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.