[en] To better understand the increasing human impact on the water cycle and the feedbacks between hydrology and society, the International Association of Hydrological Sciences (IAHS) organized the scientific decade "Panta Rhei-Everything Flows: Change in hydrology and society" (2013-2022). A key finding is the need to use integrated approaches to assess the co-evolution of human-water systems in order to avoid unintended consequences of human interventions over long periods of time. Additionally, substantial progress has been made in leveraging new data sources on human behaviour, e.g. through text mining of social media posts. Much has been learned about detecting hydrological changes and attributing them to their drivers, e.g. quantifying climate effects on floods. To achieve further progress, we recommend broadening the understanding, the discipline and training activities, while at the same time pursuing synthesis by focusing on key themes, developing innovative approaches and finding sustainable solutions to the world's water problems.
Research Center/Unit :
UEE - Urban and Environmental Engineering - ULiège
Disciplines :
Civil engineering
Author, co-author :
Kreibich, Heidi
Sivapalan, Murugesu
Aghakouchak, Amir
Addor, Nans
Aksoy, Hafzullah
Arheimer, Berit
Arnbjerg-Nielsen, Karsten
Vail- Castro, Cynthia
Cudennec, Christophe
Madruga De Brito, Mariana
Di Baldassarre, Giuliano
Finger, David
Fowler, Keirnan
Knoben, Wouter
Krueger, Tobias
Liu, Junguo
Macdonald, Elena
Mcmillan, Hilary
Mendiondo, E. Mario
Montanari, Alberto
Muller, Marc
Pande, Saket
Tian, Fuqiang
Viglione, Alberto
Wei, Yongping
Castellarin, Attilio
Loucks, Daniel Peter
Oki, Taikan
Polo, María
Savenije, Huub
Van Loon, Anne
Agarwal, Ankit
Alvarez-Garreton, Camila
Andreu, Ana
Barendrecht, Marlies
Brunner, Manuela
Cavalcante, Louise
Cavus, Yonca
Ceola, Serena
Chaffe, Pedro
Chen, Xi
Coxon, Gemma
Dandan, Zhao
Davary, Kamran
Dembélé, Moctar
Dewals, Benjamin ; Université de Liège - ULiège > Département ArGEnCo > Hydraulics in Environmental and Civil Engineering
Frolova, Tatiana
Gain, Animesh
Gelfan, Alexander
Ghoreishi, Mohammad
Grabs, Thomas
Guan, Xiaoxiang
Hannah, David
Helmschrot, Joerg
Höllermann, Britta
Hounkpè, Jean
Koebele, Elizabeth
Konar, Megan
Kratzert, Frederik
Lindersson, Sara; Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Llasat, Maria
Matanó, Alessia
Mazzoleni, Maurizio
Mejia, Alfonso
Mendoza, Pablo
Merz, Bruno; Section Hydrology, GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
Ackerman Grunfeld, D., et al., 2024. Underestimated burden of per- and polyfluoroalkyl substances in global surface waters and groundwaters. Nature Geoscience, 17 (4), 340–346. doi:10.1038/s41561-024-01402-8.
Addor, N., et al., 2017. The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth System Science, 21 (10), 5293–5313. doi:10.5194/hess-21-5293-2017.
Addor, N., et al., 2019. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges. Hydrological Sciences Journal, 65 (5), 712–725. doi:10.1080/02626667.2019.1683182.
Addor, N., and Melsen, L.A., 2019. Legacy, rather than adequacy, drives the selection of hydrological models. Water Resources Research, 55 (1), 378–390. doi:10.1029/2018WR022958.
Adger, W.N., et al., 2013. Changing social contracts in climate-change adaptation. Nature Climate Change, 3 (4), 330–333. doi:10.1038/nclimate1751.
AghaKouchak, A., et al., 2021. Anthropogenic drought: definition, challenges, and opportunities. Reviews of Geophysics, 59 (2), e2019RG000683. doi:10.1029/2019RG000683.
AghaKouchak, A., et al., 2015. Water and climate: recognize anthropogenic drought. Nature, 524 (7566), 409–411. doi:10.1038/524409a.
Aguilar, C., Montanari, A., and Polo, M.J., 2017. Real-time updating of the flood frequency distribution through data assimilation. Hydrology and Earth System Sciences, 21 (7), 3687–3700. doi:10.5194/hess-21-3687-2017.
Alam, M.F., et al., 2022. Understanding human–water feedbacks of interventions in agricultural systems with agent based models: a review. Environmental Research Letters, 17 (10), 103003. doi:10.1088/1748-9326/ac91e1.
Álamos, N., et al., 2024. The influence of human activities on streamflow reductions during the megadrought in central Chile, Hydrol. Earth System Science, 28 (11), 2483–2503. doi:10.5194/hess-28-2483-2024.
Alborzi, A., et al., 2018. Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts. Earth System Science, 13 (8), 084010. doi:10.1088/1748-9326/aad246.
Alencar, P.H., et al., 2024. Flash droughts and their impacts–using newspaper articles to assess the perceived consequences of rapidly emerging droughts. Environmental Research Letters, 19, 074048. doi:10.1088/1748-9326/ad58fa.
Alexander, S.M., et al., 2020. Qualitative data sharing and synthesis for sustainability science. Nature Sustainability, 3 (2), 81–88. doi:10.1038/s41893-019-0434-8.
Alshehhi, R., and Marpu, P.R., 2017. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 245–260. doi:10.1016/j.isprsjprs.2017.02.008.
Althoff, D., Bazame, H.C., and Nascimento, J.G., 2021. Untangling hybrid hydrological models with explainable artificial intelligence. H2Open Journal, 4 (1), 13–28. doi:10.2166/h2oj.2021.066.
Alvarez-Garreton, C., et al., 2018. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies–chile dataset. Hydrol Earth Syst. Sci, 22 (11), 5817–5846. doi:10.5194/hess-22-5817-2018.
Amirkhani, M., et al., 2022. An operational sociohydrological model to understand the feedbacks between community sensitivity and environmental flows for an endorheic lake basin, lake Bakhtegan, Iran. Journal of Hydrology, 605 (2022), 127375. doi:10.1016/j.jhydrol.2021.127375.
Anna, H., et al., September2019. Global mapping of citizen science projects for disaster risk reduction. Frontiers of Earth Science, 7. doi:10.3389/feart.2019.00226.
Annis, A., and Nardi, F., 2019. Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping. Geo-Spatial Information Science, 22 (4), 223–236. doi:10.1080/10095020.2019.1626135.
Arheimer, B., et al., 2020. Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation, Hydrol. Earth System Science, 24 (2), 535–559. doi:10.5194/hess-24-535-2020
Arheimer, B., et al., 2024. The IAHS science for solutions decade, with hydrology engaging local people in one global world (HELPING). Hydrological Sciences Journal, 69 (11), 1417–1435. doi:10.1080/02626667.2024.2355202.
Arheimer, B., Donnelly, C., and Lindström, G., 2017. Regulation of snow-fed rivers affects flow regimes more than climate change. Nature Communications, 8 (1). doi:10.1038/s41467-017-00092-8.
Arheimer, B., and Lindström, G., 2019. Detecting changes in river flow caused by wildfires, storms, urbanization, regulation, and climate across Sweden. Water Resources Research, 55 (11), 8990–9005. doi:10.1029/2019WR024759.
Arheimer, B., Nilsson, J., and Lindström, G., 2015. Experimenting with coupled hydro-ecological models to explore measure plans and water quality goals in a semi-enclosed swedish bay. Water, 7 (7), 3906–3924. doi:10.3390/w7073906.
Arheimer, B., and Pers, B.C., 2017. Lessons learned? Effects of nutrient reductions from constructing wetlands in 1996–2006 across Sweden. Ecological Engineering. 103, 404–414. doi:10.1016/j.ecoleng.2016.01.088
Arnbjerg-Nielsen, K., et al., 2022. To what extent should we ensure the explicit inclusion of water quality within the WEF nexus? Discussion of “Water quality: the missing dimension of water in the water–energy–food nexus. Hydrological Sciences Journal, 67 (8), 1287–1290. doi:10.1080/02626667.2022.2077651.
Badjana, H.M., et al., 2017. Hydrological system analysis and modelling of the Kara River Basin (West Africa) using a lumped metric conceptual model. Hydrological Sciences Journal, 62 (7), 1094–1113. doi:10.1080/02626667.2017.1307571.
Bai, X., et al., 2016. Plausible and desirable futures in the Anthropocene: a new research agenda. Global Environmental Change, 39, 351–362. doi:10.1016/j.gloenvcha.2015.09.017.
Baldassarre, D., et al., 2021. Integrating multiple research methods to unravel the complexity of human-water systems. AGU Advances, 2 (3), 3. doi:10.1029/2021av000473.
Bárdossy, A., Seidel, J., and El Hachem, A., 2021. The use of personal weather station observations to improve precipitation estimation and interpolation, Hydrol. Earth System Science, 25 (2), 583–601. doi:10.5194/hess-25-583-2021.
Barendrecht, M.H., et al., 2019. The value of empirical data for estimating the parameters of a sociohydrological flood risk model. Water Resources Research, 55 (2), 1312–1336. doi:10.1029/2018WR024128.
Barendrecht, M.H., Viglione, A., and Blöschl, G., 2017. A dynamic framework for flood risk. Water Security, 1, 3–11. doi:10.1016/j.wasec.2017.02.001.
Bartosova, A., et al., 2019. Future socioeconomic conditions may have a larger impact than climate change on nutrient loads to the Baltic Sea. Ambio, 48 (11), 1325–1336. doi:10.1007/s13280-019-01243-5.
