Beria, H., Larsen, J. R., Michelon, A., Ceperley, N. C., and Schaefli, B.:
HydroMix v1.0: A new Bayesian mixing framework for attributing uncertain
hydrological sources, Geosci. Model Dev., 13, 2433–2450,
https://doi.org/10.5194/gmd-13-2433-2020, 2020.
a
Bernal, S., Butturini, A., and Sabater, F.: Inferring nitrate sources through
end member mixing analysis in an intermittent Mediterranean stream,
Biogeochemistry, 81, 269–289,
https://doi.org/10.1007/s10533-006-9041-7, 2006.
a,
b
Burns, D. A., Mcdonnell, J. J., Hooper, R. P., Peters, N. E., Freer, J. E.,
Kendall, C., and Beven, K.: Quantifying contributions to storm runoff
through end-member mixing analysis and hydrologic measurements at the Panola
Mountain Research Watershed (Georgia, USA), Hydrol. Process., 15, 1903–1924,
https://doi.org/10.1002/hyp.246, 2001.
a
Carrera, J., Vázquez-Suñé, E., Castillo, O., and Sánchez-Vila, X.: A methodology to compute mixing ratios with uncertain end-members, Water Resour. Res., 40, 1–11,
https://doi.org/10.1029/2003WR002263, 2004.
a
Christophersen, N. and Hooper, R. P.: Multivariate analysis of stream water
chemical data: the use of Principal Components Analysis for the end-member
mixing problem, Water Resour. Res., 28, 99–107, 1992.
a,
b,
c,
d,
e,
f,
g,
h
Coifman, R. R., Kevrekidis, I. G., Lafon, S., Maggioni, M., and Nadler, B.:
Diffusion maps, reduction coordinates, and low dimensional representation of
stochastic systems, Multisc. Model. Simul., 7, 842–864, 2008. a
Delsman, J. R., Oude Essink, G. H., Beven, K. J., and Stuyfzand, P. J.:
Uncertainty estimation of end-member mixing using generalized likelihood
uncertainty estimation (GLUE), applied in a lowland catchment, Water Resour. Res., 49, 4792–4806,
https://doi.org/10.1002/wrcr.20341, 2013.
a,
b,
c,
d
Ding, C. H., Li, T., and Jordan, M. I.: Convex and semi-nonnegative matrix
factorizations, IEEE T. Pattern Anal. Mach. Intel., 32, 45–55, 2008.
a,
b,
c
Hooper, R. P.: Applying the Scientific Method to Small Catchment Studies: A
Review of the Panola Mountain Experience, Hydrol. Process., 15, 2039–2050,
https://doi.org/10.1002/hyp.255, 2001.
a
Hooper, R. P.: Diagnostic tools for mixing models of stream water chemistry,
Water Resour. Res., 39, 1055,
https://doi.org/10.1029/2002WR001528, 2003.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k
Hooper, R. P. and Christophersen, N.: Predicting episodic stream acidification in the southeastern United States: combining a long‐term acidification model and the end‐member mixing concept, Water Resour. Res., 28,
1983–1990,
https://doi.org/10.1029/92WR00706, 1992.
a,
b,
c,
d,
e
Hooper, R. P., Christophersen, N., and Peters, N. E.: Modelling streamwater
chemistry as a mixture of soilwater end-members – an application to the
Panola Mountain Catchment, Georgia, U.S.A., J. Hydrol., 116, 321–343, 1990.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k,
l,
m
Hur, J., Williams, M. A., and Schlautman, M. A.: Evaluating spectroscopic and
chromatographic techniques to resolve dissolved organic matter via end member
mixing analysis, Chemosphere, 63, 387–402,
https://doi.org/10.1016/j.chemosphere.2005.08.069, 2006.
a
Inamdar, S., Dhillon, G., Singh, S., Dutta, S., Levia, D., Scott, D., Mitchell, M., Van Stan, J., and McHale, P.: Temporal variation in end-member chemistry and its influence on runoff mixing patterns in a forested, Piedmont
catchment, Water Resour. Res., 49, 1828–1844, 2013. a
James, A. L. and Roulet, N. T.: Investigating the applicability of end-member
mixing analysis (EMMA) across scale: A study of eight small, nested catchments in a temperate forested watershed, Water Resour. Res., 42, 1–17,
https://doi.org/10.1029/2005WR004419, 2006.
a
Jung, H. Y., Hogue, T. S., Rademacher, L. K., and Meixner, T.: Impact of
wildfire on source water contributions in Devil Creek, CA: evidence from
end-member mixing analysis, Hydrol. Process., 23, 183–200,
https://doi.org/10.1002/hyp.7132, 2009.
