Search

Estimating actual crop evapotranspiration (ETc act) is a fundamental requirement for effective crop water management, aiming to achieve precision irrigation amidst changing environmental conditions. Despite the introduction of various methods for estimating ETc act values, these methods are still associated with several challenges and limitations. Motivated by the robustness of deep learning models, this study employed two hybrid models that integrate Convolution Neural Network with either Random Forests (CNN-RF) or Support Vector Machine (CNN-SVR) to estimate potato ETc act using a limited set of input features. Three models were configured and compared for each CNN-RF (CNN-RF1, CNNRF2, CNNRF3) and CNN-SVM (CNN-SVM1, CNN-SVM2, CNN-SVM3), by using different combinations of variable input features derived from meteorological data (air temperature (Ta), vapour pressure deficit (VPD), net radiation (Rn)) and MODIS satellite data (land surface temperature (LST), fraction of photosynthetically active radiation (Fpar), leaf area index (LAI)). The results demonstrated the outstanding performance of the CNN-RF models, showcasing their superiority over the CNN-SVM models. Notably CNN-RF1 model yielded the best results, achieving a normalized root mean squared error (nRMSE) of 11.7 and 31.5 %; Nash–Sutcliffe Efficiency (NSE) of 0.97 and 0.80; Correlation coefficient (CC) of 0.99 and 0.91 at training and testing phases respectively. While, both models exhibited lower performance when using remote sensing satellite-based inputs, CNN-RF2, managed to produce satisfactory estimates of nRMSE = 16.7, 40.9 %; NSE = 0.95, 0.66; CC = 0.98, 0.813; Bias = −0.41, −4.377 W/m2 during training and testing respectively. The ETc act of potato showed to have high correlation with LST (0.74–0.84) and using LST as proxy for Ta, resulted in improved model estimates compared to using satellite data exclusively. These findings suggest that, in situations where climatic data is limited, remote sensing can serve as a viable alternative for modelling potato ETc act when coupled with CNN-RF, and further improvement can be achieved by incorporating meteorological data fusion.

Shifts in plant phenology associated with climate change have received unprecedented attention from land managers, policymakers, and the scientific community, with important implications for the structure and function of the biosphere. Eddy covariance (EC) has been used to measure carbon exchange between the land surface and atmosphere, which provides an opportunity to describe the seasonality of ecosystem processes. Using BASE flux products provided by the AmeriFlux network and a non-linear modeling approach, this study developed a vegetation carbon phenology (VCP) dataset, which we termed VCPNET (version 1.0). VCPNET was developed with fine spatial and temporal scales for a total of 87 EC sites (2820 site-years of data) across North America, including nine vegetation types. We extracted phenology metrics by site to provide long-term VCP metrics across these ecosystems. Analyses revealed significant differences in photosynthetic capacity and respiration rate-derived VCP metrics across the landscape, as well as differences associated with whether daily cumulative or half-hourly maximum measurements were used. Daily carbon fluxes may be more consistent in simulating phenology behavior than 30-min maximum fluxes across all vegetation types, except in sites classified as Grassland. Overall vegetation types, the seasonality of photosynthetic capacity may exert a larger control on the net carbon uptake of the ecosystem compared to the respiration rate. Thus, model-estimated photosynthesis-activated seasons may better represent true phenology, particularly the development of above- and below-ground plant biomass in tree-covered areas, which is crucial in forecasting ecosystem carbon sequestration. In addition, a nonlinear model can robustly capture day-to-day variation in carbon flux; however, precise simulation of phenological patterns with multiple peaks, typically observed in disturbed ecosystems, must be addressed during model optimization. The cross-ecosystem phenology metrics provided by VCPNET could be utilized for regional-global analysis and calibration of satellite-based vegetation indices, which accurately scale land surface phenology. Our findings provide a prospective tool for a deeper understanding of ecosystem function, allowing a direction and foundation for optimizing phenology models and algorithms and ultimately contributing to developing new ecological theories and practices in response to climate change.

This study investigates high-frequency mapping of downward shortwave radiation (DSR) at the Earth's surface using the advanced baseline imager (ABI) instrument mounted on Geostationary Operational Environmental Satellite—R Series (GOES-R). The existing GOES-R DSR product (DSRABI) offers hourly temporal resolution and spatial resolution of 0.25°. To enhance these resolutions, we explore machine learning (ML) for DSR estimation at the native temporal resolution of GOES-R Level-2 cloud and moisture imagery product (5 min) and its native spatial resolution of 2 km at nadir. We compared four common ML regression models through the leave-one-out cross-validation algorithm for robust model assessment against ground measurements from AmeriFlux and SURFRAD networks. Results show that gradient boosting regression (GBR) achieves the best performance (R2 = 0.916, RMSE = 88.05 W·m−2) with more efficient computation compared to long short-term memory, which exhibited similar performance. DSR estimates from the GBR model through the ABI live imaging of vegetated ecosystems workflow (DSRALIVE) outperform DSRABI across various temporal resolutions and sky conditions. DSRALIVE agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal resolutions (5-min intervals) with R2 exceeding 0.85 and RMSE = 122 W·m−2. We conclude that GBR offers a promising approach for high-frequency DSR mapping from GOES-R, enabling improved applications for near-real-time monitoring of terrestrial carbon and water fluxes.

Spatial estimates of crop evapotranspiration with high accuracy from the field to watershed scale have become increasingly important for water management, particularly over irrigated agriculture in semiarid regions. Here, we provide a comprehensive assessment on patterns of annual agricultural water use over California's Central Valley, using 30-m daily evapotranspiration estimates based on Landsat satellite data. A semiempirical Priestley-Taylor approach was locally optimized and cross-validated with available field measurements for major crops including alfalfa, almond, citrus, corn, pasture, and rice. The evapotranspiration estimates explained >70% variance in daily measurements from independent sites with an RMSE of 0.88 mm day-1. When aggregated over the Valley, we estimated an average evapotranspiration of 820 ± 290 mm yr-1 in 2014. Agricultural water use varied significantly across and within crop types, with a coefficient of variation ranging from 8% for Rice (1,110 ± 85 mm yr-1) to 59% for Pistachio (592 ± 352 mm yr-1). Total water uses in 2016 increased by 9.6%, as compared to 2014, mostly because of land-use conversion from fallow/idle land to cropland. Analysis across 134 Groundwater Sustainability Agencies (GSAs) further showed a large variation of agricultural evapotranspiration among and within GSAs, especially for tree crops, e.g., almond evapotranspiration ranging from 339 ± 80 mm yr-1 in Tracy to 1,240 ± 136 mm yr-1 in Tri-County Water Authority. Continuous monitoring and assessment of the dynamics and spatial heterogeneity of agricultural evapotranspiration provide data-driven guidance for more effective land use and water planning across scales.

