• Energy Research

AbstractSoil organic carbon (SOC) in coastal wetlands, also known as “blue C,” is an essential component of the global C cycles. To gain a detailed insight into blue C storage and controlling factors, we studied 142 sites across ca. 5000 km of coastal wetlands, covering temperate, subtropical, and tropical climates in China. The wetlands represented six vegetation types (Phragmites australis, mixed of P. australis and Suaeda, single Suaeda, Spartina alterniflora, mangrove [Kandelia obovata and Avicennia marina], tidal flat) and three vegetation types invaded by S. alterniflora (P. australis, K. obovata, A. marina). Our results revealed large spatial heterogeneity in SOC density of the top 1‐m ranging 40–200 Mg C ha−1, with higher values in mid‐latitude regions (25–30° N) compared with those in both low‐ (20°N) and high‐latitude (38–40°N) regions. Vegetation type influenced SOC density, with P. australis and S. alterniflora having the largest SOC density, followed by mangrove, mixed P. australis and Suaeda, single Suaeda and tidal flat. SOC density increased by 6.25 Mg ha−1 following S. alterniflora invasion into P. australis community but decreased by 28.56 and 8.17 Mg ha−1 following invasion into K. obovata and A. marina communities. Based on field measurements and published literature, we calculated a total inventory of 57 × 106 Mg C in the top 1‐m soil across China's coastal wetlands. Edaphic variables controlled SOC content, with soil chemical properties explaining the largest variance in SOC content. Climate did not control SOC content but had a strong interactive effect with edaphic variables. Plant biomass and quality traits were a minor contributor in regulating SOC content, highlighting the importance of quantity and quality of OC inputs and the balance between production and degradation within the coastal wetlands. These findings provide new insights into blue C stabilization mechanisms and sequestration capacity in coastal wetlands.

AbstractDrylands cover more than 40% of Earth's land surface and occur at the margin of forest distributions due to the limited availability of water for tree growth. Recent elevated temperature and low precipitation have driven greater forest declines and pulses of tree mortality on dryland sites compared to humid sites, particularly in temperate Eurasia and North America. Afforestation of dryland areas has been widely implemented and is expected to increase in many drylands globally to enhance carbon sequestration and benefits to the human environment, but the interplay of sometimes conflicting afforestation outcomes has not been formally evaluated yet. Most previous studies point to conflicts between additional forest area and water consumption, in particular water yield and soil conservation/desalinization in drylands, but were generally confined to local and regional scales. Our global synthesis demonstrates that additional tree cover can amplify water consumption through a nonlinear increase in evapotranspiration—depending on tree species, age, and structure—which will be further intensified by future climate change. In this review we identify substantial knowledge gaps in addressing the dryland afforestation dilemma, where there are trade‐offs with planted forests between increased availability of some resources and benefits to human habitats versus the depletion of other resources that are required for sustainable development of drylands. Here we propose a method of addressing comprehensive vegetation carrying capacity, based on regulating the distribution and structure of forest plantations to better deal with these trade‐offs in forest multifunctionality. We also recommend new priority research topics for dryland afforestation, including: responses and feedbacks of dryland forests to climate change; shifts in the ratio of ecosystem ET to tree cover; assessing the role of scale of afforestation in influencing the trade‐offs of dryland afforestation; and comprehensive modeling of the multifunctionality of dryland forests, including both ecophysiological and socioeconomic aspects, under a changing climate.

AbstractWinter snow is an important driver of tree growth in regions where growing‐season precipitation is limited. However, observational evidence of this influence at larger spatial scales and across diverse bioclimatic regions is lacking. Here, we investigated the interannual effects of winter (here defined as previous October to current February) snow depth on tree growth across temperate China over the period of 1961–2015, using a regional network of tree ring records, in situ daily snow depth observations, and gridded climate data. We report uneven effects of winter snow depth on subsequent growing‐season tree growth across temperate China. There shows little effect on tree growth in drier regions that we attribute mainly to limited snow accumulation during winter. By contrast, winter snow exerts important positive influence on tree growth in stands with high winter snow accumulation (e.g., in parts of cold arid regions). The magnitude of this effect depends on the proportion of winter snow to pre‐growing‐season (previous October to current April) precipitation. We further observed that tree growth in drier regions tends to be increasingly limited by warmer growing‐season temperature and early growing‐season water availability. No compensatory effect of winter snow on the intensifying drought limitation of tree growth was observed across temperate China. Our findings point toward an increase in drought vulnerability of temperate forests in a warming climate.

