SIMONOVIC Slobodan P.

Topaloglu Fatih

渡邉 紹裕

田中 賢治

藤原 洋一

Research Institute for Humanity and Nature Repository

An Inverse Modeling Approach to Assess the Impacts of Climate Change onWater Resources Management at the Watershed ScaleYoichi FUJIHARA1, Slobodan P. SIMONOVIC2, Fatih TOPALOGLU3,Tsugihiro WATANABE4, and Kenji TANAKA51,4Research Institute for Humanity and Nature, 457-4 Kamigamo-motoyama, Kita-Ku,Kyoto 603-80472Department of Civil and Environmental Engineering, University of Western Ontario, London, OntarioN6A 5B9, Canada3Faculty of Agriculture, Cukurova University, Adana 01130, Turkey5Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji 611-00111. IntroductionOver the last several years, hydrologists havepaid great heed to the potential impacts of cli-matic change on water resources and hydrologicalcycling, which are traditionally assessed using atop-down approach. With this approach, outputsfrom general circulation models (GCMs) are sta-tistically or dynamically downscaled to the riverbasin scale, and the local climatic signal is theninput to a hydrological model to assess the directconsequences in the basin. However, problems re-lated to this approach arise from the incompati-bility between the temporal and spatial scales ofa GCM and a river basin. Because serious ef-fort is required to validate GCM outputs and thedownscaling techniques, many river basin author-ities consequently remain skeptical of the possibleimpacts of climate change.We attempted to improve understanding of theprocesses leading to local hydrological hazards byintroducing an inverse (or bottom-up) approachto the modeling of flood risk and vulnerability tochanging climatic conditions. The modeling ap-proach was applied to the Seyhan River Basin,Turkey.2. MethodsThe inverse modeling approach is designed toassess the vulnerability of river basin hydrologicalprocesses to climate forcing from a bottom-up per-spective. The theoretical concepts of this approachwere developed at the University of Western On-tario, Canada, by Cunderlik and Simonovic (2004,2005, in press). The approach consists of the fol-lowing four steps and is illustrated in Figure 1.1. Critical hydrological exposures, which maylead to local failures of water resource systems ina particular basin, are first identified. Critical ex-posures are analyzed together with existing guide-lines and management practices. This step is ac-complished in collaboration with local water au-thorities.2. In the next step, the identified critical hydro-logical exposures (such as floods and droughts)are transformed into corresponding critical me-teorological conditions (e.g., extreme precipita-tion events, sudden warming, and prolonged dryspells). A hydrological model is used to establishthe inverse link between hydrological and meteo-rological processes.3. General circulation model (GCM) outputsare analyzed to investigate future changes relativeto the present conditions. Afterward, a stochasticweather generator, which is one of the downscal-ing methods, is used to generate the meteorologi-cal conditions under present and future scenarios.4. In the final stage, the frequencies of criti-cal meteorological events causing specific waterresources risks are assessed from the weather gen-erator outputs. The frequencies of critical hydro-logical events are obtained using the establishedinverse link.The presented approach can be used to develophazard risk management strategies under presentand future climatic conditions for any water re-source system. The main advantages of the inverseGCM K -NN Frequency of critical meteorological eventCritical hydrological eventSimulations of hydrologic modelFrequency of critical hydrological eventFig.1 The Framework of this Studyapproach over the traditional top-down approachare its focus on specific existing water and poten-tial water resource problems, a direct link with theend user, and easy updating when new and im-proved GCM outputs become available.3. Results3.1 Identification of Critical HydrologicalEvents3.1.1 Flood eventsAdana, with a population of approximately 1.3million, is the capital of Adana Province and thefourth largest city in Turkey. Socioeconomic ac-tivities in the lower area of the Seyhan River Basinare much more vital than those of the upper area.The urban center is located around and down-stream from Seyhan Dam, which was constructedin 1956 to protect