Article Title Page
Modeling Hydrological Impacts of Climate Change in Different Climatic Zones
Author Details
Author 1 Name: Fasil Ejigu Eregno
Department: Department of Geosciences
University/Institution: University of Oslo
Town/City: Oslo
Country: Norway
And
Department: Department of Plant and Environmental Sciences
University/Institution: Norwegian University of Life Sciences
Town/City: Ås
Country: Norway
Author 2 Name: Chong-Yu Xu
Department: Department of Geosciences
University/Institution: University of Oslo
Town/City: Oslo
Country: Norway
Author 3 Name: Nils-Otto Kitterød
Department: Department of Plant and Environmental Sciences
University/Institution: Norwegian University of Life Sciences
Town/City: Ås
Country: Norway
Corresponding author: Fasil Ejigu Eregno
Corresponding Author’s Email: efasilejigu@yahoo.com
Acknowledgments (if applicable): The work presented in this paper was supported by the Department of Geosciences, University
of Oslo through the Norwegian Research Council (RCN) Environment and Development Programme (FRIMUF) project number
171783. We are grateful to the Ministry of Water Resource and National meteorology Agency of Ethiopia, and Norwegian Water
Resource and Energy Directorate (NVE), for their collaboration in providing meteorological and stream flow data. We gratefully
acknowledge Professor Yongqin David Chen, the Chinese University of Hong Kong who provided hydrological data for the
Dongjiang basin through the Project no. CUHK4627/05H of the Research Grants Council of the Hong Kong Special Administrative
Region, China.
Biographical Details (if applicable): Fasil Ejigu Eregno is a student at the Department of Plant and Environmental Sciences,
Norwegian University of Life Sciences. His main areas of interest are hydrological modeling at regional and catchment scale; Water
quality modeling; and erosion and sediment transport. Fasil Ejigu is the corresponding author and can be contacted at:
efasilejigu@yahoo.com
Chong-Yu Xu is a Professor of Hydrology at the Department of Geosciences, University of Oslo. His main area of interest are
related to hydrological modelling at global, regional and catchment scales; modelling of hydrological impact of climate and
environment changes at global, regional and catchment scales; regional evapotranspiration and its role in linking climatic and
hydrological system; regionalization of hydrological variables and model parameters; uncertainty analysis and time series analysis.
Nils-Otto Kitterød is Associate Professor in Hydrology at the Department of Plant and Environmental Sciences, Norwegian
University of Life Sciences. His main areas of interest are related to soil water, groundwater, transport of contaminants, geostatistics,
and stochastic simulation.
Structured Abstract: Purpose - Recent advances in hydrological impact studies point that the response of specific catchments to
climate change scenario using a single model approach is questionable. This study was aimed at investigating the impact of climate
change on three river basins in China, Ethiopia and Norway using WASMOD and HBV hydrological models.
Design/methodology/approach - First, hydrological models’ parameters were determined using current hydro-climatic data inputs.
Second, the historical time series of climatic data was adjusted according to the climate change scenarios. Third,
the hydrological characteristics of the catchments under the adjusted climatic conditions were simulated using the
calibrated hydrological models. Finally, comparisons of the model simulations of the current and possible future
hydrological characteristics were performed. Responses were evaluated in terms of runoff, actual
evapotranspiration and soil moisture change for incremental precipitation and temperature change scenarios.
Findings - From the results obtained it can be inferred that two equally well calibrated models gave different
hydrological response to hypothetical climatic scenarios. Our findings support the concern that climate change
analysis using lumped hydrological models may lead to unreliable conclusion.
Practical implications - Extrapolation of driving forces (temperature and precipitation) beyond the range of parameter calibration
yields unreliable response. It is beyond the scope of this study to reduce this model ambiguity, but reduction of uncertainty is a
challenge for further research.
Originality/value - The research was conducted based on the primary time series data using the existing two hydrological models to
test the magnitude differences one can expect when using different hydrological models to simulate hydrological response of climate
changes in different climate zones.
