AmeriFlux Data Research Articles

Global estimates of daily evapotranspiration using SMAP surface and root-zone soil moisture

The interplay between soil moisture and evapotranspiration modulates the water available to sustain soil evaporation and influences canopy stomatal conductance controls on vegetation transpiration. Modeling this behavior remains challenging. Indeed, satellite remote sensing based Penman-Monteith (PM) ET models tend not to directly consider soil moisture constraints on evaporation and transpiration due to a lack of consistent soil moisture data. To address this issue, we modified a PM model to include satellite enhanced surface and root zone soil moisture from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission. The resulting model was used to produce global 9-km daily ET estimates, including contributing fluxes from soil evaporation, transpiration and evaporation of canopy-intercepted precipitation. The global PM ET estimates were assessed using in situ sap flow and AmeriFlux measurements, upscaled FLUXCOM latent heat flux, as well as against other independent global ET data products (GLEAM, GLDAS, SSEBop, and LandFlux-EVAL). The modelled transpiration showed similar seasonal variation and positive correlation to in situ sap flow measurements available from several forest sites (R2 = 0.85; p < 0.01). When compared against AmeriFlux data, the PM ET estimates showed favorable agreement with annual ET measurements extracted from 34 diverse sites (R2 = 0.58; p < 0.01; RMSE = 227 mm yr−1). The PM 8-day ET results also reflected a similar hemispheric seasonality as the global FLUXCOM record (R2 = 0.94–0.98; p < 0.01), while comparisons against other global ET products showed moderate mean differences of between 49 and 107 mm yr−1 (11–25%) over the global domain. Our PM ET estimates varied up to 52% in response to SMAP surface soil moisture dynamics, displaying stronger surface (0–5 cm depth) than root zone (0–100 cm) soil moisture sensitivity. While PM ET sensitivity to soil moisture was greater in arid climate regions, it was also significant in humid climate zones, with analysis indicating that the inclusion of soil moisture predominantly acts as a sustaining influence on ET, especially in moisture limited drylands. PM ET sensitivity to temperature was stronger in humid forest regions relative to other climate and land cover regimes. Overall, the model results clarify the influence of soil moisture heterogeneity on the global ET pattern as informed by satellite-based estimates of surface and root zone soil moisture. Potential enhancements to the spatial and vertical resolution of soil moisture inputs are expected to enable further ET improvements through more realistic model representation of soil and plant available water.

Evaluation of Environmental Controls on Terrestrial Net Ecosystem Exchange of CO2: A Global Perspective From the FLUXNET Sites

AbstractNet ecosystem exchange (NEE) of CO2 is a key process modulating carbon exchanges between terrestrial ecosystems and the atmosphere; however, it remains a grand challenge to elucidate how the interactions of NEE with environmental variables vary among ecosystems and climate regimes across timescales. The FLUXNET and AmeriFlux data sets were used to diagnose the environmental controls on NEE. Based on the sites with long‐term observations (≥7 years), the results showed that the couplings between NEE and surrounding environments were stronger at daily and monthly scales than at annual scales, highlighting the temporal dependence of environmental variables influencing NEE. Moreover, the boosted regression tree method was applied to quantify the relative impacts of environmental controls on daily NEE variations. It revealed that leaf area index (LAI) and shortwave radiation (Rs) were the major divers of daily NEE variations at most sites with the average contribution of 35.5% and 27.8%, respectively. Particularly, LAI was the principal control in deciduous broadleaf forested, non‐forested, and arid sites, while Rs was the leading factor in evergreen forested sites. Meanwhile, air temperature (Ta), soil water content (SWC), and vapor pressure deficit (VPD) exerted smaller influences with the average contribution of 14.6%, 13.2%, and 8.9%, respectively. The relative impacts of LAI, Rs, SWC, and VPD also varied with aridity index, and mean annual precipitation and Ta. Furthermore, NEE was more sensitive to vegetation dynamics in drier climate regions. This study provides additional understanding of how environmental factors regulate NEE dynamics across diverse land surface and climatic conditions.

