Beyer, K., Goldstein, J., Ramakrishnan, R., and Shaft, U.: When Is “Nearest Neighbor” Meaningful?, in: Database Theory – ICDT'99, 217–235, Springer, Berlin, Germany,
https://doi.org/10.1007/3-540-49257-7_15, 1999.
a,
b
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., and Hwang, D.-U.: Complex networks: Structure and dynamics, Phys. Rep., 424, 175–308,
https://doi.org/10.1016/j.physrep.2005.10.009, 2006.
a,
b
Börner, K., Sanyal, S., and Vespignani, A.: Network science, Ann. Rev. Info. Sci. Tech., 41, 537–607,
https://doi.org/10.1002/aris.2007.1440410119, 2007.
a,
b,
c,
d
Budyko, M.: Climate and Life, Volume 18 – 1st Edition,
https://shop.elsevier.com/books/climate-and-life/budyko/978-0-12-139450-9 (last access: 20 July 2023), 1974. a
Ciulla, F. and Varadharajan, C.: Classification of River Catchments in the Contiguous United States: Code, Dataset, Similarity Patterns, and Resulting Classes, ESS-DIVE repository [code and data set],
https://doi.org/10.15485/1987555, 2023.
a,
b,
c,
d,
e,
f,
g,
h,
i
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., and Flammini, A.: Political Polarization on Twitter, ICWSM, 5, 89–96,
https://doi.org/10.1609/icwsm.v5i1.14126, 2011.
a
Czuba, J. A. and Foufoula-Georgiou, E.: A network-based framework for identifying potential synchronizations and amplifications of sediment delivery in river basins, Water Resour. Res., 50, 3826–3851,
https://doi.org/10.1002/2013WR014227, 2014.
a
Daly, C., Taylor, G. H., Gibson, W. P., Parzybok, T. W., Johnson, G. L., and Pasteris, P. A.: HIGH-QUALITY SPATIAL CLIMATE DATA SETS FOR THE UNITED STATES AND BEYOND, T. ASAE, 43, 1957–1962,
https://doi.org/10.13031/2013.3101, 2000.
a
Falcone, J. A.: GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow, Technical Report, Tech. rep., USGS [data set],
https://doi.org/10.3133/70046617, 2011.
a,
b
Falcone, J. A., Carlisle, D. M., Wolock, D. M., and Meador, M. R.: GAGES: A stream gage database for evaluating natural and altered flow conditions in the conterminous United States, Ecology, 91, 621,
https://doi.org/10.1890/09-0889.1, 2010.
a
Guo, Y., Zhang, Y., Zhang, L., and Wang, Z.: Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review, WIREs Water, 8, e1487,
https://doi.org/10.1002/wat2.1487, 2021.
a
He, N., Li, Y., Liu, C., Xu, L., Li, M., Zhang, J., He, J., Tang, Z., Han, X., Ye, Q., Xiao, C., Yu, Q., Liu, S., Sun, W., Niu, S., Li, S., Sack, L., and Yu, G.: Plant Trait Networks: Improved Resolution of the Dimensionality of Adaptation, Trends Ecol. Evol., 35, 908–918,
https://doi.org/10.1016/j.tree.2020.06.003, 2020.
a
Hill, R. A., Weber, M. H., Leibowitz, S. G., Olsen, A. R., and Thornbrugh, D. J.: The Stream-Catchment (StreamCat) Dataset: A Database of Watershed Metrics for the Conterminous United States, J. Am. Water Resour. Assoc., 52, 120–128,
https://doi.org/10.1111/1752-1688.12372, 2016.
a
Houle, M. E., Kriegel, H.-P., Kröger, P., Schubert, E., and Zimek, A.: Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?, in: Scientific and Statistical Database Management, 482–500, Springer, Berlin, Germany,
https://doi.org/10.1007/978-3-642-13818-8_34, 2010.
a
Hubbard, S. S., Varadharajan, C., Wu, Y., Wainwright, H., and Dwivedi, D.: Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry, Hydrol. Process., 34, 3175–3182,
https://doi.org/10.1002/hyp.13807, 2020.
