Semi-arid valleys in northern Utah are home to the majority of the state population and are dependent upon winter snowpack in surrounding mountains for water for irrigation, hydropower and municipal use. Water is delivered to the urban areas in the spring as discharge in rivers draining the mountains. Understanding the natural variability and cycles of wet and dry periods enables water managers to make informed water allocations. However, the complex regional climate teleconnections are not well understood and the shortness of the instrumental period does not allow for a full understanding of natural variability. Paleo proxies can be used to extend the instrumental record and better capture natural variability. This study uses dendrochronology to reconstruct streamflows of the Logan River in northern Utah over the last several centuries to provide water managers with a better understanding of natural variability. This reconstruction involved sampling and creating three Douglas-fir, one limber pine and two Rocky Mountain juniper chronologies in northern Utah. Combined with existing chronologies, three flow reconstructions of the Logan River were created: one using only within basin chronologies, one using all considered chronologies and one long chronology. Employing regional chronologies resulted in the most robust models, similar to other findings. Results indicate that the last several centuries exhibited greater variability and slightly higher mean annual flows than in the instrumental record (1922-2011). These reconstructions were created using species well established within the dendroclimatology literature such as of Douglas-fir and limber pine and the lesser used Rocky Mountain juniper. The success of Rocky Mountain juniper suggests that it can be a useful species for dendroclimatology in other areas lacking more widely recognized species in semi-arid climates (e.g., pinyon pine).

Purpose Forecasting the annual water supply in an arid area by using the water content of snow on watersheds on some particular date, such as April 1, has become a very useful practice. Although these forecasts have given results of great practical value, they have sometimes been considerably in error. Seeking to minimize error, forecasters have incorporated various additional data such as temperature and antecedent rain to improve the relation between snow measurement and measured runoff. Numerous methods have been suggested in the search for a reliable streamflow forecasting equation and various data have been used. Nearly all of the methods made some improvements, but in the attempt to minimize the number of variables, perhaps full use has not been made of all the available data. A successful streamflow forecasting method for Logan River, Cache County, Utah was suggested by Professor Cleve H. Milligan (11) and Dr. Rex L. Hurst. They utilized Fourier Series and Multiple Linear Regression as a mathematical model. In their study, four primary factors were used which are antecedent streamflow, temperature, precipitation, and snow survey data. This method has also been used in the forecasting for the Blacksmith Fork River, south of the Logan River, by Fok (5) with a high degree of accuracy. In his study, temperature and precipitation data were both measured outside the watershed and showed a lower degree of significance in the complete forecasting equation. If these data had been measured in the watershed they might have yielded greater significance in the forecasting equation. Perhaps a better factor than temperature and precipitation would be soil moisture data obtained on the watershed. Objective The major objective of this thesis is to develop a method for use of soil moisture data in an equation for streamflow forecasting for the Logan River in Northern Utah. Several Investigators have recognized the need for soil moisture data and for a method of including it in the forecasting equations (see literature review).

Maintaining the ecological health of streams is vital for sustainable water resources management. Streamflow is a primary factor influencing the structure and function of ecological communities. A quantitative understanding of how stream biota respond to variation in streamflow is required for stream bioassessment. This dissertation focuses on quantifying relationships between streamflow regime and stream macroinvertebrate richness and composition. The contribution comprises statistical models that predict stream macroinvertebrate class from streamflow regime and predict streamflow regime from watershed attributes, and a tool that helps derive watershed attribute variables used in these models. The dissertation is a collection of three papers. In the first paper 12 variables were used to represent streamflow regime at 543 sites in the western US. Principal component analysis (PCA) and K-means clustering were used to obtain statistically independent factors and streamflow regime classes. We examined the relationship between these characterizations of streamflow and macroinvertebrate richness and composition at 63 of the 543 sites where there was also biological data. This analysis identified specific aspects of the streamflow regime that were useful in predicting macroinvertebrate richness and composition and that have potential application in classification-based bioassessment and management. A regional-scale study such as this requires tools for efficiently delineating watersheds and deriving their attributes. Paper two presents a multiple watershed delineation tool that addresses issues such as a) incorrectly positioned outlets and b) large Digital Elevation Models. This tool has capabilities to delineate stream networks with the threshold that determines drainage density being objectively determined so that the resulting networks adhere to geomorphological stream network laws. It also derives a suite of geomorphological watershed attributes that were used in prediction models in paper three. In paper three, we developed statistical models to predict streamflow regime class from watershed attributes. Four popular statistical methods were used and the uncertainty associated with class predictions for each method was quantified. Paper three also identified the watershed attributes that were most important for discriminating streamflow regime classes.

