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Updated: May 15, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
A multi-temporal framework for assessing flood risk to cultural heritage: spatiotemporal evidence from Shandong
Aihui Jiang1, Zhiwei Yang2, Jie Dai3
1College of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
Abstract:
Cultural heritage sites are increasingly exposed to flood hazards, yet existing assessments often overlook heritage-specific vulnerability and rely on static spatial units. To address these limitations, this study develops a Comprehensive Heritage Flood Risk Index (CHFRI) framework that integrates hazard, exposure, vulnerability, and response capacity dimensions, with explicitly incorporation of heritage attributes into the assessment. A combined AHP-entropy weighting approach was employed to balance expert judgement with objective data, and a sensitivity analysis was conducted to confirm the robustness of spatial risk identification across different weight combinations. This framework was then applied to 2,194 heritage sites in Shandong Province over the period 2000 to 2020. Results indicated that: (1) the spatial pattern remained consistent: high-risk sites concentrated in eastern coastal (Rizhao, Weihai) and western inland cities (Dezhou, Liaocheng), while a low-risk belt extended across the central region; (2) the overall risk structure improved significantly: high-risk sites decreased from 39.88% to 27.62%, while Intermediate-risk sites increased from 37.32% to 48.63%. Among the 30 Highest-risk sites in 2000, 26 were downgraded, yet 18 new sites escalated to the Highest-risk category by 2020, including 4 national-level and 14 provincial-level sites; (3) heritage typologies showed divergent trajectories: ancient buildings and modern buildings exhibited substantial risk reduction, whereas grotto temples and stone carvings remained persistently vulnerable. The CHFRI framework provides a transferable model for climate-resilient heritage conservation and recommends future integration of AI-enhanced predictive tools.
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