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Assessing combined sewer overflows with long lead time for better surface water management a

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Mawada Abdellatif , William Atherton & Rafid Alkhaddar

a

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School of the Built Environment, Liverpool John Moores University, Liverpool, UK Published online: 10 Oct 2013.

Click for updates To cite this article: Mawada Abdellatif, William Atherton & Rafid Alkhaddar (2014) Assessing combined sewer overflows with long lead time for better surface water management, Environmental Technology, 35:5, 568-580, DOI: 10.1080/09593330.2013.837938 To link to this article: http://dx.doi.org/10.1080/09593330.2013.837938

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Environmental Technology, 2014 Vol. 35, No. 5, 568–580, http://dx.doi.org/10.1080/09593330.2013.837938

Assessing combined sewer overflows with long lead time for better surface water management Mawada Abdellatif∗ , William Atherton and Rafid Alkhaddar School of the Built Environment, Liverpool John Moores University, Liverpool, UK

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(Received 31 March 2013; final version received 2 August 2013 ) During high-intensity rainfall events, the capacity of combined sewer overflows (CSOs) can exceed resulting in discharge of untreated stormwater and wastewater directly into receiving rivers. These discharges can result in high concentrations of microbial pathogens, biochemical oxygen demand, suspended solids, and other pollutants in the receiving waters. The frequency and severity of the CSO discharge are strongly influenced by climatic factors governing the occurrence of urban stormwater runoff, particularly the amount and intensity of the rainfall. This study attempts to assess the impact of climate change (change in rainfall amount and frequency) on CSO under the high (A1FI) and low (B1) Special Report on Emissions Scenarios of the greenhouse concentration derived from three global circulation models in the north west of England at the end of the twenty-first century. Keywords: combined sewer overflow; climate change; InfoWorks CS; pollution risk; time series

1. Introduction In more recent years there have been numerous environmental problems associated with combined sewer overflows (CSOs) and other wet weather discharges from urban wastewater systems in the UK. At the beginning of the 1990s, it was estimated that there were around 25,000 CSOs in England and Wales and that 8000 were causing pollution problems and were designated as unsatisfactory.[1] The cost of improving these CSOs was forecasted to be in the order of £4.5 billion. By around 2010, it was anticipated that almost all unsatisfactory CSOs in the UK would have been eradicated but with the climate change, the problem will appear again causing further challenges for CSOs in the future. The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) states that the warming of climate system is now unequivocal, as is evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice and rising global average sea level.[2] The IPCC also reports that if greenhouse gas emissions continue at or above current rates, changes in the global climate system during the twenty-first century will very likely be larger than those observed during twentieth century. So these changes could present a significant risk to the performance of a combined sewers system infrastructure including CSOs. Thus, assessment of the system performance in future conditions is urgently needed for better understanding of potential implications of climate change and what needed to be done to reduce the impacts currently predicted.

∗ Corresponding

Evaluating regional impacts from possible climate change in the future for CSOs requires a methodology to estimate certain meteorological variables (e.g. rainfall) for the time period and geographical region of interest. In general, two physical systems are involved: the climate system in which the effect of greenhouse gasses emissions can be simulated by general circulation models (GCMs) (from which rainfall is downscaled) and the hydrological system which is the urban drainage systems. GCMs are widely used to assess the impact of climate change. Outputs from these GCMs are based upon the fundamental laws of physics embodied within each model and the assumptions on the concentration of greenhouse gases in the atmosphere.[3] As these GCMs differ in the way they simplify the climate, different models have been widely used to address these uncertainties. Outputs from GCMs reproduce the system and aggregate the process in space and time. Future projections of climate variable (e.g. rainfall) are dependent upon the choice of GCMs employed.[4] What is less clear is the extent to which local (sub-grid) scale meteorological processes will be affected as the coarse scale of the GCMs is inadequate for impact studies.[5] This limitation has been widely addressed through the use of techniques to downscale large-scale simulations from GCMs. Although climate change models contain substantial uncertainties, the consideration of climate change effects has become an important issue to estimate the possible impact on existing drainage systems (and the respective costs of possible climate change adaption measures).[6]

author. Emails: [email protected], [email protected]

