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      Assessing rainfall variability, long-term trends, and drought incidences in the Tanzania part of the Kagera River sub-basin

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          Estimates of the Regression Coefficient Based on Kendall's Tau

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            Is Open Access

            The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

            The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
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              A Non-Parametric Approach to the Change-Point Problem

              A. Pettitt (1979)
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                Author and article information

                Contributors
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                Journal
                Earth Science Informatics
                Earth Sci Inform
                Springer Science and Business Media LLC
                1865-0473
                1865-0481
                February 2025
                January 17 2025
                February 2025
                : 18
                : 2
                Article
                10.1007/s12145-025-01699-y
                09aab49f-0515-4c7a-ac11-b892eb53b9f8
                © 2025

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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