Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

Abstract. Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

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Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

10.5194/hess-2016-584 ◽

2016 ◽

Cited By ~ 2

Author(s):

Harm-Jan F. Benninga ◽

Martijn J. Booij ◽

Renata J. Romanowicz ◽

Tom H. M. Rientjes

Keyword(s):

Hydrological Model ◽

Initial Conditions ◽

Snow Accumulation ◽

Lead Times ◽

Model Parameters ◽

Streamflow Forecasting ◽

Weather Forecasts ◽

Streamflow Forecasts ◽

Forecasting System ◽

Precipitation And Temperature

Abstract. The paper presents a methodology to give insight in the performance of ensemble streamflow forecasting systems. We developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times from 1 day to 10 days for low, medium and high streamflow and related runoff generating processes. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts serve as input to a deterministic lumped hydrological (HBV) model. Due to inconsistent bias, the best streamflow forecasts were obtained without pre- and post-processing of the meteorological and streamflow forecasts. Best forecast skill, relative to alternative forecasts based on historical measurements of precipitation and temperature, is shown for high streamflow and for snow accumulation low streamflow events. Forecasts of medium streamflow events and low streamflow events generated by precipitation deficit show less skill. To improve the performance of the forecasting system for high streamflow events, in particular the meteorological forecasts require improvement. For low streamflow forecasts, the hydrological model should be improved. The study recommends improving the reliability of the ensemble streamflow forecasts by including the uncertainties in hydrological model parameters and initial conditions, and by improving the dispersion of the meteorological input forecasts.

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Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5273-2017 ◽

2017 ◽

Vol 21 (10) ◽

pp. 5273-5291 ◽

Cited By ~ 6

Author(s):

Harm-Jan F. Benninga ◽

Martijn J. Booij ◽

Renata J. Romanowicz ◽

Tom H. M. Rientjes

Keyword(s):

Hydrological Model ◽

Initial Conditions ◽

Snow Accumulation ◽

Lead Times ◽

Model Parameters ◽

Streamflow Forecasting ◽

Weather Forecasts ◽

Streamflow Forecasts ◽

Forecasting System ◽

Hydrometeorological Conditions

Abstract. The paper presents a methodology that gives insight into the performance of ensemble streamflow-forecasting systems. We have developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times ranging from 1 to 10 days for low, medium and high streamflow and different hydrometeorological conditions. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts served as inputs to a deterministic lumped hydrological (HBV) model. Due to a non-homogeneous bias in time, pre- and post-processing of the meteorological and streamflow forecasts are not effective. The best forecast skill, relative to alternative forecasts based on meteorological climatology, is shown for high streamflow and snow accumulation low-streamflow events. Forecasts of medium-streamflow events and low-streamflow events under precipitation deficit conditions show less skill. To improve performance of the forecasting system for high-streamflow events, the meteorological forecasts are most important. Besides, it is recommended that the hydrological model be calibrated specifically on low-streamflow conditions and high-streamflow conditions. Further, it is recommended that the dispersion (reliability) of the ensemble streamflow forecasts is enlarged by including the uncertainties in the hydrological model parameters and the initial conditions, and by enlarging the dispersion of the meteorological input forecasts.

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

Water ◽

10.3390/w13020162 ◽

2021 ◽

Vol 13 (2) ◽

pp. 162

Author(s):

Lyuliu Liu ◽

Ying Wu ◽

Peiqun Zhang ◽

Jianqing Zhai ◽

Li Zhang ◽

...

Keyword(s):

Water Resource Management ◽

Climate Models ◽

Initial Conditions ◽

Global Climate Models ◽

Climate System ◽

Hydrological Models ◽

Streamflow Forecasting ◽

Relative Operating Characteristic ◽

Climate System Model ◽

Seasonal Streamflow

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.

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Interactive Comment: “Assessment of an ensemble seasonal streamflow forecasting system for Australia” by James C. Bennett et al.

10.5194/hess-2017-395-rc1 ◽

2017 ◽

Author(s):

Anonymous

Keyword(s):

Streamflow Forecasting ◽

Forecasting System ◽

Seasonal Streamflow

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Does the application of multiple hydrological models improve seasonal streamflow forecasting skill?

10.5194/egusphere-egu2020-20187 ◽

2020 ◽

Author(s):

Bastian Klein ◽

Ilias Pechlivanidis ◽

Louise Arnal ◽

Louise Crochemore ◽

Dennis Meissner ◽

...

Keyword(s):

