The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTMi,n) and relevant scientific/applicable references (published papers—PPi,n) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB). This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering. The ontology soil/water concepts (semantics) of the developed network correspond to soil/water physicochemical & biological properties, classified in seven different generations that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media. The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and independent variables. All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator “ depends on” quoting existent explicit functions and equations; 2) dependent and independent variables interconnected by using the logical operator “ depends on” quoting produced implicit functions, according to Rayleigh’s method of indices; 3) dependent and independent variables interconnected by using the logical operator “ related to” based on a logical dependence among the examined nodes-concepts-variables. The aforementioned approach provides significant advantages to semantic web developers and web users by means of prompt knowledge navigation, tracking, retrieval and usage.
A Knowledge Base (KB) in knowledge engineering is a commonly accepted information structure over a discipline that combined with artificial intelligence and expert systems, where information can be easily retrieved in a rapid way and deployed in numerous applications, outlining the relationship with the software engineering, information integration and knowledge management (Studer et al., 1998). KBs form ontological mappings (Ontologies) by employing concepts (semantics) and rigid internal relationships amid its network corpus. Clearly defined concepts (conceptualization—explicitly or implicitly), shared (controlled) vocabulary and leveled up/down generations/classes (taxonomies) have to be built up inconsequential hierarchies (Genesereth & Nilsson, 1987; Gruber, 1995; Guarino, 1995; Uschold & Grüninger, 1996; Borst, 1997; Roche, 2003) to support such a structure.
Ontology-Based Knowledge Networks gradually are being applied to a vast range of disciplines, such as in soil science, by describing soil properties, processing and their interaction (Du et al., 2016; Heeptaisong & Srivihok, 2010). Ontologies as a formal description of knowledge set, with suitably placed concepts within a domain strictly bound by well-defined relationships are applied in artificial intelligence in order to provide to all users an interaction framework with various application systems i.e. communication models between (KB) users and machines (Weng & Chang, 2008).
In soil science, pollutant’s fate via infiltration/percolation is considered to be a multi-stage processing and undoubtedly a major concern in industrial ecology. Precipitated water runoffs could arise contamination problems on account of their penetration from humic topsoil to lower surface layers. Mikkelsen et al. (1997) presented a case of heavy traffic roads. Soil crust rehabilitation in the aftermath of a pollution incident, could be remarkably aided by already building up ontological structures dedicated to infiltration phenomena applied to various accidental cases (Du & Cohn, 2016).
Infiltration is approached as a complex both biological & physicochemical process during which an aquatic solution (potentially contaminated with insoluble/dispersed particles or micelles), penetrates the ground under the gravity force and/or the capillary action. The partial sub-processes which are taking place until the infiltration water reaches the groundwater table are, inter alia, common filtration, chemical reaction (depending on the layers of sedimentary rock/soil the water is passing through), biochemical conversion, sedimentation, coagulation, flocculation (Lassabatere et al., 2010).
In general, the infiltration rate depends mainly on 1) ground surface loading with (waste) water; 2) the soil porosity; and 3) the vegetation coverage. Αn introspection reveals interdependences of numerous parameters among others, type, bulk density and texture of the soil, canopy coverage and topsoil biomass production (Wang et al., 2017; Patle et al., 2018; Wood et al., 1987; Tejedor et al., 2013). The given physicochemical parameters are of variant importance/gravity in terms of inducing the evolution of the ongoing phenomenon.
In the present paper, an Ontology-Based Knowledge Network is developed of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM standards and published scientific references in order to describe the infiltration/percolation process of contaminated water. The developed/proposed Ontology-Based Knowledge Network can be adopted as a tool for the semantic representation of infiltration/percolation process of contamination water through soil structure and porous media.
In this section, a comprehensive Ontology-Based Knowledge Network design and construction is described/summarized by the below presented steps.
1) Determination of the initial ontology concept.
2) Determination and proper selection of other sub-concepts, which could be fitted in the ontology network, and correspond to soil/water physicochemical & biological properties.
3) Identification of soil/water physicochemical & biological properties through an extensive research of ASTM Standards and scientific published references.
4) Documentation of three possible ways that could justify the interconnections between all the ontology sub-concepts of our interest and relevant soil/water physicochemical & biological properties.
As regards the first step, the “infiltration rate” of contaminated water in the soil (porous medium), it was established as the initial conceptual property of our ontology network. Interconnection of the afore-mentioned properties is achieved by adopting a framework of concepts interrelated with soil/water properties and partial sub-processes which are taking place during the process.
