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印度夏季风降水不足的时空模式:对印度夏季季风作物粮食产量的影响。

Spatial and temporal pattern of deficient Indian summer monsoon rainfall (ISMR): impact on Kharif (summer monsoon) food grain production in India.

作者信息

Kumar P Vijaya, Bhavani O, Bhaskar S

机构信息

Central Research Institute for Dryland Agriculture, Santoshnagar, Saidabad (P.O.), Hyderabad, 500059, Telangana, India.

Natural Resources Management Division, Indian Council of Agriculture Research, New Delhi, 110012, India.

出版信息

Int J Biometeorol. 2023 Mar;67(3):485-501. doi: 10.1007/s00484-023-02428-0. Epub 2023 Jan 18.

Abstract

Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901-2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3-4 years) and 4 to 5 consecutive drought years (once in 120 years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R (0.8-0.88) between observed and predicted data and low RMSE (2.6-2.7%), can be employed for advanced estimation of DDP of the country and for taking country-level policy decisions by the Indian Government. For the first time, models were formulated to estimate state-wise departure in FGP (DP). In these models, novel indices viz., (i) rainfall departure and irrigation index (RDII) and (ii) physical and socio-economic index (PSEI), were used as predictor variables. These models, with R (0.71-0.75) and RMSE of 11.8-14.2(< SD of observed data), hold promise for advance estimation of production loss in states, useful for regional-level planning by the Government of India, and testing them in other countries.

摘要

尽管印度历史粮食产量(FGP)呈显著增长趋势,但印度夏季风降雨不足(ISMR)常常导致粮食产量下降。本研究旨在了解降雨不足(干旱)的时空变化及其对印度国家和地区粮食产量的影响。全国长期(1901 - 2020年)降雨量和干旱面积的百分比偏差分别呈现出不显著和显著的趋势。分区降雨量在4个和5个分区分别呈现出显著下降和上升趋势。印度西北部和西部分区出现了高频干旱年份(每3 - 4年一次)以及4至5个连续干旱年份(每120年一次)。全印度夏季粮食作物去趋势化产量与其正常产量的偏差(DDP)与印度夏季风降雨正常偏差(DRF)呈现出显著的二次关系。除了二次方程外,还建立了另一个以去趋势化作物面积、DRF和干旱面积为预测变量的多元回归模型来预测DDP。这两个模型在观测数据和预测数据之间具有较高的R值(0.8 - 0.88)和较低的均方根误差(2.6 - 2.7%),可用于该国粮食产量的提前估算以及印度政府制定国家层面的政策决策。首次建立了模型来估算各邦粮食产量偏差(DP)。在这些模型中,新的指标,即(i)降雨偏差和灌溉指数(RDII)以及(ii)自然和社会经济指数(PSEI),被用作预测变量。这些模型的R值为0.71 - 0.75,均方根误差为11.8 - 14.2(<观测数据的标准差),有望用于邦内产量损失的提前估算,对印度政府进行区域层面规划以及在其他国家进行测试都很有用。

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