Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-831-2021
https://doi.org/10.5194/hess-25-831-2021
Research article
 | 
19 Feb 2021
Research article |  | 19 Feb 2021

Objective functions for information-theoretical monitoring network design: what is “optimal”?

Hossein Foroozand and Steven V. Weijs

Related authors

BCUB - A large sample ungauged basin attribute dataset for British Columbia, Canada
Daniel Kovacek and Steven Weijs
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-508,https://doi.org/10.5194/essd-2023-508, 2024
Revised manuscript accepted for ESSD
Short summary
Technical note: “Bit by bit”: a practical and general approach for evaluating model computational complexity vs. model performance
Elnaz Azmi, Uwe Ehret, Steven V. Weijs, Benjamin L. Ruddell, and Rui A. P. Perdigão
Hydrol. Earth Syst. Sci., 25, 1103–1115, https://doi.org/10.5194/hess-25-1103-2021,https://doi.org/10.5194/hess-25-1103-2021, 2021
Short summary
Advancing catchment hydrology to deal with predictions under change
U. Ehret, H. V. Gupta, M. Sivapalan, S. V. Weijs, S. J. Schymanski, G. Blöschl, A. N. Gelfan, C. Harman, A. Kleidon, T. A. Bogaard, D. Wang, T. Wagener, U. Scherer, E. Zehe, M. F. P. Bierkens, G. Di Baldassarre, J. Parajka, L. P. H. van Beek, A. van Griensven, M. C. Westhoff, and H. C. Winsemius
Hydrol. Earth Syst. Sci., 18, 649–671, https://doi.org/10.5194/hess-18-649-2014,https://doi.org/10.5194/hess-18-649-2014, 2014
Data compression to define information content of hydrological time series
S. V. Weijs, N. van de Giesen, and M. B. Parlange
Hydrol. Earth Syst. Sci., 17, 3171–3187, https://doi.org/10.5194/hess-17-3171-2013,https://doi.org/10.5194/hess-17-3171-2013, 2013

Related subject area

Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
Uncertainty estimation of regionalised depth–duration–frequency curves in Germany
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 2075–2097, https://doi.org/10.5194/hess-27-2075-2023,https://doi.org/10.5194/hess-27-2075-2023, 2023
Short summary
FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022,https://doi.org/10.5194/hess-26-5373-2022, 2022
Short summary
Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021,https://doi.org/10.5194/hess-25-5603-2021, 2021
Short summary
Characteristics and process controls of statistical flood moments in Europe – a data-based analysis
David Lun, Alberto Viglione, Miriam Bertola, Jürgen Komma, Juraj Parajka, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 5535–5560, https://doi.org/10.5194/hess-25-5535-2021,https://doi.org/10.5194/hess-25-5535-2021, 2021
Short summary
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Manuela I. Brunner and Eric Gilleland
Hydrol. Earth Syst. Sci., 24, 3967–3982, https://doi.org/10.5194/hess-24-3967-2020,https://doi.org/10.5194/hess-24-3967-2020, 2020
Short summary

Cited articles

Alfonso, L., Lobbrecht, A., and Price, R.: Information theory–based approach for location of monitoring water level gauges in polders, Water Resour. Res., 46, W03528, https://doi.org/10.1029/2009WR008101, 2010a. a, b, c, d, e, f, g, h, i
Alfonso, L., Lobbrecht, A., and Price, R.: Optimization of water level monitoring network in polder systems using information theory, Water Resour. Res., 46, W12553, https://doi.org/10.1029/2009WR008953, 2010b. a, b, c, d, e, f, g, h
Aydin, B. E., Hagedooren, H., Rutten, M. M., Delsman, J., Oude Essink, G. H. P., van de Giesen, N., and Abraham, E.: A Greedy Algorithm for Optimal Sensor Placement to Estimate Salinity in Polder Networks, Water, 11, 1101, https://doi.org/10.3390/w11051101 2019. a
Banik, B. K., Alfonso, L., Di Cristo, C., and Leopardi, A.: Greedy Algorithms for Sensor Location in Sewer Systems, Water, 9, 856, https://doi.org/10.3390/w9110856 2017. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Barrenetxea, G., Ingelrest, F., Schaefer, G., and Vetterli, M.: The hitchhiker's guide to successful wireless sensor network deployments, in: Proceedings of the 6th ACM conference on Embedded network sensor systems, SenSys '08, 43–56, Association for Computing Machinery, Raleigh, NC, USA, https://doi.org/10.1145/1460412.1460418, 2008. a
Download
Short summary
In monitoring network design, we have to decide what to measure, where to measure, and when to measure. In this paper, we focus on the question of where to measure. Past literature has used the concept of information to choose a selection of locations that provide maximally informative data. In this paper, we look in detail at the proper mathematical formulation of the information concept as an objective. We argue that previous proposals for this formulation have been needlessly complicated.