The spatial exposure of the Chinese infrastructure system to flooding and drought hazards

Hu, Xi; Shi, Peijun; Lim, Wee Ho

doi:10.1007/s11069-015-2012-3

Cited by 27 publications

(20 citation statements)

References 42 publications

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“…The CaMa-Flood is currently the only open-source global river model available (http://hydro.iis.u-tokyo.ac.jp/ yamadai/cama-flood/index.html) that is capable of simulating backwater effect within a reasonable computational time and providing flooded area, flooded volume and flood depth. It has been widely used in flood assessment and future projection of global food risk [12,16,36].…”

Section: Evaluating Ghms-based Flood Simulation Against Gsim Datamentioning

confidence: 99%

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Tao

Sun

Gentine

et al. 2019

Environ. Res. Lett.

118

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A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971-2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics-machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment.

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Section: Evaluating Ghms-based Flood Simulation Against Gsim Datamentioning

confidence: 99%

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Tao

Sun

Gentine

et al. 2019

Environ. Res. Lett.

118

View full text Add to dashboard Cite

show abstract

“…The CaMa-Flood model has been validated extensively for its ability to simulate runoff in some of the largest catchments of the globe including the Amazon, Mississippi, Parana, Niger, Congo, Ob, Ganges, Lena, and Mekong [22,24,33]. Given the high credibility of the model in simulating river flow and flood inundation dynamics, the model has been used to assess the impacts of climate change at regional to global scales [19,20,[33][34][35][36]. This study uses the globally calibrated version of Cama-Flood model that was used to generate global scale runoff projections in Hirabayashi et al [20].…”

Section: Cama-flood Hydrodynamic Modelmentioning

confidence: 99%

Flooding Related Consequences of Climate Change on Canadian Cities and Flow Regulation Infrastructure

Gaur

Yamazaki

et al. 2019

Water

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This study discusses the flooding related consequences of climate change on most populous Canadian cities and flow regulation infrastructure (FRI). The discussion is based on the aggregated results of historical and projected future flooding frequencies and flood timing as generated by Canada-wide hydrodynamic modelling in a previous study. Impact assessment on 100 most populous Canadian cities indicate that future flooding frequencies in some of the most populous cities such as Toronto and Montreal can be expected to increase from 100 (250) years to 15 (22) years by the end of the 21st century making these cities highest at risk to projected changes in flooding frequencies as a consequence of climate change. Overall 40–60% of the analyzed cities are found to be associated with future increases in flooding frequencies and associated increases in flood hazard and flood risk. The flooding related impacts of climate change on 1072 FRIs located across Canada are assessed both in terms of projected changes in future flooding frequencies and changes in flood timings. Results suggest that 40–50% of the FRIs especially those located in southern Ontario, western coastal regions, and northern regions of Canada can be expected to experience future increases in flooding frequencies. FRIs located in many of these regions are also projected to experience future changes in flood timing underlining that operating rules for those FRIs may need to be reassessed to make them resilient to changing climate.

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“…We drive a global river routing model -Catchment-Based Macro-scale Floodplain (CaMa-Flood) -using the daily runoff of the Atmospheric and Oceanic General Circulation Models (AOGCMs) at a spatial resolution of 1° x 1°1 (Hu et al, 2017). The CaMa-Flood model routes the runoff input simulated by a land surface model into the oceans or lakes along a prescribed river network (Yamazaki et al, 2011).…”

Section: Changing Flood Hazardsmentioning

confidence: 99%

Methodology and Findings for the Exposure Analysis of the Chinese Wastewater Sector to Flooding and Earthquakes Hazards

Hu¹,

Pant²,

Zorn³

et al. 2019

Self Cite

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. This paper is a product of the Global Facility for Disaster Reduction and Recovery, Climate Change Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world.

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The spatial exposure of the Chinese infrastructure system to flooding and drought hazards

Cited by 27 publications

References 42 publications

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Flooding Related Consequences of Climate Change on Canadian Cities and Flow Regulation Infrastructure

Methodology and Findings for the Exposure Analysis of the Chinese Wastewater Sector to Flooding and Earthquakes Hazards

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