NON-ZERO LOWER LIMIT IN LOW FLOW FREQUENCY ANALYSIS?1

ABSTRACT: The implications of fitting distributions with non-zero lower limits to low flow data are examined. The 3-parameter Weibull distribution is fitted to annual minimum flow series from 60 long term stations in Canada. The relations between the estimated lower limit and three sampling variables (skewness, smallest, and largest observations) were investigated. The lower limit strongly depends on the sample skewness; it varies directly with the sample skewness, which in turn is highly influenced by the largest observation. For a given skewness, the value of the estimated lower limit is determined by the value of the smallest observation. Therefore, the lower limit cannot be accurately determined, and the resulting low flow estimates will be either too small or too high.     

ESTIMATION OF COASTAL WATER CONTAMINATION LONG-TERM TREND USING SEASONAL LINEAR MODELS1

ABSTRACT: Bivalves are used as bioindicators to assess trends of the chemical quality of coastal and marine environments due to their ability to concentrate chemicals. These shellfish are subject to seasonal physiological changes influencing the chemical concentration. Using quarterly data, we model concentration via linear regression with a biologically based seasonal component. This was applied to cadmium concentration measured in the blue mussel (Mytilus edulis) at three sites in the Seine estuary (Normandy, France). In this case we have a high concentration season from January to June and a “low concentration” season from July to December. This season definition was checked a posteriori, using box-and-whisker plots and a statistical test of comparison of pair-wise adjusted least-squares mean differences, and it appears to be very reasonable. We averaged data by season and across sites. Our final model (R²= 0.846 with N= 27 observations) includes highly significant terms: a season effect, which accounts for 45% of the total variability, a linear and a quadratic time term. Outliers were identified by high Studentized residual values and attributed to bias in the temporal sampling schemes. The methodology developed will further be used with other shellfish and/or other trace elements and organic chemicals.     

EFFECT OF SIMULATED CLIMATE CHANGE ON SNOWMELT RUNOFF MODELING IN SELECTED BASINS1

ABSTRACT: The projected increase in the concentration of CO₂ and other greenhouse gases in the atmosphere is likely to result in a global temperature increase. This paper reports on the probable effects of a temperature increase and changes in transpiration on basin discharge in two different mountain snowmelt regions of the western United States. The hydrological effects of the climate changes are modeled with a relatively simple conceptual, semi-distributed snowmelt runoff model. Based on the model results, it may be concluded that increased air temperatures will result in a shift of snowmelt runoff to earlier in the snowmelt season. Furthermore, it is shown that it is very important to include the expected change in climate-related basin conditions resulting from the modeled temperature increase in the runoff simulation. The effect of adapting the model parameters to reflect the changed basin conditions resulted in a further shift of streamflow to April and an even more significant decrease of snowmelt runoff in June and July. If the air temperatures increase by approximately 5°C and precipitation and accumulated snow amounts remain about the same, runoff in April and May, averaged for the two basins, is expected to increase by 185 percent and 26 percent, respectively. The runoff in June and July will decrease by about 60 percent each month. Overall, the total seasonal runoff decreases by about 6 percent. If increased CO₂ concentrations further change basin conditions by reducing transpiration by the maximum amounts reported in the literature, then, combined with the 5°C temperature increase, the April, May, June, and July changes would average +230 percent, +40 percent, ?55 percent, and ?45 percent, respectively. The total seasonal runoff change would be +11 percent.     

VALIDATION OF AGNPS FOR SMALL WATERSHEDS USING AN INTEGRATED AGNPS/GIS SYSTEM1

ABSTRACT: The AGNPS (AGricultural NonPoint Source) model was evaluated for predicting runoff and sediment delivery from small watersheds of mild topography. Fifty sediment yield events were monitored from two watersheds and five nested subwater-sheds in East Central Illinois throughout the growing season of four years. Half of these events were used to calibrate parameters in the AGNPS model. Average calibrated parameters were used as input for the remaining events to obtain runoff and sediment yield data. These data were used to evaluate the suitability of the AGNPS model for predicting runoff and sediment yield from small, mild-sloped watersheds. An integrated AGNPS/GIS system was used to efficiently create the large number of data input changes necessary to this study. This system is one where the AGNPS model was integrated with the GRASS (Geographic Resources Analysis Support System) GIS (Geographical Information System) to develop a decision support tool to assist with management of runoff and erosion from agricultural watersheds. The integrated system assists with the development of input GIS layers to AGNPS, running the model, and interpretation of the results.     

STATE VARIABLE MODEL FOR URBAN RAINFALL-RUNOFF PROCESS1

ABSTRACT: A nonlinear hydrologic system model has been developed for analyzing the urban rainfall-runoff process. The model is formulated as a state variable model consisting of several parameters. A search technique is employed to find the set of parameters for which the model's response best fits observed data. The model could be used in either a simulation or forecasting mode. The model is applied to observed data for the Waller Creek Watershed in Austin, Texas, to develop the model parameters for various levels of urbanization of the watershed. The trend of each parameter with respect to levels of urbanization is examined.     

