Identifying Nature-Based Solution Priority Areas for Urban Waterlogging Adaptation Under Climate Change and Urban Expansion
気候変動と都市拡大下における都市水害適応のための自然基盤ソリューション優先地域の特定 (AI 翻訳)
Chenchen Yang, Dongxu Lin, Yuhan Duan, Chenshuo Wang, Ming Lei, Zhifang Wang
🤖 gxceed AI 要約
日本語
北京市中心部を対象に、気候変動と都市拡大を考慮した将来の都市水害リスクをシミュレーションし、自然基盤ソリューション(NbS)の優先地域を特定した。交通インフラに沿った回廊状のリスクパターンが明らかになり、緑地と道路の水文接続性の弱さが適応上の課題であることを示した。
English
This study identifies priority areas for nature-based solutions (NbS) to adapt to urban waterlogging under climate change and urban expansion, using Beijing as a case. It integrates land use simulation, hydrological modeling, and species distribution modeling to map future waterlogging risk, revealing a corridor pattern linked to transportation infrastructure. The key challenge is the weak hydrological connection between road runoff and adjacent green spaces. The framework demonstrates how risk simulation can inform spatially explicit adaptation planning.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文は気候変動適応策としてのNbSの実装に焦点を当てており、日本の都市におけるグリーンインフラ計画や適応策策定に示唆を与える。特に、道路と緑地の水文接続性の重要性は、日本の都市排水計画でも考慮すべき点である。
In the global GX context
This paper provides a spatially explicit framework for prioritizing nature-based solutions for urban climate adaptation, relevant to global cities facing waterlogging risks under climate change. The emphasis on hydrological connectivity between runoff sources and sinks offers a planning insight beyond just increasing green space quantity.
👥 読者別の含意
🔬研究者:This study demonstrates a methodological framework for coupling land use change, hydrological modeling, and species distribution modeling to identify NbS priority areas, applicable to other cities.
🏢実務担当者:Urban planners can use the corridor-based risk patterns and field investigation insights to redesign roadside green spaces for better runoff retention.
🏛政策担当者:The study underscores the need to integrate hydrological connectivity into urban green space planning policies for climate adaptation.
📄 Abstract(原文)
Identifying where nature-based solutions should be prioritized has become a critical task for climate-adaptive urban stormwater management under the combined pressures of climate change and urban expansion. Taking the central urban area of Beijing as a case study, this study develops a dynamic prediction framework that incorporates the Source–Flow–Sink (SFS) process of urban waterlogging. The framework integrates a future land use simulation model (FLUS), the Soil Conservation Service (SCS) hydrological model, and the Maximum Entropy (MaxEnt) model and incorporates both climate change (RCP8.5) and urban expansion to simulate the spatial configuration of waterlogging risk in 2031. High-risk areas were then overlaid with land-cover data and open-space distribution to identify potential NbS opportunity spaces, which were further examined through field investigation. The results show that future waterlogging risk in Beijing exhibits a clear corridor-oriented pattern closely associated with transportation infrastructure. Transportation-related variables account for more than 80% of total model contribution, suggesting a strong statistical association between future waterlogging occurrence and transportation-related spatial features. Field investigation further reveals that many roadside green spaces are elevated above adjacent roads, limiting their ability to receive and retain runoff. Thus, the key adaptation challenge lies not simply in the amount of green space, but in the weak hydrological connection between runoff pathways and adjacent open spaces. While Beijing’s priority areas are mainly corridor-based, other cities may be shaped by different processes and spaces. More broadly, this study demonstrates how hydrological risk simulation can be translated into spatially explicit planning priorities and more locally grounded adaptation decisions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/land15071198first seen 2026-07-05 05:07:16
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。