Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Sciences Discussions Earth System Hydrology and 2,3 , and A. R. Paz 2 ´ 10% under the SRES emissions scenarios a in South America and one of the most im- + 6100 6099 C warming scenario is, however, associated ◦ 5% to , C. E. M. Tucci 2 + ´ elite, Campinas, Brazil ´ aulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, 13%) under the 2 51% with prescribed increases in global mean air temperature of + C. Substantial uncertainty in projected changes to mean river dis- + ◦ , W. Collischonn 1 8% to 28% to C for the HadCM3 GCM as well as uncertainties related to GCM structure. + C increase in global mean temperature. Pattern-scaled GCM-outputs are ap- ◦ ◦ − ´ obrega 2 + lia-DF CEP:70610-200, Brazil ı ´ This discussion paper is/has beenSystem under Sciences review for (HESS). the Please journal refer to Hydrology the and corresponding Earth final paper in HESS if available. EMBRAPA Monitoramento por Sat Agencia Nacional de Aguas (National Water Agency, SPS, Area 5,Instituto Quadra de 3, Pesquisas Hidr Bloco “L”, annual climate variability (e.g. sustained drought)agers is and of has particular been concern observed in to the water discharge man- of rivers around the world (e.g. Dettinger scenario or the magnitude of rise in mean global temperature. 1 Introduction The well-being of human societiesenced is by closely climate associated variability. with This climateeconomy relationship and is is thereby based influ- especially on strongstrong rain-fed in dependence agriculture regions upon where (e.g. river the sub-Saharan flow Africa) for or the where generation there of electricity is (e.g. Brazil). Multi- and from between 1 and 6 charge ( with the choice ofmost GCM. important We source conclude of that, uncertainty in derives the from case the of GCM the rather Rio than Grande the Basin, emission the emission scenarios (A1b, A2,of B1, 1 B2) to and 6 For increases the in latter, global multimodel runs meanCM4, using air MPI 6 temperature ECHAM5, GCMs (CCCMA NCARfor CGCM31, CCSM30, a CSIRO UKMO Mk30, HadGEM1) IPSL andplied to HadCM3 a as large-scale baseline, on hydrological model simulations (MGB-IPH) using of HadCM3, thebaseline mean period Rio annual (1961–1990), Grande river by Basin. discharge increases, Based relative to the We quantify uncertainty inGrande, the a impacts major tributary of of climate theportant change River basins Paran on in the Brazil discharge forsider water of uncertainty supply the and in Rio hydro-electric climate power generation. projections We associated con- with the SRES (greenhouse-gas) Abstract Hydrol. Earth Syst. Sci.www.hydrol-earth-syst-sci-discuss.net/7/6099/2010/ Discuss., 7, 6099–6128, 2010 doi:10.5194/hessd-7-6099-2010 © Author(s) 2010. CC Attribution 3.0 License. Uncertainty in climate change impactswater on resources in the RioBrazil Grande Basin, M. T. N Brazil 3 Received: 31 July 2010 – Accepted: 3Correspondence August to: 2010 W. – Collischonn Published: ([email protected]) 25 AugustPublished 2010 by Copernicus Publications on behalf of the European Geosciences Union. 1 Bras 2 5 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | C), ◦ ) was ´ a and 2 model (Motta and Tucci, ff 90%) of Brazil’s electric power ∼ 25%. (Fig. 1), which is relatively hilly, ranging erent GCMs, emission scenarios (SRES 2 + ff 6102 6101 15% and hydro-electric dam (drainage area 758 000 km ı − ´ ´ a (Robertson and Mechoso, 1998). ´ a. Approximately 60% of HEP generation in Brazil is provided er et al. (2008) evaluated the impacts of climate change on the ff C threshold of “dangerous” climate change (Todd et al., 2010). ◦ ´ a Basin and the river is also very important in terms of energy production Most analyses of climate change impacts on river discharge in South America have, One of the most important concerns related to climate change in Brazil is there- Tomasella et al. (2008) analysed the impacts of climate change on the discharge of The impacts of climate change upon river flow, including the incidence and magni- by the Paran further downstream in Paraguay