© 2013 Taylor & Francis

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Environmental Technology In recent years, continuous hydrologic modelling has been proposed to model overflows from the sewer system as an alternative to the synthetic design storm approach, which has some limitations, particularly when considering the operation of storm overflows.[7] This is because synthetic design storm has; first, a limited use for defining storms with a return period of less than one year, whereas almost all overflows operate more frequently than this; second, the synthetic data represent typical summer or typical winter storms rather than the whole range of storms that occur throughout a typical year. Therefore, a better method of considering overflow operation is to use a series of storms representing the rainfall over a long period.[8] One of the most commonly stated disadvantages of continuous simulation is that such an approach is extremely time-consuming and expensive.[7] However, with the advent of inexpensive and computationally efficient microcomputer technology this limitation is generally no longer valid. Considering climate change in the UK, projections suggest that future climate could results in increased CSO discharge frequency [9]; however, little is known about the extent of this risk. A study conducted by Tait et al. [10] indicated that climate change, which is thought to be caused by the current global warming, will produce around 20% additional increase in CSO discharge volume for a catchment in the UK by the 2020s. Another study, which has investigated CSOs discharges under the current climate in the UK, is the work by Fitz Gerald et al.[11] Providing a case study from Langstone Harbour (which is a largely urban catchment) [11] found that CSOs’ continue to be a source of intermittent pollution events leading to temporary shellfish harvesting closures, despite a major wastewater scheme to discharge continuous treated effluent offshore. Moreover, other risk of pollution apart from that contributed by stormwater is also possible. Comber et al. [12] found that domestic sources of phosphorus contribute significantly to the domestic load to sewer and that overall, domestic sources dominate loads to sewage treatment works. Estimates provided show that although the natural diet contributes 40% of the domestic phosphorus load, other potentially preventable sources contribute significantly to the estimated 44,000 tons of phosphorus entering UK sewage treatment works each year. On an international scale, CSO discharges under past, present, and future climate were also identified to be the main source of particle and organic pollution. A study by Ahyerre and Chebbo [13] to urban catchment in Paris has found that a significant amount of sediment including biochemical oxygen demand (BOD) and other material were eroded from the sewers with stormwater to the receiving river. A recent study by Nilsen et al. [14] in Norway, which used the delta method for simulation of the rainfall, found that there is an 83% increase in annual CSO discharge when comparing years of maximum annual precipitation. A pilot study of Linz in Austria [15] has been conducted to investigate urban drainage modelling under

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climate change. The study showed that there would be a total increase in spilled volume by approximately 17% for the future scenario, but Austrian requirements are met as the required efficiency rates decrease with increased rainfall. The study by Patz et al. [16] described that due to climate change, the Great Lakes region of the USA will likely be facing a combination of an increase in CSO discharges due to heavy rainfall, warmer lake waters, and lowered levels. These three aspects would increase the risk of waterborne diseases. Moreover, Kleidorfer et al. [17] compared the behaviour of different performance indicators of combined sewer systems in terms of flooding and CSO discharge for long-term environmental change effects (change in rainfall characteristics, change in impervious area, and change in dry weather flow) in Austria. They found that an increase in rainfall intensities has the highest impact on the system performance followed by variation in imperious area. Moreover, a report from the Swedish Meteorology and Hydrology Research Institute found that CSO discharges would increase from 0.8 to 2.5 million m3 in the future period of 2071–2080 under scenarios A2 and B2 for a catchment in Norway with an increase of more than 200% as stated in [18]. In the USA, weather generator approach by US Environmental Protection Agency [19] has been used to downscale the future rainfall for purpose of the assessment of CSO and results suggest that if future climate change includes increased precipitation and stormwater runoff volumes, the efficacy of CSO mitigation efforts may be diminished. The current paper presents an assessment methodology for CSOs intermittent discharges with particular emphasis on those aspects most likely to be influenced by climate change (rainfall intensity) in 2080s using generalised linear and artificial neural network (GLM-ANN) to downscale future rainfall. Three GCMs have been used in simulating future rainfall to account for any uncertainty associated with climate change scenarios. As none of the above-mentioned studies considered uncertainties from using different GCMs when assessing climate change impacts on urban infrastructure system, this study is considered as a pioneer for addressing this issue.

2. Case study and data used The study used the Windermere drainage area, in the north west of England, as a case study to demonstrate the potential impacts of climate change in this sector. The InfoWorks CS Software used for the purpose of simulation was developed by Innovyse Ltd and is the standard software used by all UK water companies to model sewer network systems. The Windermere drainage area is located in Cumbria in the north west of England (Figure 1). It covers an area of 425 ha and has a residential population of 10,930. The InfoWorks CS model of the area contains 173 sub-catchments and a total number of 633 pipes, which connects 655 manholes and 4 outfalls (2 CSO). The receiving

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Figure 1.