Bartosova, A., et al., 2021. Large-scale hydrological and sediment modeling in nested domains under current and changing climate. Journal of Hydrologic Engineering, 26 (5). doi:10.1061/(ASCE)HE.1943-5584.0002078.
Bassi, A., et al., 2024. Learning landscape features from streamflow with autoencoders. Hydrology and Earth System Sciences Discussions, 2024, 1–30.
Beck, M., and Krueger, T., 2016. The epistemic, ethical, and political dimensions of uncertainty in integrated assessment modeling. WIREs Climate Change, 7, 627–645. doi:10.1002/wcc.415.
Berg, P., Donnelly, C., and Gustafsson, D., 2018. Near-real-time adjusted reanalysis forcing data for hydrology. Hydrology Earth Systemtic Sciences, 22 (2), 989–1000. doi:10.5194/hess-22-989-2018.
Bertassello, L., Levy, M.C., and Müller, M.F., 2021. Sociohydrology, ecohydrology, and the space-time dynamics of human-altered catchments. Hydrological Sciences Journal, 66 (9), 1393–1408. doi:10.1080/02626667.2021.1948550.
Bertola, M., et al., 2020. Flood trends in Europe: are changes in small and big floods different?Hydrology and Earth System Sciences, 24 (4), 1805–1822. doi:10.5194/hess-24-1805-2020.
Bertola, M., et al., 2021. Do small and large floods have the same drivers of change? A regional attribution analysis in Europe. Hydrology and Earth System Sciences, 25 (3), 1347–1364. doi:10.5194/hess-25-1347-2021.
Bertola, M., Viglione, A., and Blöschl, G., 2019. Informed attribution of flood changes to decadal variation of atmospheric, catchment and river drivers in Upper Austria. Journal of Hydrology, 577, 123919. doi:10.1016/j.jhydrol.2019.123919.
Biswas, A.K., 2004. Integrated water resources management: a reassessment. Water International, 29 (2), 248–256. doi:10.1080/02508060408691775.
Bloomfield, J.P., et al., 2021. How is Baseflow Index (BFI) impacted by water resource management practices?, Hydrol. Earth Systemtic Sciences, 25 (10), 5355–5379. doi:10.5194/hess-25-5355-2021.
Blöschl, G., et al., 2013. Runoff predictions in ungauged basins–synthesis across processes, places and scales. Cambridge, UK: Cambridge University Press, 465.
Blöschl, G., et al., 2017. Changing climate shifts timing of European floods. Science, 357 (6351), 588–590. doi:10.1126/science.aan2506.
Blöschl, G., et al., 2019a. Twenty-three unsolved problems in hydrology (UPH) - a community perspective. Hydrological Sciences Journal, 64 (10), 1141–1158. doi:10.1080/02626667.2019.1620507
Blöschl, G., et al., 2019b. Changing climate both increases and decreases European river floods. Nature, 573 (7772), 108–111. doi:10.1038/s41586-019-1495-6.
Blum, A.G., et al., 2020. Causal effect of impervious cover on annual flood magnitude for the United States. Geophysical Research Letters, 47 (5), no–no. doi:10.1029/2019GL086480.
Bonotto, G., et al., 2022. Identifying causal interactions between groundwater and streamflow using convergent cross-mapping. Water Resources Research, 58 (8), e2021WR030231. doi:10.1029/2021WR030231.
Bou Nassar, J.A., et al., 2021. Multi-level storylines for participatory modeling–involving marginalized communities in Tz’olöj Ya. Mayan Guatemala Hydrology Earth Systemtic Sciences, 25 (3), 1283–1306. doi:10.5194/hess-25-1283-2021.
Brelsford, C., et al., 2020. Developing a sustainability science approach for water systems. Ecology & Society, 25 (2). doi:10.5751/ES-11515-250223.
Brondizio, E.S., et al., 2016. Re-conceptualizing the Anthropocene: a call for collaboration. Global Environmental Change, 39, 318–327. doi:10.1016/j.gloenvcha.2016.02.006.
Brunner, M.I., 2021. Reservoir regulation affects droughts and floods at local and regional scales. Environmental Research Letters, 16 (12), 124016. doi:10.1088/1748-9326/ac36f6.
Brunner, M.I., et al., 2023. Hydrological drought generation processes and severity are changing in the Alps. Geophysical Research Letters, 50 (2), e2022GL101776. doi:10.1029/2022GL101776.
Brunner, M.I., and Tallaksen, L.M., 2019. Proneness of European catchments to multiyear streamflow droughts. Water Resources Research, 55 (11), 8881–8894. doi:10.1029/2019WR025903.
Buarque, S., et al., 2020. Using historical source data to understand urban flood risk: a socio-hydrological modelling application at gregório creek, Brazil. Hydrological Sciences Journal, 65 (7), 1075–1083. doi:10.1080/02626667.2020.1740705.
Burt, T.P., and McDonnell, J.J., 2015b. Whither field hydrology? The need for discovery science and outrageous hydrological hypotheses. Water Resources Research, 51 (8), 5919–5928. doi:10.1002/2014WR016839.
Butsch, C., et al., 2022b. Editorial: actors and adaptive planning in water management. Frontiers in Water, 4, 991338. doi:10.3389/frwa.2022.991338.
Buytaert, W., et al., 2014. Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development. Frontiers of Earth Science, 2October. 10.3389/feart.2014.00026.
Caretta, M.A., et al., 2022. Water. In: D.C., Roberts, eds. Climate change 2022: impacts, adaptation and vulnerability. contribution of working group II to the sixth assessment report of the intergovernmental panel on climate change. Cambridge, UK and New York, NY, USA: Cambridge University Press, 551–712. doi:10.1017/9781009325844.006.
Carisi, F., et al., 2018. Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy). NHESS, 18, 2057–2079. doi:10.5194/nhess-18-2057-2018
Carnohan, S.A., et al., 2020. Climate change adaptation in rural South Africa: using stakeholder narratives to build system dynamics models in data-scarce environments. Journal of Simulation, 15 (1–2), 5–22. doi:10.1080/17477778.2020.1762516.
Carroll, S.R., et al., 2020. The CARE Principles for Indigenous Data Governance. Data Science Journal, 19 (1), 43. doi:10.5334/dsj-2020-043
Carvalho, P.N., et al., 2022. Nature-based solutions addressing the water-energy-food nexus: review of theoretical concepts and urban case studies. Journal of Cleaner Production, 338, 130652. doi:10.1016/j.jclepro.2022.130652.
Carvalho, T.M.N., de Souza Filho, F.D.A., and de Brito, M.M., 2024. Unveiling water allocation dynamics: a text analysis of 25 years of stakeholder meetings. Environmental Research Letters, 19 (4), 044066. doi:10.1088/1748-9326/ad37cd.
Cavus, Y., and Aksoy, H., 2020. Critical drought severity/intensity-duration-frequency curves based on precipitation deficit. Journal of Hydrology, 584, 124312. doi:10.1016/j.jhydrol.2019.124312.
Cavus, Y., Stahl, K., and Aksoy, H., 2022. Revisiting major dry periods by rolling time series analysis for human-water relevance in drought. Water Resources Management, 36 (8), 2725–2736. doi:10.1007/s11269-022-03171-8.
Ceola, S., et al., 2016. Adaptation of water resources systems to changing society and environment: a statement by the International association of hydrological sciences. Hydrological Sciences Journal, 61 (16), 2803–2817. doi:10.1080/02626667.2016.1230674.
Cerri, M., et al., 2021. Are openstreetmap building data useful for flood vulnerability modelling?Natural Hazards and Earth System Sciences, 21 (2), 643–662. doi:10.5194/nhess-21-643-2021.
Cervone, G., et al., 2016. Using twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study. International Journal of Remote Sensing, 37 (1), 100–124. doi:10.1080/01431161.2015.1117684.
Chagas, V.B., Chaffe, P.L., and Blöschl, G., 2022. Climate and land management accelerate the Brazilian water cycle. Nature Communications, 13 (1), 5136. doi:10.1038/s41467-022-32580-x.
Chagas, V.B.P., et al., 2020. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil. Earth System Science Data, 12 (3), 2075–2096. doi:10.5194/essd-12-2075-2020.
Chagas, V.B.P., and Chaffe, P.L.B., 2018. The role of land cover in the propagation of rainfall into streamflow trends. Water Resources Research, 54 (9), 5986–6004. doi:10.1029/2018WR022947.
Chen, J., and Rodell, M., 2021. Applications of gravity recovery and climate experiment (GRACE) in global groundwater study. In: A. Mukherjee, B. R. Scanlon, A. Aureli, S. Langan, H. Guo, A.A. McKenzie, eds. Global Groundwater. London: Elsevier, 531–543. doi:10.1016/B978-0-12-818172-0.00039-6.
Cheng, C., et al., 2022. What is the relationship between land use and surface water quality? A review and prospects from remote sensing perspective. Environ Sci Pollut Res, 29 (38), 56887–56907. doi:10.1007/s11356-022-21348-x.
Chiang, F., Mazdiyasni, O., and AghaKouchak, A., 2021. Evidence of anthropogenic impacts on global drought frequency, duration, and intensity. Nature Communications, 12 (1), 2754. doi:10.1038/s41467-021-22314-w.
Collar, N.M., et al., 2022. Linking fire-induced evapotranspiration shifts to streamflow magnitude and timing in the western United States. Journal of Hydrology, 612, 128242. doi:10.1016/j.jhydrol.2022.128242.
