National Drought Mitigation Center


Applying Season-ahead Precipitation and Streamflow Forecasts to Guide Reservoir Allocations: A Case Study of the Highland Lakes in Central Texas

September 15, 2016

Presenters: Paul Block (University of Wisconsin) and David Watkins (Michigan Technological University)


This interactive webinar will report the results from a NOAA-Sectoral Applications Research Program (SARP) grant and has been selected to help inform end-users within the National Integrated Drought Information System (NIDIS). Please feel free to invite others as you see fit. A webinar link will be sent to all participants with additional information closer to the date the date of the presentation.


Background:  Season-ahead forecasts can assist decision-makers in water, agriculture, and disaster risk management by anticipating and preparing for extreme conditions, such as floods or droughts. This talk presents advances in statistical precipitation and streamflow forecasting for managing reservoir allocations, with a case of the Highland Lakes in Central Texas. This study introduces the Nino Index Phase Analysis (NIPA) framework for forecasting hydroclimate variables on seasonal timescales utilizing the state of the El Nino Southern Oscillation (ENSO), a dominant climate signal in Central Texas, to classify the years of record into phases for prediction. Results indicate significant improvements in precipitation prediction skill, particularly for years exhibiting extreme wet or dry conditions. Preliminary integration with hydrologic models (e.g. VIC) to generate streamflow forecasts will also be presented. To complement, local predictor information will also be included. The results of this project will ultimately help guide reservoir allocation planning. Local end-users will be able to use precipitation and streamflow predictions to derive reservoir volume predictions, in accordance with their water management plan, to inform farmers downstream regarding available water for the upcoming cropping season, lending itself to better decision-making policy.