Seasonality in aerodynamic resistance across a range of North American ecosystems

Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in “big leaf” models of sensible heat flux (H). We evaluated aerodynamic resistance and surface roughness lengths for momentum (z0m) and heat (z0h) using the kB−1 parameter (ln(z0m/z0h)). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer (Rah). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with Rah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in Rah were not linked to green-up. Our findings highlight that decreases in kB−1 are an important control over Rah, explaining > 50% of seasonal variation in Rah across most sites. Decreases in kB−1 during green-up are likely caused by increasing z0h in response to higher leaf area index. Accounting for seasonal variation in kB−1 is key for predicting H as well; assuming kB−1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales.

Evapotranspiration Climatology of Indiana Using In Situ and Remotely Sensed Products

AbstractAn intercomparison of multiresolution evapotranspiration (ET) datasets with reference to ground-based measurements for the development of regional reference (ETref) and actual (ETa) evapotranspiration maps over Indiana is presented. A representative ETref equation for the state is identified by evaluating 10 years of in situ measurements (2009–19). A statewide ETref climatology is developed using the ETref equation and high-resolution surface meteorological data from the gridded surface meteorological dataset (gridMET). For ETa analyses, MODIS, Simplified Surface Energy Balance Operational dataset (SSEBop), Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.3a and 3.3b), and NLDAS (Noah and VIC) datasets are evaluated using AmeriFlux data. Thirty years of rainfall data from Climate Hazards Group Infrared Precipitation with Station Data Rainfall (CHIRPS) are used with the ET datasets to develop effective precipitation fields. Results show that the standardized Penman–Monteith equation performs as the best ETref equation with median symmetric accuracy (MSA) of 0.37, Taylor’s skill score (TSC) of 0.89, and r2 = 0.83. The analysis shows that the gridMET dataset overestimates wind speed and requires adjustment before a series of statewide ETref climatology maps are generated (1990–2020). For ETa, the MODIS and GLEAM (3.3b) datasets outperform the rest, with MSA = 0.5, TSC = 0.8, and r2 = 0.8. The state ETa dataset is generated using all MODIS data from 2003 and blending the MODIS data with GLEAM (3.3b) to cover data unavailability. Using the top-performing datasets, annual ETref for Indiana is computed as 1110 mm, ETa as 708 mm, and precipitation as 1091 mm. A marginal increasing climatological trend is found for Indiana’s ETref (0.013 mm yr−1) while ETa is found to be relatively stable. The state’s water availability, defined as rainfall minus ETa, has remained positive and stable at 0.99 mm day−1 (annual magnitude of +3820 mm).

Development of the DayCent-Photo model and integration of variable photosynthetic capacity

We integrated a photosynthetic sub-model into the daily Century model (DayCent) to improve the estimations of seasonal changes in carbon fluxes at the Niwot Ridge LTER site and the Harvard forest LTER site (DayCent-Photo). The photosynthetic sub-model, adapted from the SIPNET/PnET family of models, includes solar radiation and vapor pressure deficit controls on production, as well as temperature and water stress terms. A key feature we added to the base photosynthetic equations is the addition of a variable maximum net photosynthetic rate (Amax). We optimized the parameters controlling photosynthesis using a variation of the Metropolis-Hastings algorithm along with data-assimilation techniques. The model was optimized and validated against observed net ecosystem exchange (NEE) and estimated gross primary production (GPP) and ecosystem respiration (RESP) values for AmeriFlux sites at Niwot Ridge and Harvard forest. The inclusion of a variable Amax rate greatly improved model performance (NEE RMSE = 0.63 gC·m–2, AIC = 2099) versus a version with a single Amax parameter (NEE RMSE = 0.74 gC·m–2, AIC = 3724). DayCent-Photo was able to capture the inter-annual and seasonal flux patterns for NEE, GPP, ecosystem respiration (RESP), and daily actual evapotranspiration (AET), but tended to overestimate yearly NEE uptake. The DayCent-Photo model has been successfully set up to simulate daily NEE, GPP, RESP, and AET for deciduous forest, conifer forests, and grassland systems in the US using AmeriFlux data sets and has recently been improved to include the impact of UV radiation surface litter decay (DayCent-UV). The simulated influence of a variable Amax rate suggests a need for further studies on the process controls affecting the seasonal photosynthetic rates. The results for all of the forest and grassland sites show that maximum Amax values occurs early during the growing period and taper off toward the end of the growing season.

