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Global Institute for Water Security

Academic Background

  • Professor of Natural Hazards and Resilience, Department of Civil Engineering, University of Calgary, Calgary, Canada
  • Associate Professor of Natural Hazards and Resilience, Department of Civil Engineering, University of Calgary, Calgary, Canada
  • Assistant Professor of Statistical Hydrology and Stochastic Processes, Department of Civil, Geological & Environmental Engineering, University of Saskatchewan, Saskatoon, Canada
  • Research Associate, Stochastic modelling and global changes, University of California, Irvine, USA
  • Ph.D., Stochastic and statistical hydrology, National Technical University of Athens, Greece
  • M.Sc., Water Resources, National Technical University of Athens, Greece
  • B.Sc., Environmental Sciences, University of Aegean, Greece

Research Interests and Expertise

Simon Michael Papalexiou is Professor of Natural Hazards in the Department of Civil Engineering at the University of Calgary, Canada. His research focuses on hydroclimatic variability and extremes, advanced space-time stochastic modeling, downscaling techniques, and climate change diagnostics. He has published nearly 100 peer-reviewed articles in top-tier journals and presented at over 120 conferences worldwide. Simon leads the development of CoSMoS, a widely used software suite for stochastic modeling with a global user base. He serves as Associate Editor for Water Resources Research (AGU) and the Journal of Hydrology (Elsevier), and has reviewed for more than 50 scientific journals. He also organizes scientific workshops and regularly convenes sessions on time series modeling. He is Vice-President of the International Commission on Statistical Hydrology (ICSH-IAHS) and Team Lead for the Storms Module in the UNU Sustainability Nexus AID program. His contributions have earned several honors, including the 2024 AGU Natural Hazards Early Career Award and multiple Best Paper Awards. His work has been selected as Editor’s Choice in Science Magazine, Editor’s Highlight in Earth’s Future, and featured four times in AGU’s Eos Science News Magazine. His research has received wide media attention, with coverage by over 100 news outlets, including features on radio and television.

Research Keywords

  • Water and climate
  • Developing advanced stationary and non-stationary stochastic models to simulate hydroclimatic processes, including storms, flooding, drought, wind, etc., in space and time.
  • Understanding, quantifying, and modelling the uncertainty and variability in hydroclimatic processes.
  • Creating probabilistic tools to improve risk estimates of hydroclimatic extremes and assess changes.
  • Using big data to assess global and regional changes in extremes due to Earth System change.
  • Advancing bias-correction and downscaling methods for local-scale assessment of climate projections.
  • Forming serial complete and probabilistic ensemble datasets of meteorological forcings.

Publications

  1. Abdelmoaty, H. M., Rajulapati, C. R., Nerantzaki, S. D., & Papalexiou, S. M. (2025). Bias-corrected high-resolution temperature and precipitation projections for Canada. Scientific Data, 12(1), 191. https://doi.org/10.1038/s41597-025-04396-z
  2. Abouzied, G. A. A., Tang, G., Papalexiou, S. M., Clark, M. P., Aruffo, E., & Di Carlo, P. (2025). Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019. Geoscience Data Journal, 12(1), e267. https://doi.org/10.1002/gdj3.267
  3. Ballarin, A. S., Markonis, Y., Rakovec, O., & Papalexiou, S. M. (2025). How likely is Ukraine to experience an extreme drought in the near future? Climatic Change, 178(6), 113. https://doi.org/10.1007/s10584-025-03958-9