the city of Adana against floods.The flood of 1980 was the biggest flood onrecord and also produced the most damage (JapanInternational Cooperation Agency, 1994). Flooddamage occurred not only on the Cukurova Plainbut also in upstream areas and around tributariesof the Seyhan River. Although the recently con-structed Seyhan Dam was operating at the time,the flood of 1980 destroyed 21 buildings and dam-aged 76 others. The deluge was equivalent to aflood of 100-year probability.The flood damage in 1980 accelerated the plan-ning for and construction of the Catalan Dam.Dam construction began in 1982, and the CatalanDam has been operating since 1997. The Catalanand Seyhan dams should be able to protect down-stream areas against floods of 500-year probabil-ity. Based on these findings, 100-year and 500-year floods are identified as critical hydrologicalevents in the Seyhan River Basin.3.1.2 Drought eventsThe Lower Seyhan Irrigation Project (LSIP) inAdana was initiated by the Turkish governmentas an important irrigation project covering an areaof 204,000 ha. In 2002, approximately 1.8 Gm3of irrigation water was withdrawn from the Sey-han River for the LSIP. This withdrawal has in-creased annually, and region IV of the LSIP willbe completed within several years. Therefore, wa-ter scarcity will be a major concern in the SeyhanRiver Basin. In this study, we employ the follow-ing index as a water scarcity index:Iws =W − SQ(1)where W , S, and Q are the annual water with-drawals by all sectors, use of desalinated water,and the annual inflow at Seyhan Dam, respec-tively. When this index is higher than 0.4, the Sey-han basin is considered highly water-stressed (Al-camo et al., 2003; Oki and Kanae, 2006). Drought,represented by a water scarcity index (Iws) valuehigher than 0.4, is identified as a critical event inthe Seyhan River Basin.3.2 Hydrological Model ApplicationThe simulation region is an area of 2.75◦2.75◦ (36.5◦-39.25◦ and 34.25◦-37.0◦) with 5min latitude/longitude spatial resolution (33 33grids). Gtopo30 was used as the digital elevationmodel (DEM). The Global Land Cover Character-ization version 2.0 (GLCC-v2) was selected forthe basic land use data. However, the irrigated1011021031041007550250Discharge (m3 /s)Precipitation (mm)1972 1973 1974ObservedSimulated1011021031041007550250Discharge (m3 /s)Precipitation (mm)2000 2001 2002ObservedSimulated(a) 1972-1974 (b) 2000-2002Fig.2 Simulated Hydrograph at Station 18181011021031041007550250Discharge (m3 /s)Precipitation (mm)2000 2001 2002ObservedSimulatedFig.3 Simulated Hydrograph at the Catalan Dam (2000-2002)cropland and forest areas in the original data setare clearly underestimated. Thus, the data set wasmodified by ground truthing and normalized dif-ference vegetation index (NDVI) time series anal-ysis.Grid precipitation and temperature data forHydro-BEAM (Kojiri et al., 1998) were createdby interpolating data from 23 stations. The in-verse distance method (IDM) was employed asthe spatial interpolation method. Hydro-BEAMwas then run using the grid meteorological datafrom 1972 to 2002 to calibrate the model param-eters. The simulated hydrographs at station 1818for the first and last 3 years are shown in Figure2. These figures show that the river discharge atstation 1818 was quite well reproduced by Hydro-BEAM. Moreover, the simulated hydrographs atthe Catalan Dam for the last 3 years are shown inFigure 3. This figure also shows that the simulatedresult agreed with the observed data.3.3 Inverse Linkage3.3.1 Flood eventThe 12 biggest floods from the historical recordwere selected as the representative flood eventsin the Seyhan River Basin. Analyzing the rela-tionship between these floods and meteorologicalconditions, daily areal precipitation had a large in-fluence on the floods. The inverse link betweenfloods and areal precipitation was determined bythe following procedures.We created a synthetic precipitation data set toestablish the relationship between the peak dis-charge and areal precipitation. Synthetic areal pre-cipitation data sets (50, 100, 150, and 200 mmday−1) were created by multiplying the ratio be-tween observed areal precipitation correspondingto the flood event and the synthetic areal precipita-tion. Hydro-BEAM was run using each syntheticprecipitation data value. Figure 4 shows the rela-tionship between the peak discharge and areal pre-cipitation. After the average