Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4401-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-4401-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Aug 2018
Research article |
| 21 Aug 2018
| 21 Aug 2018
Detecting dominant changes in irregularly sampled multivariate water quality data sets
Christian Lehr
CORRESPONDING AUTHOR
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
University of Potsdam, Institute for Earth and Environmental Sciences, Potsdam, Germany
Ralf Dannowski
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Thomas Kalettka
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Christoph Merz
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Institute of Geological Sciences, Workgroup Hydrogeology, Freie Universität Berlin, Berlin, Germany
Boris Schröder
Landscape Ecology and Environmental Systems Analysis, Institute of Geoecology, Technische Universität Braunschweig, Langer
Kamp 19c, 38106 Braunschweig, Germany
Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Altensteinstraße 6, 14195 Berlin, Germany
Jörg Steidl
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Gunnar Lischeid
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
University of Potsdam, Institute for Earth and Environmental Sciences, Potsdam, Germany
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A screening method for the fast identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks is suggested and tested. The only information required is a set of time series of groundwater head readings all measured at the same instants of time. The results were used to check the data for measurement errors and to identify wells with possible anthropogenic influence.
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Cited articles
Aubert, A. H., Gascuel-Odoux, C., Gruau, G., Akkal, N., Faucheux, M., Fauvel,
Y., Grimaldi, C., Hamon, Y., Jaffrézic, A., Lecoz-Boutnik, M., Molénat, J.,
Petitjean, P., Ruiz, L., and Merot, P.: Solute transport dynamics in small,
shallow groundwater-dominated agricultural catchments: insights from a
high-frequency, multisolute 10 yr-long monitoring study, Hydrol. Earth Syst.
Sci., 17, 1379–1391, https://doi.org/10.5194/hess-17-1379-2013, 2013.
Basu, N. B., Destouni, G., Jawitz, J. W., Thompson, S. E., Loukinova, N. V.,
Darracq, A., Zanardo, S., Yaeger, M., Sivapalan, M., Rinaldo, A., and Rao, P.
S. C.: Nutrient loads exported from managed catchments reveal emergent
biogeochemical stationarity, Geophys. Res. Lett., 37, L23404,
https://doi.org/10.1029/2010GL045168, 2010.
Basu, N. B., Thompson, S. E., and Rao, P. S. C.: Hydrologic and
biogeochemical functioning of intensively managed catchments: A synthesis of
top-down analyses, Water Resour. Res., 47, W00J15,
https://doi.org/10.1029/2011WR010800, 2011.
Beudert, B., Bässler, C., Thorn, S., Noss, R., Schröder, B.,
Dieffenbach-Fries, H., Foullois, N., and Müller, J.: Bark Beetles
Increase Biodiversity While Maintaining Drinking Water Quality, Conserv.
Lett., 8, 272–281, https://doi.org/10.1111/conl.12153, 2015.
Bieroza, M. Z., Heathwaite, A. L., Mullinger, N. J., and Keenan, P. O.:
Understanding nutrient biogeochemistry in agricultural catchments: the
challenge of appropriate monitoring frequencies, Environ. Sci.-Proc. Imp.,
16, 1676–1691, https://doi.org/10.1039/C4EM00100A, 2014.
Blaen, P. J., Khamis, K., Lloyd, C., Comer-Warner, S., Ciocca, F., Thomas, R.
M., MacKenzie, A. R., and Krause, S.: High-frequency monitoring of catchment
nutrient exports reveals highly variable storm event responses and dynamic
source zone activation, J. Geophys. Res.-Biogeo., 122, 2265–2281,
https://doi.org/10.1002/2017JG003904, 2017.
Broers, H. P. and van der Grift, B.: Regional monitoring of temporal changes
in groundwater quality, J. Hydrol., 296, 192–220,
https://doi.org/10.1016/j.jhydrol.2004.03.022, 2004.
Burt, T. P., Howden, N. J. K., Worrall, F., and Whelan, M. J.: Importance of
long-term monitoring for detecting environmental change: lessons from a
lowland river in south east England, Biogeosciences, 5, 1529–1535,
https://doi.org/10.5194/bg-5-1529-2008, 2008.
