Statistical analysis of hydroclimatic time series: Uncertainty and insights

D. Koutsoyiannis, and A. Montanari, Statistical analysis of hydroclimatic time series: Uncertainty and insights, Water Resources Research, 43 (5), W05429, doi:10.1029/2006WR005592, 2007.

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[English]

Today, hydrologic research and modeling depends largely on climatological inputs, whose physical and statistical behavior are the subject of many debates in the scientific community. A relevant ongoing discussion is focused on long-term persistence (LTP), a natural behavior identified in several studies of instrumental and proxy hydroclimatic time series, which, nevertheless, is neglected in some climatological studies. LTP may reflect a long-term variability of several factors and thus can support a more complete physical understanding and uncertainty characterization of climate. The implications of LTP in hydroclimatic research, especially in statistical questions and problems, may be substantial but appear to be not fully understood or recognized. To offer insights on these implications, we demonstrate by using analytical methods that the characteristics of temperature series, which appear to be compatible with the LTP hypothesis, imply a dramatic increase of uncertainty in statistical estimation and reduction of significance in statistical testing, in comparison with classical statistics. Therefore we maintain that statistical analysis in hydroclimatic research should be revisited in order not to derive misleading results and simultaneously that merely statistical arguments do not suffice to verify or falsify the LTP (or another) climatic hypothesis.

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See also: http://dx.doi.org/10.1029/2006WR005592

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Our works referenced by this work:

1. D. Koutsoyiannis, The Hurst phenomenon and fractional Gaussian noise made easy, Hydrological Sciences Journal, 47 (4), 573–595, doi:10.1080/02626660209492961, 2002.
2. D. Koutsoyiannis, Climate change, the Hurst phenomenon, and hydrological statistics, Hydrological Sciences Journal, 48 (1), 3–24, doi:10.1623/hysj.48.1.3.43481, 2003.
3. D. Koutsoyiannis, Uncertainty, entropy, scaling and hydrological stochastics, 1, Marginal distributional properties of hydrological processes and state scaling, Hydrological Sciences Journal, 50 (3), 381–404, doi:10.1623/hysj.50.3.381.65031, 2005.
4. D. Koutsoyiannis, Uncertainty, entropy, scaling and hydrological stochastics, 2, Time dependence of hydrological processes and time scaling, Hydrological Sciences Journal, 50 (3), 405–426, doi:10.1623/hysj.50.3.405.65028, 2005.
5. D. Koutsoyiannis, A toy model of climatic variability with scaling behaviour, Journal of Hydrology, 322, 25–48, doi:10.1016/j.jhydrol.2005.02.030, 2006.
6. D. Koutsoyiannis, A. Efstratiadis, and K. Georgakakos, Uncertainty assessment of future hydroclimatic predictions: A comparison of probabilistic and scenario-based approaches, Journal of Hydrometeorology, 8 (3), 261–281, doi:10.1175/JHM576.1, 2007.

Our works that reference this work:

1. C. Cudennec, C. Leduc, and D. Koutsoyiannis, Dryland hydrology in Mediterranean regions -- a review, Hydrological Sciences Journal, 52 (6), 1077–1087, doi:10.1623/hysj.52.6.1077, 2007.
2. D. Koutsoyiannis, H. Yao, and A. Georgakakos, Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods, Hydrological Sciences Journal, 53 (1), 142–164, doi:10.1623/hysj.53.1.142, 2008.
3. D. Koutsoyiannis, A. Efstratiadis, N. Mamassis, and A. Christofides, On the credibility of climate predictions, Hydrological Sciences Journal, 53 (4), 671–684, doi:10.1623/hysj.53.4.671, 2008.
4. D. Koutsoyiannis, C. Makropoulos, A. Langousis, S. Baki, A. Efstratiadis, A. Christofides, G. Karavokiros, and N. Mamassis, Climate, hydrology, energy, water: recognizing uncertainty and seeking sustainability, Hydrology and Earth System Sciences, 13, 247–257, doi:10.5194/hess-13-247-2009, 2009.
5. D. Koutsoyiannis, A. Montanari, H. F. Lins, and T.A. Cohn, Climate, hydrology and freshwater: towards an interactive incorporation of hydrological experience into climate research—DISCUSSION of “The implications of projected climate change for freshwater resources and their management”, Hydrological Sciences Journal, 54 (2), 394–405, doi:10.1623/hysj.54.2.394, 2009.
6. D. Koutsoyiannis, A random walk on water, Hydrology and Earth System Sciences, 14, 585–601, doi:10.5194/hess-14-585-2010, 2010.
