PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems

Climate warming is expected to increase global methane (CH4 ) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4 ) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981-2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4 , with increases in FCH4 of ca. 10% or higher found in the fall from 1981-1989 to 2010-2018. The annual trends were driven by increases during summer and fall, particularly at high-CH4 -emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10-40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions.

Peatland dynamics: A review of process-based models and approaches

Despite peatlands' important feedbacks on the climate and global biogeochemical cycles, predicting their dynamics involves many uncertainties and an overwhelming variety of available models. This paper reviews the most widely used process-based models for simulating peatlands' dynamics, i.e., the exchanges of energy and mass (water, carbon, and nitrogen). 'Peatlands' here refers to mires, fens, bogs, and peat swamps both intact and degraded. Using a systematic search (involving 4900 articles), 45 models were selected that appeared at least twice in the literature. The models were classified into four categories: terrestrial ecosystem models (biogeochemical and global dynamic vegetation models, n = 21), hydrological models (n = 14), land surface models (n = 7), and eco-hydrological models (n = 3), 18 of which featured "peatland-specific" modules. By analysing their corresponding publications (n = 231), we identified their proven applicability domains (hydrology and carbon cycles dominated) for different peatland types and climate zones (northern bogs and fens dominated). The studies range in scale from small plots to global, and from single events to millennia. Following a FOSS (Free Open-Source Software) and FAIR (Findable, Accessible, Interoperable, Reusable) assessment, the number of models was reduced to 12. Then, we conducted a technical review of the approaches and associated challenges, as well as the basic aspects of each model, e.g., spatiotemporal resolution, input/output data format and modularity. Our review streamlines the process of model selection and highlights: (i) standardization and coordination are required for both data exchange and model calibration/validation to facilitate intercomparison studies; and (ii) there are overlaps in the models' scopes and approaches, making it imperative to fully optimize the strengths of existing models rather than creating redundant ones. In this regard, we provide a futuristic outlook for a 'peatland community modelling platform' and suggest an international peatland modelling intercomparison project.

Tropical Peatland Hydrology Simulated With a Global Land Surface Model

Tropical peatlands are among the most carbon-dense ecosystems on Earth, and their water storage dynamics strongly control these carbon stocks. The hydrological functioning of tropical peatlands differs from that of northern peatlands, which has not yet been accounted for in global land surface models (LSMs). Here, we integrated tropical peat-specific hydrology modules into a global LSM for the first time, by utilizing the peatland-specific model structure adaptation (PEATCLSM) of the NASA Catchment Land Surface Model (CLSM). We developed literature-based parameter sets for natural (PEATCLSM_Trop,Nat) and drained (PEATCLSM_Trop,Drain) tropical peatlands. Simulations with PEATCLSM_Trop,Nat were compared against those with the default CLSM version and the northern version of PEATCLSM (PEATCLSM_North,Nat) with tropical vegetation input. All simulations were forced with global meteorological reanalysis input data for the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. The evaluation against a unique and extensive data set of in situ water level and eddy covariance-derived evapotranspiration showed an overall improvement in bias and correlation compared to the default CLSM version. Over Southeast Asia, an additional simulation with PEATCLSM_Trop,Drain was run to address the large fraction of drained tropical peatlands in this region. PEATCLSM_Trop,Drain outperformed CLSM, PEATCLSM_North,Nat, and PEATCLSM_Trop,Nat over drained sites. Despite the overall improvements of PEATCLSM_Trop,Nat over CLSM, there are strong differences in performance between the three study regions. We attribute these performance differences to regional differences in accuracy of meteorological forcing data, and differences in peatland hydrologic response that are not yet captured by our model.

Climate Sensitivity of Peatland Methane Emissions Mediated by Seasonal Hydrologic Dynamics

Peatlands are among the largest natural sources of atmospheric methane (CH4) worldwide. Peatland emissions are projected to increase under climate change, as rising temperatures and shifting precipitation accelerate microbial metabolic pathways favorable for CH4 production. However, how these changing environmental factors will impact peatland emissions over the long term remains unknown. Here, we investigate a novel data set spanning an exceptionally long 11 years to analyze the influence of soil temperature and water table elevation on peatland CH4 emissions. We show that higher water tables dampen the springtime increases in CH4 emissions as well as their subsequent decreases during late summer to fall. These results imply that any hydroclimatological changes in northern peatlands that shift seasonal water availability from winter to summer will increase annual CH4 emissions, even if temperature remains unchanged. Therefore, advancing hydrological understanding in peatland watersheds will be crucial for improving predictions of CH4 emissions.

