Modeling summer month hydrological drought probabilities in the United States using antecedent flow conditions
Hydrological Drought Probabilities in the United States
Modeling using antecedent flow conditions
Droughts in Virginia Streams
Methods for estimating drought streamflow probabilities
Low-flow characteristics of Virginia streams
Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5-11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows.
We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification.
Northeast Region Drought Streamflow Probabilities
This application allows the display and query of drought probability in selected Northeast region streams. Maximum likelihood logistic regression is used to estimate drought probabilities. Winter streamflows are used to estimate the chance of hydrologic drought during the summer months (July, August, and September) 5 to 11 months in advance.
Drought Streamflow Probabilities for Virginia Streams
This application allows the display and query of summer (July, August, and September) drought streamflow probabilities for Virginia streams. Maximum likelihood logistic regression is used to estimate drought probabilities 5 to 11 months in advance using streamflow data from the previous winter months.
Results:
As the global climate has warmed concern has increased that climate change will influence water flow in streams and rivers. In this study we use maximum likelihood logistic regression (MLLR) to estimate hydrological drought probabilities for gaged rivers and streams throughout the U.S. from 5 to 11 months ahead of their occurrence. Our results demonstrate the utility of using well-formed MLLR models to estimate summer month hydrological drought streamflow probabilities from streamflow data collected during previous winter months. We find that modeling hydrological drought streamflow probabilities in this way exploits the explanatory power of temporally linked water flows, even without full knowledge of the nature and timing of surface and groundwater flows specific to each stream.
Equations developed using MLLR to calculate hydrological drought probabilities exhibit strong correct classification rates in most areas of the U.S., predicting hydrological drought outcomes up to 11 months in advance of their occurrence. The predictive equations may be used to communicate local and regional drought conditions, improve drought awareness, implement drought management plans, and test water allocation protocols and decision rules. Work to improve MLLR model performance using new combinations of hydrological drought threshold percentiles and new explanatory variables will leverage the explanatory power of temporally linked water flows, helping drive progress in the assessment and prediction of hydrological drought.
While past studies have reported low-flow characteristics and have applied various methods to drought modeling and evaluation, few have dealt specifically with forecasting hydrological drought. Estimates of summer month hydrological drought probability provide advanced warning of drought conditions, extending the lead time for drought awareness, management response, and making water withdrawal decisions.
Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5-11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows.
We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification.
Northeast Region Drought Streamflow Probabilities
This application allows the display and query of drought probability in selected Northeast region streams. Maximum likelihood logistic regression is used to estimate drought probabilities. Winter streamflows are used to estimate the chance of hydrologic drought during the summer months (July, August, and September) 5 to 11 months in advance.
Drought Streamflow Probabilities for Virginia Streams
This application allows the display and query of summer (July, August, and September) drought streamflow probabilities for Virginia streams. Maximum likelihood logistic regression is used to estimate drought probabilities 5 to 11 months in advance using streamflow data from the previous winter months.
Results:
As the global climate has warmed concern has increased that climate change will influence water flow in streams and rivers. In this study we use maximum likelihood logistic regression (MLLR) to estimate hydrological drought probabilities for gaged rivers and streams throughout the U.S. from 5 to 11 months ahead of their occurrence. Our results demonstrate the utility of using well-formed MLLR models to estimate summer month hydrological drought streamflow probabilities from streamflow data collected during previous winter months. We find that modeling hydrological drought streamflow probabilities in this way exploits the explanatory power of temporally linked water flows, even without full knowledge of the nature and timing of surface and groundwater flows specific to each stream.
Equations developed using MLLR to calculate hydrological drought probabilities exhibit strong correct classification rates in most areas of the U.S., predicting hydrological drought outcomes up to 11 months in advance of their occurrence. The predictive equations may be used to communicate local and regional drought conditions, improve drought awareness, implement drought management plans, and test water allocation protocols and decision rules. Work to improve MLLR model performance using new combinations of hydrological drought threshold percentiles and new explanatory variables will leverage the explanatory power of temporally linked water flows, helping drive progress in the assessment and prediction of hydrological drought.
While past studies have reported low-flow characteristics and have applied various methods to drought modeling and evaluation, few have dealt specifically with forecasting hydrological drought. Estimates of summer month hydrological drought probability provide advanced warning of drought conditions, extending the lead time for drought awareness, management response, and making water withdrawal decisions.