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