Collins, M.J., 2019. River flood seasonality in the Northeast United States: characterization and trends. Hydrological Processes, 33 (5), 687–698. doi:10.1002/hyp.13355
Conallin, J., et al., 2022. A review of the applicability of the motivations and abilities (MOTA) framework for assessing the implementation success of water resources management plans and policies. Hydrol. Earth Syst. Sci, 26 (5), 1357–1370. doi:10.5194/hess-26-1357-2022.
Coxon, G., et al., 2020. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth Syst. Sci. Data, 12 (4), 2459–2483. doi:10.5194/essd-12-2459-2020.
Coxon, G., et al., 2024. Wastewater discharges and urban land cover dominate urban hydrology signals across England and Wales. Environmental Research Letters, 19, 084016. doi:10.1088/1748-9326/ad5bf.
Csete, M.E., and Doyle, J.C., 2002. Reverse engineering of biological complexity. Science, 295 (5560), 1664–1669. doi:10.1126/science.1069981.
Cudennec, C., et al., 2018. Epistemological dimensions of the water-energy-food nexus approach. Hydrological Sciences Journal, 63 (12), 1868–1871. doi:10.1080/02626667.2018.1545097.
Cudennec, C., et al., 2020. Towards FAIR and SQUARE hydrological data. Hydrological Sciences Journal, 65 (5), 681–682. doi:10.1080/02626667.2020.1739397.
Cudennec, C., et al., 2022a. Operational, epistemic and ethical value chaining of hydrological data to knowledge and services: a watershed moment. Hydrological Sciences Journal, 67 (16), 2363–2368. doi:10.1080/02626667.2022.2150380.
Cudennec, C., Sud, M., and Boulton, G., 2022b. Governing Open Science. Hydrological Sciences Journal, 67 (16), 2359–2362. doi:10.1080/02626667.2022.2086462.
Dailey, K.R., Welch, K.A., and Lyons, W.B., 2014. Evaluating the influence of road salt on water quality of Ohio rivers over time. Applied Geochemistry, 47, 25–35. doi:10.1016/j.apgeochem.2014.05.006.
Daniel, D., Pande, P., and Rietveld, L., 2022. Endogeneity in water use behaviour across case studies of household water treatment adoption in developing countries. World Development Perspectives, 25, 100385. doi:10.1016/j.wdp.2021.100385.
Dasgupta, S., et al., 2015. Climate change and soil salinity: the case of coastal Bangladesh. Ambio, 44 (8), 815–826. doi:10.1007/s13280-015-0681-5.
Davenport, F.V., et al., 2020. Flood size increases nonlinearly across the western United States in response to lower snow-precipitation ratios. Water Resources Research, 56 (1), e2019WR025571. doi:10.1029/2019WR025571.
De Groeve, T., 2014. Current status and best practices for disaster loss data recording in EU member states: a comprehensive overview of current practice in the EU member states. JRC Scientific and Policy Report (Report JRC92290).
Di Baldassarre, G., et al., 2013. Socio-hydrology: conceptualising human-flood interactions, Hydrol. Earth System Science, 17 (8), 3295–3303. doi:10.5194/hess-17-3295-2013.2013.
Di Baldassarre, G., et al., 2015. Debates - Perspectives on socio-hydrology: capturing feedbacks between physical and social processes. Water Resources Research, 51 (6), 4770–4781. doi:10.1002/2014WR016416.
Di Baldassarre, G., et al., 2017. Drought and flood in the Anthropocene: feedback mechanisms in reservoir operation. Earth Syst. Dynam, 8 (1), 225–233. doi:10.5194/esd-8-225-2017.
Di Baldassarre, G., et al., 2018. Water shortages worsened by reservoir effects. Nature Sustainability, 1 (11), 617–622. doi:10.1038/s41893-018-0159-0.
Di Baldassarre, G., et al., 2019. Sociohydrology: scientific challenges in addressing the sustainable development goals. Water Resources Research, 55 (8), 6327–6355. doi:10.1029/2018WR023901.
Dixon, H., et al., 2022. Intergovernmental cooperation for hydrometry–what, why and how?Hydrological Sciences Journal, 67 (16), 2552–2566. doi:10.1080/02626667.2020.1764569.
Do Nascimento, T.V.M., et al., 2024. EStreams: an integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe. scientific Data, 11 (1), 879. doi:10.1038/s41597-024-03706-1.
Dottori, F., et al., 2018. Increased human and economic losses from river flooding with anthropogenic warming. Nature Climate Change, 8 (9), 781–786. doi:10.1038/s41558-018-0257-z.
Duethmann, D., et al., 2015. Attribution of streamflow trends in snow and glacier melt-dominated catchments of the Tarim River, Central Asia. - Water Resources Research, 51 (6), 4727–4750. doi:10.1002/2014WR016716.
Duethmann, D., Blöschl, G., and Parajka, J., 2020. Why does a conceptual hydrological model fail to correctly predict discharge changes in response to climate change?Hydrology and Earth System Sciences, 24 (7), 3493–3511. doi:10.5194/hess-24-3493-2020.
Dumont, A., Mayor, B., and López-Gunn, E., 2013. Is the rebound effect or Jevons paradox a useful concept for better management of water resources? Insights from the irrigation modernisation process in Spain. Aquatic procedia, 1, 64–76. doi:10.1016/j.aqpro.2013.07.006.
Eerkes-Medrano, D., Thompson, R.C., and Aldridge, D.C., 2015. Microplastics in freshwater systems: a review of the emerging threats, identification of knowledge gaps and prioritisation of research needs. Water Research, 75, 63–82. doi:10.1016/j.watres.2015.02.012.
Elshafei, Y., et al., 2014. A prototype framework for models of socio-hydrology: identification of key feedback loops and parameterization approach, Hydrol. Earth System Science, 18 (6), 2141–2166. doi:10.5194/hess-18-2141-2014.
Erban, L.E., et al., 2013. Release of arsenic to deep groundwater in the Mekong Delta, Vietnam, linked to pumping-induced land subsidence. Proceedings of the National Academy of Sciences, 110 (34), 13751–13756. doi:10.1073/pnas.1300503110.
Evers, M., et al., 2016. Collaborative decision making in sustainable flood risk management: a socio-technical approach and tools for participatory governance. Environmental Science & Policy, 55 (2), 335–344. doi:10.1016/j.envsci.2015.09.009.
Farahmand, H., et al., 2022. Anomalous human activity fluctuations from digital trace data signal flood inundation status. Environment and Planning. B, Urban Analytics and City Science, 49 (7), 1893–1911. doi:10.1177/23998083211069990.
Ferdous, M.R., et al., 2018. Socio-hydrological spaces in the jamuna river floodplain in Bangladesh. Hydrology and Earth System Sciences, 22 (10), 5159–5173. doi:10.5194/hess-22-5159-2018.
Ferraro, P.J., Sanchirico, J.N., and Smith, M.D., 2019. Causal inference in coupled human and natural systems. Proceedings of the National Academy of Sciences, 116(12), 5311–5318. doi:10.1073/pnas.1805563115.
Finger, D., Wüest, A., and Bossard, P., 2013. Effects of oligotrophication on primary production in peri-alpine lakes. Water Resour. Res, 49 (8), 4700–4710. doi:10.1002/wrcr.20355.
Fohringer, J., et al., 2015. Social media as an information source for rapid flood inundation mapping. NHESS, 15, 2725–2738. doi:10.5194/nhess-15-2725-2015.
Formetta, G., and Feyen, L., 2019. Empirical evidence of declining global vulnerability to climate-related hazards. Global Environmental Change, 57, 101920.
Fowler, K., et al., 2022. Hydrological shifts threaten water resources. Water Resources Research, 58 (8), e2021WR031210. doi:10.1029/2021WR031210.
Fowler, K., et al., 2022b. Explaining changes in rainfall–runoff relationships during and after Australia’s Millennium Drought: a community perspective. Hydrology and Earth System Sciences, 26 (23), 6073–6120. doi:10.5194/hess-26-6073-2022.
Fowler, K.J.A., et al., 2021. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia. Earth Syst. Sci. Data, 13 (8), 3847–3867. doi:10.5194/essd-13-3847-2021.
Franceschinis, C., et al., 2021. Heterogeneity in flood risk awareness: a longitudinal, latent class model approach. Journal of Hydrology, 599, 126255. doi:10.1016/j.jhydrol.2021.126255.
Frasson, R.P.D.M., et al., 2019. Will the surface water and ocean topography (SWOT) satellite mission observe floods?Geophysical Research Letters, 46 (17–18), 10435–10445. doi:10.1029/2019GL084686.
Frota, R.L., et al., 2021. Network” socio-hydrology: a case study of causal factors that shape the Jaguaribe River Basin, Ceará-Brazil. Hydrological Sciences Journal, 66 (6), 935–950. doi:10.1080/02626667.2021.1913282.
Fusinato, E., 2024. Safe development paradox: evidence and methodological insights from a systematic review. Nat Hazards, 120, 13693–13714. doi:10.1007/s11069-024-06774-z.
Garcia, M., et al., 2019. Towards urban water sustainability: analyzing management transitions in Miami, Las Vegas, and Los Angeles. Global Environmental Change, 58, 101967. doi:10.1016/j.gloenvcha.2019.101967.
Garcia, M., et al., 2022. Weathering water extremes and cognitive biases in a changing climate. Water Security, 15, 100110. doi:10.1016/j.wasec.2022.100110.
Garcia, M., Ridolfi, E., and Di Baldassarre, G., 2020. The interplay between reservoir storage and operating rules under evolving conditions. Journal of Hydrology, 590, 125270. doi:10.1016/j.jhydrol.2020.125270.
