Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration

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Abstract

A remote sensing approach is used to examine the direct and indirect effects of snow cover duration and weather conditions on the growth response of mountain grasslands located above the tree line in the French Alps. Time-integrated Normalized Difference Vegetation Index (NDVIint), used as a surrogate for aboveground primary productivity, and snow cover duration were derived from a 13-year long time series of the Moderate-resolution Imaging Spectroradiometer (MODIS). A regional-scale meteorological forcing that accounted for topographical effects was provided by the SAFRAN-CROCUS-MEPRA model chain. A hierarchical path analysis was developed to analyze the multivariate causal relationships between forcing variables and proxies of primary productivity. Inter-annual variations in primary productivity were primarily governed by year-to-year variations in the length of the snow-free period and to a much lesser extent by temperature and precipitation during the growing season. A prolonged snow cover reduces the number and magnitude of frost events during the initial growth period but this has a negligible impact on NDVIint as compared to the strong negative effect of a delayed snow melting. The maximum NDVI slightly responded to increased summer precipitation and temperature but the impact on productivity was weak. The period spanning from peak standing biomass to the first snowfall accounted for two-thirds of NDVIint and this explained the high sensitivity of NDVIint to autumn temperature and autumn rainfall that control the timing of the first snowfall. The ability of mountain plants to maintain green tissues during the whole snow-free period along with the relatively low responsiveness of peak standing biomass to summer meteorological conditions led to the conclusion that the length of the snow-free period is the primary driver of the inter-annual variations in primary productivity of mountain grasslands.

Figures

  • Figure 1. (a) Location map of the 121 polygons across the 17 climatologically defined massifs of the French Alps. (b) Number of polygons per massif.
  • Figure 2. Yearly course of NDVI and NDSI showing the different variables used in this study: date of snowmelt (TSNOWmelt), maximum NDVI (NDVImax) and date of NDVImax (TNDVImax), date of snowfall (TSNOWfall), length of the snow-free period (Psf), length of the initial growth period (Pg), length of the senescence period (Ps), and time-integrated NDVI over the growth period (NDVIint,g) and over the senescence period (NDVIint,s).
  • Figure 3. Frequency distribution of the relative contribution of Pg and Ps to Psf (a), of NDVIint,g and NDVIint,s to NDVIint (b), and of GPPint,g and GPPint,s to GPPint (c). Values were calculated for each year and for each polygon.
  • Figure 4. Inter-annual standardized anomalies for NDVImax (a), Psf (b), NDVIint (c), and GPPint (d).
  • Table 1. Variance partitioning into between-polygon and between-year components for the set of predictors and growth responses included in the path analysis.
  • Figure 5. Path analysis diagram showing the interacting effects of meteorological forcing, snow cover duration, and NDVImax on NDVIint (a, b) and GPPint (c, d). For each proxy of productivity, separate models for the period of growth (a, c) and the period of senescence (b, d) are shown. Line thickness of arrows is proportional to standardized path coefficients which are indicated on the right or above each arrow. Values in italics indicate paths that can be removed without penalizing model AIC (see Table 2). A solid line (or dotted lines) indicates a significant positive (or negative) effect at P < 0.05. Double-lined arrows correspond to fixed parameters. Abbreviations include TEMP, averaged daily mean temperature (or senescence period); PREC, averaged daily sum of precipitation; and FrEv, number of frost events. Letter g (or s) represents the initial growth period (or the senescence period), spring the months of March and April, and fall the months of October and November.
  • Table 2. Model fit of competing path models. AIC is the Akaike information criteria value and 1AIC is the difference in AIC between the best model and alternative models.
  • Table 3. Relationships between mean temperature or precipitation of polygons and the path coefficients estimated at the polygon level. Only significant relationships are shown.

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CITATION STYLE

APA

Choler, P. (2015). Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration. Biogeosciences, 12(12), 3885–3897. https://doi.org/10.5194/bg-12-3885-2015

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