Optimal Extension of Rain Gauge Monitoring Network for Rainfall Intensity and Erosivity Index Interpolation

Chebbi, A.; Bargaoui, Zoubeïda; Cunha, Maria da Conceição

doi:10.1061/(asce)he.1943-5584.0000353

Cited by 34 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A few studies also combined optimization method based on simulation tools (e.g. simulated annealing) with the kriging technique [2,8,9] to obtain the optimal rain gauge network.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Rain Gauge Network in Johor by Using Geostatistics and Particle Swarm Optimization

Aziz

Yusof²,

Daud³

et al. 2016

Geomate

View full text Add to dashboard Cite

This study proposes particle swarm optimization (PSO) approach to determine the optimal number and locations for the optimal rain gauge network in Johor state. The existing network of 84 rain gauges in Johor is also restructured into new locations by using daily rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November -February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and particle swarm optimization as the algorithm of optimization during the restructured proses. The numerical result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the proposed method can serve as an analysis tool for a decision making to assist hydrologist in the selection of prime sites for the installation of rain gauge stations.

show abstract

“…A few studies also combined optimization method based on simulation tools (e.g. simulated annealing) with the kriging technique [2,8,9] to obtain the optimal rain gauge network.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Rain Gauge Network in Johor by Using Geostatistics and Particle Swarm Optimization

Aziz

Yusof²,

Daud³

et al. 2016

Geomate

View full text Add to dashboard Cite

show abstract

“…Four different objectives are usually considered with regard to optimal rain gauge network design and assessment by the kriging‐based geostatistical approach: Expanding the existing rain gauge network with additional stations to achieve appropriate network density for the reduction of estimation uncertainty (Loof et al, ; Papamichail and Metaxa, ; Tsintikidis et al, ; Barca et al, ; Chebbi et al, ). Identifying and establishing the optimal location of additional rain gauge stations in the network to improve the estimation accuracy (Pardo‐Igúzquiza, ; Chen et al, ). Prioritizing the rain gauges with respect to their contribution in error reduction in the network (Kassim and Kottegoda, ; Cheng et al, ; Yeh et al, ). Choosing an optimal subset of stations from an existing dense rain gauge network to achieve optimum rainfall information (Shaghaghian and Abedini, ).…”

Section: Introductionmentioning

confidence: 99%

Optimal design of rain gauge network in the Middle Yarra River catchment, Australia

Adhikary

Yilmaz

Muttil

2014

Hydrological Processes

View full text Add to dashboard Cite

Abstract:Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A welldesigned rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging-based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment.

show abstract

“…Rationalization based on multivariate analyses was used to eliminate rain-gauge redundancy for the optimum rain-gauge network (Burn and Goulter 1991). Geostatistical frameworks have been utilized for optimal distribution of the rain-gauge monitoring network, because these can produce unbiased estimators with minimum error variance (Tsintikidis et al 2002;Barca et al 2008;Chen et al 2008;Cheng et al 2008;Chebbi et al 2011). In the geostatistical applications, Bastin et al (1984) and Kassim and Kottegoda (1991) used the iterative manner to select rain-gauge locations associated with minimum kriging variance.…”

Section: Introductionmentioning

confidence: 99%

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

Jung¹,

Kim²,

Baik³

et al. 2014

Terr. Atmos. Ocean. Sci.

View full text Add to dashboard Cite

A reliable network of rain gauges is a crucial component of rainfall estimation in a watershed. To provide a better evaluation method for rain-gauge networks, a new evaluation method using average inter-gauge correlation coefficients (averaged CC) for estimating an effective radius for each rain gauge was developed. In this study, averaged CCs were obtained from the values of inter-gauge correlation coefficients after choosing a minimum number of rainfall data sets as a threshold. The Nam River Basin (2400 km 2 ) and its 24 rain gauges were selected with 8 years (2003 -2010) rainfall data to validate a new evaluation method. In the spatial correlation coefficient fitting process for generating correlation distances, averaged CCs increased fitness accuracy (maximum 37%) in terms of coefficient of determination (R 2 ) compared with a commonly used method (the last value of the inter-gauge correlation coefficient as the number of data sets is increased: last CC). In the evaluation of effective radii for 8 years, the robustness of the averaged CCs was supported by lower standard deviations for all rain gauges. For the optimum coverage of rainfall estimation in terms of effective radius, the Nam River Basin requires 20 rain gauges. Investigation of altitude effects presented that the effective radii were minimally influenced by the altitude of rain-gauge locations for this area.

show abstract

Optimal Extension of Rain Gauge Monitoring Network for Rainfall Intensity and Erosivity Index Interpolation

Cited by 34 publications

References 30 publications

Optimal Design of Rain Gauge Network in Johor by Using Geostatistics and Particle Swarm Optimization

Optimal Design of Rain Gauge Network in Johor by Using Geostatistics and Particle Swarm Optimization

Optimal design of rain gauge network in the Middle Yarra River catchment, Australia

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

Contact Info

Product

Resources

About