Burt, T. P., Howden, N. J. K., Worrall, F., and McDonnell, J. J.: On the
value of long-term, low-frequency water quality sampling: avoiding throwing
the baby out with the bathwater, Hydrol. Process., 25, 828–830,
https://doi.org/10.1002/hyp.7961, 2011.
Capell, R., Tetzlaff, D., Malcolm, I., Hartley, A., and Soulsby, C.: Using
hydrochemical tracers to conceptualise hydrological function in a larger
scale catchment draining contrasting geologic provinces, J. Hydrol., 408,
164–177, https://doi.org/10.1016/j.jhydrol.2011.07.034, 2011.
Cassidy, R. and Jordan, P.: Limitations of instantaneous water quality
sampling in surface-water catchments: Comparison with near-continuous
phosphorus time-series data, J. Hydrol., 405, 182–193,
https://doi.org/10.1016/j.jhydrol.2011.05.020, 2011.
Cherobim, V. F., Huang, C.-H., and Favaretto, N.: Tillage system and time
post-liquid dairy manure: Effects on runoff, sediment and nutrients losses,
Agr. Water Manage. 184, 96–103, https://doi.org/10.1016/j.agwat.2017.01.004, 2017.
Cleveland, R., Cleveland, W., McRae, J., and Terpenning, I.: STL: A
seasonal-trend decomposition procedure based on loess, J. Off. Stat., 6,
3–73, 1990.
Cleveland, W. S.: Robust Locally Weighted Regression and Smoothing
Scatterplots, J. Am. Stat. Assoc., 74, 829–836,
https://doi.org/10.1080/01621459.1979.10481038, 1979.
Cleveland, W. S. and Devlin, S. J.: Locally Weighted Regression: An Approach
to Regression Analysis by Local Fitting, J. Am. Stat. Assoc., 83, 596–610,
https://doi.org/10.1080/01621459.1988.10478639, 1988.
Cloutier, V., Lefebvre, R., Therrien, R., and Savard, M. M.: Multivariate
statistical analysis of geochemical data as indicative of the
hydrogeochemical evolution of groundwater in a sedimentary rock aquifer
system, J. Hydrol., 353, 294–313, https://doi.org/10.1016/j.jhydrol.2008.02.015, 2008.
Cohn, T. A. and Lins, H. F.: Nature's style: Naturally trendy, Geophys. Res.
Lett., 32, L23402, https://doi.org/10.1029/2005GL024476, 2005.
Dijkstra, E. W.: A note on two problems in connexion with graphs, Numer.
Math., 1, 269–271, https://doi.org/10.1007/BF01386390, 1959.
DLR-DFD and UBA (German Aerospace Centre-German Remote Sensing Data Centre
and German Federal Environmental Agency): CORINE Land Cover 2000, Daten zur
Bodenbedeckung – Deutschland (Data on land cover – Germany), published on
DVD, 2004.
EU: Council Directive 91/676/EEC of 12 December 1991 concerning the
protection of waters against pollution caused by nitrates from agricultural
sources, Official Journal of the European Communities, 1–8, 1991.
EU: Directive 2000/60/EC of the European Parliament and of the Council of 23
October 2000 establishing a framework for Community action in the field of
water policy, Official Journal of the European Communities, 1–70, 2000.
EU: Directive 2006/118/EC of the European Parliament and of the Council of 12
December 2006 on the protection of groundwater against pollution and
deterioration, Official Journal of the European Union, 19–31, 2006.
Fitzpatrick, M., Long, D., and Pijanowski, B.: Exploring the effects of urban
and agricultural land use on surface water chemistry, across a regional
watershed, using multivariate statistics, Appl. Geochem., 22, 1825–1840,
https://doi.org/10.1016/j.apgeochem.2007.03.047, 2007.
Gámez, A. J., Zhou, C. S., Timmermann, A., and Kurths, J.: Nonlinear
dimensionality reduction in climate data, Nonlin. Processes Geophys., 11,
393-398, https://doi.org/10.5194/npg-11-393-2004, 2004.
Geng, X., Zhan, D.-C., and Zhou, Z.-H.: Supervised nonlinear dimensionality
reduction for visualization and classification, IEEE T. Syst. Man Cy. B, 35,
1098–1107, https://doi.org/10.1109/TSMCB.2005.850151, 2005.
