Measuring Recovery to Build up Metrics of Flood Resilience Based on Pollutant Discharge Data: A Case Study in East China

Abstract

Building "disaster-resilient" rather than "disaster-resistant" cities/communities requires the development of response capabilities to natural disasters and subsequent recovery. This study devises a new method to measure resilience via recovery capability to validate indicators from social, economic, infrastructural, and environmental domains. The pollutant discharge data (waste-water and waste-gas discharge/emission data) of local power plants, sewage treatment plants and main factories were used to monitor recovery process of both people's living and local industrial production as the waste water/gas is released irregularly during the short disaster-hit period. A time series analysis of such data was employed to detect the disturbance on these infrastructures from disasters and to assess community recovery capability. A recent record-breaking flash flood in Changzhou, a city in eastern-central China, was selected as a case study. We used ordinal logistic regression to identify leading proxies of flood resilience. A combination of six variables related to socioeconomic factors, infrastructure development and the environment, stood out and explained 61.4% of the variance in measured recovery capability. These findings substantiate the possibility of using recovery measurement based on pollutant discharge to validate resilience metrics, and contribute more solid evidences for policy-makers and urban planners to make corresponding measures for resilience enhancement.

Flood resilience: a systematic review

“…The lack of fluidity between perspectives of resilience has led to a clear divide in opinion as to whether there should be a united definition of resilience in order to make it more operational, or whether a single definition is undesirable. On the one hand, it is argued that no agreed definition of resilience makes it difficult to measure (Song, Huang, and Li 2017;Balsells et al 2013;De Bruijn 2004). On the other hand, a single understanding of resilience is argued to be undesirable, where the multiple frameworks to measure resilience should be seen as a positive thing towards understanding and operationalising the concept (Hegger et al 2016;Keating et al 2017).…”

“…Simulation based approaches typically involved using hydraulic modelling software to simulate flood events as a means of analysing system performance (see Birgani and Yazdandoost 2016;Owotoki et al 2006;Mugume et al 2015;Schinke et al 2016;Golz, Schinke, and Naumann 2015;Pregnolato et al 2016;Gersonius et al 2008). Alternatively, Coates et al (2016) and Song, Huang, and Li (2017) use other simulation methods, such as Agent-Based Modelling and Change Detection Analysis, respectively. The indicator methods used are all similar in principle; however they are applied differently and can be easily tailored to different contexts and hazards across geographical boundaries.…”

“…Recovery processes which focus on the shorter-term are usually associated with "bouncing back" (Birgani and Yazdandoost 2016) or establishing pre-disaster conditions, and are associated with speed (Song, Huang, and Li 2017). The time in which recovery is measured can therefore be considered a metric of resilience.…”

“…Only one of the 16 papers directly quantify recovery in a temporal sense. Song, Huang, and Li (2017) adopt a dynamic approach and measure immediate recovery over time through utilising big-data availability in China. Whereas, long-term recovery is mainly focused with the learning and adaptation process from the event (Nguyen and James 2013).…”

Assessing local resilience to typhoon disasters: A case study in Nansha, Guangzhou

“…Although the literature emphasizes the importance of disaster resilience research and management, no consensus has been reached on a clear definition of disaster resilience [ 7 , 8 ]. The concept has been taken in two main directions: some researchers have considered disaster resilience to be the product of a set of related items such as vulnerability and recovery capability [ 9 – 18 ], whereas others have considered it to be a multifaceted concept that involves indicators as proxies for resilience in a set of sub-dimensions, such as social resilience, economic resilience, and infrastructural resilience [ 1 , 19 – 21 ]. For the first direction, it could clarify the context of resilience and make it easily understood from a qualitative perspective, whereas conceptualization in the second direction could help to build a more applicable framework for resilience assessment.…”