and Argentina. HEP generation in the Rio Grande drains an area ofin approximately elevation 145 000 from km more200 m than a.m.s.l. 1800 m Agricultural abovenatural land mean and use sea planted constitutes level forestsbasin more (m cover than is a.m.s.l.) approximately 70% 20%. approximately of tosummer; 1400 the mm Mean less actual, area annual annual than and whereas evapotranspiration rainfall ismately averaged over over 950 concentrated mm. the the The whole during Rio basinstart Grande Southern is discharges of approxi- Hemisphere into the the River River Paran Paranaiba which marks the including the 2 2 The Rio Grande Basin The Rio Grande is one of the main headwater tributaries of the River Paran to date, relied uponsessments climate should be projections viewed from with(GCM) a caution structure since single is the GCM. not uncertainty The considered.on associated results stream with In flow model of this in paper, these the werange as- Rio estimate of Grande climate climate Basin change scenarios of impacts (Collischonn to South et a America al., large-scale through distributed 2007a). thethe hydrological application Critically, quantification model of the a of (MGB-IPH) range uncertaintyA1b, of between A2, applied di B1, climate B2) scenarios and enables prescribed increases in global mean air temperature (1 to 6 (Ambrizzi et al., 2007;(high Marengo, emission) 2007). and Two B2 emission (low scenarios emission)one although GCM were the (HadCM3). considered; PRECIS It A2 projections was drawfor concluded from HEP that just generation most would of face the a Brazilian reduction rivers in which discharge are used due to climate change. generated using PRECIS (Providing REgional Climates for Impacts Studies) model deviation) according to expected changes associated with climate change scenarios power (HEP) generation which relies onflows sustained (those river exceeded flow, results 90% suggested of that the low time) would decrease byfore 58%. the implicationsresources for and HEP generation. HEPproduction. is The responsible Schae country forBrazilian relies almost energy heavily sector all with on ( models a renewable to particular generate emphasis reference on timeSubsequently, series electricity. the of They stream statistical used flow models statistical for parameters several hydropower were plants. perturbed (mean and standard tions from one GCM (HadCM3)olution that using were the dynamically ETA Regional downscaled Climate tothe Model a River (RCM) 40 Tocantins km (Chou at et grid the al., Tucuru res- projected 2000). to Discharge of decrease bybaseline. 20% More for importantly the for 2080–2099 water resources period management, compared including to hydro-electric a 1970–1999 analyses of the regional impactsby of Tucci climate and change on Damiani water1984; (1994). resources Tucci was Using and conducted the Clarke,ferent IPH2 1980) GCMs, rainfall and mean runo climate stream flow predictionsprojected in for to the change 2040–2060 Brazilian by from parts between three of dif- the River Uruguaythe Basin rivers was Araguaia and Tocantinsthe that MGB-IPH flow hydrological from model central (Collischonn to et northern al., Brazil. 2007a) They driven used by climate projec- ability has been recorded2001) in and the the River River Paraguay Paran and its tributaries (Collischonntude et of al., periods ofresources sustained management high are or low important flow areas and, of in research. turn, their implications In for Brazil, water one of the first and Diaz, 2000; Peel et al., 2001; Timilsena et al., 2009). In South America, this vari- 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff erent HRUs ff ´ a generation cascade, ). Daily rainfall in each 2 generated from di ff ciency for stream flow (NS); and Nash- ´ Agua Vermelha, Furnas and Estreito) with ffi 6104 6103 erent parts of the hydrograph and the di ff , including applications for river flow forecasts based e model e 2 ff 12% of Brazil’s total (ANEEL, 2005). There are four hydropower between fitting di ∼ ff ); Nash-Sutcli V ∆ and is used for regulating