Journal of Hydrology 528 (2015) 1–16
Contents lists available at ScienceDirect
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j h y d r o l
a
,
b ,
c , d
a , e
a
a
Department of Geosciences, University of Oslo, PO Box 1047 Blindern, N-0316 Oslo, Norway b
Uni Research Climate, Allegt. 55, 5007 Bergen, Norway c
Earth System Physics (ESP) Section, Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy d
Laboratoire de Physique de l’Atmosphère et de l’Océan Siméon Fongang (LPAO-SF), Ecole Supérieure Polytechnique, Cheikh Anta Diop University (UCAD-ESP), Dakar-Fann, Dakar,
Senegal e Department of Earth Sciences, Uppsala University, Sweden a r t i c l e i n f o
Article history:
Received 11 August 2014
Received in revised form 6 May 2015
Accepted 12 May 2015
Available online 27 May 2015
This manuscript was handled by Andras
Bardossy, Editor-in-Chief, with the assistance of Luis E. Samaniego, Associate
Editor
Keywords:
Large-scale hydrological model
Climate change
RegCM4
Southern Africa
Near future s u m m a r y
This study aims to provide model estimates of changes in hydrological elements, such as
EvapoTranspiration (ET) and runoff, in Southern Africa in the near future until 2029. The climate change scenarios are projected by a high-resolution Regional Climate Model (RCM), RegCM4, which is the latest version of this model developed by the Abdus Salam International Centre for Theoretical Physics (ICTP).
The hydrological projections are performed by using a large-scale hydrological model (WASMOD-D), which has been tested and customized on this region prior to this study. The results reveal that (1) the projected temperature shows an increasing tendency over Southern Africa in the near future, especially eastward of 25 ° E, while the precipitation changes are varying between different months and sub-regions;
(2) an increase in runoff (and ET) was found in eastern part of Southern Africa, i.e. Southern Mozambique and Malawi, while a decrease was estimated across the driest region in a wide area encompassing
Kalahari Desert, Namibia, southwest of South Africa and Angola; (3) the strongest climate change signals are found over humid tropical areas, i.e. north of Angola and Malawi and south of Dem Rep of Congo; and
(4) large spatial and temporal variability of climate change signals is found in the near future over
Southern Africa. This study presents the main results of work-package 2 (WP2) of the ‘Socioeconomic
Consequences of Climate Change in Sub-equatorial Africa (SoCoCA)’ project, which is funded by the
Research Council of Norway.
Ó 2015 Elsevier B.V. All rights reserved.
1. Introduction
Global climate change affects the terrestrial hydrological system in the world, with a strong impact on water resources
( Kundzewicz et al., 2007; Intergovernmental Panel on Climate
Change (IPCC), 2007
). There is evidence that the hydrological system has already responded to the observed warming over recent decades (e.g.
Christensen et al., 2007a; Bates et al., 2008 ).
Projection of the potential effects of climate change on the time and magnitude of discharge has a strong socioeconomic value
( Vergara et al., 2007
), thus the prediction of discharge change under climate change is important for policy and decision-making.
The most common method for assessing the impact is to run a hydrological model driven by various climate projections from
⇑
Corresponding author at: Uni Research Climate, Allegt. 55, 5007 Bergen,
Norway.
E-mail address: lu.li@uni.no
(L. Li).
http://dx.doi.org/10.1016/j.jhydrol.2015.05.028
0022-1694/ Ó 2015 Elsevier B.V. All rights reserved.
General Circulation Models (GCMs) as input forcing data (e.g.
Gosling et al., 2011; Remesan et al., 2014; Liu et al., 2012
). Many studies (e.g.,
Fowler et al., 2007; Dibike and Coulibaly, 2007;
Stahl et al., 2008; Markoff and Cullen, 2008 ) have attempted to
develop methods to ‘‘downscale’’ from the global climate model scale to a finer spatial scale. Such methods can be broadly classified into four types (
Mearns et al., 2001; Arnell et al., 2003
): (1) simple interpolation of the climate model data to a finer spatial scale based on some variants, i.e. topography and coastline (
Hulme and Jenkins, 1998 ); (2) statistical downscaling using empirical
relationship between coarse-scale and local climate (
Giorgi et al.,
2001 ); (3) Regional Climate Models (RCMs) ( Hulme et al., 2002 );
and (4) global Atmospheric General Circulation Models (AGCMs) with high or variable resolution in a ‘‘time-slice’’ experiment
(
Giorgi et al., 2001 ). Among these, the use of high resolution
RCMs has become one of the most popular methods to add fine scale details, i.e. vegetation variations, topography and coastlines,

2 L. Li et al. / Journal of Hydrology 528 (2015) 1–16
Table 1
Daily WASMOD-M variables and their equations.
Variable controlled
Snow fall
Rainfall
Snow storage
Snow melt
Actual evapotranspiration
Available water
Saturated percentage area
Fast flow
Slow flow
Total flow
Land moisture
Parameter (units) a a c c
1
4
1
2
, a
2
(–)
(–)
( °
(mm
C)
1 day)
Equation sn t
¼ p t n
1 e ½ð T a a
1
Þ = ð a
1 a
2
Þ
2 r t sp t m t
¼ p t
¼
¼ sp sp sn t 1 t 1 t
þ sn t n
1 e m
½ð T a t a
2
Þ = ð a
1 o
þ a
2
Þ
2 o
þ f t s t e t
¼ min ½ ep t
ð 1 a w t
4
= ep t w t
¼ r t
þ sm spa t
¼ 1 e
þ t 1 c
1 w t
¼ ð r t
¼ w t
þ m t
ð 1 e
Þ spa t c
2 w t Þ d t sm t
¼ s t
þ f t
¼ sm t 1
þ r t
þ m t
Þ ; w t e t d t
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
Where { x } + means max( x , 0) and { x } – means min( x , 0); ep t
E c
= 0.018/30 (mm day
1
° C
2
); a
1
¼ E c
ð is the snowfall temperature and
T
þ a a
2
Þ
2
ð 100 RH Þ is the daily potential evapotranspiration, where we have used is the snow melt temperature; RH is relative humidity calculated from air temperature and dew point temperature; T a is air temperature ( ° C); p t is the precipitation in a given day; sm t 1 is the land moisture (available storage).
and to simulate patterns of current climate as well as climate change.
Moreover, in the last few years, the scientific community has undertaken a substantial effort in modelling focused on different regions around the world.
Such activities included the
AMMA-ENSEMBLE project over West Africa ( Paeth et al., 2011;
Diallo et al., 2012
) as well as the COordinated Regional climate
Downscaling EXperiment (CORDEX) framework, including focus on Africa ( http://wcrp-cordex.ipsl.jussieu.fr/
) ( Giorgi et al., 2009;
Jones et al., 2011; Endris et al., 2013; Gbobaniyi et al., 2014;
Kalognomou et al., 2013; Kim et al., 2013 ), Europe (also
PRUDENCE:
Christensen et al., 2007b
), South America (also
CLARIS;
Marengo et al., 2009; Menendez et al., 2010 ), and North
America (also NARCCAP;
Mearns et al., 2013 ). In addition, many
individual high resolution RCM climate change projection studies have also been undertaken over Africa, in particular West Africa
(e.g.
Sylla et al., 2010; Abiodun et al., 2012; Diallo et al., 2012;
Mariotti et al., 2014 ) and Southern Africa (e.g.
Arnell et al., 2003;
Tadross et al., 2005; Diallo et al., 2014 ). For instance,
Tadross et al. (2005)
projected a rainfall increase over the eastern part of
South Africa using two different RCMs, which they attributed to an increase in convective rainfall, and
Diallo et al. (2014)
found a prevailing shift towards a hydroclimatic regime of more intense and less frequent precipitation events in this region for the near future. The effectiveness of RCMs in resolving fine resolution processes interacting with the mean climate may have a strong impact on hydrological elements.
However, RCMs do not provide sufficient detail to satisfactorily address impacts on hydrology as well as water resources (
Xu et al.,
2005; Graham et al., 2007
). In order to overcome this limitation as well as to address this issue, outputs from RCMs are used as input data to drive, i.e. initialize, a hydrological model. Nowadays, RCMs have often been used to create scenarios to drive hydrological models (e.g.
Mearns et al., 1999; Leung and Wigmosta, 1999;
Bergstrom et al., 2001; Venalainen et al., 2001; Arnell et al.,
2003; Graham et al., 2011; Chen et al., 2012; Gosling, 2014
).
Arnell et al. (2003)
discussed different ways of constructing climate change scenarios. They recommended that it is better to use regional climate models to construct scenarios defining a change in climate (mean and variability) and use this information to drive a hydrological model, rather than using the future output from RCM directly, unless the RCM simulates baseline regional climate accurately.
During the past two decades, numerous large-scale global datasets have been developed for global and/or regional hydrological assessment and modelling, such as the Climate Research Unit
(CRU;
CRU, 2000
), the NEXRAD radar rainfall ( Kang and
Merwade, 2014
), the Tropical Rainfall Measuring Mission
(TRMM;
Huffman et al., 2007; Castro et al., 2015; Adjei et al.,
2014 ), and the Global Precipitation Climatology Project (GPCP;
Adler et al., 2003 ) datasets. A meteorological forcing dataset was
developed by the European Union funded WATer and global
CHange (WATCH) project ( www.eu-watch.org
), the WATCH
Forcing Data (WFD;
Weedon et al., 2010, 2011, 2014
). This dataset was derived from the reanalysis of the European Centre for
Medium-Range Weather Forecasts (ECMWF, ERA40;
Uppala et al.,
2005 ) for the period 1958–2001, and was bias-corrected ( Piani et al., 2010
) based on monthly observational data (
Mitchell and
Jones, 2005 ). Some studies using WFD have shown that it yields
acceptable simulations of river runoff in some regions ( Piani et al., 2010; Hagemann et al., 2011 ). The WFD dataset has also been
used for studying climate change impact on crop yields in sub-Saharan Africa (e.g.
Waha et al., 2013 ). Its usefulness over
Southern Africa has been investigated by
Li et al. (2013)
, who showed that it performed better than simulations based on the
TRMM dataset.
Furthermore, changes in climate as simulated by RCMs may likely be influenced by the driving climates of coarse resolution
GCMs (
Xu, 1999; Arnell et al., 2003; Mariotti et al., 2014 ). This
paper presents the main results of WP2 in ‘Socioeconomic
Consequences of Climate Change in Sub-equatorial Africa
(SoCoCA)’ project, which is funded by the Research Council of
Norway (RCN) Environment and Development Programme
(FRIMUF). In this project, the National Centre for Atmospheric
Research (NCAR) Community Atmospheric Model version 4
(CAM4;
Gent et al., 2011
) is used for global climate modelling.
CCSM, from which other boundary conditions were used in this case, is a fully coupled global climate model that provides state-of-the-art simulations for past, present, and future climate states ( http://www.ccsm.ucar.edu/ ). For several years the atmospheric part of the CCSM has been used for aerosol–cloud interaction studies by the SoCoCA participants (
Kristjánsson et al., 2005;
Storelvmo et al., 2006; Myhre et al., 2007
). It allows us to use a relatively high atmospheric resolution in CAM4, which leads to improved simulation of large-scale climate patterns over Africa.
RegCM4 ( Giorgi et al., 2012
) is the latest version of ICTP regional climate model. It has been used for a wide range of applications, from process studies, to paleoclimate and climate change simula-
tions ( Giorgi and Mearns, 1999; Giorgi et al., 2004 ). Especially,
RegCM4 includes the capability of simulating climatic effects of
desert dust, sulphates and carbonaceous aerosols ( Solmon et al.,
2006; Zakey et al., 2006
), as well as ozone (
Shalaby et al., 2012
).

