Impacts of Extreme Drought Events at Different Phenophases on the Aboveground Net Primary Productivity (Anpp) and its Photosynthetic Physiological Regulatory Process of Artemisia Ordosica
Chunyuan WANGZhi QuanGuodong DingYuanyuan ZhaoLinlin ZhangF.-S. ZhangZe-Bao ZhengBo LiuZhaoyan DiaoXuewei ShiMinghan Yu
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Climate change has intensified the frequency of extreme drought events in desert ecosystems, accompanied by uneven distribution of annual precipitation. Whether extreme precipitation events at different phenophases have equivalent impacts on desert plants is an unverified topic, yet it is crucial for understanding the mechanisms of vegetation adaptation to changes in precipitation. This study focuses on the typical desert plant Artemisia ordosica and employs in situ precipitation control experiments using rain shelters to simulate extreme drought events (30 consecutive days of precipitation removal) at three phenophases: the sprouting stage, vegetative growth stage, and flowering and fruiting stage. Against this backdrop, phenological differences in the leaf photosynthetic physiological regulatory mechanisms that affect the accumulation of Aboveground Net Primary Productivity (ANPP) in A. ordosica under extreme drought events were explored, including parameters such as photosynthetic gas exchange, chlorophyll fluorescence, and antioxidant enzymes. The findings reveal that: (1) Extreme drought events at different phenophases markedly reduced the photosynthesis of A. ordosica leaves, subsequently leading to the significantly reduction in ANPP accumulation (p<0.05). With the impact degree ordered as follows: flowering and fruiting stage > sprouting stage > vegetative growth stage; (2) During extreme drought events, A. ordosica experiences a decrease in photosynthetic gas exchange capacity and an enhancement in water use efficiency, which are stomatal regulatory responses. Additionally, there is an increase in thermal dissipation, a decline in photochemical activity parameters (such as potential photosynthetic activity of PSII, initial light energy conversion efficiency, actual photochemical quantum yield, and photochemical quenching), and an augmentation of the antioxidant enzyme system, which are non-stomatal regulatory responses; (3) During extreme drought events at different phenophases, the dominant factor leading to a decline in the photosynthetic rate of A. ordosica leaves is stomatal regulation. However, there are phenological differences in the sensitivity of stomatal and non-stomatal regulation. The stomatal regulation of A. ordosica leaves during the sprouting stage is more sensitive compared to other phenophases. Non-stomatal regulation is most sensitive during the vegetative growth stage, with a heightened sensitivity in the modulation of chlorophyll fluorescence. The study reveals differences in the photosynthetic physiological regulation of desert vegetation in response to extreme drought events at different phenophases, offering an innovative perspective on the physiological and ecological regulatory mechanisms of desert ecosystems in the face of climate change.Keywords:
Primary productivity
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Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP) and annual net primary production (NPP) are contained in MODerate Resolution Imaging Spectroradiometer (MODIS) products (MOD17), which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS) LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types.
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Net primary production links the biosphere and the climate system through the global cycling of carbon, water and nutrients. Accurate quantification of net primary productivity (NPP) is therefore critical in understanding the response of the world’s ecosystems to global climate change, and how changes in ecosystems might themselves feed back to the climate system. Twelve model estimates of long-term annual NPP for the Australian continent were reviewed. These models varied considerably in the approaches adopted and the inputs required. The model estimates ranged 5-fold, from 0.67 to 3.31 Gt C y–1. Within-continent variation was similarly large, with most of the discrepancies occurring in the arid zone of Australia, which comprises most of the continent. It is also within this zone that empirical NPP data are most lacking. Comparison with a recent global-scale analysis of six dynamic global vegetation models showed a similar level of variability in continental total NPP, 0.38 to 2.85 Gt C y–1, and similar within-continent spatial variability. As a first tentative step towards model validation the twelve NPP estimates were compared with existing field measurements, although the ability to reach definitive conclusions was limited by insufficient data, and incompatibilities between the field-based observations and the model predictions. It was concluded that the current NPP-modelling capability falls short of the accuracy required for effective application in understanding the terrestrial biospheric implications of global atmospheric / climatic change. Potential methods that could be used in future work for improving modelled estimates of Australian continental NPP and their validation are discussed. These include increasing the spatial coverage of empirical NPP estimates within arid ecosystems, the use of existing high quality site data for more detailed model exploration, and a formal model inter-comparison using uniform driver datasets to investigate more intensively differences in model behaviour and assumptions.
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ORNL DAAC: In many grasslands, aboveground net primary productivity (ANPP) is commonly estimated by measuring peak aboveground biomass. Estimates of belowground net primary productivity (BNPP), and consequently, total net primary productivity (NPP), are more difficult. We addressed one of the three main objectives of the Global Primary Productivity Data Initiative for grassland systems - to develop simple models or algorithms to estimate missing components of total system NPP.
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Based on light-use efficiency model,a net primary productivity model of grassland based on EOS/MODIS data was proposed,which considered the influence of temperature and surface water vapor pressure.All the parameters needed were retrieved from MODIS data except the monthly percentage of sunshine hour.The monthly NPP of grassland was calculated using MODIS data with 1km×1km resolution and monthly percentage of sunshine hour of 586 meteorological stations.And then the annual NPP was accumulated.The average NPP of grassland of China is 239.02gcm~(-2)a~(-1) in 2003.The result showed that the spatial distribution of NPP accord with the practical situation.
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