Dam on the uMdloti River, South Africa. Behavioral analyses of the Hazelmere reservoir were performed using plausible scenarios. Feasible alternative reservoir operation models were formulated and investigated to determine the best operating policy and power system configuration. This study determines the amounts of monthly and total annual energy that can be generated from Hazelmere reservoir based on turbines efficiencies of 75%, 85% and 90%. Optimization models were formulated to maximize hydropower generation within the constraints of existing abstractions, hydrological and system constraints. Differential evolution (DE) optimization method was adopted to resolve the optimization models. The methodology was applied for an operating season. The optimization models were formulated to maximize hydropower generation while keeping within the limits of existing irrigation demands. Differential evolution algorithm was employed to search feasible solution space for the best policy. Reservoir behavioural analysis was conducted to inspect the feasibility of generating hydropower from the Hazelmere reservoir under normal flow conditions. Optimization models were formulated to maximize hydropower generation from the dam. DE was employed to resolve the formulated models within the confines of the system constraints. It was found that 527.51 MWH of annual energy may be generated from the dam without system failure. Storage was maintained above critical levels while the reservoir supplied the full demands on the dam throughout the operating period indicating that the system yield is sufficient and there is no immediate need to augment the system. / M

published_or_final_version / abstract / Civil Engineering / Master / Master of Philosophy