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis. Therefore, the accuracy of gross primary production (GPP) estimates is expected to improve by removing these components. However, their influence in GPP estimations has not been quantitatively evaluated for deciduous forests. Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components ( FAPAR green ) for partitioning APAR green (photosynthetically active radiation absorbed by photosynthetic components). In this study, the enhanced vegetation index (EVI) estimated FAPAR green and to separate the photosynthetically active radiation absorbed by photosynthetic components ( APAR green ) from total APAR observations ( APAR total ) at two deciduous forest sites. The eddy covariance-light use efficiency (EC-LUE) algorithm was employed to evaluate the influence of non-photosynthetic components and to test the performance of APAR green in GPP estimation. The results show that the influence of non-photosynthetic components have a seasonal pattern at deciduous forest sites, large differences are observed with normalized root mean square error (RMSE*) values of APAR green -based GPP and APAR total -based GPP between tower-based GPP during the early and end stages, while slight differences occurred during peak growth seasons. In addition, daily GPP estimation was significantly improved using the APAR green -based method, giving a higher coefficient of determination and lower normalized root mean square error against the GPP estimated by the APAR total -based method. The results demonstrate the significance of partitioning APAR green from APAR total for accurate GPP estimation in deciduous forests. [ABSTRACT FROM AUTHOR]

Wetlands are the largest natural source of methane (CH4); however, the contribution of subtropical wetlands to global CH4 budgets is still unclear due to difficulties in accurately quantifying CH4 emissions from these complex ecosystems. Both direct (water management strategies) and indirect (altered weather patterns associated with climate change) anthropogenic influences are also leading to greater uncertainties in our ability to determine changes in CH4 emissions from these ecosystems. This study compares CH4 fluxes from two freshwater marshes with different hydroperiods (short versus long) in the Florida Everglades to examine temporal patterns and biophysical drivers of CH4 fluxes. Both sites showed similar seasonal patterns across years with higher CH4 release during wet seasons versus dry seasons. The long hydroperiod site showed stronger seasonal patterns and overall, emitted more CH4 than the short hydroperiod site; however, no distinctive diurnal patterns were observed. We found that air temperature was a significant positive driver of CH4 fluxes for both sites regardless of season. In addition, gross ecosystem exchange was a significant negative predictor of CH4 emissions in the dry season at the long hydroperiod site. CH4 fluxes were impacted by water level and its changes over site and season, and time scales, which are influenced by rainfall and water management practices. Thus with increasing water distribution associated the Comprehensive Everglades Restoration Plan we expect increases in CH4 emissions, and when couple with increased with projected higher temperatures in the region, these increases may be enhanced, leading to greater radiative forcing. [ABSTRACT FROM AUTHOR]

Earth System Models (ESMs) are increasingly representing agriculture due to its impact on biogeochemical cycles, local and regional climate, and fundamental importance for human society. Realistic large scale simulations may require spatially varying crop parameters that capture crop growth at various scales and among different cultivars, as well as common crop management practices, but their importance is uncertain, and they are often not represented in ESMs. In this study, we examine the impact of using constant versus spatially varying crop parameters using a novel, realistic crop rotation scenario in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2). We implemented crop rotation by using ELMv2's dynamic land unit capability, and then calibrated and validated the model against observations collected at three AmeriFlux sites in the US Midwest with corn soybean rotation. The calibrated model closely captured the magnitude and observed seasonality of carbon and energy fluxes across crops and sites. We performed regional simulations for the US Midwest using the calibrated model and found that spatially varying only a few crop parameters across the region, as opposed to using constant parameters, had a large impact, with the carbon fluxes and energy fluxes both varying by up to 40%. These results imply that large scale ESM simulations using spatially invariant crop parameters may result in biased energy and carbon fluxes estimation from agricultural land, and underline the importance of improving human‐earth systems interactions in ESMs. Plain Language Summary: Crops are increasingly being characterized in global land models because of their impact on local and regional climate. However, there is limited understanding of the impact of crop rotation and of different crop cultivars on carbon and energy fluxes from the land surface. Our study implements crop rotation and spatially varying crop parameters in the Energy Exascale Earth System Model Land Model and finds that doing so improves carbon and energy flux estimation from cropland area. These findings emphasize the importance of capturing agricultural management practices and variability in growth characteristics across different crop cultivars in global land models. Key Points: This study implements corn soybean rotation and spatially varying crop parameters in the Energy Exascale Earth System Land ModelThe model is calibrated and validated against observations collected at three AmeriFlux sites in the US MidwestWe find that spatially varying crop parameters resulted in improved flux estimation from cropland areas [ABSTRACT FROM AUTHOR]

In deciduous forests, spring leaf development and fall leaf senescence regulate the timing and duration of photosynthesis and transpiration. Being able to model these dates is therefore critical to accurately representing ecosystem processes in biogeochemical models. Despite this, there has been relatively little effort to improve internal phenology predictions in widely used biogeochemical models. Here, we optimized the phenology algorithms in a regionally developed biogeochemical model (PnET-CN) using phenology data from eight mid-latitude PhenoCam sites in eastern North America. We then performed a sensitivity analysis to determine how the optimization affected future predictions of carbon, water, and nitrogen cycling at Bartlett Experimental Forest, New Hampshire. Compared to the original PnET-CN phenology models, our new spring and fall models resulted in shorter season lengths and more abrupt transitions that were more representative of observations. The new phenology models affected daily estimates and interannual variability of modeled carbon exchange, but they did not have a large influence on the magnitude or long-term trends of annual totals. Under future climate projections, our new phenology models predict larger shifts in season length in the fall (1.1–3.2 days decade−1) compared to the spring (0.9–1.5 days decade−1). However, for every day the season was longer, spring had twice the effect on annual carbon and water exchange totals compared to the fall. These findings highlight the importance of accurately modeling season length for future projections of carbon and water cycling. [ABSTRACT FROM AUTHOR]

Estimating actual crop evapotranspiration (ET c act) is a fundamental requirement for effective crop water management, aiming to achieve precision irrigation amidst changing environmental conditions. Despite the introduction of various methods for estimating ET c act values, these methods are still associated with several challenges and limitations. Motivated by the robustness of deep learning models, this study employed two hybrid models that integrate Convolution Neural Network with either Random Forests (CNN-RF) or Support Vector Machine (CNN-SVR) to estimate potato ET c act using a limited set of input features. Three models were configured and compared for each CNN-RF (CNN-RF 1 , CNNRF 2 , CNNRF 3) and CNN-SVM (CNN-SVM 1 , CNN-SVM 2 , CNN-SVM 3), by using different combinations of variable input features derived from meteorological data (air temperature (Ta), vapour pressure deficit (VPD), net radiation (Rn)) and MODIS satellite data (land surface temperature (LST), fraction of photosynthetically active radiation (Fpar), leaf area index (LAI)). The results demonstrated the outstanding performance of the CNN-RF models, showcasing their superiority over the CNN-SVM models. Notably CNN-RF 1 model yielded the best results, achieving a normalized root mean squared error (nRMSE) of 11.7 and 31.5 %; Nash–Sutcliffe Efficiency (NSE) of 0.97 and 0.80; Correlation coefficient (CC) of 0.99 and 0.91 at training and testing phases respectively. While, both models exhibited lower performance when using remote sensing satellite-based inputs, CNN-RF 2 , managed to produce satisfactory estimates of nRMSE = 16.7, 40.9 %; NSE = 0.95, 0.66; CC = 0.98, 0.813; Bias = −0.41, −4.377 W/m2 during training and testing respectively. The ET c act of potato showed to have high correlation with LST (0.74–0.84) and using LST as proxy for Ta, resulted in improved model estimates compared to using satellite data exclusively. These findings suggest that, in situations where climatic data is limited, remote sensing can serve as a viable alternative for modelling potato ET c act when coupled with CNN-RF, and further improvement can be achieved by incorporating meteorological data fusion. • Estimation of ET c act is a fundamental requirement of crop water management. • Deep learning based Random Forest models demonstrated to be superior when estimating potato ET c act. • Satellite remote sensing data is a viable option when estimating potato ET c act. [ABSTRACT FROM AUTHOR]

Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface-atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum-flux data. At 69 forest sites, annual ha robustly predicted site-to-site and year-to-year differences in canopy heights (R2 = 0.88, 111 site-years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2 > 0.70 in 74 site-years). Our results demonstrate the potential of flux-derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large-scale and time-varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.

Drought, one of the most destructive natural disasters is projected by numerous studies to become more severe and widespread under climate change. These water limitations will have profound effects on terrestrial systems across the globe. Yet, most of the existing drought monitoring indices are based on drought stress derived from environmental conditions rather than ecosystem responses. Here, we propose using a new approach, the Normalized Ecosystem Drought Index (NEDI), coupled with modified Variable Interval Time Averaging (VITA) method, to quantify drought severity according to ecosystem transitional patterns with water availability. The method is inspired by Sprengel’s and Liebig’s Law of the Minimum for plant nutrition. Eddy covariance measurements from 60 AmeriFlux sites that cross 8 International Geosphere–Biosphere Programme (IGBP) vegetation types were used to validate the use of NEDI coupled to VITA. The results show that NEDI can reasonably depict both drought stress posed by the environment and drought responses presented by various ecosystems. Water availability becomes a dominant limiting factor for ecosystem evapotranspiration when NEDI falls below zero, and normalized evapotranspiration strength generally decreases with decreasing NEDI under this regime. The widely used self-calibrating Palmer Drought Severity Index (sc-PDSI) and Standardized Precipitation Index (SPI) have difficulty capturing ecosystem responses to water availability, although they can reasonably represent drought conditions detected in the environment. The normalization feature employed in NEDI makes it feasible to compare drought severity over different regions, seasons and vegetation types. The new drought index also provides a valuable tool for irrigation and water distribution management practices which may enhance water conservation efforts as drought conditions become more prevalent.

Long‐running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem‐Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US‐WCr and US‐Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US‐Los and US‐ALQ), and a very tall (400 m) landscape‐level tower (US‐PFa). Together, they provided over 70 site‐years of observations. The 19‐tower Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 campaign centered around US‐PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long‐term sites, but cross‐site coherence in interannual NEE in the earlier part of the record became weaker with time as non‐climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less‐insulated soils delayed start of spring green‐up. Higher CO2 increased maximum net assimilation parameters but not total gross primary productivity. Stand‐scale sites were larger net sinks than the landscape tower. Clustered, long‐term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space. Plain Language Summary: The terrestrial biosphere features the largest global sources and sinks of atmospheric carbon. Changes in growing season length, disturbance frequency, human management, increasing atmospheric carbon dioxide (CO2) concentrations, amount and timing of precipitation, and warmer air temperature all influence the carbon cycle. Observations from the global eddy covariance flux tower network have been key for diagnosing these changes. However, data from most sites are limited in length. Here, we explore how multi‐decadal carbon flux measurements from a cluster of flux towers in forests and wetlands in the upper Midwest USA respond to environmental change. Despite the proximity of the sites, year‐to‐year variation in carbon fluxes was rarely similar between sites. Surprisingly, warmer winters promoting earlier snowmelt led to later spring green‐up because soil temperature was colder. Impacts of higher CO2 and warmer temperature on annual carbon fluxes were limited but did influence factors linking carbon flux sensitivity to climate. Differences in flux magnitudes from a very tall tower flux to the network show that the whole does not seem to be simply a sum of its measured parts. More elaborate approaches may be needed to understand the processes that control carbon fluxes across large landscapes. Key Points: Multi‐decadal eddy covariance flux tower site cluster provides insight into variation of regional carbon cyclingVariation of carbon exchange in two forests, two wetlands, and a tall tower responded differently to weather, phenology, and disturbanceChallenges in upscaling fluxes indicate need for advances in aquatic observations, disturbance mapping, and flux footprint decomposition [ABSTRACT FROM AUTHOR]

The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively). [ABSTRACT FROM AUTHOR]

Keywords: AmeriFlux; dual isotope approach; Péclet effect; precipitation; stable oxygen isotopes; stomatal conductance; tree rings; vapour pressure deficit (VPD) EN AmeriFlux dual isotope approach Péclet effect precipitation stable oxygen isotopes stomatal conductance tree rings vapour pressure deficit (VPD) 809 812 4 10/13/22 20221101 NES 221101 A reply to Lin et al. (2022) 'Do changes in tree-ring 18 O indicate changes in stomatal con... There is growing scientific consensus that rising atmospheric CO SB 2 sb concentrations have caused an increase in forest water-use efficiency (WUE), but the magnitude of this effect and the relative roles of enhanced photosynthesis vs reduced stomatal conductance ( I g i SB s sb ) are still under debate (Walker I et al i ., 2020). Detecting long-term changes in stomatal conductance: challenges and opportunities of tree-ring 18O proxy Dual isotope approach, Péclet effect, AmeriFlux, precipitation, stable oxygen isotopes, stomatal conductance, tree rings, vapour pressure deficit (VPD) Simulating oxygen isotope ratios in tree ring cellulose using a dynamic global vegetation model. [Extracted from the article]

Gross primary production (GPP) models driven by fine resolution remote sensing data characterize the spatial and temporal heterogeneities in plant photosynthesis, which is largely dependent on biome-specific maximum photosynthetic capacity. The red-edge reflectance, sensitive to leaf chlorophyll content, is a good proxy of maximum photosynthetic capacity. More importantly, studies show that the red-edge reflectance-related chlorophyll content index (CIr) multiplied by the incident photosynthetic active radiation (PARin) strongly correlates to GPP estimated at carbon flux towers (GPPflux). Yet, to the best of our knowledge, there is no systematic study investigating the general relationship between fine spatial resolution CIr and GPP among biomes and the relationship between CIr and maximum photosynthetic capacity in GPP models. To provide an overview on incorporating space-borne CIr into a GPP model, we applied fine resolution Sentinel-2-derived CIr and GPPflux over 57 flux sites representative of 10 biomes. We investigated the relationship between CIr and GPPflux, and the spatio-temporal relationship between CIr and ecosystem maximum photosynthetic capacity indicated by the potential ecosystem light use efficiency (LUEpot). We also evaluated the relationship between other five vegetation indices (VIs) and GPPflux. Results showed that the CIr multiplied by PARin has a higher agreement (R2 > 0.5) with GPP than other VIs. A universal relationship exists between the CIr multiplied by PARin and GPP, except for forest biomes. The CIr also strongly (R2 > 0.5) relates to the LUEpot during the peak of the growing season. The CIr has a low spatial variance (CV = 0.25) among biomes, highlighting that CIr can be a proxy of maximum photosynthetic capacity in GPP models that do not require biome-dependent coefficients. This study provides insight for incorporating CIr into GPP models and better quantifying global terrestrial photosynthesis.