M. Abdellatif et al.

Windermere drainage area in the Northwest of England.

water for wastewater discharges from the catchment is Lake Windermere, a 16 km long freshwater lake. The lake receives direct storm discharges from the two points of spill (coming from one CSO) and also receives the final effluent from Windermere wastewater treatment work (WwTw). In the Windermere area, the larger developments were judged to be re-developments in existing developed areas and would contribute little additional area to the wastewater network.[20] Two principal data sets were employed during the calibration and validation of the daily precipitation models. First, the observed daily rainfall data were obtained for Tower Wood station from the Environment Agency for England and Wales, for the period 1961–2001. Second, the large-scale observed climatic predictors’ data were obtained from the National Centre for Environment Predictions (NCEP/NCAR). These observed data sets were used to calibrate and validate the downscale model used in this work. GCM data sets were obtained from the Canadian Climate Impacts Scenarios Group website, for three different GCM models for future projection of rainfall. These are the Hadley Centre (HadCM3) model, the Canadian Centre for Climate Modelling and Analysis (CGCM2) model, and the Commonwealth Scientific and Industrial Research Organization (CSIRO Mark2) model.

3. Rainfall model In the current study, the GLM-ANN method was used as the statistical downscale model to develop a high-resolution time series of rainfall for the period 2070–2099. The approach relates large-scale climate variables (predictors) to regional and local variable (predictand). Then the largescale output of GCM simulation is fed into this statistical model to estimate the corresponding local and regional climate characteristics.[21] MATLAB 2010b has been used for purpose of the simulation. In the following sections, descriptions of the GLM and ANN which were used to model annual rainfall occurrence and amount, respectively, are given. 3.1. Annual rainfall occurrence model Predictors–rainfall relations are assessed based on the correlation coefficient. The predictors are then selected from a range of candidate predictors based on a number of criteria such as significance, strength of correlation, and consistency of predictor across the catchment. Stepwise regression is applied for selection process as it yields the most powerful and parsimonious model and has been used in many studies.[22,23] In order to remove the inconsistencies associated with the presence of small rainfall values, a threshold is usually applied to the data, as

Environmental Technology

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rainfall values less than this threshold are considered to be dry days and represented with zero. Those equal to or greater than the threshold are considered wet days and represented with one to form a series of binary values for the occurrence model which has been screened with the predictors. In the present study, a threshold value of 0.3 mm/day is used for rainfall. The GLM is an extension of linear modelling process that allows fitting data which follow probability distributions other than the normal distribution and given by Chandler and Wheater.[24] In this paper, logistic regression, which is a special case of GLM that is employed to model wet and dry day sequences of rainfall with screened predictors following a binomial distribution, is expressed as follows Pi  = e(Xi β) , 1 − Pi

Hidden Neurons Input

(Optimal size is selected during the training)

Neurons Output

X1 Neuron

X2 Y1

X8

(1)

Figure 2.

where Pi is the probability of rain for the ith case in the data set, e the base of the natural logarithms, β the coefficients estimated from the data, and Xi the climate variables (predictors). This model is used to fit the binary series (occurrence) by employing the maximum likelihood method to estimate model parameters. Part of the data is used for model calibration and the other part is used for model verification. Use of the GLM approach offers a significant improvement over the general multiple linear regressions as distribution of the response (the rainfall) does not need to be normal. Moreover, the logistic regression, in particular, limits the predicted values in the occurrence model to lie between the interval of 0 and 1.[25] To test the performance of the occurrence model, the percent correct (PC) and Heidke skill score (HSS) indices [26] are needed to be calculated and checked, which can be obtained from a 2 × 2 contingency table.

given by

3.2. Annual rainfall amount model For this study, it was found beneficial to adopt the familiar multi-layer feed forward artificial neural network (MLFFANN) model which was used to build a non-linear relation between the observed rainfall amount series and the same selected set of climatic variables (predictors) used for the rainfall occurrence model. Figure 2 shows a representation of the neural network diagram with inputs X1 to X8 and outputs Y1 that are used in the present study. The number of neurons in the input and output layers is determined by the number of elements in the external input array and output array of the network, respectively. The appropriate number of neurons in the hidden layer is important for the success of the neural network model and is determined during the training process by reducing the mean square error. The output y of a network with 8 inputs, k log-sigmoid nodes in the hidden layer, and one linear node in the output layer is

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•

•

•

•

•

•

Feed forward neural network model.

