Due to a continual increase in the demand for water as well as an ongoing regional drought, there is an imminent need to monitor and forecast water resources in the Western United States. In particular, water resources in the IntermountainWest rely heavily on snow water storage. Thus, the need to improve seasonal forecasts of snowpack and considering new techniques would allow water resources to be more effectively managed throughout the entire water-year. Many available models used in forecasting snow water equivalent (SWE) measurements require delicate calibrations. In contrast to the physical SWE models most commonly used for forecasting, we offer a statistical model. We present a data-based statistical model that characterizes seasonal snow water equivalent in terms of a nested time-series, with the large scale focusing on the inter-annual periodicity of dominant signals and the small scale accommodating seasonal noise and autocorrelation. This model provides a framework for independently estimating the temporal dynamics of SWE for the various snow telemetry (SNOTEL) sites. We use SNOTEL data from ten stations in Utah over 34 water-years to implement and validate this model. This dissertation has three main goals: (i) developing a new statistical model to forecast SWE; (ii) bridging existing R packages into a new R package to visualize and explore spatial and spatio-temporal SWE data; and (iii) applying the newly developed R package to SWE data from Utah SNOTEL sites and the Upper Sheep Creek site in Idaho as case studies.

Since 1982, under an agreement between the California Department of Water Resources and the USDA Forest Service, snow sensors have been installed and operated in Forest Service-administered wilderness areas in the Sierra Nevada of California. The sensors are to be removed by 2005 because of the premise that sufficient data will have been collected to allow "correlation" and, by implication, prediction of wilderness snow data by nonwilderness sensors that are typically at a lower elevation. Because analysis of snow water equivalent (SWE) data from these wilderness sensors would not be possible until just before they are due to be removed, "surrogate pairs" of high- and low-elevation snow sensors were selected to determine whether correlation and prediction might be achieved. Surrogate pairs of sensors with between 5 and 15 years of concurrent data were selected, and correlation and regression were used to examine the statistical feasibility of SWE prediction after "removal" of the wilderness sensors. Of the 10 pairs analyzed, two pairs achieved a correlation coefficient of 0.95 or greater. Four more had a correlation of 0.94 for the accumulation period after the snow season was split into accumulation and melt periods. Standard errors of estimate for the better fits ranged from 15 to 25 percent of the mean April 1 snow water equivalent at the high-elevation sensor. With the best sensor pairs, standard errors of 10 percent were achieved. If this prediction error is acceptable to water supply forecasters, sensor operation through 2005 in the wilderness may produce predictive relationships that are useful after the wilderness sensors are removed

California Department of Water Resources (DWR) Snow Surveys Section has recently explored the potential use of recently developed hydrologic models to estimate snow water equivalent (SWE) for the Sierra Nevada mountain range. DWR Snow Surveys Section's initial step is to determine how well these hydrologic models compare to the trusted regression equations, currently used by DWR Snow Surveys Section. A comparison scheme was ultimately developed between estimation measures for SWE by interpreting model results for the Feather River Basin from: a) National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) gridded SWE reconstruction product, b) United States Geological Survey (USGS) Precipitation-Runoff Modeling System (PRMS), and c) DWR Snow Surveys Section regression equations. Daily SWE estimates were extracted from gridded results by computing an average SWE based on 1,000 ft elevation band increments from 3,000 to 10,000 ft (i.e. an elevation band would be from 3,000 to 4,000 ft). The dates used for processing average SWE estimates were cloud-free satellite image dates during snow ablation months, March to August, for years 2000-2012. The average SWE for each elevation band was linearly interpolated for each snow sensor elevation. The model SWE estimates were then compared to the snow sensor readings used to produce the snow index in DWR's regression equations. In addition to comparing JPL's SWE estimate to snow sensor readings, PRMS SWE variable for select hydrologic response units (HRU) were also compared to snow sensor readings. Research concluded with the application of statistical methods to determine the reliability in the JPL products and PRMS simulated SWE variable, with results varying depending on time duration being analyzed and elevation range.

Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.