The AmeriFlux network: A coalition of the willing

AmeriFlux scientists were early adopters of a network-enabled approach to ecosystem science that continues to transform the study of land-atmosphere interactions. In the 20 years since its formation, AmeriFlux has grown to include more than 260 flux tower sites in the Americas that support continuous observation of ecosystem carbon, water, and energy fluxes. Many of these sites are co-located within a similar climate regime, and more than 50 have data records that exceed 10 years in length. In this prospective assessment of AmeriFlux’s strengths in a new era of network-enabled ecosystem science, we discuss how the longevity and spatial distribution of AmeriFlux data make them exceptionally well suited for disentangling ecosystem response to slowly evolving changes in climate and land-cover, and to rare events like droughts and biological disturbances. More recently, flux towers have also been integrated into environmental observation networks that have broader scientific goals; in North America these include the National Ecological Observatory Network (NEON), Critical Zone Observatory network (CZO), and Long-Term Ecological Research network (LTER). AmeriFlux stands apart from these other networks in its reliance on voluntary participation of individual sites, which receive funding from diverse sources to pursue a wide, transdisciplinary array of research topics. This diffuse, grassroots approach fosters methodological and theoretical innovation, but also challenges network-level data synthesis and data sharing to the network. While AmeriFlux has had strong ties to other regional flux networks and FLUXNET, better integration with networks like NEON, CZO and LTER provides opportunities for new types of cooperation and synergies that could strengthen the scientific output of all these networks.

Dependence of thermal roughness length on friction velocity across land cover types: A synthesis analysis using AmeriFlux data

Using AmeriFlux data from 74 eddy covariance sites, we present a synthesis of the dependence of thermal roughness length on friction velocity across five land covers types, including deciduous broadleaf forests, evergreen needleleaf forests, croplands, grasslands, and shrublands. To analyze patterns in thermal roughness length, we estimate the ratio of momentum to thermal roughness lengths (kB−1) at half hourly scales. Because kB−1 cannot be directly measured, we infer kB−1 from surface flux and meteorological measurements using Monin-Obukhov similarity theory. The inferred kB−1 is moderately correlated with the measured friction velocity over two of the land cover types—grasslands and shrublands—but weakly correlated at croplands and forests, suggesting that kB−1 varies with friction velocity over bluff body surfaces, but not over tightly packed, permeable canopies, such as forests and croplands. This is consistent with previous work showing contrasting turbulent structures over sparse and dense vegetation canopies. To determine how this degree of correlation affects the modeling of kB−1, we estimate kB−1 using four common models, specifically those with friction velocity dependencies, and find that no single model works the best across land covers.

Evaluating atmospheric CO2 effects on gross primary productivity and net ecosystem exchanges of terrestrial ecosystems in the conterminous United States using the AmeriFlux data and an artificial neural network approach

