Risk Assessment of Shanghai Extreme Flooding Under the Land Use Change Scenario

Abstract Environmental changes have led to non-stationary flood risks in coastal cities. How to quantitatively characterize the future change trend and effectively adapt is a frontier scientific problem that needs to be solved urgently. To this end, this study uses the 2010 Shanghai land use data as the base and uses the GeoSOS-FLUS model to simulate future land use change scenarios (2030, 2050, and 2100). Based on the results of storm and flood numerical simulations, probabilistic risk, and other multidisciplinary methods, extreme storm and flood risks of various land uses (residential land, commercial and public service land, industrial land, transportation land, agricultural land, and other land) in Shanghai are analyzed and 4 adaptation strategies to deal with extreme flooding have been developed. The research results show that: 1) Under the two emission scenarios, residential, commercial and public service, and industrial land have the highest exposure assets. Under the RCP8.5 scenario, the exposure of assets in 2100, 2050, and 2030 will be 1.7 times, 1.5 times, and 1.3 times that in 2010 for 1/1000-year, respectively; the losses will be 2.7 times, 2.0 times, and 1.8 times that in 2010, respectively. 2) The spatial pattern of loss, which forms the scattered distribution of 1/10-year, is mainly distributed on both sides of the Huangpu River. For 1/1000-year, which is mainly gradually showed a strip distribution, continuous distribution of the city center, and the Qingpu-Songjiang depression in the southwest are high-risk areas for storm floods. 3) The risks are mainly distributed in the city center, the lower reaches of the Huangpu River, the northern shore of Hangzhou Bay, the Qingpu-Songjiang depression in the southwest, and Chongming Island (southwest and northeast). Our work can provide decision-making basis for risk-sensitive based urban planning, flood risk adaptation, and resilience building in Shanghai. The methodology can also provide a reference for risk assessment in other coastal areas.

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Risk assessment of shanghai extreme flooding under the land use change scenario

Natural Hazards ◽

10.1007/s11069-021-04978-1 ◽

2021 ◽

Author(s):

Xinmeng Shan ◽

Jie Yin ◽

Jun Wang

Keyword(s):

Risk Assessment ◽

Land Use ◽

Land Use Change ◽

Change Scenario ◽

Extreme Flooding

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A Cellular Automata Model for Integrated Simulation of Land Use and Transport Interactions

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030149 ◽

2021 ◽

Vol 10 (3) ◽

pp. 149

Author(s):

Nuno Pinto ◽

António P. Antunes ◽

Josep Roca

Keyword(s):

Land Use ◽

Cellular Automata ◽

Land Use Change ◽

Urban Studies ◽

Particle Swarm Algorithm ◽

Cellular Automata Model ◽

Integrated Simulation ◽

The Road ◽

Ca Model ◽

The City

Cellular automata (CA) models have been used in urban studies for dealing with land use change. Transport and accessibility are arguably the main drivers of urban change and have a direct influence on land use. Land use and transport interaction models deal with the complexity of this relationship using many different approaches. CA models incorporate these drivers, but usually consider transport (and accessibility) variables as exogenous. Our paper presents a CA model where transport variables are endogenous to the model and are calibrated along with the land use variables to capture the interdependent complexity of these phenomena. The model uses irregular cells and a variable neighborhood to simulate land use change, taking into account the effect of the road network. Calibration is performed through a particle swarm algorithm. We present an application of the model to a comparison of scenarios for the construction of a ring road in the city of Coimbra, Portugal. The results show the ability of the CA model to capture the influence of change of the transport network (and thus in accessibility) in the land use dynamics.

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Localization of Relevant Urban Micro-Consolidation Centers for Last-Mile Cargo Bike Delivery Based on Real Demand Data and City Characteristics

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211036351 ◽

2021 ◽

pp. 036119812110363

Author(s):

Christian Rudolph ◽

Alexis Nsamzinshuti ◽

Samuel Bonsu ◽

Alassane Ballé Ndiaye ◽

Nicolas Rigo

Keyword(s):

Land Use ◽

City Center ◽

Analytical Hierarchical Process ◽

Logistics Service ◽

Last Mile ◽

Logistics Service Provider ◽

Cargo Bike ◽

The City ◽

Weight Allocation ◽

Use Category

The use of cargo cycles for last-mile parcel distribution requires urban micro-consolidation centers (UMC). We develop an approach to localize suitable locations for UMCs with the consideration of three criteria: demand, land use, and type of road. The analysis considers metric levels (demand), linguistic levels (land use), and cardinal levels (type of road). The land-use category is divided into commercial, residential, mixed commercial and residential, and others. The type of road category is divided into bicycle road, pedestrian zone, oneway road, and traffic-calmed road. The approach is a hybrid multi-criteria analysis combining an Analytical Hierarchical Process (AHP) and PROMETHEE methods. We apply the approach to the city center of Stuttgart in Germany, using real demand data provided by a large logistics service provider. We compared different scenarios weighting the criteria differently with DART software. The different weight allocation results in different numbers of required UMCs and slightly different locations. This research was able to develop, implement, and successfully apply the proposed approach. In subsequent steps, stakeholders such as logistics companies and cities should be involved at all levels of this approach to validate the selected criteria and depict the “weight” of each criterion.

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FLOOD RISK ASSESSMENT UNDER HISTORICAL AND PREDICTED LAND USE CHANGE USING CONTINUOUS HYDROLOGIC MODELING

10.37099/mtu.dc.etds/917 ◽

2015 ◽

Author(s):

Jonathan T. Nelson

Keyword(s):

Risk Assessment ◽

Land Use ◽

Land Use Change ◽

Flood Risk ◽

Hydrologic Modeling ◽

Flood Risk Assessment

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Commercial and industrial land use change, job decentralization and growth controls: a spatially explicit analysis

Journal of Land Use Science ◽

10.1080/17474230903437162 ◽

2010 ◽

Vol 5 (1) ◽

pp. 45-66 ◽

Cited By ~ 9

Author(s):

Michail Fragkias ◽

Jacqueline Geoghegan

Keyword(s):

Land Use ◽

Land Use Change ◽

Spatially Explicit ◽

Industrial Land ◽

Industrial Land Use ◽

Explicit Analysis ◽

Growth Controls

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Multivariate Analysis for Assessing Sources, and Potential Risks of Polycyclic Aromatic Hydrocarbons in Lisbon Urban Soils

Minerals ◽

10.3390/min9030139 ◽

2019 ◽

Vol 9 (3) ◽

pp. 139 ◽

Cited By ~ 3

Author(s):

Anabela Cachada ◽

Ana Dias ◽

Amélia Reis ◽

Eduardo Ferreira da Silva ◽

Ruth Pereira ◽

...

Keyword(s):

Risk Assessment ◽

Polycyclic Aromatic Hydrocarbons ◽

Urban Planning ◽

Aromatic Hydrocarbons ◽

Urban Soils ◽

Cost Effective ◽

City Center ◽

Polycyclic Aromatic ◽

Potential Risks ◽

The City

Urban soils quality may be severely affected by polycyclic aromatic hydrocarbons (PAHs) contamination, as is the case of Lisbon (Portugal). However, to conduct a risk assessment analysis in an urban area can be a very difficult task due to the patchy nature and heterogeneity of these soils. Thus, the present study aims to provide an example on how to perform the first tier of a risk assessment plan in the case of urban soils using a simpler, cost effective, and reliable framework. Thus, a study was conducted in Lisbon to assess the levels of PAH, their potential risks to the environment and human health, and to identify their major sources. Source apportionment was performed by studying PAHs profiles, their relationship with potentially toxic elements, and general characteristics of soil using multivariate statistical methods. Results showed that geostatistical tools are useful for evaluating the spatial distribution and major inputs of PAHs in urban soils, as well as to identify areas of potential concern, showing their usefulness in risk assessment analysis and urban planning. Particularly, the prediction maps obtained allowed for a clear identification of areas with the highest levels of PAHs (close to the airport and in the city center). The high concentrations found in soils from the city center should be a result of long-term accumulation due to diffuse pollution mostly from traffic (through atmospheric emissions, tire debris and fuel exhaust, as well as pavement debris). Indeed, most of the sites sampled in the city center were historical gardens and parks. The calculation of potential risks based on different models showed that there is a high discrepancy among guidelines, and that risks will be extremely associated with the endpoint or parameters used in the different models. Nevertheless, this initial approach based on total levels was useful for identifying areas where a more detailed risk assessment is needed (close to the airport and in the city center). Therefore, the use of prediction maps can be very useful for urban planning, for example, by crossing information obtained with land uses, it is possible to define the most problematic areas (e.g., playgrounds and schools).

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Understanding the Shared E-scooter Travels in Austin, TX

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9020135 ◽

2020 ◽

Vol 9 (2) ◽

pp. 135 ◽

Cited By ~ 11

Author(s):

Junfeng Jiao ◽

Shunhua Bai

Keyword(s):

Higher Education ◽

Land Use ◽

Operation Time ◽

Street Connectivity ◽

Travel Patterns ◽

City Center ◽

University Of Texas ◽

The University ◽

The Relationship ◽

The City

This paper investigated the travel patterns of 1.7 million shared E-scooter trips from April 2018 to February 2019 in Austin, TX. There were more than 6000 active E-scooters in operation each month, generating over 150,000 trips and covered approximately 117,000 miles. During this period, the average travel distance and operation time of E-scooter trips were 0.77 miles and 7.55 min, respectively. We further identified two E-scooter usage hotspots in the city (Downtown Austin and the University of Texas campus). The spatial analysis showed that more trips originated from Downtown Austin than were completed, while the opposite was true for the UT campus. We also investigated the relationship between the number of E-scooter trips and the surrounding environments. The results show that areas with higher population density and more residents with higher education were correlated with more E-scooter trips. A shorter distance to the city center, the presence of transit stations, better street connectivity, and more compact land use were also associated with increased E scooter usage in Austin, TX. Surprisingly, the proportion of young residents within a neighborhood was negatively correlated with E-scooter usage.

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