Shifts in plant phenology associated with climate change have received unprecedented attention from land managers, policymakers, and the scientific community, with important implications for the structure and function of the biosphere. Eddy covariance (EC) has been used to measure carbon exchange between the land surface and atmosphere, which provides an opportunity to describe the seasonality of ecosystem processes. Using BASE flux products provided by the AmeriFlux network and a non-linear modeling approach, this study developed a vegetation carbon phenology (VCP) dataset, which we termed VCPNET (version 1.0). VCPNET was developed with fine spatial and temporal scales for a total of 87 EC sites (2820 site-years of data) across North America, including nine vegetation types. We extracted phenology metrics by site to provide long-term VCP metrics across these ecosystems. Analyses revealed significant differences in photosynthetic capacity and respiration rate-derived VCP metrics across the landscape, as well as differences associated with whether daily cumulative or half-hourly maximum measurements were used. Daily carbon fluxes may be more consistent in simulating phenology behavior than 30-min maximum fluxes across all vegetation types, except in sites classified as Grassland. Overall vegetation types, the seasonality of photosynthetic capacity may exert a larger control on the net carbon uptake of the ecosystem compared to the respiration rate. Thus, model-estimated photosynthesis-activated seasons may better represent true phenology, particularly the development of above- and below-ground plant biomass in tree-covered areas, which is crucial in forecasting ecosystem carbon sequestration. In addition, a nonlinear model can robustly capture day-to-day variation in carbon flux; however, precise simulation of phenological patterns with multiple peaks, typically observed in disturbed ecosystems, must be addressed during model optimization. The cross-ecosystem phenology metrics provided by VCPNET could be utilized for regional-global analysis and calibration of satellite-based vegetation indices, which accurately scale land surface phenology. Our findings provide a prospective tool for a deeper understanding of ecosystem function, allowing a direction and foundation for optimizing phenology models and algorithms and ultimately contributing to developing new ecological theories and practices in response to climate change. [Display omitted] • VCPNET is a vegetation phenology dataset developed using AmeriFlux data. • Phenological metrics derived from daily cumulative flux have higher ecological utility than shorter time scales. • Seasonality in photosynthetic capacity alters ecosystem C uptake more than respiration. • VCPNET can improve the scaling of land surface phenology. [ABSTRACT FROM AUTHOR]

How aquatic primary productivity influences the carbon (C) sequestering capacity of wetlands is uncertain. We evaluated the magnitude and variability in aquatic C dynamics and compared them to net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) rates within calcareous freshwater wetlands in Everglades National Park. We continuously recorded 30-min measurements of dissolved oxygen (DO), water level, water temperature (Twater), and photosynthetically active radiation (PAR). These measurements were coupled with ecosystem CO2 fluxes over 5 years (2012–2016) in a long-hydroperiod peat-rich, freshwater marsh and a short-hydroperiod, freshwater marl prairie. Daily net aquatic primary productivity (NAPP) rates indicated both wetlands were generally net heterotrophic. Gross aquatic primary productivity (GAPP) ranged from 0 to − 6.3 g C m−2 day−1 and aquatic respiration (RAq) from 0 to 6.13 g C m−2 day−1. Nonlinear interactions between water level, Twater, and GAPP and RAq resulted in high variability in NAPP that contributed to NEE. Net aquatic primary productivity accounted for 4–5% of the deviance explained in NEE rates. With respect to the flux magnitude, daily NAPP was a greater proportion of daily NEE at the long-hydroperiod site (mean = 95%) compared to the short-hydroperiod site (mean = 64%). Although we have confirmed the significant contribution of NAPP to NEE in both long- and short-hydroperiod freshwater wetlands, the decoupling of the aquatic and ecosystem fluxes could largely depend on emergent vegetation, the carbonate cycle, and the lateral C flux. [ABSTRACT FROM AUTHOR]

Linking biometric measurements of stand‐level biomass growth to tower‐based measurements of carbon uptake—gross primary productivity and net ecosystem productivity—has been the focus of numerous ecosystem‐level studies aimed to better understand the factors regulating carbon allocation to slow‐turnover wood biomass pools. However, few of these studies have investigated the importance of previous year uptake to growth. We tested the relationship between wood biomass increment (WBI) and different temporal periods of carbon uptake from the current and previous years to investigate the potential lagged allocation of fixed carbon to growth among six mature, temperate forests. We found WBI was strongly correlated to carbon uptake across space (i.e., long‐term averages at the different sites) but on annual timescales, WBI was much less related to carbon uptake, suggesting a temporal mismatch between C fixation and allocation to biomass. We detected lags in allocation of the previous year's carbon uptake to WBI at three of the six sites. Sites with higher annual WBI had overall stronger correlations to carbon uptake, with the strongest correlations to carbon uptake from the previous year. Only one site had WBI with strong positive relationships to current year uptake and not the previous year. Forests with low rates of WBI demonstrated weak correlations to carbon uptake from the previous year and stronger relationships to current year climate conditions. Our work shows an important, but not universal, role of lagged allocation of the previous year's carbon uptake to growth in temperate forests. Plain Language Summary: We compared the interannual variability of stand‐level biomass growth (estimated from annual tree‐level measurements) to carbon uptake (measured from towers monitoring gas exchange over forest canopies) to identify temporal mismatches between the two processes. We used data from multiple forested sites with long‐term measurements of carbon uptake to ask the question: is there a consistent temporal offset between the uptake of carbon and the allocation to plant biomass? We found that the relationship between growth and carbon uptake varies among sites, and there was no consistent temporal offset between the uptake of carbon and allocation to biomass growth. Sites with higher growth rates had higher interannual variability, and more apparent coupling between biomass growth and carbon uptake from the previous year. Forests with lower growth rates had weaker relationships with carbon uptake and stronger coupling with current year environmental conditions. We demonstrate that temporal lags between carbon uptake and allocation to growth are not universal among these temperate forests, and the carry‐over of uptake stored from the previous year is not as critical in slow‐growing forests, compared to fast‐growing forests, likely due to lower demands for growth. This work helps to clarify the limitations on the growth of slow‐turnover wood biomass. Key Points: We found evidence for lags in allocation of previous year's carbon uptake to wood biomass increment (WBI) at three of six sites studiedSites with high annual WBI demonstrated strong correlations to carbon uptake, with the strongest correlations including previous year uptakeWBI was less tightly coupled to carbon uptake in less productive forests that also exhibited low interannual variability in WBI [ABSTRACT FROM AUTHOR]

