scholarly journals Assessment of an ensemble seasonal streamflow forecasting system for Australia

2017 ◽  
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 (

scholarly journals Assessment of an ensemble seasonal streamflow forecasting system for Australia

2017 ◽  
Vol 21 (12) ◽  
pp. 6007-6030 ◽  
Author(s):  
James C. Bennett ◽  
Quan J. Wang ◽  
David E. Robertson ◽  
Andrew Schepen ◽  
Ming Li ◽  
...  
Keyword(s):  
Error Model ◽  
Forecast Skill ◽  
Lead Times ◽  
Rainfall Runoff ◽  
Australian Continent ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Despite an increasing availability of skilful 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 skilful 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 skilful forecasts at shorter lead times ( <  4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall–runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall–runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

scholarly journals Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

2018 ◽  
Vol 22 (12) ◽  
pp. 6257-6278 ◽  
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.

scholarly journals Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

2016 ◽  
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.

scholarly journals Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

2017 ◽  
Vol 21 (10) ◽  
pp. 5273-5291 ◽  
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.

Do you want Seamless Subseasonal Streamflow Forecasts?    Ask MuTHRE!

2021 ◽  
Author(s):  
Mark Thyer ◽  
David McInerney ◽  
Dmitri Kavetski ◽  
Richard Laugesen ◽  
Narendra Tuteja ◽  
...  
Keyword(s):  
Time Scales ◽  
Multiple Time Scales ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
High Quality ◽  
Quality Performance ◽  
Forecast Performance ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

&lt;p&gt;Sub-seasonal streamflow forecasts (with lead times of 1-30 days) provide valuable information for many consequential water resource management decisions, including reservoir operation to meet environmental flow and irrigation demands, issuance of early flood warnings, and others. A key aim is to produce &amp;#8220;seamless&amp;#8221; forecasts, with high quality performance across the full range of lead times and time scales. &amp;#160;&lt;/p&gt;&lt;p&gt;This presentation introduces the &lt;em&gt;&lt;strong&gt;Multi-Temporal Hydrological Residual Error model (MuTHRE)&lt;/strong&gt; &lt;/em&gt;to address the challenge of obtaining &amp;#8220;seamless&amp;#8221; sub-seasonal forecasts, i.e., daily forecasts with consistent high-quality performance over multiple lead times (1-30 days) and aggregation scales (daily to monthly).&lt;/p&gt;&lt;p&gt;The model is designed to overcome common errors in streamflow forecasts:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Seasonality&lt;/li&gt; &lt;li&gt;Dynamic biases due to hydrological non-stationarity&lt;/li&gt; &lt;li&gt;Extreme errors poorly represented by the common Gaussian distribution.&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;The model is evaluated comprehensively over 11 catchments in the Murray-Darling Basin, Australia, using multiple performance metrics to scrutinize forecast reliability, sharpness and bias, across a range of lead times, months and years, at daily and monthly time scales.&lt;/p&gt;&lt;p&gt;The MuTHRE model provides &amp;#8221;high&amp;#8221; improvements, in terms of reliability for&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Short lead times (up to 10 days), due to representing non-Gaussian errors&lt;/li&gt; &lt;li&gt;Stratified by month, due to representing seasonality in hydrological errors&lt;/li&gt; &lt;li&gt;Dry years, due to representing dynamic biases in hydrological errors.&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;Forecast performance also improved in terms of sharpness, volumetric bias and CRPS skill score; Importantly, improvements are consistent across multiple time scales (daily and monthly).&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;strong&gt;This study highlights the benefits of modelling multiple temporal characteristics of hydrological errors, and demonstrates the power of the MuTHRE model for producing seamless sub-seasonal streamflow forecasts that can be utilized for a wide range of applications.&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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&amp;#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&amp;#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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

scholarly journals Value of seasonal streamflow forecasts in emergency response reservoir management

2017 ◽  
Author(s):  
Sean W. D. Turner ◽  
James Bennett ◽  
David Robertson ◽  
Stefano Galelli
Keyword(s):  
Emergency Response ◽  
Research Effort ◽  
Urban Water Supply ◽  
Hydropower Generation ◽  
Reservoir Operations ◽  
Control Rules ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Seasonal Streamflow

Abstract. Considerable research effort has recently been directed at improving ensemble seasonal streamflow forecasts, and transferring these methods into operational services. This paper examines the value of forecasts when applied to a range hypothetical reservoirs. We compare forecast-informed reservoir operations with operations based on more traditional control rules established from historical records. Using synthetic forecasts, we show that forecast-informed operations can improve reservoir operations where forecasts are accurate, but that this benefit is far more likely to occur in reservoirs operated for continually adjusted objectives (e.g., for hydropower generation) than compared with those operated for emergency response objectives (e.g., urban water supply, for which water use restrictions are seldom imposed). We then test whether a modern experimental forecasting system — called Forecast Guided Stochastic Scenarios (FoGSS) — can benefit a wide range of reservoirs operated for emergency response objectives. FoGSS-informed operations improved reservoir operations in a large majority of the reservoirs tested. In the catchments where FoGSS forecasts sometimes failed to improve operations over conventional control rules, we show that this is partly due to less consistently skilful forecasts at the timing during critical decisions are made.

