Optimized Application of Biome‐BGC for Modeling the Daily GPP of Natural Vegetation Over Peninsular Spain

Sánchez-Ruíz, Sergio; Chiesi, Marta; Fibbi, Luca; Carrara, Arnaud; Maselli, Fabio; Gilabert, María Amparo

doi:10.1002/2017jg004360

Cited by 19 publications

(13 citation statements)

References 50 publications

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“…Model validation is the process of evaluating the consistency and accuracy of the modeled results relative to field measurements. Biome-BGC has been assessed by performing sensitivity analyses [35] and validations against measured data, including forest [36][37][38], cropland [21], grassland [22,39], and all ecosystems [40]. In this study, we used field data collected from previous studies to evaluate the model's performance in predicting NPP dynamics (as shown in Table 1 for supporting information).…”

Section: Simulation Design and Model Validationmentioning

confidence: 99%

Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia

Han

Luo

et al. 2018

Sustainability

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The effect of climate change on the spatio-temporal patterns of the terrestrial carbon dynamics in Central Asia have not been adequately quantified despite its potential importance to the global carbon cycle. Therefore, the modified BioGeochemical Cycles (Biome-BGC) model was applied in this study to evaluate the impacts of climatic change on net primary productivity (NPP) and net ecosystem productivity. Four vegetation types were studied during the period 1979 to 2011: cropland, grassland, forest, and shrubland. The results indicated that: (1) The climate data showed that Central Asia experienced a rise in annual mean temperature and a decline in precipitation from 1979 to 2011; (2) the mean NPP for Central Asia in 1979–2011 was 281.79 gC m−2 yr−1, and the cropland had the highest NPP compared with the other vegetation types, with a value of 646.25 gC m−2 yr−1; (3) grassland presented as a carbon source (−0.21 gC m−2 yr−1), whereas the other three types were carbon sinks; (4) the four vegetation types showed similar responses to climate variation during the past 30 years, and grassland is the most sensitive ecosystem in Central Asia. This study explored the possible implications for climate adaptation and mitigation.

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Section: Simulation Design and Model Validationmentioning

confidence: 99%

Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia

Han

Luo

et al. 2018

Sustainability

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“…Although remote sensing can be used over large areas, the response of grassland ecosystems to grazing cannot be determined because remote sensing images only provide insight into real scenarios. Model simulation combined with observational data is considered the most effective method for studying the spatiotemporal dynamics of the grassland carbon budget and the response to grazing over large areas because it allows different scenarios (e.g., grazed and ungrazed scenarios) to be evaluated in the same region via model estimation (Anh Nguyet and Kawasaki, 2017;Sanchez-Ruiz et al, 2018;Tian et al, 2020). Observational data can then be used to validate the reliability of model outputs.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of the Carbon Budget and the Response to Grazing in Qinghai Grasslands

et al. 2022

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Estimating the grassland carbon budget is critically important for ensuring that grassland resources are used sustainably. However, the spatiotemporal dynamics of the carbon budget and the response to grazing have not yet been characterized in Qinghai grasslands. Here, we estimated the gross primary productivity (GPP) and net ecosystem exchange (NEE) in Qinghai grasslands using the improved Biome-BGCMuSo model to characterize the spatiotemporal dynamics of the carbon budget and the response to grazing in this region from 1979 to 2018. The GPP of Qinghai grasslands fluctuated during the study period, with an average annual value of 118.78 gC/m2. The NEE of Qinghai grasslands fluctuated from 1979 to 2018, with an average value of −5.16 gC/m2. After 2,000, GPP increased, and NEE decreased in a fluctuating manner. There were clear regional differences in GPP and NEE. GPP was low in most areas of Qinghai, and GPP was high in eastern and southern Qinghai. The southern, southeastern, and northeastern parts of Qinghai were mainly carbon sinks, and the northwestern part of Qinghai and the region between the southeastern and northeastern parts of Qinghai were mainly carbon sources. Grazing generally decreased GPP and increased NEE in Qinghai grasslands from 1979 to 2018. There was spatial heterogeneity in the effect of grazing on GPP and NEE. Under grazing, GPP and NEE were significantly decreased mainly in eastern Qinghai, and GPP and NEE were significantly increased mainly in southern and eastern Qinghai. NEE was most affected by grazing in eastern Qinghai. The results of this study aid our understanding of the mechanism driving variation in the grassland carbon budget and provide new data that could be used to support local grassland management.

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“…The 14-class and 1 km spatial resolution land cover map generated by [25] was used to obtain a vegetation type map, which was used to simulate GPP (Section 2.3). Elevation extracted from the Shuttle Radar Topography Mission 3 arc second digital elevation model [26] and land cover data from the Spanish Land Use Information System (SIOSE) [27] were combined to obtain a forest type map [28], which was used to simulate forest respiration (Section 2.4).…”

Section: Vegetation and Forest Type Mapsmentioning

confidence: 99%

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

et al. 2022

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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%).

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Optimized Application of Biome‐BGC for Modeling the Daily GPP of Natural Vegetation Over Peninsular Spain

Cited by 19 publications

References 50 publications

Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia

Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia

Spatiotemporal Dynamics of the Carbon Budget and the Response to Grazing in Qinghai Grasslands

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

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