We analyzed 7 years (2002–2008) of micrometeorological and concurrent biological observations of carbon and water fluxes at a mature ponderosa pine forest in central Oregon in a semiarid climate. We ...sought to evaluate the extent that gross primary productivity, net ecosystem exchange, ecosystem respiration, net primary productivity, net ecosystem productivity, tree transpiration, and evapotranspiration varied seasonally and interannually in this ecosystem subjected to varying periods and severity of droughts. To explain variation, we found it necessary to define seasons functionally within a hydroecological year rather than by fixed calendar dates. The interannual variability in growing season length was large (45 days), and the end date was more variable than the onset. Plant‐available soil water was the main determinant of carbon fluxes. Spring evapotranspiration primarily used shallow water, whereas summer and fall evapotranspiration drew water from deeper in the soil profile. A multiyear drought (2001–2003) had a more severe and fundamentally different impact on carbon and water cycles than a single‐year (2005) drought because of carryover effects in soil water and carbohydrate reserves in plant tissue. Calendar year–based analysis was inadequate to diagnose drought years in precipitation and ecosystem drought response. Extension of meteorological records back to 1982 showed that anomalies were coherent across the region and that the observations represented below‐average precipitation and above‐average temperatures coherent with a warm‐phase Pacific Decadal Oscillation. The carbon sink of this seasonally water‐limited ecosystem is anticipated to increase with increasing available soil water during the growing season.
Water availability constrains the structure and function of terrestrial ecosystems and is projected to change in many parts of the world over the coming century. We quantified the response of tree ...net primary productivity (NPP), live biomass (BIO), and mean carbon residence time (CRT = BIO / NPP) to spatial variation in water availability in the western US. We used forest inventory measurements from 1953 mature stands (> 100 years) in Washington, Oregon, and California (WAORCA) along with satellite and climate data sets covering the western US. We summarized forest structure and function in both domains along a 400 cm yr−1 hydrologic gradient, quantified with a climate moisture index (CMI) based on the difference between precipitation and reference evapotranspiration summed over the water year (October–September) and then averaged annually from 1985 to 2014 (CMIwy). Median NPP, BIO, and CRT computed at 10 cm yr−1 intervals along the CMIwy gradient increased monotonically with increasing CMIwy across both WAORCA (rs = 0.93–0.96, p < 0.001) and the western US (rs = 0.93–0.99, p < 0.001). Field measurements from WAORCA showed that median NPP increased from 2.2 to 5.6 Mg C ha−1 yr−1 between the driest and wettest 5 % of sites, while BIO increased from 26 to 281 Mg C ha−1 and CRT increased from 11 to 49 years. The satellite data sets revealed similar changes over the western US, though these data sets tended to plateau in the wettest areas, suggesting that additional efforts are needed to better quantify NPP and BIO from satellites in high-productivity, high-biomass forests. Our results illustrate that long-term average water availability is a key environmental constraint on tree productivity, carbon storage, and carbon residence time in mature forests across the western US, underscoring the need to assess potential ecosystem response to projected warming and drying over the coming century.
Terrestrial ecosystem‐atmosphere exchange of carbon, water vapor, and energy has been measured for over a decade at many sites globally. To minimize measurement and analysis errors, quality assurance ...data have been collected over short periods along‐side tower instruments at AmeriFlux research sites. Theoretical and empirical error and uncertainty values have been reported for various aspects of the eddy covariance technique but until recently it has not been possible to constrain network level variation based on direct comparison of side‐by‐side measurements. Paired observations, although rare in practice, offer a possibility to obtain real‐world error estimates for flux observations and corresponding uncertainties. In this study, we report the relative instrumental errors from the AmeriFlux quality assurance and quality control (QA/QC) site intercomparisons of 84 site visits (2002–2012). Relative errors, including random and systematic instrumental errors, are presented for meteorological and radiation variables, gas concentrations, and the turbulent fluxes. The lowest relative errors (<2%) were found for the meteorological parameters, while the largest relative errors were found for latent heat and CO2 fluxes. The mean relative instrumental error for CO2 flux averaged −8.2% (underestimation by the tower instruments). Sensible and latent heat fluxes exhibited mean errors of −1.7% and −5.2%, respectively. Deviation around the mean was also largest for the turbulent fluxes, approaching 20%. Because the data collected during QA/QC site visits are used to identify and correct errors, our results represent a conservative estimate of instrumental errors in the AmeriFlux database. Overall, the presented results confirm the high quality of the network data and underline its status as a valuable data source for the research community.
