Evaluating the IITM-CFSv2 extended-range daily rainfall hindcast skills for the seasonal Kharif rice yield estimation of Warangal district, using the DSSAT crop-growth model.
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| Title: | Evaluating the IITM-CFSv2 extended-range daily rainfall hindcast skills for the seasonal Kharif rice yield estimation of Warangal district, using the DSSAT crop-growth model. |
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| Authors: | Amat, Hemadri Bhusan1,2 (AUTHOR) hbamat@gmail.com, Kandasamy, Sathya Uma Lakshmi3 (AUTHOR), Dey, Avijit4 (AUTHOR), Swain, Dillip Kumar3 (AUTHOR), Sahai, Atul Kumar4 (AUTHOR), Ashok, Karumuri1,5 (AUTHOR) |
| Source: | Journal of Earth System Science. Mar2026, Vol. 135 Issue 1, p1-15. 15p. |
| Subject Terms: | *General circulation model, *Crop growth, *Long-range weather forecasting, *Crop yields, *Precipitation forecasting, *Monsoons |
| Abstract: | Recent studies have shown that the IITM-CFSv2 general circulation model (GCM) demonstrates strong skills in extended-range prediction (ERP) hindcast products. The model can predict the onset of the Indian Summer Monsoon (ISM). It also predicts the monsoon intraseasonal oscillation and wet–dry spells over the Indian subcontinent. These predictions are skillful up to week-3 lead. The model captures the linkage between state-wise Kharif rice production (KRP) and local summer monsoon rainfall, as well as their linkage to the tropical Indo-Pacific climate drivers across various Indian states. In this study, we explore whether these IITM-CFSv2 ERP skills can be leveraged in predicting the seasonal KRP of Warangal district during 2004–2019 by using these hindcasts as inputs to the Decision Support System for Agrotechnology Transfer (DSSAT), a crop growth model. For reference, we also run the DSSAT model with observations, including the observed rainfall recorded by the India Meteorological Department (IMD). The results suggest that IITM-CFSv2 week-1 hindcasts predict the summer monsoon rainfall in the region with significant skill. The DSSAT's simulated rice yield using IMD datasets shows a moderate correlation of 0.4, statistically significant at an 85% confidence level. Similarly, simulated yields using IITM-CFSv2 ERP at week-1 and week-2 leads have correlation values of 0.48 and 0.53 at a 90% confidence level for the first transplant date. For the third transplanting date, the simulated yields using week-1 and week-2 leads also exhibit statistically significant correlations. Research highlights: The IITM-CFSv2 ERP (week-1 and week-2) hindcasts show significant skill in predicting regional monsoon rainfall, which is vital for rainfed Kharif rice cultivation. DSSAT rice yields forecast using IMD observed and IITM-CFSv2 ERP showed the highest correlation for the first transplanting date, with RMSE values from 11 to 16%. The model struggles to capture the yields for the 2nd and 3rd transplanting dates, with moderate-to-low yield correlations, which implies the importance of monsoon onset and the specified dates in a regional crop calendar. Biotic factors such as pests, weeds and diseases may affect crop yields in some cases. Refining models to improve accuracy in predictions and address biases is crucial for supporting farmers and enhancing crop yield forecasts. [ABSTRACT FROM AUTHOR] |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 191497926 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluating the IITM-CFSv2 extended-range daily rainfall hindcast skills for the seasonal Kharif rice yield estimation of Warangal district, using the DSSAT crop-growth model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Amat%2C+Hemadri+Bhusan%22">Amat, Hemadri Bhusan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> hbamat@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kandasamy%2C+Sathya+Uma+Lakshmi%22">Kandasamy, Sathya Uma Lakshmi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dey%2C+Avijit%22">Dey, Avijit</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Swain%2C+Dillip+Kumar%22">Swain, Dillip Kumar</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sahai%2C+Atul+Kumar%22">Sahai, Atul Kumar</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ashok%2C+Karumuri%22">Ashok, Karumuri</searchLink><relatesTo>1,5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Earth+System+Science%22">Journal of Earth System Science</searchLink>. Mar2026, Vol. 135 Issue 1, p1-15. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22General+circulation+model%22">General circulation model</searchLink><br />*<searchLink fieldCode="DE" term="%22Crop+growth%22">Crop growth</searchLink><br />*<searchLink fieldCode="DE" term="%22Long-range+weather+forecasting%22">Long-range weather forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Crop+yields%22">Crop yields</searchLink><br />*<searchLink fieldCode="DE" term="%22Precipitation+forecasting%22">Precipitation forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Monsoons%22">Monsoons</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recent studies have shown that the IITM-CFSv2 general circulation model (GCM) demonstrates strong skills in extended-range prediction (ERP) hindcast products. The model can predict the onset of the Indian Summer Monsoon (ISM). It also predicts the monsoon intraseasonal oscillation and wet–dry spells over the Indian subcontinent. These predictions are skillful up to week-3 lead. The model captures the linkage between state-wise Kharif rice production (KRP) and local summer monsoon rainfall, as well as their linkage to the tropical Indo-Pacific climate drivers across various Indian states. In this study, we explore whether these IITM-CFSv2 ERP skills can be leveraged in predicting the seasonal KRP of Warangal district during 2004–2019 by using these hindcasts as inputs to the Decision Support System for Agrotechnology Transfer (DSSAT), a crop growth model. For reference, we also run the DSSAT model with observations, including the observed rainfall recorded by the India Meteorological Department (IMD). The results suggest that IITM-CFSv2 week-1 hindcasts predict the summer monsoon rainfall in the region with significant skill. The DSSAT's simulated rice yield using IMD datasets shows a moderate correlation of 0.4, statistically significant at an 85% confidence level. Similarly, simulated yields using IITM-CFSv2 ERP at week-1 and week-2 leads have correlation values of 0.48 and 0.53 at a 90% confidence level for the first transplant date. For the third transplanting date, the simulated yields using week-1 and week-2 leads also exhibit statistically significant correlations. Research highlights: The IITM-CFSv2 ERP (week-1 and week-2) hindcasts show significant skill in predicting regional monsoon rainfall, which is vital for rainfed Kharif rice cultivation. DSSAT rice yields forecast using IMD observed and IITM-CFSv2 ERP showed the highest correlation for the first transplanting date, with RMSE values from 11 to 16%. The model struggles to capture the yields for the 2nd and 3rd transplanting dates, with moderate-to-low yield correlations, which implies the importance of monsoon onset and the specified dates in a regional crop calendar. Biotic factors such as pests, weeds and diseases may affect crop yields in some cases. Refining models to improve accuracy in predictions and address biases is crucial for supporting farmers and enhancing crop yield forecasts. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=191497926 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12040-025-02709-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: General circulation model Type: general – SubjectFull: Crop growth Type: general – SubjectFull: Long-range weather forecasting Type: general – SubjectFull: Crop yields Type: general – SubjectFull: Precipitation forecasting Type: general – SubjectFull: Monsoons Type: general Titles: – TitleFull: Evaluating the IITM-CFSv2 extended-range daily rainfall hindcast skills for the seasonal Kharif rice yield estimation of Warangal district, using the DSSAT crop-growth model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Amat, Hemadri Bhusan – PersonEntity: Name: NameFull: Kandasamy, Sathya Uma Lakshmi – PersonEntity: Name: NameFull: Dey, Avijit – PersonEntity: Name: NameFull: Swain, Dillip Kumar – PersonEntity: Name: NameFull: Sahai, Atul Kumar – PersonEntity: Name: NameFull: Ashok, Karumuri IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02534126 Numbering: – Type: volume Value: 135 – Type: issue Value: 1 Titles: – TitleFull: Journal of Earth System Science Type: main |
| ResultId | 1 |
