Search

We introduce a different operational approach to estimate 8-day average daily evapotranspiration (ET) using both routinely available data and the Penman-Monteith (P-M) equation for canopy transpiration and evaporation of intercepted water and Priestley and Taylor for soil evaporation. Our algorithm considered the environmental constraints on canopy resistance and ET by (1) including vapor pressure deficit (VPD), incoming solar radiation, soil moisture, and temperature constraints on stomatal conductance; (2) using leaf area index (LAI) to scale from the leaf to canopy conductance; and (3) calculating canopy resistance as a function of environmental variables such as net radiation and VPD. Remote sensing data from the Moderate Resolution Spectroradiometer (MODIS) and satellite soil moisture data were used to derive the ET model. The algorithm was calibrated and evaluated using measured ET data from 20 AmeriFlux Eddy covariance flux sites for the period of 2003–2012. We found good agreements between our 8-day ET estimates and observations with mean absolute error (MAE) ranges from 0.17 mm/day to 0.94 mm/day compared with MAE ranging from 0.28 mm/day to 1.50 mm/day for MODIS ET. Compared to MODIS ET, our proposed algorithm has higher correlations and higher Willmott’s index of agreement with observations for the majority of the Ameriflux sites. The strong relationship between the model estimated ET and the flux tower observations implies that our model has the potential to be applied to different ecosystems and at different temporal scales.

Predicted decreases in water availability across the temperate forest biome have the potential to offset gains in carbon (C) uptake from phenology trends, rising atmospheric CO2, and nitrogen deposition. While it is well established that severe droughts reduce the C sink of forests by inducing tree mortality, the impacts of mild but chronic water stress on forest phenology and physiology are largely unknown. We quantified the C consequences of chronic water stress using a 13-year record of tree growth (n = 200 trees), soil moisture, and ecosystem C balance at the Morgan–Monroe State Forest (MMSF) in Indiana, and a regional 11-year record of tree growth (n > 300 000 trees) and water availability for the 20 most dominant deciduous broadleaf tree species across the eastern and midwestern USA. We show that despite ~26 more days of C assimilation by trees at the MMSF, increasing water stress decreased the number of days of wood production by ~42 days over the same period, reducing the annual accrual of C in woody biomass by 41%. Across the deciduous forest region, water stress induced similar declines in tree growth, particularly for waterdemanding ‘mesophytic’ tree species. Given the current replacement of water-stress adapted ‘xerophytic’ tree species by mesophytic tree species, we estimate that chronic water stress has the potential to decrease the C sink of deciduous forests by up to 17% (0.04 Pg C yr � 1 ) in the coming decades. This reduction in the C sink due to mesophication and chronic water stress is equivalent to an additional 1–3 days of global C emissions from fossil fuel burning each year. Collectively, our results indicate that regional declines in water availability may offset the growth-enhancing effects of other global changes and reduce the extent to which forests ameliorate climate warming.

Terrestrial plants remove CO2 from the atmosphere through photosynthesis, a process that is accompanied by the loss of water vapour from leaves. The ratio of water loss to carbon gain, or water-use efficiency, is a key characteristic of ecosystem function that is central to the global cycles of water, energy and carbon. Here we analyse direct, long-term measurements of whole-ecosystem carbon and water exchange. We find a substantial increase in water-use efficiency in temperate and boreal forests of the Northern Hemisphere over the past two decades. We systematically assess various competing hypotheses to explain this trend, and find that the observed increase is most consistent with a strong CO2 fertilization effect. The results suggest a partial closure of stomata-small pores on the leaf surface that regulate gas exchange-to maintain a near-constant concentration of CO2 inside the leaf even under continually increasing atmospheric CO2 levels. The observed increase in forest water-use efficiency is larger than that predicted by existing theory and 13 terrestrial biosphere models. The increase is associated with trends of increasing ecosystem-level photosynthesis and net carbon uptake, and decreasing evapotranspiration. Our findings suggest a shift in the carbon- and water-based economics of terrestrial vegetation, which may require a reassessment of the role of stomatal control in regulating interactions between forests and climate change, and a re-evaluation of coupled vegetation-climate models.

Vegetation phenology is sensitive to climate change and variability, and is a first order control on the carbon budget of forest ecosystems. Robust representation of phenology is therefore needed to support model-based projections of how climate change will affect ecosystem function. A variety of models have been developed to predict species or site-specific phenology of trees. However, extension of these models to other sites or species has proven difficult. Using meteorological and eddy covariance data for 29 forest sites (encompassing 173 site-years), we evaluated the accuracy with which 11 different models were able to simulate, as a function of air temperature and photoperiod, spatial and temporal variability in the onset of spring photosynthetic activity. In parallel, we also evaluated the accuracy with which dynamics in remotely sensed vegetation indices from MODIS captured the timing of spring onset. To do this, we used a subset of sites in the FLUXNET La Thuile database located in evergreen needleleaf and deciduous broadleaf forests with distinct active and dormant seasons and where temperature is the primary driver of seasonality. As part of this analysis we evaluated predictions from refined versions of the 11 original models that include parameterizations for geographic variation in both thermal and photoperiod constraints on phenology. Results from cross-validation analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than the original models. Estimates for the timing of spring onset from MODIS were highly correlated with the onset of photosynthesis derived from flux measurements, but were biased late for needleleaf sites. Our results demonstrate that simple phenology models can be used to predict the timing of spring photosynthetic onset both across sites and across years at individual sites. By extension, these models provide an improved basis for predicting how the phenology and carbon budgets of temperature-limited forest ecosystems may change in the coming decades.