flows all over the River Paran e for the logarithms of stream flow (NSlog). These three objective functions were 3 ff As a result of the multi-objective optimization, several Pareto optimal solutions were Evapotranspiration is calculated using observed daily or mean-monthly values of A more detailed description of the MGB-IPH model and the tools used for pre- MGB-IPH has been employed in a range of large-scale river basins ranging from objective-functions, as suggested byvalidation, the Bastidas values et obtained for al. NS and (2002). NSlog were In approximately 0.9 both at calibration all but and one timization algorithm (Yapo et al.,volume 1998) bias was employed ( usingSutcli three objective-functions: calculated at several hydropower plants overseries the were basin available where (Fig. observed 1). discharge time found. A singleacceptable solution trade-o was chosen from among them with the aim of providing an temperature, sunshine hours, relative humidity,using wind the speed Penman-Monteith and equation. atmospheric Hydrological pressure using model data parameters were from calibrated validation. 1970 The to model 1980, wasapproach calibrated while described by by modifying the Collischonn values et period of al. (2007a). parameters, 1981 following The to the multi-objective MOCOM-UA 2001 op- was used for model 3.1 Model calibration and validation Initial calibration and validationcal of data the provided model by wasdense station gauge undertaken meteorological network with records. of input Rainfallrepresentation 273 meteorologi- data of stations precipitation derive (ANA, (density from 2005), ofgrid a which 1 allows fairly cell station a of per reasonable 530 theapplied spatial km on model observed was precipitation records. then calculated by an inverse distance weighted method Tapajos river basin (Collischonn et al., 2008). processing the DEMlischonn in et order al. (2007a) to and divide Paz and the Collischonn basin (2007). in individual cells is found in Col- and Araguaia rivers (Tomasella et al., 2008) and tests of TRMM rainfall data in the on quantitative precipitation forecasts (Tucci et al.,2007b; 2003, Bravo 2008; et Collischonn al., et 2009), al., simulations 2005, of the impact of climate change on the Tocantins tion based on data of thelocity, following solar variables: radiation, air and temperature, atmospheric relative pressure. humidity,in wind Runo one ve- cell is summed andusing flow three generated linear within reservoirs the cell (baseflow,is subsurface is flow routed propagated to and through the surface stream flow). the network description Stream river of flow network the model using is the given Muskingum-Cunge by Collischonn method. et al.6000 A (2007a). to full more than 1 million km following a Hydrologic Responseproach (Beven, Unit 2001; (HRU) Kouwen et orof al., Grouped 1993), soil which Response types are Unit and areasHRUs (GRU) land with (Allasia similar ap- cover et combinations al., or 2006). landusing rainfall Soil-water use. data budget and is A evapotranspiration calculated computed cell using for the contains each Penman-Monteith a HRU equa- of limited each number cell, of distinct 3 The MGB-IPH hydrological model The MGB-IPH hydrological modelal., is 2007a) a which large-scale includesspiration, flow distributed modules propagation, for model and calculating flow (Collischonnderived routing from the et a through soil-water digital a elevation budget, drainage modelis (Paz network evapotran- divided and automatically Collischonn, into 2007). square cells The connected drainage by basin channels. Each cell is further divided in parts, plants along the Rio Grandea (Marimbondo, total capacity in17 km excess of 1000 MW.including The the Furnas Itaipu reservoir hydropowerwater alone resources plant. in has the Besides a region are its volume also of importance essential for for irrigation power and urban generation, water supplies. Basin accounts for 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.88 = e (NSLog) of ff resolution using 00 6 × 00 ) CRU TS 3.0 observational dataset C (“dangerous” climate change) us- ◦ ◦ 5 . 0 × e (NS) and Log-Nash-Sutcli ◦ ff 5 . 6106 6105 C using the UKMO HadCM3 GCM as well as (3) A1b ◦ cient of variation used to generate daily data, were obtained from ffi 0.88. Nevertheless, the results using the CRU dataset can be considered = ects of reservoir operation and consumptive use of water upstream (ONS, 2007). Simulated stream flow at Agua Vermelha reservoir, which is very near to the outlet of Subsequently these results were compared to those obtained when the hydrological ff river flow increases byyear 10%. (Fig. Projected 3). increases are The not most evenly important distributed changes over the occur during the late wet season (from 5 Results and discussion 5.1 Uncertainty in greenhouse-gas emissions Table 2 presents projected changesfor the in model average runs river which employ flowemission results at of scenarios. the Agua HadCM3 An Vermelha GCM reservoir andunder increase four all in greenhouse-gas four discharge scenarios. compared In the to case the of baseline the is most severe projected emissions scenario, A2, mean (1961–1990) CRU data weremean modified temperature so was that removed. anymodel This runs trend detrended with relating CRU a dataset to “stablewith was climatology” increasing the used (i.e. global climate for no change baseline trend) model to runs. provide a basis for comparison itation were generated usingborn the (2009) ClimGen and pattern-scaling Todd et techniqueemission al. described scenarios (2010). (A1b, in A2, Scenarios Os- B1, were B2)perature generated and for of (2) (1) prescribed 1, increases greenhouse-gas 2, inemission global 3, mean scenario 4, tem- and 5, and prescribeding 6 warming six of additional 2 GCMspled from the Model World Intercomparison Climate ProjectCGCM31, Research phase Programme CSIRO (WCRP) 3 Mk30, Cou- (CMIP3)HadGEM1. multi-model IPSL dataset: CM4, Table CCCMA 1 MPI summarizes ECHAM5, the NCAR model CCSM30, runs and which UKMO were evaluated. Baseline 4 Climate projections Future climate scenarios for temperature (and in turn evapotranspiration) and precip- stream flow. average calculated using station records, and also 7% lower than the observed average naturalized flows based on thee correction of actual observed timeAgreement series between to remove the the data observed as and input is simulated notCRU hydrograph as data good calculated as results using that in obtained0.69 CRU values using and of rain 0.60, gauge Nash-Sutcli respectively. data In (Fig.and contrast, NSLog 2). the Use use of of the reasonable, station records because results the in NS seasonalityobserved. and Average the stream range flow calculated of using stream the flow CRU are data close is to 7% the lower than the considered to be identical to the mean monthly values. the basin, for 1970–1980 is presentedderived in from Fig. the 2. model This using figure bothderived shows the monthly from station hydrographs the meteorological data CRU and dataset. the gridded Observed data stream flows are also shown in the form of (Mitchell and Jones, 2005).lowing Monthly procedures data outlined were in disaggregated Todd et tothe al. basis a (2010). for daily the Daily resolution coe rain fol- the gauge Brazilian data, National which Water provides Agency (ANA).hydrological To model enable these they data were toan be re-interpolated inverse used distance within to weighted the the method.values Solar model’s from radiation the 6 was CRU estimated dataset,sure using and cloudiness data. relative humidity was Daily estimated values using vapour for pres- the variables used to calculate evapotranspiration were values less than 0.05% for calibration and less than 7%model for was validation. forced with griddeddata (precipitation meteorological totals, data. minimum and Baselinecover) maximum monthly were temperature, meteorological vapour obtained pressure, from cloud the gridded (0 of power plants shown in Fig. 1. Values of volume bias were also acceptable, with 5 5 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | to 1 − s 4%). In 3 − = 10% decrease. Q50 + ∆ C scenario. Figure 5 ◦ 16%, 8% to 18%) in the mean − C, the increase would be + ◦ 9% increase in mean river = 6 + + 10% HadGEM). A similar result Q95 erent GCMs (CCCMA, CSIRO, − ff ∆ C rise in global mean air tempera- C) /s for 95% exceedance probability ◦ ◦ 3 50%) are considerably greater than − 5%) under the A1b emissions scenario 18% (ECHAM5); two GCMs (HadGEM, for 5% duration and from 713 m + + 1 − s to 715 m 6108 6107 3 is projected for 5% exceedence probability in 1 − 28%) projected decline in mean river flow. The 1 14% CCCMA, s − − − 3 s C scenario to 50% for the 6 3 ◦ 20%) and CCCMA ( − C rise in global mean temperature (Table 5). Projected 20% (IPSL) to ◦ = C) in global mean air temperature range considerably over 2%) changes in mean annual river discharge. Three GCMs − ◦ < 30%) and HadGEM ( Q50 − ∆ in baseline to 8564 m 34%, 1 − C are summarised in Table 5. Projected changes in mean river dis- − s ◦ (baseline) to 6398 m 3 = for 95% duration. 20%) in mean river flow. 1 − 1 − s − Q95 s 3 ∆ 3 4% to Results from the six priority GCMs for a prescribed increase in global mean air tem- As reported above, the common focus in climate change studies on projected − those projected in mean riveris flow observed ( for adeclines projected in 2 the lowIPSL flows ( are much greatercontrast, than a those projected projected increase in forusing the mean low IPSL flows flow is using ( atduration odds flow with curve a for largethe such ( same GCMs, no Fig. matter 10, consideredsign the reveals of high that changes. flows the or behaviour the of low changes flows, actually is preserving the ( changes in mean riverflow can maskflow. important intra-annual For (seasonal) instance, changes in projected(Table river declines 4) in for low CCCMA flows ( under the A1b emissions scenario whole range of streamflow values, from low flows to highperature flows. of 2 charge for the same risethe (2 six applied GCMs from NCAR) project negligible ( (HadCM3, ECHAM5, CSIRO) project substantialdischarge increases ( of the Rio Grande (Fig. 9). Two GCMs (IPSL, CCCMA) project decreases HadCM3, the ECHAM5 GCM producesshows an that increase the in predictions mean ofover river increase the discharge. year, or although decrease Figure some are 7 ofintense the more reductions models or during (IPSL, less the CCCMA, evenly late HadGEM1) distributed Figure show dry the 8 season most or reveals early that wet increasing season (August or to decreasing October). results are evenly distributed over the amounts whilst the CSIRO GCM shows a negligible reduction of river flow. In addition to discharge whereas the newTwo generation other HadGEM1 GCMs model (CCCMA projects a and IPSL), suggest that river flow will decrease by larger 897 m 5.3 Uncertainty in GCM structure Model results whenECHAM, meteorological IPSL, inputs HadCM3,with from HadGEM1) those results for di obtained the by runningline A1b the (Table hydrological 4). emission model with scenario the As detrended are above, base- the compared HadCM3 GCM projects a river discharge changes are projectedto to occur January). during For the early theture, wet scenario river season which flows (November increase simulates by a overduration 6 90% curves in (Fig. December. 6), A with similarflows increasing trend for global is all air presented in the temperatures flow from resulting durations. in 5579 m increasing For the extreme scenario of 5.2 Uncertainty in prescribed warming (1All to the 6 scenarios usingincrease HadCM3 in for the increases discharge in ofin global the river mean Rio discharge temperature Grande rises project (Table8% 3). an in above The proportion the magnitude to baseline ofsummarises increasing the for the increase global changes the in mean 1 mean air monthly flows temperature for from all six scenarios. Most importantly, October). Indeed, analysis of flow-durationto curves peak (Fig. 4) flows. reveals In preferential changes the5667 m case of A2, thecontrast most to severe emission a scenario, decrease anduration. increase from from 726 m February to July). Less important changes occur during