Quantitative understanding of regional gross primary productivity (GPP) and net ecosystem exchanges (NEE) and their responses to environmental changes are critical to quantifying the feedbacks of ecosystems to the global climate system. Numerous studies have used the eddy flux data to upscale the eddy covariance derived carbon fluxes from stand scales to regional and global scales. However, few studies incorporated atmospheric carbon dioxide (CO2) concentrations into those extrapolations. Here, we consider the effect of atmospheric CO2 using an artificial neural network (ANN) approach to upscale the AmeriFlux tower of NEE and the derived GPP to the conterminous United States. Two ANN models incorporating remote sensing variables at an 8-day time step were developed. One included CO2 as an explanatory variable and the other did not. The models were first trained, validated using eddy flux data, and then extrapolated to the region at a 0.05o×0.05o (latitude×longitude) resolution from 2001 to 2006. We found that both models performed well in simulating site-level carbon fluxes. The spatially-averaged annual GPP with and without considering the atmospheric CO2 were 789 and 788gCm−2yr−1, respectively (for NEE, the values were −112 and −109gCm−2yr−1, respectively). Model predictions were comparable with previous published results and MODIS GPP products. However, the difference in GPP between the two models exhibited a great spatial and seasonal variability, with an annual difference of 200gCm−2yr−1. Further analysis suggested that air temperature played an important role in determining the atmospheric CO2 effects on carbon fluxes. In addition, the simulation that did not consider atmospheric CO2 failed to detect ecosystem responses to droughts in part of the US in 2006. The study suggests that the spatially and temporally varied atmospheric CO2 concentrations should be factored into carbon quantification when scaling eddy flux data to a region.

Carbon cycle uncertainty in the Alaskan Arctic

Abstract. Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (σ) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0 ± 9.2 kg C m−2), then gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), net ecosystem exchange (NEE) (−0.01 ± 0.19 kg C m−2 yr−1), and CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region.

Soil thermal dynamics of terrestrial ecosystems of the conterminous United States from 1948 to 2008: an analysis with a process-based soil physical model and AmeriFlux data

The spatiotemporal distribution characteristics of soil temperature are a significant, but seldom described signal of climate warming. This study examines the spatiotemporal trends in soil temperature at depths of 10, 20, and 50 cm in the conterminous US during 1948–2008. We find a warming trend of between 0.2 and 0.4 °C at all depths from 1948 to 2008. The lowest soil temperatures are in Colorado and the area where Wyoming, Idaho, and Montana meet. The coastal areas, such as Texas, Florida, and California, experienced the highest soil temperature. In addition, areas that experienced weak cooling in summer soil temperature include Texas, Oklahoma, and Arkansas. Warming was recorded in Arizona, Nevada, and Oregon. In winter, Mississippi, Alabama, and Georgia show a cooling trend, and Montana, North Dakota, and South Dakota have been warming over the 61-year period. Additionally, mix-forest areas experience slightly cooler soil temperature in comparison with air temperature. Shrubland areas experience slightly warmer soil temperature in comparison with air temperature. This study is among the first to analyze the spatiotemporal distribution characteristics of soil temperature in the conterminous US by using multiple site observational data. Improved understanding of the spatially complex responses of soil temperature shall have significant implications for future studies in climate change over the region.

Estimating net surface longwave radiation from net surface shortwave radiation for cloudy skies

This work addresses the estimation of net surface longwave radiation (NSLR) from net surface shortwave radiation (NSSR) by analysing the Surface Radiation Budget Network (SURFRAD) radiation data under cloudy conditions. A general model is developed to estimate NSLR from the NSSR for cloudy skies with a root mean square error (RMSE) of 23.16 W m−2 compared with in situ data. The model is applied to AmeriFlux data. The results show that the mean error and RMSE are –2.31 W m−2 and 29.25 W m−2, respectively, compared with the measurement of AmeriFlux. To examine the significance of the influence of seasons on the estimated NSLR, the model is proposed as a function of seasonal variation. The results show a slight improvement for winter and spring, whereas a larger error is found for autumn compared with the results obtained by the general model. The influences of land cover and elevation on the model are also investigated. The results show that the model is slightly sensitive to the normalized difference vegetation index (NDVI) and the elevation. The RMSE of the model decreases from 23.16 W m−2 to 21.04 W m−2 when the NDVI and the elevation are considered in the model.

