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The State of California is moving towards sustainable groundwater management and carbon neutrality goals. We aim to answer questions of importance to sustainable agricultural practices, in particular how to: (1) decrease unnecessary water losses from evapotranspiration (ET), (2) increase carbon sequestration, and (3) enhance overall water use efficiency. While implementing our cooperative extension program in Biometeorology, we deployed eddy covariance systems on flux towers placed on numerous farms to directly measure net water and carbon exchange between different agricultural crops and the atmosphere. Currently, we are simultaneously running up to twenty of these flux towers on any day of the year to address the seasonality of carbon and water cycling with innovative agricultural practices. Some of the most urgent questions posed by local communities, commodity boards, state agencies, NGOs and private corporations are mainly related to water use by different agricultural landscapes, but carbon fluxes are increasingly gaining attention as well. In order to explore these questions, our network of towers spans the entire Central Valley from northern California rice fields (that are increasingly being fallowed for water transfers), to the southern part of the state where transferred water is being used for perennial crops. Although some data and measurements are reported on the ET of California crops, the ever-changing adaptation practices in agricultural management are challenging previously established parameters (e.g. crop coefficients) used in irrigation management. For better spatial resolution, we often use semi-direct ET measurements, derived from residuals of observed surface energy budgets. In addition, alternative low-cost measurements, such as the surface renewal method, are actively being evaluated and we are refining different approaches for their independent use. This talk will mostly focus on ET measurements and the simultaneous quantification of water budgets of entire agricultural fields. We are designing several of our experiments to encompass a full water budget of the targeted fields. This provides a backup estimate of ET as well as an opportunity to couple soil moisture profiles, runoff, irrigation, and precipitation dynamics to eddy covariance flux tower measurements, and eventually help with the ET partitioning. This information is increasingly being sought by modelers and remote sensing scientists to calibrate their ET estimates as we work together on addressing emerging challenges in California agriculture and could potentially be used to understand the physiological response of crops to changes in plant water status and how this response could affect crop quality and productivity. Evaluation of alternative methods and models will include cross-comparisons of existing techniques with more advanced methods under development, including ACASA. We are especially excited to broaden our extension and farm education to small growers and communities that have been historically overlooked by cooperative extension and funding agencies.

Integrated quadrant analysis is a novel technique to identify and to characterize the trajectory and strength of turbulent coherent structures in the atmospheric surface layer. By integrating the three-dimensional velocity field characterized by traditional quadrant analysis with respect to time, the trajectory history of individual coherent structures can be preserved with Eulerian turbulence measurements. We develop a method to identify the ejection phase of coherent structures based on turbulence kinetic energy (TKE). Identifying coherent structures within a time series using TKE performs better than identifying them with the streamwise and vertical velocity components because some coherent structures are dominated by the cross-stream velocity component as they pass the sensor. By combining this identification method with the integrated quadrant analysis, one can animate or plot the trajectory of individual coherent structures from high-frequency velocity measurements. This procedure links a coherent ejection with the subsequent sweep and quiescent period in time to visualize and quantify the strength and the duration of a coherent structure. We develop and verify the method of integrated quadrant analysis with data from two field studies: the Eclipse Boundary Layer Experiment (EBLE) in Corvallis, Oregon in August 2017 (grass field) and the Vertical Cherry Array Experiment (VACE) in Linden, California in November 2019 (cherry orchard). The combined TKE identification method and integrated quadrant analysis are promising additions to conditional sampling techniques and coherent structure characterization because the identify coherent structures and couple the sweep and ejection components in space. In an orchard (VACE), integrated quadrant analysis verifies each coherent structure is dominated by a sweep. Conversely, above the roughness sublayer (EBLE), each coherent structure is dominated by an ejection.

