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 The benefits of pre- and postprocessing streamflow forecasts for an operational flood-forecasting system of 119 Norwegian catchments

2021 ◽  
Author(s):  
Trine J. Hegdahl ◽  
Kolbjørn Engeland ◽  
Ingelin Steinsland ◽  
Andrew Singleton
Keyword(s):  
Lead Time ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Flood Forecasting ◽  
Added Value ◽  
Flood Events ◽  
Ensemble Forecasts ◽  
Processing Scheme ◽  
Temperature And Precipitation ◽  
Streamflow Forecasts

Abstract. The novelty of this study is to evaluate the univariate and the combined effects of including both precipitation and temperature forecasts in the preprocessing together with the postprocessing of streamflow for forecasting of floods as well as all streamflow values for a large sample of catchments. A hydrometeorological forecasting chain in an operational flood forecasting setting with 119 Norwegian catchments was used. This study evaluates the added value of pre- and postprocessing methods for ensemble forecasts in a hydrometeorological forecasting chain in an operational flood forecasting setting with 119 Norwegian catchments. Two years of ECMWF ensemble forecasts of temperature (T) and precipitation (P) with a lead-time up to 9 days were used to force the operational hydrological HBV model to establish streamflow forecasts. Two approaches to preprocess the temperature and precipitation forecasts were tested. 1) An existing approach applied to the gridded forecasts using quantile mapping for temperature and a Bernoulli-gamma distribution for precipitation. 2) Bayesian model averaging (BMA) applied to catchment average values of temperature and precipitation. BMA was also used for postprocessing catchment streamflow forecasts. Ensemble forecasts of streamflow were generated for a total of fourteen schemes based on combinations of raw, preprocessed, and postprocessed forecasts in the hydrometeorological forecasting chain. The aim of this study is to assess which pre- and postprocessing approaches should be used to improve streamflow and flood forecasts and look for regional or seasonal patterns in preferred approaches. The forecasts were evaluated for two datasets: i) all streamflows and ii) flood events with streamflow above mean annual flood. Evaluations were based on reliability, continuous ranked probability score (CRPS) and -skill score (CRPSS). For the flood dataset, the critical success index (CSI) was used. Evaluations based on all streamflow data showed that postprocessing improved the forecasts only up to a lead-time of two to three days, whereas preprocessing T and P using BMA improved the forecasts for 50 %–90 % of the catchments beyond three days lead-time. However, for flood events, the added value of pre- and postprocessing is smaller. Preprocessing of P and T gave better CRPS for marginally more catchments compared to the other schemes. Based on CSI, we found that many of the forecast schemes perform equally well. Further, we found large differences in the ability to issue warnings between spring and autumn floods. There was almost no ability to predict autumn floods beyond 3 days, whereas the spring floods had predictability up to 9 days for many events and catchments. The results indicate that the ensemble forecasts have problems in predicting correct autumn precipitation, and the uncertainty is larger for heavy autumn precipitation compared to spring events when temperature driven snow melt is important. To summarize we find that the flood forecasts benefit from most pre-and postprocessing schemes, although the best processing approaches depend on region, catchment, and season, and that the processing scheme should be tailored to each catchment, lead time, season and the purpose of the forecasting.

scholarly journals Inter-comparison between retrospective ensemble streamflow forecasts using meteorological inputs from ECMWF and NOAA/ESRL in the Hudson River sub-basins during Hurricane Irene (2011)

2018 ◽  
Vol 50 (1) ◽  
pp. 166-186 ◽  
Author(s):  
F. Saleh ◽  
V. Ramaswamy ◽  
N. Georgas ◽  
A. F. Blumberg ◽  
J. Pullen
Keyword(s):  
Lead Time ◽  
Hudson River ◽  
Model Systems ◽  
Model Ensemble ◽  
Single Model ◽  
Ensemble Forecasts ◽  
Resampling Methods ◽  
Hurricane Irene ◽  
Resampling Method ◽  
Streamflow Forecasts

Abstract The objective of this work was to evaluate the benefits of using multi-model meteorological ensembles in representing the uncertainty of hydrologic forecasts. An inter-comparison experiment was performed using meteorological inputs from different models corresponding to Hurricane Irene (2011), over three sub-basins of the Hudson River basin. The ensemble-based precipitation inputs were used as forcing in a hydrological model to retrospectively forecast hourly streamflow, with a 96-hour lead time. The inputs consisted of 73 ensemble members, namely one high-resolution ECMWF deterministic member, 51 ECMWF members and 21 NOAA/ESRL (GEFS Reforecasts v2) members. The precipitation inputs were resampled to a common grid using the bilinear resampling method that was selected upon analysing different resampling methods. The results show the advantages of forcing hydrologic forecasting systems with multi-model ensemble forecasts over using deterministic and single model ensemble forecasts. The work showed that using the median of all 73 ensemble streamflow forecasts relatively improved the Nash–Sutcliffe Efficiency and lowered the biases across the examined sub-basins, compared with using the ensemble median from an individual model. This research contributes to the growing literature that demonstrates the promising capabilities of multi-model systems to better describe the uncertainty in streamflow predictions.