Gilliom, R. J. and Helsel, D. R.: Estimation of Distributional Parameters for
Censored Trace Level Water Quality Data: 1. Estimation Techniques, Water
Resour. Res., 22, 135–146, https://doi.org/10.1029/WR022i002p00135, 1986.
Glynn, E., Chen, J., and Mushegian, A.: Detecting periodic patterns in
unevenly spaced gene expression time series using Lomb–Scargle periodograms,
Bioinformatics, 22, 310–316, https://doi.org/10.1093/bioinformatics/bti789, 2006.
Graeber, D., Gelbrecht, J., Pusch, M. T., Anlanger, C., and von Schiller, D.:
Agriculture has changed the amount and composition of dissolved organic
matter in Central European headwater streams, Sci. Total Environ., 438,
435–446, https://doi.org/10.1016/j.scitotenv.2012.08.087, 2012.
Grayson, R. and Blöschl, G. (Ed.): Spatial patterns in catchment
hydrology: observations and modelling, Cambridge University Press Cambridge,
UK, 2000.
Haag, I. and Westrich, B.: Processes governing river water quality identified
by principal component analysis, Hydrol. Process., 16, 3113–3130,
https://doi.org/10.1002/hyp.1091, 2002.
Halliday, S. J., Wade, A. J., Skeffington, R. A., Neal, C., Reynolds, B.,
Rowland, P., Neal, M., and Norris, D.: An analysis of long-term trends,
seasonality and short-term dynamics in water quality data from Plynlimon,
Wales, Sci. Total Environ., 434, 186–200,
https://doi.org/10.1016/j.scitotenv.2011.10.052, 2012.
Hannemann, M. and Schirrmeister, W.: Paläohydrogeologische Grundlagen der
Entwicklung der Süss-/Salzwassergrenze und der Salzwasseraustritte in
Brandenburg, Brandenburg Geowissenschaftliche Beiträge, 5, 61–72, 1998.
Helsel, D. R.: More Than Obvious: Better Methods for Interpreting Nondetect
Data, Environ. Sci. Technol., 39, 419A–423A, https://doi.org/10.1021/es053368a, 2005.
Helsel, D. R.: Fabricating data: How substituting values for nondetects can
ruin results, and what can be done about it, Chemosphere, 65, 2434–2439,
https://doi.org/10.1016/j.chemosphere.2006.04.051, 2006.
Helsel, D. R.: Statistics for Censored Environmental Data Using Minitab and
R, 2nd ed., John Wiley & Sons, 2012.
Hocke, K.: Phase estimation with the Lomb-Scargle periodogram method, Annales
Geophysicae, 16, 356–358, 1998.
Hocke, K. and Kämpfer, N.: Gap filling and noise reduction of unevenly
sampled data by means of the Lomb-Scargle periodogram, Atmos. Chem. Phys., 9,
4197–4206, https://doi.org/10.5194/acp-9-4197-2009, 2009.
Hooda, P. S., Edwards, A. C., Anderson, H. A., and Miller, A.: A review of
water quality concerns in livestock farming areas, Sci. Total Environ., 250,
143–167, https://doi.org/10.1016/S0048-9697(00)00373-9, 2000.
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,
https://doi.org/10.1016/0022-1694(90)90131-G, 1990.
Horne, J. and Baliunas, S.: A prescription for period analysis of unevenly
sampled time series, Astrophys. J., 302, 757–763, 1986.
Hotelling, H.: Analysis of a complex of statistical variables into principal
components, J. Educ. Psychol., 24, 417–441, https://doi.org/10.1037/h0071325, 1933.
Howden, N. J. K., Burt, T. P., Worrall, F., and Whelan, M. J.: Monitoring
fluvial water chemistry for trend detection: hydrological variability masks
trends in datasets covering fewer than 12 years, J. Environ. Monitor., 13,
514–521, https://doi.org/10.1039/C0EM00722F, 2011.
Jessen, S., Postma, D., Thorling, L., Müller, S., Leskelä, J., and
Engesgaard, P.: Decadal variations in groundwater quality: A legacy from
nitrate leaching and denitrification by pyrite in a sandy aquifer, Water
Resour. Res., 53, 184–198, https://doi.org/10.1002/2016WR018995, 2017.
