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<p>Reliable assessment of water balance (WB) under climate change requires an explicit treatment of uncertainty, particularly for precipitation (Pr). This study evaluated near‑future hydro‑climatic projections (2024–2054) for Lashkenar Village, northern Iran, using CMIP6 models under the SSP2‑4.5 scenario. A structured framework was applied, combining lead–lag correction, bias correction, statistical downscaling, and multi‑model ensembling. Among the tested approaches, Support Vector Regression (SVR) for downscaling and Bayesian Model Averaging (BMA) for ensembling provided the most robust performance, with precipitation correlation of <i>R</i> = 0.97 and temperature <i>R</i> = 0.6. Expanded uncertainty and dynamic Bootstrapping analyses quantified precipitation‑driven uncertainty, showing that approximately ± 11% of annual Pr propagated into WB components, equivalent to ± 0.38&#xa0;m of groundwater fluctuation. Change‑point detection identified 2014 (temperature) and 2021 (precipitation) as critical shifts, with subsequent trends indicating warming (+ 0.018&#xa0;°C yr⁻¹) and declining rainfall (-1.03&#xa0;mm yr⁻¹). Traditional Mann–Kendall tests failed to capture these subtle transitions, whereas the proposed Skewness‑Torque (ST) framework and Yearly Skewness Trend (YST) method revealed distributional asymmetry, time lags, and resilience thresholds, providing early‑warning signals of regime shifts. Beyond conventional trend tests, these skewness‑based diagnostics represent a methodological advance that is applicable to other data‑scarce basins. Results suggest that Lashkenar is transitioning toward drought conditions, underscoring the need for adaptive water management strategies. The framework illustrates how carefully adjusted CMIP6 outputs, alongside Water Balance estimates with quantified uncertainty, can inform climate-resilient planning in data-scarce mountainous catchments.</p>

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Water resources projection using CMIP6 global climate models and water balance uncertainty

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月18日

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  • Farhad Daliri,
  • Vijay P. Singh

摘要

Reliable assessment of water balance (WB) under climate change requires an explicit treatment of uncertainty, particularly for precipitation (Pr). This study evaluated near‑future hydro‑climatic projections (2024–2054) for Lashkenar Village, northern Iran, using CMIP6 models under the SSP2‑4.5 scenario. A structured framework was applied, combining lead–lag correction, bias correction, statistical downscaling, and multi‑model ensembling. Among the tested approaches, Support Vector Regression (SVR) for downscaling and Bayesian Model Averaging (BMA) for ensembling provided the most robust performance, with precipitation correlation of R = 0.97 and temperature R = 0.6. Expanded uncertainty and dynamic Bootstrapping analyses quantified precipitation‑driven uncertainty, showing that approximately ± 11% of annual Pr propagated into WB components, equivalent to ± 0.38 m of groundwater fluctuation. Change‑point detection identified 2014 (temperature) and 2021 (precipitation) as critical shifts, with subsequent trends indicating warming (+ 0.018 °C yr⁻¹) and declining rainfall (-1.03 mm yr⁻¹). Traditional Mann–Kendall tests failed to capture these subtle transitions, whereas the proposed Skewness‑Torque (ST) framework and Yearly Skewness Trend (YST) method revealed distributional asymmetry, time lags, and resilience thresholds, providing early‑warning signals of regime shifts. Beyond conventional trend tests, these skewness‑based diagnostics represent a methodological advance that is applicable to other data‑scarce basins. Results suggest that Lashkenar is transitioning toward drought conditions, underscoring the need for adaptive water management strategies. The framework illustrates how carefully adjusted CMIP6 outputs, alongside Water Balance estimates with quantified uncertainty, can inform climate-resilient planning in data-scarce mountainous catchments.