L. Li et al. / Journal of Hydrology 528 (2015) 1–16 3
Fig. 1.
Topographic map with GRDC stations of continental Southern Africa and the three sub-regions.
In this study, we quantified potential impacts of climate change, estimated from RegCM4 nested within CAM4, on the hydrology over continental Southern Africa, which is an area especially vulnerable to climate change. Despite this high vulnerability, only a few high-resolution (i.e. up to 25 km) regional climate projections have been made for the Southern African region (e.g.
Hudson and
Jones, 2002; Tadross et al., 2005; Engelbrecht et al., 2009, 2013;
Diallo et al., 2014
). Studies based on hydrological models driven
by RCMs are even fewer and are not up to date ( Arnell, 1999;
Arnell et al., 2003 ). Besides, most of these studies looked at the
later future scenarios at the end of 21st century (i.e.
Arnell et al.,
2003; Nohara et al., 2006 ). The primary purpose of this work is
to provide model estimates of changes in hydrological elements, such as ET and runoff, in Southern Africa in the near future until
2029, driven by a high-resolution RegCM4. This study is based on the previous work of
Li et al. (2013) , who tested and customized
the applicability of the large-scale hydrological model, the daily version of Water And Snow balance MODelling system at macro scale (WASMOD-D), in simulating runoff and other water balance components in the Southern Africa region.
2. Models and data
2.1. The climate models
The regional climate modelling system RegCM4 described in
Giorgi et al. (2012)
is the latest version of the RCM developed at
the ICTP. The model, building on previous versions ( Giorgi et al.,
1993a,b; Pal et al., 2007
), is a primitive equation, sigma vertical coordinate model with dynamics based on the hydrostatic version of the National Centre for Atmospheric Research/Pennsylvania
State University’s Mesoscale Meteorological model version 5
(NCAR/PSU’s MM5;
Grell et al., 1994 ). A fully detailed description
of RegCM4 can be found in
Giorgi et al. (2012)
.
Initial and 6 hourly lateral boundary conditions necessary to run RegCM4 for the present days and near future scenario simulations are taken from NCAR CAM4. CAM4 has been run at a horizontal resolution of 0.9
° 1.25
° (Lat Lon) with 26 vertical layers, forced by prescribed Sea Surface Temperature (SST) and sea ice concentration. Two simulations with different prescribed SSTs have been performed for the present day time slice (1990–2009).
For the first simulation (hereafter referred RegCM4/R1), the
Hadley centre SST dataset (HadSST) is prescribed to both CAM4 and RegCM4, while the CCSM4 SST is used in the second simulation
(hereafter referred RegCM4/R2) for both models. For the near future time slice (2010–2029), projected SSTs from CCSM4, under the Representative Concentration Pathway 4.5 (RCP 4.5) climate scenario, have been used in both simulations. RegCM4 was integrated over the Southern Africa domain with a horizontal grid spacing of 25 km and 18 vertical levels for the period 1990–
2029. For brevity, details for the CAM4 and RegCM4 simulation design can be found in
Diallo et al. (2014) , who also assessed
RegCM4 skills to faithfully simulate the main Southern African climate features. They conclude that RegCM4 reproduces reasonably the mean spatial distribution, the extreme precipitation events, the month-to-month variability of rainfall and temperature and their associated circulation features. In this paper the projected change

4 L. Li et al. / Journal of Hydrology 528 (2015) 1–16
Fig. 2.
1990–2001 spatial distribution of annual (a) precipitation, (b) temperature, (c) runoff and (d) actual evapotranspiration from WFD and RegCM4 (R1 and R2 experiments, see text).

from the RegCM4/R2 model run is used to drive a hydrological model in order to investigate the change in hydrological elements for the near future period, whereas the RegCM4/R1 experiment is used for comparison with observations in the past.
2.2. The large-scale hydrological model
L. Li et al. / Journal of Hydrology 528 (2015) 1–16 5 based on calibrations of WASMOD-D on 22 basins in the region
(
Fig. 1
). More information about these 22 stations, i.e. station name, river name, latitude, longitude and drainage area, can be found in
Table 1
in
Li et al. (2013)
. The global mean method considers that the physical attributes of catchments are represented by their mean values. More detail of the hydrological model set up and calibration can be found in the study by
Li et al. (2013)
.
Runoff, evapotranspiration and other hydrological components were simulated in this study at a spatial resolution of 0.5
° 0.5
° , using a daily version of the Water And Snow balance MODelling system at the macro scale (WASMOD-D;
Widén-Nilsson et al.,
2009; Gong et al., 2011; Li et al., 2013 ), developed based on
WASMOD (
Xu et al., 1996; Xu, 2002
). The input data included daily values of precipitation, temperature and Potential Evapo
Transpiration (PET) from RegCM4. WASMOD-D calculates actual evapotranspiration, and separates runoff into fast flow and slow flow (see
Table 1
), plus daily snow accumulation and melt which is not so relevant in this study.
The global mean method (
Jin et al., 2009
) was used to obtain regional parameter values, and the water balance components from 1990 to 1999 for continental Southern Africa were simulated
2.3. Data
(
The 0.5
° period from 1958 to 2001 was taken as the ‘observed’ forcing data
Li et al., 2013
2005
0.5
° gridded daily rainfall from WFD covering the
). WFD consists of meteorological variables needed for running hydrological models, derived from ERA-40 reanalysis
(
Uppala et al., 2005
). The data are derived from this reanalysis product ( www.ecmwf.int/research/era/do/get/era-40 uea.ac.uk/~timm/grid/CRU_TS_2_1.html
;
) via sequential interpolation to half-degree resolution, elevation correction and monthly-scale adjustments based on CRU-TS2.1 (correctedtemperature, diurnal temperature range, cloud-cover) (
Mitchell
www.cru.
and Jones,
). On the other hand, the Global Precipitation Climatology
Fig. 3.
The spatial distribution of difference (RegCM4–WFD) between RegCM4/R1 and WFD in precipitation (Preci), temperature (Tmp), runoff and actual EvapoTranspiration
(ET) (waterbody areas have been blanked) over 1990–2001.

