Strong and Rapid Postfire Recovery of Vegetation Productivity in Australia.

Investigating the attributions of evapotranspiration (ET) and gross primary production (GPP) changes is of great importance for regional, sustainable water resources and ecological management in semi-arid regions. Based on the simulation conducted during 2000–2019 by improving water-carbon coupling Distributed Time Variant Gain Model, the trends of ET and GPP were estimated and the driving factors were identified via 10 experimental scenarios in the water source area of the Xiong’an New Area in North China. The results show significant increases both in ET and GPP by 2.4 mm/a and 6.0 gC/m2/a in the region, respectively. At the annual scale, increasing precipitation dominates the ET uptrend. Air temperature, humidity and the interactive effects also contribute to the ET uptrend, and the contributions are 12.8%, 2.0% and 2.3%, respectively, while elevated atmospheric CO2 concentration (eCO2) and solar dimming lead to ET changes of about −7.2% and −12.4%, respectively. For the GPP changes, the increase in GPP is mainly caused by eCO2, increasing precipitation and rising temperature with the contributions of 56.7%, 34.8% and 27.8%, respectively. Solar dimming, humidity and windspeed contribute −6.8%, −4.8% and −3.5% of the GPP changes. Compared to climate change, land use and cover change has smaller effects on both ET and GPP for the few changes in land coverage. At the seasonal scale, ET and GPP increase to a greater extent during the growing season in spring and summer than in autumn and winter. Precipitation, temperature and eCO2 are generally the main causes for ET and GPP changes. Meanwhile, the decreasing humidity and rising temperature are dominant factors for ET and GPP increases, respectively, in winter. Furthermore, solar dimming has strong effects on ET reduction in autumn. The contribution of the interactive effects is much higher on a seasonal scale than annual scale, contributing to considerable decreases in ET and GPP in spring, increases in ET in autumn and winter, and an increase in GPP in winter. This study highlights the importance of considering water-carbon coupling on the attributions of ET and GPP changes and the differentiation of the effects by the abovementioned influential factors at annual and seasonal scales.

ABSTRACTChina has the world's third‐largest area of marsh wetlands that plays a critical role in regional and global carbon cycle. Vegetation gross primary productivity (GPP) is a commonly used indicator of carbon sequestration of wetland ecosystems. Although climate change has significantly changed the marsh GPP, the changes in GPP and climatic effects in the marshes of China remian unclear. Using MODIS GPP and climate data during 2001–2020, this study examined the spatiotemporal changes in marsh GPP and its response to climate change in China. The results indicate that the annual averaged GPP over marshes of China increased significantly (4.56 g C/m2/year), with an average value of 517.39 g C/m2 from 2001 to 2020. Increased annual precipitation significantly enhances the regionally averaged GPP of marshes across China, while mean temperature does not have a significant effect on marsh GPP. Although temperature did not have an obvious effect on the national average GPP, it had a significant effect on the GPP in some marsh regions of China. This study found for the first time the asymmetric influences of daytime and nighttime temperatures on marsh GPP. Specifically, nighttime temperature had a larger positive impact on marsh GPP compared to daytime temperature in China. Increased daytime temperature could decrease annual GPP, while increased nighttime temperature increases the GPP. At a regional scale, the impact of climate change on marsh GPP varies significantly across climate regions. In the Tibetan Plateau marsh region, the increases in annual maximum temperature, annual minimum temperatures, and spring precipitation contribute to the increase in annual marsh GPP. Spatially, the partial correlation results have obvious spatial heterogeneities across China. This study highlights the distinct influences of seasonal climatic change on marsh GPP of different regions and suggests that the asymmetric impacts of day and night temperatures should be fully considered inaccurately simulating and predicting the vegetation productivity in China marsh.

Evapotranspiration (ETa) and gross primary productivity (GPP) are two of the key flux indices in a basin system. This study investigated the evolutions of ETa and GPP and the contributions of climate variables and vegetation index over multiple land cover types, simulated by the VIP model (Vegetation Interfaces Processes, RS version) with Terra-Modis 250 m Normalized Difference Vegetation Index (NDVI) dataset in the Ziya-Daqing basins (ZDB), China, during 2001–2015. The nonparametric Mann-Kendall test was used to analyze the changes in ETa and GPP. Contributions to the variations of ETa and GPP were explored using a differential equation method. The analyses revealed that ETa and GPP were high in the eastern plain, and spatial distributions were mainly associated with distributions of the major land-cover types. ETa and GPP showed positive trends in the western Taihang Mountains and negative trends in the eastern plain region. The annual ETa and GPP increased from 2001 to 2015 in most land-use types except cropland and urban. The decreases in ETa and GPP were primarily attributed to solar dimming in cropland and urban. The increases in ETa and GPP mainly occurred in the natural land-cover types (e.g., mixed forest and grassland) as a tradeoff between the positive effect of greening and the negative effect of declined radiation. In ZDB, the contributions of net radiation, air temperature, wind speed, and leaf area index to the change in ETa were − 0.681%, − 0.379%, − 0.032%, and 1.442%, respectively. The contributions of net short-wave radiation, air temperature, relative humidity, and fraction of photosynthetically active radiation to the change in GPP were − 2.411%, 0.085%, − 0.020%, and 10.224%, respectively. Thus, the “greening” was the major reason for ETa and GPP increases in natural vegetations, whereas solar dimming was the major reason for ETa and GPP decreases in cropland.

