The default behavior for adding and solving with noisemodels has changed from Pastas 1.5. Find more information here

Bibliography#

Pastas is based on a lot of scientific literature. Here the references are listed for all the methods implemented in Pastas. This list is automatically generated from a public Zotero library (collection References). For a list of studies using Pastas we refer to the Publications page of this website.

Aka74

H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716–723, December 1974. doi:10.1109/TAC.1974.1100705.

Aka79

H. Akaike. A Bayesian extension of the minimum AIC procedure of autoregressive model fitting. Biometrika, 66(2):237–242, August 1979. URL: http://biomet.oxfordjournals.org/content/66/2/237, doi:10.1093/biomet/66.2.237.

BRLK04

David B. Baker, R. Peter Richards, Timothy T. Loftus, and Jack W. Kramer. A NEW FLASHINESS INDEX: CHARACTERISTICS AND APPLICATIONS TO MIDWESTERN RIVERS AND STREAMS1. JAWRA Journal of the American Water Resources Association, 40(2):503–522, April 2004. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1111/j.1752-1688.2004.tb01046.x (visited on 2023-01-17), doi:10.1111/j.1752-1688.2004.tb01046.x.

BHvGG06

W. L. Berendrecht, A. W. Heemink, F. C. van Geer, and J. C. Gehrels. A non-linear state space approach to model groundwater fluctuations. Advances in Water Resources, 29(7):959–973, July 2006. URL: http://www.sciencedirect.com/science/article/pii/S0309170805002113, doi:10.1016/j.advwatres.2005.08.009.

BM13

J. P. Bloomfield and B. P. Marchant. Analysis of groundwater drought building on the standardised precipitation index approach. Hydrology and Earth System Sciences, 17(12):4769–4787, 2013. URL: https://hess.copernicus.org/articles/17/4769/2013/, doi:10.5194/hess-17-4769-2013.

Bru99

G. A. Bruggeman. Analytical solutions of geohydrological problems. Volume Eq. 123.32. Elsevier, Amsterdam, 1999.

CB00

B Clausen and B.J.F Biggs. Flow variables for ecological studies in temperate streams: groupings based on covariance. Journal of Hydrology, 237(3):184–197, November 2000. URL: https://www.sciencedirect.com/science/article/pii/S0022169400003061, doi:10.1016/S0022-1694(00)00306-1.

CBC+19

R. A. Collenteur, M. Bakker, R. Caljé, S. A. Klop, and F. Schaars. Pastas: Open Source Software for the Analysis of Groundwater Time Series. Groundwater, 57(6):877–885, November 2019. location=USA, The Netherlands. URL: https://doi.org/10.1111/gwat.12925, doi:10.1111/gwat.12925.

CBKB21

R. A. Collenteur, M. Bakker, G. Klammler, and S. Birk. Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data. Hydrology and Earth System Sciences, 25(5):2931–2949, 2021. location=Austria. URL: https://hess.copernicus.org/articles/25/2931/2021/, doi:10.5194/hess-25-2931-2021.

Col74

Robert K. Colwell. Predictability, Constancy, and Contingency of Periodic Phenomena. Ecology, 55(5):1148–1153, August 1974. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.2307/1940366 (visited on 2023-01-17), doi:10.2307/1940366.

Ede47

J. H. Edelman. Over de berekening van grondwaterstroomingen (About the calculation of groundwater flow). PhD thesis, Delft University of Technology Delft, The Netherlands, 1947.

Ell87

Aaron M. Ellison. EFFECT OF SEED DIMORPHISM ON THE DENSITY-DEPENDENT DYNAMICS OF EXPERIMENTAL POPULATIONS OF ATRIPLEX TRIANGULARIS (CHENOPODIACEAE). American Journal of Botany, 74(8):1280–1288, August 1987. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1002/j.1537-2197.1987.tb08741.x (visited on 2023-01-17), doi:10.1002/j.1537-2197.1987.tb08741.x.

