Building high-resolution projections of temperature potential changes using statistical downscaling for the future period 2026–2100 in the highland region of Yemen – A supportive approach for empowering environmental planning and decision-making

IF 5.4 Q1 ENVIRONMENTAL SCIENCES
Ali H. AL-Falahi , Naeem Saddique , Uwe Spank , Christian Bernhofer
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引用次数: 0

Abstract

Environmental resources and ecological systems are significantly affected by the steady rise of the global temperature. However, the degree of temperature change at the regional and local levels is uncertain. The uncertainty arises from various factors, but mostly due to the short length of ground data and dependency of local studies on the large-scale and spatially coarse output of Global Climate Models (GCMs). Therefore, the output of GCM cannot be directly used in impact assessment studies at a regional and local level. In this study, the Statistical Down-Scaling Model (SDSM) is employed to investigate the magnitude of temperature changes (Minimum and Maximum Temperature) for the future period 2026–2100. The SDSM builds relationships between large-scale predictors and local climate variables, allowing for finer-resolution projections at a regional level. The study utilized the Climate Hazard Infra-Red Temperature with Station (CHIRTS-daily) to complete daily missing records in more than 90 ground stations. Additionally, predictors of the National Center for Environmental Prediction (NCEP) for the historical period (1961–2010) and the Canadian Earth System Model (CanESM2) for the future period (2026–2100) are employed to calibrate SDSM and to build finer-resolution scenarios under two representative concentration pathways; RCP2.6 and RCP8.5. The methodology additionally involved validating the SDSM performance using observed historical data before applying it to future projections. The findings indicate that both minimum and maximum temperatures (T-min and T-max) will increase, with a more pronounced rise in minimum temperature (T-min). Over the future period (2026–2100), the projected average temperature rise is 1.10 °C (T-max) and 1.43 °C (T-min) under RCP2.6. For RCP8.5, the projected average increases are 1.56 °C and 2.3 °C for T-max and T-min, respectively. Overall, the most significant increase is projected to occur in the 2090s (2076–2100) under RCP8.5, particularly in the lowlands and wadis of Al Mahwit and Raymah governorate. In these areas, the minimum temperature (T-min) exhibited an increased absolute value of up to 3.2 °C. This high rise in temperatures is expected to result in increased evapotranspiration, prolonged droughts, and possibly breakouts of some plant diseases and pests. This would require effective adaptation measures such as harvesting rainwater and growing short-time and heat-resistance crops. Engaging in field visits and social discussions added depth to the study by introducing various traditional methods and indigenous practices. Valuable resources for future efforts to mitigate the potential impacts of climate change are offered by these insights.
利用统计降尺度技术对也门高原地区未来 2026-2100 年期间的气温潜在变化进行高分辨率预测--增强环境规划和决策能力的辅助方法
环境资源和生态系统受到全球气温持续上升的严重影响。然而,区域和地方层面的温度变化程度并不确定。造成这种不确定性的因素有很多,但主要是由于地面数据的长度较短,以及地方研究对全球气候模型(GCMs)大尺度和空间粗放输出结果的依赖。因此,全球气候模式的输出结果不能直接用于区域和地方层面的影响评估研究。本研究采用了统计降尺度模型 (SDSM) 来研究未来 2026-2100 年期间的气温变化幅度(最低气温和最高气温)。SDSM 建立了大尺度预测因子与当地气候变量之间的关系,可在区域层面上进行分辨率更高的预测。该研究利用气候灾害红外温度与站点(CHIRTS-daily)来完成 90 多个地面站的每日缺失记录。此外,还采用了国家环境预报中心(NCEP)对历史时期(1961-2010 年)和加拿大地球系统模式(CanESM2)对未来时期(2026-2100 年)的预测,以校准 SDSM,并在两种代表性浓度路径(RCP2.6 和 RCP8.5)下建立更精细的分辨率情景。该方法还包括在将 SDSM 应用于未来预测之前,利用观测到的历史数据验证 SDSM 的性能。研究结果表明,最低气温和最高气温(T-min 和 T-max)都将升高,其中最低气温(T-min)的升高更为明显。在未来时期(2026-2100 年),在 RCP2.6 条件下,预计平均气温上升 1.10 ℃(T-max)和 1.43 ℃(T-min)。对于 RCP8.5,T-max 和 T-min 的预计平均温度升幅分别为 1.56 ℃ 和 2.3 ℃。总体而言,在 RCP8.5 条件下,预计 2090 年代(2076-2100 年)的增幅最大,尤其是在 Al Mahwit 和 Raymah 省的低地和瓦迪斯。在这些地区,最低气温 (T-min) 的绝对值增加了 3.2 °C。气温升高预计将导致蒸散量增加、干旱持续时间延长以及一些植物病虫害的爆发。这就需要采取有效的适应措施,如收集雨水和种植短时耐热作物。实地考察和社会讨论通过介绍各种传统方法和本土做法,增加了研究的深度。这些见解为今后努力减轻气候变化的潜在影响提供了宝贵的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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