GIS and ADM cross-cutting thematics

ADM basics and GIS fundamentals have been recalled and we can now focus on possible links between the two disciplines. Indeed, cross-cutting thematics appear to be relevant both for spatial modeling of atmospheric dispersion and for the technical coupling, espe-cially regarding coordinates systems, scales and time.

2.2.1 Coordinate systems

Coordinate systems are used both by mathematicians and geographers and constitute the base of space representation for atmospheric dispersion models and GIS applications.

Many ADM systems are based on three dimensional Cartesian coordinate system that provide the three physical dimensions of space (length, width, and height) according to a frame gathering thex, they andz-axis whereas most GIS use spherical coordinate systems based on ellipsoids and angles calculation to determine latitude (ϕ) and longitude (λ) values. The main differences between the two coordinate systems cited above are shown by figure 2.2.

Figure 2.2: Cartesian (left) and Geographic (right) coordinate systems

Although GIS can display absolute x,y,z triplets, this is not a correct way to produce maps and location based analysis. Indeed, as geographic coordinate systems represent the surface of the Earth on a plane, a suitable map projection is needed in order to represent

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Gaussian coordinates according to a coordinate system that best respect the reality of the Globe. Each projection preserves or approximates basic metric properties such as shapes, areas, distances and scales [29]. The purpose of the map and the place it represents determine which projection should be use to build the map and minimize distortion.

Given that atmospheric dispersion models are often designed to produce Cartesianx,y,z formated outputs, and that GIS supports by nature many projection types and matching coordinates transformation algorithms, we can easily conclude that both aspects should be able to communicate with each other through input/output processing. This would allow one to make use of ADM outputs in a geographic coordinate system and so to obtain a pesticide cloud in the "digital reality" of its landscape. More details on this concept are given in sections 7.1 and 7.2.2.

2.2.2 Scales

Scale has several meanings that are important to detailed, as it is once again used in different ways by atmospheric modelers and geographers. GIS community commonly differentiates four connotations for scale, as suggested by Quattrochi and Goodchild in [30]. Thecartographic scalefirst refers to the size on the map divided by the size on the real world, which induces that small-scaled maps represents large areas [31]. The geographic scale is used to define the spatial extent of the study area. As for theoperational scale, it describes the scale at which a phenomenon operates. A fourth definition of scale is linked toresolution, which can be defined as the smallest differentiable part of a spatial dataset and is helpful to define finer and coarser scales. For example, DEMs which present the smallest pixel size are of finer scale.

According to physicians, scale refers to the size and the spatial extent (also called domain) of physical processes. As an example, micro-scale (occurs over distances from 2mm to 2km),meso-scale (from 2 to 2000km) andmacro-scale (500 to 10000km) are well-known terms and commonly used to describe local to global atmospheric and meteorologic phenomenons. However, some phenomena like atmospheric dispersion operate on several scales [32] that have to be taken into account in their modeling scheme. Multi scaled models are thereby needed in pesticide ADM, for example to make a canopy flow model, a volatilization model and a transport model interact, and so to model the whole process at best. Scale variations are an important research topic and many ADM studies are focusing on it to couple validated models operating from micro to meso scales, or to enhance existing atmospheric models as presented in [33].

ADM uses therefore both geographic and operational scales to determine the validity and the efficiency of a particular model. The coupling of ADM and GIS also implies to adapt a model’s inputs/outputs to the cartographic scale, and to find the best way to map processes within a GIS environment. Resolution also presents several interesting issues regarding the use of DEM as input data in order to take realistic topographies into account. Scale and scale changes are therefore major interests for both the GIS and ADM communities, and this topic is more widely detailed and exploited in part 5.

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2.2.3 Time

Time and temporality are also essential to many GIS applications. Although the con-cept of storing attribute information to spatial objects within a GIS is relatively a basic task, adding the time support raises new difficulties [34]. Temporal GIS applications are subject to numerous researches in the spatial modeling communities because many GIS based projects lack of linkage between space and time. Working with temporal GIS layers is a new challenging issue as it could greatly enhance spatial information in many cases, as regards about environmental spatial analysis.