GCEW (Global Commission on the Economics of Water), 2023.The What, Why and How of the World Water Crisis.Paris, France: OECD Environment Directorate Climate, Biodiversity and Water Division. https://watercommission.org/wp-content/uploads/2023/03/Why-What-How-of-Water-Crisis-Web.pdf [Accessed 20 March 2025].
Genova, P., and Wei, Y.P., 2023. A socio-hydrological model for assessing water resource allocation and water environmental regulations in the Maipo River Basin. Journal of Hydrology, 617 (2023), 129159. doi:10.1016/j.jhydrol.2023.129159.
Genova, P., Wei, Y.P., and Olivares, M., 2022. Evolution of water environmental regulations in Chile since 1900. Water Policy, 24 (8), 1306–1324.
Ghoreishi, M., et al., 2021. Peering into agricultural rebound phenomenon using a global sensitivity analysis approach. Journal of Hydrology, 602, 126739. doi:10.1016/j.jhydrol.2021.126739.
Giuliani, M., and Castelletti, A., 2016. Is robustness really robust? How different definitions of robustness impact decision-making under climate change. Clim. Change, 135 (3–4), 409–424. doi:10.1007/s10584-015-1586-9.
Gleick, P.H., and Palaniappan, M., (2010). Peak water limits to freshwater withdrawal and use. Proceedings of the National Academy of Sciences, 107(25), 11155–11162.
Godinez-Madrigal, J., Van Cauwenbergh, N., and van der Zaag, P., 2020. Unraveling intractable water conflicts: the entanglement of science and politics in decision-making on large hydraulic infrastructure. Hydrol. Earth Syst. Sci, 24 (10), 4903–4921. doi:10.5194/hess-24-4903-2020.
Gohari, A., et al., 2013. Water transfer as a solution to water shortage: a fix that can backfire. Journal of Hydrology, 491, 23–39. doi:10.1016/j.jhydrol.2013.03.021.
Gonzales, P., and Ajami, N., 2017. Social and structural patterns of drought-related water conservation and rebound. Water Resources Research, 53 (12), 10619–10634. doi:10.1002/2017wr021852.
Gooch, G., and Huitema, D., 2008. Participation in water management: theory and practice. In: J.G., Timmerman, C., Pahl-Wostl, and J., Möltgen eds. The adaptiveness of IWRM: Analysing european iwrm research. international water association publishing. London, UK: IWA Publishing, 27–44 doi:10.2166/9781780401911.
Gudmundsson, L., et al., 2021. Globally observed trends in mean and extreme river flow attributed to climate change. Science, 371 (6534), 1159–1162. doi:10.1126/science.aba3996.
Gupta, H.V., et al., 2014. Large-sample hydrology: a need to balance depth with breadth. Hydrol. Earth Syst. Sci, 18, 463–477. doi:10.5194/hess-18-463-2014.
Haeffner, M., et al., 2017. Accessing blue spaces: social and geographic factors structuring familiarity with, use of, and appreciation of urban waterways. Landscape and Urban Planning, 167, 136–146. doi:10.1016/j.landurbplan.2017.06.008.
Hall, C.A., et al., 2022. A hydrologist’s guide to open science. Hydrology and Earth System Sciences, 26 (3), 647–664. doi:10.5194/hess-26-647-2022.
Hall, J., et al., 2014. Understanding flood regime changes in Europe: a state-of-the-art assessment. Hydrology and Earth System Sciences, 18 (7), 2735–2772. doi:10.5194/hess-18-2735-2014.
Hanrahan, B.R., et al., 2018. Winter cover crops reduce nitrate loss in an agricultural watershed in the central US. Agriculture, Ecosystems & Environment, 265, 513–523. doi:10.1016/j.agee.2018.07.004.
Harman, C., and Troch, P.A., 2014. What makes Darwinian hydrology” Darwinian”? Asking a different kind of question about landscapes. Hydrology and Earth System Sciences, 18 (2), 417–433. doi:10.5194/hess-18-417-2014.
Hayashi, Y., et al., 2021. A transdisciplinary engagement with Australian Aboriginal water and the hydrology of a small bedrock island. Hydrological Sciences Journal, 66 (13), 1845–1856. doi:10.1080/02626667.2021.1974025.
Heal, K.V., et al., 2021. Water quality: the missing dimension of water in the water–energy–food nexus. Hydrological Sciences Journal, 66 (5), 745–758. doi:10.1080/02626667.2020.1859114.
Heal, K.V., et al., 2022. Ensuring consideration of water quality in nexus approaches in the science–practice continuum. Hydrological Sciences Journal, 67 (8), 1291–1293. doi:10.1080/02626667.2022.2077652.
Hersbach, H., et al., 2020. The ERA5 global reanalysis. Meteorology Social, 146 (730), 1999–2049. doi:10.1002/QJ.3803.
Hipsey, M.R., and Arheimer, B., 2013. Challenges for water-quality research in the new IAHS decade on hydrology under societal and environmental change. IAHS Publishing, 361, 17–29.
Höge, M., et al., 2023. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland. Earth Systemtic Science Data, 15 (12), 5755–5784. doi:10.5194/essd-15-5755-2023.
Höllermann, B., and Evers, M., 2019. Coping with uncertainty in water management: qualitative system analysis as a vehicle to visualize the plurality of practitioners’ uncertainty handling routines. Journal of Environmental Management, 235, 213–223. doi:10.1016/j.jenvman.2019.01.034.
Hou, X., et al., 2022. Global mapping reveals increase in lacustrine algal blooms over the past decade. Nature Geoscience, 15 (2), 130–134. doi:10.1038/s41561-021-00887-x
Hrachowitz, M., et al., 2013. A decade of Predictions in Ungauged Basins (PUB)–a review. Hydrological Sciences Journal, 58 (6), 1198–1255. doi:10.1080/02626667.2013.803183.
Huang, H., et al., 2022. Changes in mechanisms and characteristics of western US floods over the last sixty years. Geophysical Research Letters, 49 (3), e2021GL097022. doi:10.1029/2021GL097022.
Huggins, X., et al., 2022. Hotspots for social and ecological impacts from freshwater stress and storage loss. Nat Commun, 13 (1), 439. doi:10.1038/s41467-022-28029-w.
Hund, S.V., et al., 2018. Groundwater recharge indicator as tool for decision makers to increase socio-hydrological resilience to seasonal drought. Journal of Hydrology, 563, 1119–1134. doi:10.1016/j.jhydrol.2018.05.069.
Hundecha, Y., and Merz, B., 2012. Exploring the relationship between changes in climate and floods using a model-based analysis. Water Resources Research, 48 (4). doi:10.1029/2011WR010527.
IPCC, 2012. In: C.B. Field, ed. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge, England: Cambridge Univ. Press, 582.
IPCC, 2022: Climate Change 2022: impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.
ISC, 2023. UN 2023 Water Conference: ISC Policy Brief. Paris: International Science Council. https://council.science/publications/water-policy-brief/
Jackson, F.L., et al., 2018. A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland’s Atlantic salmon rivers under climate change. Science of the Total Environment, 612, 1543–1558. doi:10.1016/j.scitotenv.2017.09.010.
Jackson, F.L., et al., 2021. A deterministic river temperature model to prioritize management of riparian woodlands to reduce summer maximum river temperatures. Hydrological Processes, 35 (8), e14314. doi:10.1002/hyp.14314.
Jan, S., et al., April2019. Virtual Staff Gauges for Crowd-Based Stream Level Observations. Frontiers of Earth Science, 7. doi:10.3389/feart.2019.00070.
Jasechko, S., et al., 2020. Groundwater level observations in 250,000 coastal US wells reveal scope of potential seawater intrusion. Nature Communications, 11 (1), 3229. doi:10.1038/s41467-020-17038-2.
Jia, J., Cui, W., and Liu, J., 2022. Urban catchment-scale blue-green-gray infrastructure classification with unmanned aerial vehicle images and machine learning algorithms. Frontiers in Environmental Science, 9, 778598. doi:10.3389/fenvs.2021.778598.
Jollymore, A., et al., 2017. Citizen science for water quality monitoring: data implications of citizen perspectives. Journal of Environmental Management, 200 (September), 456–467. doi:10.1016/j.jenvman.2017.05.083.
Kallis, G., 2010. Coevolution in water resource development: the vicious cycle of water supply and demand in Athens, Greece. Ecological Economics, 69 (4), 796–809. doi:10.1016/j.ecolecon.2008.07.025.
Kam, J., Stowers, K., and Kim, S., 2019. Monitoring of drought awareness from google trends: a case study of the 2011–17 California drought. Weather, Climate, and Society, 11 (2), 419–429. doi:10.1175/wcas-d-18-0085.1.
Kandasamy, J., et al., 2014. Socio-hydrologic drivers of the pendulum swing between agricultural development and environmental health: a case study from Murrumbidgee River basin, Australia. Hydrology and Earth System Sciences, 18 (3), 1027–1041. doi:10.5194/hess-18-1027-2014.
Kates, R.W., et al., (2006). Reconstruction of New Orleans after Hurricane Katrina: a research perspective. Proceedings of the national Academy of Sciences, 103(40), 14653–14660.
Ke, Q., et al., 2020. Urban pluvial flooding prediction by machine learning approaches–a case study of Shenzhen city, China. Advances in Water Resources, 145, 103719. doi:10.1016/j.advwatres.2020.103719.
Kellermann, P., et al., 2020. The object-specific flood damage database HOWAS 21. - Natural Hazards and Earth System Sciences (NHESS), 20 (9), 2503–2519. doi:10.5194/nhess-20-2503-2020.
