Conceptualising the Experiment

The study was based on the concept of fraction of attributable risk-the key concept underlying the risk- based approach in probabilistic event attribution. In the context of this study, the approach was used to determine how the probability of exceeding peak stream flows experienced in 2015 (at 1-day, 10-day, and 30-day durations) varies in “factual” and “counter-factual” climates and in different land use and land cover scenarios. By analysing the differences in the exceedance probabilities in the two climate states, it would be possible to determine the fraction of the risk that is attributable to climate change due to anthropogenic greenhouse gas emissions. The conceptualisation and setting up of the experiment had to be consistent with the framing of the attribution question which, in this case, was to determine how the likelihood of experiencing a flood of the January 2015 magnitude changes as a result of anthropogenic emissions against a background of changing land use and cover. Flood risk analysis may focus on metrics or features such as stream flow, area under inundation, and damage caused. In this study, however, the analysis was limited to stream flow, specifically 1-day, 10-day and 30-day maximum flows, given data availability, model capability, as well as the level of complexity which the analyses of those other feature demands.

The study attempted to answer the question of how two external factors-climate change and land use and land cover change-might have, exclusively or jointly, influenced the risk of experiencing the 2015 floods. In order, to achieve this, a hydrological model was run with precipitation from two different sets of climate model ensemble simulations. The first set is the “factual” climate simulation where the climate model is forced with both natural and anthropogenic emissions of greenhouse gases consistent with observations of the atmospheric conditions as at the time of the occurrence of the event while the other is a hypothesised “counter-factual” case where the model is forced with emissions from natural sources only to mimic a climatic state without anthropogenic influences from greenhouse gas emissions. In the subsequent sections of this report, the outputs from the “counter-factual” and “factual” climate model simulations used to drive the hydrological model may be collectively referred to as “attribution data”. The hydrological model was forced with the attribution data in order to determine how runoff varies in the factual and counter-factual climates.

To account for the impact of land use and land cover change, the hydrological model would be conditioned on land use characteristics. Two land use scenarios were identified based on the land use and cover mapping of the catchment as well as land use data available from sources described later. The first land use scenario (historical) was based on the land use and cover in 1990 and the second scenario (current) was based on the 2010 land use and cover in the catchment in which case it was assumed that that land characteristics during the time of occurrence of the event closely resembled the land characteristics in 2010.

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Each climate-based simulation would therefore be replicated once but with altered land characteristics in the hydrological model. Thus, a total of four sets of experiments would be conducted, in which the two climate-scenario based simulations were repeated for the two land use scenarios. The first set was a combination of the counter-factual simulation conditioned by historical land use which offered as a control. The second set was a combination of the counter-factual climate scenario and historical land use in order to test the effect of land use alone. The third set was a combination of the factual-climate scenario and historical land use which would test the how the risk of flooding varies with climate in the event that land characteristics had remained as they were in 1990. The fourth set would offer and understanding of how the risk of flooding varies as a function of both land use and cover change and climate change from anthropogenic influences.

The process is summarised in the illustration in figure 5.1. The key output from each simulation was the discharge or run-off as a function of the input data and the land characteristics on which the simulation was conditioned. The experiment yielded a total of four sets of outputs from which probability density functions of discharge under different climate and land use scenarios were derived for the daily run-off outputted from the model simulations. The threshold was set based on the annual peak run-off for the 2014/15 season. For purposes of this study, the threshold will be referred to as Q2015. The PDFs fitted to the data were used to determine the exceedance probability of Q2015 for sets of simulations. The fraction of attributable risk was determined by comparing the differences in the exceedance probabilities of the three experiments against the control.

Figure 5.1 Framework summarising the experimental set up

From a risk perspective, there are two fundamental functions to determine the change in the risk and to determine how much of that change is attributable to climate change as a result of anthropogenic emission of greenhouse gases or land use and cover change or a change in both factors. The probability ratio (PR) is used to determine the change in the risk or probability of occurrence of an extreme event and this is expressed as PR=P1/P0 where P1 and P0 are the exceedance probabilities of the extreme event in the factual and counter-factual climates respectively. The fraction of attributable risk (FAR) is used to determine the proportion of the risk attributable to anthropogenic global warming and is expressed as FAR= (1-P0/P1). Given that this study had three experimental scenarios and a control, PR and FAR were determined for the exceedance probability for a flood of Q2015 magnitude from the probability distributions for each of the three experimental scenarios, relative to the probability distribution of the

GCM Statistical Downscaling Hydrological model + Land use A Hydrological model + Land use B Hydrological model + Land use A Hydrological model + Land use B Probability Distribution 1 Probability Distribution 2 Probability Distribution 3 Probability Distribution 4 Pre-Industrial Current

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control, expressed as Pa, Pb and Pc for scenarios a, b and c respectively. With P0 denoting the exceedance probability for a flood of the Q2015 magnitude in the control scenario, the FARs for the three sets of scenarios were derived based on the probability relationships summarised in table in table 5.1. The detailed application of this conceptualisation in determining the FAR from the simulation outputs of this study is highlighted in section 5.8 of this chapter.

Table 5.1 Summary of experimental scenarios and their corresponding FAR calculation in relation to the control

Description Probability

Ratio Fraction Attributable Risk of Control Counterfactual climate x

Historical land use. In the context of this study this is the designated control against which the other scenarios (‘treatments’) are evaluated for probability ratio and FAR. The exceedance probability for a Q2015 flood for the control is denoted as P0

Scenario A Counterfactual climate x current land use. The exceedance probability for a Q2015 flood for this scenario is denoted as Pa.

Pa/P0 1-(P0/Pa)

Scenario B Factual climate x historical land use. The exceedance probability for a Q2015 flood for this scenario is denoted as Pb.

Pb/P0 1-(P0/Pb)

Scenario C Factual climate x current land use. The exceedance probability for a Q2015 flood for this scenario is denoted Pc.

Pc/P0 1-(P0/Pc)