7. G. G. Anagnostopoulos, D. Koutsoyiannis, A. Christofides, A. Efstratiadis, and N. Mamassis, A comparison of local and aggregated climate model outputs with observed data, Hydrological Sciences Journal, 55 (7), 1094–1110, doi:10.1080/02626667.2010.513518, 2010.
8. G. Di Baldassarre, A. Montanari, H. F. Lins, D. Koutsoyiannis, L. Brandimarte, and G. Blöschl, Flood fatalities in Africa: from diagnosis to mitigation, Geophysical Research Letters, 37, L22402, doi:10.1029/2010GL045467, 2010.
9. H. Tyralis, and D. Koutsoyiannis, Simultaneous estimation of the parameters of the Hurst-Kolmogorov stochastic process, Stochastic Environmental Research & Risk Assessment, 25 (1), 21–33, 2011.
10. D. Koutsoyiannis, and A. Langousis, Precipitation, Treatise on Water Science, edited by P. Wilderer and S. Uhlenbrook, 2, 27–78, doi:10.1016/B978-0-444-53199-5.00027-0, Academic Press, Oxford, 2011.
11. D. Koutsoyiannis, 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, 2011.
12. D. Koutsoyiannis, A. Paschalis, and N. Theodoratos, Two-dimensional Hurst-Kolmogorov process and its application to rainfall fields, Journal of Hydrology, 398 (1-2), 91–100, doi:10.1016/j.jhydrol.2010.12.012, 2011.
13. D. Koutsoyiannis, Hurst-Kolmogorov dynamics as a result of extremal entropy production, Physica A: Statistical Mechanics and its Applications, 390 (8), 1424–1432, doi:10.1016/j.physa.2010.12.035, 2011.
14. S.M. Papalexiou, D. Koutsoyiannis, and A. Montanari, Can a simple stochastic model generate rich patterns of rainfall events?, Journal of Hydrology, 411 (3-4), 279–289, 2011.
15. Y. Markonis, and D. Koutsoyiannis, Climatic variability over time scales spanning nine orders of magnitude: Connecting Milankovitch cycles with Hurst–Kolmogorov dynamics, Surveys in Geophysics, 34 (2), 181–207, doi:10.1007/s10712-012-9208-9, 2013.
16. D. Koutsoyiannis, Hydrology and Change, Hydrological Sciences Journal, 58 (6), 1177–1197, doi:10.1080/02626667.2013.804626, 2013.
17. F. Lombardo, E. Volpi, D. Koutsoyiannis, and S.M. Papalexiou, Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology, Hydrology and Earth System Sciences, 18, 243–255, doi:10.5194/hess-18-243-2014, 2014.
18. C. Pappas, S.M. Papalexiou, and D. Koutsoyiannis, A quick gap-filling of missing hydrometeorological data, Journal of Geophysical Research-Atmospheres, 119 (15), 9290–9300, doi:10.1002/2014JD021633, 2014.
19. S. Ceola, A. Montanari, and D. Koutsoyiannis, Toward a theoretical framework for integrated modeling of hydrological change, WIREs Water, 1 (5), 427–438, doi:10.1002/wat2.1038, 2014.
20. A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014.
21. D. Koutsoyiannis, Generic and parsimonious stochastic modelling for hydrology and beyond, Hydrological Sciences Journal, 61 (2), 225–244, doi:10.1080/02626667.2015.1016950, 2016.
22. P.E. O’Connell, D. Koutsoyiannis, H. F. Lins, Y. Markonis, A. Montanari, and T.A. Cohn, The scientific legacy of Harold Edwin Hurst (1880 – 1978), Hydrological Sciences Journal, 61 (9), 1571–1590, doi:10.1080/02626667.2015.1125998, 2016.
23. Y. Markonis, and D. Koutsoyiannis, Scale-dependence of persistence in precipitation records, Nature Climate Change, 6, 399–401, doi:10.1038/nclimate2894, 2016.
24. Y. Markonis, S. C. Batelis, Y. Dimakos, E. C. Moschou, and D. Koutsoyiannis, Temporal and spatial variability of rainfall over Greece, Theoretical and Applied Climatology, doi:10.1007/s00704-016-1878-7, 2016.
25. A. Tegos, H. Tyralis, D. Koutsoyiannis, and K. H. Hamed, An R function for the estimation of trend signifcance under the scaling hypothesis- application in PET parametric annual time series, Open Water Journal, 4 (1), 66–71, 6, 2017.
26. H. Tyralis, and D. Koutsoyiannis, On the prediction of persistent processes using the output of deterministic models, Hydrological Sciences Journal, 62 (13), 2083–2102, doi:10.1080/02626667.2017.1361535, 2017.
