The intensity and frequency of precipitation events, especially extreme precipitation events (EPE), are increasingly recognized for their importance in regulating ecosystem functions (e.g., C ...fluxes). Here we assessed the effects of precipitation events on ecosystem carbon exchange following the precipitation events. Naturally occurring precipitation events and CO2 exchange were measured by applying an eddy covariance technique and meteorological tower between 2008 and 2010 in a desert grassland in Inner Mongolia, China. The results indicate that the accumulative net ecosystem exchange (NEE) of carbon was −11.08 gC⋅m−2, −15.62 gC⋅m−2 and 26.68 gC m−2 in 2008, 2009 and 2010, respectively. The magnitude and timing of precipitation controlled the variation in accumulative NEE. Summer and autumn precipitation produced stronger impacts than those in the spring. The desert grassland appeared more sensitive to precipitation than other grassland ecosystems, with a threshold of ∼1–2 mm precipitation. During the 3-year study period, EPEs significantly enhanced the carbon sink strength due to discrepant sensitivity of gross ecosystem productivity (GEP) and ecosystem respiration (Reco) to precipitation events. NEE response time to EPE (TL) lasted 5–17 days. Total accumulative carbon was - 40.32 gC m−2, which was 91% more than the 3-year average of NEE during TL and 33% more than the growing season average. GEP and Reco were disproportionally promoted by 52% and 37% with EPEs during TL. A nonlinear equation (NEE = α + βte-γt) was developed with high confidence (R2 = 0.84) to model the changes of NEE following the precipitation event. The sensitivity response of carbon exchange processes to larger precipitation events suggests that the carbon sink strength of the Inner Mongolian desert grassland will be elevated with the increase in the frequency and intensity of extreme precipitation. This study provides updated evidence regarding how precipitation events alter ecosystem carbon sequestration and helps mitigate the uncertainty when quantifying the carbon budget of a semi-arid ecosystem with a different future climate. Further studies are crucial to investigate the long-term responses of carbon exchange processes to variations in precipitation patterns and characterize the responses of biological components of the carbon balance following precipitation events.
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•Variation in precipitation patterns (magnitude and timing) had changed NEE dynamics.•Summer and autumn precipitation produced stronger impacts on carbon exchange of a desert grassland, which have a threshold of ∼1–2 mm precipitation.•Extreme precipitation events significantly enhanced carbon sink strength of a desert grassland in Inner Mongolia, China.•The changes of NEE following the precipitation event were described by a non-linear algorithm (NEE = α + β te- γ t) with high confidence (R2 = 0.84).
The biomass of the subtropical forests of China is an important component of the global carbon cycle. Recently, several above ground biomass (AGB) maps have been produced using a variety of ...approaches to assess the carbon stock of the subtropical forest in China. However, due to the lack of reliable ground observations and the limitations of AGB mapping methods at regional scales, estimates of the spatial distribution of AGB vary greatly, leading to large uncertainties in the carbon stock estimations. In this study, we produced a new 1-km spatial resolution AGB map by synthesizing an unprecedented number of ground AGB observations from published studies, and developed an AGB mapping method using a combination of ground observations, MODIS data, forest cover/gain/loss maps based on Landsat, GLAS forest canopy height, and climatic and terrain data. In addition, we validated our estimates using independent testing data and compared our estimates with three previous AGB maps. The results indicate that the total AGB stock in the subtropical forest of China is (266 ± 9.1) × 106 Mg, with an average AGB of 123.2 Mg/ha. Based on sixteen explanatory variables, our ensemble mean model explains 75% of the variance in forest AGB, with an RMSE of 45.5 Mg/ha. Comparison using all observation data shows that our map has a significantly lower RMSE and bias than previous maps, where the RMSE and bias tended to vary with forest type. This study not only improved the accuracy of AGB estimation for the subtropical forests but also highlighted the importance of forest type for regional AGB estimation.
•Produced a new AGB map for the subtropical forest by integrating multisource data•Improved the accuracy of AGB mapping compared to previous maps•Revealed the importance of forest types for AGB estimation
▶ Home landscapes with more trees are generally preferred. ▶ Students who majored in social sciences are more inclined to choose a neat, well-kept environment around their homes, while wildlife ...science students prefer more natural landscape. ▶ This study also found that senior students and students from large cities also prefer well-maintained and artificial landscapes. ▶ Students who are members of an environmental group, and those whose parents have a better education, are more likely to choose a more natural landscape.
