The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8

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.

The GEOframe-NewAge Modelling System Applied in a Data Scarce Environment

“…Each part of the hydrological cycle is implemented as a component, i.e., a self-contained building block, module, or unit of code (e.g., [23,24]). Components can be joined together to obtain a modeling solution that can accomplish a complicated task (e.g., [25,26]). GEOframe-NewAge was chosen for its flexibility, its solid informatic structure, and its good performance in several applications (e.g., [14,15,[26][27][28][29][30][31]).…”

“…More than 50 components are available, and they can be grouped into nine categories, as follows: The basin can be discretized into Hydrological Response Units (HRUs), i.e., hydrologically similar parts, such as a catchment or a hillslope or one of its parts, using the components for geomorphic and DEM analyses introduced in [32] and reviewed in [33]. The meteorological forcing data in the centroid of each HRU can be spatially interpolated using a geostatistical approach, such as the Kriging technique [26,34]. The radiation budget includes both shortwave and longwave radiation components [25,29].…”

“…Figure 4 shows the workflow of the components used and their connections for each HRU centroid. The modeling solution adopted in this work allows for the simulation of the entire hydrological budget: from the spatialization of the temperature data (SIK-K component [26]) to the runoff production (Embedded reservoirs component).…”

“…The meteorological input forcings are spatially interpolated using Kriging techniques (the SIK-K component). Its parameters were obtained following the procedure reported in [26]. First, the semivariance was analyzed, and experimental semivariograms were fitted using the best theoretical models of the 10 available ones.…”

Geostatistical Methods in Water Distribution Network Design - A Case Study

“…in which values z(x i +h), z(x i ) correspond to the values of water supply pressure in points x i and x i +h separated from each other by the distance h; n h number of pairs (x i , x i +h) values of water supply pressure in points separated by the distance h, used in calculation of the semivariogram γ * (h) function. Equation 2and the zero load condition (3) create together the equation system referred to as the ordinary kriging [11][12][13]…”