STREAM-CHANNEL INCISION FOLLOWING DRAINAGE-BASIN URBANIZATION1

ABSTRACT: Urbanization of a drainage basin results in pervasive hydrologic changes that in turn initiate long-term changes in stream channels. Increases in peak discharges and in durations of high flows result in either quasi-equilibrium channel expansion, where cross-section area increases in near-proportion to the discharge increase, or catastrophic channel incision, where changes occur far out of proportion to the discharge increases that initiated them. Field data and hydrologic modeling of rapidly urbanizing basins in King County, Washington, define conditions of flow, topography, geology, and channel roughness that identify streams susceptible to incision. Channel slope and geologic material are particularly critical; thus simple map overlays, nearly irrespective of contributing drainage area, provide a valuable planning tool for identification of susceptible terrain. Where such conditions exist, basal shear stress provides a quantifiable parameter for predicting likely problems, although knickpoints are typical in such settings and confound simple calculation of sediment-transport rates. Where urbanization proceeds in such areas, effective mitigation of the incision hazards requires a degree of stormwater control far in excess of standards typically applied to present development activity.     

HYDROLOGIC EFFECTS OF CLIMATE CHANGE IN THE DELAWARE RWER BASIN1

ABSTRACT: The Thornthwaite water balance and combinations of temperature and precipitation changes representing climate change were used to estimate changes in seasonal soil-moisture and runoff in the Delaware River basin. Winter warming may cause a greater proportion of precipitation in the northern part of the basin to fall as rain, which may increase winter runoff and decrease spring and summer runoff. Estimates of total annual runoff indicate that a 5 percent increase in precipitation would be needed to counteract runoff decreases resulting from a warming of 2°C; a 15 percent increase for a warming of 4°C. A warming of 2° to 4°C, without precipitation increases, may cause a 9 to 25 percent decrease in runoff. The general circulation model derived changes in annual runoff ranged from ?39 to +9 percent. Results generally agree with those obtained in studies elsewhere. The changes in runoff agree in direction but differ in magnitude. In this humid temperate climate, where precipitation is evenly distributed over the year, decreases in snow accumulation in the northern part of the basin and increases in evapotranspiration throughout the basin could change the timing of runoff and significantly reduce total annual water availability unless precipitation were to increase concurrently.     

PARTITIONING SMALL SCALE SPATIAL VARIABILITY OF RUNOFF AND EROSION ON SAGEBRUSH RANGELAND1

ABSTRACT: Most hydrologic models require input parameters which represent the variability found across an entire landscape. The estimation of such parameters is very difficult, particularly on rangeland. Improved model parameter estimation procedures are needed which incorporate the small-scale and temporal variability found on rangeland. This study investigates the use of a surface soil classification scheme to partition the spatial variability in hydrologic and interrill erosion processes in a sagebrush plant community. Four distinct microsites were found to exist within the sagebrush coppice-dune dune-interspace complex. The microsites explained the majority of variation in hydrologic and interrill erosion response found on the site and were discernable based on readily available soil and vegetation information. The variability within each microsite was quite low and was not well correlated with soil and vegetation properties. The surface soil classification scheme defined in this study can be quite useful for defining sampling procedures, for understanding hydrologic and erosion processes, and for parameterizing hydrologic models for use on sagebrush range-land.     

BAYESIAN MODELS OF FORECASTED TIME SERIES1

Bayesian Processor of Forecasts (BPF) combines a prior distribution, which describes the natural uncertainty about the realization of a hydrologic process, with a likelihood function, which describes the uncertainty in categorical forecasts of that process, and outputs a posterior distribution of the process, conditional upon the forecasts. The posterior distribution provides a means of incorporating uncertain forecasts into optimal decision models. We present fundamentals of building BPF for time series. They include a general formulation, stochastic independence assumptions and their interpretation, computationally tractable models for forecasts of an independent process and a first-order Markov process, and parametric representations for normal-linear processes. An example is shown of an application to the annual time series of seasonal snowmelt runoff volume forecasts.     

