AN OVERVIEW OF ENSEMBLE STREAMFLOW PREDICTION
STUDIES IN KOREA
DAE-IL JEONG, YOUNG-OH KIM
School of Civil, Urban & Geosystems Engineering, Seoul National University, San 56-1,
Sillim-dong, Gwanak-gu, Seoul, 151-742, Korea
NAM-IL KIM
websoulus Ltd, San 4-2, Bongcheon-dong, Gwanak-gu, Seoul, 151-050, Korea
ICK-HWAN KO
Water Resources Research Institute, Korea Water Resources Corporation, 462-1,
Jeonmin-dong, Daejeon, 305-790, Korea
In 2001, the Hydrology Research Group (HRG) at Seoul National University introduced
the Ensemble Streamflow prediction (ESP) approach for probabilistic forecasting in
Korea. The long-term probabilistic forecasting system, with GUI, was recently developed
for the Geum river basin, and a monthly optimal multi-reservoir operating system is
currently being applied for the Nakdong and Geum river basin, which couples the ESP
forecasts with an optimization model using sampling stochastic dynamic programming.
In addition to the practical aspects, the resent research experience on ESP has raised
some academic issues on ways of improving the accuracy of ESP in Korea. This paper
briefly covers the past ESP studies and addresses some future research needs in
connection with the ESP in Korea.
INTRODUCTION
Ensemble Streamflow Prediction (ESP) is a well-known probabilistic forecasting
technique in operational hydrology. It currently serves as a key component of the 21
century advanced hydrologic prediction system for the National Weather Service in the
United States and has recently become a popular research topic (e.g. Georgakakos and
Krzysztofowicz, 2001). In Korea, Kim et al. (2001) introduced ESP as an alternative
probabilistic forecasting technique for improving the Water Supply Outlook, which is
published monthly by the Ministry of Construction and Transportation. That study
motivated the Korea Water Resources Corporation (KOWACO) to establish a long-term
probabilistic forecasting system using the ESP for the 5 major river basins in Korea. So
far, ESP has only been applied to important multi purpose dams and transport sites in 3
major river basins in Korea. Figure 1 shows the ESP application sties of the 3 major
basins in Korea. Based on these applications, numerous unique strategies for improving
the accuracy of ESP in Korea have been established. This paper briefly covers the past
ESP studies and addresses some future research needs in connection with the ESP in
Korea.
1
2
Figure 1. ESP application sites of 3 major basins
ENSEMBLE STREAMFLOW PREDICTION
Basic Theory
Ensemble Streamflow Prediction runs a rainfall-runoff model that uses meteorological
inputs to generate an ensemble of possible streamflow hydrographs. The ESP procedure
is shown in Figure 2. A generated streamflow ensemble is a function of the hydrological
states at the initial forecast time in the rainfall-runoff model, and thus the technique is
also referred to as the conditional Monte Carlo simulation approach (Day, 1985).
Figure 2. ESP procedure
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ESP has been widely used as a hydrologic forecasting technique. “Special issue:
Probabilistic and Ensemble Forecasting” (Georgakakos and Krzysztofowicz, 2001) in the
Journal of Hydrology reported the resent ESP studies in hydrology. The Ensemble
approach also is an active research topic in meteorology. The numerous papers on this
topic published in Monthly Weather Review, Journals of Climate and Journal of
Hydrometeorology and other relevant journals.
Probabilistic Forecasts and their skill
A categorical probabilistic forecast to issue forecasts, using ESP scenarios, and the
Average Hit Score (AHS) to measure ESP forecast performance, were employed,
respectively. To issue a categorical probabilistic forecast, the whole streamflow range
was divided into three categories: below-normal, normal and above-normal flow. The
three flow categories were divided according to the 33.3 and 66.7% quantiles estimated
from the historical flow data. If no forecasting technique was available, our forecasts
relied on this type of equally-spaced 33.3% probability forecasts, also known as the
‘naïve forecast’. After the flow categories were quantified, the best probability density
function (pdf) was fitted to generated a runoff ensemble (ESP pdf), and then probabilities
were assigned to each flow category using the ESP pdf.
