The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8
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Published:2018-06-13
Issue:6
Volume:11
Page:2189-2207
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Bancheri Marialaura, Serafin Francesco, Bottazzi Michele, Abera WuletawuORCID, Formetta Giuseppe, Rigon Riccardo
Abstract
Abstract. This work presents a software package for the interpolation of climatological variables, such as temperature and precipitation, using kriging techniques. The purposes of the paper are (1) to present a geostatistical software that is easy to use and easy to plug in to a hydrological model; (2) to provide a practical example of an accurately designed software from the perspective of reproducible research; and (3) to demonstrate the goodness of the results of the software and so have a reliable alternative to other, more traditional tools. A total of 11 types of theoretical semivariograms and four types of kriging were implemented and gathered into Object Modeling System-compliant components. The package provides real-time optimization for semivariogram and kriging parameters. The software was tested using a year's worth of hourly temperature readings and a rain storm event (11 h) recorded in 2008 and retrieved from 97 meteorological stations in the Isarco River basin, Italy. For both the variables, good interpolation results were obtained and then compared to the results from the R package gstat.
Publisher
Copernicus GmbH
Reference72 articles.
1. Abera, W., Formetta, G., Borga, M., and Rigon, R.: Estimating the water budget components and their variability in a pre-alpine basin with JGrass-NewAGE, Adv. Water Resour., 104, 37–54, 2017. 2. Adams, B. M., Bohnhoff, W., Dalbey, K., Eddy, J., Eldred, M., Gay, D., Haskell, K., Hough, P. D., and Swiler, L. P.: Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 5.0 user's manual, Sandia National Laboratories, Tech. Rep. SAND2010-2183, 2009. 3. Adhikary, S. K., Muttil, N., and Yilmaz, A. G.: Genetic programming-based ordinary kriging for spatial interpolation of rainfall, J. Hydrol. Eng., 21, 04015062, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001300, 2015. 4. Aidoo, E. N., Mueller, U., Goovaerts, P., and Hyndes, G. A.: Evaluation of geostatistical estimators and their applicability to characterise the spatial patterns of recreational fishing catch rates, Fish. Res., 168, 20–32, 2015. 5. Argent, R. M.: An overview of model integration for environmental applications–components, frameworks and semantics, Environ. Modell. Softw., 19, 219–234, 2004.
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