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

Abstract

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 (NDVI int ), 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 NDVI int 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 NDVI int and this explained the high sensitivity of NDVI int 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 snowfree 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.

Resilience as a framework for analyzing the adaptation of mountain summer pasture systems to climate change

“…We used an iterative process to combine: (1) an analysis of the tools used by extension officers and the way they are used in the field, which may differ from the initial objective and reveal insights into the functioning of the system (Gross et al 2011); (2) 10 working sessions between April 2013 and February 2015 with 13 experts (Table 1, among which are three of the authors of this paper) involved for part or all of the process (Fig. 2); (3) a selection of scientific research to confirm certain hypotheses regarding ecological functioning (Jonas et al 2008, Choler 2015 or to derive inspiration from management models focusing on the management of climate variability (Moulin et al 2001); and (4) a panel of diversified case studies among the sample of cases from the Sentinel summer pastures program (sheep and cattle, local and transhumant farmers, individual and collective organizations) that were used as examples for proof of concept. The conceptual model is mainly based on scientific and technical knowledge, but extension officers also tried to report empirical knowledge learned from their exchanges with herders.…”

On the Importance of High-Resolution Time Series of Optical Imagery for Quantifying the Effects of Snow Cover Duration on Alpine Plant Habitat

“…Growing season length, as determined by snow cover duration, has been associated with (i) patterns of taxonomic and functional diversity in alpine plant communities [23,24] as well as diversity patterns across plant and microbe trophic levels [25]; and (ii) ecosystem functioning, including primary productivity and phenology [26,27], and nutrient cycling [28]. Turnover in alpine plant diversity typically occurs over short distances (<50 m) in the form of complex mosaics that vary according to topographic heterogeneity [29], which necessitates quantifying changes in environmental conditions at this scale [30].…”

“…For example, time-integrated values of NDVI have been used as a surrogate for aboveground primary productivity in mountainous environments [27,76]. High-resolution data will enable relating these optically-derived proxies of ecosystem functioning to community-scale diversity metrics, whether taxonomic or functional.…”

Analysis of Spatial-Temporal Variation of Land Surface Temperature, Vegetation and Snow Cover in Lar National Park of Iran

“…In addition, it should be considered that climate change in Iran's rangeland will affect approximately 81 million various livestock that depends on rangelands to supply water and food (UNFCCC2002, Amiri et al 2010) Vegetation phenology and production in the cold and wet rangelands primarily determined by the accumulation of snowmelt. The time of snow cover indirectly controls the distribution of many plant species by enhancing the length of the growing season (Billings and Bliss 1959, May et al 1982, Paudel and Andersen 2013, Choler 2015. Increasing temperature may result in a sped up snow melting and is at least partly responsible for enhancing vegetation activities.…”

“…In regions where water availability is not a limiting factor, higher temperatures are expected to allow for plant upslope migration and increased vegetative cover (Sawyer and Stephen 2014), shrub expansion (Fraser et al 2014) and earlier snowmelt (Choler 2015). Lillesand et al 2014 stated that "the normalized difference vegetation index is preferred to the simple index for global vegetation monitoring because the NDVI helps compensate for changing illumination conditions, surface slope, aspect, and other extraneous factors".…”