The infiltration rate could be measured and monitored by well-established techniques fully described by standard methods and practices recommended by a widely recognized standardization organization such as the American Society for Testing & Material (ASTM) and relative scientific published papers. This is indispensable for obtaining results comparable with the ones obtained in similar experimental models, under similar conditions, since some of these observations are obtained with more precise measurements that are described through standardized methods i.e. ASTM standards and scientific published papers.
According to the structure of the network, all the nodes/concepts/physicochemical and biological properties are interconnected by using the principles set by dependent and independent variables. This set of dependent and independent variables justified and documented by three possible ways, as they are describing below:
1) Dependent and independent variables interconnected by using the logical operator “depends on” quoting existent explicit functions and equations;
2) Dependent and independent variables interconnected by using the logical operator “depends on” quoting produced implicit functions, by implementing Rayleigh’s method of indices;
3) Dependent and independent variables interconnected by using the logical operator “related to” based on a logical dependence between the examined nodes-concepts-variables.
The last Rayleigh’s method of indices is based on the fundamental principle of dimensional homogeneity of physical variables involved in this problem. The dependent variable is identified and expressed as a product of all the independent variables raised to an unknown integer exponent. Equating the indices of n fundamental dimensions of the variables involved, n independent equations are obtained. Finally, these n equations are solved to obtain the dimensionless groups.
Under the form of the directed/developed ontology network, shown in FigureA2 of the Appendix, it can be easily supported by a computer program through several ready-to-use packages already available in the market. All these nodes/concepts/properties are represented by ASTM standards and scientific published papers in an integrated Ontology-Based Knowledge Network, FigureA1 of the Appendix, a part of which is shown in Figure1 of the current page.
Each node of ASTM standard and published paper has a set of references to other ASTM standards or/and published papers. All the nodes of scientific published papers and ASTM standards are symbolized as PPi,n and ASTMi,n respectively, where i denotes the generation number, and n denotes the member number of each generation. Starting from a certain ASTM standard, related with the topic under consideration (e.g. “Infiltration Rate Determination Using Double-Ring Infiltrometer with Sealed-Inner Ring”, according to D5093-15 ASTM Standard Test Method, in the case of the present work), we may represent it as well as its references with points or vertices or nodes interconnected with arcs directed from the initial standard to its references. These references are all members of the first generation with the initial standard as unique parent. Each member of the first generation has other referenced ASTM standards or/and published papers of its own, creating a second generation, and so forth until the seventh generation is achieved in our case. Evidently, the initial standard as well as any
member of the i generation may be a member of one or more generations i.e. a member of the i (i = 0, 1, 2, …) generation may also appear as a member of the j generation, provided that j ≥ i + 2.
Values, indicating dependence of applying a standard or/and published paper (especially of carrying out a standard test) on the previous application of another standard or/and published paper of the next generation, are assigned to each arc, so that a network is obtained from the directed multi-graph. These values vary from zero, indicating no dependence whatsoever, up to one, indicating full dependence (e.g.
In the application example of current page, the selected initial “Standard Test Method for Field Measurement of Infiltration Rate Using Double-Ring Infiltrometer with Sealed-Inner Ring”, under the code number ASTM D5093-15 (2015), has four arcs leading to corresponding referenced scientific published papers (PP1,1 & PP1,2) and ASTM standards (ASTM1,3 & ASTM1,4) as shown in Figures 1-3. The same procedure continues to the next generation and so forth until the seventh generation is formed. The integrated Ontology-Based Knowledge Network with all its ASTM standards and scientific published papers is presented in FigureA1 of the Appendix. However, a partial network that includes all the nodes of the first three generations is presented in Figure1.
The dependence indices are considered as Boolean parameters, obtaining the values one or zero (meaning “referenced” or “not referenced”, respectively) for sake of simplicity.
