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Liquid-liquid equilibrium (LLE) data for water + diethylene glycol dimethyl ether + {methyl isobutyl carbinol, methyl isobutyl ketone, methyl isopropyl ketone, methyl tert-butyl ether, n-octanol and 2-ethyl-1-hexanol} was measured at 298.2 K under 101.3 kPa. The effect of separating diethylene glycol dimethyl ether from water is evaluated by the distribution coefficient and the separation coefficient. The Othmer-Tobias and Hand equations were chosen to examine the experimental LLE data. Meanwhile, the data was correlated by the NRTL and UNIQUAC thermodynamic models with AAD% less than 0.55 and RMSD% less than 0.77. The separation performance of six solvents was compared using the σ-profile analysis via the exploration of the intermolecular interaction.

At the ecosystem scale, water-use efficiency (WUE) is defined broadly as the ratio of carbon assimilated to water evaporated by an ecosystem. WUE is an important aspect of carbon and water cycling and has been used to assess forest ecosystem responses to climate change and rising atmospheric CO2 concentrations. This study investigates the influence of meteorological and radiation variables on forest WUE by analyzing an 18 year (1998–2015) half-hourly time series of carbon and water fluxes measured with the eddy covariance technique in an old-growth conifer forest in the Pacific Northwest, USA. Three different metrics of WUE exhibit an overall increase over the period 1998–2007 mainly due to an increase in gross primary productivity (GPP) and a decrease in evapotranspiration (ET). However, the WUE metrics did not exhibit an increase across the period from 2008 to 2015 due to a greater reduction in GPP relative to ET. The strength of associations among particular meteorological variables and WUE varied with the scale of temporal aggregation used. In general, vapor pressure deficit and air temperature appear to control WUE at half-hourly and daily time scales, whereas atmospheric CO2 concentration was identified as the most important factor controlling monthly WUE. Carbon and water fluxes and the consequent WUE showed a weak correlation to the Standard Precipitation Index, while carbon fluxes were strongly dependent on the combined effect of multiple climate factors. The inferred patterns and controls on forest WUE highlighted have implications for improved understanding and prediction of possible adaptive adjustments of forest physiology in response to climate change and rising atmospheric CO2 concentrations.

Author(s): Jiang, Yueyang; Kim, John B; Trugman, Anna T; Kim, Youngil; Still, Christopher J

In recent years, the rapid expansion of urban distribution business and the increase use of traditional fuel logistics trucks have resulted in a lot of environmental, traffic and resource problems. “Green logistics” has become the direction of urban distribution transformation, and replacing fuel vehicles with pure electric logistics vehicles has become a way to meet the requirements of urban end distribution. In this paper, the characteristics of electric logistics vehicles are combined with the constraints of cargo capacity to meet the demand for sufficient power and driving range in the distribution process. Charging operations are carried out using charging piles at fixed locations when necessary, and the time windows of customers at each node are considered and penalty factors are set so that the distribution path of electric logistics vehicles can be well planned, which makes the total logistics cost lowest.

In the circumpolar north (45–90°N), permafrost plays an important role in vegetation and carbon (C) dynamics. Permafrost thawing has been accelerated by the warming climate and exerts a positive feedback to climate through increasing soil C release to the atmosphere. To evaluate the influence of permafrost on C dynamics, changes in soil temperature profiles should be considered in global C models. This study incorporates a sophisticated soil thermal model (STM) into a dynamic global vegetation model (LPJ-DGVM) to improve simulations of changes in soil temperature profiles from the ground surface to 3 m depth, and its impacts on C pools and fluxes during the 20th and 21st centuries. With cooler simulated soil temperatures during the summer, LPJ-STM estimates ~ 0.4 Pg C yr− 1 lower present-day heterotrophic respiration but ~ 0.5 Pg C yr− 1 higher net primary production than the original LPJ model resulting in an additional 0.8 to 1.0 Pg C yr− 1 being sequestered in circumpolar ecosystems. Under a suite of projected warming scenarios, we show that the increasing active layer thickness results in the mobilization of permafrost C, which contributes to a more rapid increase in heterotrophic respiration in LPJ-STM compared to the stand-alone LPJ model. Except under the extreme warming conditions, increases in plant production due to warming and rising CO2, overwhelm the e nhanced ecosystem respiration so that both boreal forest and arctic tundra ecosystems remain a net C sink over the 21st century. This study highlights the importance of considering changes in the soil thermal regime when quantifying the C budget in the circumpolar north.

