Pathogenic contamination is a significant factor contributing to the degradation of surface water both globally and within the United States. This leads to negative economic impacts, sickness, and, in severe cases, fatalities. As the world's population grows, pollution increases, placing more stress on water resources, particularly in arid regions. The situation is made worse by climate change. The forecasted expansion of arid and semi-arid land areas and alterations in precipitation patterns could have a significant impact on those living in poverty and dry regions. This dissertation aims to investigate previously undocumented threats to water quality through understanding pathogen drivers in arid and semi-arid environments and documenting wastewater infrastructure on Tribal lands. Specifically, I first investigated how ephemeral streams (common in arid and semiarid areas) impact the presence of pathogens in surface waters by identifying the main drivers of E. coli concentration from a series of proposed predictors. Second, I identified unknown potential sources of water quality impairments on Tribal lands, which are mainly rural and in arid or semiarid areas, focusing on wastewater infrastructure in these systems. I specifically quantified populations served by wastewater treatment plants and then used a remote sensing approach to identify possible unpermitted wastewater lagoons that often serve as the only wastewater infrastructure in some areas. The findings revealed unique insights that could help aid water management in arid and semiarid regions as well as in rural areas.

Soil moisture (θ) is a fundamental variable controlling the exchange of water and energy at the land surface. As a result, the characterization of the statistical properties of θ across multiple scales is essential for many applications including flood prediction, drought monitoring, and weather forecasting. Empirical evidences have demonstrated the existence of emergent relationships and scale invariance properties in θ fields collected from the ground and airborne sensors during intensive field campaigns, mostly in natural landscapes. This dissertation advances the characterization of these relations and statistical properties of θ by (1) analyzing the role of irrigation, and (2) investigating how these properties change in time and across different landscape conditions through θ outputs of a distributed hydrologic model. First, θ observations from two field campaigns in Australia are used to explore how the presence of irrigated fields modifies the spatial distribution of θ and the associated scale invariance properties. Results reveal that the impact of irrigation is larger in drier regions or conditions, where irrigation creates a drastic contrast with the surrounding areas. Second, a physically-based distributed hydrologic model is applied in a regional basin in northern Mexico to generate hyperresolution θ fields, which are useful to conduct analyses in regions and times where θ has not been monitored. For this aim, strategies are proposed to address data, model validation, and computational challenges associated with hyperresolution hydrologic simulations. Third, analyses are carried out to investigate whether the hyperresolution simulated θ fields reproduce the statistical and scaling properties observed from the ground or remote sensors. Results confirm that (i) the relations between spatial mean and standard deviation of θ derived from the model outputs are very similar to those observed in other areas, and (ii) simulated θ fields exhibit the scale invariance properties that are consistent with those analyzed from aircraft-derived estimates. The simulated θ fields are then used to explore the influence of physical controls on the statistical properties, finding that soil properties significantly affect spatial variability and multifractality. The knowledge acquired through this dissertation provides insights on θ statistical properties in regions and landscape conditions that were never investigated before; supports the refinement of the calibration of multifractal downscaling models; and contributes to the improvement of hyperresolution hydrologic modeling.

Population growth within drylands is occurring faster than growth in any other ecologic zone, putting pressure on already stressed water resources. Because the availability of surface water supplies in drylands tends to be highly variable, many of these populations rely on groundwater. A critical process contributing to groundwater recharge is the interaction between ephemeral channels and groundwater aquifers. Generally, it has been found that ephemeral channels contribute to groundwater recharge when streamflow infiltrates into the sandy bottoms of channels. This process has traditionally been studied in channels that drain large areas (10s to 100s km2). In this dissertation, I study the interactions between surface water and groundwater via ephemeral channels in a first-order watershed located on an arid piedmont slope within the Jornada Experimental Range (JER) in the Chihuahuan Desert. To achieve this, I utilize a combination of high-resolution observations and computer simulations using a modified hydrologic model to quantify groundwater recharge and shed light on the geomorphic and ecologic processes that affect the rate of recharge. Observational results indicate that runoff generated within the piedmont slope contributes significantly to deep percolation. During the short-term (6 yr) study period, we estimated 385 mm of total percolation, 62 mm/year, or a ratio of percolation to rainfall of 0.25. Based on the instrument network, we identified that percolation occurs inside channel areas when these receive overland sheetflow from hillslopes. By utilizing a modified version of the hydrologic model, TIN-based Real-time Integrated Basin Simulator (tRIBS), that was calibrated and validated using the observational dataset, I quantified the effects of changing watershed properties on groundwater recharge. Distributed model simulations quantify how deep percolation is produced during the streamflow generation process, and indicate that it plays a significant role in moderating the production of streamflow. Sensitivity analyses reveal that hillslope properties control the amount of rainfall necessary to initiate percolation while channel properties control the partitioning of hillslope runoff into streamflow and deep percolation. Synthetic vegetation experiments show that woody plant encroachment leads to increases in both deep percolation and streamflow. Further woody plant encroachment may result in the unexpected enhancement of dryland aquifer sustainability.

