Data and Models

Climate Extremes, Attribution and Forecasting

Floods of the Last Interglacial and of the pre-industrial periods
This datasets consists of global maps of simulated river run-off and flood for two past periods: the pre-industrial (AD 1850) and the Last Interglacial (ca. 125,000 years ago). These are the results of modelling with the global hydrological model PCR-GLOBWB and the global flood model CaMa-Flood, included in the paper Scussolini et al. 2020. 

Link: https://zenodo.org/record/3677737#.YHlJ-udcJZU

Related publication:

  • Scussolini, P., Eilander, D., Sutanudjaja, E.H., Ikeuchi, H., Hoch, J.M., Ward, P.J., Bakker, P., Otto-Bliesner, B.L., Guo C, Stepanek, C., Zhang, Q., Braconnot, P., Guarino, M.-V., Muis, S., Yamazaki, D., Veldkamp, T.I.E. & Aerts, J.C.J.H. (2020). Global River Discharge and Floods in the Warmer Climate of the Last Interglacial. Geophysical Research Letters, 47(18), luue2020GL089375. doi: https://doi.org/10.1029/2020GL089375

Dataset of precipitation proxies for the Last Interglacial
This is the global database of published proxies for precipitation during the Last Interglacial, compared to the modern/pre-industrial period, presented in the paper Scussolini et al. 2019.

Link: https://advances.sciencemag.org/content/suppl/2019/11/18/5.11.eaax7047.DC1 

Related publication: 

  • Scussolini, P., Bakker, P. , Guo, C., Stepanek, C., Zhang, Q., Braconnot, P., Cao, J.,  Guarino, M.-V., Coumou, D., Prange, M., Ward, P.J., Renssen, H., Kageyama, M., Otto-Bliesner, B. & Aerts, J.C.J.H. (2019). Agreement between reconstructed and modeled boreal precipitation of the Last Interglacial. Science Advances, 5(11), eaax7047. doi: https://doi.org/10.1126/sciadv.aax7047

RGCPD python package
RG-CPD is a framework to process 3-dimensional climate data, such that relationships based on correlation can be tested for conditional dependence, i.e. causality. These causal teleconnections can be used to forecast a target variable of interest.

Link: https://github.com/semvijverberg/RGCPD

Related publication:  

  • Vijverberg, S., Schmeits, M., van der Wiel, K. & Coumou, D. (2020). Sub-seasonal statistical forecasts of eastern United States hot temperature events. Monthly Weather Review, 148(12), 4799–4822. doi: https://doi.org/10.1175/MWR-D-19-0409.1

Flood, Drought and Multi-Risk Assessment

Global Flood Monitor
The global food monitor detects flood events globally in real-time using flood-related tweets in 12 languages. 

Link: www.globalfloodmonitor.org

Related publications:

  • de Bruijn, J.A., de Moel, H., Jongman, B. et al. (2019). A global database of historic and real-time flood events based on social media. Scientific Data, 6, 311 (2019). https://doi.org/10.1038/s41597-019-0326-9
  • de Bruijn, J.A., de Moel, H., Jongman, B. et al. (2018). TAGGS: Grouping Tweets to Improve Global Geoparsing for Disaster Response. Journal of Geovisualization and Spatial Analysis, 2(2). https://doi.org/10.1007/s41651-017-0010-6

Synthetic Tropical cyclOne geneRation Model (STORM)
STORM generates 10,000 years of synthetic tropical cyclones from any meteorological dataset, preserving the climate statistics as in the original input dataset. 

Links: https://data.4tu.nl/articles/dataset/STORM_IBTrACS_present_climate_synthetic_tropical_cyclone_tracks/12706085 (synthetic tracks) and https://data.4tu.nl/articles/dataset/STORM_tropical_cyclone_wind_speed_return_periods/12705164 (return periods).

Related publications:

  • Bloemendaal, N., Haigh, I.D. de Moel, H. et al. (2020). Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data,7, 40. https://doi.org/10.1038/s41597-020-0381-2  (synthetic tracks). 
  • Bloemendaal, N., de Moel, H., Muis, S. et al. (2020). Estimation of global tropical cyclone wind speed probabilities using the STORM dataset. Scientific Data,7, 377. https://doi.org/10.1038/s41597-020-00720-x (return periods).

FLOPROS
Global database of FLOod PROtection Standards, comprising information in the form of the flood return period associated with protection measures, at different spatial scales.

Link: https://nhess.copernicus.org/articles/16/1049/2016/nhess-16-1049-2016-supplement.zip 

Related publication:

  • Scussolini, P., Aerts, J.C.J.H., Jongman, B.,  Bouwer, L.M.,  Winsemius, H.C, de Moel, H.& Ward, P. J. (2016). FLOPROS: an evolving global database of flood protection standards. Natural Hazards and Earth System Sciences, 16(5), 1049–1061. doi: https://doi.org/10.5194/nhess-16-1049-2016


WISC
High resolution windstorm damage assessment model (open access). High resolution dataset of windstorm tracks, windstorm footprints, and damages available through copernicus and the Climate Data Store (CDS, upcoming).

Link: https://wisc.climate.copernicus.eu/wisc/#/ 

Related publication: 


GLOFRIS
A state-of-the-art global scale river and coastal flood risk model. It has been used to assess global flood risk under current conditions, future global change, and under different states of climate variability. It is also used to assess the benefits and costs of disaster risk reduction and adaptation measures and has been applied in studies for organisations including the World Bank, OECD, and Netherlands Environmental Assessment Agency. Together with the World Resources Institute, we used the results of GLOFRIS to develop the Aqueduct Floods webtool.