Mangrove forests of the tropical and subtropical regions provide critical ecosystem services, fulfill important socio-economic and environmental functions, and support coastal livelihoods. These forests are also among the most vulnerable ecosystems, both to anthropogenic disturbance and climate change. Yet, no map or published study exists showing detailed spatiotemporal trends of mangrove deforestation at local to regional scales. This study uses change point and mixed-pixel analyses with a time series (2000–2010) of high-fidelity imagery from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) for tracking deforestation of mangroves in the Mahakam Delta of Indonesia at 250 m spatial scale and a 3-monthly temporal interval. The results show that a total of 21,000 ± 152 ha of mangrove land in the Mahakam Delta were deforested and converted to shrimp ponds in 11 years. In 2000, the deforested lands covered 47% of the entire delta, while in 2010 it increased to 75%. Deforestation rates varied in each year, but peaked in 2002 and declined since then. The combination of high-fidelity time series data and a robust method of change detection resulted in a virtual reconstruction of 11 years of drastic land change history of the delta's mangrove areas at a consistent spatiotemporal scale. We anticipate that the methodology developed in this study will be useful to reconstruct deforestation histories of other places as well. Also, our findings can potentially be used to further explore the socioeconomic drivers of mangrove deforestation so that reliable, appropriate and sustainable methods of local and regional scale adaptation/mitigation strategies can be developed.

a b s t r a c t The eddy-covariance method often underestimates fluxes under stable, low-wind conditions at night when turbulence is not well developed. The most common approach to resolve the problem of nighttime flux underestimation is to identify and remove the deficit periods using friction-velocity (u*) threshold filters (u* Th ). This study modifies an accepted method for u* Th evaluation by incorporating change-point

Terrestrial ecosystems are highly sensitive to climatic changes in early and late growing seasons. Land surface phenology (LSP), the study of the timing of recurring seasonal pattern of variation in vegetated land surfaces observed from synoptic sensors, has thus received much attention due to its role as a surrogate in detecting the impact of climate change. Although several studies have been conducted on the growing season LSP, studies on the net carbon uptake phenology (CUP) defined as the detrended zero-crossing timing of net ecosystem productivity from a source to a sink in spring and vice versa in autumn, have been scarce. Here we present a CUP determination approach using the commonly available remote sensing data in four selected temperate and boreal deciduous forest CO2 flux tower sites. We test a hypothesis that the mean monthly surface temperature and LSP derived from remote sensing observations explain the CUP both in spring and autumn seasons. Our approach predicts the observed CUP in spring and autumn within 8 day mean errors, equivalent to the temporal resolution of the 8-day composite remote sensing dataset used in this study for the four flux tower sites. The results from this study will have a large implication for global change studies with increasing amount of valuable remote sensing data to be used for monitoring CUP beyond the footprints of CO2 flux towers.

Studies of global hydrologic cycles, carbon cycles and climate change are greatly facilitated when. global estimates of evapotranspiration (E) are available. We have developed an air-relative-humidity-based two-source (ARTS) E model that simulates the surface energy balance, soil water balance, and environmental constraints on E. It uses remotely sensed leaf area index (L-ai) and surface meteorological data to estimate E by: 1) introducing a simple biophysical model for canopy conductance (G(c)), defined as a constant maximum stomatal conductance g(smax) of 12.2 mm s(-1) multiplied by air relative humidity (R-h) and L-ai (G(c) = g(srnax) x R-h X L-ai); 2) calculating canopy transpiration with the G(c)-based Penman-Monteith (PM) E model; 3) calculating soil evaporation from an air-relative-humidity-based model of evapotranspiration (Yan & Shugart, 2010); 4) calculating total E (E-0) as the sum of the canopy transpiration and soil evaporation, assuming the absence of soil water stress; and 5) correcting E-0 for soil water stress using a soil water balance model. This physiological ARTS E model requires no calibration. Evaluation against eddy covariance measurements at 19 flux sites, representing a wide variety of climate and vegetation types, indicates that daily estimated E had a root mean square error = 0.77 mm d(-1). bias = -0.14 mm d(-1), and coefficient of determination, R-2 = 0.69. Global, monthly, 0.5 degrees-gridded ARTS E simulations from 1984 to 1998, which were forced using Advanced Very High Resolution Radiometer Lai data, Climate Research Unit climate data, and surface radiation budget data, predicted a mean annual land E of 58.4 x 10(3) km(3). This falls within the range (58 x 10(3)-85 x 10(3) km(3)) estimated by the Second Global Soil Wetness Project (GSWP-2: Dirmeyer et al., 2006). The ARTS E spatial pattern agrees well with that of the global E estimated by GSWP-2. The global annual ARTS E increased by 15.5 mm per decade from 1984 to 1998, comparable to an increase of 9.9 mm per decade from the model tree ensemble approach (Jung et al., 2010). These comparisons confirm the effectivity of the ARTS E model to simulate the spatial. pattern and climate response of global E. This model is the first of its kind among remote-sensing-based PM E models to provide global land E estimation with consideration of the soil water balance. (C) 2012 Elsevier Inc. All rights reserved.

Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by ecosystems and is an important component of the terrestrial carbon cycle. Empirical light use efficiency (LUE) models and process-based Farquhar, von Caemmerer, and Berry (FvCB) photosynthetic models are widely used for GPP estimation. In this paper, the MODIS GPP algorithm using the LUE approach and the Boreal Ecosystem Productivity Simulator (BEPS) based on the FvCB model in which a sunlit and shaded leaf separation scheme is evaluated against GPP values derived from eddy-covariance (EC) measurements in a variety of ecosystems. Although the total GPP values simulated using these two models agree within 89% when they are averaged for the conterminous U.S., there are systematic differences between them in terms of their spatial and temporal distribution patterns. The spatial distribution of MODIS GPP therefore differs substantially from that produced by BEPS. These differences may be due to an inherent problem of the LUE modeling approach. When a constant maximum LUE value is used for a biome type, this simplification cannot properly handle the contribution of shaded leaves to the total canopy-level GPP. When GPP is modeled by BEPS as the sum of sunlit and shaded leaf GPP, the problem is minimized, i.e., at the low end, the relative contribution of shaded leaves to GPP is small and at the high end, the relative contribution of shaded leaves is large. Compared with monthly and annual GPP derived from eddy covariance data at 40 tower sites in North America, BEPS performed better than the MODIS GPP algorithm. The difference between MODIS and BEPS GPP widens as with the fraction of shaded leaves increases. The simpler LUE modeling approach should therefore be further improved to reduce this bias issue for effective estimation of regional and temporal GPP distributions.

