The water cycle is expected to undergo significant change. A warmer climate causes more water to evaporate from both land and oceans; in turn, a warmer atmosphere can hold more water – roughly four percent more water for every 1ºF rise in temperature. As a result, water availability is becoming less predictable in many places. Higher temperatures and more extreme, less predictable, weather conditions are projected to affect availability and distribution of rainfall, snowmelt, river flows and groundwater. Droughts are anticipated to becoming more frequent and more severe.
Considering how important water is as a resource to our economic activity and the healthy functioning of ecosystems, understanding water availability is a critical metric across sectors. This includes understanding changes in water availability in the past and projections of what water might be available in the future.
Climate Engine has partnered with Planet Monitoring to run its water availability index across Planet’s high resolution imagery. This provides the benefits of having global coverage and high resolution so that the water availability story for any location across the world can be understood.
Planet is a private Earth imaging company who provides daily global coverage of the Earth at high spatial resolution. Daily images are composted into a weekly basemap based on image quality. Data is collected using a constellation of over 150 individual satellites called doves. Currently, three generations of doves are used in this dataset: Dove-Classic, Dove-R, and SuperDove. Planet data is available using surface reflectance.
SIMS provides actual evapotranspiration (ETa) estimates for agricultural areas. This product is based on the evapotranspiration (ET) model Satellite Irrigation Management Support (SIMS) developed by Melton et al 2012. ET data is useful for evaluation consumptive use estimates in surface water and groundwater dependent agricultural regions.
SIMS utilizes satellite imagery and the United States Department of Agriculture Cropland Data Layers as input for the SIMS model. This model uses the normalized difference vegetative index (NDVI) in conjunction with crop type to estimate a fraction of reference ET (ETf) which is used to convert reference ET to ETa. ETf is linearly interpolated between available images and applied to a grass reference ET time series sourced from GridMET (Abatzoglou 2012) resulting in a time series of daily ETa images.
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