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Increasing the resilience of the UK water sector to climate change using probabilistic weather generator information 1

C.N.P. Harris , A.D. Quinn and J. Bridgeman 1: School of Civil Engineering, University of Birmingham, UK. cnh014@bham.ac.uk Aims 

To u se a probabilistic w eather generator approach for d eterm ining the im pact of clim ate change on w ater resou rce resilience on t he Stoke and Lad d ered ge d rou ght zones.

To d evelop an approach for expand ing the u se of single-station w eather generation by applying an au to-regressive m od el to the in form ation in ord er to create pseu d o-spatial w eather generator inform ation over short d istances.

To qu antify the m ost robu st clim ate change ad aptation processes in the research area u sing a risk -based approach that w ou ld be replicable in ind u stry.

1. Weather Generator Data

2. Auto-regressive model

Weather generators (WGs) enable clim ate change im p act assessm ents to be cond u cted at greater resolu tion in sp ace and tim e than global clim ate m od els alone allow . They are usefu l for stu d ies of w ater resou rce p rovision, w here the sequ ence of events is im p ortant. Precipitation and p otential evapotransp iration (PET) sequ ences are created by the UKCP09 w eather generator (UKCP09WG) for variou s feasible fu tu res. The H Yd rological SIm u lation Mod el (H YSIM) calcu lates channel flow s u sing these sequ ences and a param eterisation of the catchm ent area. 10000

An ap p roach to broad en the u sability of the p oint-based UKCP09WG is d eveloped that u ses an au toregressive m od el to create ‘p seu d o-sp atial’ rainfall sequ ences. ‘Dau ghter’ p recipitation sequ ences are created from the ‘m other’ sequ ence u sing: P 2 = {[P UC(r k0)) + P-1(r k-1) + P-2(r k-2)] + E} ∆μ Where, P 2 = Second ary precip itation sequ ence P UC = Original w eather generator sequ ence Rk...n = Cross-correlation coefficient of lag k d ays E = Rand om elem ent ∆μ = Scaling valu e to m atch m ean of instru m ental d ata at P 2

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Figure 1. Flow Duration Curves (FDCs) at Upper Chu rnet (left), Solom on’s H ollow (m id d le) and Deep H ayes (right) d uring the sim ulated baseline p eriod (blue lines) and instrum ental record (black lines). Solid lines: w hole year, d ashed lines: w inter m onths (ON DJFM), d otted lines: sum m er m onths (AMJJAS). Precipitation sequences at Solom on’s H ollow and Deep H ayes w ere d erived from the ‘m other’ sequence at Upper Chu rnet. Reprod u ction of instrum ental flow s are generally good , valid ating the WG, autoregressive m od el and H ysim .

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3. Quantifying risk of water shortage

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Reservoir capacity (%)

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Figu re 2. An exam p le of sim u lated Tittesw orth Reservoir level (blu e) falling below a control cu rve (red ) signifying the failu re of the system to m aintain w ater levels above a certain Level of Service (LoS) for that year.

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5. Smart Adaptation

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The large am ou nt of w ater resou rce fu tu res prod u ced enables statistically robu st clim ate change ad aptation m easu res to be d eterm ined . This im p roves u p on trad itional techniqu es of u sing one (or a few ) p rojections of the fu tu re, w hich can lead to m alad aptation. Und er fu tu re clim ate cond itions, the probability of not m eeting a pre-d eterm ined level of acceptable risk (i.e. a m axim u m of y years in w hich level of service of severity x is not m et over the tim e p eriod t) is increased . Charts su ch as figu re 5 can be u sed to visu alise how su ccessfu l certain approaches are at red u cing risk (i.e. by calcu lating how m u ch of the d istribu tion is kept below an acceptable valu e for risk)

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Precip itation in the 2080s varies across the m od elling range, bu t m ore intense w inter rainfall and less su m m er rainfall are generally exhibited , cau sing greater w ater resou rce vu lnerability as patterns of rainfall shift from those on w hich the system is d esigned . Ep isod es of m eteorological and hyd rological su m m er d rou ght increase in intensity as the centu ry progresses. Flow s in the stu d ied catchm ents d ecrease (figu re 3), reservoir storage volu m e falls (figure 4), d rou ght risk in the area increases (figu re 5), and m onthly reservoir yield is red u ced .

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Reservoir % full

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4. Future drought risk

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Sim u lations of fu tu re w ater availability in the resou rce zone are created u sing the Aqu ator w ater resou rce m od el. Risk of w ater shortage, based on w ater levels falling below a pred eterm ined control cu rve (a ‘d rou ght trigger’) at Tittesw orth Reservoir, is calcu lated across the range of clim atic u ncertainty.

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Probability of Exceedance (%)

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Figure 3. FDC for the Upper Churnet Catchm ent in Figure 4. Volum e Duration Curve for Tittesw orth the baseline period (black) and the 2080s (blue). Solid Reservoir d uring average cond itions in the baseline lines represent w hole year flow s, d ashed lines are period (black) com pared to 2080s average (solid blue w inter m onths (ON DJFM) and d otted lines d enote line) and a range of ten 2080s sim ulations (d otted sum m er (AMJJAS). blue lines).

Conclusions Severity of drought

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Frequency of drought

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Risk of drought per year

Figu re 5. Changes in d rou ght risk of d ifferent severities in the baseline p eriod (blu e), instru m ental 1961-90 record (green) and 2080s (black). Each p oint rep resents an ind ivid ual sequ ence at Tittesw orth Reservoir, thu s show ing a range of potential d rou ght risks to the Stoke and Lad d ered ge d rought zones. Circles: risk of a hosep ip e ban p er year; triangles: risk of p assing d rou ght w arning control line p er year; squares: risk of p assing storage alert line p er year; d iam ond s: risk of falling below control cu rve p er year.

Figu re 5 show s the broad range of p otential w ater shortage risk fu tu res that cou ld effect the Stoke and Lad d ered ge d rou ght zones in the 2080s. In the final resu lts, 20 su b-sam p led sim u lations w ill be created for each tim e horizon and em issions scenario.

1) The u nd er-estim ation of instru m ental d rou ght risks in the sim u lated baseline period s m eans that cau tion shou ld be taken w hen interp reting the fu tu re d rou ght risk resu lts. They shou ld be seen as a percentage change in risk from an im p recise realisation of 20th centu ry cond itions, rather than a d efinite alteration of risk from a point in the past to a point in the fu tu re 2) The resu lts for longer tim e horizons (2050s and 2080s) shou ld be view ed as low -end p rojections and a large am ou nt of head room shou ld be ad d ed accord ingly. The resu lts can, how ever, accu rately id entify changes to norm al operating cond itions and trend s in su m m er d rou ght risk, w hich rep resent significant tools to w ater resou rce d ecision m akers. 3) By ap p lying the p rincip als of robu st d ecision m aking and risk assessm ent, it is p ossible to significantly increase the resilience of d rou ght zones to potential fu tu re cond itions. Contact: CN H 014@bham .ac.u k Acknow ledgements: The au thors w ou ld like to thank Severn Trent Water, H yd roLogic, Oxford Scientific Softw are and Mott MacDonald for their inpu t into the p roject.

Increasing the reslience of the UK water sector to climate change  

Christopher Harris, University of Birmingham, UK--- Increasing the reslience of the UK water sector to climate change using probabilistic we...

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