Understanding feedbacks of ecosystem carbon sequestration to climate change is an urgent step in developing future ecosystem models. Using 187 site-years of flux data observed at 24 sites covering three plant functional types (i.e. evergreen forests (EF), deciduous forests (DF) and non-forest ecosystems (NF) (e.g., crop, grassland, wetland)) in North America, we present an analysis of both interannual and spatial relationships between annual net ecosystem production (NEP) and phenological indicators, including the flux-based carbon uptake period (CUP) and its transitions, degree-day-derived growing season length (GSL), and spring and autumn temperatures. Diverse responses were acquired between annul NEP and these indicators across PFTs. Forest ecosystems showed consistent patterns and sensitivities in the responses of annual NEP to CUP and its transitions both interannually and spatially. The NF ecosystems, on the contrary, exhibited different trends between interannual and spatial relationships. The impact of CUP onset on annual NEP in NF ecosystems was interannually negative but spatially positive. Generally, the GSL was observed to be a likely good indicator of annual NEP for all PFTs both interannually and spatially, although with relatively moderate correlations in NF sites. Both spring and autumn temperatures were positively correlated with annual NEP across sites while this potential was greatly reduced temporally with only negative impacts of autumn temperature on annual NEP in DF sites. Our analysis showed that DF ecosystems have the highest efficiency in accumulating NEP from warmer spring temperature and prolonged GSL, suggesting that future climate warming will favor deciduous species over evergreen species, and supporting the earlier observation that ecosystems with the greatest net carbon uptake have the longest GSL.

Decadal trends in delay of the end of the season (EOS) have been recently observed across a large fraction of the forested areas in the Northern hemisphere. However, the spatial patterns of EOS variability and its environmental forcings at local scale are largely unknown within deciduous forests. In this study, we investigated short- and long-term changes in EOS and its relationship with variability of air temperature and precipitation across the deciduous forests of the Eastern USA from 1989 to 2008. We used high-resolution (1 km2) satellite data in conjunction with meteorological measurements. Our results show strong evidence of widespread delay in EOS throughout larger areas than what was previously reported. Equally important, the results show that EOS variability and EOS response to summer air temperature varied significantly across the Eastern USA. EOS response to climate variability was in general correlated with the latitude of the forest, but different patterns for different areas were observed as well. No clear relationship was observed between EOS and precipitation, probably because of the complexity of the link between water relations and senescence mechanisms and controls in deciduous trees. Overall, our results show the importance of local scale heterogeneity (likely driven by both biotic and abiotic factors) in determining significantly different patterns in the relationship between EOS and climate variability across Eastern USA.

a b s t r a c t The eddy covariance technique provides long-term continuous monitoring of site-specific net ecosystem exchange of CO2 (NEE) across a large range of forest types. However, these NEE estimates only represent fluxes at the scale of the tower footprint and need to be scaled up to quantify NEE over regions or conti- nents. In the present study, we expanded a method developed previously and generated a new NEE model exclusively based on the Moderate Resolution Imaging Spectroradiometer (MODIS) products, including enhanced vegetation index (EVI), land surface water index (LSWI), land surface temperature (LST) and Terra nighttime LST� . This method, in our previous research, provided substantially good predictions of NEE and well reflected the seasonal dynamics of the deciduous broadleaf forest at the Harvard forest site. Studying NEE of forests in the middle-latitude regions of the Northern Hemisphere is significant because it may help to understand the 'missing carbon sink' from terrestrial ecosystems. In this study we selected eight eddy flux sites to represent the major forest ecosystems in the northern United States. Compared with the model based on a single site, we also established the general models that apply to evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF), respectively. The results showed that our simpler model based entirely on MODIS products promised well to estimate NEE by the eddy covariance technique. The modeled annual mean NEE from DBF deviated from the measured NEE by 44.4%, whereas the modeled NEE from ENF was extremely close to the measured NEE within 5.5%. In the end, we also validated both general models for ENF and DBF using independent flux sites. It demonstrated this method

The Sundarbans is the world's largest remaining single block of mangrove forest, covering approximately 1 million ha (~ 10,000 km2) of the Ganges–Brahmaputra delta along the coastal areas of India and Bangladesh. Sea level rise and alteration of water flows of the Himalayan headwaters are among the major disturbances threatening these coastal areas. But very few studies exist on the dynamics or current status of the Sundarbans coastline. We used Landsat images spanning from 1973 to 2010, and an algorithm that we developed, to consistently estimate the spatiotemporal dynamics of erosion and accretion for four different time intervals and the whole study period. Our results show that the direction and extent of erosion and accretion rates varied throughout the different periods. Erosion was the highest in the 1973–1979 interval, with 23.2 km2 year−1 of land loss. However, that rate substantially declined in the following periods, reaching a rate of 7–10 km2 year−1. Accretion showed a rate of 10 km2 year−1 between 1973 and 1989, but substantially declined to ~ 4 km2 year−1 between 1989 and 2010. Accretion rate has declined in the recent years but erosion rate has remained relatively high. As a result the delta front has undergone a net erosion of ~ 170 km2 of coastal land in the 37 years of our study period. These numbers are significantly higher than the previously reported rates and magnitudes of erosion in this area. The methods and maps developed in this study may be helpful in management planning of this vulnerable coastline.

The role of different sky conditions on diffuse PAR fraction (ϕ), air temperature (Ta), vapor pressure deficit (vpd) and GPP in a deciduous forest is investigated using eddy covariance observations of CO2 fluxes and radiometer and ceilometer observations of sky and PAR conditions on hourly and growing season timescales. Maximum GPP response occurred under moderate to high PAR and ϕ and low vpd. Light response models using a rectangular hyperbola showed a positive linear relation between ϕ and effective quantum efficiency (α = 0.023ϕ + 0.012, r2 = 0.994). Since PAR and ϕ are negatively correlated, there is a tradeoff between the greater use efficiency of diffuse light and lower vpd and the associated decrease in total PAR available for photosynthesis. To a lesser extent, light response was also modified by vpd and Ta. The net effect of these and their relation with sky conditions helped enhance light response under sky conditions that produced higher ϕ. Six sky conditions were classified from cloud frequency and ϕ data: optically thick clouds, optically thin clouds, mixed sky (partial clouds within hour), high, medium and low optical aerosol. The frequency and light responses of each sky condition for the growing season were used to predict the role of changing sky conditions on annual GPP. The net effect of increasing frequency of thick clouds is to decrease GPP, changing low aerosol conditions has negligible effect. Increases in the other sky conditions all lead to gains in GPP. Sky conditions that enhance intermediate levels of ϕ, such as thin or scattered clouds or higher aerosol concentrations from volcanic eruptions or anthropogenic emissions, will have a positive outcome on annual GPP, while an increase in cloud cover will have a negative impact. Due to the ϕ/PAR tradeoff and since GPP response to changes in individual sky conditions differ in sign and magnitude, the net response of ecosystem GPP to future sky conditions is non-linear and tends toward moderation of change.

