Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-831-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-831-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Objective functions for information-theoretical monitoring network design: what is “optimal”?
Hossein Foroozand
Department of Civil Engineering, University of British Columbia, Vancouver, British Columbia, Canada
Steven V. Weijs
CORRESPONDING AUTHOR
Department of Civil Engineering, University of British Columbia, Vancouver, British Columbia, Canada
Related authors
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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
Preprint under review for ESSD
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We made a dataset for British Columbia describing the terrain, soil, land cover, and climate of over 1 million watersheds. The attributes are often used in hydrology because they are related to the water cycle. The data is meant to be used for water resources problems that can benefit from lots of basins and their attributes. The data and instructions needed to build the dataset from scratch are freely available. The permanent home for the data is https://doi.org/10.5683/SP3/JNKZVT.
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
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Computer models should be as simple as possible but not simpler. Simplicity refers to the length of the model and the effort it takes the model to generate its output. Here we present a practical technique for measuring the latter by the number of memory visits during model execution by
Strace, a troubleshooting and monitoring program. The advantage of this approach is that it can be applied to any computer-based model, which facilitates model intercomparison.
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
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
FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
Characteristics and process controls of statistical flood moments in Europe – a data-based analysis
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Numerical investigation on the power of parametric and nonparametric tests for trend detection in annual maximum series
Spatially dependent flood probabilities to support the design of civil infrastructure systems
Technical note: Stochastic simulation of streamflow time series using phase randomization
Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 1: theoretical development
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 2: numerical application
Characterizing the spatial variations and correlations of large rainstorms for landslide study
Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic
Dealing with uncertainty in the probability of overtopping of a flood mitigation dam
Flood frequency analysis of historical flood data under stationary and non-stationary modelling
Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland
Towards modelling flood protection investment as a coupled human and natural system
A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation
Examination of homogeneity of selected Irish pooling groups
Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models
Design flood hydrographs from the relationship between flood peak and volume
Introducing empirical and probabilistic regional envelope curves into a mixed bounded distribution function
HESS Opinions "A random walk on water"
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
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Design rainfall volumes at different duration and frequencies are necessary for the planning of water-related systems and facilities. As the procedure for deriving these values is subjected to different sources of uncertainty, here we explore different methods to estimate how precise these values are for different duration, locations and frequencies in Germany. Combining local and spatial simulations, we estimate tolerance ranges from approx. 10–60% for design rainfall volumes in Germany.
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
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A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
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
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This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
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
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We investigate statistical properties of observed flood series on a European scale. There are pronounced regional patterns, for instance: regions with strong Atlantic influence show less year-to-year variability in the magnitude of observed floods when compared with more arid regions of Europe. The hydrological controls on the patterns are quantified and discussed. On the European scale, climate seems to be the dominant driver for the observed patterns.
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
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Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency domain instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.
Vincenzo Totaro, Andrea Gioia, and Vito Iacobellis
Hydrol. Earth Syst. Sci., 24, 473–488, https://doi.org/10.5194/hess-24-473-2020, https://doi.org/10.5194/hess-24-473-2020, 2020
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We highlight the need for power evaluation in the application of null hypothesis significance tests for trend detection in extreme event analysis. In a wide range of conditions, depending on the underlying distribution of data, the test power may reach unacceptably low values. We propose the use of a parametric approach, based on model selection criteria, that allows one to choose the null hypothesis, to select the level of significance, and to check the test power using Monte Carlo experiments.
Phuong Dong Le, Michael Leonard, and Seth Westra
Hydrol. Earth Syst. Sci., 23, 4851–4867, https://doi.org/10.5194/hess-23-4851-2019, https://doi.org/10.5194/hess-23-4851-2019, 2019
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While conventional approaches focus on flood designs at individual locations, there are many situations requiring an understanding of spatial dependence of floods at multiple locations. This research describes a new framework for analyzing flood characteristics across civil infrastructure systems, including conditional and joint probabilities of floods. This work leads to a new flood estimation paradigm, which focuses on the risk of the entire system rather than each system element in isolation.
Manuela I. Brunner, András Bárdossy, and Reinhard Furrer
Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019, https://doi.org/10.5194/hess-23-3175-2019, 2019
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This study proposes a procedure for the generation of daily discharge data which considers temporal dependence both within short timescales and across different years. The simulation procedure can be applied to individual and multiple sites. It can be used for various applications such as the design of hydropower reservoirs, the assessment of flood risk or the assessment of drought persistence, and the estimation of the risk of multi-year droughts.
