Wetland Restoration as a Climate Solution: Assessing the carbon, greenhouse gas, and biophysical impacts of restoring degraded agricultural peatlands to freshwater deltaic wetlands

The biosphere removes nearly a quarter of anthropogenic greenhouse gas emissions each year through biogeochemical processes. These fluxes, along with biophysical exchanges of energy and water, play an important role in local to global climate dynamics and human well-being. With an urgent need to decrease the amount of greenhouse gases in the atmosphere to avoid runaway climate change, much recent work has focused on identifying and quantifying the role that terrestrial ecosystems could play in mitigating climate change. Restoration of coastal and deltaic wetlands, with their often carbon-rich organic soils and high productivity, presents an attractive but largely untested land-based climate mitigation strategy. The benefits associated with wetland restoration stem from two key areas. First, drained agricultural peat soils can be large greenhouse gas sources. Second, the slow decomposition rates of inundated wetland soil organic matter along with high productivity leads to soil carbon accumulation and protection. While these tenets are generally widely appreciated, they have rarely been tested and measured at the ecosystem scale, over multiple years. In this dissertation work, I studied the coupled biogeochemical and biophysical impacts of a long-term wetland restoration ecological experiment in the Sacramento-San Joaquin River Delta in California, USA. By continuously measuring greenhouse gas and energy fluxes at the ecosystem scale over a variety of land cover types, including four restored wetlands of various ages and structures, I was able to characterize the carbon, greenhouse gas, and biophysical impacts of degraded peat soil restoration to freshwater deltaic wetlands. I find that these restored freshwater deltaic wetlands are highly productive, sequestering carbon in the soil as productivity outpaces ecosystem respiration. This productivity comes at the cost of substantial methane emissions, however, making these wetlands greenhouse gas neutral to sources over a century. Despite this fact, transitions from high-emission degraded peat soil agricultural land uses to restored deltaic wetlands often reduce greenhouse gas emissions overall. Furthermore, I analyze how the biophysical impacts of this restoration activity – the changes to the way the ecosystems exchange heat and water – affect the surface temperature and boundary layer. I find that along with potential biogeochemical benefits, restored deltaic wetlands have an evaporative cooling effect due to rougher, wetter canopies. These findings shed light on the viability of freshwater wetland restoration as a land-based climate mitigation solution. Restored wetlands should be part of a climate solution, but aren’t a ‘quick fix’. Ecosystem restoration is a dynamic process, with interannual variability, succession, and disturbance influencing the long-term performance of these ecosystems. Despite the incurred methane emissions in our restored wetlands, the flooded conditions effectively inhibit heterotrophic soil respiration and thus sequester carbon and create soil, refilling the deeply subsided ‘islands’ that have formed over the past century and a half. Other positive co-benefits, like local to regional biophysical cooling, along with habitat enhancement, must be considered to understand the full potential of restored wetlands as a part of the climate solution

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Wetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments. Wetland methane emissions contribute to global warming, and are oversimplified in climate models. Here the authors use eddy covariance measurements from 48 global sites to demonstrate seasonal hysteresis in methane-temperature relationships and suggest the importance of microbial processes.Peer reviewe

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

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 halfhourly 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).Peer reviewe

Wetland Restoration as a Climate Solution: Assessing the carbon, greenhouse gas, and biophysical impacts of restoring degraded agricultural peatlands to freshwater deltaic wetlands

