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Tethys Platform is an open source framework for developing web-based applications for Earth Observation data. Our experience shows that Tethys significantly lowers the barrier for cloud-based app development, simplifies the process of accessing scalable distributed cloud computing resources and leverages additional software for data and computationally intensive modeling. The Tethys software development kit allows users to create web apps for visualizing, analyzing, and modeling Earth Observation data. Tethys platform provides a collaborative environment for scientists to develop and deploy several Earth Observation web applications across multiple Tethys portals. We work in partnership with leading regional organizations worldwide to help developing countries use information provided by earth-observing satellites and geospatial technologies for managing climate risks and land use. This paper highlights the several Tethys portals and web applications that were developed as part of this effort. Implementation of the Tethys framework has significantly improved the Application Readiness Level metric for several NASA projects and the potential impact of Tethys to replicate and scale other applied science programs.

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Since 2002, National Aeronautics and Space Administration’s Gravity Recovery and Climate Experiment (GRACE) allows scientists of various disciplines to analyze and map changes in total water storage globally. Although the raw data are available to the public, the process of viewing, manipulating, and analyzing GRACE data can be difficult for those without strong technological backgrounds in programming or geospatial software. This is particularly true for water managers in developing countries, where GRACE data could be a valuable asset for sustainable water resource management. To address this problem, we developed an open‐source utility for subsetting GRACE datasets to selected regions of interest and packaged that utility in a web application that allows water managers to quickly and easily access and visualize GRACE data. For a selected region, we use data from the three Global Land Data Assimilation System land surface models: Noah, variable infiltration capacity, and Catchment Land Surface Model, and decompose the GRACE data for total water storage into surface water, soil moisture, and groundwater components along with associated uncertainty estimates. The resulting groundwater data are in the form of groundwater storage changes over time, and a better understanding of groundwater storage trends helps water managers to more effectively respond to drought and agricultural demand in a selected region and thereby more sustainably manage groundwater resources. We demonstrate these uses in a case study from the Hindu‐Kush Himalayan region.