4 Summary and Conclusions
Using annual maxima precipitation and streamflow across a large sample of catchments, this study has empirically assessed the relationship between temporal changes in precipitation extremes and changes in annual flood magnitude. The spatial pattern of trends detected from precipitation extremes is weakly correlated to the spatial pattern of trends detected from AMAX streamflow over 671 CONUS catchments, with a coefficient of determination of less than 0.2.
A weak linkage between annual precipitation extremes and annual floods is apparent across the CAMELS catchments, with the vast majority of catchments have less than 50% of annual flood events directly linked to precipitation extremes (85%, 90%, and 73% of all catchments for AMAX precipitation, AMAX wet-month precipitation and AMAX effective precipitation respectively). Catchments with a high snow-to-rain ratio (indicated by fsnow value) generally have a low causal relationship between precipitation extremes and floods, but the impact of snow presence is not uniform. The co-variation between extreme precipitation intensity and flood magnitude is also low, with more than 60% of catchments having an R2 of less than 0.5, regardless of which precipitation extreme metrics being used. Using a snow-soil routine to correct the actual amount of precipitation modulating floods has led to a substantially improved predictability for changes in floods, suggesting that future trend detection studies should focus more on the catchment attributes such as soil profile and impervious area.
Notwithstanding the complex processes driving floods, this study has quantitatively assessed the limitation of using changes in precipitation as a proxy for potential changes in floods. The findings indicate that the intensity of daily precipitation extremes is a weak predictor for temporal changes in annual maxima of daily streamflow, even for catchments with a relatively high causal relationship. This study highlights the need for additional efforts to investigate the non-linear responses of floods to climate changes using a larger sample of catchments, which would hopefully achieve a universal understanding of how floods might evolve. For instance, the approach presented in this study can be applied for other large sample datasets [Addor et al. , 2019; Alvarez-Garreton et al. , 2018; Coxon et al. , 2020; Gudmundsson et al. , 2018] to quantify the contribution of extreme precipitation to historical changes in floods for other parts of the world.
Acknowledgments and Data
Hong Xuan Do is currently funded by the School for Environment and Sustainability, University of Michigan through Grant U064474. The authors appreciate the developers of the CAMELS dataset for making this asset publicly available. Hydrometerological data is freely available at https://dx.doi.org/10.5065/D6MW2F4D [Newman et al. , 2014] while the catchment attributes, including the fraction of precipitation falling as snow is freely available at https://doi.org/10.5065/D6G73C3Q [Addor et al. , 2017b].
Reference
Addor, N., A. J. Newman, N. Mizukami, and M. P. Clark (2017a), The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci. Discuss. , 2017 , 1-31.
Addor, N., A. J. Newman, N. Mizukami, and M. P. Clark (2017b), Catchment attributes for large-sample studies, Boulder, CO: UCAR/NCAR, edited.
Addor, N., H. X. Do, C. Alvarez-Garreto, G. Coxon, K. Fowler, and P. Mendoza (2019), Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges, Hydrological Sciences Journal .
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.
Berghuijs, W. R., R. A. Woods, C. J. Hutton, and M. Sivapalan (2016), Dominant flood generating mechanisms across the United States,Geophysical Research Letters , 43 (9), 4382-4390.
Berghuijs, W. R., S. Harrigan, P. Molnar, L. J. Slater, and J. W. Kirchner (2019), The Relative Importance of Different Flood-Generating Mechanisms Across Europe, Water Resources Research , 55 (6), 4582-4593.
Blöschl, G., J. Hall, A. Viglione, R. A. Perdigão, J. Parajka, B. Merz, D. Lun, B. Arheimer, G. T. Aronica, and A. Bilibashi (2019), Changing climate both increases and decreases European river floods,Nature , 573 (7772), 108-111.
Blöschl, G., et al. (2017), Changing climate shifts timing of European floods, Science , 357 (6351), 588.
Bosilovich, M. G., S. D. Schubert, and G. K. Walker (2005), Global Changes of the Water Cycle Intensity, Journal of Climate ,18 (10), 1591-1608.
