nasa-gesdisc/es-publications-researchareas huggingface dataset:
Effect of free-ranging cattle on mammalian diversity: an Austral Yungas case study (2022)

Abstract
            
              Extensive cattle ranging is an important economic activity in mountains, with diverse effects on native mammal communities. The effects of cattle
              Bos taurus
              can be negative, positive or neutral, mostly depending on the stocking rate. We examined the effect of cattle on the diversity and abundance of native mammalian species in the Austral Yungas region of Argentina, considering environmental variables, land protection status, and human influence. Using 12,512 trap-nights from 167 camera-trap stations over 11 years (20092019), we calculated a relative abundance index using camera events and used generalized linear models to estimate the effect of cattle on small mammals, large herbivores, species of conservation concern and felids. Cattle had different effects on each group of native mammals. We observed a lower abundance of large native herbivores and the absence of small mammals in areas with high cattle abundance. The tapir
              Tapirus terrestris,
              jaguar
              Panthera onca
              and white-lipped peccary
              Tayassu pecari
              are rare in the Yungas and therefore potentially vulnerable to extinction there. Conservation of small felids and low cattle abundance could be compatible, but felids are threatened by other anthropogenic influences. Native mammalian diversity and richness were related to land protection status. The entire ecoregion is potentially suitable for cattle, suggesting the potential for further threats, and that cattle should be excluded from strictly protected areas. To ensure extensive cattle ranging is compatible with wildlife conservation in areas where exclusion is not possible, we recommend improved management of cattle and moderate stocking rates.

Research areas: Ecosystems, Agriculture
Assessing The Effects Of A Winter Wheat Cover Crop On Soil Water Use, Cotton Yield, And Soil Organic Carbon In No-Till Cotton Production Systems (2022)

Highlights
            
            
              
                
                  Soil water depleted by the cover crop was quickly replenished by spring precipitation.
                
                
                  Higher soil water was maintained during the cotton growing season under the cover crop treatment.
                
                
                  Cover crops improved soil organic carbon under both irrigated and dryland conditions.
                
                
                  Cover crops enhanced crop water productivity under both irrigated and dryland conditions.
                
              
            
            
              Abstract.
              Cover crops provide many soil health benefits to agricultural systems. An interest in growing cover crops in cotton (Gossypium hirsutum L.) production systems has been increasing in the Texas Rolling Plains (TRP) region. Due to limited rainfall and groundwater availability, producers in this semi-arid region are concerned that winter cover crops can reduce soil water availability for a subsequent cotton crop. In addition, the long-term effects of cover crops on soil organic carbon (SOC) are not well studied in this region. The overall goal of this study was to assess the long-term effects of growing a winter wheat (Triticum aestivum) cover crop on soil water, SOC, seed cotton yield, and crop water productivity (CWP) under irrigated and dryland cotton production systems using the Decision Support System for Agrotechnology Transfer (DSSAT) model. Measured data from cover crop experiments conducted at the Texas A&M AgriLife Research Station at Chillicothe from 2011 to 2020 were used to evaluate the CROPGRO-Cotton and CERES-Wheat modules in the DSSAT Cropping System Model. The average percent error (PE) between the simulated and measured seed cotton yield was 0.3% and -0.9%, and that between the simulated and measured aboveground wheat biomass was 1.4% and -3.4% during the calibration and evaluation periods, respectively. For the simulation of SOC, the PE was 8.5% and 7.6% during the calibration and evaluation periods, respectively. Long-term (2001 to 2020) simulations showed that soil water was reduced substantially by the winter wheat cover crop before its termination. However, depleted soil water was quickly replenished by spring precipitation and maintained at a higher level during the cotton growing season. Winter wheat cover crops can potentially improve SOC in irrigated and dryland cotton production systems. The CWP of cotton also improved with a winter wheat cover crop under both irrigated and dryland conditions. We concluded that cover crops could potentially improve soil health and provide a sustainable environment for TRP cotton production.  Keywords: CERES-Wheat, CROPGRO-Cotton, DSSAT, Soil health, Texas Rolling Plains (TRP).

Research areas: Agriculture
Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands (2021)

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).