EVALUATION OF A DEPTH PROPORTIONAL INTAKE DEVICE FOR AUTOMATIC PUMPING SAMPLERS1

ABSTRACT: A depth proportional intake boom for portable pumping samplers was used to collect suspended sediment samples in two coastal streams for three winters. The boom pivots on the stream bed while a float on the downstream end allows debris to depress the boom and pass without becoming trapped. This equipment modifies point sampling by maintaining the intake nozzle at the same proportion of water depth regardless of stage. Data taken by pumping samplers with intakes mounted on the boom were compared with depth integrated hand samples. Pumped samples showed higher concentrations than depth integrated hand samples. Results suggested that cross-sectional sampling can give high precision with proper placement and calibration of a boom mounted intake.     

A UNIFYING SET OF PROBABILITY TRANSFORMS FOR STOCHASTIC HYDROLOGY1

Abstract: An ordered set of probability sensity functions was derived form transforms of the normal distribution. Variate transforms are based on orders of exponentiation. The set of distributions includes the log-normal. a partial basis for use of these functins in hydrology is demonstrated by establishing some required properties. A concept of mixed samples of zero and nonzero elements forming nonseparable sets of virtual and real elements is introduced to establish a physical lower limit of sampled diata independent of functional bounds.     

NONPARAMETRIC TEST PERFORMANCE FOR TRENDS IN WATER QUALITY WITH SAMPLING DESIGN APPLICATIONS1

ABSTRACT: In this paper four nonparametric tests for monotonic trend detection are compared with respect to their power and accuracy. The importance of comparing powers at equal empirical significance levels rather than nominal levels is stressed. Therefore, an appropriate graphical method is presented. The effect of the sampling frequency is also assessed using Monte Carlo simulations and a trajectory representation that visualizes the dynamics of the trade-off between the type I and type II errors. These methods are applied to compare four nonparametrical tests (seasonal Mann. Kendall, modified seasonal Mann-Kendall, covariance eigenvalue and covariance inversion) under several conditions. It is concluded with respect to the power that it is not worthwhile for the modified seasonal Mann-Kendall test applied to the AR(1) process considered in this paper to increase the sampling frequency from monthly to biweekly for detecting a monotonic trend of 5 percent, 10 percent, or 15 percent of the process variance. Under these conditions the seasonal Mann-Kendall test is highly liberal, while the covariance inversion and the covariance eigenvalue test are conservative. This research is situated in the development of an efficient sampling design for the Flemish water quality monitoring network.     

ESTIMATION OF EPISODIC STREAM ACIDIFICATION BASED ON MONTHLY OR ANNUAL SAMPLING1

ABSTRACT: Programs of monthly or annual stream water sampling will rarely observe the episodic extremes of acidification chemistry that occur during brief, unpredictable runoff events. When viewed in the context of data from several streams, however, baseflow measurements of variables such as acid neutralizing capacity, pH and NO₃· are likely to be highly correlated with the episodic extremes of those variables from the same stream and runoff season. We illustrate these correlations for a water chemistry record, nearly two years in length, obtained from intensive sampling of 13 small Northeastern U.S. streams studied during USEPA's Episodic Response Project. For these streams, simple regression models estimate episodic extremes of acid neutralizing capacity, pH, NO₃·, Ca²⁺, SO₄^2?, and total dissolved Al with good relative accuracy from statistics of monthly or annual index samples. Model performances remain generally stable when episodic extremes in the second year of sampling are predicted from first-year models. Monthly or annual sampling designs, in conjunction with simple empirical models calibrated and maintained through intensive sampling every few years, may estimate episodic extremes of acidification chemistry with economy and reasonable accuracy. Such designs would facilitate sampling a large number of streams, thereby yielding estimates of the prevalence of episodic acidification at regional scales.     

EVALUATION OF THE MAXEY-EAKIN METHOD FOR ESTIMATING RECHARGE TO GROUND-WATER BASINS IN NEVADA1

AESTRACT An evaluation of the Maxey-Eakin method for calculating recharge to ground-water basins in Nevada was performed. The evaluation consisted of comparing Maxey-Eakin estimates with independent estimates of recharge, and analyzing the nature of the differences between the groups of estimates. In the comparison with the Maxey-Eakin estimates, two different groups of independent estimates were used: (1) 40 recharge estimates that were identified from water budgets contained in reports by the Nevada Department of Conservation and Natural Resources and (2) 27 recharge estimates that were identified from previous studies that used models. The results of the comparisons indicate generally good agreement between the Maxey-Eakin estimates and both groups of independent estimates. To quantify this agreement, an analysis was conducted to estimate the uncertainty in the Maxey-Eakin method. The analysis produced an upper bound on the standard deviation of the Maxey-Eakin estimate for a given basin. For the group of 40 water-budget estimates, the upper bound on the standard deviation for an individual basin is 4,800 acre-ft/yr, and the corresponding coefficient of variation of the Maxey-Eakin estimate is no greater than 44 percent. For the group of 27 model estimates, the upper bound on the standard deviation is 4,100 acre-ft/yr, and the corresponding coefficient of variation is no greater than 24 percent.