The effectiveness of the categorical probabilistic forecast was measured using the
AHS. If a flow category, k, occurs, a score of 100Pr[k] can be obtained, where k falls
under the below-normal, the normal or the above normal flow categories, and Pr[k] is the
probability assigned to the category in which that flow occurs. This was repeated for the
entire verification period. The resulting average hit score was denoted by Pravg. If Pravg is
higher than 33.3%, which implies a naïve forecast, then the forecasting technique is
regarded as being capable because the probabilistic forecast performs better than the
naïve forecast.
SUMMARY OF ESP STUDIES IN KOREA
Improving the Korean Water Supply Outlook
The ‘Water Supply Outlook’ (WSO) is an official long-term forecasting effort in Korea,
which is issued monthly by the River Planning Division of the Ministry of Construction
and Transportation (MOCT) and the Korea Institute of Construction Technology (KICT).
The forecasting technique used in the Korean WSO (called KWSO hereafter) runs a
rainfall-runoff model developed by the KICT (called the KICT model hereafter), using
rainfall data predicted during an individual forecast month as the primary input variable.
As the amount of rainfall is unknown at the date of the runoff forecast, the KWSO
considers three estimation scenarios: of low, average and high rainfall, which naturally
result in low, average and high runoff forecasts, respectively. The average rainfall
scenario is represented by the average historical rainfall data value for the month being
considered, while the low and high rainfall scenarios are based on a 20% decrease and
increase about the average rainfall, respectively. Kim et al. (2001) pointed out that the
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KWSO and ESP techniques were similar in their modeling structures as they both use a
rainfall-runoff model and a set of multiple rainfall scenarios. The difference being the
second employed considerably more scenarios than the first. Therefore, they suggested
the ESP technique as a means of improving the KWSO.
The ESP technique was applied to obtain 1-month runoff forecasts for July through
to December, at Gongju in the Gum River basin, during a 5 year period from 1995 to
1999, i.e. for a total of 30-month ([6months form July to December]x[5years form 1995
to 1999]). The same rainfall-runoff model (i.e. the KICT model) and model parameters
used in the KWSO were used in the ESP application. The observed rainfall data for the
76-year period from 1919 through 1994 were used as the meteorological inputs, and thus
an ensemble of 76 runoff scenarios were generated each month. This study compared the
50% quantile of the ESP pdf and the average runoff forecast of the KWSO as
representative forecasts of the ESP and the KWSO, with the observed runoff, and showed
that the ESP technique outperformed the KWSO forecast. The categorical forecast was
issued using the generated runoff scenarios, which was verified by the AHS. The overall
AHS was 38.3, which indicated that the ESP performed better than the naïve forecast and
that the former technique was applicable in Korea.
ESP using Climate Forecast Information
Jeong and Kim (2002) applied ESP to obtain1-month ahead inflow forecasts at Chungju
Dam in the Han River basin, and then re-examined its applicability to Korea. They also
tested two probability (or weight) adjustment methods, called the Croley and the PDFRatio methods, which employ climate forecast information to adjust the scenario
probabilities, and compared those methods with the equal weighting method. A monthly
inflow ensemble for the Chungju Dam was generated using the TANK rainfall-runoff
model with historical precipitation data (1966~1995). The performance of the generated
ensembles was evaluated for a 48-month verification period, from January, 1996 to
December, 1999. Through this study, the scenario probabilities of the generated ensemble
were calculated using climate forecast information.
In Korea, however, the Korea Meteorological Administration has not as yet officially
issued any seasonal forecast. Therefore, this study artificially generated a time series of
probabilistic precipitation forecasts for the 48-month verification period, as follows. The
entire precipitation range was divided into three categories: below-normal, normal and
above-normal, using the 33.3 and 66.7% quantiles (denoted by r33.3 and r66.7, respectively,
in Fig. 3). These quantiles were estimated from a pdf fit to the historical precipitation
data observed from 1966 to 1995. A probability of 0.5 was then assigned to the category
where the historical precipitation occurred, while 0.25 probabilities were assigned to the
other two categories for each forecasted month of the verification period.
Table 1 shows the AHS of the equal weighting, Croley and PDF-Ratio methods.
From Table 1, it could be concludes that: 1) the ESP forecast was useful because the
equal weighting method was superior to the naïve forecast, and 2) the use of probabilistic
precipitation forecasts would result in more accurate probabilistic inflow forecasts once
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either of the scenario weighting schemes have been used, because the Croley and the
PDF-Ratio methods perform better than the equal weighting method, even though the
Croley and PDF-Ratio methods were very competitive.