The interconnections between all nodes/concepts which represents soil/water physicochemical and biological properties and sub-processes of the integrated
| ASTM D5093 | PP1,1 | PP1,2 | ASTM1,3 | ASTM1,4 | |
|---|---|---|---|---|---|
| ASTM D5093 | 0 | 1 | 1 | 1 | 1 |
| PP1,1 | 0 | 0 | 0 | 0 | 0 |
| PP1,2 | 0 | 0 | 0 | 0 | 0 |
| ASTM1,3 | 0 | 0 | 0 | 0 | 0 |
| ASTM1,4 | 0 | 0 | 0 | 0 | 0 |
| PP1,1 | PP1,2 | ASTM1,3 | ASTM1,4 | ASTM2,1 | ASTM2,2 | ASTM2,3 | PP2,4 | |
|---|---|---|---|---|---|---|---|---|
| PP1,1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| PP1,2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| ASTM1,3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| ASTM1,4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| ASTM2,1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ASTM2,2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ASTM2,3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| PP2,4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Ontology-Based Knowledge Network of FigureA2 of the Appendix, are presented and depicted with arcs. Each single arc consists of a starting point and a final point edge. In order to find and justify the correlations between all the nodes/concepts of FigureA1 & FigureA2 of the Appendix, the selected concepts are corresponding to dependent and independent physical variables. According to the structure of the network, a dependent physical variable is the starting point of an arc that ends up (final pointed arc edge), to an independent variable. All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator “depends on” quoting existent explicit functions and equations, 2) dependent and independent variables interconnected by using the logical operator “depends on” quoting produced implicit functions, according to Rayleigh’s method of indices, presented in Tables A1-A7 of the Appendix 3) dependent and independent variables interconnected by using the logical operator “related to” based on a logical dependence between the examined nodes-concepts-variables. All the above-described methods are detailed presented in the “contribution” column of Tables A1-A7 in the Appendix along with a Terminology Tablein (SI units) of TableA8.
PP1,1: Lassabatere et al. (2010), Effect of the settlement of sediments on water infiltration in two urban infiltration basins, Geoderma, 156(3-4), 316-325.
PP1,2: Assouline (2013), Infiltration into soils: Conceptual approaches and solutions, Water resources research, 49(4), 1755-1772.
ASTM1,3: ASTM C1585-20 (2020), Standard Test Method for Measurement of Rate of Absorption of Water by Hydraulic-Cement Concretes.
ASTM1,4: ASTM D3385-18 (2018), Standard Test Method for Infiltration Rate of Soils in Field Using Double-Ring Infiltrometer.
ASTM2,1: ASTM D3385-18 (2018), Standard Test Method for Infiltration Rate of Soils in Field Using Double-Ring Infiltrometer.
ASTM2,2: ASTM D5126-16e1 (2016), Standard Guide for Comparison of Field Methods for Determining Hydraulic Conductivity in Vadose Zone.
ASTM2,3: ASTM C1792-14 (2014), Standard Test Method for Measurement of Mass Loss versus Time for One-Dimensional Drying of Saturated Concretes
PP2,4: Di Prima et al. (2016), Testing a new automated single ring infiltrometer for Beerkan infiltration experiments, Geoderma, 262, 20-34.
The network presented on FigureA1 of the Appendix is structured by using all relevant ASTM standardization available to build up rigid constructed generations. Nonetheless, no other international fully approved standardized methodology was adopted (e.g. ISO, DIN, BS, EN etc.). A terminology Tableis an integral part of the Ontology-Based Knowledge Network and is a very useful medium to avoid scientists of boundary disciplines misapprehension/misleading to even basic concepts.
The following presented paradigm could be a characteristic one to hydraulic engineers and earth scientists that their activities are located in between interdisciplinary knowledge areas. The term “infiltration” forms a continuum with “percolation” and “seepage” terms, so that several authors use them interchangeably, as quasi synonyms. From a topological point of view infiltration is considered to be the water movement into the soil while percolation refers to the water path within/through the soil until it reaches the water table. Consequently, it seems that there is a first interface between the infiltration and percolation zones as well as another subsequent interface between percolation zone and water table. On the other hand, seepage is the slow escape/leakage of water on or near the earth surface simulating a downhill route possible formed by natural phenomena or local constitution of earth, implying difference in permeability enhanced by the presence of clay-loam soils and certain minerals.
The presented multi-generation ontological network, which employs standardization of techniques/methods relevant to sedimentation during and after infiltration, is merely the beginning of a forthcoming profound interdisciplinary expansion. The corresponding network of standards and recommended practices might receive further enrichment, incorporated e.g. ISO/EN standards and more profound phenomenological knowledge related to porous media infiltration. This could be achieved by presented new interconnections among conceptual levels and deeper generation formation analysis upon the already proposed structure.
The authors declare no conflicts of interest regarding the publication of this paper.