6 L. Li et al. / Journal of Hydrology 528 (2015) 1–16
Fig. 4.
Change (near future minus reference) in 2-m air temperature (in ° C) for May–October (MJJASO, panel (a)), November–April (NDJFMA, panel (b)) and annul mean (panel
(c)) from RegCM4/R2.
Fig. 5.
Mean precipitation change in the near future (expressed in %) with respect to the reference period for May–October (MJJASO, panel (a)), November–April (NDJFMA, panel (b)) and annual mean (panel (c)) from RegCM4/R2.
Centre full data product Version 4 (GPCCv4) ( orias.dwd.de/GPCC/
GPCC_Visualiser ;
Rudolf and Schneider, 2005; Schneider et al.,
2008
) monthly observations are used in combination with separate precipitation gauge corrections for rainfall and snowfall.
Fig. 1
shows the topography and locations of the Global Runoff
Data Centre (
GRDC, 2010 ) stations and three sub-regions in
Southern Africa. The three sub-regions are chosen for analysis in this paper, considering their rather homogeneous rainfall annual cycle patterns (
Liebmann et al., 2012; Kalognomou et al., 2013
).
They were defined by
Kalognomou et al. (2013)
, who evaluated the ability of 10 RCMs participating in CORDEX-Africa simulating precipitation climatology over Southern Africa. Sub-region 1, including Zambia, Malawi, Zimbabwe, northeast of Mozambique, half of Botswana, and north and east part of Angola, lies in the tropical rainfall regime where rainfall is convective and influenced by the position of InterTropical Convergence Zone (ITCZ). Rainfall is highly seasonal with an annual peak of precipitation in January–
February and the wet season (November–April) precipitation is accounting for more than 85% of annual total precipitation.
Sub-region 2, including southern half of Botswana, south part of
Mozambique, northeast part of South Africa, Swaziland and
Lesotho, lies in a subtropical regime, which also experiences convective rainfall resulting from the action of a semi-permanent thermal low pressure system in the austral summer. The fraction of wet season (November–April) precipitation varies from 85% to less than 50%. Sub-region 3, including west coast of South Africa, experiences a Mediterranean climate with wet conditions in late autumn and winter, and with the annual peak of precipitation in
June–July. The wet season (April–August) is accounting for about
60–70% of total annual precipitation, and the precipitation is generally low throughout this narrow sub-region. The rainfall is produced by transient mid-latitude low-pressure systems. More details on rainfall and weather regimes in these regions can be found in the studies by
Liebmann et al. (2012)
and
Kalognomou et al. (2013)
.
Discharge data from 22 gauging stations (
Fig. 1
) were obtained from GRDC. After quality control for record length and completeness, unrealistic runoff coefficients, precipitation and discharge

Fig. 6.
Mean monthly relative change in precipitation (Preci), temperature (Temp) and Potential EvapoTranspiration (PET) from RegCM4/R2 (2010–2029 vs 1990–
2009) in the three sub-regions of Southern Africa.
data consistency, etc., 22 discharge stations in southern Africa were retained for parameter regionalisation. These 22 stations locate in 18 rivers, mainly in Zambia, Namibia and South Africa.
More details can be found in the study by
Li et al. (2013)
. The
Southern Africa land cover and land use data were used to identify waterbodies (i.e. lakes) in the study. The data was downloaded from
Global Land Cover 2000 database
jrc.ec.europa.eu/products/glc2000/products.php
were blanked in the results since no lakes or reservoirs are considered in current hydrological modelling practices.
2.4. Climate change and variability index
L. Li et al. / Journal of Hydrology 528 (2015) 1–16
3. Results and discussions
(GLC 2000, http://bioval.
). The waterbodies
The climate change vs climate variability was estimated by the
Signal-to-Noise ratio (SN) in this study. According to the study of
Jung et al. (2012)
the value of SN > 1, i.e. when a change in the signal is larger than the simulated monthly variability, suggests a case with a climate change signal that can clearly be distinguished from variability. SN is estimated as follows:
7
3.1. Hydrological performance by RegCM4 in the historical period
The RegCM4 simulated climate data were upscaled to
0.5
° 0.5
° by arithmetic average, because this is the resolution of the baseline climate data used by the large-scale hydrological model. In order to assess the performance of RegCM4, the simulation result from RegCM4/R1 is used for comparison with WFD
(observations) in the historical period since the observed SST from
HadSST is used in this period in RegCM4/R1. Precipitation is the immediate source of water for the land surface hydrological budget, whose uncertainty will strongly impact the model calibration results.
Fig. 2
shows the average annual precipitation, temperature, runoff and actual evapotranspiration (1990–2001) in Southern
Africa from WFD, RegCM4/R1 and RegCM4/R2, from which we can see that (1) in general, the spatial distributions of annual precipitation from the two RegCM4 runs are rather similar to WFD, with low rainfall in south-western regions around Namibia as well as in Kalahari desert and with high rainfall towards north-eastern regions; (2) the precipitation from RegCM4/R2 is overestimated across the central north and northeast part of the region including
Zambia, Malawi and Tanzania, while that from RegCM4/R1 is slightly underestimated in Malawi and Tanzania; (3) the precipitation from RegCM4 is larger than that from WFD in southeast coastal area around Swaziland and Lesotho; (4) the temperatures in the RegCM4 runs look more similar to WFD than what is the case for precipitation. The largest difference locates at the humid tropical areas, i.e. Tanzania, Malawi and Mozambique; (5) the simulation by RegCM4/R2 overestimates runoff across the northeast part of the region and produces larger estimates of runoff in the humid tropical area, due to the overestimation in rainfall. Similar results have been found in the study of
Arnell et al. (2003) ; and
(6) the largest deviations from WFD of the annual actual evapotranspiration simulated in RegCM4/R1and RegCM4/R2 are located in the humid tropical areas, i.e.
Tanzania, Malawi and
Mozambique. RegCM4/R1 underestimates while the RegCM4/R2 overestimates the actual evapotranspiration in these north-eastern parts of the region. In general, RegCM4 simulates precipitation and temperature fairly well compared with the
WFD reanalysis data in Southern Africa for the historical period.
Fig. 3
shows the difference in precipitation, temperature, runoff and ET between WFD and RegCM4/R1. The annual differences in precipitation, runoff and ET are within 100 mm/y over most of the region, while the temperature bias is less than 1 ° C in much of the region, with slightly larger negative biases near the east coast and somewhat higher biases, both positive and negative, along the west coast. The largest underestimation of precipitation can be found in the northeastern, i.e. Lake Malawi region and
Tanzania, and northwestern region, i.e. north Angola, which results in the largest difference in runoff and ET as well. Overestimation of precipitation can be found over Drakensburg Mountain and
Lesotho highlands. The regional precipitation bias of the RegCM4 is similar to the bias from other CORDEX RCMs in Southern
Africa (e.g.
Kalognomou et al., 2013
).
SN ¼ j X fut r
X press j
ð 1 where X fut and X press represent the mean monthly values of variable
X for the near future (2010–2029) and present (1990–2009) time slices, respectively; while r is the standard deviation of monthly values of this variable for the present time slice.
Þ
3.2. Near future climate change and variability
RegCM4/R2 is used to build a climate change scenario in this study, where the same SSTs, projected from CCSM4, have been used for both historical and future simulations. The projected change in 2-m temperature between the near future (2010–2029) and the reference period (1990–2009) under the RCP4.5 emission scenario is presented in
Fig. 4
a–c. RegCM4 projects a general increase in the mean 2-m air temperature throughout most of

8 L. Li et al. / Journal of Hydrology 528 (2015) 1–16
(a)
1000
60
6
800
40
600 4
20
400
2 0
200
-20
0
0
1 2 3 4 5 6 7 8 9 101112
Month
1 2 3 4 5 6 7 8 9 101112
Month
(b)
-40
1 2 3 4 5 6 7 8 9 101112
Month
1000
800
600
3
2
30
20
10
0
400
1
-10
200
0
-20
0
1 2 3 4 5 6 7 8 9 101112
Month
-1
1 2 3 4 5 6 7 8 9 101112
Month
(c)
-30
1 2 3 4 5 6 7 8 9 101112
Month
200
25
150
20
1
100
0.5
15
10
50
0
0
5
0
-50
-5
-100
1 2 3 4 5 6 7 8 9 101112
Month
-0.5
1 2 3 4 5 6 7 8 9 101112
Month
-10
1 2 3 4 5 6 7 8 9 101112
Month
Fig. 7.
Boxplots of monthly change in precipitation, temperature and Potential EvapoTranspiration (PET) from RegCM4/R2 (2010–2029 vs 1990–2009) over (a) sub-region 1,
(b) sub-region 2 and (c) sub-region 3 (Note y -axis differences in sub-figures.) the domain. The projected increase in 2-m air temperature is larger than 0.6
° C in most of the land area with highest values in the northwest of the domain (up to 2 ° C) while the lowest warming during the May–October period is predicted around Drakensberg
Mountain and northwestern Namibia. Overall, the change in mean
2-m air temperature is consistent with the expected warmer future climate.
The corresponding projected precipitation changes are displayed in
Fig. 5
a–c. During the May–October (MJJASO) period
(
Fig. 5 a), predicted precipitation from RegCM4 remains unchanged
over most of the region except in the northwest region. However, rainfall is predicted to increase/decrease over the area eastward/westward of 25 ° E during the November–April (NDJFMA)
period ( Fig. 5
b) and for the annual mean (
Fig. 5 c). Over the
Malawian region, this increase is greater than 20%. Increases greater than 30% are found near Lake Malawi and towards south of Mozambique. Moreover, the driest region is located in a wide area encompassing Kalahari Desert, Namibia and Angola. The highest rainfall decrease is projected in regions experiencing the strongest warming (
Fig. 4 a–c), suggesting a lack of moisture, thus
leading to a reduction of evaporative cooling, which in turn enhances the warming.
The mean monthly changes (2010–2029 vs 1990–2009) of precipitation, temperature and Potential EvapoTranspiration (PET) derived from RegCM4/R2 in the three sub-regions are shown in
Fig. 6 , from which we can see that (1) the mean monthly

