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Modeling is an important tool in watershed management. In much of the world, data needed for modeling, both for model inputs and for model evaluation, are very limited or non-existent. The overall objective of this research was to enable development of watershed management plans for agricultural dominated systems under situations where data are scarce. First, uncertainty of the SWAT model's outputs due to input parameters, specifically soils and high resolution digital elevation models, which are likely to be lacking in data-poor environments, was quantified using Monte Carlo simulation. Two sources of soil parameter values (SSURGO and STATSGO) were investigated, as well as three levels of DEM resolution (10, 30, and 90 m). Uncertainty increased as the input data became coarser for individual soil parameters. The combination of SSURGO and the 30 m DEM proved to adequately balance the level of uncertainty and the quality of input datasets. Second, methods were developed to generate appropriate soils information and DEM resolution for data-poor environments. The soils map was generated based on lithology and slope class, while the soil attributes were generated by linking surface soil texture to soils characterized in the SWAT soils database. A 30 m resolution DEM was generated by resampling a 90 m DEM, the resolution that is readily available around the world, by direct projection using a cubic convolution method. The effect of the generated DEM and soils data on model predictions was evaluated in a data-rich environment. When all soil parameters were varied at the same time, predictions based on the derived soil map were comparable to the predictions based on the SSURGO map. Finally, the methodology was tested in a data-poor watershed in Bolivia. The proposed methodologies for generating input data showed how available knowledge can be employed to generate data for modeling purposes and give the opportunity to incorporate uncertainty in the decision making process in data-poor environments. / Ph. D.

This research incorporates streambank erosion and failure processes into a distributed watershed model and evaluates the impacts of climate change on the processes driving streambank sediment mobilization at a watershed scale. Excess sediment and nutrient loading are major water quality concerns for streams and receiving waters. Previous work has established that in addition to surface and road erosion, streambank erosion and failure are primary mechanisms that mobilize sediment and nutrients from the landscape. This mechanism and other hydrological processes driving sediment and nutrient transport are likely to be highly influenced by anticipated changes in climate, particularly extreme precipitation and flow events. This research has two primary goals: to develop a physics-based watershed model with more inclusive representation of sediment by including simulation of streambank erosion and geotechnical failure; and to investigate the impacts of climate change on unstable streams and suspended sediment mobilization by overland erosion, erosion of roads, and the erosion as well as failure of streambanks. This advances mechanistic simulation of suspended sediment mobilization and transport from watersheds, which is particularly valuable for investigating the impacts of climate and land use changes, as well as extreme events. Model development involved coupling two existing physics-based models: the Bank Stability and Toe Erosion Model (BSTEM) and the Distributed Hydrology Soil Vegetation Model (DHSVM). This approach simulates streambank erosion and failure in a spatially explicit environment. The coupled model is applied to the Mad River watershed in central Vermont as a test case. I then use the calibrated Mad River model to predict the response in watershed sediment loading to future climate scenarios that specifically represent local temperature and precipitation trends for the northeastern US, particularly changing trends in the frequency and magnitude of extreme precipitation. Overall the streambank erosion and failure processes are captured in the coupled model approach. Although the presented calibration of the model underestimates suspended sediment concentrations resulting from relatively small storm/flow events, it still improves prediction of cumulative loads and in some cases suspended sediment concentrations during elevated flow events in comparison to model results without including BSTEM. Increases in temperature affect the timing and magnitude of snow melt and spring flows, as well as associated sediment mobilization, in the watershed. Increases in annual precipitation and in extreme precipitation events produce increases in annual as well as peak discharge and sediment loads in the watershed. This research adds to the body of evidence indicating that streambank erosion and failure can be a major source of suspended sediment, and thereby a major source of phosphorus as well. It also shows that local climate trends in the Northeast are likely to result in higher peak discharges and sediment yields from meso-scale, high-gradient watersheds that encompass headwater forested streams and agricultural floodplains. One limitation was that we could not drive the model with meteorological data that represented changes in both temperature and precipitation, highlighting the need for improved climate predictions. This coupled model approach could be parameterized for alternative watersheds and be re-applied to answer various questions related to erosion processes and sediment transport in a watershed. These findings have important implications for resource allocation and targeted watershed management strategies.