27. T. Iliopoulou, S.M. Papalexiou, Y. Markonis, and D. Koutsoyiannis, Revisiting long-range dependence in annual precipitation, Journal of Hydrology, 556, 891–900, doi:10.1016/j.jhydrol.2016.04.015, 2018.
28. H. Tyralis, P. Dimitriadis, D. Koutsoyiannis, P.E. O’Connell, K. Tzouka, and T. Iliopoulou, On the long-range dependence properties of annual precipitation using a global network of instrumental measurements, Advances in Water Resources, 111, 301–318, doi:10.1016/j.advwatres.2017.11.010, 2018.
29. Y. Markonis, Y. Moustakis, C. Nasika, P. Sychova, P. Dimitriadis, M. Hanel, P. Máca, and S.M. Papalexiou, Global estimation of long-term persistence in annual river runoff, Advances in Water Resources, 113, 1–12, doi:10.1016/j.advwatres.2018.01.003, 2018.
30. I. Tsoukalas, C. Makropoulos, and D. Koutsoyiannis, Simulation of stochastic processes exhibiting any-range dependence and arbitrary marginal distributions, Water Resources Research, 54 (11), 9484–9513, doi:10.1029/2017WR022462, 2018.
31. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, 575, 354–380, doi:10.1016/j.jhydrol.2019.05.017, 2019.
32. T. Iliopoulou, and D. Koutsoyiannis, Revealing hidden persistence in maximum rainfall records, Hydrological Sciences Journal, 64 (14), 1673–1689, doi:10.1080/02626667.2019.1657578, 2019.
33. G. Papacharalampous, H. Tyralis, D. Koutsoyiannis, and A. Montanari, Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale, Advances in Water Resources, 136, 103470, doi:10.1016/j.advwatres.2019.103470, 2020.
34. D. Koutsoyiannis, Revisiting the global hydrological cycle: is it intensifying?, Hydrology and Earth System Sciences, 24, 3899–3932, doi:10.5194/hess-24-3899-2020, 2020.
35. T. Iliopoulou, and D. Koutsoyiannis, Projecting the future of rainfall extremes: better classic than trendy, Journal of Hydrology, 588, doi:10.1016/j.jhydrol.2020.125005, 2020.
36. Z. W. Kundzewicz, I. Pińskwar, and D. Koutsoyiannis, Variability of global mean annual temperature is significantly influenced by the rhythm of ocean-atmosphere oscillations, Science of the Total Environment, 747, 141256, doi:10.1016/j.scitotenv.2020.141256, 2020.
37. A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Generalized storage-reliability-yield framework for hydroelectric reservoirs, Hydrological Sciences Journal, 66 (4), 580–599, doi:10.1080/02626667.2021.1886299, 2021.
38. P. Dimitriadis, D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021.
39. D. Koutsoyiannis, and P. Dimitriadis, Towards generic simulation for demanding stochastic processes, Sci, 3, 34, doi:10.3390/sci3030034, 2021.
40. D. Koutsoyiannis, Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, Edition 3, ISBN: 978-618-85370-0-2, 391 pages, doi:10.57713/kallipos-1, Kallipos Open Academic Editions, Athens, 2023.

Works that cite this document: View on Google Scholar or ResearchGate

Other works that reference this work (this list might be obsolete):

1. Hamed, K.H., Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis, Journal of Hydrology, 349(3-4), 350-363, 2008.
2. Barnett, T.P., and D.W. Pierce, When will Lake Mead go dry?, Water Resources Research, 44, W03201, doi:10.1029/2007WR006704, 2008.
3. Khaliq, M.N., T.B.M.J. Ouarda, P. Gachon and L. Sushama, Temporal evolution of low-flow regimes in Canadian rivers, Water Resources Research, 44 (8), W08436, 2008.
4. Komnitsas, K., and K. Modis, Geostatistical risk estimation at waste disposal sites in the presence of hot spots, J. Hazard. Mater., 164 (2-3), 1185-1190, 2009.
5. Halley, J. M., Using models with long-term persistence to interpret the rapid increase of earth’s temperature, Physica A: Statistical Mechanics and its Applications, 388(12), 2492-2502, 2009.
6. Khaliq, M., T. Ouarda, P. Gachon, L. Sushama and A. St-Hilaire, Identification of hydrological trends in the presence of serial and cross correlations: A review of selected methods and their application to annual flow regimes of Canadian rivers, Journal of Hydrology, 368(1-4), 117-130, 2009.
