Dataset Information

Daily Cropland Soil NO_x Emissions Identified by TROPOMI and SMAP.

ABSTRACT: We use TROPOMI (TROPOspheric Monitoring Instrument) tropospheric nitrogen dioxide (NO₂) measurements to identify cropland soil nitrogen oxide (NO_x = NO + NO₂) emissions at daily to seasonal scales in the U.S. Southern Mississippi River Valley. Evaluating 1.5 years of TROPOMI observations with a box model, we observe seasonality in local NO_x enhancements and estimate maximum cropland soil NO_x emissions (15-34 ng N m^-2 s^-1) early in growing season (May-June). We observe soil NO_x pulsing in response to daily decreases in volumetric soil moisture (VSM) as measured by the Soil Moisture Active Passive (SMAP) satellite. Daily NO₂ enhancements reach up to 0.8 × 10¹⁵ molecules cm^-2 4-8 days after precipitation when VSM decreases to ~30%, reflecting emissions behavior distinct from previously defined soil NO_x pulse events. This demonstrates that TROPOMI NO₂ observations, combined with observations of underlying process controls (e.g., soil moisture), can constrain soil NO_x processes from space.

SUBMITTER: Huber DE

PROVIDER: S-EPMC7757188 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Daily Cropland Soil NOx Emissions Identified by TROPOMI and SMAP.

Huber Daniel E DE Steiner Allison L AL Kort Eric A EA

Geophysical research letters 20201113 22

We use TROPOMI (TROPOspheric Monitoring Instrument) tropospheric nitrogen dioxide (NO2) measurements to identify cropland soil nitrogen oxide (NOx = NO + NO2) emissions at daily to seasonal scales in the U.S. Southern Mississippi River Valley. Evaluating 1.5 years of TROPOMI observations with a box model, we observe seasonality in local NOx enhancements and estimate maximum cropland soil NOx emissions (15-34 ng N m-2 s-1</sup ...[more]

PMID: 33380760

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Project description:This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m3 m-3 and 0.001 m3 m-3, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m3 m-3, but increased the root zone bias by 0.014 m3 m-3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to a skill degradation in other land surface variables.

Project description:Seagrass meadows store globally significant organic carbon (Corg ) stocks which, if disturbed, can lead to CO2 emissions, contributing to climate change. Eutrophication and thermal stress continue to be a major cause of seagrass decline worldwide, but the associated CO2 emissions remain poorly understood. This study presents comprehensive estimates of seagrass soil Corg erosion following eutrophication-driven seagrass loss in Cockburn Sound (23 km2 between 1960s and 1990s) and identifies the main drivers. We estimate that shallow seagrass meadows (<5 m depth) had significantly higher Corg stocks in 50 cm thick soils (4.5 ± 0.7 kg Corg /m2 ) than previously vegetated counterparts (0.5 ± 0.1 kg Corg /m2 ). In deeper areas (>5 m), however, soil Corg stocks in seagrass and bare but previously vegetated areas were not significantly different (2.6 ± 0.3 and 3.0 ± 0.6 kg Corg /m2 , respectively). The soil Corg sequestration capacity prevailed in shallow and deep vegetated areas (55 ± 11 and 21 ± 7 g Corg m-2 year-1 , respectively), but was lost in bare areas. We identified that seagrass canopy loss alone does not necessarily drive changes in soil Corg but, when combined with high hydrodynamic energy, significant erosion occurred. Our estimates point at ~0.20 m/s as the critical shear velocity threshold causing soil Corg erosion. We estimate, from field studies and satellite imagery, that soil Corg erosion (within the top 50 cm) following seagrass loss likely resulted in cumulative emissions of 0.06-0.14 Tg CO2-eq over the last 40 years in Cockburn Sound. We estimated that indirect impacts (i.e. eutrophication, thermal stress and light stress) causing the loss of ~161,150 ha of seagrasses in Australia, likely resulted in the release of 11-21 Tg CO2 -eq since the 1950s, increasing cumulative CO2 emissions from land-use change in Australia by 1.1%-2.3% per annum. The patterns described serve as a baseline to estimate potential CO2 emissions following disturbance of seagrass meadows.

Dataset Information

Daily Cropland Soil NO_x Emissions Identified by TROPOMI and SMAP.

Publications

Daily Cropland Soil NO<sub>x</sub> Emissions Identified by TROPOMI and SMAP.

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