Rapid urbanization and population growth occurring in the cities of South Western

United States have led to significant modifications in its environment at local and

regional scales. Both local and regional climate changes are expected to have massive

impacts on the hydrology of Colorado River Basin (CRB), thereby accentuating the need

of study of hydro-climatic impacts on water resource management in this region. This

thesis is devoted to understanding the impact of land use and land cover (LULC) changes

on the local and regional hydroclimate, with the goal to address urban planning issues

and provide guidance for sustainable development.

In this study, three densely populated urban areas, viz. Phoenix, Las Vegas and

Denver in the CRB are selected to capture the various dimensions of the impacts of land

use changes on the regional hydroclimate in the entire CRB. Weather Research and

Forecast (WRF) model, incorporating the latest urban modeling system, is adopted for

regional climate modeling. Two major types of urban LULC changes are studied in this

Thesis: (1) incorporation of urban trees with their radiative cooling effect, tested in

Phoenix metropolitan, and (2) projected urban expansion in 2100 obtained from

Integrated Climate and Land Use Scenarios (ICLUS) developed by the US

Environmental Protection Agency for all three cities.

The results demonstrated prominent nocturnal cooling effect of due to radiative

shading effect of the urban trees for Phoenix reducing urban surface and air temperature

by about 2~9 °C and 1~5 °C respectively and increasing relative humidity by 10~20%

during an mean diurnal cycle. The simulations of urban growth in CRB demonstratedii

nocturnal warming of about 0.36 °C, 1.07 °C, and 0.94 °C 2m-air temperature and

comparatively insignificant change in daytime temperature, with the thermal environment

of Denver being the most sensitive the urban growth. The urban hydroclimatic study

carried out in the thesis assists in identifying both context specific and generalizable

relationships, patterns among the cities, and is expected to facilitate urban planning and

management in local (cities) and regional scales.

This doctoral thesis investigates the predictability characteristics of floods and flash floods by coupling high resolution precipitation products to a distributed hydrologic model. The research hypotheses are tested at multiple watersheds in the Colorado Front Range (CFR) undergoing warm-season precipitation. Rainfall error structures are expected to propagate into hydrologic simulations with added uncertainties by model parameters and initial conditions. Specifically, the following science questions are addressed: (1) What is the utility of Quantitative Precipitation Estimates (QPE) for high resolution hydrologic forecasts in mountain watersheds of the CFR?, (2) How does the rainfall-reflectivity relation determine the magnitude of errors when radar observations are used for flood forecasts?, and (3) What are the spatiotemporal limits of flood forecasting in mountain basins when radar nowcasts are used into a distributed hydrological model?. The methodology consists of QPE evaluations at the site (i.e., rain gauge location), basin-average and regional scales, and Quantitative Precipitation Forecasts (QPF) assessment through regional grid-to-grid verification techniques and ensemble basin-averaged time series. The corresponding hydrologic responses that include outlet discharges, distributed runoff maps, and streamflow time series at internal channel locations, are used in light of observed and/or reference data to diagnose the suitability of fusing precipitation forecasts into a distributed model operating at multiple catchments. Results reveal that radar and multisensor QPEs lead to an improved hydrologic performance compared to simulations driven with rain gauge data only. In addition, hydrologic performances attained by satellite products preserve the fundamental properties of basin responses, including a simple scaling relation between the relative spatial variability of runoff and its magnitude. Overall, the spatial variations contained in gridded QPEs add value for warm-season flood forecasting in mountain basins, with sparse data even if those products contain some biases. These results are encouraging and open new avenues for forecasting in regions with limited access and sparse observations. Regional comparisons of different reflectivity -rainfall (Z-R) relations during three summer seasons, illustrated significant rainfall variability across the region. Consistently, hydrologic errors introduced by the distinct Z-R relations, are significant and proportional (in the log-log space) to errors in precipitation estimations and stream flow magnitude. The use of operational Z-R relations without prior calibration may lead to wrong estimation of precipitation, runoff magnitude and increased flood forecasting errors. This suggests that site-specific Z-R relations, prior to forecasting procedures, are desirable in complex terrain regions. Nowcasting experiments show the limits of flood forecasting and its dependence functions of lead time and basin scale. Across the majority of the basins, flood forecasting skill decays with lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Both precipitation and flood forecasting skills are noticeably reduced for lead times greater than 30 minutes. Scale dependence of hydrologic forecasting errors demonstrates reduced predictability at intermediate-size basins, the typical scale of convective storm systems. Overall, the fusion of high resolution radar nowcasts and the convenient parallel capabilities of the distributed hydrologic model provide an efficient framework for generating accurate real-time flood forecasts suitable for operational environments.