7. Khaliq, M., T. Ouarda, and P. Gachon, Identification of temporal trends in annual and seasonal low flows occurring in Canadian rivers: The effect of short- and long-term persistence, Journal of Hydrology, 369(1-2), 183-197, 2009.
8. Déry, S. J., K. Stahl, R. D. Moore, P. H. Whitfield, B. Menounos, and J. E. Burford, Detection of runoff timing changes in pluvial, nival, and glacial rivers of western Canada, Water Resour. Res., 45, W04426, doi:10.1029/2008WR006975, 2009.
9. Kumar, S., V. Merwade, J. Kam, and K. Thurner, Streamflow trends in Indiana: Effects of long term persistence, precipitation and subsurface drains, Journal of Hydrology, 374(1-2), 171-183, 2009.
10. Hamed, K. H., Effect of persistence on the significance of Kendall’s tau as a measure of correlation between natural time series, The European Physical Journal, 174 (1), 65-79, 2009.
11. Villarini, G., F. Serinaldi, J. A. Smith, and W. F. Krajewski, On the stationarity of annual flood peaks in the continental United States during the 20th century, Water Resour. Res., 45, W08417, doi:10.1029/2008WR007645, 2009.
12. Fatichi, S., S. M. Barbosa, E. Caporali and M. E. Silva, Deterministic versus stochastic trends: Detection and challenges, Journal Of Geophysical Research-Atmospheres, 114, D18121, doi:10.1029/2009JD011960, 2009.
13. Allamano, P., P. Claps and F. Laio, Global warming increases flood risk in mountainous areas, Geophysical Research Letters, 36, Art. No. L24404, DOI: 10.1029/2009GL041395, 2009.
14. Zhang, Z., A. D. Dehoff, R. D. Pody and J. W. Balay, Detection of Streamflow Change in the Susquehanna River Basin, Water Resources Management, 24 (10), 1947-1964, 2010.
15. Ehsanzadeh, E., and K. Adamowski, Trends in timing of low stream flows in Canada: impact of autocorrelation and long-term persistence, Hydrological Processes, 24, 970–980, 2010.
16. Modis, K., K. Vatalis, G. Papantonopoulos, and C. Sachanidis Uncertainty management of a hydrogeological data set in a greek lignite basin, using BME, Stochastic Environmental Research and Risk Assessment, 24 (1), 47-56, 2010.
17. Clarke, R. T., On the (mis)use of statistical methods in hydro-climatological research, Hydrol. Sci. J., 55(2), 139–144, 2010.
18. Villarini, G., and J. A. Smith, Flood peak distributions for the eastern United States, Water Resour. Res., 46, W06504, doi:10.1029/2009WR008395, 2010.
19. Nayak, A., D. Marks, D. G. Chandler and M. Seyfried, Long-term snow, climate, and streamflow trends at the Reynolds Creek Experimental Watershed, Owyhee Mountains, Idaho, United States, Water Resour. Res., 46, W06519, doi:10.1029/2008WR007525, 2010.
20. Stahl, K., H. Hisdal, J. Hannaford, L. M. Tallaksen, H. A. J. van Lanen, E. Sauquet, S. Demuth, M. Fendekova, and J. Jódar, Streamflow trends in Europe: evidence from a dataset of near-natural catchments, Hydrol. Earth Syst. Sci., 14, 2367-2382, doi:10.5194/hess-14-2367-2010, 2010.
21. Schmocker-Fackel, P., and F. Naef, Changes in flood frequencies in Switzerland since 1500, Hydrol. Earth Syst. Sci., 14, 1581-1594, doi: 10.5194/hess-14-1581-2010, 2010.
22. Khaliq M. N., and P. Gachon, Pacific decadal oscillation climate variability and temporal pattern of winter flows in Northwestern North America, Journal of Hydrometeorology, 11 (4), 917-933, 2010.
23. Dupuis, D.J., Statistical modeling of the monthly Palmer drought severity index, Journal of Hydrologic Engineering, 15 (10), 796-807, art. no. 004010QHE, 2010.
24. #Walter, M., and R. M. Vogel, Increasing trends in peak flows in the northeastern united states and their impacts on design, Proceedings of the 2nd Joint Federal Interagency Conference on Sedimentation and Hydrologic Modeling, Las Vegas, Nevada, USA, 2010.
25. Barco, J., T. S. Hogue, M. Girotto, D. R. Kendall and M. Putti, Climate signal propagation in southern California aquifers, Water Resour. Res., 46, W00F05, doi: 10.1029/2009WR008376, 2010.
26. Botter, G., Stochastic recession rates and the probabilistic structure of stream flows, Water Resources Research, 46 (12), art. no. W12527, doi: 10.1029/2010WR009217, 2010.
