Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2235-2020
https://doi.org/10.5194/hess-24-2235-2020
Research article
 | 
08 May 2020
Research article |  | 08 May 2020

Optimal design of hydrometric station networks based on complex network analysis

Ankit Agarwal, Norbert Marwan, Rathinasamy Maheswaran, Ugur Ozturk, Jürgen Kurths, and Bruno Merz

Related authors

Preface: Estimating and predicting natural hazards and vulnerabilities in the Himalayan region
Wolfgang Schwanghart, Ankit Agarwal, Kristen Cook, Ugur Ozturk, Roopam Shukla, and Sven Fuchs
Nat. Hazards Earth Syst. Sci., 24, 3291–3297, https://doi.org/10.5194/nhess-24-3291-2024,https://doi.org/10.5194/nhess-24-3291-2024, 2024
Short summary
Performance analysis of physically-based (HEC-RAS, CADDIES) and AI-based (LSTM) flood models for two case studies
Marina Batalini de Macedo, Nikunj K. Mangukiya, Maria Clara Fava, Ashutosh Sharma, Roberto Fray da Silva, Ankit Agarwal, Maria Tereza Razzolini, Eduardo Mario Mendiondo, Narendra K. Goel, Mathew Kurian, and Adelaide Cássia Nardocci
Proc. IAHS, 386, 41–46, https://doi.org/10.5194/piahs-386-41-2024,https://doi.org/10.5194/piahs-386-41-2024, 2024
Short summary
Spatial distribution of bedrock landslides over the landscape evolution in NW Himalayan River catchments
Abhishek Kashyap, Mukunda Dev Behera, Anand Kumar Pandey, and Ankit Agarwal
EGUsphere, https://doi.org/10.5194/egusphere-2022-533,https://doi.org/10.5194/egusphere-2022-533, 2022
Preprint archived
Short summary
Multi-mission altimetry data to evaluate hydrodynamic model-based stage-discharge rating curves in flood-prone Mahanadi River, India
Pankaj R. Dhote, Joshal K. Bansal, Vaibhav Garg, Praveen K. Thakur, and Ankit Agarwal
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-101,https://doi.org/10.5194/nhess-2022-101, 2022
Preprint withdrawn
Short summary
Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach
Jürgen Kurths, Ankit Agarwal, Roopam Shukla, Norbert Marwan, Maheswaran Rathinasamy, Levke Caesar, Raghavan Krishnan, and Bruno Merz
Nonlin. Processes Geophys., 26, 251–266, https://doi.org/10.5194/npg-26-251-2019,https://doi.org/10.5194/npg-26-251-2019, 2019
Short summary

Related subject area

Subject: Engineering Hydrology | Techniques and Approaches: Mathematical applications
Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events
Andreas Hänsler and Markus Weiler
Hydrol. Earth Syst. Sci., 26, 5069–5084, https://doi.org/10.5194/hess-26-5069-2022,https://doi.org/10.5194/hess-26-5069-2022, 2022
Short summary
Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration
Yohei Sawada and Risa Hanazaki
Hydrol. Earth Syst. Sci., 24, 4777–4791, https://doi.org/10.5194/hess-24-4777-2020,https://doi.org/10.5194/hess-24-4777-2020, 2020
Short summary
Flood trends along the Rhine: the role of river training
S. Vorogushyn and B. Merz
Hydrol. Earth Syst. Sci., 17, 3871–3884, https://doi.org/10.5194/hess-17-3871-2013,https://doi.org/10.5194/hess-17-3871-2013, 2013
Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates
J. López and F. Francés
Hydrol. Earth Syst. Sci., 17, 3189–3203, https://doi.org/10.5194/hess-17-3189-2013,https://doi.org/10.5194/hess-17-3189-2013, 2013
A decision tree model to estimate the value of information provided by a groundwater quality monitoring network
A. I. Khader, D. E. Rosenberg, and M. McKee
Hydrol. Earth Syst. Sci., 17, 1797–1807, https://doi.org/10.5194/hess-17-1797-2013,https://doi.org/10.5194/hess-17-1797-2013, 2013

Cited articles

Adhikary, S. K., Yilmaz, A. G., and Muttil, N.: Optimal design of rain gauge network in the Middle Yarra River catchment, Australia, Hydrol. Process., 29, 2582–2599, https://doi.org/10.1002/hyp.10389, 2015. 
Agarwal, A.: Unraveling spatio-temporal climatic patterns via multi-scale complex networks, Universität Potsdam, Potsdam, 2019. 
Agarwal, A., Marwan, N., Rathinasamy, M., Merz, B., and Kurths, J.: Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach, Nonlin. Processes Geophys., 24, 599–611, https://doi.org/10.5194/npg-24-599-2017, 2017. 
Agarwal, A., Marwan, N., Maheswaran, R., Merz, B., and Kurths, J.: Quantifying the roles of single stations within homogeneous regions using complex network analysis, J. Hydrol., 563, 802–810, https://doi.org/10.1016/j.jhydrol.2018.06.050, 2018a. 
Agarwal, A., Maheswaran, R., Marwan, N., Caesar, L., and Kurths, J.: Wavelet-based multiscale similarity measure for complex networks, Eur. Phys. J. B, 91, 296, https://doi.org/10.1140/epjb/e2018-90460-6, 2018b. 
Download
Short summary
In the climate/hydrology network, each node represents a geographical location of climatological data, and links between nodes are set up based on their interaction or similar variability. Here, using network theory, we first generate a node-ranking measure and then prioritize the rain gauges to identify influential and expandable stations across Germany. To show the applicability of the proposed approach, we also compared the results with existing traditional and contemporary network measures.