Journal of Geophysical Research: Biogeosciences最新文献

The timing and progression of the spring thaw transition in high northern latitudes (HNL) coincides with warmer temperatures and landscape thawing, promoting increased soil moisture and growing season onset of gross primary productivity (GPP), heterotrophic respiration (HR), and evapotranspiration (ET). However, the relative order and spatial pattern of these events is uncertain due to vast size and remoteness of the HNL. We utilized satellite environmental data records (EDRs) derived from complementary passive microwave and optical sensors to assess the progression of spring transition events across Alaska and Northern Canada from 2016 to 2020. Selected EDRs included land surface and soil freeze-thaw status, solar-induced chlorophyll fluorescence (SIF) signifying canopy photosynthesis, root zone soil moisture (RZSM), and GPP, HR, and ET as indicators of ecosystem carbon and water-energy fluxes. The EDR spring transition maps showed thawing as a precursor to rising RZSM and growing season onset. Thaw timing was closely associated with ecosystem activation from winter dormancy, including seasonal increases in SIF, GPP, and ET. The HR onset occurred closer to soil thawing and prior to GPP activation, reducing spring carbon (CO₂) sink potential. The mean duration of the spring transition spanned ∼6 ± 1.5 weeks between initial and final onset events. Spring thaw timing and maximum RZSM were closely related to active layer thickness in HNL permafrost zones, with deeper active layers showing generally earlier thawing and greater RZSM. Our results confirm the utility of combined satellite EDRs for regional monitoring and better understanding of the complexity of the spring transition.

The Amazon River plume (ARP) has been shown to support high rates of nitrogen fixation and primary production. However, nitrogen fixation alone cannot account for total primary production determined in the region, hinting that other nitrogen uptake processes might play a role. For the first time, we measured nitrate uptake rates in the ARP during three cruises in May 2018, June 2019 and April/May 2021, along with primary production rates and an analysis of phytoplankton community composition via high performance liquid chromatography. Based on a classification according to the salt content the region was divided into estuarine (ES), mesohaline (MH) and oceanic (OC) stations. Primary production was light limited near the river mouth at ES stations and was maximal off the coasts of French Guiana and Suriname, where also nitrate uptake was highest with rates of 11.4 mmol m⁻² d⁻¹. The role of eddies pinching off a deflecting plume are discussed as possible reason for higher nutrient concentrations at the MH stations. Surprisingly, at most MH stations north of 5°N, nitrate uptake rates were low despite the presence of sufficient substrate concentration (up to 1.44 μM nitrate). Diatoms, dinoflagellates or Synechococcus sp. dominated phytoplankton communities. OC stations showed lowest productivity rates in accordance with oligotrophic conditions. However, rates seem to be sufficient to completely deplete the remaining riverine nitrate, preventing its export to the open ocean.

Vegetation absorption is one major form of carbon storage. The earliest spatial distribution of the Net Primary Production (NPP), an index to estimate how much carbon is absorbed, could extend back to the 1980s from satellite imagery. Our study reconstructed a time series annual NPP maps in the southern Indiana since 1940 with point-by-point regression models and ring-width index (RWI) from 16 tree-ring chronologies. Our RWI-NPP model had a good performance using Random Forest (RF) regression comprehensively considering both normal and dry years. The RWI-NPP model performance gap between forest and grassland is acceptable. We also found that the tendency (model of the tendency = −0.50) based on the combination of real NPP data and simulated NPP data were opposite to the one (slope = 18.70) only based on real NPP data where the extended data set could correct some bias caused by limited data. There is a huge NPP fluctuation in the recent years (2010–2013) which is highly likely to be caused by the combination of higher frequency of extreme climate events and the intensive land-use and land-cover change. We assume that most of the vegetation pixels had the same growing pattern with the plot in Morgan Monroe Flux Tower whose dominant species is ACSH (35.66%) or the plot in Hoot Woods whose dominant species is FRAM (34.41%). This study is novel in the assessment of the spatial distribution patterns of NPP since 1940. We can witness how the NPP changes within the last 70 years.

