Natural Resources, School of


Date of this Version



Earth Syst. Sci. Data, 15, 2009–2023, 2023


© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.


As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of sociohydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023,

C0-authors include: Heidi Kreibich1, Kai Schröter1,65, Giuliano Di Baldassarre39,40, Anne F. Van Loon2, Maurizio Mazzoleni2, Guta Wakbulcho Abeshu3, Svetlana Agafonova4, Amir AghaKouchak5, Hafzullah Aksoy6, Camila Alvarez-Garreton7, Blanca Aznar9, Laila Balkhi10, Marlies H. Barendrecht2, Sylvain Biancamaria11, Liduin Bos-Burgering12, Chris Bradley13, Yus Budiyono14, Wouter Buytaert15, Lucinda Capewell13, Hayley Carlson10, Yonca Cavus16,17,18, Anaïs Couasnon2, Gemma Coxon19,20, Ioannis Daliakopoulos21, Marleen C. de Ruiter2, Claire Delus22, Mathilde Erfurt18, Giuseppe Esposito23, Didier François22, Frédéric Frappart66, Jim Freer19,20,24, Natalia Frolova4, Animesh K. Gain25, Manolis Grillakis26, Jordi Oriol Grima9, Diego A. Guzmán27, Laurie S. Huning28,5, Monica Ionita29,67,46, Maxim Kharlamov30,4, Dao Nguyen Khoi31,49, Natalie Kieboom32, Maria Kireeva4, Aristeidis Koutroulis33, Waldo Lavado-Casimiro35, Hong-Yi Li3, Maria Carmen LLasat36,37, David Macdonald38, Johanna Mård39,40, Hannah Mathew-Richards32, Andrew McKenzie38, Alfonso Mejia41, Eduardo Mario Mendiondo42, Marjolein Mens43, Shifteh Mobini44,34, Guilherme Samprogna Mohor45, Viorica Nagavciuc44,29, Thanh Ngo-Duc47, Huynh Thi Thao Nguyen48, Pham Thi Thao Nhi31,48, Olga Petrucci23, Nguyen Hong Quan48,49, Pere Quintana-Seguí50, Saman Razavi51,52,10, Elena Ridolfi68, Jannik Riegel53, Md Shibly Sadik54, Nivedita Sairam1, Elisa Savelli39,40, Alexey Sazonov30,4, Sanjib Sharma55, Johanna Sörensen44, Felipe Augusto Arguello Souza42, Kerstin Stahl18, Max Steinhausen1, Michael Stoelzle18, Wiwiana Szali ´nska56, Qiuhong Tang57, Fuqiang Tian58, Tamara Tokarczyk56, Carolina Tovar59, Thi Van Thu Tran48, Marjolein H. J. van Huijgevoort60, Michelle T. H. van Vliet61, Sergiy Vorogushyn1, ThorstenWagener45,20,62, YuelingWang57, Doris E. Wendt62, Elliot Wickham63, Long Yang64, Mauricio Zambrano-Bigiarini7,8, and Philip J. Ward2,69