Microscale drivers of summer CO2 fluxes in the Svalbard High Arctic tundra

High-Arctic ecosystems are strongly affected by climate change, and it is still unclear whether they will become a carbon source or sink in the next few decades. In turn, such knowledge gaps on the drivers and the processes controlling CO2 fluxes and storage make future projections of the Arctic carbon budget a challenging goal. During summer 2019, we extensively measured CO2 fluxes at the soil–vegetation–atmosphere interface, together with basic meteoclimatic variables and ecological characteristics in the Bayelva river basin near Ny Ålesund, Spitzbergen, Svalbard (NO). By means of multi-regression models, we identified the main small-scale drivers of CO2 emission (Ecosystem Respiration, ER), and uptake (Gross Primary Production, GPP) in this tundra biome, showing that (i) at point scale, the temporal variability of fluxes is controlled by the classical drivers, i.e. air temperature and solar irradiance respectively for ER and GPP, (ii) at site scale, the heterogeneity of fractional vegetation cover, soil moisture and vegetation type acted as additional source of variability for both CO2 emissions and uptake. The assessment of the relative importance of such drivers in the multi-regression model contributes to a better understanding of the terrestrial carbon dioxide exchanges and of Critical Zone processes in the Arctic tundra.

Fires Drive Long-Term Environmental Degradation in the Amazon Basin

The Amazon Basin is undergoing extensive environmental degradation as a result of deforestation and the rising occurrence of fires. The degradation caused by fires is exacerbated by the occurrence of anomalously dry periods in the Amazon Basin. The objectives of this study were: (i) to quantify the extent of areas that burned between 2001 and 2019 and relate them to extreme drought events in a 20-year time series; (ii) to identify the proportion of countries comprising the Amazon Basin in which environmental degradation was strongly observed, relating the spatial patterns of fires; and (iii) examine the Amazon Basin carbon balance following the occurrence of fires. To this end, the following variables were evaluated by remote sensing between 2001 and 2019: gross primary production, standardized precipitation index, burned areas, fire foci, and carbon emissions. During the examined period, fires affected 23.78% of the total Amazon Basin. Brazil had the largest affected area (220,087 fire foci, 773,360 km2 burned area, 54.7% of the total burned in the Amazon Basin), followed by Bolivia (102,499 fire foci, 571,250 km2 burned area, 40.4%). Overall, these fires have not only affected forests in agricultural frontier areas (76.91%), but also those in indigenous lands (17.16%) and conservation units (5.93%), which are recognized as biodiversity conservation areas. During the study period, the forest absorbed 1,092,037 Mg of C, but emitted 2908 Tg of C, which is 2.66-fold greater than the C absorbed, thereby compromising the role of the forest in acting as a C sink. Our findings show that environmental degradation caused by fires is related to the occurrence of dry periods in the Amazon Basin.

Effects of Habitat-Specific Primary Production on Fish Size, Biomass, and Production in Northern Oligotrophic Lakes

Ecological theory predicts that the relative distribution of primary production across habitats influence fish size structure and biomass production. In this study, we assessed individual, population, and community-level consequences for brown trout (Salmo trutta) and Arctic char (Salvelinus alpinus) of variation in estimated habitat specific (benthic and pelagic) and total whole lake (GPPwhole) gross primary production in 27 northern oligotrophic lakes. We found that higher contribution of benthic primary production to GPPwhole was associated with higher community biomass and larger maximum and mean sizes of fish. At the population level, species-specific responses differed. Increased benthic primary production (GPPBenthic) correlated to higher population biomass of brown trout regardless of being alone or in sympatry, while Arctic char responded positively to pelagic primary production (GPPPelagic) in sympatric populations. In sympatric lakes, the maximum size of both species was positively related to both GPPBenthic and the benthic contribution to GPPWhole. In allopatric lakes, brown trout mean and maximum size and Arctic char mean size were positively related to the benthic proportion of GPPWhole. Our results highlight the importance of light-controlled benthic primary production for fish biomass production in oligotrophic northern lakes. Our results further suggest that consequences of ontogenetic asymmetry and niche shifts may cause the distribution of primary production across habitats to be more important than the total ecosystem primary production for fish size, population biomass, and production. Awareness of the relationships between light availability and asymmetric resource production favoring large fish and fish production may allow for cost-efficient and more informed management actions in northern oligotrophic lakes.

