Seasonal Streamflow Forecasts Research Articles

Evaluating Streamflow Forecasts in Hydro‐Dominated Power Systems—When and Why They Matter

AbstractThe value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large‐scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled‐water energy model that is subject to reservoir inflow forecasts with different levels of predictive performance. We implement the modeling framework on a real‐world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro‐dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.

Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling

Abstract. Dynamical (i.e., model-based) methods are widely used by forecasting centers to generate seasonal streamflow forecasts, building upon process-based hydrological models that require parameter specification (i.e., calibration). Here, we investigate the extent to which the choice of calibration objective function affects the quality of seasonal (spring–summer) streamflow hindcasts produced with the traditional ensemble streamflow prediction (ESP) method and explore connections between hindcast skill and hydrological consistency – measured in terms of biases in hydrological signatures – obtained from the model parameter sets. To this end, we calibrate three popular conceptual rainfall-runoff models (GR4J, TUW, and Sacramento) using 12 different objective functions, including seasonal metrics that emphasize errors during the snowmelt period, and produce hindcasts for five initialization times over a 33-year period (April 1987–March 2020) in 22 mountain catchments that span diverse hydroclimatic conditions along the semiarid Andes Cordillera (28–37∘ S). The results show that the choice of calibration metric becomes relevant as the winter (snow accumulation) season begins (i.e., 1 July), enhancing inter-basin differences in hindcast skill as initializations approach the beginning of the snowmelt season (i.e., 1 September). The comparison of seasonal hindcasts shows that the hydrological consistency – quantified here through biases in streamflow signatures – obtained with some calibration metrics (e.g., Split KGE (Kling–Gupta efficiency), which gives equal weight to each water year in the calibration time series) does not ensure satisfactory seasonal ESP forecasts and that the metrics that provide skillful ESP forecasts (e.g., VE-Sep, which quantifies seasonal volume errors) do not necessarily yield hydrologically consistent model simulations. Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. Finally, the choice of calibration metric generally affects the magnitude, rather than the sign, of correlations between hindcast quality attributes and catchment descriptors, the baseflow index and interannual runoff variability being the best predictors of forecast skill. Overall, this study highlights the need for careful parameter estimation strategies in the forecasting production chain to generate skillful forecasts from hydrologically consistent simulations and draw robust conclusions on streamflow predictability.

Accurate statistical seasonal streamflow forecasts developed by incorporating remote sensing soil moisture and terrestrial water storage anomaly information

Water managers need improved seasonal streamflow forecasts in drought-prone regions like the U.S. Great Plains, where the inter-annual variability in rainfall and streamflow is high and seasonal streamflow forecasting systems are not well developed. Recent research has demonstrated that in-situ soil moisture measurements can be used to produce accurate streamflow forecasts in the Great Plains where rainfall runoff is the predominant source of streamflow. However, an inadequate density of in-situ soil moisture monitoring locations makes producing such forecasts impossible in many locations. Therefore, our objective was to develop and quantify the accuracy of seasonal streamflow forecasts for rainfall-dominated watersheds using remotely sensed data. Precipitation and remotely sensed soil moisture and terrestrial water storage anomaly data were incorporated into a multiple linear regression (MLR) model to produce seasonal streamflow forecasts for five watersheds in the Great Plains. The best MLR models had Nash-Sutcliffe Efficiency (NSE) values ranging from 0.71 to 0.93, indicating good to very good model performance. In one watershed with nearby in-situ soil moisture data, the use of remotely sensed soil moisture data led to greater forecast accuracy than did the inclusion of the sparse in-situ data. Our results show that the use of remote sensing data in MLR models can provide accurate seasonal streamflow forecasts in rainfall-dominated regions which currently lack such information.