L. Li et al. / Journal of Hydrology 528 (2015) 1–16 9
Fig. 8.
Mean annual (a) runoff (waterbody areas have been blanked), (b) actual EvapoTranspiration (ET) (mm/y) from RegCM4/R2 (2010–2029) and the relative change of mean annual (c) runoff (waterbody areas have been blanked) and (d) actual EvapoTranspiration (ET) from RegCM4/R2 (2010–2029 vs 1990–2009).
temperature and potential evapotranspiration generally increase, but the change in precipitation is more variable between different months and different sub-regions for the near future (2010 until
2029); (2) the temperatures from all three sub-regions are increasing from January to December except September in sub-region 2;
(3) there is a decrease in precipitation before July in sub-region
3, with the largest decrease (more than 20%) of precipitation in June; (4) the changes in mean monthly PET are in the range between 7.7% and +14.8% with an average of +1.7% in sub-region 1, 6.8% and +11.7% with an average of +2.3% in sub-region 2 and 1.4% and +11.8% with an average of +5.4% in sub-region 3; (5) the change in mean monthly PET corresponds well to the change in mean monthly temperature, i.e. only decrease of temperature in September in sub-region 2 is accompanied by negative change in PET, and also the highest increase in PET in
December in sub-regions 1 and 2 is in accordance with the large increase in temperature. The annual changes in PET and temperature are in line with those found by
Arnell (1999)
. The annual global average PET as calculated by the Penman–Monteith formula increases by 7.5–10% by the 2020s, but the percentage of annual change in PET in our sub-regions or in Southern Africa was not specifically given in the study of
Arnell (1999) .
In order to investigate the large spatial variability in changes over Southern Africa,
Fig. 7
shows boxplots of monthly change in precipitation, temperature and PET from RegCM4/R2 (2010–2029 vs 1990–2009) over the three sub-regions. It can be seen that (1) there is a large spatial variation in changes in sub-region 1; (2) outliers in the boxplots in
Fig. 7
a, illustrates that some areas in sub-region 1, i.e. south of Mozambique, have large increases in precipitation and temperature (
Fig. 4
); (3) for all sub-regions,
September turns out to have the largest increase and variability in precipitation, in particular in sub-region 1, while there is only a small increase in temperature in sub-regions 1 and 3 and even decrease in sub-region 2, which means it will be getting wetter and colder in September in sub-region 2; (4) the monthly changes in PET correspond to the variability in change in temperature in all sub-regions; (5) in
Fig. 7 c, there are fewer outliers in sub-region 3.
The boxplots figure also demonstrates that there are large

10
(a)
200
150
100
L. Li et al. / Journal of Hydrology 528 (2015) 1–16
200
50
0
-50
-100
1 2 3 4 5 6 7 8 9 10 11 12
Month
(b)
200
150
100
50
0
-50
-100
1 2 3 4 5 6 7 8 9 10 11 12
Month
(c)
200
150
100
150
100
50
0
-50
1 2 3 4 5 6 7 8 9
Month
10 11 12
200
150
100
50
0
-50
1 2 3 4 5 6 7 8 9 10 11 12
Month
50
50
0
-50
0
-100
1 2 3 4 5 6 7 8 9 10 11 12
Month
-50
1 2 3 4 5 6 7 8 9
Month
10 11 12
Fig. 9.
Boxplot of future monthly relative change of runoff and actual EvapoTranspiration (ET) from RegCM4/R2 in (a) sub-region 1, (b) sub-region 2 and (c) sub-region 3.
uncertainties in precipitation and temperature change from
RegCM4/R2, especially in sub-region 1.
3.3. Near future hydrological change and variability
Fig. 8
shows the spatial distribution of mean annual runoff and actual EvapoTranspiration (ET) as well as the relative change of runoff and ET in the near future RegCM4/R2 (2010–2029) simulation. The runoff is in the range 0–300 mm/y over most parts of the
region ( Fig. 8
a) and the annual actual evapotranspiration in the range 0–1500 mm/y in the near future (
Fig. 8 b). The relative
change in runoff and ET shows: (1) in general, there is a wider increase in runoff, especially over the north-eastern area and sub-region 1, while there is a decrease over eastern part of
Namibia, the Kalahari desert, Angola and South Africa, which is consistent with the projected decrease in precipitation in those regions (
Fig. 5 c); (2) the mean annual ET decreases over the north-
west and south of Southern Africa, including Angola and South
Africa in the near future; (3) there is an increase in ET in Malawi and Lake Tanganyika region which may likely be tied to the combination of precipitation increase and temperature rise in these regions. The annual runoff and actual evapotranspiration both increase over the north-eastern region. The study of
Arnell et al.
(2003)
identified relative runoff change in Southern Africa in the

L. Li et al. / Journal of Hydrology 528 (2015) 1–16 11
Fig. 10.
Coefficients of Variation (CV) from WFD (1990–2001) and Signal to Noise (SN) from RegCM4/R2 (2010–2029 vs 1990–2009) of (a) precipitation, (b) actual
EvapoTranspiration (ET) and (c) runoff.
later future (2071–2100), based on RCMs, and found a comparable pattern in changes over Southern Africa. In their study, about 40% decrease was found over Angola, Namibia and South Africa, while
10–30% increase was found in northeast part of Southern Africa.
In our study, the west and south parts of the study domain, i.e.
eastern part of Namibia, the Kalahari Desert, Angola and South
Africa, will also be getting drier, resulting in a decrease of more than 25% in runoff.
Boxplots of near future monthly relative change in runoff and
ET over the three sub-regions are shown in
Fig. 9 , which investi-
gates the variability of monthly relative changes in RegCM4/R2.
It is seen that (1) the variability of runoff and ET in sub-regions 1 and 2 is highly reduced compared to that of precipitation, which shows that the nonlinearity of hydrological processes has a strong ability of water regulation; (2) there are still many outliers in
sub-regions 1 and 2 ( Fig. 9 a), resulting from the large variability
in precipitation and temperature in the regions ( Fig. 7 a); and (3)
there is a considerable variability in runoff and ET in sub-region
3 as well.
3.4. Climate change vs variability
Fig. 10
shows the spatial distribution of the variability of monthly precipitation, runoff and ET in WFD (1990–2001) and the SN ratios of precipitation, ET and runoff in
RegCM4/R2 (2010–2029). The figure demonstrates that the
Coefficients of Variation (CV) of monthly precipitation, ET and runoff in Southern Africa have the similar spatial distributions, with largest CV located in the driest area in west coastal
Namibia, while the smallest CVs are over wet areas, including the southeast coastal area and the north part of the region.
Similar CV spatial distributions have been found in the study of
Arnell et al. (2003) , which investigated the CV of annual
runoff during 1961–1990. In their study, the largest CV of annual runoff locates at the joint area of Namibia, Botswana and South Africa. The SNs of the three variables are under
0.3 over most of the area in Southern Africa, except Malawi and the boundary region between Tanzania and Democratic
Republic of Congo, where the SNs are over 1. In sub-region
1, the SNs of runoff and ET are mostly under 0.4. Besides, there is a relatively high SN ratio in precipitation across central northern part of the region including northern part of Angola and southern part of Dem Rep of Congo, which is the wettest area with rainfall over 1000 mm/y. The SN over most of
Southern Africa, except the Malawian region, is less than 1.0, which means that the past climate variability is larger than the near future climate change. More detailed results can be seen in
Table 2
and
Fig. 11 .

