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Terms, Statistics, and Performance Measures for Maximum Likelihood Logistic Regression Models Estimating Hydrological Drought Probabilities in the United States (2017)

Dates

Publication Date
Start Date
1884
End Date
2014

Citation

Austin, S.H., and Nelms, D.L., 2017, Terms, statistics, and performance measures for maximum likelihood logistic regression models estimating hydrological drought probabilities in the United States (2017): U.S. Geological Survey data release, https://doi.org/10.5066/F7HH6H8H.

Summary

A table is presented listing: (1) USGS Gage Station Numbers, (2) Model Identification Tags, (3) Model Term Estimates, (4) Model Term Fit Statistics, and (5) Model Performance Indices for Maximum Likelihood Logistic Regression (MLLR) Models estimating hydrological drought probabilities in the United States. Models were developed using streamflow daily values (DV) readily available from the U.S. Geological Survey National Water Information System (NWIS) and mean monthly streamflows readily computed from NWIS streamflow DV. Models were prepared for 9,144 sites throughout the United States as described in: Modeling Summer Month Hydrological Drought Probabilities In The United States Using Antecedent Flow Conditions by Samuel H. Austin [...]

Contacts

Point of Contact :
Samuel H Austin
Originator :
Samuel H. Austin, David L. Nelms
Metadata Contact :
Samuel H Austin
Publisher :
U.S. Geological Survey
Distributor :
Samuel H Austin
USGS Mission Area :
Water Resources
SDC Data Owner :
Virginia-West Virginia Water Science Center

Attached Files

Click on title to download individual files attached to this item.

Austin-NDS P2 JAWRA Data Release File CSV.csv 41.05 MB
1. Austin-NDS P2 JAWRA Data Release METADATA XML.xml
Original FGDC Metadata

View
19 KB

Purpose

The dataset describes terms used in each MLLR model.

Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/F7HH6H8H

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