Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-2701-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-2701-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Jun 2021
Data description paper |
| 15 Jun 2021
| 15 Jun 2021
Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions
Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Tanja C. Portele
Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Patrick Laux
Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Augsburg University, Institute of Geography, Alter Postweg 118, 86159 Augsburg, Germany
Harald Kunstmann
Karlsruhe Institute of Technology (KIT), Campus Alpin, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Augsburg University, Institute of Geography, Alter Postweg 118, 86159 Augsburg, Germany
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Cited
19 citations as recorded by crossref.
- Seasonal fire danger forecasts for supporting fire prevention management in an eastern Mediterranean environment: the case of Attica, Greece A. Karali et al. 10.5194/nhess-23-429-2023
- Combined Modes of the Northern Stratosphere, Tropical Oceans, and East Asian Spring Rainfall: A Novel Method to Improve Seasonal Forecasts of Precipitation J. Rao et al. 10.1029/2022GL101360
- Evaluating prospects for subseasonal-to-seasonal forecast-based anticipatory action from a global perspective J. Lala et al. 10.1016/j.wace.2022.100510
- Improving the Spring Air Temperature Forecast Skills of BCC_CSM1.1 (m) by Spatial Disaggregation and Bias Correction: Importance of Trend Correction C. Duan et al. 10.3390/atmos12091143
- Assessment of climate change impacts on the hydro-wind-solar energy supply system Y. Zhang et al. 10.1016/j.rser.2022.112480
- PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China H. Xia & K. Wang 10.1016/j.atmosres.2024.107705
- Improving Global Subseasonal to Seasonal Precipitation Forecasts Using a Support Vector Machine‐Based Method G. Yin et al. 10.1029/2023JD038929
- Projected dryness/wetness pattern and influence factors in China under the CMIP6 scenarios for 2021–2100 J. Yang et al. 10.1080/19475705.2024.2415529
- Future changes in precipitation over the upper Yangtze River basin based on bias correction spatial downscaling of models from CMIP6 H. Wu et al. 10.1088/2515-7620/ac620e
- Evaluating Bias-Correction Methods for Seasonal Dynamical Precipitation Forecasts S. Golian & C. Murphy 10.1175/JHM-D-22-0049.1
- Seasonal sub-basin-scale runoff predictions: A regional hydrometeorological Ensemble Kalman Filter framework using global datasets M. Borne et al. 10.1016/j.ejrh.2022.101146
- Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series R. Behling et al. 10.1038/s41598-022-24712-6
- Downscaled compound heatwave and heavy-precipitation analyses for Guangdong, China in the twenty-first century J. Ren et al. 10.1007/s00382-023-06712-y
- Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices W. Atiah et al. 10.1016/j.heliyon.2023.e17604
- Probabilistic predictions for meteorological droughts based on multi-initial conditions M. Torres-Vázquez et al. 10.1016/j.jhydrol.2024.131662
- Simulating Potential Impacts of Future Climate Change on Post-Rainy Season Sorghum Yields in India K. Chadalavada et al. 10.3390/su14010334
- Using a new local high resolution daily gridded dataset for Attica to statistically downscale climate projections K. Varotsos et al. 10.1007/s00382-022-06482-z
- Regional-scale seasonal forecast of surface water availability in a semi-arid environment: The case of Ceará State in Northeast of Brazil E. Rottler et al. 10.1016/j.ejrh.2024.102058
- Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America T. Portele et al. 10.3389/feart.2021.669427
18 citations as recorded by crossref.
- Seasonal fire danger forecasts for supporting fire prevention management in an eastern Mediterranean environment: the case of Attica, Greece A. Karali et al. 10.5194/nhess-23-429-2023
- Combined Modes of the Northern Stratosphere, Tropical Oceans, and East Asian Spring Rainfall: A Novel Method to Improve Seasonal Forecasts of Precipitation J. Rao et al. 10.1029/2022GL101360
- Evaluating prospects for subseasonal-to-seasonal forecast-based anticipatory action from a global perspective J. Lala et al. 10.1016/j.wace.2022.100510
- Improving the Spring Air Temperature Forecast Skills of BCC_CSM1.1 (m) by Spatial Disaggregation and Bias Correction: Importance of Trend Correction C. Duan et al. 10.3390/atmos12091143
- Assessment of climate change impacts on the hydro-wind-solar energy supply system Y. Zhang et al. 10.1016/j.rser.2022.112480
- PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China H. Xia & K. Wang 10.1016/j.atmosres.2024.107705
- Improving Global Subseasonal to Seasonal Precipitation Forecasts Using a Support Vector Machine‐Based Method G. Yin et al. 10.1029/2023JD038929
- Projected dryness/wetness pattern and influence factors in China under the CMIP6 scenarios for 2021–2100 J. Yang et al. 10.1080/19475705.2024.2415529
- Future changes in precipitation over the upper Yangtze River basin based on bias correction spatial downscaling of models from CMIP6 H. Wu et al. 10.1088/2515-7620/ac620e
- Evaluating Bias-Correction Methods for Seasonal Dynamical Precipitation Forecasts S. Golian & C. Murphy 10.1175/JHM-D-22-0049.1
- Seasonal sub-basin-scale runoff predictions: A regional hydrometeorological Ensemble Kalman Filter framework using global datasets M. Borne et al. 10.1016/j.ejrh.2022.101146
- Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series R. Behling et al. 10.1038/s41598-022-24712-6
- Downscaled compound heatwave and heavy-precipitation analyses for Guangdong, China in the twenty-first century J. Ren et al. 10.1007/s00382-023-06712-y
- Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices W. Atiah et al. 10.1016/j.heliyon.2023.e17604
- Probabilistic predictions for meteorological droughts based on multi-initial conditions M. Torres-Vázquez et al. 10.1016/j.jhydrol.2024.131662
- Simulating Potential Impacts of Future Climate Change on Post-Rainy Season Sorghum Yields in India K. Chadalavada et al. 10.3390/su14010334
- Using a new local high resolution daily gridded dataset for Attica to statistically downscale climate projections K. Varotsos et al. 10.1007/s00382-022-06482-z
- Regional-scale seasonal forecast of surface water availability in a semi-arid environment: The case of Ceará State in Northeast of Brazil E. Rottler et al. 10.1016/j.ejrh.2024.102058
Latest update: 21 Jan 2025
Short summary
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.
Semi-arid regions depend on the freshwater resources from the rainy seasons as they are crucial...
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