ALPMON FINAL REPORT - ARC systems research

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Contract ENV4-CT96-0359 ALPMON 2.3.4.3.2 SCS-CN sub-model The runoff/infiltration component of the FSTAB model is based on the SCS-CN (Soil Conservation Service – Curve Number) sub-model (USDA, 1986, Boughton, 1989; Hawkins et al. 1985). The applications domain of SCS-CN are: Urban, Agricultural, Forest and Rangeland. Input data of SCS-CN are: soil characteristics, land use an land cover, antecedent moisture conditions, slope (in the EPIC and SWRRB implementations), daily rainfall volume (for direct runoff and infiltration volume computation). In the present use of the SCS-CN sub-model, the main output data is an adimensional index for daily runoff or infiltration potential. 2.3.4.3.3 DA sub-model The DA sub-model is derived from an assessment of the most relevant instability factors present on scientific literature on landslides, debris-flows and mud-flows, analysed by means of the Discriminant Analysis, a multivariate statistical methodology already applied on this field by other authors (Carrara et al.,1991, Busoni et al., 1995; Rowbotham and Dudycha, 1998). The Discriminant Analysis approach can be summarised on the following steps: I. Identify from a-priori knowledge of the phenomenon a set of parameters which could affect stability. II. Measure the above parameters over a significant number of ground truth points on the test site area. III. For each ground truth point assess the stability from field evidence using the following classification: Stable / Unstable / Uncertain. IV. Apply the Discriminant Analysis to the dataset to find out the Discriminant Function i.e. the axis which better discriminates the three clusters of ground truth points classified as Stable, Unstable and Uncertain. The Discriminant Analysis gives a quantitative assessment of the contribution of each parameter to the stability. Once the Discriminant Function is computed over the ground truth points, it is possible to use it as a model to process any point of the distributed dataset (i.e. data over a regular grid derived from ALPMON land cover and other auxiliary sources) and to assign it a stability index. 2.3.4.3.4 FSTAB Model The FSTAB (Fuzzy Stability) model integrates the output from the DA and SCS-CN sub-models by means of an inference engine, based on fuzzy logic, which gives a reasoned erosion risk index, called MEHI (Mass Erosion Hazard Index). The inference engine for the assessment of global risk, is based on fuzzy logic and uses a set of predefined decision rules. There is an increasing number of fuzzy logic based methodologies for slope stability assessment in the last decade (CHOWDHURY, 1988; OKIMURA et al.,1993; YOSHIMURA, 1996). The reason for this interest in fuzzy logic is the possibility to model complex phenomena and at the same time manage the uncertain nature of some model parameters and processes. Model and data uncertainty play a special role in hazard assessment and non-deterministic nature of the processes needs of a combined approach. The DA sub-model allows to define a degree of stability based on static conditions depending on geomorphological and soil features. However, infiltration potential, storm intensity and amount are important triggers for instability processes. In fact, different infiltration potentials in adjacent areas may give an explanation of the great variability on mass movement generation in similar morphological situations. The SCS-CN sub-model accounts for the dynamic component related to infiltration potential. 2.3.4.4 Implementation of Remote Sensing into the Model The data flow (Figure 10) illustrates all the of parameters involved in the erosion risk model. The primary parameters (violet) have been processed to derive secondary parameters used input for the sub-models DA and SCS-CN. It is evident that all the required soil parameters have been completely estimated indirectly because no primary information over soil were available. Geological field survey JR, RSDE, ALU, LMU, Seibersdorf, WSL 46
Contract ENV4-CT96-0359 ALPMON has been used for the calibration of the DA sub-model while geomorphology has been used for the tuning and validation of the FSTAB model. IRS-1C Pan TM DTM Communes DTM Communes GeologicalMaps Land Systems Communes Geomorphic Maps Aerial Photos Land Cover Slope Aspect Geology Geomorphology Geological Field Survey Soil Type DA Soil Depth MEHI Impermeable Subsoil Hydrologic Soil Group JR, RSDE, ALU, LMU, Seibersdorf, WSL 47 CN INF Figure 10: Erosion risk data flow Critical Rainfall Volume 2.3.4.5 Results The main issue of the FSTAB model is the Mass Erosion Hazard Index (MEHI). MEHI was computed at a pixel resolution of 30 metres over all the test site a part from pure rocks areas and slopes deeper than 45% because on these areas rock falls are prevailing over mass erosion and these two phenomena are too different to be modelled by the same tool which models the mass erosion risk. It must be pointed out that the FSTAB differs from other erosion models because it does not provide an erosion rate index, which would not in any case help preventing natural disasters, but a parameter representing the propensity towards the generation of mass erosion. The assessment of results was therefore based on comparisons both qualitative and quantitative of MEHI vs. the geomorphology because it was the most significant available information about the actual and potential erosion risk in the test site area.
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