k

wj(2) zj + b(2) ,  8 Zj = f wij(1) Xi + bj(1)

Y1 =

(2)

1

1

=

1 + exp



1 8 1

−wij(1) xi + bj(1)

,

(3)

where xi corresponds to the ith input, the coefficients wj(2) and b(2) (wij(1) and bj(1) ) are the weights and biases from the output (hidden) layers, and f is the log-sigmoid transfer function. Different transfer functions can be applied to ANN; for the purpose of this study, logistic and linear functions are used in the hidden and output layers, respectively. MLFF-ANNs have been applied successfully to solve numerous difficult and diverse problems. They have been trained under supervision with a highly popular algorithm known as the error back-propagation algorithm.[27] There are many variations of the backpropagation algorithm, including gradient descent and the faster algorithms using heuristic or optimization techniques.[28] In this study, the Levenberg–Marquardt back-propagation (LMBP) algorithm method for ANN training was used. The LMBP, one of the second-order non-linear optimization techniques, is usually faster and more reliable than any of the other back-propagation techniques.[29] The LMBP uses the approximate Hessian matrix that can be represented as H = J TJ ,

(4)

where J is the Jacobian matrix, which contains the first derivatives of the ANN errors with respect to weights and biases. The LMBP algorithm uses this approximation to the

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Hessian matrix in the following update:

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W = −[J T J + μI ]−1 J T e,

have been processed in accordance with the Environment Agency criteria.[30] (5)

where (e) is the residual error vector and (μ) is a variable small scalar that controls the learning process. When the scalar (μ) is zero, Equation (5) is equivalent to Newton’s method, using the approximate Hessian matrix. When (μ) is large, Equation (5) is equivalent to the gradient descent method with a small step size. Newton’s method is faster and more accurate near an error minimum than the gradient descent method; thus, the aim is to shift towards Newton’s method as quickly as possible. In practice, LMBP is faster in finding better optima for a variety of problems than the other usual methods.[29] The hybrid GLM-ANN downscaling model is then used to simulate future rainfall using a set of global circulation model outputs (for a specific scenario emission) as predictors (this set should corresponds to the NCEP set of climatic variables used earlier in building the downscaling model). Then the occurrence model is run using GCM predictors and dry and wet days of the future rainfall amount will be determined based on the occurrences model. If the probability results from occurrences model is equal to or greater than a specific threshold, then the amount estimated from the GLM-ANN model is taken as the amount of rainfall for that particular day, and if the probability of occurrence is less than this threshold, the rainfall amount is taken as zero or dry day. 4.

CSOs modelling

For assessing the possible effects of climate change on CSO, urban drainage models are run with climate change for a typical 10 years continuous time-series data. Discharges from CSOs to receiving waters are evaluated by aid of a rainfall-runoff and transport model based on long-term simulations with (i) historical rainfall of the base period for 1961–1990 and (ii) predicted rainfall derived from a global climate model for 2070–2099. The CSOs discharge results Table 1.

4.1.

Selected typical 10 years continuous time series rainfall

An annual rainfall time-series is a sequence of rainfall events that is statistically representative of the annual pattern at a given location and it is normally used for modelling of CSOs. The annual typical 10-year rainfall series will be used for shellfish and bathing water drivers. The downscaled 30 years daily rainfall series is first disaggregated to hourly using the Bartlett–Lewis Rectangular Pulses (BLRP) model. Then a 10-year series is selected from the 30 years hourly data by carrying out comparison between years based on annual and monthly rainfall totals Environment Agency, 2009. The years should be ranked in terms of variance from the annual and monthly totals and from the average values for the baseline and future data set. The series of 10 consecutive years from the 30 years with the lowest overall variance should be selected. The ranking systems should therefore be set up in order to select the 10 years closest to the average year (cf. [31]). 4.2.

Sewers system flow model

Combined sewer systems in an urban area are built to collect and transport foul flow (dry weather flow [DWF]) and storm runoff to the treatment works and CSO discharge points. The main constituents of DWF are population-generated flows from residential properties within the network, trade flow, commercial flow and infiltration from groundwater into the sewerage system network. For purposes of this study, the Windermere combined sewer model used contains a population-generated flow of (128 l/h/d) with diurnal profile during the day (Table 1). Total trade flow of 0.40 l/s and commercial flow of 8.93, and annual infiltration flow of 20.39 l/s is used. The selected 10 continuous years of the stormwater are applied to the sewers model to generate the runoff after

Diurnal flow and pollutants load for per capita for domestic waste.

