DOI: 10.5281/zenodo.10908410
title: COM-DROUGHTS Drought Indices

description: Data Description:

The COM-DROUGHTS Drought Indices dataset consists of daily time series of three drought indicators for meteorological, agricultural, and hydrological drought for 52 lower lying catchments (mean altitude <1500 m asl) in Switzerland for the period 1991-2099. The time series were derived from the newest transiently simulated climate and hydrological scenarios (CH2018, Hydro-CH2018) for Switzerland. Time series are provided on catchment-scale (aggregated).

Following Stagge et al. 2015, all years have been harmonized to 365 days by excluding the last day of the year in case of leap years.

The time series data is available for 3 representative concentration paths (RCPs, see e.g., Moss et al. 2010 / van Vuuren et al. 2011):- RCP2.6: 8 GCM-RCM model chains- RCP4.5: 16 GCM-RCM model chains- RCP8.5: 20 GCM-RCM model chains

Descriptive information is available for:- catchment and climate characteristics (catchment_climate_characteristics.csv)- model calibration and validation statistics (catchment_calibration_validation.csv)- GCM-modelchains and RCP-scenarios (models_scenario_info.csv)

Drought Indices:

1) Meteorological drought: SPI3- represented by the Standardized Precipitation Index (McKee et al. 1993) on a three-monthly basis- derived from the CH2018 climate scenarios for Switzerland (daily mean precipitation aggregated on catchment-area).2) Agricultural drought: ET/PET- represented by the relative evapo(transpi)ration (ET/PET; see e.g., Fuhrer and Jasper 2009) - derived from daily time series of evapotranspiration (ET) and potential evapotranspiration (PET) (output of the Hydro-CH2018 Runoff ensemble / hydrological model simulations; see Muelchi et al. 2021)).3) Hydrological drought: M7Q- represented by the 7-day average streamflow (M7Q)- derived from daily runoff time series (Hydro-CH2018 Runoff ensemble / hydrological model simulations; see Muelchi et al. 2021).

Data Organization:

- Dataset provided as .zip-File- Data files are provided in .csv-Format- A README.txt describes folder, data structure and file-naming convention.- info-files with keys and information on catchments and GCM-RCM chains

Drought indices data is structured as follows:> Folder: INDEX_NAME--> containing all data-files: 1 File per GCM_RCM model chain containing daily time series for all 52 catchments and index (variations)

File-Naming Convention: [INDEX_NAME]_[SCENARIO]_[GCM_RCM_RESOLUTION_SCENARIO].csvExample: SPI/SPI_RCP26_KNMI-RACMO_HADGEM_EUR44_RCP26.csv

Further reading & methodological details:

The following paper based on this dataset provides more detailed information on the datasets, the underlying methods and associated datasets and literature for further reading. 



von Matt, C. N., Muelchi, R., Gudmundsson, L., and Martius, O.: Compound droughts under climate change in Switzerland, Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, 2024.

https://nhess.copernicus.org/articles/24/1975/2024/nhess-24-1975-2024.html


Funding:

The COM-DROUGHTS project was funded by the Swiss Federal Office for the Environment (FOEN).

References:

1) CH2018 Project Team, 2018. CH2018 - Climate Scenarios for Switzerland. National Centre for Climate Services. https://doi.org/doi: 10.18751/Climate/Scenarios/CH2018/1.02) Fuhrer, J., Jasper, K., 2009. Bewässerungsbedürftigkeit von Acker- und Grasland im heutigen Klima. Agrarforschung 10, 396–401.3) McKee, T., Doesken, N., Kleist, J., 1993. THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES. Eight conference on applied climatology, 17-22 January 1993, Anaheim, California.4) Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010.5) Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., Martius, O., 2021. An ensemble of daily simulated runoff data (1981–2099) under climate change conditions for 93 catchments in Switzerland (Hydro-CH2018-Runoff ensemble). Geoscience Data Journal 9, 46–57. https://doi.org/10.1002/gdj3.1176) Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., and Stahl, K.: Candidate Distributions for Climatological Drought Indices (SPI and SPEI), International Journal of Climatology, 35, 4027–4040, https://doi.org/10.1002/joc.4267, 2015.7) van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative concentration pathways: an overview, Climatic Change, 109, 5, https://doi.org/10.1007/s10584-011-0148-z, 2011.