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WIREs Water

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A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies

Overview

Sara Lindersson, Luigia Brandimarte, Johanna Mård, Giuliano Di Baldassarre

Published Online: Mar 06 2020

DOI: 10.1002/wat2.1424

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Abstract The availability of planetary‐scale geospatial datasets that can support the study of water‐related disasters in the Anthropocene is rapidly growing. We review 124 global and free datasets allowing spatial (and temporal) analyses of floods, droughts and their interactions with human societies. Our collection of datasets is available in a descriptive list for download at https://zenodo.org/record/3368882. The purpose of providing an overview of datasets across a wide range of hydrological and socioeconomic variables is to highlight research opportunities across scientific disciplines for the study of the water‐society interplay. Our collection of datasets confirms that the availability of geospatial data capturing hydrological hazards and exposure is far more mature than those capturing vulnerability aspects. We do not only highlight the unprecedented opportunities associated with these global datasets for the study of water‐related disasters in the Anthropocene, but also discuss the challenges associated with their exploitation. These challenges include: (a) time varying datasets advised not to be used in time series analyses; (b) fine spatial resolution datasets advised not to be used in local scale studies; (c), datasets built by a wide variety of data sources prohibiting systematic uncertainty assessments; and (d) datasets built by covariate variables preventing interaction studies. This article is categorized under: Engineering Water > Planning Water Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

Number of articles (2000–2018) in Web of Science Core Collection database (Clarivate Analytics, ) containing the terms “flood” or “drought” or “hydrological extreme” when refined by “global.” The five most common article categories are Environmental sciences, Meteorology atmospheric sciences, Geosciences multidisciplinary, Water resources, and Ecology

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Data dependencies among datasets of different categories. Arrow direction shows which dataset is using the other as data source. Symbol size indicates how frequently it is used by other datasets in this data collection. Only direct relationships among the datasets are included in this figure. Indirect dependency relationships, for instance if two datasets are based on the same satellite imagery, are therefore not shown here. Layout of network chart follows ordination, produced in software NetDraw (Borgatti, ). Full descriptions of all datasets, with respective references, are available at https://zenodo.org/record/3368882

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Temporal and spatial resolutions of raster datasets by category. Only raster datasets with specified spatial resolutions are included in this figure. Datasets with static or irregular temporal resolutions are not included in this figure. Full descriptions of all datasets, with respective references, are available at https://zenodo.org/record/3368882

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Time period and resolution of datasets grouped by categories. Thick bars indicate datasets with annual temporal resolutions or finer. Dots with thin lines indicate datasets representing individual year(s). Arrowheads indicate that temporal coverage extends further back in time than displayed in this chart. Datasets without specified temporal representations, for instance static datasets based on a variety of data sources, are not included in this figure. Full descriptions of all datasets, with respective references, are available at https://zenodo.org/record/3368882

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Temporal resolution and data format of datasets by category. Static datasets are snapshots and do not capture change over time. Irregular datasets include datasets with irregular recurrence time and event‐based datasets. Tabular data format includes both tables with and without geographic representation, such as coordinates. Datasets lacking specification of temporal resolution or data format, for example due to variations between measuring stations, are not included in this figure. Some datasets offer multiple temporal resolutions and data formats, for which all alternatives are included in this figure

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Categories and subcategories of the 124 freely accessible global datasets included in this review. Included variables all relate to hydrological extremes and societies, based on published conceptualizations of human–water disaster systems. Number of datasets in each category is given underneath the title. The width of the categories and sub‐categories indicate the number of datasets. All datasets are described in the data table available at https://zenodo.org/record/3368882

