Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain

Regulation of biophysical drivers on carbon and water fluxes over a warm-temperate plantation in northern China

Plantations have great potential for carbon sequestration and play a vital role in the water cycle. However, it is challenging to accurately estimate the carbon and water fluxes of plantations, and the impact of biophysical drivers on the coupling of carbon and water fluxes is not well understood. Thus, we modified the phenology module of the Biome-BGC model and optimized the parameters with the aim of simulating the gross primary productivity (GPP), evapotranspiration (ET) and water use efficiency (WUE) of a warm-temperate plantation in northern China from 2009 to 2020. Photosynthetically active radiation (PAR) showed significant positive correlations on GPP and WUE during the first stage of the growing season (S1: from early April to late July). Active accumulated temperature (Taa) mainly controlled the changes in GPP and ET during the second stage (S2: between the end of July and early November). Throughout the growing season, soil water content dominated daily GPP and WUE, whereas Taa regulated ET. The optimized Biome-BGC model performed better than the original model in simulating GPP and ET. Compared with the values simulated by the original model, root mean square error decreased by 7.89 % and 15.97 % for the simulated GPP and ET, respectively, while the determination coefficient increased from 0.77 to 0.81 for simulated GPP and from 0.51 to 0.62 for simulated ET. The results of this study demonstrated that the optimized model more accurately assessed carbon sequestration and water consumption in plantations.

Growing-season carbon budget of alpine meadow ecosystem in the Qinghai Lake Basin: a continued carbon sink through this century according to the Biome-BGC model

BACKGROUND

The alpine meadow is one of the most important ecosystems in the Qinghai-Tibet Plateau (QTP), and critically sensitive to climate change and human activities. Thus, it is crucial to precisely reveal the current state and predict future trends in the carbon budget of the alpine meadow ecosystem. The objective of this study was to explore the applicability of the Biome-BGC model (BBGC) in the Qinghai Lake Basin (QLB), identify the key parameters affecting the variation of net ecosystem exchange (NEE), and further predict the future trends in carbon budget in the QLB.

RESULTS

The alpine meadow mainly acted as carbon sink during the growing season. For the eco-physiological factors, the YEL (Yearday to end litterfall), YSNG (Yearday to start new growth), CLEC (Canopy light extinction coefficient), FRC:LC (New fine root C: new leaf C), SLA (Canopy average specific leaf area), C:Nleaf (C:N of leaves), and FLNR (Fraction of leaf N in Rubisco) were confirmed to be the top seven parameters affecting carbon budget of the alpine meadow. For the meteorological factors, the sensitivity of NEE to precipitation was greater than that to vapor pressure deficit (VPD), and it was greater to radiation than to air temperature. Moreover, the combined effect of two different meteorological factors on NEE was higher than the individual effect of each one. In the future, warming and wetting would enhance the carbon sink capacity of the alpine meadow during the growing season, but extreme warming (over 3.84 ℃) would reduce NEE (about 2.9%) in the SSP5-8.5 scenario.

CONCLUSION

Overall, the alpine meadow ecosystem in the QLB generally performs as a carbon sink at present and in the future. It is of great significance for the achievement of the goal of carbon neutrality and the management of alpine ecosystems.

Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series

The main objective of this study is to analyze the spatial and temporal variability of gross and net primary production (GPP and NPP) in Peninsular Spain across 15 years (2004–2018) and determine the relationship of those carbon fluxes with precipitation and air temperature. A time series study of daily GPP, NPP, mean air temperature, and monthly standardized precipitation index (SPI) at 1 km spatial resolution is conducted to analyze the ecosystem status and adaptation to changing environmental conditions. Spatial variability is analyzed for vegetation and specific forest types. Temporal dynamics are examined from a multiresolution analysis based on the wavelet transform (MRA-WT). The Mann–Kendall nonparametric test and the Theil–Sen slope are applied to quantify the magnitude and direction of trends (increasing or decreasing) within the time series. The use of MRA-WT to extract the annual component from daily series increased the number of statistically significant pixels. At pixel level, larger significant GPP and NPP negative changes (p-value < 0.1) are observed, especially in southeastern Spain, eastern Mediterranean coastland, and central Spain. At annual temporal scale, forests and irrigated crops are estimated to have twice the GPP of rainfed crops, shrublands, grasslands, and sparse vegetation. Within forest types, deciduous broadleaved trees exhibited the greatest annual NPP, followed by evergreen broadleaved and evergreen needle-leaved tree species. Carbon fluxes trends were correlated with precipitation. The temporal analysis based on daily TS demonstrated an increase of accuracy in the trend estimates since more significant pixels were obtained as compared to annual resolution studies (72% as to only 17%).

Study on the Relationship between Topological Characteristics of Vegetation Ecospatial Network and Carbon Sequestration Capacity in the Yellow River Basin, China

Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (p < 0.05, R2 = 0.59). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (p < 0.01, R2 = 0.49). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting.