Advanced

Climate Change Effects on Rainfall and Management of Urban Flooding

Rana, Arun LU (2013) In Lund University
Abstract (Swedish)
Popular Abstract in Swedish

Översvämningar i städer ökar på grund av ökad urbanisering samt

klimatförändringar och klimatvariabilitet. Denna avhandling presenterar effekterna av klimatförändringar och intensiv nederbörd i städer samt handhavandet av översvämning i urban områden i Mumbai, Indien och i Skåne samt Göteborg. En rad statistiska och analytiska verktyg har tillämpats för att studera nederbördstrender och

extrema nederbördshändelser i två områden. I Mumbai har effekten av klimatförändringar studerats med hjälp av nio GCM-simuleringar (General Circulation Model) med bias-korrektur genom distributionsbaserad skalering (DBS) och för Göteborg har GCM-utdata och observationer använts för att... (More)
Popular Abstract in Swedish

Översvämningar i städer ökar på grund av ökad urbanisering samt

klimatförändringar och klimatvariabilitet. Denna avhandling presenterar effekterna av klimatförändringar och intensiv nederbörd i städer samt handhavandet av översvämning i urban områden i Mumbai, Indien och i Skåne samt Göteborg. En rad statistiska och analytiska verktyg har tillämpats för att studera nederbördstrender och

extrema nederbördshändelser i två områden. I Mumbai har effekten av klimatförändringar studerats med hjälp av nio GCM-simuleringar (General Circulation Model) med bias-korrektur genom distributionsbaserad skalering (DBS) och för Göteborg har GCM-utdata och observationer använts för att karakterisera nederbörd.

Genom att använda en DBS-processad projektion av högupplöst data har en konsekvensanalys (klimat- och extremvärdesstatistik) genomförts för den framtida perioden 2010–2099. Det har också gjorts en trendanalys med Students t-test och Mann-Kendall-testet. Vidare har Random Cascade-modellering tillämpats på nederbördsdata för att skapa högupplöst data för Mumbai. Metoden kan användas för att utarbeta IDF-kurvor. Samma skapade data har använts i översvämningsmodellering och på så vis har översvämningskartor utarbetats. Nederbördstrender för månad, säsong och år har studerats för Mumbai (1951-2004). För Skåne och Göteborg har trender för dygns- och flerdygnsnederbörd studerats. Långsiktiga trender har framställts med Mann-Kendall-testet, Students t-test och linjär regression. Nederbördstrenderna för Mumbai har kunnat styrkas med klimatindicier genom multivariabla, statistiska verktyg: PCA och SVD. PCA har även använts för att beskriva variation i RCM-genererad nederbörd i Göteborg. Dagvattensystemet i Mumbai respektive Göteborg har analyserats analytiskt. Slutligen har en integrerad tvådimensionell (2D) hydrodynamisk avrinningsmodell använts för att simulera översvämning från dagvatten i de metropolitiska områdena av Mumbai, Indien. Resultaten visar på stor variation i nederbörd i Mumbai. Det ses en signifikant, nedåtgående trend för långsiktiga, sydvästliga monsunregn. Dessutom ses en minskad

genomsnittlig, maximal dygnsnederbörd. Det ses att sydvästliga monsunregn i Mumbai är negativt korrelerade med Indiska oceanens dipol, El Niño–sydlig oscillation och East Atlantic Pattern. I Skåne och Göteborg har däremot den årliga nederbörden ökat signifikant på grund av ökande nederbördsmängder om vintern. Det ses en ökning av årshögsta dygnsnederbörden på en plats, där har det högsta värdet ofta uppmätts om vintern. Antalet kraftiga nederbördshändelser med korta

återkomstperioder har ökat, men antalet av de extrema händelserna har inte ökat. Utvärderingen av jämförelseperioden med hjälp av DBS-biaskorrektur visade att mätt och skalerad nederbördsdata är starkt korrelerade och att skalerad data kan användas för att representera olika statistiska värden såsom medel, varians och extremvärden.