The AmeriFlux data activity and data system: an evolving collection of data management techniques, tools, products and services

Abstract. The Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL), USA has provided scientific data management support for the US Department of Energy and international climate change science since 1982. Among the many data archived and available from CDIAC are collections from long-term measurement projects. One current example is the AmeriFlux measurement network. AmeriFlux provides continuous measurements from forests, grasslands, wetlands, and croplands in North, Central, and South America and offers important insight about carbon cycling in terrestrial ecosystems. To successfully manage AmeriFlux data and support climate change research, CDIAC has designed flexible data systems using proven technologies and standards blended with new, evolving technologies and standards. The AmeriFlux data system, comprised primarily of a relational database, a PHP-based data interface and a FTP server, offers a broad suite of AmeriFlux data. The data interface allows users to query the AmeriFlux collection in a variety of ways and then subset, visualize and download the data. From the perspective of data stewardship, on the other hand, this system is designed for CDIAC to easily control database content, automate data movement, track data provenance, manage metadata content, and handle frequent additions and corrections. CDIAC and researchers in the flux community developed data submission guidelines to enhance the AmeriFlux data collection, enable automated data processing, and promote standardization across regional networks. Both continuous flux and meteorological data and irregular biological data collected at AmeriFlux sites are carefully scrutinized by CDIAC using established quality-control algorithms before the data are ingested into the AmeriFlux data system. Other tasks at CDIAC include reformatting and standardizing the diverse and heterogeneous datasets received from individual sites into a uniform and consistent network database, generating high-level derived products to meet the current demands from a broad user group, and developing new products in anticipation of future needs. In this paper, we share our approaches to meet the challenges of standardizing, archiving and delivering quality, well-documented AmeriFlux data worldwide to benefit others with similar challenges of handling diverse climate change data, to further heighten awareness and use of an outstanding ecological data resource, and to highlight expanded software engineering applications being used for climate change measurement data.

An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data

A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring of the Earth system at a large scale. In this paper, we developed an artificial neural network (ANN) model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data (normalized difference vegetation index, land surface temperature and surface net radiation) for the period 2002–2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination (R2) value for comparison between estimated and measured ET was 0.77, the root-mean-square error was 0.62 mm/d, and the mean residual was − 0.28. The simple model developed in this paper captured the seasonal and interannual variation features of ET on the whole. However, the accuracy of estimated ET depended on the vegetation types, among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.

Integrating empirical–modeling approaches to improve understanding of terrestrial ecology processes

Recent decades have seen tremendous increases in the quantity of empirical ecological data collected by individual investigators, as well as through research networks such as FLUXNET (Baldocchi et al., 2001). At the same time, advances in computer technology have facilitated the development and implementation of large and complex land surface and ecological process models. Separately, each of these information streams provides useful, but imperfect information about ecosystems. To develop the best scientific understanding of ecological processes, and most accurately predict how ecosystems may cope with global change, integration of empirical and modeling approaches is necessary. However, true integration - in which models inform empirical research, which in turn informs models (Fig. 1) - is not yet common in ecological research (Luo et al., 2011). The goal of this workshop, sponsored by the Department of Energy, Office of Science, Biological and Environmental Research (BER) program, was to bring together members of the empirical and modeling communities to exchange ideas and discuss scientific practices for increasing empirical - model integration, and to explore infrastructure and/or virtual network needs for institutionalizing empirical - model integration (Yiqi Luo, University of Oklahoma, Norman, OK, USA). The workshop included presentations and small group discussions that coveredmore » topics ranging from model-assisted experimental design to data driven modeling (e.g. benchmarking and data assimilation) to infrastructure needs for empirical - model integration. Ultimately, three central questions emerged. How can models be used to inform experiments and observations? How can experimental and observational results be used to inform models? What are effective strategies to promote empirical - model integration?« less

Estimating the net ecosystem exchange for the major forests in the northern United States by integrating MODIS and AmeriFlux data

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 continents. 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 performed well for estimating NEE.

