scholarly journals Skill of a global forecasting system in seasonal ensemble streamflow prediction

2017 ◽  
Vol 21 (8) ◽  
pp. 4103-4114 ◽  
Author(s):  
Naze Candogan Yossef ◽  
Rens van Beek ◽  
Albrecht Weerts ◽  
Hessel Winsemius ◽  
Marc F. P. Bierkens
Keyword(s):  
Information Service ◽  
Warning System ◽  
Skill Score ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Set Up ◽  
Global Water

Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

scholarly journals Skill of a global forecasting system in seasonal ensemble streamflow prediction

2016 ◽  
Author(s):  
Naze Candogan Yossef ◽  
Rens van Beek ◽  
Albrecht Weerts ◽  
Hessel Winsemius ◽  
Marc F. P. Bierkens
Keyword(s):  
Information Service ◽  
Brier Score ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Set Up ◽  
Framework Programme Project ◽  
Seasonal Streamflow

Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system FEWS-World which has been set up within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the global hydrological model PCR-GLOBWB. We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the ECMWF and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the skill from the ESP ensembles, which contain no actual information on weather, serves as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier Score to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual to theoretical skill in order to quantify the percentage of the theoretical skill that is achieved. The results suggest that the skill of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

scholarly journals Lagged Ensembles, Forecast Configuration, and Seasonal Predictions

2013 ◽  
Vol 141 (10) ◽  
pp. 3477-3497 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Geographical Location ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Negative Influence ◽  
Positive Influence ◽  
Prediction Skill ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts

Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

scholarly journals Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

2016 ◽  
Author(s):  
Louise Crochemore ◽  
M.-H. Ramos ◽  
Florian Pappenberger
Keyword(s):  
Bias Correction ◽  
Linear Scaling ◽  
Drought Risk ◽  
Seasonal Precipitation ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Distribution Mapping ◽  
Seasonal Streamflow

Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias corrected precipitation and streamflow ensemble forecasts in sixteen catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy, and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e., ESP method), is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contribute mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

The benefit of pre- and postprocessing streamflow forecasts for 119 Norwegian catchments, evaluated within the frame of an operational flood-forecasting system

2020 ◽  
Author(s):  
Trine Jahr Hegdahl ◽  
Kolbjørn Engeland ◽  
Ingelin Steinsland ◽  
Andrew Singleton
Keyword(s):  
Lead Time ◽  
Bayesian Model Averaging ◽  
Weather Forecasting ◽  
False Alarms ◽  
Flood Events ◽  
Ensemble Forecasts ◽  
Flood Warning ◽  
Temperature And Precipitation ◽  
Streamflow Forecasts ◽  
Forecasting System

<p>In this work the performance of different pre- and postprocessing methods and schemes for ensemble forecasts were compared for a flood warning system.  The ECMWF ensemble forecasts of temperature (T) and precipitation (P) were used to force the operational hydrological HBV model, and we estimated 2 years (2014 and 2015) of daily retrospect streamflow forecasts for 119 Norwegian catchments. Two approaches were used to preprocess the temperature and precipitation forecasts: 1) the preprocessing provided by the operational weather forecasting service, that includes a quantile mapping method for temperature and a zero-adjusted gamma distribution for precipitation, applied to the gridded forecasts, 2)  Bayesian model averaging (BMA) applied to the catchment average values of temperature and precipitation. For the postprocessing of catchment streamflow forecasts, BMA was used. Streamflow forecasts were generated for fourteen schemes with different combinations of the raw, pre- and postprocessing approaches for the two-year period for lead-time 1-9 days.</p><p>The forecasts were evaluated for two datasets: i) all streamflow and ii) flood events. The median flood represents the lowest flood warning level in Norway, and all streamflow observations above median flood are included in the flood event evaluation dataset. We used the continuous ranked probability score (CRPS) to evaluate the pre- and postprocessing schemes. Evaluation based on all streamflow data showed that postprocessing improved the forecasts only up to a lead-time of 2 days, while preprocessing T and P using BMA improved the forecasts for 50% - 90% of the catchments beyond 2 days lead-time. However, with respect to flood events, no clear pattern was found, although the preprocessing of P and T gave better CRPS to marginally more catchments compared to the other schemes.</p><p>In an operational forecasting system, warnings are issued when forecasts exceed defined thresholds, and confidence in warnings depends on the hit and false alarm ratio. By analyzing the hit ratio adjusted for false alarms, we found that many of the forecasts seemed to perform equally well. Further, we found that there were large differences in the ability to issue correct warning levels between spring and autumn floods. There was almost no ability to predict autumn floods beyond 2 days, whereas the spring floods had predictability up to 9 days for many events and catchments.</p><p>The results underline differences in the predictability of floods depending on season and the flood generating processes, i.e. snowmelt affected spring floods versus rain induced autumn floods. The results moreover indicate that the ensemble forecasts are less good at predicting correct autumn precipitation, and more emphasis could be put on finding a better method to optimize autumn flood predictions. To summarize we find that the flood forecasts will benefit from pre-/postprocessing, the optimal processing approaches do, however, depend on region, catchment and season.</p>

scholarly journals Skilful seasonal forecasts of streamflow over Europe?

