Earth’s Future Supporting Information for Implications of freshwater flux data from the CMIP5 multi-model output across a set of Northern Hemisphere drainage basins Arvid Bring*1,2, Shilpa M. Asokan2,3, Fernando Jaramillo2, Jerker Jarsjö2, Lea Levi2,4, Jan Pietroń2, Carmen Prieto2,3, Peter Rogberg2 and Georgia Destouni2,3 1 Water Systems Analysis Group, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USA. 2 Department of Physical Geography, and the Bolin Centre for Climate Research, Stockholm University, Sweden. 3 Navarino Environmental Observatory (NEO), Messinia, Greece. 4 Department of Land and Water Resources Engineering, KTH Royal Institute of Technology, Stockholm, Sweden, and Department of Applied Hydraulics, Faculty of Civil Engineering, Architecture and Geodesy, University of Split, Croatia. *Corresponding author: arvid.bring@unh.edu. Contents of this file Supporting methods Figures S1 to S5 Tables S1 to S2 Introduction This file contains an extended methods section that describes in detail how we processed climate model and hydrological observation data. Supporting tables and figures provide more detail on the drainage basins we investigated and the results for individual models in the CMIP5 multi-model ensemble. Supporting methods 1 We synthesized CMIP5 and observational data for six Northern Hemisphere drainage basins (maps in Supporting Fig S5) that all constitute hydrological basins, for which the water balance can be established. Four of these regions (the Arctic, Sava, Selenga, and Sweden) are comprised of river basins (31 in total), one of drainage to a terminal water body (the Aral Sea), and one of drainage through a stretch of coastline (Greece). We selected the CMIP5 models with data available for temperature (T; termed nearsurface air temperature and abbreviated as tas in CMIP5 datasets), precipitation (P; CMIP5: precipitation/pr), surface runoff (R; CMIP5: total runoff/mrro) and evapotranspiration (ET; CMIP5: evaporation/evspsbl; this includes conversion of both liquid and solid phases to vapor from underlying surface and vegetation) for the historical, RCP2.6 and RCP8.5 experiments (23 models in total, listed in Supporting Table S1). To avoid model bias, only one member per model family was used, and for those models providing more than one realization, the preferred choice was r1i1p1 (r: realization, i: initialisation, p: perturbation). For some models, the r1i1p1 realization was corrupted or not available and had to be replaced. The NorESM1-M model was later excluded due to erroneous evapotranspiration data for the RCP2.6 scenario, leaving 22 models. For these models we calculated area-weighted annual averages of all parameters, also computing net annual water balance (ΔS) as P – R – ET, for all the drainage basins in Table 1. For comparison with observations, we area-averaged annual temperature and precipitation data from the CRU TS3.10/CRU TS3.10.01 datasets [Harris et al., 2014] for all basins, and applied correction factors for undercatch [Adam and Lettenmaier, 2003] and orographic effects [Adam et al., 2006]. The undercatch corrections act mainly through a global extrapolation of adjustments for various gauge types, and their corresponding catch ratios, in a World Meteorological Organization compilation. The correction for orographic effects is based on water balance calculations for a selected set of reference basins, which is then extended onto a correction domain over the world’s main mountainous regions. We based runoff data on downstream station