OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia

Georgy Ayzel,Oxana Erina,Liubov Kurochkina,Dmitriy Sokolov,Vsevolod Moreydo,Natalia Varentsova

doi:10.3390/w11081546

Abstract

The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data—GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.

Highlights

Flood-related hazards are among the most devastating weather-driven natural disasters which affect the population in vulnerable areas and cause high economic losses throughout the world [1,2,3].A steadily rising world population alongside an increase in natural disasters highlights the importance of developing early-warning weather systems [4,5]
We selected two basins located in the European part of Russia as pilot study areas to evaluate the performance of OpenForecast as an operational runoff forecasting system (Figure 1): (1) Moskva R. at Barsuki village (755 km2 ) and (2) Seraya R. at Novinki village (293 km2 )
Ayzel [32], who indicated the limited robustness of GR4J model for runoff predictions in three basins located in the north of Russia

Summary

Introduction

Flood-related hazards are among the most devastating weather-driven natural disasters which affect the population in vulnerable areas and cause high economic losses throughout the world [1,2,3]. A steadily rising world population alongside an increase in natural disasters highlights the importance of developing early-warning weather systems [4,5]. Such systems are not limited to providing only timely and reliable runoff forecasts to inform local communities about possible flooding, but can be used by local authorities and businesses as proxies in water resource management and planning, water quality prediction, and economic loss mitigation.

Objectives

Methods

Results

Conclusion