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ClimateSERV allows development practitioners, scientists/researchers, and government decision-makers to visualize and download historical rainfall data, vegetation condition data, and 180-day forecasts of rainfall and temperature to improve understanding of, and make improved decisions for, issues related to agriculture and water availability.

In SERVIR regions, where long-term ground observations of rainfall are sparse, there is a critical need for satellite and model-derived rainfall data for predicting droughts, estimating crop yields, and more. Decision-makers need a way to accurately asses how severe a drought will be, how it compares to past droughts, and its potential effect on crop yields. Such assessments require accurate estimations of rainfall variations in space and time. It is important to place an evolving dryer-than-normal season into historical context in order to analyze the severity of rainfall deficits. Until now, such analyses used rainfall data from specific points on the Earth’s surface. However, that data fails to show the region-wide variability that reveals comprehensive rainfall patterns.

Application Purpose

SERVIR has created a user-friendly, web-based tool – ClimateSERV – that provides several important datasets together in one system to help decision-makers in SERVIR’s data-sparse regions assess the evolving situation via holistic analysis of water and agriculture. Using ClimateSERV, development practitioners, scientists/researchers, and government decision-makers can readily analyze historical rainfall for the past 30 years and compare it with the best available forecasts for the next 180 days for their defined area of interest to improve the understanding of, and make improved decisions for, issues related to agriculture and water availability.

Application Uses

With this tool, decision-makers can download, view, graph, and interpret the CHIRPS, eMODIS NDVI, and NMME seasonal forecast data in a web-based user interface. ClimateSERV can help decision-makers assess and monitor large-scale rainfall patterns, analyze how those patterns may be affected by climate change, determine likelihood of drought, and infer crop condition. Kenya Meteorological Service field offices are already using the data to provide climate resilience guidance to farmers. For example, KMS’s Kericho office is using the CHIRPS dataset to downscale seasonal climate outlooks for farmers’ use in planning crop cultivars and planting times. SERVIR hubs plan to train end-users in their regions to use and analyze the CHIRPS, NMME, and NDVI data through ClimateSERV. Use of seasonal forecasts by end-users has been increasing. ClimateSERV will assist in the conversion of these large datasets into actionable information.