Fis95

Nicholas I Fisher. Statistical analysis of circular data. cambridge university press, 1995.

FMHLG13

Daniel Foreman-Mackey, David W. Hogg, Dustin Lang, and Jonathan Goodman. Emcee: The MCMC Hammer. \pasp, 125(925):306, March 2013. _eprint: 1202.3665. doi:10.1086/670067.

GSK+12

Ladislav Gaál, Ján Szolgay, Silvia Kohnová, Juraj Parajka, Ralf Merz, Alberto Viglione, and Günter Blöschl. Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology. Water Resources Research, April 2012. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1029/2011WR011509 (visited on 2023-01-17), doi:10.1029/2011WR011509.

HSGM00

David M. Hannah, Barnaby P. G. Smith, Angela M. Gurnell, and Glenn R. McGregor. An approach to hydrograph classification. Hydrological Processes, 14(2):317–338, February 2000. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1002/(SICI)1099-1085(20000215)14:2<317::AID-HYP929>3.0.CO;2-T (visited on 2023-01-17), doi:10.1002/(SICI)1099-1085(20000215)14:2<317::AID-HYP929>3.0.CO;2-T.

HH85

J. A. Hartigan and P. M. Hartigan. The Dip Test of Unimodality. The Annals of Statistics, 13(1):70–84, 1985. Publisher: Institute of Mathematical Statistics. URL: http://www.jstor.org/stable/2241144 (visited on 2023-01-17).

HHSB19

B. Heudorfer, E. Haaf, K. Stahl, and R. Barthel. Index-based characterization and quantification of groundwater dynamics. Water Resources Research, May 2019. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR024418 (visited on 2019-05-24), doi:10.1029/2018WR024418.

Hothers00

Robert C Hilborn and others. Chaos and nonlinear dynamics: an introduction for scientists and engineers. Oxford University Press on Demand, 2000.

HKCCB15

Tobias Houska, Philipp Kraft, Alejandro Chamorro-Chavez, and Lutz Breuer. SPOTting Model Parameters Using a Ready-Made Python Package. PLOS ONE, 10(12):e0145180, December 2015. Publisher: Public Library of Science. URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145180 (visited on 2023-02-16), doi:10.1371/journal.pone.0145180.

HJ89

JMR Hughes and B James. A hydrological regionalization of streams in Victoria, Australia, with implications for stream Ecology. Marine and Freshwater Research, 40(3):303–326, 1989. URL: https://doi.org/10.1071/MF9890303.

KK07

D. Kavetski and G. Kuczera. Model smoothing strategies to remove microscale discontinuities and spurious secondary optima in objective functions in hydrological calibration. Water Resources Research, March 2007. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1029/2006WR005195, doi:10.1029/2006WR005195.

Kir09

James W. Kirchner. Catchments as simple dynamical systems: Catchment characterization, rainfall-runoff modeling, and doing hydrology backward. Water Resources Research, February 2009. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1029/2008WR006912 (visited on 2023-01-17), doi:10.1029/2008WR006912.

KFP12

H. Kling, M. Fuchs, and M. Paulin. Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology, 424-425:264 – 277, 2012. URL: http://www.sciencedirect.com/science/article/pii/S0022169412000431, doi:https://doi.org/10.1016/j.jhydrol.2012.01.011.

KG99

M. Knotters and J. G. De Gooijer. TARSO modeling of water table depths. Water Resources Research, 35(3):695–705, 1999. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/1998WR900049, doi:10.1029/1998WR900049.

MVCVDW13

Kristine Martens, Marc Van Camp, Dirk Van Damme, and Kristine Walraevens. Groundwater dynamics converted to a groundwater classification as a tool for nature development programs in the dunes. Journal of Hydrology, 499:236–246, August 2013. URL: https://www.sciencedirect.com/science/article/pii/S0022169413004988, doi:10.1016/j.jhydrol.2013.06.045.