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References

Abatzoglou,, J. T., Dobrowski,, S. Z., Parks,, S. A., & Hegewisch,, K. C. (2018). TerraClimate, a high‐resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5, 170191. https://doi.org/10.1038/sdata.2017.191
AghaKouchak,, A. (2015). A multivariate approach for persistence‐based drought prediction: Application to the 2010–2011 East Africa drought. Journal of Hydrology, 526, 127–135. https://doi.org/10.1016/j.jhydrol.2014.09.063
Ahmed,, K., Shahid,, S., Wang,, X., Nawaz,, N., & Khan,, N. (2019). Spatiotemporal changes in aridity of Pakistan during 1901–2016. Hydrology and Earth System Sciences, 23(7), 3081–3096. https://doi.org/10.5194/hess-23-3081-2019
Albrecht,, F. (2018). Natural hazard events and social capital: The social impact of natural disasters. Disasters, 42(2), 336–360. https://doi.org/10.1111/disa.12246
Anh,, D. T. L., & Aires,, F. (2019). River discharge estimation based on satellite water extent and topography: An application over the Amazon. Journal of Hydrometeorology, 20(9), 1851–1866. https://doi.org/10.1175/jhm-d-18-0206.1
Anseeuw,, W., Lay,, J., Messerli,, P., Giger,, M., & Taylor,, M. (2013). Creating a public tool to assess and promote transparency in global land deals: The experience of the land matrix. The Journal of Peasant Studies, 40(3), 521–530. https://doi.org/10.1080/03066150.2013.803071
Barbarossa,, V., Huijbregts,, M. A. J., Beusen,, A. H. W., Beck,, H. E., King,, H., & Schipper,, A. M. (2018). FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015. Scientific Data, 5, 180052. https://doi.org/10.1038/sdata.2018.52
Barredo,, J. I. (2007). Major flood disasters in Europe: 1950–2005. Natural Hazards, 42(1), 125–148. https://doi.org/10.1007/s11069-006-9065-2
Beck,, H. E., Wood,, E. F., Pan,, M., Fisher,, C. K., Miralles,, D. G., van Dijk,, A. I. J. M., … Adler,, R. F. (2019). MSWEP V2 global 3‐hourly 0.1° precipitation: Methodology and quantitative assessment. Bulletin of the American Meteorological Society, 100, 473–500. https://doi.org/10.1175/bams-d-17-0138.1
Beck,, H. E., Zimmermann,, N. E., McVicar,, T. R., Vergopolan,, N., Berg,, A., & Wood,, E. F. (2018). Present and future Köppen–Geiger climate classification maps at 1‐km resolution. Scientific Data, 5, 180214. https://doi.org/10.1038/sdata.2018.214
Bennett,, M. M., & Smith,, L. C. (2017). Advances in using multitemporal night‐time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sensing of Environment, 192, 176–197. https://doi.org/10.1016/jsse.2017.01.005
Bernauer,, T., Böhmelt,, T., Buhaug,, H., Gleditsch,, N. P., Tribaldos,, T., Weibust,, E. B., & Wischnath,, G. (2012). Water‐related intrastate conflict and cooperation (WARICC): A new event dataset. International Interactions, 38(4), 529–545. https://doi.org/10.1080/03050629.2012.697428
Best,, J. (2019). Anthropogenic stresses on the world`s big rivers. Nature Geoscience, 12(1), 7–21. https://doi.org/10.1038/s41561-018-0262-x
Borgatti,, S. P. (2002). Netdraw network visualization. Harvard, MA: Analytic Technologies.
Brocca,, L., Tarpanelli,, A., Filippucci,, P., Dorigo,, W., Zaussinger,, F., Gruber,, A., & Fernández‐Prieto,, D. (2018). How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products. International Journal of Applied Earth Observation and Geoinformation, 73, 752–766. https://doi.org/10.1016/j.jag.2018.08.023
Busker,, T., de Roo,, A., Gelati,, E., Schwatke,, C., Adamovic,, M., Bisselink,, B., … Cottam,, A. (2019). A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry. Hydrology and Earth System Sciences, 23(2), 669–690. https://doi.org/10.5194/hess-23-669-2019
Campana,, P. E., Zhang,, J., Yao,, T., Andersson,, S., Landelius,, T., Melton,, F., & Yan,, J. (2018). Managing agricultural drought in Sweden using a novel spatially‐explicit model from the perspective of water‐food‐energy nexus. Journal of Cleaner Production, 197, 1382–1393. https://doi.org/10.1016/j.jclepro.2018.06.096
Carrão,, H., Naumann,, G., & Barbosa,, P. (2016). Mapping global patterns of drought risk: An empirical framework based on sub‐national estimates of hazard, exposure and vulnerability. Global Environmental Change, 39, 108–124. https://doi.org/10.1016/j.gloenvcha.2016.04.012
Carroll,, M. L., DiMiceli,, C. M., Townshend,, J. R. G., Sohlberg,, R. A., Hubbard,, A. B., & Wooten,, M. R. (2017). MOD44W: Global MODIS water maps user guide. Retrieved from https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/mod44w_user_guide_atbd_v6.pdf
Center for International Earth Science Information Network, %26 Columbia University. (2017a). Documentation for the global population count grid time series estimates. Retrieved from 10.7927/H40R9MBW
Center for International Earth Science Information Network, %26 Columbia University. (2017b). Documentation for the gridded population of the World, Version 4 (GPWv4), revision 10 data sets. Retrieved from 10.7927/H4B56GPT
Centre for Research on the Epidemiology of Disasters (2019). In D. Guha‐Sapir, (Ed.), EM‐DAT: The emergency events database. Brussels, Belgium: Université catholique de Louvain (UCL), CRED. Retrieved from https://www.emdat.be/
