Process refinement contributed more than parameter optimization to improve the CoLM's performance in simulating the carbon and water fluxes in a grassland

Li, Yuzhen; Li, Longhui; Dong, Jibao; Bai, Jie; Yuan, Xiuliang; Song, Shikai; Zhao, Hongfei; Chen, Xi; Li, Yali

doi:10.1016/j.agrformet.2020.108067

Cited by 8 publications

(2 citation statements)

References 91 publications

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“…Since it considers various biogeochemical, biogeophysical, and hydrological processes between the land‐atmosphere interface, the CoLM can capture key parameters such as soil moisture, sensible, latent heat fluxes, and canopy assimilation rate of photosynthesis (i.e., GPP) (Dai et al., 2003). Worldwide comprehensive validation has shown that the CoLM has competent performance in simulating GPP dynamics at the global scale (Chen et al., 2011; Li et al., 2020) (see also Figure S1 in Supporting Information S1). To drive CoLM, climate forcing (e.g., precipitation, temperature, radiation, and humidity) and land surface background (e.g., LULC, leaf area index (LAI), soil property, and topography) are needed.…”

Section: Methodsmentioning

confidence: 99%

Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity

Hou

Zhou

et al. 2022

Earth's Future

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Anthropogenic land use/land cover (LULC) change alters terrestrial gross primary productivity (GPP), the largest land‐atmosphere carbon exchanges. Identifying the impacts of LULC changes on future terrestrial GPP has been challenging due to the scarcity of standardized future LULC for ecosystem models. Here, we present eight scenario‐based projections of global spatially explicit LULC at 1‐km resolution over the period 2015–2100 with a Future Land Use Simulation model—consistent with the Shared Socioeconomic Pathways and Representative Concentration Pathways. Twenty computational experiments with different LULC patterns, climate forcing, and CO2 concentrations were conducted to quantify their contributions to future GPP dynamics. Results show that the global terrestrial GPP would decline in the 21st century in most LULC scenarios due to urbanization, agricultural expansion, and deforestation. Moreover, the contribution of LULC changes to global GPP dynamics ranges from 3.43% to 10.78% when CO2 fertilization effect (CFE) is not modeled during 2000–2100 (7%–9% of the terrestrial area is dominated by LULC change). However, this value may range from 10.92% to 16.16% during 2000–2050 and 1.41%–14.57% during 2050–2100. The contribution of LULC even reached 56.08% during 2050–2100 in Southeast Asia due to deforestation. Despite the relatively important role of LULC to GPP dynamics, it becomes trivial globally when incorporating CFE into the model (i.e., LULC accounts for 1.24%–2.51% during 2000–2100). Our findings emphasize the strategic role of CFE in enhancing global GPP and highlight the quantitatively nontrivial role of LULC at the regional scale.

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Section: Methodsmentioning

confidence: 99%

Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity

Hou

Zhou

et al. 2022

Earth's Future

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“…Nevertheless, discrepancies still exist among LSMs because of their different hydrological and biophysical parameterizations schemes, insufficiently expressed land surface processes so far, as well as uncertainties in regional driven data [21]. Fortunately, their simulation accuracy is being improved in recent years by process refinement, parameter optimization and assimilation with remotely sensed observations [22][23][24]. Compared to the LE (i.e., EvapoTranspiration (ET)), few long-term global H estimations are available because of the lack of surface parameters that are difficult to measure directly, such as the thermal roughness length imposing a dynamic impact on turbulent exchange efficiency by controlling the lower boundary conditions at the land-atmosphere interface [25,26].…”

Section: Introductionmentioning

confidence: 99%

Partitioning Global Surface Energy and Their Controlling Factors Based on Machine Learning

et al. 2020

Self Cite

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As two competitive pathways of surface energy partitioning, latent (LE) and sensible (H) heat fluxes are expected to be strongly influenced by climate change and wide spread of global greening in recent several decades. Quantifying the surface energy fluxes (i.e., LE and H) variations and controlling factors is still a challenge because of the discrepancy in existing models, parameterizations, as well as driven datasets. In this study, we assessed the ability of random forest (RF, a machine learning method) and further predicted the global surface energy fluxes (i.e., LE and H) by combining FLUXNET observations, climate reanalysis and satellite-based leaf area index (LAI). The results show that the surface energy fluxes variations can be highly explained by the established RF models. The coefficient of determination (R2) ranges from 0.66 to 0.89 for the LE, and from 0.53 to 0.90 for the H across 10 plant functional types (PFTs), respectively. Meanwhile, the root mean square error (RMSE) ranges from 12.20 W/m2 to 21.94 W/m2 for the LE and from 12.05 W/m2 to 22.34 W/m2 for the H at a monthly scale, respectively. The important influencing factors in building RF models are divergent with respect to LE and H, but the solar radiation is common to both LE and H and to all 10 PFTs in this study. We also found a contrasting trend of LE and H: a positive trend in LE and a negative trend in H during 1982–2016 and these contrasting trends are dominated by the elevated CO2 concentration level. Our study suggested an important role of the CO2 concentration in determining surface energy partitioning which is needed to be considered in future studies.

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Performance of the ecosystem demography model (EDv2.2) in simulating gross primary production capacity and activity in a dryland study area

Dashti

Pandit

Glenn

et al. 2021

Agricultural and Forest Meteorology

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Process refinement contributed more than parameter optimization to improve the CoLM's performance in simulating the carbon and water fluxes in a grassland

Cited by 8 publications

References 91 publications

Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity

Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity

Partitioning Global Surface Energy and Their Controlling Factors Based on Machine Learning

Performance of the ecosystem demography model (EDv2.2) in simulating gross primary production capacity and activity in a dryland study area

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