Phosphorus pollution is a major water quality issue impacting the environment and human health. Traditional methods limit the frequency and extent of total phosphorus (TP) measurements across many rivers. However, remote sensing can accurately estimate riverine TP; nevertheless, no large-scale assessment of riverine TP using remote sensing exists. Large-scale models using remote sensing can provide a fast and consistent method for TP measurement, important for data generalization and accessing extensive spatial-temporal change in TP. Our study uses remote sensing and machine learning to estimate the TP in rivers in the contiguous United States (CONUS). Initially, we developed a national scale matchup data set for Landsat detectable rivers (river width >30 m) using in situ TP and surface reflectance. We used in situ data from the Water Quality Portal (WQP), alongside water surface reflectance data from Landsat 5, 7, and 8 spanning from 1984 to 2021. Then, we used this data set to develop a machine learning (ML) model using different preprocessing methods and algorithms. We found that using high-level vegetation in the clustering approach and over-sampling or under-sampling our training data in the sampling approach improved our model estimation accuracy. We compared XGBLinear, XGBTree, Regularized Random Forest (RRF), and K-Nearest neighbors ML algorithms and selected XGBLinear as the best model with an R² of 0.604, RMSE of 0.103 mg/L, mean average error of 0.83, and NSE of 0.602. Finally, we identified human footprint, elevation, river area, and soil erosion as the main attributes influencing the accuracy of estimated TP from the ML model.

Explicitly representing the world's most frequently cultivated winter wheat in land surface model (LSM) is important for understanding carbon and energy cycling over cropland and its interactions with climate, which is crucial for global food security. However, in the latest version of Noah-MP-Crop LSM, winter wheat is significantly underrepresented. This study improved the winter-wheat parameterization in Noah-MP-Crop model by optimizing the phenological scheme, incorporating vernalization process, and calibrating several key parameters associated with winter wheat photosynthesis and carbon allocations. Focusing on the North China Plain as area representative region, model performance in simulating crop dynamic growth, carbon flux, and energy fluxes was validated at both site and regional scales. Results showed that the simulated phenological development matched well with the real-world phenological records. A comparison between the simulated results by the default and developed parameterizations revealed the significant improvements in the reproductions of leaf area index (LAI) and gross primary production (GPP). The determination coefficient (R²) value of GPP was increased from 0.15 to 0.46 to 0.39–0.91. Simulations of energy fluxes showed smaller improvements, with R² values increasing from 0.46 to 0.67 to 0.61–0.84 for latent heat (LE) and 0.18–0.55 to 0.25–0.61 for sensible heat. Additionally, the mean average error of net radiation was reduced. Improvements in spatial and temporal variations of LAI, GPP, and LE in regional simulation were also observed. This work can facilitate incorporating winter wheat cultivation and its interactions with climate system, particularly when coupling the Noah-MP-Crop model with the widely used Weather Research and Forecasting model.

Soils in forested ecosystems are extremely heterogeneous and represent a critical component of terrestrial ecosystems. Despite their substantial ecological value, the geographic characteristics, ecological processes, and coexistence of microbial communities in forest soils remain poorly understood. Here, we investigated the biodiversity dynamics, environmental influences, community assembly, and co-occurrence patterns of bacterial and fungal communities in surface and subsurface soils across 47 Chinese forest sites. The biogeographic characteristics determined using high-throughput sequencing data sets revealed evident spatial patterns of bacterial and fungal α and β diversity, assembly processes, and co-occurrence relationship, with greater variation in the bacterial than in fungal communities. Both fungal and bacterial communities showed significant spatial separations regulated by community assembly processes, co-occurrence patterns, and soil variables. The microbial dissimilarity was lower in high latitudes than in low latitudes, which was consistent with the lower deterministic processes and relatively higher co-occurrence associations in high latitudes than in low latitudes. Additionally, there were significant associations of soil dissolved organic matter (DOM) characteristics (e.g., its content, aromaticity, and molecular weight) with biodiversity dissimilarities, microbial assembly process balances, and microbial co-occurrence relationships in bacterial and fungal communities; they clearly indicate the key role of DOM in regulating microbial biogeographic patterns in forest soil ecosystems. Collectively, our study enhances the understanding of biogeographic patterns and coexistence theories in forest soil microbial ecosystems.

