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- Liu, Li, et al.
(författare)
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ERA5-Land overestimates runoff coefficient but underestimates runoff recession rate in the central Tibetan permafrost region
- 2024
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Ingår i: JOURNAL OF HYDROLOGY-REGIONAL STUDIES. - 2214-5818. ; 53
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Tidskriftsartikel (refereegranskat)abstract
- Study region: The source region of Yangtze River (SRYR) Study focus: Better understanding the influence of soil freeze/thaw (F/T) activity on runoff production is vital for cold regions like the Qinghai-Tibet Plateau. While global reanalysis products offer full spatial coverage and fine temporal resolution for runoff estimates, their accuracy in depicting runoff dynamics within permafrost-dominated regions remains uncertain. To address this gap, a thorough assessment of the ERA5-Land runoff products within the SRYR was undertaken to determine their effectiveness in representing specific permafrost hydrological mechanisms. New hydrological insights for the region: Our analysis reveals that ERA5-Land adequately captures the intra-annual variability of runoff, but tends to overestimate runoff coefficients while underestimate runoff recession rates. The monthly runoff coefficient fluctuates during the thaw season, showing decreasing trends from May to increasing trends after July. The runoff recession rate generally shifts from acceleration to retardation during the freezing period (October-March), influenced significantly by snow cover and soil F/T dynamics. There's a discernible delay in the observed transition time (0.3 days/a) of runoff recession from 1980 to 2020, closely linked (R>0.35, p<0.05) to soil F/T indices. Notably, the simulated transition time lacks significant trends, and displays contrasting correlations with soil F/T indices. This highlights a critical need for the ERA5-Land model to refine its representation of soil F/T dynamics, particularly snow physics and soil thermal schemes.
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| 2. |
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| 3. |
- Xing, Xiuli, et al.
(författare)
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Modeling China's terrestrial ecosystem gross primary productivity with BEPS model : Parameter sensitivity analysis and model calibration
- 2023
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Ingår i: Agricultural and Forest Meteorology. - 0168-1923. ; 343
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Tidskriftsartikel (refereegranskat)abstract
- Terrestrial ecosystems are the largest sink for carbon, and their ecosystem gross primary productivity (GPP) regulates variations in atmospheric carbon dioxide (CO2) concentrations. Current process-based ecosystem models used for estimating GPP are subject to large uncertainties due to poorly constrained parameter values. In this study, we implemented a global sensitivity analysis (GSA) on parameters in the Boreal Ecosystem Productivity Simulator (BEPS) considering the parameters’ second-order impacts. We also applied the generalized likelihood estimation (GLUE) method, which is flexible for a multi-parameter calibration, to optimize the GPP simulation by BEPS for 10 sites covering 7 plant functional types (PFT) over China. Our optimized results significantly reduced the uncertainty of the simulated GPP over all the sites by 17 % to 82 % and showed that the GPP is sensitive to not only the photosynthesis-related parameters but also the parameters related to the soil water uptake as well as to the energy balance. The optimized GPP across South China showed that the mix forest, shrub, and grass have a higher GPP and are more controlled by the soil water availability. This study showed that the GLUE method together with the GSA scheme could constrain the ecosystem model well when simulating GPP across multiple ecosystems and provide a reasonable estimate of the spatial and temporal distribution of the ecosystem GPP over China. We call for more observations from more sites, as well as data on plant traits, to be collected in China in order to better constrain ecosystem carbon cycle modeling and understand its response to climate change.
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| 4. |
- Zhang, Wenxin, et al.
(författare)
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Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000–2018
- 2023
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 1879-1026 .- 0048-9697.
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Tidskriftsartikel (refereegranskat)abstract
- Southwest China has been the largest terrestrial carbon sink in China over the past 30 years, but has recently experienced a succession of droughts caused by high precipitation variability, potentially threatening vegetation productivity in the region. Yet, the impact of precipitation anomalies on the vegetation primary productivity is poorly known. We used an asymmetry index (AI) to explore possible asymmetric productivity responses to precipitation anomalies in Southwest China from 2003 to 2018, using a precipitation dataset, combined with gross primary productivity (GPP), net primary productivity (NPP), and vegetation optical depth (VOD) products. Our results indicate that the vegetation primary productivity of Southwest China shows a negative asymmetry, suggesting that the increase of vegetation primary productivity during wet years exceeds the decrease during dry years. However, this negative asymmetry of vegetation primary productivity was shifted towards a positive asymmetry during the period of analysis, suggesting that the resistance of vegetation to drought, has increased with the rise in the occurrence of drought events. Among the different biomes, grassland vegetation primary productivity had the highest sensitivity to precipitation anomalies, indicating that grasslands are more flexible than other biomes and able to adjust primary productivity in response to precipitation anomalies. Furthermore, our results showed that the asymmetry of vegetation primary productivity was influenced by the effects of temperature, precipitation, solar radiation, and anthropogenic and topographic factors. These findings improve our understanding of the response of vegetation primary productivity to climate change over Southwest China.
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