Giakoumatos, S. D. V., & Gkionakis, A. K. T. (2021). Development of an Ontology-Based Knowledge Network by Interconnecting Soil/Water Concepts/Properties, Derived from Standards Methods and Published Scientific References Outlining Infiltration/Percolation Process of Contaminated Water. Journal of Geoscience and Environment Protection, 9, 25-52. https://doi.org/10.4236/gep.2021.91003
| Row | Column | Node | Appendix references | Concept | Depends on/related to | Contribution |
|---|---|---|---|---|---|---|
| 0 Generation | ||||||
| 0 | 1 | ASTM D5093-15 | [ | Infiltration rate | 0 | 1 |
| 1st Generation | ||||||
| Row | Column | Node | Appendix references | Concept | Depends on/related to | Contribution |
| 1 | 1 | PP1,1 | [ | Hydraulic conductivity | ASTM D5093-15 | The infiltration rate q(t) depends on saturated hydraulic conductivity (Ks) according to the equation [ |
| 1 | 2 | PP1,2 | [ | Soil sorptivity | ASTM D5093-15 | The infiltration rate q(t) into a porous material depends on soil sorptivity (S), for narrow pore size distributions in accordance with the equation 14, on page 1758, on [ |
| 1 | 3 | ASTM1,3 | [ | Soil Sorptivity | ASTM D5093-15 | The infiltration rate q(t) depends on soil sorptivity (S), according to the implicit function: q ( t ) = ( z t ) ∗ f ( ( S h ) , ( t 2 ∗ γ ρ w ∗ z 3 ) , ( γ ψ ∗ z ) , ( R i ) , ( t ∗ γ μ ∗ z ) , ( z v z ∗ t ) , ( t 2 ∗ γ ρ s ∗ z 3 ) , ( z 2 k i n t r ) , ( S c ) , ( z H ) , ( z t 0.5 ∗ S ) ) where the terms, definitions and dimensions of ( q ( t ) , z , t , S h , γ , ρ w , ψ , R i , μ , v z , ρ s , k i n t r , S c , H , S ) are presented in detail in the terminology ( |
| 1 | 4 | ASTM1,4 | [ | Hydraulic conductivity | ASTM D5093-15 | The infiltration rate q(t) depends on hydraulic conductivity (K) according to the implicit function: q ( t ) = ( H t ) ∗ f ( ( S h ) , ( S c ) , ( μ ψ ∗ t ) , ( H g ∗ t 2 ) , ( H v z ∗ t ) , ( H 2 A ) , ( R e ) , ( μ ∗ t ρ s ∗ H 2 ) , ( H 2 k i n t r ) , ( H 2 t * D ) , ( R i ) , ( μ ∗ H t ∗ γ ) , Φ , θ , S e , n , i ) where the terms, definitions and dimensions of ( q ( t ) , H , t , S h , S c , μ , ψ , g , v z , A , R e , ρ s , k i n t r , D , R i , γ , Φ , θ , S e , n , i ) are presented in detail in the terminology ( |
| 2nd Generation | ||||||
|---|---|---|---|---|---|---|
| Row | Column | Node | Appendix references | Concept | Depends on/ Related to | Contribution |
| 2 | 1 | ASTM2,1 | [ | Intrinsic Permeability | PP1,1 | Hydraulic conductivity (K) depends on intrinsic permeability (kintr), according to the implicit function: K = ( v z ) ∗ f ( ( v z q ) , ( ρ ∗ v z 2 ψ ) , ( 1 R i ) , ( 1 R e ) , ( H 2 A i n f ) , ( ρ ρ s ) , ( H 2 k i n t r ) , ( H v z ∗ t ) , ( v z ∗ H D ) , ( ρ ∗ v z 2 ∗ H σ ) , Φ , θ , S e , n , i ) The terms, definitions and dimensions of ( K , v z , q , ρ , ψ , R i , R e , H , A i n f , ρ s , k i n t r , t , D , σ , Φ , θ , S e , n , i ) are presented in detail in the below terminology ( |
| 2 | 2 | ASTM2,2 | [ | Hydraulic conductivity | PP1,2 | Soil sorptivity (S) depends on hydraulic conductivity (K), according to the implicit function: S = ( z t 0.5 ) ∗ f ( ( z q ∗ t ) , ( S h ) , ( R e ) , ( γ ψ ∗ z ) , ( R i ) , ( t ∗ γ μ ∗ z ) , ( z v z ∗ t ) , ( t 2 ∗ γ ρ s ∗ z 3 ) , ( z 2 k i n t r ) , ( S c ) , ( z H ) , ( z h c ) , Φ , θ , n ) Where the terms, definitions and dimensions of ( S , z , t , q , S h , R e , γ , ψ , R i , μ , v z , ρ s , k i n t r , S c , H , h c , Φ , θ , n ) are presented in detail in the below terminology ( |