Observations of net ecosystem exchange (NEE) of carbon and its biophysical drivers have been collected at the AmeriFlux site in the Morgan-Monroe State Forest (MMSF) in Indiana, USA since 1998. Thus, this is one of the few deciduous forest sites in the world, where a decadal analysis on net ecosystem productivity (NEP) trends is possible. Despite the large interannual variability in NEP, the observations show a significant increase in forest productivity over the past 10 years (by an annual increment of about 10 g C m � 2 yr � 1 ). There is evidence that this trend can be explained by longer vegetative seasons, caused by extension of the vegetative activity in the fall. Both phenological and flux observations indicate that the vegetative season extended later in the fall with an increase in length of about 3 days yr � 1 for the past 10 years. However, these changes are responsible for only 50% of the total annual gain in forest

A new methodology for establishing the spatial representativeness of tower albedo measurements that are routinely used in validation of satellite retrievals from global land surface albedo and reflectance anisotropy products is presented. This method brings together knowledge of the intrinsic biophysical properties of a measurement site, and the surrounding landscape to produce a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns at separate seasonal periods throughout the year. Variogram functions extracted from Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo using multiple spatial and temporal thresholds were used to assess the degree to which a given point (tower) measurement is able to capture the intrinsic variability of the immediate landscape extending to a satellite pixel. A validation scheme was implemented over a wide range of forested landscapes, looking at both deciduous and coniferous sites, from tropical to boreal ecosystems. The experiment focused on comparisons between tower measurements of surface albedo acquired at local solar noon and matching retrievals from the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/albedo algorithm. Assessments over a select group of field stations with comparable landscape features and daily retrieval scenarios further demonstrate the ability of this technique to identify measurement sites that contain the intrinsic spatial and seasonal features of surface albedo over sufficiently large enough footprints for use in modeling and remote sensing studies. This approach, therefore, improves our understanding of product uncertainty both in terms of the representativeness of the field data and its relationship to the larger satellite pixel.

Labile carbon (C), which is principally comprised of non-structural carbohydrates, is an essential intermediary between C assimilation and structural growth in deciduous forests. We developed a new approach that combined meteorological and biometric C cycling data for a mixed deciduous forest in Michigan, USA, to provide novel estimates of whole-ecosystem labile C production and reallocation to structural net primary production (NPP). We substantiated inferred seasonal patterns of labile C production and reallocation to structural NPP with measurements of Populus grandidentata and Quercus rubra wood non-structural carbohydrate concentration and mass over two years. Our analysis showed that 55% of annual net canopy C assimilate ( A c ) was first allocated to labile C production rather than to immediate structural NPP. Labile C produced during the latter half of summer later supported dormant-season structural growth and respiration, with 34% of structural NPP in a given year requiring labile C stored during previous years. Seasonal changes in wood non-structural carbohydrate concentration and mass generally corroborated inferred temporal patterns of whole-ecosystem labile C production and reallocation to structural NPP. Our findings confirm that disparities can arise between same-year meteorological and biometric net ecosystem production when meteorologically measured C assimilation and biometrically measured growth are asynchronous because of temporary photosynthate allocation to labile C storage. We conclude that a broader understanding of labile C production and reallocation at the ecosystem scale is important to interpreting lagged canopy C cycling and structural growth processes.

This paper presents a novel theoretical framework for the study of individual tree sap flow that incorporates both spatial and temporal variability in sap velocities. In this formulation, the instantaneous sap velocity at any point in the radial profile of xylem tissue is defined as the product of a time-invariant sap velocity distribution and a time-varying term which is defined as a stem conductance. We hypothesize that the characteristic distribution of sap velocity is relatively uniform both within individual trees and between trees of the same species if location in the xylem tissue is expressed in normalized units relative to the total xylem depth (i.e. tree size). Experimental evidence confirms our hypothesis in the case of a population of sugar maples in a mixed deciduous forest and dwarf apple trees in an orchard, despite the fact that observations were drawn from a wide range of tree sizes and under varying soil moisture levels and atmospheric conditions that determines water demand. Furthermore, profiles of sap velocity and resulting integrals of total sap flow exhibit significant reduction in bias (by 30–40%) in comparison to prior methods used to extrapolate point observations of sap velocity. The method we describe exhibits the greatest improvement when only a small fraction of the total sapwood is measured, which is the typical scenario for most applications. While these results require further confirmation in order to be generalized, they nevertheless offer the basis to improve both the specific sampling strategies used to estimate whole-tree transpiration using sap velocity probes as well as methods employed to upscale water use of individual trees to larger scales for evaluation of landscape water balance.

In this work we present experimental evidence in support of a new approach for investigating the dependence of sap velocity on atmospheric water demand and soil moisture supply. In this method, sap velocity is defined as the product of two components: the first describes the ‘shape’ of the radial profile of sap velocity, which is consistent through time and is likely linked to the species-specific anatomical and structural properties of the conducting xylem; the second, which we define as stem conductance, captures the time-dependent component of sap velocity that is mostly governed by shifts in atmospheric water demand and individual tree water supply. The heat pulse technique was used to estimate radial profiles of sap velocity and transpiration from a sample of 16 mature sugar maples (Acer saccharum) located along a topographic transect in a mixed deciduous forest. Our results demonstrate that: (1) stem conductance is strongly correlated with bulk air conditions (with confidence intervals for all the sampled trees greater than 99% and average R2 of 0.43, 0.57, 0.54 for vapor pressure deficit (VPD), PPFD and net radiation, respectively) and atmospheric water demand (average R2 equal to 0.73) on an hourly basis and that it is independent of tree size; (2) sensitivity of stem conductance to atmospheric water demand in sugar maples is also correlated to variation in local soil water availability (P-value = 0.014, R2 = 0.43) which arises due to a mild topographic gradient (i.e. 20 m of relative relief along 140 m) and relatively shallow soil. Although the sampled trees were subjected to a wide range of atmospheric water demands and soil moistures, the response to changes in environmental conditions is entirely explainable by dynamics of stem conductance rather than the relative fraction of sap flow along the radial profile, as some of the previous studies reported. Overall, our results confirm our theoretical approach and the possibility of partitioning sap velocity variability between either xylem properties or changes in environmental conditions.

