Spectral approach to model mountain lake catchment through landscape attributes

Casals-Carrasco, Pilar; Catalan, Jordi; Madhavan, B. Babu; Gond, Valéry

doi:10.1117/12.509950

Cited by 1 publication

(3 citation statements)

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“…Maximum lake depth was measured during the survey while the remaining variables were obtained from maps, using manual and computer‐assisted GIS methods (Hansen et al. , 2002; Casals‐Carrasco et al. , 2004).…”

Section: Methodsmentioning

confidence: 99%

“…Lithology was obtained from geological maps (Casals‐Carrasco et al. , 2004), and was described as the per cent of area composed of five main classes of rocks: igneous plutonic, siliceous sedimentary, siliceous metamorphic, sedimentary and metamorphic carbonate and igneous volcanic.…”

Section: Methodsmentioning

confidence: 99%

“…Morphological variables were lake depth, lake area, catchment area, maximum altitude within the catchment, and the catchment-to-lake ratio. Maximum lake depth was measured during the survey while the remaining variables were obtained from maps, using manual and computer-assisted GIS methods (Hansen et al, 2002;Casals-Carrasco et al, 2004).…”

Section: Environmental Variablesmentioning

confidence: 99%

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Regionalisation of chemical variability in European mountain lakes

et al. 2009

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1. We carried out a coordinated survey of mountain lakes covering the main ranges across Europe (including Greenland), sampling 379 lakes above the local tree line in 2000. The objectives were to identify the main sources of chemical variability in mountain lakes, define a chemical classification of lakes, and develop tools to extrapolate our results to regional lake populations through an empirical regionalisation or upscaling of chemical properties. 2. We investigated the main causes of chemical variability using factor analysis (FA) and empirical relationships between chemistry and several environmental variables. Weathering, sea salt inputs, atmospheric deposition of N and S, and biological activity in soils of the catchment were identified as the major drivers of lake chemistry. 3. We tested discriminant analysis (DA) to predict the lake chemistry. It was possible to use the lithology of the catchments to predict the range of Ca 2+ and SO 4 2) into which a lake of unknown chemistry will decrease. Lakes with lower SO 4 2) concentrations have little geologically derived S, and better reflect the variations in atmospheric S loading. The influence of marine aerosols on lakewater chemistry could also be predicted from the minimum distance to the sea and altitude of the lakes. 4. The most remarkable result of FA was to reveal a factor correlated to DOC (positively) and NO 3 ) (negatively). This inverse relationship might be the result either of independent processes active in the catchment soils and acting in an opposite sense, or a direct interaction, e.g. limitation of denitrification by DOC availability. Such a relationship has been reported in the recent literature in many sites and at all scales, appearing to be a global pattern that could reflect the link between the C and N cycles. 5. The concentration of NO 3 ) is determined by both atmospheric N deposition and the processing capacity of the catchments (i.e. N uptake by plants and soil microbes). The fraction of the variability in NO 3 ) because of atmospheric deposition is captured by an independent factor in the FA. This is the only factor showing a clear pattern when mapped over Europe, indicating lower N deposition in the northernmost areas. 6. A classification has been derived which takes into account all the major chemical features of the mountain lakes in Europe. FA provided the criteria to establish the most important factors influencing lake water chemistry, define classes within them, and classify the surveyed lakes into each class. DA can be used as a tool to scale up the classification to unsurveyed lakes, regarding sensitivity to acidification, marine influence and sources of S.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Environmental Variablesmentioning

confidence: 99%

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