Sawam region averages for srep

Abstract: Increasing frequencies of droughts require proactive preparedness, particularly in semi-arid regions. As forecasting of such hydrometeorological extremes several months ahead allows for necessary climate proofing, we assess the potential economic value of the seasonal forecasting system SEAS5 for decision making in water management. For seven drought-prone regions analyzed in America, Africa, and Asia, the relative frequency of drought months significantly increased from 10 to 30% between 1981 and 2018. We demonstrate that seasonal forecast-based action for droughts achieves potential economic savings up to 70% of those from optimal early action. For very warm months and droughts, savings of at least 20% occur even for forecast horizons of several months. Our in-depth analysis for the Upper-Atbara dam in Sudan reveals avoidable losses of 16 Mio US$ in one example year for early-action based drought reservoir operation. These findings stress the advantage and necessity of considering seasonal forecasts in hydrological decision making. TechnicalInfo: The dataset contains the time series (ensemble forecasts, reanalysis data) and shape files which have been used for computing the different indicators in the article "Seasonal forecasts offer economic benefit for hydrological decision making in semi-arid regions".

Cite this as

Portele, Tanja, Lorenz, Christof (2021). Dataset: Sawam region averages for srep. https://doi.org/10.35097/441

DOI retrieved: 2021

Additional Info

Field Value
Imported on January 12, 2023
Last update August 4, 2023
License CC BY 4.0 Attribution
Source https://doi.org/10.35097/441
Author Portele, Tanja
More Authors
Lorenz, Christof
Source Creation 2021
Publishers
Karlsruhe Institute of Technology (KIT)
Production Year 2021
Publication Year 2021
Subject Areas
Name: Geological Science

Name: Environmental Science and Ecology

Name: Other
Additional: Climate Science

Related Identifiers
Identifier: 10.1038/s41598-021-89564-y
Type: DOI
Relation: IsSupplementTo