Time 00:00–01:00 01:00–02:00 02:00–03:00 03:00–04:00 04:00–05:00 05:00–06:00 06:00–07:00 07:00–08:00 08:00–09:00 09:00–10:00 10:00–11:00 11:00–12:00

Flow multiplier

NH3 multiplier

BOD/SS multiplier

Time

Flow multiplier

NH3 multiplier

BOD/SS multiplier

0.250 0.08 0.07 0.80 0.11 0.58 1.63 2.12 1.83 1.68 1.51 1.23

0.87 0.87 0.87 0.87 0.87 0.87 1.30 1.74 1.30 0.87 0.87 0.87

0.60 0.60 0.60 0.60 0.60 0.60 0.90 1.20 1.20 1.20 1.20 1.20

12:00–13:00 13:00–14:00 14:00–15:00 15:00–16:00 16:00–17:00 17:00–18:00 18:00–19:00 19:00–20:00 20:00–21:00 21:00–22:00 22:00–23:00 23:00–00:00

1.40 1.12 0.92 0.95 1.16 1.26 1.44 1.16 1.05 1.11 0.96 0.55

0.87 0.87 0.87 0.87 0.87 0.87 0.87 0.87 0.87 0.87 0.87 0.87

1.20 1.20 1.20 1.20 1.20 1.20 0.65 0.60 0.75 0.66 0.73 0.6

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Environmental Technology

Figure 3.

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Hydrograph comparisons for sewer flow model verification for a flow monitor in the catchment.

being disaggregated to 5-min rainfall time series employing the StormPac programme. The transformation of a rainfall hyetograph into a surface runoff hydrograph involves two principal parts. First, losses due to antecedent conditions and evapotranspiration are deducted from the rainfall which is between 1 and 3 mm/day and assumed to be constant in the future simulation. Second, the resulting effective rainfall is transformed by surface routing into an overland flow hydrograph. For urban catchments in the UK, the percentage runoff coefficient can be estimated from Wallingford or New UK volume models.[32] Then effective rainfall hyetograph can be transformed into runoff after accounting for all the above-mentioned catchment losses (overland flow). In this process, the runoff moves across the surface of the sub-catchment to the nearest entry point to the sewerage system using the flood routing model.[32] Before applying the DWF and storm flow, the sewers model need to be tested. The hydraulic performance of the sewer system is often calibrated using short-term flow surveys and so achieves a reasonably accurate correspondence between simulated and recorded data. Model parameters which have been estimated through the flow survey are considered to be constant in the future. Flow survey is done by measuring the depth and velocity of sewer flows at key location throughout a network and attempting to recreate the same hydrograph peaks, volume and general shape in the model by comparing flow, depth, and velocity hydrographs. Location of the monitors is crucial, whilst a particular location may be desirable, the practicalities of sitting a monitor may be influenced by its location. Steep Pipe Methodology has considered the value of locating

flow monitors in a variety of different pipe diameters and gradients. Flow monitors record depth and velocities and therefore the cross-sectional area of the flow and the velocity can be used to determine the flow rate. The model is built and verified in the period between February 2008 and May 2008. An example of a successful verification results for the model at one site in the catchment is shown in Figure 3 for a flow monitor installed at a sewer upstream Windermere wastewater treatment works. The verification plots show that a perfect match was obtained for the flow depth and discharge in the link, an indication for good representation and performance of the model. After the model has been verified it will be ready for the simulation using the inputs mention above, then a hydraulic comparison can be carried out to investigate the performance of the CSOs in the future with the assumption that DWF is constant. 4.3. Sewers system water quality model The water quality loadings are swept into the sewerage network and transported to the WwTw. However, during rainfall events, CSOs come into operation and spill events occur. The hydraulic model can report on flow rates and, when run in water quality mode, pollutant concentration entering the water course can also be determined. The parameters used for water quality analysis in this paper are the concentration of ammonia (NH3 ), BOD. There are four entry points of pollutants into the sewerage network. These are domestic wastewater, trade effluent waste, surface runoff and gully pots.

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M. Abdellatif et al. Table 2. Human discharges to the sewer. g/day

SS BOD NH3

55.2 55.2 6.6

0.5

Correlation (%)

Parameter

0.7

Zero-correlation Partialcorrelation

0.3 0.1 –0.1 –0.3

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5. 5.1.

