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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.

<p> There is an undisputed need to increase accuracy of snow cover estimation in regions comprised of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their annual water supply, such as the Western United States, Central Asia, and the Andes. Presently, the most pertinent monitoring and research needs related to alpine snow cover area (SCA) are: (1) to improve SCA monitoring by providing detailed fractional snow cover (FSC) products which perform well in temporal/spatial heterogeneous forested and/or alpine terrains; and (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management. </p><p> To address the above, the presented research approach is based on Landsat Fractional Snow Cover (Landsat-FSC), as a measure of the temporal/spatial distribution of alpine SCA. A fusion methodology between remotely sensed multispectral input data from Landsat TM/ETM+, terrain information, and IKONOS are utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) are used to capture the multi-scale information content of the input data compositions by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat and terrain input data compositions. The ANN Landsat-FSC algorithm is validated (RMSE ~ 0.09; mean error ~ 0.001-0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. ANN input data selections are evaluated to determine the nominal data information requirements for FSC estimation. Snow/non-snow multispectral and terrain input data are found to have an important and multi-faced impact on FSC estimation. Constraining the ANN to linear modeling, as opposed to allowing unconstrained function shapes, results in a weak FSC estimation performance and therefore provides evidence of non-linear bio-geophysical and remote sensing interactions and phenomena in complex mountain terrains. The research results are presented for rugged areas located in the San Juan Mountains of Colorado, and the hilly regions of Black Hills of Wyoming, USA. </p>

<p> In this paper, we study solute transport in an individual fracture and the surrounding porous rock. Specifically, we consider a parallel-plate model of a single fracture that allows for the diffusion of solute within the matrix and the adsorption of solute to the fracture walls. We developed two stochastic particle-tracking methods to numerically solve for the concentration of the fracture model. The first is the hi-res method which captures the solute dynamics at a micro-scale. The second algorithm we develop, the upscaled method, captures the large-scale dynamics of the system at vastly reduced computational cost. We verified the accuracy of these methods by comparing their results to numerical results from the literature. We also compared the efficiency of the developed particle tracking methods to an existing particle tracking method from the literature in the case of no interface absorption.</p>

<p> The hydrogeologic controls, flow velocities and paths, groundwater delineation, and physical characteristics in a joint controlled dendritic conduit-spring system have been characterized. The known conduit branches from C My Shovel (CM) Cave with 4475 meters (m) of measurable passages and tributaries. Surface entrance to CM Cave is located 1360 m upstream from discharge at Magic Springs. </p><p> Four storm events were measured characterizing the dynamics. The maximum discharge of these four events was 1.2 m³/s (41 ft³/s) with 0.08 m³/s (3 ft³/s) baseflow conditions at Magic Springs. The characteristic shape and response of discharge are well defined with a rise time between 5.5 and 6.5 hours (hr). The half flow period time (t_0.5) ranges between 12.9 and 15.7 hr, depending on peak discharge. The rise time and t0.5 occur in less than one day and the conduit volume exceeds 0.5 x 10⁶ m³. The conduit-spring system drains within 3.7 to 7.5 days after the storm event. The thermal effects are event driven, maintaining 85% of the temperature change over 1300 m. The spring discharge has total dissolved solids around 350 mg/L and is chemically stable. </p><p> The field component of this study include a karst density survey, four dye traces, and continuous monitoring of specific conductance, pressure, temperature, water-level stage height, and discharge at Magic Springs and in the conduit below CM Cave. The general karst density survey identifies caves and dolines within given area. There is a sinking stream that transfers flow from Spring Branch Creek into the conduit system and two focused regions in a karst plain having densities of 20 and 44 karst features/0.16 km². </p><p> Hydrographs and chemographs show patterns interpreted as pulses of dilute water recharging through exposed caves, sinkholes, and sinking streams. These pulses have minimal reaction with the rock or matrix during recharge, which is superimposed on baseflow from the joint controlled dendritic conduit-spring system in this karst terrane. </p><p> The groundwater drainage basin has been defined. The dye tracing results identified groundwater piracy across surface water divides and helped define the groundwater drainage basin. Groundwater velocities were measured between 1800 m/d and 3000 m/d under baseflow conditions. The discharge at Magic Springs under these four storm events showed velocities between 8,700 and 15,120 m/d. </p><p> An autosampler and charcoal packets were both employed during dye tracing. Both detected fluorescence from all four injection sites. The measured velocities ranged between 1865 up to 2929 m/d under baseflow conditions. All dye trace tests were conducted under baseflow. Under baseflow conditions, dye was only traced to the Magic Springs locations from the eleven charcoal monitoring locations.</p>