Abstract

Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the Model Conditional Processor and the Ensemble Model Output Statistics method. Using 2-years of reforecasts with daily timesteps this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations both in terms of the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead-times (up to 15 days) but is largest at short lead-times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time-series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end-users to make more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Highlights

  • Preparedness for floods is greatly improved through the use of streamflow forecasts resulting in less damage and fewer fatalities (Field et al, 2012; Pappenberger et al, 2015a)

  • These uncertainties are introduced throughout the system and are often categorised as meteorological uncertainties which propagate to the streamflow forecasts from the numerical weather prediction (NWP) systems, and hydrological uncertainties which account for all other sources of uncertainty including those from the initial hydrological conditions and errors in the hydrological model (Krzysztofowicz, 1999)

  • This section focuses on the overall impact of post-processing at all 522 of the evaluated stations across the European Flood Awareness System (EFAS) domain and aims to address the research question: Does the post-processing method provide improved forecasts?

Read more

Summary

Introduction

Preparedness for floods is greatly improved through the use of streamflow forecasts resulting in less damage and fewer fatalities (Field et al, 2012; Pappenberger et al, 2015a). The European Flood Awareness System (EFAS), part of the European Commis sion’s Copernicus Emergency Management Service, supports local authorities by providing continental-scale medium-range streamflow forecasts up to 15 days ahead (Thielen et al, 2009; Smith et al, 2016). These streamflow forecasts are produced by driving a hydrological model with an ensemble of meteorological forecasts from multiple numerical weather prediction (NWP) systems including two NWP ensembles and two deterministic NWP forecasts (Smith et al, 2016). According to Krzysztofowicz (1999) and Todini (2008), a reliable forecast will include the total predictive uncertainty which is the probability of a future event occurring conditioned on all the information available when the forecast is produced

Objectives
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.