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Historically wildfires have been beneficial to forests, however, human developments have encroached on forests when wildfire was artificially suppressed by federal and state agencies. The area burned by wildfire each year has increased twenty-fold in the past three decades. Large, high severity fires pose increased threats to human and aquatic communities within and downstream of the burned area due to post-wildfire effects on flooding and sedimentation. We need to understand the impacts of wildfires to be able to mitigate their damages and to recognize their potential benefits. This research addresses the questions: 1) Do wildfires impact rural and urban economies differently and what are managers doing to adapt management strategies? 2) Do floods increase after wildfire, and if so, by how much? 3) Do wildfires affect fish habitat, and if so, how? Chapter 2 provides insight into both positive and negative economic impacts on rural and urban economies after a wildfire, and brings to light manager’s inability to change their management strategies due to constraints such as budget limitations. Chapter 3 measures how floods change in nine basins after a wildfire occurred, and reveals that floods may increase up to 880 percent after a fire. Chapter 4 demonstrates that fish habitat is significantly altered after wildfires and why change is harmful to the fish. This work shows that wildfire significantly changes the burned and surrounding area, and that more work is needed for a better understanding of how to predict how a specific area will respond to wildfire.

Linear forecasting models have played major roles in many applications for over a century. If error terms in models are normally distributed, linear models are capable of producing the most accurate forecasting results. The central limit theorem (CLT) provides theoretical support in applying linear models. / During the last two decades, nonlinear models such as neural network models have gradually emerged as alternatives in modeling and forecasting real processes. In hydrology, neural networks have been applied to rainfall-runoff estimation as well as stream and peak flow forecasting. Successful nonlinear methods rely on the generalized central limit theorem (GCLT), which provides theoretical justifications in applying nonlinear methods to real processes in impulsive environments. / This dissertation will attempt to predict the daily stream flow of White Clay Creek by making intensive comparisons of linear and nonlinear forecasting methods. Data are modeled and forecasted by seven linear and nonlinear methods: The random walk with drift method; the ordinary least squares (OLS) regression method; the time series Autoregressive Integrated Moving Average (ARIMA) method; the feed-forward neural network (FNN) method; the recurrent neural network (RNN) method; the hybrid OLS regression and feed-forward neural network (OLS-FNN) method; and the hybrid ARIMA and recurrent neural network (ARIMA-RNN) method. The first three methods are linear methods and the remaining four are nonlinear methods. The OLS-FNN method and the ARIMA-RNN method are two completely new nonlinear methods proposed in this dissertation. These two hybrid methods have three special features that distinguish them from any existing hybrid method available in literature: (1) using the OLS or ARIMA residuals as the targets of followed neural networks; (2) training two neural networks in parallel for each hybrid method by two objective functions (the minimum mean squares error function and the minimum mean absolute error function); and (3) using two trained neural networks to obtain respective forecasting results and then combining the forecasting results by a Bayesian Model Averaging technique. Final forecasts from hybrid methods have linear components resulting from the regression method or the ARIMA method and nonlinear components resulting from feed-forward neural networks or recurrent neural networks. / Forecasting performances are evaluated by both root of mean square errors (RMSE) and mean absolute errors (MAE). Forecasting results indicate that linear methods provide the lowest RMSE forecasts when data are normally distributed and data lengths are long enough, while nonlinear methods provide a more consistent RMSE and MAE forecasts when data are non-normally distributed. Nonlinear neural network methods also provide lower RMSE and MAE forecasts than linear methods even for data that are normally distributed but with small data samples. The hybrid methods provide the most consistent RMSE and MAE forecasts for data that are non-normally distributed. / The original flow is differenced and log differenced to get two differenced series: The difference series and the log difference series. These two series are then decomposed based on stochastic process decomposition theorems to produce two, three and four variables that are used as input variables in regression models and neural network models. / By working on an increment series, either difference series or log difference series, instead of the original flow series, we get two benefits: First we have a clear time series model. The secondary benefit is from the fact that the original flow series is an autocorrelated series and an increment series is approximately an independently ditributed series. For an independently ditributed series, parameters such as Mean and Standard Deviation can be calculated easily. / The length of data during modeling is in practice very important. Model parameters and forecasts are estimated from 30 data samples (1 month), 90 data samples (3 months), 180 data samples (6 months), and 360 data samples (1 year).

Nutrient mitigation capacity of low-grade weirs in agricultural drainage ditches

Effect of implementing best management practices on water and habitat quality in the Upper Strawberry River Watershed, Fulton County, Arkansas, USA

The earth has a finite supply of fresh water operating within a specific natural cycle. Due to population increases, massive industrialization of developing nations, and a culture of water consumption based on endlessness, the world is facing a massive crisis of freshwater shortage. Past and present solutions to local crisis have focused on supply management, when the real solution is demand management. Demand is founded on societal habits, cultural practices, and an individually based perception of water's value. The built environment mirrors this perception, where architecture as a cultural construct becomes an access terminal for various resource infrastructures. This thesis proposes an architecture that renders visible the cyclic specificity and finitude of water by proposing a new typology of public building that experientially transforms the inherited habits of citizens towards a balanced perception of water.

Arizona like many other semi-arid regions in the world is facing a suite of policy issues that stem from water scarcity and security of supply issues intersecting with growing and competing water demands. A vexing issue in southern Arizona has been the preservation of riparian habitat. The study of environmental economics provides researchers with techniques to estimate the value of natural resources, such as riparian habitat, to level the playing field in policy discussions on development and water management. In Appendices B-D results from two hedonic property analyses suggest that homebuyers, one of the main consumers of riparian habitat in urban areas, have preferences for greener and higher condition riparian habitat and furthermore that they are willing to pay property premiums to benefit from this resource. There is also some evidence that riparian habitat conservation and restoration can be self-financing. The economics of another water using sector in the state, the recreation sector, specifically winter-based recreation, is assessed in Appendix E. The analysis finds that although ski areas in Arizona are subject to large inter-year variability in terms of snowfall and season length that snowmaking adaptations, a technology that is water-intensive, is financially feasible in the medium term as a climate variability and climate change adaptation. Nevertheless, ski areas in the state are likely to face increased financial pressures if climate change scenarios are realized and will have to implement other adaptation strategies to remain viable. Finally, water competition in the state between Indian and non-Indian users and the techniques used to dispel such tensions, namely water settlements, are discussed in Appendix F. The research finds that settlements offer opportunities for win-win agreements between the settling tribe and other water users in the same watersheds and for the introduction of new water supply management tools that benefit signatory and non-signatory parties alike.