Keywords: Climate change , Hydrological modelling , River basin , China , Ethiopia , Norway
Article Classification: Research paper
MODELING HYDROLOGICAL IMPACTS OF CLIMATE CHANGE IN
DIFFERENT CLIMATIC ZONES
INTRODUCTION
Climate change may cause significant impacts on water resources by resulting changes in the
hydrological cycle. Increasing temperature will lead to greater amounts of water vapour in the
atmosphere and the hydrological cycle will be intensified with more precipitation. However, the
extra precipitation will not be equally distributed around the globe. Some parts of the world may
see significant reductions in precipitation, or alterations in the timing of wet and dry seasons and
would lead to increases in both floods and droughts (Seino et al., 1998; Walsh et al., 2012; Parry
et al., 2012; Zhang et al, 2012). The spatial change in amount, intensity, and frequency of the
precipitation will affect the magnitude and frequency of river flows; consequently, it will
substantially affect the water resources at local and regional levels.
Quantitative estimates of the hydrological effects of climate change at local and regional scales
are essential for understanding and solving the potential water resource management problems
associated with water supply for domestic and industrial water use, power generation, and
agriculture (Steele-Dunne et al., 2008; Chen et al., 2012a). Therefore, the ability to understand
the hydrologic response of climate change will help policy makers to guide planning and form
more resilient infrastructure in the future.
Hydrological models provide a framework to conceptualise and investigate the relationships
between climate, and water resources (Xu, 1999; Jothityangkoon et al., 2001; Xu et al., 2005;
Chen et al., 2012b; Jung et al., 2012; Jiang et al., 2012; Yang et al., 2012). A number of studies
have investigated hydrological impact of climate change in different regions (e.g. Xu, 2000;
Christensen et al., 2004; Jha et al., 2006, Beldring et al., 2008; Steele-Dunne et al., 2008;
Graham and Jacob, 2000; Engeland et al., 2001). Jiang et al. (2007) employ comparison of
hydrological impact of climate change using six hydrological models in Dongjiang basin and the
result showed that significant differences exist in the predicted runoff, actual evapotranspiration
and soil moisture between hydrological models. The key differences between the study presented
here and those studies are the use of two hydrological models rather than single hydrological
models and testing the magnitude difference in three different climate zones. The two models
used to investigate the hydrological impact of climate change are HBV-light, daily water balance
model described in (Seibert. J., 1998) and WASMOD, monthly water balance model described in
(Xu, 2002; Xu et al., 1996). Another issue with modelling hydrological impact of climate change
is how to capture the full range of climate change scenarios. A number of different methods exist
to construct climate change scenarios that include techniques utilising climate analogues,
synthetic scenarios, and general circulation model (GCM) scenarios (Carter et al., 1995). In this
study, with the goal of testing the magnitude difference of hydrological model prediction in
different climate zones, hypothesized scenarios are applied. Given the differences of approaches
and downscaling techniques, the use of hypothesized scenarios as input to catchment-scale
hydrological models is widely used (e.g., Xu, 2000; Graham and Jacob, 2000; Engeland et al.,
2001; Boorman and Sefton, 1997). The main objective of this study is to test the magnitude
differences one can expect when using different hydrological models in different regions to
predict hydrological impact of climate change.
MATERIALS AND METHODS
Study areas and data
The study was conducted on three river basins in three continents representing a range of
geographic and climatic conditions (Fig 1). (1) Dongjiang River Basin: is one of the Zhujiang
sub-basins located in Guangdong and Jiangxi provinces in southern China. The drainage area of
the river basin is 25,555 Km2 and flows from north-east to south-west direction. For this study,
areal rainfall was calculated from the records of the 51 stations using the Thiessen polygon
method, mean daily temperature from 8 stations and evaporation data from 5 stations were
organized in order to use as model input for the period of 1978 – 1988, (2) Didessa River Basin:
is located in the South Western part of Ethiopia. The catchment area encompasses approximately
9981 Km2 up to the river gauge near Arjo. Didessa river is a part of the upper Blue Nile
drainage system and it covers around 5.4 % of the upper Blue Nile basin area and the river is the
largest tributary of upper Blue Nile river which contributes 10.7 % of the total discharge
(Conway, 2000).