| 2 | 3 | ASTM2,3 | [ | Hydraulic Diffusivity | ASTM1,3 | Soil sorptivity (S) depends on hydraulic diffusivity (D), according to the implicit function: S = ( z t 0.5 ) ∗ f ( ( S h ) , ( M ρ ∗ z 3 ) , ( M ψ ∗ t 2 ∗ z ) , ( z g ∗ t 2 ) , ( μ ∗ t ∗ z M ) , ( M ρ s ∗ z 3 ) , ( z 2 k i n t r ) , ( S c ) , ( M t 2 ∗ γ ) ) Where the terms, definitions and dimensions of ( S , z , t , S h , M , ρ , ψ , g , μ , ρ s , k i n t r , S c , γ ) are presented in detail in the terminology ( |
| 2 | 4 | PP2,4 | [ | Effective liquid saturation | ASTM1,4 | Hydraulic conductivity (Κ) depends on effective saturation or degree of saturation (Se), according to Van Genuchten-Mualem model [ |
| 3rd Generation | ||||||
|---|---|---|---|---|---|---|
| Row | Column | Node | Appendix references | Concept | Depends on/ Related to | Contribution |
| 3 | 1 | PP3,1 | [ | Total porosity | ASTM2,1 | The intrinsic permeability (kint) depends on & can be calculated by combining Hagen-Poiseuille flow equation to Darcy’s law and depends on the total porosity (Φ) according to equation (eq. 2, page 274): k i n t = Φ 8 ∗ ∫ 0 ∞ f ( r ) ∗ d r ∫ 0 ∞ f ( r ) ∗ r 2 ∗ d r , where the terms, definitions and dimensions of ( k i n t , Φ , f ( r ) , r ) are presented in detail in the terminology ( |
| 3 | 2 | PP3,2 | [ | Water content | ASTM2,2 | Relative hydraulic conductivity (Kr) depends on water content (θ) according to the Mualem equation (1976), (eq.21, page 515): K r = Θ 1 / 2 ∗ [ ∫ 0 Θ 1 h ( x ) ∗ d x / ∫ 0 1 1 h ( x ) ∗ d x ] 2 , Θ = θ − θ r θ s − θ r where the terms, definitions and dimensions of ( K r , Θ , h , θ ) are presented in detail in the terminology ( |
| 3 | 3 | ASTM3,3 | [ | Hydraulic conductivity | ASTM2,3 | Hydraulic diffusivity (D) depends on Hydraulic conductivity (K) according to the implicit equation: D = ( q ∗ d i n n e r ) ∗ f ( ( K q ) , ( 1 R e ) , ( K 2 ∗ ρ s ψ ) , ( K 2 g ∗ d i n n e r ) , ( K 2 ∗ d i n n e r 2 ∗ ρ s 2 ρ 2 ∗ v z 2 ∗ d g r a i n 2 ) , ( d i n n e r 2 k i n t r ) , ( d i n n e r K ∗ ( t i − t p ) ) , ( d i n n e r K ∗ ( t p − t f ) ) , ( d i n n e r z ) , ( K 2 ∗ d i n n e r ∗ ρ s σ ) , ( d i n n e r H ) , Φ , θ ) where the terms, definitions and dimensions of ( D , q , R e , d i n n e r , K , ρ s , ψ , g , ρ , v z , d g r a i n , k i n t r , t i , t p , t f , z , σ , H , Φ , θ ) are presented in detail in the terminology ( |
| 3 | 4 | PP3,4 | [ | Pressure head | PP2,4 | Water retention (θ) is depended on pressure head (h) according to Van Genuchten model (1980) which predicts the hydraulic conductivity (Κ) from the water retention curve and is expressed as the following given relationship (eq. 2 & 3, page 892): θ = ( 1 1 + ( a ∗ h ) b ) c ( θ s − θ r ) + θ r , where the terms, definitions and dimensions of ( θ , a , h , b , c , θ s , θ r ) are presented in detail in the terminology ( |
| 3 | 5 | ASTM3,5 | [ | Liquid viscosity | ASTM2,1 | Intrinsic permeability (kintr) depends on liquid viscosity (μ), according to the implicit equation: k i n t r = h c 2 ∗ f ( ( h c q ∗ t ) , ( S h ) , ( h c v z * t ) , ( S c ) , ( ρ s ∗ h c 2 ψ ∗ t 2 ) , ( ρ s ∗ h c 2 μ ∗ t ) , ( R i ) , ( h c 2 t ∗ D ) , ( R e ) , ( h c z ) , ( C a ) , ( C i t ∗ ( d C i d t ) ) , ( T i h c ∗ ( d T d z ) ) , Φ , θ , S e , n , i , K s ) Where the terms, definitions and dimensions of ( k i n t r , h c , q , t , S h , v z , S c , ρ s , ψ , μ , R i , D , R e , z , C a , C i , T i , T , Φ , θ , S e , n , i , K s , d C i d t ) are presented in detail in the terminology ( |
