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Wildfire modifies the short- and long-term exchange of carbon between terrestrial ecosystems and the atmosphere, with impacts on ecosystem services such as carbon uptake. Dry western US forests historically experienced low-intensity, frequent fires, with patches across the landscape occupying different points in the fire-recovery trajectory. Contemporary perturbations, such as recent severe fires in California, could shift the historic stand-age distribution and impact the legacy of carbon uptake on the landscape. Here, we combine flux measurements of gross primary production (GPP) and chronosequence analysis using satellite remote sensing to investigate how the last century of fires in California impacted the dynamics of ecosystem carbon uptake on the fire-affected landscape. A GPP recovery trajectory curve of more than five thousand fires in forest ecosystems since 1919 indicated that fire reduced GPP by 157.4 ± 7.3 g C m − 2 y − 1 ( mean ± SE, n = 1926 ) in the first year after fire, with average recovery to prefire conditions after ∼ 12 y. The largest fires in forested ecosystems reduced GPP by 393.8 ± 15.7 g C m − 2 y − 1 ( n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly 9.9 ± 3.5 MMT CO 2 (3-y rolling mean) in cumulative forgone carbon uptake due to the legacy of fires on the landscape, complicating the challenge of maintaining California’s natural and working lands as a net carbon sink. Understanding these changes is paramount to weighing the costs and benefits associated with fuels management and ecosystem management for climate change mitigation.

Since the 1930s, California’s Sierra Nevada has warmed by an average of 1.2∘C. Warming directly primes forests for easier wildfire ignition, but the change in climate also affects vegetation species composition. Different types of vegetation support unique fire regimes with distinct probabilities of catastrophic wildfire, and anticipating vegetation transitions is an important but undervalued component of long-term wildfire management and adaptation. Vegetation transitions are more likely where the climate has become unsuitable but the species composition remains static. This vegetation climate mismatch (VCM) can result in vegetation conversions, particularly after a disturbance like wildfire. Here we produce estimates of VCM within conifer-dominated forests in the Sierra Nevada. Observations from the 1930s Wieslander Survey provide a foundation for characterizing the historical relationship between Sierra Nevada vegetation and climate before the onset of recent, rapid climate change. Based on comparing the historical climatic niche to the modern distribution of conifers and climate, ∼19.5% of modern Sierra Nevada coniferous forests are experiencing VCM, 95% of which is below an elevation of 2356 m. We found that these VCM estimates carry empirical consequences: likelihood of type-conversion increased by 9.2% for every 10% decrease in habitat suitability. Maps of Sierra Nevada VCM can help guide long-term land management decisions by distinguishing areas likely to transition from those expected to remain stable in the near future. This can help direct limited resources to their most effective uses—whether it be protecting land or managing vegetation transitions—in the effort to maintain biodiversity, ecosystem services, and public health in the Sierra Nevada.

Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation (E) and transpiration (T) would greatly enhance our understanding of the water cycle and its response to climate change. While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E. Here, we report a novel ET partitioning method using Artificial Neural Networks (ANN) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento-San Joaquin Delta, California, USA, as well as leaf-level T data for validation. The four wetlands vary in structure from some with large areas of open water and little vegetation to very densely vegetated wetlands, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R2across all sites. The order of variable inclusion (and importance) was: vapor pressure deficit (VPD) > gap-filled sensible heat flux (H_gf) > air temperature (Tair) > friction velocity (u∗) > other variables. Overall, 36 ANNs were analyzed. The model using VPD, H_gf, Tair, and u∗(F11), showed an average testing R2value across all sites of 0.853. In comparison with the model that included all 10 variables (F36), F11 generally performed better during validation with independent data. In comparison to other methods described in the literature, the ANN method generated more consistent T/ET partitioning results especially for more complex sites with large E contributions. Our method improves the understanding of T/ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T/ET partitioning in other systems, increasing our knowledge of the global water cycle.

