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Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts (version 2)

As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2023-001_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2022-002_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.

Inventory of dams in Germany

The inventory of dams in Germany contains information on name, date of construction, the start of operation, state, river, dam height, crest length, lake area, lake volume, purpose of the dam, dam type, building characteristics, and coordinates. The inventory is a zip-file composed of 3 tab-delimited files and 1 shapefile. The shapefile contains all 530 dams with all 15 columns and can be opened with every GIS program. The geographic coordinate system used is WGS 1984. The file 2020-005_Speckhann-et-al_Dams_in_Germany_v.1.0.txt has the same information as the shapefile, i.e. contains 530 dams with the same 15 columns and it is delimitated using tab. The 2020-005_Speckhann-et-al_Abreviation.txt file contains 4 different tables which presents every abbreviation used at the inventory. The abbreviations were used for several applications: dam building characteristics, purpose of the dams, German states, and dam’s type. They were separated in 4 different tables (Building characteristics, Purpose, States and Type). The Building Characteristics are related to the structural formation of the dams, for example embankment dam is listed as “EDD”. All abbreviations regarding the building characteristics of the dam can be visualized at Table 2 at the Data description. The Purpose of the dams was divided into 8 categories: energy production, flood control, recreational use, water supply, industrial and agricultural water supply, fishing, transport and nature protection. At the inventory there are multi-purposes dams and single-purposes dams, i.e. a dam might have more than one purpose. The States in Germany were also abbreviated at the inventory using 2 letters. Due to no observed entries at the inventory for Berlin, Bremen and Hamburg, those states are not shown at Table 4. The types of dams were also abbreviated. 2020-005_Speckhann-et-al_Source_v.1.0.txt contains the name of every dam and the main source used for the obtention of the information. .

Model output for: “Feeding ten billion people is possible within four terrestrial planetary boundaries”

The netCDF data stored here represent crop production simulations from the LPJmL biosphere model underlying the different steps of the U-turn portrayed in the main paper by Gerten et al. The LPJmL data cover the entire globe with a spatial resolution of 0.5° for the baseline period as well as for different scenarios reflecting the studied ways to restrict crop production through maintaining planetary boundaries on the one hand and the various opportunities to increase food supply within the boundaries on the other hand (see paper, specifically Figs. 1 & 2, Table 2). The stored variable is crop production (fresh matter) multiplied by the fractional coverage of different crop functional types, per 0.5° grid cell. The data are provided in one netCDF file for each scenario. An overview of the scenarios assigned to the folder names is given in the file inventory. The data support the study: Gerten, D., Heck, V., Jägermeyr, J., Bodirsky, B.L., Fetzer, I., Jalava, M., Kummu, M., Lucht, W., Rockström, J., Schaphoff, S., Schellnhuber, H.J.: Feeding ten billion people is possible within four terrestrial planetary boundaries. Nature Sustainability (2020).

Compound flood drivers for northwestern Europe in high-resolution EURO-CORDEX Simulations

This dataset comprises time series of 6-hourly surges and the daily streamflow records simulated from hydrodynamic-hydrologic modelling to quantify the compound effects of surges and peak river discharge over northwestern Europe. We simulate the surge height (m) and river discharge (m3/s) at the vicinity of the coast in the reference (1981–2005) and projected (2040–2069) periods using time series of high-resolution (0.11⁰, which is about 12 km) regional dynamically downscaled meteorological forcings from the World Climate Research Program CORDEX (COordinated Regional Climate Downscaling EXperiment) framework (Nikulin et al., 2011) (https://esg-dn1.nsc.liu.se/search/esgf-liu/) for Europe, forced by five host (or parent)-GCMs from the CMIP5 project. Given data availability, we use meteorological forcing dataset from SHMI’s Rossby Centre regional atmospheric model (RCA4; Strandberg et al., 2015) driven by five host GCMs participating in CMIP5, i.e., CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, IPSL-IPSL-CM5A-MR, MOHC-HadGEM2-ES, and MPI-M-MPI-ESM-LR. For each host GCM, the first ensemble member (r1i1p1) of climate realization has been used except the ICHEC-EC-EARTH model, r12i1p1 realization has been used. All simulations have the same physical version (p1) and initialization method (i1) but differ in initial states (i.e., r1 and r12). After 2005, the future scenarios diverge, and we investigate projected change in compound flood climatology during 2040 – 2069 using business as usual RCP8.5 scenario to cover extremes. While we simulate surge at 33 tide gauges using hydrodynamic model Delft3D (Delft3D-FLOW, 2014), the simulation of discharge from 39 stream gauges is performed using the global hydrological and water use model, WaterGAP 2.2d (Müller Schmied et al., 2014). Since we are mostly interested in the meteorological phenomena that drive the compound flood mechanism, we focus on modeling of surges and do not simulate tides. The individual datasets of the surge and discharge time series for each host GCMs in the GCM-RCM chains are available in the sub-folders ‘Discharge’ and ‘Surge’ under the zip-file ‘CF_drivers’.

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