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