Texas has experienced some of the largest rainfall and flood events in the United States. Heavy flooding during the rainy season causes extensive property damage and loss of human life. 
Real-time data processing and flood forecasting can play a vital role in minimizing such damages. However, in order to forecast these conditions it is necessary to first obtain reliable rainfall data. Rain gage networks are generally sparse and insufficient to capture the spatial variability across a single watershed, let alone the entire state. These networks are also limited in providing the real-time data necessary for flood studies. The use of data from weather radar systems as an input to runoff estimation calculations, such as the NRCS- Curve Number Method, would help alleviate these problems.
The objective of this research is to develop near real-time runoff maps for Texas using precipitation data from the NEXRAD radar rainfall network. This will provide information useful for flood mitigation, reservoir operation, and watershed and water resource management practices.
 Source: Smith, J. A., M. L. Baeck, J. E. Morrison, and P. Sturdevant-Rees, 2000. Catastrophic Rainfall and Flooding in Texas. Journal of Hydrometeorology. American Meteorological Society. 1:5-25.
Soil moisture estimates are essential for calculating irrigation water requirements, scheduling irrigation, and water allocation from reservoirs. It is also a good measure of crop water stress during growing season. Since direct measure of soil moisture from AVHRR is impossible, AVHRR data along with NEXRAD data will be used with water balance models for estimating soil moisture.
Potential Evapotranspiration (PET)
Potential Evapotranspiration (PET), or reference evapotranspiration, is currently estimated using data from sparsely located weather stations around the state. More than 70% of the precipitation falling on the United States is returned to the atmosphere through evapotranspiration. Hence accurate estimation of ET is essential for calculating irrigation water requirements and water balance calculations.
Advanced Very High Resolution Radiometer (AVHRR) is a sensor aboard the NOAA series of polar orbiting earth satellites that have been in operation for more than three decades. Currently, NOAA-14, NOAA-16 and NOAA-17 satellites are in orbit. The main purpose of these satellites is to forecast weather and monitor regional climatic conditions. However, their potential for monitoring crop growth, assessing crop yield and monitoring forest cover has been realized only during the past decade. AVHRR is a broadband scanner, sensing in the visible (Channel 1), near-infrared (Channel 2) and thermal infrared portions (Channel 3, Channel 4 and Channel 5) of the electromagnetic spectrum. The spectral ranges of different channels are given in Table 1.
|1||0.58 - 0.68|
|2||0.73 - 1.10|
|3||3.55 - 3.93|
|4||10.3 - 11.3|
|5||11.5 - 12.5|
The satellite receiving system, located at the Blackland Research Center(BRC) in Temple, TX, acquires daily raw data from the three active AVHRR satellites. An automatic data processing system has been developed for radiometric, geometric, and atmospheric corrections. Besides this standard processing, algorithms developed by various researchers have been refined in computing Normalized Difference Vegetation Index(NDVI) and Land Surface Temperature (LST).
NDVI is a measure of vegetation condition and health. It is computed from the reflectance measured in visible and infrared channels (Channel 1 and Channel 2) of the AVHRR satellite using a simple formula:
|NDVI =||ch1 - ch2|
|ch2 + ch1|
NDVI and LST maps for Texas will be available soon through the Real-Time Vegetation Monitoring System maintained by the BRC in Temple, TX.