records or estimates of the ratio of evapotranspiration to precipitation (details below; data sources listed in Supporting Table S2). For this comparison, observed temperature and precipitation values were 2 aggregated only for years for which there was also runoff data available (periods listed in Supporting Table S2). Water-balance derived evapotranspiration was determined from the observed precipitation and runoff data as ET = P – R, assuming the long-term net annual water balance to be zero over the multiple decades considered. This assumption is justified because any other potential water balance deviations are likely to be shortterm, while this study focuses on long-term changes. An evaluation of alternative assumptions of storage changes was performed for the Swedish basins [Destouni et al., 2013], and these effects are generally small. We subsequently calculated averages, maximum and minimum values, and standard deviation of all parameters for the periods 1961-1990, 1961-1980, 1986-2005 (both models and observations), 2010-2039 and 2070-2099 (models only). We used the RCP8.5 experiment for the period 2010-2039 and 2070-2099 and the historical experiment for all other periods. Data for the RCP2.6 experiment for 2010-2039 and 2070-2099 are also included for comparison in the supporting material (Supporting Fig. S3), but patterns in our results are generally similar in that and the RCP8.5 experiments. Runoff data assembly We based runoff data on observations at discharge monitoring stations, listed in Supporting Table S2. For Greece, runoff observations were limited, and the Greek drainage area was identified from all catchment areas with discharge outlet points along the Greek mainland coastline, through the ArcGIS watershed algorithm applied to the Hydro1k Digital Elevation Model from the United States Geological Survey. Hence, all Greek islands were excluded, but upstream catchment areas outside the Greek mainland territory with outlet points along Greek coastline are included (see Supporting Fig. S5c). The Sava river basin was delineated from river network information in the Catchment Characterization and Modeling River and Catchment Database [Vogt et al., 2007] and the Hydro1k Digital Elevation Model from the United States Geological Survey, based on the most downstream station at Sremska Mitrovica. 3 For the eight rivers in the Arctic, runoff at the most downstream stations of thee of them (Kolyma, Mackenzie, and Pechora) were extended by correlation with data from an upstream monitoring stations with more complete records. This was done to fill in breaks in records at the most downstream station. The R2 values for downstream-upstream interstation correlation for years when both stations had data available were (average monthly/flow-weighted monthly): Kolyma 0.60/0.88, Mackenzie 0.73/0.76, Pechora 0.52/0.69. The share of the basin area for which runoff was extrapolated from the upstream station corresponds to 31% for Kolyma, 7% for the Mackenzie, and 21% for Pechora. Of the 45 years investigated, extrapolated values of downstream runoff comprised 17 years for Kolyma, 7 for Mackenzie, and 26 for Pechora. Greece water balance estimates Due to lack of direct runoff observations for most of Greece, we based evapotranspiration estimates partly on reasonable runoff data from only three Greek basins (see Supporting Table S2; map in Supporting Fig. S5c). As these are all located in northern Greece, we also complemented the Greek runoff and evapotranspiration estimates with independent