Changes in productivity partitioning induced by precipitation extremes increase inaccuracy of grassland carbon estimation.

The fraction of net primary productivity (NPP) allocated to belowground organs (fBNPP) in grasslands is a critical parameter in global carbon cycle models; moreover, understanding the effect of precipitation changes on this parameter is vital to accurately estimating carbon sequestration in grassland ecosystems. However, how fBNPP responds to temporal precipitation changes along a gradient from extreme drought to extreme wetness, remains unclear, mainly due to the lack of long-term data of belowground net primary productivity (BNPP) and the fact that most precipitation experiments did not have a gradient from extreme drought to extreme wetness. Here, by conducting both a precipitation gradient experiment (100-500 mm) and a long-term observational study (34 years) in the Inner Mongolia grassland, we showed that fBNPP decreased linearly along the precipitation gradient from extreme drought to extreme wetness due to stronger responses in aboveground NPP to drought and wet conditions than those of BNPP. Our further meta-analysis in grasslands worldwide also indicated that fBNPP increased when precipitation decreased, and the vice versa. Such a consistent pattern of fBNPP response suggests that plants increase the belowground allocation with decreasing precipitation, while increase the aboveground allocation with increasing precipitation. Thus, the linearly decreasing response pattern in fBNPP should be incorporated into models that forecast carbon sequestration in grassland ecosystems; failure to do so will lead to underestimation of the carbon stock in drought years and overestimation of the carbon stock in wet years in grasslands.

Estimating forest floor carbon stocks in woodland formations in Spain.

The forest floor C stock needs to be accurately estimated in order to quantify its contribution to nutrient cycling and other ecological processes as well as for reporting purposes under international agreements. Hence, a modelling approach was used which involved testing three different types of models (GLM, GAM and random forest) to determine which one provided the best estimates of forest floor C stocks. The dataset employed contained over 1650 observations from different available sources embracing different climatic, topographic and biotic variables to be tested in the model. The approach that provided the best estimation of forest floor C stock was the random forest method, with forest type, latitude, altitude, canopy cover, mean summer temperature, annual accumulated temperature, summer precipitation, water deficit and the normalized difference vegetation index (NDVI) as covariates. To obtain a robust forecast, several iterations of the model were performed to estimate forest floor C stocks from the mean of the predictions. The model estimated a forest floor C stock of 0.148 ± 0.081 Pg, equivalent to a biomass of 0.381 ± 0.214 Pg, for a wooded area of almost 184,000 km2 in peninsular Spain and the Balearic Islands. The predictions were also presented in the form of a map showing the spatial distribution of the forest floor C stock. The results revealed a mean forest floor C stock of 8 Mg C ha-1 for Spanish forests and identified differences between coniferous (10.1 Mg C ha-1) and hardwood forests (6.3 Mg C ha-1).

Assessing changes of carbon stock in dipterocarp forest due to hydro-electric dam construction in Malaysia.

Deforestation and forest degradation are among the leading global concerns, as they could reduce the carbon sink and sequestration potential of the forest. The impoundment of Kenyir River, Hulu Terengganu, Malaysia, in 1985 due to the development of hydropower station has created a large area of water bodies following clearance of forested land. This study assessed the loss of forest carbon due to these activities within the period of 37 years, between 1972 and 2019. The study area consisted of Kenyir Lake catchment area, which consisted mainly of forests and the great Kenyir Lake. Remote sensing datasets have been used in this analysis. Satellite images from Landsat 1-5 MSS and Landsat 8 OLI/TRIS that were acquired between the years 1972 and 2019 were used to classify land uses in the entire landscape of Kenyir Lake catchment. Support vector machine (SVM) was adapted to generate the land-use classification map in the study area. The results show that the total study area includes 278,179 ha and forest covers dominated the area for before and after the impoundment of Kenyir Lake. The assessed loss of carbon between the years 1972 and 2019 was around 8.6 million Mg C with an annual rate of 0.36%. The main single cause attributing to the forest loss was due to clearing of forest for hydro-electric dam construction. However, the remaining forests surrounding the study area are still able to sequester carbon at a considerable rate and thus balance the carbon dynamics within the landscapes. The results highlight that carbon sequestration scenario in Kenyir Lake catchment area shows the potential of the carbon sink in the study area are acceptable with only 17% reduction of sequestration ability. The landscape of the study area is considered as highly vegetated area despite changes due to dam construction.