The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP. Plain Language Summary: Most widely used computer models of the land surface neglect canopy‐induced turbulence in calculations of heat, water, and momentum exchanges. Accounting for canopy‐induced turbulence is important because coherent eddies that form near the canopy top are responsible for generating most of the transportation of heat, water, and momentum, in and directly above the canopy. In 2007, 2008 scientists Ian N. Harman and John J. Finnigan developed a methodology that adapts contemporarily used equations in operational LSMs to account for canopy‐induced turbulence. In this study, we create a new physics option for the Noah with Multi‐Parameterization (Noah‐MP) LSM that accounts for canopy‐induced turbulence based on the Harman and Finnigan, 2007–2008 methodology. The primary focus of this study is to quantify differences between Noah‐MP snowpack simulations using the classical physics option that neglects canopy‐induced turbulence with the new physics option that accounts for canopy‐induced turbulence. Overall, simulations using the new physics option tend to have better agreement with ground‐based SWE observations across 647 validation sites within the western United States. We intend for the new physics scheme to improve weather and hydrological applications for operational modeling systems that rely on the Noah‐MP LSM. Key Points: This study presents a new physics option for Noah‐MP that accounts for canopy‐induced turbulence in the roughness sublayerSimulated SWE over shrublands using the new turbulence scheme agree more with observations than those using the traditional MOST schemeDifferences in snow simulations are mainly attributable to differences in aerodynamic resistance to sensible heat below the canopy top [ABSTRACT FROM AUTHOR]

Many secondary deciduous forests of eastern North America are approaching a transition in which mature early‐successional trees are declining, resulting in an uncertain future for this century‐long carbon (C) sink. We initiated the Forest Accelerated Succession Experiment (FASET) at the University of Michigan Biological Station to examine the patterns and mechanisms underlying forest C cycling following the stem girdling‐induced mortality of >6,700 early‐successional Populus spp. (aspen) and Betula papyrifera (paper birch). Meteorological flux tower‐based C cycling observations from the 33‐ha treatment forest have been paired with those from a nearby unmanipulated forest since 2008. Following over a decade of observations, we revisit our core hypothesis: that net ecosystem production (NEP) would increase following the transition to mid‐late‐successional species dominance due to increased canopy structural complexity. Supporting our hypothesis, NEP was stable, briefly declined, and then increased relative to the control in the decade following disturbance; however, increasing NEP was not associated with rising structural complexity but rather with a rapid 1‐yr recovery of total leaf area index as mid‐late‐successional Acer, Quercus, and Pinus assumed canopy dominance. The transition to mid‐late‐successional species dominance improved carbon‐use efficiency (CUE = NEP/gross primary production) as ecosystem respiration declined. Similar soil respiration rates in control and treatment forests, along with species differences in leaf physiology and the rising relative growth rates of mid‐late‐successional species in the treatment forest, suggest changes in aboveground plant respiration and growth were primarily responsible for increases in NEP. We conclude that deciduous forests transitioning from early to middle succession are capable of sustained or increased NEP, even when experiencing extensive tree mortality. This adds to mounting evidence that aging deciduous forests in the region will function as C sinks for decades to come. [ABSTRACT FROM AUTHOR]

Abstract The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP.

The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively).

A long‐standing goal of ecology has been to understand the cycling of carbon in forests. This has taken on new urgency with the need to address a rapidly changing climate. Forests serve as long‐term stores for atmospheric CO2, but their continued ability to take up new carbon is dependent on future changes in climate and other factors such as age. We have been measuring many aspects of carbon cycling at an unmanaged evergreen forest in central Maine, USA, for over 25 years. Here we use these data to address questions about the magnitude and control of carbon fluxes and quantify flows and uncertainties between the different pools. A key issue was to assess whether recent climate change and an aging tree population were reducing annual C storage. Total ecosystem C stocks determined from inventory and quantitative soil pits were about 23,300 g C m−2 with 46% in live trees, and 48% in the soil. Annual biomass increment in trees at Howland Forest averaged 161 ± 23 g C m−2 yr−1, not significantly different from annual net ecosystem production (NEP = −NEE) of 211 ± 40 g C m−2 y−1 measured by eddy covariance. Unexpectedly, there was a small but significant trend of increasing C uptake through time in the eddy flux data. This was despite the period of record including some of the most climate‐extreme years in the last 125. We find a surprising lack of influence of climate variability on annual carbon storage in this mature forest. Plain Language Summary: Trees remove carbon dioxide (CO2) from the atmosphere by photosynthesis and store it in chemical form in wood and other plant tissues. Much of the stored carbon ends up in the soil. Plants and soil organisms use some of the stored carbon to provide energy for growth and plant maintenance processes which releases CO2 back to the atmosphere. The movement of carbon atoms into and out of the forest and through the plants and soils is termed the "carbon cycle". We have been studying the carbon cycle of the Howland Forest in central Maine, an unmanaged evergreen forest with most trees between 100 and 200 years old. Over the last 25 years the forest has stored almost 3.5 tons of CO2 per acre each year, even though that timespan has included the warmest, wettest, and driest years in the last 125. Although the forest is maturing, it is storing on average a bit more carbon each year and there is as yet no clear reason why. Forest growth is a natural solution to the problem of too much CO2 in the atmosphere. This research helps show the longer‐term stability and viability of forests as natural climate solutions. Key Points: An unmanaged evergreen forest has been a net carbon sink for over 25 years and the sink strength has increased significantly with timeAnnual average carbon increment measured by eddy flux or inventory data are not significantly differentThe forest remained a robust C sink in the warmest, wettest, and driest years since 1895, indicating resistance to climate variability [ABSTRACT FROM AUTHOR]

