COMPARISON OF REGRESSION AND TIME-SERIES METHODS FOR SYNTHESIZING MISSING STREAMFLOW RECORDS1

ABSTRACT: Regression and time-series techniques have been used to synthesize and predict the stream flow at the Foresta Bridge gage from information at the upstream Pohono Bridge gage on the Merced River near Yosemite National Park. Using the available data from two time periods (calendar year 1979 and water year 1986), we evaluated the two techniques in their ability to model the variation in the observed flows and in their ability to predict stream flow at the Foresta Bridge gage for the 1979 time period with data from the 1986 time period. Both techniques produced reasonably good estimates and forecasts of the flow at the downstream gage. However, the regression model was found to have a significant amount of autocorrelation in the residuals, which the time-series model was able to eliminate. The time-series technique presented can be of great assistance in arriving at reasonable estimates of flow in data sets that have large missing portions of data.     

REGIONAL FLOOD EQUATIONS FOR THE PROVINCES OF ONTARIO AND QUEBEC1

ABSTRACT: When nonparametric frequency analysis was performed on 183 stations from Ontario and Quebec, unimodal and multimodal maximum annual flood density functions were discovered. In order to determine generating mechanisms, a monthly partitioning of the annual maximum floods was undertaken. The timing of the floods revealed that the unimodal distributions reflected a single flood generating mechanism while the multi-modal densities reflected two or more mechanisms. Based on the division of the flood series by mechanisms, nine homogeneous regions were delineated. L-moment distributional homogeneity tests along with smaller standard errors for the regional equations supported the delineation.     

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.     

ESTIMATING REGIONAL SNOW WATER EQUIVALENT WITH A SIMPLE SIMULATION MODEL1

A simple simulation model designed to monitor snow-packs of the central Sierra Nevada is described. The model estimates average snow water equivalent for rectangular subregions in the area. Static subregion characteristics, daily precipitation and mean and minimum air temperatures measured at three index stations are the only needed input values. A water balance technique simulates daily snowpack changes in each subregion. Reasonable basinwide water equivalent values are produced. The procedure should be useful for estimating snow water distribution in large mountainous watersheds.     

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS1

ABSTRACT: A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that includsion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.     

IDENTIFICATION OF SOLUTE LOADING SOURCES TO A SURFACE STREAM1

ABSTRACT: A study was performed to identify sources of solute loading to the Dirty Devil River and its major tributaries, in southeastern Utah. A primary goal was to determine the contribution of gypsum dissolution to total dissolved solids concentration, and its potential increase in the future if salinity control measures are instituted. Synoptic field data were collected during the low flow period in October 1983. Data were analyzed using the geochemistry models WATEQF and BALANCE to postulate mineral reactions leading to solute loading. Three known sources of solute loading, involving two different geochemical mechanisms, were clearly discernable. Two additional areas of possible gypsum dissolution were located.     

MEETING THE CHALLENGE OF POLICY-RELEVANT SCIENCE: LESSONS FROM A WATER RESOURCE PROJECT1

ABSTRACT: Water resource scientists face complex tasks in evaluating aspects of water projects, but relatively few assessment procedures have been applied and accepted as standard applications. Decision-makers often rely on environmental assessments to evaluate the value and operation of projects. There is often confusion about scientists' role in policy decisions. The scientist can affect policy-making as an expert withess, an advocate or a surrogate. By understanding the policy process, scientists can make their work more “policy relevant.” Using the Terror Lake hydro project in Alaska as a guide, three lessons are discussed: (1) not all problems are able to be solved with technology; (2) policy-relevant technology is rarely imposed on a problem; and (3) the scientist need not just react to the policy process, but can have an impact on how that process unfolds.     

DERIVING THE NONLINEAR RISK-BENEFIT ALGORITHM FOR RESERVOIRS1

ABSTRACT: The Nonlinear Risk-Benefit (NRB) Algorithm includes risk as one of the objectives in a multiple-objective optimization problem. The NRB Algorithm is derived by extending the Surrogate Worth Trade-Off method to quadratic programming. This category of problem is common in water resources planning and design, especially multipurpose reservoir systems. Consequently, an example is given using the algorithm for optimally operating a multipurpose reservoir.     

论区域环境规划与区域开发环境影响评价在区域开发环境管理中的作用和地位

回顾了国内外区域开发环境管理的发展历程，简要论述了区域环境规划与区域开发环境影响评价在区域开发环境管理中的作用和地位以及两者的相互关系。     

ECONOMIC RESOURCE MULTIPLIERS FOR REGIONAL IMPACT ANALYSIS1

ABSTRACT: Tradeoffs between regional economic development and resource use is a question often confronting local decisionmakers. A resource-interindustry model can be used to depict the interrelationships between regional economic sectors as to household income and/or employment and resource use. A resource-interindustry model was developed for Humboldt and Lander Counties in Nevada which shows the tradeoffs between regional household income (wages, salaries, profits, and rents) and/or employment and water usage. Water income and water employment multipliers can be ranked, enabling decisionmakers to realize sectors which require greatest regional water usage to regional household income and/or employment.