The Colorado Basin River Forecast Center (CBRFC) provides forecasts of streamflow for purposes such as flood warning and water supply. Much of the water in these basins comes from spring snowmelt, and the forecasters at CBRFC currently employ a suite of models that include a temperature-index snowmelt model. While the temperature-index snowmelt model works well for weather and land cover conditions that do not deviate from those historically observed, the changing climate and alterations in land use necessitate the use of models that do not depend on calibrations based on past data. This dissertation reports work done to overcome these limitations through using a snowmelt model based on physically invariant principles that depends less on calibration and can directly accommodate weather and land use changes. The first part of the work developed an ability to update the conditions represented in the model based on observations, a process referred to as data assimilation, and evaluated resulting improvements to the snowmelt driven streamflow forecasts. The second part of the research was the development of web services that enable automated and efficient access to and processing of input data to the hydrological models as well as parallel processing methods that speed up model executions. These tasks enable the more detailed models and data assimilation methods to be more efficiently used for streamflow forecasts.

Analytical baseflow separation techniques such as those used in the automated hydrograph separation program HYSEP rely on a single input parameter that defines the period of time after which surface runoff ceases and all streamflow is considered baseflow. In HYSEP, this input parameter is solely a function of drainage basin contributing area. This method cannot be applied universally since in most regions the time of surface runoff cessation is a function of a number of different hydrologic and hydrogeologic basin characteristics, not just contributing drainage area. This study demonstrates that streamflow conductivity can be used as a natural tracer that integrates the different hydrologic and hydrogeologic basin characteristics that influence baseflow response. Used as an indicator of baseflow as a component of total flow, streamflow conductivity allows for an empirical approach to hydrograph separation using a simple mass balance algorithm. Although conductivity values for surface-water runoff and ground-water baseflow must be identified to apply this mass balance algorithm, field studies show that assumptions based on streamflow at low flow and high flow conditions are valid for estimating these end member conductivities. The only data required to apply the mass balance algorithm are streamflow conductivity and discharge measurements. Using minimal data requirements, empirical hydrograph separation techniques can be applied that yield reasonable estimates of baseflow. This procedure was performed on data from 10 USGS gaging stations for which reliable, real-time conductivity data are available. Comparison of empirical hydrograph separations using streamflow conductivity data with analytical hydrograph separations demonstrates that uncalibrated, graphical estimation of baseflow can lead to substantial errors in baseflow estimates. Results from empirical separations can be used to calibrate the runoff cessation input parameter used in analytical separation for each gaging station. In general, collection of stream conductivity data at gaging stations is relatively recent, while discharge measurements may extend many decades into the past. Results demonstrate that conductivity data available for a relatively short period of record can be used to calibrate the runoff cessation input parameter used for analytical separation. The calibrated analytical method can then be applied over a much longer period record since discharge data are the only requirement.