The biosphere removes nearly a quarter of anthropogenic greenhouse gas emissions each year through biogeochemical processes. These fluxes, along with biophysical exchanges of energy and water, play an important role in local to global climate dynamics and human well-being. With an urgent need to decrease the amount of greenhouse gases in the atmosphere to avoid runaway climate change, much recent work has focused on identifying and quantifying the role that terrestrial ecosystems could play in mitigating climate change. Restoration of coastal and deltaic wetlands, with their often carbon-rich organic soils and high productivity, presents an attractive but largely untested land-based climate mitigation strategy. The benefits associated with wetland restoration stem from two key areas. First, drained agricultural peat soils can be large greenhouse gas sources. Second, the slow decomposition rates of inundated wetland soil organic matter along with high productivity leads to soil carbon accumulation and protection. While these tenets are generally widely appreciated, they have rarely been tested and measured at the ecosystem scale, over multiple years. In this dissertation work, I studied the coupled biogeochemical and biophysical impacts of a long-term wetland restoration ecological experiment in the Sacramento-San Joaquin River Delta in California, USA. By continuously measuring greenhouse gas and energy fluxes at the ecosystem scale over a variety of land cover types, including four restored wetlands of various ages and structures, I was able to characterize the carbon, greenhouse gas, and biophysical impacts of degraded peat soil restoration to freshwater deltaic wetlands. I find that these restored freshwater deltaic wetlands are highly productive, sequestering carbon in the soil as productivity outpaces ecosystem respiration. This productivity comes at the cost of substantial methane emissions, however, making these wetlands greenhouse gas neutral to sources over a century. Despite this fact, transitions from high-emission degraded peat soil agricultural land uses to restored deltaic wetlands often reduce greenhouse gas emissions overall. Furthermore, I analyze how the biophysical impacts of this restoration activity – the changes to the way the ecosystems exchange heat and water – affect the surface temperature and boundary layer. I find that along with potential biogeochemical benefits, restored deltaic wetlands have an evaporative cooling effect due to rougher, wetter canopies. These findings shed light on the viability of freshwater wetland restoration as a land-based climate mitigation solution. Restored wetlands should be part of a climate solution, but aren’t a ‘quick fix’. Ecosystem restoration is a dynamic process, with interannual variability, succession, and disturbance influencing the long-term performance of these ecosystems. Despite the incurred methane emissions in our restored wetlands, the flooded conditions effectively inhibit heterotrophic soil respiration and thus sequester carbon and create soil, refilling the deeply subsided ‘islands’ that have formed over the past century and a half. Other positive co-benefits, like local to regional biophysical cooling, along with habitat enhancement, must be considered to understand the full potential of restored wetlands as a part of the climate solution

An Ecosystem-Scale Flux Measurement Strategy to Assess Natural Climate Solutions.

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

Climate‐Driven Limits to Future Carbon Storage in California's Wildland Ecosystems

