The National Water Center (NWC) started using the National Water Model (NWM) in 2016. The NWM delivers state-of-the-science hydrologic forecasts in the nation. The NWM aims at operationally forecasting streamflow in more than 2,000,000 river reaches while currently river forecasts are issued for 4,000. The NWM is a specific configuration of the community WRF-Hydro Land Surface Model (LSM) which has recently been introduced to the hydrologic community. The WRF-Hydro model, itself, uses another newly-developed LSM called Noah-MP as the core hydrologic model. In WRF-Hydro, Noah-MP results (such as soil moisture and runoff) are passed to routing modules. Riverine water level and discharge, among other variables, are outputted by WRF-Hydro. The NWM, WRF-Hydro, and Noah-MP have recently been developed and more research for operational accuracy is required on these models. The overarching goal in this dissertation is improving the ability of these three models in simulating and forecasting hydrological variables such as streamflow and soil moisture. Therefore, data assimilation (DA) is implemented on these models throughout this dissertation. State-of-the art DA is a procedure to integrate observations obtained from in situ gages or remotely sensed products with model output in order to improve the model forecast. In the first chapter, remotely sensed satellite soil moisture data are assimilated into the Noah-MP model in order to improve the model simulations. The performances of two DA techniques are evaluated and compared in this chapter. To tackle the computational burden of DA, Massage Passing Interface protocols are used to augment the computational power. Successful implementation of this algorithm is demonstrated to simulate soil moisture during the Colorado flood of 2013. In the second chapter, the focus is on the WRF-Hydro model. Similarly, the ability of DA techniques in improving the performance of WRF-Hydro in simulating soil moisture and streamflow is investigated. The results of chapter 2 show that the assimilation of soil moisture can significantly improve the performance of WRF-Hydro. The improvement can reach 58% depending on the study location. Also, assimilation of USGS streamflow observations can improve the performance up to 25%. It was also observed that soil moisture assimilation does not affect streamflow. Similarly, streamflow assimilation does not improve soil moisture. Therefore, joint assimilation of soil moisture and streamflow using multivariate DA is suggested. Finally, in chapter 3, the uncertainties associated with flood forecasting are studied. Currently, the only uncertainty source that is taken into account is the meteorological forcings uncertainty. However, the results of the third chapter show that the initial condition uncertainty associated with the land state at the time of forecast is an important factor that has been overlooked in practice. The initial condition uncertainty is quantified using the DA. USGS streamflow observations are assimilated into the WRF-Hydro model for the past ten days before the forecasting date. The results show that short-range forecasts are significantly sensitive to the initial condition and its associated uncertainty. It is shown that quantification of this uncertainty can improve the forecasts by approximately 80%. The findings of this dissertation highlight the importance of DA to extract the information content from the observations and then incorporate this information into the land surface models. The findings could be beneficial for flood forecasting in research and operation.

Government Island, located in the Columbia River approximately 16 km (10 mi) upstream of the confluence with the Willamette River, is a wetland mitigation site prompted by expansion of the southwest quadrant of Portland International Airport. The purpose of the study is to predict water levels in two enclosed lowland areas, Jewit Lake and Southeast Pond, based on levels of the Columbia River, precipitation, and evapotranspiration. Mitigation is intended to convert 1.13 km2 (237 acres) of seasonally flooded wetland to 1.27 km2 (267 acres) of semi-permanently flooded wetland and seasonally flooded wetland. Flooding of the wetland is most likely to occur December through January and May through early June when Columbia River water levels at Government Island exceed 3.6 m (12 ft) m.s.l. Flooding of Jewit Lake occurs through a channel connecting the wetland to the Columbia River. A groundwater model (MODFLOW) was parameterized to simulate the hydrology of the wetland. Observations of the subsurface stratigraphy in 25 soil pits, bucket auger cores, and during installation of water monitoring devices were used to estimate thickness and lateral extent of a confining unit that overlies an aquifer. Climatological data for 1994 and water levels were entered into MODFLOW to calibrate rates of water movement through the subsurface. Periods of drying for Jewit Lake and Southeast Pond were predicted based on precipitation and actual evapotranspiration rates expected to be present in the study area between June and December. Results of groundwater modeling show that Jewit Lake will maintain surface water above 3.6 m (12 ft) in most years. Southeast Pond is expected to dry annually as mitigation is unlikely to change the hydrology of Southeast Pond. Groundwater modeling predicted the types of wetlands present at different elevations by evaluating periods of drying within the wetland using the U.S. Fish and Wildlife Service classification of wetlands method. Results suggest that Jewit Lake will be converted to semipermanently flooded wetland below 3.6 m (12 ft) in elevation. Southeast Pond will remain a seasonally flooded wetland as a result of mitigation.

This project presents the development and design of a comprehensive interface coupled with a geodatabase (ArcGISwat 2003), for the Soil and Water Assessment Tool (SWAT). SWAT is a hydrologically distributed, lumped parameter model that runs on a continuous time step. The quantity and extensive detail of the spatial and hydrologic data, involved in the input and output, both make SWAT highly complex. A new interface, that will manage the input/output (I/O) process, is being developed using the Geodatabase object model and concepts from hydrological data models such as ArcHydro. It also incorporates uncertainty analysis on the process of modeling. This interface aims to further direct communication and integration with other hydrologic models, consequently increasing efficiency and diminishing modeling time. A case study is presented in order to demonstrate a common watershed-modeling task, which utilizes SWAT and ArcGIS-SWAT2003.

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.

Not available / text

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.

This project presents the development and design of a comprehensive interface coupled with a geodatabase (ArcGISwat 2003), for the Soil and Water Assessment Tool (SWAT). SWAT is a hydrologically distributed, lumped parameter model that runs on a continuous time step. The quantity and extensive detail of the spatial and hydrologic data, involved in the input and output, both make SWAT highly complex. A new interface, that will manage the input/output (I/O) process, is being developed using the Geodatabase object model and concepts from hydrological data models such as ArcHydro. It also incorporates uncertainty analysis on the process of modeling. This interface aims to further direct communication and integration with other hydrologic models, consequently increasing efficiency and diminishing modeling time. A case study is presented in order to demonstrate a common watershed-modeling task, which utilizes SWAT and ArcGIS-SWAT2003.

Thesis (M.S. - Renewable Natural Resources)--University of Arizona, 1980. / Includes bibliographical references.

Thesis (M.S. - Hydrology and Water Resources Administration)--University of Arizona, 1982. / Includes bibliographical references (leaves 121-124).