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<p>Basin-scale management often requires prioritization across fewer than 20 institutionally defined assessment units, limiting the information available for uncertainty evaluation. This study develops the Small-Sample Uncertainty Assessment Protocol (SSUAP), which combines sample-size-responsive confidence thresholds, six plausible component-weight scenarios, binary priority classification, and confidence-matched management guidance without fitting probability distributions to the small cross-sectional sample. The framework was applied to five Upper Yangtze River sub-basins using climatic, hydrological, land-use, vegetation, and soil data for 1991–2024. High/Low priority classifications remained unchanged across all six weight scenarios, yielding complete pairwise classification agreement. Upper Stream and Wujiang were consistently classified as High Priority, while Jialing, Jinsha, and Mintuo remained Low Priority. Under the primary formula-derived thresholds, 20% of the sub-basins received High confidence and 80% received Moderate confidence. Under the broader operational bands of 0.20 and 0.40, the corresponding distribution was 40% High and 60% Moderate because Upper Stream shifted to High confidence. The composite and texture-based erosion classifications agreed for three of five sub-basins. Controlled simulations at representative sample sizes from (<i>n</i> = 3) to (<i>n</i> = 20) showed increasing confidence-tier utilization with sample size and mean ensemble agreement of approximately 0.93. SSUAP provides a transparent procedure for separating intervention priority from implementation confidence in institutionally constrained basin assessments, while requiring further validation across independent applications.</p>

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Managing Basin Vulnerability Under Uncertainty: A Confidence-Based Framework for Small-Sample Water Resources Decisions

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Fatima Zahra Kherazi,
  • Dongying Sun,
  • Charafa El Rhadiouini,
  • Jan Muhammad Sohu,
  • Sonia Najam Shaikh,
  • Sanam Soomro

摘要

Basin-scale management often requires prioritization across fewer than 20 institutionally defined assessment units, limiting the information available for uncertainty evaluation. This study develops the Small-Sample Uncertainty Assessment Protocol (SSUAP), which combines sample-size-responsive confidence thresholds, six plausible component-weight scenarios, binary priority classification, and confidence-matched management guidance without fitting probability distributions to the small cross-sectional sample. The framework was applied to five Upper Yangtze River sub-basins using climatic, hydrological, land-use, vegetation, and soil data for 1991–2024. High/Low priority classifications remained unchanged across all six weight scenarios, yielding complete pairwise classification agreement. Upper Stream and Wujiang were consistently classified as High Priority, while Jialing, Jinsha, and Mintuo remained Low Priority. Under the primary formula-derived thresholds, 20% of the sub-basins received High confidence and 80% received Moderate confidence. Under the broader operational bands of 0.20 and 0.40, the corresponding distribution was 40% High and 60% Moderate because Upper Stream shifted to High confidence. The composite and texture-based erosion classifications agreed for three of five sub-basins. Controlled simulations at representative sample sizes from (n = 3) to (n = 20) showed increasing confidence-tier utilization with sample size and mean ensemble agreement of approximately 0.93. SSUAP provides a transparent procedure for separating intervention priority from implementation confidence in institutionally constrained basin assessments, while requiring further validation across independent applications.