of peak discharge foreach synthetic precipitation was obtained, the in-verse linkage was established as follows:Q = aP 2 + bP + c (2)where Q is the peak discharge at the Catalan Dam,P is the areal precipitation, and a, b, and c are pa-rameters.50 100 150 200103104Peak Discharge at the Catalan Dam (m3 /s)Areal Precipitation (mm)AverageFig.4 Inverse Link (Peak Discharge)3.3.2 Drought eventIn this study, the water scarcity index (Iws) wasused as the index of drought. An advance anal-ysis revealed that the water scarcity index can beapproximated by areal annual precipitation. Theinverse link between water scarcity and areal an-nual precipitation was obtained by the followingprocedures.Data for all years were used to establish the re-lationship between the water scarcity index andareal annual precipitation. To create synthetic datain which the annual precipitation was set to halfthe value of the original data, we multiplied by0.5. Hydro-BEAM was then run for the data ofall years and the synthetic data. Figure 5 illus-trates the relationship between the water scarcityindex and areal annual precipitation. The inverselinkage was obtained as follows:Iws = exp(aP + b) (3)where Iws is the water scarcity index for a year, Pis the areal annual precipitation, and a and b areparameters.200 400 600 800 100000.51Water Scarcity IndexAreal Annual Precipitation (mm)Fig.5 Inverse Link (Water Scarcity Index)3.3.3 GCM ProjectionsThis study employed nine state-of-the-artGCMs that were also used in the most recent phaseof the Coupled Model Intercomparison Project(CMIP; Covey et al., 2003). From the atmosphericdata set, monthly precipitation and monthly meantemperature data under IPCC SRES A2 scenariowere collected for each model. Two subset peri-ods (present: 1961-1990 and future: 2070-2099)were set to investigate the temperature and precip-itation changes.Figure 6(a) indicates the ensemble averaged an-nual temperature of the nine GCMs, which projectthe rising annual temperatures in all regions; Fig-ure 6(b) shows the ensemble averaged annual pre-cipitation of the nine GCMs, which project the de-creasing annual precipitation in the Mediterraneanregion, including Turkey. The decreasing precip-itation and rising temperature predicted for theMediterranean region suggest that climate changewill have serious impacts on the hydrology andwater resources of the Seyhan River Basin. Weset the decreasing precipitation scenario for theweather generator based on these GCM analyses.3.4 Frequency of Critical HydrologicalEventsWe adopted a modified version of the k-nearestneighbor approach (Sharif and Burn, 2004, 2006),which is one of the stochastic weather genera-tors. The k-nn approach was applied to gener-0°30°60°0°30°60°0° 30° 60° 90° 120° 150° 180°0° 30° 60° 90° 120° 150° 180°−6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6degreesC0°30°60°0°30°60°0° 30° 60° 90° 120° 150° 180°0° 30° 60° 90° 120° 150° 180°−300 −250 −200 −150 −100 −50 0 50 100 150 200 250 300mm/year(a) Temperature (b) PrecipitationFig.6 Temperature and Precipitation Changesate the meteorological conditions corresponding tothe scenarios (decreased precipitation) for a 100-year period. The frequency of critical meteorolog-ical events causing flooding was assessed from theweather generator outputs. The flood return peri-ods, which were inversely estimated using equa-tion (2), are shown in Figure 7. For 100-yearflood, the future peak discharge is almost the sameas that at present. However, the 500-year flood un-der present conditions will decrease to a flood of250-year magnitude under the decreasing precipi-tation conditions. Thus, critical flood events willoccur less frequently under the decreasing precip-itation scenario.The frequency of critical meteorological eventscausing drought was also assessed from theweather generator outputs. The return periods,which were inversely estimated using equation (3),are shown in Figure 8. Drought, indicated by Iwshigher than 0.4, has about a 6-year probability un-der present conditions. However, the decreasingprecipitation scenario indicates that droughts willoccur more often, at a 2-year probability.4. ConclusionsThis study introduced an inverse bottom-up ap-proach to the modeling of flood and drought riskunder changing climatic conditions. The approachis focused on specific extant water resource prob-lems, has a direct link with end-users, and canbe easily