Scholarlink

<p>Droughts are complex natural phenomena that significantly impact ecosystems, economies, and societies, particularly under the influence of climate change. This review focuses on meteorological, agricultural, and hydrological droughts across seasonal to multi-decadal timescales, covering major hydroclimatic settings including arid, semi-arid, monsoon-dominated, temperate, and Mediterranean regions. Accurate characterization and prediction of droughts are crucial for developing effective mitigation and adaptation strategies. This review provides a structured comparative synthesis of recent advancements in climate modeling, downscaling, and bias-correction techniques used for drought characterization and impact assessment under changing climate conditions. Literature was synthesized using four evaluation criteria: process realism, spatial applicability, uncertainty treatment, and implementation feasibility. Climate models, including GCMs, RCMs, and Earth System Models, are assessed in terms of their ability to represent drought drivers, persistence, extremes, and regional variability. Dynamical, statistical, and hybrid downscaling approaches are compared with respect to spatial refinement, data requirements, transferability, and performance across contrasting hydroclimatic regions. Bias-correction methods such as linear scaling, quantile mapping, variance adjustment, and emerging machine-learning approaches are evaluated for their capacity to reduce systematic errors while preserving drought-relevant statistics. Particular emphasis is placed on uncertainty propagation across the climate model–downscaling–bias correction chain, where errors can accumulate and alter projected drought frequency, duration, and severity. Existing integrated workflows are reviewed to identify methodological gaps related to multi-model ensembles, non-stationarity, observational limitations, and cross-scale consistency. Finally, the review concludes that future drought assessments should move toward ensemble-based, uncertainty-aware, and AI-assisted frameworks that jointly evaluate climate forcing, post-processing choices, and drought metrics to improve risk-informed adaptation planning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrating Climate Modelling, Downscaling, and Bias Correction for Drought Studies: Current Approaches and Future Directions

  • 期刊论文

  • 发表日期：

    2026年7月24日

查看全文
  • Chinthu Naresh,
  • Aneesh Mathew

摘要

Droughts are complex natural phenomena that significantly impact ecosystems, economies, and societies, particularly under the influence of climate change. This review focuses on meteorological, agricultural, and hydrological droughts across seasonal to multi-decadal timescales, covering major hydroclimatic settings including arid, semi-arid, monsoon-dominated, temperate, and Mediterranean regions. Accurate characterization and prediction of droughts are crucial for developing effective mitigation and adaptation strategies. This review provides a structured comparative synthesis of recent advancements in climate modeling, downscaling, and bias-correction techniques used for drought characterization and impact assessment under changing climate conditions. Literature was synthesized using four evaluation criteria: process realism, spatial applicability, uncertainty treatment, and implementation feasibility. Climate models, including GCMs, RCMs, and Earth System Models, are assessed in terms of their ability to represent drought drivers, persistence, extremes, and regional variability. Dynamical, statistical, and hybrid downscaling approaches are compared with respect to spatial refinement, data requirements, transferability, and performance across contrasting hydroclimatic regions. Bias-correction methods such as linear scaling, quantile mapping, variance adjustment, and emerging machine-learning approaches are evaluated for their capacity to reduce systematic errors while preserving drought-relevant statistics. Particular emphasis is placed on uncertainty propagation across the climate model–downscaling–bias correction chain, where errors can accumulate and alter projected drought frequency, duration, and severity. Existing integrated workflows are reviewed to identify methodological gaps related to multi-model ensembles, non-stationarity, observational limitations, and cross-scale consistency. Finally, the review concludes that future drought assessments should move toward ensemble-based, uncertainty-aware, and AI-assisted frameworks that jointly evaluate climate forcing, post-processing choices, and drought metrics to improve risk-informed adaptation planning.