Quantifying the uncertainties caused by resistance parameterizations is fundamental for understanding, improving, and developing terrestrial evapotranspiration (ET) models. Using high-density eddy covariance (EC) tower observations in a heterogeneous oasis in northwest China, this study evaluates the impacts of resistances on the estimation of latent heat flux (LE), the energy equivalent of ET, by comparing resistance parameterizations with different complexities under one- and two-source Penman–Monteith (PM) equations. The results showed that the mean absolute percent error (MAPE) for the LE estimates from the one- and two-source PM equations varied from 32% to 53%, and the uncertainties were caused mainly by the resistance parameterizations. Calibrating the parameters required in the resistance estimations could improve the performance of the PM equations; specifically, the MAPEs for the one-source PM equations were approximately 16%, whereas they were 38% for the two-source PM equations, emphasizing that multiple resistances result in increased uncertainties. The following conclusions were reached: 1) the empirical and biophysical parameters required in resistance estimations were responsible for the uncertainty; 2) increasingly complex resistance parameterizations resulted in greater uncertainties in LE estimates; and 3) models without resistance parameterizations exhibited reduced uncertainties in LE estimates.

Achieving a diverse and inclusive community requires establishing a culture of genuine equality for all members. Our purpose in writing this book is to share our collective knowledge about how to challenge the forces that enable and sustain discrimination in the workplace as informed by our experience developing and implementing the UC Davis ADVANCE program. The program’s goal is to create an inclusive academic community that reflects and serves the diverse population of California. In this introductory chapter, we emphasize the need to move from affirmative action programs, which have played an important role in fostering diversity, to a focus on institutional transformation, which requires not only changes in policy but also shifts in academic culture. Much of what we cover in this book is broadly applicable beyond academia and will interest those wanting to understand and address challenges to diversity, equity, and inclusion in their own organizations. Since workplaces differ in their goals, priorities, and culture, the book is not a “how to” manual but rather a collective effort to share with readers the approaches we took, the information we gathered, what we observed and experienced, and the lessons we learned along the way.

Ideally, higher education systems are meritocracies in which advancement or promotion is based on demonstrated accomplishment and scholarly impact. “Merit” is believed to be associated with innate intellectual ability, dedication to learning and knowledge generation, mastery of a field of study, and recognition by others of comparable training and academic standing. Evaluations of accomplishment are dutifully (and often wishfully) believed to be wholly objective despite an abundance of evidence to the contrary. Unfortunately, implicit bias and other barriers to inclusion are pervasive within meritocracies. For members of marginalized groups, their social identity may diminish how their accomplishments are perceived and valued; conversely, the accomplishments of those with privileged identities may be over-valued. Moreover, what counts as “valuable” is itself not objective or neutral but rather reflects socially-constructed and culturally-specific priorities. Because academic merit and reward systems, as well as local cultures, can intentionally as well as unintentionally reinforce and hence perpetuate bias and barriers to inclusion, one of our UC Davis ADVANCE initiatives centered on review of all policies and practices affecting faculty advancement. We appraised the potential for bias in hiring, promotion, progression, and retention of faculty. We also evaluated the importance of culture in replicating barriers to inclusion.

The impact of climate variables on monthly reference evapotranspiration (ETo) is a critical issue in water resources management and irrigation planning. The spatio-temporal contribution of climate ...

Surface renewal (SR) is a biometeorological technique that uses high-frequency air temperature measurements above a plant canopy to estimate sensible heat flux. The sensible heat flux is then used to estimate latent heat flux as the residual of a surface energy balance equation. SR previously relied on calibration against other methods (e.g., eddy covariance) to obtain accurate measurements of sensible heat flux, and this need for calibration limited the use of SR to research applications. Our group recently showed that compensating for the frequency response characteristics of SR thermocouples causes the calibration factor to converge near the theoretically predicted value of 0.5 (Shapland et al., Agric For Meteorol 189:36–47, 2014). This led to the development of an inexpensive, stand-alone SR system to measure sensible heat flux without the need for calibration, and here we evaluated the SR system in a mature vineyard containing a weighing lysimeter. Vineyard evapotranspiration (ET) measured with SR was strongly and positively correlated with that from the lysimeter, eddy covariance, and a soil water budget approach. ET measured with the various techniques responded similarly to changes in the microclimatic conditions (i.e., day to day variability) and when water was withheld from the entire vineyard for an extended period. A stress index, calculated using reference and actual ET from SR and lysimetry, was correlated to leaf water potential, stomatal conductance, and volumetric soil water content measurements, but some of these relationships were more variable than others. Our results suggest that the new SR method could potentially be used as a low-cost tool to provide growers with field-specific estimates of crop water use and stress for irrigation management in vineyards.

