THE AGRO-HYDROLOGICAL MODELLING IN LANDSCAPE – VARIOUS SCALES APPROACHES
Martina Nováková, Rastislav Skalský, Jozef Takáč
Soil Science and Conservation Research Institute, Gagarinova 10, 842 10 Bratislava, Slovakia,
m.novakova@vupop.sk, r.skalsky@vupop.sk, j.takac@vupop.sk
Process-based soil-plant-atmosphere system (PRAT) models (further referred as simulation models)
are scientific tools for gathering, storing and maintenance of the knowledge in the scientific disciplines
such as soil hydrology, plant physiology, pedology, biogeochemistry and ecology. Simulation models
are also widely applied in the various-scale landscape modelling as they can produce exact and in
time dynamic information on many important landscape characteristics (e.g. primary production,
greenhouse-gases sequestration and emission, water cycling and balance in ecosystem, elements
transport and leaching). Landscape planning, land evaluation and other environmental decision support activities motivate many of such applications.
Landscape-scale application of the simulation models brings some additional modelling problems
compared to the field-scale application. These include particularly the necessity of the model inputs/outputs appropriate spatial representation across the area of interest and securing the scientific
reliability of the obtained spatially distributed modelling results.
Identification of the soil nitrate leaching hot-spots in the Lehnice municipality surrounding area, identification of the soil nitrate leaching spatial and temporal pattern in the Žitný ostrov lowland region
(south-western Slovakia), and crop yield forecasting system for Slovakia provide good examples of the
selected simulation models (DAISY, WOFOST) application in local, regional, and national scales.
Based on the examples we try to identify the differences in model input/output data management as
well as we search for the main sources of uncertainties. Our findings are as follows:
In the local-scale the simulation model is applied within the defined grid of points. All mandatory landscape and management data for running the model (e.g. hydro-physical characteristics of soil, nitrogen input) and also some output data (e.g. groundwater contamination by nitrates) are directly measured and available for each location. The model calibration and validation is possible. Thanks to number of the point measurements and predictable heterogeneity of the landscape in the relatively small
area, spatial variability of the modelling outputs can be described geostatistically and output values
interpolated over the whole area. Uncertainties of the modelling come from the model itself and spatial
arrangement and density of the measurement points. Uncertainties can be easily expressed quantitatively.
Regional-scale application of the simulation model is facing the problem of landscape data availability
(climate, soil, management data). The measured data are not available for each desired location. The
simplifications in spatial organisation of the inputs or outputs have to be done. Geographical analysis
including all assumed landscape characteristics is done in order to get the representative landscape
units which correspond with the measured data. Model calibration and validation is possible in the
measured points. The inputs/outputs spatial pattern cannot be described geostatistically as there are
not enough points in the region and spatial heterogeneity over the relatively large region is too high.
The uncertainties of the modelling come from the model itself and spatial model. The spatial model
uncertainties reflect the data interpretation methodology and they are only hard to quantify in case
when datasets with independently measured data or very detailed environmental statistics are not up
to hand.
National scale modelling fully depends on the availability of the existing sources data. Model calibration and validation is possible, but both the problem with number of the point-measurement locations
and its representativeness for the area is striking the process. Not all mandatory input data for the
simulation models are available from the existing datasets. Along with spatial units delineation and its
characterisation by the quantitative data on landscape (e.g. soil layer specific analytical values) the
missing inputs have to be estimated (e.g. pedotransfer functions application for the hydro-physical soil
properties estimation from available soil texture data). Both the interpretation and estimation of the
input data significantly simplify the reality in the landscape. Thus, the uncertainties coming from the
spatial model fairly outweighs those coming from the simulation model itself. The possibility of the
modelling uncertainties quantitative evaluation depends on the data interpretation methodology and
availability of the appropriate national-level environmental statistics.
We can conclude that the successful and scientifically sound landscape-scale application of the process-based PRAT models depends mostly on the input data availability and quality. On the other hand,
the process-based PRAT models, which are designed to work on the field scale and are highdemanding for inputs are – while applied in the landscape scale – always very uncertain in their results.
The further research activities should be therefore aimed not only on PRAT system itself treated on
the most detailed level (micro scale) but also towards the PRAT system modelling in the real landscape context (macro scale).
Key words: process-based models of the soil-plant-atmosphere system, landscape modelling, geographical data
Acknowledgement: Some ideas presented here are outcome of the work on the 7th FP project CCTAME (Climate Change: Terrestrial Adaptation and Mitigation in Europe).