AN OVERVIEW OF ENSEMBLE STREAMFLOW PREDICTION STUDIES IN KOREA

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

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 Pr avg. If Pr avg 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 3

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 PDF- Ratio 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 r 33.3 and r 66.7, 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 4

5 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 Equal Croley PDF-Ratio AHS 38.5 46.6 45.9 Figure 3. Synthetic probabilistic forecast of precipitation Error (CMS) 200 150 100 50 0-50 -100-150 -200 Et Eh Em Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 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

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%. 6 66.6 TANK SSE OLC SSE/OLC AHS (%) 33.3 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg. month 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

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. 7 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

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). 8