<p> Streams in the Kenai River watershed are characterized by a fish-rich environment, with competing interests between commercial industry and recreational users. Resource managers strive to balance the needs of both these user groups while maintaining the sustainability of the resource. The ability to estimate future river conditions could help maintain the resource, and a strong, sustainable economy on the Kenai Peninsula. </p><p> This research used the Army Corps of Engineers Hydrological Modeling System (HMS), which transforms rainfall to river discharge. The main goal was to define a set of parameters that were calibrated using an event based strategy, and concurrent rainfall and discharge data. The model was calibrated and validated in three sub-basins located in different environmental settings (i.e. lowlands, mid, and high elevation). In addition, the Kenai River watershed, as a whole, was modeled. </p><p> Due to limited concurrent datasets, a combination of current and historic rainfall and discharge data was used in the calibration. Over the period of time between the historic data and the current data, no major changes in the watershed were detected. </p><p> Model results at the sub-basin and watershed scale provided reasonable results over the modeling period. Each sub-basin maintained errors below 10% for the calibration and only slight increase in the error for the verification trials. It was found that during an extreme precipitation event, the model did not perform within reasonable bounds.</p>

<p> Accurate projections of future sea level rise require detailed modeling of the relevant processes affecting glacier and ice sheet dynamics. Although sophisticated high-resolution ice sheet models have been developed in recent years, some processes are still not well understood. Through a combination of field experiments, numerical modeling, and theoretical analyses, this research explores several processes affecting dynamics of the Greenland ice sheet, particularly in a changing climate as melt increases further inland: a) A novel, low-cost in-situ method of inferring firn permeability is presented, which is especially useful in regions of the ice sheet experiencing increased melt and refrozen solid ice layers in the firn. b) Thermo-mechanically coupled flow line modeling of the Greenland ice sheet interior reveals insights about the distribution of temperate ice and sensitivity to different modeling parameters. c) A subglacial hydrology model is introduced (SHAKTI: Subglacial Hydrology and Kinetic, Transient Interactions) that allows for the coexistence of laminar and turbulent flow regimes and flexible geometry configurations that include both sheetlike and channelized drainage systems, while including melt from viscous dissipation. Application of the SHAKTI model to marine-terminating Store Glacier in west Greenland suggests a channelized system develops near the terminus with high meltwater input and collapses to a sheetlike system with low input, with some residual channel structure extending inland from the front. d) Heat transfer coefficients are obtained through modeling of internal viscous and turbulent dissipation (appropriate for subglacial and englacial hydrology) compared to the case of heated walls (the classical experimental case upon which most heat transfer coefficients are based). A difference of about a factor of two is found between the heat transfer coefficients for heated walls and the internal dissipation case.</p><p>

<p> 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&rsquo;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&ndash;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&rsquo;s regression equations. In addition to comparing JPL&rsquo;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.</p>

