Ensemble Streamflow Prediction System Research Articles

Snowpack driven streamflow predictability under future climate: contrasting changes across two western Canadian river basins

Abstract Anthropogenic climate change induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the variable infiltration capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a test-bed ensemble streamflow prediction system by treating VIC simulated snow water equivalent (SWE) as a known predictor, and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041-2070 and 2071-2100 future periods against 1981-2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin, and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.

Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years

AbstractEnsemble forecasting applied to the field of hydrology is currently an established area of research embracing a broad spectrum of operational situations. This work catalogs the various pathways of ensemble streamflow forecasting based on an exhaustive review of more than 700 studies over the last 40 years. We focus on the advanced state of the art in the model‐based (dynamical) ensemble forecasting approaches. Ensemble streamflow prediction systems are categorized into three leading classes: statistics‐based streamflow prediction systems, climatology‐based ensemble streamflow prediction systems and numerical weather prediction‐based hydrological ensemble prediction systems. For each ensemble approach, technical information, as well as details about its strengths and weaknesses, are provided based on a critical review of the studies listed. Through this literature review, the performance and uncertainty associated with the ensemble forecasting systems are underlined from both operational and scientific viewpoints. Finally, the remaining key challenges and prospective future research directions are presented, notably through hybrid dynamical ‐ statistical learning approaches, which obviously present new challenges to be overcome in order to allow the successful employment of ensemble streamflow forecasting systems in the next decades. Targeting students, researchers and practitioners, this review provides a detailed perspective on the major features of an increasingly important area of hydrological forecasting.

Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach

Abstract. Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.

Summer drought predictability over Europe: empirical versus dynamical forecasts

Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts—System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction.

Modification of input datasets for the Ensemble Streamflow Prediction based on large-scale climatic indices and weather generator

Summary Ensemble Streamflow Prediction (ESP) provides an efficient tool for seasonal hydrological forecasts. In this study, we propose a new modification of input data series for the ESP system used for the runoff volume prediction with a lead of one month. These series are not represented by short historical weather datasets but by longer generated synthetic weather data series. Before their submission to the hydrological model, their number is restricted by relations among observed meteorological variables (average monthly precipitation and temperature) and large-scale climatic patterns and indices (e.g. North Atlantic Oscillation, sea level pressure values and two geopotential heights). This modification was tested over a four-year testing period using the river basin in central Europe. The LARS-WG weather generator proved to be a suitable tool for the extension of the historical weather records. The modified ESP approach proved to be more efficient in the majority of months compared both to the original ESP method and reference forecast (based on probability distribution of historical discharges). The improvement over traditional ESP was most obvious in the narrower forecast interval of the expected runoff volume. The inefficient forecasts of the modified ESP scheme (compared to traditional ESP) were conditioned by an insufficient restriction of input synthetic weather datasets by the climate forecast.

Monthly and seasonal streamflow forecasts using rainfall‐runoff modeling and historical weather data

Well‐validated rainfall‐runoff models are able to capture the relationships between rainfall and streamflow and to reliably estimate initial catchment states. While future streamflows are mainly dependent on initial catchment states and future rainfall, use of the rainfall‐runoff models together with estimated future rainfall can produce skilful forecasts of future streamflows. This is the basis for the ensemble streamflow prediction system, but this approach has not been explored in Australia. In this paper, two conceptual rainfall‐runoff models, together with rainfall ensembles or analogues based on historical rainfall and the Southern Oscillation index (SOI), were used to forecast streamflows at monthly and 3‐monthly scales at two catchments in east Australia. The results showed that both models forecast monthly streamflow well when forecasts for all months were evaluated together, but their performance varied significantly from month to month. Best forecasting skills were obtained (both monthly and 3 monthly) when the models were coupled with ensemble forcings on the basis of long‐term historical rainfall. SOI‐based resampling of forcings from historical data led to improved forecasting skills only in the period from September to December at the catchment in Queensland. For 3 month streamflow forecasts, best skills were in the period from April to June at the catchment in Queensland and in the period from October to January for the catchment in New South Wales, both of which were the periods after the rainy season. The forecasting skills are indicatively comparable to the statistical forecasting skills using a Bayesian joint probability approach. The potential approaches for improved hydrologic modeling through conditional parameterization and for improved forecasting skills through advanced model updating and bias corrections are also discussed.

A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system

Abstract. Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using streamflow observations in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.

A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

Abstract. The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.