a
Jeong, H., Mason, S. P., Barabási, A.-L., and Oltvai, Z. N.: Lethality and centrality in protein networks, Nature, 411, 41–42,
https://doi.org/10.1038/35075138, 2001.
a
Köppen, W.: Klassifikation der klimate nach Temperatur, Niederschlag und Yahreslauf, Pet. Mitt., 64, 193–203, 1918. a
Krakovská, A., Mezeiová, K., and Budáčová, H.: Use of False Nearest Neighbours for Selecting Variables and Embedding Parameters for State Space Reconstruction, Journal of Complex Systems, 2015, 932750,
https://doi.org/10.1155/2015/932750, 2015.
a,
b
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., and Matias, Y.: Caravan – A global community dataset for large-sample hydrology, Sci. Data, 10, 61,
https://doi.org/10.1038/s41597-023-01975-w, 2023.
a
Kumar, J., Mills, R. T., Hoffman, F. M., and Hargrove, W. W.: Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets, Procedia Comput. Sci., 4, 1602–1611,
https://doi.org/10.1016/j.procs.2011.04.173, 2011.
a
MacQueen, J.: Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, vol. 5.1, pp. 281–298, University of California Press, Ewing, NJ, USA,
https://projecteuclid.org/ebooks/berkeley-symposium-on-mathematical-statistics-and-probability/Some-methods-for-classification-and-analysis-of-multivariate-observations/chapter/Some-methods-for-classification-and-analysis-of-multivariate-observations/bsmsp/1200512992 (last access: 4 March 2024), 1967.
a,
b
McDonnell, J. J., Sivapalan, M., Vaché, K., Dunn, S., Grant, G., Haggerty, R., Hinz, C., Hooper, R., Kirchner, J., Roderick, M. L., Selker, J., and Weiler, M.: Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology, Water Resour. Res., 43, W07301,
https://doi.org/10.1029/2006WR005467, 2007.
a
McMillan, H. K., Gnann, S. J., and Araki, R.: Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes, Water Resour. Res., 58, e2021WR031 751,
https://doi.org/10.1029/2021WR031751, 2022.
a
Newman, M.: Networks, Oxford University Press, Oxford, England, UK,
https://global.oup.com/academic/product/networks-9780198805090 (last access: 4 March 2024), 2018.
a,
b,
c
Olden, J. D. and Poff, N. L.: Redundancy and the choice of hydrologic indices for characterizing streamflow regimes, River Res. Appl., 19, 101–121,
https://doi.org/10.1002/rra.700, 2003.
a,
b,
c,
d
Ombadi, M. and Varadharajan, C.: Urbanization and aridity mediate distinct salinity response to floods in rivers and streams across the contiguous United States, Water Research, 220, 118664,
https://doi.org/10.1016/j.watres.2022.118664, 2022.
a
Pearson, K.: Note on Regression and Inheritance in the Case of Two Parents, Proceedings of the Royal Society of London Series I, 58, 240–242, 1895. a
Pearson, K.: LIII. On lines and planes of closest fit to systems of points in space, The London, Edinburgh, and Dublin philosophical magazine and journal of science, 2, 559–572,
https://doi.org/10.1080/14786440109462720, 1901.
a
Rinaldo, A., Banavar, J. R., and Maritan, A.: Trees, networks, and hydrology, Water Resour. Res., 42, W06D07,
https://doi.org/10.1029/2005WR004108, 2006.
a
Rodríguez-Iturbe, I. and Rinaldo, A.: Fractal River Basins, Cambridge University Press, Cambridge, England, UK,
https://www.cambridge.org/us/universitypress/subjects/earth-and-environmental-science/hydrology-hydrogeology-and-water-resources/fractal-river-basins-chance-and-self-organization?format=PB&isbn=9780521004053 (last access: 4 March 2024), 2001. a
Rosvall, M., Axelsson, D., and Bergstrom, C. T.: The map equation, Eur. Phys. J. Spec. Top., 178, 13–23,
https://doi.org/10.1140/epjst/e2010-01179-1, 2009.
