Katelyn P. Driscoll | USGS Science

USGS2019

Management opportunities and research priorities for Great Plains grasslands

The Great Plains Grassland Summit: Challenges and Opportunities from North to South was held April 10-11, 2018 in Denver, Colorado. The geographical focus for the summit was the entire Great Plains. The summit was designed to provide syntheses of information about key grassland topics of interest in the Great Plains; networking and learning channels for managers, researchers and stakeholders; and working sessions for sharing input and ideas about challenges and future research and management opportunities. The summit was convened to better understand Great Plains stressors and resource demands and how to manage them, and to discuss methods for improved collaboration among natural resource managers, scientists, and stakeholders. Over 200 stakeholders, who collectively were affiliated with all of the Great Plains states, attended the summit. Attendees included university researchers, government scientists, and individuals affiliated with federal and state agencies, tribes, the private sector, and non-governmental organizations (NGOs). Plenary speakers provided syntheses of current knowledge on key topics to help stage working sessions on working lands, native plants and pollinators, native wildlife and biological diversity, invasive species, wildland and prescribed fire, energy development, and weather, water, and climate. The summit steering committee designed one suite of questions that were asked of participants in each working session. This report is a digest of the input from those who attended the seven working sessions and responded to the structured questions.

Colorado, Illinois, Iowa, Kansas, Minnesota, Misso↗

USGS2017

A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

Many approaches have been developed for measuring or estimating actual evapotranspiration ( ET a ), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ET a . Several remote sensing methods can be used to estimate ET a at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ET a estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ET a and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ET a that are currently applied successfully in the United States. The METRIC model can produce maps of ET a at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ET a against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ET a . The SSEBop model has been used to produce maps of ET a over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ET a estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ET a estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth Engine TM has been used to implement METRIC with automated calibration for regional-scale estimates of ET a using Landsat data. The U.S. Geological Survey also is using Google Earth Engine TM to implement SSEBop for estimating ET a in the United States at a continental scale using Landsat data.

Scientific Investigations Report↗