For this study, areal rainfall records were calculated using the Thiessen polygon method from 10
rainfall stations for the period from 1985 to 1999, and (3) Elverum Basin: is a part of Glomma
basin and located in the south eastern part of Norway. The drainage area of the basin up to
Elverum river gauge station is 15449 Km2. The hydrology of the area is characterized by low
flow during winter caused by snow accumulation and high flow during snow melt in spring or
early summer (Henny A.J. et al., 2008). All the hydro-climatic data were acquired from
Norwegian Water Resource and Energy Directorate (NVE) and Norwegian Meteorological
institute (eKlima).
In order to evaluate the models’ performance at different catchment scales, small nested
catchments of Shuntian, Dembi, and Hummelvoll which are found within Dongjiang, Didessa,
and Elverum basins respectively were also examined.
Figure 1. Location of study river basins
Approach
The study follows four steps (Xu, 1999; Xu et al., 2005). (1) The parameters of hydrological
models were determined in the study basins using current climatic inputs and observed river
flows from model calibration, (2) the historical time series of climatic data were adjusted
according to the climate change scenarios, (3) the hydrological characteristics of the catchments
under the adjusted climate were simulated using the calibrated hydrological models, and (4)
comparisons of the model simulations of the current and possible future hydrological
characteristics were performed.
Climate Change Scenarios
A number of different methods exist to construct climate change scenarios that include
techniques utilising climate analogues, synthetic scenarios, general circulation model (GCM)
scenarios (Carter, 1995). In this study, we used Synthetic scenarios that describe techniques
where particular climatic elements are changed by a realistic but arbitrary amount, often
according to a qualitative interpretation of climate model simulations for a region. For example,
adjustments of baseline temperatures by +1, +2, +3 and +4°C and baseline precipitation by ±5,
±10, ±15 and ±20 % could represent various magnitudes of future change (IPCC, 2007). This
type of climate change scenarios have been used to study the effects of climate change on water
resources in many previous studies (e.g., Xu, 2000; Chen et al., 2007; Boorman and Sefton,
1997; Panagoulia and Dimou, 1997a,b; Varis et al., 2004). The main advantages of synthetic
scenarios are it is simple to apply, transparent, and easily interpreted by policy makers and nonspecialists. In addition, they capture a wide range of possible changes in climate, offering a
useful tool for evaluating the sensitivity of an exposure unit to changing climate. Since
individual variables can be altered independently of each other, synthetic scenarios also help to
describe the relative sensitivities to changes in different climatic variables. In order to cover a
wide range of climate variability, ten Synthetic scenarios were derived from combinations of two
absolute temperature changes and five relative precipitation changes (Table 1).
Table 2. Hypothetical climate change scenarios
Scenarios
∆T (oC)
∆P (%)
1
2
-20
2
2
-10
3
2
0
4
2
+10
5
2
+20
6
4
-20
7
4
-10
8
4
0
9
4
+10
10
4
+20
Hydrological Models
For this study, we selected WASMOD lumped conceptual hydrological model and HBV semidistributed conceptual models based on (1) the nature of physical processes that interact to
produce the phenomena under investigation, (2) availability of the required information, (3) wide
applicability and popularity of the models, and (4) the acquaintance with the models.
HBV Model
The HBV model version used in this study is HBV light (Seibert, 2005). The model runs on daily
time step to simulate daily discharge using daily precipitation, temperature and potential
evaporation as inputs. Precipitation is simulated to be either snow if the temperature is below the
threshold temperature TT (°C) or rain otherwise. Snow melt is calculated with the degree-day
method (Eq. 1). Liquid water within the snow pack refreezes when air temperature fall below TT
according to a refreezing coefficient, CFR (-) (Eq. 2).