The North American Monsoon (NAM) is characterized by high inter- and intra-seasonal variability, and potential climate change effects have been forecasted to increase this variability. The potential effects of climate change to the hydrology of the southwestern U.S. is of interest as they could have consequences to water resources, floods, and land management. I applied a distributed watershed model, the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS), to the Beaver Creek basin in Arizona. This sub-basin of the Verde River is representative of the regional topography, land cover, and soils distribution. As such, it can serve to illustrate the utility of distributed models for change assessment studies. Model calibration was performed utilizing radar-based NEXRAD data, and comparisons were done to two additional sources of precipitation data: ground-based stations and the North American Land Data Assimilation System (NLDAS). Comparisons focus on the spatiotemporal distributions of precipitation and stream discharge. Utilizing the calibrated model, I applied scenarios from the HadCM3 General Circulation Model (GCM) which was dynamically downscaled by the Weather Research and Forecast (WRF) model, to refine the representation of Arizona's regional climate. Two time periods were examined, a historical 1990-2000 and a future 2031-2040, to evaluate the hydrologic consequence in the form of differences and similarities between the decadal averages for temperature, precipitation, stream discharge and evapotranspiration. Results indicate an increase in mean air temperature over the basin by 1.2 ºC. The average decadal precipitation amounts increased between the two time periods by 2.4 times that of the historical period and had an increase in variability that was 3 times the historical period. For the future period, modeled streamflow discharge in the summer increased by a factor of 3. There was no significant change in the average evapotranspiration (ET). Overall trends of increase precipitation and variability for future climate scenarios have a more significant effect on the hydrologic response than temperature increases in the system during NAM in this study basin. The results from this study suggest that water management in the Beaver Creek will need to adapt to higher summer streamflow amounts.

Ponderosa pine forests are a dominant land cover type in semiarid montane areas. Water supplies in major rivers of the southwestern United States depend on ponderosa pine forests since these ecosystems: (1) receive a significant amount of rainfall and snowfall, (2) intercept precipitation and transpire water, and (3) indirectly influence runoff by impacting the infiltration rate. However, the hydrologic patterns in these ecosystems with strong seasonality are poorly understood. In this study, we used a distributed hydrologic model evaluated against field observations to improve our understandings on spatial controls of hydrologic patterns, appropriate model resolution to simulate ponderosa pine ecosystems and hydrologic responses in the context of contrasting winter to summer transitions. Our modeling effort is focused on the hydrologic responses during the North American Monsoon (NAM), winter and spring periods. In Chapter 2, we utilized a distributed model explore the spatial controls on simulated soil moisture and temporal evolution of these spatial controls as a function of seasonal wetness. Our findings indicate that vegetation and topographic curvature are spatial controls. Vegetation controlled patterns during dry summer period switch to fine-scale terrain curvature controlled patterns during persistently wet NAM period. Thus, a climatic threshold involving rainfall and weather conditions during the NAM is identified when high rainfall amount (such as 146 mm rain in August, 1997) activates lateral flux of soil moisture and frequent cloudy cover (such as 42% cloud cover during daytime of August, 1997) lowers evapotranspiration. In Chapter 3, we investigate the impacts of model coarsening on simulated soil moisture patterns during the NAM. Results indicate that model aggregation quickly eradicates curvature features and its spatial control on hydrologic patterns. A threshold resolution of ~10% of the original terrain is identified through analyses of homogeneity indices, correlation coefficients and spatial errors beyond which the fidelity of simulated soil moisture is no longer reliable. Based on spatial error analyses, we detected that the concave areas (~28% of hillslope) are very sensitive to model coarsening and root mean square error (RMSE) is higher than residual soil moisture content (~0.07 m3/m3 soil moisture) for concave areas. Thus, concave areas need to be sampled for capturing appropriate hillslope response for this hillslope. In Chapter 4, we investigate the impacts of contrasting winter to summer transitions on hillslope hydrologic responses. We use a distributed hydrologic model to generate a consistent set of high-resolution hydrologic estimates. Our model is evaluated against the snow depth, soil moisture and runoff observations over two water years yielding reliable spatial distributions during the winter to summer transitions. We find that a wet winter followed by a dry summer promotes evapotranspiration losses (spatial averaged ~193 mm spring ET and ~ 600 mm summer ET) that dry the soil and disconnect lateral fluxes in the forested hillslope, leading to soil moisture patterns resembling vegetation patches. Conversely, a dry winter prior to a wet summer results in soil moisture increases due to high rainfall and low ET during the spring (spatially averaged 78 mm ET and 232 mm rainfall) and summer period (spatially averaged 147 mm ET and 247 mm rainfall) which promote lateral connectivity and soil moisture patterns with the signature of terrain curvature. An opposing temporal switch between infiltration and saturation excess runoff is also identified. These contrasting responses indicate that the inverse relation has significant consequences on hillslope water availability and its spatial distribution with implications on other ecohydrological processes including vegetation phenology, groundwater recharge and geomorphic development. Results from this work have implications on the design of hillslope experiments, the resolution of hillslope scale models, and the prediction of hydrologic conditions in ponderosa pine ecosystems. In addition, our findings can be used to select future hillslope sites for detailed ecohydrological investigations. Further, the proposed methodology can be useful for predicting responses to climate and land cover changes that are anticipated for the southwestern United States.