abstract: Cities are, at once, a habitat for humans, a center of economic production, a direct consumer of natural resources in the local environment, and an indirect consumer of natural resources at regional, national, and global scales. These processes do not take place in isolation: rather they are nested within complex coupled natural-human (CNH) systems that have nearby and distant teleconnections. Infrastructure systems—roads, electrical grids, pipelines, damns, and aqueducts, to name a few—have been built to convey and store these resources from their point of origin to their point of consumption. Traditional hard infrastructure systems are complemented by soft infrastructure, such as governance, legal, economic, and social systems, which rely upon the conveyance of information and currency rather than a physical commodity, creating teleconnections that link multiple CNH systems. The underlying structure of these systems allows for the creation of novel network methodologies to study the interdependencies, feedbacks, and timescales between direct and indirect resource consumers and producers; to identify potential vulnerabilities within the system; and to model the configuration of ideal system states. Direct and indirect water consumption provides an ideal indicator for such study because water risk is highly location-based in terms of geography, climate, economics, and cultural norms and is manifest at multiple geographic scales. Taken together, the CNH formed by economic trade and indirect water exchange networks create hydro-economic networks. Given the importance of hydro-economic networks for human well-being and economic production, this dissertation answers the overarching research question: What information do we gain from analyzing virtual water trade at the systems level rather than the component city level? Three studies are presented with case studies pertaining to the State of Arizona. The first derives a robust methodology to disaggregate indirect water flows to subcounty geographies. The second creates city-level metrics of hydro-economic vulnerability and functional diversity. The third analyzes the physical, legal, and economic allocation of a shared river basin to identify vulnerable nodes in river basin hydro-economic networks. This dissertation contributes to the literature through the creation of novel metrics to measure hydro-economic network properties and to generate insight into potential US hydro-economic shocks. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2016

<p> If given the correct remotely sensed information, machine learning can accurately describe soil moisture conditions in a heterogeneous region at the large scale based on soil moisture readings at the small scale through rule transference across scale. This paper reviews an approach to increase soil moisture resolution over a sample region over Australia using the Soil Moisture Active Passive (SMAP) sensor and Landsat 8 only and a validation experiment using Sentinal-2 and the Advanced Microwave Scanning Radiometer (AMSR-E) over Nevada. This approach uses an inductive localized approach, replacing the need to obtain a deterministic model in favor of a learning model. This model is adaptable to heterogeneous conditions within a single scene unlike traditional polynomial fitting models and has fixed variables unlike most leaning models. For the purposes of this analysis, the SMAP 36 km soil moisture product is considered fully valid and accurate. Landsat bands coinciding in collection date with a SMAP capture are down sampled to match the resolution of the SMAP product. A series of indices describing the Soil-Vegetation-Atmosphere Triangle (SVAT) relationship are then produced, including two novel variables, using the down sampled Landsat bands. These indices are then related to the local coincident SMAP values to identify a series of rules or trees to identify the local rules defining the relationship between soil moisture and the indices. The defined rules are then applied to the Landsat image in the native Landsat resolution to determine local soil moisture. Ground truth comparison is done via a series of grids using point soil moisture samples and air-borne L-band Multibeam Radiometer (PLMR) observations done under the SMAPEx-5 campaign (Panciera 2013). This paper uses a random forest due to its highly accurate learning against local ground truth data yet easily understandable rules. The predictive power of the inferred learning soil moisture algorithm did well with a mean absolute error of 0.054 over an airborne L-band retrieved surface over the same region. The validation experiment also demonstrated a strong linkage to the soil moisture, but the algorithm suffered from a lack of training data over such a small site. However, soil moisture estimation still exhibited a mean average error (MAE) of 0.028, compared to a 0.129 MAE of a deterministic model built upon the Air Force Weather Model.</p><p>

<p>Quantifying groundwater recharge is of crucial importance for sustainable groundwater management. While many recharge quantification techniques have been devised, few provide spatially and temporally distributed estimates for regional-scale water resource assessments. In this study, a GIS-based and USGS-developed recharge quantification tool ? the Soil Water Balance (SWB) model ? was applied to produce fine-tuned recharge constraints and document spatial and temporal dynamics of recharge. SWB has, as of yet, been tested solely in coastal and continental temperate-humid climate zones. This study expands testing of SWB to a Mediterranean climate zone, focusing on Catalina Island, California. Catalina has experienced significant water supply issues due to a prolonged drought. Using available climate, land use/land cover and hydrology data, the SWB model yields annual recharge values for the time period 2008-2014 of 0.05 mm/year to over 82 mm/year. Results of this thesis provide information on spatial and temporal patterns of groundwater recharge on Catalina Island.

