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Exploring the ecohydrological impacts of woody plant encroachment in paired watersheds of the Sonoran Desert, Arizona

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Woody plant encroachment is a worldwide phenomenon linked to water availability in semiarid systems. Nevertheless, the implications of woody plant encroachment on the hydrologic cycle are poorly understood, especially at

Woody plant encroachment is a worldwide phenomenon linked to water availability in semiarid systems. Nevertheless, the implications of woody plant encroachment on the hydrologic cycle are poorly understood, especially at the catchment scale. This study takes place in a pair of small semiarid rangeland undergoing the encroachment of Prosopis velutina Woot., or velvet mesquite tree. The similarly-sized basins are in close proximity, leading to equivalent meteorological and soil conditions. One basin was treated for mesquite in 1974, while the other represents the encroachment process. A sensor network was installed to measure ecohydrological states and fluxes, including precipitation, runoff, soil moisture and evapotranspiration. Observations from June 1, 2011 through September 30, 2012 are presented to describe the seasonality and spatial variability of ecohydrological conditions during the North American Monsoon (NAM). Runoff observations are linked to historical changes in runoff production in each watershed. Observations indicate that the mesquite-treated basin generates more runoff pulses and greater runoff volume for small rainfall events, while the mesquite-encroached basin generates more runoff volume for large rainfall events. A distributed hydrologic model is applied to both basins to investigate the runoff threshold processes experienced during the NAM. Vegetation in the two basins is classified into grass, mesquite, or bare soil using high-resolution imagery. Model predictions are used to investigate the vegetation controls on soil moisture, evapotranspiration, and runoff generation. The distributed model shows that grass and mesquite sites retain the highest levels of soil moisture. The model also captures the runoff generation differences between the two watersheds that have been observed over the past decade. Generally, grass sites in the mesquite-treated basin have less plant interception and evapotranspiration, leading to higher soil moisture that supports greater runoff for small rainfall events. For large rainfall events, the mesquite-encroached basin produces greater runoff due to its higher fraction of bare soil. The results of this study show that a distributed hydrologic model can be used to explain runoff threshold processes linked to woody plant encroachment at the catchment-scale and provides useful interpretations for rangeland management in semiarid areas.

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  • 2013

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Improvements in flood forecasting in mountain basins through a physically-based distributed model

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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

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.

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  • 2012

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Assessing the effects of climate change in a semiarid basin utilizing a fully distributed hydrologic model: a case study of Beaver Creek, Arizona

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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

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.

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  • 2012

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Optimization model for design of vegetative filter strips for stormwater management and sediment control

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Vegetative filter strips (VFS) are an effective methodology used for storm water management particularly for large urban parking lots. An optimization model for the design of vegetative filter strips

Vegetative filter strips (VFS) are an effective methodology used for storm water management particularly for large urban parking lots. An optimization model for the design of vegetative filter strips that minimizes the amount of land required for stormwater management using the VFS is developed in this study. The resulting optimization model is based upon the kinematic wave equation for overland sheet flow along with equations defining the cumulative infiltration and infiltration rate.

In addition to the stormwater management function, Vegetative filter strips (VFS) are effective mechanisms for control of sediment flow and soil erosion from agricultural and urban lands. Erosion is a major problem associated with areas subjected to high runoffs or steep slopes across the globe. In order to effect economy in the design of grass filter strips as a mechanism for sediment control & stormwater management, an optimization model is required that minimizes the land requirements for the VFS. The optimization model presented in this study includes an intricate system of equations including the equations defining the sheet flow on the paved and grassed area combined with the equations defining the sediment transport over the vegetative filter strip using a non-linear programming optimization model. In this study, the optimization model has been applied using a sensitivity analysis of parameters such as different soil types, rainfall characteristics etc., performed to validate the model

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  • 2015

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Assessing land-atmosphere interactions through distributed footprint sampling at two eddy covariance towers in semiarid ecosystems of the Southwestern U.S

