UNCERTAINTY IN HYDROLOGICAL PREDICTIONS DUE TO
INADEQUATE REPRESENTATION OF CLIMATE
VARIABILITY IMPACTS
ANTHONY S. KIEM, HAPUARACHCHIGE P. HAPUARACHCHI, HIROSHI
ISHIDAIRA, JUN MAGOME and KUNIYOSHI TAKEUCHI
Takeuchi-Ishidaira Lab., Interdisciplinary Graduate School of Medicine and
Engineering, University of Yamanashi, Takeda 4-3-11, Kofu, Yamanashi 400-8511,
JAPAN
The influence of multi-temporal climate variability represents a significant source of
uncertainty in hydrological modelling. This study investigates the impact of the El
Niño/Southern Oscillation (ENSO) on precipitation and discharge in the Mekong River
basin. The physically based distributed hydrological model BTOPMC is also utilized to
determine: 1) the ability of BTOPMC to replicate ENSO related variability in observed
discharge and therefore the physical soundness of the model, and 2) the uncertainty in
hydrological predictions due to ENSO in the Mekong Basin. It is shown that precipitation
and discharge in the Mekong is significantly influenced by the ENSO and that impacts on
discharge are enhanced due to ENSO related climate variability also influencing
precipitation-runoff transformation processes. BTOPMC is found to adequately replicate
the majority of the variability in the hydrological process, indicating that BTOPMC
satisfactorily captures most of the ‘true’ physics of the Mekong Basin’s hydrological
response. However, differences exist between simulated and observed ‘high’ daily flows
(top 10%), especially during El Niño years, suggesting inadequate representations linking
the impact of climate variability on precipitation intensity (and frequency) and the
resulting variations in the hydrological process.
INTRODUCTION
Large-scale climate phenomena, such as the El Niño/Southern Oscillation (ENSO), are
associated with significant spatial and temporal variability in the magnitude, frequency
and intensity of precipitation in many parts of the world e.g. [Ropelewski and Halpert, 8;
Wooldridge et al., 10; Xu et al., 11]. Several studies in have also shown that similar
relationships exist between climate variability and streamflow [e.g. Amarasekera et al., 1;
Kiem et al., 7]. Anomalies in other important hydro-meteorological variables, such as
temperature and evaporation, have also been linked to ENSO and other sources of multitemporal climate variability [Diaz and Markgraf, 4; Wooldridge et al., 10]. This seasonal
to multi-decadal variability of hydro-meteorological variables, combined with the
complex heterogeneity of the land surface through soils, vegetation and topography,
results in precipitation-runoff responses that are inherently non-stationary and non-linear
[Blöschl and Sivapalan, 3; Wooldridge et al., 10]. Wooldridge et al. [10] showed that in
Eastern Australia the synergistic influence of ENSO on runoff response results in
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enhanced streamflow variability when compared with the impact of ENSO on
precipitation. Therefore, it is important to realize that climate variability impacts on
hydrological processes are more complex than annual or seasonal precipitation averages
reveal and that it is possible for discharge to be markedly influenced by climate
variability, even if minimal impact is evident in the precipitation.
However, significant variability also exists in the way ENSO (and also non-ENSO
related variability) influences hydro-meteorological variables in space and time [e.g.
Kiem et al., 7; Xu et al., 11]. Data driven and calibrated hydrological models do not
adequately account for the uncertainty associated with the influence of climate variability
on hydrological processes. Physically based models provide a more realistic way of
approximating all the possible combinations of climatic influences (both ENSO and nonENSO related) on the hydrological process – provided they adequately capture and
replicate the physical processes that contribute to the precipitation-runoff transformation
and that they quantify the uncertainty associated with their predictions.
This study investigates the spatial distribution of ENSO related climate variability
impacts on precipitation and streamflow in the large (>550000 km2) Mekong River basin.
In addition, hydrological simulations made using the physically based distributed
hydrological model BTOPMC (block-wise use of TOPMODEL with Muskingum-Cunge
flow routing method [Takeuchi et al., 10; Ao et al., 2] are analyzed to determine how
well BTOPMC reproduces the impacts of ENSO on hydrological processes in the
Mekong Basin - and therefore how well the true ‘physics’ of the basin’s precipitationrunoff relationships are captured.