Over the last two and half decades, strong evidence showed that the terrestrial ecosystems are acting as a net sink for atmospheric carbon. However the spatial and temporal patterns of variation in the sink are not well known. In this study, we examined latitudinal patterns of interannual variability (IAV) in net ecosystem exchange (NEE) of CO2 based on 163 site-years of eddy covariance data, from 39 northern-hemisphere research sites located at latitudes ranging from ~29°N to ~64°N. We computed the standard deviation of annual NEE integrals at individual sites to represent absolute interannual variability (AIAV), and the corresponding coefficient of variation as a measure of relative interannual variability (RIAV). Our results showed decreased trends of annual NEE with increasing latitude for both deciduous broadleaf forests and evergreen needleleaf forests. Gross primary production (GPP) explained a significant proportion of the spatial variation of NEE across evergreen needleleaf forests, whereas, across deciduous broadleaf forests, it is ecosystem respiration (Re). In addition, AIAV in GPP and Re increased significantly with latitude in deciduous broadleaf forests, but AIAV in GPP decreased significantly with latitude in evergreen needleleaf forests. Furthermore, RIAV in NEE, GPP, and Re appeared to increase significantly with latitude in deciduous broadleaf forests, but not in evergreen needleleaf forests. Correlation analyses showed air temperature was the primary environmental factor that determined RIAV of NEE in deciduous broadleaf forest across the North American sites, and none of the chosen climatic factors could explain RIAV of NEE in evergreen needleleaf forests. Mean annual NEE significantly increased with latitude in grasslands. Precipitation was dominant environmental factor for the spatial variation of magnitude and IAV in GPP and Re in grasslands.

For scientific study and practical irrigation management, the estimate of plant transpiration and water use rates is needed. Several methods are available to measure water use by plants, and among them, sap flow estimation techniques are currently popular, although the interpretation of data may be complex due to several assumptions. In this study, we present a combined approach to measure transpiration rates in mature, field-grown dwarf apple trees (‘Empire’/M.9) in a humid climate. Sap flow gauges (heat pulse method) were used to estimate sap flow rates on apples, over a period of 10 weeks. The gauges were first calibrated with simultaneous measurements of whole-canopy gas exchange using chambers mounted on the same plants for short periods (5 days). Mid-summer transpiration rates were peaked at about 2.5 l day −1 m −2 of leaf area but generally averaged about 2 l day −1 m −2 of leaf area. Calculated basal crop coefficients were generally lower than those reported in literature, variable and were a function of the vapor pressure deficit, thus confirming the concerns of using crop coefficients with reference grass in humid climates especially when applied to tall, discontinuous canopies that are well coupled with the bulk air.

Predicting the impact of drought on forest ecosystem processes requires an understanding of trees' species-specific responses to drought, especially in the Eastern USA, where species composition is highly dynamic due to historical changes in land use and fire regime. Here, we adapted a framework that classifies trees' water-use strategy along the spectrum of isohydric to anisohydric behavior to determine the responses of three canopy-dominant species to drought. We used a collection of leaf-level gas exchange, tree-level sap flux and stand-level eddy covariance data collected in south-central Indiana from 2011 to 2013, which included an unusually severe drought in the summer of 2012. Our goal was to assess how patterns in the radial profile of sap flux and reliance on hydraulic capacitance differed among species of contrasting water-use strategies. In isohydric species, which included sugar maple (Acer saccharum Marsh.) and tulip poplar (Liriodendron tulipifera L.), we found that the sap flux in the outer xylem experienced dramatic declines during drought, but sap flux at inner xylem was buffered from reductions in water availability. In contrast, for anisohydric oak species (Quercus alba L. and Quercus rubra L.), we observed relatively smaller variations in sap flux during drought in both inner and outer xylem, and higher nighttime refilling when compared with isohydric species. This reliance on nocturnal refilling, which occurred coincident with a decoupling between leaf- and tree-level water-use dynamics, suggests that anisohydric species may benefit from a reliance on hydraulic capacitance to mitigate the risk of hydraulic failure associated with maintaining high transpiration rates during drought. In the case of both isohydric and anisohydric species, our work demonstrates that failure to account for shifts in the radial profile of sap flux during drought could introduce substantial bias in estimates of tree water use during both drought and non-drought periods.

Ongoing shifts in the species composition of Eastern US forests necessitate the development of frameworks to explore how species-specific water-use strategies influence ecosystem-scale carbon (C) cycling during drought. Here, we develop a diagnostic framework to classify plant drought-response strategies along a continuum of isohydric to anisohydric regulation of leaf water potential (Ψ L). The framework is applied to a 3-year record of weekly leaf-level gas exchange and Ψ measurements collected in the Morgan-Monroe State Forest (Indiana, USA), where continuous observations of the net ecosystem exchange of CO2 (NEE) have been ongoing since 1999. A severe drought that occurred in the middle of the study period reduced the absolute magnitude of NEE by 55 %, though species-specific responses to drought conditions varied. Oak species were characterized by anisohydric regulation of Ψ L that promoted static gas exchange throughout the study period. In contrast, Ψ L of the other canopy dominant species was more isohydric, which limited gas exchange during the drought. Ecosystem-scale estimates of NEE and gross ecosystem productivity derived by upscaling the leaf-level data agreed well with tower-based observations, and highlight how the fraction of isohydric and anisohydric species in forests can mediate net ecosystem C balance.