As a global climate phenomenon, the El Ni??o-Southern Oscillation (ENSO) involves the coupling of the ocean and the atmosphere. Most climate prediction studies have, by far, only investigated the teleconnections between global climatic anomalies and the ???surface??? predictors of ENSO. The prediction models resulting from these studies have generally suffered from inadequate, if not the lack of, skill across the so-called boreal ???spring barrier???. This is illustrated in the first part of this thesis where the applicability of the SOI phase for long-lead rainfall projections in Australia is discussed. With the increasing availability of subsurface ocean temperature data, the characteristics of the Pacific Ocean???s heat content and its role in ENSO are now better understood. The second part of this thesis investigated the predictability of ENSO using the thermocline as a predictor. While the persistence and SST-based ENSO hindcasts dropped in skill across the spring barrier, the thermocline-based hindcasts remained skillful even up to a lag of eighteen months. Continuing on the favorable results of ENSO prediction, the third part of this thesis extended the use of the thermocline in the prediction of Australia???s rainfall and streamflow. When compared to models that use ???surface??? predictors, the model that incorporated thermocline information resulted in more skillful projections of rainfall and streamflow especially at long lead-times. More importantly, significant increases in skill of autumn and winter projections demonstrate the ability of the subsurface ocean to retain some climatic memory across the predictability barrier. This resilience can be attributed to the high persistence of the ocean heat content during the first half of the year. Based on weighting, the model averaging exercise also affirmed the superiority of the ???subsurface??? model over the ???surface??? models in terms of streamflow projections. The encouraging findings of this study could have far-reaching implications not only to the science of ENSO prediction but also to the more pragmatic realm of hydrologic forecasting. What this study has demonstrated is an alternative predictor that is suitable for the long range forecasting of ENSO, rainfall and streamflow. With better hydrologic forecasting comes significant improvement in the management of reservoirs which eventually leads to an increase in the reliability and sufficiency of water supply provision.

Water managers throughout the world are challenged with managing scarce resources and therefore rely heavily on forecasts to allocate and meet various water demands. The need for improved streamflow and snowpack forecast models is of the utmost importance. In this research, the use of oceanic and atmospheric variables as predictors was investigated to improve the long lead-time (three to nine months) forecast of streamflow and snowpack. Singular Value Decomposition (SVD) analysis was used to identify a region of Pacific and Atlantic Ocean SSTs and a region of 500 mbar geopotential height (Z500mb) that were teleconnected with streamflow and snowpack. The resulting Pacific and Atlantic Ocean SSTs and Z500mb regions were used to create indices that were then used as predictors in a non-parametric forecasting model. The majority of forecasts resulted in positive statistical skill, which indicated an improvement of the forecast over the climatology or no-skill forecast. The results indicated that derived indices from SSTs were better suited for long lead-time (six to nine month) forecasts of streamflow and snowpack while the indices derived from Z500mb improved short lead-time (3 month) forecasts. In all, the results of the forecast model indicated that incorporating oceanic-atmospheric climatic variability in forecast models can lead to improved forecasts for both streamflow and snowpack.

Tree rings have been used as a proxy in reconstructing streamflow in the western U.S. for many years, but few reconstructions have been attempted in the eastern United States. Clear limitations exist for streamflow reconstructions in the eastern U.S. compared to the western U.S., but value can be established as demonstrated in this research. The primary goal of this research was to reconstruct streamflow using data from five headwater gages in the Appalachian Mountains of North Carolina. These gages are located on the Valley River, the Oconaluftee River, the Nantahala River, the Little Tennessee River, and the Watauga River. Tree-ring chronologies were used to reconstruct streamflow. Tree-ring chronology predictors were selected using a seasonal correlation analysis. Seasonal correlation analysis revealed May-June-July (MJJ) streamflow variability being highly correlated with tree-ring chronologies in the study region and vicinity. Stepwise linear regression methods were used to reconstruct MJJ streamflow. The reconstructions for the Valley, Oconaluftee, and Nantahala Rivers were considered acceptable reconstructions because the models explained approximately 50% of the total variance in historic period MJJ streamflow records. These three streamflow reconstruction models have predictive skill indicated by a positive reduction of error (RE) values. The root mean square error (RMSE) statistic was 11.5 million cubic meters (MCM) for the Valley River (26% of the mean reconstructed MJJ flow), 15.9 MCM for the Oconaluftee River (16% of the mean reconstructed MJJ flow), and 8.2 MCM for the Nantahala River (20% of the mean reconstructed MJJ flow). Analysis of the reconstructed streamflow data for these three rivers revealed low flow periods from 1710 to 1712 at all three sites. The research presented here shows the potential benefit of using tree-ring chronologies to reconstruct streamflow in the Tennessee Valley region by demonstrating the ability of proxy-based reconstructions to provide useful data beyond the instrumental record. These useful data include identification of extreme wet or dry periods and oscillations in the historical reconstructions that are not visible in the instrumental data.