A Study on Sensitivities of Tropical Forest GPP Responding to the Characteristics of Drought—A Case Study in Xishuangbanna, China

Droughts that occur in tropical forests (TF) are expected to significantly impact the gross primary production (GPP) and the capacity of carbon sinks. Therefore, it is crucial to evaluate and analyze the sensitivities of TF-GPP to the characteristics of drought events for understanding global climate change. In this study, the standardized precipitation index (SPI) was used to define the drought intensity. Then, the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) was utilized to simulate the dynamic process of GPP corresponding to multi-gradient drought scenarios—rain and dry seasons × 12 level durations × 4 level intensities. The results showed that drought events in the dry season have a significantly greater impact on TF-GPP than drought events in the rainy season, especially short-duration drought events. Furthermore, the impact of drought events in the rainy season is mainly manifested in long-duration droughts. Due to abundant rainfall in the rainy season, only extreme drought events caused a significant reduction in GPP, while the lack of water in the dry season caused significant impacts due to light drought. Effective precipitation and soil moisture stock in the rainy season are the most important support for the tropical forest dry season to resist extreme drought events in the study area. Further water deficit may render the tropical forest ecosystem more sensitive to drought events.

Modelos da Produtividade Primária Bruta em área de floresta tropical em sazonalmente seca, usando dados reflectância da vegetação de caatinga

R E S U M OArtigo recebido em XX/XX/2021 e aceito em XX/XX/2021 Os estudos da troca de energia nos ecossistemas fornecem informações importantes para a compreensão da Produtividade nos ecossistemas. A vegetação é um dos principais elementos da biosfera terrestre sendo responsável pela avaliação e funcionamento da atividade fotossintética bem como para as trocas de carbono entre os ecossistemas e a atmosfera. Neste contexto, a PPB é utilizada para avaliar, planejar e gerenciar os recursos ambientais frente as mudanças climáticas globais. Esse estudo tem por objetivo avaliar a Produção Primária Bruta no Bioma da Caatinga em Pernambuco. O estudo foi realizado na área de Floresta Tropical Sazonalmente Seca, a Caatinga no Estado de Pernambuco. Utilizou-se a refletância da superfície do produto (MOD09) a partir do MODIS/TERRA satélite do sensor, a refletância de superfície (SR) Landsat-8 e a reflectancia a superficie do fieldspec. Foram adquiridas nove cenas para o produto (MOD09), seis cenas para a refletância de superfície (SR) Landsat-8 e as mesmas datas das imagens foram utilizados os espectros de campo (filedspec). Foi realizada a seleção de amostras espectrais na imagem (espectros de referência), considerando o ponto espectral do local de coleta. Os modelos foram construídos a partir das combinações das bandas (ρ_350, ρ_351, ρ_352, ..., ρ_2500) suas transformações (ρ, 1/ρ, ln⁡(ρ), log_10⁡(ρ), √ρ, ρ^2, e^ρ). Os desempenhos dos modelos foram avaliados utilizando dois índices estatísticos, um de tendência (coeficiente de Pearson– r) e outro de desvio (Erro médio quadrático (RMSE– RMSE), e o PBIAS. Os resultados apontaram que os modelos calibrados demostraram bom desempenho na previsão com o uso das bandas do sensor OLI/Landsat 8 e do MODIS/Terra (MOD09GA). Models of Gross Primary Productivity in a seasonally dry tropical forest area using reflectance data from the Caatinga vegetationA B S T R A C TThe studies of energy exchange in ecosystems provide important information for the understanding of Productivity in ecosystems. Vegetation is one of the main elements of the terrestrial biosphere and is responsible for the evaluation and functioning of photosynthetic activity as well as for carbon exchanges between ecosystems and the atmosphere. In this context, a PPB is used to assess, plan and manage environmental resources in the face of global climate change. This study aims to evaluate a Gross Primary Production in the Caatinga Biome in Pernambuco. The study was carried out in the Seasonally Dry Tropical Forest, a Caatinga in the State of Pernambuco. Use the product's surface reflectance (MOD09) from the sensor's MODIS / TERRA satellite and the Landsat-8 surface reflectance (SR), nine scenes for the product (MOD09), six scenes for surface reflectance (SR) Landsat-8 and similar data with fieldspec. A selection of spectral members in the image (reference spectra) was carried out, considering the spectral point of the collection site. The models were built from the combinations of the bands (ρ_350, ρ_351, ρ_352, ..., ρ_2500) their transformations (ρ, 1/ρ, ln⁡(ρ), log_10⁡(ρ), √ρ, ρ^2, e^ρ). The performances of the models were taken using two statistical indices, one of trend (Pearson's coefficient - r) and another of deviation (Mean square error (RMSE - RMSE), and PBIAS. The results showed that the calibrated models showed good performance in prediction using the OLI / Landsat 8 and MODIS / Terra (MOD09GA) bands.Keyword: Remote sensing, FieldSpec®3, Caatinga

Simulation of Carbon Exchange from a Permafrost Peatland in the Great Hing’an Mountains Based on CoupModel

Climate change is accelerating its impact on northern ecosystems. Northern peatlands store a considerable amount of C, but their response to climate change remains highly uncertain. In order to explore the feedback of a peatland in the Great Hing’an Mountains to future climate change, we simulated the response of the overall net ecosystem exchange (NEE), ecosystem respiration (ER), and gross primary production (GPP) during 2020–2100 under three representative concentration pathways (RCP2.6, RCP6.0, and RCP8.5). Under the RCP2.6 and RCP6.0 scenarios, the carbon sink will increase slightly until 2100. Under the RCP8.5 scenario, the carbon sink will follow a trend of gradual decrease after 2053. These results show that when meteorological factors, especially temperature, reach a certain degree, the carbon source/sink of the peatland ecosystem will be converted. In general, although the peatland will remain a carbon sink until the end of the 21st century, carbon sinks will decrease under the influence of climate change. Our results indicate that in the case of future climate warming, with the growing seasons experiencing overall dryer and warmer environments and changes in vegetation communities, peatland NEE, ER, and GPP will increase and lead to the increase in ecosystem carbon accumulation.