Enhanced ecosystem carbon storage is a key component of many climate mitigation pathways. The State of California has set an ambitious goal of carbon neutrality by 2045, relying in part on enhanced carbon sequestration in natural and working lands. We used statistical modeling, including random forests and climate analogues, to explore the climate-driven challenges and uncertainties associated with the goal of long-term carbon sequestration in forests and shrublands. We found that seasonal patterns of temperature and precipitation are strong controllers of the spatial distribution of aboveground live carbon. RCP8.5 projections of temperature and precipitation were estimated to drive decreases of 16.1 ± 7.5% in aboveground live carbon by the end of the century, with coastal areas of central and northern California and low/mid-elevation mountain areas being most vulnerable. With RCP4.5 projections, declines were less severe, with 8.8 ± 5.3% carbon loss. In either scenario, the increased temperature systematically caused biomass declines, and the spread of projected precipitation across 32 CMIP5 models introduced substantial uncertainty in the magnitude of that decline. Projected changes in the environmental niche for the 20 most biomass-dominant tree species revealed widespread replacement of conifers by oak species in low elevations of central and northern California, with corresponding decline in carbon storage depending on expected migration rates. The spatial patterns of vulnerability we identify may allow policymakers to assess where carbon sequestration in aboveground biomass is an appropriate part of a climate mitigation portfolio, and where future climate-driven carbon losses may be a liability.Data and code to accompany "Climate-driven limits to future carbon storage in California's wildland ecosystems" AGU Advances, 2021 Corresponding author: Shane Coffield [email protected] This archive contains input data, model output, Python scripts, and Google Earth Engine (GEE) scripts. Python and GEE scripts can also be accessed directly via https://github.com/scoffiel/carbon_projections https://code.earthengine.google.com/?accept_repo=users/scoffiel/carbon_projections git clone https://earthengine.googlesource.com/users/scoffiel/carbon_projections Data overview: input_data: contains all processed data needed to run models in Python. All were derived from public sources. Processed raster layers are 1/8-degree resolution in EPSG:4326 projection. Climate – Bias-Crrected Spatial Downscaled (BCSD) CMIP5 data for 2006-2099 which has been compiled into 6 different netcdf files in Python scripts #1-2. For RCP4.5 and RCP8.5 scenarios, we generated a "climate_present" file for 2006-2099 average and "climate_future" file for 2090-2099 average with dimensions for 32 mdels (in order of most drying to wetting), 8 variables (4 seasons of T & P), and lat/lon. An additional "climate_present_10yrs" file maintains all 10 years f data needed for calculating interannual variability in the climate analogs approach (script #9). These climate data are the driver variables for all models. Units: °C for mean daily temperature and mm/day for precipitation Web source: https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html Citation: Brekke et al., 2013; Maurer et al., 2007 carbn_eighth.tif – abveground live wildland carbon for California for 2014. Rescaled from raw 30m data obtained from the California Air Resources Board (30m data available upon request from CARB). This is generated by GEE script #1 and is the target dataset for training RF regression models of carbon density in Python script #5. Units: ton C/ha Web source: https://ww2.arb.ca.gov/nwl-inventory Citation: CARB 2018, Gonzalez et al., 2015 landcver_eighth.tif – dminant land cover class generated from 30m National Land Cover Database (NLCD) for 2016 in GEE script #3. This is the target dataset for training RF classification models of vegetation type in Python script #8 Units: 1 for shrub/grass, 2 for forest Web source: https://www.mrlc.gov/data/nlcd-2016-land-cover-conus Citation: Homer et al., 2020 valid_fractin.tif – fractin of each 1/8-degree pixel which is comprised of herbaceous, shrub, or forest landcover, also derived from the NLCD dataset. Generated in GEE script #2. landcver_mask_eighth.tif – Mask layer with "1" fr all areas of the Western US that are at least 50% wildland cover, also derived from the NLCD dataset. Generated in GEE script #5 elev_eighth.tif – Derived frm 30m USGS elevation data in GEE script #3 Units: m Web source: https://lpdaac.usgs.gov/products/srtmgl1v003/ Citation: NASA JPL 2013 ffsets.zip – shapefiles f 32 forest carbon offset project polygons in California, collected from https://webmaps.arb.ca.gv/ARBOCIssuanceMap/ . Used in Pythn script #7 to assess vulnerability of these areas. ecregion_carbon_densities.tiff – frest carbon density averaged by EPA Level III ecoregions using CARB AGL carbon layer; generated in GEE script #4 and used in Python script #8 to estimate carbon change associated with vegetation type conversions. Units: ton C/ha cci_eighth.tif – abveground live carbon density for the western US and Mexico for 2017, derived from the ESA Climate Change Initiative global biomass dataset. Generated in GEE script #6 and used for climate analogs approach in Python script #9. Units: ton biomass/ha (converted to carbon in Python) Web source: https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084 Citation: Santoro & Cartus, 2019 lemma_39spp_eighth.tif – abveground live carbon density for 39 species in California, compiled from 30m data from Oregon State for 2012 via GEE script #7 and used as target variables in species niche models (Python script #11). Each band is one species, ordered by most total biomass to least. Units: ton biomass/ha (converted to carbon in Python) Web source: https://lemma.forestry.oregonstate.edu/data Citation: Kennedy et al., 2018 model_output: contains subfolders corresponding to the four approaches discussed in the manuscript. For all approaches, we provide projections of carbon change (ton C/ha) for 6 scenarios: RCP4.5 & RCP8.5 x dry/mean/wet. Randm forest regression of carbon density Randm forest classification of dominant vegetation type Climate analgs Randm forest regression of 20 individual species' carbon density Google Earth Engine code overview Carbon_data: rescales 30m CARB carbon data layer (available upon request from CARB) to 1/8-degree to match the BCSD climate dataset, including masking out water/ag/urban landcover Valid_land_fraction: calculates the fraction of sub-gridcell area that is allowed to support aboveground carbon (excludes water/ag/urban/barren cover) Elevation: rescales 30m USGS elevation data to 1/8-degree to match the BCSD climate dataset Landcover: rescales 30m NLCD land cover data to 1/8 degree (forest, shrub/grass, null) Landcover_mask: creates a 1/8-degree layer masking out any areas of the western US that are not 50% wildland cover (for climate analogs analysis) Cci_biomass: rescales 100m CCI biomass data to 1/8-degree for US and Mexico Lemma_spp: reformats LEMMA species-level data into one raster layer with one band for each species' density at 1/8 degree Python code overview Process_climate.py: processes raw BCSD monthly climate data into combined netcdf files Process_climate_10yrs.py: duplicate of script 1 to process raw BCSD climate data, but modified slightly to maintain all 10 years of data in the present. This is needed for calculating the interannual variability in the climate analogs approach. Plot_clim_change.py: generates maps of mean annual T & P change for RCP4.5 & RCP8.5 (Fig 1) Plot_clim_spread.py: generates maps of spread of precipitation across 32 models for RCP8.5 (FigS1) and dry vs. wet models averages for RCP8.5 (FigS2) RF_carbon_model.py: approach #1. Models present-day distribution of CARB carbon layer based on climate data. Project future carbon and change RF_model_spread.py: rebuilds RF regression models from script 5, for each of the 32 climate models. Compares 3 different runs: T & P both change, T only (P constant), and P only (T constant). Offsets_change.py: compares RF regression model results from script 5 for all forests vs. carbon offset projects RF_veg_class_model.py: approach #2. Models present-day distribution of NLCD forest-vs-shrub layer based on climate data. Projects future land cover, change, and associated carbon change Climate_analogs.py. approach #3. Matches future climate pixels with their present analogue using Mahalanobis distance. Projects future carbon density by assigning that of the present analog. 3 runs: full domain (25-49 lat and -125 - -100 lon), 500 km radius, 100 km radius Analog_whittaker_plots.py: approach #3 supplementary figure - Whittaker scatter plots of mean annual P vs. T, showing how CA's gridcells shift Species_models.py: approach #4. Fits RF regression models to each of the top 20 tree spp in California. Project future carbon and change. Applies restrictions on distance between spp present and future locations (migration scenarios)