Liquid-liquid equilibrium (LLE) data for {water + furfuryl alcohol + solvent (isopropyl ether, tert-butyl methyl ether, dichloromethane and methyl isobutylcarbinol)} ternary systems were determined at T = 298.2 K and p = 101.3 kPa. The distribution coefficients and separation factors were evaluated and the results showed that those solvents can effectively for furfuryl alcohol recovery from aqueous solutions. The Othmer-Tobias, Bachman and Hand equations were employed to validate the reliability of the experimental LLE data. Additionally, the experimental LLE data were correlated using the NRTL and UNIQUAC models and the binary interaction parameters were optimized. The root mean square deviations (RMSD) between the correlated data by NRTL and UNIQUAC models and the experimental LLE data were less than 0.36%, which demonstrated that both models can correlate experimental LLE data successfully.

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.

As climate warms, changes in the carbon (C) balance of arctic tundra will play an important role in the global C balance. The C balance of tundra is tightly coupled to the nitrogen (N) and phosphorus (P) cycles because soil organic matter is the principal source of plant-available nutrients and determines the spatial variation of vegetation biomass across the North Slope of Alaska. Warming will accelerate these nutrient cycles, which should stimulate plant growth. We applied the multiple element limitation model to investigate the spatial distribution of soil organic matter and vegetation on the North Slope of Alaska and examine the effects of changes in N and P cycles on tundra C budgets under climate warming. The spatial variation of vegetation biomass on the North Slope is mainly determined by nutrient mineralization, rather than air temperature. Our simulations show substantial increases in N and P mineralization with climate warming and consequent increases in nutrient availability to plants. There are distinctly different changes in N versus P cycles in response to warming. N is lost from the region because the warming-induced increase in N mineralization is in excess of plant uptake. However, P is more tightly cycled than N and the small loss of P under warming can be compensated by entrainment of recently weathered P into the ecosystem cycle. The increase in nutrient availability results in larger C gains in vegetation than C losses from soils and hence a net accumulation of C in the ecosystems. The ongoing climate warming in Arctic enhances mineralization and leads to a net transfer of nutrient from soil organic matter to vegetation, thereby stimulating tundra plant growth and increased C sequestration in the tundra ecosystems. The C balance of the region is predominantly controlled by the internal nutrient cycles, and the external nutrient supply only exerts a minor effect on C budget.

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.

Recent fire activity throughout Alaska has increased the need to understand postfire impacts on soils and permafrost vulnerability. Our study utilized data and modeling from a permafrost and ecosystem gradient to develop a mechanistic understanding of the short- and long-term impacts of tundra and boreal forest fires on soil thermal dynamics. Fires influenced a variety of factors that altered the surface energy budget, soil moisture, and the organic-layer thickness with the overall effect of increasing soil temperatures and thaw depth. The postfire thickness of the soil organic layer and its impact on soil thermal conductivity was the most important factor determining postfire soil temperatures and thaw depth. Boreal and tundra ecosystems underlain by permafrost experienced smaller postfire soil temperature increases than the nonpermafrost boreal forest from the direct and indirect effects of permafrost on drainage, soil moisture, and vegetation flammability. Permafrost decreased the loss of the insulating soil organic layer, decreased soil drying, increased surface water pooling, and created a significant heat sink to buffer postfire soil temperature and thaw depth changes. Ecosystem factors also played a role in determining postfire thaw depth with boreal forests taking several decades longer to recover their soil thermal properties than tundra. These factors resulted in tundra being less sensitive to postfire soil thermal changes than the nonpermafrost boreal forest. These results suggest that permafrost and soil organic carbon will be more vulnerable to fire as climate warms.

This study aims to assess how high-latitude vegetation may respond under various climate scenarios during the 21st century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble Lund-Potsdam-Jena (LPJ) simulations for the northern high latitudes (45oN and polewards) for the period 1900–2100. The LPJ Dynamic Global Vegetation Model (LPJ-DGVM) was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to climate variability, compared with boreal forest trees and C3 perennial grasses. This sensitivity would result in a unanimous northward greenness migration due to anomalous warming in the northern high latitudes. Temporally, boreal needleleaved evergreen plants are projected to decline considerably, and a large portion of C3 perennial grass is projected to disappear by the end of the 21st century. In contrast, the area of temperate trees would increase, especially under the most extreme A1FI scenario. As the warming continues, the northward greenness expansion in the Arctic region could continue.

Large wildland fires are major disturbances that strongly influence the carbon cycling and vegetation dynamics of Canadian boreal ecosystems. Although large wildland fires have recently received much scrutiny in scientific study, it is still a challenge for researchers to predict large fire frequency and burned area. Here, we use monthly climate and elevation data to quantify the frequency of large fires using a Poisson model, and we calculate the probability of burned area exceeding a certain size using a compound Poisson process. We find that the Poisson model simulates large fire occurrence well during the fire season (May through August) using monthly climate, and the threshold probability calculated by the compound Poisson model agrees well with historical records. Threshold probabilities are significantly different among different Canadian ecozones, with the Boreal Shield ecozone always showing the highest probability. The fire prediction model described in this study and the derived information will facilitate future quantification of fire risks and help improve fire management in the region.