Centre for Research on the Epidemiology of Disasters & United Nations Office for Disaster Risk Reduction. (2018). Economic losses, poverty %26 disasters: 1998–2017. Retrieved from https://www.preventionweb.net/publications/view/61119
Ceola,, S., Laio,, F., & Montanari,, A. (2019). Global‐scale human pressure evolution imprints on sustainability of river systems. Hydrology and Earth System Sciences, 23(9), 3933–3944. https://doi.org/10.5194/hess-23-3933-2019
Chen,, J., Cao,, X., Peng,, S., & Ren,, H. (2017). Analysis and applications of GlobeLand30: A review. International Journal of Geo‐Information, 6(8), 230. https://doi.org/10.3390/ijgi6080230
Chen,, S.‐A., Michaelides,, K., Grieve,, S. W. D., & Singer,, M. B. (2019). Aridity is expressed in river topography globally. Nature, 573(7775), 573–577. https://doi.org/10.1038/s41586-019-1558-8
Chuvieco,, E., Lizundia‐Loiola,, J., Pettinari,, M. L., Ramo,, R., Padilla,, M., Tansey,, K., … Plummer,, S. (2018). Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data, 10(4), 2015–2031. https://doi.org/10.5194/essd-10-2015-2018
Clarivate Analytics. (2019). Web of Science core collection database. Retrieved from www.webofknowledge.com
Craig,, C. A., Feng,, S., & Gilbertz,, S. (2019). Water crisis, drought, and climate change in the Southeast United States. Land Use Policy, 88, 104110. https://doi.org/10.1016/j.landusepol.2019.104110
Crochemore,, L., Isberg,, K., Pimentel,, R., Pineda,, L., Hasan,, A., & Arheimer,, B. (2019). Lessons learnt from checking the quality of openly accessible river flow data worldwide. Hydrological Sciences Journal, 1–13. https://doi.org/10.1080/02626667.2019.1659509
Cuthbert,, M. O., Gleeson,, T., Moosdorf,, N., Befus,, K. M., Schneider,, A., Hartmann,, J., & Lehner,, B. (2019). Global patterns and dynamics of climate–groundwater interactions. Nature Climate Change, 9(2), 137–141. https://doi.org/10.1038/s41558-018-0386-4
de Graaf,, I. E. M., Gleeson,, T., van Beek,, L. P. H., Sutanudjaja,, E. H., & Bierkens,, M. F. P. (2019). Environmental flow limits to global groundwater pumping. Nature, 574(7776), 90–94. https://doi.org/10.1038/s41586-019-1594-4
Defourny,, P., Boettcher,, M., Bontemps,, S., Brockmann,, C., Kirches,, G., Lamarche,, C., … Wevers,, J. (2017). Land cover CCI product user guide version 2.0. Retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf
Di Baldassarre,, G., Kemerink,, J. S., Kooy,, M., & Brandimarte,, L. (2014). Floods and societies: The spatial distribution of water‐related disaster risk and its dynamics. WIREs Water, 1(2), 133–139. https://doi.org/10.1002/wat2.1015
Di Baldassarre,, G., Martinez,, F., Kalantari,, Z., & Viglione,, A. (2017). Drought and flood in the Anthropocene: Feedback mechanisms in reservoir operation. Earth System Dynamics, 8(1), 225–233. https://doi.org/10.5194/esd-8-225-2017
Di Baldassarre,, G., Montanari,, A., Lins,, H., Koutsoyiannis,, D., Brandimarte,, L., & Blöschl,, G. (2010). Flood fatalities in Africa: From diagnosis to mitigation. Geophysical Research Letters, 37(22), L22402. https://doi.org/10.1029/2010gl045467
Di Baldassarre,, G., Nohrstedt,, D., Mård,, J., Burchardt,, S., Albin,, C., Bondesson,, S., … Parker,, C. F. (2018). An integrative research framework to unravel the interplay of natural hazards and vulnerabilities. Earth`s Future, 6(3), 305–310. https://doi.org/10.1002/2017EF000764
Di Baldassarre,, G., Viglione,, A., Carr,, G., Kuil,, L., Yan,, K., Brandimarte,, L., & Blöschl,, G. (2015). Debates‐perspectives on socio‐hydrology: Capturing feedbacks between physical and social processes: A socio‐hydrological approach to explore flood risk changes. Water Resources Research, 51(6), 4770–4781. https://doi.org/10.1002/2014WR016416
Di Baldassarre,, G., Wanders,, N., AghaKouchak,, A., Kuil,, L., Rangecroft,, S., Veldkamp,, T. I. E., … Van Loon,, A. F. (2018). Water shortages worsened by reservoir effects. Nature Sustainability, 1(11), 617–622. https://doi.org/10.1038/s41893-018-0159-0
Dobson,, J. E., Bright,, E. A., Coleman,, P. R., Durfee,, R. C., & Worley,, B. A. (2000). LandScan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing, 66(7), 849–857.
Döll,, P., Müller Schmied,, H., Schuh,, C., Portmann,, F. T., & Eicker,, A. (2014). Global‐scale assessment of groundwater depletion and related groundwater abstractions. Combining Hydrological Modeling with Information from Well Observations and GRACE Satellites, 50(7), 5698–5720. https://doi.org/10.1002/2014wr015595
Dorigo,, W., Wagner,, W., Albergel,, C., Albrecht,, F., Balsamo,, G., Brocca,, L., … Lecomte,, P. (2017). ESA CCI soil moisture for improved earth system understanding: State‐of‐the art and future directions. Remote Sensing of Environment, 203, 185–215. https://doi.org/10.1016/j.rse.2017.07.001
Dorigo,, W. A., Xaver,, A., Vreugdenhil,, M., Gruber,, A., Hegyiová,, A., Sanchis‐Dufau,, A. D., … Drusch,, M. (2013). Global automated quality control of in situ soil moisture data from the international soil moisture network. Vadose Zone Journal, 12(3), 1–21. https://doi.org/10.2136/vzj2012.0097
Dottori,, F., Alfieri,, L., Salamon,, P., Bianchi,, A., Feyen,, L., & Hirpa,, F. A. (2016). Flood hazard map of the world. Retrieved from http://data.jrc.ec.europa.eu/collection/floods
