GENERALIZED ESTIMATES FROM STREAMFLOW DATA OF ANNUAL AND SEASONAL GROUND-WATER RECHARGE RATES IN NEW HAMPSHIRE DRAINAGE BASINS

The development of large ground-water supplies associated with rapid population growth in New Hampshire has raised concern regarding the sustainability of the water supply and the balancing of competing demands between various water users. This study has developed regression equations to estimate generalized annual and seasonal ground-water recharge rates 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 flows from upland basin areas, and infiltration from streams running through 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 hydrograph separation in each drainage basin evaluated in this study. Regression equations were then developed relating annual and seasonal ground-water-recharge to the corresponding precipitation values as determined at the centroid of each drainage basin. Average annual and seasonal precipitation data were then used to compute a set of normalized ground-water-recharge values that reflected the long-term average 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 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.