Probabilistic Visibility Forecasting Using Bayesian Model Averaging

2011 ◽  
Vol 139 (5) ◽  
pp. 1626-1636 ◽  
Author(s):  
Richard M. Chmielecki ◽  
Adrian E. Raftery
Keyword(s):  
Relative Humidity ◽  
Pacific Northwest ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Weather Forecasting ◽  
Model Averaging ◽  
Ensemble Forecasts ◽  
Predictive Probability ◽  
The Pacific ◽  
Visibility Forecasting

Bayesian model averaging (BMA) is a statistical postprocessing technique that has been used in probabilistic weather forecasting to calibrate forecast ensembles and generate predictive probability density functions (PDFs) for weather quantities. The authors apply BMA to probabilistic visibility forecasting using a predictive PDF that is a mixture of discrete point mass and beta distribution components. Three approaches to developing predictive PDFs for visibility are developed, each using BMA to postprocess an ensemble of visibility forecasts. In the first approach, the ensemble is generated by a translation algorithm that converts predicted concentrations of hydrometeorological variables into visibility. The second approach augments the raw ensemble visibility forecasts with model forecasts of relative humidity and quantitative precipitation. In the third approach, the ensemble members are generated from relative humidity and precipitation alone. These methods are applied to 12-h ensemble forecasts from 2007 to 2008 and are tested against verifying observations recorded at Automated Surface Observing Stations in the Pacific Northwest. Each of the three methods produces predictive PDFs that are calibrated and sharp with respect to both climatology and the raw ensemble.

scholarly journals Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenchao Ma ◽  
Yuta Ishitsuka ◽  
Akira Takeshima ◽  
Kenshi Hibino ◽  
Dai Yamazaki ◽  
...  
Keyword(s):  
Lead Time ◽  
Grid Point ◽  
Scale Model ◽  
Flood Warning ◽  
Forecast Lead Time ◽  
Extreme Flood ◽  
Break Time ◽  
Forecasting System ◽  
Dike Break ◽  
Severe Flooding

AbstractFloods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding.

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.

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 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 A comparison between ensemble and deterministic hydrological forecasts in an operational context

2011 ◽  
Vol 29 ◽  
pp. 85-94 ◽  
Author(s):  
M.-A. Boucher ◽  
F. Anctil ◽  
L. Perreault ◽  
D. Tremblay
Keyword(s):  
Weather Forecasting ◽  
Ensemble Forecasts ◽  
Meteorological Model ◽  
Resources Management ◽  
Weather Forecasts ◽  
Hydropower Production ◽  
Forecasting System ◽  
The Cost ◽  
Ensemble Weather Forecasts

Abstract. Ensemble forecasts can greatly benefit water resources management as they provide useful information regarding the uncertainty of the situation at hand. However, weather forecasting systems are evolving and the cost for reanalysis and reforecasts is prohibitive. Consequently, series of ensemble weather forecasts from a particular version of the forecasting system are often short. In this case study, we consider a hydrological event that took place in 2003 on the Gatineau watershed in Canada and caused management difficulties in a hydropower production context. The weather ensemble forecasting system in place at that time is now obsolete, but we show that with minimal post-processing of the forecasts, it is still beneficial to exploit ensemble rather than deterministic forecasts, even if the latter emerge from a more advanced meteorological model and possess superior spatial resolution.

scholarly journals Estimating the benefits of single value and probability forecasting for flood warning

2011 ◽  
Vol 8 (4) ◽  
pp. 6639-6681 ◽  
Author(s):  
J. S. Verkade ◽  
M. G. F. Werner
Keyword(s):  
Flood Risk ◽  
Lead Time ◽  
False Alarms ◽  
Lead Times ◽  
Flood Warning ◽  
Probabilistic Forecasts ◽  
Response Systems ◽  
The Cost ◽  
Warning Response

Abstract. Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed floods, or surprises. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the benefits of FFWRSs. In the present paper, a method to estimate the benefits of (imperfect) FFWRSs in reducing flood risk is presented. These benefits include not only the reduction of flood losses due to a warning response, but also consider the costs of the warning response itself, as well as the costs associated with forecasting uncertainty. The method allows for estimation of the benefits of FFWRSs that use either deterministic or probabilistic forecasts. Through application to a case study, it is shown that FFWRSs using a probabilistic forecast have the potential to realise higher benefits at all lead-times. However, it is also shown that provision of warning at increasing lead-time does not necessarily lead to an increasing reduction of flood risk, but rather that an optimal lead-time at which warnings are provided can be established as a function of forecast uncertainty and the cost-loss ratio of the user receiving and responding to the warning.

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