  4. Nerantzaki, S.D., Abdelmoaty, H.M., Papalexiou, S.M., Newman, A.J., (2025). The Influence of Atmospheric Drivers, Environmental Factors, and Urban Land Use on Extreme Hourly Precipitation Trends over the CONtiguous United States for 40 years at 4-km resolution (CONUS404). Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2025.178407
  5. Papalexiou, S. M., Mascaro, G., Pendergrass, A. G., Mamalakis, A., de Brito, M. M., Andreadis, K. M., et al. (2025). Sustainability Nexus AID: storms. Sustainability Nexus Forum, 33(1), 1. https://doi.org/10.1007/s00550-024-00544-y
  6. Tang, G., Clark, M. P., Knoben, W. J. M., Liu, H., Gharari, S., Arnal, L., …, & Papalexiou, S. M., (2025). Uncertainty hotspots in global hydrologic modeling: the impact of precipitation and temperature forcings. Bulletin of the American Meteorological Society, 106(1), E146–E166. https://doi.org/10.1175/BAMS-D-24-0007.1
  7. Vogel, R. M., Papalexiou, S. M., Lamontagne, J. R., & Dolan, F. (2025). When Heavy Tails Disrupt Statistical Inference. The American Statistician, 79(2), 221–235. https://doi.org/10.1080/00031305.2024.2402898
  8. Zaerpour, M., Hatami, S., Ballarin, A. S., Papalexiou, S. M., Pietroniro, A., & Nazemi, A. (2025). Agriculture’s impact on water–energy balance varies across climates. Proceedings of the National Academy of Sciences, 122(12), e2410521122. https://doi.org/10.1073/pnas.2410521122
  9. Abdelmoaty, H. M., Papalexiou, S. M., Gaur, A., & Markonis, Y. (2024). Investigating Catchment-Scale Daily Snow Depths of CMIP6 in Canada. Geophysical Research Letters, 51(12), e2024GL109664. https://doi.org/10.1029/2024GL109664
  10. Abdelmoaty, H. M., Papalexiou, S. M., Nerantzaki, S., Mascaro, G., Gaur, A., Lu, H., et al. (2024). Snow depth time series Generation: Effective simulation at multiple time scales. Journal of Hydrology X, 23, 100177. https://doi.org/10.1016/j.hydroa.2024.100177
  11. Ballarin, André S., Wendland, E., Zaerpour, M., Hatami, S., Meira Neto, A. A., & Papalexiou, S. M. (2024). Frequency Rather Than Intensity Drives Projected Changes of Rainfall Events in Brazil. Earth’s Future, 12(1), e2023EF004053. https://doi.org/10.1029/2023EF004053
  12. Ballarin, A. S., Oliveira, P. T., S. M. Uchôa, J. G. S. M., Lima, C. H. R., Zaerpour, M., Vargas Godoy, M. R.,…, Papalexiou, S.M., & Wendland, E. (2024). Escalating threat of human-perceived heatwaves in Brazil. Environmental Research Communications. https://doi.org/10.1088/2515-7620/ad9140
  13. Ballarin, André Simões, Vargas Godoy, M. R., Zaerpour, M., Abdelmoaty, H. M., Hatami, S., Gavasso‐Rita, Y. L., Wendland, E., & Papalexiou, S. M. (2024). Drought intensification in Brazilian catchments: implications for water and land management. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ad3e18
  14. Gavasso-Rita, Y. L., Papalexiou, S. M., Li, Y., Elshorbagy, A., Li, Z., & Schuster-Wallace, C. (2024). Crop models and their use in assessing crop production and food security: A review. Food and Energy Security, 13(1), e503. https://doi.org/10.1002/fes3.503
  15. Grimaldi, S., Cappelli, F., Papalexiou, S. M., Petroselli, A., Nardi, F., Annis, A., et al. (2024). Optimizing sensor location for the parsimonious design of flood early warning systems. Journal of Hydrology X, 24, 100182. https://doi.org/10.1016/j.hydroa.2024.100182
  16. Liu, H., Clark, M. P., Gharari, S., Sheikholeslami, R., Freer, J., Knoben, W. J. M., Marsh, C., & Papalexiou, S. M. (2024). An Improved Copula-Based Framework for Efficient Global Sensitivity Analysis. Water Resources Research, 60(1), e2022WR033808. https://doi.org/10.1029/2022WR033808
  17. Markonis, Y., Vargas Godoy, M. R., Pradhan, R. K., Pratap, S., Thomson, J. R., Hanel, M., …, & Papalexiou, S. M. (2024). Spatial partitioning of terrestrial precipitation reveals varying dataset agreement across different environments. Communications Earth & Environment, 5(1), 1–10. https://doi.org/10.1038/s43247-024-01377-9