Kelly-Quinn, M., et al., 2022. Opportunities, Approaches and Challenges to the Engagement of Citizens in Filling Small Water Body Data Gaps. Hydrobiologia. August, 1–21. doi:10.1007/s10750-022-04973-y.
Kemter, M., et al., 2020. Joint trends in flood magnitudes and spatial extents across Europe. Geophysical Research Letters, 47 (7), e2020GL087464. doi:10.1029/2020GL087464.
Khazaei, B., et al., 2019. Climatic or regionally induced by humans? Tracing hydro-climatic and land-use changes to better understand the Lake Urmia tragedy. Journal of Hydrology, 569, 203–217. doi:10.1016/j.jhydrol.2018.12.004.
Kim, S., Shao, W., and Kam, J., 2019. Spatiotemporal Patterns of US Drought Awareness. Palgrave Communications, 5 (1). doi:10.1057/s41599-019-0317-7.
Kingston, D., et al., 2020. Moving beyond the catchment scale: value and opportunities in large-scale hydrology to understand our changing world. Hydrological Processes, 34 (10), 2292–2298. doi:10.1002/hyp.13729.
Knighton, J., et al., (2021). Flood risk behaviors of United States riverine metropolitan areas are driven by local hydrology and shaped by race. Proceedings of the National Academy of Sciences, 118(13), e2016839118.
Koutsoyiannis, D., 2011. Hurst-Kolmogorov dynamics and uncertainty. Journal of the American Water Resources Association, 47 (3), 481–495. doi:10.1111/j.1752-1688.2011.00543.x.
Koutsoyiannis, D., and Montanari, A., 2015. Negligent killing of scientific concepts: the stationarity case. Hydrological Sciences Journal, 60 (7–8), 7–1183. doi:10.1080/02626667.2014.959959.
Kratzert, F., et al., 2019. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth System Science, 23 (12), 5089–5110. doi:10.5194/hess-23-5089-2019.
Kratzert, F., et al., 2023. Caravan - A global community dataset for large-sample hydrology. Sci Data, 10 (1), 61. doi:10.1038/s41597-023-01975-w.
Kreibich, H., et al., 2017. Adaptation to flood risk - results of international paired flood event studies. Earth’s Future, 5 (10), 953–965. doi:10.1002/2017EF000606.
Kreibich, H., et al., 2019. How to improve attribution of changes in drought and flood impacts. Hydrological Sciences Journal, 64 (1), 1–18. doi:10.1080/02626667.2018.1558367.
Kreibich, H., et al., 2022a. Critical research in the water-related multi-hazard field. Nature Sustainability, 5 (2), 90–91. doi:10.1038/s41893-021-00833-0.
Kreibich, H., et al., 2022b. The challenge of unprecedented floods and droughts in risk management. - Nature, 608 (7921), 80–86. doi:10.1038/s41586-022-04917-5
Kreibich, H., et al., 2023. Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts. - Earth System Science Data, 15 (5), 2009–2023. doi:10.5194/essd-15-2009-2023.
Kryvasheyeu, Y., et al., 2016. Rapid Assessment of Disaster Damage Using Social Media Activity. Science Advances, 2 (3), e1500779. doi:10.1126/sciadv.1500779.
Kvas, A., et al., 2024. Evaluating long-term water storage trends in small catchments and aquifers from a joint inversion of 20 years of GRACE/GRACE-FO mission data. - Geophysical Journal International, 236 (2), 1002–1012. doi:10.1093/gji/ggad468.
Lan, T., et al., 2020. Detection and attribution of abrupt shift in minor periods in human-impacted streamflow. Journal of Hydrology, 584, 124637. doi:10.1016/j.jhydrol.2020.124637.
Lane, S.N., 2014. Acting, predicting and intervening in a socio-hydrological world. Hydrology and Earth System Sciences, 18 (3), 927–952. doi:10.5194/hess-18-927-2014.
Lemos, M.C., 2015. Usable climate knowledge for adaptive and co-managed water governance. Current Opinion in Environmental Sustainability, 12, 48–52. doi:10.1016/j.cosust.2014.09.005.
Leong, C., 2018. The role of narratives in sociohydrological models of flood behaviors. Water Resources Research, 54 (4), 3100–3121. doi:10.1002/2017WR022036.
Levy, M.C., et al., 2018. Land use change increases streamflow across the arc of deforestation in Brazil. Geophysical Research Letters, 45 (8), 3520–3530. doi:10.1002/2017GL076526.
Lienert, J., et al., 2022. The role of multi-criteria decision analysis in a transdisciplinary process: co-developing a flood forecasting system in western Africa. Hydrol. Earth Syst. Sci, 26 (11), 2899–2922. doi:10.5194/hess-26-2899-2022.
Lins, H.F., and Cohn, T.A., 2011. Stationarity: wanted dead or alive?Journal of the American Water Resources Association, 47 (3), 475–480. doi:10.1111/j.1752-1688.2011.00542.x.
Liu, J., et al., 2017a. Challenges in operationalizing the water-energy-food nexus. Hydrological Sciences Journal, 62 (11), 1714–1720. doi:10.1080/02626667.2017.1353695.
Liu, J., et al., 2017b. Water scarcity assessments in the past, present, and future. Earth’s Future, 5 (6), 545–559. doi:10.1002/2016EF000518.
Liu, J., et al., 2019. On knowledge generation and use for sustainability. Nature Sustainability, 2 (2), 80–82. doi:10.1038/s41893-019-0229-y.
Liu, J., Liu, Q., and Yang, H., 2016. Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality. Ecological Indicators, 60, 434–441. doi:10.1016/j.ecolind.2015.07.019.
Liu, K., et al., 2022. Assessment of ecological water scarcity in China. Environmental Research Letters, 17 (10), 104056. doi:10.1088/1748-9326/ac95b0.
Llasat, M.C., et al., 2016. Trends in flash flood events versus convective precipitation in the Mediterranean region: the case of Catalonia. Journal of Hydrology, 541, 24–37. doi:10.1016/j.jhydrol.2016.05.040.
Löschner, L., et al., 2016. Scientist–stakeholder workshops: a collaborative approach for integrating science and decision-making in Austrian flood-prone municipalities. Environmental Science & Policy, 55, 345–352. doi:10.1016/j.envsci.2015.08.003.
Lowry, C.S., and Fienen, M.N., 2013. CrowdHydrology: crowdsourcing Hydrologic Data and Engaging Citizen Scientists. Ground Water, 51 (1), 151–156. doi:10.1111/j.1745-6584.2012.00956.x.
Ma, T., et al., 2020. Pollution exacerbates China’s water scarcity and its regional inequality. Nature Communications, 11 (1), 1–9. doi:10.1038/s41467-019-13993-7.
Mahé, G., et al., 2021. The UNESCO FRIEND-Water program: accelerates, shares and transfers knowledge and innovation in hydrology across the world in the frame of the Intergovernmental Hydrological Program (IHP. PIAHS, 384, 5–18. doi:10.5194/piahs-384-5-2021.
Mao, G., et al., 2021. Comprehensive comparison of artificial neural networks and long short-term memory networks for rainfall-runoff simulation. Physics and Chemistry of the Earth, 123, 103026. doi:10.1016/j.pce.2021.103026.
Mao, G., and Liu, J., 2019. WAYS v1: a hydrological model for root zone water storage simulation on a global scale. Geoscientific Model Development, 12 (12), 5267–5289. doi:10.5194/gmd-12-5267-2019.
Marais, J., et al., 2016. A Review of the Topologies Used in Smart Water Meter Networks: a Wireless Sensor Network Application. Journal of Sensors, 2016, 9857568. doi:10.1155/2016/9857568.
Mård, J., Di Baldassarre, G., and Maxxoleni, M., 2018. Nighttime light data reveal how flood protection shapes human proximity to rivers. Scientific Advances, 4, eaar5779. doi:10.1126/sciadv.aar5779.
Matalas, N.C., 2012. Comment on the announced death of stationarity. Journal of Water Resources Planning and Management, 138 (4), 311–312. doi:10.1061/(ASCE)WR.1943-5452.0000215.
Mazzoleni, M., et al., 2021. Water management, hydrological extremes, and society: modeling interactions and phenomena. Ecology and Society, 26 (4). doi:10.5751/ES-12643-260404.
McAneney, J., et al., 2019. Normalised insurance losses from Australian natural disasters: 1966–2017. Environmental Hazards, 18 (5), 414–433. doi:10.1080/17477891.2019.1609406.
McMillan, H., et al., 2016. PantaRhei 2013–2015: global perspectives on hydrology, society and change. Hydrological Sciences Journal, 61 (7), 1174–1191. doi:10.1080/02626667.2016.1159308.
Medema, W., McIntosh, B.S., and Jeffrey, P.J., 2008. From Premise to Practice: a Critical Assessment of Integrated Water Resources Management and Adaptive Management Approaches in the Water Sector. ECOLOGY AND SOCIETY, 13 (2), 29. doi:10.5751/ES-02611-130229.
Meier, M.H.E., et al., 2014. Ensemble Modeling of the Baltic Sea Ecosystem to Provide Scenarios for Management. AMBIO, 43 (1), 37–48. doi:10.1007/s13280-013-0475-6.
Melsen, L.A., Vos, J., and Boelens, R., 2018. What is the role of the model in socio-hydrology? Discussion of “Prediction in a socio-hydrological world”*. Hydrological Sciences Journal, 63 (9), 1435–1443. doi:10.1080/02626667.2018.1499025.