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide (CO2 ) and mitigating climate change has received increasing attention in recent years. While recent studies have shown that methane (CH4 ) emissions can potentially offset the carbon burial rates in low-salinity coastal wetlands, there is hitherto a paucity of direct and year-round measurements of ecosystem-scale CH4 flux (FCH4 ) from mangrove ecosystems. In this study, we examined the temporal variations and biophysical drivers of ecosystem-scale FCH4 in a subtropical estuarine mangrove wetland based on 3 years of eddy covariance measurements. Our results showed that daily mangrove FCH4 reached a peak of over 0.1 g CH4 -C m-2 day-1 during the summertime owing to a combination of high temperature and low salinity, while the wintertime FCH4 was negligible. In this mangrove, the mean annual CH4 emission was 11.7 ± 0.4 g CH4 -C m-2 year-1 while the annual net ecosystem CO2 exchange ranged between -891 and -690 g CO2 -C m-2 year-1 , indicating a net cooling effect on climate over decadal to centurial timescales. Meanwhile, we showed that mangrove FCH4 could offset the negative radiative forcing caused by CO2 uptake by 52% and 24% over a time horizon of 20 and 100 years, respectively, based on the corresponding sustained-flux global warming potentials. Moreover, we found that 87% and 69% of the total variance of daily FCH4 could be explained by the random forest machine learning algorithm and traditional linear regression model, respectively, with soil temperature and salinity being the most dominant controls. This study was the first of its kind to characterize ecosystem-scale FCH4 in a mangrove wetland with long-term eddy covariance measurements. Our findings implied that future environmental changes such as climate warming and increasing river discharge might increase CH4 emissions and hence reduce the net radiative cooling effect of estuarine mangrove forests.

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET). ISSN:0168-1923 ISSN:1873-2240

The concentration of nitrous oxide (N

Salinity gradients across estuaries influence wetland carbon storage, methane (CH4) biogeochemistry, and plant productivity. Estuarine freshwater wetlands may experience increases in salinity during drought; however, the impact of salinization on greenhouse gas (GHG) emissions is uncertain. We measured ecosystem-scale GHG emissions from a wetland experiencing salinization during the 2011–2017 California drought and used information theory analyses to quantify couplings and causal interactions between CH4 fluxes and two dominant environmental drivers; gross ecosystem photosynthesis and temperature. Machine learning models were then used to estimate salinization-induced changes in CH4 fluxes and plant productivity. We observed dynamic CH4 flux-driver relationships across the salinization disturbance event, where temperature connections strengthened, and productivity connections dampened during salinization. Annual gross ecosystem productivity reduced by 64% during peak salinization, whereas annual CH4 emissions only reduced by 10%, suggesting that other CH4 substrate sources compensated for reductions in recent photosynthate. Our results demonstrate the value of applying information theory and machine learning approaches to ecological analyses and suggest that drought-induced salinization may increase GHG emissions from estuarine freshwater wetlands.

Eddy covariance measurement systems provide direct observation of the exchange of greenhouse gases between ecosystems and the atmosphere, but have only occasionally been intentionally applied to quantify the carbon dynamics associated with specific climate mitigation strategies. Natural climate solutions (NCS) harness the photosynthetic power of ecosystems to avoid emissions and remove atmospheric carbon dioxide (CO2), sequestering it in biological carbon pools. In this perspective, we aim to determine which kinds of NCS strategies are most suitable for ecosystem-scale flux measurements and how these measurements should be deployed for diverse NCS scales and goals. We find that ecosystem-scale flux measurements bring unique value when assessing NCS strategies characterized by inaccessible and hard-to-observe carbon pool changes, important non-CO2 greenhouse gas fluxes, the potential for biophysical impacts, or dynamic successional changes. We propose three deployment types for ecosystem-scale flux measurements at various NCS scales to constrain wide uncertainties and chart a workable path forward: "pilot", "upscale", and "monitor". Together, the integration of ecosystem-scale flux measurements by the NCS community and the prioritization of NCS measurements by the flux community, have the potential to improve accounting in ways that capture the net impacts, unintended feedbacks, and on-the-ground specifics of a wide range of emerging NCS strategies.