Dottori,, F., Salamon,, P., Bianchi,, A., Alfieri,, L., Hirpa,, F. A., & Feyen,, L. (2016). Development and evaluation of a framework for global flood hazard mapping. Advances in Water Resources, 94, 87–102. https://doi.org/10.1016/j.advwatres.2016.05.002
Doxsey‐Whitfield,, E., MacManus,, K., Adamo,, S. B., Pistolesi,, L., Squires,, J., Borkovska,, O., & Baptista,, S. R. (2015). Taking advantage of the improved availability of census data: A first look at the gridded population of the world, version 4. Papers in Applied Geography, 1(3), 226–234. https://doi.org/10.1080/23754931.2015.1014272
Ehrlich,, D., Melchiorri,, M., Florczyk,, A. J., Pesaresi,, M., Kemper,, T., Corbane,, C., … Siragusa,, A. (2018). Remote sensing derived built‐up area and population density to quantify global exposure to five natural hazards over time. Remote Sensing, 10(9), 1378. https://doi.org/10.3390/rs10091378
Esch,, T., Heldens,, W., Hirner,, A., Keil,, M., Marconcini,, M., Roth,, A., … Strano,, E. (2017). Breaking new ground in mapping human settlements from space—The global urban footprint. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 30–42. https://doi.org/10.1016/j.isprsjprs.2017.10.012
European Union. (2016). EU SCIENCE HUB. Earth observation. Retrieved from https://ec.europa.eu/jrc/en/research-topic/earth-observation
Falkenmark,, M., & Chapman,, T. (1989). Comparative hydrology: An ecological approach to land and water resources. Paris: UNESCO.
Famiglietti,, J. S., Cazenave,, A., Eicker,, A., Reager,, J. T., Rodell,, M., & Velicogna,, I. (2015). Satellites provide the big picture. Science, 349(6249), 684–685. https://doi.org/10.1126/science.aac9238
Fang,, Y., Ceola,, S., Paik,, K., McGrath,, G., Rao,, P. S. C., Montanari,, A., & Jawitz,, J. W. (2018). Globally universal fractal pattern of human settlements in river networks. Earth`s Future, 6(8), 1134–1145. https://doi.org/10.1029/2017ef000746
Ficklin,, D. L., Abatzoglou,, J. T., Robeson,, S. M., Null,, S. E., & Knouft,, J. H. (2018). Natural and managed watersheds show similar responses to recent climate change. Proceedings of the National Academy of Sciences of the United States of America, 115(34), 8553–8557. https://doi.org/10.1073/pnas.1801026115
Florke,, M., Schneider,, C., & McDonald,, R. I. (2018). Water competition between cities and agriculture driven by climate change and urban growth. Nature Sustainability, 1(1), 51–58. https://doi.org/10.1038/s41893-017-0006-8
Ford,, T. W., & Quiring,, S. M. (2019). Comparison of contemporary in situ. Model, and Satellite Remote Sensing Soil Moisture with a Focus on Drought Monitoring, 55(2), 1565–1582. https://doi.org/10.1029/2018wr024039
Frasson,, R. P. d. M., Schumann,, G. J.‐P., Kettner,, A. J., Brakenridge,, G. R., & Krajewski,, W. F. (2019). Will the surface water and ocean topography (SWOT) satellite Mission observe floods? Geophysical Research Letters, 46(17–18), 10435–10445. https://doi.org/10.1029/2019gl084686
Gao,, X., Liang,, S., & He,, B. (2019). Detected global agricultural greening from satellite data. Agricultural and Forest Meteorology, 276‐277, 107652. https://doi.org/10.1016/j.agrformet.2019.107652
Giglio,, L., Boschetti,, L., Roy,, D. P., Humber,, M. L., & Justice,, C. O. (2018). The collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217, 72–85. https://doi.org/10.1016/j.rse.2018.08.005
Gilbert,, M., Nicolas,, G., Cinardi,, G., Van Boeckel,, T. P., Vanwambeke,, S. O., Wint,, G. R. W., & Robinson,, T. P. (2018). Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Scientific Data, 5, 180227. https://doi.org/10.1038/sdata.2018.227
Gorelick,, N., Hancher,, M., Dixon,, M., Ilyushchenko,, S., Thau,, D., & Moore,, R. (2017). Google earth engine: Planetary‐scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
GRAIN. (2016). The global farmland grab in 2016. How big, how bad? Retrieved from https://www.grain.org/article/entries/5492-the-global-farmland-grab-in-2016-how-big-how-bad
Gründemann,, G. J., Werner,, M., & Veldkamp,, T. I. E. (2018). The potential of global reanalysis datasets in identifying flood events in Southern Africa. Hydrology and Earth System Sciences, 22(9), 4667–4683. https://doi.org/10.5194/hess-22-4667-2018
Gudmundsson,, L., & Seneviratne,, S. I. (2016). Anthropogenic climate change affects meteorological drought risk in Europe. Environmental Research Letters, 11(4), 044005. https://doi.org/10.1088/1748-9326/11/4/044005
Guo,, H., Bao,, A., Ndayisaba,, F., Liu,, T., Jiapaer,, G., El‐Tantawi,, A. M., & De Maeyer,, P. (2018). Space‐time characterization of drought events and their impacts on vegetation in Central Asia. Journal of Hydrology, 564, 1165–1178. https://doi.org/10.1016/j.jhydrol.2018.07.081
Gupta,, H. V., Perrin,, C., Blöschl,, G., Montanari,, A., Kumar,, R., Clark,, M., & Andréassian,, V. (2014). Large‐sample hydrology: A need to balance depth with breadth. Hydrology and Earth System Sciences, 18(2), 463–477. https://doi.org/10.5194/hess-18-463-2014
