Drought-Induced Carbon and Water Use Efficiency Responses in Dryland Vegetation of Northern China

CMIP6 ESMs overestimate greening and the photosynthesis trends in Dryland East Asia

The Dryland East Asia (DEA) is one of the largest inland arid regions, and vegetation is very sensitive to climate change. The complex environment in DEA with defects of modeling construction make it difficult to simulate and predict changes in vegetation structure and productivity. Here, we use the emergent constraint (EC) method to constrain the future interannual leaf area index (LAI) and gross primary productivity (GPP) trends in DEA, under four scenarios of the latest Sixth Coupled Model Intercomparison Project (CMIP6) model ensemble. LAI and GPP increase in all scenarios in the near term (2015-2050), with continued growth in SSP370 and SSP585 and stasis in SSP126 and SSP245 in the far term (2051-2100). However, after building effective EC relationships, the constrained increasing trends of LAI (GPP) are reduced by 43.5 %-53.9 % (30.5 %-50.0 %) compared with the uncertainties of the original ensemble, which are reduced by 10.0 %-45.7 % (4.6 %-34.3 %). We also extend the EC in moving windows and grid cells, further strengthening the robustness of the constraints, especially by illustrating spatial sources of these emergent relationships. Overestimations of LAI and GPP trends suggest that current CMIP6 models may be insufficient to capture the complex relationships between climate change and vegetation dynamics in DEA; however, these models can be adjusted based on established emergent relationships.

Spatio-temporal patterns and control mechanism of the ecosystem carbon use efficiency across the Mongolian Plateau

Carbon use efficiency (CUE) is a crucial parameter that reflects the carbon storage within ecosystems, providing insight into the potential for carbon sequestration at the ecosystem scale and its feedback on climate change. The Mongolian Plateau exemplifies an arid and semi-arid region with a delicate ecological environment that displays heightened sensitivity to global climate change. Understanding the variation and control of CUE is critical for assessing regional carbon. However, few studies have focused on the interaction of factors influencing CUE; furthermore, how CUE responds to climate change and anthropogenic activities remains unclear. Here, we aimed to investigate spatiotemporal patterns and their control mechanisms by generating CUE data based on multi-source remote sensing data. CUE demonstrated a slow downward trend from 2000 to 2018, with higher values in relatively dry-cool regions and lower values in relatively humid-warm regions. Furthermore, CUE values were ranked by biome as follows: grassland > sandy vegetation > cropland > shrubs > forest, driven by climate characteristics, vegetation coverage, water stress, stand age, and management practices. Additionally, climatic factors affected CUE more than the soil variables, except for alpine meadows. The climate factors of precipitation (PPT), index of water availability (IWA) (QPPT = 0.487, QIWA = 0.444), and soil factors, e.g., pH and soil organic content (SOC) (QPH = 0.397, QSOC = 0.372), had the greatest influence on CUE. Finally, most two explanatory factors interacted to effectively enhance the explanation of CUE; the synergy of the IWA and PPT contributed the most to CUE (QIWA∩PPT = 0.604). Moreover, the joint effect of climate change and anthropogenic activities was identified as the major contributor (68 %) to the decline in CUE within this region. This study presents compelling evidence highlighting the importance of considering climate change and anthropogenic disturbances in ecosystem management and conservation efforts in arid and semi-arid regions.

Assessment of the spatiotemporal characteristics of vegetation water use efficiency in response to drought in Inner Mongolia, China

