Shakeel Muhammad, Abbas Hussnain, Ali Zulfiqar, Tariq Aqil, Almazroui Mansour, Kader Shuraik
College of Statistical Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, 54590, Punjab, Pakistan.
Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi State, MS, 39762-9690, USA.
J Environ Manage. 2025 Sep;392:126692. doi: 10.1016/j.jenvman.2025.126692. Epub 2025 Jul 26.
Future drought characterization often relies on Multi-Modal Ensembles (MMEs) of Global Climate Models (GCMs), particularly from the Coupled Model Intercomparison Project Phase 6 (CMIP6). However, the reliability of projections is often hindered by insufficient ranking methodologies for GCMs and inadequate handling of outliers in regional aggregation. This study presents a novel framework to enhance the reliability of drought projections and standardization by introducing innovative ranking, aggregation, and projection methods. The framework is not limited to a specific region but is adaptable to diverse climatic and geographic contexts. The proposed methodology employs Mutual Information (MI) to evaluate the performance of GCM in simulating historical precipitation, followed by comprehensive rating metrics (CRM) to rank models effectively. A novel regional aggregation technique addresses outlier influence, ensuring robust multi-model ensembles. The approach incorporates top-performing GCMs into MMEs using advanced geometric and regression methods, validated using the Kling-Gupta efficiency with knowable moments (KGE). A Gaussian-Norm Weighted Drought Index (GNWDI) was also introduced, offering enhanced drought standardization within the Standardized Precipitation Index (SPI) framework. Applying this framework in Punjab, Pakistan, using 22 GCMs, enabled the identification of high-performing models such as MIROC-ES2L, CMCC-CM2-SR5, and IPSL-CM6A-LR. Future drought trends for 2015-2100 were projected under three Shared Socioeconomic Pathways (SSPs). Results revealed a rise in extreme droughts and wet conditions under high emission scenarios (SSP5-8.5), highlighting the intensification of drought severity over extended periods. Specifically, under SSP5-8.5, the average probability of extreme droughts (ED) across all time scales is approximately 0.0221, which remains comparable to lower emission scenarios but shows slightly elevated values at longer time scales, such as 48 months (0.025). Additionally, severe wet (SW) conditions notably increase under SSP5-8.5, with the probability rising from 0.044 at 1 month to 0.051 at 12 and 24 months, and peaking at 0.051 again at 48 months, suggesting more frequent extreme hydrological swings under intensified climate forcing. This study significantly advances drought projection techniques by addressing critical gaps in model ranking, aggregation, and standardization. The framework offers a reliable, regionally adaptable tool for policymakers and researchers, enabling proactive drought management and improved climate resilience under varying emission scenarios.
未来干旱特征描述通常依赖于全球气候模型（GCMs）的多模式集合（MMEs），尤其是来自耦合模式比较计划第6阶段（CMIP6）的模型。然而，由于GCMs的排名方法不足以及区域聚合中对异常值处理不当，预测的可靠性常常受到阻碍。本研究提出了一个新颖的框架，通过引入创新的排名、聚合和预测方法来提高干旱预测的可靠性和标准化程度。该框架不限于特定区域，而是适用于各种气候和地理环境。所提出的方法采用互信息（MI）来评估GCM在模拟历史降水方面的性能，随后使用综合评级指标（CRM）对模型进行有效排名。一种新颖的区域聚合技术解决了异常值的影响，确保了稳健的多模型集合。该方法使用先进的几何和回归方法将表现最佳的GCM纳入MMEs，并使用具有已知矩的克林 - 古普塔效率（KGE）进行验证。还引入了高斯范数加权干旱指数（GNWDI），在标准化降水指数（SPI）框架内提供了增强的干旱标准化。在巴基斯坦旁遮普省应用该框架，使用22个GCM，识别出了诸如MIROC - ES2L、CMCC - CM2 - SR5和IPSL - CM6A - LR等高性能模型。在三种共享社会经济路径（SSPs）下预测了2015 - 2100年的未来干旱趋势。结果显示，在高排放情景（SSP5 - 8.5）下，极端干旱和湿润状况有所增加，突出了长期干旱严重程度的加剧。具体而言，在SSP5 - 8.5情景下，所有时间尺度上极端干旱（ED）的平均概率约为0.0221，这与低排放情景相当，但在较长时间尺度（如48个月，概率为0.025）下略有升高。此外，在SSP5 - 8.5情景下，严重湿润（SW）状况显著增加，概率从1个月时的0.044上升到12个月和24个月时的0.051，并在48个月时再次达到峰值0.051，表明在强化气候强迫下极端水文波动更加频繁。本研究通过解决模型排名、聚合和标准化方面的关键差距，显著推进了干旱预测技术。该框架为政策制定者和研究人员提供了一个可靠的、区域适应性强的工具，能够在不同排放情景下实现主动的干旱管理并提高气候适应能力。
