• Energy Research

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R2 > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions.

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R2 > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions.

Earth system models describe the physical, chemical and biological processes that govern our global climate. While it is difficult to single out one component as being more important than another in these sophisticated models, terrestrial vegetation is a critical player in the biogeochemical and biophysical dynamics of the Earth system. There is much debate, however, as to how plant diversity and function should be represented in these models.Plant functional types (PFTs) have been adopted by modellers to represent broad groupings of plant species that share similar characteristics (e.g. growth form) and roles (e.g. photosynthetic pathway) in ecosystem function. In this review, the PFT concept is traced from its origin in the early 1800s to its current use in regional and global dynamic vegetation models (DVMs). Special attention is given to the representation and parameterization of PFTs and to validation and benchmarking of predicted patterns of vegetation distribution in high-latitude ecosystems. These ecosystems are sensitive to changing climate and thus provide a useful test case for model-based simulations of past, current and future distribution of vegetation.Models that incorporate the PFT concept predict many of the emerging patterns of vegetation change in tundra and boreal forests, given known processes of tree mortality, treeline migration and shrub expansion. However, representation of above- and especially below-ground traits for specific PFTs continues to be problematic. Potential solutions include developing trait databases and replacing fixed parameters for PFTs with formulations based on trait co-variance and empirical trait-environment relationships. Surprisingly, despite being important to land-atmosphere interactions of carbon, water and energy, PFTs such as moss and lichen are largely absent from DVMs. Close collaboration among those involved in modelling with the disciplines of taxonomy, biogeography, ecology and remote sensing will be required if we are to overcome these and other shortcomings.

Earth system models describe the physical, chemical and biological processes that govern our global climate. While it is difficult to single out one component as being more important than another in these sophisticated models, terrestrial vegetation is a critical player in the biogeochemical and biophysical dynamics of the Earth system. There is much debate, however, as to how plant diversity and function should be represented in these models.Plant functional types (PFTs) have been adopted by modellers to represent broad groupings of plant species that share similar characteristics (e.g. growth form) and roles (e.g. photosynthetic pathway) in ecosystem function. In this review, the PFT concept is traced from its origin in the early 1800s to its current use in regional and global dynamic vegetation models (DVMs). Special attention is given to the representation and parameterization of PFTs and to validation and benchmarking of predicted patterns of vegetation distribution in high-latitude ecosystems. These ecosystems are sensitive to changing climate and thus provide a useful test case for model-based simulations of past, current and future distribution of vegetation.Models that incorporate the PFT concept predict many of the emerging patterns of vegetation change in tundra and boreal forests, given known processes of tree mortality, treeline migration and shrub expansion. However, representation of above- and especially below-ground traits for specific PFTs continues to be problematic. Potential solutions include developing trait databases and replacing fixed parameters for PFTs with formulations based on trait co-variance and empirical trait-environment relationships. Surprisingly, despite being important to land-atmosphere interactions of carbon, water and energy, PFTs such as moss and lichen are largely absent from DVMs. Close collaboration among those involved in modelling with the disciplines of taxonomy, biogeography, ecology and remote sensing will be required if we are to overcome these and other shortcomings.

AbstractSoil net nitrogen mineralization rate (Nmin), which is critical for soil nitrogen availability and plant growth, is thought to be primarily controlled by climate and soil physical and/or chemical properties. However, the role of microbes on regulating soil Nmin has not been evaluated on the global scale. By compiling 1565 observational data points of potential net Nmin from 198 published studies across terrestrial ecosystems, we found that Nmin significantly increased with soil microbial biomass, total nitrogen, and mean annual precipitation, but decreased with soil pH. The variation of Nmin was ascribed predominantly to soil microbial biomass on global and biome scales. Mean annual precipitation, soil pH, and total soil nitrogen significantly influenced Nmin through soil microbes. The structural equation models (SEM) showed that soil substrates were the main factors controlling Nmin when microbial biomass was excluded. Microbe became the primary driver when it was included in SEM analysis. SEM with soil microbial biomass improved the Nmin prediction by 19% in comparison with that devoid of soil microbial biomass. The changes in Nmin contributed the most to global soil NH4+‐N variations in contrast to climate and soil properties. This study reveals the complex interactions of climate, soil properties, and microbes on Nmin and highlights the importance of soil microbial biomass in determining Nmin and nitrogen availability across the globe. The findings necessitate accurate representation of microbes in Earth system models to better predict nitrogen cycle under global change.