12 L. Li et al. / Journal of Hydrology 528 (2015) 1–16
Table 2
Signal to noise ratio for selected hydrological variables in three sub-regions.
Models
R1
R2
Months
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
P: precipitation; ET: actual EvapoTranspiration; Q: runoff;
Bold numbers show the largest value in each column.
ET
0.51
0.53
0.50
0.38
0.27
0.18
0.12
0.09
0.05
0.06
0.11
0.36
0.17
0.20
0.22
0.34
0.31
0.22
0.15
0.10
0.12
0.16
0.11
0.14
Subregion 1
P
0.69
0.53
0.32
0.16
0.05
0.02
0.02
0.02
0.04
0.08
0.30
0.73
0.39
0.27
0.63
0.40
0.11
0.02
0.02
0.01
0.20
0.06
0.18
0.34
Q
0.86
0.95
0.83
0.46
0.19
0.08
0.03
0.02
0.01
0.01
0.07
0.47
0.29
0.40
0.59
0.65
0.33
0.14
0.06
0.03
0.06
0.10
0.06
0.13
Subregion 2
P
0.55
0.57
0.35
0.20
0.12
0.09
0.04
0.03
0.07
0.17
0.21
0.47
0.44
0.38
0.52
0.28
0.16
0.03
0.03
0.03
0.18
0.19
0.13
0.29
ET
0.47
0.60
0.59
0.45
0.33
0.19
0.12
0.09
0.06
0.09
0.16
0.31
0.22
0.32
0.40
0.39
0.32
0.22
0.18
0.14
0.12
0.16
0.16
0.15
Q
0.51
0.77
0.73
0.52
0.29
0.12
0.06
0.03
0.02
0.05
0.12
0.30
0.33
0.58
0.82
0.56
0.34
0.17
0.10
0.05
0.05
0.11
0.14
0.16
Subregion 3
P
0.06
0.13
0.15
0.43
0.23
0.33
0.42
0.13
0.14
0.12
0.31
0.08
0.06
0.12
0.13
0.24
0.27
0.47
0.32
0.08
0.49
0.11
0.32
0.08
ET
0.18
0.13
0.15
0.24
0.24
0.20
0.23
0.25
0.17
0.14
0.26
0.29
0.15
0.09
0.11
0.18
0.25
0.32
0.24
0.13
0.14
0.27
0.31
0.29
Table 2
summarises the regional average of SN ratios for precipitation, ET and runoff in the three sub-regions. Main findings are that
(1) in sub-region 1, the precipitation has largest SN (0.63) in March, while both ET and runoff have the maximum SNs (0.34 and 0.65) in
April; (2) in sub-region 2, the maximum SNs of precipitation, ET and runoff (0.52, 0.40 and 0.82) all maxima are found in March; (3) in sub-region 3, the largest precipitation SN (0.49) is found in
September, while the maximum SNs of ET and runoff (0.32 and
0.45) are found in June and November, respectively. However, the
SN of ET in November and that of runoff in June are also large. The variability of SN of monthly ET and runoff in different sub-regions is shown in
Fig. 11
, from which can be seen that (1) for sub-regions 1 and 2, the SN values of runoff and ET are close to or greater than 0.5 during February to April, which indicates that the large SN ratios are predominantly in the wet season in those two sub-regions; (2) for sub-region 3, large SN ratios occur in June–July, which is the annual peak precipitation period, and October–
November; and (3) furthermore, the SN of runoff has larger variability than that of ET for all sub-regions. The SN ratios of runoff and ET are strongly related. Compared with
Fig. 7
, although the relative change of precipitation in September from sub-region 1 is quite large, the SN signal is moderate because of large nature variability.
3.5. Discussion on uncertainties
In this study, the bias-corrected reanalysis WFD data were used as ‘‘observations’’, since high quality rainfall observation data are not available in the Southern Africa region. The quality of WFD data has previously been discussed and compared with TRMM, as these two global gridded datasets were used for calibration of hydrological model in an earlier study (
Li et al., 2013
). In general, the mean annual precipitation from WFD is larger than TRMM, especially over Angola. The runoff simulated using WFD was larger over northwestern of Angola than that from TRMM. Based on the earlier investigation, it should be noted that there is an unavoidable uncertainty in the precipitation of WFD, which of course results in uncertainty in runoff and ET in the historical period as well. The runoff and ET have been blanked over the lakes since the lakes are not considered in the hydrological simulations.
Regarding the difference between the simulations from WFD and
RegCM4/R1, the precipitation, temperature, runoff and ET biases are shown in
Fig. 3
, which shows that the temperature of
RegCM4 is underestimated in Mozambique, Zimbabwe and south part of South Africa, while overestimation is found in the west part, i.e. Namib Desert. Further, there was an underestimation of precipitation in the northeastern part of the domain especially over Lake
Malawi region while a positive precipitation bias is found in the southeastern parts of the region, especially over high topography areas. This is the case also in most models in the CORDEX ensemble, as described by
Kalognomou et al. (2013) . In their study, 10
regional climate models (50 km) from CORDEX were compared to a number of observational datasets, i.e. GPCP. There are many sources of differences between RCMs, including physics packages
(e.g. microphysics and boundary layer schemes, land surface models), as well as boundary conditions (taken from various global models or reanalysis products). Besides, from the investigation of precipitation change in
Fig. 6
, we can also see that there is a substantial spatial variability and uncertainty in precipitation in the
RegCM4/R2 future climate change scenarios. Furthermore, the uncertainty in runoff and ET also comes from the large-scale hydrological model WASMOD-D that we used, which lacks Lake module. Besides, WASMOD-D was calibrated with the observed discharge from GRDC, and the quality and quantity of discharge data over Southern Africa are a challenge. Uncertainty also comes from the regionalisation of WASMOD-D, which was based on the calibrations from 22 discharge stations over Southern Africa.
Most of the stations locate in Zambia, which is in sub-region 1; two stations locate in sub-region 2 and only one in sub-region 3.
4. Conclusions
Based on the latest version of the ICTP Regional Climate Model, i.e. RegCM4, nested within the global atmospheric model CAM4
Q
0.09
0.06
0.10
0.23
0.19
0.22
0.30
0.29
0.19
0.18
0.34
0.22
0.09
0.05
0.08
0.16
0.21
0.40
0.31
0.17
0.32
0.36
0.45
0.25

(1)
2
1.5
1
L. Li et al. / Journal of Hydrology 528 (2015) 1–16
2
1.5
1
0.5
0.5
(2)
2
0
1 2 3 4 5 6 7 8 9 10 11 12
Month
1.5
1.5
0
1 2 3 4 5 6 7 8 9 10 11 12
Month
2
1
0.5
1
0.5
(3)
2
0
1 2 3 4 5 6 7 8 9 10 11 12
Month
1.5
1.5
0
1 2 3 4 5 6 7 8 9 10 11 12
Month
2
13
1
0.5
1
0.5
0
1 2 3 4 5 6 7 8 9 10 11 12
Month
0
1 2 3 4 5 6 7 8 9 10 11 12
Month
Fig. 11.
Boxplot of Signal to Noise (SN) of runoff and actual EvapoTranspiration (ET) from RegCM4/R2 (2010–2029 vs 1990–2009) in (a) sub-region 1, (b) sub-region 2 and (c) sub-region 3.
run at the University of Oslo and the WASMOD-D model, we investigated the potential impact of climate change on the hydrological system over continental Southern Africa in the near future (2010–
2029) under the RCP4.5 climate emission scenario. The predicted changes in precipitation, temperature, runoff and actual evapotranspiration have been evaluated in terms of their spatial and temporal variability. The following conclusions are drawn from this study.
The hydrological WASMOD-D model driving by outputs from
RegCM4 in Southern Africa yielded results in fairly well agreement with that driving by WFD bias-corrected reanalysis data for the historical period (1990–2009). It indicates that RegCM4 gave a satisfied projection for hydrological simulation over the historical period, which provides a reliable base for future projection of water resources in the region.
The results from RegCM4 indicate the temperature will increase over continental Southern Africa in the near future (2010–2029), while the precipitation is predicted to increase/decrease over the area eastward/westward of 25 ° E during November–April and for the annual mean. The projected runoff changes in the near future by the WASMOD-D model largely follow projected changes in precipitation and modified by changes of temperature in some regions. Generally, increase of runoff was found over eastern region of Southern Africa, i.e. Southern Mozambique and the

