OPTIMIZING WELL PLACEMENT IN A COASTAL AQUIFER: OUTER CAPE COD,MASSACHUSETTS1

ABSTRACT: An inverse‐simulation approach is used to determine optimal strategies for developing public water‐supply systems in a shallow, coastal aquifer on the outermost arm of the Cape Cod peninsula in Massachusetts. Typically a forward simulation (or “trial and error”) approach is used to find best pumping strategies, but the chances of finding success with this tact diminish as the number of potential options grows large. Well locations and pumping rates are optimized with respect to: (1) providing sufficient water to areas of water‐quality impairment, (2) minimizing impacts to nearby surface waters, (3) preventing saltwater contamination due to overpumping, and (4) minimizing financial cost of well development. Potential well sites and water‐supply scenarios are separated into “politically‐based” and “resource‐based” categories to gain insight into the degree that pre‐existing political boundaries hinder best management practices. The approach provides a promising tool in transboundary water‐resources settings because it allows stakeholders to find solutions that best meet everyone's goals, as opposed to pursuing options that will create conflict, or are less than optimal.     

USING SOIL TEXTURE TO ESTIMATE SATURATED HYDRAULIC CONDUCTIVITY AND THE IMPACT ON RAINFALL-RUNOFF SIMULATIONS1

ABSTRACT: In this paper a new set of soil texture data is used to estimate the spatial distribution of saturated hydraulic conductivity values for a small rangeland catchment. The estimates of conductivity are used to re-excite and re-evaluate a quasi-physically based rainfall-runoff model. The performance of the model is significantly reduced with conductivity estimates gleaned from soil texture data rather than the infiltration data used in our previous efforts.     

LANDSAT MID-INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL-MOISTURE ASSESSMENT1

ABSTRACT: Landsat satellite Thematic Mapper (TM) data were used to assess regional soil moisture conditions. The mid-infrared (MIR) data of TM band 7 were overlain onto four principal land-use categories (Agricultural/Irrigated, Urban/Clearings, Forest/ Wetlands, Water) using a geographic information system (GIS). M data were used to assess four qualitative surface soil-moisture conditions (water/very wet, wet, moist, and dry) within each land-use category of a 208,354 ha southwestern Florida study area. The MIR response was inversely related to the qualitative surface soil-moisture content. Integration of Landsat TM MIR data with land use through GIS appears to be a useful technique for high-resolution regional soil moisture assessment, and further research to reline this technique is recommended.     

DISCHARGE CHARACTERISTICS OF PERFORATED PIPE FOR USE IN INFILTRATION TRENCHES1

ABSTRACT: Infiltration trenches are an effective stormwater management alternative for the control of urban runoff from small areas. Perforated pipes buried within the gravel of an infiltration trench are used to distribute the inflowing runoff along the length of the trench. Laboratory tests are described that characterize the hydraulics of the orifices in perforated pipes. The results show that the steady-state exfiltration of water from the pipe into a surrounding gravel trench can be described by the orifice equation.     

SPATIAL DISTRIBUTION OF POINT SOIL MOISTURE ESTIMATES USING LANDSAT TM DATA AND FUZZY-C CLASSIFICATION1

ABSTRACT: Many hydrologic models have input data requirements that are difficult to satisfy for all but a few well-instrumented, experimental watersheds. In this study, point soil moisture in a mountain watershed with various types of vegetative cover was modeled using a generalized regression model. Information on sur-ficial characteristics of the watershed was obtained by applying fuzzy set theory to a database consisting of only satellite and a digital elevation model (DEM). The fuzzy-c algorithm separated the watershed into distinguishable classes and provided regression coefficients for each ground pixel. The regression model used the coefficients to estimate distributed soil moisture over the entire watershed. A soil moisture accounting model was used to resolve temporal differences between measurements at prototypical measurement sites and validation sites. The results were reasonably accurate for all classes in the watershed. The spatial distribution of soil moisture estimates corresponded accurately with soil moisture measurements at validation sites on the watershed. It was concluded that use of the regression model to distribute soil moisture from a specified number of points can be combined with satellite and DEM information to provide a reasonable estimation of the spatial distribution of soil moisture for a watershed.     