Understanding the changes in gross primary production (GPP), which is the total carbon fixation by terrestrial ecosystems through vegetation photosynthesis, due to land use conversion in a tourism city is important for carbon cycle studies. Satellite data from Landsat 5, Landsat 7 and Landsat 8 and meteorological data are used to calculate annual GPP for 1995, 2003 and 2014, respectively, using the vegetation production model (VPM) in the tourism city Denpasar, Bali, Indonesia. Five land use types generated from topographic maps in three different years over the past two decades are used to quantify the impacts of land use changes on GPP estimation values. Analysis was performed for two periods to determine changes in land use and GPP value as well as their speed. The results demonstrated that urban land development, namely, the increase of settlement areas due to tourism activity, had overall negative effects on terrestrial GPP. The total GPP of the whole area decreased by 7793.96 tC year−1 (12.65%) during the study period. The decline is due to the conversion of agriculture and grassland area into settlements, which caused the city to lose half of its ability to uptake carbon through vegetation. However, although forest area is declining, forest maintenance and restoration by making them protection areas has been helpful in preventing a drastic decline in GPP value over the past two decades. This study provides information that is useful for carbon resource management, tourism, policy making and scholars concerned about carbon reduction in a tourism city.

Abstract. Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are uncertain. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP between 1981 and 2010, driven by common climate forcing, LULCC and CO2 data, was found to be 7.4±1.8 Pg C yr−1. In general, climate was the dominant control factor of the annual trends, IAV and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China, where vegetation is dense. LULCC decreased the IAV of China's total GPP by ∼7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation, and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.

Analysis of drought events and their impacts on vegetation productivity based on the integrated surface drought index in the Hanjiang River Basin, China

<strong class="journal-contentHeaderColor">Abstract.</strong> Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO<span class="inline-formula">₂</span> fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (<span class="inline-formula"><i>r</i>&gt;0.84</span>), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of <span class="inline-formula"><i>r</i>=0.49</span>) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop<span class="inline-formula">+</span>SH) and extratropical Northern Hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop<span class="inline-formula">+</span>SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO<span class="inline-formula">₂</span> fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO<span class="inline-formula">₂</span> fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO<span class="inline-formula">₂</span> fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop<span class="inline-formula">+</span>SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products.

Fire is a major disturbance factor in Daxing'anling region, with important impacts on carbon balance of forest ecosystems. Fire severity and the distinction of microclimates induced by different topography are the primary factors driving the restoration of post-fire net primary productivity (NPP). In this study, we examined the influence of fire severity and topographic factors on the restoration of forest NPP in the Genhe forest region. The spatial and temporal restoration process of post-fire NPP were simulated by combining with MTCLIM and 3PGS model based on multiyear Landsat TM satellite (2008-2012) and climate (1980-2010) data. The results showed that the 3PGS-MTCLIM model could precisely estimate the spatial distribution of NPP at small scales, with a good correlation between simulated and observed values (R2=0.828). The percentage of declined NPP in the year following the fire ranged 43%-80%, and the average NPP recovery period for this region was about 10 years by comparing pre- and post-fire NPP. Fire severity had significant impacts on post-fire recovery. The stronger the fire intensity, the longer the recovery period was needed. The NPP recovered relatively slower after a period of fast-speed recovery. Among the three topographic factors, elevation was the strongest one affecting forest NPP restoration, followed by slope and aspect.

Sun-Induced fluorescence at 760 nm (F760) is increasingly being used to predict gross primary production (GPP) through light use efficiency (LUE) modeling, even though the mechanistic processes that link the two are not well understood. We analyzed the effect of nitrogen (N) and phosphorous (P) availability on the processes that link GPP and F760 in a Mediterranean grassland manipulated with nutrient addition. To do so, we used a combination of process-based modeling with Soil-Canopy Observation of Photosynthesis and Energy (SCOPE), and statistical analyses such as path modeling. With this study, we uncover the mechanisms that link the fertilization-driven changes in canopy nitrogen concentration (N%) to the observed changes in F760 and GPP. N addition changed plant community structure and increased canopy chlorophyll content, which jointly led to changes in photosynthetic active radiation (APAR), ultimately affecting both GPP and F760. Changes in the abundance of graminoids, (%graminoids) driven by N addition led to changes in structural properties of the canopy such as leaf angle distribution, that ultimately influenced observed F760 by controlling the escape probability of F760 (Fesc). In particular, we found a change in GPP–F760 relationship between the first and the second year of the experiment that was largely driven by the effect of plant type composition on Fesc, whose best predictor is %graminoids. The P addition led to a statistically significant increase on light use efficiency of fluorescence emission (LUEf), in particular in plots also with N addition, consistent with leaf level studies. The N addition induced changes in the biophysical properties of the canopy that led to a trade-off between surface temperature (Ts), which decreased, and F760 at leaf scale (F760leaf,fw), which increased. We found that Ts is an important predictor of the light use efficiency of photosynthesis, indicating the importance of Ts in LUE modeling approaches to predict GPP.