Comprehensive Assessment of Performances of Long Time-Series LAI, FVC and GPP Products over Mountainous Areas: A Case Study in the Three-River Source Region, China

Vegetation biophysical products offer unique opportunities to examine long-term vegetation dynamics and land surface phenology (LSP). It is important to understand the time-series performances of various global biophysical products for global change research. However, few endeavors have been dedicated to assessing the performances of long-term change characteristics or LSP extraction derived from different satellite products, especially in mountainous areas with highly fragmented and rugged surfaces. In this paper, we assessed the time-series characteristics and LSP detections of Global LAnd Surface Satellite (GLASS) leaf area index (LAI), fractional vegetation cover (FVC), and gross primary production (GPP) products across the Three-River Source Region (TRSR). The performances of products’ temporal agreements and their statistical relationship as a function of topographic indices and heterogeneous pixels, respectively, were investigated through intercomparison among three products during the period 2000 to 2018. The results show that the phenological differences between FVC and two other products are beyond 10 days over more than 35% of the pixels in TRSR. The long-term trend of FVC diverges significantly from GPP and LAI for 13.96% of the total pixels, and the percentages of mismatched pixels between FVC and two other products are 33.24% in the correlation comparison. Moreover, good agreements are observed between GPP and LAI, both in terms of LSP and interannual variations. Finally, the LSP and long-term dynamics of the three products exhibit poor performances on heterogeneous surfaces and complex topographic areas, which reflects the potential impacts of environmental factors and algorithmic imperfections on the quality and performances of different products. Our study highlights the spatiotemporal disparities in detections of surface vegetation activity in mountainous areas by using different biophysical products. Future global change studies may require multiple high-quality satellite products with long-term stability as data support.

Integration of Forest Growth Component in the FEST-WB Distributed Hydrological Model: The Bonis Catchment Case Study

In this paper, the FEST-FOREST model is presented. A FOREST module is written in the FORTRAN-90 programming language, and was included in the FEST-WB distributed hydrological model delivering the FEST-FOREST model. FEST-FOREST is a process-based dynamic model allowing the simulation at daily basis of gross primary production (GPP) and net primary production (NPP) together with the carbon allocation of a homogeneous population of trees (same age, same species). The model was implemented based on different equations from literature, commonly used in Eco-hydrological models. This model was developed within the framework of the INNOMED project co-funded under the ERA-NET WaterWorks2015 Call of the European Commission. The aim behind the implementation of the model was to simulate in a simplified mode the forest growth under different climate change and management scenarios, together with the impact on the water balance at the catchment. On a first application of the model, the results are considered very promising when compared to field measured data.

Distinguishing ecological outcomes of pathways in Grain for Green Program in the subtropical areas of China

Effective forestation policies are urgently required across the globe under the initiative of UN Decade on Ecosystem Restoration. Rather than simply planting trees, such initiatives involve complex components of societal and biophysical systems. However, the underlying pathways by which forestation influences the ecological outcomes are not well understood, especially lacking a unified quantification framework. In this study, such a framework was developed to reveal the pathways in which reforestation programs influenced ecological outcomes through identifying the linkages among reforestation efforts, societal changes, land system changes, and ecological outcomes. The framework was applied in the reforestation program of Grain for Green Program (GFGP), to explore that how the GFGP influenced vegetation dynamics and ecosystem functioning in Guizhou Province of China through direct and indirect pathways. Two independent remote-sensing-based indicators: the enhanced vegetation index (EVI), derived from Moderate Resolution Imaging Spectroradiometer (MODIS), and gross primary production (GPP), obtained from the Solar-induced chlorophyll fluorescence (SIF) fine resolution dataset GOSIF, were combined with inventory data and land use maps to detect changes in social and ecological outcomes. Using the Structural Equation Model (SEM) to perform the framework, the results showed that the GFGP positively contributed to the increasing greenness and GPP of the study area through the direct conservation pathway. Although the implementation of GFGP encouraged outmigration and led to a decrease in farmland area, GFGP on greenness and GPP showed negative indirect effects because of the difficulty of reforestation during land-use conversion from farmland to forest land. This study revealed divergent impacts of the reforestation program through multiple pathways, which could provide valuable information for other parts of the globe to design ecological restoration policies more precisely.