Skip to main content

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


Publication Date
Start Date
End Date


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,


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 [...]


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 and 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


The dataset describes terms used in each MLLR model.

Additional Information


Type Scheme Key
DOI doi:10.5066/F7HH6H8H

Item Actions

View Item as ...

Save Item as ...

View Item...