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https://doi.org/10.5194/essd-2020-177
© Author(s) 2020. This work is distributed under
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https://doi.org/10.5194/essd-2020-177
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Oct 2020

27 Oct 2020

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This preprint is currently under review for the journal ESSD.

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

Christof Lorenz1, Tanja C. Portele1, Patrick Laux1,2, and Harald Kunstmann1,2 Christof Lorenz et al.
  • 1Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
  • 2Augsburg University, Institute of Geography, Alter Postweg 118, 86159 Augsburg, Germany

Abstract. Seasonal forecasts have the potential to substantially improve water management particularly in water scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface re-run of the latest ECMWF reanalysis ERA5. By that, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1°. This is exemplary performed over 4 predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco (Brazil), the Tekeze-Atbara and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo-Chira (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts. Furthermore, the lead-dependent drift effects are remarkably reduced in the BCSD-forecasts. However, our analysis also showed that computing monthly averages from daily bias-corrected forecasts can lead to statistical inconsistencies particularly during periods and seasons with strong temporal climate gradients or heteroscedasticity. During such periods, particularly the lowest- and highest-lead forecasts can show remaining biases. Our dataset covers the whole (re-)forecast period from 1981 to 2019, for which we provide bias-corrected and spatially disaggregated daily ensemble forecasts for precipitation, average, minimum and maximum temperature as well as for shortwave radiation from the initial date to the coming 214 days. This sums up to more than 100,000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the 5 analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi. org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: Tekeze-Atbara and Blue Nile Basins (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo-Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test-bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions.

Christof Lorenz et al.

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Christof Lorenz et al.

Data sets

Seasonal Water Resources Management for Semiarid Areas: Bias-corrected and spatially disaggregated seasonal forecasts for the Karun Basin (Iran) Lorenz, Christof, Portele, Tanja C., Laux, Patrick, and Kunstmann, Harald https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD

Seasonal Water Resources Management for Semiarid Areas: Bias-corrected and spatially disaggregated seasonal forecasts for the Rio São Francisco Basin (Brazil) Lorenz, Christof, Portele, Tanja C., Laux, Patrick, and Kunstmann, Harald https://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD

Seasonal Water Resources Management for Semiarid Areas: Bias-corrected and spatially disaggregated seasonal forecasts for the Tekeze-Atbara and Blue Nile Basins (Ethiopia/Eritrea/Sudan) Lorenz, Christof, Portele, Tanja C., Laux, Patrick, and Kunstmann, Harald https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD

Seasonal Water Resources Management for Semiarid Areas: Bias-corrected and spatially disaggregated seasonal forecasts for the Catamayo-Chira Basin (Ecuador/Peru) Lorenz, Christof, Portele, Tanja C., Laux, Patrick, and Kunstmann, Harald https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD

Christof Lorenz et al.

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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 pro-active 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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