Quantification of terrestrial ecosystem carbon dynamics in the conterminous United States combining a process-based biogeochemical model and MODIS and AmeriFlux data

Abstract. Satellite remote sensing provides continuous temporal and spatial information of terrestrial ecosystems. Using these remote sensing data and eddy flux measurements and biogeochemical models, such as the Terrestrial Ecosystem Model (TEM), should provide a more adequate quantification of carbon dynamics of terrestrial ecosystems. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI) and carbon flux data of AmeriFlux to conduct such a study. We first modify the gross primary production (GPP) modeling in TEM by incorporating EVI and LSWI to account for the effects of the changes of canopy photosynthetic capacity, phenology and water stress. Second, we parameterize and verify the new version of TEM with eddy flux data. We then apply the model to the conterminous United States over the period 2000–2005 at a 0.05° × 0.05° spatial resolution. We find that the new version of TEM made improvement over the previous version and generally captured the expected temporal and spatial patterns of regional carbon dynamics. We estimate that regional GPP is between 7.02 and 7.78 Pg C yr−1 and net primary production (NPP) ranges from 3.81 to 4.38 Pg C yr−1 and net ecosystem production (NEP) varies within 0.08–0.73 Pg C yr−1 over the period 2000–2005 for the conterminous United States. The uncertainty due to parameterization is 0.34, 0.65 and 0.18 Pg C yr−1 for the regional estimates of GPP, NPP and NEP, respectively. The effects of extreme climate and disturbances such as severe drought in 2002 and destructive Hurricane Katrina in 2005 were captured by the model. Our study provides a new independent and more adequate measure of carbon fluxes for the conterminous United States, which will benefit studies of carbon-climate feedback and facilitate policy-making of carbon management and climate.

MODIS 위성 영상 기반의 일차생산성 알고리즘 입력 기상 자료의 신뢰도 평가: 미국 Corn Belt 지역을 중심으로

Investigation of the exchange between biosphere and atmosphere at regional, continental, and global scales can be directed to combining remote sensing with carbon cycle process to estimate vegetation productivity. NASA Earth Observing System (EOS) currently produces a regular global estimate of gross primary productivity (GPP) and annual net primary productivity (NPP) of the entire terrestrial earth surface at 1 km spatial resolution. While the MODIS GPP algorithm uses meteorological data provided by the NASA Data Assimilation Office (DAO), the sub-pixel heterogeneity or complex terrain are generally reflected due to coarse spatial resolutions of the DAO data (a resolution of ). In this study, we estimated inputs retrieved from MODIS products of the AQUA and TERRA satellites with 5 km spatial resolution for the purpose of finer GPP and/or NPP determinations. The derivatives included temperature, VPD, and solar radiation. Seven AmeriFlux data located in the Corn Belt region were obtained to use for evaluation of the input data from MODIS. MODIS-derived air temperature values showed a good agreement with ground-based observations. The mean error (ME) and coefficient of correlation (R) ranged from to and from 0.83 to 0.98, respectively. VPD somewhat coarsely agreed with tower observations (ME

Comparative hydrology across AmeriFlux sites: The variable roles of climate, vegetation, and groundwater