Burnash, R. J. C., R. L. Ferral, and R. A. McGuire (1973), A generalized streamflow simulation system: Conceptual modeling for digital computers , US Department of Commerce, National Weather Service, and State of California ….
Clausius, R. (1850), Über die bewegende Kraft der Wärme und die Gesetze, welche sich daraus für die Wärmelehre selbst ableiten lassen,Annalen der Physik , 155 (3), 368-397.
Coxon, G., et al. (2020), CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data Discuss. , 2020 , 1-34.
Do, H. X., S. Westra, and L. Michael (2017), A global-scale investigation of trends in annual maximum streamflow, Journal of Hydrology .
Do, H. X., L. Gudmundsson, M. Leonard, and S. Westra (2018), The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data , 10 (2), 765-785.
Do, H. X., S. Westra, M. Leonard, and L. Gudmundsson (2020a), Global-Scale Prediction of Flood Timing Using Atmospheric Reanalysis,Water Resources Research , 56 (1), e2019WR024945.
Do, H. X., et al. (2020b), Historical and future changes in global flood magnitude – evidence from a model–observation investigation,Hydrol. Earth Syst. Sci. , 24 (3), 1543-1564.
Donat, M. G., et al. (2013), Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset, Journal of Geophysical Research: Atmospheres , 118 (5), 2098-2118.
Eltahir, E. A. B. (1998), A soil moisture–rainfall feedback mechanism: 1. Theory and observations, Water resources research ,34 (4), 765-776.
Findell, K. L., and E. A. B. Eltahir (1997), An analysis of the soil moisture-rainfall feedback, based on direct observations from Illinois,Water Resources Research , 33 (4), 725-735.
Gronewold, A. D., and C. A. Stow (2014), Water loss from the Great Lakes, Science , 343 (6175), 1084-1085.
Gudmundsson, L., H. X. Do, M. Leonard, and S. Westra (2018), The Global Streamflow Indices and Metadata Archive (GSIM) - Part 2: Time Series Indices and Homogeneity Assessment, edited, PANGAEA.
Gudmundsson, L., M. Leonard, H. X. Do, S. Westra, and S. I. Seneviratne (2019), Observed trends in global indicators of mean and extreme streamflow, Geophysical Research Letters , 46 (2), 756-766.
Guerreiro, S. B., H. J. Fowler, R. Barbero, S. Westra, G. Lenderink, S. Blenkinsop, E. Lewis, and X.-F. Li (2018), Detection of continental-scale intensification of hourly rainfall extremes,Nature Climate Change , 8 (9), 803-807.
Hock, R. (2003), Temperature index melt modelling in mountain areas,Journal of Hydrology , 282 (1), 104-115.
Hodgkins, G. A., R. W. Dudley, S. A. Archfield, and B. Renard (2019), Effects of climate, regulation, and urbanization on historical flood trends in the United States, Journal of Hydrology , 573 , 697-709.
Hodgkins, G. A., et al. (2017), Climate-driven variability in the occurrence of major floods across North America and Europe,Journal of Hydrology , 552 , 704-717.
Huntington, T. G. (2006), Evidence for intensification of the global water cycle: Review and synthesis, Journal of Hydrology ,319 (1), 83-95.
Ivancic, T., and S. Shaw (2015), Examining why trends in very heavy precipitation should not be mistaken for trends in very high river discharge, Climatic Change , 1-13.
Keenan, R. J., G. A. Reams, F. Achard, J. V. de Freitas, A. Grainger, and E. Lindquist (2015), Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015, Forest Ecology and Management , 352 , 9-20.
Kundzewicz, Z. W., D. Graczyk, T. Maurer, I. Przymusińska, M. Radziejewski, C. Svensson, and M. Szwed (2004), Detection of change in world-wide hydrological time series of maximum annual flow, Global Runoff Date Centre, Koblenz, Germany.
Lambin, E. F., H. J. Geist, and E. Lepers (2003), Dynamics of land-use and land-cover change in tropical regions, Annual review of environment and resources , 28 (1), 205-241.