<p> Despite a rising interest in intermittent river systems, landscape influences on long-term wetting and drying patterns of streamflow are not well understood. There has been a significant increase in the presence of intermittent rivers worldwide due to climate change and subsequent increases in groundwater abstraction, and these effects are intensified in already arid regions such as the American Southwest. Consequently, the spatial extent of wet and dry reaches of Arizona&rsquo;s Agua Fria River, Cienega Creek, and San Pedro River has been documented by citizen scientists for several years. Citizen science involves the use of trained members of the public for data collection, and the analysis of datasets produced from citizen science projects have become a huge asset to the scientific community. Here, we synthesize the most current data (1999&ndash;2016) to determine what stream and valley characteristics act as drivers for patterns of surface water flow. Geologic, geomorphic, and land cover characteristics of these rivers were analyzed via aerial imagery and Digital Elevation Models within ArcGIS 10.3 in conjunction with the Soil and Water Assessment Tool model. Principal Component Analysis was used in order to assess trends across sites. A set of landscape intermittency metrics was produced and then further analyzed using Multiple Linear Regression. We found that land cover had a significant (p-value &lt; 0.01) positive correlation with reach average (i.e., the proportion of channel wet). Physical watershed and channel characteristics each had a negative correlation with both intermittency metrics (i.e., wet/dry status and reach average). However, their results were not significant to the 0.05 level. This study begins to shed light on the drivers of landscape intermittency patterns of desert streams and demonstrates the utility of citizen science in regard to the study of intermittent river systems.</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>

<p> The Interior Alaska boreal forest ecosystem is one of the largest ecosystems on earth and lies between the warmer southerly temperate and colder Arctic regions. The ecosystem is underlain by discontinuous permafrost. The presence or absence of permafrost primarily controls water pathways and ecosystem composition. As a result, the region hosts two distinct ecotypes that transition over a very short spatial scale&mdash;often on the order of meters. Accurate mesoscale hydrological modeling of the region is critical as the region is experiencing unprecedented ecological and hydrological changes that have regional and global implications. However, accurate representation of the landscape heterogeneity and mesoscale hydrological processes has remained a big challenge. This study addressed this challenge by developing a simple landscape model from the hill-slope studies and in situ measurements over the past several decades. The new approach improves the mesoscale prediction of several hydrological processes including streamflow and evapotranspiration (ET). </p><p> The impact of climate induced landscape change under a changing climate is also investigated. In the projected climate scenario, Interior Alaska is projected to undergo a major landscape shift including transitioning from a coniferous-dominated to deciduous-dominated ecosystem and from discontinuous permafrost to either a sporadic or isolated permafrost region. This major landscape shift is predicted to have a larger and complex impact in the predicted runoff, evapotranspiration, and moisture deficit (precipitation minus evapotranspiration). Overall, a large increase in runoff, evapotranspiration, and moisture deficit is predicted under future climate. Most hydrological climate change impact studies do not usually include the projected change in landscape into the model. In this study, we found that ignoring the projected ecosystem change could lead to an inaccurate conclusion. Hence climate induced vegetation and permafrost changes must be considered in order to fully account the changes in hydrology.</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>

<p> Droughts reduce water resources necessary for human survival, economic development, and to sustain healthy ecosystems. Our ability to monitor and forecast droughts has grown dramatically in the past decades due to improved hydrological modeling made possible by satellite data and high computing power. However, there is still a large gap of knowledge regarding the mechanisms behind drought onset, development, and recovery. This gap prevents us from being able to forecast every severe drought and from being more confident about the effects of climate change. This thesis proposes a paradigm shift from droughts as local events to droughts as dynamic hazards that can travel in space. In this framework, droughts become the frame of reference, opening new possibilities for drought assessment and forecasting. Here, droughts are shown to have traveled across continents between 1979&mdash;2009. Patterns of frequent and common directions of displacement are identified. Precipitation recycling is proposed as an important mechanism behind these observed dynamics, and a detailed study of moisture sources over North America from 1980&mdash;2016 is carried out. This work shows that drought conditions can propagate downwind, especially from the U.S. Southwest to the U.S. Midwest, and from the northwest of Mexico and Central America to the center and south of Mexico. The effect of local precipitation recycling on drought intensification is quantified and shown to be highest in the north of Mexico and the U.S. Southwest. In a study of climate change's impacts on droughts, large biases are found in the climate models' representation of the hydrologic cycle and land-atmospheric coupling. This is shown to affect the models' drought projections by the end of the twenty-first century. Finally, this thesis includes a study of drought impacts on electricity generation and on <i>CO</i>₂, <i> SO</i>₂, and <i>NO_x</i> emissions from the power sector in the American West under current and future climates. This work advances the understanding of how droughts propagate through the hydrologic cycle locally and across continents, opening new opportunities for seasonal forecasting. It also includes a rigorous drought impact study on the electricity sector that provides useful information to stakeholders and decision makers.</p><p>