Jeong and Kim (2002) performed an error analysis on their ESP probabilistic inflow
forecast. The total error (Et) was disaggregated into the hydrologic (or modeling) error
(Eh) and the meteorological error (Em) (Figure 4). This error analysis showed that the
hydrologic error dominated the monthly inflow forecast during the dry season (Jan, Feb,
Apr, Oct, Nov and Dec from Fig. 4), while the meteorological (or random) error
dominated the monthly inflow forecast during the wet season (Jun, Jul, Aug and Sep
from Fig 4). Based on this result, they suggested that the rainfall-runoff model used for
their ESP study should be improved during the dry season, while the meteorological
forecast was necessary during the wet season.
Table 1. AHS of three weighting methods
AHS
Equal
Croley
PDF-Ratio
38.5
46.6
45.9
Et
Eh
Em
200
150
Error (CMS)
Figure 3. Synthetic probabilistic
forecast of precipitation
100
50
0
-50
Jan
Feb
Mar
Apr May Jun
Jul
Aug Sep
Oct
Nov Dec
-100
-150
-200
Forecast month
Figure 4. Result of Error Analysis (1994)
Improving Rainfall-runoff Models
To deal with the hydrologic error of the ESP forecast during the dry season, Jeong and
Kim (2004) developed a new rainfall-runoff model using an artificial neural network,
which can be used for the ESP. This study used two types of ANN, namely the Single
Neural Network (SNN) and the Ensemble Neural Network (ENN), to improve the
simulation capability of the rainfall-runoff model of the ESP forecasting system for the
monthly inflow to the Daecheong Dam in the Geum River basin. The ANN models were
compared to a conceptual rainfall-runoff model, TANK, and through this the ANN
models were verified as superior to the TANK model.
Kim et al. (2003) applied combining, correction and correction-then-combining
methods to improve the existing rainfall-runoff model, TANK, for ESP probabilistic
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forecasting. The ESP technique was also applied to forecast the probabilistic inflow
forecasts one-month in advance, from 1996 to 2001. For each forecasted month,
historical rainfall and evaporation scenarios for the period from 1981 to 1995 were input
into the two rainfall-runoff models, i.e., TANK and ENN, to generate streamflow
scenarios. Thus, an ensemble of 15 runoff scenarios was generated for each forecasted
month. The runoff scenarios of the two rainfall-runoff models were then combined and/or
corrected.
Optimal Linear Correction (OLC) (a simple correction method using linear
regression), Sum of Squared Error (SSE) (a combining method using time-varying
weights) and SSE/OLC (a correction then combining method, using OLC and SSE) were
applied to the ESP forecast. Figure 5 compares the monthly AHS of the TANK, OLC,
SSE and SSE/OLC methods. The overall AHSs (Avg. of X axis) of the 4 rainfall-runoff
methods were greater than 33.3%, which indicates that the ESP performed better than the
naïve forecast. The forecast performance was considerably improved with the OLC, SSE
and SSE/OLC methods, especially for November, December and January (i.e. the dry
season) when the modeling uncertainty was more important than the meteorological
uncertainty, as mentioned in the Introduction. The OLC was a simple correction method,
but improved the ESP accuracy of the TANK method during the dry season. The SSE
method, which combined the TANK and ENN methods with time-varying weights,
performed better than the OLC, but the SSE/OLC performed best of all, producing an
average hit score of 47.2%.
AHS (%)
66.6
TANK
SSE
OLC
SSE/OLC
33.3
0
Jan
Feb
Mar
Apr
May
Jun
Jul
month
Aug
Sep
Oct
Nov
Dec
Avg.
Figure 5. Average hit score of TANK, OLC, SSE, and SSE/OLC method (%)
Development of ESP systems
With the cooperation of the Hydrology Research Group at Seoul National University, the
KOWACO recently developed ESP systems applying the SSARR (Streamflow Synthesis
and Reservoir Regulation) model, a rainfall-runoff model, to the Geum River basin in
Korea. The SSARR Model is a watershed simulation model developed by the US Army
Corps of Engineers, which simulates the runoff from rainfall and snow melt and river
systems, including the operation of reservoirs and diversions in both short and long term
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time scales. Graphical user interfaces for ESP have also been designed to allow the
developed computer systems to become more practical.
