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Evaluating the performance of RegCM4 in studies on irrigated and rainfed cotton crops

Abstract

With the changing climate, reliable climate projections are essential for agriculture risk management. The present study aims to explore the output of a regional climate model (RCM) at different climatic regimes and its applications in crop simulation models. Here, a comparative study of the cotton crop growth and yield response for Akola in the central and Hisar northern agroclimatic zone of India represents rainfed and irrigated growing regions of cotton, respectively. The RegCM4 projections and its bias-corrected values of temperature and precipitation data for the period 1971–2005 are compared with the observations to assess its reliability with the crop simulation models as weather inputs. The results signify that the RCM model is wet, which implies that, it shows high rainfall intensity in terms of frequency as a number of rainy days and amount. The model also shows night warming as there is a significant decline in maximum temperature and minimal decline in minimum temperature, thus there is a reduced diurnal temperature difference. Overall model highly underestimates temperature and overestimates rainfall. Strikingly reduced numbers of intense warm and cold events are simulated. Model is highly biased for rainfall events >0 mm/day and 5mm/day, and moderately biased for rainfall >5 mm/day. Precipitation bias-correction, using quantile mapping approach, shows excellent agreement at an annual scale. But precipitation variability could not be captured that well as it is a ‘distribution-based method'. However, it worked well in the irrigated Hisar region than the rainfed Akola region. The bias-corrected RegCM4 climate inputs are utilized in Decision Support System for Agro-technology Transfer (DSSAT) simulations for cotton yields, Leaf Area Index (LAI) and ball number at maturity/m2 (NM) for both regions. Bias-corrected outputs are in better agreement with corresponding observations than non-bias-corrected outputs in both regions. Future research could apply these simulated model data complemented with reliable bias correction techniques to explicitly study climate change's impact on crop productivity.

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Acknowledgements

For the present study, data from the field study has been a part of the FASAL project. ‘Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL)’, which is operational at Ministry of Agriculture, under Govt. of India in collaboration with India Meteorological Department (IMD), Space Application Centre (SAC) Mahalanobis National Crop Forecasting Centre (MNCFC) and Institute of Economic Growth (IEG). For this IMD collaborates with Agromet Field units (AMFUs) located at different State Agricultural Universities (SAUs), ICAR, IITs, etc., to develop yield forecasts. For the climate projections, we would like to gratefully acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, the former coordinating body of CORDEX and responsible panel for CMIP5. The climate modelling groups are sincerely thanked for producing and making available their model output. The authors thank the Earth System Grid Federation (ESGF) infrastructure and the Climate Data Portal hosted at the Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology (IITM) for providing CORDEX South Asia data.

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AS has carried out main computation and first draft; APD has conceived, discussed and edited ms; KKS has provided data needed and discussed; UM has provided finer details on crop-related domain; and PM has provided discourse on climate.

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Correspondence to A P Dimri.

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Communicated by Kavirajan Rajendran

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Shikha, A., Dimri, A.P., Singh, K.K. et al. Evaluating the performance of RegCM4 in studies on irrigated and rainfed cotton crops. J Earth Syst Sci 130, 198 (2021). https://doi.org/10.1007/s12040-021-01705-z

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Keywords

  • Climate change
  • cotton
  • RegCM4
  • bias-correction
  • quantile mapping
  • DSSAT
  • irrigated
  • rainfed