Results and discussion

Calibration and performance of the downscale model Determination of appropriate predictors for the downscaling model is very important. This process tends to drop out those variables that have less influence on the rainfall in a specific location to avoid over-fitting based on stepwise regression (also known as forward regression). Figure 4 represents the selected predictors for the occurrence and mount model, which consisted of a set of 8 variables describing regional climatic conditions (definitions for each of the predictors is given in Table 3) from a range of 26 climate variables. The bar chart in Figure 4 shows that the values of correlation coefficient for rainfall occurrence and these predictors ranges between 0.01 and 0.5 for zero correlation and between 0.08 and 0.28 for partial correlation. Despite the apparently low correlation coefficient, it was found that these relations are statistically significant at the 5% level of significance. The predictors have then been employed to construct the rainfall occurrence model using daily observed rainfall

ncepp _v (+1)

ncepp _zh

ncepp_z (+1)

ncepr850

nceprhum(+1)

ncepr500

ncepp8_u

–0.7

ncepmslp (+1)

–0.5

One other area that needs to be highlighted is the state of the pollutants. Each pollutant can exist in two forms either dissolved or attached pollutants. Some pollution can only exist in one form such as NH3 , whilst BOD can come in dissolved or attached forms. The attached pollutants are linked to sediment fractions. The quantity attached to the sediment fraction is given by a potency factor of 0.56 applied in this study. The actual mass of pollutant is calculated by multiplying the sediment mass by the potency factor.[8] The model treats sediments and attached pollutants separately from dissolved pollutants. For domestic waste these load parameters are standard and were based on the average load of human discharge to the sewer according to a research programme carried out by Construction Industry Research and Information Association-CIRIA (Table 2) and the concentration varies with time in a fairly repeatable daily (diurnal) pattern (Table 1). Trade pollutant load is less than 1 l/s flow rate and is not required to be explicitly represented in the model separately and it can be represented as equivalent of population load. The washoff parameters can be split into two, the surface pollutants dealing with the attached components and the gully pots dealing with the dissolved component, these are again mainly default values.

Figure 4. rainfall.

Selected large-scale climate variables for annual daily

Table 3.

Predictors definition.

Code ncepmslp (+1) ncepp8− u ncepr500 nceprhum(+1) ncepr850 ncepp p− z (+1) ncepp − zh ncepp − − v(+1)

Variable Lagged forward mean sea level pressure 850 hpa zonal velocity Lagged forward surface vorticity Lagged forward near surface relative humidity 850 hpa relative humidity Lagged forward vorticity Surface divergence Lagged forward surface meridional velocity

in the period 1961–1987 for calibration and in the period 1988–2001 for verification. Table 4 reflects the accuracy of the rainfall occurrence model in terms of PC, HSS and Bias (B). If HSS = 1, it means a perfect forecast; and if HSS = 0, it means that the model has no skill at all; however, if HSS < 0, the forecast is worse. Equivalently, if B = 1, it means the forecast is unbiased; however, if B > 1, it means there is an over forecast and if B < 1, it means there is an under forecast. PC usually ranges from zero for no correct forecasts obtained by the model to one when all model forecasts are correct. The results in Table 4 show that the developed occurrence model is capable of predicting rainfall occurrence with sufficient accuracy as dictated by the higher values of PC (>80%) in both calibration and verification periods. This is attributed to the nature of rainfall in the Lakes Districts as it is more frequent with high intensity. Longer rainfall series with higher intensity would usually result in a better calibrated occurrence model. Rainfall amount prediction is considered as the most difficult variable to predict. The rainfall evolution involves a complex process that is not only driven by the dynamic change in atmosphere, but also affected by the land– surface characteristics. ANNs were trained, using the sum of squares and back-propagation algorithm of Levenberg– Marquard to model the daily rainfall time series of the Tower Wood Station. Ninety per cent of the observed daily rainfall in the period 1 January 1960 to 31 December 2001 is used

Environmental Technology Table 4. PC, HSS and B for annual rainfall occurrence models for both calibration (1961–1987) and verification (1988–2001) periods. PC

HSS

B

Calibration Verification All

0.82 0.80 0.81

0.63 0.60 0.62

1.00 1.02 1.01

Observed

GLM-ANN

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2000 Rainfall (mm)

Period

575

1500

1000

500

Observed

250

GLM-ANN 0 1961

Rinfall (mm)

150

1966

1971

1976

1981 Year

1986

1991

1996

2001

Figure 6. Inter-annual variability for observed and modelled rainfall during the calibration and verification periods (1961–2001).

