Global analyses

We perform global analyses that involves numerous human-water systems along gradients of social and hydrological conditions, and reveal whether the dynamics described above are site-specific or generic patterns, and whether their emergence is random or depends on identifiable circumstances.
We take advantage of the opportunity offered by the current proliferation of global datasets, providing useful information over the past decades (25-40 years). In the project, the following information are used:

  • Hydrological extremes: worldwide river flow archives (Hannah et al., 2011), outcomes of global hydrological models (Bierkens, 2015), drought and flood inundation maps derived from satellite imagery (Di Baldassarre et al., 2011) and flood observatories (DFO, 2017), global maps of surface water dynamics (Pekel et al., 2016);
  • Impacts: global database of losses and fatalities (CRED, 2016; Figure B2.8);
  • Society: worldwide population data, human settlement maps (Linard and Tatem, 2011), satellite nighttime as proxies of economic activity and population distribution (Ceola et al., 2015), combined with day-time light in low-income countries (Jean et al., 2016);
  • Human alterations: global land-use maps, worldwide datasets of irrigation (Siebert et al., 2015), information about flood protection standards in different countries (Scussolini et al., 2016), dams and reservoirs (GRanD; Lehner et al., 2011).

Examples of worldwide datasets that will be exploited in this project (Lindersson et al. 2020)

Using these datasetes we perform a worldwide spatio-temporal analysis looking for emerging patterns, but without assuming the existence of a single causal link between e.g. hydrological and demographic change but we account for reciprocal effects between multiple key variables and identify the social and hydrological circumstances in which certain tendencies emerge. To this end, the explanatory analysis is  interlinked with the competing hypotheses developed with the dynamic models developed within this project.

The global nature of this analysis will permit unravelling whether the dynamics resulting from the interplay are specific contingencies, or general tendencies that emerge under identifiable conditions. Thus, this global study is expected to provide key insights about what can be generalised, and what cannot be generalised.

References:

  • Bierkens, M.F.P. (2015). Global hydrology 2015: State, trends, and directions, Water Resour. Res., 51, 4923–4947.
  • Ceola, S., Laio, F., and A. Montanari (2015). Human-impacted waters: New perspectives from global highresolution monitoring, Water Resour. Res., 51, 7064–7079.
  • CRED, Centre for Research on the Epidemiology of Disasters (2017). The International Disaster Database, www.emdat.be.
  • DFO, Dartmouth Flood Observatory (2017). Space-based Measurement and Modeling of Surface Water for Research, Humanitarian, and Water Management Applications, http://floodobservatory.colorado.edu/.
  • Di Baldassarre, G., G. Schumann, L. Brandimarte, P.D. Bates (2011). Timely low resolution SAR imagery to support floodplain modelling: a case study review, Surveys in Geophysics, 32(3), 255-269.
  • Hannah, D. M., Demuth, S., van Lanen, H. A., et al. (2011). Large-scale river flow archives: importance, current status and future needs. Hydrological Processes, 25(7), 1191-1200.
  • Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and S. Ermon (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794.
  • Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., and Nilsson, C. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9(9), 494-502.
  • Linard, C., and Tatem A.J. (2011). Population mapping of poor countries. Nature, 474, 36.
  • Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418–422.
  • Scussolini, P., Aerts, J. C. J. H., Jongman, B., Bouwer, L. M., Winsemius, H. C., de Moel, H., and P.J. Ward (2016). FLOPROS: an evolving global database of flood protection standards, Natural Hazards Earth Syst.Sci., 16, 1049-1061
  • Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N., and Scanlon, B. R. (2015). A global data set of the extent of irrigated land from 1900 to 2005. Hydrology and Earth System Sciences, 19(3), 1521-1545.