Large-scale constructed wetlands for nutrient removal from stormwater runoff: An everglades restoration project

The South Florida Water Management District (SFWMD) constructed a wetland south of Lake Okeechobee to begin the process of removing nutrients (especially phosphorus) from agricultural stormwater runoff entering the Everglades. The project, called the Everglades Nutrient Removal (ENR) project, is a prototype for larger, similarly constructed wetlands that the SFWMD will build as part of the Everglades restoration program. This innovative project is believed to be one of the largest agricultural stormwater cleanup projects in the United States, if not in the world. This publication describes the ENR project's design, construction, and proposed operation, as well as the proposed research program to be implemented over the next few years.     

THE EFFECTS OF A MOVING RAINSTORM ON DIRECT RUNOFF PROPERTIES1

ABSTRACT: The effects of a moving rainstorm on flood runoff characteristics were investigated. A flood hydrograph simulation model called “FH-Model” and a natural watershed were used. A hypothetical rainstorm of 50 years recurrence interval, 75 mm depth, and 4 hours duration was used to show the effects of velocity and direction of the moving rainstorm on the runoff characteristics. Compared with an equivalent stationary rainstorm (ESRS), the peak flow caused by a rainstorm moving in a downstream direction with a speed equal to channel velocity, V, was 27.5 percent higher and the peak flow caused by the same rainstorm moving in an upstream direction was 21.7 percent smaller. These percentages reduced to 10.5 percent and 8.6 percent for storms moving downstream and upstream, respectively, at three times the channel velocity, 3V. There were negligible differences in the time of peak, T_p between runoff caused by storms moving downstream and runoff produced by ESRS. However, T_p for a storm moving upstream at V velocity was 82 percent higher than that produced by ESRS, but was reduced to 27 percent higher when the storm velocity was 3V.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

EFFECTS OF INITIAL ABSTRACTION AND URBANIZATION ON ESTIMATED RUNOFF USING CN TECHNOLOGY1

ABSTRACT: Few studies have been conducted to explore the effects of initial abstraction on estimated direct runoff despite the widespread use of the curve number (CN) method in many hydrologic models to estimate direct runoff. In this study, use of a 5 percent ratio of initial abstraction (I_a) to storage (S) to estimate daily direct runoff with modified CN values for a 5 percent I_a/S value was investigated using the Long‐Term Hydrologic Impact Assessment (L‐THIA) geographic information system (GIS). In addition, the effects on estimated runoff of altering the hydrologic soil group due to urbanization were investigated. The L‐THIA model was applied to the Indiana Little Eagle Creek watershed with 5 percent and 20 percent I_a/S values, considering hydrologic soil group alteration due to urbanization. The results indicate that uses of a 5 percent l_a/S and modified CN values and Hydrologic Soil Group D for urbanized areas in model runs can improve long term direct runoff prediction.     

ASSESSMENT OF POSSIBLE IMPACTS OF CLIMATE CHANGE IN AN URBAN CATCHMENT1

ABSTRACT: Stationarity of rainfall statistical parameters is a fundamental assumption in hydraulic infrastructure design that may not be valid in an era of changing climate. This study develops a framework for examining the potential impacts of future increases in short duration rainfall intensity on urban infrastructure and natural ecosystems of small watersheds and demonstrates this approach for the Mission/Wagg Creek watershed in British Columbia, Canada. Nonstationarities in rainfall records are first analyzed with linear regression analysis, and the detected trends are extrapolated to build potential future rainfall scenarios. The Storm Water Management Model (SWMM) is used to analyze the effects of increased rainfall intensity on design peak flows and to assess future drainage infrastructure capacity according to the derived scenarios. While the framework provided herein may be modified for cases in which more complex distributions for rainfall intensity are needed and more sophisticated stormwater management models are available, linear regressions and SWMM are commonly used in practice and are applicable for the Mission/Wagg Creek watershed. Potential future impacts on stream health are assessed using methods based on equivalent total impervious area. In terms of impacts on the drainage infrastructure, the results of this study indicate that increases in short duration rainfall intensity may be expected in the future but that they would not create severe impacts in the Mission/Wagg Creek system. The equivalent levels of imperviousness, however, suggest that the impacts on stream health could be far more damaging.