updated when new and improved GCMoutputs become available because the analysis ofGCM outputs is the last step in the proposedmethodology.2000 3000 40000200400600Return Period (year)Peak Discharge (m3/s)PresentFutureFig.7 Return Period (Peak Discharge)0.2 0.4 0.605101520253035Return Period (year)Water Scarcity IndexPresentFutureFig.8 Return Period (Water Scarcity Index)The study case presented one possible applica-tion through its assessment of flood and droughtrisk in the Seyhan River Basin, Turkey, underpresent and future climatic conditions. Based ondata and reports for the Seyhan River Basin, 100-year and 500-year floods were identified as criti-cal hydrological events. In addition, drought, in-dicated by Iws higher than 0.4, was identified as acritical event in the Seyhan River Basin. The iden-tified critical floods and drought were transformedinto corresponding critical meteorological condi-tions using a Hydro-BEAM simulation. Basedon analysis of the outputs of nine state-of-the-artGCMs, the decreased precipitation scenario wasset for the weather generator. The k-nn was thenused to generate meteorological conditions corre-sponding to the scenario for a 100-year period.The frequencies of critical meteorological eventscausing flooding and drought were assessed fromthe weather generator outputs. The frequency offlood and drought were then estimated using theestablished inverse link. The results suggest thatthe 500-year return period flood under the presentcondition will decrease to a 250-year return pe-riod flood under the decreasing precipitation con-ditions. Drought, with 6-year probability underthe present condition, has 2-year probability underthe future condition.AcknowledgmentsThis research was supported financially by theProject Impact of Climate Changes on Agricul-tural Production System in Arid Areas (ICCAP),administered by the Research Institute for Human-ity and Nature (RIHN) and the Scientific and Tech-nical Research Council of Turkey (TUBITAK). Inaddition, this research was also supported finan-cially in part by Japan Society for the Promotionof Science (JSPS) Grant-in-Aid No. 16380164.ReferencesAlcamo, J., P. Doll, T. Henrichs, F. Kaspar, B.Lehner, T. Rosch, and S. Siebert (2003) Globalestimates of water withdrawals and availabilityunder current and future business-as-usual con-ditions, Hydrological Sciences Journal, 48(3),339-348.Covey, C., K.M. Achuta Rao, U. Cubasch, P.Jones, S.J. Lambert, M.E. Mann, T.J. Phillips,and K.E. Taylor (2003) An overview of re-sults from the Coupled Model IntercomparisonProject (CMIP), Global and Planetary Change,37, 103-133.Cunderlik, J.M., and S.P. Simonovic (2004) In-verse modeling of water resources risk and vul-nerability to changing climatic conditions, Pro-ceedings of the 57th CWRA Annual Congress,Water and climate change: Knowledge for bet-ter adaptation, 16-18 June, Montreal, Canada.Cunderlik, J.M., and S.P. Simonovic (2005) Hy-drological extremes in a southwestern Ontarioriver basin under future climate conditions,Journal of Hydrologic Sciences, 50(4), 631-654.Cunderlik, J.M., and S.P. Simonovic (in press)Inverse flood risk modeling under changing cli-matic conditions, Hydrological Processes.Japan International Cooperation Agency (1994)Feasibility study on flood control, forecastingand warning system for Seyhan River Basin,5-6.Kojiri, T., A. Tokai, and Y. Kinai (1998) Assess-ment of river basin environment though simu-lation with water quality and quantity, Annualsof Disaster Prevention Research Institute, KyotoUniversity, 41(B-2), 119-134.Oki, T., and S. Kanae (2006) Global hydrologicalcycles and world water resources, Science, 313,1068-1072.Sharif, M., and D.H. Burn (2004) Developmentand application of k-nearest neighbor weathergenerating model, Proceedings of the 57thCWRA Annual Congress, Water and climatechange: Knowledge for better adaptation, 16-18June, Montreal, Canada.Sharif, M., and D.H. Burn (2006) Simulating cli-mate change scenarios using an improved k-nearest neighbor model, Journal of Hydrology,325, 179-196.

An Inverse Modeling Approach to Assess the Impacts of Climate Change on Water Resources Management at the Watershed Scale

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An Inverse Modeling Approach to Assess the Impacts of Climate Change on Water Resources Management at the Watershed Scale

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