Key Points
First comprehensive analysis of instrumental errors/uncertainties for flux network
Turbulent fluxes exhibit highest error values
The network‐wide data quality was found to be encouraging
The diverse vegetation types and carbon pools of the U.S. Pacific Northwest (PNW) are tightly coupled to fire regimes that depend on climate and fire suppression. To realistically assess the effects ...of twenty‐first‐century climate change on PNW fire and carbon dynamics, we developed a new fire suppression rule for the MC1 dynamic general vegetation model that we ran under three climate change scenarios. Climate projections from the CSIRO Mk3, MIROC 3.2 medres, and Hadley CM3 general circulation models, forced by the A2 CO2 emissions scenario, were downscaled to a 30 arc‐second (∼0.6 km2) grid. Future climates amplify the already strong seasonality of temperature and precipitation across the domain. Simulations displayed large increases in area burned (76%–310%) and burn severities (29%–41%) by the end of the twenty‐first century. The relatively dry ecosystems east of the Cascades gain carbon in the future despite projections of more intense wildfires, while the mesic maritime forests lose up to 1.2 Pg C from increased burning. Simulated fire suppression causes overall carbon gains yet leaves ecosystems vulnerable to large future fires. Overall, our simulations suggest the Pacific Northwest has the potential to sequester ∼1 Pg C over the next century unless summer droughts severely intensify fire regimes.
Key Points
Mesic forests may be vulnerable to future fires and emit large amounts of carbon
Fire intensities will increase and suppression will become less effective
Dry ecosystems display increased productivity, carbon sequestration, and fire frequencies under climate change
There are two broad approaches to quantifying landscape C dynamics – by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps ...or biases. An alternative approach to generating C budgets has been to use process‐based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques – which combine stock and flux observations with a dynamic model – improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3‐year period, and include eddy flux and soil CO2 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)=−251±197 g C m−2 over the 3 years, compared with an estimate of −419±29 g C m−2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5 g C m−2 day−1, but the uncertainty on assimilated estimates averaged 0.47 g C m−2 day−1, and only exceeded 0.5 g C m−2 day−1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long‐running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote‐sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis – which might be provided indirectly by remotely sensed data – improves the analysis of NEE.
This study quantifies the short-term effects of low-, moderate-, and high-severity fire on carbon pools and fluxes in the Eastern Cascades of Oregon. We surveyed 64 forest stands across four fires ...that burned 41,000 ha (35%) of the Metolius Watershed in 2002 and 2003, stratifying the landscape by burn severity (overstory tree mortality), forest type (ponderosa pine PP and mixed-conifer MC), and prefire biomass. Stand-scale C combustion ranged from 13 to 35% of prefire aboveground C pools (area − weighted mean = 22%). Across the sampled landscape, total estimated pyrogenic C emissions were equivalent to 2.5% of statewide anthropogenic CO₂ emissions from fossil fuel combustion and industrial processes for the same 2-year period. From low- to moderate- to high-severity ponderosa pine stands, average tree basal area mortality was 14, 49, and 100%, with parallel patterns in mixed-conifer stands (29, 58, 96%). Despite this decline in live aboveground C, total net primary productivity (NPP) was only 40% lower in high- versus low-severity stands, suggesting strong compensatory effects of non-tree vegetation on C uptake. Dead wood respiratory losses were small relative to total NPP (range: 10-35%), reflecting decomposition lags in this seasonally arid system. Although soil C, soil respiration, and fine root NPP were conserved across severity classes, net ecosystem production (NEP) declined with increasing severity, driven by trends in aboveground NPP. The high variability of C responses across this study underscores the need to account for landscape patterns of burn severity, particularly in regions such as the Pacific Northwest, where non-stand-replacement fire represents a large proportion of annual burned area.
