THE SENSITWITY OF NORTHERN SIERRA NEVADA STREAMFLOW TO CLIMATE CHANGE1

ABSTRACT: The sensitivity of streamflow to climate change was investigated in the American, Carson, and Truckee River Basins, California and Nevada. Nine gaging stations were used to represent streamflow in the basins. Annual models were developed by regressing 1961–1991 streamflow data on temperature and precipitation. Climate-change scenarios were used as inputs to the models to determine streamflow sensitivities. Climate-change scenarios were generated from historical time series by modifying mean temperatures by a range of +4°C to—4°C and total precipitation by a range of +25 percent to -25 percent. Results show that streamflow on the warmer, lower west side of the Sierra Nevada generally is more sensitive to temperature and precipitation changes than is streamflow on the colder, higher east side. A 2°C rise in temperature and a 25-percent decrease in precipitation results in stream-flow decreases of 56 percent on the American River and 25 percent on the Carson River. A 2°C decline in temperature and a 25-percent increase in precipitation results in streamflow increases of 102 percent on the American River and 22 percent on the Carson River.     

EMPIRICAL MODELS FOR TROPHIC STATE IN SOUTHEASTERN U.S. LAKES AND RESERVOIRS1

ABSTRACT: A cross-sectional data set of 80 lakes and reservoirs in nine southeastern states was examined to specify and parameterize trophic state relationships. The relationships fitted are based on measurements of several limnological variables taken over the course of a growing season or year in each of the lakes. The trophic state models relate phosphorus and nitrogen loading to inlake phosphorus and nitrogen concentrations, which in turn are related to maximum chlorophyll level, Secchi disk depth, dominant algal species, and hypolimnetic dissolved oxygen status. Due to the empirical nature of the study, causal conclusions are limited; rather, the models are most useful for prediction of average growing season conditions related to trophic state.     

ANALYTICAL REGRESSION STAGE ANALYSIS FOR DEVILS HOLE,DEATH VALLEY NATIONAL PARK,NEVADA1

ABSTRACT: Devils Hole is a collapse depression connected to the regional carbonate aquifer of the Death Valley ground water flow system. Devils Hole pool is home to an endangered pupfish that was threatened when irrigation pumping in nearby Ash Meadows lowered the pool stage in the 1960s. Pumping at Ash Meadows ultimately ceased, and the stage recovered until 1988, when it began to decline, a trend that continued until at least 2004. Regional ground water pumping and changes in recharge are considered the principal potential stresses causing long term stage changes. A regression was found between pumpage and Devils Hole water levels. Though precipitation in distant mountain ranges is the source of recharge to the flow system, the stage of Devils Hole shows small change in stage from 1937 to 1963, a period during which ground water withdrawals were small and the major stress on stage would have been recharge. Multiple regression analyses, made by including the cumulative departure from normal precipitation with pumpage as independent variables, did not improve the regression. Drawdown at Devils Hole was calculated by the Theis Equation for nearby pumping centers to incorporate time delay and drawdown attenuation. The Theis drawdowns were used as surrogates for pumpage in multiple regression analyses. The model coefficient for the regression, R²= 0.982, indicated that changes in Devils Hole were largely due to effects of pumping at Ash Meadows, Amargosa Desert, and Army 1.     

江苏省城市防汛决策支持系统研究

在分析江苏省城市防汛问题的基础上，设计了其防汛决策支持系统；根据江苏省城市洪涝灾情特点研建和选用了适宜的数学模型，该模型可以迅速、可靠和正确地模拟城市雨情、水情、灾情的发展过程和可能后果，为城市防汛决策提供多层次的信息服务和多种支持手段。     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

CONSTRUCTING A PROBLEM SOLVING ENVIRONMENT TOOL FOR HYDROLOGIC ASSESSMENT OF LAND USE CHANGE1

ABSTRACT: This paper describes the integration of a comprehensive hydrological model known as the Hydrological Simulation Program Fortran (HSPF) into a problem solving environment (PSE) for watershed management. The original PSE concept was a structure providing web‐based access to a suite of models, including HSPF and other models of in‐stream hydrodynamics, biological impacts and economic effects, for the watershed‐wide assessment of alternative land use scenarios. The present paper describes only the HSPF integration into the PSE program. Example applications to the 148 square kilometer (57 square mile) Back Creek subwatershed in the upper Roanoke River system (1,479 square kilometers or 571 square miles) in southwest Virginia are used to illustrate important concepts and linkages between land development and hydrological change using hypothetical' what if'scenarios. The features of HSPF and its limitations in this context are discussed. The paper as such is a proof‐of‐concept paper and not a completion report. It is intended to describe the PSE tool building process rather than analysis of the many possible simulation outcomes. However, the dominance of raw imperviousness as a contributor to hydrograph response is apparent in all the PSE simulations described in this paper.     