|---|---|---|---|---|---|---|
| 3 | 6 | ASTM3,6 | [ | Gravitational acceleration | ASTM2,1 | Intrinsic permeability (kintr) depends on gravitational acceleration (g), according to the implicit equation: k i n t r = h c 2 ∗ f ( ( h c q ∗ t ) , ( S h ) , ( h c v z * t ) , ( S c ) , ( ρ s ∗ h c 2 ψ ∗ t 2 ) , ( ρ s ∗ h c 2 μ ∗ t ) , ( R i ) , ( h c 2 t ∗ D ) , ( R e ) , ( h c z ) , ( C a ) , ( C i t ∗ ( d C i d t ) ) , ( T i h c ∗ ( d T d z ) ) , Φ , θ , S e , n , i , K s ) Where the terms, definitions and dimensions of ( k i n t r , d g r a i n , q , t , S h , R e , S c , μ , ψ , ρ s , d r i n g , R i , D , σ , h c ) are presented in detail in the terminology ( |
| 4th Generation | ||||||
|---|---|---|---|---|---|---|
| Row | Column | Node | Appendix references | Concept | Depends on/ Related to | Contribution |
| 4 | 1 | PP4,1 | [ | Soil mass density | PP3,1 | According to Nimmo (2004) equation α = ( 1 − ρ s ρ p ) (equation 1, page 3), total porosity ( α ) or (Φ) depends on soil bulk density (ρs) and soil mass density or particle density (ρp), where the terms, definitions and dimensions of ( α , ρ s , ρ p ) are presented in detail in the terminology ( |
| 4 | 2 | PP4,2 | [ | Soil organic matter | PP3,2 | Water content is related to Soil Organic Matter (SOM). Soil organic matter content and composition effects on water content and soil water potential (pages 2282, 2285)1. |
| 4 | 3 | PP4,3 | [ | Effective liquid saturation | ASTM3,3 | Hydraulic conductivity (K) depends on Effective liquid saturation (Se) according to Kozeny’s approach and Corey’s effective saturation definition (Mualem 1976), (equation 1 & 2, page 513): K ( θ ) = K s ∗ ( S e ) n , S e = θ − θ r θ s − θ r Where the terms, definitions and dimensions of ( K ( θ ) , K s , S e , θ , θ r , θ s , n ) are presented in detail in the terminology ( |
| 4 | 4 | PP4,4 | [ | Interfacial tension | PP3,4 | Pressure head (h), depends on interfacial tension (σ) according to Young-Laplace equation, (equation 3, page 1011): h = − 2 ∗ σ ∗ cos φ ρ ∗ r ∗ g Where the terms, definitions and dimensions of ( h , σ , φ , ρ , r , g ) are presented in detail in the terminology ( |
| 4 | 5 | PP4,5 | [ | Shear modulus/ Modulus of rigidity | ASTM3,5 | The viscosity (η) of the fluid depends on & can be varied by the variation of the angular relaxation frequency (ω1) and the shear modulus (c44), according to the equation: η = c 44 ω 1 , (equation 9, page 239), where the terms, definitions and dimensions of ( η , c 44 , ω 1 ) are presented in detail in the below terminology ( |
| 4 | 6 | PP4,6 | [ | Time | ASTM3,6 | The gravitational acceleration (g) related to time. |
| 5th Generation | ||||||
|---|---|---|---|---|---|---|
| Row | Column | Node | Appendix references | Concept | Depends on/ Related to | Contribution |
| 5 | 1 | PP5,1 | [ | Soil water retention | PP4,1 | The relation of pore-size to particle-size distribution is certain. Large pores can be associated with large and smaller particles (page 8). Consequently, pore-size and particle size distribution are related to measured soil water retention (page 5)2 |
| 5 | 2 | PP5,2 | [ | Soil texture | PP4,2 | Evidence suggests that soil texture influences the content, the distribution and the composition of soil organic matter (SOM) (pages 207, 208). Consequently, soil organic matter is related to soil texture.3 |
| 5 | 3 | PP5,3 | [ | Capillary pressure | PP4,3 | Effective liquid saturation (Se) depends on Capillary pressure (Pc), according to Corey approximation equation (equation 10, page 10) S e = ( c P c ) 2 , where the terms, definitions and dimensions of ( S e , c , P c ) are presented in detail in the below terminology ( |