Environmental observatory networks (EONs) provide information to understand and forecast the spatial and temporal dynamics of Earth's biophysical processes. Consequently, representativeness analyses are important to provide insights for improving EONs' management, design, and interpretation of their value‐added products. We assessed the representativeness of registered FLUXNET sites (n = 41, revised on September 2018) across Latin America (LA), a region of great importance for the global carbon and water cycles, which represents 13% of the world's land surface. Nearly 46% of registered FLUXNET sites are located in evergreen broad‐leaf forests followed by sites in woody savannas (∼20%). Representativeness analyses were performed using a 0.05° spatial grid for multiple environmental variables, gross primary productivity (GPP), and evapotranspiration (ET). Our results showed a potential representativeness of 34% of the surface area for climate properties, 36% for terrain parameters, 34% for soil resources, and 45% when all aforementioned environmental variables were summarized into a principal component analysis. Furthermore, there was a 48% potential representativeness for GPP and 34% for ET. Unfortunately, data from these 41 sites are not all readily available for the scientific community, limiting synthesis studies and model benchmarking/parametrization. The implication is that global/regional data‐driven products are forced to use information from FLUXNET sites outside LA to predict patterns in LA. Representativeness could increase to 86% (for GPP) and 80% (for ET) if 200 sites are optimally distributed. We discussed ongoing challenges, the need to enhance interoperability and data sharing, and promote monitoring efforts across LA to increase the accuracy of regional‐to‐global data‐driven products. Plain Language Summary: Environmental observatory networks (EONs) provide information to better understand, model, and forecast the spatial and temporal dynamics of Earth's biophysical processes. Consequently, representativeness analyses of EON's are important to provide insights for improving EONs' management, design, and interpretation of their value‐added products (e.g., datasets, model predictions). We assessed the representativeness of registered FLUXNET sites (n = 41, revised on September 2018) across Latin America (LA), a region of great importance for the global carbon and water cycles, which represents nearly 13% of the world's land surface area. Representativeness analyses were performed using multiple environmental variables, gross primary productivity (GPP), and evapotranspiration (ET) across LA. Our results showed potential spatial representativeness of 34% of the surface area for climate properties, 36% for terrain parameters, 34% for soil resources, and 45% when all aforementioned environmental variables were summarized. Furthermore, there was a 48% potential representativeness for GPP and 34% for ET. Our results represent a best‐case scenario because data from these 41 sites is not all readily available for the scientific community. We discussed the need to enhance collaboration, promote the participation of active/inactive sites to share information with local, regional and international networks, and promote monitoring efforts across this region of the world. Key Points: There is a need to promote data sharing and improve the representativeness of environmental networks across Latin AmericaCurrently, 48% of gross primary productivity and 34% of evapotranspiration patterns are represented by FLUXNET sitesData‐driven products are forced to use information outside Latin America to predict patterns within this region [ABSTRACT FROM AUTHOR]

In October 2020, under COVID‐19 quarantine, AmeriFlux held its largest and one of its most successful annual meetings. Historically, ∼100 scientists attend; this meeting had over 400 registrants and participants. Participants expressed that this was among the best virtual meetings that they had ever attended, and 100% of post‐meeting survey respondents stated that they would attend again. Feedback revealed the meeting fostered a strong sense of connection to the AmeriFlux community, especially among early career and international scientists. How did a feeling of strong connection to the community arise from the seemingly cold and isolated structures of virtual meetings? The meeting emphasized Diversity, Equity, and Inclusion (DEI), which resulted in an unexpected enhancement of science communication and community connections. Additionally, the meeting experimented with an online virtual gaming‐like world (Gather.town), where users controlled video/audio‐enabled avatars in a conference center and poster hall environment to create spontaneous conversations and discussions. In lieu of a social‐bonding field trip, participants showed videos of their field sites accompanied by informal banter, which were watched in group settings in Gather. Social mixers were structured over Zoom breakout rooms that were limited in size to promote participation with accessible games. Science talks were selected based on appeal to a demographically diverse organizing committee, which enhanced appeal to a broad meeting audience. Finally, breakout reports were given not in the format of bullet point slides, but instead as creatively improvized fairytales, which dramatically enhanced engagement. Here, we describe some of the process that went into the AmeriFlux 2020 meeting. Keeping with the theme of experimenting and fairytales, we present this narrative in the form of a fairytale; and, without further ado... Key Points: Diversity, Equity, and Inclusion are keys to enhanced science communication and community connectionsLean‐in to experimenting with novel meeting tools and options for improved virtual meetingsStory telling as a means of effective science communication and engagement [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a major pathway for water loss from many ecosystems, and its seasonal variation affects soil moisture and net ecosystem CO2 exchange. We developed an algorithm to estimate ET using a semi-empirical Priestley–Taylor (PT) approach, which can be applied at a range of spatial scales. We estimated regional net radiation (Rnet) at monthly time scales using MODerate resolution Imaging Spectroradiometer (MODIS) albedo and land surface temperature. Good agreement was found between satellite-based estimates of monthly Rnet and field-measured Rnet, with a RMSE of less than 30 W m− 2. An adjustable PT coefficient was parameterized as a function of leaf area index and soil moisture based on observations from 27 AmeriFlux eddy covariance sites. The biome specific optimization using tower-based observations performed well, with a RMSE of 17 W m− 2 and a correlation of 0.90 for predicted monthly latent heat. We implemented the approach within the hydrology module of the CASA biogeochemical model, and used it to estimate ET at a 1 km spatial resolution for the conterminous United States (CONUS). The RMSE of modeled ET was reduced to 21.1 mm mon− 1, compared to 27.1 mm mon− 1 in the original CASA model. The monthly ET rates averaged over the Mississippi River basin were similar to those derived using GRACE satellite measurements and river discharge data. ET varied substantially over the CONUS, with annual mean values of 110 ± 76 mm yr− 1 in deserts, 391 ± 176 mm yr− 1 in savannas and grasslands, and 840 ± 234 mm yr− 1 in broadleaf forests. The PT coefficient was the main driver for the spatial variation of ET in arid areas, whereas Rnet controlled ET when mean annual precipitation was higher than approximately 400 mm yr− 1.

Evapotranspiration (ET) is one of the most critical parameters in water- and energy-related domains. Two basic assumptions with respect to soil-moisture variation have been widely investigated for the retrieval of ET based on the trapezoid methods. Specifically, soil moisture within the surface and root-zone layers was assumed to vary synchronously in most of the earlier analyses. However, several recent investigations assumed that soil moisture within the upper soil layer should be dried up before the root-zone layer is stressed. To this end, the retrieval of ET under different aridity conditions over North American grasslands was investigated with the two assumptions, and the estimated ET was assessed using the flux data collected from eight AmeriFlux sites. Based on the available data from 2002 to 2018, results showed that the “asynchronous-assumed” method can obtain better ET estimates than the “synchronous-assumed” method over semiarid and subhumid areas, whereas the “synchronous-assumed” method can obtain better ET estimates in humid areas. Moreover, because of the different closure techniques used for the ET correction, no consistent conclusions could be found for the arid conditions to determine which trapezoid was better. Specifically, it was found that the cases of surface soil with zero water availability that were defined by the asynchronous-assumed trapezoid method rarely occur, even in arid areas, which indicated that the critical boundary that determines whether the root-zone layer begins to be water-stressed may need to be redefined. [ABSTRACT FROM AUTHOR]

We test the relationship between canopy photosynthesis and reflected near‐infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near‐infrared and red radiation from vegetation, with light‐emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near‐infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near‐infrared radiation were highly correlated with measurements of canopy photosynthesis on half‐hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near‐infrared radiation were greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient‐rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient‐poor, mineral soils. Reflected near‐infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near‐infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications. Key Points: Reflected near‐infrared radiation is a strong proxy for canopy photosynthesisThis proxy works for a wide range of ecosystem structure and functionThe method has promise to be used for a variety of applications relating to carbon and water use [ABSTRACT FROM AUTHOR]

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.

