Drought Forecasting for Streams and Groundwaters in 

Northeastern United States

Background

When rainfall is lower than normal 

over an extended period, streamflows 

decline, groundwater levels fall, and 

hydrological drought can occur. Droughts 

can reduce the water available for 

societal needs, such as public and private 

drinking-water supplies, farming, and 

industry, and for ecological health, such 

as maintenance of water quality and 

natural ecosystems. Recent droughts 

in the northeastern United States have 

highlighted the need for new scientific 

tools to forecast the probability of 

future droughts so water managers and 

the public can be better prepared for 

these events when they happen. Two 

recent U.S. Geological Survey (USGS) 

studies provide tools that can forecast 

the probabilities of summer droughts 

for streams (Austin and Nelms, 2017) 

and the probabilities of groundwaterlevel declines below specified targets or 

thresholds (Dudley and others, 2017). 

These tools provide promising methods 

for identifying and anticipating probable 

streamflow and groundwater droughts 

specific to the northeastern United 

States. USGS Water Science Centers 

in the northeastern United States have 

acted together to use these methods 

for numerous streamflow gages and 

groundwater-level monitoring wells, 

and to make the results of the analyses 

available on the world wide web. 

This fact sheet describes the drought 

forecasting techniques used in a study 

to predict droughts for streamflow and 

groundwater in the northeastern United 

States. 

Overview

Tools for drought forecasting were 

developed and applied to 1,168 streamflow gages and 23 groundwater-level 

monitoring wells in the northeastern 

United States using methods described 

by Austin and Nelms (2017) and Dudley 

and others (2017). The forecasting tools 

use statistical modeling approaches to 

anticipate and plan for potential streamflow droughts from 1 to 11 months in 

advance of their occurrence and groundwater droughts 1 to 3 months in advance 

of their occurrence.

Results from the models in the form 

of graphs and maps are available in an 

interactive USGS web tool (Kandel and 

others, 2019) (https://va.water.usgs.gov/

webmap/drought_ne/), which is updated 

monthly when the most current hydrologic streamflow or groundwater-level 

data are available for each location. 

Using constant projections of drought 

potential, local, state, and regional managers and planners can identify potential 

streamflow and groundwater droughts 

with extended lead time, affording the 

opportunity to anticipate, initiate, and 

adjust drought management actions ahead 

of an impending drought.

Drought Forecasting Techniques

Statistical analyses called logistic 

regression models were prepared for each 

gaged stream and groundwater well in the 

web tool. Logistic regression calculates 

the probability of an event occurrence, 

based on the predictors (explanatory 

variables) used in the model. Each 

stream forecast model provides summer 

month hydrological drought streamflow 

probabilities using streamflow data 

collected during the previous winter 

months. Groundwater well forecast 

models provide hydrological drought 

predictions using data from several 

explanatory variables: current and 

previous groundwater levels, nearby 

streamflow, base-flow estimates, 

precipitation, drought severity indices, 

and atmosphere-ocean indices.

These tools provide promising methods 

for identifying and anticipating probable 

streamflow and groundwater droughts 

specific to the northeastern United States.

U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2019–3015

April 2019
Streamflow Probabilities

Maximum Likelihood Logistic 

Regression (MLLR) models (fig. 1) 

are used to predict the chance of 

streamflow exceeding, or not exceeding, 

a particular drought threshold, describe 

the odds of a particular outcome using 

a predictive variable, and estimate 

the probability response as a smooth 

function of a factor. In the development 

of this tool, the probabilities of daily 

streamflow exceeding (moving above) 

and not exceeding (staying below) 

a 20th percentile drought threshold 

during summer of the year (July 

through September) are estimated as 

functions of monthly mean streamflow 

during the previous winter (October 

through February). Studies have shown 

that well-formed MLLR models can 

provide accurate predictions of drought 

streamflow probability and that some 

of the best performing models deal 

with areas for which drought is of high 

concern to water-resource managers. 

In one ad hoc test of 5,950 predictions 

of September drought streamflow 

probabilities across the United States, 

using streamflow data from the previous 

October, the overall rate of correct 

prediction exceeded 90 percent (Austin 

and Nelms, 2017).

16 20 24 28 32 36 40 44 48 52 56

January mean (daily value)

0.00

0.25

0.50

0.75

1.00

Daily value > quantile 20 percent 

September daily value period-of-record

Yes

No

0.00 0.20 0.40 0.60 0.80 1.00

1-specificity false positive

True positive sensitivity 0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Figure 1. An example Maximum Likelihood Logistic Regression model and Companion 

Receiver Operating Characteristic Validation Curve (inset) with area under curve of 0.85, 

indicating high prediction accuracy, estimating drought streamflow probability in September 

(y-axis) as a function of mean daily streamflow from the previous January (x-axis). (Station 

number 14054500, Brown Creek Near La Pine, Oregon, 1922–91. Daily value [DV] is the 

average streamflow during a single day, measured in cubic feet per second; period-ofrecord, the full interval of time over which streamflow daily values have been recorded at a 

particular streamgage; Yes, the probability of DV exceeding drought flow [no drought], y-axis 

fraction above the regression line; No, the probability of DV not exceeding drought flow 

[drought], y-axis fraction below the regression line) Figure modified from Austin and Nelms, 

(2017, fig. 6).
Groundwater-Level Probabilities

The groundwater drought forecast 

models used in this study are logistic 

regression models that estimate the 

probability that future groundwater 

levels will be at or less than the low 

10th percentile groundwater-level value 

among all available data for each well 

(fig. 2). The ability of forecast models to 

estimate the probability of groundwater 

levels at or less than the 10th percentile 

varies among the wells analyzed, and 

as a result, statistically valid predictions 

may be available 1–3 months into the 

future for any month of the year. The 

explanatory variables used in the forecast 

models represent current conditions at 

the time of forecasting. For example, for 

a 1-month forecast model, the current 

month-end values for groundwater levels 

and Palmer Drought Severity Index 

could provide the input data to forecast 

the probability that a groundwater level 

will be at or less than the 10th percentile 

threshold at the end of the next month.