27. #Paiva R., W. Collischonn and E. B. Schnetterling, Climate change impacts on water resources in the Quarai River Basin, Section 6.1 in: Modelling the Impact of Climate Change on Water Resources (C. F. Fung, A. Lopez and M. New, eds.), 136-147, Wiley-Blackwell, ISBN: 978-1-4051-9671-0, 2011.
28. Barbosa, S. M., Testing for deterministic trends in global sea surface temperature, Journal of Climate, 24 (10), 2516-2522, 2011.
29. Dery, S. J., T. J. Mlynowski, M. A. Hernandez-Henriquez and F. Straneo, Interannual variability and interdecadal trends in Hudson Bay streamflow, Journal of Marine Systems, 88 (3), 341-351, 2011.
30. Villarini, G., J. A. Smith, M. L. Baeck, R. Vitolo, D. B. Stephenson and W. F. Krajewski, On the frequency of heavy rainfall for the midwest of the United States, Journal of Hydrology, 400 (1-2), 103-120, 2011.
31. Halley, J. M., and D. Kugiumtzis, Nonparametric testing of variability and trend in some climatic records, Climatic Change, 107(3-4), 267-276, 2011.
32. Ouarda, T. B. M. J., and S. El-Adlouni, Bayesian nonstationary frequency analysis of hydrological variables, Journal of the American Water Resources Association, 47(3), 496-505, 2011.
33. Villarini, G., J. A. Smith, M. L. Baeck, and W. F. Krajewski, Examining flood frequency distributions in the Midwest U.S., Journal of the American Water Resources Association, 47(3), 447-463, 2011.
34. Lins, H. F., and T. A. Cohn, Stationarity: wanted dead or alive? Journal of the American Water Resources Association, 47(3), 475-480, 2011.
35. Avery, G. H., Scientific misconduct: A response to Davies and Fielding, World Medical & Health Policy, 3 (2), Art. 12, DOI: 10.2202/1948-4682.1166, 2011.
36. Frank, P. Imposed and neglected uncertainty in the global average surface air temperature index, Energy and Environment, 22 (4), 407-424, 2011.
37. Hamed, K. H., The distribution of Kendall’s tau for testing the significance of cross-correlation in persistent data, Hydrol. Sci. J., 56 (5), 841–853, 2011.
38. Morin, E., To know what we cannot know: Global mapping of minimal detectable absolute trends in annual precipitation, Water Resour. Res., 47, W07505, doi: 10.1029/2010WR009798, 2011.
39. Hodgkins, G. A., and R. W. Dudley, Historical summer base flow and stormflow trends for New England rivers, Water Resour. Res., 47, W07528, doi: 10.1029/2010WR009109, 2011.
40. Savina, M., P. Molnar and P. Burlando, Seasonal long-term persistence in radar precipitation in complex terrain, Water Resources Research, 47 (10), W10506, doi: 10.1029/2010WR010170, 2011.
41. Ehsanzadeh, E., G.. van der Kamp and C. Spence, The impact of climatic variability and change in the hydroclimatology of Lake Winnipeg watershed, Hydrological Processes, 26 (18), 2802-2813, 2012.
42. Armstrong, W. H., M. J. Collins and N. P. Snyder, Increased frequency of low-magnitude floods in New England, Journal of the American Water Resources Association, 48 (2), 306-320, 2012.
43. #Machiwal, D., and M. K. Jha, Exploring trends in climatological time series of Orissa, India using nonparametric trend tests, Hydrologic Time Series Analysis: Theory and Practice, Springer, Netherlands, 222-248, 2012.
44. #Machiwal, D., and M. K. Jha, Analysis of streamflow trend in the Susquehanna River basin, USA, Hydrologic Time Series Analysis: Theory and Practice, Springer, Netherlands, 181-200, 2012.
45. Montanari, A., Hydrology of the Po River: looking for changing patterns in river discharge, Hydrology and Earth System Sciences, 16, 3739-3747, doi:10.5194/hess-16-3739-2012, 2012.
46. Forsythe, K. W., B. Schatz, S. J. Swales, L.-J. Ferrato and D. M. Atkinson, Visualization of Lake Mead surface area changes from 1972 to 2009, ISPRS International Journal of Geo-Information, 1, 108-119, 2012.
47. #Merz, B., Z. W. Kundzewicz, J. Delgado, Y. Hundecha and H. Kreibich, Detection and attribution of changes in flood hazard and risk, Changes of Flood Risk in Europe, IAHS-AISH Publication (SPEC. ISS. 10), (ed. Z. W. Kundzewicz), 435-458, 2012.