the low flow season (August to 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | http: ˜ ao das 18%, with ´ ogicas, + lia, 2007. ı ´ ´ aticas Globais e ¨ oschl and Mon- ´ arios regionalizados 20% to ˆ es modelos regionais. ˜ oes Hidrol − C increase in temperature ◦ ´ eculo XXI, Bras ˜ ao do clima atual e definic¸ ect power generation capacity, ff ˜ oes de clima usando tr 6110 6109 13%. Under a rise in global mean air temper- + C. A very consistent trend of increasing discharge ◦ ´ orio brasileiro ao longo do S 28% to ˜ ao da Biodiversidade – DCBio Clim Mudanc¸as − ´ eculo XXI: Projec¸ C to 6 ◦ ´ Aguas: HidroWeb – Sistema de Informac¸ 10%) and prescribed increases in global mean air temperature , access: 1 August 2005. + Financial assistance for the first activities of this research was provided ´ erio do Meio Ambiente - MMA, Secretaria de Biodiversidade e Florestas – ´ ogico (CNPq). This work was supported, in part, by a grant from the UK Nat- ´ aticas para o territ 5% to C, projected changes in mean river flow range from ◦ 50%). For the latter, a very clear trend is evident of increasing river flow + 2 + ˜ oes clim ˆ + encia Nacional de ´ orio 3, Minist fico e Tecnol ı ´ 8% to de clima no BrasilRelat para o S SBF, Diretoria de Conservac¸ Efeitos sobre a Biodiversidade –alterac¸ Sub projeto: Caracterizac¸ //hidroweb.ana.gov.br in South America, in:Sivapalan, Predictions M., Wagener, in T., Ungauged Uhlenbrook,and Basins: S., Kumar, P., Zehe, Promises IAHS E., Publ., and 303, Lashmi, Progress, 360–370, V., edited Liang, IAHS by: Press, X., Wallingford, Tachikawa, UK, Y., 2006. Finally, the analysis made here for the Rio Grande should be replicated at the na- These results are in accordance with findings of other authors who suggest that the Quantified uncertainty in hydrological projections increases substantially when GCM + Ambrizzi, T., Rocha, R., Marengo, J. A., Pisnitchenko, I., and Alves, L.: Cen Allasia, D., Collischonn, W., Silva, B. C., and Tucci, C. E. M.: Large basin simulation experience ANA, Ag to early reviews ofclarity. the manuscript by Richard Taylor and Julian Thompson that improvedReferences its ural and Environmental Researchthe Council Earth (NERC), under System the (QUEST) Quantifying programme and (Ref. Understanding NE/E001890/1). The authors are very grateful by FINEP/CT-Hidro Financiadora de Estudos eand Projetos) Technology (MCT). from The the first BrazilianAgency Ministry author (ANA). of acknowledges Science the The support fourthCient of author Brazilian was National Water supported by Conselho Nacional de desenvolvimento related to GCMs. Acknowledgements. in river flow, for example,hydropower could plants lead to in acceleration the ingeneration, the which Amazon would pace Basin not of or be construction justified. of the new increase intional fossil scale, fuel in order thermoelectric to assess if other river basins show the same level of uncertainty power plants. In the Brazilian case, for instance, an erroneous prediction of reduction impacting planning decisions on the necessity and timing of the construction of new ature of at least two GCM showing no important changes inchoice average flows of at the all. GCM isof the climate largest change quantified on source rivertanari, flow of 2010; (Bates uncertainty Paiva et in and al., projectedshould Collischonn, 2008; be impacts 2010). Kay exercised in et Our results al., results basedagement 2009; on indicate Bl decisions projections that from may extreme a caution single follow.Grande GCM. and Mistaken the A man- Parana, 10% for example, increase/decrease would in possibly a discharge of the River SRES emission scenarios where slight decreases in low flowsstructure are projected. is considered.1961–1990 baseline Projected for changes the samesix in greenhouse priority mean gas GCMs emission vary river scenario from discharge (A1b) using relative the to the mean air temperature ofis 1 projected to occurinput if to climate the hydrological projections model. from Meanscenarios river a discharge ( single increases under GCM, SRES HadCM3, emissions ( are used as with increasing mean globalthe air annual temperature. flow of For thebaseline. every Rio Low 1 (Q95) Grande and increases high by (Q05) 8 flows to are also 9%, projected in to relation increase to except for the the 1961–1990 Uncertainty in the impact ofthe climate change most on important the rivers dischargein of in terms the Brazil Rio of for Grande, (1) one hydro-electric(2) of GCM power SRES structure generation, emission using was a assessed scenarios priority (A1B, subset A2, of B1, six B2) CMIP3/IPCC-AR4 GCMs, and prescribed increases in global 6 Conclusions 5 5 25 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | lia, ı ´ , J. 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C., Kundzewicz, Z. W., Wu, S., and Palutikof, J. P. (Eds.): Climate Change and Wa- Bastidas, L. A., Gupta, H. V., and Sorooshian, S.: Emerging paradigms in the calibration of hy- ANEEL, Ag 5 5 30 25 20 15 30 25 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | model. ff dricos, ABRH, 12, ı ´ C over baseline ◦ C over baseline scenario ◦ 1 to 6 plus 2 Hadley Center Model + 6113 6114 CC 2040–2069 C 2040–2069 C 2040–2069 C 2040–2069 2040–2069 CC 2040–2069 C 2040–2069 C 2040–2069 C 2040–2069 C 2040–2069 2040–2069 C 2040–2069 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ 2 3 4 5 6 2 2 2 2 2 2 1 + + + + + + + + + + + + Hydrological model runs. HadCM3 HadCM3 HadCM3 HadCM3 HadCM3 CCCMA CGCM31 CSIRO Mk30 MPI ECHAM5 UKMO HadGEM1 IPSL CM4 NCAR CCSM30 detrend 1961-90 CRU-TSBaseline CRU-TSONS-naturalized flowsMGB-IPH 2040–2069 detrend 1930–2002 1930–2002 1970–1980 observed HadCM3HadCM3HadCM3HadCM3 A2 B1 B2 2006–2100 2006–2100 2006–2100 UKMO HadGEM1CCCMA CGCM31CSIRO mk3.0 A1B A1BMPI ECHAM5 A1BIPSL CM4 2006–2100 A1BNCAR 2006–2100 CCSM30 2006–2100 A1B A1B 2006–2100 A1B 2006–2100 2006–2100 ModelHadCM3 Scenario A1B Length 2006–2100 Obs. models, J. Hydrol., 204, 83–97, 1998. forecast based on climateRes., and 39(7), hydrological 1181, modeling: doi:10.1029/2003WR002074, 2003. Uruguay River basin, Waterterm Resour. flow forecasting indoi:10.1002/asl.165, the 2008. Rio Grande watershed (Brazil), Atmos. Sci. Lett., 9(2), 53–56, In: Hydrological forecasting,1980. Proceedings of the Oxford symposium IAHS,Portuguese), 129, Revista 425–454, Brasileira de Engenharia2, – 1994. Caderno de Recursos H ing uncertainty in thescales impact – of a climate unified change approach, on Hydrol. water Earth resources Syst. at Sci. riverStudy Discuss., basin of in and the preparation, global climate 2010. the change Tocantins impacts river on basin, 2008 surface (in water Portuguese). resources and groundwater levels in climate variability and long-termdoi:10.1016/j.jhydrol.2008.11.035, Colorado 2009. river basin streamflow, J. Hydrol., 365, 289–301, V. L., Pereira,PPE/COPPE/UFRJ, A., Rio and de Janeiro, Cunha, 2008. S. H. F.: Climate change: energy security, Final Report, Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Multi-objective global optimization for hydrologic Tucci, C. E. M., Collischonn, W., Clarke, R. T., Paz, A. R., and Allasia, D.: Short- and long- Table 1. Tucci, C. E. M., Clarke, R. T., Collischonn, W., Dias, P. L. S., and Sampaio, G.: Long term flow Tucci, C. E. M. and Clarke, R. T.: Adaptive forecasting with a conceptualTucci, rainfall-runo C. E. M. and Damiani, A.: Potential impacts of climate change on the Uruguay river (in Tomasella, J., Rodriguez, D. A., Cuartas, L. A., Ferreira, M., Ferreira, J. C., and Marengo, J.: Todd, M. C., Taylor, R. G., Osborn, T., Kingston, D. G., Arnell, N. W., and Gosling, S.: Quantify- Timilsena, J., Piechota, T., Tootle, G., and Singh, A:. Associations of interdecadal/interannual 5 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent mean erent green- ff ff 8% 5667 7% 2508 2% 726 + + − C baseline ◦ 6 50% 26% 53% + + + + C ◦ 5 41% 23% 43% 5% 6127, 5% 2686, 3% 709, + + + + + + − C ◦ 4 33% 20% 33% + + + + Model HadCM3 C 13% 5924, 10% 2629, 2% 707, ◦ + + − 3 24% 16% 23% + 6116 6115 + + + C ◦ 2 16% 15% 15% + + + + 9% 2748, 12% 6398, 2% 715, + C + − ◦ 8% 7% 8% 1 + + + + Scenario A1B Scenario A2 Scenario B1 