Analysen av framtida långsiktiga klimatförutsägelser visar en signifikant, positiv trend för fyra av de nio modeller som använts för att studera extrem dygnsnederbörd för perioden 2010–2099. När det gäller Göteborg pekar resultaten på att högupplösta RCM-modeller kan användas för studier av klimatpåverkan. För en halvårsperiod

kunde det konstateras en mycket god överensstämmelse mellan modellerade och observerade tidsserier vid Random Cascade-modellering för tidsperioder som var längre än en halvtimme när högupplöst data fanns att tillgå. IDF-kurvorna som utarbetats visade att den gällande dimensioneringsstandarden för Mumbai har en återkomstperiod på under ett år. Därmed står det klart att årliga översvämningsproblem

i Mumbai är att förvänta. Detta understryks av resultaten från översvämningsmodelleringen och de analytiska studierna. (Less)
Abstract
Flooding in urban basins is intensifying due to increasing urbanization and climate change and variability. This thesis presents how the effects of climate change and high-intensive rainfall on the urban drainage system and management of flooding in urban areas of were studied in Mumbai, India and Southern Sweden, including Skåne

and Gothenburg. Various statistical and analytical tools were applied to study trends and extreme events in two study areas. The impact of climate change on Mumbai was studied using nine GCM simulations with bias correction using DBS methodology. For Gothenburg, RCM output and observations were used to predict the characteristics of rainfall. Through use of transient DBS processed projection data, an... (More)
Flooding in urban basins is intensifying due to increasing urbanization and climate change and variability. This thesis presents how the effects of climate change and high-intensive rainfall on the urban drainage system and management of flooding in urban areas of were studied in Mumbai, India and Southern Sweden, including Skåne

and Gothenburg. Various statistical and analytical tools were applied to study trends and extreme events in two study areas. The impact of climate change on Mumbai was studied using nine GCM simulations with bias correction using DBS methodology. For Gothenburg, RCM output and observations were used to predict the characteristics of rainfall. Through use of transient DBS processed projection data, an impact analysis (climate and extreme value statistics) was performed for the future period of the years 2010 to 2099. Trend analysis using the student t-test and the Mann-Kendall test was also performed. Further, Random Cascade modelling was applied on daily rainfall data to reproduce high temporal resolution data for Mumbai. The method can be used for development of IDF curves. The generated data were used for flood modelling in the area and the generation of flood maps. Trends for monthly, seasonal, and annual precipitation were studied for Mumbai (1951-2004). For Southern Sweden, daily and multi-day precipitation trends were studied. Long-term precipitation trends were determined using the Mann-Kendall test, the student t-test, and linear regression. The trends for rainfall in Mumbai were corroborated with climatic indices using multivariate statistical tools, namely PCA and SVD. PCA was also used for explaining variability in RCM-generated precipitation in Gothenburg. Analytical analyses were made of the drainage systems in Mumbai and Gothenburg. Finally, an integrated two dimensional (2D) hydrodynamic runoff model was used to simulate storm-water flooding and related processes in the metropolitan areas of Mumbai, India. The analysis revealed a high degree of variability in rainfall over Mumbai. A significant decreasing trend for long-term southwest monsoon rainfall was found. Also, a decrease in average maximum daily rainfall was indicated. The southwest monsoon

rainfall over Mumbai was found to be inversely related to the Indian Ocean dipole, the El Ninõ-Southern Oscillation, and the East Atlantic Pattern. In Southern Sweden, however, annual precipitation has increased significantly due to increasing winter precipitation. There is an increasing trend for maximum annual daily precipitation at

one location where the annual maximum often occurs in winter. The number of events with short return periods is increasing, but the number of other extreme events has not increased. Evaluation of the baseline period using the DBS bias correction method showed that observed and scaled rainfall data are strongly correlated and that

these can represent various key statistics including mean, variance, and extreme values. The analysis of future long-term climate projections revealed a positive significant trend for 4 out of 9 model simulations for daily extreme rainfall during the period 2010-2099. In the case of Gothenburg, the results obtained pointed towards

the usefulness of high resolution RCMs for impact studies. In random cascade modelling, very good agreement between modelled and observed disaggregation rainfall series was found for time scales larger than 1/2 h when short-term data were available. Established IDF-curves showed that the current design standard for Mumbai City has a return period of less than one year. Thus, annual recurring flooding problems in Mumbai appear evident. This was further emphasized in results