Greenhouse gases (GHGs) are produced during the composting process, but few studies have measured emissions from a full-scale windrow of composting green-waste. This is important for evaluating com...

Recent prolonged droughts in California have emphasized the urgent need to implement more efficient water management practices for high value tree crops. Accurate estimation of evapotranspiration (ET), a main component of consumptive water use, is critical for improving management of micro-irrigated pistachio orchards grown in the San Joaquin Valley of California. We estimated ET of three mature commercial pistachio orchards on non-saline and increasingly saline soils in 2015 and 2016, using the Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) method and Landsat 8 satellite observations. Based on a comparison with field observations at 8 sites, we modified the parameterizations of the momentum roughness length and net radiation for pistachio tree crops and reduced the uncertainty of daily ET estimates. When compared with field data, the recalibrated METRIC ET estimates had an R2 of 0.59, a mean absolute error of 1.1 mm/day, and a RMSE of 1.4 mm/day during Landsat overpass dates (n = 72). The METRIC ET map captured the temporal dynamics and spatial heterogeneity both within and among the orchards. The mean annual crop season estimated ET (mid-March to mid-October in 2016) with remote sensing decreased by 32% from 1064 ± 99 mm in the non-salt affected control orchard to 725 ± 82 mm in the orchard with the highest level of soil-water salinity. The ET reduction was consistent with canopy volume differences among the study orchards, as shown by summer Normalized Difference Vegetation Index (NDVI) from Landsat observations, e.g., 0.72 ± 0.06 in the control vs. 0.52 ± 0.06 in the most saline orchard. The available energy was controlled mostly by canopy features and explained 64% of daily ET variation among all Landsat pixels and satellite overpass days. The normalized differenced water index (NDWI) could be considered as an important parameter to capture the partitioning of available energy for ET (R2 = 0.38), suggesting that the lower soil osmotic potential in saline orchards further reduced crop ET.

The eddy-covariance (EC) method is the most direct micrometeorological approach to measure the surface–atmosphere trace gases under turbulent conditions. The measurement of trace gas fluxes by eddy covariance requires fast-response instruments (higher than 1 Hz) to measure the turbulent fluctuations of vertical wind velocity and trace gas mixing ratio. For many trace gas species, such fast-response analyzers are either not available or do not meet the required precision, long-term stability, and ease of operation under field conditions. Thus, a range of alternative flux measurement techniques have been developed to relax the requirement for fast response time and precision of analysis of the targeted trace gases. These alternative trace gas flux measurement methods are based on surface layer gradients, eddy accumulation, and disjunct eddy-covariance approaches. Of these, the two latter are presented in this chapter. In addition, the surface layer renewal approach, which can be used to understand turbulent exchange processes, and the nocturnal boundary layer Keeling plot approach for determination of the isotopic composition of emitted gases under stable conditions are also described.

In this study, we evaluated the effect of land cover, atmospheric subsidence, and advection on the annual dynamics of atmospheric boundary layer (ABL) height from two contrasting sites. The first site is the Walker Branch forest, a deciduous forest of temperate climate, complex topography, and cloudy summers. The second site is the Sacramento-San Joaquin River Delta, a site of Mediterranean climate, flat terrain on a local scale, and clear summers. After testing a new algorithm to calculate ABL heights from 915 MHz radar wind profilers, we evaluated a hierarchy of three slab models to recreate the diurnal and annual patterns of ABL growth. We found that the lower ABL heights in the Delta, particularly during late summer, are driven by the combined effects of increased atmospheric subsidence and marine air advection. In both sites, the annual pattern of ABL height was strongly correlated to total daily incoming radiation, and in the Delta, the annual pattern of ABL height closely followed the seasonal patterns of sensible heat flux from a mosaic of different land covers. A land composite of latent and sensible heat fluxes obtained through a meso-network of eddy covariance measurements and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission resulted in higher model skill, thus showing that land cover heterogeneity is an important driver of ABL growth. Model simulations show that in the Delta, restoring agricultural land to wetlands with large open water areas could result in a reduction of ABL height during those months with low subsidence and advection.