Research areas: Greenhouse Gases
Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO2 (2016)

Abstract. A new model for greenhouse gas transport has been developed based on Environment and Climate Change Canada's operational weather and environmental prediction models. When provided with realistic posterior fluxes for CO2, the CO2 simulations compare well to NOAA's CarbonTracker fields and to near-surface continuous measurements, columns from the Total Carbon Column Observing Network (TCCON) and NOAA aircraft profiles. This coupled meteorological and tracer transport model is used to study the predictability of CO2. Predictability concerns the quantification of model forecast errors and thus of transport model errors. CO2 predictions are used to compute modeldata mismatches when solving flux inversion problems and the quality of such predictions is a major concern. Here, the loss of meteorological predictability due to uncertain meteorological initial conditions is shown to impact CO2 predictability. The predictability of CO2 is shorter than that of the temperature field and increases near the surface and in the lower stratosphere. When broken down into spatial scales, CO2 predictability at the very largest scales is mainly due to surface fluxes but there is also some sensitivity to the land and ocean surface forcing of meteorological fields. The predictability due to the land and ocean surface is most evident in boreal summer when biospheric uptake produces large spatial gradients in the CO2 field. This is a newly identified source of uncertainty in CO2 predictions but it is expected to be much less significant than uncertainties in fluxes. However, it serves as an upper limit for the more important source of transport error and loss of predictability, which is due to uncertain meteorological analyses. By isolating this component of transport error, it is demonstrated that CO2 can only be defined on large spatial scales due to the presence of meteorological uncertainty. Thus, for a given model, there is a spatial scale below which fluxes cannot be inferred simply due to the fact that meteorological analyses are imperfect. These unresolved spatial scales correspond to small scales near the surface but increase with altitude. By isolating other components of transport error, the largest or limiting error can be identified. For example, a model error due to the lack of convective tracer transport was found to impact transport error on the very largest (wavenumbers less than 5) spatial scales. Thus for wavenumbers greater than 5, transport model error due to meteorological analysis uncertainty is more important for our model than the lack of convective tracer transport.

Research areas: Greenhouse Gases
Spatiotemporal variability of atmospheric CO2 concentration and controlling factors over sugarcane cultivation areas in southern Brazil (2021)

With the advancement of remote sensing, it is now possible to identify and characterize greenhouse gas emissions under deferment land uses. Given the above, this study aimed to characterize the spatialtemporal variability and the main factors controlling the average atmospheric CO2 column (Xco2) in the macroregion of Ribeirao Preto (MRP), Sao Paulo, a significant sugarcane producer in Brazil. We obtained remote sensing data from January 2015 to December 2018. The variables used were Xco2 and sun-induced fluorescence of chlorophyll (SIF) by NASA's Orbiting Carbon Observatory-2 satellite (OCO-2), relative humidity (RH), global radiation (Qg), and the average temperature at 2 m (T2m) by the NASA-POWER platform, and leaf area index (LAI) and evapotranspiration by PenmanMonteith (ET) by MODIS sensor. We evaluated the data in trimesters averages, where descriptive statistics, Pearson correlation and linear regression have been applied. The spatial distribution was made by the inverse distance weighted (IDW). The minimum (390.40  0.41 ppm) and maximum (394.75  0.34 ppm) mean of Xco2 was observed in the first quarter of 2015 and third quarter of 2017. The Xco2 obtained negative correlations with the SIF (0.81), LAI (0.81), RH (0.74), ET (0.84), and Qg (0.51). Hotspots and coldspots of Xco2 tend to vary over the years. We conclude that the temporal variation of Xco2 above sugarcane areas in southern Brazil is well represented by a periodic function. Our results indicate photosynthesis and soil exposure after harvest are factors that could act as source and sink of CO2.

Research areas: Greenhouse Gases
Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data (2022)