Large fires are a major disturbance in Canadian forests and exert significant effects on both the climate system and ecosystems. During the last century, extremely large fires accounted for the majority of Canadian burned area. By making an instaneous change over a vast area of ecosystems, extreme fires often have significant social, economic, and ecological consequences. Since extreme values of fire size always situate in the upper tail of a cumulative probability distribution, the mean and variance alone are not sufficient to fully characterize those extreme events. To characterize the large fire behaviors in the upper tail, the authors in this study applied three extreme value distribution functions: (i) the generalized extreme value (GEV) distribution, (ii) the generalized Pareto distribution (GPD), and (iii) the GEV distribution with a Poisson point process (PP) representation to fit the Canadian historical fire data of the period 1959–2010. The analysis was conducted with the whole data set and different portions of the data set according to ignition sources (lightning-caused or human-caused) and ecozone classification. It is found that (i) all three extreme statistical models perform well to characterize extreme fire events, but the GPD and PP models need extra care to fit the nonstationary fire data, (ii) anthropogenic and natural extreme fires have significantly different extreme statistics, and (iii) fires in different ecozones exhibit very different characteristics in the view of statistics. Further, estimated fire return levels are comparable with observations in terms of the magnitude and frequency of an extreme event. These statistics of extreme values provide valuable information for future quantification of large fire risks and forest management in the region.

To investigate the underlying mechanisms that control long-term recovery of tundra carbon (C) and nutrients after fire, we employed the Multiple Element Limitation (MEL) model to simulate 200-yr post-fire changes in the biogeochemistry of three sites along a burn severity gradient in response to increases in air temperature, CO2 concentration, nitrogen (N) deposition, and phosphorus (P) weathering rates. The simulations were conducted for severely burned, moderately burned, and unburned arctic tundra. Our simulations indicated that recovery of C balance after fire was mainly determined by the internal redistribution of nutrients among ecosystem components (controlled by air temperature), rather than the supply of nutrients from external sources (e.g., nitrogen deposition and fixation, phosphorus weathering). Increases in air temperature and atmospheric CO2 concentration resulted in (1) a net transfer of nutrient from soil organic matter to vegetation and (2) higher C : nutrient ratios in vegetation and soil organic matter. These changes led to gains in vegetation biomass C but net losses in soil organic C stocks. Under a warming climate, nutrients lost in wildfire were difficult to recover because the warming-induced acceleration in nutrient cycles caused further net nutrient loss from the system through leaching. In both burned and unburned tundra, the warming-caused acceleration in nutrient cycles and increases in ecosystem C stocks were eventually constrained by increases in soil C : nutrient ratios, which increased microbial retention of plant-available nutrients in the soil. Accelerated nutrient turnover, loss of C, and increasing soil temperatures will likely result in vegetation changes, which further regulate the long-term biogeochemical succession. Our analysis should help in the assessment of tundra C budgets and of the recovery of biogeochemical function following fire, which is in turn necessary for the maintenance of wildlife habitat and tundra vegetation.

Fire frequency has dramatically increased in the tundra of northern Alaska, USA, which has major implications for the carbon budget of the region and the functioning of these ecosystems, which support important wildlife species. We investigated the postfire succession of plant and soil carbon (C), nitrogen (N), and phosphorus (P) fluxes and stocks along a burn severity gradient in the 2007 Anaktuvuk River fire scar in northern Alaska. Modeling results indicated that the early regrowth of postfire tundra vegetation was limited primarily by its canopy photosynthetic potential, rather than nutrient availability, because of the initially low leaf area and relatively high inorganic N and P concentrations in soil. Our simulations indicated that the postfire recovery of tundra vegetation was sustained predominantly by the uptake of residual inorganic N (i.e., in the remaining ash), and the redistribution of N and P from soil organic matter to vegetation. Although residual nutrients in ash were higher in the severe burn than the moderate burn, the moderate burn recovered faster because of the higher remaining biomass and consequent photosynthetic potential. Residual nutrients in ash allowed both burn sites to recover and exceed the unburned site in both aboveground biomass and production five years after the fire. The investigation of interactions among postfire C, N, and P cycles has contributed to a mechanistic understanding of the response of tundra ecosystems to fire disturbance. Our study provided insight on how the trajectory of recovery of tundra from wildfire is regulated during early succession.