  18. Rajulapati, C. R., Tesemma, Z., Shook, K., Papalexiou, S. M., & Pomeroy, J. W. (2024). A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections. Journal of Hydrology, 645, 132080. https://doi.org/10.1016/j.jhydrol.2024.132080
  19. Tang, G., Wood, A. W., Newman, A. J., Clark, M. P., & Papalexiou, S. M. (2024). GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications. Geoscientific Model Development, 17(3), 1153–1173. https://doi.org/10.5194/gmd-17-1153-2024
  20. Vargas Godoy, M. R., Papalexiou, S. M., & Markonis, Y. (2024). HYADES - A Global Archive of Annual Maxima Daily Precipitation. Scientific Data, 11(1), 298. https://doi.org/10.1038/s41597-024-03109-2
  21. Vargas Godoy, M. R., Markonis, Y., Rakovec, O., Jenicek, M., Dutta, R., …, Papalexiou, S. M., & Hanel, M. (2024). Water cycle changes in Czechia: a multi-source water budget perspective. Hydrology and Earth System Sciences, 28(1), 1–19. https://doi.org/10.5194/hess-28-1-2024
  22. Volpi, E., Grimaldi, S., Aghakouchak, A., Castellarin, A., Chebana, F., Papalexiou, S. M., et al. (2024). The legacy of STAHY: Milestones, achievements, challenges, and open problems in statistical hydrology. Hydrological Sciences Journal, 0(ja). https://doi.org/10.1080/02626667.2024.2385686
  23. Whitfield, P. H., Abdelmoaty, H., Nerantzaki, S., & Papalexiou, S. M. (2024). The 2021 heatwave results in simultaneous but different hydrological responses over Canada west of 100°W. Journal of Hydrology, 130824. https://doi.org/10.1016/j.jhydrol.2024.130824
  24. Zaerpour, M., Hatami, S., Ballarin, A. S., Knoben, W. J. M., Papalexiou, S. M., Pietroniro, A., & Clark, M. P. (2024). Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain. Communications Earth & Environment, 5(1), 1–14. https://doi.org/10.1038/s43247-024-01891-w
  25. Zaerpour, M., Papalexiou, S. M., Pietroniro, A., & Nazemi, A. (2024). How extreme are flood peak distributions? A quasi-global analysis of daily discharge records. Journal of Hydrology, 631, 130849. https://doi.org/10.1016/j.jhydrol.2024.130849
  26. Zaghloul, M. A., & Papalexiou, S. M. (2024). Generation Mechanisms and Probabilistic Assessment of Peak Spring Streamflow in the Canadian Prairies. Stochastic Environmental Research and Risk Assessment, 38(3), 1071–1088. https://doi.org/10.1007/s00477-023-02614-x
  27. Zaghloul, M. A., Elshorbagy, A., & Papalexiou, S. M. (2024). Enhancing regional flood frequency analysis by integrating site-similarity measures with watershed modeling. Journal of Hydrology, 641, 131754. https://doi.org/10.1016/j.jhydrol.2024.131754
  28. Papalexiou, S. M., Serinaldi, F., & Clark, M. P. (2023). Large-domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1000 Sites. Water Resources Research, e2022WR034094. https://doi.org/10.1029/2022WR034094
  29. Nerantzaki, S. D., Papalexiou, S. M., Rajulapati, C. R., & Clark, M. P. (2023). Nonstationarity in High and Low-Temperature Extremes: Insights from a Global Observational Dataset by Merging Extreme-Value Methods. Earth’s Future, e2023EF003506. https://doi.org/10.1029/2023EF003506
  30. Mascaro, G., Papalexiou, S. M., & Wright, D. B. (2023). Advancing Characterization and Modeling of Space-Time Correlation Structure and Marginal Distribution of Short-Duration Precipitation. Advances in Water Resources, 177, 104451. https://doi.org/10.1016/j.advwatres.2023.104451
  31. Tang, G., Clark, M. P., Knoben, W. J. M., Liu, H., Gharari, S., Arnal, L., Hylke E. B., Wood, A. W., Newman, A., & Papalexiou S. M. (2023). The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins. Water Resources Research, 59(6), e2022WR033767. https://doi.org/10.1029/2022WR033767