The oil and gas (O&amp;G) sector is estimated to be the largest contributor to anthropogenic methane (CH<SUB>4</SUB>) emissions in Colorado. Since 2004, the State of Colorado has implemented multiple regulations to significantly reduce emissions from the O&amp;G sector. The Denver-Julesburg Basin (DJ Basin) is a significant O&amp;G producing region in northern Colorado, and O&amp;G production here has steadily increased over the last decade. To assess CH<SUB>4</SUB> trends in Northern Colorado, we selected CH<SUB>4</SUB> retrievals from the NASA Atmospheric Infrared Sounder (AIRS) instrument for 2003-2020. The study grid cell includes Denver, Boulder, and much of the dense O&amp;G production in the DJ Basin. We computed mean June-August ascending node AIRS 700 hPa CH<SUB>4</SUB> for each year and subtracted mean June-August CH<SUB>4</SUB> sampled at NOAA's Niwot Ridge (NWR) station, a high-altitude background site. Differences represent estimated enhancement over background. Linear regression shows an annual change of −2.84 ppb ± 0.8 ppb from 2012 to 2020 (R<SUP>2</SUP> 0.90) and an estimated reduction of 56% for 2012-2020, despite substantial increases in O&amp;G production. Local CH<SUB>4</SUB> enhancement is strongly correlated with surface measurements of ethane at Platteville which is in the center of the O&amp;G fields (correlation coefficient 0.96), and this is evidence that reductions in O&amp;G emissions are driving reductions in CH<SUB>4</SUB>. We conclude that AIRS CH<SUB>4</SUB> can be used to measure the efficacy of emissions control programs in this region and that regulatory requirements are having an effect.

Research areas: Greenhouse Gases
Cold season emissions dominate the Arctic tundra methane budget (2016)

Significance
            Arctic ecosystems are major global sources of methane. We report that emissions during the cold season (September to May) contribute 50% of annual sources of methane from Alaskan tundra, based on fluxes obtained from eddy covariance sites and from regional fluxes calculated from aircraft data. The largest emissions were observed at the driest site (<5% inundation). Emissions of methane in the cold season are linked to the extended zero curtain period, where soil temperatures are poised near 0 C, indicating that total emissions are very sensitive to soil climate and related factors, such as snow depth. The dominance of late season emissions, sensitivity to soil conditions, and importance of dry tundra are not currently simulated in most global climate models.
          , 
            
              Arctic terrestrial ecosystems are major global sources of methane (CH
              4
              ); hence, it is important to understand the seasonal and climatic controls on CH
              4
              emissions from these systems. Here, we report year-round CH
              4
              emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for 50% of the annual CH
              4
              flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the zero curtain period, when subsurface soil temperatures are poised near 0 C. The zero curtain may persist longer than the growing season, and CH
              4
              emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH
              4
              derived from aircraft data demonstrate the large spatial extent of late season CH
              4
              emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12  5 (95% confidence interval) Tg CH
              4
              y
              1
              , 25% of global emissions from extratropical wetlands, or 6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH
              4
              emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.

Research areas: Greenhouse Gases
Large N2O emissions from cryoturbated peat soil in tundra (2009)

Nitrous oxide is a potent greenhouse gas whose concentration is increasing in the atmosphere. So far, the highest terrestrial nitrous oxide emissions have been measured in agricultural and tropical soils, and nitrous oxide emissions from northern natural soils have been considered negligible. Pristine tundra, one of the largest natural land cover types in the world, is a mosaic of different surface types including bare surfaces created by cryoturbation. Here we used a static chamber method to measure nitrous oxide emissions from the discontinuous permafrost zone in subarctic East European tundra. We show that nitrous oxide emissions from bare peat surfaces in the region, known as peat circles, range between 0.9 and 1.4g nitrous oxide m<SUP>-2</SUP> from June to October, and are equivalent to those from tropical and agricultural soils. Extrapolation of our data to the whole Arctic reveals that the emissions from these hot spots could amount to ~0.1Tg nitrous oxide yr<SUP>-1</SUP>, corresponding to 4% of the global warming potential of Arctic methane emissions at present. Therefore, not only carbon, but also nitrogen stored in permafrost soils, has to be considered when assessing the present and future climatic impact of tundra.

Research areas: Greenhouse Gases
The Transpolar Drift conveys methane from the Siberian Shelf to the central Arctic Ocean (2018)

Methane sources and sinks in the Arctic are poorly quantified. In particular, methane emissions from the Arctic Ocean and the potential sink capacity are still under debate. In this context sea ice impact on and the intense cycling of methane between sea ice and Polar surface water (PSW) becomes pivotal. We report on methane super- and under-saturation in PSW in the Eurasian Basin (EB), strongly linked to sea ice-ocean interactions. In the southern EB under-saturation in PSW is caused by both inflow of warm Atlantic water and short-time contact with sea ice. By comparison in the northern EB long-time sea ice-PSW contact triggered by freezing and melting events induces a methane excess. We reveal the Ttranspolar Drift Stream as crucial for methane transport and show that inter-annual shifts in sea ice drift patterns generate inter-annually patchy methane excess in PSW. Using backward trajectories combined with 18O signatures of sea ice cores we determine the sea ice source regions to be in the Laptev Sea Polynyas and the off shelf regime in 2011 and 2015, respectively. We denote the Transpolar Drift regime as decisive for the fate of methane released on the Siberian shelves.