Watersheds can be characterized as complex space‐time filters that transform incoming fluxes of energy, water, and nutrients into variable output signals. The behavior of these filters is driven by climate, geomorphology, and ecology and, accordingly, varies from site to site. We investigated this variation by exploring the behavior of evapotranspiration signals from 14 different AmeriFlux sites. Evapotranspiration is driven by water and energetic forcing and is mediated by ecology and internal redistribution of water and energy. As such, it integrates biological and physical controls, making it an ideal signature to target when investigating watershed filtering. We adopted a paradigmatic approach (referred to as the null model) that couples the Penman‐Monteith equation to a soil moisture model and explored the deviations between the predictions of the null model and the observed AmeriFlux data across the sites in order to identify the controls on these deviations and their commonalities and differences across the sites. The null model reproduced evapotranspiration fluxes reasonably well for arid, shallow‐rooted systems but overestimated the effects of water limitation and could not reproduce seasonal variation in evapotranspiration at other sites. Accounting for plant access to groundwater (or deep soil moisture) reserves and for the effects of soil temperature on limiting evapotranspiration resolved these discrepancies and greatly improved prediction of evapotranspiration at multiple time scales. The results indicate that site‐specific hydrology and climatic factors pose important controls on biosphere‐hydrosphere interactions and suggest that plant–water table interactions and early season phenological controls need to be incorporated into even simple models to reproduce the seasonality in evapotranspiration.

Drought Indices Based on the Climate Forecast System Reanalysis and Ensemble NLDAS

Abstract Drought indices derived from the Climate Forecast System Reanalysis (CFSR) are compared with indices derived from the ensemble North American Land Data Assimilation System (NLDAS) and the North American Regional Reanalysis (NARR) over the United States. Uncertainties in soil moisture, runoff, and evapotranspiration (E) from three systems are assessed by comparing them with limited observations, including E from the AmeriFlux data, soil moisture from the Oklahoma Mesonet and the Illinois State Water Survey, and streamflow data from the U.S. Geological Survey (USGS). The CFSR has positive precipitation (P) biases over the western mountains, the Pacific Northwest, and the Ohio River valley in winter and spring. In summer, it has positive biases over the Southeast and large negative biases over the Great Plains. These errors limit the ability to use the standardized precipitation indices (SPIs) derived from the CFSR to measure the severity of meteorological droughts. To compare with the P analyses, the Heidke score for the 6-month SPI derived from the CFSR is on average about 0.5 for the three-category classification of drought, floods, and neutral months. The CFSR has positive E biases in spring because of positive biases in downward solar radiation and high potential evaporation. The negative E biases over the Great Plains in summer are due to less P and soil moisture in the root zone. The correlations of soil moisture percentile between the CFSR and the ensemble NLDAS are regionally dependent. The correlations are higher over the area east of 100°W and the West Coast. There is less agreement between them over the western interior region.

Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data

In this study, we used the remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS), meteorological and eddy flux data and an artificial neural networks (ANNs) technique to develop a daily evapotranspiration (ET) product for the period of 2004–2005 for the conterminous U.S. We then estimated and analyzed the regional water-use efficiency (WUE) based on the developed ET and MODIS gross primary production (GPP) for the region. We first trained the ANNs to predict evapotranspiration fraction (EF) based on the data at 28 AmeriFlux sites between 2003 and 2005. Five remotely-sensed variables including land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), leaf area index (LAI) and photosynthetically active radiation (PAR) and ground-measured air temperature and wind velocity were used. The daily ET was calculated by multiplying net radiation flux derived from remote sensing products with EF. We then evaluated the model performance by comparing modeled ET with the data at 24 AmeriFlux sites in 2006. We found that the ANNs predicted daily ET well ( R 2 = 0.52–0.86). The ANNs were applied to predict the spatial and temporal distributions of daily ET for the conterminous U.S. in 2004 and 2005. The ecosystem WUE for the conterminous U.S. from 2004 to 2005 was calculated using MODIS GPP products (MOD17) and the estimated ET. We found that all ecosystems' WUE-drought relationships showed a two-stage pattern. Specifically, WUE increased when the intensity of drought was moderate; WUE tended to decrease under severe drought. These findings are consistent with the observations that WUE does not monotonously increase in response to water stress. Our study suggests a new water-use efficiency mechanism should be considered in ecosystem modeling. In addition, this study provides a high spatial and temporal resolution ET dataset, an important product for climate change and hydrological cycling studies for the MODIS era.