GROUND-WATER RECHARGE THROUGH ACTWE SAND DUNES IN NORTHWESTERN NEVADA1

ABSTRACT: Most water-resouree investigations in semiarid basins of the Great Basin in western North America conclude that ground-water recharge from direct precipitation on the valley floor is negligible. However, many of these basins contain large areas covered by unvegetated, active sand dunes that may act as conduits for ground-water recharge. The potential for this previously undocumented recharge was investigated in an area covered by sand dunes in Desert Valley, northwestern Nevada, using a deep percolation model. The model uses daily measurements of precipitation and temperature th determine energy and moisture balances, from which estimates of long-term mean annual recharge are made. For the study area, the model calculated a mean annual recharge rate of as much as 1.3 inches per year, or 17 percent of the long-term mean precipitation. Model simulations also indicate that recharge would be virtually zero if the study area were covered by vegetation rather than dunes.     

COUPLING OF NONPARAMETRIC FREQUENCY AND L-MOMENT ANALYSES FOR MIXED DISTRIBUTION IDENTIFICATION 1

ABSTRACT: Both L-moment and nonparametric frequency analyses were performed on a series of annual maximum floods from New Brunswick, Canada. The L-moment analysis concluded that the data were generated from a unimodal Generalized Extreme Value (GEV) distribution. However, the nonparametric frequency analysis indicated that a majority of stations followed nonunimodal mixed distributions since peak flows occur during different seasons and are the result of different generating mechanisms. The coupling of L-moment and nonparametric analyses facilitates mixed distribution identification. Thus, the nonparametric method helps in identifying underlying probability distribution, especially when samples arise from mixed distributions.     

CENTRAL NEBRASKA RIVER BASINS,NEBRASKA1

ABSTRACT: The Central Nebraska Basins (NAWQA) study unit includes the Platte River and two major tributaries, the Loup and Elkhorn Rivers. Platte River flows are variable in the western part of the study unit because of diversions, but the Loup and Elkhorn Rivers originate in an area of dune sand covered by grassland that generates consistent base flows. More frequent runoff in the eastern part of the study unit also sustains stream flow. Ground water in the study unit has no regional confining units and the system is a water table aquifer throughout. Macroinvertebrate and fish taxa at biological sampling sites in the state were related to stream flow. One of the four wetland complexes identified in the study unit includes habitat for threatened and endangered bird species. The study unit is an agricultural area that includes row crops, both irrigated and nonirrigated in the eastern and southern parts, and rangeland in the Sand Hills of the western part. A water quality assessment will be based on the differences in environmental setting in each of four subunits within the study unit.]     

PROBABILISTIC GREAT LAKES HYDROLOGY OUTLOOKS1

ABSTRACT: The Great Lakes Environmental Research Laboratory developed a semiautomatic software package for making hydrological outlooks for the Great Lakes. These include basin moisture storages, basin runoff, lake heat storage, lake evaporation, heat fluxes, and net lake supplies, one or more full months into the future. The package combines GLERL's rainfall-runoff and lake evaporation models with near real-time data reduction techniques to represent current system states. Users select historical meteorologic record segments as candidate future scenarios to generate deterministic near real-time hydrological outlooks. GLERL has extended the package to make probabilistic outlooks for a decision-maker who must estimate the risk associated with his decisions. GLERL matches National Weather Service meteorologic outlook probabilities by selecting groups of historical meteorologic sequences, and constructs embedded outlook intervals for each hydrologic variable of interest. Interval probabilities are assigned from comparisons over a recent evaluation period. This physically-based approach for generating outlooks offers the ability, as compared to other statistically-based approaches, to incorporate improvements in the understanding, of process dynamics as they occur in the future and to respond reasonably to conditions initial to a forecast (such as heat and moisture storages), not observed in the past.     

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.