estimates for southern Greece, as reported in the official Greek water management plan for western Peloponnese [Greek Water Management Authority, 2013]. The western Peloponnese estimates are for two Greek water management drainage basins (GR29 and GR32; map in Supporting Fig. S5c), for which evapotranspiration estimates can be obtained as a share of total precipitation, ET/P in two different ways: directly from reported ET and P values, and indirectly from also reported runoff values as (P-R)/P. The resulting ET/P estimates span then a range of physically possible evapotranspiration values, which are indicated with error bars in Figure 3a. References Adam, J. C., and D. P. Lettenmaier (2003), Adjustment of global gridded precipitation for systematic bias, J. Geophys. Res., 108(D9), 1–14, doi:10.1029/2002JD002499. Adam, J. C., E. A. Clark, D. P. Lettenmaier, and E. F. Wood (2006), Correction of global precipitation products for orographic effects, J. Clim., 19(1), 15–38. 4 Destouni, G., F. Jaramillo, and C. Prieto (2013), Hydroclimatic shifts driven by human water use for food and energy production, Nat. Clim. Change, 3, 213–217, doi:10.1038/nclimate1719. Greek Water Management Authority (2013), Water Management Plan for the River Basins of the Water Management District of Western Peloponnese (GR01), Government Journal (Second Volume), Athens. Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister (2014), Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset, Int. J. Climatol., 34, 623–642, doi:10.1002/joc.3711. Vogt, J., P. Soille, R. Colombo, M. L. Paracchini, and A. de Jager (2007), Development of a pan-European River and Catchment Database, in Digital Terrain Modelling, edited by D. R. J. Peckham and D. G. Jordan, pp. 121–144, Springer Berlin Heidelberg. Historical annual average water fluxes (mm yr-1) 1961-1990 Historical annual temperature (K) 1961-1990 a Temperature b Precipitation c d Evapotranspiration Runoff e Net Water Balance BNU-ESM BNU-ESM CCSM4 CCSM4 CNRM-CM5 CNRM-CM5 CSIRO-Mk3-6-0 CSIRO-Mk3-6-0 CanESM2 CanESM2 FGOALS-g2 FGOALS-g2 FIO-ESM FIO-ESM GFDL-CM3 GFDL-CM3 GFDL-ESM2G GFDL-ESM2G GISS-E2-H GISS-E2-H GISS-E2-R GISS-E2-R IPSL-CM5A-LR IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5A-MR MIROC-ESM MIROC-ESM MIROC-ESM-CHEM MIROC-ESM-CHEM MIROC5 MIROC5 MPI-ESM-LR MPI-ESM-LR MPI-ESM-MR MPI-ESM-MR MRI-CGCM3 MRI-CGCM3 NorESM1-M NorESM1-M NorESM1-ME NorESM1-ME bcc-csm1-1 bcc-csm1-1 bcc-csm1-1-m bcc-csm1-1-m 260 270 280 290 K Aral Arctic Greece Sava Selenga Sweden 0 500 1000 1500 0 250 500 750 0 500 1000 -300 -150 0 150 300 mm yr-1 0° C Figure S1. Historical CMIP5 temperature and water fluxes. Modeled temperature and water fluxes by the studied set of CMIP5 models for the historical experiment 1961-1990. 5 Projected annual average water fluxes (mm yr-1) rcp 8.5 2010-2039 Projected annual temperature (K) rcp 8.5 2010-2039 a Temperature b Precipitation c d Evapotranspiration Runoff e Net Water Balance BNU-ESM BNU-ESM CCSM4 CCSM4 CNRM-CM5 CNRM-CM5 CSIRO-Mk3-6-0 CSIRO-Mk3-6-0 CanESM2 CanESM2 FGOALS-g2 FGOALS-g2 FIO-ESM FIO-ESM GFDL-CM3 GFDL-CM3 GFDL-ESM2G GFDL-ESM2G GISS-E2-H GISS-E2-H GISS-E2-R GISS-E2-R IPSL-CM5A-LR IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5A-MR MIROC-ESM MIROC-ESM MIROC-ESM-CHEM MIROC-ESM-CHEM MIROC5 MIROC5 MPI-ESM-LR MPI-ESM-LR MPI-ESM-MR MPI-ESM-MR MRI-CGCM3 MRI-CGCM3 NorESM1-M NorESM1-M NorESM1-ME NorESM1-ME bcc-csm1-1 bcc-csm1-1 bcc-csm1-1-m bcc-csm1-1-m 260 270 280 290 300 K Aral Arctic Greece Sava Selenga Sweden 0 500 1000 1500 0 250 500 750 0 500 1000 -300 -150 0 150 300 mm yr-1 0° C Figure S2. Near-future CMIP5 temperature and water fluxes. Projections of temperature and water fluxes by the studied set of CMIP5 models for the RCP8.5 experiment 20102039. 