Hannah,, D. M., Demuth,, S., van Lanen,, H. A. J., & Looser,, U. (2011). Large‐scale river flow archives: Importance, current status and future needs. Hydrological Processes, 25(7), 1191–1200. https://doi.org/10.1002/hyp.7794
Harris,, I., Jones,, P. D., Osborn,, T. J., & Lister,, D. H. (2014). Updated high‐resolution grids of monthly climatic observations—The CRU TS3.10 dataset. International Journal of Climatology, 34(3), 623–642. https://doi.org/10.1002/joc.3711
Hengl,, T., Mendes de Jesus,, J., Heuvelink,, G. B. M., Ruiperez Gonzalez,, M., Kilibarda,, M., Blagotić,, A., … Kempen,, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748
Huang,, C., Chen,, Y., Zhang,, S. Q., & Wu,, J. P. (2018). Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Reviews of Geophysics, 56(2), 333–360. https://doi.org/10.1029/2018rg000598
Jägermeyr,, J., Gerten,, D., Schaphoff,, S., Heinke,, J., Lucht,, W., & Rockström,, J. (2016). Integrated crop water management might sustainably halve the global food gap. Environmental Research Letters, 11(2), 025002. https://doi.org/10.1088/1748-9326/11/2/025002
Jongman,, B., Ward,, P. J., & Aerts,, J. (2012). Global exposure to river and coastal flooding: Long term trends and changes. Global Environmental Change‐Human and Policy Dimensions, 22(4), 823–835. https://doi.org/10.1016/j.gloenvcha.2012.07.004
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, 112(18), E2271–E2280. https://doi.org/10.1073/pnas.1414439112
Kidd,, C., Becker,, A., Huffman,, G. J., Muller,, C. L., Joe,, P., Skofronick‐Jackson,, G., & Kirschbaum,, D. B. (2017). So, how much of the Earth`s surface is covered by rain gauges? Bulletin of the American Meteorological Society, 98(1), 69–78. https://doi.org/10.1175/bams-d-14-00283.1
Kidd,, C., & Huffman,, G. (2011). Global precipitation measurement. Meteorological Applications, 18(3), 334–353. https://doi.org/10.1002/met.284
Killough,, B. (2018). Overview of the Open Data Cube Initiative. Paper presented at the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018 (pp. 8629–8632). https://doi.org/10.1109/IGARSS.2018.8517694.
Klisch,, A., & Atzberger,, C. (2016). Operational drought monitoring in Kenya using MODIS NDVI time series. 8(4), 267. Retrieved from https://www.mdpi.com/2072-4292/8/4/267
Koks,, E. E., Rozenberg,, J., Zorn,, C., Tariverdi,, M., Vousdoukas,, M., Fraser,, S. A., … Hallegatte,, S. (2019). A global multi‐hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10, 2677. https://doi.org/10.1038/s41467-019-10442-3
Kovács,, G. (1984). Proposal to construct a coordinating matrix forcomparative hydrology. Hydrological Sciences Journal, 29(4), 435–443. https://doi.org/10.1080/02626668409490961
Kreibich,, H., Di Baldassarre,, G., Vorogushyn,, S., Aerts,, J. C. J. H., Apel,, H., Aronica,, G. T., … Merz,, B. (2017). Adaptation to flood risk: Results of international paired flood event studies. Earth`s Future, 5(10), 953–965. https://doi.org/10.1002/2017ef000606
Kummu,, M., Taka,, M., & Guillaume,, J. H. A. (2018). Gridded global datasets for gross domestic product and human development index over 1990–2015. Scientific Data, 5, 180004. https://doi.org/10.1038/sdata.2018.4
Lamarche,, C., Santoro,, M., Bontemps,, S., d`Andrimont,, R., Radoux,, J., Giustarini,, L., … Arino,, O. (2017). Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sensing, 9(1), 36. https://doi.org/10.3390/rs9010036
Le Coz,, J., Patalano,, A., Collins,, D., Guillen,, N. F., Garcia,, C. M., Smart,, G. M., … Braud,, I. (2016). Crowd sourced data for flood hydrology: Feedback from recent citizen science projects in Argentina, France and New Zealand. Journal of Hydrology, 541, 766–777. https://doi.org/10.1016/j.jhydrol.2016.07.036
Lehmann,, J., Coumou,, D., & Frieler,, K. J. C. C. (2015). Increased record‐breaking precipitation events under global warming, 132(4), 501–515. https://doi.org/10.1007/s10584-015-1434-y
Lehner,, B. (2013). HydroSHEDS technical documentation version 1.2. Washington, DC: World Wildlife Fund. Retrieved from https://www.hydrosheds.org
Lehner,, B. (2014). HydroBASINS technical documentation version 1.c. Washington, DC: World Wildlife Fund. Retrieved https://www.hydrosheds.org
Lehner,, B., & Döll,, P. (2004). Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296(1), 1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028
Lehner,, B., & Grill,, G. (2013). Global river hydrography and network routing: Baseline data and new approaches to study the world`s large river systems. Hydrological Processes, 27(15), 2171–2186. https://doi.org/10.1002/hyp.9740
Lehner,, B., Liermann,, C. R., Revenga,, C., Vörösmarty,, C., Fekete,, B., Crouzet,, P., … Wisser,, D. (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. Retrieved from. http://www.jstor.org/stable/23034466
Lehner,, B., Verdin,, K., & Jarvis,, A. (2008). New global hydrography derived from Spaceborne elevation data. Eos, Transactions American Geophysical Union, 89(10), 93–94. https://doi.org/10.1029/2008EO100001
Leyk,, S., Gaughan,, A. E., Adamo,, S. B., de Sherbinin,, A., Balk,, D., Freire,, S., … Pesaresi,, M. (2019). The spatial allocation of population: A review of large‐scale gridded population data products and their fitness for use. Earth System Science Data, 11(3), 1385–1409. https://doi.org/10.5194/essd-11-1385-2019