As a potential carbon sink, mangroves play an important role in climate mitigation. India houses several major global mangrove patches, which remain vulnerable to climate change. The ecosystem-atmosphere CO₂ exchange is most accurately measured by the eddy covariance method, whereas satellites provide the biophysical parameters for a wider area. In the present study, the Sentinel-2 satellite data is used to map the land cover types in the Pichavaram mangrove forest and identify two major dominant species (Rhizophora spp. and Avicennia marina), which indicated more than 95% classification accuracy. We used 2 years (2017 and 2018) of in situ gross primary productivity (GPP) and leaf area index (LAI) measurements and rectified the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and LAI products from 2010 to 2018. The modified MODIS GPP and LAI products were used to develop machine learning models, that is, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to study the climate influence on mangrove productivity. The RF model (R² = 0.85 and root mean square error (RMSE) = 0.2) outperformed the XGBoost model (R² = 0.75 and RMSE = 0.26) and was used to project the impact of climate change on the mangrove GPP for two extreme climate change scenarios, namely SSP1-1.26 and SSP5-8.5. The GPP increases and decreases in future during wet and dry periods, respectively. Overall, the projected GPP indicated a reduction of 3.73%–20.3% from 2050 to 2060 and of 4.82%–28.15% from 2090 to 2100, compared to its current average (from 2010 to 2018).

Extensive reservoir construction has fragmented more than 70% of the world's rivers, significantly impacting river connectivity and carbon cycling. However, the response of riverine dissolved organic matter (DOM) to reservoir influence and its potential downstream effects remains unclear. In this study, we employed multiple analytical techniques, including Fourier transform ion cyclotron resonance mass spectrometry, radiocarbon dating, and environmental factor analysis, to investigate the dynamic changes in DOM and its controlling factors under different hydrological management regimes in the LongTan Reservoir, the largest reservoir in the Pearl River, which is the second largest river in China by water discharge. Our results indicate that the molecular diversity of riverine DOM is reduced in the reservoir. Oxygen-rich and heteroatomic compounds, such as those containing nitrogen, sulfur, and phosphorus, are preferentially removed through enhanced photo- and biodegradation processes in the reservoir, particularly during the storage period. This leads to DOM that is enriched with oxygen-poor compounds and shows a biodegraded Δ¹⁴C value downstream. This study highlights that the composition of riverine DOM is significantly altered by the reservoir, but these effects could potentially be mitigated by optimizing the outlet location.

Grassland phenology is highly sensitive to climate change. Here, we investigate the spatiotemporal patterns of start (start of season (SOS)) and end (end of season (EOS)) dates of the growing season and quantify changes in their climatic controls over the arid Central Asian grassland ecosystems during 1982–2015, which may improve the model performance by considering shifts in primary drivers under ongoing climate change. Our results suggest that temperature played a positive role in advancing the SOS date, with the control of temperature on SOS getting stronger as preseason conditions become warmer but not drier. For autumn phenology, rapid increase in temperature after 1999 in combination with reductions in precipitation jointly contributed to a shift from delayed to advanced EOS. The areas that EOS regulated by either temperature or precipitation have changed between the two subperiods. Our findings suggest that the dynamic controls of temperature and precipitation on grassland phenology and the difference between spring and autumn phenology should be built into phenological models more accurately.

Installation of subsurface drainage systems has profoundly altered the nitrogen cycle in agricultural regions across the globe, facilitating substantial loss of nitrate (NO₃⁻) to surface water systems. Lack of understanding of the sources and processes controlling NO₃⁻ loss from tile-drained agroecosystems hinders the development of management strategies aimed at reducing this loss. The natural abundance nitrogen and oxygen isotopes of NO₃⁻ provide a valuable tool for differentiating nitrogen sources and tracking the biogeochemical transformations acting on NO₃⁻. This study combined multi-years of tile drainage measurements with NO₃⁻ isotopic analysis to examine NO₃⁻ source and transport mechanisms in a tile-drained corn-soybean field. The tile drainage NO₃⁻ isotope data were supplemented by characterization of the nitrogen isotopic composition of potential NO₃⁻ sources (fertilizer, soil nitrogen, and crop biomass) in the field and the oxygen isotopic composition of NO₃⁻ produced by nitrification in soil incubations. The results show that NO₃⁻ isotopes in tile drainage were highly responsive to tile discharge variation and fertilizer input. After accounting for isotopic fractionations during nitrification and denitrification, the isotopic signature of tile drainage NO₃⁻ was temporally stable and similar to those of fertilizer and soybean residue during unfertilized periods. This temporal invariance in NO₃⁻ isotopic signature indicates a nitrogen legacy effect, possibly resulting from N recycling at the soil microsite scale and a large water storage for NO₃⁻ mixing. Collectively, these results demonstrate how combining field NO₃⁻ isotope data with knowledge of isotopic fractionations can reveal mechanisms controlling NO₃⁻ cycling and transport under complex field conditions.