from flood modelling and analytical studies. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • professor Arnbjerg-Nielsen, Karsten, Urban Water Engineering (UWE) - DTU Environment, Department of Environmental Engineering, Technical University of Denmark.
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Climate Change, Extreme Events, Intensive Rainfall, Urban Drainage, Intensity-Duration-Frequency, Statistical analysis, Flood Management
in
Lund University
pages
216 pages
defense location
Lecture hall V:C, Department of Building and Environmental Technology, John Ericssons väg 1, Lund University Faculty of Engineering.
defense date
2013-09-27 10:15
ISBN
978-91-7473-636-6
language
English
LU publication?
yes
id
623e63d6-56c4-4290-9190-6b00ed0217a6 (old id 4002945)
date added to LUP
2013-09-05 15:23:55
date last changed
2016-09-19 08:45:17
@misc{623e63d6-56c4-4290-9190-6b00ed0217a6,
  abstract     = {Flooding in urban basins is intensifying due to increasing urbanization and climate change and variability. This thesis presents how the effects of climate change and high-intensive rainfall on the urban drainage system and management of flooding in urban areas of were studied in Mumbai, India and Southern Sweden, including Skåne<br/><br>
and Gothenburg. Various statistical and analytical tools were applied to study trends and extreme events in two study areas. The impact of climate change on Mumbai was studied using nine GCM simulations with bias correction using DBS methodology. For Gothenburg, RCM output and observations were used to predict the characteristics of rainfall. Through use of transient DBS processed projection data, an impact analysis (climate and extreme value statistics) was performed for the future period of the years 2010 to 2099. Trend analysis using the student t-test and the Mann-Kendall test was also performed. Further, Random Cascade modelling was applied on daily rainfall data to reproduce high temporal resolution data for Mumbai. The method can be used for development of IDF curves. The generated data were used for flood modelling in the area and the generation of flood maps. Trends for monthly, seasonal, and annual precipitation were studied for Mumbai (1951-2004). For Southern Sweden, daily and multi-day precipitation trends were studied. Long-term precipitation trends were determined using the Mann-Kendall test, the student t-test, and linear regression. The trends for rainfall in Mumbai were corroborated with climatic indices using multivariate statistical tools, namely PCA and SVD. PCA was also used for explaining variability in RCM-generated precipitation in Gothenburg. Analytical analyses were made of the drainage systems in Mumbai and Gothenburg. Finally, an integrated two dimensional (2D) hydrodynamic runoff model was used to simulate storm-water flooding and related processes in the metropolitan areas of Mumbai, India. The analysis revealed a high degree of variability in rainfall over Mumbai. A significant decreasing trend for long-term southwest monsoon rainfall was found. Also, a decrease in average maximum daily rainfall was indicated. The southwest monsoon<br/><br>
rainfall over Mumbai was found to be inversely related to the Indian Ocean dipole, the El Ninõ-Southern Oscillation, and the East Atlantic Pattern. In Southern Sweden, however, annual precipitation has increased significantly due to increasing winter precipitation. There is an increasing trend for maximum annual daily precipitation at<br/><br>
one location where the annual maximum often occurs in winter. The number of events with short return periods is increasing, but the number of other extreme events has not increased. Evaluation of the baseline period using the DBS bias correction method showed that observed and scaled rainfall data are strongly correlated and that<br/><br>
these can represent various key statistics including mean, variance, and extreme values. The analysis of future long-term climate projections revealed a positive significant trend for 4 out of 9 model simulations for daily extreme rainfall during the period 2010-2099. In the case of Gothenburg, the results obtained pointed towards<br/><br>
the usefulness of high resolution RCMs for impact studies. In random cascade modelling, very good agreement between modelled and observed disaggregation rainfall series was found for time scales larger than 1/2 h when short-term data were available. Established IDF-curves showed that the current design standard for Mumbai City has a return period of less than one year. Thus, annual recurring flooding problems in Mumbai appear evident. This was further emphasized in results<br/><br>
from flood modelling and analytical studies.},
  author       = {Rana, Arun},
  isbn         = {978-91-7473-636-6},
  keyword      = {Climate Change,Extreme Events,Intensive Rainfall,Urban Drainage,Intensity-Duration-Frequency,Statistical analysis,Flood Management},
  language     = {eng},
  pages        = {216},
  series       = {Lund University},
  title        = {Climate Change Effects on Rainfall and Management of Urban Flooding},
  year         = {2013},
}