Soil moisture dominates the variation of gross primary productivity during hot drought in drylands

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was dominated by the CO2 fertilization effect (Global: 83.9 %), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. Trends after 2000 were different from the full time-series, showing that satellite-based GPP products suggested weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs kept increasing. The inconsistencies are very likely caused by the contrasting performances between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks.

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was dominated by the CO2 fertilization effect (Global: 83.9 %), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. Trends after 2000 were different from the full time-series, showing that satellite-based GPP products suggested weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs kept increasing. The inconsistencies are very likely caused by the contrasting performances between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks.

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was dominated by the CO2 fertilization effect (Global: 83.9 %), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. Trends after 2000 were different from the full time-series, showing that satellite-based GPP products suggested weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs kept increasing. The inconsistencies are very likely caused by the contrasting performances between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks.

BackgroundClimate change is altering the fire regime and compromising the post-fire recovery of vegetation worldwide. To understand the factors influencing post-fire vegetation cover restoration, we calculated the recovery of vegetation in 200,000 hectares of western Mediterranean forest burned by 268 wildfires over a 27-year period (1988–2015). We used time series of the Tasseled Cap Transformation Brightness (TCTB) spectral transformation over Landsat imagery to calculate vegetation recovery. Then, we quantified the importance of the main drivers of post-fire vegetation recovery (climate, fire severity, and topography) along an aridity gradient (semi-arid, sub-humid, and humid) using Random Forest models.ResultsIn most models (99.7%), drought duration was the most important factor, negatively affecting post-fire recovery especially in the extremes of the aridity gradient. Fire severity was the second most important factor for vegetation cover recovery, with its effect varying along the aridity gradient: there was a positive relationship between fire severity and recovery in sub-humid and humid areas, while semi-arid areas showed the opposite pattern. Topographic variables were the least important driver and had a marginal effect on post-fire recovery. Additionally, semi-arid areas exhibited a low mean recovery rate, indicating limitations in the short-term recovery after a fire.ConclusionsOur study highlights the key role that drought duration plays in the recovery of vegetation after wildfires in the Mediterranean basin and, particularly, in forests located in climatically extreme areas. The results suggest that the predicted increase in drought duration coupled with a higher frequency and intensity of large fires may modify the structure and composition of Mediterranean forest ecosystems. Our analysis provides relevant information to evaluate and design adaptive management strategies in post-fire recovery hotspots of Mediterranean forest ecosystems.

IntroductionThe increasing frequency of compound heat events (CHEs), including compound dry-hot events (CDHEs) and compound humid-hot events (CHHEs), poses significant threats to terrestrial ecosystems. While previous studies have examined the independent and combined effects of drought and heat on vegetation productivity, the specific roles of CHHEs and the differential impacts of CDHEs and CHHEs remain poorly understood.MethodsUsing Gross Primary Productivity (GPP) estimated from satellite-based near-infrared reflectance (NIRv), monthly meteorological data and the Standardized Precipitation Evapotranspiration Index (SPEI), this study calculated the Standardized Compound Event Indicator (SCEI) to quantify the severity of CHEs, and investigated the immediate and lagged effects of CDHEs and CHHEs on global GPP from 2001 to 2018.ResultsOur results demonstrated that CDHEs occurred more frequently and with greater severity than CHHEs during the study period. The immediate effects of CDHEs reduced GPP in 68% of vegetated areas, whereas CHHEs enhanced GPP in 58% of vegetated areas. Globally, CDHEs and CHHEs caused net GPP changes of −5.26 Pg C yr−1 and 1.67 Pg C yr−1, respectively. In contrast, GPP in the polar zone, boreal shrubs, and boreal grasslands increased during CDHEs and decreased during CHHEs, with average net GPP changes of 0.17 Pg C yr−1 and −0.04 Pg C yr−1, respectively. Additionally, lag effects were most prominent in the periods of 0 to 3 months and 10 to 12 months post-event.DiscussionThese findings highlight the contrasting impacts of compound dry- and humid-hot events on ecosystem carbon fluxes and provide a better understanding of global carbon cycles under climate extremes.