This book provides an overview of the impacts of climate change on short-duration rainfall extremes and the related consequences at the scales relevant to urban hydrology. Also the state-of-the-art practices to study these impacts are presented. Key messages provided by the different chapters are:
• Chapter 1:
○ Current discussions of climate change impacts on hydrology generally focus on large scale hydrological impacts, for instance on floods, low flows, groundwater recharge, and droughts along major rivers.
○ Future scenarios offer little information on the fine temporal and spatial scale hydrological impacts of relevance for urban drainage applications.
○ This is despite the fact that higher risk of flooding caused by direct runoff from rainfall is a key impact of climate change.
○ Investigation of such impact poses particular difficulties due to the small scales involved.
• Chapter 2:
○ Statistical modelling of the total short-term rainfall process is mainly performed using either point process or multifractal theory. Extreme value theory is used to describe extremes, either annual maxima or peaks-over-threshold values.
○ For design purposes, the statistical properties of rainfall extremes are compactly described in the form of Intensity-Duration-Frequency (IDF) curves which are often used together with design storms.
○ A key objective of rainfall analysis is to develop tools for scale shifts. Areal Reduction Factors (ARFs) are used to convert between point extremes and spatial averages. Multifractals, expressed in the form of a random cascade process, are generically suited for rainfall disaggregation.
○ Rainfall statistics and stochastic models that are typically used in support of urban drainage studies, such as IDF curves, design storms and rainfall generators, are typically derived from historical series, under the intrinsic assumption that these series are stationary. This assumption is likely to be violated in a changing climate.
• Chapter 3:
○ Difficulties exist in identifying climate change trends in historical series of rainfall extremes because of limited data sets as well as short- and long-term persistence and instrumental or environmental changes.
○ Extrapolations of historical trends to future decades can be made but the future trends will have a higher degree of uncertainty.
○ For these reasons, as is the case for any modelling application, it is recommended to combine the empirical data with the results from physically-based modelling by means of climate models.
• Chapter 4:
○ Climate models make projections on the response of the atmosphere to external forcing including GHG concentration. Such projections are inherently probabilistic and it is important to treat them as such in further analyses.
○ Global Climate Models (GCMs) produce results at spatial and temporal scales that are far too coarse for urban drainage applications.
○ GCMs typically have poor accuracy in simulating precipitation extremes, because of the simplified representation of clouds, convection and land-surface processes at their coarse spatial scale, and because of the complexity of the feedbacks or mesoscale circulations that are not resolved in the models.
○ GHG emission scenarios used to date are based on a broad range of socio-economic scenarios.
New scenarios are based on sets of future pathways of GHG concentrations (Representative Concentration Pathways; RCPs) in the atmosphere and levels of radiative forcing.
• Chapter 5:
○ Increasing the resolution of a climate model for a particular area of interest can be achieved with dynamical downscaling using a high-resolution Regional Climate Model (RCM) or Limited Area Model (LAM) nested in a GCM.
○ This type of models may be run on personal computers, not only at the synoptic scale but also at the smaller meso-scale area or even at the scale of a single city. Fine-scale LAM simulations may provide useful insight in for example how local geographical features affect fine-scale rainfall intensities.
○ Currently, urban drainage impact analysis generally have to rely on the synoptic-scale RCM simulations from public databases, given that only these provide easy access to sufficiently long simulation runs (30 years or more) for an ensemble of scenario simulations.
• Chapter 6:
○ Existing assessments of RCM rainfall output generally conclude that biases exist and that the grid-scale representation of short-duration rainfall extremes differs widely from local observations. This results in the necessity for bias correction and downscaling using empirical-statistical methods, before using the data in impact modelling.
○ Both biases and future changes differ between projections, depending on global model, regional model and emission scenario. An ensemble approach therefore is recommended where a set of scenarios is considered, leading to a range of plausible impacts.
○ Good modelling practise indeed includes consideration of uncertainties, but this becomes even more important in climate change impact analysis, certainly in urban hydrology.
○ At the same time, it must be recognised that the total uncertainty of climate projections is likely to be larger than that exhibited by an ensemble of models, because the models share the same level of process understanding and sometimes even the same parameterization schemes and code.
• Chapter 7:
○ Several methods for statistical downscaling exist, each with their own assumptions, all based on the combined use of historical data and climate model results.
○ Methods exist that do not make direct use of the rainfall results of climate models, but that project rainfall changes from changes in other more reliable climate model outputs such as atmospheric circulation and temperature.
○ There is generally limited possibility to validate the statistical downscaling assumptions. Good practice therefore involves assessment of the uncertainties associated with the downscaling step.
○ Application of different downscaling methods should be recommended, but limited to the methods for which assumptions–that can only be partly tested–are found valid for the specific study area.
○ Statistical bias correction, temporal and spatial downscaling can be performed separately, or combined. Due to lack of accurate long-term spatial rainfall statistics, spatial downscaling and bias correction of rainfall intensities at given temporal scales are difficult to separate, hence are commonly combined.
• Chapter 8:
○ Due to the difficulties and uncertainties in climate change impact modelling and analysis on the urban scales, caution must be exercised when interpreting climate change scenarios.
○ Typical increases in rainfall intensities at small urban hydrology scales range between 10% and 60%
from historical control periods in the recent past (typically 1961–1990) up to 2100.
○ Consideration of an ensemble approach where several climate forcing scenarios, climate models, initial states and statistical downscaling techniques are considered, allows the order of magnitude of the uncertainty associated with each aspect to be assessed.
○ Whatever methods are adopted, the resulting change should not be interpreted as an exact number but only as indicative of the expected magnitude of change within the next 20 to 100 years.
• Chapter 9:
○ Climate change impacts on extreme short-duration rainfall events may have significant impacts in terms of surcharge of urban drainage systems and pluvial flooding. Results so far indicate more problems with sewer surcharging, sewer flooding and more frequent CSO spills.
○ There are also many other types of severe consequences at the scales relevant to urban hydrology, such as sedimentation, environmental/water quality, damage to infrastructure, and even socio-economic and cultural effects.
○ Next to regional differences in extreme rainfall and other meteorological changes, the precise impacts will also depend on local topography and on urban planning practices.
○ Extreme rainfall changes in the range 10–60% may lead to changes in flood and CSO frequencies and volumes in the range 0–400% depending on the system characteristics. This is because floods and overflows are due to exceedance of runoff or sewer flow thresholds and react to rainfall (changes) in a highly non-linear way.
• Chapter 10:
○ Urban planners and designers of urban drainage infrastructure can use the projected changes in precipitation and other key input to start accounting for the effects of future climate change. The sections of the urban drainage system with insufficient capacity to convey future design flows can be upgraded over the next few decades as part of a program of routine and scheduled replacement and renewal of aging infrastructure.
○ The large uncertainties that currently exist should not be an argument for delaying climate change impact investigations or adaptation actions. Instead, uncertainties should be accounted for and flexible and sustainable solutions aimed at. An adaptive approach has to be established that both provides inherent flexibility and reversibility and also avoids closing off options. This is different from the traditional engineering approach, which is rather static and is often based on design rules set by engineering communities without much public debate.
○ This adaptive approach involves active learning, hence recognizing that flexibility is required as understanding increases.