Klesse S, Etzold S, Frank D (2016) Integrating tree-ring and inventory-based measurements of aboveground biomass growth: research opportunities and carbon cycle consequences from a large snow breakage event in the Swiss Alps. European Journal of Forest Research, 135 (2): 297-311. [10.1007/s10342-015-0936-5]


Dendrochronology, Aboveground biomass increment, Interannual variability, Drought, Disturbance


The temporal variability of the forest sink is associated with high uncertainties in both its magnitude and the driving ecological and climatic processes. In this study, we assess the inter-annual variability (IAV) of carbon uptake using annually resolved aboveground biomass increment (ABI) estimates from 272 pseudorandomly sampled trees at a long-term monitoring plot in the dry valley of the Valais in Switzerland. Over the 1950–2011 period, the mean ABI is 86.8 g C m-2 year-1 with an IAV of ±31 %. The IAV is largely driven by hydrological conditions throughout the water year from previous August to current August (r SPEI = 0.56; 1st differenced r = 0.75, p < 0.001). During extremely dry years (such as 1972, 1976, 1998, and 2011), the carbon accumulation was reduced up to 63 % from the long-term mean. Furthermore, our analysis explores possible biases of annual ABI derived from manual band dendrometers in permanent plot inventories caused by water status related changes in tree size. During the snow breakage event in March 2012 and subsequent management activities, 17 % of the standing biomass was killed. We estimate that the remaining trees will take ~16 years to make up for the loss of this disturbance, assuming a similar growth rate of the remaining trees as during the previous 60 years and that a potentially drier climate and the increased water availability for the remaining trees will balance each other. We demonstrate that well-replicated, representative tree-ring datasets have a huge potential to complement shorter-term and lower-resolution forest inventory monitoring data. Integrating tree-ring and plot data allow one to gain knowledge about annual changes in forest productivity even before monitoring started and help ecosystem managers to better adapt their strategies.

LWF Classification

Network: LWF, Sites: Lens, Category: ISI,