More frequent, intense, and more extended droughts and heatwaves are challenging agricultural productivity and management. This study introduces a new modeling approach to represent the impact of heat and water stress on energy and mass fluxes from orchards. A photosynthesis model that explicitly accounts for dynamic responses of Rubisco and RuBP limitations to stress was linked to the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). ACASA is a multilayer soil-vegetation-atmosphere numerical model based on higher-order closure of turbulence equations to calculate plant-atmosphere exchanges of carbon dioxide, water, and heat. Field measurements of leaf area index, stomatal conductance, and photosynthesis were performed to estimate model parameters. Model results were compared to data from an eddy covariance flux tower deployed at an almond orchard. Overall, this new approach, ACASA-DynPM, is in closer agreement with observations of H, λE, and Fc compared to ACASA. On days of high temperatures and water stress, ACASA-DynPM outperforms ACASA. Thus, we argue that accounting for plant physiological stress might be especially relevant when estimating energy and mass fluxes over intensively irrigated agricultural regions. Such biases can have implications for the modeled degree of crop stress and water demands, and eventually, it can impact climate regional estimates when coupled with an atmospheric model.

The carbon-nitrogen (CN) biogeochemistry and the prognostic canopy structure scheme used in the Community Land Model version 4.5 (CLM4.5) are integrated into the multiple canopy layer higher order closure Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). The ACASA-CN model inherits the advantages from ACASA and CLM4.5, and it is the first model that represents fully prognostic terrestrial feedbacks with a higher order closure turbulence scheme and vertically resolved canopy structure. The simulations are evaluated in terms of ecological, biogeophysical and biogeochemical aspects at six AmeriFlux eddy covariance sites encompassing a variety of vegetation types and microclimatic conditions across the continental United States. Our model evaluation shows that ACASA-CN reasonably simulates surface layer properties, such as vegetation structure and plant phenology. Our results indicate that the use of a higher order closure turbulence scheme with multiple canopy layer representation is critical to land carbon cycle simulation. Based on our simulations, water vapor exchange between the land surface and the atmosphere is primarily through plant transpiration (78–88%), and canopy evapotranspiration is enhanced with the use of CN biogeochemistry. Our results indicate that the ratio of transpiration to evapotranspiration exhibits a two-stage variation pattern, and the transpiration fraction decreases in dense canopies.

Radiation frost happens frequently in the Yangtze River Delta region, which causes high economic loss in agriculture industry. It occurs because of heat losses from the atmosphere, plant and soil in the form of radiant energy, which is strongly associated with the micrometeorological characteristics. Multidimensional and nonlinear micrometeorological data enhances the difficulty in predicting the radiation frost. Support vector machines (SVMs), a type of algorithms, can be supervised learning which widely be employed for classification or regression problems in research of precision agriculture. This paper is the first attempt of using SVMs to build prediction models for radiation frost. Thirty-two kinds of micrometeorological parameters, such as daily mean temperature at six heights (Tmean0.5, Tmean1.5, Tmean2.0, Tmean3.0, Tmean4.5 and Tmean6.0), daily maximum and minimum temperatures at six heights (Tmax0.5, Tmax1.5, Tmax2.0, Tmax3.0, Tmax4.5 and Tmax6.0, and Tmin0.5, Tmin1.5, Tmin2.0, Tmin3.0, Tmin4.5 and Tmin6.0), daily mean relative humidity at six heights (RH0.5, RH1.5, RH2.0, RH3.0, RH4.5 and RH6.0), net radiation (Rn), downward short-wave radiation (Rsd), downward long-wave radiation (Rld), upward long-wave radiation (Rlu), upward short-wave radiation (Rsu), soil temperature (Tsoil) and soil heat flux (G) and daily average wind speed (u) were collected from November 2016 to July 2018. Six combinations inputs were used as the basis dataset for testing and training. Three types of kernel functions, such as linear kernel, radial basis function kernel and polynomial kernel function were used to develop the SVMs models. Five-fold cross validation was conducted for model fitting on training dataset to alleviate over-fitting and make prediction results more reliable. The results showed that an SVM with the radial basis function kernel (SVM-BRF) model with all the 32 micrometeorological data obtained high prediction accuracy in training and testing sets. When the single type of data (temperature, humidity and radiation data) was used for the SVM without any functions, prediction accuracy was better than that with functions. The SVM-BRF model had the best prediction accuracy when using the multidimensional and nonlinear micrometeorological data. Considering the complexity level of the model and the accuracy of prediction, micrometeorological data at the canopy height with the SVM-BRF model has been recommended for radiation frost prediction in Yangtze River Delta and probably could be applied in elsewhere with the similar terrains and micro-climates.