| 5 | 4 | ASTM5,4 | [ | Capillary Pressure head | PP4,4 | Interfacial tension (γ) depends on capillary pressure head (hc), according to the implicit function: γ = ( z t 2 ) ∗ f ( ( z q ∗ t ) , ( S h ) , ( z v z ∗ t ) , ( S c ) , ( μ ψ ∗ t ) , ( t ∗ μ ρ s ∗ z 2 ) , ( R i ) , ( z h c ) , ( z 2 t ∗ D ) , ( R e ) , ( z S ∗ t 0.5 ) ) where the terms, definitions and dimensions of ( γ , z , t , q , S h , v z , S c , μ , ψ , ρ s , R i , h c , D , R e , S ) are presented in detail in the below terminology ( |
| 5 | 5 | ASTM5,5 | [ | Thermal diffusivity | PP4,1 | Soil mass density (ρs) depends on thermal diffusivity (Dth), according to implicit function: ρ s = ( ( λ ∗ T 3 D t h 3 ∗ ( d T d z ) 2 ) * f ( ( D t h 2 ∗ ( d T d z ) 2 C ∗ T 3 ) , ( T 2 t ∗ D t h ∗ ( d T d z ) 2 ) , ( T 3 V s ∗ ( d T d z ) 3 ) ) ( λ ∗ T 6 D t h 3 ∗ M s ∗ ( d T d z ) 5 ) , ( λ ∗ T D t h ∗ ( d T d z ) ∗ k t h ) , Φ , n ) where the terms, definitions and dimensions of ( ρ s , λ , T , D t h , z , C , v s , M s , k t h , Φ , n ) are presented in detail in the below terminology ( |
| 6th Generation | ||||||
|---|---|---|---|---|---|---|
| Row | Column | Node | Appendix references | Concept | Depends on/ Related to | Contribution |
| 6 | 1 | PP6,1 | [ | Soil organic matter | PP5,1 | Results show that soil water retention is related to soil organic matter (SOM) (page 3086). Soil organic matter affects soil water retention because of its affinity to water and its influence on soil structure and bulk density (page 3087)4 |
| 6 | 2 | PP6,2 | [ | Type of rock of soil parent material | PP5,2 | The soil texture heterogeneity is controlled by the type of rock that constitutes the soil parent material (pages 157, 163). Consequently, soil texture is related to type of rock of soil parent material5 |
| 6 | 3 | PP6,3 | [ | Total Porosity | PP5,3 | The capillary pressure (Pc) depends on the porosity (Φ), (equation 3.4.1.14, page 58). P c = ( Φ K ) 0 , 5 ∗ σ ∗ J ( s ) , where the terms, definitions and dimensions of ( P c , Φ , K , σ , J ( s ) , s ) are presented in detail in the below terminology ( |
| 6 | 4 | PP6,4 | [ | Pore size distribution | ASTM5,4 | In van Genuchten’s model, the capillary pressure (Pc) is depended on the pore size distribution (n), (eq. 2, page 3619): P c = � − 1 ∗ ( S e ( − 1 m ) − 1 ) 1 n , where the terms, definitions and dimensions of ( P c , � , S e , m , n ) are presented in detail in the below terminology ( |
| 6 | 5 | ASTM6,5 | [ | Thermal conductivity | ASTM5,5 | Thermal diffusivity (Dth) depends on thermal conductivity (λ), according to the implicit function: D t h = ( V s 2 3 t ) f ( ( V s 1 3 ∗ M s λ ∗ t 3 ∗ T ) , ( V s 2 3 C ∗ t 2 ∗ T ) , ( M s ρ s ∗ V s ) , ( T V s 1 3 ∗ ( d T d z ) ) , ( M s t 2 ∗ T ∗ k t h ) , Φ , n ) where the terms, definitions and dimensions of ( D t h , V s , t , M s , λ , T , C , ρ s , z , k t h , Φ , n ) are presented in detail in the below terminology ( |
| 6 | 6 | ASTM6,6 | [ | Heat transfer rate | ASTM5,5 | Thermal diffusivity (Dth) depends on heat transfer rate (qH), according to the implicit function: D t h = α s = ( z 2 t ) ∗ f ( ( ρ s ∗ z 4 λ s ∗ T i ∗ t 3 ) , ( z 2 C s ∗ t 2 ∗ T i ) , ( z 3 V s ) , ( T i ( d T d z ) ∗ z ) , ( B i ) , ( z 2 A ) , ( ρ s ∗ z 5 t 2 ∗ q H ) ) Where the terms, definitions and dimensions of ( D t h , z , t , ρ s , λ s , T i , C s , V s , B i , A , q H ) are presented in detail in the below terminology ( |
