Ensemble Streamflow Prediction Forecasts Research Articles

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.

The Colorado River Basin Operational Prediction Testbed: A Framework for Evaluating Streamflow Forecasts and Reservoir Operations

ABSTRACTThe Bureau of Reclamation (Reclamation) plays a central management role in the Colorado River Basin (CRB), with an increasing focus on meeting the needs of stakeholders during the current drought. One aspect of this role involves generating five‐year projections of reservoir operating conditions in the federal multi‐reservoir system. These projections are the basis for estimating the probability of shortage conditions, which are relied on by stakeholders, and are particularly important during drought. Currently, Ensemble Streamflow Prediction (ESP) forecasts drive Reclamation's Colorado River Mid‐term Modeling System to produce probabilistic reservoir projections to be used in risk‐based analysis and decision support for the first two years of the outlook period. The lack of significant forecast skill beyond the first year motivates interest in alternative forecasting approaches. The CRB Operational Prediction Testbed was created to provide a quantitative and consistent framework for assessing the skill of streamflow forecasts and their impact on associated reservoir system projections. Reservoir system projections are evaluated by analyzing Lakes Powell and Mead operations, including projected pool elevation and operating tiers. In an initial application of this testbed, ESP forecasts were compared to experimental streamflow forecasts to assess their skill impact on two‐year reservoir projections, which are critical information for managing drought.

Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

Abstract. Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the ensemble streamflow prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39 493 catchments in Sweden, understand their spatio-temporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatio-temporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slow-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in seven unique regions with similar hydrological behaviour. Overall, these results contribute to identifying in which areas and seasons and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service, but also, most importantly, for guiding decision-making in critical services such as hydropower management and risk reduction.

Evaluation of the potential of using subsets of historical climatological data for ensemble streamflow prediction (ESP) forecasting

Streamflow forecasting is a crucial task for hydropower operations, flood forecasting and water resource use optimization. Ensemble Streamflow Prediction (ESP) is commonly used in the case of long-term lead times (a few months or seasons ahead). ESP covers the use of historical meteorological scenarios in driving a hydrological model to generate an ensemble of possible future streamflow. Many studies have evaluated methods for selecting optimal subsets of scenarios to improve forecasting skill, and indeed, this is still an ongoing area of research. In this study, we propose a procedure that calculates the maximum potential skill of a classic ESP forecast. A genetic algorithm (GA) is used to determine the best possible set of climatological scenarios given any ensemble size. Along with providing a direct estimate of the ESP forecasting potential in hindcast experiments, the method can be used as a reference for comparing other methods to ESP. The procedure is also used to compare classical ESP, a well-established forecasting method, with two new methods, namely, the Analogue method and the Contingency Table (CT) approach. A discriminant analysis is finally implemented to attempt to identify key features of ESP members that performed well as compared to their counterparts using historic climatology and climate indices. It is shown while there exists a potential for improvement, a lot of research must still be realized to exploit this potential. The procedure was tested over two basins in Canada. In general, results showed that for any forecast date, decreasing the ensemble size led to a higher potential for better forecasting skills. However, the method does not yet allow identifying the subset of the entire climatology to be used to maximize the ESP forecast performance.

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Abstract. This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories.

Evaluation of Seasonal Streamflow Forecasting

Long-term streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. In this paper, a probabilistic forecast framework based on Ensemble Streamflow Prediction (ESP) technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. The present study employs the ESP-based approach using the TopNet hydrological model. The objective of this present paper is to evaluate and assess the uncertainty due to initial condition of the catchments and forcing (meteorological input to the model) data for (ESP) based streamflow forecasting using the TopNet hydrological model in New Zealand catchments. An ensemble of streamflow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the four catchments on New Zealand’s South Island. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23% to 69% (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The major uncertainty associated with ESP forecast is combination of uncertainty due to initial state and climate forcing. The analysis indicates that the sensitivity of flow forecast to initial condition uncertainty depends on the hydrological regime experienced by the basin during the forecast period. On average, the relative importance of initial condition is greatest within two weeks to months of the start of the simulation for all catchment and all season. After this time period uncertainty in forecast is mainly due to uncertainty in forcing data. Finding of this study can be valuable tool for water resource managements.