Melt = CFMAX *(T(t) −TT)
(1)
Refreezing = CFR*CFMAX *(TT −T(t))
(2)
Rainfall and snow melt are divided into water filling the soil box and groundwater recharge
depending on the relation between water content of the soil box (SM (mm)) and its largest value
(FC (mm)) (Eq. 3). Actual evapotranspiration from the soil box equals the potential
evapotranspiration if SM/FC is above LP (-), while a linear reduction is used when SM/FC is
below LP (Eq. 4).
rech arg e  SM (t ) 
=

p (t )
 FC 
BETA
 SM (t ) 
Eact = E pot * min 
,1
 FC * LP 
(3)
(4)
Groundwater recharge is added to the upper groundwater box (SUZ (mm)). Runoff from the
groundwater boxes is computed as the sum of two or three linear outflow equations (K0, K1 and
K2 (d-1)) depending on whether SUZ is above a threshold value, UZL (mm), or not (Eq. 5). This
runoff is finally transformed to give the simulated runoff (mm d-1) (Eq. 6). The model has in total
10 parameters to be calibrated.
QGW (t ) = K 2 * SLZ + K1 * SUZ + K 0 * max( SUZ − SLZ , 0)
Qaim (t ) =
MAXBAS
∑
C (i ) * QGW (t − i + 1)
i =1
i
(5)
2
MAXBAS
4
where, c(i ) = ∫
− u−
*
du
MAXBAS
2
MAXBAS 2
i −1
(6)
WASMOD model
The Water And Snow balance MODeling system (WASMOD) is a conceptual lumped modelling
system developed by (Xu, 2002), and different versions of the model have been widely applied
for runoff simulation at catchment, regional and global scales (Gong et al., 2009; Jin et al., 2010;
Widen-Nilsson et al., 2007, 2009; Li et al., 2011, 2012). In this study a monthly time step
WASMOD is used which requires monthly values of areal precipitation, potential
evapotranspiration and air temperature as inputs. Temperature-index function is used to separate
rainfall rt and snowfall st and then snowfall is added to the snowpack spt (the first storage) at the
end of the month, of which a fraction mt melts and contributes to the soil-moisture storage smt.
The soil storage contributes to evapotranspiration et , to a fast component of flow ft and to base
flow bt. All the above mentioned processes are governed by six parameters (a1 - a6) and the
principal equations for the parameters are presented in Table 2.
Table 3. Principal equations for the parameters of WASMOD model
Snowfall
st = pt{1 – exp[–(ct – a)/(a1 – a2)]2}+
a1 ≥ a2
Rainfall
r t = pt – s t
Snow storage
spt = spt-1 + st – mt
Snowmelt
mt = spt{1 – exp[–(ct – a2)/(a1 – a2)]2}+
Potential evapotranspiration
ept = [1 + a3(ct – cm)]epm
Actual evapotranspiration
et = min{wt[1 – exp(–a4ept)], ept}
0 ≤ a4 ≤ 1
+
2
Slow flow
bt = a5(sm t-1)
a5 ≥ 0
+
2
Fast flow
ft = a6(sm t-1) (mt + nt)
a6 ≥ 0
Water balance
smt = smt-1 + rt + mt – et – bt – ft
+
wt = rt + sm t-1 is the available water; sm+t-1 is the available storage; nt = rt – ept(1 – exp(rt/ept)) is
the active rainfall; pt and ct are monthly precipitation and air temperature respectively; and epm
and cm are long-term monthly averages. ai (i = 1, …, 6) are the model parameters. The
superscript plus means x+ = max(x,0).
Model Calibration and Validation
The parameters of WASMOD and HBV models were determined through the calibration
procedure. For HBV model, Monte Carlo procedure was used to investigate the best parameter
values using the results of a large number of model runs with randomly generated parameter sets.
