Improving Drought Monitoring and Prediction Using LIS and Satellite Products

Prolonged droughts in different semi-arid regions have increased awareness and research efforts to better understand and predict their impact and effects on water resources allocation and demand. In part of the research effort, land surface models (LSMs) are being enhanced through improved land surface parameters and variables derived from in-situ and satellite products. These enhanced models produce a wide range of water and energy budget variables that can be used in modeling and predicting drought conditions. The Land Information System (LIS) project is one effort in enhancing the ability of these LSMs to incorporate improved land surface parameters and assimilate appropriate, quality-controlled remote sensing and in-situ fields. LIS has been customized to run at different scales, with different models, and with different types of these datasets.

One region that LIS is being customized for involves the Middle Rio Grande River in New Mexico. The LIS software is being set-up in order to conduct studies on water consumption and land cover /land use impacts. The satellite datasets used in this work include the TERRA and AQUA MODIS datasets, Landsat, and ASTER. The MODIS land cover type product has been merged with a local, high-resolution land use dataset employed by decision support tools, like the USBR AWARDS ET Toolbox, for use along the Middle Rio Grande River area. This merged dataset has been setup for use in LIS to produce more high-resolution, heterogeneous moisture and energy flux fields, so more of the land use characterization can be captured in monitoring evapotranspiration and other drought-relevant variables. Evaluations of other high resolution information like soil type, elevation and other vegetation-based products are also made for specialized LIS model runs. These evaluations and results from LSM experiment runs will be compared against other datasets and presented at the meeting.