Li,, L., Skidmore,, A., Vrieling,, A., & Wang,, T. (2019). A new dense 18‐year time series of surface water fraction estimates from MODIS for the Mediterranean region. Hydrology and Earth System Sciences, 23(7), 3037–3056. https://doi.org/10.5194/hess-23-3037-2019
Mao,, Y., Zhou,, T., Leung,, L. R., Tesfa,, T. K., Li,, H.‐Y., Wang,, K., … Getirana,, A. (2019). Flood inundation generation mechanisms and their changes in 1953–2004 in global Major River basins. Journal of Geophysical Research: Atmospheres, 124(22), 11672–11692. https://doi.org/10.1029/2019jd031381
Mård,, J., Di Baldassarre,, G., & Mazzoleni,, M. (2018). Nighttime light data reveal how flood protection shapes human proximity to rivers. Science Advances, 4(8), eaar5779. https://doi.org/10.1126/sciadv.aar5779
Masih,, I., Maskey,, S., Mussá,, F. E. F., & Trambauer,, P. (2014). A review of droughts on the African continent: A geospatial and long‐term perspective. Hydrology and Earth System Sciences, 18(9), 3635–3649. https://doi.org/10.5194/hess-18-3635-2014
McCabe,, M. F., Rodell,, M., Alsdorf,, D. E., Miralles,, D. G., Uijlenhoet,, R., Wagner,, W., … Wood,, E. F. (2017). The future of earth observation in hydrology. Hydrology and Earth System Sciences, 21(7), 3879–3914. https://doi.org/10.5194/hess-21-3879-2017
Mohammad,, P., & Goswami,, A. (2019). Temperature and precipitation trend over 139 major Indian cities: An assessment over a century. Modeling Earth Systems and Environment, 5(4), 1481–1493. https://doi.org/10.1007/s40808-019-00642-7
Musa,, Z. N., Popescu,, I., & Mynett,, A. (2015). A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation. Hydrology and Earth System Sciences, 19(9), 3755–3769. https://doi.org/10.5194/hess-19-3755-2015
National Oceanic and Atmospheric Administration. (2017). README for version 1 nighttime VIIRS day/night band composites. Retrieved from https://data.ngdc.noaa.gov/instruments/remote-sensing/passive/spectrometers-radiometers/imaging/viirs/dnb_composites/v10/README_dnb_composites_v1.txt
Nemani,, R., Votava,, P., Michaelis,, A., Melton,, F., & Milesi,, C. (2011). Collaborative supercomputing for global change science. Eos, Transactions American Geophysical Union, 92(13), 109–110. https://doi.org/10.1029/2011eo130001
Palacios‐Lopez,, D., Bachofer,, F., Esch,, T., Heldens,, W., Hirner,, A., Marconcini,, M., … Reinartz,, P. (2019). New perspectives for mapping global population distribution using world settlement footprint products. Sustainability, 11(21), 6056 Retrieved from https://www.mdpi.com/2071-1050/11/21/6056
Palmer,, M., & Ruhi,, A. (2018). Measuring Earth`s rivers. Science, 361(6402), 546–547. https://doi.org/10.1126/science.aau3842
Pande,, S., & Sivapalan,, M. (2017). Progress in socio‐hydrology: A meta‐analysis of challenges and opportunities. WIREs Water, 4(4), 18. https://doi.org/10.1002/wat2.1193
Parrens,, M., Bitar,, A. A., Frappart,, F., Paiva,, R., Wongchuig,, S., Papa,, F., … Kerr,, Y. (2019). High resolution mapping of inundation area in the Amazon basin from a combination of L‐band passive microwave, optical and radar datasets. International Journal of Applied Earth Observation and Geoinformation, 81, 58–71. https://doi.org/10.1016/j.jag.2019.04.011
Pekel,, J.‐F., Cottam,, A., Gorelick,, N., & Belward,, A. S. (2016). High‐resolution mapping of global surface water and its long‐term changes. Nature, 540, 418–422. https://doi.org/10.1038/nature20584
Pérez‐Hoyos,, A., Rembold,, F., Kerdiles,, H., & Gallego,, J. (2017). Comparison of global land cover datasets for cropland monitoring. Remote Sensing, 9(11), 1118. https://doi.org/10.3390/rs9111118
Policelli,, F., Slayback,, D., Brakenridge,, B., Nigro,, J., Hubbard,, A., Zaitchik,, B., … Jung,, H. (2017). The NASA global flood mapping system. In V. Lakshmi, (Ed.), Remote sensing of hydrological extremes (pp. 47–63). Cham: Springer International Publishing.
Rosser,, J. F., Leibovici,, D. G., & Jackson,, M. J. (2017). Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards, 87(1), 103–120. https://doi.org/10.1007/s11069-017-2755-0
Schneider,, U., Becker,, A., Finger,, P., Meyer‐Christoffer,, A., & Ziese,, M. (2018). GPCC full data monthly product version 2018 at 0.25°: Monthly land‐surface precipitation from rain‐gauges built on GTS‐based and historical data. https://doi.org/10.5676/DWD_GPCC/FD_M_V2018_025
Schumann,, G., Bates,, P. D., Horritt,, M. S., Matgen,, P., & Pappenberger,, F. (2009). Progress in integration of remote sensing‐derived flood extent and stage data and hydraulic models. Reviews of Geophysics, 47(4), RG4001. https://doi.org/10.1029/2008rg000274
Schwatke,, C., Dettmering,, D., Bosch,, W., & Seitz,, F. (2015). DAHITI—An innovative approach for estimating water level time series over inland waters using multi‐mission satellite altimetry. Hydrology and Earth System Sciences, 19(10), 4345–4364. https://doi.org/10.5194/hess-19-4345-2015
Shaeri Karimi,, S., Saintilan,, N., Wen,, L., & Valavi,, R. (2019). Application of machine learning to model wetland inundation patterns across a large semiarid floodplain. Water Resources Research, 55(11), 8765–8778. https://doi.org/10.1029/2019wr024884
