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Terrestrial open water evaporation is difficult to measure both in situ and remotely yet is critical for understanding changes in reservoirs, lakes, and inland seas from human management and climatically altered hydrological cycling. Multiple satellite missions and data systems (e.g., ECOSTRESS, OpenET) now operationally produce evapotranspiration (ET), but the open water evaporation data produced over millions of water bodies are algorithmically produced differently than the main ET data and are often overlooked in evaluation. Here, we evaluated the open water evaporation algorithm, AquaSEBS, used by ECOSTRESS and OpenET against 19 in situ open water evaporation sites from around the world using MODIS and Landsat data, making this one of the largest open water evaporation validations to date. Overall, our remotely sensed open water evaporation retrieval captured some variability and magnitude in the in situ data when controlling for high wind events (instantaneous: r2 = 0.71; bias = 13% of mean; RMSE = 38% of mean). Much of the instantaneous uncertainty was due to high wind events (u > mean daily 7.5 m·s-1) when the open water evaporation process shifts from radiatively-controlled to atmospherically-controlled; not accounting for high wind events decreases instantaneous accuracy significantly (r2 = 0.47; bias = 36% of mean; RMSE = 62% of mean). However, this sensitivity minimizes with temporal integration (e.g., daily RMSE = 1.2-1.5 mm·day-1). To benchmark AquaSEBS, we ran a suite of 11 machine learning models, but found that they did not significantly improve on the process-based formulation of AquaSEBS suggesting that the remaining error is from a combination of the in situ evaporation measurements, forcing data, and/or scaling mismatch; the machine learning models were able to predict error well in and of itself (r2 = 0.74). Our results provide confidence in the remotely sensed open water evaporation data, though not without uncertainty, and a foundation by which current and future missions may build such operational data.

This research investigates ecological responses to drought by developing a conceptual framework of vegetation response and investigating how multiple measures of drought can improve regional drought monitoring. We apply this approach to a case study of a recent drought in Guanacaste, Costa Rica. First, we assess drought severity with the Standard Precipitation Index (SPI) based on a 64‐yr precipitation record derived from a combination of Global Precipitation Climatology Center data and satellite observations from Tropical Rainfall Measuring Mission and Global Precipitation Measurement. Then, we examine spatial patterns of precipitation, vegetation greenness, evapotranspiration (ET), potential evapotranspiration (PET), and evaporative stress index (ESI) during the drought years of 2013, 2014, and 2015 relative to a baseline period (2002–2012). We compute wet season (May–October) anomalies for precipitation at 0.25° spatial resolution, normalized difference vegetation index (NDVI) at 30‐m spatial resolution, and ET, PET and ESI derived with the Priestley‐Taylor Jet Propulsion Laboratory (PT‐JPL) model at 1‐km spatial resolution. We assess patterns of landscape response across years and land cover types including three kinds of forest (deciduous, old growth, and secondary), grassland, and cropland. Results show that rainfall in Guanacaste reached an all‐time low in 2015 over a 64‐yr record (wet season SPI = −3.46), resulting in NDVI declines. However, ET and ESI did not show significant anomalies relative to a baseline, drought‐free period. Forests in the region exhibited lower water stress compared to grasslands and had smaller declines, and even some increases, in NDVI and ET during the drought period. This work highlights the value of using multiple measures to assess ecosystem responses to drought. It also suggests that agricultural land management has an opportunity to integrate these findings by emulating some of the characteristics of drought‐resilient ecosystems in managed systems. [ABSTRACT FROM AUTHOR]