An Efficient Method to Correct Under-Dispersion in Ensemble Streamflow Prediction of Inflow Volumes for Reservoir Optimization

Ensemble streamflow prediction (ESP) has been widely used to gain insight on possible future inflows to hydropower reservoirs. However underestimation of climate, model structure and initial condition uncertainty often leads to under-dispersed ESP forecasts. In this paper, we present a novel approach called “Hindcast-mode Uncertainty Estimation” (HUE) to efficiently add variability in ESP forecasts to reduce their under-dispersion. The method was tested on a Canadian catchment used by Rio Tinto – Aluminium division to produce hydropower for their aluminium smelting plants. This project was focused on correcting long-term predictions of freshet runoff volumes to optimize drawdown volumes, with up to 6 months of lead time. It was found that by adding an error term to the hydrological model’s snow water equivalent (SWE) state variable at the time of forecast in hindcasting mode, the resulting simulation could be forced to perfectly reproduce the freshet runoff volume. This error term was computed for all years on record which enabled modeling of the error’s distribution. This distribution can then be sampled from to add noise to the model’s SWE at the start of a new ESP forecast. Results show that the current winter ESP forecasts are strongly under-dispersed for the freshet runoff volume estimation and that the proposed method is able to widen the ESPs to correct the under-dispersion problem. This was validated by using Talagrand diagrams which shifted from a U-shape (prior to HUE) to a uniform distribution (with HUE). The project objectives of correcting the ESP forecast’s under-dispersion in spring runoff estimations was thus attained with minimal effort, bypassing the need to perform more complex ensemble data assimilation techniques.

Value of long‐term streamflow forecasts to reservoir operations for water supply in snow‐dominated river catchments

AbstractWe present a forecast‐based adaptive management framework for water supply reservoirs and evaluate the contribution of long‐term inflow forecasts to reservoir operations. Our framework is developed for snow‐dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter‐annual time horizons. We quantify and bound the contribution of seasonal and inter‐annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one‐year‐long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter‐annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast‐based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter‐annual component of the ESP forecast contributes 20–60% of the total forecast value.

Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand

Streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. Despite operational application of Ensemble Streamflow Prediction (ESP) for long-range streamflow forecasts in United States of America by the National Weather Service River Forecast System, no such approach has been explored in New Zealand. The objective of the present paper is to explore ESP-based forecasts in New Zealand catchments, highlighting its capability for seasonal flow forecasting. In this paper, a probabilistic forecast framework based on ESP technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. In the present study employs the ESP-based approach using the TopNet hydrological model with a range of past forcing data and current initial conditions. An ensemble stream flow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the Rangitata, Ahuriri, and Hooker and Jollie catchments in South Island, New Zealand. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23 to 69 % (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The ESP based forecast exhibited higher skill for a greater percentage of the forecasting period than climatology. As a result, the ESP forecast can provide better over all information for integrated water resources management purpose. ESP-based forecasts using the TopNet hydrological model have potential as tools for water resource management in New Zealand catchments.

Post-processing of ensemble forecasts in low-flow period

For water supply, navigational, ecological protection or water quality control purposes, there is a great need in knowing the likelihood of the river level falling below a certain threshold. Ensemble streamflow prediction (ESP) based on simulations of deterministic hydrologic models is widely used to assess this likelihood. Raw ESP results can be biased in both the ensemble means and the spreads. In this study, we applied a modified general linear model post-processor (GLMPP) to correct these biases. The modified GLMPP is built on the basis of regression of simulated and observed streamflow calculated on the basis of canonical events, instead of the daily values as is carried out in the original GLMPP. We conducted the probabilistic analysis of post-processed ESP results falling below pre-specified low-flow levels at seasonal time scale. Raw ESP forecasts from the 1980 to 2006 periods by four different land surface models (LSMs) in eight large river basins in the continental USA are included in the analysis. The four LSMs are Noah, Mosaic, variable infiltration capacity and Sacramento models. The major results from this study are as follows: (1) a modified GLMPP was proposed on the basis of canonical events; (2) post-processing can improve the accuracy and reduce the uncertainty of hydrologic forecasts; (3) post-processing can help deal with the effect of human activity; and (4) raw simulation results from different models vary greatly in different basins. However, post-processing can always remove model biases under different conditions. Copyright © 2014 John Wiley & Sons, Ltd.

Seasonal Hydrologic Forecasting: Do Multimodel Ensemble Averages Always Yield Improvements in Forecast Skill?