Incorporation of seasonal climate forecasts in the ensemble streamflow prediction system

Summary A technique for incorporating 0–3 months lead temperature and precipitation forecasts from two Canadian numerical weather prediction (NWP) models into the ensemble streamflow prediction (ESP) system is presented. The technique involves downscaling monthly NWP forecast outputs to station locations using the model output statistics (MOS) approach and then temporally disaggregating the monthly forecasts into daily input weather data suitable for driving a hydrologic model. The daily weather sequence for a desired month is generated by a nearest neighbor re-sampling of one of the years in the historical record, and then modifying the daily weather data for the same month of the re-sampled year so as to reproduce the MOS-based monthly forecast value. Streamflow forecasts from the MOS-based scheme are compared to pre-ESP and post-ESP re-sampling schemes without seasonal climate forecast guidance. In the pre-ESP scheme, daily weather inputs for the hydrologic model were conditionally re-sampled from historical records. In the post-ESP scheme, streamflow traces produced by the climatic ESP system were conditionally re-sampled. The three schemes were applied to the Bow and Castle rivers, both located in the headwaters of the South Saskatchewan River basin in the province of Alberta, Canada. Correlations between the MOS-based median forecast and observed flow for the Castle River were consistently higher than those based on the pre-ESP and post-ESP schemes. Other skill measures showed mixed results, with the MOS-based forecasts being more skillful in some cases and less skillful in others. All three schemes exhibited better skill for above-normal flow categories than for below-normal categories. It is also shown that considerable improvement in the ESP forecast skill could be achieved through more accurate simulation of streamflow, particularly for forecast issue dates late in the water year.

The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations

Ensemble forecasting is widely used in meteorology and, increasingly, in hydrology to quantify and propagate uncertainty. In practice, ensemble forecasts cannot account for every source of uncertainty, and many uncertainties are difficult to quantify accurately. Thus, ensemble forecasts are subject to errors, which may be correlated in space and time and may be systematic. Ensemble verification is necessary to quantify these errors, and to better understand the sources of predictive error and skill in particular modeling situations. The Ensemble Verification System (EVS) is a flexible, user-friendly, software tool that is designed to verify ensemble forecasts of numeric variables, such as temperature, precipitation and streamflow. It can be applied to forecasts from any number of discrete locations, which may be issued with any frequency and lead time. The EVS can also produce and verify forecasts that are aggregated in time, such as daily precipitation totals based on hourly forecasts, and can aggregate verification statistics across several discrete locations. This paper is separated into four parts. It begins with an overview of the EVS and the structure of the Graphical User Interface. The verification metrics available in the EVS are then described. These include metrics that verify the forecast probabilities and metrics that verify the ensemble mean forecast. Several new verification metrics are also presented. Following a description of the Application Programming Interface, the procedure for adding a new metric to the EVS is briefly outlined. Finally, the EVS is illustrated with two examples from the National Weather Service (NWS), one focusing on ensemble forecasts of precipitation from the NWS Ensemble Pre-Processor and one focusing on ensemble forecasts of streamflow from the NWS Ensemble Streamflow Prediction system. The conclusions address future enhancements to, and applications of, the EVS.

On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

Abstract Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins.

확률론적 중장기 댐 유입량 예측 (II) 앙상블 댐 유입량 예측을 위한 GDAPS 활용

This study develops ESP (Ensemble Streamflow Prediction) system by using medium-term numerical weather prediction model which is GDAPS(T213) of KMA. The developed system forecasts medium- and long-range exceedance Probability for streamflow and RPSS evaluation scheme is used to analyze the accuracy of probability forecasts. It can be seen that the daily probability forecast results contain high uncertainties. A sensitivity analysis with respect to forecast time resolution shows that uncertainties decrease and accuracy generally improves as the forecast time step increase. Weekly ESP results by using the GDAPS output with a lead time of up to 28 days are more accurately predicted than traditional ESP results because conditional probabilities are stably distributed and uncertainties can be reduced. Therefore, it can be concluded that the developed system will be useful tool for medium- and long-term reservoir inflow forecasts in order to manage water resources.

Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction

AbstractPrevious ensemble streamflow prediction (ESP) studies in Korea reported that modelling error significantly affects the accuracy of the ESP probabilistic winter and spring (i.e. dry season) forecasts, and thus suggested that improving the existing rainfall‐runoff model, TANK, would be critical to obtaining more accurate probabilistic forecasts with ESP. This study used two types of artificial neural network (ANN), namely the single neural network (SNN) and the ensemble neural network (ENN), to provide better rainfall‐runoff simulation capability than TANK, which has been used with the ESP system for forecasting monthly inflows to the Daecheong multipurpose dam in Korea. Using the bagging method, the ENN combines the outputs of member networks so that it can control the generalization error better than an SNN. This study compares the two ANN models with TANK with respect to the relative bias and the root‐mean‐square error. The overall results showed that the ENN performed the best among the three rainfall‐runoff models. The ENN also considerably improved the probabilistic forecasting accuracy, measured in terms of average hit score, half‐Brier score and hit rate, of the present ESP system that used TANK. Therefore, this study concludes that the ENN would be more effective for ESP rainfall‐runoff modelling than TANK or an SNN. Copyright © 2005 John Wiley & Sons, Ltd.