With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon-LAnd Model Intercomparison Project (C-LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) -Carnegie-Ames-Stanford Approach′ (CASA′) and carbon-nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO 2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1-3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4PgCyr-1 for CASA′ vs. 1.2PgCyr-1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988-2004 based on comparison with atmospheric inversion results from TRANSCOM (r =0.66 for CASA′ and r =0.73 for CN). Adding (CASA′) or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C-LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community-wide platform for model-data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C-LAMP are publicly available on the Earth System Grid. © 2009 Blackwell Publishing Ltd.

• Evapotranspiration was measured by concurrent above- and below-canopy eddy covariance. • Total forest evapotranspiration was 606 mm yr−1, with 275 mm yr−1 from the understory. • Transpiration from trees accounted for 47% of evapotranspiration. • Interannual variability in evapotranspiration and transpiration was small. • First multi-year study of evapotranspiration from eastern Sierra Nevada, California. Evapotranspiration (ET) from the land surface to the atmosphere is a major component of Earth's water cycle, and comprises both transpiration (T) of xylem water from plants and evaporation (E) of water from soils and vegetation surfaces. These two component fluxes respond differently to changes in temperature, water availability and atmospheric CO 2 concentrations. Concurrent eddy covariance (EC) measurements above and below forest canopies provide a promising approach to partition ET into E and T. However, below-canopy EC measurements are rare, and questions remain regarding their spatial variability, canopy coupling, and temporal dynamics. To address these challenges, we measured and partitioned ET over more than three years, using concurrent above- and below-canopy EC towers in a montane forest at Sagehen Creek in California's Sierra Nevada mountains. This is the establishing study for the AmeriFlux site US-SHC. The main environmental control for ET was available energy; other important controls were canopy & soil temperature, soil moisture, vapor pressure deficit, and wind speed. Below-canopy measurements at two locations within the above-canopy footprint were similar to one another, suggesting low spatial heterogeneity in understory ET near the creek at our Sagehen site. We observed a total forest ET of 606 ± 50 mm yr−1 with 275 ± 17 mm yr−1 measured in the understory (all mean ± SD) during the water years 2018–2020. Interannual variability in ET and T was small despite large variability in precipitation totals; thus the P–ET water balance was mainly driven by variations in water supply. Partitioning the components of total forest ET at Sagehen with concurrent EC measurements showed that on average, 67–74% of ET originated from T (47% from trees and 20–27% from understory vegetation), while 26–33% were from E (mostly from the understory). Our results demonstrate the potential of concurrent above- and below-canopy EC measurements for ET partitioning. [ABSTRACT FROM AUTHOR]

Soil moisture data, obtained from four AmeriFlux sites in the US, were examined using an ecohydrological framework. Sites were selected for the analysis to provide a range of plant functional type, climate, soil particle size distribution, and time series of data spanning a minimum of two growing seasons. Soil moisture trends revealed the importance of measuring water content at several depths throughout the rooting zone; soil moisture at the surface (0-10 cm) was approximately 20-30%) less than that at 50-60 cm. A modified soil moisture dynamics model was used to generate soil moisture probability density functions at. each site. Model calibration results demonstrated that the commonly used soil matric potential values for finding the vegetation stress point and field content may not be appropriate, particularly for vegetation adapted to a water-controlled environment. Projections of future soil moisture patterns suggest that two of the four sites will become severely stressed by climate change induced alterations to the precipitation regime. (C) 2006 Elsevier Ltd. All rights reserved.

Evapotranspiration, a major component in terrestrial water balance and net primary productivity models, is difficult to measure and predict. This study compared five models of potential evapotranspiration (PET) applied to a ponderosa pine forest ecosystem at. an AmeriFlux site in Northern California. The AmeriFlux sites are research forests across the United States, Canada, Brazil, and Costa Rica with instruments on towers that measure carbon, water, and energy fluxes into and out of the ecosystems. The evapotranspiration models ranged from simple temperature and solar radiation-driven equations to physically-based combination approaches and included reference surface and surface cover-dependent algorithms. For each evapotranspiration model, results were compared against mean daily latent heat from half-hourly measurements recorded on a tower above the forest canopy. All models calculate potential evapotranspiration (assuming well-watered soils at field capacity), rather than actual evapotranspiration (based on soil moisture limitations), and thus overpredicted values from the dry summer seasons of 1997 and 1998. A soil moisture function was integrated to estimate actual evapotranspiration, resulting in improved accuracy in model simulations. A modified Priestley-Taylor model performed well given its relative simplicity. (c) 2004 Elsevier Ltd. All rights reserved.

Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.

Multiple lines of evidence suggest that plant water-use efficiency (WUE)—the ratio of carbon assimilation to water loss—has increased in recent decades. Although rising atmospheric CO2 has been proposed as the principal cause, the underlying physiological mechanisms are still being debated, and implications for the global water cycle remain uncertain. Here, we addressed this gap using 30-y tree ring records of carbon and oxygen isotope measurements and basal area increment from 12 species in 8 North American mature temperate forests. Our goal was to separate the contributions of enhanced photosynthesis and reduced stomatal conductance to WUE trends and to assess consistency between multiple commonly used methods for estimatingWUE. Our results show that tree ring-derived estimates of increases in WUE are consistent with estimates from atmospheric measurements and predictions based on an optimal balancing of carbon gains and water costs, but are lower than those based on ecosystemscale flux observations. Although both physiological mechanisms contributed to rising WUE, enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to species that experienced moisture limitations. This finding challenges the hypothesis that rising WUE in forests is primarily the result of widespread, CO2-induced reductions in stomatal conductance. [ABSTRACT FROM AUTHOR]

