Estimates of low-flow statistics, flow durations, and ground-water-recharge rates are needed to assist water-resource managers in assessing surface-water resources and ground-water availability. Often these estimates are required at ungaged sites where no observed streamflow data are available for analysis. Regression equations for estimating low-flow statistics and flow durations, and for estimating ground-water-recharge rates at ungaged sites have been developed for New Hampshire. However, use of these equations requires numerous input parameters, such as basin and climatic characteristics. This report describes a customized geographic information system (GIS) application, the New Hampshire Watershed Tool, that automates the measurement of the characteristics used for input to the regression equations and calculates the corresponding flow statistics and ground-water-recharge rates.

This report presents regression equations to estimate generalized annual and seasonal ground-water-recharge rates in drainage basins in New Hampshire. The ultimate source of water for a ground-water withdrawal is aquifer recharge from a combination of precipitation on the aquifer, ground-water flow from upland basin areas, and infiltration from streambeds to the aquifer. An assessment of ground-water availability in a basin requires that recharge rates be estimated under `normal' conditions and under assumed drought conditions. Recharge equations were developed by analyzing streamflow, basin characteristics, and precipitation at 55 unregulated continuous record stream-gaging stations in New Hampshire and in adjacent states. In the initial step, streamflow records were analyzed to estimate a series of annual and seasonal ground-water-recharge components of streamflow in each drainage basin evaluated in this study. Regression equations were then developed relating the series of annual and seasonal ground-water-recharge values to the corresponding series of annual and seasonal precipitation values as determined at the centroid of each drainage basin. This resulted in one equation for each of the 55 basins for each of the four seasonal periods and the annual period, or a total of 275 regression equations. Average annual and seasonal precipitation data for 1961-90 were then used to compute a set of normalized ground-water-recharge values that reflected the long-term average annual and seasonal variations (normalized) and mean recharge characteristics of each drainage basin. Ordinary-least-squares regression was applied in the process of selecting 10 out of 93 possible basin and climatic characteristics for further testing in the development of the equations for computing the generalized estimate of annual and seasonal ground-water recharge based on the set of normalized recharge values. Generalized-least-squares regression was used for the final parameter estimation and error evaluation. The following basin and climatic characteristics were found to be statistically significant predictors for at least one of the dependent variables: average annual, summer, and spring precipitation as determined at U.S. Geological Survey stream-gaging stations; average annual basin-centroid precipitation; average mean annual basin temperature; average minimum winter basin temperature; percent coniferous forest in a basin; percent mixed coniferous and deciduous forest in a basin; average fall basin-centroid precipitation; and average annual snowcover. These 10 basin and climatic characteristics were selected because they were statistically significant based on several statistical parameters that evaluated which combination of characteristics contributed the most to the predictive accuracy of the regression-equation models. A geographic information system is required to measure the values of the predictor variables for the equations developed in the study. The average annual normalized ground-water recharge was 21.0 in. This value was determined by generalized-least-squares (GLS) regression analysis for all of the basins used in the normalized ground-water recharge analysis for precipitation from 1961-90. The average winter (January 1-March 15) ground-water recharge was 4.3 in., average spring (March 16-May 31) ground-water recharge was 9.0 in., average summer (June 1-October 31) ground-water recharge was 4.0 in., and average fall (November 1-December 31) ground-water recharge was 3.6 in. Normalized ground-water recharge ranged annually from 12.3 to 31.8 in., for winter from 2.30 to 7.82 in., for spring from 5.16 to 13.7 in., for summer from 1.45 to 10.2 in., and for fall from 2.21 to 6.06 in.

Estimates of the magnitude and frequency of streamflow are needed to safely and economically design bridges, culverts, and other structures in or near streams. These estimates also are used for managing floodplains, identifying flood-hazard areas, and establishing flood-insurance rates, but may be required at ungaged sites where no observed flood data are available for streamflow-frequency analysis. This report describes equations for estimating flow-frequency characteristics at ungaged, unregulated streams in Vermont. In the past, regression equations developed to estimate streamflow statistics required users to spend hours manually measuring basin characteristics for the stream site of interest. This report also describes the accompanying customized geographic information system (GIS) tool that automates the measurement of basin characteristics and calculation of corresponding flow statistics. The tool includes software that computes the accuracy of the results and adjustments for expected probability and for streamflow data of a nearby stream-gaging station that is either upstream or downstream and within 50 percent of the drainage area of the site where the flow-frequency characteristics are being estimated. The custom GIS can be linked to the National Flood Frequency program, adding the ability to plot peak-flow-frequency curves and synthetic hydrographs and to compute adjustments for urbanization.

Decision makers need viable methods for the interpretation of local, regional, and national-highway runoff and urban-stormwater data including flows, concentrations and loads of chemical constituents and sediment, potential effects on receiving waters, and the potential effectiveness of various best management practices (BMPs). Valid (useful for intended purposes), current, and technically defensible stormwater-runoff models are needed to interpret data collected in field studies, to support existing highway and urban-runoffplanning processes, to meet National Pollutant Discharge Elimination System (NPDES) requirements, and to provide methods for computation of Total Maximum Daily Loads (TMDLs) systematically and economically. Historically, conceptual, simulation, empirical, and statistical models of varying levels of detail, complexity, and uncertainty have been used to meet various data-quality objectives in the decision-making processes necessary for the planning, design, construction, and maintenance of highways and for other land-use applications. Water-quality simulation models attempt a detailed representation of the physical processes and mechanisms at a given site. Empirical and statistical regional water-quality assessment models provide a more general picture of water quality or changes in water quality over a region. All these modeling techniques share one common aspect-their predictive ability is poor without suitable site-specific data for calibration. To properly apply the correct model, one must understand the classification of variables, the unique characteristics of water-resources data, and the concept of population structure and analysis. Classifying variables being used to analyze data may determine which statistical methods are appropriate for data analysis. An understanding of the characteristics of water-resources data is necessary to evaluate the applicability of different statistical methods, to interpret the results of these techniques, and to use tools and techniques that account for the unique nature of water-resources data sets. Populations of data on stormwater-runoff quantity and quality are often best modeled as logarithmic transformations. Therefore, these factors need to be considered to form valid, current, and technically defensible stormwater-runoff models. Regression analysis is an accepted method for interpretation of water-resources data and for prediction of current or future conditions at sites that fit the input data model. Regression analysis is designed to provide an estimate of the average response of a system as it relates to variation in one or more known variables. To produce valid models, however, regression analysis should include visual analysis of scatterplots, an examination of the regression equation, evaluation of the method design assumptions, and regression diagnostics. A number of statistical techniques are described in the text and in the appendixes to provide information necessary to interpret data by use of appropriate methods. Uncertainty is an important part of any decisionmaking process. In order to deal with uncertainty problems, the analyst needs to know the severity of the statistical uncertainty of the methods used to predict water quality. Statistical models need to be based on information that is meaningful, representative, complete, precise, accurate, and comparable to be deemed valid, up to date, and technically supportable. To assess uncertainty in the analytical tools, the modeling methods, and the underlying data set, all of these components need be documented and communicated in an accessible format within project publications.