EMS Annual Meeting Abstracts
Vol. 18, EMS2021-402, 2021
https://doi.org/10.5194/ems2021-402
EMS Annual Meeting 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Downscaling global seasonal weather forecasts for crop yield forecasting over Zimbabwe

Sinclair Chinyoka and Gert-Jan Steeneveld
Sinclair Chinyoka and Gert-Jan Steeneveld
  • Wageningen University, Meteorology and Air Quality Section, Wageningen, Netherlands (gert-jan.steeneveld@wur.nl)

This study focuses on the assessment of the impact of downscaling seasonal forecasts from the Climate Forecast System version 2 (CFSv2) using the Weather Research and Forecasting (WRF) mesoscale model over Zimbabwe on a spatial resolution of 21km and 7km for Southern Africa and Zimbabwe respectively. We used a 7-day re-initialization simulation strategy for 212 days per season and was repeated for eights seasons between 2010 and 2018. The impact of downscaling global seasonal forecasts was further evaluated in crop forecasting using the WOrld FOod STudies (WOFOST) model. Statistical analysis of the forecasted seasonal rainfall revealed a reduction of the bias from about -2 mm/day from CFSv2 forecasts to about 0.5mm/day from WRF forecasts in most parts of the country. We also found that an improvement in seasonal tercile rainfall prediction from 25%, 50%, and 75% by CFSv2 in three different regions to about 62.5% by WRF in all regions. Substantial improvement was achieved in Standard Precipitation Index-driven seasonal forecasts with two regions with a percent correct of 75% and region 2 with 100% by WRF compared to 62.5% by CFSv2 in all regions. Hence, the characterization of seasonal rainfall in terms of drought forecasts is better than the tercile rainfall prediction system and will be more beneficial to farmers in Zimbabwe. WRF seasonal rain forecasts improved both in magnitude and in forecasting the onset of the growing season. This was indicated by the accumulated absolute maize yield error which factored in a miss of onset of the growing season by each model. WRF outperformed CFSv2 for maize and sorghum yield forecasts in 6, 6, and 8 (out of 8) seasons in Karoi, Masvingo, and Gweru sites respectively. WRF forced crop simulations reduced mean absolute percent error of maize yield by 12.2% and sorghum yield by 9.3 % from CFSv2 forced simulations. Our results also show that maize will be more productive and less risky at Karoi and Masvingo and sorghum at the Gweru site. In our view, there should be no farming of both maize and sorghum at Beitbridge due to the high risk of crop failure unless a proper irrigation system is in place.

How to cite: Chinyoka, S. and Steeneveld, G.-J.: Downscaling global seasonal weather forecasts for crop yield forecasting over Zimbabwe, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-402, https://doi.org/10.5194/ems2021-402, 2021.

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