STUDY REGION AND DATA
The Mekong River (Figure 1), starting from the Qinghai province in China and passing
through Myanmar, Thailand, Laos, Cambodia and Vietnam, is the 12th longest river in
the world (~ 4,800 kilometres) and the longest and most important in south-east Asia. In
order to investigate the relationship between ENSO and hydrological variability in the
Mekong Basin (and to calibrate and validate the BTOPMC model) daily precipitation
data (1972-2000) from 64 stations throughout the Mekong basin and daily discharge data
(1972-1992) at Pakse is used - this data is provided by the ‘Mekong River Commission’,
‘China Meteorological Administration’ and the ‘Institute of Atmospheric Physics at the
Chinese Academy of Sciences’. Figure 1 shows the spatial distribution of the 64
precipitation stations and the location of Pakse discharge station. The upstream total
drainage area of Pakse is 545000 km2 and the long-term average annual (January to
December) precipitation of the basin is approximately 1570 mm. However, the actual
annual averages vary significantly in space and time (see Figure 2).
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Figure 1. Location of the Mekong river basin and the discharge (Pakse) and precipitation
stations used in this study
Spatial distribution of precipitation in the Mekong Basin
Figure 2 shows the average annual precipitation (January 1972 to December 2000) for
each of the 64 precipitation stations used in this study. It can be seen that marked
deviations from the annual basin average of approximately 1570 mm are apparent. The
northern third of the basin is noticeably ‘drier’ (<800mm/year on average) than the long
term annual basin average and the basin becomes progressively ‘wetter’ as the latitude
approaches the equator, with the majority of the southern third of the basin wetter than
the long term annual basin average. The ‘wettest’ area of the basin is the region around
station 170406.
To illustrate the temporal variability (and to further demonstrate the spatial
variability) of Mekong Basin precipitation Figure 2 also displays box plots showing the
monthly distributions of precipitation from January 1972 to December 2000 for six
‘representative’ stations. Although monthly distributions for only six stations are shown,
these ‘representative’ stations adequately illustrate precipitation patterns of the nearby
stations that have similar average annual precipitation. From the box plots in Figure 2 it
can be seen that the majority of the Mekong Basin’s precipitation occurs between May
and October. The large ‘boxes’ (inter-quartile ranges) and substantial differences between
minimum and maximum values for the May to October monthly distributions also
indicates that significant year to year variability exists during these months.
600
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56125 (32.2 N, 96.5 E)
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56751 (25.7 N, 100.2 E)
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Precipitation (mm)
Precipitation (mm)
56125
Precipitation (mm)
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209902 (20.0 N, 99.3 E)
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Precipitation (mm)
Precipitation (mm)
170107
Precipitation (mm)
1200 170406 (18.0 N, 104.2 E)
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170107 (17.5 N, 101.2 E)
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130204 (13.7 N, 102.5 E)
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1 2 3 4 5 6 7 8 9 10 11 12
Figure 2. Average annual (1st January 1972 to 31st December 2000) precipitation at 64
precipitation stations and monthly precipitation distributions at 6 ‘representative’ gauges
(Note that the y-axis on the box plot for station 170406 has a maximum of 1200mm)
ENSO IMPACTS ON PRECIPITATION AND DISCHARGE
To determine the impacts of ENSO in the Mekong Basin each year is assigned an ENSO
classification (either El Niño, La Niña or Neutral) based on the six-month October to
March average Multivariate ENSO Index (MEI) value. This method and ENSO index has
been demonstrated to be the most robust for the time period being investigated [Kiem and
Franks, 6]. May to October monthly precipitation totals at each precipitation station (and
monthly discharge totals at Pakse) are then stratified into El Niño, La Niña and Neutral
phases based on the ENSO classification for the relevant year. Stratified precipitation
(and discharge) totals for each month (May to October) at each gauge are then compared
to determine the relationship between different ENSO phases and precipitation (and
discharge). The ENSO analysis is only performed from May to October as Figure 2
shows that minimal annual variation occurs in the precipitation totals during the Mekong
Basin ‘dry’ season (November to April). Therefore, it is assumed (and has been
confirmed by the authors) that the impacts of ENSO on hydro-climatic data in the
Mekong Basin ‘dry’ season are also minimal.