  32. Rajulapati, C. R., & Papalexiou, S. M. (2023). Precipitation Bias Correction: A Novel Semi-parametric Quantile Mapping Method. Earth and Space Science, 10(4), e2023EA002823. https://doi.org/10.1029/2023EA002823
  33. Abdelmoaty, H. M., & Papalexiou, S. M. (2023). Changes of Extreme Precipitation in CMIP6 Projections: Should We use Stationary or Nonstationary Models? Journal of Climate, 1, 1–40. https://doi.org/10.1175/JCLI-D-22-0467.1
  34. Marra, F., Amponsah, W., & Papalexiou, S. M. (2023). Non-asymptotic Weibull tails explain the statistics of extreme daily precipitation. Advances in Water Resources, 173, 104388. https://doi.org/10.1016/j.advwatres.2023.104388
  35. Yousfi, N., El Adlouni, S., Papalexiou, S. M., & Gachon, P. (2023). Mixture Probability Models with Covariates: Applications in Estimating Risk of Hydroclimatic Extremes. Journal of Hydrologic Engineering, 28(4), 04023011. https://doi.org/10.1061/JHYEFF.HEENG-5831
  36. Papalexiou, S. M. (2022). Rainfall Generation Revisited: Introducing CoSMoS-2s and Advancing Copula-Based Intermittent Time Series Modeling. Water Resources Research, 58(6), e2021WR031641. https://doi.org/10.1029/2021WR031641
  37. AghaKouchak, A., Pan, B., Mazdiyasni, O., Sadegh, M., Jiwa, S., Zhang, W., et al. (2022). Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2238), 20210288. https://doi.org/10.1098/rsta.2021.0288
  38. Grimaldi, S., Volpi, E., Langousis, A., Papalexiou, S. M., Luciano De Luca, D., Piscopia, R., et al. (2022). Continuous hydrologic modelling for small and ungauged basins: A comparison of eight rainfall models for sub-daily runoff simulations. Journal of Hydrology, 610, 127866. https://doi.org/10.1016/j.jhydrol.2022.127866
  39. Gu, X., Ye, L., Xin, Q., Zhang, C., Zeng, F., Nerantzaki, S. D., & Papalexiou, S. M. (2022). Extreme Precipitation in China: A Review on Statistical Methods and Applications. Advances in Water Resources, 163, 104144. https://doi.org/10.1016/j.advwatres.2022.104144
  40. Hobbi, S., Papalexiou, S. M., Rupa Rajulapati, C., Nerantzaki, S. D., Markonis, Y., Tang, G., & Clark, M. P. (2022). Detailed investigation of discrepancies in Köppen-Geiger climate classification using seven global gridded products. Journal of Hydrology, 612, 128121. https://doi.org/10.1016/j.jhydrol.2022.128121
  41. Lipoth, J., Tereda, Y., Papalexiou, S. M., Spiteri, R. J., Lipoth, J., Tereda, Y., et al. (2022). A new very simply explicitly invertible approximation for the standard normal cumulative distribution function. AIMS Mathematics, 7(7), 11635–11646. https://doi.org/10.3934/math.2022648
  42. Nerantzaki, S. D., & Papalexiou, S. M. (2022). Assessing extremes in hydroclimatology: A review on probabilistic methods. Journal of Hydrology, 605, 127302. https://doi.org/10.1016/j.jhydrol.2021.127302
  43. Pradhan, R. K., Markonis, Y., Vargas Godoy, M. R., Villalba-Pradas, A., Andreadis, K. M., Nikolopoulos, E. I., et al. (2022). Review of GPM IMERG performance: A global perspective. Remote Sensing of Environment, 268, 112754. https://doi.org/10.1016/j.rse.2021.112754
  44. Rajulapati, C. R., Abdelmoaty, H. M., Nerantzaki, S. D., & Papalexiou, S. M. (2022). Changes in the risk of extreme temperatures in megacities worldwide. Climate Risk Management, 36, 100433. https://doi.org/10.1016/j.crm.2022.100433
  45. Rajulapati, C. R., Gaddam, R. K., Nerantzaki, S. D., Papalexiou, S. M., Cannon, A. J., & Clark, M. P. (2022). Exacerbated heat in large Canadian cities. Urban Climate, 42, 101097. https://doi.org/10.1016/j.uclim.2022.101097
  46. Schuster-Wallace, C. J., Dickson-Anderson, S. E., Papalexiou, S. M., & Ganzouri, A. E. (2022). Design and Application of the Tank Simulation Model (TSM): Assessing the Ability of Rainwater Harvesting to Meet Domestic Water Demand. Journal of Environmental Informatics, 40(1), 16–29. https://doi.org/doi:10.3808/jei.202200477
