Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales-review and recommendations.

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales-sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

Predicting the decline in daily maximum transpiration rate of two pine stands during drought based on constant minimum leaf water potential and plant hydraulic conductance.

The effect of drought on forest water use is often estimated with models, but comprehensive models require many parameters, and simple models may not be sufficiently flexible. Many tree species, Pinus species in particular, have been shown to maintain a constant minimum leaf water potential above the critical threshold for xylem embolism during drought. In such cases, prediction of the relative decline in daily maximum transpiration rate with decreasing soil water content is relatively straightforward. We constructed a soil-plant water flow model assuming constant plant conductance and daily minimum leaf water potential, but variable conductance from soil to root. We tested this model against independent data from two sites: automatic shoot chamber data and sap flow measurements from a boreal Scots pine (Pinus sylvestris L.) stand; and sap flow measurements from a maritime pine (Pinus pinaster Ait.) stand. To focus on soil limitations to water uptake, we expressed daily maximum transpiration rate relative to the rate that would be obtained in wet soil with similar environmental variables. The comparison was successful, although the maritime pine stand showed carry-over effects of the drought that we could not explain. For the boreal Scots pine stand, daily maximum transpiration was best predicted by water content of soil deeper than 5 cm. A sensitivity analysis revealed that model predictions were relatively insensitive to the minimum leaf water potential, which can be accounted for by the importance of soil resistance of drying soil. We conclude that a model with constant plant conductance and minimum leaf water potential can accurately predict the decline in daily maximum transpiration rate during drought for these two pine stands, and that including further detail about plant compartments would add little predictive power, except in predicting recovery from severe drought.