The canopy structural and functional impacts on land surface modeling of energy and carbon fluxes were investigated by a series of simulations conducted at AmeriFlux eddy covariance sites. Canopy structures were described by different degrees of complexity of Leaf Area Index (LAI) datasets. The monthly climatological LAI datasets applied in the Weather Research and Forecasting (WRF) Model and the Community Earth System Model (CESM) were used to represent static ecological conditions. The LAI remotely sensed by the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to represent time-varying ecological conditions with natural variability. To investigate the sensitivity of different canopy profile representations, all of these LAI datasets were used to assign the necessary ecological information for single and multiple canopy layer land surface models to simulate a seven-year period across a variety of vegetation covers. The results show that a more realistic canopy profile representation (i.e., multiple layers), both in terms of structural and functional treatments, improves biogeophysical and biogeochemical simulations. The root mean square errors for the simulated evapotranspiration and Net Ecosystem Exchange are reduced by 10% and 15%, respectively when the ecological information is represented by a more realistic time-varying LAI dataset instead of a static LAI dataset with no geographical sensitivity. A land surface model with multiple canopy layers and a realistic ecological dataset, which can better represent ecosystem structural and functional responses to microclimate conditions, is thus recommended for long-term climate projections.

We thank the principal investigators of the AmeriFlux network, and the U.S. Department of Energy’s Office of Science for funding the AmeriFlux data resources. We thank the U.S. Department of Energy Lawrence Berkeley Lab Ameriflux Network Management Project for core site support. This research was supported through the National Science Foundation award EF1137306/MIT subaward 5710003122 to the University of California Davis; and other government, industry and foundation sponsors of the MIT Joint Program on the Science and Policy of Global Change. For a complete list of sponsors and U.S. government funding sources, please visit http://globalchange.mit.edu/sponsors/all.

Quantifying the uncertainties induced by resistance parameterization is fundamental to understanding, improving, and developing terrestrial evapotranspiration (ET) models. Using high-density eddy covariance (EC) tower observations in a heterogeneous oasis in Northwest China, this study evaluates the impact of resistances on latent heat flux (LE) estimations, the energy equivalent of ET, by comparing resistance parameterizations with varied complexity under one- and two-source Penman-Monteith (PM) equations. We then discuss possible solutions for reducing such uncertainties by employing a three-temperature (3T) model, which does not explicitly include resistance-related parameters. The results show that the mean absolute percent error (MAPE) varied from 32 % to 39 % for the LE estimates from the one- and two-source PM equations. When only surface resistance (rs) was parameterized under the one-source network, then the uncertainty (defined as the difference between MAPEs) dropped to 12 %. When both rs and aerodynamic resistance (ra) were parameterized differently under the one- and two-source networks, then the uncertainties in the estimates were 11~23 %, emphasizing that multiple resistances add uncertainties. Additionally, the 3T model performed better than the PM equations, with MAPE of 19 %. The results suggest that 1) although prior calibration of the parameters required in resistance estimations can improve the PM-based LE estimates, resistance parameterization process can generate obvious uncertainties, 2) more complex resistance parameterizations leads to more uncertainty in the LE estimation, and 3) the relatively simple 3T model avoids resistance parameterization, thus introducing less uncertainty in the LE estimation.

