A framework for long‐term scenario analysis in the <scp>T</scp>aihu <scp>B</scp>asin, <scp>C</scp>hina

Cheng, Xiaotao; Evans, E. P.; Wu, Haoyun; Thorne, Colin R.; Han, Songjun; Simm, Jonathan

doi:10.1111/jfr3.12024

Cited by 14 publications

(11 citation statements)

References 12 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Procedure. The flood risk assessment framework is adopted in this paper from the China/UK scientific cooperation project "China-UK Scenario Analysis Technology for River Basin Flood Risk Management in the Taihu Basin" [26]. The methodology contains a number of steps ( Figure 3).…”

Section: 2mentioning

confidence: 99%

A Three-Parameter S-Shaped Function of Flood Return Period and Damage

Cheng

et al. 2016

Advances in Meteorology

View full text Add to dashboard Cite

With growing flood risk due to increased urbanization, flood damage assessment and flood risk management must be reconsidered. To demonstrate and assess the new features and trends of flood risk in urbanized areas, a novel S-shaped function of return period and damage(R-D)is proposed. The function contains three parameters, which are defined as the maximum flood damageA, critical return periodRc, and integrated loss coefficientk. A basic framework for flood damage assessment was established to evaluate flood damage in the Taihu Basin under various scenarios. The simulation results were used to construct the floodR-Dfunctions. The study results show that the floodR-Dmodel based on the Gompertz function agrees well with the mutability of flood damage in the highly urbanized basin when the flood scale exceeds the defense capability. TheR-Dfunction can be utilized for timely and effective flood damage assessment and prediction. It can describe the impacts of socioeconomic development, urbanization degree, and flood control capability improvements well. The turning points of the function curve can be used as gradation criteria for rational strategy development associated with flood hazards.

show abstract

Section: 2mentioning

confidence: 99%

A Three-Parameter S-Shaped Function of Flood Return Period and Damage

Cheng

et al. 2016

Advances in Meteorology

View full text Add to dashboard Cite

show abstract

“…Until today only few studies addressed large‐scale flood risk assessments, e.g. the RASP project in England and Wales (Hall et al ., , ) or more recently the Taihu Basin Foresight Project in China (Cheng et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Continuous, large‐scale simulation model for flood risk assessments: proof‐of‐concept

Falter

Dũng

Vorogushyn

et al. 2014

J Flood Risk Management

View full text Add to dashboard Cite

In this paper we present the Regional Flood Model (RFM), a process‐based model cascade developed for large‐scale basins. The objective of this study is to demonstrate that flood risk assessments, based on a continuous simulation approach, including rainfall–runoff, 1D river network, 2D hinterland inundation and damage estimation models, are feasible at the scale of large catchments. RFM is applied to the German part of the Elbe catchment including around 2700 river‐km. For this proof‐of‐concept study, simulations are performed continuously over the period of 1990–2003. Simplification of equations and parallelisation enable the continuous 2D hydrodynamic inundation simulation with reasonable run‐times on a relatively high resolution of 100 m. As uncertainties are introduced with each module along the model chain, results are evaluated, where possible, with observed data. Results indicate that uncertainties are significant, especially for hydrodynamic simulations. This is basically a consequence of low data quality and disregarding dike breach effects in the simulations. Reliable information on overbank cross‐sections and dikes is expected to considerably improve the results. We conclude that the large‐scale simulation of catchment processes, inundation and damage, driven by long‐term climate data, is viable within a continuous simulation framework. It has the potential to provide a spatially consistent, large‐scale picture of flood risk.

show abstract

“…The hydrological processes of the Taihu Plain are also affected by tide level changes to some extent [37,38]. Meanwhile, more and more hydraulic structures, including sluices and pumps, have been constructed for water resources management and flood control [39]. Overall, the dense population, rapid industrialization, and heavy urbanization have put considerable pressure on the hydrology of the region.…”

Section: Study Areamentioning

confidence: 99%

“…The capacity of water abstraction for water management across the entire region has been enhanced significantly after 1989, which is almost consistent with the identified change point in the water level series. The alteration of the hydrological regime in the Taihu Plain due to these hydraulic structures has been shown by several studies [25,39]. The water abstraction from the Yangtze River to the Taihu Plain through pumping stations has been mainly concentrated during the dry season (from September to April) since 2000 [59,60].…”

Section: Impacts Of Human Activities On Water Level Changesmentioning

confidence: 99%

Unraveling the Role of Human Activities and Climate Variability in Water Level Changes in the Taihu Plain Using Artificial Neural Network

Wang

Tabari

et al. 2019

Water

View full text Add to dashboard Cite

Water level, as a key indicator for the floodplain area, has been largely affected by the interplay of climate variability and human activities during the past few decades. Due to a nonlinear dependence of water level changes on these factors, a nonlinear model is needed to more realistically estimate their relative contribution. In this study, the attribution analysis of long-term water level changes was performed by incorporating multilayer perceptron (MLP) artificial neural network. We took the Taihu Plain in China as a case study where water level series (1954–2014) were divided into baseline (1954–1987) and evaluation (1988–2014) periods based on abrupt change detection. The results indicate that climate variables are the dominant driver for annual and seasonal water level changes during the evaluation period, with the best performance of the MLP model having precipitation, evaporation, and tide level as inputs. In the evaluation period, the contribution of human activities to water level changes in the 2000s is higher than that in the 1990s, which indicates that human activities, including the rapid urbanization, are playing an important role in recent years. The influence of human activities, especially engineering operations, on water level changes in the 2000s is more evident during the dry season (March-April-May (MAM) and December-January-February (DJF)).

show abstract

A framework for long‐term scenario analysis in the Taihu Basin, China

Cited by 14 publications

References 12 publications

A Three-Parameter S-Shaped Function of Flood Return Period and Damage

A Three-Parameter S-Shaped Function of Flood Return Period and Damage

Continuous, large‐scale simulation model for flood risk assessments: proof‐of‐concept

Unraveling the Role of Human Activities and Climate Variability in Water Level Changes in the Taihu Plain Using Artificial Neural Network

Contact Info

Product

Resources

About