The magnitude and pace of photosynthetic recovery after wildfire in California ecosystems

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 [Formula: see text] g C m[Formula: see text] y[Formula: see text]([Formula: see text]) in the first year after fire, with average recovery to prefire conditions after [Formula: see text] y. The largest fires in forested ecosystems reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text] (n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly [Formula: see text] MMT CO[Formula: see text] (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

California’s COVID-19 economic shutdown reveals the fingerprint of systemic environmental racism

Racial and ethnic minorities in the United States often experience higher-than-average exposures to air pollution. However, the relative contribution of embedded institutional biases to these disparities can be difficult to disentangle from physical environmental drivers, socioeconomic status, and cultural or other factors that are correlated with exposures under status quo conditions. Over the spring and summer of 2020, rapid and sweeping COVID-19 shelter-in-place orders around the world created large perturbations to local and regional economic activity that resulted in observable changes in air pollution concentrations, compositions, and distributions. Here, we use the pandemic-related emergency order and subsequent economic slowdown to causally estimate pollution exposure disparities in California. Using both public ground-based sensor data and a citizen-science network of monitors for respirable particulate matter (PM2.5), along with satellite records of nitrogen dioxide (NO2), we show that the initial sheltering-in-place period produced disproportionate air pollution reduction benefits for Asian, Hispanic/Latinx, and low- income communities. By linking these pollution data with weather, geographic, socioeconomic, and mobility data in difference-in-differences models, we demonstrate that these disparate pollution reductions cannot be explained by environmental conditions, geography, income, or local economic activity and are instead driven by non-local activity. This study thus provides causally-identified evidence of systemic racial and ethnic bias in pollution control under business-as-usual conditions