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions, including their seasonality, due to quasi-continuous and high temporal resolution of flux measurements, coincident measurements of carbon, water, and energy fluxes, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we 1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4- community-product/). FLUXNET-CH4 includes half-hourly and daily gap-filled and non gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we 2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally, because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands and because freshwater wetlands are a substantial source of total atmospheric CH4 emissions; and 3) provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions, but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20° S to 20° N) the spring onset of elevated CH4 emissions starts three days earlier, and the CH4 emission season lasts 4 days longer, for each degree C increase in mean annual air temperature. On average, the onset of increasing CH4 emissions lags soil warming by one month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling, and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). The FLUXNET-CH4 dataset provides an open-access resource for CH4 flux synthesis, has a range of applications, and is unique in that it includes coupled measurements of important CH4 drivers such as GPP and temperature. Although FLUXNET-CH4 could certainly be improved by adding more sites in tropical ecosystems and by increasing the number of site-years at existing sites, it is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4408468. Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/, and a complete list of the 79 individual site data DOIs is provided in Table 2 in the Data Availability section of this document., Earth System Science Data Discussions, ISSN:1866-3591

Inundated wetlands can potentially sequester substantial amounts of soil carbon (C) over the long-term because of slow decomposition and high primary productivity, particularly in climates with long growing seasons. Restoring such wetlands may provide one of several effective negative emission technologies to remove atmospheric CO2 and mitigate climate change. However, there remains considerable uncertainty whether these heterogeneous ecotones are consistent net C sinks and to what degree restoration and management methods affect C sequestration. Since wetland C dynamics are largely driven by climate, it is difficult to draw comparisons across regions. With many restored wetlands having different functional outcomes, we need to better understand the importance of site-specific conditions and how they change over time. We report on 21 site-years of C fluxes using eddy covariance measurements from five restored fresh to brackish wetlands in a Mediterranean climate. The wetlands ranged from 3 to 23 years after restoration and showed that several factors related to restoration methods and site conditions altered the magnitude of C sequestration by affecting vegetation cover and structure. Vegetation established within two years of re-flooding but followed different trajectories depending on design aspects, such as bathymetry-determined water levels, planting methods, and soil nutrients. A minimum of 55% vegetation cover was needed to become a net C sink, which most wetlands achieved once vegetation was established. Established wetlands had a high C sequestration efficiency (i.e. the ratio of net to gross ecosystem productivity) comparable to upland ecosystems but varied between years undergoing boom-bust growth cycles and C uptake strength was susceptible to disturbance events. We highlight the large C sequestration potential of productive inundated marshes, aided by restoration design and management targeted to maximise vegetation extent and minimise disturbance. These findings have important implications for wetland restoration, policy, and management practitioners.

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.

