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94. Chapman T (1999) A comparison of algorithms for stream flow recession and baseflow
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97. Marmonier P, Archambaud G, Belaidi N et al (2012) The role of organisms in hyporheic
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98. Boano F, Harvey JW, Marion A et al (2014) Hyporheic flow and transport processes:
mechanisms, models, and biogeochemical implications. Rev Geophys 52:603–679. https://
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99. Couturier C, Charru M, Doublet S, Pointereau P (2017) Le scénario Afterres 2050. Solagro
100. Ministère de l’Ecologie du Développement Durable et de l’Energie (2012) Explore 2070:
prospective socio-économique et démographique – pressions anthropiques
101. AGRESTE (2009) La statistique agricole annuelle: Présentation générale
102. Koerner M, Cinotti B, Jussy J-H, Benoit M (2000) Evolution des surfaces boisées en France
depuis le début du XIXème siècle: identification et localisation des boisements des territoires
agricoles abandonnés. Rev For Fr 3:249–269. https://doi.org/10.4267/2042/5359
103. Boé J, Terray L, Habets F, Martin E (2007) Statistical and dynamical downscaling of the Seine
basin climate for hydro-meteorological studies. Int J Climatol 27:1643–1655. https://doi.org/
10.1002/joc.1602
104. Dayon G, Boé J, Martin E (2015) Transferability in the future climate of a statistical
downscaling method for precipitation in France. J Geophys Res 120:1023–1043. https://doi.
org/10.1002/2014JD022236
105. Habets F, Boone A, Champeaux J et al (2008) The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France. J Geophys Res 113:D06113. https://doi.org/10.1029/
2007JDOO8548
106. Hattermann FF, Vetter T, Breuer L et al (2018) Sources of uncertainty in hydrological climate
impact assessment: a cross-scale study. Environ Res Lett 13:15006. https://doi.org/10.1088/
1748-9326/aa9938
107. Her Y, Yoo S-H, Cho J et al (2019) Uncertainty in hydrological analysis of climate change:
multi-parameter vs. multi-GCM ensemble predictions. Sci Rep 9:4974. https://doi.org/10.
1038/s41598-019-41334-7
108. Ashraf Vaghefi S, Iravani M, Sauchyn D et al Regionalization and parameterization of a
hydrologic model significantly affect the cascade of uncertainty in climate-impact projections.
Climate Dynam. https://doi.org/10.1007/s00382-019-04664-w
88
N. Flipo et al.
effect on wetland stream benthic productivity, Trout Lake watershed, northern Wisconsin,
USA. J Hydrol 320:370–384. https://doi.org/10.1016/j.jhydrol.2005.07.029
89. Yager RM (1998) Deftecting influential observations in nonlinear regression modelling of
groundwater flow. Water Resour Res 34:1623–1633
90. Brutsaert W, Nieber JL (1977) Regionalized drought flow hydrographs from a mature glaciated plateau. Water Resour Res 13:637–643. https://doi.org/10.1029/WR013i003p00637
91. Arnold JG, Muttiah RS, Srinivasan R, Allen PM (2000) Regional estimation of base flow and
groundwater recharge in the Upper Mississippi river basin. J Hydrol 227:21–40. https://doi.
org/10.1016/S0022-1694(99)00139-0
92. Lyne V, Hollick M (1979) Stochastic time variable rainfall-runoff modelling. In: Proceedings
of the hydrology and water resources symposium, Perth, 10–12 September,1979. Institution of
Engineers National Conference Publication, pp 89–92
93. Chapman T (1991) Comment on “Evaluation of automated techniques for base flow and
recession analyses” by R.J. Nathan and T.A. McMahon. Water Resour Res 27:1783–1784
94. Chapman T (1999) A comparison of algorithms for stream flow recession and baseflow
separation. Hydrol Process 13:701–714
95. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. {P}art {I}, a
discussion of principles. J Hydrol 10:282–290
96. Newcomer ME, Hubbard SS, Fleckenstein JH et al (2018) Influence of hydrological perturbations and riverbed sediment characteristics on hyporheic zone respiration of CO2 and N2.
Eur J Vasc Endovasc Surg 123:902–922. https://doi.org/10.1002/2017JG004090
97. Marmonier P, Archambaud G, Belaidi N et al (2012) The role of organisms in hyporheic
processes: gaps in current knowledge, needs for future research and applications. Int J Limnol
48:253–266
98. Boano F, Harvey JW, Marion A et al (2014) Hyporheic flow and transport processes:
mechanisms, models, and biogeochemical implications. Rev Geophys 52:603–679. https://
doi.org/10.1002/2012RG000417
99. Couturier C, Charru M, Doublet S, Pointereau P (2017) Le scénario Afterres 2050. Solagro
100. Ministère de l’Ecologie du Développement Durable et de l’Energie (2012) Explore 2070:
prospective socio-économique et démographique – pressions anthropiques
101. AGRESTE (2009) La statistique agricole annuelle: Présentation générale
102. Koerner M, Cinotti B, Jussy J-H, Benoit M (2000) Evolution des surfaces boisées en France
depuis le début du XIXème siècle: identification et localisation des boisements des territoires
agricoles abandonnés. Rev For Fr 3:249–269. https://doi.org/10.4267/2042/5359
103. Boé J, Terray L, Habets F, Martin E (2007) Statistical and dynamical downscaling of the Seine
basin climate for hydro-meteorological studies. Int J Climatol 27:1643–1655. https://doi.org/
10.1002/joc.1602
104. Dayon G, Boé J, Martin E (2015) Transferability in the future climate of a statistical
downscaling method for precipitation in France. J Geophys Res 120:1023–1043. https://doi.
org/10.1002/2014JD022236
105. Habets F, Boone A, Champeaux J et al (2008) The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France. J Geophys Res 113:D06113. https://doi.org/10.1029/
2007JDOO8548
106. Hattermann FF, Vetter T, Breuer L et al (2018) Sources of uncertainty in hydrological climate
impact assessment: a cross-scale study. Environ Res Lett 13:15006. https://doi.org/10.1088/
1748-9326/aa9938
107. Her Y, Yoo S-H, Cho J et al (2019) Uncertainty in hydrological analysis of climate change:
multi-parameter vs. multi-GCM ensemble predictions. Sci Rep 9:4974. https://doi.org/10.
1038/s41598-019-41334-7
108. Ashraf Vaghefi S, Iravani M, Sauchyn D et al Regionalization and parameterization of a
hydrologic model significantly affect the cascade of uncertainty in climate-impact projections.
Climate Dynam. https://doi.org/10.1007/s00382-019-04664-w
88
N. Flipo et al.