a,
b
Kronholm, S. C. and Capel, P. D.: A comparison of high-resolution specific
conductance-based end-member mixing analysis and a graphical method for
baseflow separation of four streams in hydrologically challenging agricultural watersheds, Hydrol. Process., 29, 2521–2533,
https://doi.org/10.1002/hyp.10378, 2015.
a
Kuha, J.: AIC and BIC: Comparisons of assumptions and performance, Sociolog. Meth. Res., 33, 188–229, 2004. a
Li, X., Ding, Y., Han, T., Kang, S., Yu, Z., and Jing, Z.: Seasonal controls
of meltwater runoff chemistry and chemical weathering at Urumqi Glacier No. 1
in central Asia, Hydrol. Process., 33, 3258–3281,
https://doi.org/10.1002/hyp.13555, 2019.
a,
b
Liu, F., Bales, R. C., Conklin, M. H., and Conrad, M. E.: Streamflow generation from snowmelt in semi-arid, seasonally snow-covered, forested catchments, Valles Caldera, New Mexico, Water Resour. Res., 44, W12443,
https://doi.org/10.1029/2007WR006728, 2008a.
a,
b
Liu, F., Parmenter, R., Brooks, P. D., Conklin, M. H., and Bales, R. C.:
Seasonal and interannual variation of streamflow pathways and biogeochemical
implications in semi-arid, forested catchments in Valles Caldera, New Mexico,
Ecohydrology: Ecosystems, Land and Water Process Interactions,
Ecohydrogeomorphology, 1, 239–252, 2008b. a
Liu, F., Conklin, M. H., and Shaw, G. D.: Insights into hydrologic and
hydrochemical processes based on concentration-discharge and end-member
mixing analyses in the mid-Merced River Basin, Sierra Nevada, California, Water Resour. Res., 53, 832–850, 2017. a
Lv, Y., Gao, L., Geris, J., Verrot, L., and Peng, X.: Assessment of water
sources and their contributions to streamflow by end-member mixing analysis
in a subtropical mixed agricultural catchment, Agr. Water Manage., 203, 411–422,
https://doi.org/10.1016/j.agwat.2018.03.013, 2018.
a,
b
Neal, C., Robson, A., Reynolds, B., and Jenkins, A.: Prediction of future
short-term stream chemistry – a modelling approach, J. Hydrol., 130, 87–103,
https://doi.org/10.1016/0022-1694(92)90105-5, 1992.
a
Neill, C., Chaves, J. E., Biggs, T., Deegan, L. A., Elsenbeer, H., Figueiredo, R. O., Germer, S., Johnson, M. S., Lehmann, J., Markewitz, D., and Piccolo, M. C.: Runoff sources and land cover change in the Amazon: An end-member mixing analysis from small watersheds, Biogeochemistry, 105, 7–18,
https://doi.org/10.1007/s10533-011-9597-8, 2011.
a,
b
Popp, A. L., Scheidegger, A., Moeck, C., Brennwald, M. S., and Kipfer, R.:
Integrating Bayesian Groundwater Mixing Modeling With On-Site Helium Analysis to Identify Unknown Water Sources, Water Resour. Res., 55, 10602–10615,
https://doi.org/10.1029/2019WR025677, 2019.
a,
b
Thurau, C., Kersting, K., Wahabzada, M., and Bauckhage, C.: Convex non-negative matrix factorization for massive datasets, Knowledge Inform. Syst., 29, 457–478,
https://doi.org/10.1007/s10115-010-0352-6, 2011.
a,
b,
c,
d,
e,
f
Valder, J. F., Long, A. J., Davis, A. D., and Kenner, S. J.: Multivariate
statistical approach to estimate mixing proportions for unknown end members,
J. Hydrol., 460-461, 65–76,
https://doi.org/10.1016/j.jhydrol.2012.06.037, 2012.
a,
b
Wagstaff, K., Cardie, C., Rogers, S., and Schroedl, S.: Constrained
k-means
clustering with background knowledge, in: Proceedings of the Eighteenth
International Conference on Machine Learning, vol. 1, 577–584,
https://web.cse.msu.edu/~cse802/notes/ConstrainedKmeans.pdf (last access: 6 May 2020), 2001.
a,
b
Yang, L. and Hur, J.: Critical evaluation of spectroscopic indices for organic matter source tracing via end member mixing analysis based on two contrasting sources, Water Res., 59, 80–89,
https://doi.org/10.1016/j.watres.2014.04.018, 2014.
a,
b