The regular monitoring of evapotranspiration from satellites has been limited because of discontinuous temporal coverage, resulting in snapshots at a particular point in space and time. We developed ...a temporal upscaling scheme using satellite-derived instantaneous estimates of evapotranspiration to produce a daily-sum evapotranspiration averaged over an 8-day interval. We tested this scheme against measured evapotranspiration data from 34 eddy covariance flux towers covering seven plant functional types from boreal to tropical climatic zones. We found that the ratio of a half-hourly-sum of potential solar radiation (extraterrestrial solar irradiance on a plane parallel to the Earth's surface) between 10:00
hh and 14:00
hh to a daily-sum of potential solar radiation provides a robust scaling factor to convert a half-hourly measured evapotranspiration to an estimate of a daily-sum; the estimated and measured daily sum evapotranspiration showed strong linear relation (
r
2
=
0.92) and small bias (−2.7%). By comparison, assuming a constant evaporative fraction (the ratio of evapotranspiration to available energy) during the daytime, although commonly used for temporal upscaling, caused 13% underestimation of evapotranspiration on an annual scale. The proposed temporal upscaling scheme requires only latitude, longitude and time as input. Thus it will be useful for developing continuous evapotranspiration estimates in space and time, which will improve continuous monitoring of hydrological cycle from local to global scales.
Plant trait measurements are needed for evaluating ecological responses to environmental conditions and for ecosystem process model development, parameterization, and testing. We present a ...standardized dataset integrating measurements from projects conducted by the Terrestrial Ecosystem Research and Regional Analysis- Pacific Northwest (TERRA-PNW) research group between 1999 and 2014 across Oregon and Northern California, where measurements were collected for scaling and modeling regional terrestrial carbon processes with models such as Biome-BGC and the Community Land Model. The dataset contains measurements of specific leaf area, leaf longevity, leaf carbon and nitrogen for 35 tree and shrub species derived from more than 1,200 branch samples collected from over 200 forest plots, including several AmeriFlux sites. The dataset also contains plot-level measurements of forest composition, structure (e.g., tree biomass), and productivity, as well as measurements of soil structure (e.g., bulk density) and chemistry (e.g., carbon). Publically-archiving regional datasets of standardized, co-located, and geo-referenced plant trait measurements will advance the ability of earth system models to capture species-level climate sensitivity at regional to global scales.
Abstract
Forest preservation is crucial for protecting biodiversity and mitigating climate change. Here we assess current forest preservation in the western United States using spatial data and find ...that beyond the 18.9% (17.5 Mha) currently protected, an additional 11.1% (10.3 Mha) is needed to achieve 30% preservation by 2030 (30 × 30). To help meet this regional preservation target, we developed a framework that prioritizes forestlands for preservation using spatial metrics of biodiversity and/or carbon within each ecoregion. We show that meeting this preservation target would lead to greater protection of animal and tree species habitat, current carbon stocks, future carbon accumulation, and forests that are important for surface drinking water. The highest priority forestlands are primarily owned by the federal government, though substantial areas are also owned by private entities and state and tribal governments. Establishing Strategic Forest Reserves would help protect biodiversity and carbon for climate adaptation and mitigation.
Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the ...relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (RECO) in deciduous forests. Previous studies showed that empirical RECOmodels can be substantially improved by considering the biotic dependency of RECOon the short‐term productivity (e.g., daily gross primary production, GPP) in addition to the well‐known environmental controls of temperature and water availability. Here, we use a model‐data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECOat 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data‐oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled RECOwhen compared to a version of the model that does not consider the physiological phenology. The reduction of the model‐observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model‐error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECOin deciduous forests by including the phenological cycle of the canopy.
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