Abstract Multimodel techniques have proven useful in improving forecast skill in many applications, including hydrology. Seasonal hydrologic forecasting in large basins represents a special case of hydrologic modeling, in which postprocessing techniques such as temporal aggregation and time-varying bias correction are often employed to improve forecast skill. To investigate the effects that these techniques have on the performance of multimodel averaging, the performance of three hydrological models [Variable Infiltration Capacity, Sacramento/Snow-17, and the Noah land surface model] and two multimodel averages [simple model average (SMA) and multiple linear regression (MLR) with monthly varying model weights] are examined in three snowmelt-dominated basins in the western United States. These evaluations were performed for both simulating and forecasting [using the Ensemble Streamflow Prediction (ESP) method] monthly discharge, with and without monthly bias corrections. The single best bias-corrected model outperformed the multimodel averages of raw models in both retrospective simulations and ensemble mean forecasts in terms of RMSE. Forming an MLR multimodel average from bias-corrected models added only slight improvements over the best bias-corrected model. Differences in performance among all bias-corrected models and multimodel averages were small. For ESP forecasts, both bias correction and multimodel averaging generally reduced the RMSE of the ESP ensemble means at lead times of up to 6 months in months when flow is dominated by snowmelt, with the reduction increasing as lead time decreased. The primary reason for this is that aggregating simulated streamflows from daily to monthly time scales increases model cross correlation, which in turn reduces the effectiveness of multimodel averaging in reducing those components of model error that bias correction cannot address. This effect may be stronger in snowmelt-dominated basins because the interannual variability of winter precipitation is a common input to all models. It was also found that both bias correcting and multimodel averaging using monthly varying parameters yielded much greater error reductions than methods using time-invariant parameters.

How Essential is Hydrologic Model Calibration to Seasonal Streamflow Forecasting?

Abstract Hydrologic model calibration is usually a central element of streamflow forecasting based on the ensemble streamflow prediction (ESP) method. Evaluation measures of forecast errors such as root-mean-square error (RMSE) are heavily influenced by bias, which in turn is readily reduced by calibration. On the other hand, bias can also be reduced by postprocessing (e.g., “training” bias correction schemes based on retrospective simulation error statistics). This observation invites the question: How much is forecast error reduced by calibration, beyond what can be accomplished by postprocessing to remove bias? The authors address this question through retrospective evaluation of forecast errors at eight streamflow forecast locations distributed across the western United States. Forecast periods of length ranging from 1 to 6 months are investigated, for forecasts initiated from 1 December to 1 June, which span the period when most runoff occurs from snowmelt-dominated western U.S. rivers. ESP forecast errors are evaluated both for uncalibrated forecasts to which a percentile mapping bias correction approach is applied, and for forecasts from an objectively calibrated model without explicit bias correction. Using the coefficient of prediction (Cp), which essentially is a measure of the fraction of variance explained by the forecast, the authors find that the reduction in forecast error as measured by Cp that is achieved by bias correction alone is nearly as great as that resulting from hydrologic model calibration.

Use of Bayesian Merging Techniques in a Multimodel Seasonal Hydrologic Ensemble Prediction System for the Eastern United States

Abstract Skillful seasonal hydrologic predictions are useful in managing water resources, preparing for droughts and their impacts, energy planning, and many other related sectors. In this study, a seasonal hydrologic ensemble prediction system is developed and evaluated over the eastern United States, with a focus on the Ohio River basin. The system uses a hydrologic model (i.e., the Variable Infiltration Capacity model) as the central element for producing ensemble predictions of soil moisture, snow, and streamflow with lead times up to six months. One unique feature of this system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate forecasts from multiple climate models with observed climatology in a Bayesian framework, such that the uncertainties related to the atmospheric forcings can be better quantified while the signals from individual models are combined. Simultaneously, climate model forecasts are downscaled to an appropriate spatial scale for hydrologic predictions. When generating daily meteorological forcing, the system uses the rank structures of selected historical forcing records to ensure reasonable weather patterns in space and time. Seasonal hydrologic predictions are made with this system, using seasonal climate forecast from the NCEP Climate Forecast System (CFS), and from a combination of the NCEP CFS and seven climate models in the European Union’s Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (CFS+DEMETER). Forecasts of these two types are made for the summer periods (May to October) of 1981–99 and are compared to forecasts produced with the traditional Ensemble Streamflow Prediction (ESP) approach used in operational seasonal streamflow predictions. The forecasts from this system for the summer of 1988 show very promising skill in precipitation, soil moisture, and streamflow over the Ohio River basin, especially the multimodel CFS+DEMETER forecast. The evaluation with all 19 summer forecasts shows that the multimodel CFS+DEMETER forecast is significantly better than the ESP forecast during the first two months of the forecasts. The advantage is marginal to moderate when only the CFS forecast is used. This study validates the approach of using seasonal climate predictions from dynamic climate models in hydrological predictions, and it also emphasizes the need for international collaborations to develop multimodel seasonal predictions.

Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.

Optimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction

This study presents state-of-the-art optimization techniques for enhancing reservoir operations which use sampling stochastic dynamic programming (SSDP) with ensemble streamflow prediction (ESP). SSDP used with historical inflow scenarios (SSDP/Hist) derives an off-line optimal operating policy through a backward-moving solution procedure. In contrast, SSDP used with monthly forecasts of ESP (SSSDP/ESP) reoptimizes the off-line policy. These stochastic models are used to derive a monthly joint operating policy during the drawdown period of the Geum River multireservoir system in Korea. A cross-validation test of 1,900 simulation runs demonstrates that: (1) proposed stochastic models that explicitly include inflow uncertainty are superior to those that do not; (2) updating policy with ESP forecasts is appropriate in this reservoir system; (3) the lower dam of the Geum River multireservoir system should maintain elevation of 66.5m during the beginning of the drawdown period to avoid significant increase in the downstream water shortages; and (4) forecasting accuracy may result in considerable effects on joint reservoir operations.