No-tillage with limited supplementary irrigation is promising for improving crop growth and ensuring sustainable crop yield in semi-arid and semi-humid areas defined by their water scarcity and uneven seasonal precipitation distribution. Choosing effective irrigation methods and scheduling to improve irrigation water use efficiency (IWUE) and reduce the risk of soil nitrate leaching has become increasingly necessary for crop management. Field experiments were conducted on no-tillage summer maize over four consecutive years from 2014 to 2017 in a semi-humid and drought-prone region of Northwest China. Four irrigation strategies were used, i.e. rain-fed (RF, no irrigation), conventional flood irrigation (CK), full drip irrigation (FI) and deficit drip irrigation (DI, 75% FI). The results indicated that supplemental irrigation mitigated the drought stress resulting in improving maize growth and a stable, sustainable yield. Plant height, leaf area index and aboveground biomass of the DI treatment were lower than those of the CK and FI treatments, while the DI treatment produced insignificantly lower grain yield and increased grain nitrogen content. The DI treatment promoted the transfer of nutrients from vegetative organs to reproductive organs. The IWUE of the DI treatment was increased by 22.1%–27.3% and 28.4%–48.6% compared with the CK and FI treatments during the four growing seasons, respectively. Soil NO3−-N of the CK treatment was mainly distributed below 40 cm soil depth, while most soil NO3−-N remained above 40 cm in the FI and DI treatments, implying lower levels of nitrate leaching compared with the conventional flood irrigation. In conclusion, deficit drip supplementary irrigation is a sustainable agricultural production strategy that improves water use efficiency, maintains stable yield and alleviates the risk of soil nitrate pollution in the no-tillage agrisystem of arid and semi-arid regions.

Plant physiological responses to stress and subsequent damage have not been successfully integrated into leaf, plant or land surface models. Water deficit leads to a series of plant responses: stomatal closure decreases evaporative cooling leading to higher leaf temperature, which it can affect photosynthetic dissipation of absorbed energy, especially under high irradiance. Excess absorbed energy by PSII results in photoinhibition, which is not typically included when modeling photosynthesis using the Farquhar-von Caemmerer-Berry (FvCB) model. We introduce a novel approach to represent plant-atmosphere interactions: a non-steady state model of photoinhibition linked to the RuBP-regeneration component of the FvCB model that accounts for the interactions between high temperature, irradiance, and water deficits. Gas exchange and chlorophyll fluorescence dynamics showed that, under severe water stress, the maximum quantum efficiency of PSII (Fv/Fm) quickly decreases, and it takes days to recover. We found a clear relationship between maximum electron transport rate and Fv/Fm and that short-term acclimation to irradiance affected photosynthetic FvCB model parameters. Fv/Fm functioned well as a coupling variable, able to scale RuBP-regeneration limited photosynthesis parameters proportional to photoinhibition. These results indicate that plant stress, photo-inhibitory damage, and recovery can be explicitly represented when modeling photosynthesis.

The ACASA (Advanced Canopy-Atmosphere-Soil Algorithm) model, with a higher order closure for tall vegetation, has already been successfully tested and validated for spruce forests. The aim of this paper is the further application for a clearing with a heterogeneous structure of the underlying surface. The comparison with flux data shows a good agreement with a footprint aggregated tile approach of the model. However, the results of a comparison with a tile approach on the basis of the mean land use classification of the clearing is not significantly different. It is assumed that the footprint model is not accurate enough to separate small scale heterogeneities. All measured fluxes are corrected for energy balance closure with a Bowen ratio and a buoyancy flux correction method. The comparison with the model – where the energy balance is closed – shows that the buoyancy correction for Bowen ratios > 1.5 better fits the measured data. For lower Bowen ratios, the correction probably lies between both methods, but the amount of available data was too small to make a conclusion. With an assumption of a similarity between water and carbon dioxide fluxes, no correction of the net ecosystem exchange is necessary for Bowen ratios > 1.5.