The availability and variability of snowmelt has become a serious concern because of increased water demand, and because of the high degree of uncertainty related to climate variability posing a threat to the magnitude and timing of this precious resource. Understanding the geophysical controls and interannual variability of the spatial patterns of seasonal montane snowpacks are critical for understanding the effects of a warmer climate on the snowpack water storage. To explicitly resolve snow hydrological controls in complex montane environments, it is necessary to provide high resolution spatially and temporally distributed estimates of snow water equivalent, while also taking into consideration the uncertainties in the system. Toward this end, this dissertation developed a retrospective data assimilation technique (SWE reanalysis) that aimed to optimally merge VIS-NIR remote sensing data into a snow prediction model, and at the same time, account for the limitations of measurements, forcings, and model errors. The SWE reanalysis was: first developed and implemented over a small region, in order to investigate the performance of the methods under their nominal scenarios; second implemented for the full Landsat-5 record (27 year) over a regional scale domain in order to test accuracy and gain insight on the spatial and interannual controls on the SWE patterns; third extended to the entire Sierra Nevada in order to benchmark the reanalysis for its application to the full Sierra Nevada and to preliminarly [i.e. preliminarily] understand what are the spatial controls on SWE patterns. The key findings of this dissertation can be summarized as follows: 1) The SWE reanalysis approach provided accurate spatially and continuous estimates of SWE and of its uncertainties due to measurement, forcings, and model errors. 2) The methods were found to be robust to input errors such as biases in solar radiation and precipitation, and robust to the number of available VIS-NIR observations. 3) The application of the methods over the Kern watershed for the full Landsat-5 record suggested that SWE accumulation patterns were in general not interannually consistent and that the interannual variability was dependent on whether a dry or wet year was analyzed. 4) The trend test analysis showed that peak-SWE and day-of-peak have not drastically changed over the analyzed 27 years for the Kern River watershed, but suggested that the lower elevations may be more susceptible to climate variability and change. 5) Elevation was found to be the primary control on spatial patterns of peak-SWE and day-of-peak for the entire Sierra Nevada range; however different patterns were found across the watersheds of the Sierra Nevada depending on their location. Ultimately, the methods can be applied to the full Sierra Nevada and other montane regions over the modern remote sensing record to generate a dataset that should be useful to scientists and practitioners not only in hydrology, but other fields where seasonal snow processes are a key driver such as biogeochemistry, mountain meteorology, and water resource management.

Principles of Snow Hydrology describes the factors that control the accumulation, melting and runoff of water from seasonal snowpacks over the surface of the earth. The book addresses not only the basic principles governing snow in the hydrologic cycle, but also the latest applications of remote sensing, and techniques for modeling streamflow from snowmelt across large mixed land-use river basins. Individual chapters are devoted to climatology and distribution of snow, snowpack energy exchange, snow chemistry, ground-based measurements and remote sensing of snowpack characteristics, snowpack management, and modeling snowmelt runoff. Many chapters have review questions and problems with solutions available online. This book is a reference book for practicing water resources managers and a text for advanced hydrology and water resources courses which span fields such as engineering, earth sciences, meteorology, biogeochemistry, forestry and range management, and water resources planning.

Accurately estimating basin‐wide snow water equivalent (SWE) is the most important unsolved problem in mountain hydrology. Models that rely on remotely sensed inputs are especially needed in ranges with few surface measurements. The NASA Airborne Snow Observatory (ASO) provides estimates of SWE at 50 m spatial resolution in several basins across the Western U.S. during the melt season. Primarily, water managers use this information to forecast snowmelt runoff into reservoirs; another impactful use of ASO measurements lies in validating and improving satellite‐based snow estimates or models that can scale to whole mountain ranges, even those without ground‐based measurements. We compare ASO measurements from 2013 to 2015 to four methods that estimate spatially distributed SWE: two versions of a SWE reconstruction method, spatial interpolation from snow pillows and courses, and NOAA's Snow Data Assimilation System (SNODAS). SWE reconstruction downscales energy forcings to compute potential melt, then multiplies those values by satellite‐derived estimates of fractional snow‐covered area to calculate snowmelt. The snowpack is then built in reverse from the date the snow is observed to disappear. The two SWE reconstruction models tested include one that employs an energy balance calculation of snowmelt, and one that combines net radiation and degree‐day approaches to estimate melt. Our full energy balance model, without ground observations, performed slightly better than spatial interpolation from snow pillows, having no systematic bias and 26% mean absolute error when compared to SWE from ASO. Both reconstruction models and interpolation were more accurate than SNODAS.