Augmentation of groundwater monitoring networks using information theory and ensemble modeling with pedotransfer functions

Yakirevich, A.; Pachepsky, Yakov; Gish, T. J.; Guber, Andrey; Kuznetsov, M.; Cady, R. E.; Nicholson, Tom

doi:10.1016/j.jhydrol.2013.07.032

Cited by 17 publications

(16 citation statements)

References 33 publications

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“…In environmental science, information-theoretic concepts such as the "Shannon entropy" have found widespread use in various applications (e.g. Brunsell, 2010;Weijs et al, 2013a;Yakirevich et al, 2013), ranging from uncertainty assessment in 3-D geological models (Schweizer et al, 2017) to the delineation of water 135 resource zones in Japan (Kawachi et al, 2001). For an introduction to, and detailed review of, information theoretic concepts we refer the reader to Cover and Thomas (2005), Singh (2013), and Weijs and van de Giesen (2013).…”

Section: The Role Of Surface Topography In Hydrological Modelingmentioning

confidence: 99%

On the dynamic nature of hydrological similarity

Loritz¹,

Gupta²,

Jackisch³

et al. 2018

Preprint

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Abstract:The increasing diversity and resolution of spatially distributed data on terrestrial systems greatly enhances the potential of hydrological modeling. Optimal and parsimonious use of these data sources implies, however, that we better understand a) which system characteristics exert primary controls 15 on hydrological dynamics and b) to what level of detail do those characteristics need to be represented in a model.In this study we develop and test an approach to explore these questions that draws upon information theoretic and thermodynamic reasoning, using spatially distributed topographic information as a straightforward example. Specifically, we subdivide a meso-scale catchment into 105 hillslopes and 20 represent each by a two dimensional numerical hillslope model. These hillslope models differ exclusively with respect to topography related parameters derived from a digital elevation model; the remaining setup and meteorological forcing for each are identical. We analyze the degree of similarity of simulated discharge and storage among the hillslopes as a function of time by examining the Shannon information entropy. We furthermore derive a 'compressed' catchment model by clustering the hillslope models into 25 functional groups of similar runoff generation using normalized mutual information as a distance measure.Our results reveal that, within our given model environment, only a portion of the entire amount of topographic information stored within a digital elevation model is relevant for the simulation of 2 group', which leads to no substantial loss in model performance. Importantly, we find that the concept of hydrological similarity is not necessarily time-invariant. On the contrary, the Shannon entropy as measure for diversity in the simulation ensemble shows a distinct annual pattern, with periods of highly redundant simulations, reflecting coherent and organized dynamics, and periods where hillslopes operate in distinctly different ways. 35We conclude that the proposed approach provides a powerful framework for understanding and diagnosing how and when process organization and functional similarity of hydrological systems emerges in time. Our approach is neither restricted to the model, nor to model targets or the data source we selected in this study. Overall, we propose that the concepts of hydrological systems acting similarly (and thus giving rise to redundancy) or displaying unique functionality (and thus being irreplaceable) are not 40 mutually exclusive. They are in fact of complementary nature, and systems operate by gradually changing to different levels of organization in time.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Section: The Role Of Surface Topography In Hydrological Modelingmentioning

confidence: 99%

On the dynamic nature of hydrological similarity

Loritz¹,

Gupta²,

Jackisch³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…It is instead ideal for an optimal monitoring network to be designed to allow for the best placement of monitoring stations and to determine the ideal measurement frequencies. The merit of using information theory entropy has been shown in several cases of groundwater network design [31][32][33]38,44,48,53,58].…”

Section: Soil Moisture and Groundwater Networkmentioning

confidence: 99%

“…These include the use of entropy measures in both single and multi-objective optimization problems and are used in network reduction [32,33], expansion [38,58] and redesign [44], as well as have been used to highlight vulnerable areas in an area that should be monitored [53]. In identifying vulnerable areas in the Victoria County Groundwater Conservation District (VCGCD) in Texas, USA, Uddameri and Andruss [53] developed a monitoring priority index (MPI) based on a weighted stakeholder preference to highlight the areas of interest.…”