<p> Snow is a key component of the Earth&rsquo;s energy balance, climate, environment, and a major source of freshwater in many regions. Seasonal and perennial snow cover affect up to 50% of the Northern Hemisphere landmass, which accounts for vast regions of the Earth that influence climate, culture, and commerce significantly. Information on snow properties such as snow cover, depth, and wetness is important for making hydrological forecasts, monitoring climate change, weather prediction, and issuing snowmelt runoff, flash flood, and avalanche warnings. Hence, adequate knowledge of the areal extent of snow and its properties is essential for hydrologists, water resources managers, and decision-makers. </p><p> The use of infrared (IR) and microwave (MW) remote sensing (RS) has demonstrated the capability of estimating the presence of snow cover and snowpack properties with accuracy. However, there are few publicly accessible, operational RS-based snow depth products, and these only provide the depth of recently accumulated dry snow because retrievals lose accuracy drastically for wet snow (late winter - early spring). Furthermore, it is common practice to assume snow grain size and wetness to be constant to retrieve certain snow properties (e.g. snow depth). This approach is incorrect because these properties are space- and time- dependent, and largely impact the MW signal scattering. Moreover, the remaining operational snow depth products have not been validated against in-situ observations; which is detrimental to their performance and future calibrations. </p><p> This study is focused on the discovery of patterns in geospatial data sets using data mining techniques for mapping snow depth globally at 10 km spatial resolution. A methodology to develop a RS MW-based snow depth and water equivalent (SWE) product using regression tree algorithms is developed. The work divided into four main segments includes: (1) validation of RS-based IR and MW-retrieved Land Surface Temperature (LST) products, (2) studying snow wetness by developing, validating, and calibrating a Snow Wetness Profiler, (3) development of a regression tree algorithm capable of estimating snow depth based on radiative (MW observations) and physical snowpack properties, and (4) development of a global MW-RS-based snow depth product built on the regression tree algorithm. </p><p> A predictive model based on Regression Tree (RT) is developed in order to model snow depth and water equivalent at the Cooperative Remote Sensing Science and Technology Center &ndash; Snow Analysis and Field Experiment (CREST-SAFE). The RT performance analyzed based on contrasting training error, true prediction error, and variable importance estimates. The RT algorithm is then taken to a broader scale, and Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission &ndash; Water 1 (GCOM-W1) MW brightness temperature measurements were used to provide snow depth and SWE estimates. These SD and SWE estimates were evaluated against twelve (12) Snow Telemetry (SNOTEL) sites owned by the National Resources Conservation Service (NRCS) and JAXA&rsquo;s own snow depth product. Results demonstrated that a RS MW-based RT algorithm is capable of providing snow depth and SWE estimates with acceptable accuracy for the continental United States, with some limitations. The major setback to the RT algorithm is that it will only provide estimates based on the data with which it was trained. Therefore, it is recommended that the work be expanded, and data from additional in-situ stations be used to re-train the RT algorithm. The CREST snow depth and water equivalent product, as it was named, is currently operational and publicly accessible at https://www.noaacrest.org//snow/products/. </p><p>

<p> Recent research and technological advances in the field of hydrology and water resources call for parallel educational reforms at the undergraduate level. This thesis describes the design, development, and evaluation of a series of undergraduate learning modules that engage students in investigative and inquiry-based learning experiences and introduces data analysis and numerical modeling skills. The modules are situated in the coastal hydrologic basins of Louisiana, USA. Centered on the current crisis of coastal land loss in the region, the modules immerse students in a suite of active-learning experiences in which they prepare and analyze data, reproduce model simulations, interpret results, and balance the beneficial and detrimental impacts of several real-world coastal restoration projects. The modules were developed using a web-based design that includes geospatial visualization via a built-in map-interface, textual instructions, video tutorials, and immediate feedback mechanisms. Following pilot implementations, an improvement-focused evaluation was conducted to examine the effectiveness of the modules and their potential for advancing students&rsquo; experiences with modeling-based analysis in hydrology and water resources. Both qualitative and quantitative data was collected including Likert-scale surveys, student performance grades, informal interviews, and text-response surveys. Students&rsquo; perceptions indicated that data and modeling-driven pedagogy using local real-world projects contributed to their learning and served as an effective supplement to instruction. The evaluation results also pointed out some key aspects on how to design effective and conducive undergraduate learning experiences that adopt technology-enhanced, data and modeling-based strategies, and how to pedagogically strike a balance between sufficient module complexity, ensurance of students&rsquo; continuous engagement, and flexibility to fit within existing curricula limitations. Additionally, to investigate how such learning modules can achieve large scale adoption, a total of 100 interviews were conducted with academic instructors and practicing professionals in the field of hydrology and water resources engineering. Key perspectives indicate that future efforts should appease hindering factors such as steep learning curves, lack of assessment data, refurbishment requirements, rigidness of material, time limitations.</p><p>