A scalable flood-resilience-index for measuring climate change adaptation: Munich city.

Climate change is affecting the frequency and intensity of rainfall extreme events worldwide. Despite the growing global awareness, developing flood resilient cities has proven to be a major challenge. This paper investigates the application of an event-based scalable Flood Resilience Index (FRI) for assessing climate change adaptation. Flood resilience is represented by three dimensions: physical, social and economic. A household climate adaptation is adopted consisting of a combination of a flood-proof gate with an indoor tank and a submersible pump system implemented in all houses. The climate related impact under a high-emission scenario (RCP8.5) is analysed for Munich with the CRCM5 Large-Ensemble. Results show that for Munich extreme heavy rainfall events are increasing. The FRI can successfully identify households and districts which: a) are mostly affected by heavy rainfall, b) benefit the most from the climate adaptation, and c) are the most resilient. For the most severe future scenario investigated the climate adaptation measure was able to improve 57% of all affected buildings within Maxvorstadt to an FRI equal to 1.0 during the event and recovery phase.

Leandro J ，Chen KF ，Wood RR ，Ludwig R ... -  《-》

Evaluating the response and adaptation of urban stormwater systems to changed rainfall with the CMIP6 projections.

Yao Y ，Li J ，Jiang Y ，Huang G ... -  《-》

A framework for quantifying climate-informed heavy rainfall change: Implications for adaptation strategies.

To understand the influence of climate change on heavy rainfalls and reduce the consequential multidimensional risks, we develop a climate-informed and adaptation strategies-related framework by using the information on heavy rainfalls and various socioeconomic factors. For this purpose, we firstly quantify the spatiotemporal characteristics of heavy rainfalls with various durations (1 h to multiple days) and return periods (2-year to 50-year) for the flood-prone country Cambodia, as a case study, during the historical period (1980-2005), mid-century (2040-2065), and late-century (2070-2095), using the latest three hourly climate model datasets under RCP 8.5 and 1 hourly ERA5 reanalysis datasets. A novel conditional artificial neural network (CANN) model is employed for temporal disaggregation to obtain the monthly maximum of 1 hourly rainfall in the future periods and subsequently, a zero-inflated generalized extreme value function (ZIGEV) is applied for extreme value analysis (EVA) to obtain rainfall intensity with different return periods. Secondly, the province-level flood risk change maps are developed based on a novel flood risk change index. The combination of CANN and ZIGEV performs better in EVA than traditional approaches by reducing the uncertainty from the stationarity assumption of temporal disaggregation and bias in the disaggregated rainfall. Rainfall intensity is projected to increase more in higher return periods and shorter durations towards the late-century, predominantly over Southern and Central Cambodia. Projected rainfall intensity-duration-frequency (IDF) curves in the capital city, Phnom Penh, reveal that the occurrence frequency of heavy rainfall in a given duration (e.g., 48 h) is likely to become ~10-fold in the mid-century. Results of province-level flood risk change maps indicate that Southeastern and Northwestern regions should be prioritized for employing adaption strategies. Our results will assist the policymakers in further mapping the flood susceptibility and vulnerability in different spatiotemporal scales across various communities and localities in the country and beyond.

Zhao W ，Abhishek ，Kinouchi T ，Ang R ，Zhuang Q ... -  《-》

An uncertainty-based framework to quantifying climate change impacts on coastal flood vulnerability: case study of New York City.

Zahmatkesh Z ，Karamouz M 《-》

Can flood resilience of green-grey-blue system cope with future uncertainty?

Urban flooding is becoming a great global concern due to growing cities, while climate change and urbanization may pose daunting challenges to both environment and humans. The integrated green-grey-blue (IGGB) system has gained interests worldwide to mitigate flood issues, however, how IGGB system acts in urban flood resilience and whether it can address future uncertainties have not been fully understood. In this study, a new framework, which combined an evaluation index system and coupling model, was constructed to quantify urban flood resilience (FR) and its responses to future uncertainties. The results showed that higher FR upstream than downstream; however, upstream FR declined approximately twice as much as downstream when faced with climate change and urbanization. Generally, climate change appeared to have a greater impact on urban flood resilience than urbanization, resulting to 3.20%-4.28% and 2.08%-4.09% FR reduction, respectively. The IGGB system could greatly improve robustness against future uncertainty, due to the fact that the IGGB without low impact development facilities (LIDs) was about 2 times in FR decline compared with IGGB with LIDs. The increase of LIDs proportion could diminish the impact of climate change, which shifted the dominant factor affecting FR from the interaction between urbanization and climate change to urbanization. Notably, a threshold of 13% construction land increase was quantified, beyond which negative effects of rainfall become dominant again. The results could guide IGGB design and urban flooding management in other similar regions.

Yin D ，Zhang X ，Cheng Y ，Jia H ，Jia Q ，Yang Y ... -  《-》