The dissertation focuses on the application of entropy theory in hydrologic analysis and simulation, namely, rainfall analysis, streamflow simulation and drought analysis. The extreme value distribution has been employed for modeling extreme rainfall values. Based on the analysis of changes in the frequency distribution of annual rainfall maxima in Texas with the changes in duration, climate zone and distance from the sea, an entropy-based distribution is proposed as an alternative distribution for modeling extreme rainfall values. The performance of the entropy based distribution is validated by comparing with the commonly used generalized extreme value (GEV) distribution based on synthetic and observed data and is shown to be preferable for extreme rainfall values with high skewness. An entropy based method is proposed for single-site monthly streamflow simulation. An entropy-copula method is also proposed to simplify the entropy based method and preserve the inter-annual dependence of monthly streamflow. Both methods are shown to preserve statistics, such as mean, standard deviation, skenwess and lag-one correlation, well for monthly streamflow in the Colorado River basin. The entropy and entropy-copula methods are also extended for multi-site annual streamflow simulation at four stations in the Colorado River basin. Simulation results show that both methods preserve the mean, standard deviation and skewness equally well but differ in preserving the dependence structure (e.g., Pearson linear correlation). An entropy based method is proposed for constructing the joint distribution of drought variables with different marginal distributions and is applied for drought analysis based on monthly streamflow of Brazos River at Waco, Texas. Coupling the entropy theory and copula theory, an entropy-copula method is also proposed for constructing the joint distribution for drought analysis, which is illustrated with a case study based on the Parmer drought severity index (PDSI) data in Climate Division 5 in Texas.

Extreme rainfall events and consequent floods are being observed more frequently in the Western Black Sea region in Turkey as climate changes. In this study, application of a flood early warning system is intended by using and calibrating a combined model system. A regional-scale hydro-meteorological model system, consisting of Weather Research and Forecasting (WRF) model, NOAH land surface model and fully distributed NOAH-Hydro hydrologic models, is used for simulations of 25 heavy-rainfall and major flooding events observed in the Western Black Sea region between years 2000 and 2011. The performance of WRF model system in simulating precipitation is tested with 3-dimensional variational (3DVAR) data assimilation scheme. WRF-derived precipitation with and without data assimilation and Multi Precipitation Estimates (MPE) are used in NOAH-Hydro model to simulate streamflow for flood events. Statistical precipitation analyses show that WRF model with 3DVAR improved precipitation up to 12% with respect to no-assimilation. MPE algorithm generally underestimates rainfall and it also showed lower performance than WRF model with and without data assimilation. Depending on reliability of precipitation inputs, NOAH-Hydro model produces reasonable flood hydrographs both in structure and volume. After model calibration is performed using assimilated precipitation inputs in Bartin Basin, NOAH-Hydro model reduced the average error in streamflow by 23.24% and 53.57% with calibration for testing events. With calibrated parameters, NOAH-Hydro model forced by WRF non-assimilated precipitation input also reduced the error in streamflow but with lower rates (16.67% and 40.72%). With a proper model calibration and reliable precipitation inputs, hydrologic modeling system is capable of simulating flood events.