Merz, B., et al., 2012. HESS Opinions ‘More efforts and scientific rigour are needed to attribute trends in flood time series’. - Hydrology and Earth System Sciences, 16 (5), 1379–1387. doi:10.5194/hess-16-1379-2012.
Merz, B., et al., 2015. Charting unknown waters - On the role of Surprise in flood risk assessment and management. Water Resources Research, 51 (8), 6399–6416. doi:10.1002/2015WR017464.
Merz, B., et al., 2021. Causes, impacts and patterns of disastrous river floods. - Nature Reviews Earth & Environment, 2 (9), 592–609. doi:10.1038/s43017-021-00195-3.
Metin, A.D., et al., 2018. How do changes along the risk chain affect flood risk?- Natural Hazards and Earth System Sciences (NHESS), 18 (11), 3089–3108. doi:10.5194/nhess-18-3089-2018.
Michaelis, T., Brandimarte, L., and Mazzoleni, M., 2020. Capturing flood-risk dynamics with a coupled agent-based and hydraulic modelling framework. Hydrological Sciences Journal, 65 (9), 1458–1473. doi:10.1080/02626667.2020.1750617.
Milly, P.C., et al., 2015. On critiques of “Stationarity is dead: whither water management?”. Water Resources Research, 51 (9), 7785–7789. doi:10.1002/2015WR017408.
Milly, P.C.D., et al., 2008. Stationarity Is Dead: whither Water Management?Science, 319 (5863), 573–574. doi:10.1126/science.1151915.
Mondino, E., et al., 2021. Longitudinal survey data for diversifying temporal dynamics in flood risk modelling. Nat. Hazards Earth Syst. Sci, 21 (9), 2811–2828. doi:10.5194/nhess-21-2811-2021.
Montanari, A., et al., 2013. “Panta Rhei-Everything Flows”: change in hydrology and society-The IAHS Scientific Decade 2013-2022. Hydrological Sciences Journal, 58 (6), 1256–1275. doi:10.1080/02626667.2013.809088.
Montanari, A., et al., 2023. Why the 2022 Po River drought is the worst in the past two centuries. Science Advances, 9 (32), 8304. doi:10.1126/sciadv.adg8304.
Mostert, and Mostert, E., 2018. An alternative approach for socio-hydrology: case study research. Hydrol. Earth Syst. Sci, 22 (1), 317–329. doi:10.5194/hess-22-317-2018.
Mullen, C., et al., 2022. Hydro economic asymmetries and common-pool overdraft in transboundary aquifers. Water Resources Research, 58 (11), e2022WR032136. doi:10.1029/2022WR032136.
Müller, M.F., et al., 2016. Impact of the Syrian refugee crisis on land use and transboundary freshwater resources. Proceedings of the national academy of sciences, 113(52), pp.14932–14937.
Müller, M.F., et al., 2024. Mapping the landscape of water and society research: promising combinations of compatible and complementary disciplines. Wiley Interdisciplinary Reviews: Water, 11 (2), e1701. doi:10.1002/wat2.1701.
Müller, M.F., and Levy, M.C., 2019. Complementary vantage points: integrating hydrology and economics for sociohydrologic knowledge generation. Water Resources Research, 55 (4), 2549–2571. doi:10.1029/2019WR024786.
Müller, M.F., Roche, K.R., and Dralle, D.N., 2021. Catchment processes can amplify the effect of increasing rainfall variability. Environmental Research Letters, 16 (8), 084032. doi:10.1088/1748-9326/ac153e.
Muneepeerakul, R., John, M.A., and M, J., 2020. The emergence and resilience of self-organized governance in coupled infrastructure systems. PNAS, 117 (9), 4617–4622. doi:10.1073/pnas.1916169117.
Mustafa, A., et al., 2018. Effects of spatial planning on future flood risks in urban environments. Journal of Environmental Management, 225, 193–204. doi:10.1016/j.jenvman.2018.07.090.
Nardi, F., et al., 2022. Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges. Hydrological Sciences Journal, 67 (16), 2534–2551. doi:10.1080/02626667.2020.1849707.
Nath, S., and Kirschke, S., 2023. Ground Water Monitoring through Citizen Science: a Review of Project Designs and Results. Ground Water, 61 (4), 481–493. February. doi:10.1111/gwat.13298.
Newman, A.J., et al., 2015. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth System Science, 19 (1), 209–223. doi:10.5194/hess-19-209-2015.
Nicollier, V., Cordeiro Bernardes, M.E., and Kiperstok, A., 2022. What governance failures reveal about water resources management in a municipality of Brazil. Sustainability, 14, 2144. doi:10.3390/su14042144.
Njue, N., et al., 2019. Citizen Science in Hydrological Monitoring and Ecosystem Services Management: state of the Art and Future Prospects. The Science of the Total Environment, 693 (November), 133531. doi:10.1016/j.scitotenv.2019.07.337.
Nlend, B., et al., 2018. The impact of urban development on aquifers in large coastal cities of West Africa: present status and future challenges. Land Use Policy, 75, 352–363. doi:10.1016/j.landusepol.2018.03.007
Ojha, T., Misra, S., and Raghuwanshi, N.S., 2015. Wireless sensor networks for agriculture: the state-of-the-art in practice and future challenges. Computers and Electronics in Agriculture, 118, 66–84. doi:10.1016/j.compag.2015.08.011.
Olson, M., 1965. The logic of collective action: public goods and the theory of groups. Cambridge, MA: Harvard University Press.
Oral, H.V., et al., 2021. Management of Urban Waters with Nature-Based Solutions in Circular Cities—Exemplified through Seven Urban Circularity Challenges. Water, 13 (23), 3334. doi:10.3390/w13233334.
Ostrom, E., 1990. Governing the commons: the evolution of institutions for collective action. New York, US: Cambridge university press.
Pande, S., et al., 2022. Never ask for a lighter rain but a stronger umbrella. Frontiers in Water, 3, 822334. doi:10.3389/frwa.2021.822334.
Papagiannaki, K., et al., 2022. Developing a large-scale dataset of flood fatalities for territories in the Euro-Mediterranean region, FFEM-DB. - Scientific Data, 9 (1), 166. doi:10.1038/s41597-022-01273-x.
Paprotny, D., et al., 2018. Trends in flood losses in Europe over the past 150 years. Nature Communications, 9 (1), 1985. doi:10.1038/s41467-018-04253-1.
Paprotny, D., et al., 2021. A probabilistic approach to estimating residential losses from different flood types. - Natural Hazards, 105 (3), 2569–2601. doi:10.1007/s11069-020-04413-x.
Park, C.E., et al., 2018. Keeping global warming within 1.5°C restrains emergence of aridification. Nature Climate Change, 8 (1), 70–74. doi:10.1038/s41558-017-0034-4.
Payet-Burin, R., et al., 2019. WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the water-energy-food-climate nexus. Hydrology and Earth System Sciences, 23 (10), 4129–4152. doi:10.5194/hess-23-4129-2019.
Pekel, J.-F., et al., 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540 (7633), 418–422. doi:10.1038/nature20584.
Penny, G., et al., 2021. Trust and incentives for transboundary groundwater cooperation. Advances in Water Resources, 155, 104019. doi:10.1016/j.advwatres.2021.104019.
Penny, G., Bolster, D., and Müller, M.F., 2022. Social dilemmas and poor water quality in household water systems. Hydrology and Earth System Sciences, 26 (4), 1187–1202. doi:10.5194/hess-26-1187-2022.
Pham, L.D.M.H., et al., 2022. Socio-hydrological approach for farmer adaptability to hydrological changes: a case study in salinity-controlled areas of the Vietnamese Mekong Delta. Hydrological Sciences Journal, 67 (4), 495–507. doi:10.1080/02626667.2022.2030865.
Phlips, E.J., et al., 2020. Hurricanes, El Niño and harmful algal blooms in two sub-tropical Florida estuaries: direct and indirect impacts. Scientific Reports, 10 (1), 1910. doi:10.1038/s41598-020-58771-4
Pimentel, R., Herrero, J., and Polo, M.J., 2017. Quantifying snow cover distribution in semiarid regions combining satellite and terrestrial imagery. Remote Sensing, 9 (10), 995. doi:10.3390/rs9100995.
Polo, M.J., et al., 2019. The Guadalfeo Monitoring Network (Sierra Nevada, Spain): 14 years of measurements to understand the complexity of snow dynamics in semiarid regions. Earth System Science Data, 11 (1), 393–407. doi:10.5194/essd-11-393-2019.
Pouladi, P., et al., 2020. Socio-hydrological framework for investigating farmers’ activities affecting the shrinkage of Urmia Lake; hybrid data mining and agent-based modelling Hydrol. Sci. J, 65, 1249–1261.
Praharaj, S., et al., 2021. Estimating Impacts of Recurring Flooding on Roadway Networks: a Norfolk, Virginia Case Study. Natural Hazards, 107 (3), 2363–2387. doi:10.1007/s11069-020-04427-5.
Quesnel, K.J., and Ajami, N.K., 2017. Changes in Water Consumption Linked to Heavy News Media Coverage of Extreme Climatic Events. Science Advances, 3 (10), e1700784. doi:10.1126/sciadv.1700784.
Rahman, M.M., et al., 2019. Salinization in large river deltas: drivers, impacts and socio-hydrological feedbacks. Water Security, 6, 100024. doi:10.1016/j.wasec.2019.100024.
Ramachandran, R., Bugbee, K., and Murphy, K., 2021. From Open Data to Open Science. Earth & Space Science, 8 (5), e2020EA001562. doi:10.1029/2020EA001562.