  47. Tang, G., Clark, M. P., & Papalexiou, S. M. (2022). EM-Earth: The Ensemble Meteorological Dataset for Planet Earth. Bulletin of the American Meteorological Society, 103(4), E996–E1018. https://doi.org/10.1175/BAMS-D-21-0106.1
  48. Wang, W., Yin, S., Gao, G., Papalexiou, S. M., & Wang, Z. (2022). Increasing trends in rainfall erosivity in the Yellow River basin from 1971 to 2020. Journal of Hydrology, 610, 127851. https://doi.org/10.1016/j.jhydrol.2022.127851
  49. Papalexiou, S. M., Serinaldi, F., & Porcu, E. (2021). Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57(8), e2020WR029466. https://doi.org/10.1029/2020WR029466
  50. Papalexiou, S. M., Rajulapati, C. R., Andreadis, K. M., Foufoula-Georgiou, E., Clark, M. P., & Trenberth, K. E. (2021). Probabilistic Evaluation of Drought in CMIP6 Simulations. Earth’s Future, e2021EF002150. https://doi.org/10.1029/2021EF002150
  51. Abdelmoaty, H. M., Papalexiou, S. M., Rajulapati, C. R., & AghaKouchak, A. (2021). Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation. Earth’s Future, 9(10), e2021EF002196. https://doi.org/10.1029/2021EF002196
  52. Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., et al. (2021). The Abuse of Popular Performance Metrics in Hydrologic Modeling. Water Resources Research, 57(9), e2020WR029001. https://doi.org/10.1029/2020WR029001
  53. Markonis, Y., Pappas, C., Hanel, M., & Papalexiou, S. M. (2021). A cross-scale framework for integrating multi-source data in Earth system sciences. Environmental Modelling & Software, 139, 104997. https://doi.org/10.1016/j.envsoft.2021.104997
  54. Moccia, B., Papalexiou, S. M., Russo, F., & Napolitano, F. (2021). Spatial variability of precipitation extremes over Italy using a fine-resolution gridded product. Journal of Hydrology: Regional Studies, 37, 100906. https://doi.org/10.1016/j.ejrh.2021.100906
  55. Moustakis, Y., Papalexiou, S. M., Onof, C. J., & Paschalis, A. (2021). Seasonality, Intensity, and Duration of Rainfall Extremes Change in a Warmer Climate. Earth’s Future, 9(3), e2020EF001824. https://doi.org/10.1029/2020EF001824
  56. Papacharalampous, G., Tyralis, H., Papalexiou, S. M., Langousis, A., Khatami, S., Volpi, E., & Grimaldi, S. (2021). Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity. Science of The Total Environment, 767, 144612. https://doi.org/10.1016/j.scitotenv.2020.144612
  57. Rajulapati, C. R., Papalexiou, S. M., Clark, M. P., & Pomeroy, J. W. (2021). The Perils of Regridding: Examples using a Global Precipitation Dataset. Journal of Applied Meteorology and Climatology, 1(aop). https://doi.org/10.1175/JAMC-D-20-0259.1
  58. Sheikholeslami, R., Gharari, S., Papalexiou, S. M., & Clark, M. P. (2021). VISCOUS: A Variance-Based Sensitivity Analysis Using Copulas for Efficient Identification of Dominant Hydrological Processes. Water Resources Research, 57(7), e2020WR028435. https://doi.org/10.1029/2020WR028435
  59. Shook, K., Papalexiou, S. M., & Pomeroy, J. W. (2021). Quantifying the effects of Prairie depressional storage complexes on drainage basin connectivity. Journal of Hydrology, 593, 125846. https://doi.org/10.1016/j.jhydrol.2020.125846
  60. Tang, G., Clark, M. P., Papalexiou, S. M., Newman, A. J., Wood, A. W., Brunet, D., & Whitfield, P. H. (2021). EMDNA: an Ensemble Meteorological Dataset for North America. Earth System Science Data, 13(7), 3337–3362. https://doi.org/10.5194/essd-13-3337-2021
  61. Tang, G., Clark, M. P., & Papalexiou, S. M. (2021a). SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019. Journal of Climate, 34(16), 6493–6511. https://doi.org/10.1175/JCLI-D-21-0067.1
  62. Tang, G., Clark, M. P., & Papalexiou, S. M. (2021b). The use of serially complete station data to improve the temporal continuity of gridded precipitation and temperature estimates. Journal of Hydrometeorology, 1(aop). https://doi.org/10.1175/JHM-D-20-0313.1