AbstractSoil net nitrogen mineralization rate (Nmin), which is critical for soil nitrogen availability and plant growth, is thought to be primarily controlled by climate and soil physical and/or chemical properties. However, the role of microbes on regulating soil Nmin has not been evaluated on the global scale. By compiling 1565 observational data points of potential net Nmin from 198 published studies across terrestrial ecosystems, we found that Nmin significantly increased with soil microbial biomass, total nitrogen, and mean annual precipitation, but decreased with soil pH. The variation of Nmin was ascribed predominantly to soil microbial biomass on global and biome scales. Mean annual precipitation, soil pH, and total soil nitrogen significantly influenced Nmin through soil microbes. The structural equation models (SEM) showed that soil substrates were the main factors controlling Nmin when microbial biomass was excluded. Microbe became the primary driver when it was included in SEM analysis. SEM with soil microbial biomass improved the Nmin prediction by 19% in comparison with that devoid of soil microbial biomass. The changes in Nmin contributed the most to global soil NH4+‐N variations in contrast to climate and soil properties. This study reveals the complex interactions of climate, soil properties, and microbes on Nmin and highlights the importance of soil microbial biomass in determining Nmin and nitrogen availability across the globe. The findings necessitate accurate representation of microbes in Earth system models to better predict nitrogen cycle under global change.

Permafrost play an important role in regulating global climate system. We analyzed the gross primary productivity (GPP), net primary productivity (NPP), and evapotranspiration (ET) derived from MODIS and three earth system models participated in the Coupled Model Inter-comparison Project Phase 6 (CMIP6) in the Asian permafrost region. The water use efficiency (WUE) was further computed. The simulated GPP, NPP, and ET show slightly increasing trends during historical period (1900-2014) and strong increasing trends in projection period (2015-2100), and projected impacts of climate change on all variables are greater under high-emission scenarios than low-emission scenarios. Further analysis revealed higher increases in GPP and NPP than that of ET, indicating that vegetation carbon sequestration governs the growing WUE under historical and projected periods in this region. The GPP, NPP and ET showed higher changing rates in western, central and southeast areas of this region, and WUE (WUEGPP, and WUENPP) shows the similar spatial pattern. Compared to MODIS-derived GPP, NPP, and ET during 2000-2014, Earth system models yield the best estimates for NPP, while slight underestimations for GPP and ET, and thus slight overestimations for WUEGPP and WUENPP. This study highlights the predominant role of vegetation activity in regulating regional WUE in Asian permafrost region under future climate change. Vegetation domination of the growing water use efficiency implies that the permafrost region may continue acting efficiently in sequestrating atmospheric carbon in terms of water consumption throughout the 21st century.

Permafrost play an important role in regulating global climate system. We analyzed the gross primary productivity (GPP), net primary productivity (NPP), and evapotranspiration (ET) derived from MODIS and three earth system models participated in the Coupled Model Inter-comparison Project Phase 6 (CMIP6) in the Asian permafrost region. The water use efficiency (WUE) was further computed. The simulated GPP, NPP, and ET show slightly increasing trends during historical period (1900-2014) and strong increasing trends in projection period (2015-2100), and projected impacts of climate change on all variables are greater under high-emission scenarios than low-emission scenarios. Further analysis revealed higher increases in GPP and NPP than that of ET, indicating that vegetation carbon sequestration governs the growing WUE under historical and projected periods in this region. The GPP, NPP and ET showed higher changing rates in western, central and southeast areas of this region, and WUE (WUEGPP, and WUENPP) shows the similar spatial pattern. Compared to MODIS-derived GPP, NPP, and ET during 2000-2014, Earth system models yield the best estimates for NPP, while slight underestimations for GPP and ET, and thus slight overestimations for WUEGPP and WUENPP. This study highlights the predominant role of vegetation activity in regulating regional WUE in Asian permafrost region under future climate change. Vegetation domination of the growing water use efficiency implies that the permafrost region may continue acting efficiently in sequestrating atmospheric carbon in terms of water consumption throughout the 21st century.