This study explores students’ preferences toward natural and wild versus clean and neat residential landscapes using preference survey data. Based on the rating scores of four housing landscape designs, multinomial logit models were used to explore the potential influential factors on people's preferences, especially the wildness or neatness of the home landscape. The results suggest that students in agricultural economics, horticulture, and social sciences are more inclined to choose a neat, well-kept environment around their homes. In contrast, wildlife science students prefer more natural landscapes. This study also found that senior students and students from large cities also prefer well-maintained and artificial landscapes. Also, students who are members of an environmental group, and those whose parents have a better education, are more likely to choose a more natural landscape. The results would provide additional information for planners, developers, engineers, architects and foresters in building more livable communities which are aesthetically appealing but also ecologically sound.
The Tibetan Plateau has experienced higher-than-global-average climate warming in recent decades, resulting in many significant changes in ecosystem structure and function. Among them is albedo, ...which bridges the causes and consequences of land surface processes and climate. The plateau is covered by snow/ice and vegetation in the non-growing season (nGS) and growing season (GS), respectively. Based on the MODIS products, we investigated snow/ice cover and vegetation greenness in relation to the spatiotemporal changes of albedo on the Tibetan Plateau from 2000 through 2013. A synchronous relationship was found between the change in GSNDVI and GSalbedo over time and across the Tibetan landscapes. We found that the annual average albedo had a decreasing trend, but that the albedo had slightly increased during the nGS and decreased during the GS. Across the landscapes, the nGSalbedo fluctuated in a synchronous pattern with snow/ice cover. Temporally, monthly snow/ice coverage also had a high correspondence with albedo, except in April and October. We detected clear dependencies of albedo on elevation. With the rise in altitude, the nGSalbedo decreased below 4000 m, but increased for elevations of 4500-5500 m. Above 5500 m, the nGSalbedo decreased, which was in accordance with the decreased amount of snow/ice coverage and the increased soil moisture on the plateau. More importantly, the decreasing albedo in the most recent decade appeared to be caused primarily by lowered growing season albedo.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Deep-time records from greenhouse climate periods (e.g., the Late Cretaceous) provide a reference point for understanding how high atmospheric CO2 concentrations influence precipitation in the ...mid-latitude Northern Hemisphere (e.g., East Asia). In this study, we quantitatively reconstruct mean annual precipitation (MAP) in East Asia during the latest Cretaceous through the earliest Paleogene (~76–65.5 Ma), based on a well-studied paleosol sequence from the Sifangtai and Mingshui Formations from the SK-1n scientific borehole in the Songliao Basin, northeastern China. We use several proxies, including sedimentary-based observational proxies (e.g., depth to the calcic horizon, DTC) and elemental geochemistry proxies in the paleosol B horizon (e.g., the chemical index of alteration minus potassium, CIA-K; the calcium‑magnesium weathering index, CALMAG), which show the consistency with weathering proxies and previously published isotopic records. Changes in the MAP are associated with warming and cooling events. In the warciaming period (e.g., at ~69.5–68.5 Ma), an increase in the land-sea thermal contrast led to an expanded, enhanced, poleward-shifted thermal low-pressure system over the East Asian continent, which triggered an enhanced hydrological cycle and increasing MAP in the Songliao Basin. During the cooling period (e.g., at ~72.5–69.5 Ma and ~68.5–66.5 Ma), weakened East Asian monsoon and strengthened equatorward-shifted westerlies allowed for colder and arid air masses to encroach upon the Songliao Basin, which led to decreased MAP. Changes in MAP across the K-Pg boundary coincide with climate fluctuations and catastrophic geological events. Furthermore, our work compares three warming intervals in deep-time (middle Maastrichtian, late Maastrichtian and earliest Paleogene) with Shared Socio-economic Pathway scenarios used by the IPCC for the end of the 21st century, and indicates MAP increases in East Asia with ongoing anthropogenic CO2 emissions.
•The first high-resolution quantitative precipitation record in mid-latitude East Asia during ~76−65.5 Ma..•Changes in the mean annual precipitation (MAP) associated with climate fluctuations and catastrophic geological events.•Geological records suggest MAP in East Asia will likely increase under ongoing anthropogenic CO2 emissions.