Within micrometeorology the term modeling is not uniquely defined. It refers to various methods covering a range of complexity extending from simple regressions up to complicated numerical models . In applied meteorology (agro meteorology and hydro meteorology) simple analytical models are very common. Modeling of evaporation is particularly important but sophisticated numerical methods are not yet widely used in this research area. The following chapter describes different types of models and their limitations beginning with simple analytical methods up to numerical models of near-surface energy and matter transport. The application of models in heterogeneous terrain receives special attention and related flux averaging approaches are addressed in a separate subchapter.

The process of composting is a source of greenhouse gases (GHG) that contribute to climate change. We monitored three field-scale green waste compost windrows over a one-year period to measure the seasonal variance of the GHG fluxes. The compost pile that experienced the wettest and coolest weather had the highest average CH4 emission of 254 ± 76 g C day−1 dry weight (DW) Mg−1 and lowest average N2O emission of 152 ± 21 mg N day−1 DW Mg−1compared to the other seasonal piles. The highest N2O emissions (342 ± 41 mg N day−1 DW Mg−1) came from the pile that underwent the driest and hottest weather. The compost windrow oxygen (O2) concentration and moisture content were the most consistent factors predicting N2O and CH4 emissions from all seasonal compost piles. Compared to N2O, CH4 was a higher contributor to the overall global warming potential (GWP) expressed as CO2 equivalents (CO2 eq.). Therefore, CH4 mitigation practices, such as increasing O2 concentration in the compost windrows through moisture control, feedstock changes to increase porosity, and windrow turning, may reduce the overall GWP of composting. Based on the results of the present study, statewide total GHG emissions of green waste composting were estimated at 789,000 Mg of CO2 eq., representing 2.1% of total annual GHG emissions of the California agricultural sector and 0.18% of the total state emissions.

International audience; Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (epsilon(msh)) was 2.63 to 4.59 times that of sunlit leaves (epsilon(msu)). Generally, the relationships of epsilon(msh) and epsilon(msu) with epsilon(max) were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR.

The measurement of carbon exchange between vegetation and the atmosphere is vital to quantify the impact of environmental variables on the carbon sequestration capacity of forests, and to predict how they will respond to future climate. In this study we use proximal remote sensing, defined as observations made from non-contact radiometric or imaging sensors in close proximity to the forest canopy (10?20?m), as an intermediate upscaling tool between direct measurements of carbon fluxes and satellite-derived estimations of primary productivity in a tropical dry forest (TDF) in Jalisco, Mexico. Two broad-band vegetation indices (VIs), the normalized difference VI and the enhanced vegetation index 2 (EVI2), were calculated from proximally sensed canopy properties, validated with field estimates of the fraction of absorbed photosynthetically active radiation by photosynthetic tissue (f APARgreen), and compared to estimates of gross primary productivity (GPP) and net ecosystem exchange of CO2, measured from a flux tower. The VIs captured the phenology of the TDF, both under typical summer rainfall and during an atypically-dry wet season in El Ni?o of 2009. The VIs also tracked a secondary leaf-flushing in the dry season of 2010. Our study suggests that (1) VIs are the best predictors of gross carbon uptake, able to explain up to 86% of variations in GPP; (2) VIs are accurate predictors of the photosynthetic capacity of green tissue, able to explain up to 99% of f APARgreen variation; and (3) VIs and soil water content can be used to develop an empirical model that captures the seasonal trajectory of GPP from high respiration after the rain pulses, to rapid leaf development, and finally to slow senescence as the soil dries out. Proximal remote sensing constitutes a useful tool to link field-base measurements of carbon fluxes to satellite- or airborne-derived estimates of carbon exchange.

The plant canopy-atmosphere exchanges of heat, water vapor, carbon dioxide, and other trace materials are critical topics in plant biometeorology. Evapotranspiration and the ecosystem-atmospheric carbon balance are two ubiquitous examples of plant canopyatmosphere exchange. Therefore, the development and refinement of practical and inexpensive measurement and estimation techniques of such canopy-atmosphere exchanges is very important.