Scenario B2 baseline , % change) , % change) , % change) 1 1 1 Hydrological modelling results using the same GCM (HadCM3) and di Hydrological modelling results using the same GCM (HadCM3) and di − − − , % change) , % change) , % change) s s s 1 1 1 3 3 3 − − − s s s Average river flow(m 2666, 2865, 3070, 3283, 3495, 3715, 2475 95% duration flow(m 765, 801, 826, 854, 874, 897, 713 5% duration flow(m 6037, 6405, 6873, 7430, 7988, 8564, 5579 3 3 3 (m (m 5% duration flow 6335, (m Average river flow95% 2731, duration flow 710, global temperature increase scenarios. Baseline calculated using 30 years, from 2040 to 2069. Table 3. house emission scenarios. Baseline calculated using 95 years, from 2006 to 2100. Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 6% 10% 22% 20% 34% 12% − − − − − − 2% 6% 8% + − + 50% 28% 20% − − − 1% 3% 16% 2% 9% 12% − + − − + + 8% 18% 17% 18% 12% 13% + + + + 6118 6117 + + 6% 8% 8% 6% 1% 2% + + + − − − erent global circulation models. Baseline calculated using 30 ff 4% 16% 30% 10% 14% erent global circulation models. Baseline calculated using 95 years, − − − − − ff CCCMA CSIRO ECHAM5 HadGEM1 NCAR IPSL baseline CCCMA CSIRO ECHAM5 HadCM3 IPSL HadGEM1 baseline Hydrological modelling results using the same greenhouse emission scenario (A1B) , % change) , % change) 0% , % change) Hydrological modelling results using the same mean global temperature rise scenario , % change) , % change) , % change) 1 1 1 1 1 1 − − − − − − s s s s s s C) and projections from di 3 3 3 3 3 3 ◦ 2 95% duration flow(m 5% 596, duration flow(m 758, 5575, 6045, 835, 6569, 600, 5756, 673, 6045, 468, 4892, 713 5579 Average river flow(m 2382, 2677, 2924, 2445, 2534, 1989, 2475 95% duration flow(m 5% 507, duration flow(m 681, 5123, 5636, 787, 6357, 710, 6335, 362, 4520, 565, 5353, 726 5667 Average river flow(m 2152, 2446, 2831, 2731, 1816, 2247, 2508 + years, from 2040 to 2069. Table 5. ( Table 4. and projections from di from 2006 to 2100. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | main (b) 6120 6119 regional drainage including the study area (Rio Grande basin) and (a) Calculated stream flow hydrographs at Agua Vermelha reservoir using CRU and rain- Maps of gauge data compared to the observed naturalized hydrograph. Fig. 2. Fig. 1. hydropower plants in the Rio Grande basin. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent SRES emission sce- ff erent SRES emission scenarios ff and relative to the detrended 1961–1990 (a) 6122 6121 . (b) Projected changes in mean monthly river flow under di Projected mean monthly flow duration curves under di Fig. 4. using HadCM3 in the Rio Grande basin along with the detrended 1961–1990 baseline. baseline narios using HadCM3 in the Rio Grande basin Fig. 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and relative to the detrended (a) 6124 6123 . (b) Projected changes in mean monthly river flow under prescribed increases in global Projected mean monthly flow duration curves under prescribed increases in global mean Fig. 6. air temperature using HadCM3baseline. in the Rio Grande basin along with the detrended 1961–1990 mean air temperature using HadCM31961–1990 in baseline the Rio Grande basin Fig. 5. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and relative to the detrended 1961–1990 (a) 6126 6125 . (b) Projected mean monthly flow duration curves under the A1b SRES emissions scenario Projected changes in mean monthly river flow under the A1b SRES emissions scenario Fig. 8. from six priority GCMs in the Rio Grande basin along with the detrended 1961–1990 baseline. Fig. 7. from six priority GCMs in the Rio Grande basin baseline Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and relative to the detrended (a) 6128 6127 . (b) C from six priority GCMs in the Rio Grande basin along with the detrended ◦ 2 + Projected changes in mean monthly flow duration curves under a mean global tem- Projected changes in mean monthly river flow under a mean global temperature rise C from six priority GCMs in the Rio Grande basin ◦ 2 + Fig. 10. perature rise of 1961–1990 baseline. 1961–1990 baseline Fig. 9. of