Ledingham, J., D. Archer, E. Lewis, H. Fowler, and C. Kilsby (2019), Contrasting seasonality of storm rainfall and flood runoff in the UK and some implications for rainfall-runoff methods of flood estimation,Hydrology Research , 50 (5), 1309-1323.
Lins, H. F., and J. R. Slack (1999), Streamflow trends in the United States, Geophysical research letters , 26 (2), 227-230.
Merz, R., and G. Blöschl (2003), A process typology of regional floods,Water Resources Research , 39 (12).
Milly, P. C. D., J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer (2008), Stationarity Is Dead: Whither Water Management?, Science , 319 (5863), 573-574.
Newman, A. J., K. Sampson, M. P. Clark, A. Bock, R. J. Viger, and D. Blodgett (2014), A large-sample watershed-scale hydrometeorological dataset for the contiguous USA, UCAR/NCAR, doi , 10 , D6MW2F4D.
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 Syst. Sci. ,19 (1), 209-223.
Papalexiou, S. M., and A. Montanari (2019), Global and Regional Increase of Precipitation Extremes Under Global Warming, Water Resources Research , 55 (6), 4901-4914.
Rao, C. R. (1973), Linear statistical inference and its applications , 625 pp., Wiley New York.
Sharma, A., C. Wasko, and D. P. Lettenmaier (2018), If Precipitation Extremes Are Increasing, Why Aren’t Floods?, Water Resources Research , 0 (0).
Slater, L. J., M. B. Singer, and J. W. Kirchner (2015), Hydrologic versus geomorphic drivers of trends in flood hazard, Geophysical Research Letters , 42 (2), 370-376.
Stahl, K., L. M. Tallaksen, J. Hannaford, and H. A. J. van Lanen (2012), Filling the white space on maps of European runoff trends: estimates from a multi-model ensemble, Hydrol. Earth Syst. Sci. ,16 (7), 2035-2047.
Stein, L., F. Pianosi, and R. Woods (2020), Event-based classification for global study of river flood generating processes, Hydrological Processes , 34 (7), 1514-1529.
Thornton, P. E., S. W. Running, and M. A. White (1997), Generating surfaces of daily meteorological variables over large regions of complex terrain, Journal of Hydrology , 190 (3), 214-251.
Van den Dool, H., J. Huang, and Y. Fan (2003), Performance and analysis of the constructed analogue method applied to US soil moisture over 1981–2001, Journal of Geophysical Research: Atmospheres ,108 (D16).
Villarini, G., and J. A. Smith (2010), Flood peak distributions for the eastern United States, 46 (6).
Wasko, C., R. Nathan, and M. C. Peel (2020), Changes in Antecedent Soil Moisture Modulate Flood Seasonality in a Changing Climate, Water Resources Research , 56 (3), e2019WR026300.
Westra, S., L. A. Alexander, and F. W. Zwiers (2013), Global Increasing Trends in Annual Maximum Daily Precipitation, Journal of Climate ,26 (11), 15.
Westra, S., H. J. Fowler, J. P. Evans, L. V. Alexander, P. Berg, F. Johnson, E. J. Kendon, G. Lenderink, and N. M. Roberts (2014), Future changes to the intensity and frequency of short-duration extreme rainfall, Reviews of Geophysics , 52 (3), 522-555.
Woods, R. A. (2009), Analytical model of seasonal climate impacts on snow hydrology: Continuous snowpacks, Advances in Water Resources , 32 (10), 1465-1481.
Yamazaki, D., F. O’Loughlin, M. A. Trigg, Z. F. Miller, T. M. Pavelsky, and P. D. Bates (2014), Development of the Global Width Database for Large Rivers, Water Resources Research , 50 (4), 3467-3480.
Ye, S., H.-Y. Li, L. R. Leung, J. Guo, Q. Ran, Y. Demissie, and M. Sivapalan (2017), Understanding Flood Seasonality and Its Temporal Shifts within the Contiguous United States, Journal of Hydrometeorology , 18 (7), 1997-2009.