100 50

100 0 0

1

2

3

4

5

6 7 Month

8

9

10

11

Figure 5. Comparison between monthly observed and modelled precipitation data during the calibration and verification periods (1961–2001).

for model calibration, 5% for validation during the training to avoid over fitting, and another 5% for testing after the run is terminate. The selection of these percentages has been selected randomly instead of applying sequence of years, which appear to produce better results. Figure 5 shows comparison plots of monthly rainfall amounts between the observed and modelled series for the whole 1961–2001 period. The plot demonstrate a good degree of agreement between the observed and modelled monthly rainfalls, though the model tends to underestimate the summer months and overestimate the winter months. Clearly, it can be deduced from these plots that the model is able to reproduce the monthly rainfall. Figure 6 shows the inter-annual variability at the Tower Wood Station, for the observed and modelled rainfall time series in the period 1961–2001. The monthly and yearly accumulations appear to have been adequately captured by the model, an important requirement when assessing climate impacts on a hydrological system. Another diagnostic test for reproduction of rainfall values is a plot of quantiles of observed versus simulated values as presented in Figure 7. It can be observed that the GLMANN model follows the 45◦ line, suggesting that the model is closer to the observed rainfall distribution. However, the model is slightly underestimating the annual extreme rainfall. Moreover, Table 5 shows the statistics to evaluate the performance of the amount model. The model shows better skill in predicting rainfall amount in terms of correlation, 70% and root mean sure error (RMSE). However, for the

12

Simulated daily rainfall (mm)

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200

80

60

40

20

0 0

20

40

60

80

100

Observed daily rainfall (mm)

Figure 7. Quantile – Quantile for daily rainfall for the calibration and verification periods (1961–2001). Table 5. Statistical properties for observed and simulated daily rainfall during the calibration and verification periods (1961–2001). Method Observed Simulated GLM-ANN

RMSE Correlation Autocorrelation STD 5.91

0.70

0.23 0.41

7.85 4.50

autocorrelation and standard deviation, the hybrid GLMANN model was not able to capture these statists very well, which could be attributed to the intensive rainfall in the area which makes the rainfall highly skewed. The annual biases in autocorrelation and standard deviation of observed and simulated daily rainfall amounts are up to 78% and 43%, respectively. Based on model test results discussed above, the rainfall occurrence and amount models built and calibrated for the Tower Wood Station can be considered skillful enough

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for use to downscale future rainfall in the Windermere catchment for the purpose of climate impacts assessment.

5.2. Projected future rainfall To assess possible future changes in the rainfall, the derived model for both precipitation occurrence and amounts were used in conjunction with data from three GCMs, namely, CCGM2, CSIRO, and HadCM3. Data sets for both the A1FI and B1 emission scenarios from each of the GCMs were used as inputs to the occurrence and amounts models discussed in Section 3.1. Data from different GCMs and emissions scenarios are usually employed as significant uncertainties are associated with prediction of future climate. The uncertainties can be due to the way the global circulation model represents the climate physics, or the way the emission scenario portrays the future. After simulating the future 30 years daily rainfall series at the Tower Wood Station (for the period 2071–2099, or the 2080s), the rainfall series needs to be temporally downscaled (or disaggregated) to an hourly scale. The BLRP model is selected to perform the disaggregation from daily to hourly rainfall (see [33] for details of fitting the model). The best 10 typical years has been selected to represent the 30 years future rainfall for simulation of the combined sewer overflows. Figure 8(a) and 8(b) represents an example

for validation check performed on the selected 10 years in 2080s for scenarios A1FI and B1 from HadCM3 GCM. The figures show the annual average monthly rainfall from the 10 selected years and the 30 years for scenario A1FI and B1, respectively. The plots in the two figures ensure that the annual variations in the selected rainfall are sufficiently represented by the original 30 years rainfall downscaled from the GCMs. Figure 9(a) and 9(b) represents another check for consistency of the daily rainfall frequency in the two series for scenarios A1FI and B1 of HadCM3 GCM. The daily frequency plots in both figures demonstrate that the 10 years rainfall series is sufficiently representative of the rainfall in the Windermere catchment, and can be used to model discharge from CSOs in the catchment. Statistically, the rainfall selected is very similar to that generated by the downscaling model and the pattern of the rainfall Table 6. Ten best typical years for current (selected from the period 1990–1990) and future rainfall for the three GCMs (selected from the period 2070–2099). Scenario Current period HadCM3 CSIRO CGCM2

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Environmental Technology distribution is also in good agreement with the two series. Table 6 lists the 10 typical years selected from the 30 years daily rainfall series simulated by each GCM. The pattern of the annual bathing season (period between May and September in the UK) rainfall of the selected 10 typical years for the high (A1FI) and low (B1)