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5e 5n 6e 6n 7e 7n 8e 8n 9e 9n 10e 10n
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56751 (25.7 N, 100.2 E)
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Precipitation (mm)
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170107
(white circle)
(white
circle)
Precipitation (mm)
1200 170406 (18.0 N, 104.2 E)
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5e 5n 6e 6n 7e 7n 8e 8n 9e 9n 10e 10n
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170107 (17.5 N, 101.2 E)
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5e 5n 6e 6n 7e 7n 8e 8n 9e 9n 10e 10n
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130204 (13.7 N, 102.5 E)
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Figure 3. Ratio of mean non-El Niño to mean El Niño May to October precipitation totals
and monthly (May to Oct.) precipitation distributions during El Niño (e) and non-El Niño
(n) at 6 ‘representative’ gauges. (Note: y-axis for station 170406 has a max. of 1200mm)
It was found that no significant difference existed between the precipitation (and
discharge) in the La Niña and Neutral phases and therefore these two groups were
combined to form a ‘non-El Niño’ group. Figure 3 shows the ratio of mean non-El Niño
to mean El Niño May to October total precipitation. A ratio of greater than one indicates
that non-El Niño events are ‘wetter’ than El Niño years. In addition, for the stations with
a ratio greater than one a Students t-test is performed to determine if the increase in
precipitation during non-El Niño years is significant. A p-value of less than 0.1 (the
<10% level) is considered to indicate a statistically significant increase. From Figure 3 it
can be seen that the average May to October precipitation is higher during non-El Niño
years for the majority (57 out of 64) of stations, and that for 26 stations this difference is
significant at the <10% level. Figure 3 also shows the monthly distributions (May to
October) for the six ‘representative’ stations. The important point to gain from these box
plots is that even though the tendency is for El Niño months to be ‘drier’ than the same
months occurring in non-El Niño years there is still marked variability within El Niño
and non-El Niño distributions – even for stations where a significant difference between
mean El Niño and mean non-El Niño May to October totals exists. This suggests that
non-ENSO related climate variability also plays an important role in the Mekong Basin.
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1050
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en
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(c)
Figure 4. El Niño and non-El Niño
distributions of May to October
(a) basin average precipitation,
(b) observed discharges at Pakse and
(c) runoff ratios.
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(a)
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El Nino
non-El Nino
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en Qsim
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Pakse (15.1 N, 105.8 E)
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Discharge (x10 m /s)
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Ratio of Simulated/Observed
May to Oct Total Discharge
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1200
1100
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May to Oct Total ObsQ/Basin Av. P
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May to Oct Total Q (x10 m /s)
Basin Av. May to Oct Total P(mm)
Figure 4a illustrates the impacts that ENSO has on May to October total precipitation
averaged over the whole Mekong Basin while Figures 4b and 4c demonstrate the impact
that ENSO has on discharge at Pakse. The mean basin average May to October total
precipitation is 8% higher in non-El Niño years than it is for El Nino (this difference is
significant at the <5% level). However, when the observed average May to October total
discharge at Pakse is analyzed it is found that non-El Niño years are on average more
than 16% wetter than El Nino (also significant at the <5% level). Figure 4c shows that
this enhancement of ENSO influences on discharge when compared to precipitation is
because the runoff ratio during non-El Niño years is higher (~8%) than it is during El
Niño. This implies that, in addition to the precipitation in non-El Niño events tending to
be higher than El Niño, a given unit of precipitation will result in much more discharge
during the non-El Niño phase. This result demonstrates that ENSO influences both
precipitation and the processes controlling the precipitation-runoff transformation in the
Mekong Basin. The question is how well does the physically based distributed
hydrological model BTOPMC replicate this ENSO related variability?
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2
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.01 .1
(b)
1
5 10 20
50 70
Percent
90 95 99 99.9
Figure 5. El Niño and non-El Niño (a)
distributions of the ratio of simulated to observed
May to October total discharge, (b) probability
distributions of the daily observed and simulated
discharge between May and October at Pakse
HYDROLOGICAL SIMULATIONS FOR THE MEKONG USING BTOPMC
Simulated daily discharge at Pakse is obtained for the same period as the observed
discharge data (1972-1992) using the latest version of BTOPMC [Takeuchi et al., 10; Ao
et al., 2]. The updated version of BTOPMC incorporates a new approach to estimate
lateral transmissivity of soil (T0) which eliminates the need for subjective soil
reclassification methods and more accurately approximates the true physical
characteristics of the catchment [Hapuarachchi et al., 5]. Overall model evaluation
statistics indicate that BTOPMC adequately simulates the observed discharge at Pakse.
The Nash-Sutcliffe coefficient equals 82.7% for the 1972-77 calibration period and
81.7% for the 1978-1992 validation, while the simulated to observed discharge ratio
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equals 1.016 for calibration and 1.02 for validation. However, of greater interest for this
study is how well BTOPMC replicates the ENSO related variability evident in the
Mekong Basin – and therefore how well the actual physical processes contributing to
hydrological variability are represented. This is assessed by stratifying the simulated
discharge into ENSO groups and comparing the May to October El Niño and non-El
Niño distributions with each other and with the ENSO-stratified observed discharge
distributions to determine whether the distinct El Niño and non-El Niño responses are
reproduced.