Using the best parameter set, the first one year period used as a warm up period to initialize the
model before actual calibration and the remaining periods were divided in such a way that twothird of the data was used for the calibration and one-third of the data was used for validation. In
the case of WASMOD, an automatical optimization is used. After the specification procedure,
two-third of the data was used for calibration and the remaining one-third of the data for
validation.
Among the many model performance indicators, the Nash–Sutcliffe model efficiency coefficient
(E) has been widely used to quantitatively describe the accuracy of model output. The coefficient
can range from minus infinity to one with higher value indicating better performance and it is
defined as:
∑ (Q
E = 1−
∑ (Q
obs
− Qsim ) 2
obs
− Qobs ) 2
(7)
Where Qobs and Qsim represent observed and simulated discharge respectively and Qobs is
observed mean value. The value of E represents the extent to which the simulated value is the
better predictor of the observed mean. In addition to Nash–Sutcliffe model efficiency coefficient
(E), Root mean square error (RMSE) and Relative volume error (RVE) were also applied.
The root mean square error (RMSE) is the measure of differences between values predicted by a
model and values actually observed. The root mean square error (RMSE) is defined as;
RM SE =
∑
n
t =1
( Q obst − Q sim t ) 2
n
(8)
Where Qobs and Qsim represent observed and simulated discharge respectively and n is a number
of observations. Since the errors are squared before they are averaged, it gives relatively high
weight to large errors. The value of RMSE can ranges from 0 to ∞ and lower values are better.
Relative volume of error (RVE) tells whether the model simulation is biased as compared with
observation. It is defined as;
RVE (%) =
∑ (Q − Q
∑ (Q )
obs
sim
)
×100
obs
(9)
Where, Qobs and Qsim represent observed and simulated discharge respectively.
RESULT AND DISCUSSION
Evaluation of model performance in reproducing historical records
Statistical analysis was conducted to evaluate the performance of the models. The result of
statistical analysis for the calibration and validation period is presented in Table 3. The value of
Nash–Sutcliffe coefficient (E) indicates that both models are performed quite well in all
catchments and it ranges from 0.88 to 0.96 for calibration period and from 0.80 to 0.95 for
validation period. The corresponding low error (RMSE and RVE) increased the confidence of
the models performance to simulate the historical records at acceptable accuracy.
Norway
Ethiopia
China
Country
2411
15449
Elverum
Hummelvoll
1806
9981
Didessa
Dembi
1357
25555
(Km )
2
Area
Shuntian
Dongjiang
sub-basins
Basins and
1980-1988
WASMOD
1985-1992
WASMOD
1980-1988
1985-1992
HBV
HBV
1987-1993
WASMOD
1987-1993
WASMOD
1987-1993
1987-1992
HBV
HBV
1978-1983
WASMOD
1978-1983
WASMOD
1978-1983
1978-1983
HBV
HBV
Period
Model
specified calibration and validation period
0.89
0.90
0.92
0.90
0.88
0.88
0.89
0.89
0.94
0.96
0.91
0.91
E
14.93
12.58
11.49
10.8
30.00
27.55
11.6
11.59
21.6
16.37
15.57
15.87
RMSE
Calibration
-1.87
0.90
2.65
0.40
-3.85
2.38
-1.2
-0.38
-2.24
0.95
-1.09
1.64
RVE (%)
1989-1995
1989-1995
1993-1997
1993-1997
1994-1998
1994-1998
1993-1996
1993-1996
1984-1988
1984-1988
1984-1988
1984-1988
Period
0.80
0.90
0.85
0.90
0.87
0.85
0.90
0.87
0.90
0.95
0.83
0.84
E
17.45
12.2
16.21
11.40
24.5
25.19
11.14
11.18
2.23
15.53
16.24
15.44
RMSE
Validation
-8.40
-0.80
-1.28
-15.5
-0.88
-16.22
-5.32
-11.36
-8.59
-4.42
4.04
8.98
RVE (%)
Table 4. Model performance statistics obtained from WASMOD and HBV simulation for different basins and sub-basins during the
Also, the statistical result shows that no significant difference exists between the two models in
reproducing the historical records.