48. Burn, D. H., J. Hannaford, G. A. Hodgkins, P. H. Whitfield, R. Thorne and T. Marsh, Reference hydrologic networks II. Using reference hydrologic networks to assess climate-driven changes in streamflow, Hydrological Sciences Journal, 57 (8), 1580-1593, 2012.
49. Gil-Alana, L. A., U.K. Rainfall data: a long-term persistence approach, J. Appl. Meteor. Climatol., 51, 1904–1913, 2012.
50. #United States Environmental Protection Agency, Technical Documentation, Climate Change Indicators in the United States 2012, United States Environmental Protection Agency, 2012.
51. Sang, Y.-F., A review on the applications of wavelet transform in hydrology time series analysis, Atmospheric Research, 122, 8-15,2013.
52. Kumar, S., V. Merwade, J. L. Kinter III and D. Niyogi, Evaluation of temperature and precipitation trends and long-term persistence in CMIP5 20th century climate simulations, Journal of Climate, 26 (12), 4168-4185, 2013.
53. Montanari, A., and G. Di Baldassarre, Data errors and hydrological modelling: the role of model structure to propagate observation uncertainty, Advances in Water Resources, 51, 498-504, 2013.
54. Murphy, C., S. Harrigan, J. Hall and R. L. Wilby, Climate-driven trends in mean and high flows from a network of reference stations in Ireland, Hydrological Sciences Journal, 58 (4), 58 (4), 797-812, 2013.
55. Unger-Shayesteh, K., S. Vorogushyn, D. Farinotti, A. Gafurov, D. Duethmann, A. Mandychev and B. Merz, What do we know about past changes in the water cycle of Central Asian headwaters? A review, Global and Planetary Change, 10.1016/j.gloplacha.2013.02.004, 2013.
56. Hrachowitz, M., H.H.G. Savenije, G. Blöschl, J.J. McDonnell, M. Sivapalan, J.W. Pomeroy, B. Arheimer, T. Blume, M.P. Clark, U. Ehret, F. Fenicia, J.E. Freer, A. Gelfan, H.V. Gupta, D.A. Hughes, R.W. Hut, A. Montanari, S. Pande, D. Tetzlaff, P.A. Troch, S. Uhlenbrook, T. Wagener, H.C. Winsemius, R.A. Woods, E. Zehe, and C. Cudennec, A decade of Predictions in Ungauged Basins (PUB) — a review, Hydrological Sciences Journal, 58(6), 1198-1255, 2013.
57. Hodgkins, G. A., The importance of record length in estimating the magnitude of climatic changes: an example using 175 years of lake ice-out dates in New England, Climatic Change, 10.1007/s10584-013-0766-8, 2013.
58. #Slingo, J., Statistical models and the global temperature record, Met Office, 2013.
59. #Hodgkins, G. A., and R. W. Dudley, Modeled future peak streamflows in four coastal Maine rivers, U.S. Geological Survey Scientific Investigations Report 2013–5080, Reston, Virginia, USA, 18 p., 2013.
60. Dudley, R. W., and G. A. Hodgkins, Historical groundwater trends in Northern New England and relations with streamflow and climatic variables, Journal of the American Water Resources Association, 10.1111/jawr.12080, 2013.
61. Peterson, T. C., R. R. Heim Jr., R. Hirsch, D. P. Kaiser, H. Brooks, N. S. Diffenbaugh, R. M. Dole, J. P. Giovannettone, K. Guirguis, T. R. Karl, R. W. Katz, K. Kunkel, D. Lettenmaier, G. J. McCabe, C. J. Paciorek, K. R. Ryberg, S. Schubert, V. B. S. Silva, B. C. Stewart, A. V. Vecchia, G. Villarini and R. S. Vose, Monitoring and understanding changes in heat waves, cold waves, floods, and droughts in the United States: state of knowledge, Bull. Amer. Meteor. Soc., 94, 821–834, 2013.
62. #Loukas, A., and L. Vasiliades, Review of applied methods for flood-frequency analysis in a changing environment in Greece, In: A review of applied methods in Europe for flood-frequency analysis in a changing environment, Floodfreq COST action ES0901: European procedures for flood frequency estimation (ed. by H. Madsen et al.), Centre for Ecology & Hydrology, Wallingford, UK, 2013.
63. #Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai, Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013.
64. #Viglione, A., A. Montanari and G. Blöschl, Challenges of reservoir planning and management in a changing world, Considering Hydrological Change in Reservoir Planning and Management, Proceedings of H09, IAHS-IAPSO-IASPEI Assembly (IAHS Publ. 362), Gothenburg, Sweden, 2013.