Quantifying streamflow predictability across North America on sub-seasonal to seasonal timescales

2021 ◽  
Author(s):  
Louise Arnal ◽  
Martyn Clark ◽  
Vincent Vionnet ◽  
Vincent Fortin ◽  
Alain Pietroniro ◽  
...  
Keyword(s):  
North America ◽  
Climate Prediction ◽  
Forecast Skill ◽  
Streamflow Forecasting ◽  
Water Sector ◽  
Forecast System ◽  
Streamflow Forecast ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Seasonal Streamflow

&lt;div&gt; &lt;p&gt;&lt;span&gt;&lt;span&gt;Sub-seasonal to seasonal streamflow forecasts represent critical operational inputs for many water sector applications of societal relevance, such as spring flood early warning, water supply, hydropower generation, and irrigation scheduling. However, the skill of such forecasts has not risen greatly in recent decades despite recognizable advances in many relevant capabilities, including hydrologic modeling and S2S climate prediction. In order to build a continental-scale forecasting system that has value at the local scale, the sources and nature of predictability in the forecasts should be quantified and communicated. This can additionally help to target science investments for tangible improvements in the sub-seasonal to seasonal streamflow forecasting skill.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;&lt;span&gt;&lt;span&gt;As part of the Canada-based Global Water Futures (GWF) program, we are advancing capabilities for probabilistic sub-seasonal to seasonal streamflow forecasts over North America. The overall aim is to improve sub-seasonal to seasonal streamflow forecasts for a range of water sector applications. We are implementing an array of forecasting methods that integrate state-of-the-art mechanistic models and statistical methods. These include, for instance, a &lt;/span&gt;&lt;/span&gt;&lt;span&gt;probabilistic sub-seasonal to seasonal streamflow forecasting system based on quantile regression of snow water equivalent observations, and a system based on the ESP approach (Day, 1985). &lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;&lt;span&gt;To guide forecast system developments over North America, we are currently quantifying streamflow predictability for different hydroclimatic regimes, forecast initialization times, and lead times, against both streamflow simulations and observations to quantify the effect of model errors. Building on the work from Wood et al. (2016) and Arnal et al. (2017), we are disentangling the dominant predictability sources (i.e., initial hydrological conditions and atmospheric forcings) of sub-seasonal to seasonal streamflow across North American watersheds. The results provide insights into the elasticity of predictability, i.e., the increase in streamflow forecast skill possible by improving a specific component of the forecast system, and will inform the forecasting system development.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;&lt;span&gt;&lt;span&gt;Arnal Louise, Wood Andrew W., Stephens Elisabeth, Cloke Hannah L., Pappenberger Florian, 2017: An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity. Journal of Hydrometeorology, doi: 10.1175/JHM-D-16-0259.1&lt;/span&gt;&lt;/span&gt;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;Day, Gerald N., 1985: Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management, doi:10.1061/(ASCE)0733-9496(1985)111:2(157)&lt;/p&gt; &lt;/div&gt;&lt;p&gt;Wood, Andrew W., Tom Hopson, Andy Newman, Levi Brekke, Jeff Arnold, and Martyn Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology, doi: 10.1175/JHM-D-14-0213.1&lt;/p&gt;

scholarly journals Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts

2018 ◽  
Author(s):  
Fitsum Woldemeskel ◽  
David McInerney ◽  
Julien Lerat ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
...  
Keyword(s):  
Skill Score ◽  
Climatic Conditions ◽  
Residual Error ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Processing Scheme ◽  
Error Models ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Forecasting System

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 three residual error models based on the logarithmic (Log), log-sinh (Log-Sinh) and Box-Cox with λ = 0.2 (BC0.2) transformation schemes and identifies the best performing scheme for post-processing monthly and seasonal (3-months) 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 using the three residual error models substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme significantly 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 error models. This study is significant because the reliable and sharper forecasts obtained using the BC0.2 post-processing scheme will help water managers and users of the forecasting service to make better-informed decisions in planning and management of water resources.

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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