  63. Tyralis, H., Papacharalampous, G., Langousis, A., & Papalexiou, S. M. (2021). Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms. Remote Sensing, 13(3), 333. https://doi.org/10.3390/rs13030333
  64. Vargas Godoy, M. R., Markonis, Y., Hanel, M., Kyselý, J., & Papalexiou, S. M. (2021). The Global Water Cycle Budget: A Chronological Review. Surveys in Geophysics. https://doi.org/10.1007/s10712-021-09652-6
  65. Zaerpour, M., Papalexiou, S. M., & Nazemi, A. (2021). Informing Stochastic Streamflow Generation by Large-Scale Climate Indices at Single and Multiple Sites. Advances in Water Resources, 104037. https://doi.org/10.1016/j.advwatres.2021.104037
  66. AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou S.M., Ragno, E., Sadegh, M., (2020). Climate Extremes and Compound Hazards in a Warming World. Annual Review of Earth and Planetary Sciences, 48(1), 519–548. https://doi.org/10.1146/annurev-earth-071719-055228
  67. Brunner, M. I., Papalexiou, S.M., Clark, M. P., & Gilleland, E. (2020). How Probable Is Widespread Flooding in the United States? Water Resources Research, 56(10). https://doi.org/10.1029/2020WR028096
  68. Papalexiou, S.M., Rajulapati, C. R., Clark, M. P., & Lehner, F. (2020). Robustness of CMIP6 Historical Global Mean Temperature Simulations: Trends, Long‐Term Persistence, Autocorrelation, and Distributional Shape. Earth’s Future, 8(10). https://doi.org/10.1029/2020EF001667
  69. Papalexiou, S.M., & Serinaldi, F. (2020). Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity. Water Resources Research, 56(2). https://doi.org/10.1029/2019WR026331
  70. Rajulapati, C. R., Papalexiou, S.M., Clark, M. P., Razavi, S., Tang, G., & Pomeroy, J. W. (2020). Assessment of Extremes in Global Precipitation Products: How Reliable Are They? Journal of Hydrometeorology, 21(12), 2855–2873. https://doi.org/10.1175/JHM-D-20-0040.1
  71. Salas, J. D., Anderson, M. L., Papalexiou, S.M., & Frances, F. (2020). PMP and Climate Variability and Change: A Review. Journal of Hydrologic Engineering, 25(12), 03120002. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002003
  72. Tang, G., Clark, M. P., Papalexiou, S.M., Ma, Z., & Hong, Y. (2020). Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sensing of Environment, 240, 111697. https://doi.org/10.1016/j.rse.2020.111697
  73. Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S.M., Vionnet, V., & Whitfield, P. H. (2020b). SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018. Earth System Science Data, 12(4), 2381–2409. https://doi.org/10.5194/essd-12-2381-2020
  74. Zaghloul, M., Papalexiou, S.M., Elshorbagy, A., & Coulibaly, P. (2020). Revisiting flood peak distributions: A pan-Canadian investigation. Advances in Water Resources, 145, 103720. https://doi.org/10.1016/j.advwatres.2020.103720
  75. Markonis, Y., Papalexiou, S.M., Martinkova, M., & Hanel, M. (2019). Assessment of Water Cycle Intensification Over Land using a Multisource Global Gridded Precipitation DataSet. Journal of Geophysical Research: Atmospheres, 124(21), 11175–11187. https://doi.org/10.1029/2019JD030855
  76. Nerantzaki, S. D., & Papalexiou, S.M. (2019). Tails of extremes: Advancing a graphical method and harnessing big data to assess precipitation extremes. Advances in Water Resources, 134, 103448. https://doi.org/10.1016/j.advwatres.2019.103448
  77. Papalexiou, S.M., & Montanari, A. (2019). Global and Regional Increase of Precipitation Extremes under Global Warming. Water Resources Research, 55(6), 4901–4914. https://doi.org/10.1029/2018WR024067
  78. Iliopoulou, T., Papalexiou, S. M., Markonis, Y., & Koutsoyiannis, D. (2018). Revisiting long-range dependence in annual precipitation. Journal of Hydrology, 556, 891–900. https://doi.org/10.1016/j.jhydrol.2016.04.015
