My current research at Texas Tech involves the application of GIS and remote sensing for detecting and monitoring atmospheric and land surfaces processes associated with the management of a target threatened bird species (lesser prairie chicken). More details will be shared here once we have some preliminary results.

Lesser Prairie Chicken (Wiki)

Supervised classification of the study area

The objective of this study is to produce an observationally based monthly evapotranspiration (ET) product using the simple water balance equation across the conterminous United States (CONUS). We adopted the best quality ground- and satellite-based observations of the water budget components, i.e., precipitation, runoff, and water storage change, while ET is computed as the residual. Precipitation data is provided by the bias-corrected PRISM observation-based precipitation dataset, while runoff comes from observed monthly streamflow values at 592 USGS stream gauging stations that have been screened by strict quality controls. We developed a land surface model-based downscaling approach to disaggregate the monthly GRACE equivalent water thickness data to daily, 0.125º values. The derived ET computed as the residual from the water balance equation is evaluated against three sets of existing ET products. The similar spatial patterns and small differences between the reconstructed ET in this study and the other three products show the reliability of the observationally based approach. The new ET product and the disaggregated GRACE data provide a unique, important hydro-meteorological data set that can be used to evaluate the other ET products as a benchmark dataset, assess recent hydrological and climatological changes, and terrestrial water and energy cycle dynamics across the CONUS. These products will also be valuable for studies and applications in drought assessment, water resources management, and climate change evaluation.

USGS stream gauging stations

Multi-year average annual ET

Flood disasters have significant impacts on the development of communities globally, often causing loss of life and property. It is increasingly important to create a globally shared flood cyber-infrastructure (CyberFlood) to collect, organize, and manage flood databases that visually provide useful information back to both authorities and the public in real-time. The community shared CyberFlood infrastructure described in this study uses cloud computing services and crowdsourcing data collection methods to provide on-demand, location-based visualization as well as statistical analysis and graphing capabilities. It also involves public participation, allowing the public to submit their entries of flood events to help the community to archive comprehensive information of flood events, past and present. The Global Flood Inventory (GFI) is used as a primary database to develop this cyber-infrastructure. The GFI, which contains detailed information of global flood events from 1998 to 2008, was developed and made available for community use. In order to expand and update the existing inventory, a crowdsourcing methodology is employed which enables web-based data entry for the public to report or record their personal accounts of local flood events. This step is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Cloud computing is further integrated into this cyber-infrastructure by utilizing public cloud services provided by Google, which effectively accelerates the speed during data processing and visualization over the Internet. As a cloud-based cyber-infrastructure, people can access this infrastructure from all over the world through the Internet or mobile phones. The shared vision is to better serve the global water community by providing essential flood information, aided by the state-of-the-art cloud computing and crowd-sourcing technology. This CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters (e.g. floods, landslide, and droughts).

Real time visualization

CyberFlood iOS app screenshot