Section: Soil Moisture and Groundwater Networkmentioning

confidence: 99%

“…It may also be the case that the existing groundwater monitoring network is not adequate and additional monitoring stations are needed. Yakirevich et al [58] developed a method that utilizes minimum cross entropy (MCE) to sequentially add monitoring stations to a network. MCE was used as a metric to quantify the difference between two variants of a Hydrus-3D model [80], and the monitoring stations were added to the network where the difference between models was largest.…”

Section: Soil Moisture and Groundwater Networkmentioning

confidence: 99%

See 1 more Smart Citation

Entropy Applications to Water Monitoring Network Design: A Review

Keum

Kornelsen

Leach

et al. 2017

Entropy

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Abstract:Having reliable water monitoring networks is an essential component of water resources and environmental management. A standardized process for the design of water monitoring networks does not exist with the exception of the World Meteorological Organization (WMO) general guidelines about the minimum network density. While one of the major challenges in the design of optimal hydrometric networks has been establishing design objectives, information theory has been successfully adopted to network design problems by providing measures of the information content that can be deliverable from a station or a network. This review firstly summarizes the common entropy terms that have been used in water monitoring network designs. Then, this paper deals with the recent applications of the entropy concept for water monitoring network designs, which are categorized into (1) precipitation; (2) streamflow and water level; (3) water quality; and (4) soil moisture and groundwater networks. The integrated design method for multivariate monitoring networks is also covered. Despite several issues, entropy theory has been well suited to water monitoring network design. However, further work is still required to provide design standards and guidelines for operational use.

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“…A crucial step of the Bayesian theory is the sampling algorithm. There are several commonly used sampling algorithms, such as the Metropolis-Hastings algorithm, the Gibbs algorithm and the adaptive Metropolis algorithm [30][31][32][33][34][35][36][37][38][39][40]. Bayesian theory can obtain the posterior probability density function of hydrogeological elements, but the premise is that the distribution of hydrogeological parameters is known a priori.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Flow Determination Using an Interval Parameter Perturbation Method

Dong

Tian

Zhan

et al. 2017

Water

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Abstract:Groundwater flow simulation often inevitably involves uncertainty, which has been quantified by a host of methods including stochastic methods and statistical methods. Stochastic methods and statistical methods face great difficulties in applications. One of such difficulties is that the statistical characteristics of random variables (such as mean, variance, covariance, etc.) must be firstly obtained before the stochastic methods can be applied. The dilemma is that one is often unclear about such statistical characteristics, given the limited available data.To overcome the problems met by stochastic methods, this study provides an innovative approach in which the hydrogeological parameters and sources and sinks of groundwater flow are represented by bounded but uncertain intervals of variables called interval of uncertainty variables (IUVs) and this approach is namely the interval uncertain method (IUM). IUM requires only the maximum and minimum values of the variable. By utilizing the natural interval expansion, an interval-based parametric groundwater flow equation is established, and the solution of that equation can be found. Using a hypothetical steady-state flow case as an example, one can see that when the rate of change is less than 0.2, the relative error of this method is generally limited to less than 5%; when the rate of change is less than 0.3, the relative error of this method can be kept within 10%. This research shows that the proposed method has smaller relative errors and higher computational efficiency than the Monte Carlo methods. It is possible to use this method to analyze the uncertainties of groundwater flow when it is difficult to obtain the statistical characteristics of the hydrogeological systems. The proposed method is applicable in linear groundwater flow system. Its validity in nonlinear flow systems such as variably saturated flow or unconfined flow with considerable variation of water table will be checked in the future.

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Augmentation of groundwater monitoring networks using information theory and ensemble modeling with pedotransfer functions

Cited by 17 publications

References 33 publications

On the dynamic nature of hydrological similarity

On the dynamic nature of hydrological similarity

Entropy Applications to Water Monitoring Network Design: A Review

Groundwater Flow Determination Using an Interval Parameter Perturbation Method

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