Jolliffe, I.: Principal Component Analysis, 2nd ed., Springer, 2002.
Jolliffe, I. T. and Cadima, J.: Principal component analysis: a review and
recent developments, Philos. T. R. Soc S.-A, 374, 20150202,
https://doi.org/10.1098/rsta.2015.0202, 2016.
Jørgensen, J. C., Jacobsen, O. S., Elberling, B., and Aamand, J.:
Microbial Oxidation of Pyrite Coupled to Nitrate Reduction in Anoxic
Groundwater Sediment, Environ. Sci. Technol., 43, 4851–4857,
https://doi.org/10.1021/es803417s, 2009.
Kalettka, T. and Rudat, C.: Hydrogeomorphic types of glacially created kettle
holes in North-East Germany, Limnologica – Ecology and Management of Inland
Waters, 36, 54–64, https://doi.org/10.1016/j.limno.2005.11.001, 2006.
Kalettka, T. and Steidl, J.: Measurement of stream water chemical
ingredients, Quillow catchment, Germany, https://doi.org/10.4228/ZALF.1998.265, 2014.
Kendall, M.: Rank Correlation Methods, Charles Griffin Book Series, 1990.
Kim, D. and Oh, H.-S.: EMD: A Package for Empirical Mode Decomposition and
Hilbert Spectrum, R J., 1, 40–46, 2009.
Kim, D. and Oh, H.-S.: EMD: Empirical Mode Decomposition and Hilbert
Spectral Analysis, 2014.
Kirchner, J., Feng, X., and Neal, C.: Fractal stream chemistry and its
implications for contaminant transport in catchments, Nature, 403, 524–527,
2000.
Kirchner, J., Feng, X., Neal, C., and Robson, A.: The fine structure of
water-quality dynamics: the (high-frequency) wave of the future, Hydrol.
Process., 18, 1353–1359, https://doi.org/10.1002/hyp.5537, 2004.
Kirchner, J. W. and Neal, C.: Universal fractal scaling in stream chemistry
and its implications for solute transport and water quality trend detection,
P. Natl. Acad. Sci., 110, 12213–12218, https://doi.org/10.1073/pnas.1304328110, 2013.
Koutsoyiannis, D.: Nonstationarity versus scaling in hydrology, J. Hydrol.,
324, 239–254, https://doi.org/10.1016/j.jhydrol.2005.09.022, 2006.
Kroeze, C., Hofstra, N., Ivens, W., Löhr, A., Strokal, M., and van
Wijnen, J.: The links between global carbon, water and nutrient cycles in an
urbanizing world – the case of coastal eutrophication, Curr. Opin. Env.
Sust., 5, 566–572, https://doi.org/10.1016/j.cosust.2013.11.004, 2013.
Lee, J. A. and Verleysen, M.: Nonlinear Dimensionality Reduction, Springer,
2007.
Lehr, C., Pöschke, F., Lewandowski, J., and Lischeid, G.: A novel method
to evaluate the effect of a stream restoration on the spatial pattern of
hydraulic connection of stream and groundwater, J. Hydrol., 527, 394–401,
https://doi.org/10.1016/j.jhydrol.2015.04.075, 2015.
Lehr, C., Kalettka, T., Merz, C., Steidl, J., and Lischeid, G.: R scripts for
the detection of dominant changes in irregularly sampled multivariate water
quality data sets, https://doi.org/10.4228/ZALF.2017.340, 2018.
Lins, H. F. and Cohn, T. A.: Stationarity: Wanted Dead or Alive?, J. Am.
Water Resour. As., 47, 475–480, https://doi.org/10.1111/j.1752-1688.2011.00542.x, 2011.
Lischeid, G. and Bittersohl, J.: Tracing biogeochemical processes in stream
water and groundwater using non-linear statistics, J. Hydrol., 357, 11–28,
https://doi.org/10.1016/j.jhydrol.2008.03.013, 2008.
Lischeid, G. and Kalettka, T.: Grasping the heterogeneity of kettle hole
water quality in Northeast Germany, Hydrobiologia, 689, 63–77,
https://doi.org/10.1007/s10750-011-0764-7, 2012.