14
Malawian, while a decrease was estimated across the driest region in a wide area encompassing Kalahari Desert, Namibia, southwest of South Africa and Angola. The change of ET has a quite similar spatial and temporal distribution as runoff in Southern Africa.
The climate variability in the historical period is larger than the climate change signal over most areas of Southern Africa, except in
Malawi and Lake Tanganyika regions where the strongest climate change signals are found in precipitation as well as in evapotranspiration and runoff. It is noted that there is a high spatial and temporal variability in the results from RegCM4, especially in sub-region 1, the largest sub-region with least spatial and temporal homogeneity of climate variables in the study.
Acknowledgments
L. Li et al. / Journal of Hydrology 528 (2015) 1–16
SoCoCa
SN
SST
TRMM
WASMOD
WASMOD-D
WATCH
WFD
WP
This study was funded by the Research Council of Norway
(RCN) project 190159/V10 (SoCoCA) and project 216576
(NORINDIA). Finally, we would like to thank the three anonymous reviewers who gave valuable comments on an earlier version of this manuscript.
Appendix A
Abbreviation
AMMA-ENSEMBLE African Monsoon Multidisciplinary
CAM4
CCSM
Analyses-ENSEMBLE
Community Atmosphere Model version 4
Community Climate System Model
CORDEX
CRU
CV
ECWMF
COordinated Regional climate
Downscaling EXperiment
Climate Research Unit
Coefficients of Variation
European Centre for Medium-Range
Weather Forecasts
ECMWF Re-Analysis data ERA-40
ET
GCM
GLC 2000
GPCCv4
EvapoTranspiration
General Circulation Model
Global Land Cover 2000 database
Global Precipitation Climatology Centre full data product Version 4
Global Runoff Data Centre GRDC
HadSST
ICTP
IPCC
Hadley centre SST data set
Abdus Salam International Centre for
Theoretical Physics
Intergovernmental Panel on Climate
ITCZ
NARCCAP
NCAR
PET
PRUDENCE
PSU’s MM5
RCM
RCN
RCP 4.5
RegCM4
Change
InterTropical Convergence Zone
North American Regional Climate Change
Assessment Program
National Center for Atmospheric
Research
Potential EvapoTranspiration
Prediction of Regional scenarios and
Uncertainties for Defining EuropeaN
Climate change risks and Effects
Pennsylvania State University’s
Mesoscale Meteorological model version
5
Regional Climate Model
Research Council of Norway
Representative Concentration Pathway
4.5
REGional Climate Model version 4 of ICTP
Socioeconomic Consequences of Climate change in Sub-equatorial Africa
Signal-to-Noise ratio
Sea Surface Temperature
Tropical Rainfall Measuring Mission
Water And Snow balance MODelling system
Water And Snow balance MODelling system at macro scale-Daily version
WATer and global CHange project
WATCH Forcing Data
Work Package
References
Abiodun, B.J., Adeyewa, Z.D., Oguntunde, P.G., Salami, A.T., Ajayi, V.O., 2012.
Modeling the impacts of reforestation on future climate in West Africa. Theor.
Appl. Climatol. 110, 77–96.
http://dx.doi.org/10.1007/s00704-012-0614-1 .
Adjei, K.A., Ren, L.L., Appiah-Adjei, E.K., Odai, S.N., 2014. Application of satellitederived rainfall for hydrological modelling in the data-scarce Black Volta transboundary basin. Hydrol. Res. 111.
http://dx.doi.org/10.2166/nh.2014.111
, in press.
Adler, R.F., Huffman, G.J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B.,
Schneider, U., Curtis, S., Bovin, D., Gruber, A., Susskind, J., Arkin, P., Nelkin, E.,
2003. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeorol. 4, 1147–1167 .
Arnell, N.W., 1999. Climate change and global water resources. Global Environ.
Change 9, S31–S49 .
Arnell, N., Hudson, D., Jones, R., 2003. Climate change scenarios from a regional climate model: estimating change in runoff in southern Africa. J. Geophys. Res.
108 (D16), 4519.
http://dx.doi.org/10.1029/2002JD002782 .
Bates, B.C., Kundzewicz, Z.W., Wu, S., Palutikof, J.P., 2008. Climate Change and
Water. Technical Paper of the Intergovernmental Panel on Climate Change. IPCC
Secretariat, Geneva.
Bergstrom, S., Carlsson, B., Gardelin, M., Lindstrom, G., Pettersson, A.,
Rummukainen, M., 2001. Climate change impacts on runoff in Sweden: assessments by global climate models, dynamical downscaling and hydrological modeling. Clim. Res. 16, 101–112.
http://dx.doi.org/10.3354/ cr016101 .
Castro, L.M., Salas, M.M., Fernández, B., 2015. Evaluation of TRMM Multi-satellite precipitation analysis (TMPA) in a mountainous region of the central Andes range with a Mediterranean climate. Hydrol. Res. 46 (1), 89–105.
http:// dx.doi.org/10.2166/nh.2013.096
.
Chen, H., Xu, C.-Y., Guo, S.L., 2012. Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J. Hydrol. 434–435, 36–45 .
Christensen, J.H., Carter, T.R., Rummukainen, M., Amanatidis, G., 2007a. Evaluating the performance and utility of regional climate models: the PRUDENCE project.
Clim. Change 81, 1–6.
http://dx.doi.org/10.1007/s10584-006-9211-6 .
Christensen, J.H., Hewitson, B., Busuioc, A., et al., 2007b. Regional climate projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M.,
Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: the Physical
Science Basis. Contribution of Working Group 1 to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge
University Press, New York.
CRU, 2000. Climate Research Unit, University of East Anglia < http://www.cru.uea.
ac.uk
> (accessed 09.12).
Diallo, I., Sylla, M.B., Giorgi, F., Gaye, A.T., Camara, M., 2012. Multimodel GCM-RCM ensemble based projections of temperature and precipitation over West Africa for the early 21st century. Int. J. Geophys. 19 http://dx.doi.org/10.1155/2012/
972896 (Article ID 972896).
Diallo, I., Giorgi, F., Sandeep, S., Stordal, F., Giuliani, G., 2014. Evaluation of RegCM4 driven by CAM4 over Southern Africa: mean climatology, interannual variability and daily extremes of wet season temperature and precipitation. Theor. Appl.
Climatol.
http://dx.doi.org/10.1007/s00704-014-1260-6 .
Dibike, Y.B., Coulibaly, P., 2007. Validation of hydrologic models for climate scenario simulation: the case of Saguenay watershed in Québec. Hydrol. Process. 21 (23),
3123–3135 .
Endris, H.S., Omondi, P., Jain, S., Lennard, C., Hewitson, B., Chang’a, L., Awange, J.L.,
Dosio, A., Ketiem, P., Nikulin, G., Panitz, H.-J., Büchner, M., Stordal, F., Tazalika, L.,
2013. Assessment of the performance of CORDEX regional climate models in simulating eastern Africa rainfall. J. Clim. 26 (21), 8453–8475 .
Engelbrecht, F.A., McGregor, J.L., Engelbrecht, C.J., 2009. Dynamics of the conformalcubic atmospheric model projected climate-change signal over southern Africa.
Int. J. Climatol. 29, 1013–1033.
http://dx.doi.org/10.1002/joc.1742
.
Engelbrecht, C.J., Engelbrecht, F.A., Dyson, L.L., 2013. High-resolution modelprojected change in mid-tropospheric closed-lows and extreme rainfall events over southern Africa. Int. J. Climatol. 33, 173–187.
http://dx.doi.org/
10.1002/joc.3420
.