Seasonal streamflow forecasting in South America’s largest rivers

Study regionSouth America large basins (>5000 km²) Study focusThis work represents a first assessment of seasonal streamflow forecasts in South America based on a continental-scale application of a large scale hydrologic-hydrodynamic model and ECMWF's seasonal forecasting system precipitation forecasts (SEAS5-SSF) with bias correction. Seasonal streamflow forecasts were evaluated against a reference model run. Forecast skill was estimated relative to the Ensemble Streamflow Prediction (ESP) method. New hydrological insightsWe observed that bias correction was essential to obtain positive skill of SEAS5-SSF over ESP, which remained a hard to beat benchmark, especially in regions with high seasonality, and highly dependent on initial conditions. SEAS5-SSF skill was found to be dependent on initialization month, basin and lead time. Rivers where the skill is higher were Amazon, Araguaia, Tocantins and Paraná.

Seasonal Streamflow Forecasting for Fresh Water Reservoir Management in the Netherlands: An Assessment of Multiple Prediction Systems

Abstract For efficient management of the Dutch surface water reservoir Lake IJssel, (sub)seasonal forecasts of the water volumes going in and out of the reservoir are potentially of great interest. Here, streamflow forecasts were analyzed for the river Rhine at Lobith, which is partly routed through the river IJssel, the main influx into the reservoir. We analyzed seasonal forecast datasets derived from the European Flood Awareness System (EFAS), the Swedish Meteorological and Hydrological Institute (SMHI) European Hydrological Predictions for the Environment (E-HYPE), and Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), which differ in their underlying hydrological formulation, but are all forced by meteorological forecasts from ECMWF’s fifth generation seasonal forecast system (SEAS5). We postprocessed the streamflow forecasts using quantile mapping (QM) and analyzed several forecast quality metrics. Forecast performance was assessed based on the available reforecast period, as well as on individual summer seasons. QM increased forecast skill for nearly all metrics evaluated. Averaged over the reforecast period, forecasts were skillful for up to 4 months in spring and early summer. Later in summer the skillful period deteriorated to 1–2 months. When investigating specific years with either low- or high-flow conditions, forecast skill increased with the extremity of the event. Although raw forecasts for both E-HYPE and EFAS were more skillful than HTESSEL, bias correction based on QM can significantly reduce the difference. In operational mode, the three forecast systems show comparable skill. In general, dry conditions can be forecasted with high success rates up to 3 months ahead, which is very promising for successful use of Rhine streamflow forecasts in downstream reservoir management. Significance Statement Lake IJssel is the Netherlands’ largest freshwater reservoir, with its main water source coming from a branch of the river Rhine. We investigate whether seasonal forecasts of river discharge can help in managing the lake level to create extra buffer capacity for dry periods. We compare three seasonal forecast systems and assess their quality. We find that statistical corrections are needed for all systems to be used. In spring discharge can be predicted up to 4 months ahead due to snow processes. In summer this time is shorter, but it increases with event extremity: severe low-flow events can be predicted longer ahead. This offers potential for water managers to base their lake management on other similar reservoirs.

Seasonal catchment memory of high mountain rivers in the Tibetan Plateau

Rivers originating in the Tibetan Plateau are crucial to the population in Asia. However, research about quantifying seasonal catchment memory of these rivers is still limited. Here, we propose a model able to accurately estimate terrestrial water storage change (TWSC), and characterize catchment memory processes and durations using the memory curve and the influence/domination time, respectively. By investigating eight representative basins of the region, we find that the seasonal catchment memory in precipitation-dominated basins is mainly controlled by precipitation, and that in non-precipitation-dominated basins is strongly influenced by temperature. We further uncover that in precipitation-dominated basins, longer influence time corresponds to longer domination time, with the influence/domination time of approximately six/four months during monsoon season. In addition, the long-term catchment memory is observed in non-precipitation-dominated basins. Quantifying catchment memory can identify efficient lead times for seasonal streamflow forecasts and water resource management.