Lischeid, G., Krám, P., and Weyer, C.: Tracing Biogeochemical Processes
in Small Catchments Using Non-linear Methods, edited by: Müller, F.,
Baessler, C., Schubert, H., and Klotz, S., Springer, the Netherlands, 2010.
Lischeid, G., Kalettka, T., Merz, C., and Steidl, J.: Monitoring the phase
space of ecosystems: Concept and examples from the Quillow catchment,
Uckermark, Ecol. Indic., 65, 55–65, https://doi.org/10.1016/j.ecolind.2015.10.067, 2016.
Lomb, N.: Least-squares frequency analysis of unequally spaced data,
Astrophys. Space Sci., 39, 447–462, 1976.
Mahecha, M. D., Martínez, A., Lischeid, G., and Beck, E.: Nonlinear
dimensionality reduction: Alternative ordination approaches for extracting
and visualizing biodiversity patterns in tropical montane forest vegetation
data, Ecol. Inform., 2, 138–149, https://doi.org/10.1016/j.ecoinf.2007.05.002, 2007.
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259,
1945.
Massmann, G., Tichomirowa, M., Merz, C., and Pekdeger, A.: Sulfide oxidation
and sulfate reduction in a shallow groundwater system (Oderbruch Aquifer,
Germany), J. Hydrol., 278, 231–243, https://doi.org/10.1016/S0022-1694(03)00153-7, 2003.
Massmann, G., Pekdeger, A., and Merz, C.: Redox processes in the Oderbruch
polder groundwater flow system in Germany, Appl. Geochem., 19, 863–886,
https://doi.org/10.1016/j.apgeochem.2003.11.006, 2004.
McLeod, A.: Kendall: Kendall rank correlation and Mann-Kendall trend test,
available at: https://CRAN.R-project.org/package=Kendall (last access:
8 October 2017), 2011.
Meals, D. W., Dressing, S. A., and Davenport, T. E.: Lag Time in Water
Quality Response to Best Management Practices: A Review, J. Environ. Qual.,
39, 85–96, 2010.
Merz, C. and Steidl, J.: Measurement of groundwater heads, Quillow catchment,
Germany, https://doi.org/10.4228/ZALF.2000.272, 2014a.
Merz, C. and Steidl, J.: Measurement of ground water chemical ingredients,
Quillow catchment, Germany, https://doi.org/10.4228/ZALF.2000.266, 2014b.
Merz, C. and Steidl, J.: Data on geochemical and hydraulic properties of a
characteristic confined/unconfined aquifer system of the younger Pleistocene
in northeast Germany, Earth Syst. Sci. Data, 7, 109–116,
https://doi.org/10.5194/essd-7-109-2015, 2015.
Merz, C., Steidl, J., and Dannowski, R.: Parameterization and regionalization
of redox based denitrification for GIS-embedded nitrate transport modeling in
Pleistocene aquifer systems, Environ. Geol., 58, 1587,
https://doi.org/10.1007/s00254-008-1665-6, 2009.
Milliman, J. D., Farnsworth, K. L., Jones, P. D., Xu, K. H., and Smith, L.
C.: Climatic and anthropogenic factors affecting river discharge to the
global ocean, 1951–2000, Global Planet. Change, 62, 187–194,
https://doi.org/10.1016/j.gloplacha.2008.03.001, 2008.
Molenat, J., Gascuel-Odoux, C., Ruiz, L., and Gruau, G.: Role of water table
dynamics on stream nitrate export and concentration in agricultural headwater
catchment (France), J. Hydrol., 348, 363–378,
https://doi.org/10.1016/j.jhydrol.2007.10.005, 2008.
Neal, C.: The water quality functioning of the upper River Severn, Plynlimon,
mid-Wales: issues of monitoring, process understanding and forestry, Hydrol.
Earth Syst. Sci., 8, 521–532, https://doi.org/10.5194/hess-8-521-2004, 2004.
Neal, C., Reynolds, B., Rowland, P., Norris, D., Kirchner, J. W., Neal, M.,
Sleep, D., Lawlor, A., Woods, C., Thacker, S., Guyatt, H., Vincent, C.,
Hockenhull, K., Wickham, H., Harman, S., and Armstrong, L.: High-frequency
water quality time series in precipitation and streamflow: From fragmentary
signals to scientific challenge, Sci. Total Environ., 434, 3–12,
https://doi.org/10.1016/j.scitotenv.2011.10.072, 2012.