Summary Although it is increasingly being recognized that roots play a key role in soil carbon (C) dynamics, the magnitude and direction of these effects are unknown. Roots can accelerate soil C losses by provisioning microbes with energy to decompose organic matter or impede soil C losses by enhancing microbial competition for nutrients. We experimentally reduced belowground C supply to soils via tree girdling, and contrasted responses in control and girdled plots for three consecutive growing seasons. We hypothesized that decreases in belowground C supply would have stronger effects in plots dominated by ectomycorrhizal (ECM) trees rather than arbuscular mycorrhizal (AM) trees. In ECM-dominated plots, girdling decreased the activity of enzymes that break down soil organic matter (SOM) by c. 40%, indicating that, in control plots, C supply from ECM roots primes microbial decomposition. In AM-dominated plots, girdling had little effect on SOM-degrading enzymes, but increased the decomposition of AM leaf litter by c. 43%, suggesting that, in control plots, AM roots may intensify microbial competition for nutrients. Our findings indicate that root-induced changes in soil processes depend on forest composition, and that shifts in the distribution of AM and ECM trees owing to climate change may determine soil C gains and losses.

replying to C. D. Holmes Nature507,http://dx.doi.org/10.1038/nature13113(2014) Forests have become more efficient at using water over the past two decades1. A series of hypotheses exist to explain this trend, but the only credible explanation to date is a response to rising atmospheric CO2. Keenan et al.1 show that the observed trend is physiologically plausible, but is much larger than expected from conventional theory and experimental evidence. This has led to suggestions that processes other than increased atmospheric CO2 may have contributed to the observed trend2. One such process that has yet to be examined is the effect of tropospheric ozone on forest water-use efficiency (WUE). In the accompanying Comment3, Holmes reports that ozone concentrations have declined in the northeastern and midwestern USA by about 50% from 1995 to 2010. Using empirical relationships, he estimates that this decline could explain roughly 15% of the reported increase in WUE over North America, and a significantly lower proportion of the trend in Europe.

Given that forests represent the primary terrestrial sink for atmospheric CO2 , projections of future carbon (C) storage hinge on forest responses to climate variation. Models of gross primary production (GPP) responses to water stress are commonly based on remotely sensed changes in canopy 'greenness' (e.g., normalized difference vegetation index; NDVI). However, many forests have low spectral sensitivity to water stress (SSWS) - defined here as drought-induced decline in GPP without a change in greenness. Current satellite-derived estimates of GPP use a vapor pressure deficit (VPD) scalar to account for the low SWSS of forests, but fail to capture their responses to water stress. Our objectives were to characterize differences in SSWS among forested and nonforested ecosystems, and to develop an improved framework for predicting the impacts of water stress on GPP in forests with low SSWS. First, we paired two independent drought indices with NDVI data for the conterminous US from 2000 to 2011, and examined the relationship between water stress and NDVI. We found that forests had lower SSWS than nonforests regardless of drought index or duration. We then compared satellite-derived estimates of GPP with eddy-covariance observations of GPP in two deciduous broadleaf forests with low SSWS: the Missouri Ozark (MO) and Morgan Monroe State Forest (MMSF) AmeriFlux sites. Model estimates of GPP that used VPD scalars were poorly correlated with observations of GPP at MO (r(2) = 0.09) and MMSF (r(2) = 0.38). When we included the NDVI responses to water stress of adjacent ecosystems with high SSWS into a model based solely on temperature and greenness, we substantially improved predictions of GPP at MO (r(2) = 0.83) and for a severe drought year at the MMSF (r(2) = 0.82). Collectively, our results suggest that large-scale estimates of GPP that capture variation in SSWS among ecosystems could improve predictions of C uptake by forests under drought.

During the project period we continued to conduct long-term (multi-year) measurements, analysis, and modeling of energy and mass exchange in and over a deciduous forest in the Midwestern United States, to enhance the understanding of soil-vegetation-atmosphere exchange of carbon. At the time when this report was prepared, results from nine years of measurements (1998 - 2006) of above canopy CO2 and energy fluxes at the AmeriFlux site in the Morgan-Monroe State Forest, Indiana, USA (see Table 1), were available on the Fluxnet database, and the hourly CO2 fluxes for 2007 are presented here (see Figure 1). The annual sequestration of atmospheric carbon by the forest is determined to be between 240 and 420 g C m-2 a-1 for the first ten years. These estimates are based on eddy covariance measurements above the forest, with a gap-filling scheme based on soil temperature and photosynthetically active radiation. Data gaps result from missing data or measurements that were rejected in qua)lity control (e.g., during calm nights). Complementary measurements of ecological variables (i.e. inventory method), provided an alternative method to quantify net carbon uptake by the forest, partition carbon allocation in each ecosystem components, and reduce uncertainty on annual net ecosystem productivity (NEP). Biometricmore » datasets are available on the Fluxnext database since 1998 (with the exclusion of 2006). Analysis for year 2007 is under completion.« less

[1] Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such asemax (LUE), Vcmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).

[1] Seasonal dynamics of atmospheric carbonyl sulfide (OCS) at regional and continental scales and plant OCS exchange at the leaf level have shown a close relationship with those for CO2. CO2 has both sinks and sources within terrestrial ecosystems, but the primary terrestrial exchange for OCS is thought to be leaf uptake, suggesting potential for OCS uptake as a proxy for gross primary production (GPP). We explored the utility of OCS uptake as a GPP proxy in micrometeorological studies of biosphere-atmosphere CO2 exchange by applying theoretical concepts from earlier OCS studies to estimate GPP. We partitioned measured net ecosystem exchange (NEE) using the ratio of measured vertical mole fraction gradients of OCS and CO2. At the Harvard Forest AmeriFlux site, measured CO2 and OCS vertical gradients were correlated and were related to NEE and GPP, respectively. Estimates of GPP from OCS-based NEE partitioning were similar to those from established environmental regression techniques, providing evidence that OCS uptake can potentially serve as a GPP proxy. Measured vertical CO2 mole fraction gradients at five other AmeriFlux sites were used to project anticipated vertical OCS mole fraction gradients to provide indication of potential OCS signal magnitudes at sites where no OCS measurements were made. Projected OCS gradients at sites with short canopies were greater than those in forests, including measured OCS gradients at Harvard Forest, indicating greater potential for OCS uptake as a GPP proxy at these sites. This exploratory study suggests that continued investigation of linkages between OCS and GPP is warranted.

Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 2000-2006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 ± 145 g C m−2 y−1 during the spring transition period, and +75 ± 130 g C m−2 y−1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology, and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately, and therefore will misrepresent the seasonality and interannual variability of key biosphere-atmosphere feedbacks and interactions in coupled global climate models., JRC.H.2-Air and Climate

Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site-years of eddy covariance data, from 79 research sites located at latitudes ranging from similar to 3 degrees S to similar to 70 degrees N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site-specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual temperature can be considered as BR in empirical models. A strong correlation was found between the mean annual ER and mean annual gross primary production (GPP). Consequently, GPP, which is typically more accurately modeled, can be used to estimate BR. A light use efficiency GPP model (i.e., EC-LUE) was applied to estimate global GPP, BR and ER with input data from MERRA (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate resolution Imaging Spectroradiometer). The global ER was 103 Pg C yr (-1), with the highest respiration rate over tropical forests and the lowest value in dry and high-latitude areas.

[1] Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties are then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.

Thermal adaptation of gross primary production and ecosystem respiration has been well documented over broad thermal gradients. However, no study has examined their interaction as a function of temperature, i.e. the thermal responses of net ecosystem exchange of carbon (NEE). In this study, we constructed temperature response curves of NEE against temperature using 380 site-years of eddy covariance data at 72 forest, grassland and shrubland ecosystems located at latitudes ranging from ~29° N to 64° N. The response curves were used to define two critical temperatures: transition temperature (Tb) at which ecosystem transferring from carbon source to sink and optimal temperature (To) at which carbon uptake is maximized. Tb was strongly correlated with annual mean air temperature. To was strongly correlated with mean temperature during the net carbon uptake period across the study ecosystems. Our results suggested that ecosystem CO2 flux switched from source to sink when air temperature reached annual mean temperature in spring and reached maximum uptake at mean temperature of the net carbon uptake period. Our results imply that the net ecosystem exchange of carbon adapt to the temperature across the geographical range due to intrinsic connections between vegetation primary production and ecosystem respiration. Thermal adaptation of gross primary production and ecosystem respiration has been well documented over broad thermal gradients. However, no study has examined their interaction as a function of temperature, i.e. the thermal responses of net ecosystem exchange of carbon (NEE). In this study, we constructed temperature response curves of NEE against temperature using 380 site-years of eddy covariance data at 72 forest, grassland and shrubland ecosystems located at latitudes ranging from ~29° N to 64° N. The response curves were used to define two critical temperatures: transition temperature (Tb) at which ecosystem transferring from carbon source to sink and optimal temperature (To) at which carbon uptake is maximized. Tb was strongly correlated with annual mean air temperature. To was strongly correlated with mean temperature during the net carbon uptake period across the study ecosystems. Our results suggested that ecosystem CO2 flux switched from source to sink when air temperature reached annual mean temperature in spring and reached maximum uptake at mean temperature of the net carbon uptake period. Our results imply that the net ecosystem exchange of carbon adapt to the temperature across the geographical range due to intrinsic connections between vegetation primary production and ecosystem respiration.

International audience; [1] Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site-years, 10 biomes, and includes two large-scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model-data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the efficiency with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependance on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well.

A coupled Bayesian model selection and geostatistical regression modeling approach is adopted for empirical analysis of gross primary productivity (GPP) at six AmeriFlux sites, including the Kennedy Space Center Scrub Oak, Vaira Ranch, Tonzi Ranch, Blodgett Forest, Morgan Monroe State Forest, and Harvard Forest sites. The analysis is performed at a continuum of temporal scales ranging from daily to monthly, for a period of seven years. A total of 10 covariates representing environmental stimuli and indices of plant physiology are considered in explaining variations in GPP. Similar to other statistical methods, the proposed approach estimates regression coefficients and uncertainties associated with the covariates in a selected regression model. However, unlike traditional regression methods, the presented approach also estimates the uncertainty associated with the selection of a single "best" model of GPP. In addition, the approach provides an enhanced understanding of how the importance of specific covariates changes with temporal resolutions. An examination of trends in the importance of specific covariates reveals scaling thresholds above or below which covariates become significant in explaining GPP. Results indicate that most sites (especially those with a stronger seasonal cycle) exhibit at least one prominent scaling threshold between daily to 20-day temporal scale. This demonstrates that environmental variables that explain GPP at synoptic scales are different from those that capture its seasonality. At shorter time scales, radiation, temperature, and vapor pressure deficit exert most significant influence on GPP at most examined sites. However, at coarser time scales, the importance of these covariates in explaining GPP declines. Overall, unique best models are identified at most sites at the daily scale, whereas multiple competing models are identified at larger time scales. In addition, the selected models are able to explain a larger fraction of the observed variability for sites exhibiting strong seasonality.

Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate-carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid-and high-latitudes, (2) a strong function of dryness at mid-and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45 degrees N). The sensitivity of NEE to mean annual temperature breaks down at similar to 16 degrees C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence.

International audience; Micrometeorological measurements of nighttime ecosystem respiration can be systematically biased when stable atmospheric conditions lead to drainage flows associated with decoupling of air flow above and within plant canopies. The associated horizontal and vertical advective fluxes cannot be measured using instrumentation on the single towers typically used at micrometeorological sites. A common approach to minimize bias is to use a threshold in friction velocity, u*, to exclude periods when advection is assumed to be important, but this is problematic in situations when in-canopy flows are decoupled from the flow above. Using data from 25 flux stations in a wide variety of forest ecosystems globally, we examine the generality of a novel approach to estimating nocturnal respiration developed by van Gorsel et al. (van Gorsel, E., Leuning, R., Cleugh, H.A., Keith, H., Suni, T., 2007. Nocturnal carbon efflux: reconciliation of eddy covariance and chamber measurements using an alternative to the u*-threshold filtering technique. Tellus 59B, 397–403, Tellus, 59B, 307-403). The approach is based on the assumption that advection is small relative to the vertical turbulent flux (FC) and change in storage (FS) of CO2 in the few hours after sundown. The sum of FC and FS reach a maximum during this period which is used to derive a temperature response function for ecosystem respiration. Measured hourly soil temperatures are then used with this function to estimate respiration RRmax. The new approach yielded excellent agreement with (1) independent measurements using respiration chambers, (2) with estimates using ecosystem lightresponse curves of Fc + Fs extrapolated to zero light, RLRC, and (3) with a detailed process-based forest ecosystem model, Rcast. At most sites respiration rates estimated using the u*-filter, Rust, were smaller than RRmax and RLRC. Agreement of our approach with independent measurements indicates that RRmax provides an excellent estimate of nighttime ecosystem respiration