Figure 2. Comparisons of mean monthly observed runoff with WASMOD and HBV simulated runoff in
each catchment
Comparisons of mean monthly runoff values simulated by WASMOD and HBV models with the
observed values are depicted in Fig. 2. There is a good agreement in the mean monthly observed
runoff with both model simulations. Our results demonstrate that both models were able to
reproduce the dynamics of monthly runoff hydrograph for all catchments.
Generally, the statistical results and visual observation of observed and calculated runoff graph
show that both WASMOD and HBV models can reproduce historical monthly runoff series at all
tested climate zone catchments with an acceptable accuracy. No significant difference exists
between the two models in reproducing the historical records. The main purpose of comparing
the observed runoff with model simulated value is to check the capability of the models in
reproducing the historical records at acceptable accuracy on different climate zones in order to
make sure that the simulations under climate change conditions will be predicted well.
Model simulation corresponding to future climate change scenarios
After calibrating the hydrological models with the historical record, the next step in the
investigation was to simulate flows corresponding to future climate conditions. The results were
plotted as a percentage of change from the simulated long-term annual and monthly water
balance components, namely runoff, evapotranspiration and soil moisture content. This will help
in identifying any specific trend in the change of monthly and annual runoff, actual
evapotranspiration and soil moisture storage in the Didessa, Dongjiang and Elverum river basins
corresponding to the different future climate change scenarios.
Change in mean annual runoff
The sensitivity of river basins to the changing precipitation and temperature input is evaluated
based on the runoff at the catchment outlet. The scale of the catchment also influences the runoff
at the outlet. In order to investigate the impact of precipitation and temperature change on mean
annual runoff change at different catchment scales, nested small catchment within the river
basins was identified, namely, Shuntian catchment within Dongjiang basin, Dembi catchment
within Didessa basin, and Hummelvoll within Elverum basin. Fig. 3 illustrates that the mean
annual runoff change using all ten scenarios at different catchment scale. It is seen from the
figure that; (1) the general pattern of annual changes of runoff simulated by both models are
somehow similar. Scenarios with decreased precipitation (i.e. scenarios 1, 2, 6, 7) result in
decreased runoff for both models and for all catchments regardless of the magnitude of
temperature increase (i.e. 2 or 4°C). Scenarios with increased precipitation (i.e. scenarios 4, 5, 9,
10) result in increased runoff for both models and for all catchments except one case. For
scenarios 3 and 8 (i.e. precipitation does not change while temperature increases 2 and 4°C,
respectively) HBV model shows a decrease in runoff for all the catchment while WASMOD
shows a slight increase in runoff for Hummelvoll catchment, which is in a cold region covered
with snow for more than a half year. And (2) the magnitude of the runoff change depends on the
scenario, the model and the climate region.
Figure 3. Mean annual runoff change estimated by WASMOD model (upper graph) and HBV model
(lower graph) using 10 scenarios in different catchments
More detailed changes of runoff with respect to different scenarios can be seen from Fig. 4,
which shows that; (1) Annual runoff change is more sensitive for change in precipitation in
Didessa basin (located in the South-western of Ethiopia) as compared with other basins under
both model predictions (Fig. 4). The change ranges from about -65 to +40% as simulated by
HBV and from about -50 to +30% as simulated by WASMOD for the scenarios with temperature
increases by 4°C. (2) On the other hand, the annual runoff change in Dongjiang basin (located in
Southeast China) is least sensitive for precipitation change on the annual base. The change
ranges from about -40 to 0% as simulated by HBV and from about -40 to +15% as simulated by
WASMOD. Form Elverum basin the result of HBV is similar with Dongjiang basin and the
result of WASMOD shows a slight difference with Dongjiang basin on the annual changes.