Seasonal Streamflow Forecast in the Tocantins River Basin, Brazil: An Evaluation of ECMWF-SEAS5 with Multiple Conceptual Hydrological Models

The assessment of seasonal streamflow forecasting is essential for appropriate water resource management. A suitable seasonal forecasting system requires the evaluation of both numerical weather prediction (NWP) and hydrological models to represent the atmospheric and hydrological processes and conditions in a specific region. In this paper, we evaluated the ECMWF-SEAS5 precipitation product with four hydrological models to represent seasonal streamflow forecasts performed at hydropower plants in the Legal Amazon region. The adopted models included GR4J, HYMOD, HBV, and SMAP, which were calibrated on a daily scale for the period from 2014 to 2019 and validated for the period from 2005 to 2013. The seasonal streamflow forecasts were obtained for the period from 2017 to 2019 by considering a daily scale streamflow simulation comprising an ensemble with 51 members of forecasts, starting on the first day of every month up to 7 months ahead. For each forecast, the corresponding monthly streamflow time series was estimated. A post-processing procedure based on the adjustment of an autoregressive model for the residuals was applied to correct the bias of seasonal streamflow forecasts. Hence, for the calibration and validation period, the results show that the HBV model provides better results to represent the hydrological conditions at each hydropower plant, presenting NSE and NSElog values greater than 0.8 and 0.9, respectively, during the calibration stage. However, the SMAP model achieves a better performance with NSE values of up to 0.5 for the raw forecasts. In addition, the bias correction displayed a significant improvement in the forecasts for all hydrological models, specifically for the representation of streamflow during dry periods, significantly reducing the variability of the residuals.

Exploiting multiple hydrologic forecasts to inform real-time reservoir operation for drought mitigation

To timely prompt anticipatory operations and relieve prolonged droughts effectively, multiple hydrological forecasts that provide thorough information about future streamflow need to be employed. However, it is challenging to integrate multiple forecasts in reservoir optimization and obtain a proper trading-off between current and future water supply benefits. This study proposes a novel Model Predictive Control (MPC) that can not only utilize the streamflow forecast but also utilize other two categorical forecasts, including the regime state forecast to capture the long-term persistence of the streamflow process and the annual streamflow volume state forecast. In the novel MPC, a discount factor depending on the annual streamflow volume state forecast weighs up the current deficit cost decided by the streamflow forecast and the future cost function determined by the regime state forecast. Taking the Biliuhe Reservoir in China as a case study, the value of each forecast in drought mitigation is evaluated. Results show that with an appropriate discount factor, using seasonal streamflow forecast can relieve intense deficits for extreme droughts and avoid unnecessary intense limitations for slight droughts. Utilizing the annual streamflow volume state forecast contributes to guaranteeing a reliable water supply. And employing the regime state forecast relieves the intensity of severe water deficit. By incorporating these hydrological forecasts, a 31.86% performance gain can be obtained with only a 5.03% reliability decrease compared to the baseline Stochastic Dynamic Programming informed by no forecast information. Nevertheless, forecast value is beset by forecast uncertainty. For streamflow forecast, forecast value decreases with increasing forecast uncertainty. But for the other two categorical forecasts, their forecast value depends on not only the forecast accuracy but also the hydrologic conditions and forecast bias.

Unfolding the relationship between seasonal forecast skill and value in hydropower production: a global analysis

Abstract. The potential benefits of seasonal streamflow forecasts for the hydropower sector have been evaluated for several basins across the world but with contrasting conclusions on the expected benefits. This raises the prospect of a complex relationship between reservoir characteristics, forecast skill, and value. Here, we unfold the nature of this relationship by studying time series of simulated power production for 735 headwater dams worldwide. The time series are generated by running a detailed dam model over the period 1958–2000 with three operating schemes: basic control rules, perfect forecast-informed operations, and realistic forecast-informed operations. The realistic forecasts are issued by tailored statistical prediction models – based on lagged global and local hydroclimatic variables – predicting seasonal monthly dam inflows. As expected, results show that most dams (94 %) could benefit from perfect forecasts. Yet, the benefits for each dam vary greatly and are primarily controlled by the time-to-fill value and the ratio between reservoir depth and hydraulic head. When realistic forecasts are adopted, 25 % of dams demonstrate improvements with respect to basic control rules. In this case, the likelihood of observing improvements is controlled not only by design specifications but also by forecast skill. We conclude our analysis by identifying two groups of dams of particular interest: dams that fall in regions expressing strong forecast accuracy and having the potential to reap benefits from forecast-informed operations and dams with a strong potential to benefit from forecast-informed operations but falling in regions lacking forecast accuracy. Overall, these results represent a first qualitative step toward informing site-specific hydropower studies.

Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi‐Model Climate Forecasts

AbstractIn the Colorado River Basin (CRB), ensemble streamflow prediction (ESP) forecasts drive operational planning models that project future reservoir system conditions. CRB operational seasonal streamflow forecasts are produced using ESP, which represents climate using an ensemble of meteorological sequences of historical temperature and precipitation, but do not typically leverage additional real‐time subseasonal‐to‐seasonal climate forecasts. Any improvements to streamflow forecasts would help stakeholders who depend on operational projections for decision making. We explore incorporating climate forecasts into ESP through variations on an ESP trace weighting approach, focusing on Colorado River unregulated inflows forecasts to Lake Powell. The k‐nearest neighbors (kNN) technique is employed using North American Multi‐Model Ensemble one‐ and three‐month temperature and precipitation forecasts, and preceding three‐month historical streamflow, as weighting factors. The benefit of disaggregated climate forecast information is assessed through the comparison of two kNN weighting strategies; a basin‐wide kNN uses the same ESP weights over the entire basin, and a disaggregated‐basin kNN applies ESP weights separately to four subbasins. We find in general that climate‐informed forecasts add greater marginal skill in late winter and early spring, and that more spatially granular disaggregated‐basin use of climate forecasts slightly improves skill over the basin‐wide method at most lead times.

Behind every robust result is a robust method: Perspectives from a case study and publication process in hydrological modelling

Behind every robust result is a robust method: Perspectives from a case study and publication process in hydrological modelling

Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil

Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil

Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil

Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil

Predictability of Seasonal Streamflow Forecasting Based on CSM: Case Studies of Top Three Largest Rivers in China

Accurate seasonal streamflow forecasting is important in reservoir operation, watershed planning, and water resource management, and streamflow forecasting is often based on hydrological models driven by coupled global climate models (CGCMs). To understand streamflow forecasting predictability, this study considered the three largest rivers in China and explored deterministic and probabilistic skill metrics on the monthly scale according to ensemble streamflow hindcasts from the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) driven by multiple climate forcings from the climate system model by the Beijing Climate Center (BCC_CSM1.1m). The effects of initial conditions (ICs) and meteorological forcings (MFs) on skill were investigated using the conventional ensemble streamflow prediction (ESP) and reverse-ESP (revESP). The results revealed the following: (1) Skill declines as lead time increases, and forecasting is generally the most skillful for lead month 1; (2) skill is higher for dry rivers than wet rivers, and higher for dry target months than wet months for the Yellow and Yangtze Rivers, suggesting greater skill in potential drought forecasting than flood forecasting; (3) the relative operating characteristic (ROC) area is greater for abnormal terciles than the near-normal tercile for all three rivers, greater for the above-normal tercile than the below-normal tercile for the Yellow and Yangtze Rivers, but slightly greater for the below-normal tercile than the above-normal tercile for the Xijiang River; and (4) the influence of ICs outweighs that of MFs in dry months, and the period of influence varies from 1 to 3 months; however, the influence of MFs is dominant in wet target months. These findings will help improve the understanding of both the seasonal streamflow forecasting predictability based on coupled climate system/hydrological models and of streamflow forecasting for variable rivers and seasons.

Use of Seasonal Streamflow Forecasts for Flood Mitigation with Adaptive Reservoir Operation: A Case Study of the Chao Phraya River Basin, Thailand, in 2011

Predicting streamflow can help water managers make policy decisions for individual river basins. In 2011, heavy rainfall from May until October resulted in the largest flood event in the history of Thailand. This event created difficulty for water managers, who lacked information to make predictions. Studies on the 2011 Thai flood have proposed alternative reservoir operations for flood mitigation. However, no study to date has used predictive information to determine how to control reservoirs and mitigate such extreme floods. Thus, the objective of this study is to update and develop a method for using streamflow predictive data to support adaptive reservoir operation with the aim of mitigating the 2011 flood. The study area was the Chao Phraya River Basin, one of the most important basins in Thailand. We obtained predictive information from a hydrological model with a reservoir operation module using an ensemble of seasonal precipitation data from the European Centre for Medium–Range Weather Forecasts (ECMWF). The six-month ECMWF prediction period was used to support the operation plan for mitigating flooding in 2011 around each reservoir during the wet season. Decision-making for reservoir operation based on seasonal predictions was conducted on a monthly time scale. The results showed that peak river discharge decreased slightly, by around 4%, when seasonal predictive data were used. Moreover, changing the reservoir operation plan and using seasonal predictions decreased the peak river discharge by around 20%.