Ecosystem water use efficiency (eWUE) can be used to obtain a better comprehension of the ecosystem water-carbon cycle. This study aimed to characterize the regional-scale responses and adaptations of different vegetation categories to drought changes and the spatiotemporal characteristics of WUE and associated drought factors for nine vegetation categories in Inner Mongolia, China, from 2000 to 2020. This study estimated drought, the association between drought and eWUE among varying vegetation categories, and the differences in eWUE between the drought stage and the post-drought stage by analyzing the spatiotemporal variations in eWUE of different vegetation categories based on MODIS ET (evapotranspiration), GPP (gross primary productivity), and temperature vegetation drought index data. The results illustrated the following: (1) the multi-year mean eWUE from 2000 to 2020 was 1.03 g·m^-2·mm^-1, with an overall significantly increasing trend of 0.008 g·m^-2·mm^-1 and eWUE decreasing from northeast to southwest. (2) The rank of vegetation types in Inner Mongolia according to multi-year mean eWUE was evergreen coniferous forest > savanna > evergreen broadleaf forest > forested grassland > farmland > deciduous broadleaf forest > mixed forest > closed scrub > grassland. All vegetation categories illustrated an increasing trend in eWUE over time. (3) eWUE was inversely associated with drought in the drought stage and a clear effect of drought legacy was identified in which harsh drought impacted the eWUE of the ecosystem, whereas eWUE was positively associated with drought. (4) The eWUE values of ecosystems increased significantly after drought, indicating that ecosystems that are adapted to drought show high capacity to recovery from drought stress.

The Effect of Vegetative Coverage and Altitude on the Vegetation Water Consumption in the Alpine Inland River Basin of the Northeastern Qinghai–Tibet Plateau

Estimating accurately the vegetation water consumption (VWC) in the Qinghai Lake Basin (QLB) is conducive to the effective utilization and management of water resources in the QLB, which is of great significance to the construction of a national park in the QLB. We used Geographic Information System (GIS) technology and remote sensing (RS) technology based on potential evapotranspiration data to calculate the VWC in the QLB from 2000 to 2020, and analyzed the influencing factors of the VWC in the QLB from 2000 to 2020. The results showed that (1) the average value of the VWC in the QLB varied from 242.96 mm to 287.99 mm, the average value of the VWC was 267.07 mm, and the average value of the total VWC was 79.05 × 108 m3 from 2000 to 2020. (2) In terms of spatial variation of the VWC, the VWC in the QLB did not increase significantly from 2000 to 2014, however, the VWC in the QLB showed a significant increase from 2015 to 2020. (3) As the altitude gradient increases, the VWC in the QLB from 2000 to 2020 showed a significant downward trend with the increase in altitude. When the altitude increases by 100 m, the value of the VWC decreases by 13.47 mm from 2000 to 2014 and 22.8 mm from 2015 to 2020, respectively. (4) Exploring the influencing factors of the VWC in the QLB from 2000 to 2020, the results showed that the VWC was mainly affected by the average annual precipitation and normalized difference vegetation index (NDVI) from 2000 to 2014. It was mainly affected by the combined effects of annual temperature, precipitation, and vegetation coverage from 2015 to 2020. The VWC was mainly affected by the average annual temperature, precipitation, and vegetation coverage along the altitude gradient from 2000 to 2014. It was mainly affected by the average annual temperature and vegetation coverage in the QLB from 2015 to 2020. Obviously, vegetation coverage was the most important factor affecting the VWC regardless of spatial or altitude gradient variations.

Quantitative analysis of the contributions of land use change and CO2 fertilization to carbon use efficiency on the Tibetan Plateau

Carbon use efficiency (CUE) is a key element in the vegetation carbon cycle, and determines how vegetation allocates carbon. Here, our research provides the spatio-temporal variations of CUE on the Tibetan Plateau (TP) based on ensemble simulations from 12 terrestrial ecosystem models. Moreover, the experimental design of simulations adds one time-varying driver at a time, thus quantitative analysis of the response of CUE to climate factors (i.e., temperature, precipitation and radiation), land use and land cover change (LULCC), and CO₂ fertilization can be investigated. Results show that average CUE value of the multi-model simulations (0.583 ± 0.064) on the TP is slightly lower than that derived from the satellite-based product, the Moderate Resolution Imaging Spectroradiometer (0.646). However, CUE varies greatly among models due to differences in simulating plant photosynthetic productivity and respiratory rate, with range of 0.489-0.661. LULCC and CO₂ fertilization contribute 4.24% and 0.79% of the annual mean CUE, respectively. Among the climatic factors, temperature and precipitation have positive correlations with CUE over most areas of the TP while solar radiation shows a negative impact.