Figure 4. Mean annual runoff change simulated by WASMOD and HBV models
Change in mean monthly runoff
Figure 5 shows that; (1) Large difference exists between two models especially for the scenarios
with precipitation decrease. (2) Dongjiang has smallest seasonal variability whereas Elverum has
the largest seasonal variability. (3) The largest change is observed at Elverum in April, this
reflecting the fact that the temperature increase is sufficient enough to cause snow melt so as to
create peak runoff.
Figure 5. Comparison of mean monthly change in runoff simulated by WASMOD (left graph) and HBV
(right graph) for scenario 1=a, scenario 5=b, scenario 6=c, scenario 10=d
Change in mean annual evapotranspiration
Similar to the change in annual runoff, the changes in annual evapotranspiration also vary
between the regions when climate change scenarios are used to drive the two models. It is seen
from Figure 6 that; (1) there is large difference between models. (2) The annual actual
evapotranspiration change in Elverum basin is highly sensitive for temperature change scenario,
whereas for precipitation change scenario, the change is relatively less. This reflects that the
limiting factor for evaporation in the area is energy than moisture. (3) Actual evapotranspiration
change in Didessa and Dongjiang basin is more sensitive for precipitation change scenarios than
temperature change scenarios on the annual base. This is due to the fact that moisture is the
limiting factor in the regions.
Figure 6. Mean annual actual evapotranspiration change simulated by WASMOD and HBV models
Change in mean monthly evapotranspiration
Fig. 7 illustrates changes in mean monthly actual evapotranspiration (AET). The result indicates
that; (1) there is a remarkable difference between the two models’ predictions, scenarios and the
regions. (2) Dongjiang basin has relatively less seasonal variation than the other regions. (3)
Elverum has highest seasonal variations of actual evapotranspiration relative to the other regions.
The basin is more sensitive for temperature change scenario than precipitation change scenario.
(4) Didessa basin has highest seasonal variation for precipitation increase scenarios, while
seasonal variation is less for precipitation decrease scenarios.
Figure 7. Comparison of mean monthly change in actual evapotranspiration simulated by WASMOD (left
graph) and HBV (right graph) for scenario 1=a, scenario 5=b, scenario 6=c, scenario 10=d
Change in mean annual soil moisture storage
The percent change of mean annual soil moisture in response to precipitation and temperature
change scenarios are shown in Figure 8. In general, Figure 8 shows that; (1) there is large
difference between the two models. (2) The difference in annual soil moisture change simulated
by WASMOD model is larger than HBV simulation in Didessa basin as compare to the other
basins. (3) Annual soil moisture change is less sensitive for precipitation change scenario in
Elverum basin under both model predictions, while Dongjiang basin also less sensitive for
precipitation change scenario under WASMOD simulation but relatively the sensitivity increases
under HBV model simulation. (4) The magnitude changes in mean annual soil moisture content
of the three basins are similar when the change in precipitation increases by 20%. But when
precipitation reduced by 20%, the mean annual soil moisture content reduced by far in the case
of Didessa and Dongjiang basins as compared with Elverum basin.
Figure 8. Mean annual soil moisture storage change simulated by WASMOD and HBV models
Change in mean monthly soil moisture storage
Mean monthly soil moisture content change detected by the two models for the selected climate
change scenarios were plotted in Fig. 9. The figure shows that; (1) the patterns of change as well
as the magnitudes of change between the two models are quite different.
(2) Dongjiang has the smallest seasonal soil moisture variation whereas Didessa has the largest
seasonal variation for different precipitation and temperature change scenarios. (3) In Didessa
basin, soil moisture content is more sensitive for precipitation change scenarios as compare to
temperature change scenarios.