L. Li et al. / Journal of Hydrology 528 (2015) 1–16
Fowler, H.J., Blenkinsop, S., Tebaldi, C., 2007. Review linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int. J. Climatol. 27, 1547–1578.
http://dx.doi.org/
10.1002/joc.1556
.
Gbobaniyi, E., Sarr, A., Sylla, M.B., Diallo, I., Lennard, C., et al., 2014. Climatology, annual cycle and interannual variability of precipitation and temperature in
CORDEX simulations over West Africa. Int. J. Climatol. 34, 2241–2257.
http:// dx.doi.org/10.1002/joc.3834
.
Gent, P.R., Danabasoglu, G., Donner, L.J., Holland, M.M., Hunke, E.C., Jayne, S.R.,
Lawrence, D.M., Neale, R.B., Rasch, P.J., Vertenstein, M., Worley, P.H., Yang, Z.L.,
Zhang, M., 2011. The community climate system model version 4. J. Clim.
http:// dx.doi.org/10.1175/2011JCLI4083.1
.
Giorgi, F., Mearns, L.O., 1999. Introduction to special section: regional climate modeling revisited. J. Geophys. Res. 104 (D6), 6335–6352 .
Giorgi, F., Marinucci, M.R., Bates, G., 1993a. Development of a second generation regional climate model (RegCM2). I. Boundary layer and radiative transfer processes. Mon. Weather Rev. 121, 2794–2813 .
Giorgi, F., Marinucci, M.R., Bates, G., DeCanio, G., 1993b. Development of a second generation regional climate model (RegCM2). II. Convective processes and assimilation of lateral boundary conditions. Mon. Weather Rev. 121, 2814–
2832 .
Giorgi, F., Hewitson, B., Christensen, J., Hulme, M., Von Storch, H., Whetton, P., Jones,
R., Mearns, L., Fu, C., 2001. Regional climate information: evaluation and projections. In: Houghton, J.T., et al. (Eds.), Climate Change 2001: The Scientific
Basis, Contribution of Working Group 1 to the Third Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge Univ. Press, New York, pp. 583–638.
Giorgi, F., Bi, X., Pal, J.S., 2004. Mean, interannual variability and trends in a regional climate change experiment over Europe. II. Climate change scenario (2071–
2100). Clim. Dyn. 23, 839–859 .
Giorgi, F., Jones, C., Asrar, G., 2009. Addressing climate information need at the regional level: the CORDEX framework. WMO Bull. 58 (3), 175–183 .
Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M.B., Bi, X., Elguindi, N., Diro,
G.T., Nair, V., Giuliani, G., Turuncoglu, U.U., Cozzini, S., Güttler, I., O’Brien, T.A.,
Tawfik, A.B., Shalaby, A., Zakey, A.S., Steiner, A.L., Stordal, F., Sloan, L.C.,
Brankovic, C., 2012. RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim. Res. 52, 7–29.
http://dx.doi.org/10.3354/ cr01018 .
Global Land Cover 2000 database, 2003. European Commission, Joint Research
Centre < http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php
>.
Gong, L., Halldin, S., Xu, C.Y., 2011. Global scale river routing – an efficient time delay algorithm based on HydroSHEDS high resolution hydrography. Hydrol.
Process. 25 (7), 1114–1128 .
Gosling, R., 2014. Assessing the impact of projected climate change on drought vulnerability in Scotland. Hydrol. Res. 45 (6), 806–886.
http://dx.doi.org/
10.2166/nh.2014.148
.
Gosling, S.N., Taylor, R.G., Arnell, N.W., Todd, M.C., 2011. A comparative analysis of projected impacts of climate change on river runoff from global and catchmentscale hydrological models. Hydrol. Earth Syst. Sci. 15, 279–294.
http:// dx.doi.org/10.5194/hess-15-279-2011 .
Graham, L.P., Hagemann, S., Jaun, S., Beniston, M., 2007. On interpreting hydrological change from regional climate models. Clim. Change 81, 97–122.
http://dx.doi.org/10.1007/s10584-006-9217-0 .
Graham, L.P., Andersson, L., Horan, M., Kunz, R., Lumsden, T., Schulze, R., Warburton,
M., Wilk, J., Yang, W., 2011. Using multiple climate projections for assessing hydrological response to climate change in the Thukela River Basin, South
Africa. Phys. Chem. Earth, Parts A/B/C 36, 727–735 .
GRDC, 2010. The Global Runoff Data Centre, 56002 Koblenz, Germany.
Grell, G., Dudhia, J., Stauffer, D.R., 1994. A Description of the Fifth Generation Penn
State/NCAR Mesoscale Model (MM5). National Center for Atmospheric Research
Tech Note NCAR/TN-398 + STR, NCAR, Boulder, CO.
Hagemann, S., Chen, C., Haerter, J.O., Heinke, J., Gerten, D., Piani, C., 2011. Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J. Hydrometeorol. 12, 556–578 .
Hudson, D.A., Jones, R.G., 2002. Regional climate model simulations of present-day and future climates of southern Africa. Hadley Cent. Tech. Note, vol. 39. Met Off.
Hadley Cent., Exeter, U.K., pp. 42.
Huffman, G.J., Adler, R.F., Bolvin, D.T., Gu, G., Nelkin, E.J., Bowman, K.P., Hong, Y.,
Stocker, E.F., Wolff, D.B., 2007. The TRMM multisatellite precipitation analysis: quasi-global, multi-year, combined-sensor precipitation estimates at fine scale.
J. Hydrometeorol. 8, 38–55 .
Hulme, M., Jenkins, G., 1998. Climate change scenarios for the United Kingdom: scientific report. UKCIP Tech. Rep., Clim. Res. Unit, vol. 1. Univ. of East Anglia,
Norwich, England, pp. 80.
Hulme, M., Turnpenny, J.R., Jenkins, G.J., 2002. Climate change scenarios for the
United Kingdom: the UKCIP02 briefing report. Tyndall Centre for Climate
Change Research, Norwich, UK, pp. 15.
Intergovernmental Panel on Climate Change (IPCC), 2007. Climate Change 2007:
Impacts, Adaptation and Vulnerability. In: Parry, M., et al. (Eds.), Contribution of
Working Group II to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge Univ. Press, Cambridge, U.K.
Jin, X., Xu, C.-Y., Zhang, Q., Chen, Y.D., 2009. Regionalization study of a conceptual hydrological model in Dongjiang Basin, south China. Quatern. Int. 208 (1–2),
129–137 .
Jones, C., Giorgi, F., Asrar, G., 2011. The Coordinated Regional Downscaling
Experiment (CORDEX). An International Downscaling Link to CMIP5. CLIVAR
15
Exchanges, No. 56, International CLIVAR Project Office, Southampton, United
Kingdom, pp. 52.
Jung, G., Wagner, S., Kunstmann, H., 2012. Joint climate–hydrology modeling: an impact study for the data-sparse environment of the Volta Basin in West Africa.
Hydrol. Res. 43 (3), 231–248.
http://dx.doi.org/10.2166/nh.2012.044
.
Kalognomou, E.A. et al., 2013. A diagnostic evaluation of precipitation in CORDEX models over southern Africa. J. Clim. 26, 9477–9506.
http://dx.doi.org/10.1175/
JCLI-D-12-00703.1
.
Kang, K., Merwade, V., 2014. The effect of spatially uniform and non-uniform precipitation bias correction methods on improving NEXRAD rainfall accuracy for distributed hydrologic modeling. Hydrol. Res. 45 (1), 23–42.
http:// dx.doi.org/10.2166/nh.2013.194
.
Kim, J., Waliser, D.E., Mattmann, C.A., Goodale, C.E., Hart, A.F., Zimdars, P.A.,
Crichton, D.J., Jones, C., Nikulin, G., Hewitson, B., Jack, C., Lennard, C., Favre, A.,
2013. Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors. Clim. Dyn.
http://dx.doi.org/10.1007/s00382-013-1751-7 .
Kristjánsson, J.E., Iversen, T., Kirkevåg, A., Selland, Ø., Debernard, J.B., 2005.
Response of the climate system to aerosol direct and indirect forcing: role of cloud feedbacks. J. Geophys. Res. 110, D24206.
http://dx.doi.org/10.1029/
2005JD006299 .
Kundzewicz, Z.W., Mata, L.J., Arnell, N.W., Döll, P., Kabat, P., Jiménez, B., Miller, K.A.,
Oki, T., Sen Z., Shiklomanov, I.A., 2007. Freshwater resources and their management. In: Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J.,
Hanson, C.E.
(Eds.), Climate Change 2007: Impacts, Adaptation and
Vulnerability. Contribution of Working Group II to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge
University Press, Cambridge, UK, pp. 173–210.
Leung, L.R., Wigmosta, M.S., 1999. Potential climate change impacts on mountain watersheds in the Pacific Northwest. J. Am. Water Resour. Assoc.
35, 1463–1471 .
Li, L., Ngongondo, C.S., Xu, C.-Y., Gong, L., 2013. Comparison of the global TRMM and
WFD precipitation datasets in driving a large-scale hydrological model in
Southern Africa. Hydrol. Res. 44 (5), 770–788 .
Liebmann, B., Bladé, I., Kiladis, G.N., Carvalho, L.M.V., Senay, G.B., Allured, D., Leroux,
S., Funk, C., 2012. Seasonality of African precipitation from 1996 to 2009. J. Clim.
25 (12), 4304–4322 .
Liu, Z., Zongxue, X., Guobin, F., Zhijun, Y., 2012. Assessing the hydrological impacts of climate change in the headwater catchment of the Tarim River basin, China.
Hydrol. Res. 44 (5), 834–849 .
Marengo, J., Jones, R., Alves, L.M., Valverde, M.C., 2009. Future change of temperature and precipitation extremes in South America as derived from the
PRECIS regional climate model system. Int. J. Climatol. 29, 2241–2255.
http:// dx.doi.org/10.1002/joc.1863
.
Mariotti, L., Diallo, I., Coppola, E., Giorgi, F., 2014. Seasonal and intraseasonal changes of African monsoon climate in 21st century CORDEX projections.
Climatic Change 125, 53–65.
http://dx.doi.org/10.1007/s10584-
014-1097-0 .
Markoff, M.S., Cullen, A.C., 2008. Impact of climate change on Pacific Northwest hydropower. Clim. Change 87, 451–469.
http://dx.doi.org/10.1007/s10584-007-
9306-8 .
Mearns, L.O., Mavromatis, T., Tsvetsinskaya, E., Hays, C., Easterling, W., 1999.
Comparative responses of EPIC and CERES crop models to high and low resolution climate change scenarios. J. Geophys. Res. 104, 6623–6646 .
Mearns, L.O., Hulme, M., Carter, T.R., Leemans, R., Lal, M., Whetton, P., 2001. Climate scenario development. In: Houghton, J.T., et al. (Eds.), Climate Change 2001: The
Scientific Basis. Contribution of Working Group 1 to the Third Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge Univ.
Press, New York, pp. 739–768.
Mearns, L.O., Sain, S., Leung, L.R., Bukovsky, M.S., McGinnis, S., Biner, S., Caya, D.,
Arritt, R.W., Gutowski, W., Takle, E., Snyder, M., Jones, R.G., Nunes, A.M.B.,
Tucker, S., Herzmann, D., McDaniel, L., Sloan, L., 2013. Climate change projections of the North American Regional Climate Change Assessment
Program (NARCCAP). Clim. Change 120, 965–975.
http://dx.doi.org/10.1007/ s10584-013-0831-3 .
Menendez, C.G., de Castro, M., Sorensson, A.A., Boulanger, J.P., Participating CLARIS
Modeling Groups, 2010. CLARIS project: towards climate downscaling in South
America. Meteorol. Z. 19 (4), 357–362.
http://dx.doi.org/10.1127/0941-2948/
2010/0459 .
Mitchell, T.D., Jones, P.D., 2005. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J.
Climatol. 25 (6), 693–712 .
Myhre, G., Nilsen, J.S., Gulstad, L., Shine, K.P., Rognerud, B., Isaksen, I.S.A., 2007.
Radiative forcing due to stratospheric water vapour from CH
4 oxidation.
Geophys. Res. Lett. 34 (1), L01807 .
Nohara, D., Kitoh, A., Hosaka, M., Oki, T., 2006. Impact of climate change on river discharge projected by multimodel ensemble. J. Hydrometeorol. 7, 1076–1089.
http://dx.doi.org/10.1175/JHM531.1
.
Paeth, H., Hall, N.M.J., Gaertner, M.A., Alonso, M.D., Moumouni, S., Polcher, J., Ruti,
P.M., Fink, A.H., Gosset, M., Lebel, T., Gaye, A.T., Rowell, D.P., Moufouma-Okia,
W., Jacob, D., Rockel, B., Giorgi, F., Rummukainen, M., 2011. Progress in regional downscaling of West African precipitation. Atmos. Sci. Lett. 12, 75–82.
http:// dx.doi.org/10.1002/asl.306
.
Pal, J.S., Girogi, F., Bi, X., Elguindi, N., Solmon, F., Gao, X., Rauscher, S.A., Francisco, R.,
Zakey, A., Winter, J., Ashfaq, M., Syed, F.S., Bell, J.L., Diffenbaugh, N.S.,
Karmacharya, J., Konare, A., Martinez, D., da Rocha, R.P., Sloan, L.C., Steiner, A.,
2007. Regional climate modeling for the developing world – the ICTP RegCM3