When the simulated El Niño and non-El Niño distributions of May to October totals
are compared it is found that the differences between the two is very similar to the
differences in the observed data with Figure 5a showing that the majority of simulated
May to October totals are less than 5% (10%) higher or lower than observed for non-El
Niño (El Niño) years. The simulated average May to October total discharge during nonEl Niño years is approximately 14% wetter than El Nino (compared with 16% for the
observed data) and as with the observed data this difference is also significant at the <5%
level. However, when the daily simulated and observed discharges are compared for the
El Niño and non-El Niño groups it is found that some discrepancies exist. The NashSutcliffe coefficient calculated only on daily values from May to October in El Niño
years is only 63% compared with 76% for non-El Niño events indicating that even
though the May to October total discharge is reasonably well reproduced in all ENSO
phases some problems exist at the daily scale, especially during El Niño years. Figure 5b
shows that the probability distributions of the daily observed and simulated discharge
between May and October are well matched up to the 90 th percentile. However, for
discharges in the top 10th percentile the simulated and observed probability distributions
begin to diverge markedly, especially for El Niño events. Figure 5b (and the low NashSutcliffe coefficient for El Niño years) indicates that BTOPMC has some problems
replicating the precipitation-runoff processes that control the timing and magnitude of
high flow events, particularly in El Niño years.
CONCLUSIONS
Precipitation and discharge in the Mekong is significantly influenced by the ENSO, with
mean basin average precipitation (discharge at Pakse) approximately 8% (16%) higher
during non-El Niño years. Results indicate that enhancement of ENSO impacts on
discharge is due to ENSO related climate variability also influencing the processes
controlling the precipitation-runoff transformation in the Mekong Basin. BTOPMC
adequately replicates the majority of ENSO related variability in the hydrological
process, indicating that BTOPMC satisfactorily captures most of the ‘true’ physics of the
Mekong Basin’s hydrological response. However, differences exist between simulated
and observed high daily flows (top 10%), especially during El Niño years. This indicates
that the processes associated with generating peak flows are not well represented in
BTOPMC. Preliminary investigations suggest the impact of climate variability on
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precipitation frequency and intensity and the resulting variation in canopy interception
and infiltration excess is inadequately represented. It is also important to note that the
climate related variability in precipitation, discharge and hydrological processes
illustrated in this study is extremely difficult to replicate in a hydrological model that is
not physically based. As shown here, and in previous studies [e.g. Kiem et al., 7], the
impacts of ENSO vary markedly from event to event and also on decadal-multi-decadal
time-scales. There is also non-ENSO related variability to consider along with the
possibility that the frequency and magnitude of climate impacts could change in the
future. Therefore, to adequately approximate and replicate all possible hydrological
responses with a non-physical based model would require extensive data records in order
to calibrate or ‘train’ the model how to perform – even then an event or combination of
events may occur that did not occur in the calibration data period, resulting in inaccurate
hydrological simulations.
Identification of the physical mechanisms which cause non-stationary and non-linear
precipitation-runoff relationships, and the incorporation of this insight into physically
based hydrological models, is crucial for successful prediction in ungauged basins. If the
processes which cause climate related variations in the precipitation-runoff relationship
can be identified, and adequately approximated via physically based model parameters,
then the uncertainty associated with predicted discharges when these models are applied
to ungauged basins will be significantly reduced, especially for areas where the influence
of climate variability is marked.
REFERENCES
[1] Amarasekera, K.N., Lee, R.F., Williams, E.R. and Eltahir, E.A., “ENSO and the
natural variability in the flow of tropical rivers”, J. Hydrology, Vol. 200, No. 1,
(1997), pp 24-39.
[2] Ao, T.Q., Yoshitani, J., Takeuchi, K., Fukami, K., Mutsuura, T. and Ishidaira, H.,
“Effects of sub-basin scale on runoff simulation in distributed hydrological model:
BTOPMC”, in “Weather Radar Information and Distributed Hydrological
Modeling” (Proc. HS03 held at IUGG2003), Sapporo, (2003), pp 227-233.
[3] Blöschl, G. and Sivapalan, M., “Scale issues in hydrological modelling: a review” in
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[6] Kiem, A.S. and Franks, S.W., “On the identification of ENSO-induced rainfall and
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[9] Takeuchi, K., Ao, T.Q. and Ishidaira, H., “Introduction of block-wise use of
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[10] Wooldridge, S.A., Franks, S.W. and Kalma, J.D., “Hydrological Implications of the
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