65. #Murphy, C., S. Harrigan, J. Hall and R. L.Wilby, Hydrodetect: The Identification and Assessment of Climate Change Indicators for an Irish Reference Network of River Flow Stations, Climate Change Research Programme (CCRP) Report Series No. 27, pp 1-66. ISBN 978-1-84095-507-1, Environmental Protection Agency, Co. Wexford, Ireland, 2013.
66. #Murphy, C., S. Harrigan, J. Hall and R. L.Wilby, Hydrodetect: the identification and assessment of climate change indicators for an Irish reference network of river flow stations – an overview, Irish National Hydrology Conference, 3-15, 2013.
67. Kundzewicz, Z.W., S. Kanae, S. I. Seneviratne, J. Handmer, N. Nicholls, P. Peduzzi, R. Mechler, L. M. Bouweri, N. Arnell, K. Mach, R. Muir-Wood, G. R. Brakenridge, W. Kron, G. Benito, Y. Honda, K. Takahashi, and B. Sherstyukov, Flood risk and climate change: global and regional perspectives, Hydrological Sciences Journal, 2014.
68. Lovejoy, S., and D. Schertzer, The Weather and Climate: Emergent Laws and Multifractal Cascades, Cambridge University Press, 2013.
69. Armstrong, W. H., M. J. Collins and N. P. Snyder, Hydroclimatic flood trends in the northeastern United States and linkages with large-scale atmospheric circulation patterns, Hydrological Sciences Journal, 59 (9), 1636-1655, 2014.
70. Hall, J., B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, J. Kriaučiūnienė, Z.W. Kundzewicz, M. Lang, M. C. Llasat, N. Macdonald, N. McIntyre, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, C. Neuhold, J. Parajka, R. A. P. Perdigão, L. Plavcová, M. Rogger, J. L. Salinas, E. Sauquet, C. Schär, J. Szolgay, A. Viglione and G. Blöschl, Understanding flood regime changes in Europe: a state-of-the-art assessment, Hydrol. Earth Syst. Sci., 18, 2735-2772, 10.5194/hess-18-2735-2014, 2014.
71. Campos, J. N.B., F. A. Souza Filho and H. V.C. Lima, Risks and uncertainties in reservoir yield in highly variable intermittent rivers: Case of the Castanhão Reservoir in semi-arid Brazil, Hydrological Sciences Journal, 59 (6), 1184-1195, 2014.
72. Szolgayova, E., G. Laaha, G. Blöschl and C. Bucher, Factors influencing long range dependence in streamflow of European rivers, Hydrological Processes, 28 (4), 1573-1586, 2014.
73. Dinpashoh, Y., R. Mirabbasi, D. Jhajharia, H. Abianeh and A. Mostafaeipour, Effect of short term and long-term persistence on identification of temporal trends, J. Hydrol. Eng., 19(3), 617–625, 2014.
74. Panagoulia, D., and E. I. Vlahogianni, Non-linear dynamics and recurrence analysis of extreme precipitation for observed and general circulation model generated climates, Hydrological Processes, 28(4), 2281–2292, 2014.
75. Panagoulia, D., P. Economou and C. Caroni, Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change, Environmetrics, 25 (1), 29-43, 2014.
76. Condon, L. E., and R. M. Maxwell, Groundwater-fed irrigation impacts spatially distributed temporal scaling behavior of the natural system: a spatio-temporal framework for understanding water management impacts, Environmental Research Letters, 9 (3), 034009, 2014.
77. Graf, R., Reference statistics for the structure of measurement series of groundwater levels (Wielkopolska Lowland - western Poland), Hydrological Sciences Journal, 10.1080/02626667.2014.905689, 2014.
78. Sagarika, S., A. Kalra and S. Ahmad, Evaluating the effect of persistence on long-term trends and analyzing step changes in streamflows of the continental United States, Journal of Hydrology, 10.1016/j.jhydrol.2014.05.002, 2014.
79. #United States Environmental Protection Agency, Technical Documentation, Climate Change Indicators in the United States 2014, United States Environmental Protection Agency, 2014.
80. Yang, G., and L. C. Bowling, Detection of changes in hydrologic system memory associated with urbanization in the Great Lakes region, Water Resources Research, 50 (5), 3750-3763, 2014.
81. Di, C., X. Yang and X. Wang, A four-stage hybrid model for hydrological time series forecasting, PLoS ONE 9 (8), e104663, 10.1371/journal.pone.0104663, 2014.
