Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-2701-2021
https://doi.org/10.5194/essd-13-2701-2021
Data description paper
 | 
15 Jun 2021
Data description paper |  | 15 Jun 2021

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Christof Lorenz, Tanja C. Portele, Patrick Laux, and Harald Kunstmann

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Abatzoglou, J. T. and Brown, T. J.: A comparison of statistical downscaling methods suited for wildfire applications, Int. J. Climatol., 32, 772–780, https://doi.org/10.1002/joc.2312, 2012. a, b
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Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis, NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA, https://doi.org/10.7289/V5C8276M, 2009. a
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Semi-arid regions depend on the freshwater resources from the rainy seasons as they are crucial for ensuring security for drinking water, food and electricity. Thus, forecasting the conditions for the next season is crucial for proactive water management. We hence present a seasonal forecast product for four semi-arid domains in Iran, Brazil, Sudan/Ethiopia and Ecuador/Peru. It provides a benchmark for seasonal forecasts and, finally, a crucial contribution for improved disaster preparedness.
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