Cong Jiang, Lihua Xiong, Lei Yan, Jianfan Dong, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 23, 1683–1704, https://doi.org/10.5194/hess-23-1683-2019, https://doi.org/10.5194/hess-23-1683-2019, 2019
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We present the methods addressing the multivariate hydrologic design applied to the engineering practice under nonstationary conditions. A dynamic C-vine copula allowing for both time-varying marginal distributions and a time-varying dependence structure is developed to capture the nonstationarities of multivariate flood distribution. Then, the multivariate hydrologic design under nonstationary conditions is estimated through specifying the design criterion by average annual reliability.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 1993–2005, https://doi.org/10.5194/hess-22-1993-2018, https://doi.org/10.5194/hess-22-1993-2018, 2018
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A new method is proposed to solve the stochastic unsteady open-channel flow system in only one single simulation, as opposed to the many simulations usually done in the popular Monte Carlo approach. The derivation of this new method gave a deterministic and linear Fokker–Planck equation whose solution provided a powerful and effective approach for quantifying the ensemble behavior and variability of such a stochastic system, regardless of the number of parameters causing its uncertainty.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 2007–2021, https://doi.org/10.5194/hess-22-2007-2018, https://doi.org/10.5194/hess-22-2007-2018, 2018
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A newly proposed method is applied to solve a stochastic unsteady open-channel flow system (with an uncertain roughness coefficient) in only one simulation. After comparing its results to those of the Monte Carlo simulations, the new method was found to adequately predict the temporal and spatial evolution of the probability density of the flow variables of the system. This revealed the effectiveness, strength, and time efficiency of this new method as compared to other popular approaches.
Liang Gao, Limin Zhang, and Mengqian Lu
Hydrol. Earth Syst. Sci., 21, 4573–4589, https://doi.org/10.5194/hess-21-4573-2017, https://doi.org/10.5194/hess-21-4573-2017, 2017
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Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in space, which causes more landslides in the area of intense rainfall. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a trend surface and a random field of residuals. The scales of fluctuation of the residuals are found between 5 km and 30 km.
Elena Shevnina, Ekaterina Kourzeneva, Viktor Kovalenko, and Timo Vihma
Hydrol. Earth Syst. Sci., 21, 2559–2578, https://doi.org/10.5194/hess-21-2559-2017, https://doi.org/10.5194/hess-21-2559-2017, 2017
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This paper presents the probabilistic approach to evaluate design floods in a changing climate, adapted in this case to the northern territories. For the Russian Arctic, the regions are delineated, where it is suggested to correct engineering hydrological calculations to account for climate change. An example of the calculation of a maximal discharge of 1 % exceedance probability for the Nadym River at Nadym is provided.
Eleni Maria Michailidi and Baldassare Bacchi
Hydrol. Earth Syst. Sci., 21, 2497–2507, https://doi.org/10.5194/hess-21-2497-2017, https://doi.org/10.5194/hess-21-2497-2017, 2017
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In this research, we explored how the sampling uncertainty of flood variables (flood peak, volume, etc.) can reflect on a structural variable, which in our case was the maximum water level (MWL) of a reservoir controlled by a dam. Next, we incorporated additional information from different sources for a better estimation of the uncertainty in the probability of exceedance of the MWL. Results showed the importance of providing confidence intervals in the risk assessment of a structure.
M. J. Machado, B. A. Botero, J. López, F. Francés, A. Díez-Herrero, and G. Benito
Hydrol. Earth Syst. Sci., 19, 2561–2576, https://doi.org/10.5194/hess-19-2561-2015, https://doi.org/10.5194/hess-19-2561-2015, 2015
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A flood frequency analysis using a 400-year historical flood record was carried out using a stationary model (based on maximum likelihood estimators) and a non-stationary model that incorporates external covariates (climatic and environmental). The stationary model was successful in providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.
