ANALYSIS AND MODELING OF LONG-TERM STREAM TEMPERATURES ON THE STEAMBOAT CREEK BASIN,OREGON: IMPLICATIONS FOR LAND USE AND FISH HABITAT1

ABSTRACT: Steamboat Creek basin is an important source of timber and provides crucial spawning and rearing habitat for anadromous steelhead trout (Oncorhynchus mykiss). Because stream temperatures are near the upper limit of tolerance for the survival of juvenile steelhead, the possible long-term effect of clear-cut logging on stream temperatures was assessed. Twenty-year (1969–1989) records of summer stream temperature and flow from four tributaries and two reaches of Steamboat Creek and Boulder Creek (a nearby unlogged watershed) were analyzed. Logging records for the Steamboat Creek basin and air temperature records also were used in the analysis. A time-series model of the components of stream temperature (seasonal cycle of solar radiation, air temperature, streamflow, an autoregressive term of order 1, and a linear trend variable) was fitted to the water-temperature data. The linear trend variable was significant in all the fitted models except Bend Creek (a tributary fed by cool ground-water discharge) and Boulder Creek. Because no trends in either climate (i.e., air temperature) or streamflow were found in the data, the trend variable was associated with the pre-1969 loss and subsequent regrowth of riparian vegetation and shading canopies.     

CONFLICTING PERSPECTWES ABOUT DETECTION LIMITS AND ABOUT THE CENSORING OF ENVIRONMENTAL DATA1

ABSTRACT: Current conventions for reporting analytical results from environmental samples brings the objectives of laboratory scientists into conflict with those of environmental scientists. The objective of chemical analyses is to provide estimates of the true composition of samples. Reported results must reflect the analytical uncertainty. Current conventions require left-censoring of those results below the Limit of Detection. The objective of statistical interpretation of environmental data is to provide estimates of the characteristics of ecosystems. Such statistical analyses are often confounded by left-censoring of analytical results. We review the different points of view and propose a compromise which recognizes these conflicting perspectives.     

AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

STATISTICAL METHODS AND SAMPLING DESIGN FOR ESTIMATING STEP TRENDS IN SURFACE-WATER QUALITY1

ABSTRACT: This paper addresses two components of the problem of estimating the magnitude of step trends in surface water quality. The first is finding a robust estimator appropriate to the data characteristics expected in water-quality time series. The Hodges-Lehmann class of estimators is found to be robust in comparison to other nonparametric and moment-based estimators. A seasonal Hodges-Lehmann estimator is developed and shown to have desirable properties. Second, the effectiveness of various sampling strategies are examined using Monte Carlo simulation coupled with application of this estimator. The simulation is based on a large set of total phosphorus data from the Potomac River. To assure that the simulated records have realistic properties, the data are modeled in a multiplicative fashion incorporating flow, hysteresis, seasonal, and noise components. The results demonstrate the importance of balancing the length of the two sampling periods and balancing the number of data values between the two periods. The inefficiency of sampling at frequencies much in excess of 12 samples per year is demonstrated. Rotational sampling designs are discussed, and efficient designs, at least for this river and constituent, are shown to involve more than one year of active sampling at frequencies of about 12 per year.     

A TAU-LIKE TEST FOR TREND IN THE PRESENCE OF MULTIPLE CENSORING POINTS1

ABSTRACT: A common problem arises in testing for trends in water quality when observations are reported as “less than detection limit.” If a single detection limit is used for the entire study, existing non-parametric statistical methods, modified for ties, are applicable. If, however, the detection limit varies during the course of the study, resulting in multiple detection limits, then the commonly used trend detection methods are not appropriate. A statistic similar to Kendall's tau, but based on expected ranks, is proposed. Monte Carlo simulations show that the normal approximation to the distribution of this statistic is quite good, even for small samples and a large proportion of censored observations. The statistic is also shown to have greater power than the ad-hoc method of treating all observations less than the target censored observation as tied.     

DESIGN BASIS FOR EVERGLADES STORMWATER TREATMENT AREAS1

ABSTRACT: The State of Florida (1994) has adopted a plan for addressing Everglades eutrophication problems by reducing anthropogenic phosphorus loads. The plan involves implementation of Best Management Practices in agricultural watersheds and construction of regional treatment marshes (Stormwater Treatment Areas or STA's). This paper describes the development, testing, and application of a mass-balance model for sizing STA's to achieve treatment objectives. The model is calibrated and tested against peat and water-column data collected in Water Conservation Area-2A (WCA-2A), where phosphorus dynamics and eutrophication impacts have been intensively studied. The 26-year-average rate of phosphorus accretion in peat is shown to be proportional to average water-column phosphorus concentration, with a proportionality constant of 10.2 m/yr (90 percent Confidence Interval = 8.9 to 11.6 m/yr). Spatial and temporal variations in marsh water-column data suggest that drought-induced recycling of phosphorus was important during periods of low stage in WCA-2A. Maintaining wet conditions will be important to promote phosphorus removal in STA's. Sensitivity analysis of STA performance is conducted over the range of uncertainty in model parameter estimates to assess the adequacy of the model as a basis for STA design.     

FREQUENCY-DURATION ANALYSIS OF DISSOLVED-OXYGEN CONCENTRATIONS IN TWO SOUTHWESTERN WISCONSIN STREAMS1

ABSTRACT: Historically, dissolved-oxygen (DO) data have been collected in the same manner as other water-quality constituents, typically at infrequent intervals as a grab sample or an instantaneous meter reading. Recent years have seen an increase in continuous water-quality monitoring with electronic dataloggers. This new technique requires new approaches in the statistical analysis of the continuous record. This paper presents an application of frequency-duration analysis to the continuous DO records of a cold and a warm water stream in rural southwestern Wisconsin. This method offers a quick, concise way to summarize large time-series data bases in an easily interpretable manner. Even though the two streams had similar mean DO concentrations, frequency-duration analyses showed distinct differences in their DO-concentration regime. This type of analysis also may be useful in relating DO concentrations to biological effects and in predicting low DO occurrences.     

FLOOD FREQUENCY ANALYSIS FOR SMALL WATERSHEDS IN SOUTHERN ILLINOIS1

ABSTRACT: Twenty-two gaging stations were selected for developing a regional flood frequency curve for small (area less than 2 square miles) watersheds in southern Illinois. Five probability functions were compared, and the extreme value type I function was selected to develop the regional flood curve. The curve was generated with the index flood method and also another empirical method that related the function parameters to the watershed area. Estimated peak discharges with various return periods were compared with the results obtained from multiple regression analysis.     

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.     

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.