Master of Science / Biological & Agricultural Engineering / Stacy L. Hutchinson / Increasing attentiveness to climate change and the dependence of human life on natural resources has spurred awareness about the detrimental impacts of human activity on the environment. Ecosystem services, or the benefits that humans derive from ecosystems, have changed more in the past 50 years than in any other comparable period in human history (Carpenter et al., 2009).The dilemma of managing the trade-off between immediate human needs and maintaining the ability of the Earth to provide ecosystem services is considered to be one of the largest challenges of this century (Foley et al., 2005). The ecosystem service concept aims maximize the provision of services across an entire ecosystem to achieve overall ecosystem health through land management, policy, and economic decisions. The intent of this research was to improve such decisions by increasing the understanding about the relationship between urban best management practices and freshwater provision, erosion regulation, and flood regulation ecosystem services. Fifty-six land management scenarios with varying densities of BMP application were simulated using the Stormwater Management Model (SWMM). The ecosystem services resulting from these land management scenarios were quantified using indices developed by Logsdon and Chaubey (2013). Results demonstrate that the application of bioretention cells improve both freshwater provision and erosion regulation services immediately downstream from the implementation site, and an increase in erosion regulation services was observed at the greater watershed scale. There was no change in the provision of freshwater, erosion regulation, or flood regulation services observed by the application of green roofs or rain barrels at either scale of analysis.

abstract: This dissertation advances spatial decision support system development theory by using a geodesign approach to evaluate design alternatives for such systems, including the impacts of the spatial model, technical spatial data, and user interface tools. These components are evaluated with a case study spatial decision support system for watershed management in the Niantic River watershed in Connecticut, USA. In addition to this case study, this dissertation provides a broader perspective on applying the approach to spatial decision support systems in general. The spatial model presented is validated, the impacts of the model are considered. The technical spatial data are evaluated using a new method developed to quantify data fitness for use in a spatial decision support system. Finally, the tools of the user interface are assessed by applying a conceptual framework and evaluating the resulting tools via user survey. / Dissertation/Thesis / Doctoral Dissertation Geography 2014

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A distributed approach can improve functionality of H/WQ (Hydrology and Water Quality) modeling by facilitating a way to explicitly incorporate spatial characteristics of a watershed into the model. The time-area approach, with its intuitive and inherently distributed concept, provides a simple method to simulate runoff mechanisms. This study developed a distributed model based on the time-area approach with the goal of improved utility and efficiency in H/WQ modeling. Uncertainty is always introduced into watershed modeling because of imperfect knowledge and scale dependant spatial heterogeneity and temporal variability. Uncertainty analysis can provide a modeler, policy maker, and stakeholder with reliability information, better understanding, and better communication about the modeling results. This study quantified uncertainty of the model parameter and output through uncertainty analysis in order to assess risk in watershed management. The main goal of this study was to develop a hydrology and sediment transport model capable of routing overland flow using a time-area concept and providing reliability of the modeling results in a probabilistic manner through uncertainty analysis. The HYSTAR (HYdrology and Sediment transport simulation using Time-ARea method) model incorporates a modified Curve Number (CN) method and the newly devised time-area routing method to estimate runoff. HYSTAR is capable of simulating direct runoff, base flow, soil moisture, and sediment load in a distributed manner and in an hourly time step. In the model, the modified CN and a continuity equation are used to calculate infiltration of the routed runoff as well as rainfall on every overland cell. The effective direct runoff volume is distributed over downstream areas using the newly developed routing method. A direct runoff hydrograph is constructed directly through the discrete convolution of the time-area histogram and the effective direct runoff volume map without employing a unit hydrograph. In addition, sediment transport is simulated using the routing method and the sediment transport capacity approach without using a delivery ratio. The sensitivity analysis found that the CN and root zone depth were the most critical parameters for runoff simulation with HYSTAR. The model provided acceptable performance in predicting runoff and sediment load of a subwatershed of the Owl Run Watershed (ORD) with the Nash-Sutcliffe efficiency coefficient and coefficient of determination greater than 0.5. However, it failed to reproduce runoff for a subwatershed of Polecat Creek Watershed (PCA), where data show that runoff is not immediately responsive to rainfall. Uncertainty analysis revealed that the confidence intervals of the simulated monthly runoff and sediment load corresponded to 9.7 % and 10.2 % of their averages, respectively, at a significance level of 0.05. In addition, the average ranges of variation created by the Digital Elevation Model (DEM) and National Land Cover Data (NLCD) errors in the simulated monthly runoff and sediment load were equivalent to 7.5 % and 15.9 % of the average of their calibrated values, respectively. Based on the uncertainty analysis results, the Margin of Safety (MOS) of Total Maximum Daily Load (TMDL) were explicitly quantified as corresponding to 7.0 % and 21.3 % of the average of the simulated runoff and sediment load for ORD at significance level of 0.05. In conclusion, the HYSTAR model provided a new way to explicitly simulate runoff and sediment load of a watershed in a distributed manner. The approach developed here retains the simplicity of a unit hydrograph approach without employing numerical methods. Uncertainty analysis found that parameter uncertainty had greater impact on the model output than did expected Geographic Information System (GIS) data errors. In addition, the impact of the topographic data error on the model output was greater than was that of the land cover data error. Finally, this study provided a proof that a 5 to 10 % MOS that many TMDL studies consider underestimates modeling uncertainty. / Ph. D.

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