Precipitation and streamflow data for computing lag to peak at selected stations in Maine
January 2, 2018
These are rainfall and stream stage data collected at gaging sites in Maine. Data were collected from March to October from 2008 to 2015 and include stage data at crest stage gages, ratings to convert stage data to streamflow data, previously unpublished rainfall data, rainfall binned into specified time intervals, and storm files combining rainfall and streamflow data. Data were collected, compiled and combined in order to analyze the lag to peak (the time between the center of volume of the rainfall and the peak streamflow) for small basins in Maine.
Citation Information
Publication Year | 2018 |
---|---|
Title | Precipitation and streamflow data for computing lag to peak at selected stations in Maine |
DOI | 10.5066/F7PK0F3D |
Authors | Pamela Lombard, David J Holtschlag |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | New England Water Science Center |
Rights | This work is marked with CC0 1.0 Universal |
Related Content
Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model
We test the use of a mixed-effects model for estimating lag to peak for small basins in Maine (drainage areas from 0.8 to 78 km2). Lag to peak is defined as the time between the center of volume of the excess rainfall during a storm event and the resulting peak streamflow. A mixed-effects model allows for multiple observations at sites without violating model assumptions inherent in traditional or
Authors
Pamela J. Lombard, David Holtschlag
Related Content
Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model
We test the use of a mixed-effects model for estimating lag to peak for small basins in Maine (drainage areas from 0.8 to 78 km2). Lag to peak is defined as the time between the center of volume of the excess rainfall during a storm event and the resulting peak streamflow. A mixed-effects model allows for multiple observations at sites without violating model assumptions inherent in traditional or
Authors
Pamela J. Lombard, David Holtschlag