Accessing the Drought 

Probabilities

An interactive, online map resource 

tool (fig. 3) providing real-time access 

to, and visualization of, streamflow and 

groundwater drought probabilities for 

each northeastern United States gaged 

basin and groundwater well included in 

this study is available at https://va.water.

usgs.gov/webmap/drought_ne/.

Broadening Analyses to Enhance 

Predictions

Additions to the data used for 

the models could enhance and further 

strengthen drought predictions. For 

example, adding explanatory variables 

such as measurements of snowpack 

or National Oceanic and Atmospheric 

Administration precipitation forecasts 

could strengthen the model response, as 

could extending and shifting the temporal 

reach of predictive models to months 

later than February and intervals larger 

than 1 month. Empirical testing and 

fine-tuning of model explanatory and 

response variables using attributes of 

unique landscape settings or particular 

regions of the country could improve 

model robustness and reliability. 

Potentially significant advances in 

predictive capability and model utility are 

possible with new support from USGS 

Water Science Centers in the northeastern 

United States.

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

2007

−2

−1

0

1

2

Standardized groundwater level (dimensionless)

0.0

0.2

0.4

0.6

0.8

1.0

Probability

Monthly estimated probabilities 

that the groundwater level 

will be at or less than the 

10th percentile threshold 

3 months later

Monthly groundwater levels

Groundwater levels at or less than 

the 10th percentile low threshold

EXPLANATION

Figure 2. Example of a 3-month groundwater drought forecast model. Figure modified from 

Dudley and others (2017, fig. 3).
EXPLANATION

No data

0 to <5

5 to <10

10 to <20

20 to <30

30 to <40

40 to <50

≥50

Drought probability, in percent Drought probability values

July

Sept. Aug.

Figure 3. Example of the online 

interactive map resource tool that 

provides real-time access to, and 

visualization of, streamflow and 

groundwater drought probabilities. 

Drought probability values for each 

gaging station are shown as a triad of 

three color-coded circles representing 

July, August, and September drought 

estimates (Kandel and others, 2019).

For More Information on the Forecasting Tool

For sites in Connecticut, Massachusetts, 

Maine, Rhode Island, New Hampshire, 

and Vermont, contact 

Director, New England Water Science Center, 

(603) 226-7807, 

10 Bearfoot Rd, 

Northborough, MA 01532, 

https://www.usgs.gov/centers/

new-england-water

For sites in Maryland, Delaware, 

and Washington D.C, contact 

Director, Maryland-Delaware-DC 

Water Science Center, 

(443) 498-5501, 

5522 Research Park Drive, 

Catonsville, MD 21228, 

https://www.usgs.gov/centers/

md-de-dc-water

For sites in New Jersey, contact 

Director, New Jersey Water Science Center, 

(609) 771-3901, 

3450 Princeton Pike, Suite 110, 

Lawrenceville, NJ 08648, 

https://www.usgs.gov/centers/nj-water

For sites in New York, contact 

Director, New York Water Science Center, 

(518) 285-5661, 

425 Jordan Road, 

Troy, NY 12180-8349, 

https://www.usgs.gov/centers/ny-water

For sites in Pennsylvania, contact 

Director, Pennsylvania Water Science Center, 

(717) 730-6950, 

215 Limekiln Road, 

New Cumberland, PA 17070, 

https://www.usgs.gov/centers/pa-water

For sites in Virginia and West Virginia, contact 

Director, Virginia and West Virginia Water 

Science Center, 

(804) 261-2600, 

1730 East Parham Road, 

Richmond, VA 23228, 

https://www.usgs.gov/centers/va-wv-water

References Cited

Austin, S.H., and Nelms, D.L., 2017, Modeling summer month hydrological drought 

probabilities in the United States using 

antecedent flow conditions: Journal of the 

American Water Resources Association, 

v. 53, p. 1133–1146, accessed November, 

15, 2018, at https://doi.org/10.1111/1752-

1688.12562.

Dudley, R.W., Hodgkins, G.A., and Dickinson, J.E., 2017, Forecasting the probability of future groundwater levels 

declining below specified low thresholds 

in the conterminous U.S.: Journal of the 

American Water Resources Association, 

v. 53, p. 1424–1436, accessed November, 

15, 2018, at https://doi.org/10.1111/1752-

1688.12582.

Kandel, C., Austin, S.H., and Rapp, J.L., 

2019, Drought Streamflow Probabilities in 

the Northeast Region: U.S. Geological Survey Web-Mapping Application, accessed 

April, 3, 2019, at https://va.water.usgs.gov/

webmap/drought_ne/.

By Samuel H. Austin and Robert W. Dudley

Banner image by Hendrik Cornelissen, Unsplash; 

image of stream on p. 1 by Nathan Anderson, 

Unsplash; image of stream on p. 2–3 by Arjan 

Wilmsen, Unsplash. ISSN 2327-6916 (print)

ISSN 2327-6932 (online)

https://doi.org/10.3133/fs20193015