82. Bracken, C., B. Rajagopalan and E. Zagona, A hidden Markov model combined with climate indices for multidecadal streamflow simulation, Water Resources Research, 50 (10), 7836-7846, 2014.
83. Sang Y.-F., C. Liu, Z. Wang, J. Wen and L. Shang, Energy-based wavelet de-noising of hydrologic time series, PLoS ONE, 9 (10), e110733, 10.1371/journal.pone.0110733, 2014.
84. Marani, M., and S. Zanetti, Long-term oscillations in rainfall extremes in a 268 year daily time series, Water Resources Research, 51 (1), 639-647, 2015.
85. Padilla, A., K. Rasouli and S.J. Déry, Impacts of variability and trends in runoff and water temperature on salmon migration in the Fraser River Basin, Canada, Hydrological Sciences Journal, 60 (3), 523-533, 2015.
86. Hertig, E., C. Beck, E. Hertig, H. Wanner and J. Jacobeit, A review of non-stationarities in climate variability of the last century with focus on the North Atlantic-European sector, Earth-Science Reviews, 147, 1-17, 2015.
87. Mortsch, L., S. Cohen and G. Koshida, Climate and water availability indicators in Canada: Challenges and a way forward. Part II – Historic trends, Canadian Water Resources Journal, 40 (2), 146-159, 2015.
88. #Yu, X., T.A. Cohn and J.R. Stedinger, Flood frequency analysis in the context of climate change, World Environmental and Water Resources Congress 2015: Floods, Droughts, and Ecosystems - Proceedings of the 2015 World Environmental and Water Resources Congress, 2376-2385, 2015.
89. Kalra, A., S. Sagarika and S. Ahmad, Spatial and temporal evaluation of hydroclimatic variables in the Colorado river basin, World Environmental and Water Resources Congress 2015: Floods, Droughts, and Ecosystems - Proceedings of the 2015 World Environmental and Water Resources Congress, 1118-1127, 2015.
90. Cheng, C., A. Sa-Ngasoongsong, O. Beyca, T. Le, H. Yang, Z. Kong and S.T.S. Bukkapatnam, Time series forecasting for nonlinear and non-stationary processes: A review and comparative study, IIE Transactions (Institute of Industrial Engineers), 47 (10), 1053-1071, 2015.
91. Lara, A., A. Bahamondez, A. González-Reyes, A.A. Muñoz, E. Cuq and C. Ruiz-Gómez, Reconstructing streamflow variation of the Baker River from tree-rings in Northern Patagonia since 1765, Journal of Hydrology, 10.1016/j.jhydrol.2014.12.007, 2015.
92. Serinaldi, F., and C.G. Kilsby, The importance of prewhitening in change point analysis under persistence, Stochastic Environmental Research and Risk Assessment, 10.1007/s00477-015-1041-5, 2015.
93. Westerberg, I.K., and C. Birkel, Observational uncertainties in hypothesis testing: investigating the hydrological functioning of a tropical catchment, Hydrol. Process., 10.1002/hyp.10533, 2015.
94. Kundzewicz, Z.W., Climate change track in river floods in Europe, Proc. IAHS, 369, 189–194, 10.5194/piahs-369-189-2015, 2015.
95. Serinaldi, F., Can we tell more than we can know? The limits of bivariate drought analyses in the United States, Stochastic Environmental Research and Risk Assessment, 10.1007/s00477-015-1124-3, 2015.
96. Hu, Z., Q. Li, X. Chen, Z. Teng, C. Chen, G. Yin and Y. Zhang, Climate changes in temperature and precipitation extremes in an alpine grassland of Central Asia, Theoretical and Applied Climatology, 10.1007/s00704-015-1568-x, 2015.
97. Fan, L., H. Wang, W. Lai and C. Wang, Administration of water resources in Beijing: Problems and countermeasures, Water Policy, 17 (4), 563-580, 2015.
98. Di Baldassarre, G., L. Brandimarte, and K. Beven, The seventh facet of uncertainty: wrong assumptions, unknowns and surprises in the dynamics of human-water systems, Hydrological Sciences Journal, doi:10.1080/02626667.2015.1091460, 2015.
99. Munshi, J., A robust test for OLS trends in daily temperature data, Social Science Research Network, doi:10.2139/ssrn.2631298, 2015.
100. Tan, X., and T. Y. Gan, Multifractality of Canadian precipitation and streamflow, International Journal of Climatology, doi:10.1002/joc.5078, 2017.

Tagged under: Course bibliography: Hydrometeorology, Climate stochastics, Works discussed in weblogs, Hurst-Kolmogorov dynamics, Papers initially rejected, Scaling, Stochastics, Uncertainty