Academic literature on the topic 'Hydrologic sciences|Civil engineering|Water resources management'

<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>

<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> Agricultural drought is a creeping disaster that overshadows the vegetative cover in general and cropland specifically in Iraq, a country that was well known for its agricultural production and fertile soil. In the recent years, the arable lands in Iraq experienced increasing land degradation that led to desertification, economic losses, food insecurity, and deteriorating environment. Remote sensing is employed in this study and four different indices are utilized, each of which is derived from MODIS satellite mission products. Agricultural drought maps are produced from 2003 to 2015 after masking the vegetation cover. Year 2008 was found the most severe drought year during the study period, where drought covered 37% of the vegetated land. This part of the study demonstrated the capability of remote sensing in fulfilling the need of an early warning system for agricultural drought over such a data-scarce region.</p><p> This study also aims to monitor hydrological drought. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived monthly Terrestrial Water Storage (TWS) is the hydrological drought indicator, that is used to calculate the deficit. Severity of drought events are calculated by integrating monthly water deficit over the drought period. In addition, drought recovery time is assessed depending on the estimated deficit. Major drought events are classified into several levels of severity by applying a drought monograph approach. The results demonstrated that GRACE TWS is a reliable indicator for drought assessment over Iraq, and provides useful information for decision makers which can be utilized in developing drought adaptation and mitigation strategies. </p><p>

Doctor of Philosophy<br>Department of Biological & Agricultural Engineering<br>Stacy L. Hutchinson<br>Precipitation impacts hydrologic structures, agricultural production, water resources management, and recreational activities, all of which significantly affect a state’s economy. Water control structure design is based on the maximum runoff rate resulting from storms with a specific return period and duration. The Rainfall Frequency Atlas (National Weather Service Technical Paper 40, 1961) (TP-40) provided statistical rainfall analysis as the basis for hydrologic structure design until the information was updated for Kansas in February 2013 (National Oceanic and Atmospheric Administration Atlas 14, volume 8) (Atlas-14). With growing concern about the effects of global climate change and predictions of more precipitation and extreme weather events, it is necessary to explore rainfall distribution patterns using the most current and complete data available. In this work, the changes in rainfall patterns were studied using the daily rainfall data from 23 stations in Kansas and 15 stations from adjacent states with daily rainfall data of 1890 through 2012. Analysis showed an increase in extreme precipitation events in Kansas with increase in magnitude from the northwest to southeast part of the state. A comparison of results of the TP-40 analysis to period 1980–2009, showed that approximately 84% of the state had an increase in short-term rainfall event magnitudes. In addition, trend analyzes on the total annual rainfall indicated a gradual increase at 21 out of 23 stations, including eight statistically significant trends. A change-point analysis detected a significant sudden change at twelve stations as early as 1940 and as recently as 1980. The increasing trend, particularly after the significant change-points, is useful in updating water management plans and can assist with agricultural production decisions such as crop selection and new plant variety development. A comparison between 10-yr, 24-hr storms from TP-40 and Atlas-14 indicated a change of -12% to 5% in Kansas. However, the number of exceedances from the 10-yr, 1-, 2-, 3-, 4-, 7-, and 10-day storms demonstrated a tendency towards more exceedances, particularly in the last five decades. Results of this study are useful for hydrologic structure design and water resources management in order to prevent accepting additional risk of failure because of the current changing climate.