6 SUPPLEMENTARY"FIGURE"3" a" b"mm yr b" -1! c" mm yr -1! d"mm yr -1! e"mm yr -1! Figure S3. Scenario differences in projected temperature and water fluxes. Ensemble Supplementary" 3:"Scenario" projected" mean, standardFigure" deviation anddifferences" range ofin" CMIP5 simulation results for (a) temperature, (b) temperature"and" fluxes." precipitation, (c) water" evapotranspiration, (d) runoff, and (e) net annual water balance for a set of six Northern Hemisphere drainage basin regions, and for the RCP2.6 and RCP8.5 experiments considering the periods 2010-2039 and 2070-2099. Error bars denote one standard deviation of model means. 7 Historical change to annaul average water fluxes (mm yr-1) from 1961-1980 to 1986-2005 Historical annual temperature change (K) from 1961-1980 to 1986-2005 a Temperature b Precipitation c Runoff d Evapotranspiration e Net Water Balance BNU-ESM BNU-ESM CCSM4 CCSM4 CNRM-CM5 CNRM-CM5 CSIRO-Mk3-6-0 CSIRO-Mk3-6-0 CanESM2 CanESM2 FGOALS-g2 FGOALS-g2 FIO-ESM FIO-ESM GFDL-CM3 GFDL-CM3 GFDL-ESM2G GFDL-ESM2G GISS-E2-H GISS-E2-H GISS-E2-R GISS-E2-R IPSL-CM5A-LR IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5A-MR MIROC-ESM MIROC-ESM MIROC-ESM-CHEM MIROC-ESM-CHEM MIROC5 MIROC5 MPI-ESM-LR MPI-ESM-LR MPI-ESM-MR MPI-ESM-MR MRI-CGCM3 MRI-CGCM3 NorESM1-M NorESM1-M NorESM1-ME NorESM1-ME bcc-csm1-1 bcc-csm1-1 bcc-csm1-1-m bcc-csm1-1-m -0.5 0 0.5 1 1.5 K Aral Arctic Greece Sava Selenga Sweden -80 -40 0 40 80 -80 -40 0 40 80 -80 -40 0 40 80 -100 -50 0 50 100 mm yr-1 Figure S4. Historical changes in CMIP5 temperature and water fluxes. Changes in modeled temperature and water fluxes for the studied set of CMIP5 models, from the period 1961-1980 to the period 1985-2005 in the historical experiment. 8 SUPPLEMENTARY!FIGURE!5! b a ! ! c d e f ! ! ! ! Supplementary,Figure,5:,Detailed,maps,of,drainage,basins.,Maps!of!the!(a)!Aral!Sea,!(b)!Arctic,!(c)!Greece,!(d)! Figure S5. Detailed maps of drainage basins. Maps of the (a) Aral, (b) Arctic, (c) Sava,!(e)!Selenga,!and!(f)!Sweden!drainage!basins.!Basin!names!in!(b)!are!abbreviated!as!Ne:!Nelson,!Ma:! Greece, (d) Sava, (e) Selenga, and (f) Sweden drainage basins. Basins in (b) are Mackenzie,!Ko:!Kolyma,!Le:!Lena,!Ye:!Yenisey,!Pe:!Pechora,!and!SD:!Severnaya!Dvina.!Basins!are!further! abbreviated as Ne: Nelson, Ma: Mackenzie, Ko: Kolyma, Le: Lena, Ye: Yenisey, Pe: described!in!the!Supplementary!Methods.! Pechora, and SD: Severnaya Dvina. 9 Table S1. List of models included. Model Name BCC-CSM1.1 BCC-CSM1.1(m) BNU-ESM CanESM2 CCSM4 CNRM-CM5 CSIRO-Mk3.6.0 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GISS-E2-H GISS-E2-R IPSL-CM5A-LR IPSL-CM5A-MR MIROC-ESM MIROC-ESM-CHEM MIROC5 MPI-ESM-MR MPI-ESM-LR MRI-CGCM3 NorESM1-M* NorESM1-ME Institution Beijing Climate Center, China Meteorological Administration College of Global Change and Earth System Science, Beijing Normal University Canadian Centre for Climate Modelling and Analysis National Center for Atmospheric Research Centre National de Recherches Météorologiques / Centre Européen de Recherche et Formation Avancée en Calcul Scientifique Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences and CESS,Tsinghua University The First Institute of Oceanography, SOA, China NOAA Geophysical Fluid Dynamics Laboratory NASA Goddard Institute for Space