Rangecroft, S., et al., 2018. Hydrological modelling as a tool for interdisciplinary workshops on future drought. Progress in Physical Geography: Earth and Environment, 42 (2), 237–256. doi:10.1177/0309133318766.
Rangecroft, S., et al., 2021. Guiding principles for hydrologists conducting interdisciplinary research and fieldwork with participants. Hydrological Sciences Journal, 66 (2), 214–225. doi:10.1080/02626667.2020.1852241.
Rangecroft, S., et al., 2023. Unravelling and understanding local perceptions of water quality in the Santa basin, Peru. Journal of Hydrology, 625, 129949. doi:10.1016/j.jhydrol.2023.129949.
Rangecroft, S., et al., 2024. GC Insights: lessons from participatory water quality research in the upper Santa River basin, Peru. Geoscience Communication, 7 (2), 145–150. doi:10.5194/gc-7-145-2024.
Riedlinger, D., and Berkes, F., 2001. Contributions of Traditional Knowledge to Understanding Climate Change in the Canadian Arctic. The Polar Record, 37 (203), 315–328. doi:10.1017/S0032247400017058.
Roby, N.A., et al., 2018. A Novel Search Algorithm for Quantifying News Media Coverage as a Measure of Environmental Issue Salience. Environmental Modelling & Software, 101, 249–255. doi:10.1016/j.envsoft.2017.12.012.
Rockström, J., et al., 2009. A safe operating space for humanity. Nature, 461 (7263), 472–475. doi:10.1038/461472a.
Rojas, R., Feyen, L., and Watkiss, P., 2013. Climate change and river floods in the European Union: socio-economic consequences and the costs and benefits of adaptation. Global Environmental Change, 23 (6), 1737–1751. doi:10.1016/j.gloenvcha.2013.08.006.
Roobavannan, M., et al., 2018. Norms and values in sociohydrological models. Hydrology and Earth System Sciences, 22 (2), 1337–1349. doi:10.5194/hess-22-1337-2018.
Rudari, R., et al., 2017. Overview of loss data storage at global scale.In: D., Molinari, Menoni, S., Ballio, F., eds. Flood Damage Survey and Assessment, 3, 31–51. doi:10.1002/9781119217930.ch3. Geoph. Monog. Series, chap.
Rusca, M., et al., 2023. Unprecedented droughts are expected to exacerbate urban inequalities in Southern Africa. Nature Climate Change, 13 (1), 98–105. doi:10.1038/s41558-022-01546-8.
Ruska, M., and Di Baldassarre, G., 2019. Interdisciplinary critical geographies of water: capturing the mutual shaping of society and hydrological flows. Water, 11 (10), 1973. doi:10.3390/w11101973.
Safaei-Moghadam, A., Tarboton, D., and Minsker, B., 2023. Estimating the Likelihood of Roadway Pluvial Flood Based on Crowdsourced Traffic Data and Depression-Based DEM Analysis. Natural Hazards and Earth System Sciences, 23 (1), 1–19. doi:10.5194/nhess-23-1-2023.
Sauer, I.J., et al., 2021. Climate signals in river flood damages emerge under sound regional disaggregation. Nature Communications, 12 (1), 2128. doi:10.1038/s41467-021-22153-9.
Savelli, E., et al., 2021. Don’t blame the rain: social power and the 2015–2017 drought in Cape Town. Journal of Hydrology, 594, 125953. doi:10.1016/j.jhydrol.2020.125953.
Savelli, E., and Mazzoleni, M., 2023. Urban water crises driven by elites’ unsustainable consumption. Nature Sustainability, 6 (8), 929–940. doi:10.1038/s41893-023-01100-0.
Savenije, H.H.G., Hoekstra, A.Y., and van der Zaag, P., 2014. Evolving water science in the Anthropocene. Hydrology and Earth System Sciences, 18 (1), 319–332. doi:10.5194/hess-18-319-2014.
Schoppa, L., et al., 2020. Probabilistic Flood Loss Models for Companies. - Water Resources Research, 56 (9), e2020WR027649. doi:10.1029/2020WR027649.
Schoppa, L., et al., 2022. Augmenting a socio-hydrological flood risk model for companies with process-oriented loss estimation. Hydrological Sciences Journal, 67 (11), 1623–1639. doi:10.1080/02626667.2022.2095207.
Schoppa, L., et al., 2024. Projecting flood risk dynamics for effective long-term adaptation. - Earth’s Future, 12 (3), e2022EF003258. doi:10.1029/2022EF003258.
Schrieks, T., et al., 2021. Integrating behavioral theories in agent-based models for agricultural drought risk assessments. Frontiers in Water, 3, 686329. doi:10.3389/frwa.2021.686329.
Scotti, V., Giannini, M., and Cioffi, F., 2020. Enhanced flood mapping using synthetic aperture radar (SAR) images, hydraulic modelling, and social media: a case study of Hurricane Harvey (Houston, TX). J. Flood Risk Manag, 13 (4), e12647. doi:10.1111/jfr3.12647.
See, L., March2019. A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding. Frontiers of Earth Science, 7. doi:10.3389/feart.2019.00044.
Serinaldi, F., and Kilsby, C.G., 2015. Stationarity is undead: uncertainty dominates the distribution of extremes. Advances in Water Resources, 77, 17–36. doi:10.1016/j.advwatres.2014.12.013.
Shao, S., et al., 2022. Nonstationary analysis of hydrological drought index in a coupled human-water system: application of the GAMLSS with meteorological and anthropogenic covariates in the Wuding River basin, China. Journal of Hydrology, 608, 127692. doi:10.1016/j.jhydrol.2022.127692.
Sherbinin, A.D., et al., 2021. The Critical Importance of Citizen Science Data. Frontiers in Climate, 3March. 10.3389/fclim.2021.650760.
Shrestha, A., et al., 2022. Socio-hydrological modeling of the tradeoff between flood control and hydropower provided by the Columbia River Treaty, Hydrol. Earth System Science, 26 (19), 4893–4917. doi:10.5194/hess-26-4893-2022.
Singh, N.K., and Borrok, D.M., 2019. A Granger causality analysis of groundwater patterns over a half-century. Scientific Reports, 9 (1), 12828. doi:10.1038/s41598-019-49278-8.
Sivapalan, M., et al., 2014. Sociohydrology: use-inspired water sustainability science for the Anthropocene. Earth’s Future, 2 (4), 225–230. doi:10.1002/2013EF000164.
Sivapalan, M., and Blöschl, G., 2015. Time scale interactions and the coevolution of humans and water. Water Resour. Res, 51 (9), 6988–7022. doi:10.1002/2015WR017896.
Sivapalan, M., and Blöschl, G., 2017. The growth of hydrological understanding: technologies, ideas, and societal needs shape the field. Water Resources Research, 53 (10), 8137–8146. doi:10.1002/2017WR021396.
Sivapalan, M., Savenije, H.H.G., and Blöschl, G., 2012. Socio-hydrology: a new science of people and water. Hydrological Processes, 26 (8), 1720–1276. doi:10.1002/hyp.8426.
Smith, L., et al., 2017. Assessing the Utility of Social Media as a Data Source for Flood Risk Management Using a Real-Time Modelling Framework. Journal of Flood Risk Management, 10 (3), 370–380. doi:10.1111/jfr3.12154.
Souza, F.A.A., et al., 2022. Droughts in São Paulo: challenges and lessons for a water-adaptive society. Urban Water Journal, 20 (10), 1682–1694. doi:10.1080/1573062X.2022.2047735.
Srinivasan, V., et al., 2016. Prediction in a socio-hydrological world. hydrological Sciences Journal, 1–8. doi:10.1080/02626667.2016.1253844.
Srinivasan, V., et al., 2017a. Prediction in a socio-hydrological world. Hydrological Sciences Journal, 62 (3), 338–345. doi:10.1080/02626667.2016.1253844.
Srinivasan, V., et al., 2017b. A dynamic framework for water security. Water Security, 1, 12–20. doi:10.1016/j.wasec.2017.03.001
Stahl, K., et al., 2016. Impacts of European drought events: insights from an international database of text-based reports. Natural Hazards and Earth System Sciences, 16 (3), 801–819. doi:10.5194/nhess-16-801-2016.
Steinhausen, M., et al., 2022. Drivers of future fluvial flood risk change for residential buildings in Europe. - Global Environmental Change, 76, 102559. doi:10.1016/j.gloenvcha.2022.102559.
Stevens, A.J., Clarke, D., and Nicholls, R.J., 2016. Trends in reported flooding in the UK: 1884–2013. Hydrological Sciences Journal, 61 (1), 50–63. doi:10.1080/02626667.2014.950581.
Tamburino, L., Di Baldassarre, G., and Vico, G., 2020. Water management for irrigation, crop yield and social attitudes: a socio-agricultural agent-based model to explore a collective action problem. Hydrological Sciences Journal, 65 (11), 1815–1829. doi:10.1080/02626667.2020.1769103.
Tanoue, M., Hirabayashi, Y., and Ikeuchi, H., 2016. Global-scale river flood vulnerability in the last 50 years. Scientific Reports, 6 (1), 36021. doi:10.1038/srep36021.
Tauro, F., et al., 2018. Measurements and observations in the XXI century (MOXXI): innovation and multi-disciplinarity to sense the hydrological cycle. Hydrological Sciences Journal, 63 (2), 169–196. doi:10.1080/02626667.2017.1420191.
Teweldebrihan, M.D., Pande, S., and McClain, M., 2020. The dynamics of farmer migration and resettlement in the Dhidhessa River Basin, Ethiopia. Hydrological Sciences Journal, 65 (12), 1985–1993. doi:10.1080/02626667.2020.1789145.