a,
b
Salton, G.: Introduction to modern information retrieval, McGraw-Hill, 1983. a
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911,
https://doi.org/10.5194/hess-15-2895-2011, 2011.
a
Scott, J. and Carrington, P. J.: The Sage Handbook of Social Network Analysis, SAGE Publications Inc, Thousand Oaks, CA, USA,
https://us.sagepub.com/en-us/nam/the-sage-handbook-of-social-network-analysis/book277881 (last access: 4 March 2024), 2023.
a,
b
Serrano, M. Á, Boguñá, M., and Vespignani, A.: Extracting the multiscale backbone of complex weighted networks, P. Natl. Acad. Sci. USA, 106, 6483–6488,
https://doi.org/10.1073/pnas.0808904106, 2009.
a
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality (complete samples), Biometrika, 52, 591–611,
https://doi.org/10.2307/2333709, 1965.
a
Sivakumar, B., Singh, V. P., Berndtsson, R., and Khan, S. K.: Catchment Classification Framework in Hydrology: Challenges and Directions, J. Hydrol. Eng., 20, A4014002,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000837, 2015.
a,
b
Spearman, C.: The Proof and Measurement of Association between Two Things, Am. J. Psychol., 100, 441–471,
https://www.jstor.org/stable/1422689 (last access: 25 December 2023), 1987. a
Tejedor, A., Longjas, A., Edmonds, D. A., Zaliapin, I., Georgiou, T. T., Rinaldo, A., and Foufoula-Georgiou, E.: Entropy and optimality in river deltas, P. Natl. Acad. Sci. USA, 114, 11651–11656,
https://doi.org/10.1073/pnas.1708404114, 2017.
a
Tejedor, A., Longjas, A., Passalacqua, P., Moreno, Y., and Foufoula-Georgiou, E.: Multiplex Networks: A Framework for Studying Multiprocess Multiscale Connectivity Via Coupled-Network Theory With an Application to River Deltas, Geophys. Res. Lett., 45, 9681–9689,
https://doi.org/10.1029/2018GL078355, 2018.
a
Troch, P. A., Lahmers, T., Meira, A., Mukherjee, R., Pedersen, J. W., Roy, T., and Valdés-Pineda, R.: Catchment coevolution: A useful framework for improving predictions of hydrological change?, Water Resour. Res., 51, 4903–4922,
https://doi.org/10.1002/2015WR017032, 2015.
a
USGS: U.S. Geological Survey, National Water Information System data available on the World Wide Web (Water Data for the Nation),
http://waterdata.usgs.gov/nwis/ (last access: 1 September 2022), 2022.
a,
b
Varadharajan, C., Hendrix, V. C., Christianson, D. S., Burrus, M., Wong, C., Hubbard, S. S., and Agarwal, D. A.: BASIN-3D: A brokering framework to integrate diverse environmental data, Comput. Geosci., 159, 105024,
https://doi.org/10.1016/j.cageo.2021.105024, 2022.
a
Wagener, T., Sivapalan, M., Troch, P., and Woods, R.: Catchment Classification and Hydrologic Similarity, Geography Compass, 1, 901–931,
https://doi.org/10.1111/j.1749-8198.2007.00039.x, 2007.
a,
b
Wainwright, H. M., Uhlemann, S., Franklin, M., Falco, N., Bouskill, N. J., Newcomer, M. E., Dafflon, B., Siirila-Woodburn, E. R., Minsley, B. J., Williams, K. H., and Hubbard, S. S.: Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions, Hydrol. Earth Syst. Sci., 26, 429–444,
https://doi.org/10.5194/hess-26-429-2022, 2022.
a
Wolock, D. M. and McCabe, G. J.: Estimates of runoff using water-balance and atmospheric general circulation models, JAWRA Journal of the American Water Resources Association, 35, 1341–1350, 1999. a