ASSEMBLY OF MAP‐BASED STREAM NARRATIVES TO FACILITATE STAKEHOLDER INVOLVEMENT IN WATERSHED MANAGEMENT1

ABSTRACT: Watershed stewardship activities throughout North America have evolved into a process that requires more involvement in planning and decision making by community stakeholders. Active involvement of all stakeholders in the process of watershed stewardship is dependent on effective exchange of information among participants, and active involvement of a wide range of stakeholders from “communities of place” as well as those from “communities of interest.” We developed a map‐based stream narrative tool as a means to: (a) assemble a wealth of incompletely documented, “traditional” ecological or natural history observations for rivers or streams; and (b) promote a higher level of active involvement by community stakeholders in contributing to information‐based, watershed management. Creation of stream narratives is intended for use as a tool to actively engage local stakeholders in the development of a more comprehensive information system to improve management for multiple stewardship objectives in watersheds. Completion of map‐based stream narrative atlases provides a valuable supplement to other independent efforts to assemble observations and knowledge about land‐based natural resources covering entire watersheds. We are confident that completion of stream narrative projects will make a valuable addition to the information and decision making tools that are currently available to the public and resource agencies interested in advancing the cause of community‐based approaches to watershed and ecosystem management.     

Application of a GIS-based stream buffer generation model to environmental policy evaluation

In this article a GIS method is presented for riparian environmental buffer generation. It integrates a scientifically tested buffer width delineation model into a GIS framework. Using the generally available data sets, it determines buffer widths in terms of local physical conditions and expected effectiveness. Technical burdens of data management, computation, and result presentation are handled by the GIS. The case study in which the method was used to evaluate the stream buffer regulations in a North Carolina county demonstrates its capability as a decision support tool to facilitate environmental policy formulation and evaluation, and environmental dispute resolution.     

DEVELOPMENT OF WATERSIIED MODELS FOR TWO SIERRA NEVADA BASINS USING A GEOGRAPHIC INFORMATION SYSTEM1

ABSTRACT: Techniques were developed using vector and raster data in a geographic information system (GIS) to define the spatial variability of watershed characteristics in the north-central Sierra Nevada of California and Nevada and to assist in computing model input parameters. The U.S. Geological Survey's Precipitation-Runoff Modeling System, a physically based, distributed-parameter watershed model, simulates runoff for a basin by partitioning a watershed into areas that each have a homogeneous hydrologic response to precipitation or snowmelt. These land units, known as hydrologic-response units (HRU's), are characterized according to physical properties, such as altitude, slope, aspect, land cover, soils, and geology, and climate patterns. Digital data were used to develop a GIS data base and HRIJ classification for the American River and Carson River basins. The following criteria are used in delineating HRU's: (1) Data layers are hydrologically significant and have a resolution appropriate to the watershed's natural spatial variability, (2) the technique for delineating HRU's accommodates different classification criteria and is reproducible, and (3) HRU's are not limited by hydrographic-subbasin boundaries. HRU's so defined are spatially noncontiguous. The result is an objective, efficient methodology for characterizing a watershed and for delineating HRU's. Also, digital data can be analyzed and transformed to assist in defining parameters and in calibrating the model.     

DETERMINING SIGNIFICANCE AND PRECISION OF ESTIMATED PARAMETERS FOR RUNOFF FROM SEMIARID WATERSHEDS1

ABSTRACT Significant parameters for predicting thunderstorm runoff from small semiarid watersheds are determined using data from the Walnut Gulch watershed in southern Arizona. Based on these data, thunderstorm rainfall is dominant over watershed parameters for predicting runoff from multiple linear regression equations. In some cases antecedent moisture added significantly to the models. A technique is developed for estimating precision of predicted values from multiple linear regression equations. The technique involves matrix methods in estimating the variance of mean predicted values from a regression equation. The estimated variance of the mean predicted value is then used to estimate the variance of an individual predicted value. A computer program is developed to implement these matrix methods and to form confidence limits on predicted values based on both a normality assumption and the Chebyshev inequality.