scholarly journals Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems

2022 ◽  
Vol 26 (1) ◽  
pp. 197-220
Author(s):  
Emixi Sthefany Valdez ◽  
François Anctil ◽  
Maria-Helena Ramos
Keyword(s):  
Uncertainty Quantification ◽  
Lead Time ◽  
Initial Conditions ◽  
Model Structure ◽  
Sources Of Uncertainty ◽  
Hydrological Forecasting ◽  
Streamflow Forecasts ◽  
Catchment Size ◽  
Forecasting System

Abstract. This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: system A, forcing, system B, forcing and initial conditions, system C, forcing and model structure, and system D, forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to 7 d of forecast horizon. The evaluation is carried out across 30 catchments in the province of Quebec (Canada) and over the 2011–2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast attribute, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable, and in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation of how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems.

scholarly journals Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems

2021 ◽  
Author(s):  
Emixi Sthefany Valdez ◽  
François Anctil ◽  
Maria-Helena Ramos
Keyword(s):  
Uncertainty Quantification ◽  
Lead Time ◽  
Initial Conditions ◽  
Model Structure ◽  
Sources Of Uncertainty ◽  
Hydrological Forecasting ◽  
Streamflow Forecasts ◽  
Catchment Size ◽  
Forecasting System

Abstract. This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: A) forcing, B) forcing and initial conditions, C) forcing and model structure, and D) forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to seven days of forecast horizon. The evaluation is carried out across 30 catchments in the Province of Quebec (Canada) and over the 2011–2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable and, in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation on how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems.

scholarly journals Accounting for three sources of uncertainty in ensemble hydrological forecasting

2016 ◽  
Vol 20 (5) ◽  
pp. 1809-1825 ◽  
Author(s):  
Antoine Thiboult ◽  
François Anctil ◽  
Marie-Amélie Boucher
Keyword(s):  
Ensemble Kalman Filter ◽  
Lead Time ◽  
Initial Conditions ◽  
Forecast Horizon ◽  
Meteorological Forcing ◽  
Total Uncertainty ◽  
Sources Of Uncertainty ◽  
Forecasting Performance ◽  
Respective Contribution ◽  
Different Sources

Abstract. Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims to untangle the sources of uncertainty by studying the combination of these tools and assessing their respective contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stages in the forecasting process by using different means. Their combination outperforms any of the tools used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial conditions uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improving the general forecasting performance and prevents this performance from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the EnKF tuning to avoid overlapping in error deciphering.

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 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.

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>

Evaluation of a short-term ensemble water level forecasting system: case of the Chaudière River

2021 ◽  
Author(s):  
Mohammed Amine Bessar ◽  
François Anctil ◽  
Pascal Matte
Keyword(s):  
Water Level ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Hydraulic Model ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Streamflow Prediction ◽  
Ensemble Prediction System ◽  
Sources Of Uncertainty ◽  
Forecasting System

<p>The quality of water level predictions is highly dependent on the success of the flow forecasts that inform the hydraulic model. Ensemble predictions, by considering several sources of uncertainty, provide more accurate and reliable forecasts. In this project, we aim to evaluate a water level ensemble prediction system coupling a hydraulic model to an ensemble streamflow prediction system accounting for 3 sources of uncertainty: meteorological data, hydrological processing (multimodel) and data assimilation to update the initial conditions. The hydraulic model is previously calibrated and validated and the roughness coefficients are adapted as a function of flow according to predefined relationships developed for several river segments. The forecasts reliability and accuracy are then assessed at each layer of the forecasting system and the outcomes are illustrated comparing the ensembles skills and reliability for the considered events. Overall, the results show that accounting of the hydrometeorological uncertainty improves the performances of the water level forecasts for different lead times.</p>

scholarly journals Factors affecting the quality of XBT data – results of analyses on profiles from the Western Mediterranean Sea

Ocean Science ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 59-75 ◽  
Author(s):  
F. Reseghetti ◽  
M. Borghini ◽  
G. M. R. Manzella
Keyword(s):  
Initial Conditions ◽  
Western Mediterranean ◽  
Temperature Correction ◽  
Rate Coefficients ◽  
Factors Affecting ◽  
Western Mediterranean Sea ◽  
Forecasting System ◽  
A New Technique ◽  
Environmental Prediction