The developed ESP systems were composed of three parts: ESP Run, ESP Scenarios
and ESP Results. At ‘ESP Run’, one can select the forecast lead time, historical
precipitation and temperature scenarios, and then run the ESP system. ‘ESP Scenarios’
shows the tables and graphs of the generated ESP streamflow scenarios. ‘ESP Results’
present probabilistic forecast results at the forecasting sites. It calculates the categorical
probabilities, such as below-normal, normal and above-normal flows, compares the
empirical and forecasted cumulative probabilities, and presents key statistics of the
forecasted streamflow scenarios.
Figure 6. GUI of the developed ESP systems
Reservoir Operations using ESP
The primary advantage of the ESP technique is that one can quantify the uncertainty
associated with the probabilistic forecasts. This ensemble forecasts make it possible to
explicitly or implicitly incorporate the streamflow uncertainty into a probabilistic
(stochastic) optimization technique for operating a water resources system (Faber and
Stedinger, 2001).
The Hydrology Research Group at Seoul National University and the KOWACO
recently developed the monthly optimal multi-reservoir operating systems for the
Nakdong and Geum river basins in Korea, which coupled the ESP forecasts with an
optimization model using sampling stochastic dynamic programming. The main
advantage of the coupled system was that an operation policy could be updated with new
calculated ESP forecasts every month.
FUTURE STUDIES
An issue for the future that may arise in ESP is how to incorporate meteorological
information into ESP. Over the last decade, a greater amount of information has become
available due to advances in understanding climatic systems, particularly due to the better
understanding of the ENSO phenomenon and the improvement in meteorological models
such as GCM. Climatic information may be usefully employed, with the aim of reducing
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meteorological uncertainty in ESP, especially during the wet season in Korea. Jeong and
Kim (2002) compared the scenario weighting schemes devised by Croley (2000) and
Stedinger and Kim (2002) to incorporate a meteorological forecast into ESP. When the
probabilistic precipitation forecasts are available these two weighting schemes can be
applied immediately to the present ESP system. Coupling the GCM meteorological
ensemble forecast with the rainfall-runoff model of ESP is another future issue. In the
short-term forecasting, the forecasted meteorological ensemble would more valuable than
the observed meteorological ensemble. Other minor issues include how long the initial
condition influences the ESP accuracy, how many scenarios are adequate in ESP in order
to obtain the most accurate forecasts.
ACKNOWLEDGEMENT
This research was supported by a grant (code 1-6-1) from Sustainable Water Resources
Research Center of 21st Century Frontier Research Program and was also supported by
the Research Institute of Engineering Science (RIES), Seoul National University, Seoul,
Korea.
REFERENCES
[1] Croley II, T. E., “Using meteorology probability forecasts in operational hydrology”,
ASCE Press, Reston, VA., (2000).
[2] Faber B.A. and Stedinger J. R., “Reservoir optimization using sampling SDP with
ensemble streamflow prediction (ESP) forecasts”, Journal of Hydrology, Vol. 249,
No. 1-4, (2001), pp 113– 133.
[3] Georgakakos K. P. and Krzysztofowicz R. Ed., “Probabilistic and Ensemble
Forecasting in Hydrology”, Special Issue, Journal of Hydrology, Vol. 249. (2001).
[4] Jeong D. I. and Kim Y. -O., “Forecasting monthly inflow to Chungju dam using
ensemble streamflow prediction”, Journal of Korean Society of Civil Engineers, Vol.
22, No. 3-B, (2002), pp 321-331 (in Korean).
[5] Jeong D. I. and Kim Y. –O., “Rainfall-runoff models using artificial neural networks
for ensemble streamflow prediction”, Hydrological processes, (2004), (submitted).
[6] Kim Y. –O., Jeong D. I. and Kim H. S., “Improving Water Supply Outlooks in
Korea with ensemble streamflow prediction”, Water International, Vol. 26, No. 4,
(2001), pp 563-568.
[7] Kim Y. –O., Jeong D. I. and Ko I. H., “Combining rainfall-runoff models for
improving ensemble streamflow prediction”, Journal of Hydrology, (2003),
(submitted).
[8] Stedinger J., and Kim Y. –O., “Updating ensemble probabilities based on climate
forecasts”, Conf. On Water Resources Planning and Management Session C2 (CD),
ASCE, Roanoke, VA, (2002).