Integrated Seasonal Drought Forecast - Adaptive Management System for the Lower Colorado River Basin in Texas

Background

drought forecast

The Lower Colorado River Authority (LCRA) in Austin, Texas manages the Highland Lakes reservoir system in Central Texas, which is comprised of a series of six lakes on the Lower Colorado River. This system provides water to approximately 1.1 million people in Central Texas, hydropower to a 55-county area, supports rice farming along the Texas Gulf Coast, and sustains in-stream flows in the Lower Colorado River and freshwater inflows to Matagorda Bay.  The current, prolonged drought conditions are severely taxing the LCRA’s system, making allocation and management decisions exceptionally challenging, and affecting the ability of stakeholders to conduct proper planning.

Reliable seasonal hydrologic forecasts will allow the LCRA to make water allocation decisions more confidently and at longer lead times (e.g., 3-6 months), ultimately translating into increased benefits for the LCRA and its constituents.  The potential benefits of forecasts at shorter time scales (e.g., weekly), for environmental releases and irrigation withdrawals, will also add up significantly over the course of a drought event.  However, we expect the benefits of forecasts to be constrained by current planning practices and operating policies, and that decision processes tailored to the new information can provide additional benefits. 

This research will help overcome obstacles to the use of seasonal forecasts, as well as identify opportunities for insuring against economic losses and stakeholder conflicts associated with droughts.  Specific questions that will be addressed in this research are as follows:

In addressing these questions, the project team will work closely with senior water managers at the LCRA, who will review climate science products and interim forecast model results as they pertain to an integrated decision support model and the LCRA Water Management Plan. 

Remote Sensing Based Soil Moisture Indices

As part of the CSI-SARP: Coping with Drought project, which looks to develop an integrated seasonal drought forecast adaptive-management system, we are developing soil moisture indices using Synthetic Aperture Radar (SAR) and Optical satellite data (Landsat). Time series analysis and multi-parameter methods will be used to account for vegetative biomass, the primary confounding factor when using SAR to estimate soil moisture. Time series analysis reduces the influence of non-changing factors such as vegetation, while highlighting the variable factors, such as soil moisture.   Landsat satellite images, which are free from the United States Geological Survey (USGS), have been used to calculate three different indices that can reveal relatively wetter and drier areas in the images: the Soil Moisture Index (SMI), which uses surface temperature and the Normalized Difference Vegetation Index (NDVI); the Vegetation Condition Index (VCI), which compares values between different dates; and the Vegetation Temperature Condition Index (VTCI), which also uses imagery reflectance and temperature values. Soil moisture data loggers were installed at Mason Mountain Wildlife Management Area in Mason, Texas to allow us to track the changes in SAR backscatter as it pertains to soil moisture. Upcoming work will focus on integrating these remote sensing results into the drought prediction modeling efforts as part of the project’s second year. This project is funded by the NOAA Climate Program Office, Sectoral Applications Research Program (SARP).

For Additional Information

David Watkins (PI)
Professor, Civil and Environmental Engineering
Michigan Technological University
906.487.1640
dwatkins@mtu.edu

Laura Bourgeau-Chavez
Senior Research Scientist, MTRI
Michigan Technological University
734.913.6873
lchavez@mtu.edu

Colin Brooks
Senior Research Scientist, MTRI
Michigan Technological University
734.913.6858
cnbrooks@mtu.edu

Bradfield Lyon
Research Scientist, IRI
Columbia University
845.680.4475
blyon@iri.columbia.edu

Paul Block
Assistant Professor, Civil and Environmental Engineering
University of Wisconsin-Madison
608.263.8792
pblock2@wisc.edu

 

 

Study Sites at Mason Mountain Wildlife Area

Soil Moisture Data Logger Installation

Principal Component Analysis of RADARSAT-2 imagery for the Mason Mountain Wildlife Management area