Thompson, J.J., et al., 2021. The Utility of Google Trends as a Tool for Evaluating Flooding in Data-scarce Places. Area. doi:10.1111/area.12719.
Thompson, S.E., et al., 2013. Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene. Hydrology and Earth System Sciences, 17 (12), 5013–5039. doi:10.5194/hess-17-5013-2013.
Thorslund, J., et al., 2021. Common irrigation drivers of freshwater salinisation in river basins worldwide. Nature Communications, 12 (1), 4232. doi:10.1038/s41467-021-24281-8.
Tian, F., et al., 2019. Dynamics and driving mechanisms of asymmetric human water consumption during alternating wet and dry periods. Hydrol. Sci. J, 64 (5), 507–524. doi:10.1080/02626667.2019.1588972.
Treuer, G., et al., 2017. A narrative method for analyzing transitions in urban water management: the case of the Miami- Dade Water and Sewer Department. Water Resources Research, 53 (1), 891–908. doi:10.1002/2016wr019658.
Troy, T.J., et al., 2015. Moving sociohydrology forward: a synthesis across studies. Hydrol. Earth Syst. Sci, 19 (8), 3667–3679. doi:10.5194/hess-19-3667-2015.
Turner, S.W.D., et al., 2021. Water storage and release policies for all large reservoirs of conterminous United States. Journal of Hydrology, 603A, 126843. doi:10.1016/j.jhydrol.2021.126843.
Uchôa, J.G.S.M., et al., 2024. Widespread potential for streamflow leakage across Brazil. Nat Commun, 15 (1), 10211. doi:10.1038/s41467-024-54370-3
UNESCO (United Nations Educational Scientific and Cultural Organization). 2021. Recommendation on open science. https://unesdoc.unesco.org/ark:/48223/pf0000379949.locale=en [Accessed5July2024]
United Nations (2018). Sustainable Development Goal 6 Synthesis Report 2018 on Water and Sanitation. New York.
Uysal, G., et al., 2024. Historical synthesis of the International Commission on Water Resources Systems. Hydrological Sciences Journal, 69 (16), 2372–2390. doi:10.1080/02626667.2024.2412726.
Vanelli, F.M., Kobiyama, M., and Mariana Madruga, D.B., 2022. To Which Extent Are Socio-Hydrology Studies Truly Integrative? The Case of Natural Hazards and Disaster Research. Hydrology and Earth System Sciences, 26 (8), 2301–2317. doi:10.5194/hess-26-2301-2022.
Van Loon, A.F., et al., 2015. Hydrological Drought Types in Cold Climates: quantitative Analysis of Causing Factors and Qualitative Survey of Impacts. Hydrology and Earth System Sciences, 19 (4), 1993–2016. doi:10.5194/hess-19-1993-2015.
Van Loon, A.F., et al., 2016. Drought in a human-modified world: reframing drought definitions, understanding, and analysis approaches. Hydrology and Earth System Sciences, 20 (9), 3631–3650. doi:10.5194/hess-20-3631-2016.
Van Loon, A.F., et al., 2022. Streamflow droughts aggravated by human activities despite management. Environ. Res. Lett, 17 (4), 044059. doi:10.1088/1748-9326/ac5def.
Van Meter, K.J., Van Cappellen, P., and Basu, N.B., 2018. Legacy nitrogen may prevent achievement of water quality goals in the Gulf of Mexico. Science, 360 (6387), 427–430. doi:10.1126/science.aar4462.
van Nooijen, R.R.P., and Kolechkina, A.G., 2021. Stability analysis of non-linear sampled data systems with time varying sample period and delay in the feedback loop. IFAC-PapersOnLine, 54 (9), 776–782. doi:10.1016/j.ifacol.2021.06.138.
Van Oel, P.R., et al., 2018. Diagnosing drought using the downstreamness concept: the effect of reservoir networks on drought evolution. Hydrological Sciences Journal, 63 (7), 979–990. doi:10.1080/02626667.2018.1470632.
van Vliet, M., Flörke, M., and Wada, Y., 2017. Quality matters for water scarcity. Nature Geosci, 10 (11), 800–802. doi:10.1038/ngeo3047.
Veigel, N., Kreibich, H., and Cominola, A., 2023. Interpretable Machine Learning Reveals Potential to Overcome Reactive Flood Adaptation in the Continental US. - Earth’s Future, 11 (9), e2023EF003571. doi:10.1029/2023EF003571.
Versteeg, N., et al., 2021. Adaptive Planning, Monitoring, and Evaluation for Long-Term Impact: insights From a Water Supply Case in Bangladesh. Frontiers in Water, 2, 621971. doi:10.3389/frwa.2020.621971.
Viglione, A., et al., 2016. Attribution of regional flood changes based on scaling fingerprints. Water Resources Research, 52 (7), 5322–5340. doi:10.1002/2016WR019036.
Villarini, G., and Wasko, C., 2021. Humans, climate and streamflow. Nature Climate Change, 11 (9), 725–726. doi:10.1038/s41558-021-01137-z.
Volpi, E., et al., 2024. The legacy of STAHY: milestones, achievements, challenges, and open problems in statistical hydrology. Hydrological Sciences Journal, 69 (14), 1913–1949. doi:10.1080/02626667.2024.2385686.
Vorogushyn, S., and Merz, B., 2013. Flood trends along the Rhine: the role of river training. - Hydrology and Earth System Sciences, 17 (10), 3871–3884. doi:10.5194/hess-17-3871-2013.
Vousdoukas, M.I., et al., 2018. Climatic and socioeconomic controls of future coastal flood risk in Europe. Nature Climate Change, 8 (9), 776–780. doi:10.1038/s41558-018-0260-4.
Wagenaar, D., et al., 2018. Regional and Temporal Transferability of Multivariable Flood Damage Models. - Water Resources Research, 54 (5), 3688–3703. doi:10.1029/2017WR022233.
Walker, D.W., Smigaj, M., and Tani, M., 2021. The Benefits and Negative Impacts of Citizen Science Applications to Water as Experienced by Participants and Communities. WIREs. Water, 8 (1). doi:10.1002/wat2.1488.
Wang, H., et al., 2017. Impacts of the dam-orientated water-sediment regulation scheme on the lower reaches and delta of the Yellow River (Huanghe): a review. Global and Planetary Change, 157, 93–113. doi:10.1016/j.gloplacha.2017.08.005.
Wang, H., et al., 2024. Anthropogenic climate change has influenced global river flow seasonality. Science, 383 (6686), 1009–1014. doi:10.1126/science.adi9501.
Ward, P.J., et al., 2020. The need to integrate flood and drought disaster risk reduction strategies. Water Security, 11, 100070. doi:10.1016/j.wasec.2020.100070
Wens, M., et al., 2020. Simulating Small-Scale Agricultural Adaptation Decisions in Response to Drought Risk: an empirical agent-based Model for Semi-Arid Kenya. Frontiers in Water, 2. doi:10.3389/frwa.2020.00015.
Wens, M.L.K., et al., 2022. Education, financial aid, and awareness can reduce smallholder farmers’ vulnerability to drought under climate change. Natural Hazards and Earth System Sciences, 22 (4), 1201–1232. doi:10.5194/nhess-22-1201-2022.
Wens, M.L.K., et al., 2021. Complexities of drought adaptive behaviour: linking theory to data on smallholder farmer adaptation decisions. International Journal of Disaster Risk Reduction, 63, 102435. doi:10.1016/j.ijdrr.2021.102435.
Wesselink, A., Kooy, M., and Warner, J., 2017. Socio-hydrology and hydrosocial analysis: toward dialogues across disciplines. Wiley Interdiscip. Rev. Water, 4 (2), e1196. doi:10.1002/wat2.1196.
White, G.F., 1945. Human adjustment to floods. Research Paper, 29. Department of Geography. University of Chicago. 225.
Wilkinson, M., et al., 2016. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3 (1), 160018. doi:10.1038/sdata.2016.18
Woolley, A.W., et al., 2010. Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science, 330 (6004), 686–688. doi:10.1126/science.1193147.
Xia, J., et al., 2021. Perspectives on eco-water security and sustainable development in the Yangtze River Basin. Geosci. Lett, 8 (18). doi:10.1186/s40562-021-00187-7.
Yang, Y., et al., 2021. Streamflow stationarity in a changing world. Environmental Research Letters, 16 (6), 064096. doi:10.1088/1748-9326/ac08c1.
Yasinskii, S.V., et al., 2018. Current Problems in Organizing Water Protection Zones at Water Bodies: case Study of the Uglich Reservoir. Water Resour, 45 (4), 490–502. doi:10.1134/S0097807818040206
Young, G., et al., 2015. Hydrological sciences and water security: an overview. PIAHS, 366, 1–9. doi:10.5194/piahs-366-1-2015.
Yu, D.J., et al., 2017. Incorporating institutions and collective action into a sociohydrological model of flood resilience. Water Resources Research, 53 (2), 1–18. doi:10.1002/2016WR019746.
Yu, D.J., et al., 2022. On capturing human agency and methodological interdisciplinarity in socio-hydrology research. Hydrol. Sci. J, 67 (13), 1905–1916. doi:10.1080/02626667.2022.2114836.
Yu, Q., et al., 2023. Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese Loess Plateau. Journal of Hydrology, 617, 129115. doi:10.1016/j.jhydrol.2023.129115.
Zhao, D., et al., 2019. Explaining virtual water trade: a spatial-temporal analysis of the comparative advantage of land, labor and water in China. Water Research, 153, 304–314. doi:10.1016/j.watres.2019.01.025.