이수기 저수지 운영을 위한 앙상블 유량예측의 효용성

Korea Water Resources Corporation(KOWACO) has developed the Integrated Real-time Water Management System(IRWMS) that calculates monthly optimal ending target storages by using Sampling Stochastic Dynamic Programming(SSDP) with Ensemble Streamflow Prediction(ESP) running on the day of each month. This system, however, has a shortcoming: it cannot reflect the hydrolmeteorologic variations in the middle of the month. To overcome this drawback, in this study updated ESP forecasts three times each month by using the observed precipitation series from the day of the month to the forecast day and the historical precipitation ensemble for the remaining days. The improved accuracy and its effect on the reservoir operations were quantified as a result. SSDP/ESP21 that reflects within-a-month hydrolmeteorologic states saves in water shortage on average than SSDP/ESP01. In addition, the simulation result demonstrated that the effect of ESP accuracy on the reduction of water shortage became more important when the total runoff was low during the drawdown period.

Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3°C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases the accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; East Fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as “truth” to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow.

Verification of National Weather Service Ensemble Streamflow Predictions for Water Supply Forecasting in the Colorado River Basin

The Ensemble Streamflow Prediction (ESP) system, developed by the National Weather Service (NWS), uses conceptual hydrologic models and historical data to generate a set, or ensemble, of possible streamflow scenarios conditioned on the initial states of a given basin. Using this approach, simulated historical probabilistic forecasts were generated for 14 forecast points in the Colorado River basin, and the statistical properties of the ensembles were evaluated. The median forecast traces were analyzed using “traditional” verification measures; these forecasts represented “deterministic ESP forecasts.” The minimum-error and historical traces were examined to evaluate the median forecasts and the forecast system. Distribution-oriented verification measures were used to analyze the probabilistic information contained in the entire forecast ensemble. Using a single-trace prediction, for example, the median, resulted in a loss of valuable uncertainty information about predicted seasonal volumes that is provided by the entire ensemble. The minimum-error and historical traces revealed that there are errors in the data, calibration, and models, which are part of the uncertainty provided by the probabilistic forecasts, but are not considered in the median forecast. The simulated ESP forecasts more accurately predicted future streamflow than climatology forecasts and, on average, provided useful information about the likelihood of future streamflow magnitude with a lead time of up to 7 months. Overall, the forecast provided stronger probability statements and became more reliable at shorter lead times. The distribution-oriented verification approach was shown to be applicable to ESP outlooks and appropriate for extracting detailed performance information, although interpretation of the results is complicated by inadequate sample sizes.

Improving Water Supply Outlook in Korea with Ensemble Streamflow Prediction

The purpose of this study is to propose an alternative forecasting approach for improving the current water supply outlook in Korea. Using a rainfall-runoff model, the existing technique for the water supply outlook in Korea produces monthly low, average, and high runoff forecasts. The proposed technique is called Ensemble Streamflow Prediction (ESP), and is currently implemented by the National Weather Service in the U.S.A. ESP appears particularly valid in Korea where the historical rainfall record is much more comprehensive than the historical streamflow record. This study applies ESP to runoff forecasting for a river basin in Korea to examine its applicability. An ensemble of one-month ahead runoff forecasts at the Gongju gauging station in the Keum River basin, Korea, was generated for each month. The resulting ESP forecasts were compared with the corresponding observed runoff data as well as the existing forecasts. Although this study is limited to one case study, the following conclusions can be made: (1) the ESP technique dominates the existing forecasting techniques in terms of both systematic and random errors; and (2) ESP is more accurate when high flows occur.

Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts

The National Weather Service (NWS) produces ensemble streamflow prediction (ESP) forecasts. These forecasts are used as the basis of a Sampling Stochastic Dynamic Programming (SSDP) model to optimize reservoir operations. The SSDP optimization algorithm, which is driven by individual streamflow scenarios rather than a Markov description of streamflow probabilities, allows the ESP forecast traces to be employed directly, taking full advantage of the description of streamflow variability, and temporal and spatial correlations captured within the traces. Frequently-updated ESP forecasts in a real-time SSDP reservoir system optimization model (and a simpler two-stage decision model) provide more efficient operating decisions than a sophisticated SSDP model employing historical time series coupled with snowmelt-season volume forecasts. Both models were driven by an appropriately weighted and representative subset of the original forecast and streamflow samples.