Articles | Volume 27, issue 20
https://doi.org/10.5194/hess-27-3733-2023
https://doi.org/10.5194/hess-27-3733-2023
Technical note
 | 
23 Oct 2023
Technical note |  | 23 Oct 2023

Technical note: Seamless extraction and analysis of river networks in R

Luca Carraro

Related subject area

Subject: Ecohydrology | Techniques and Approaches: Modelling approaches
Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California
Stephen K. Adams, Brian P. Bledsoe, and Eric D. Stein
Hydrol. Earth Syst. Sci., 27, 3021–3039, https://doi.org/10.5194/hess-27-3021-2023,https://doi.org/10.5194/hess-27-3021-2023, 2023
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Improving regional climate simulations based on a hybrid data assimilation and machine learning method
Xinlei He, Yanping Li, Shaomin Liu, Tongren Xu, Fei Chen, Zhenhua Li, Zhe Zhang, Rui Liu, Lisheng Song, Ziwei Xu, Zhixing Peng, and Chen Zheng
Hydrol. Earth Syst. Sci., 27, 1583–1606, https://doi.org/10.5194/hess-27-1583-2023,https://doi.org/10.5194/hess-27-1583-2023, 2023
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A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest
Marissa Kivi, Noemi Vergopolan, and Hamze Dokoohaki
Hydrol. Earth Syst. Sci., 27, 1173–1199, https://doi.org/10.5194/hess-27-1173-2023,https://doi.org/10.5194/hess-27-1173-2023, 2023
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Does non-stationarity induced by multiyear drought invalidate the paired-catchment method?
Yunfan Zhang, Lei Cheng, Lu Zhang, Shujing Qin, Liu Liu, Pan Liu, and Yanghe Liu
Hydrol. Earth Syst. Sci., 26, 6379–6397, https://doi.org/10.5194/hess-26-6379-2022,https://doi.org/10.5194/hess-26-6379-2022, 2022
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Effects of a biased LAI data assimilation system on hydrological variables and carbon uptake over Europe
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
EGUsphere, https://doi.org/10.5194/egusphere-2022-1137,https://doi.org/10.5194/egusphere-2022-1137, 2022
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Cited articles

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Amatulli, G., Garcia Marquez, J., Sethi, T., Kiesel, J., Grigoropoulou, A., Üblacker, M. M., Shen, L. Q., and Domisch, S.: Hydrography90m: a new high-resolution global hydrographic dataset, Earth Syst. Sci. Data, 14, 4525–4550, https://doi.org/10.5194/essd-14-4525-2022, 2022. a
Baldan, D., Cunillera-Montcusí, D., Funk, A., and Hein, T.: Introducing `riverconn': an R package to assess river connectivity indices, Environ. Model. Softw., 156, 105470, https://doi.org/10.1016/j.envsoft.2022.105470, 2022. a, b
Basu, N. B., Rao, P. S. C., Thompson, S. E., Loukinova, N. V., Donner, S. D., Ye, S., and Sivapalan, M.: Spatiotemporal averaging of in-stream solute removal dynamics, Water Resour. Res., 47, W00J06, https://doi.org/10.1029/2010WR010196, 2011. a
Bertuzzo, E., Casagrandi, R., Gatto, M., Rodríguez-Iturbe, I., and Rinaldo, A.: On spatially explicit models of cholera epidemics, J. Roy. Soc. Interf., 7, 321–333, https://doi.org/10.1098/rsif.2009.0204, 2010. a
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Mathematical models are key to the study of environmental processes in rivers. Such models often require information on river morphology from geographic information system (GIS) software, which hinders the use of replicable workflows. Here I present rivnet, an R package for simple, robust, GIS-free extraction and analysis of river networks. The package is designed so as to require minimal user input and is oriented towards ecohydrological, ecological and biogeochemical modeling.