L. Gaál, P. Molnar, and J. Szolgay
Hydrol. Earth Syst. Sci., 18, 1561–1573, https://doi.org/10.5194/hess-18-1561-2014, https://doi.org/10.5194/hess-18-1561-2014, 2014
P. E. O'Connell and G. O'Donnell
Hydrol. Earth Syst. Sci., 18, 155–171, https://doi.org/10.5194/hess-18-155-2014, https://doi.org/10.5194/hess-18-155-2014, 2014
A. I. Requena, L. Mediero, and L. Garrote
Hydrol. Earth Syst. Sci., 17, 3023–3038, https://doi.org/10.5194/hess-17-3023-2013, https://doi.org/10.5194/hess-17-3023-2013, 2013
S. Das and C. Cunnane
Hydrol. Earth Syst. Sci., 15, 819–830, https://doi.org/10.5194/hess-15-819-2011, https://doi.org/10.5194/hess-15-819-2011, 2011
B. A. Botero and F. Francés
Hydrol. Earth Syst. Sci., 14, 2617–2628, https://doi.org/10.5194/hess-14-2617-2010, https://doi.org/10.5194/hess-14-2617-2010, 2010
L. Mediero, A. Jiménez-Álvarez, and L. Garrote
Hydrol. Earth Syst. Sci., 14, 2495–2505, https://doi.org/10.5194/hess-14-2495-2010, https://doi.org/10.5194/hess-14-2495-2010, 2010
B. Guse, Th. Hofherr, and B. Merz
Hydrol. Earth Syst. Sci., 14, 2465–2478, https://doi.org/10.5194/hess-14-2465-2010, https://doi.org/10.5194/hess-14-2465-2010, 2010
D. Koutsoyiannis
Hydrol. Earth Syst. Sci., 14, 585–601, https://doi.org/10.5194/hess-14-585-2010, https://doi.org/10.5194/hess-14-585-2010, 2010
Cited articles
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
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
Bayat, B., Hosseini, K., Nasseri, M., and Karami, H.: Challenge of rainfall
network design considering spatial versus spatiotemporal variations, J. Hydrol., 574, 990–1002, https://doi.org/10.1016/j.jhydrol.2019.04.091,
2019. a
Bennett, A., Nijssen, B., Ou, G., Clark, M., and Nearing, G.: Quantifying Process Connectivity With Transfer Entropy in Hydrologic Models, Water Resour. Res., 55, 4613–4629,
https://doi.org/10.1029/2018WR024555,
2019. a
Bernardo, J. M.: Expected Information as Expected Utility, Ann. Stat., 7, 686–690, https://doi.org/10.1214/aos/1176344689, 1979. a
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T.: A fast and elitist
multiobjective genetic algorithm: NSGA-II, IEEE T.
Evolut. Computat., 6, 182–197, https://doi.org/10.1109/4235.996017, 2002. a
Fahle, M., Hohenbrink, T. L., Dietrich, O., and Lischeid, G.: Temporal
variability of the optimal monitoring setup assessed using information
theory, Water Resour. Res., 51, 7723–7743, https://doi.org/10.1002/2015WR017137,
2015. a, b
Foroozand, H. and Weijs, S. V.: Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency
in Ensemble Model Simulation, Entropy, 19, 520,
https://doi.org/10.3390/e19100520, 2017. a
Foroozand, H., Radić, V., and Weijs, S. V.: Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures, Entropy, 20, 207, https://doi.org/10.3390/e20030207, 2018. a
Franzen, S. E., Farahani, M. A., and Goodwell, A. E.: Information Flows: Characterizing Precipitation-Streamflow Dependencies in the Colorado Headwaters With an Information Theory Approach, Water Resour. Res., 56, e2019WR026133,
https://doi.org/10.1029/2019WR026133,
2020. a
Goodwell, A. E. and Kumar, P.: Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting
environmental variables, Water Resour. Res., 53, 5920–5942,
https://doi.org/10.1002/2016WR020216,
2017. a, b
Khorshidi, M. S., Nikoo, M. R., Taravatrooy, N., Sadegh, M., Al-Wardy, M., and
Al-Rawas, G. A.: Pressure sensor placement in water distribution networks for
leak detection using a hybrid information-entropy approach, Inform. Sci., 516, 56–71, https://doi.org/10.1016/j.ins.2019.12.043,
2020. a
Kollat, J. B., Reed, P. M., and Kasprzyk, J. R.: A new epsilon-dominance
hierarchical Bayesian optimization algorithm for large multiobjective