Satellite Determination of Peatland Water Table Temporal Dynamics by Localizing Representative Pixels of A SWIR-Based Moisture Index

The OPtical TRApezoid Model (OPTRAM) is a physically-based approach for remote soil moisture estimation. OPTRAM is based on the response of short-wave infrared (SWIR) reflectance to vegetation water status, which in turn responds to changes of root-zone soil moisture. In peatlands, the latter is tightly coupled to water table depth (WTD). Therefore, in theory, the OPTRAM index might be a useful tool to monitor WTD dynamics in peatlands, although the sensitivity of OPTRAM index to WTD changes will likely depend on vegetation cover and related rooting depth. In this study, we aim at identifying those locations (further called ‘best pixels’) where the OPTRAM index is most representative of overall peatland WTD dynamics. In peatlands, the high saturated hydraulic conductivity of the upper layer largely synchronizes the temporal WTD fluctuations over several kilometers, i.e., even though the mean and amplitude of the WTD dynamics may vary in space. Therefore, it can be assumed that the WTD time series, either measured at a single location or simulated for a grid cell with the PEATland-specific adaptation of the NASA Catchment Land Surface Model (PEATCLSM), are representative of the overall peatland WTD dynamics. We took advantage of this concept to identify the ‘best pixel’ of all spatially distributed OPTRAM pixels within a peatland, as that pixel with the highest time series Pearson correlation (R) with WTD data accounting for temporal autocorrelation. The OPTRAM index was calculated based on various remotely sensed images, namely, Landsat, MODIS, and aggregated Landsat images at MODIS resolution for five northern peatlands with long-term WTD records, including both bogs and fens. The ‘best pixels’ were dominantly covered with mosses and graminoids with little or no shrub or trees. However, the performance of OPTRAM highly depended on the spatial resolution of the remotely sensed data. The Landsat-based OPTRAM index yielded the highest R values (mean of 0.7 across the ‘best pixels’ in five peatlands). Our study further indicates that, in the absence of historical in situ data, PEATCLSM can be used as an alternative to localize ‘best pixels’. This finding enables the future applicability of OPTRAM to monitor WTD changes in peatlands on a global scale.

On the Potential of Sentinel-1 for High Resolution Monitoring of Water Table Dynamics in Grasslands on Organic Soils

For soils with shallow groundwater and high organic carbon content, water table depth (WTD) is a key parameter to describe their hydrologic state and to estimate greenhouse gas emissions (GHG). Since the microwave backscatter coefficient (σ0) is sensitive to soil moisture, the application of Sentinel-1 satellite data might support the monitoring of these climate-relevant soils at high spatial resolution (~100 m) by detecting spatial and temporal changes in local field and water management. Despite the low penetration depth of the C-band, σ0 is influenced by shallow WTD fluctuations via the soil hydraulic connection between the water table and surface soil. Here, we analyzed σ0 at 60 monitoring wells in a drained temperate peatland with degraded organic soils used as extensive grassland. We evaluated temporal Spearman correlation coefficients between σ0 and WTD considering the soil and vegetation information. To account for the effects of seasonal vegetation changes, we used the cross-over (incidence) angle method. Climatologies of the slope of the incidence angle dependency derived from two years of Sentinel-1 data and their application to the cross-over angle method did improve correlations, though the effect was minor. Overall, averaged over all sites, a temporal Spearman correlation coefficient of 0.45 (±0.17) was obtained. The loss of correlation during summer (higher vegetation, deeper WTD) and the effects of cuts and grazing are discussed. The site-specific general wetness level, described by the mean WTD of each site was shown to be a major factor controlling the strength of the correlation. Mean WTD deeper than about −0.60 m lowered the correlations across sites, which might indicate an important limit of the application.

ACC Deaminase Producing PGPR Bacillus amyloliquefaciens and Agrobacterium fabrum along with Biochar Improve Wheat Productivity under Drought Stress

Drought stress retards wheat plant’s vegetative growth and physiological processes and results in low productivity. A stressed plant synthesizes ethylene which inhibits root elongation; however, the enzyme 1-Aminocyclopropane-1-Carboxylate (ACC) deaminase catabolizes ethylene produced under water stress. Therefore, the ACC deaminase producing plant growth promoting rhizobacteria (PGPR) can be used to enhance crop productivity under drought stress. Biochar (BC) is an organically active and potentially nutrient-rich amendment that, when applied to the soil, can increase pore volume, cation exchange capacity and nutrient retention and bioavailability. We conducted a field experiment to study the effect of drought tolerant, ACC deaminase producing PGPR (with and without timber waste BC) on plant growth and yield parameters under drought stress. Two PGPR strains, Agrobacterium fabrum or Bacillus amyloliquefaciens were applied individually and in combination with 30 Mg ha−1 BC under three levels of irrigation, i.e., recommended four irrigations (4I), three irrigations (3I) and two irrigations (2I). Combined application of B. amyloliquefaciens and 30 Mg ha−1 BC under 3I, significantly increased growth and yield traits of wheat: grain yield (36%), straw yield (50%), biological yield (40%). The same soil application under 2I resulted in greater increases in several of the growth and yield traits: grain yield (77%), straw yield (75%), above- and below-ground biomasses (77%), as compared to control; however, no significant increases in chlorophyll a, b or total, and photosynthetic rate and stomatal conductance in response to individual inoculation of a PGPR strain (without BC) were observed. Therefore, we suggest that the combined soil application of B. amyloliquefaciens and BC more effectively mitigates drought stress and improves wheat productivity as compared to any of the individual soil applications tested in this study.