Restoring degraded peat soils to wetlands can be an attractive and efficient measure with many benefits including carbon sequestration, water quality improvement, food and habitat for wildlife, flood control, and opportunities for recreation. Agricultural lands which are restored to wetlands will start rebuild soils and reverse land subsidence. Using eddy covariance towers in four wetlands that were restored in 1997, 2010, 2013 and 2016 in the Sacramento-San Joaquin Delta in California, we saw high carbon sequestration potentials and peat accumulation. Since soil restoration takes place gradually, it is important to specify the critical turning-points in the process of improving soil microbial community structure and nitrogen cycling. In August 2018, soil samples from four wetlands with different restoration ages in the Delta were collected for chemical and microbial analyses. The bacterial and archaeal 16S rRNA genes and functional genes involved in nitrogen cycling (nirS, nirK, nosZ-I, nosZ-II, bacterial and archaeal amoA, nifH, nrfA, and ANAMMOX-specific genes) in soils were determined using a quantitative PCR method. Soil chemical parameters such as C%, N%, Al, Mn, Fe and two different organic and inorganic P pools were analysed as well. Preliminary results indicate significant dissimilarities in the abundance of soil bacterial and archaeal communities, as well as nirS, nirK, nosZ, nifH, nrfA and archaeal amoA gene-possessing microbial communities in different wetlands. Data analysis showed several statistically significant relationships between target gene parameters and soil chemical parameters that were different when comparing the sites with the restoration age. It is clear, that the complexity of the relationships increases as the wetland gets older. For example, in younger wetlands the availability of C and N plays a crucial role in gene abundances while in the oldest wetland, the most important chemical parameters were different phosphorus forms. This might indicate that more than 20 years of C and N accumulation has led to the availability of phosphorus for N transformation now to be the main limiting factor. Another important finding was that the design criteria can also determine how the wetland acts in terms of nitrogen gas emissions. For example, one of the wetlands was designed with more varied bathymetry that includes many open channels and a fluctuating water table. We saw that the nifH gene-possessing microbes that are responsible for molecular N fixing are highly abundant in open water areas while at the same time this wetland has also the highest abundance of nir genes that control N2O production by denitrifiers. Our study demonstrates that the design of the wetland can have a significant impact on N-transforming processes, but most importantly at some age, restored wetlands become more similar to natural wetlands.

Water is a limited and valuable resource in California. A large proportion of the fresh water for southern California is supplied by the Sacramento and San Joaquin rivers. With recent efforts to restore large areas of land in the Sacramento–San Joaquin Delta region from farmland to managed wetlands, it is important to investigate the effect of wetland restoration on the local water cycle. We measured evapotranspiration using the eddy covariance method over six different sites in the Sacramento–San Joaquin Delta from 2013 to 2016. The three restored wetlands sites differed in time since restoration and wetland structure, i.e. the area of open water compared to closed vegetation. The three agricultural sites were a flooded rice field, an alfalfa field, and a grazed cattle pasture. In most years, annual evapotranspiration was significantly lower at the drained agricultural sites, with 652 ± 131 mm (2 year average ± standard deviation, SD) for the pasture and 901 ± 24 mm (3 year average ± SD) for the alfalfa field, compared to the two open water wetlands, with 1091 ± 144 mm (4 year average ± SD) for the Mayberry wetland and 1140 ± 67 mm (3 year average ± SD) at the East End wetland. Annual evapotranspiration at the flooded rice site (975 ± 50 mm, 4 year average ± SD) or the closed vegetation wetland (West Pond wetland, 996 ± 63 mm, 4 year average ± SD) was not significantly different from the drained alfalfa site across individual years. Our analysis showed that the structural difference between the wetlands, specifically the fraction of open water compared to closed vegetation, has a large impact on evapotranspiration dynamics. Through analysis of normalized equilibrium evaporation and nighttime evapotranspiration measurements we deduced that evapotranspiration at the wetland with a low fraction of open water surfaces was almost entirely dominated by plant transpiration with very little contribution from evaporation, despite the fact that the site was flooded and water was readily available. Both evaporation and transpiration contributed substantially to evapotranspiration at the two wetlands with larger fraction of open water surfaces. At the closed canopy site evaporation from subcanopy water seemed to be inhibited through two mechanisms: first, the closed canopy prevented heating of the water column and led to significantly cooler water temperatures, which reduced surface vapor pressure. Second, the closed canopy decoupled the water surface from the atmosphere and inhibited turbulent transport of water vapor away from the water surface. Our results provide valuable insights into the water use in California wetlands and can inform decisions on how to maximize water conservation during wetland restoration.