Atmospheric dispersion models are also largely based on time. First, emissions and me-teorological datasets are generally presented as time-series that often have to be processed to be used for ADM. Then, temporal scales also matter in the representativity of trans-port models as both the chemical and physical properties of a pesticide cloud are time-dependent and can be strongly modified depending on the date/time of the observation.

Therefore, many models deal with the migration times of particles which makes it possible to know the fate of pesticide clouds or even the concentrations at a given moment, or for a given period.

ADM is time-dependent by nature and so it integrates time series and migration times in calculations, whereas GIS do not support temporality in their basic data models. Despite this apparent incompatibility, several attempts are being made to design temporal GIS databases and to support dynamic phenomenon natively into GIS as underlined by Wilson and Burrough in [35]. The premises of "4D-GIS" and geospatial virtual reality have started, but time is not a native feature of GIS, and this is a huge limitation to render dynamic physical processes such as pesticide atmospheric dispersion.

2.2.4 Conclusion

This chapter has recalled some of the GIS fundamentals in order to point out their assets for ADM. DEM have been described as they will be widely use for the intended coupling, as well as the vector and raster model that will both be employed, notably regarding pesticide clouds rendering within GIS.

GIS and ADM cross-cutting thematics have been highlighted and coordinate systems, scales and time now appear to be the base of our coupling. These three general concepts will be used all along the study in conjunction with the identified ADM most important parameters (namely emissions, winds and topography) both for the modeling and the technical coupling.

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Chapter 3

Coupling ADM and GIS

Both ADM and GIS basics have been succinctly described. This chapter now aims to present available and validated techniques for coupling ADM and GIS. This enumeration of coupling techniques is then followed by a non-exhaustive review of existing ADM/GIS couplings. This review tackles both CFD based and GIS based couplings and allows us to reach conclusions on ADM/GIS couplings methods both for ADM and GIS scientific communities.

3.1 Coupling techniques

Nyerges proposed a conceptual framework for coupling external spatial based models and GIS [36], composed of four categories with increasing intensity of coupling. These are described in many coupling reviews examples, and sometimes reduced to only two cate-gories (tight and loose coupling), referring to the traditional basic methods for coupling computer models [37].

3.1.1

The model and the GIS are running on separate hardware and software environments and the data transfer between the two is done "manually" by the user. This does not represents a proper coupling, but a simple way to load the model’s outputs in the GIS, subjected to data formats and projection processing. All the required steps appear to be quite cumbersome for the user.

3.1.2

The loose coupling describes an approach where integration interfaces are developed with minimal assumptions between the GIS and the external model, thus reducing the risk that a change in one application will force a change in the another one. In other words, we can define loose coupling as a programming method according to which systems are linked by a communication network but ruled by their own functional logic [14]. This implies some input and output data flows and so most of the time it implies some file

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formatting and format conversion. The loose coupling is used in many examples of GIS-based coupling due to its rather easy setup.

3.1.3

The tight coupling refers to the approach according which models or softwares are gathered in a single system and are dependent upon each other, thus avoiding the input-s/outputs processing. In the case of GIS-based coupling, this means that the model and the GIS are working with specific shared modules such as specific functions or databases.

The data models of the GIS and the model may still be different but automated data trans-fer is possible through a standardized graphical user interface. This method improves the coupling as the user needs to pay attention to the data integrity, but such couplings are more complex to build than loose coupling as it requires much more programming.

3.1.4

In this approach, GIS and models are sharing the same data model through a common interface which greatly improves the interaction between GIS and models. It is even more extended than coupling, as the model often represents a native GIS class that has to be developed. Fully integrated GIS-based applications have not been many so far because they imply important programming tasks due to the limitations of the GIS or the model.

However, once the integration is effective, it becomes easier to add new functionalities, since an Application Programming Interface (API) can be proposed.