Regression Equations for Monthly and Annual Mean and Selected Percentile Streamflows for Ungaged Rivers in Maine

Abstract

In an effort to delineate hydrologic conditions in Maine, the U.S. Geological Survey, in cooperation with the Maine Department of Transportation, used streamflow data to develop dependent variables for 130 regression equations for estimating monthly and annual mean and 1, 5, 10, 25, 50, 75, 90, 95, and 99 percentile streamflows for ungaged, unregulated rivers in Maine. Daily streamflow data from 24 rural unregulated basins with drainage areas between 14.9 and 1,419 square miles in Maine and northern New Hampshire were used in the derivation of the equations. Streamflow data collected from October 1, 1982, through September 30, 2012, were used to derive the dependent variables for this study to represent current [2015] hydrologic conditions in Maine and northern New Hampshire. Weighted least squares regression techniques were used to derive the final coefficients and measures of uncertainty for the regression equations. Eight basin characteristics serve as the explanatory variables: drainage area, distance from the coast, mean and maximum basin elevation, mean basin slope, mean basin percentage of hydrologic soil group A, fraction of sand and gravel aquifers, and percentage of open water. The largest average errors of prediction are associated with regression equations for the lowest streamflows derived for months during which the lowest streamflows of the year occur (such as the 5 and 1 monthly percentiles for August and September). The regression equations have been derived on the basis of streamflow and basin characteristics data for unregulated, rural drainage basins without substantial streamflow or drainage modifications (for example, diversions and (or) regulation by dams or reservoirs, tile drainage, irrigation, channelization, and impervious paved surfaces), therefore using the equations for regulated or urbanized basins with substantial streamflow or drainage modifications will yield results of unknown error. Input basin characteristics derived using techniques or datasets other than those documented in this report or using values outside the ranges used to develop these regression equations also will yield results of unknown error.

Statistical Models to Predict and Assess Spatial and Temporal Low‐Flow Variability in New England Rivers and Streams

“…It is possible that I was able to discriminate among multiple effects due to the restricted time period of my analyses (1985–2015), whereas earlier models have used all available data. Predictive models for summer/July/August median flows and/or annual 7Q10 values have been developed by USGS for five of the six states in New England (Ries and Friesz ; Flynn ; Dudley , ; Ahearn ; Bent et al. ), but no models have been specifically developed for baseflows during summer months (New Hampshire models also include observations from Vermont, Maine, and Massachusetts).…”

“…Determination of low‐flow statistics through regional regression analysis typically has produced estimates with wide confidence intervals (Smakhtin ) and was often based on assumptions of stationarity (Ries and Friesz ; Flynn ; Lombard et al. ; Dudley , ; Lombard , ; Bent et al. ).…”

Working towards a more holistic set of hydrologic principles to teach nonhydrologists: Five simple concepts within catchment hydrology

“…A USGS model to estimate inflows from tributaries on the Great Lakes makes use of this principle to extrapolate gage streams to ungaged streams (Luukkonen, Holtschlag, Reeves, Hoard, & Fuller, 2015). Additionally, the numerous USGS reports that established the basis for StreamStats developed regression relationships in which the dominant explanatory variable is nearly always watershed area raised to a power near one (e.g., Dudley, 2015;Gotvald, 2017).…”

Regional regression models of percentile flows for the contiguous US: Expert versus data-driven independent variable selection

“…The 1-km grid was spatially interpolated using BFI values at 8,249 gauges with at least ten years of daily 28 streamflow data (see Wolock (2003) for more information on the methodology). Although the BFI grid was produced using 29 gauged data, it is a pre-existing dataset that can be used to create independent variables for predicting the percentile flows of 30 ungauged basins, as previously demonstrated (Dudley, 2015;Hope and Bart, 2011;Yuan, 2013 for all of the streamflow data and the chosen time period overlapped with the most streamflow data. Precipitation depths 5 (mm) were weighted according to the fraction of the grid cell located within the basin boundary.…”