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Abstract
Driven by both climate change and urbanization, extreme rainfall events are becoming more frequent and having an increasing impact on urban commuting. Using hourly rainfall data and "metro" origin–destination (OD) flow data in Shanghai, China, this study uses the Prophet time series model to calculate the predicted commuting flows during rainfall events and then quantifies the spatiotemporal variations of commuting flows due to rainfall at station and OD levels. Our results show the following: 1) In general, inbound commuting flows at metro stations tend to decrease with hourly rainfall intensity, varying across station types. The departure time of commuters is usually delayed by rainfall, resulting in a significant stacking effect of inbound flows at metro stations, with a pattern of falling followed by rising. The sensitivity of inbound flows to rainfall varies at different times, high at 0700 and 1700 LT and low at 0800, 0900, 1800, and 1900 LT because of the different levels of flexibility of departure time. 2) Short commuting OD flows (≤15 min) are more affected by rainfall, with an average increase of 7.3% and a maximum increase of nearly 35%, whereas long OD flows (>15 min) decrease slightly. OD flows between residential and industrial areas are more affected by rainfall than those between residential and commercial (service) areas, exhibiting a greater fluctuation of falling followed by rising. The sensitivity of OD flows to rainfall varies across metro lines. The departure stations of rainfall-sensitive lines are mostly distributed in large residential areas that rely heavily on the metro in the morning peak hours and in large industrial parks and commercial centers in the evening peak hours. Our findings reveal the spatiotemporal patterns of commuting flows resulting from rainfall at a finer scale, which provides a sound basis for spatial and temporal response strategies. This study also suggests that attention should be paid to the surges and stacking effects of commuting flows at certain times and areas during rainfall events.

Abstract. This article presents a flood risk assessment for
Shanghai, which provides an indication of what buildings (including
residential, commercial, office, and industrial) will be exposed to flooding
and its damage. Specifically, this assessment provides a risk assessment
that buildings may face after construction. To achieve the flood risk
assessment on buildings, we developed an integrated flood model and
collected data on building shape and number of floors, land use, and
construction costs for different building types in Shanghai. The extreme
compound flood scenarios (1/200-, 1/500-, 1/1000-, and 1/5000-year floods) and
building metadata were aggregated using a risk analysis chain. According
to the damage for different flood scenarios, the average annual loss (AAL)
can be calculated and is referred to as building flood risk. The AAL of
residential, commercial, office, and industrial buildings is USD 12.3, 2.5,
3.7, and 3.4 million, respectively. Among the 15 (non-island) districts
in Shanghai, Pudong has the highest AAL. The risk analysis chain developed
in this study can be reproduced for other megacities. The results provide a
clear picture for future building flood risks which links directly to
disaster risk management, which implies the extent of flood risk in building
types, sub-districts, and districts related to the Shanghai Master Plan.
This assessment takes into consideration future climate change scenarios,
information for scenario-based decision making, and a cost–benefit analysis
for extreme flood risk management in Shanghai. We also discussed different
potential adaptation options for flood risk management.