We calibrated the Multiple Element Limitation (MEL) model to Alaskan arctic tundra to simulate recovery of thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could significantly alter regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as the climate warms. We simulated recovery following TEF stabilization and did not address initial, short-term losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we modeled a range of post-stabilization conditions by varying the initial size of SOM stocks and nutrient supply rates. Simulations indicate that nitrogen (N) losses after the TEF stabilizes are small, but phosphorus (P) losses continue. Vegetation biomass recovered 90% of its undisturbed C, N, and P stocks in 100 years using nutrients mineralized from SOM. Because of low litter inputs but continued decomposition, younger SOM continued to be lost for 10 years after the TEF began to recover, but recovered to about 84% of its undisturbed amount in 100 years. The older recalcitrant SOM in mineral soil continued to be lost throughout the 100-year simulation. Simulations suggest that biomass recovery depended on the amount of SOM remaining after disturbance. Recovery was initially limited by the photosynthetic capacity of vegetation but became co-limited by N and P once a plant canopy developed. Biomass and SOM recovery was enhanced by increasing nutrient supplies, but the magnitude, source, and controls on these supplies are poorly understood. Faster mineralization of nutrients from SOM (e.g., by warming) enhanced vegetation recovery but delayed recovery of SOM. Taken together, these results suggest that although vegetation and surface SOM on TEFs recovered quickly (25 and 100 years, respectively), the recovery of deep, mineral soil SOM took centuries and represented a major ecosystem C loss.

[1] Thawing and freezing processes are key components in permafrost dynamics, and these processes play an important role in regulating the hydrological and carbon cycles in the northern high latitudes. In the present study, we apply a well-developed soil thermal model that fully couples heat and water transport, to simulate the thawing and freezing processes at daily time steps across multiple sites that vary with vegetation cover, disturbance history, and climate. The model performance was evaluated by comparing modeled and measured soil temperatures at different depths. We use the model to explore the influence of climate, fire disturbance, and topography (north- and south-facing slopes) on soil thermal dynamics. Modeled soil temperatures agree well with measured values for both boreal forest and tundra ecosystems at the site level. Combustion of organic-soil horizons during wildfire alters the surface energy balance and increases the downward heat flux through the soil profile, resulting in the warming and thawing of near-surface permafrost. A projection of 21st century permafrost dynamics indicates that as the climate warms, active layer thickness will likely increase to more than 3 meters in the boreal forest site and deeper than one meter in the tundra site. Results from this coupled heat-water modeling approach represent faster thaw rates than previously simulated in other studies. We conclude that the discussed soil thermal model is able to well simulate the permafrost dynamics and could be used as a tool to analyze the influence of climate change and wildfire disturbance on permafrost thawing.

This study aims to assess how high-latitude vegetation may respond under various climate scenarios during the 21st century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble Lund-Potsdam-Jena (LPJ) simulations for the northern high latitudes (45(o)N and polewards) for the period 1900-2100. The LPJ Dynamic Global Vegetation Model (LPJ-DGVM) was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to climate variability, compared with boreal forest trees and C3 perennial grasses. This sensitivity would result in a unanimous northward greenness migration due to anomalous warming in the northern high latitudes. Temporally, boreal needleleaved evergreen plants are projected to decline considerably, and a large portion of C3 perennial grass is projected to disappear by the end of the 21st century. In contrast, the area of temperate trees would increase, especially under the most extreme A1FI scenario. As the warming continues, the northward greenness expansion in the Arctic region could continue.

Wildfire is a major disturbance in boreal terrestrial ecosystems. Characterizing fire regimes and projecting fire recurrence intervals for different biomes are important in managing those ecosystems and quantifying carbon dynamics of those ecosystems. This study used Canadian wildfire datasets, 1980–1999, to characterize relationships between number of fires and burned area for 13 ecozones and to calculate wildfire recurrence intervals in each ecozone. For the study period, wildfires were found to follow power–law relationships between frequency densities (number of fires normalized to unit bins) and burned areas in all ecozones. Power–law frequency–area relationships also held for both anthropogenic fires and natural fires in the 1980s and 1990s. For each Canadian ecozone using the parameters of the power–law frequency–area distributions, fire recurrence intervals were then calculated for wildfires equal to or larger than a given size of burned area. Fire recurrence intervals ranged from 1 to 32 years for burned areas >2 km2, and from 1 to 100 years for burned areas >10 km2 in every 10 000-km2 spatial area for each ecozone. The information obtained through characterizing the wildfires and the fire recurrence intervals calculated in this study will provide guidance to wildfire risk managers throughout Canada. The findings of this study will also be a benefit to future efforts in quantifying carbon dynamics in Canadian boreal terrestrial ecosystems.