Sharma,, S., Blagrave,, K., Magnuson,, J. J., O`Reilly,, C. M., Oliver,, S., Batt,, R. D., … Woolway,, R. I. (2019). Widespread loss of lake ice around the Northern Hemisphere in a warming world. Nature Climate Change, 9(3), 227–231. https://doi.org/10.1038/s41558-018-0393-5
Sheffield,, J., Wood,, E. F., Chaney,, N., Guan,, K., Sadri,, S., Yuan,, X., … Ogallo,, L. (2014). A drought monitoring and forecasting system for sub‐Sahara African water resources and food security. Bulletin of the American Meteorological Society, 95(6), 861–882. https://doi.org/10.1175/bams-d-12-00124.1
Shiru,, M. S., Shahid,, S., Chung,, E.‐S., & Alias,, N. (2019). Changing characteristics of meteorological droughts in Nigeria during 1901–2010. Atmospheric Research, 223, 60–73. https://doi.org/10.1016/j.atmosres.2019.03.010
Siebert,, S., Kummu,, M., Porkka,, M., Doll,, P., Ramankutty,, N., & 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. https://doi.org/10.5194/hess-19-1521-2015
Smith,, A., Bates,, P. D., Wing,, O., Sampson,, C., Quinn,, N., & Neal,, J. (2019). New estimates of flood exposure in developing countries using high‐resolution population data. Nature Communications, 10(1), 1814. https://doi.org/10.1038/s41467-019-09282-y
Socioeconomic Data and Applications Center, & Center for International Earth Science Information Network. (2015). Global estimated net migration grids by decade: 1970–2000 documentation. New York, NY: Columbia University Retrieved from http://sedac.ciesin.columbia.edu/downloads/docs/popdynamics/popdynamics-global-est-net-migration-grids-1970-2000-documentation.pdf
Spinoni,, J., Barbosa,, P., De Jager,, A., McCormick,, N., Naumann,, G., Vogt,, J. V., … Mazzeschi,, M. (2019). A new global database of meteorological drought events from 1951 to 2016. Journal of Hydrology: Regional Studies, 22, 100593. https://doi.org/10.1016/j.ejrh.2019.100593
Spinoni,, J., Naumann,, G., Carrao,, H., Barbosa,, P., & Vogt,, J. (2014). World drought frequency, duration, and severity for 1951–2010. International Journal of Climatology, 34(8), 2792–2804. https://doi.org/10.1002/joc.3875
Sun,, Q., Miao,, C., Duan,, Q., Ashouri,, H., Sorooshian,, S., & Hsu,, K.‐L. (2018). A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Reviews of Geophysics, 56(1), 79–107. https://doi.org/10.1002/2017rg000574
Sutanto,, S. J., van der Weert,, M., Wanders,, N., Blauhut,, V., & Van Lanen,, H. A. J. (2019). Moving from drought hazard to impact forecasts. Nature Communications, 10(1), 4945. https://doi.org/10.1038/s41467-019-12840-z
Sutanudjaja,, E. H., van Beek,, R., Wanders,, N., Wada,, Y., Bosmans,, J. H. C., Drost,, N., … Bierkens,, M. F. P. (2018). PCR‐GLOBWB 2: A 5 arcmin global hydrological and water resources model. Geoscientific Model Development, 11(6), 2429–2453. https://doi.org/10.5194/gmd-11-2429-2018
Thebo,, A. L., Drechsel,, P., Lambin,, E. F., & Nelson,, K. L. (2017). A global, spatially‐explicit assessment of irrigated croplands influenced by urban wastewater flows. Environmental Research Letters, 12(7), 074008. https://doi.org/10.1088/1748-9326/aa75d1
Trigg,, M. A., Birch,, C. E., Neal,, J. C., Bates,, P. D., Smith,, A., Sampson,, C. C., … Fewtrell,, T. J. (2016). The credibility challenge for global fluvial flood risk analysis. Environmental Research Letters, 11(9), 094014. https://doi.org/10.1088/1748-9326/11/9/094014
United Nations. (2015). Resolution adopted by the General Assembly on 25 September 2015. 70/1. Transforming our world: The 2030 Agenda for Sustainable Development. New York, NY: Author. Retrieved from https://sustainabledevelopment.un.org/post2015/transformingourworld
United Nations Environment Programme. (2016). A snapshot of the World`s water quality: Towards a global assessment. Nairobi, Kenya: Author. Retrieved from https://uneplive.unep.org/media/docs/assessments/unep_wwqa_report_web.pdf
United Nations Environment Programme. (2019). GEMStat database of the global environment monitoring system for freshwater (GEMS/water) programme. Nairobi, Kenya: Author Retrieved from https://gemstat.org/
United Nations Office for Disaster Risk Reduction. (2015). GAR 2015 global assessment report on disaster risk reduction, making development sustainable: The future of disaster risk management. Geneva, Switzerland, Switzerland: Author. Retrieved from www.preventionweb.net/english/hyogo/gar/2015/en/gar-pdf/GAR2015_EN.pdf
United Nations Office for Disaster Risk Reduction. (2019). Global assessment report on disaster risk reduction. Geneva, Switzerland, Switzerland: Author. Retrieved from https://gar.unisdr.org/report-2019
United States Department of Agriculture. (2019). Global reservoirs/lakes (G‐REALM). Washington, DC: Author. Retrieved from https://ipad.fas.usda.gov/cropexplorer/global_reservoir/Default.aspx#Note
Van Loon,, A. F., Gleeson,, T., Clark,, J., Van Dijk,, A. I. J. M., Stahl,, K., Hannaford,, J., … Van Lanen,, H. A. J. (2016). Drought in the Anthropocene. Nature Geoscience, 9(2), 89–91. https://doi.org/10.1038/ngeo2646
