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... However, most of the available tools to assess the impact of disruption in transport systems rest on network theory and the concept of percolation 5,6 . Based on data on vehicles, passengers and freight, impacts are measured by the increase in travel time 7 , loss of accessibility 8 or quantities of blocked flows 9,10 . Such indicators can then inform infrastructure planning 11 . ...
... The model is applied to the United Republic of Tanzania. Combining data on the transport sector from Pant et al. 9 and OpenStreetMap contributors 37 , the input-output table for the country 38 , a listing of Tanzanian firms with their basic characteristics and the results of a dedicated firm survey that collected data on inventories and vulnerability to disruptions 39 , we created a supply chain model that fully embeds the transport network 40 . A set of firmsone per sector-is modelled on each important node of the transport network, along with households, who buy firms' outputs. ...
... According to the World Bank's observers, the country is subject to small, frequent floods, which often interrupt roads for a few days, but also to larger floods that severely damage the transport network, which then takes very long to get repaired. Pant et al. 9 reported 13 such sizeable transport-disrupting events between 2014 and 2016. Using a comprehensive set of scenarios, we estimate the indirect economic losses caused by road disruptions for the country's households and international buyers. ...
Building resilience against shocks has become a pillar of sustainability. By understanding how the different components of an economy interact in times of crisis, we can design resilience strategies that go beyond building walls or dams. We formulate an original agent-based model to explore a crucial pathway through which a disaster affects the economy: the transport–supply chain nexus. The model simulates the behaviour of firms facing transport and supply disruptions and estimates the resulting indirect losses. As an illustration, the model is used to assess the criticality of Tanzanian roads, which are vulnerable to floods. We report three main results. First, the model generates maps that identify the transport infrastructure assets that are most critical for specific supply chains: roads that are most important for food security are different from those supporting international trade, for instance. Second, economic losses from transport disruptions increase non-linearly with the duration of disruptions, highlighting the benefit from fast repairs. Third, by combining economic and transport modelling, we can consider a broader spectrum of interventions. Beyond strengthening the transport system, it is also possible to make supply chains more resilient to disruption with, for instance, sourcing decisions or inventory management.
As the adverse effects of climate change are increasingly becoming unavoidable, calls for improving climate adaptation assessments have gathered interest at the global scale. Infrastructure policymakers and practitioners are now interested in understanding climate vulnerabilities and risks that capture the systemic nature of failure propagation seen across interconnected networks. This would help inform adaptation planning objectives meant to improve systemic resilience. This paper presents recent technical methodological and tool-based advances made in climate vulnerability, risk, and adaptation modeling of large-scale infrastructure networks. These methodologies adopt a bottom-up approach that focuses on creating data-rich representations of infrastructure network attributes, resource flows, and socio-economic indicators that are all used for quantifying direct and indirect risks to network assets exposed to extreme climate hazards at multiple scales. Insights from different case studies are presented to show how such methodologies have been used in practice for informing different policy needs. The paper concludes by identifying the existing gaps and future opportunities for such bottom-up infrastructure network vulnerability, risk, and adaptation assessment methodologies.
The paper provides a first quantitative estimate of the potential number of people and value of assets exposed to coastal
flooding in Dar es Salaam, Tanzania. The study used an elevation-based geographic information system-analysis based on physical
exposure and socio-economic vulnerability under a range of climate and socio-economic scenarios. It particularly considered
a worst-case scenario assuming even if defences (natural and/or man-made) exist, they are subjected to failure under a 100-year
flood event. About 8% of Dar es Salaam lies within the low-elevation coastal zone (below the 10 m contour lines). Over 210,000
people could be exposed to a 100-year coastal flood event by 2070, up from 30,000 people in 2005. The asset that could be
damaged due to such event is also estimated to rise from US$35 million (2005) to US$10 billion (2070). Results show that socio-economic
changes in terms of rapid population growth, urbanisation, economic growth, and their spatial distribution play a significant
role over climate change in the overall increase in exposure. However, the study illustrates that steering development away
from low-lying areas that are not (or less) threatened by sea-level rise and extreme climates could be an effective strategic
response to reduce the future growth in exposure. Enforcement of such policy where informal settlements dominate urbanisation
(as in many developing countries) could undoubtedly be a major issue. It should be recognised that this analysis only provides
indicative results. Lack of sufficient and good quality observational local climate data (e.g. long-term sea-level measurements),
finer-resolution spatial population and asset distribution and local elevation data, and detailed information about existing
coastal defences and current protection levels are identified as limitations of the study. As such, it should be seen as a
first step towards analysing these issues and needs to be followed by more detailed, city-based analyses.
Inter tropical convergence zones
D G Andrews
J R Holton
C B Leovy
Andrews, D. G., Holton, J. R., & Leovy, C. B. (2003). Inter tropical convergence zones. Atmospheric Sciences, 56, 374-399.
Spatial analysis of human exposure and vulnerability to coastal flooding in Dar es Salaam
S S Mutanga
Mutanga, S. S., Mwiruki, B., & Ramoelo, A. (2014). Spatial analysis of human exposure and vulnerability to coastal flooding in Dar es
Salaam, United Republic of Tanzania. Africa Insight, 43(4), 171-186.
Transport Risk Analysis for The United Republic of Tanzania-Final Report Oxford Infrastructure Analytics Ltd
Flood List. (2016d). http://floodlist.com/africa/ Tanzania-floods-morogoro-region-leave-5-dead-april-2016. Accessed July 08, 2017.
Transport Risk Analysis for The United Republic of Tanzania-Final Report
Oxford Infrastructure Analytics Ltd.