Fuzzy Postprocessing to Advance the Quality of Continental Seasonal Hydrological Forecasts for River Basin Management

AbstractStreamflow forecasting services driven by seasonal meteorological forecasts from dynamic prediction systems deliver valuable information for decision-making in the water sector. Moving beyond the traditional river basin boundaries, large-scale hydrological models enable a coordinated, efficient, and harmonized anticipation and management of water-related risks (droughts, floods). However, the use of forecasts from such models at the river basin scale remains a challenge, depending on how the model reproduces the hydrological features of each particular river basin. Consequently, postprocessing of forecasts is a crucial step to ensure usefulness at the river basin scale. In this paper we present a methodology to postprocess seasonal streamflow forecasts from large-scale hydrological models and advance their quality for local applications. It consists of fuzzy logic systems that bias-adjust seasonal forecasts from a large-scale hydrological model by comparing its modeled streamflows with local observations. The methodology is demonstrated using forecasts from the pan-European hydrological model E-HYPE at the Jucar River basin (Spain). Fuzzy postprocessed forecasts are compared to postprocessed forecasts derived from a quantile mapping approach as a benchmark. Fuzzy postprocessing was able to provide skillful streamflow forecasts for the Jucar River basin, keeping most of the skill of raw E-HYPE forecasts and also outperforming quantile-mapping-based forecasts. The proposed methodology offers an efficient one-to-one mapping between large-scale modeled streamflows and basin-scale observations preserving its temporal dependence structure and can adapt its input set to increase the skill of postprocessed forecasts.

Application of regression in seasonal flow forecasting for Upper Indus Basin of Pakistan

Water managers in Pakistan need timely and accurate seasonal flow forecasts like in several areas of the world to allocate water for various kinds of water usages like canal operations for irrigation, reservoir operations, and strategies to respond to extreme cases. Various hydrological models are deployed that include University of British Columbia Watershed Model (UBCWM) and snowmelt runoff model (SRM) to seasonal streamflow forecasts in Pakistan. Here, I assess the approach that employed snow water equivalent (SWE), temperature (T), precipitation (P), and February month flow (Feb) to forecasts Kharif (April–September) season with no need for intensive hydrological modeling in a skillful way for Upper Indus Basin (UIB) at Tarbela Dam. For Indus, I compare this approach results with Indus River System Authority (IRSA), UBCWM, and SRM. This approach uses multiple linear regression (MLR) to develop regression function for forecast seasonal flow volume. This regression approach provides much skillful flow forecasts that is consistent in uncertainty spread for the Indus. The regression forecast accuracy with a mean absolute percentage error (MAPE) is 7.95% with regard to statistical approach used by IRSA, UBCWM, and SRM as 10.25%, 11.05%, and 8.91%, respectively. This approach even allows improvement of 1% (volume ~ 0.6 km3) and is very simple to apply with requirement of four input data that are easy to procure or download and process. With this information in hand, a regression function can generate seasonal forecast for the authorities in Pakistan.

Impact of Geology on Seasonal Hydrological Predictability in Alpine Regions by a Sensitivity Analysis Framework

Catchment geology has a major influence on the relative impact of the main seasonal hydrological predictability sources (initial conditions (IC), climate forcing (CF)) on the forecast skill as it defines the system’s persistence. A quantification of its effect, though, on the contribution of the predictability sources to the forecast skill has not been previously investigated. In this work we apply the End Point Blending (EPB) framework to assess the contribution of IC and CF to the seasonal streamflow forecast skill over two catchments that represent the end members of a set of catchments of contrasting geology, hence contrasting hydrological response: a highly-permeable, hence slow-responding catchment and a fast-responding catchment of low permeability. Our results show that the contribution of IC in the slow-responding catchment is higher by up to 44% for forecasts initialized in winter and spring and by up to 21% for forecasts initialized in summer. IC are important for up to 4 months of lead in the slow-responding catchment and 2 months of lead in the flashier catchment. Our analysis highlights the added value of the EPB in comparison to the traditional ESP/revESP approach for identifying the sources of seasonal hydrological predictability, on the basis of catchment geology.