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| 7th Generation | ||||||
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| Row | Column | Node | Appendix references | Concept | Depends on/ Related to | Contribution |
| 7 | 1 | PP7,1 | [ | Soil temperature | PP6,1 | Results show that soil organic matter (SOM) is related to soil temperature. Soil temperature is an important factor which affects soil organic matter (SOM) decomposition rate (pages 399, 403, 404)6 |
| 7 | 2 | PP7,2 | [ | Effective porosity | PP6,3 | Effective porosity (Φe) depends on the porosity (Φ) according to the (equation 19, page 23). Φ e = ( 1 − S r ) ∗ Φ where the terms, definitions and dimensions of ( Φ e , S r , Φ ) are presented in detail in the below terminology ( |
| 7 | 3 | PP7,3 | [ | Soil water retention | PP6,4 | Statistics of the soil pore radius distribution function g(r), is related to the water retention curve of Kosugi’s further modified water retention model in which two parameters with physical significance are involved (page 1).7 |
| 7 | 4 | ASTM7,4 | [ | Soil temperature | ASTM6,5 | Thermal conductivity (λs) depends on soil temperature (T), according to the implicit function: λ s = ( ρ s ∗ z 4 t 3 ∗ T i ) ∗ f ( ( z 2 C s ∗ t 2 ∗ T i ) , ( z 2 t ∗ a s ) , ( z 3 V s ) , ( T i ( d T d z ) ∗ z ) , ( ρ s ∗ z 3 t 3 ∗ T i ∗ k t h ( s ) ) , ( ρ s ∗ z 3 t 3 ∗ T i ∗ k t h ( w ) ) , ( ρ s ∗ z 4 t 3 ∗ T i ∗ λ w ) , ( P r ) , ( S t ) , ( z 2 t 2 ∗ T i ∗ C w ) , ( z 2 A ) , ( z t ∗ v z ) , ( z 3 V ) , ( ρ s ∗ z 2 t ∗ μ ) , Φ , n , i ) where the terms, definitions and dimensions of ( λ s , ρ s , z , t , λ w , T i , z , C s , C w , k t h ( w ) , k t h ( s ) , V , d T d z , μ , v z , A , K t h , Φ , n , i ) are presented in detail in the terminology ( |
| 7 | 5 | ASTM7,5 | [ | Specific heat capacity | ASTM6,5 | Thermal conductivity (λs) depends on specific heat capacity (C), according to the implicit function: λ s = ( V s 1 3 ∗ M s t 3 ∗ ( Δ T ) ) ∗ f ( ( V s 2 3 C ∗ t 2 ∗ Δ T ) , ( M s ρ s ∗ V s ) , ( V s 2 3 t ∗ a s ) , ( Δ T V s 1 3 ∗ ( d T d z ) ) , ( M s t 3 ∗ ( Δ T ) ∗ k t h ) , ( V s 2 3 A ) , Φ , n ) where the terms, definitions and dimensions of ( λ s , V s , M s , t , T , C , ρ s , a s , k t h , A , Φ , n ) are presented in detail in the terminology ( |
| 7 | 6 | PP7,6 | [ | Ground thermal/heat flux | ASTM6,6 | The heat transfer rate depends on ground heat/thermal flux (Go), according to the equation 1, page 214: R n = H + L E + G o where the terms, definitions and dimensions of ( R n , H , L E , G o ) are presented in detail in the below terminology ( |
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Terminology Table—SI Units
| Parent material defined as the reference to unconsolidated mass from which the solum is developed by pedogenic processes (soil survey manual) | |
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| Particle size distribution is the fractions of the various soil separates in a soil sample, often expressed as mass percentages. | [ |
| Soil organic matter (SOM) is the organic matter component of soil, consisting of plant and animal detritus at various stages of decomposition, cells and tissues of soil microbes, and substances that soil microbes synthesize | |
| Soil texture is a classification instrument used both in the field and laboratory to determine soil classes based on their physical texture | [ |
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