Quantification of an ecosystem's carbon balance and its components is pivotal for understanding both ecosystem functioning and global cycling. Several methods are being applied in parallel to estimate the different components of the CO2 balance. However, different methods are subject to different sources of error. Therefore, it is necessary that site level component estimates are cross-checked against each other before being reported. Here we present a two-step approach for testing the accuracy and consistency of eddy covariance–based gross primary production (GPP) and ecosystem respiration (Re) estimates with biometric measurements of net primary production (NPP), autotrophic (Ra) and heterotrophic (Rh) respiration. The test starts with closing the CO2 balance to account for reasonable errors in each of the component fluxes. Failure to do so within the constraints will classify the flux estimates on the site level as inconsistent. If the CO2 balance can be closed, the test continues by comparing the closed site level Ra/GPP with the Rh/GPP ratio. The consistency of these ratios is then judged against expert knowledge. Flux estimates of sites that pass both steps are considered consistent. An inconsistent ratio is not necessarily incorrect but provides a signal for careful data screening that may require further analysis to identify the possible biological reasons of the unexpected ratios. We reviewed the literature and found 16 sites, out of a total of 529 research forest sites, that met the data requirements for the consistency test. Thirteen of these sites passed both steps of the consistency cross-check. Subsequently, flux ratios (NPP/GPP, Rh/NPP, Rh/Re, and Re/GPP) were calculated for the consistent sites. Similar ratios were observed at sites which lacked information to check consistency, indicating that the flux data that are currently used for validating models and testing ecological hypotheses are largely consistent across a wide range of site productivities. Confidence in the output of flux networks could be further enhanced if the required fluxes are independently estimated at all sites for multiple years and harmonized methods are used.

The (18)O and (2)H of water vapor serve as powerful tracers of hydrological processes. The typical method for determining water vapor delta(18)O and delta(2)H involves cryogenic trapping and isotope ratio mass spectrometry. Even with recent technical advances, these methods cannot resolve vapor composition at high temporal resolutions. In recent years, a few groups have developed continuous laser absorption spectroscopy (LAS) approaches for measuring delta(18)O and delta(2)H which achieve accuracy levels similar to those of lab-based mass spectrometry methods. Unfortunately, most LAS systems need cryogenic cooling and constant calibration to a reference gas, and have substantial power requirements, making them unsuitable for long-term field deployment at remote field sites. A new method called Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) has been developed which requires extremely low-energy consumption and neither reference gas nor cryogenic cooling. In this report, we develop a relatively simple pumping system coupled to a dew point generator to calibrate an ICOS-based instrument (Los Gatos Research Water Vapor Isotope Analyzer (WVIA) DLT-100) under various pressures using liquid water with known isotopic signatures. Results show that the WVIA can be successfully calibrated using this customized system for different pressure settings, which ensure that this instrument can be combined with other gas-sampling systems. The precisions of this instrument and the associated calibration method can reach approximately 0.08 per thousand for delta(18)O and approximately 0.4 per thousand for delta(2)H. Compared with conventional mass spectrometry and other LAS-based methods, the OA-ICOS technique provides a promising alternative tool for continuous water vapor isotopic measurements in field deployments.

Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) and Leaf Area Index (LAI) from the MODerate Resolution Imaging Spectroradiometer (MODIS) to constrain empirical temperature, light, moisture and structural vegetation parameters of a prognostic phenology model. We find that data assimilation better constrains structural vegetation parameters than climate control parameters. Improvements are largest for drought-deciduous ecosystems where correlation of predicted versus satellite-observed FPAR and LAI increases from negative to 0.7-0.8. Data assimilation effectively overcomes the cloud- and aerosol-related deficiencies of satellite data sets in tropical areas. Validation with a 49-year-long phenology data set reveals that the temperature-driven start of season (SOS) is light limited in warm years. The model has substantial skill (R = 0.73) to reproduce SOS inter-annual and decadal variability. Predicted SOS shows a higher inter-annual variability with a negative bias of 5-20 days compared to species-level SOS. It is however accurate to within 1-2 days compared to SOS derivedmore » from net ecosystem exchange (NEE) measurements at a FLUXNET tower. The model only has weak skill to predict end of season (EOS). Use of remote sensing data assimilation for phenology model development is encouraged but validation should be extended with phenology data sets covering mediterranean, tropical and arctic ecosystems.« less

[1] Eddy-covariance-based estimates of net ecosystem exchange are subject to various sources of systematic bias and random measurement uncertainty. Here we concentrate on the cumulative effect of random uncertainty on annual estimates of net ecosystem productivity of carbon (NEP). A 8-year data set of eddy covariance measurements over a mixed deciduous forest at the Morgan-Monroe State Forest (MMSF, Indiana, USA) was used, in conjunction with a 6-day period of paired observations with the AmeriFlux portable system, to evaluate two different approaches to estimate measurement system uncertainty, and an analogous method to estimate the uncertainty in a standard parametric model used to fill data gaps in the annual time series. The cumulative annual uncertainty was obtained by Monte Carlo simulation, separately for the observations and the model estimates. Our results indicate that the overall uncertainty of annual NEP is dominated by the contribution of the gap-filling model, even at relatively small gap fractions of 20%. The magnitude of random uncertainty in NEP varied between ±10–12 gC m−2 yr−1 (i.e., 3–4% of annual NEP at MMSF for years 1999–2006). Thus it must be expected that random uncertainty of eddy-covariance-based NEP is small compared to other potential sources of systematic bias, but we note that very little is known about the long-term cumulative covariate effects of systematic bias in the measured flux.