To address the limitations of accurately mapping small artificial water bodies and quantifying their contribution to CH4 emissions, the overarching aim of this proposal is to provide better annual regional CH4 flux estimates from inland waters of the Southeastern US by constraining the spatial and temporal dynamics of small water bodies.

Floods are the most signifigant climate change-related hazards in terms of life loss and economic damage. Effective and immediate disaster response management can reduce the impact of floods, but it requires near real-time information on flood occurrence, typically derived based on Earth Observation data. Our work has applied machine learning algorithms to map floods globally. We also showed that we can detect more ephemeral flooding events when using data from three sensors provided by NASA’s Harmonized Landsat-Sentinel 2 data product, HLS.

Fresh water stored by small on-farm reservoirs (SRs), which are small water bodies, is a fundamental component of surface hydrology and is critical for meeting global irrigation needs. Farmers use SRs to store water during the wet season for crop irrigation during the dry season. SRs can also contribute to downstream water stress by decreasing stream discharge and peak flow in the watersheds where they are built. There are more than 2.6 million SRs in the US alone, and many SRs were constructed during the last 40 years. Despite their importance for crop irrigation, SRs are poorly quantified bc (1) they are small (< 50 ha), (2) there are millions of them around the world, and (3) they occur on private land. Satellite data allow us to quantify SR water area and volume across space and time.

Industrial agriculture disproportionately affects minority, low-income, and Tribal communities, propagating environmental injustice. Concentrated Animal Feeding Operations (CAFOs) apply massive amounts of untreated waste to nearby farmlands. Even though the environmental health impacts of CAFOs are well documented, most studies rely almost exclusively on known CAFO locations from public records, which are incomplete.

We integrated analyzing time-series of satellite and hydro-climatic data to quantify flooding, surface water, and their drivers of change and vegetation response to flooding across Australia’s breadbasket, the Murray-Darling Basin (MDB), a large (>1 million km2) dryland basin the size of the U.S. 4 corner states from 1986 to 2011. The three decades investigated represented a period of extreme hydroclimatic variability, including the Millennium Drought (1999-2009), the worst drought recorded for southeastern Australia, followed by some of the wettest years on record (2010-11 La Niña years).

Located in the southwest of Western Australia, the Swan Coastal Plain (SCP) is a global biodiversity hotspot with over 1,500 wetlands. More than 70% of the wetlands have been lost since European settlement. The SCP is located in an area affected by recent climate change that also experiences rapid urban development, as the city of Perth has been expanding over time, so has ground water abstraction for urban consumption. We used Landsat TM and ETM+ imagery from 1999 to 2011 to automatically derive a spatially and temporally explicit time-series of surface water body extent on the SCP based on decision trees. We provided the first spatio-temporally dynamic map of the water bodies of the SCP.