Link: recent data can be downloaded from the Aqueduct Floods webtool www.wri.org/floods

Selected publications:

Scientific

  • Ward, P.J., Jongman, B., Sperna Weiland, F., Bouwman, A., van Beek, R., Bierkens, M.F.P., Ligtvoet, W. & Winsemius, H.C. (2013). Assessing flood risk at the global scale: model setup, results, and sensitivity. Environmental Research Letters, 8, 044019. doi: emptyemptyhttps://doi.org/10.1088/1748-9326/8/4/044019
  • Winsemius, H.C., van Beek, R., Jongman, B., Ward, P.J. & Bouwman, A. (2013). A framework for global river flood risk assessments. Hydrology and Earth System Sciences, 17, 1871–1892. doi: https://doi.org/10.5194/hess-17-1871-2013
  • Ward, P.J., Jongman, B., Kummu, M., Dettinger, M.D., Sperna Weiland, F.C. & Winsemius, H.C. (2014). Strong influence of El Niño Southern Oscillation on flood risk around the world. Proceedings of the National Academy of Sciences of the United States of America, 111(44), 15659-15644. doi: emptyhttps://doi.org/10.1073/pnas.1409822111
  • Jongman, B., Winsemius, H.C., Aerts, J.C.J.H., Coughlan de Perez, E., van Aalst, M.K., Kron, W. & Ward, P.J. (2015). Declining vulnerability to river floods and the global benefits of adaptation. Proceedings of the National Academy of Sciences of the United States of America. E2271–E2280. doi:
  • Winsemius, H.C., Aerts, J.C.J.H., van Beek, L.P.H., Bierkens, M.F.P., Bouwman, A., Jongman, B., Kwadijk, J., Ligtvoet, W., Lucas, P.L., van Vuuren, D.P. & Ward, P.J. (2016). Global drivers of future river flood risk. Nature Climate Change, 6, 381–385, doi: 10.1038/nclimate2893
  • Ward, P.J., Jongman, B., Aerts, J.C.J.H., Bates, P.D., Botzen, W.J.W., Diaz Loaiza, A., Hallegatte, S., Kind, J.M., Kwadijk, J., Scussolini, P. & Winsemius, H.C. (2017). A global framework for future costs and benefits of river-flood protection in urban areas. Nature Climate Change, 7, 642–646, doi: 10.1038/NCLIMATE3350 
  • Haer, T., Botzen, W.J.W., van Roomen, V., Connor, H., Zavala-Hidalgo, J., Eilander, M.D. & Ward, P.J. (2018). Coastal and river flood risk analyses for guiding economically optimal flood adaptation policies: A country-scale study for Mexico. Philosophical Transactions of the Royal Society A. Mathematical, Physical and Engineering Sciences, 376, doi: 10.1098/rsta.2017.0329
  • Englhardt, J., de Moel, H., Huyck, C.K., de Ruiter, M.C., Aerts, J.C.J.H. & Ward, P.J. (2019). Enhancement of large-scale flood risk assessments using building-material-based vulnerability curves for an object-based approach in urban and rural areas. Natural Hazards and Earth System Sciences, 19, 1703–1722, doi: 10.5194/nhess-19-1703-2019 
  • Tiggeloven, T., de Moel, H., Winsemius, H.C., Eilander, D., Erkens, G., Gebremedhin, E., Diaz Loaiza, A., Kuzma, S., Luo, T., Iceland, C., Bouwman, A., Van Huijstee, J., Ligtvoet, J. & Ward, P.J. (2020). Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. Natural Hazards and Earth System Sciences, 20, 1025–1044. doi: 10.5194/nhess-20-1025-2020 

Applications 

  • Sadoff, C.W., Hall, J.W., Grey, D., Aerts, J.C.J.H., Ait-Kadi, M., Brown, C., Cox, A., Dadson, S., Garrick, D., Kelman, J., McCornick, P., Ringler, C., Rosegrant, M., Whittington, D. & Wiberg, D., (2015). Securing Water, Sustaining Growth: Report of the GWP/OECD Task Force on Water Security and Sustainable Growth. Oxford, UK: University of Oxford, 180pp. 
  • Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D. & Vogt-Schilb, A. (2016). Shock Waves: Managing the Impacts of Climate Change on Poverty. Washington, DC: World Bank. 
  • PBL (2018). The Geography of Future Water Challenges. The Hague: PBL.

Socio-Hydrological Feedbacks and Risk Management

Integrated risk and behaviour ABM
European scale agent-based model that captures dynamic adaptation decisions of households and governments to floods.

Link:

Related publications:

  • Haer, T., Botzen, W.J.W. & Aerts, J.C.J.H. (2019). Advancing disaster policies by integrating dynamic adaptive behaviour in risk assessments using an agent-based modelling approach. Environmental Research Letters, 14(4). doi: https://doi.org/10.1088/1748-9326/ab0770
  • Haer, T., Husby, T.G., Botzen, W.J.W. & Aerts, J.C.J.H. (2020). The safe development paradox: An agent-based model for flood risk under climate change in the European Union. Global Environmental Change, 60. doi: https://doi.org/10.1016/j.gloenvcha.2019.102009