Research areas: Greenhouse Gases
Using Landsat time-series and LiDAR to inform aboveground forest biomass baselines in northern Minnesota, USA (2016)

The publicly accessible archive of Landsat imagery and increasing regional-scale LiDAR acquisitions offer an opportunity to periodically estimate aboveground forest biomass (AGB) from 1990 to the present to align with the reporting needs of National Greenhouse Gas Inventories (NGHGIs). This study integrated Landsat time-series data, a state-wide LiDAR dataset, and a recent cycle of the national forest inventory (NFI) records in Minnesota, USA, to obtain a spatially explicit inventory of AGB across a large region of space and time back to the 1990 baseline used by the US NGHGI. Pixel-level polynomial models were fit to 6 time-series metrics of Landsat data to obtain fitted predictors that were ultimately coupled with the NFI data in a nonparametric modeling framework to map temporal AGB baselines. Eighteen candidate models, formulated using different combinations of LiDAR and Landsat metrics, revealed that the model using both Landsat and LiDAR metrics consistently performed better than the alternative models. The RMSE of the model using both Landsat and LiDAR was 27.2 Mg ha1, against 31.39 Mg ha1 for the model using only LiDAR metrics. We conclude that the fitted Landsat-based model (RMSE = 47.64 Mg ha1) provides acceptable accuracy for the 1990-baseline mapping of AGB.

Research areas: Greenhouse Gases, Land Surface/Agriculture Indicators
Seasonality and Budgets of Soil Greenhouse Gas Emissions From a Tropical (2020)

Limited information on greenhouse gas emissions from tropical dry forest soils still hinders the assessment of the sources/sinks from this ecosystem and their contribution at global scales. Particularly, rewetting events after the dry season can have a significant effect on soil biogeochemical processes and associated exchange of greenhouse gases. This study evaluated the temporal variation and annual fluxes of CO<SUB>2</SUB>, N<SUB>2</SUB>O, and CH<SUB>4</SUB> from soils in a tropical dry forest successional gradient. After a prolonged drought of 5 months, large emissions pulses of CO<SUB>2</SUB> and N<SUB>2</SUB>O were observed at all sites following first rain events, caused by the "Birch effect," with a significant effect on the net ecosystem exchange and the annual emissions budget. Annual CO<SUB>2</SUB> emissions were greatest for the young forest (8,556 kg C ha<SUP>-1</SUP> yr<SUP>-1</SUP>) followed by the older forest (7,420 kg C ha<SUP>-1</SUP> yr<SUP>-1</SUP>) and the abandoned pasture (7,224 kg C ha<SUP>-1</SUP> yr<SUP>-1</SUP>). Annual emissions of N<SUB>2</SUB>O were greatest for the forest sites (0.39 and 0.43 kg N ha<SUP>-1</SUP> yr<SUP>-1</SUP>) and least in the abandoned pasture (0.09 kg N ha<SUP>-1</SUP> yr<SUP>-1</SUP>). CH<SUB>4</SUB> uptake was greatest in the older forest (-2.61 kg C ha<SUP>-1</SUP> yr<SUP>-1</SUP>) followed by the abandoned pasture (-0.69 kg C ha<SUP>-1</SUP> yr<SUP>-1</SUP>) and the young forest (-0.58 kg C ha<SUP>-1</SUP> yr<SUP>-1</SUP>). Fluxes were mainly influenced by soil moisture, microbial biomass, and soil nitrate and ammonium concentrations. Annual CO<SUB>2</SUB> and N<SUB>2</SUB>O soil fluxes of tropical dry forests in this study and others from the literature were much lower than the annual fluxes in wetter tropical forests. Conversely, tropical dry forests and abandoned pastures are on average stronger sinks for CH<SUB>4</SUB> than wetter tropical forests.

Research areas: Greenhouse Gases