  79. Markonis, Y., Moustakis, Y., Nasika, C., Sychova, P., Dimitriadis, P., Hanel, M., Máca, P., Papalexiou, S.M. (2018). Global estimation of long-term persistence in annual river runoff. Advances in Water Resources, 113, 1–12. https://doi.org/10.1016/j.advwatres.2018.01.003
  80. Papaioannou, G., Efstratiadis, A., Vasiliades, L., Loukas, A., Papalexiou, S.M., Koukouvinos, A., et al. (2018). An Operational Method for Flood Directive Implementation in Ungauged Urban Areas. Hydrology, 5(2), 24. https://doi.org/10.3390/hydrology5020024
  81. Papalexiou, S.M. (2018). Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency. Advances in Water Resources, 115, 234–252. https://doi.org/10.1016/j.advwatres.2018.02.013
  82. Papalexiou, S.M., AghaKouchak, A., & Foufoula-Georgiou, E. (2018). A Diagnostic Framework for Understanding Climatology of Tails of Hourly Precipitation Extremes in the United States. Water Resources Research, 54(9), 6725–6738. https://doi.org/10.1029/2018WR022732
  83. Papalexiou, S.M., AghaKouchak, A., Trenberth, K. E., & Foufoula‐Georgiou, E. (2018). Global, Regional, and Megacity Trends in the Highest Temperature of the Year: Diagnostics and Evidence for Accelerating Trends. Earth’s Future, 6(1), 71–79. https://doi.org/10.1002/2017EF000709
  84. Papalexiou, S.M., Markonis, Y., Lombardo, F., AghaKouchak, A., & Foufoula‐Georgiou, E. (2018). Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes. Water Resources Research, 54(10), 7435–7458. https://doi.org/10.1029/2018WR022726
  85. Tsoukalas, I., Papalexiou, S. M., Efstratiadis, A., & Makropoulos, C. (2018). A Cautionary Note on the Reproduction of Dependencies through Linear Stochastic Models with Non-Gaussian White Noise. Water, 10(6), 771. https://doi.org/10.3390/w10060771
  86. Papalexiou, S.M., & Koutsoyiannis, D. (2016). A global survey on the seasonal variation of the marginal distribution of daily precipitation. Advances in Water Resources, 94, 131–145. https://doi.org/10.1016/j.advwatres.2016.05.005
  87. Papalexiou, S.M., Dialynas, Y. G., & Grimaldi, S. (2016). Hershfield factor revisited: Correcting annual maximum precipitation. Journal of Hydrology, 542, 884–895. https://doi.org/10.1016/j.jhydrol.2016.09.058
  88. Lombardo, F., Volpi, E., Koutsoyiannis, D., & Papalexiou, S.M. (2014). Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology. Hydrology and Earth System Sciences, 18(1), 243–255. https://doi.org/10.5194/hess-18-243-2014
  89. Pappas, C., Papalexiou, S.M., & Koutsoyiannis, D. (2014). A quick gap filling of missing hydrometeorological data. Journal of Geophysical Research: Atmospheres, 119(15), 9290–9300. https://doi.org/10.1002/2014JD021633
  90. Papalexiou, S.M., & Koutsoyiannis, D. (2013). Battle of extreme value distributions: A global survey on extreme daily rainfall. Water Resources Research, 49(1), 187–201. https://doi.org/10.1029/2012WR012557
  91. Papalexiou, S.M., Koutsoyiannis, D., & Makropoulos, C. (2013). How extreme is extreme? An assessment of daily rainfall distribution tails. Hydrology and Earth System Sciences, 17(2), 851–862. https://doi.org/10.5194/hess-17-851-2013
  92. Papalexiou, S.M., & Koutsoyiannis, D. (2012). Entropy based derivation of probability distributions: A case study to daily rainfall. Advances in Water Resources, 45, 51–57. https://doi.org/10.1016/j.advwatres.2011.11.007
  93. Papalexiou, S.M., Koutsoyiannis, D., & Montanari, A. (2011). Can a simple stochastic model generate rich patterns of rainfall events? Journal of Hydrology, 411(3–4), 279–289. https://doi.org/10.1016/j.jhydrol.2011.10.008
  94. Papalexiou, S.M., & Koutsoyiannis, D. (2006). A probabilistic approach to the concept of Probable Maximum Precipitation. Advances in Geosciences, 7, 51–54. https://doi.org/10.5194/adgeo-7-51-2006