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn,
D., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M.
H. H., Szoecs, E., and Wagner, H.: vegan: Community Ecology Package,
available at: https://CRAN.R-project.org/package=vegan, R-package
version 2.4-4, last access: 8 October 2017.
Pearson, K. F.: LIII. On lines and planes of closest fit to systems of points
in space, Philos. Mag., 2, 559–572, https://doi.org/10.1080/14786440109462720, 1901.
Pierson-Wickmann, A.-C., Aquilina, L., Martin, C., Ruiz, L., Molénat, J.,
Jaffrézic, A., and Gascuel-Odoux, C.: High chemical weathering rates in
first-order granitic catchments induced by agricultural stress, Chem. Geol.,
265, 369–380, https://doi.org/10.1016/j.chemgeo.2009.04.014, 2009.
Press, W., Flannery, B., Teukolsky, S., and Vetterling, W.: Numerical
recipes, Cambridge University Press, 2007.
Raymond, P. A., Oh, N.-H., Turner, R. E., and Broussard, W.:
Anthropogenically enhanced fluxes of water and carbon from the Mississippi
River, Nature, 451, 449–452, https://doi.org/10.1038/nature06505, 2008.
R Core Team: A Language and Environment for Statistical Computing. R
Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/ (last access: 8 October 2017), 2017.
Rode, M., Wade, A. J., Cohen, M. J., Hensley, R. T., Bowes, M. J., Kirchner,
J. W., Arhonditsis, G. B., Jordan, P., Kronvang, B., Halliday, S. J.,
Skeffington, R. A., Rozemeijer, J. C., Aubert, A. H., Rinke, K., and Jomaa,
S.: Sensors in the Stream: The High-Frequency Wave of the Present, Environ.
Sci. Technol., 50, 10297–10307, https://doi.org/10.1021/acs.est.6b02155, 2016.
Scanlon, B. R., Jolly, I., Sophocleous, M., and Zhang, L.: Global impacts of
conversions from natural to agricultural ecosystems on water resources:
Quantity versus quality, Water Resour. Res., 43, W03437,
https://doi.org/10.1029/2006WR005486, 2007.
Scargle, J.: Studies in astronomical time series analysis. II-Statistical
aspects of spectral analysis of unevenly spaced data, Astrophys. J., 263,
835–853, 1982.
Scargle, J.: Studies in astronomical time series analysis. III-Fourier
transforms, autocorrelation functions, and cross-correlation functions of
unevenly spaced data, Astrophys. J., 343, 874–887, 1989.
Schilli, C., Lischeid, G., and Rinklebe, J.: Which processes prevail?:
Analyzing long-term soil solution monitoring data using nonlinear statistics,
Geoderma, 158, 412–420, https://doi.org/10.1016/j.geoderma.2010.06.014, 2010.
Schuetz, T., Gascuel-Odoux, C., Durand, P., and Weiler, M.: Nitrate sinks and
sources as controls of spatio-temporal water quality dynamics in an
agricultural headwater catchment, Hydrol. Earth Syst. Sci., 20, 843–857,
https://doi.org/10.5194/hess-20-843-2016, 2016.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau,
J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934,
1968.
Singh, A. and Nocerino, J.: Robust estimation of mean and variance using
environmental data sets with below detection limit observations, Chemometr.
Intell. Lab., 60, 69–86, https://doi.org/10.1016/S0169-7439(01)00186-1, 2002.
Sivakumar, B.: Dominant processes concept in hydrology: moving forward,
Hydrol. Process., 18, 2349–2353, https://doi.org/10.1002/hyp.5606, 2004.
Stumm, W. and Morgan, J.: Aquatic chemistry, Wiley, 1996.
Tenenbaum, J., Silva, V., and Langford, J.: A global geometric framework for
nonlinear dimensionality reduction, Science, 290, 2319–2323, 2000.
Tesmer, M., Möller, P., Wieland, S., Jahnke, C., Voigt, H., and Pekdeger,
A.: Deep reaching fluid flow in the North East German Basin: origin and
processes of groundwater salinisation, Hydrogeol. J., 15, 1291–1306,
https://doi.org/10.1007/s10040-007-0176-y, 2007.