Abstract. EXpendable BathyThermograph (XBT) temperature profiles collected in the framework of the Mediterranean Forecasting System – Toward Environmental Prediction (MFS-TEP) project have been compared with CTD measurements. New procedures for the quality control of recorded values have been developed and checked. Some sources of possible uncertainties and errors, such as the response time of the apparatus (XBT probe, thermistor and readout chain), or the influence of initial conditions are also analysed. To deal with the high homogeneity of Mediterranean waters, a new technique to compute the fall rate coefficients, that give a better reproduction of the depth of thermal structures, has been proposed, and new customized coefficients have been calculated. After the application of a temperature correction, the overall uncertainties in depth and in temperature measurements have been estimated.

scholarly journals Forecast convergence score: a forecaster's approach to analysing hydro-meteorological forecast systems

2011 ◽  
Vol 29 ◽  
pp. 27-32 ◽  
Author(s):  
F. Pappenberger ◽  
K. Bogner ◽  
F. Wetterhall ◽  
Y. He ◽  
H. L. Cloke ◽  
...  
Keyword(s):  
Lead Time ◽  
Decision Rules ◽  
Ensemble Forecast ◽  
Annual Maximum ◽  
Forecast Verification ◽  
Time Threshold ◽  
Catchment Size ◽  
Verification Tools ◽  
Forecasting System ◽  
Flood Warnings

Abstract. In this paper the properties of a hydro-meteorological forecasting system for forecasting river flows have been analysed using a probabilistic forecast convergence score (FCS). The focus on fixed event forecasts provides a forecaster's approach to system behaviour and adds an important perspective to the suite of forecast verification tools commonly used in this field. A low FCS indicates a more consistent forecast. It can be demonstrated that the FCS annual maximum decreases over the last 10 years. With lead time, the FCS of the ensemble forecast decreases whereas the control and high resolution forecast increase. The FCS is influenced by the lead time, threshold and catchment size and location. It indicates that one should use seasonality based decision rules to issue flood warnings.

scholarly journals Assessing sensitivity and persistence of updated initial conditions through Particle filter and EnKF for streamflow forecasting

2020 ◽  
Author(s):  
Gaia Piazzi ◽  
Guillaume Thirel ◽  
Charles Perrin
Keyword(s):  
Particle Filter ◽  
Predictive Accuracy ◽  
Initial Conditions ◽  
Internal State ◽  
Streamflow Forecasting ◽  
Parameter Uncertainties ◽  
Sources Of Uncertainty ◽  
Model State ◽  
Streamflow Forecasts ◽  
The Impact

<p>Skillful streamflow forecasts provide a key support to several water-related applications. Ensemble forecasting systems are gaining a widespread interest, since they allow accounting for different sources of uncertainty. Because of the critical impact of the initial conditions (ICs) on the forecast accuracy, it is essential to improve their estimates via data assimilation (DA). This study aims at assessing the sensitivity of the DA-based estimation of forecast ICs to several sources of uncertainty and to the update of different model states and parameters of a conceptual rainfall-runoff model. The performance of two sequential ensemble-based techniques are compared, namely Ensemble Kalman filter and Particle filter, in terms of both efficiency and temporal persistence of the updating effect through the assimilation of observed discharges at the forecast time. Several experiments specifically address the impact of the meteorological, model state and parameter uncertainties over 232 catchments in France. Results show that the benefit of the DA-based estimation of ICs for forecasting is the largest when focusing on the level of the model routing store, which is the internal state the most correlated to streamflow. While the EnKF-based forecasts outperform the PF-based ones when accounting for the meteorological uncertainty, the representation of the model state uncertainty allows greatly improving the accuracy of the PF-based predictions, with a longer-lasting updating effect (up to 10 days). Conversely, the forecasting skill is undermined when accounting for the parameter uncertainty, due to the change in the hydrological responsiveness through the update of both the production and routing store levels. A further effort is focused on assessing the impact of the spatial resolution of the hydrological model on the predictive accuracy of DA-based streamflow forecasts.</p>

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