NS70

J. E. Nash and J. V. Sutcliffe. River flow forecasting through conceptual models part I—A discussion of principles. Journal of hydrology, 10(3):282–290, 1970. Publisher: Elsevier.

NON+19

Matt Newville, Renee Otten, Andrew Nelson, Antonino Ingargiola, Till Stensitzki, Dan Allan, Austin Fox, Faustin Carter, Michał, Dima Pustakhod, Yoav Ram, Glenn, Christoph Deil, Stuermer, Alexandre Beelen, Oliver Frost, Nicholas Zobrist, Gustavo Pasquevich, Allan L. R. Hansen, Tim Spillane, Shane Caldwell, Anthony Polloreno, andrewhannum, Julius Zimmermann, Jose Borreguero, Jonathan Fraine, deep-42-thought, Benjamin F. Maier, Ben Gamari, and Anthony Almarza. Lmfit/lmfit-py 1.0.0. December 2019. URL: https://doi.org/10.5281/zenodo.3588521, doi:10.5281/zenodo.3588521.

OBM19

C. Obergfell, M. Bakker, and K. Maas. Identification and explanation of a change in the groundwater regime using time series analysis. Groundwater, April 2019. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/gwat.12891, doi:10.1111/gwat.12891.

Org08

World Meteorological Organization. Manual on low-flow estimation and prediction. World meteorological organization, 2008.

OKAP10

Ludovic Oudin, Alison Kay, Vazken Andréassian, and Charles Perrin. Are seemingly physically similar catchments truly hydrologically similar? Water Resources Research, November 2010. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1029/2009WR008887 (visited on 2023-01-17), doi:10.1029/2009WR008887.

Par33

Maurice Pardé. Fleuves et rivières. Collection Armand Colin. Section de Géographie (France) fre no. 155, 1933. Publisher: Colin.

PW14

T. J. Peterson and A. W. Western. Nonlinear time-series modeling of unconfined groundwater head. Water Resources Research, 50(10):8330–8355, October 2014. URL: http://onlinelibrary.wiley.com/doi/10.1002/2013WR014800/abstract, doi:10.1002/2013WR014800.

RMHK11

K. Rehfeld, N. Marwan, J. Heitzig, and J. Kurths. Comparison of correlation analysis techniques for irregularly sampled time series. Nonlin. Processes Geophys., 18(3):389–404, June 2011. URL: https://www.nonlin-processes-geophys.net/18/389/2011/, doi:10.5194/npg-18-389-2011.

Ric90

R. Peter Richards. Measures of Flow Variability and a New Flow-Based Classification of Great Lakes Tributaries. Journal of Great Lakes Research, 16(1):53–70, January 1990. URL: https://www.sciencedirect.com/science/article/pii/S0380133090713986, doi:10.1016/S0380-1330(90)71398-6.

RBPB96

Brian D. Richter, Jeffrey V. Baumgartner, Jennifer Powell, and David P. Braun. A Method for Assessing Hydrologic Alteration within Ecosystems. Conservation Biology, 10(4):1163–1174, August 1996. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1046/j.1523-1739.1996.10041163.x (visited on 2023-01-17), doi:10.1046/j.1523-1739.1996.10041163.x.

Sil81

B. W. Silverman. Using Kernel Density Estimates to Investigate Multimodality. Journal of the Royal Statistical Society: Series B (Methodological), 43(1):97–99, September 1981. Publisher: John Wiley & Sons, Ltd. URL: https://doi.org/10.1111/j.2517-6161.1981.tb01155.x (visited on 2023-01-17), doi:10.1111/j.2517-6161.1981.tb01155.x.

Sug78

Nariaki Sugiura. Further analysis of the data by Akaike's information criterion and the finite corrections. Communications in Statistics - Theory and Methods, 7(1):13–26, 1978. Publisher: Taylor & Francis. doi:10.1080/03610927808827599.