California is expected to experience great spatial/temporal variations evaporation. These variations arise from strong north‐south, east‐west gradients in rainfall and vegetation, strong interannual variability in rainfall (±30%) and strong seasonal variability in the supply and demand for moisture. We used the Breathing Earth System Simulator to evaluate the rates and sums of evaporation across California, over the 2001–2017 period. Breathing Earth System Simulator is a bottom‐up, biophysical model that couples subroutines that calculate the surface energy balance, photosynthesis, and stomatal conductance. The model is forced with high‐resolution remote sensing data (1 km).The questions we address are as follows: How much water is evaporated across the natural and managed ecosystems of California? How much does evaporation vary during the booms and busts in annual rainfall? and Is evaporation increasing with time due to a warming climate? Mean annual evaporation, averaged over the 2001–2017 period, was relatively steady (393 ± 21 mm/year) given the high interannual variation in precipitation (519 ± 140 mm/year). No significant trend in evaporation at the statewide level was detected over this time period, despite a background of a warming climate. Irrigated agricultural crops and orchards, at 1‐km scale, use less water than inferred estimates for individual fields. This leaves the potential for sharing water, a scarce resource, more equitably among competing stakeholders, for example, farms, fish, people, and ecosystems. Plain Language Summary: Many stakeholders are contending for the limited water budget that is available to California, the world's fifth largest economy. Yet the amount of water used by natural and managed ecosystems across the state is not well known. We produced a new, process‐oriented estimate of statewide water use by natural and managed ecosystems using a biophysical model forced with satellite remote sensing. Despite the booms and busts in rainfall over the 2001 to 2017 period, we find that statewide water use is conservative, compared to the annual variability in rainfall. Nor do we detect that statewide evaporation is increasing as the climate has warmed over this period. We find that crops use less water than conventional wisdom because a subset of fields across a 1‐km pixel are fallow and are at peak leaf area and maximum evaporation potential for a relatively short period. Forests, on the other hand, use more water than conventional wisdom because they have a long growing season and absorb more energy than crops. Our intent is to provide water managers with new information on water use to better share water among the various stakeholders, for example, agricultural, cities, fish, ground water reservoirs, and water quality. Key Points: We produced new granular information (1‐km resolution) on evaporation across California using the biophysical model, Breathing Earth System SimulatorAnnual variations in evaporation were highly damped (393 mm/year +/− 5%) relative to the booms and busts in rainfall between 2001 and 2017 (+/− 28%)Statewide evaporation is not increasing with global warming, over 17 years of inspection, because feedbacks dampen the response to a warming climate [ABSTRACT FROM AUTHOR]

Eddy covariance data are invaluable for determining ecosystem water use strategies under soil water stress. However, existing stress inference methods require numerous subjective data processing and model specification assumptions whose effect on the inferred soil water stress signal is rarely quantified. These uncertainties may confound the stress inference and the generalization of ecosystem water use strategies across multiple sites and studies. In this research, we quantify the sensitivity of soil water stress signals inferred from eddy covariance data to the prevailing data and modeling assumptions (i.e., their robustness) to compile a comprehensive list of sites with robust soil water stress signals and assess the performance of current stress inference methods. To accomplish this, we identify the most prevalent assumptions from the literature and perform a digital factorial experiment to extract probability distributions of plausible soil water stress signals and model performance at 151 FLUXNET2015 and AmeriFlux-FLUXNET sites. We develop a new framework that summarizes these probability distributions to classify and rank the robustness of each site's soil water stress signal, which we display with a user-friendly heat map. We estimate that only 5%–36% of sites exhibit a robust soil water stress signal due to deficient model performance and poorly constrained ecosystem water use parameters. We also find that the lack of robustness is site-specific, which undermines grouping stress signals by broad ecosystem categories or comparing results across studies with differing assumptions. Lastly, existing stress inference methods appear better suited for eddy covariance sites with grass/annual vegetation. Our findings call for more careful and consistent inference of ecosystem water stress from eddy covariance data. [Display omitted] • Most eddy covariance sites do not have a robust soil water stress signal. • Deficient model performance is a key contributor to the lack of robustness. • The robustness of ecosystem soil water stress signals is site-specific. • Grass/annual vegetation sites have more robust stress signals than forested sites. [ABSTRACT FROM AUTHOR]

• Mixing layer heights were assessed from radiosonde, ceilometer, and reanalysis data. • The relative influence of daily surface fluxes was examined for all sky conditions. • Results emphasized the complexity of bi-directional land-atmosphere interactions. The atmospheric mixing layer height (MLH) is a critical variable for understanding and constraining ecosystem and climate dynamics. Past MLH estimation efforts have largely relied on data with low temporal (radiosondes) or spatial (reanalysis) resolutions. This study is unique in that it utilized continuous point-based ceilometer- and radiosonde-derived measurements of MLH at surface flux tower sites to identify the surface influence on MLH dynamics. We found a strong correlation (R2 = 0.73-0.91) between radiosonde MLH and ceilometer MLH at two sites with co-located observations. Seasonally, mean MLH was the highest at all sites during the summer, while the highest annual mean MLH was found at the warm and dry sites, dominated by high sensible heat fluxes. At daily time scales, surface fluxes of sensible heat, latent heat, and vapor pressure deficit had the largest influence on afternoon MLH. However, at best, the identified forcing variables and surface fluxes only accounted for ∼38-65% of the variability in MLH under all sky conditions, and ∼53-76% of the variability under clear skies. These results highlight the difficulty in using single-point observations to explain MLH dynamics but should encourage the use of ceilometers or similar atmospheric measurements at surface flux sites in future studies. [ABSTRACT FROM AUTHOR]

Abstract Background To improve estimates of net primary production for terrestrial ecosystems of the continental United States, we evaluated a new image fusion technique to incorporate high resolution Landsat land cover data into a modified version of the CASA ecosystem model. The proportion of each Landsat land cover type within each 0.004 degree resolution CASA pixel was used to influence the ecosystem model result by a pure-pixel interpolation method. Results Seventeen Ameriflux tower flux records spread across the country were combined to evaluate monthly NPP estimates from the modified CASA model. Monthly measured NPP data values plotted against the revised CASA model outputs resulted in an overall R2 of 0.72, mainly due to cropland locations where irrigation and crop rotation were not accounted for by the CASA model. When managed and disturbed locations are removed from the validation, the R2 increases to 0.82. Conclusions The revised CASA model with pure-pixel interpolated vegetation index performed well at tower sites where vegetation was not manipulated or managed and had not been recently disturbed. Tower locations that showed relatively low correlations with CASA-estimated NPP were regularly disturbed by either human or natural forces.

Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by adapting a light use efficiency (LUE, ε) model to include in situ meteorological data and biophysical parameters. The LUEc uses four scalars to quantify the impacts of temperature, water stress, and phenology on ε. This study continues the original investigation in using the LUEc, originally limited to three AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3) by applying gridded meteorological data sets and remotely sensed green leaf area index (gLAI) to estimate daily GPP over a larger spatial extent. This was achieved by including data from four additional AmeriFlux locations in the U.S. Corn Belt for a total of seven locations. Results show an increase in error (RMSE = 3.5 g C m−2 d−1) over the original study in which in situ data were used (RMSE = 2.6 g C m−2 d−1). This is attributed to poor representation of gridded weather inputs (vapor pressure and incoming solar radiation) and application of gLAI algorithms to sites in Iowa, Minnesota, and Illinois, calibrated using data from Nebraska sites only, as well as uncertainty due to climatic variation. Despite these constraints, the study showed good correlation between measured and LUEc-modeled GPP (R2 = 0.80 and RMSE of 3.5 g C m−2 d−1). The decrease in model accuracy is somewhat offset by the ability to function with gridded weather datasets and remotely sensed biophysical data. The level of acceptable error is dependent upon the scope and objectives of the research at hand; nevertheless, the approach holds promise in developing regional daily estimates of GPP.