Vicente‐Serrano,, S. M., Van der Schrier,, G., Beguería,, S., Azorin‐Molina,, C., & Lopez‐Moreno,, J.‐I. (2015). Contribution of precipitation and reference evapotranspiration to drought indices under different climates. Journal of Hydrology, 526, 42–54. https://doi.org/10.1016/j.jhydrol.2014.11.025
Vogel,, E., Donat,, M. G., Alexander,, L. V., Meinshausen,, M., Ray,, D. K., Karoly,, D., … Frieler,, K. (2019). The effects of climate extremes on global agricultural yields. Environmental Research Letters, 14(5), 054010. https://doi.org/10.1088/1748-9326/ab154b
Vogel,, R. M., Lall,, U., Cai,, X. M., Rajagopalan,, B., Weiskel,, P. K., Hooper,, R. P., & Matalas,, N. C. (2015). Hydrology: The interdisciplinary science of water. Water Resources Research, 51(6), 4409–4430. https://doi.org/10.1002/2015wr017049
Wada,, Y., Bierkens,, M. F. P., de Roo,, A., Dirmeyer,, P. A., Famiglietti,, J. S., Hanasaki,, N., … Wheater,, H. (2017). Human–water interface in hydrological modelling: Current status and future directions. Hydrology and Earth System Sciences, 21(8), 4169–4193. https://doi.org/10.5194/hess-21-4169-2017
Wada,, Y., van Beek,, L. P. H., Wanders,, N., & Bierkens,, M. (2013). Human water consumption intensifies hydrological drought worldwide. Environmental Research Letters, 8(3), 034036. https://doi.org/10.1088/1748-9326/8/3/034036
Waldner,, F., Schucknecht,, A., Lesiv,, M., Gallego,, J., See,, L., Pérez‐Hoyos,, A., … Defourny,, P. (2019). Conflation of expert and crowd reference data to validate global binary thematic maps. Remote Sensing of Environment, 221, 235–246. https://doi.org/10.1016/j.rse.2018.10.039
Wang,, Y., Liu,, G., & Guo,, E. (2019). Spatial distribution and temporal variation of drought in Inner Mongolia during 1901–2014 using standardized precipitation evapotranspiration index. Science of the Total Environment, 654, 850–862. https://doi.org/10.1016/j.scitotenv.2018.10.425
Ward,, P. J., Jongman,, B., Salamon,, P., Simpson,, A., Bates,, P., De Groeve,, T., … Winsemius,, H. C. (2015). Usefulness and limitations of global flood risk models. Nature Climate Change, 5, 712–715. https://doi.org/10.1038/nclimate2742
Wens,, M., Johnson,, J. M., Zagaria,, C., & Veldkamp,, T. I. E. (2019). Integrating human behavior dynamics into drought risk assessment—A sociohydrologic, agent‐based approach. WIREs Water, 6(4), e1345. https://doi.org/10.1002/wat2.1345
Wilkinson,, M. D., Dumontier,, M., Aalbersberg,, I. J., Appleton,, G., Axton,, M., Baak,, A., … Mons,, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
Winsemius,, H. C., Aerts,, J. C. J. H., van Beek,, L. P. H., Bierkens,, M. F. P., Bouwman,, A., Jongman,, B., … Ward,, P. J. (2015). Global drivers of future river flood risk. Nature Climate Change, 6, 381–385. https://doi.org/10.1038/nclimate2893
World Meteorological Organization. (2016). The global observing system for climate: Implementation needs. GCOS‐200. Geneva, Switzerland: Author. Retrieved from https://library.wmo.int/doc_num.php?explnum_id=3417
Wu,, H., Kimball,, J. S., Zhou,, N., Alfieri,, L., Luo,, L., Du,, J., & Huang,, Z. (2019). Evaluation of real‐time global flood modeling with satellite surface inundation observations from SMAP. Remote Sensing of Environment, 233, 111360. https://doi.org/10.1016/j.rse.2019.111360
Yamazaki,, D., O`Loughlin,, F., Trigg,, M. A., Miller,, Z. F., Pavelsky,, T. M., & Bates,, P. D. (2014). Development of the global width database for large rivers. Water Resources Research, 50(4), 3467–3480. https://doi.org/10.1002/2013WR014664
Yamazaki,, D., Trigg,, M. A., & Ikeshima,, D. (2015). Development of a global ~90 m water body map using multi‐temporal Landsat images. Remote Sensing of Environment, 171, 337–351. https://doi.org/10.1016/j.rse.2015.10.014
Yao,, F., Wang,, J., Wang,, C., & Crétaux,, J.‐F. (2019). Constructing long‐term high‐frequency time series of global lake and reservoir areas using Landsat imagery. Remote Sensing of Environment, 232, 111210. https://doi.org/10.1016/j.rse.2019.111210
Zarfl,, C., Lumsdon,, A. E., Berlekamp,, J., Tydecks,, L., & Tockner,, K. J. A. S. (2015). A global boom in hydropower dam construction. Aquatic Sciences, 77(1), 161–170. https://doi.org/10.1007/s00027-014-0377-0
Zhao,, G., & Gao,, H. (2018). Automatic correction of contaminated images for assessment of reservoir surface area dynamics. Geophysical Research Letters, 45(12), 6092–6099. https://doi.org/10.1029/2018gl078343
Zhu,, Z., Wulder,, M. A., Roy,, D. P., Woodcock,, C. E., Hansen,, M. C., Radeloff,, V. C., … Scambos,, T. A. (2019). Benefits of the free and open Landsat data policy. Remote Sensing of Environment, 224, 382–385. https://doi.org/10.1016/j.rse.2019.02.016
Zwarteveen,, M., Kemerink‐Seyoum,, J. S., Kooy,, M., Evers,, J., Guerrero,, T. A., Batubara,, B., … Wesselink,, A. (2017). Engaging with the politics of water governance. WIREs Water, 4(6), e1245. https://doi.org/10.1002/wat2.1245

Further Reading

Frasson,, R. P. d. M., Pavelsky,, T. M., Fonstad,, M. A., Durand,, M. T., Allen,, G. H., Schumann,, G., … Yang,, X. (2019). Global relationships between river width, slope, catchment area, meander wavelength, sinuosity, and discharge. Geophysical Research Letters, 46(6), 3252–3262. https://doi.org/10.1029/2019gl082027

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