An algorithm for optimisation of a rain gauge network based on geostatistics and entropy concepts using GIS

Mahmoudi-Meimand, Hadi; Nazif, Sara; Abbaspour, Rahim Ali; Sabokbar, H A Faraji

doi:10.1080/14498596.2015.1030789

Cited by 19 publications

(10 citation statements)

References 12 publications

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“…The screening for potential stations involves the selection of areas within the study area as potential site layout areas. Previous rain-measuring station network layout designs primarily divided the whole research area according to a certain grid and then considered all grids as potential candidate stations directly [22,43], or manually selected individual grids as potential points according to the kriging interpolation accuracy (kriging variance) and other constraints [32,73]. The former requires considerable redundant calculations, especially for large study areas, and data with high spatial and temporal resolution; thus, this method is often used with radar data [43].…”

Section: Filtering Potential Stationsmentioning

confidence: 99%

A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

et al. 2020

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A well-designed rain gauge network can provide precise and detailed rainfall data for earth science research; meanwhile, satellite precipitation data has been developed to generate more real spatial features, which provides new data support for the improvement of ground station network design methods. In this paper, satellite precipitation data are introduced into the design of a rain gauge network and an optimized method for designing a rain gauge network that comprehensively considers the information content, spatiotemporality, and accuracy (ISA) of the data is proposed. After screening the potential stations, the average spatial information index of the rain gauge network, which is calculated from remote sensing data, is used to address the shortcomings of applying spatial information from single-use measurement data. Then, the greedy ranking algorithm is used to rank the order in which the rain gauges are added to the network. The results of the rain gauge network design in the upper reaches of the Chaobai river show that compared with two methods that do not consider spatiality or use only measured data to consider spatiality, the proposed method performs better in terms of the spatial layout and accuracy verification. This study provides new ideas and references for the design of hydrological station networks and explores the use of remote sensing data for the layout of ground-based station networks.

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Section: Filtering Potential Stationsmentioning

confidence: 99%

A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

et al. 2020

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“…Many of these approaches are initialized by building a network around a central station usually selected as the station with the highest marginal entropy [43,[62][63][64]. In urban Rome, Ridolfi et al [62] selected stations for the precipitation network by sequentially finding the next station that minimized the conditional entropy of the network and adding that station to the network.…”

Section: Precipitation Networkmentioning

confidence: 99%

“…Mahmoudi-Meimand et al [43] presented a methodology to add stations to a network based on a multi-variate cost function. Precipitation data were spatially interpolated from existing stations using the kriging approach where the kriging error associated with the rainfall estimation is calculated as the kriging error variance.…”

Section: Precipitation Networkmentioning

confidence: 99%

“…Precipitation data were spatially interpolated from existing stations using the kriging approach where the kriging error associated with the rainfall estimation is calculated as the kriging error variance. Their method selected the station that maximized transinformation entropy and minimized error variance using a weighted average of both measures as an objective [43]. This approach balanced the information content in the network with the errors in the interpolation method.…”

Section: Precipitation Networkmentioning

confidence: 99%

“…For precipitation, this can be challenging because data at shorter time scales in particular are well known to be non-normal. Most studies use the kriging approach for interpolation [43,63,64] and address the need for normally distributed data using a Box-Cox transform. Samuel and Coulibaly [67] addressed the interpolation problem by using the external data from the Snow Data Assimilation System (SNODAS) for candidate stations.…”

Section: Precipitation Networkmentioning

confidence: 99%

See 2 more Smart Citations

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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Application of information fusion techniques and satellite products in the optimal redesign of rain gauge networks

Behboudian

Kerachian

Hosseini

2021

Stoch Environ Res Risk Assess

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An algorithm for optimisation of a rain gauge network based on geostatistics and entropy concepts using GIS

Cited by 19 publications

References 12 publications

A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

Entropy Applications to Water Monitoring Network Design: A Review

Application of information fusion techniques and satellite products in the optimal redesign of rain gauge networks

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