Spatial Significant Wave Height Variation Assessment and Its Estimation

Altunkaynak, Abdüsselam; Özger, Mehmet

doi:10.1061/(asce)0733-950x(2005)131:6(277)

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…The stations are located at depths ranging from 47 m to 4599 m. In the study area, while larger significant wave heights appear in the winter season, relatively smaller waves occur in the summer season. The annual spatial average significant wave height at the study area is 2.31 m. Previously, Altunkaynak (2005) and Altunkaynak and Ozger (2005) applied the TPCSV and PCSV techniques do determine significant wave height using data from the same stations. The same data were used in the method developed in this study, i.e., SPCSV.…”

Section: Data Used and Analysismentioning

confidence: 99%

“…Sen and Habib (1998) developed the standard areal dependency approach to make point and spatial estimations of a variable. Later, Altunkaynak and Ozger (2005) used PCSV to predict significant wave height. In the PCSV approach, it is assumed that the highest correlation exists between the pivot station and the closest station to the pivot station, and the lowest correlation exists between the pivot station and the farthest station from the pivot station.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of significant wave height using spatial function

Altunkaynak

2015

Ocean Engineering

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Section: Data Used and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of significant wave height using spatial function

Altunkaynak

2015

Ocean Engineering

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“…Although Kriging is one of the most developed regional prediction method, it fails to define the influence radius which delineate the borders of the contribution areas. Kriging method has been applied to mining (Matius et al, 2004), tunnel (Öztürk and Nasuf, 2002), hydrology (Altunkaynak et al, 2003;Ş en and Habib, 1998), hydraulics and ocean engineering (Altunkaynak, 2005;Altunkaynak andÖzger, 2005). This approach is also often used in geostatistics to determine the parameters of regional variability.…”

Section: Introductionmentioning

confidence: 99%

Estimation of streamflow by slope regional dependency function

Altunkaynak

2008

Hydrol. Earth Syst. Sci.

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Abstract. Kriging is one of the most developed methodologies in the regional variable modeling. However, one of its drawbacks is that the influence radius can not be determined by this method. In which distance and in what ratio that pivot station is influenced from adjacent sites is rather often encountered problem in practical applications. Regional weighting functions obtained from available data consist of several broken lines. Each line has different slopes which represent the similarity and the contribution of adjacent stations as a weighting coefficient. The approach in this study is called as Slope Regional Dependency Function (SRDF). The main idea of this approach is to express the variability in value differences γ and distances together. Originally proposed SRDF and Trigonometric Point Cumulative SemiVariogram (TPCSV) methods are used to predict streamflow. TPCSV and Point Cumulative Semi-Variogram (PCSV) approaches are also compared with each other. Prediction performance of all the three methods revealed a relative error less than 10% which is acceptable for most engineering applications. It is shown that SRDF outperforms PCSV and TPCSV with very high differences. It can be used for missing data completion, determination of measurement sites location, calculation of influence radius, and determination of regional variable potential. The proposed method is applied for the 38 stream flow measurement sites located in the Mississippi River basin.

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Streamflow estimation using optimal regional dependency function

Altunkaynak

2009

Hydrological Processes

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Abstract:The determination of spatial dependency of regionalized variable (ReV) is important in engineering studies. Regional dependency function that leads to calculation of weighting coefficients is required in order to make regional or point-wise estimations. After obtaining this dependency function, it is possible to complete missing records in the time series and locate new measurement station. Also determination of regional dependency function is also useful to understand the regional variation of ReV. Point Cumulative Semi-Variogram (PCSV) is another methodology to understand the regional dependency of ReV related to the magnitude and the location. However, this methodology is not useful to determine the weighting coefficient, which is required to make regional and point-wise estimations. However, in Point Semi-Variogram (PSV) proposed here, weighting coefficient depends on both magnitude and location. Although the regional dependency function has a fluctuating structure in PSV approach, this function gradually increases with distance in PCSV. The study area is selected in Mississippi river basin with 38 streamflow stations used for PCSV application before. It is aimed to compare two different geostatistical models for the same data set. PSV method has an ability to determine the value of variable along with optimum number of neighbour stations and influence radius. PSV and slope PSV approaches are compared with the PCSV. It was shown that slope slope point semi-variogram (SPSV) approaches had relative error below 5%, and PSV and PCSV methods revealed relative errors below 10%.

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Spatial Significant Wave Height Variation Assessment and Its Estimation

Cited by 10 publications

References 17 publications

Prediction of significant wave height using spatial function

Prediction of significant wave height using spatial function

Estimation of streamflow by slope regional dependency function

Streamflow estimation using optimal regional dependency function

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