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5.3. Impact of climate change on CSO In the UK, Urban Pollution Management procedure has been developed to manage the wastewater discharges from sewers and sewage treatment under wet weather conditions such that the requirements of the receiving water are met in a cost-effective way.[34] Moreover, the Water Framework Directive (The European Parliament and the [35] is designed to improve and integrate the way water bodies are managed throughout Europe. It integrates many of the previous Directives affecting water quality requirements and requires ‘good chemical and ecological status coastal waters by 2015’. Although these efforts have been implemented for current climate, the receiving water for CSO discharges is anticipated to face other challenges with climate change in the future.

Four indicators have been chosen for assessment of sewer system performance, the mean annual overflow volume, the number of overflows per year and the total BOD and NH3 concentration of annual overflow. Figures 11 and 12 show the impact of climate change on CSOs spilling as a result of future rainfall obtained from the three GCMs for annual and bathing season (May–September) in the 2080s (2070–2099). In Figure 11, HadCM3 and CSIRO predict an increase in annual spilling volume under scenario A1FI, with a decrease in CGM2. This could be due to an increase in the average annual rainfall projected by the two GCMs, but not by the CGCM2 (Figure 10). However, under the low scenario B1, all the GCMs predict a drop in CSO spill volume, which could cause a significant pollution by increasing the BOD concentration by 26% in HadCM3 and 8.6% as in CSIRO. For the A1FI scenario, the increase in the CSOs spilling volume could cause the pollutant concentration to be diluted, leading to a drop in the BOD concentration. The same pattern happened for NH3 , which could also adversely affect the environment (the receiving water). In terms of CSOs spill frequency, only results obtained from HadCM3 predict a

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scenario all GCMs indicated a decrease in annual rainfall except CGCM2 which showed no change. The bathing season rainfall is the only period in which all models agree that there will be a decrease in rainfall as shown in Figure 10(b), with slight variation in the decrease amongst the models.

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Environmental Technology rise in the number of spill frequency under both scenarios emission. In bathing seasons, the CSOs total spill volume and spill frequency are predicted to decrease by the three GCMs under both scenarios (Figure 12) due to a reduction in rainfall amount (as explained in Figure 10). So as expected, a very slight rise in the BOD concentration is predicted by the HadCM3 and CSIRO GCMs, which tend to have nearly the same pattern under the two emission scenarios considered. Predictions from the CGCM2 GCM have found to have the lowest impact during the bathing season. There is no detection of increase in the NH3 during the bathing season, as predicted by all GCM, which could be attributed to the significant drop in the volume of washoff flow, one of the main sources for NH3 load from the gully pots, when runoff enters the sewerage system. In guidance produced by the Environment Agency (for England and Wales), it specified three spills per bathing season as a design standard for CSOs impacting on bathing waters.[30] Furthermore, for shellfish waters, the Environment Agency specification is no more than 10 spills per annum. So the results of the CSO spill frequency in Figure 11, 12 indicate that the Windermere catchment has unsatisfactory CSOs for both shellfish and bathing water which can lead to potential pollution risk and also can cause athletic problems to Lake Windermere. 6. Conclusion The possible increase in peak rainfall intensities will have an impact on emitted pollutants to receiving waters leading to failures to meet legal obligations under European Union Legislation. This paper assesses the CSOs performance adopting rainfall downscaling from GCM. The rainfall downscale model shows reasonable accuracy, especially in terms of extreme events; however, there are some limitations in its capability to capture the rainfall variability. The downscaled rainfall time series, obtained using inputs from three GCMs, are applied to an urban drainage model to assess the performance of its CSOs. A clearer picture emerges for the annual and bathing season periods by the 2080s, with most models projecting an increase in annual rainfall and a decrease in bathing season rainfall, the ranges between the ‘driest’ and ‘wettest’ models being significant. The paper indicates that projected future risk of pollution is varied among the GCMs, with a potential high BOD and NH3 concentrations from CSOs spill being associated with HadCM3 as maximum of up to 26% and 5%, respectively, when annual rainfall drops by 5% under scenario A1FI. Acknowledgements This paper is part of a research conducted at Liverpool John Moores University. Special thanks from the authors are due to MWH UK Ltd, United Utilities, Innovyse Ltd, and WRc for their

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support during the period of the research. The views expressed in the paper are those of the authors and not necessarily those of the collaborating bodies.

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