최적선형보정을 이용한 앙상블 유량예측 시스템의 개선

일단위 강우-유출모형인 SSARR모형을 이용하여 한강, 낙동강, 섬진강유역에 월 앙상블 유량예측 시스템을 구축하였다. 우선 SSARR모형의 월 평균 유출량에 대한 모의정확성을 평가한 결과 한강과 낙동강유역에서는 과소추정하는 경향이 뚜렷하였으며, 섬진강유역에서는 모의오차의 분산이 커 정확성 개선이 필요하였다. 최적선형 보정기법을 적용하여 SSARR모형의 모의유량을 보정한 결과, 섬진강을 제외한 한강과 낙동강유역의 검증지점에서는 모의 정확성이 크게 개선되었다. 또한 1998년부터 2003년까지 월 앙상블 유량예측을 실시하여 예측 정확성을 평가하였다. 한강과 낙동강유역에서 최적선형 보정기법을 이용할 경우 앙상블 유량예측 정확성이 크게 개선되었으나, 섬진강유역은 개선효과가 미비하였다. A monthly Ensemble Streamflow Prediction (ESP) system was developed by applying a daily rainfall-runoff model known as the Streamflow Synthesis and Reservoir Regulation (SSARR) model to the Han, Nakdong, and Seomjin River basins in Korea. This study first assesses the accuracy of the averaged monthly runoffs simulated by SSARR for the 3 basins and proposes some improvements. The study found that the SSARR modeling of the Han and Nakdong River basins tended to significantly underestimate the actual runoff levels and the modeling of the Seomjin River basinshowed a large error variance. However, by implementing optimal linear correction (OLC), the accuracy of the SSARR model was considerably improved in predicting averaged monthly runoffs of the Han and Nakdong River basins. This improvement was not seen in the modeling of the Seomjin River basin. In addition, the ESP system was applied to forecast probabilistic runoff forecasts one month in advance for the 3 river basins from 1998 to 2003. Considerably improvement was also achieved with OLC in probabilistic forecasting accuracy for the Han and Nakdong River basins, but not in that of the Seomjin River basin.

Incorporating Medium-Range Numerical Weather Model Output into the Ensemble Streamflow Prediction System of the National Weather Service

Abstract This study introduces medium-range meteorological ensemble inputs of temperature and precipitation into the Ensemble Streamflow Prediction component of the National Weather Service River Forecast System (NWSRFS). The Climate Diagnostics Center (CDC) produced a reforecast archive of model forecast runs from a dynamically frozen version of the Medium-Range Forecast (MRF) model. This archive was used to derive statistical relationships between MRF variables and historical basin-average precipitation and temperatures. The latter are used to feed the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two sets of ESP reforecasts were produced: A control run based on historically observed temperature and precipitation and an experimental run based on MRF-derived temperature and precipitation. This study found the MRF reforecasts to be generally superior to the control reforecasts, although there were situations when the downscaled MRF output actually degraded the forecast. Forecast improvements were most pronounced during the rising limb of the hydrograph—at this time accurate temperature forecasts improve predictions of the rate of snowmelt. Further improvements in streamflow forecasts at short forecast lead times may be possible by incorporating output from high-resolution regional atmospheric models into the NWSRFS.

Distributions-Oriented Verification of Ensemble Streamflow Predictions

Abstract Ensemble streamflow prediction systems produce forecasts in the form of a conditional probability distribution for a continuous forecast variable. A distributions-oriented approach is presented for verification of these probability distribution forecasts. First, a flow threshold is used to transform the ensemble forecast into a probability forecast for a dichotomous event. The event is said to occur if the observed flow is less than or equal to the threshold; the probability forecast is the probability that the event occurs. The distributions-oriented approach, which has been developed for meteorological forecast verification, is then applied to estimate forecast quality measures for a verification dataset. The results are summarized for thresholds chosen to cover the range of possible flow outcomes. To aid in the comparison for different thresholds, relative measures are used to assess forecast quality. An application with experimental forecasts for the Des Moines River basin illustrates the app...