VdL58

DA Kraijenhoff Van de Leur. A study of non-steady groundwater flow with special reference to a reservoir coefficient. De Ingenieur, 70(19):B87–B94, 1958.

VM10

E. J. M. Veling and K. Maas. Hantush Well Function revisited. Journal of Hydrology, 393(3):381–388, November 2010. URL: http://www.sciencedirect.com/science/article/pii/S0022169410005500, doi:10.1016/j.jhydrol.2010.08.033.

VGO+20

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C. J. Carey, Ilhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, Aditya Vijaykumar, Alessandro Pietro Bardelli, Alex Rothberg, Andreas Hilboll, Andreas Kloeckner, Anthony Scopatz, Antony Lee, Ariel Rokem, C. Nathan Woods, Chad Fulton, Charles Masson, Christian Häggström, Clark Fitzgerald, David A. Nicholson, David R. Hagen, Dmitrii V. Pasechnik, Emanuele Olivetti, Eric Martin, Eric Wieser, Fabrice Silva, Felix Lenders, Florian Wilhelm, G. Young, Gavin A. Price, Gert-Ludwig Ingold, Gregory E. Allen, Gregory R. Lee, Hervé Audren, Irvin Probst, Jörg P. Dietrich, Jacob Silterra, James T. Webber, Janko Slavič, Joel Nothman, Johannes Buchner, Johannes Kulick, Johannes L. Schönberger, José Vinícius de Miranda Cardoso, Joscha Reimer, Joseph Harrington, Juan Luis Cano Rodríguez, Juan Nunez-Iglesias, Justin Kuczynski, Kevin Tritz, Martin Thoma, Matthew Newville, Matthias Kümmerer, Maximilian Bolingbroke, Michael Tartre, Mikhail Pak, Nathaniel J. Smith, Nikolai Nowaczyk, Nikolay Shebanov, Oleksandr Pavlyk, Per A. Brodtkorb, Perry Lee, Robert T. McGibbon, Roman Feldbauer, Sam Lewis, Sam Tygier, Scott Sievert, Sebastiano Vigna, Stefan Peterson, Surhud More, Tadeusz Pudlik, Takuya Oshima, Thomas J. Pingel, Thomas P. Robitaille, Thomas Spura, Thouis R. Jones, Tim Cera, Tim Leslie, Tiziano Zito, Tom Krauss, Utkarsh Upadhyay, Yaroslav O. Halchenko, Yoshiki Vázquez-Baeza, and SciPy 1.0 Contributors. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, February 2020. URL: https://doi.org/10.1038/s41592-019-0686-2, doi:10.1038/s41592-019-0686-2.

vAB05

J. R. von Asmuth and M. F. P. Bierkens. Modeling irregularly spaced residual series as a continuous stochastic process. Water Resources Research, 41(12):W12404, December 2005. URL: http://onlinelibrary.wiley.com/doi/10.1029/2004WR003726/abstract, doi:10.1029/2004WR003726.

vABM02

J. R. von Asmuth, M. F. P. Bierkens, and K. Maas. Transfer function-noise modeling in continuous time using predefined impulse response functions. Water Resources Research, 38(12):23–1–23–12, 2002. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2001WR001136, doi:10.1029/2001WR001136.

vAOB12

J. R. von Asmuth, T. N. Olsthoorn, and M. F. P. Bierkens. Groundwater System Identification through Time Series Analysis. March 2012. URL: http://resolver.tudelft.nl/uuid:b6ccd472-9b9d-4810-aa19-3a0b046017e0.

WSH06

Xiaozhe Wang, Kate Smith, and Rob Hyndman. Characteristic-Based Clustering for Time Series Data. Data Mining and Knowledge Discovery, 13(3):335–364, November 2006. URL: https://doi.org/10.1007/s10618-005-0039-x, doi:10.1007/s10618-005-0039-x.