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Regionalization of Surface-Water Statistics Using Multiple Linear Regression

Techniques and Methods 4-A12
By: , , , and 
https://doi.org/10.3133/tm4A12

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Abstract

This report serves as a reference document in support of the regionalization of surface-water statistics using multiple linear regression. Streamflow statistics are quantitative characterizations of hydrology and are often derived from observed streamflow records. In the absence of observed streamflow records, as at unmonitored or ungaged locations, other techniques are required. Multiple linear regression is one tool that is widely used to regionalize or transfer information from gaged to ungaged locations. This report provides the background to support regression-based regionalization of streamflow statistics. This background includes tools for data assembly, exploratory data analysis, model estimation in a least-squares framework, and model evaluation.

Suggested Citation

Farmer, W.H., Kiang, J.E., Feaster, T.D., and Eng, K., 2019, Regionalization of surface-water statistics using multiple linear regression (ver. 1.1, February 2021): U.S. Geological Survey Techniques and Methods, book 4, chap. A12, 40 p., https://doi.org/10.3133/tm4A12.

ISSN: 2328-7055 (online)

Table of Contents

  • Abstract
  • Introduction
  • Data Assembly
  • Exploratory Data Analysis
  • Model Estimation
  • Model Evaluation
  • Model Application and Documentation
  • Conclusions
  • References Cited
  • Appendix 1. Glossary of Terms
  • Appendix 2. Glossary of Symbols
Publication type Report
Publication Subtype USGS Numbered Series
Title Regionalization of surface-water statistics using multiple linear regression
Series title Techniques and Methods
Series number 4-A12
DOI 10.3133/tm4A12
Edition Version 1.0: August 29, 2019; Version 1.1: February 18, 2021
Year Published 2019
Language English
Publisher U.S. Geological Survey
Publisher location Reston, VA
Contributing office(s) WMA - Integrated Modeling and Prediction Division
Description Report: v, 40 p.; Data Release; Version History
Online Only (Y/N) Y
Google Analytic Metrics Metrics page
Additional publication details