Theil, H.: A rank-invariant method of linear and polynomial regression
analysis I:, Proceedings of the Royal Netherlands Academy of Sciences, 53,
386–392, 1950a.
Theil, H.: A rank-invariant method of linear and polynomial regression
analysis II:, Proceedings of the Royal Netherlands Academy of Sciences, 53,
521–525, 1950b.
Theil, H.: A rank-invariant method of linear and polynomial regression
analysis III:, Proceedings of the Royal Netherlands Academy of Sciences, 53,
1397–1412, 1950c.
Tunaley, C., Tetzlaff, D., Lessels, J., and Soulsby, C.: Linking
high-frequency DOC dynamics to the age of connected water sources, Water
Resour. Res., 52, 5232–5247, https://doi.org/10.1002/2015WR018419, 2016.
Usunoff, E. J. and Guzmán-Guzmán, A.: Multivariate Analysis in
Hydrochemistry: An Example of the Use of Factor and Correspondence Analyses,
Ground Water, 27, 27–34, https://doi.org/10.1111/j.1745-6584.1989.tb00004.x, 1989.
Van der Maaten, L., Postma, E., and van den Herik, H.: Dimensionality
Reduction: A Comparative Review, TiCC-TR 2009-005, 2009.
Wade, A. J., Palmer-Felgate, E. J., Halliday, S. J., Skeffington, R. A.,
Loewenthal, M., Jarvie, H. P., Bowes, M. J., Greenway, G. M., Haswell, S. J.,
Bell, I. M., Joly, E., Fallatah, A., Neal, C., Williams, R. J., Gozzard, E.,
and Newman, J. R.: Hydrochemical processes in lowland rivers: insights from
in situ, high-resolution monitoring, Hydrol. Earth Syst. Sci., 16,
4323–4342, https://doi.org/10.5194/hess-16-4323-2012, 2012.
Weyer, C., Peiffer, S., Schulze, K., Borken, W., and Lischeid, G.: Catchments
as heterogeneous and multi-species reactors: An integral approach for
identifying biogeochemical hot-spots at the catchment scale, J. Hydrol., 519,
1560–1571, https://doi.org/10.1016/j.jhydrol.2014.09.005, 2014.
Weymann, D., Geistlinger, H., Well, R., von der Heide, C., and Flessa, H.:
Kinetics of N2O production and reduction in a nitrate-contaminated
aquifer inferred from laboratory incubation experiments, Biogeosciences, 7,
1953–1972, https://doi.org/10.5194/bg-7-1953-2010, 2010.
Wriedt, G., Spindler, J., Neef, T., Meißner, R., and Rode, M.:
Groundwater dynamics and channel activity as major controls of in-stream
nitrate concentrations in a lowland catchment system?, J. Hydrol., 343,
154–168, https://doi.org/10.1016/j.jhydrol.2007.06.010, 2007.
Yue, S., Pilon, P., Phinney, B., and Cavadias, G.: The influence of
autocorrelation on the ability to detect trend in hydrological series,
Hydrol. Process., 16, 1807–1829, https://doi.org/10.1002/hyp.1095, 2002.
Zhang, Y.-C., Slomp, C. P., Broers, H. P., Passier, H. F., and Cappellen, P.
V.: Denitrification coupled to pyrite oxidation and changes in groundwater
quality in a shallow sandy aquifer, Geochim. Cosmochim. Ac., 73, 6716–6726,
https://doi.org/10.1016/j.gca.2009.08.026, 2009.
Zhang, Q., Harman, C. J., and Kirchner, J. W.: Evaluation of statistical
methods for quantifying fractal scaling in water-quality time series with
irregular sampling, Hydrol. Earth Syst. Sci., 22, 1175–1192,
https://doi.org/10.5194/hess-22-1175-2018, 2018.
Short summary
We suggested and tested an exploratory approach for the detection of dominant changes in multivariate water quality data sets with irregular sampling in space and time. The approach is especially recommended for the exploratory assessment of existing long-term low-frequency multivariate water quality monitoring data.
We suggested and tested an exploratory approach for the detection of dominant changes in...