Maintaining flows and quality of water resources is critical to support ecosystem services and consumptive needs. Understanding impacts of changes in climate and land use on ecohydrologic processes in a watershed is vital to sustaining water resources for multiple uses. This study completes a continental and regional scale assessment using statistical and simulation modeling to investigate ecohydrologic impacts within watershed systems. Watersheds across the continental United States have diverse hydrogeomorphic characters, mean temperatures, soil moistures, precipitation and evaporation patterns that influence runoff processes. Changes in climate affect runoff by impacting available soil moisture, evaporation, precipitation and vegetative patterns. A one percent increase in annual soil moisture may cause a five percent increase in runoff in watersheds across the continent. Low soil moisture and high temperatures influence runoff patterns in specific regions. Spring runoff is increased by the influence Spring soil moisture, Winter and Spring evaporation, and Winter and Spring evaporation. Spring runoff is decreased by increases in Winter and Spring temperatures and increases in the vegetation index. Winter runoff is affected by maximum vegetative index, temperature, soil moisture, evaporation and precipitation. Contributing factors to runoff are influenced by geomorphic and seasonal variations requiring strategies that are site-specific and use system-wide information. Regional scale watershed analysis investigates the influence of landscape metrics on temporal streamflow processes in multiple gauged watersheds in Massachusetts, U.S.A. Time of concentration, recession coefficient, base flow index, and peak flow are hydrologic metrics used to relate to landscape metrics derived using FRAGSTAT software. Peak flow increases with increasing perimeter-area fractal dimensions, and Contagion index and decreases as Landscape Shape Index increases. There was an increasing trend in the fractal dimension over time indicative of more complex shape of patches in watershed. Base flow index and recession coefficient fluctuated from low to high decreasing recently. This could be indicative of open space legislation, conservation efforts and reforestation within the state in the last ten years. Coastal systems provide valuable ecosystem services and are vulnerable to impacts of changes in climate and continental land use patterns. Effects of land use and climate change on runoff, suspended sediments, total nitrogen and total phosphorus are simulated for coastal watersheds around the Boston Bay ecosystem. The SWAT (Soil and Water Assessment Tool) model, a continuous-time, semi distributed, process-based model, is used to simulate the watershed ecohydrologic process affecting coastal bodies. Urbanization in watersheds increased runoff by as much as 80% from the baseline. Land use change poses a major threat to water quality impacts affecting coastal ecosystems. Total nitrogen increased average of 53.8% with conservative changes in climate and land use. Total phosphorus increased an average of 57.3% with conservative changes in land use and climate change. Climate change alone causes up to 40% increase in runoff and when combined with a 3.25% increase in urban development runoff increased an average of 114%. Coastal ecosystems are impacted by nutrient runoff from watersheds. Continued urbanization and changes in climate will increase total nitrogen, total phosphorus and suspended sediments in coastal ecosystems. Continental scale runoff is affected by soil moisture and vegetative cover. Cover crops, low tillage farm practices and natural vegetation contribute to less runoff. Developing policies that encourage protection of soil structure could minimize runoff and aid in maintaining sustainable water resources. Best Management Practices and Low impact development at the national level with continued stormwater legislation directed towards sustainable land use policy will improve water quantity and quality. Fragmentation observed in Massachusetts increases the number of urban parcels and decreases the size of forested areas. Faster runoff patterns are observed but recent land management may be changing this runoff pattern. Municipal and state zoning ordinance to preserve open space and large forest patches will restrict urban growth to specific regions of a watershed. This could improve quantities of water available to ecosystems. Increases in total nitrogen, phosphorus and suspended sediments to coastal ecosystems can be minimized with use of riparian buffers and Best Management Practices within coastal watersheds. Urbanization and climate change threatens coastal ecosystems and national policy to preserve and restrict development of coastal areas will preserve coastal ecosystem services.

Uncertainty in climate change plays a major role in watershed systems. The increase in variability and intensity in temperature and precipitation affects hydrologic cycle in spatial and temporal dimensions. Predicting uncertainty in climate change impacts on watershed systems can help to understand future climate-induced risk on watershed systems and is essential for designing policies for mitigation and adaptation. Modeling the temporal patterns of uncertainties is assessed in the New England region for temperature and precipitation patterns over a long term. The regional uncertainty is modeled using Python scripting and GIS to analyze spatial patterns of climate change uncertainties over space and time. The results show that the regional uncertainty is significant in variation for changes in location and climatic scenarios. Watershed response to climate change under future scenarios is assessed using hydrologic simulation modeling for the Connecticut River watershed. Changes in water budgets are assessed for each of the subbasins using spatial analysis and process modeling using GIS and Soil and Water Assessment tool (SWAT). The results show that climate change uncertainty in precipitation and temperature can lead to uncertainty in both quantity and quality in the watershed system. A spatiotemporal, dynamic model was applied to subbasins within the Chicopee River Watershed to estimate climate change uncertainty impacts at a micro scale. These changes were assessed relative to changes in land use and climatic change. The results show that there is a significant potential for climate change to increase evaporation, watershed runoff and soil erosion rates and this varied with climate change uncertainty. Finally, water sustainability gradient analysis was applied to the Volga River watershed in Russia to assess potential climate change impacts by combining with downscaled Global Circulation Model estimates and spatial assessment. Results show that runoff and evapotranspiration are projected to increase with potential for more localized floods and drought events effecting both water resources and food supply. Overall results show that climate change uncertainty can impact watershed systems and spatial and temporal assessments is important for developing strategies for adaptation to climatic change conditions at local and regional scales.