Financing rapid community reconstruction after catastrophic disaster: lessons from the 2008 Wenchuan earthquake in China

Catastrophic disasters can change the course of urban development and challenge the long-run sustainability of cities and regions. How to rapidly reconstruct communities impaired by catastrophic disaster is a world-wide challenge. The reconstruction after the 2008 Wenchuan earthquake in China was an unusual case of very rapid reconstruction after a catastrophic disaster. Over US$147 billion was invested to rebuild the damaged areas within 3 years. The reconstruction was not simply building back what was destroyed, but was used as an opportunity to advance national goals for urbanization, rural transformation, and poverty reduction. In this article, we review how the reconstruction was planned, budgeted, and financed in the sociopolitical context of 2008 China. Particularly, we discuss two innovative programs, namely pair assistance and land-based financing. Despite the unique circumstances of China, lessons can be learned to speed up post-disaster reconstruction and urban development in other countries. Conversely, this case illustrates that a narrow focus on physical reconstruction may overlook broader economic and social issues.

     

Guest Editorial to the Special Issue: Lessons Learned from post-2008 Wenchuan Earthquake Community Recovery

Identifying the spatial extent of volcanic ash clouds in the atmosphere and forecasting their direction and speed of movement has important implications for the safety of the aviation industry, community preparedness and disaster response at ground level. Nine regional Volcanic Ash Advisory Centres were established worldwide to detect, track and forecast the movement of volcanic ash clouds and provide advice to en route aircraft and other aviation assets potentially exposed to the hazards of volcanic ash. In the absence of timely ground observations, an ability to promptly detect the presence and distribution of volcanic ash generated by an eruption and predict the spatial and temporal dispersion of the resulting volcanic cloud is critical. This process relies greatly on the heavily manual task of monitoring remotely sensed satellite imagery and estimating the eruption source parameters (e.g. mass loading and plume height) needed to run dispersion models. An approach for automating the quick and efficient processing of next generation satellite imagery (big data) as it is generated, for the presence of volcanic clouds, without any constraint on the meteorological conditions, (i.e. obscuration by meteorological cloud) would be an asset to efforts in this space. An automated statistics and physics-based algorithm, the Automated Probabilistic Eruption Surveillance algorithm is presented here for auto-detecting volcanic clouds in satellite imagery and distinguishing them from meteorological cloud in near real time. Coupled with a gravity current model of early cloud growth, which uses the area of the volcanic cloud as the basis for mass measurements, the mass flux of particles into the volcanic cloud is estimated as a function of time, thus quantitatively characterising the evolution of the eruption, and allowing for rapid estimation of source parameters used in volcanic ash transport and dispersion models.