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Land-atmosphere interactions of semiarid shrublands have garnered significant scientific interest. One of the main tools used for this research is the eddy covariance (EC) method, which measures fluxes of energy,

Land-atmosphere interactions of semiarid shrublands have garnered significant scientific interest. One of the main tools used for this research is the eddy covariance (EC) method, which measures fluxes of energy, water vapor, and carbon dioxide. EC fluxes can be difficult to interpret due to complexities within the EC footprint (i.e. the surface conditions that contribute to the flux measurements). Most EC studies use a small number of soil probes to estimate the land surface states underlying the measured fluxes, which likely undersamples the footprint-scale conditions, especially in semiarid shrublands which are characterized by high spatial and temporal variability. In this study, I installed a dense network of soil moisture and temperature probe profiles in the footprint region of an EC tower at two semiarid sites: a woody savanna in southern Arizona and a mixed shrubland in southern New Mexico. For data from May to September 2013, I link land surface states to EC fluxes through daily footprints estimated using an analytical model. Novel approaches are utilized to partition evapotranspiration, estimate EC footprint soil states, connect differences in fluxes to footprint composition, and assess key drivers behind soil state variability. I verify the hypothesis that a small number of soil probes poorly estimates the footprint conditions for soil moisture, due to its high spatial variability. Soil temperature, however, behaves more consistently in time and space. As such, distributed surface measurements within the EC footprint allow for stronger ties between evapotranspiration and moisture, but demonstrate no significant improvement in connecting sensible heat flux and temperature. I also find that in these systems vegetation cover appears to have stronger controls on soil moisture and temperature than does soil texture. Further, I explore the influence of footprint vegetation composition on the measured fluxes, which reveals that during the monsoon season evaporative fraction tends to increase with footprint bare soil coverage for the New Mexico site and that the ratio of daily transpiration to evapotranspiration increases with grass coverage at the Arizona site. The thesis results are useful for understanding the land-atmosphere interactions of these ecosystems and for guiding future EC studies in heterogeneous landscapes.

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  • 2013

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The shift of precipitation maxima on the annual maximum series using regional climate model precipitation data

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Ten regional climate models (RCMs) and atmosphere-ocean generalized model parings from the North America Regional Climate Change Assessment Program were used to estimate the shift of extreme precipitation due to

Ten regional climate models (RCMs) and atmosphere-ocean generalized model parings from the North America Regional Climate Change Assessment Program were used to estimate the shift of extreme precipitation due to climate change using present-day and future-day climate scenarios. RCMs emulate winter storms and one-day duration events at the sub-regional level. Annual maximum series were derived for each model pairing, each modeling period; and for annual and winter seasons. The reliability ensemble average (REA) method was used to qualify each RCM annual maximum series to reproduce historical records and approximate average predictions, because there are no future records. These series determined (a) shifts in extreme precipitation frequencies and magnitudes, and (b) shifts in parameters during modeling periods. The REA method demonstrated that the winter season had lower REA factors than the annual season. For the winter season the RCM pairing of the Hadley regional Model 3 and the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model had the lowest REA factors. However, in replicating present-day climate, the pairing of the Abdus Salam International Center for Theoretical Physics' Regional Climate Model Version 3 with the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model was superior. Shifts of extreme precipitation in the 24-hour event were measured using precipitation magnitude for each frequency in the annual maximum series, and the difference frequency curve in the generalized extreme-value-function parameters. The average trend of all RCM pairings implied no significant shift in the winter annual maximum series, however the REA-selected models showed an increase in annual-season precipitation extremes: 0.37 inches for the 100-year return period and for the winter season suggested approximately 0.57 inches for the same return period. Shifts of extreme precipitation were estimated using predictions 70 years into the future based on RCMs. Although these models do not provide climate information for the intervening 70 year period, the models provide an assertion on the behavior of future climate. The shift in extreme precipitation may be significant in the frequency distribution function, and will vary depending on each model-pairing condition. The proposed methodology addresses the many uncertainties associated with the current methodologies dealing with extreme precipitation.

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  • 2013