Central Europe ◽

Bayesian Model Averaging ◽

Forecast Skill ◽

Hydrological Models ◽

Streamflow Forecasting ◽

Post Processing ◽

Seasonal Forecasts ◽

Streamflow Forecasts ◽

Forecasting System ◽

Seasonal Streamflow

Many sectors, such as hydropower, agriculture, water supply and waterway transport, need information about the possible evolution of meteorological and hydrological conditions in the next weeks and months to optimize their decision processes on a long term. With increasing availability of meteorological seasonal forecasts, hydrological seasonal forecasting systems have been developed all over the world in the last years. Many of them are running in operational mode. On European scale the European Flood Awareness System EFAS and SMHI are operationally providing seasonal streamflow forecasts. In the context of the EU-Horizon2020 project IMPREX additionally a national scale forecasting system for German waterways operated by BfG was available for the analysis of seasonal forecasts from multiple hydrological models.Statistical post processing tools could be used to estimate the predictive uncertainty of the forecasted variable from deterministic / ensemble forecasts of a single / multi-model forecasting system. Raw forecasts shouldn&#8217;t be used directly by users without statistical post-processing because of various biases. To assess the added potential benefit of the application of a hydrological multi-model ensemble, the forecasting systems from EFAS, SMHI and BfG were forced by re-forecasts of the ECMWF&#8217;s Seasonal Forecast System 4 and the resulting seasonal streamflow forecasts have been verified for 24 gauges across Central Europe. Additionally two statistical forecasting methods - Ensemble Model Output Statistics EMOS and Bayesian Model Averaging BMA - have been applied to post-process the forecasts.Overall, seasonal flow forecast skill is limited in Central Europe before and after post-processing with a current predictability of 1-2 months. The results of the multi-model analysis indicate that post-processing of raw forecasts is necessary when observations are used as reference. Post-processing improves forecast skill significantly for all gauges, lead times and seasons. The multi-model combination of all models showed the highest skill compared to the skill of the raw forecasts and the skill of the post-processed results of the individual models, i.e. the application of several hydrological models for the same region improves skill, due to the different model strengths.

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Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

Hydrology and Earth System Sciences ◽

10.5194/hess-22-6257-2018 ◽

2018 ◽

Vol 22 (12) ◽

pp. 6257-6278 ◽

Cited By ~ 10

Author(s):

Fitsum Woldemeskel ◽

David McInerney ◽

Julien Lerat ◽

Mark Thyer ◽

Dmitri Kavetski ◽

...

Keyword(s):

Skill Score ◽

Climatic Conditions ◽

Streamflow Forecasting ◽

Post Processing ◽

Processing Scheme ◽

Wide Range ◽

Streamflow Forecasts ◽

Forecasting System ◽

Seasonal Streamflow ◽

Sharpness Metrics

Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources. Highlights. Uncorrected and post-processed streamflow forecasts (using three transformations, namely Log, Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments. Post-processing enhances streamflow forecast reliability, increasing the percentage of catchments with reliable predictions from 50 % to over 90 %. The BC0.2 transformation achieves substantially better forecast sharpness than the Log-Sinh and Log transformations, particularly in dry catchments.

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Assessment of an ensemble seasonal streamflow forecasting system for Australia

10.5194/hess-2017-395 ◽

2017 ◽

Cited By ~ 1

Author(s):

James C. Bennett ◽

Quan J. Wang ◽

David E. Robertson ◽

Andrew Schepen ◽

Ming Li ◽

...

Keyword(s):

Lead Times ◽

Streamflow Forecasting ◽

Forecast Horizon ◽

Australian Continent ◽

Wide Range ◽

Streamflow Forecasts ◽

Ephemeral Rivers ◽

Forecasting System ◽

Rainfall Runoff Model ◽

Seasonal Streamflow

Abstract. Despite an increasing availability of skillful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skillful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skillful forecasts at shorter lead times (

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Snow Data Assimilation via an Ensemble Kalman Filter

Journal of Hydrometeorology ◽

10.1175/jhm505.1 ◽

2006 ◽

Vol 7 (3) ◽

pp. 478-493 ◽

Cited By ~ 157

Author(s):

Andrew G. Slater ◽

Martyn P. Clark

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Snow Water Equivalent ◽

Initial Conditions ◽

Temporal Correlation ◽

Real Data ◽

Hydrologic Model ◽

Streamflow Forecasting ◽

Meteorological Forcing ◽

Ultimate Objective

Abstract A snow data assimilation study was undertaken in which real data were used to update a conceptual model, SNOW-17. The aim of this study is to improve the model’s estimate of snow water equivalent (SWE) by merging the uncertainties associated with meteorological forcing data and SWE observations within the model. This is done with a view to aiding the estimation of snowpack initial conditions for the ultimate objective of streamflow forecasting via a distributed hydrologic model. To provide a test of this methodology, the authors performed experiments at 53 stations in Colorado. In each case the situation of an unobserved location is mimicked, using the data at any given station only for validation; essentially, these are withholding experiments. Both ensembles of model forcing data and assimilated data were derived via interpolation and stochastic modeling of data from surrounding sources. Through a process of cross validation the error for the ensemble of model forcing data and assimilated observations is explicitly estimated. An ensemble square root Kalman filter is applied to perform assimilation on a 5-day cycle. Improvements in the resulting SWE are most evident during the early accumulation season and late melt period. However, the large temporal correlation inherent in a snowpack results in a less than optimal assimilation and the increased skill is marginal. Once this temporal persistence is removed from both model and assimilated observations during the update cycle, a result is produced that is, within the limits of available information, consistently superior to either the model or interpolated observations.

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Improving statistical forecasts of seasonal streamflows using hydrological model output

Hydrology and Earth System Sciences ◽

10.5194/hess-17-579-2013 ◽

2013 ◽

Vol 17 (2) ◽

pp. 579-593 ◽

Cited By ~ 37

Author(s):

D. E. Robertson ◽

P. Pokhrel ◽

Q. J. Wang

Keyword(s):

Hydrological Model ◽

Historical Record ◽

Base Flow ◽

Seasonal Forecasting ◽

Streamflow Forecasting ◽

Eastern Australia ◽

True Source ◽

Hybrid Forecasting ◽

Forecasting System ◽

Seasonal Streamflow

Abstract. Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

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