The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions

Abstract. The area to be cropped in irrigation districts needs to be planned according to the allocated water, which in turn is a function of the available water resource. Initially conservative estimates of future (in)flows in rivers and reservoirs may lead to unnecessary reduction of the water allocated. Though water allocations may be revised as the season progresses, inconsistency in allocation is undesirable to farmers as they may then not be able to use that water, leading to an opportunity cost in agricultural production. We assess the benefit of using reservoir inflow estimates derived from seasonal forecast datasets to improve water allocation decisions. A decision model is developed to emulate the feedback loop between simulated reservoir storage and water allocations to irrigated crops and is evaluated using inflow forecasts generated with the Forecast Guided Stochastic Scenarios (FoGSS) model, a 12-month ensemble streamflow forecasting system. Two forcings are used to generate the forecasts: ensemble streamflow prediction – ESP (historical rainfall) – and POAMA (calibrated rainfall forecasts from the POAMA climate prediction system). We evaluate the approach in the Murrumbidgee basin in Australia, comparing water allocations obtained with an expected reservoir inflow from FoGSS against the allocations obtained with the currently used conservative estimate based on climatology as well as against allocations obtained using observed inflows (perfect information). The inconsistency in allocated water is evaluated by determining the total changes in allocated water made every 15 d from the initial allocation at the start of the water year to the end of the irrigation season, including both downward and upward revisions of allocations. Results show that the inconsistency due to upward revisions in allocated water is lower when using the forecast datasets (POAMA and ESP) compared to the conservative inflow estimates (reference), which is beneficial to the planning of cropping areas by farmers. Overconfidence can, however, lead to an increase in undesirable downward revisions. This is more evident for dry years than for wet years. Over the 28 years for which allocation decisions are evaluated, we find that the accuracy of the available water estimates using the forecast ensemble improves progressively during the water year, especially 1.5 months before the start of the cropping season in November. This is significant as it provides farmers with additional time to make key decisions on planting. Our results show that seasonal streamflow forecasts can provide benefit in informing water allocation policies, particularly by earlier establishing final water allocations to farmers in the irrigation season. This allows them to plan better and use water allocated more efficiently.

Differing roles of base and fast flow in ensemble seasonal streamflow forecasting: An experimental investigation

Seasonal streamflow forecasting, which is important for water resources management, is generally a challenging task in hydrology. This paper focuses on the consideration of base and fast flow for the forecasting of seasonal streamflow. Taking the three tributaries of the Pearl River in South China as case study, five forecasting experiments, which forecast the average daily flow in the three-month period by using the average daily flow in the antecedent month, are devised to elucidate the different roles of base, fast, and total flow. The recursive digital filter (RDF) model is employed for the separation of base flow from fast flow; the Bayesian joint probability (BJP) model is applied to generate ensemble forecasts under cross validation; and the Schaake shuffle is used to establish temporal structure to link ensemble members when adding up forecasts of base flow and fast flow. Forecasts in the different experiments are evaluated in terms of bias, reliability, and skill, and the relationship between correlation coefficient and forecast skill is illustrated. The results show that unbiased and reliable seasonal forecasts are generally obtained in the experiments. Correlation coefficients of base flow are overall higher than those of total flow while correlation coefficients of fast flow are lower. Forecast skill tends to increase with correlation coefficient. Meanwhile, fast flow can lead to extremely large total flow in the antecedent month, which can substantially enlarge the forecasts and diminish the forecast skill when the corresponding observation is not as large. By contrast, base flow tends to always contribute to forecast skill. The separation of base flow from fast flow, which accounts for the different dynamical processes of fast and base flow, can reduce the impact of fast flow and improve the forecast skill of total flow.