monitoring network design problems, Adv. Water Resour., 31,
828–845, https://doi.org/10.1016/j.advwatres.2008.01.017,
2008. a
Konapala, G., Kao, S.-C., and Addor, N.: Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach, Water Resour. Res., 56,
e2020WR027340, https://doi.org/10.1029/2020WR027340,
2020. a
Krstanovic, P. F. and Singh, V. P.: Evaluation of rainfall networks using
entropy: I. Theoretical development, Water Resour. Manag., 6,
279–293, https://doi.org/10.1007/BF00872281, 1992. a
McGill, W. J.: Multivariate information transmission, Psychometrika, 19,
97–116, https://doi.org/10.1007/BF02289159, 1954. a
Mishra, A. K. and Coulibaly, P.: Developments in hydrometric network design: A review, Rev. Geophys., 47, RG2001, https://doi.org/10.1029/2007RG000243,
2009. a, b
Mogheir, Y., Singh, V. P., and de Lima, J. L. M. P.: Spatial assessment and
redesign of a groundwater quality monitoring network using entropy theory, Gaza Strip, Palestine, Hydrogeol. J., 14, 700–712,
https://doi.org/10.1007/s10040-005-0464-3, 2006. a
Nadeau, D. F., Brutsaert, W., Parlange, M. B., Bou-Zeid, E., Barrenetxea, G.,
Couach, O., Boldi, M.-O., Selker, J. S., and Vetterli, M.: Estimation of
urban sensible heat flux using a dense wireless network of observations,
Environ. Fluid Mech., 9, 635–653, https://doi.org/10.1007/s10652-009-9150-7, 2009. a
Neumann, J. v. and Morgenstern, O.: Theory of Games and Economic Behavior, Princeton University Press, Princeton, New Jersey, United States, 1953. a
Raso, L., Weijs, S. V., and Werner, M.: Balancing Costs and Benefits in Selecting New Information: Efficient Monitoring Using Deterministic Hydro-economic Models, Water Resour. Manag., 32,
339–357, https://doi.org/10.1007/s11269-017-1813-4, 2018. a
Ridolfi, E., Montesarchio, V., Russo, F., and Napolitano, F.: An entropy approach for evaluating the maximum information content achievable by an urban rainfall network, Nat. Hazards Earth Syst. Sci., 11, 2075–2083, https://doi.org/10.5194/nhess-11-2075-2011, 2011. a, b, c
Shannon, C. E.: A mathematical theory of communication, Bell Syst. Tech. J., 27, 379–423, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x, 1948. a, b
Simoni, S., Padoan, S., Nadeau, D. F., Diebold, M., Porporato, A., Barrenetxea, G., Ingelrest, F., Vetterli, M., and Parlange, M. B.: Hydrologic response of
an alpine watershed: Application of a meteorological wireless sensor
network to understand streamflow generation, Water Resour. Res., 47, W10524, https://doi.org/10.1029/2011WR010730,
2011. a, b
Watanabe, S.: Information Theoretical Analysis of Multivariate Correlation, IBM J. Res. Dev., 4, 66–82,
https://doi.org/10.1147/rd.41.0066, 1960. a
Weijs, S. and Foroozand, H.: hydroinfotheory/monitoring_network_objectives: v1.0.0 initial release with paper acceptance (Version v1.0.0), Zenodo, https://doi.org/10.5281/zenodo.4323537, 2020. a
Weijs, S. V., van de Giesen, N., and Parlange, M. B.: Data compression to define information content of hydrological time series, Hydrol. Earth Syst. Sci., 17, 3171–3187, https://doi.org/10.5194/hess-17-3171-2013, 2013a. a, b
Weijs, S. V., Van de Giesen, N., and Parlange, M. B.: HydroZIP: How Hydrological Knowledge can Be Used to Improve Compression of Hydrological Data, Entropy, 15, 1289–1310, https://doi.org/10.3390/e15041289, 2013b. a
Weijs, S. V., Foroozand, H., and Kumar, A.: Dependency and Redundancy: How Information Theory Untangles Three Variable Interactions in Environmental Data, Water Resour. Res., 54, 7143–7148,
https://doi.org/10.1029/2018WR022649,
2018. a
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.
In monitoring network design, we have to decide what to measure, where to measure, and when to...