Data Release: Evaluating the Robustness of PCMCI+ for Causal Discovery of Flood Drivers

This dataset contains the data created and analyzed in "Evaluating the Robustness of PCMCI+ for Causal Discovery of Flood Drivers" by Peter Miersch, Wiebke Günther, Jakob Runge, and Jakob Zscheischler (DOI: TO BE ADDED AFTER PUBLICATION). The corresponding code is available at https://github.com/petermiersch/PCMCI_Robustness Dataset Overview This dataset consists of hydrometeorological observations and simulations for 45 gauged basins in Europe. Each of the basins is identified by an ID from the Global Runoff Database Centre (GRDC). The basins were selected by the following criteria: part of the GRDC runoff dataset at least 30 years of observational continuous discharge data between 100 km^2 and 10000 km^2 diverse flood-generating processes according to Jiang et al. (2022) For a detailed description of the methods used and the dataset creation, please see our corresponding paper. Technical Description The observed and simulated dataset range from 1950 to 2021. In the observed dataset, missing (unobserved) discharge values are left blank. The resampled dataset spans 1000 years, indicated as year 1000 to year 1999 The three datasets (observed, simulated, and resampled) contain the following variables: Name Description time date [YYYY-MM-DD] pre precipitation [mm] tavg average daily temperature [°C] snow simulated accumulated snow [mm of accumulated precipitation] SM simulated soil moisture [%] Q discharge at station [m3/s] All three datasets (simulated, observed, resampled) basin averaged values of all variables are stored as <id>.csv. All variables are basin averages. Grid points at the edges are weighted by the proportion that within the catchment boundaries. Additionally, <id>_peak_indices_USWRC.txt contains the discharge peaks used in the study. In the observed and simulated dataset, meterological variables are derived from the E-OBS observational dataset version 26e (Cornes, R., G. van der Schrier, E.J.M. van den Besselaar, and P.D. Jones. 2018: An Ensemble Version of the E-OBS Temperature and Precipitation Datasets, J. Geophys. Res. Atmos., 123. doi:10.1029/2017JD028200) Observed runoff is from the Global Runoff Database Centre (GRDC) (http://www.bafg.de/GRDC) For hydrological simulations with the mesoscale Hydrological model mHM we use Digital elevation model from USGS (Observation, E. R. & Center, S. E. Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), 10.5066/F7J38R2N (2017).) Soil map from SOILGRIDS (Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 12, e0169748, 10.1371/journal.pone.0169748 (2017).) Land cover from ESA (Arino, O. et al. Global land cover map for 2009 (GlobCover 2009), 10.1594/PANGAEA.787668 (2012).) LAI climatology from NASA Global Inventory, Monitoring, and Modelling Studies (GIMMS) (Tucker, C. J. et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote. Sens. 26, 4485–4498, 10.1080/01431160500168686 (2005).) The summary_statistics.csv and summary_statistics.html contain descriptive statistics of the dataset.