Gis Applications In Agrometeorology

A GIS generally refers to a description of the characteristics and tools used in the organization and management of geographical data. The term GIS is currently applied to computerised storage, processing and retrieval systems that have hardware and software specially designed to cope with geographically referenced spatial data and corresponding informative attribute. Spatial data are commonly in the form of layers that may depict topography or environmental elements. Nowadays, GIS technology is becoming an essential tool for combining various map and satellite information sources in models that simulate the interactions of complex natural systems. A GIS can be used to produce images, not just maps, but drawings, animations, and other cartographic products.

The increasing world population, coupled with the growing pressure on the land resources, necessitates the application of technologies such as GIS to help maintain a sustainable water and food supply according to the environmental potential. The "sustainable rural development" concept envisages an integrated management of landscape, where the exploitation of natural resources, including climate, plays a central role. In this context, agrometeorology can help reduce inputs, while in the framework of global change, it helps quantify the contribution of ecosystems and agriculture to carbon budget (Maracchi, 1991). Agroclimatological analysis can improve the knowledge of existing problems allowing land planning and optimization of resource management. One of the most important agroclimatological applications is the climatic risk evaluation corresponding to the possibility that certain meteorological events could happen, damaging crops or infrastructure.

At the national and local level, possible GIS applications are endless. For example, agricultural planners might use geographical data to decide on the best zones for a cash crop, combining data on soils, topography, and rainfall to determine the size and location of biologically suitable areas. The final output could include overlays with land ownership, transport, infrastructure, labour availability, and distance to market centres.

The ultimate use of GIS lies in its modelling capability, using real world data to represent natural behaviour and to simulate the effect of specific processes. Modelling is a powerful tool for analyzing trends and identifying factors that affect them, or for displaying the possible consequences of human activities that affect the resource availability.

In agrometeorology, to describe a specific situation, we use all the information available on the territory: water availability, soil types, forest and grasslands, climatic data, geology, population, land-use, administrative boundaries and infrastructure (highways, railroads, electricity or communication systems). Within a GIS, each informative layer provides to the operator the possibility to consider its influence to the final result. However more than the overlap of the different themes, the relationship of the numerous layers is reproduced with simple formulas or with complex models. The final information is extracted using graphical representation or precise descriptive indexes.

In addition to classical applications of agrometeorology, such as crop yield forecasting, uses such as those of the environmental and human security are becoming more and more important. For instance, effective forest fire prevention needs a series of very detailed information on an enormous scale. The analysis of data, such as the vegetation coverage with different levels of inflammability, the presence of urban agglomeration, the presence of roads and many other aspects, allows the mapping of the areas where risk is greater. The use of other informative layers, such as the position of the control points and resource availability (staff, cars, helicopters, aeroplanes, fire fighting equipment, etc.), can help the decision-makers in the management of the ecosystems. Monitoring the resources and the meteorological conditions therefore allows, the consideration of the dynamics of the system, with more adherence to reality. For instance, Figure 1 shows the informative layers used for the evaluation of fire risk in Tuscany (Italy). The final map is the result of the integration of satellite data with territorial data, through the use of implemented GIS technologies (Romanelli et al, 1998).

Figure 1. Informative layers for the evaluation of fire risk index (Maracchi etal, 2000).

These maps of fire risk, constitute a valid tool for foresters and for organisation of the public services. At the same time, this new informative layer may be used as the base for other evaluations and simulations. Using meteorological data and satellite real-time information, it is possible to diversify the single situations, advising the competent authorities when the situation moves to hazard risks. Modelling the ground wind profile and taking into account the meteorological conditions, it is possible to advise the operators of the change in the conditions that can directly influence the fire, allowing the modification of the intervention strategies.

An example of preliminary information system to country scale is given by the SISP (Integrated information system for monitoring cropping season by meteorological and satellite data), developed to allow the monitoring of the cropping season and to provide an early warning system with useful information about evolution of crop conditions (Di Chiara and Maracchi, 1994). The SISP uses:

• Statistical analysis procedures on historical series of rainfall data to produce agroclimatic classification;

• A crop (millet) simulation model to estimate millet sowing date and to evaluate the effect of the rainfall distribution on crop growth and yield;

• NOAA-NDVI image analysis procedures in order to monitor vegetation condition;

• Analysis procedures of Meteosat images of estimated rainfall for early prediction of sowing date and risk areas.

The results of SISP application shown for Niger (Fig. 2) are charts and maps, which give indications to the expert of the millet conditions during the season in Niger, with the possibility to estimate the moment of the harvest and final production. SISP is based on the simulation of the millet growth and it gives an index of annual productivity by administrative units. These values, multiplied to a yield statistical factor, allow estimation of absolute production.

By means of such systems based on modelling and remote sensing, it is possible to extract indices relative to the main characteristics of the agricultural season and conditions of natural systems. This system is less expensive, easily transferable and requires minor informative layers, adapting it to the specific requirements of the users.

Niamey, cropping season 1993

Niamey, cropping season 1993

Characterization of land productivity at N ger CeSIA Index for miller _.■"'

Elaborated on 10 yeats (1981-90) and 120 stations

^IIH jIMM tft

J Rainfall : Cultural Coeff. Water balance

Characterization of land productivity at N ger CeSIA Index for miller _.■"'

Elaborated on 10 yeats (1981-90) and 120 stations

Longitude

Figure 2. Examples of outputs of SISP (Maracchi et al, 2000).

Longitude

Figure 2. Examples of outputs of SISP (Maracchi et al, 2000).

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