OpenForecast is the first national-scale operational runoff forecasting system in Russia. The presented data supports a research article on a long-term assessment of OpenForecast performance in 2020-2021. File listing: calibration_vs_hindcast.npy -- Python dictionary that provides results of efficiency assessment for calibration and evaluation (hindcast) periods in terms of NSE and KGE metrics for individual hydrological models (GR4JNSE, GR4JKGE, HBVNSE, HBVKGE). hindcast_vs_forecast.npy -- Python dictionary that provides results of efficiency assessment for hindcast, pre-operational hindcast, and forecast periods in terms of NSE and KGE metrics for individual hydrological models (GR4JNSE, GR4JKGE, HBVNSE, HBVKGE), as well as their ensemble mean (ENS). meteo_forecast.npy -- Python dictionary that reports correlation coefficients between ICON and ERA5 reanalysis for air temperature and precipitation forecasts. users.csv -- daily numbers of OpenForecast users. Sample code for data access: import numpy as np import pandas as pd calibration_hindcast = np.load("calibration_vs_hindcast.npy", allow_pickle=True).item() hindcast_forecast = np.load("hindcast_vs_forecast.npy", allow_pickle=True).item() meteo_forecasts = np.load("meteo_forecast.npy", allow_pickle=True).item() users = pd.read_csv("users.csv", index_col=0, parse_dates=True, dayfirst=True) % pandas dataframe for the GR4J_KGE model efficiency in terms of NSE for calibration and hindcast periods calibration_hindcast["GR4J_KGE"]["NSE"] % pandas dataframe for the ensemble mean efficiency in terms of NSE for hindcast and forecast periods hindcast_forecast["ENS"]["NSE"] % pandas dataframe for correlation coefficients between ICON and ERA5 for precipitation forecasts meteo_forecasts["P"]["Correlation"] % available keys of Python dictionaries could be checked as follows calibration_hindcast.keys()