Project Team: Oaxaca Water Resources Team
Team Location: Wise County Clerk of Court’s Office, Wise, Virginia; and TecnolÌ_gico de Monterrey Campus Saltillo, Saltillo, Mexico
Giovanni Colberg (University of North Dakota)
Yanina G. Colberg (University of North Dakota)
Laura Esqueda (TecnolÌ_gico de Monterrey, Saltillo Campus)
Enrique Benavides (TecnolÌ_gico de Monterrey, Saltillo Campus)
Dr. DeWayne Cecil (Global Science and Technology, Inc.)
Dr. Kenton Ross (NASA DEVELOP National Science Advisor)
Droughts are an increasingly global threat. Scarcity of water means less access to drinking water for people, and brings consequences to crop yields and cattle. Oaxaca is one of three most biologically diverse states of the country of Mexico as well as one of the country’s top producers of grain, agave, peanuts, mango and sugar cane.åÊ Cattle also is a huge industry in this state, using approximately 32 percent of state land.åÊ In 2013, 43 percent of Mexico was suffering from drought conditions.åÊ Oaxaca was rationing water by 40 percent, causing major loss of crops and cattle which cost the state and the economy millions of dollars.åÊ By partnering with local and federal agencies such as Centro Mario Molina (CMM), National Institute of Statistics and Geography (INEGI), and the National Water Commission (CONAGUA), this project focused on producing a drought severity index report of the region by utilizing NASA’s Earth Observation System (EOS) including Aqua and Terra MODIS, and TRMM Precipitation Radar. MODIS surface reflectance data were utilized to produce vegetation and water indices. MODIS Land Surface Temperature (LST) combined with TRMM rainfall data and Vegetation Indices were used in the Scaled Drought Condition Index (SDCI) model to derive the drought severity index. Surface reflectance data from MODIS was used to calculate Normalized Differential Drought Index (NDDI). Vegetation Health Index (VHI) was computed from scaled LST and scaled Vegetation Indices. The Standardized Precipitation Index (SPI) was used to compare the outputs of SDCI model, NDDI and VHI. The project also compared the differences in accuracy between TRMM rainfall data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Socioeconomic data was used to estimate the percentage of most severely affected human and cattle population within the study area. The final results of the project will be incorporated in a climate change report that will be completed by the end of the year by the CMM.
Back to VPS page.