2017 ◽  
Author(s):  
Louise Arnal ◽  
Hannah L. Cloke ◽  
Elisabeth Stephens ◽  
Fredrik Wetterhall ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Lead Time ◽  
Climate Model ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
Seasonal Streamflow

Abstract. This paper presents a Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts, benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only. However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to seven months of lead time, for certain months within a season. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making. Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for most of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting.

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

2008 ◽  
Vol 9 (6) ◽  
pp. 1301-1317 ◽  
Author(s):  
Guillaume Thirel ◽  
Fabienne Rousset-Regimbeau ◽  
Eric Martin ◽  
Florence Habets
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Streamflow Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Ensemble Streamflow Prediction ◽  
Prediction Systems ◽  
Set Up ◽  
The Impact

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.

scholarly journals Seasonal streamflow forecasts for Europe – II. Explanation of the skill

2016 ◽  
Author(s):  
Wouter Greuell ◽  
Wietse H. P. Franssen ◽  
Ronald W. A. Hutjes
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Initial Conditions ◽  
Irrigation Management ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Infiltration Capacity ◽  
Meteorological Forcing ◽  
Streamflow Forecasts ◽  
Hydrological Prediction

Abstract. Seasonal predictions can be exploited among others to optimize hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the second of two papers dealing with a model-based system built to produce seasonal hydrological forecasts (WUSHP: Wageningen University Seamless Hydrological Prediction system), applied here to Europe. Whereas the first paper presents the development and the skill evaluation of the system, this paper provides explanations for the skill. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with meteorological forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4 (S4). WUSHP is probabilistic. For the assessment of skill, hindcast simulations (1981–2010) were carried out. To explain skill, we first looked at the forcing and found considerable skill in the precipitation forecasts of the first lead month but hardly any significant skill for later lead months. Seasonal forecasts for temperature have more skill. Skill in summer temperature is related to climate change and more or less independent of lead time. Skill in February and March is unrelated to climate change. Sources of skill in runoff were isolated with Ensemble Streamflow Prediction (ESP) experiments. These revealed that beyond the second lead month simulations with forcing that is identical for all years (ESPall) produce more skill in runoff than the simulations forced with S4 output (Full Hindcasts). This occurs because interannual variability of the S4 forcing has insufficient skill while it adds noise. Other ESP-experiments show that in Europe initial conditions of soil moisture form the dominant source of skill in runoff. From April to July, at the end of the melt season, initial conditions of snow contribute significantly to the skill, also when forecasts start much earlier. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Finally, predictability of evapotranspiration was analysed in some detail, leading among others to the conclusion that it is due to all potential sources of skill but mostly to forcing.

German Near-Real Time Ensemble Hydroclimate Forecasting System

2021 ◽  
Author(s):  
Husain Najafi ◽  
Stephan Thober ◽  
Oldrich Rakovec ◽  
Pallav Kumar Shrestha ◽  
Luis Samaniego-Eguiguren
Keyword(s):  
Real Time ◽  
Heat Waves ◽  
Initial Conditions ◽  
Weather Forecast ◽  
Time Range ◽  
Ensemble Forecasts ◽  
Medium Range Weather Forecast ◽  
Operational Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System

<p>Helmholtz Centres are developing a research infrastructure in Germany to investigate the interactions of short-term events and long-term trends across Earth compartments under the Modular Observation Solutions for Earth System initiate (MOSES- https://www.ufz.de/moses/). A near-real time hydroclimate forecasting system at sub-seasonal to seasonal time range (HS2S) is developed for MOSES to provide tailored information for early warning of extreme events. </p><p>Here, we introduce two components of the HS2S which benefits from operational forecasts provided by the European Center for Medium-range Weather Forecast (ECMWF). The first component is weekly averaged forecasts of two atmospheric variables (total precipitation and maximum air temperature) which are bias corrected using a trend-preserving approach. The second component is German hydrological forecasting system. We use the mesoscale Hydrological Model (mHM- https://www.ufz.de/mhm) for generating hydrological initial conditions and ensemble forecasting. The same approach by German Drought Monitor (www.ufz.de/duerremonitor) is applied to interpolate near-real time in-situ observations from the German Meteorological Service (DWD) into 1-km grids. Then 51 real-time atmospheric daily ensemble forecasts from ECMWF ensemble extended product are bias corrected to generate of soil moisture and streamflow forecasts up to 30-day in advance. By post-processing mHM ensemble forecasts, an overview of drought conditions for the next 30-days horizon is disseminated online over Germany (https://www.ufz.de/moses/index.php?en=47304). Hydroclimate forecast are updated operationally twice a week to support MOSES event-driven campaigns for flood, drought and heat waves and to understand the predictability and skill of near-real time hydroclimate forecasts in Central Europe based on the state-of-the-art models and tools.  </p>

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

<p>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.</p><p>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’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’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.</p><p>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.</p>

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.

Sign in / Sign up

Export Citation Format

Share Document