The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: Combined sewer system

Semádeni-Davies, Annette; Hernebring, Claes; Svensson, Gilbert; Gustafsson, Lars-Göran

doi:10.1016/j.jhydrol.2007.05.028

Cited by 294 publications

(154 citation statements)

References 21 publications

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“…One important reason was due to the substitution of green spaces and water bodies by the impervious surface, which changed the natural hydrological process and increased the storm water runoff and peak flows. This ultimately led to a higher flood risk [27,68].…”

Section: Results Of Partial Redundancy Analysis At Multiple Scalesmentioning

confidence: 99%

“…This could be attributed to the fact that the clustered pattern of the impervious surface accelerated the flow rate without reducing the runoff amount [70,71], which subsequently gave rise to flow accumulation in a short time and transcended drainage ability. Several studies observed that buildings constituted an obstacle to the water flow path since they could reduce the flood depth and delay the time of the flood [46,68]. However, this type of interference from buildings might be limited, especially in extreme rainstorms.…”

Section: Fractionmentioning

confidence: 99%

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Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Zhang

Cheng

et al. 2018

Sustainability

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An impervious surface is considered one of main factors affecting urban waterlogging. Previous studies found that spatial pattern (composition and configuration) of impervious surfaces affected urban waterlogging. However, their relative importance remains unknown, and the scale-effect of the spatial pattern on urban waterlogging has been ignored. To move forward, our research studied the relationship between spatial patterns on the impervious surface and its subcategories (building and pavement) on urban waterlogging risk spots using Pearson correlation, partial redundancy analysis and performed at three grid scales (1 km × 1 km, 3 km × 3 km, 5 km × 5 km) and the catchment scale based on different spatial resolution land cover maps (2 m, 10 m and 30 m). We identified positively-correlated metrics with urban waterlogging risk spots, such as the composition of impervious surface (i.e., total impervious surface, building, pavement) and the aggregation metric of the total impervious surface at most scales, as well as two negatively correlated metrics (i.e., proximity metric of building, fragmentation metric of total impervious surface). Furthermore, the total variance of urban waterlogging risk spots explained by the spatial pattern of the total impervious surface and its subcategories increased with studied grid and catchment scales while decreasing from a fine to a coarse resolution. The relative contribution of the impervious surface composition and configuration to the variation of urban waterlogging risk spots varied across scales and among impervious surface types. The composition contributed more than the configuration did for the total impervious surface at both grid and catchment scales. Similar to total impervious surface, the composition of buildings was more important than its configuration was at all the grid scales, while the configuration of buildings was more important at the catchment scale. Contrary to the total impervious surface, the configuration of pavement at both the grid and catchment scales mattered more than their compositions did. Furthermore, the composition of the building was more important than that of pavement, but its configuration mattered less. Our study could provide a multi-scale landscape perspective with detailed suggestions for controlling the area of impervious surface and optimizing its spatial configuration in urban waterlogging risk mitigation and urban planning.

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Section: Results Of Partial Redundancy Analysis At Multiple Scalesmentioning

confidence: 99%

Section: Fractionmentioning

confidence: 99%

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Zhang

Cheng

et al. 2018

Sustainability

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“…As a result, the soil in these areas has lost rainwater absorption capacity, making cities more vulnerable to flooding in the presence of heavy rain events [3,4]. Additionally, weather phenomena such as global warming have increased the frequency, intensity, and duration of rain events in many areas [2,5]. All these circumstances have caused considerable increments in both wastewater and rainwater within cities, thereby increasing the risk of CSO and flooding events.…”

Section: Introductionmentioning

confidence: 99%

Modeling and real-time control of urban drainage systems: A review

Garcia¹,

Barreiro-Gómez

Escobar³

et al. 2015

Advances in Water Resources

179

113

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Urban drainage systems (UDS) may be considered large-scale systems given their large number of associated states and decision actions, making challenging their real-time control (RTC) design. Moreover, the complexity of the dynamics of the UDS makes necessary the development of strategies for the control design. This paper reviews and discusses several techniques and strategies commonly used for the control of UDS. Moreover, the models to describe, simulate, and control the transport of wastewater in UDS are also reviewed.

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“…In the DC method, there is no need to identify the bias. Instead, the absolute or relative delta change factors (DCF) are assessed by comparing the climate model outputs representing present-day and future climate (Semadeni- Davies et al 2008;Olsson et al 2009). The observed variable is then rescaled and used as input for impact models.…”

Section: Perturbation Of Observed Datamentioning

confidence: 99%

Projected Change—Models and Methodology

Wibig

Maraun

Benestad

et al. 2015

Regional Climate Studies

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General (global) circulation models (GCMs) are a useful tool for studying how climate may change in the future. Although GCMs have high temporal resolution, their spatial resolution is low. To simulate the future climate of the Baltic Sea region, it is necessary to downscale GCM data. This chapter describes the two conceptually different ways of downscaling: regional climate models (RCMs) nested in GCMs and using empirical and/or statistical relations between large-scale variables from GCMs and small-scale variables. There are many uncertainties in climate models, including uncertainty related to future land use and atmospheric greenhouse gas concentrations, limits on the amount of input data and their accuracy, and the chaotic nature of weather. The skill of methods for describing regional climate futures is also limited by natural climate variability. For the Baltic Sea area, the lack of an oceanic component in RCMs and poor representation of forcing by aerosols and changes in land use are major limitations. IntroductionThe development of general circulation models (GCMs) has created a useful tool for projecting how climate may change in the future. Such models describe the climate at a set of grid points, regularly distributed in space and time and with the same density over land and ocean. Their temporal resolution is relatively high, but their spatial resolution is limited by computing power. Many important processes, such as cloud formation, convection, and precipitation, occur at spatial scales much smaller than the distance between grid points. This means that these so-called sub-grid processes are not explicitly simulated by the models, but must be approximated with simplifying algorithms referred to as parameterisations. The low spatial resolution also means that the topography, coastline, and processes at the land-air, ocean-air, and land-ocean boundaries are coarsely represented in GCMs.

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The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: Combined sewer system

Cited by 294 publications

References 21 publications

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Modeling and real-time control of urban drainage systems: A review

Projected Change—Models and Methodology

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