AbstractCritical Zone Science (CZS) explores the deep evolution of landscapes from the base of the groundwater or the saprolite‐rock interface to the top of vegetation, the zone that supports all terrestrial life. Here we propose a framework for CZS to evolve further as a discipline, building on 1st generation CZOs in natural systems and 2nd generation CZOs in human‐modified systems, to incorporate human behaviour for more holistic understanding in a 3rd generation of CZOs. This concept was tested in the China‐UK CZO programme (2016–2020) that established four CZOs across China on different lithologies. Beyond conventional CZO insights into soil resources, biogeochemical cycling and hydrology across scales, surveys of farmers and local government officials led to insights into human‐environment interactions and key pressures affecting the socio‐economic livelihoods of local farmers. These learnings combined with the CZS data identified knowledge exchange (KE) opportunities to unravel diverse factors within the Land‐Water‐Food Nexus, that could directly improve local livelihoods and environmental conditions, such as reduction in fertilizer use, contributing toward Sustainable Development Goals (SDGs) and environmental policies. Through two‐way local KE, the local cultural context and socio‐economic considerations were more readily apparent alongside the environmental rationale for policy and local action to improve the sustainability of farming practices. Seeking solutions to understand and remediate CZ degradation caused by human‐decision making requires the co‐design of CZS that foregrounds human behavior and the opinions of those living in human modified CZOs. We show how a new transdisciplinary CZO approach for sustainable Earth futures can improve alignment of research with the practical needs of communities in stressed environments and their governments, supporting social‐ecological and planetary health research agendas and improving capacity to achieve SDGs.

AbstractFeedbacks between the intertwined water and carbon cycles in semi‐arid mountain ecosystems can introduce large uncertainties into projections of carbon storage. In this study, we sought to understand the influence of key mechanisms on carbon balances, focusing on an ecosystem whose complex terrain and large interannual variability in precipitation adds to its vulnerability to warming. We applied a dynamic vegetation‐ecosystem model (Lund‐Potsdam‐Jena General Ecosystem Simulator) to simulate water‐carbon interactions in the 104,512 km2 Mediterranean‐climate ecosystems of California's Sierra Nevada for 1950–2099. Our 48 scenarios include a combination of carbon dioxide (CO2) increase, air temperature change, and varying plant rooting depths. We found that with warming (+2 and +5°C), water limitations on growth and enhanced soil respiration reduce carbon storage; however, CO2 fertilization and associated enhanced water‐use efficiency offset this loss. Using the 4 km model resolution to capture steep mountain precipitation gradients, plus accounting for the several meters of actual root‐accessible water storage in the region, were also important. With warming accompanied by CO2 fertilization our projections show that the Sierra Nevada sequestering at least 200 Tg (2 kg m−2) carbon, versus carbon loss with warming alone. The increase reflects coniferous forests growing at high elevations, and some increase in broadleaved forests at low‐to‐intermediate elevations. Importantly, uncertainty in fire disturbance could shift our finding from carbon sink to source. The improved mechanistic understanding of these feedbacks can advance modeling of carbon‐water interactions in mountain‐ecosystem under a warmer and potentially drier climate.

AbstractClimate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.

AbstractHemiboreal and boreal forests growing at the southern margin of the permafrost distribution are vulnerable to climate warming. However, how climate warming threatens the growth of dominant tree species that are distributed on permafrost remains to be determined, particularly in synchrony with warming‐induced permafrost degradation. Tree growth in the permafrost region of southern Siberia was hypothesized to be highly sensitive to temperature increasing and warming‐induced permafrost degradation. To test this hypothesis, we sampled the tree ring width of 535 trees of dominant species, larch (including Larix gmelinii and L. sibirica) and white birch (Betula platyphylla), in ten hemiboreal to boreal forest plots within different permafrost zones. The relationships between the tree ring basal area index (BAI) and temperature, precipitation, and the Palmer drought severity index (PDSI) were compared among plots located in two permafrost zones. In the isolated permafrost zone, white birch grows better than larch and is not drought‐stressed (p < .05). We suggest that the deep‐rooted white birch benefits from the water from thawing permafrost, while the growth of the shallow‐rooted larch is stressed by drought. In the sporadic discontinuous permafrost zone, both white birch and larch benefited from permafrost melting, but the sensitivity of larch growth to PDSI is still significant (p < .05), indicating drought is still an important climatic factor limiting the growth of larch. Our results imply that the permafrost degradation caused by climate warming affects tree growth by creating the root layer additional water source. In the future, it is necessary to focus on monitoring permafrost degradation to better predict forest dynamics at the southern margin of the permafrost distribution.