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Global Institute for Water Security

Academic Background

  • 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 is an Associate Professor in the Department of Civil Engineering at the University of Calgary, Canada, and also holds the position of Adjunct Professor at the University of Saskatchewan. His current research focuses on hydroclimatic variability and extremes, including the development of advanced space-time stochastic models, downscaling schemes, and climate change diagnostics. He has authored over 86 articles in leading journals and actively participated in more than 100 conference presentations. Simon serves as an Associate Editor for AGU's Water Resources Research and Elsevier's Journal of Hydrology. He also led a special issue on hydroclimatic extremes in Advances in Water Resources. His involvement in the academic community extends to reviewing articles for over 50 journals, convening scientific sessions, and organizing workshops on time series modeling. Simon leads the development of CoSMoS-R, a widely used software in stochastic modeling with a global user base. Beyond academia, his research has received extensive media coverage, featured in over 100 news outlets and broadcasted on radio and TV. His work has garnered several accolades, including being 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, among other recognitions.

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. Abouzied, G. A. A., Guoqiang, T., Papalexiou, S. M., Clark, M. P., Aruffo, E., & Di Carlo, P. (2024). Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019. Geoscience Data Journal. (accepted)
  2. Papalexiou, S. M., Mascaro, G., Pendergrass, A. G., Mamalakis, A., Madruga de Brito, M., Andreadis, K. M., et al. (2024). Sustainability Nexus AID: storms. Sustainability Nexus Forum. (accepted)
  3. Vogel, R. M., Papalexiou, S. M., Lamontagne, J. R., & Dolan, F. (2024). When Heavy Tails Disrupt Statistical Inference. The American Statistician. (accepted)
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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
  75. 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
  76. 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
  77. 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
  78. 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
  79. 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
  80. 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
  81. 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
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