Assessment of rainfed wheat productivity in a changing climate in Irbid, Jordan using statistical downscaling and Random Forest Regression prediction under RCP4.5 & 8.5 pathways

IF 5.4 Q1 ENVIRONMENTAL SCIENCES Environmental and Sustainability Indicators Pub Date : 2025-02-01 DOI:10.1016/j.indic.2024.100545
Tasnim Dheif Allah Althalaj , Fayha Muhammed Al-Shibli , Amani Abdullah Alassaf
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Abstract

Jordan is confronted with substantial risks linked to climate change and is proactively striving to manage resources sustainably by aligning its initiatives with the Sustainable Development Goals (SDGs). This study examines the impacts of climate change on rainfed wheat productivity in Irbid Governorate during the historical period (1994–2021) and simulated for the future (2024–2100) using climate models (SDGs 1, 2, 11, 12, 13, & 15). Monthly precipitation, mean temperature, and annual wheat yield data were collected for the period (1994–2021) and analyzed using the Mann-Kendall test with Sen's slope to investigate the historical trends and elaborate Phi-k correlation coefficient to determine their association. Monthly precipitation and mean temperature projections were collected from CSIRO-MK3.6.0 & GFDL-ESM2M CMIP5 ensemble models under two concentration pathways: RCP4.5 and RCP8.5. The projections were biased and corrected by the dynamic bilinear interpolation method and Delta, EQM, and QM statistical downscaling to enhance the projections' reliability and performance in capturing the region's climate. Taylor diagram was utilized to choose the best model to represent observed data. Cumulative Distribution Function and Probability Density Function Curves were plotted to describe the changes over decades based on climate models. Future wheat yields were predicted using the Random Forest Regression model. The results revealed a non-significant increase in total annual precipitation of 1.8% and 13.8% and a rise in annual mean temperature of 5.4% and 2.7% for Irbid and Baqura stations, respectively during the baseline timeframe on which wheat yield increased by 21.3%. The CSIRO model outperformed the GFDL model with greater fidelity in simulating historical monthly precipitation and mean temperatures. The CSIRO-MK3.6.0 model indicates precipitation and temperature shifts for near and far future periods. Temperature increases are expected to have more severe impacts in the far future (2073–2100) while projected decreases in precipitation of 0.06 mm/day and 0.10 mm/day under RCP4.5 and RCP8.5 affect yield reductions by 11.42 kg/ha and 12.21 kg/ha. Temperature increases of 2.7 °C and 4.1 °C under RCP 4.5 & 8.5 and the responding yield decreases to 24.83 kg/ha and 33.40 kg/ha, respectively. The study highlights the need for enhanced adaptation strategies; cultivating resilient wheat varieties, promoting crop rotation, improving meteorological monitoring and risk response approaches, and providing timely weather forecasts to mitigate the adverse effects of climate change on wheat productivity and ensure sustainable agriculture in Jordan.
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约旦正面临着与气候变化相关的巨大风险,并正积极努力通过使其举措与可持续发展目标(SDGs)保持一致来可持续地管理资源。本研究利用气候模型(可持续发展目标 1、2、11、12、13、& 15)研究了历史时期(1994-2021 年)和模拟未来(2024-2100 年)气候变化对伊尔比德省雨水灌溉小麦生产力的影响。收集了历史时期(1994-2021 年)的月降水量、平均气温和小麦年产量数据,并使用 Mann-Kendall 检验和 Sen's 坡度分析历史趋势,以及精心设计的 Phi-k 相关系数确定它们之间的联系。月降水量和平均气温的预测数据来自 CSIRO-MK3.6.0 & 和 GFDL-ESM2M CMIP5 模型在两种浓度路径下的预测:RCP4.5和RCP8.5。通过动态双线性插值法和 Delta、EQM 和 QM 统计降尺度,对预测结果进行了偏差和修正,以提高预测结果的可靠性和对该地区气候的捕捉能力。利用泰勒图来选择代表观测数据的最佳模型。根据气候模型绘制了累积分布函数和概率密度函数曲线,以描述几十年来的变化。使用随机森林回归模型预测了未来的小麦产量。结果显示,在基线时间范围内,伊尔比德和巴卡拉站的年降水总量分别增加了 1.8%和 13.8%,年平均气温分别上升了 5.4%和 2.7%,而在此基础上,小麦产量增加了 21.3%。CSIRO 模型在模拟历史月降水量和平均气温方面比 GFDL 模型更为逼真。CSIRO-MK3.6.0 模型显示了近期和远期的降水和气温变化。气温升高预计将对远期(2073-2100 年)产生更严重的影响,而在 RCP4.5 和 RCP8.5 条件下,降水量预计将分别减少 0.06 毫米/天和 0.10 毫米/天,导致减产 11.42 千克/公顷和 12.21 千克/公顷。在 RCP4.5 和 RCP8.5 条件下,温度分别升高 2.7 ℃ 和 4.1 ℃,响应的产量分别减少 24.83 千克/公顷和 33.40 千克/公顷。该研究强调了加强适应战略的必要性;培育抗逆性强的小麦品种,推广轮作,改进气象监测和风险应对方法,及时提供天气预报,以减轻气候变化对小麦生产力的不利影响,确保约旦农业的可持续发展。
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来源期刊
Environmental and Sustainability Indicators
Environmental and Sustainability Indicators Environmental Science-Environmental Science (miscellaneous)
CiteScore
7.80
自引率
2.30%
发文量
49
审稿时长
57 days
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