The concentration of nitrous oxide (N2O), an ozone-depleting greenhouse gas, is rapidly increasing in the atmosphere. Most atmospheric N2O originates in terrestrial ecosystems, of which the majority can be attributed to microbial cycling of nitrogen in agricultural soils. Here, we demonstrate how the abundance of nitrogen cycling genes vary across intensively managed agricultural fields and adjacent restored wetlands in the Sacramento-San Joaquin Delta in California, USA. We found that the abundances of nirS and nirK genes were highest at the intensively managed organic-rich cornfield and significantly outnumber any other gene abundances, suggesting very high N2O production potential. The quantity of nitrogen transforming genes, particularly those responsible for denitrification, nitrification and DNRA, were highest in the agricultural sites, whereas nitrogen fixation and ANAMMOX was strongly associated with the wetland sites. Although the abundance of nosZ genes was also high at the agricultural sites, the ratio of nosZ genes to nir genes was significantly higher in wetland sites indicating that these sites could act as a sink of N2O. These findings suggest that wetland restoration could be a promising natural climate solution not only for carbon sequestration but also for reduced N2O emissions.

Corrections accounting for air density fluctuations due to heat and water vapour fluxes must be applied to the measurement of eddy-covariance fluxes when using open-path sensors. Experimental tests and ecosystem observations have demonstrated the important role density corrections play in accurately quantifying carbon dioxide $$(\hbox {CO}_{2})$$ fluxes, but less attention has been paid to evaluating these corrections for methane $$(\hbox {CH}_{4})$$ fluxes. We measured $$\hbox {CH}_{4}$$ fluxes with open-path sensors over a suite of sites with contrasting $$\hbox {CH}_{4}$$ emissions and energy partitioning, including a pavement airfield, two negligible-flux ecosystems (drained alfalfa and pasture), and two high-flux ecosystems (flooded wetland and rice). We found that density corrections successfully re-zeroed fluxes in negligible-flux sites; however, slight overcorrection was observed above pavement. The primary impact of density corrections varied over negligible- and high-flux ecosystems. For negligible-flux sites, corrections led to greater than 100% adjustment in daily budgets, while these adjustments were only 3–10% in high-flux ecosystems. The primary impact to high-flux ecosystems was a change in flux diel patterns, which may affect the evaluation of relationships between biophysical drivers and fluxes if correction bias exists. Additionally, accounting for density effects to high-frequency $$\hbox {CH}_{4}$$ fluctuations led to large differences in observed $$\hbox {CH}_{4}$$ flux cospectra above negligible-flux sites, demonstrating that similar adjustments should be made before interpreting $$\hbox {CH}_{4}$$ cospectra for comparable ecosystems. These results give us confidence in $$\hbox {CH}_{4}$$ fluxes measured by open-path sensors, and demonstrate that density corrections play an important role in adjusting flux budgets and diel patterns across a range of ecosystems.

Seasonal phenological dynamics of vegetation hold important clues on ecosystem performance towards management goals, such as carbon uptake, and thus should be considered in projections of their targeted services. However, in wetlands spatio-temporal heterogeneity due to mixing of open water, soil, green and dead vegetation makes it difficult to generalize ecosystem functioning across different regions. Remote sensing observations can provide spatially-explicit, cost-effective phenology indicators; however, little is known about their capacity to indicate the links between wetland ecosystem structure and function. Here we assessed this potential by comparing one-year Enhanced Vegetation Index (EVI) from satellite products at high (5m; RapidEye) and low (30m; Landsat) spatial resolutions with eddy covariance time series of net carbon exchange, field digital camera (phenocam) greenness and water temperature among three floristically similar restored wetlands in California, USA. Phenological timing differed by wetland site: depending on satellite, the range in site-median start of greening was up to 28 days, end of greening – up to 73 days, start of senescence – up to 79 days, and end of senescence – up to 10 days. Key transition dates from satellite inputs agreed with seasonal changes in net carbon exchange, phenocam greenness and water temperatures, suggesting that phenological contrasts could result in part from site differences in vegetation configuration and litter affecting the exposure of canopy, soil and water to sunlight and thus sub-canopy microclimate and ecosystem functioning. Yet, the agreement between satellite inputs was non-systematic, with the greatest disparities at the more heterogeneous, less vegetated site. Phenological model fitting uncertainty increased with greater spatial resolution, highlighting the tradeoff between the accuracy of representing vegetation and the complexity of local seasonal variation. These findings highlight the sensitivity of satellite-derived phenology to structural and functional heterogeneity of ecosystems and call for more rigorous spatially-explicit analyses to inform assessments of restoration and management outcomes.