16 L. Li et al. / Journal of Hydrology 528 (2015) 1–16 and RegCNET. Bull. Am. Meteorol. Soc. 88 (9), 1395–1409.
http://dx.doi.org/
10.1175/BAMS-88-9-1395 .
Piani, C., Haerter, J., Coppola, E., 2010. Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol .
99 (1), 187–192 .
Remesan, R., Bellerby, T., Frostick, L., 2014. Hydrological modelling using data from monthly GCMs in a regional catchment. Hydrol. Process. 28, 3241–3263.
http:// dx.doi.org/10.1002/hyp.9872
.
Rudolf, B., Schneider, U., 2005. Calculation of gridded precipitation data for the global land-surface using in situ gauge observations. In: Proceedings of the 2nd
Workshop of the International Precipitation Working Group IPWG, Monterey
October 2004, EUMETSAT, pp. 231–247 (ISBN 92-9110-070-6, ISSN 1727-432X).
Schneider, U., Fuchs, T., Meyer-Christoffer, A., Rudolf, B., 2008. Global Precipitation
Analysis Products of the GPCC <ftp://ftp.dwd.de/pub/data/gpcc/PDF/GPCC_ intro_products_v2011.pdf>.
Shalaby, A., Zakey, A.S., Tawfik, A.B., Solmon, F., Giorgi, F., Stordal, F., Sillman, S.,
Zaveri, R.A., Steiner, A.L., 2012. Implementation and evaluation of online gasphase chemistry within a regional climate model (RegCM-CHEM4). Geosci.
Model Dev. 5, 741–760 .
Solmon, F., Giorgi, F., Liousse, C., 2006. Aerosol modeling for regional climate studies: application to anthropogenic particles and evaluation over a European/
African domain. Tellus B 58, 51–72 .
Stahl, K., Moore, R.D., Shea, J.M., Hutchinson, D., Cannon, A.J., 2008. Coupled modeling of glacier and streamflow response to future climate scenarios. Water
Resour. Res. 44, W02422.
http://dx.doi.org/10.1029/2007WR005956 .
Storelvmo, T., Kristjansson, J.E., Myhre, G., Johnsrud, M., Stordal, F., 2006. Combined observational and modeling based study of the aerosol indirect effect. Atmos.
Chem. Phys. 6, 3583–3601 .
Sylla, M.B., Gaye, A.T., Jenkins, G.S., Pal, J.S., Giorgi, F., 2010. Consistency of projected drought over the Sahel with changes in the monsoon circulation and extremes in a regional climate model projections. J. Geophys. Res. 115, D16108.
http:// dx.doi.org/10.1029/2009JD012983 .
Tadross, M., Jack, C., Hewitson, B., 2005. On RCM-based projections of change in southern African summer climate. Geophys. Res. Lett. 32 (23), L23713 .
Uppala, S.M., Kållberg, P.W., Simmons, A.J., Andrae, U., Bechtold, V.D.C., Fiorino, M.,
Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S.,
Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M.,
Berg, L.V.D., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A.,
Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B.J.,
Isaksen, L., Janssen, P.A.E.M., Jenne, R., Mcnally, A.P., Mahfouf, J.-F., Morcrette, J.-
J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A.,
Vasiljevic, D., Viterbo, P., Woollen, J., 2005. The ERA-40 re-analysis. Q. J. R.
Meteorol. Soc. 131, 2961–3012.
http://dx.doi.org/10.1256/qj.04.176
.
Venalainen, A., Tuomenvirta, H., Lahtinen, R., Heikinheimo, M., 2001. The influence of climate warming on soil frost on snow-free surfaces in Finland. Clim. Change
50, 111–128 .
Vergara, W., Deeb, A.M., Valencia, A.M., Bradley, R.S., Francou, B., Zarzar, A.,
Grünwaldt, A., Haeussling, S.M., 2007. Economic impacts of rapid glacier retreat in the Andes. Eos Trans. AGU 88 (25).
http://dx.doi.org/10.1029/2007EO250001 .
Waha, K., Muller, C., Rolinski, S., 2013. Separate and combined effects of temperature and precipitation change on maize yields in sub-Saharan Africa for mid- to late-21st century. Global Planet. Change 106, 1–12 .
Weedon, G.P., Gomes, S., Viterbo, P., Österle, H., Adam, J.C., Bellouin, N., Boucher, O.,
Best, M., 2010. The WATCH forcing data 1958–2001: a meteorological forcing dataset for land surface-and hydrological-Rep., WATCH Tech. Rep. 22, 41
< http://www.eu-watch.org/publications/technical-reports > (accessed 09.12).
Weedon, G.P., Gomes, S., Viterbo, P., Shuttleworth, W., Blyth, E., Österle, H., Adam, J.,
Bellouin, N., Boucher, O., Best, M., 2011. Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeorol. 12 (5), 823–848 .
Weedon, G.P., Balsamo, G., Bellouin, N., Gomes, S., Best, M.J., Viterbo, P., 2014. The
WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50.
http://dx.doi.org/
10.1002/2014WR015638 .
Widén-Nilsson, E., Gong, L., Halldin, S., Xu, C.-Y., 2009. Model performance and parameter behavior for varying time aggregations and evaluation criteria in the
WASMOD-M global water balance model. Water Resour. Res. 45 (5), W05418 .
Xu, C.-Y., 1999. From GCMs to river flow: a review of downscaling techniques and hydrologic modeling approaches. Prog. Phys. Geog. 23 (2), 229–249 .
Xu, C.-Y., 2002. WASMOD – the water and snow balance modelling system. In:
Singh, V.P., Frevert, D.K. (Eds.), Mathematical Models of Small Watershed
Hydrology and Applications. Water Resources Publications, Chelsea, Michigan,
USA, pp. 555–590 .
Xu, C.-Y., Seibert, J., Halldin, S., 1996. Regional water balance modelling in the
NOPEX area: development and application of monthly water balance models. J.
Hydrol. 180, 211–236 .
Xu, C.-Y., Widén, E., Halldin, S., 2005. Modelling hydrological consequences of climate change – progress and challenges. Adv. Atmos. Sci. 22 (6), 789–797 .
Zakey, A.S., Solmon, F., Giorgi, F., 2006. Implementation and testing of a desert dust module in a regional climate model. Atmos. Chem. Phys. 6, 4687–4704 .