In the western United States, mechanical thinning and prescribed fire are common forest management practices aimed at reducing potential wildfire severity and restoring historic forest structure, yet ...their effects on forest microclimate conditions are not well understood. We collected microclimate data between 1998 and 2003 in a mixed-conifer forest in California's Sierra Nevada. Air and soil temperatures, relative humidity, photosynthetically active radiation (PAR), wind speed, soil heat flux, and soil volumetric moisture were measured at the center of 18 four-ha plots. Each plot was assigned one of six combinations of thinning and burning treatments, and each treatment was thus given three replications. We found that spatial variability in microclimate, quantified as standard deviations among monthly values of each microclimatic variable across different locations (
n
≤
18), was significantly high and was influenced primarily by elevation and canopy cover. The combination of thinning and burning treatments increased air temperature from 58.1% to 123.6%. Soil temperatures increased in all thinned plots. Air moisture variables indicated that treatments made air drier, but soil moisture increased in the range 7.9–39.8%, regardless of treatment type. PAR increased in the range 50.4–254.8%, depending on treatment type. Treatments combining thinning and burning increased wind speed by 15.3–194.3%. Although soil heat flux increased dramatically in magnitude in some plots, overall treatment effects on
G were not statistically significant. We discussed the significance and implications of the spatial variability of microclimate and the treatment effects to various ecological processes and to forest management.
Aims
This review was developed to introduce the essential components and variants of structural equation modeling (SEM), synthesize the common issues in SEM applications, and share our views on SEM’s ...future in ecological research.
Methods
We searched the Web of Science on SEM applications in ecological studies from 1999 through 2016 and summarized the potential of SEMs, with a special focus on unexplored uses in ecology. We also analyzed and discussed the common issues with SEM applications in previous publications and presented our view for its future applications.
Results
We searched and found 146 relevant publications on SEM applications in ecological studies. We found that five SEM variants had not commenly been applied in ecology, including the latent growth curve model, Bayesian SEM, partial least square SEM, hierarchical SEM, and variable/model selection. We identified ten common issues in SEM applications including strength of causal assumption, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, report of results, estimation of sample size, and the fit of model.
Conclusions
In previous ecological studies, measurements of latent variables, explanations of model parameters, and reports of key statistics were commonly overlooked, while several advanced uses of SEM had been ignored overall. With the increasing availability of data, the use of SEM holds immense potential for ecologists in the future.
We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site‐level ...gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5° × 0.5° spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross‐validation analyses revealed good performance of MTE in predicting among‐site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 1018 yr−1), H (164 ± 15 J × 1018 yr−1), and GPP (119 ± 6 Pg C yr−1) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr−1) was likely underestimated by 5–10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.
•SVM was superior to the individual methods.•SVM improved the accuracy of the ET simulation.•SVM has provided a powerful tool for global ET estimation.
Terrestrial evapotranspiration (ET) for each ...plant functional type (PFT) is a key variable for linking the energy, water and carbon cycles of the atmosphere, hydrosphere and biosphere. Process-based algorithms have been widely used to estimate global terrestrial ET, yet each ET individual algorithm has exhibited large uncertainties. In this study, the support vector machine (SVM) method was introduced to improve global terrestrial ET estimation by integrating three process-based ET algorithms: MOD16, PT-JPL and SEMI-PM. At 200 FLUXNET flux tower sites, we evaluated the performance of the SVM method and others, including the Bayesian model averaging (BMA) method and the general regression neural networks (GRNNs) method together with three process-based ET algorithms. We found that the SVM method was superior to all other methods we evaluated. The validation results showed that compared with the individual algorithms, the SVM method driven by tower-specific (Modern Era Retrospective Analysis for Research and Applications, MERRA) meteorological data reduced the root mean square error (RMSE) by approximately 0.20 (0.15) mm/day for most forest sites and 0.30 (0.20) mm/day for most crop and grass sites and improved the squared correlation coefficient (R2) by approximately 0.10 (0.08) (95% confidence) for most flux tower sites. The water balance of basins and the global terrestrial ET calculation analysis also demonstrated that the regional and global estimates of the SVM-merged ET were reliable. The SVM method provides a powerful tool for improving global ET estimation to characterize the long-term spatiotemporal variations of the global terrestrial water budget.
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