Figure 9. Comparison of mean monthly change in soil moisture simulated by WASMOD (left graph) and
HBV (right graph) for scenario 1=a, scenario 5=b, scenario 6=c, scenario 10=d
Implication of the study on climate change management
This section highlights key findings and policy implications that would be useful for policy
makers to consider with respect to climate change adaptation. The knowledge of changes of
hydrological variables due to climate change will be important to develop resilience
infrastructure and proper resource management in a given region. For reliable projection of
potential ranges of impacts from scenarios of future change, technical improvement of
hydrological models is a valuable strategy.
The hydrological impact of climate change will vary in different regions. In this particular study,
the vulnerability of different representative basins investigated through hydrological modelling
by using the same climate change scenario.
As retrieved from the study, in tropical climatic region (Didessa basin), the seasonal as well as
annual runoff variation is very high for increasing climate change scenarios. If we focus on
precipitation decrease and temperature increase scenarios as most Global Circulation Models
predicted for the region, the region will affected by moisture stress. This projected future water
stress and scarcity will have serious impacts on the socio-economic development of the countries
affected and will likely adversely affect their food production levels and development plans.
Understanding the magnitude of situation through hydrological modeling will help to create
appropriate adaptation strategies
In the polar region (Elverum basin), the variation of annual runoff is less whereas seasonal
variability is very high. As temperatures rise due to climate change in the region, winter snow
melts increases which lead to a change in the timing of the peaks runoff. Understanding the
magnitude of seasonal variability and the time of peak flow in the region through hydrological
modeling, will help for hydropower dams operational plan, flood control strategic plan. On the
other hand, reduction of low flows in summer and autumn may have large impacts on water
resource availability. Therefore hydrological modeling of climate change will help as a tool for
climate change adaptation strategies.
CONCLUSION
The main focus of the study is to test the magnitude differences one can expect when using
different hydrological models to simulate hydrological response of climate changes in different
climate zones as compared to their capacities in simulating historical water balance components.
To achieve the main goal two well-known models (i.e. HBV and WASMOD) are applied on six
catchments with different size and climate regions, i.e. two in Norway with seasonal snow
coverage, two in subtropical region in Southeast China and two in Southwest Ethiopia with least
intra-annual temperature variations. The following conclusions are drawn from the study.
1. The result of statistical analysis for calibration and validation shows that both models can
reproduce the historical runoff with acceptable accuracy for each basin.
2. Large differences exist between the two models under climate change conditions when
climate change scenarios incorporated to predicted runoff, actual evapotranspiration and
soil moisture especially at the monthly time scales.
3. The differences depend on the models, climate change scenarios, the seasons, and the
regions where the study is conducted and the hydrological variables under examination.
In general, the basins in southwest Ethiopia show a largest change in annual runoff and
annual soil moisture. While the catchments in Norway show largest increase in annual
evapotranspiration with the increase of temperature, indicating that this region is more of
energy limited region for evapotranspiration, At monthly time scale, large differences are
found for the changes in runoff, evapotranspiration and soil moisture between the results
of the two models and between the study regions. In general monthly changes in runoff
and evapotranspiration in the seasonally snow covered basins in Norway have shown
largest seasonal pattern, while the monthly changes in the basins in subtropical region in
southeast China have shown least seasonal pattern.
4. Remarkable differences between the two models results are found for all the catchments
when climate change scenarios are used to drive the models, and the largest differences
are found at monthly time scale.
5. The results of this study demonstrate that, hydrological impact of climate change
predicted by any particular hydrological model represents only the result of that model
for the specific region where the study is conducted. The purpose of this paper was to
demonstrate how two equally well calibrated models gave different hydrological response
to hypothetical climatic scenarios. It is important to emphasize that the climatic scenarios
used as input for the simulations, represents significant extrapolation of temperature and
precipitation used for parameter calibration of the hydrological models applied in this
study. The diverging responses indicate clearly the limitation in lumped hydrological
modeling. Extrapolation of driving forces (temperature and precipitation) beyond the
range of parameter calibration yields unreliable response. It is beyond the scope of this
study to reduce this model ambiguity, but reduction of uncertainty is a challenge for
further research.
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