Studies Institut Pierre-Simon Laplace Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for MarineEarth Science and Technology Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology) Meteorological Research Institute Norwegian Climate Centre 10 Table S2. List of discharge stations and data sources. Region Arctic River Station coordinates or name Basin area* (km2) Years with missing data Data source Nelson Mackenzie Kolyma Lena Yenisey Ob Severnaya Dvina Pechora Kelsey generating station Arctic Red River Kolymskoye Kusur Igarka Salekhard Ust’ Pinega Oksino 1.01 × 106 1.68 × 106 5.26 × 105 2.43 × 106 2.44 × 106 2.95 × 106 3.48 × 105 3.12 × 105 – 1961-1965 2001-2005 – 2000, 2002-2004 2000 1982, 2000, 2001 1989, 1999, 2000 1 1 2, 3 2, 3 2, 3 2, 3 2, 3 2, 3 Amu Darya Syr Darya Kziljar Karateren 0.68 × 106 0.71 × 106 2003-2005 2003-2005 4 4 Mesta-Nestos Almopaios Aliakmon Sava Selenga Temenos Prof. Ilias Ilarion Sremska Mitrovica Mostovoy 4.95 × 103 9.93 × 102 5.00 × 103 9.22 × 104 4.40 × 105 1961-1965, 1990-2005 1961-1980, 1995-2005 1961-1962, 1988-2005 – – Råneälven Luleälven Piteälven Umeälven Öreälven Ångermanälven Moälven Indalsälven Ljusnan Dalälven Norrström Nyköpingsån Vättern Emån Alsterån Helge Å Lagan Viskan Vänern Ytterholmen Bodens Krv Sikfors Krv Sorsele 2 Torrböle 2 Sollefteå Krv Västersel Hammarforsen Svegs Krv Långhag Övre Stockholm Christ.Hlm Regl.Damm Skärblacka Blankaström Getebro Torsebro Krv Ängabäcks Krv Åsbro 3 Vargöns Krv 1.01 × 103 2.49 × 104 1.08 × 104 6.06 × 103 2.86 × 103 3.06 × 104 1.47 × 103 2.38 × 104 8.48 × 103 2.51 × 104 2.26 × 104 3.59 × 103 1.33 × 104 3.94 × 103 1.33 × 103 3.66 × 103 5.48 × 103 2.16 × 103 4.69 × 104 – 2002-2005 1995, 2002 – – – – – – – – – – – – 2000 2003 – – 5 5 5 6 7 8 8 9 9 8 9 9 8 9 8 9 9 8 9 8 8 8 8 8 9 Aral† Greece Sava Selenga Sweden† * Refers to the area upstream of the corresponding monitoring station. † For the Aral and Sweden regions, all climate data were calculated not for the individual catchments but for their combined drainage area. Data sources referred to in Table S2 1. Water Survey of Canada HYDAT Hydrometric Data, accessed at http://www.wsc.ec.gc.ca/applications/H2O/index-eng.cfm on June 18, 2013. 2. R-ArcticNET database at University of New Hampshire, accessed at http://www.rarcticnet.sr.unh.edu/v4.0/index.html on June 10, 2013. 3. ArcticRIMS database at University of New Hampshire, accessed at http://rims.unh.edu on June 12, 2013. 4. Mamatov, S. (2003), Study of the groundwater contribution to the Aral Sea region water supply and water quality: Strategies for reversibility and pollution control – INTAS project 1014, EU-INTAS Aral Sea Basin Call 2000, Group CR5 Status Report, European Commission, Bruxelles, Belgium. 5. Global Runoff Data Centre, Koblenz, Germany, accessed at http://www.bafg.de on June 3, 2013. 6. Institute for the development of water resources "Jaroslav Černi", 2011, and Levi, L., F. Jaramillo, R. Andričević, and G. Destouni (2015), Hydroclimatic changes and drivers in the Sava River Catchment and comparison with Swedish catchments, Ambio, doi: 10.1007/s13280-015-0641-0, in press. 7. Hydrometeorolgical Centre of Russia, data provided as part of collaborative project on the Selenga River. 8. Swedish Meteorological and Hydrological Institute: Vattenweb, accessed at http://vattenweb.smhi.se on October 15, 2011. 9. Destouni, G., F. Jaramillo, and C. Prieto (2013), Hydroclimatic shifts driven by human water use for food and energy production, Nat. Clim. Change, 3, 213–217, doi:10.1038/nclimate1719. 11