© 2018 John Wiley & Sons Ltd Wetlands are the largest source of methane (CH4) globally, yet our understanding of how process-level controls scale to ecosystem fluxes remains limited. It is particularly uncertain how variable soil properties influence ecosystem CH4emissions on annual time scales. We measured ecosystem carbon dioxide (CO2) and CH4fluxes by eddy covariance from two wetlands recently restored on peat and alluvium soils within the Sacramento–San Joaquin Delta of California. Annual CH4fluxes from the alluvium wetland were significantly lower than the peat site for multiple years following restoration, but these differences were not explained by variation in dominant climate drivers or productivity across wetlands. Soil iron (Fe) concentrations were significantly higher in alluvium soils, and alluvium CH4fluxes were decoupled from plant processes compared with the peat site, as expected when Fe reduction inhibits CH4production in the rhizosphere. Soil carbon content and CO2uptake rates did not vary across wetlands and, thus, could also be ruled out as drivers of initial CH4flux differences. Differences in wetland CH4fluxes across soil types were transient; alluvium wetland fluxes were similar to peat wetland fluxes 3 years after restoration. Changing alluvium CH4emissions with time could not be explained by an empirical model based on dominant CH4flux biophysical drivers, suggesting that other factors, not measured by our eddy covariance towers, were responsible for these changes. Recently accreted alluvium soils were less acidic and contained more reduced Fe compared with the pre-restoration parent soils, suggesting that CH4emissions increased as conditions became more favorable to methanogenesis within wetland sediments. This study suggests that alluvium soil properties, likely Fe content, are capable of inhibiting ecosystem-scale wetland CH4flux, but these effects appear to be transient without continued input of alluvium to wetland sediments.

The ability to accurately monitor and anticipate changes in consumptive water use associated with changing land use and land management is critical to developing sustainable water management strategies in water-limited climatic regions. In this paper, we present an application of a remote sensing data fusion technique for developing high spatiotemporal resolution maps of evapotranspiration (ET) at scales that can be associated with changes in land use. The fusion approach combines ET map timeseries developed using an multi-scale energy balance algorithm applied to thermal data from Earth observation platforms with high spatial but low temporal resolution (e.g., Landsat) and with moderate resolution but frequent temporal coverage (e.g., MODIS (Moderate Resolution Imaging Spectroradiometer)). The approach is applied over the Sacramento-San Joaquin Delta region in California—an area critical to both agricultural production and drinking water supply within the state that has recently experienced stresses on water resources due to a multi-year (2012–2017) extreme drought. ET “datacubes” with 30-m resolution and daily timesteps were constructed for the 2015–2016 water years and related to detailed maps of land use developed at the same spatial scale. The ET retrievals are evaluated at flux sites over multiple land covers to establish a metric of accuracy in the annual water use estimates, yielding root-mean-square errors of 1.0, 0.8, and 0.3 mm day−1 at daily, monthly, and yearly timesteps, respectively, for all sites combined. Annual ET averaged over the Delta changed only 3 mm year−1 between water years, from 822 to 819 mm year−1, translating to an area-integrated total change in consumptive water use of seven thousand acre-feet (TAF). Changes were largest in areas with recorded land-use change between water years—most significantly, fallowing of crop land presumably in response to reductions in water availability and allocations due to the drought. Moreover, the time evolution in water use associated with wetland restoration—an effort aimed at reducing subsidence and carbon emissions within the inner Delta—is assessed using a sample wetland chronosequence. Region-specific matrices of consumptive water use associated with land use changes may be an effective tool for policymakers and farmers to understand how land use conversion could impact consumptive use and demand.

The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ET models. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento⁻San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University of Wisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth’s Radiant Energy System (CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m−2·d−1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d−1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts.