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Various climate and watershed characteristics determine the runoff coefficient (RC), and their interactions are complicated. Understanding the driving factors of the RC is important for understanding the long-term water balance and how it might change. Using the upper reaches of the Huaihe River Basin as the study area, remote sensing data were used to produce a RC map. The geographical detector was selected to quantify the individual and interactive influences of 13 driving factors on the RC. The results revealed that moderate resolution imaging spectroradiometer evapotranspiration (ET) data can be used to produce a mean average RC map based on the water balance equation. The dominant factors influencing the RC were found to vary at different scales. Precipitation had the largest correlation coefficient with the RC at the watershed scale. For the pixel scale, results from the geographical detector indicated that actual evapotranspiration (AET) and precipitation had the highest explanatory rate for the RC in the small watershed region and the whole study area (0.785 and 0.248, respectively). Climate factors, elevation, and normalized difference vegetation index had a substantial influence on the RC. Any two factors exhibited bilinear or nonlinear enhanced relationships in their interactions. The largest interactions between the factors were AET and precipitation, which exceeded 0.900. This study serves to better understand and explain runoff’s complex interrelationships.

The errors-in-variables (EIV) model is used for data processing in the field of geodesy. However, the EIV model may be ill-posed. By analyzing the decreasing regularization (D-regularization) characteristic of solutions for EIV models, algorithms using traditional methods such as singular value decomposition or the Tikhonov function can directly determine the irrationality of a model. When an EIV model is ill-posed, solutions in which the observation errors in the coefficient matrix are simulated by variables make the ill-posed nature of the model more serious. This is because the simulated observation errors are subtracted from the coefficient matrix in subsequent computations, which reduces the singular value of the normal matrix. This point is verified using an example. To account for the D-regularization of solutions in EIV models, a modified algorithm is derived by classifying the models into two categories, and the regularization parameters are iteratively revised based on the mean squared error. Finally, some conclusions are drawn from two separate examples. [ABSTRACT FROM AUTHOR]

The European land carbon uptake has been heavily impacted by several recent severe droughts, yet quantitative estimates of carbon uptake anomalies are uncertain. Atmospheric CO2 inverse models (AIMs) provide observation‐based estimates of the large‐scale carbon flux dynamics, but how well they capture drought impacts on the terrestrial carbon uptake is poorly known. Here we assessed the capacity of state‐of‐the‐art AIMs in monitoring drought impacts on the European carbon uptake over 2001–2015 using observations of environmental variability and vegetation function and made comparisons with bottom‐up estimates of carbon uptake anomalies. We found that global inversions with only limited surface CO2 observations give divergent estimates of drought impacts. Regional inversions assimilating denser CO2 observations over Europe demonstrated some improved consistency, with all inversions capturing a reduction in carbon uptake during the 2012 drought. However, they failed to capture the reduction caused by the 2015 drought. Finally, we found that a set of inversions that assimilated satellite XCO2 or assimilated environmental variables plus surface CO2 observations better captured carbon uptake anomalies induced by both the 2012 and 2015 droughts. In addition, the recent Orbiting Carbon Observatory—2 XCO2 inversions showed good potential in capturing drought impacts, with better performances for larger‐scale droughts like the 2018 drought. These results suggest that surface CO2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, and satellite XCO2 and ancillary environmental data can be used to improve observational constraints in atmospheric inversion systems. Plain Language Summary: Atmospheric CO2 inverse models (AIMs) are useful tools for quantifying the response of large‐scale carbon uptake to climate extremes, but their capacity for monitoring drought impacts, particularly at regional scales, is not fully explored. In this study, we assessed the capacity of state‐of‐the‐art AIMs for monitoring drought impacts on the European land carbon uptake over 2001–2015 using a large array of observational and model data sets. We found: (a) global inversions with only limited surface CO2 observations face a great challenge in monitoring drought impacts on the European carbon uptake; (b) Regional inversions assimilated denser CO2 observations over Europe, for the EUROCOM project, demonstrated some improved consistency but are still deficient, showing divergent estimates in interannual variability of carbon uptake for most years; and (c) A set of inversion systems that assimilated satellite XCO2 or assimilated environmental variables plus surface CO2 observations better captured annual and seasonal anomalies caused by droughts. Our study demonstrates that surface CO2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, whereby satellite XCO2 and ancillary environmental data can offer observational constraints for improving the estimates. Key Points: Global inversions with only limited surface CO2 observations give divergent estimates of drought impacts on the European carbon uptakeRegional inversions assimilating denser CO2 observations over Europe demonstrate some improved consistency but are still deficientThe inversions assimilating satellite XCO2 or environmental variables in addition to surface CO2 largely improve the estimates [ABSTRACT FROM AUTHOR]

Precipitation plays a dominant role in regulating terrestrial carbon fluxes. In concert with global warming, aridity has been increasing during recent decades in most parts of the world. How global terrestrial carbon fluxes respond to this change, however, is still unclear. Using a remote-sensing-driven, process-based model, the Boreal Ecosystem Productivity Simulator (BEPS), this study investigated the responses of global terrestrial carbon fluxes to meteorological drought, which were characterized by the standardized precipitation evapotranspiration index (SPEI). The results showed that the response of terrestrial carbon fluxes to drought exhibited distinguishable spatial heterogeneity. In most regions, terrestrial carbon fluxes responded strongly to drought. With an increase in annual water balance (annual precipitation minus annual potential evapotranspiration), the response of carbon fluxes to drought became weaker. The lagged time of terrestrial carbon fluxes responding to drought decreased with the increasing strength of carbon fluxes in response to drought. The sensitivity of terrestrial carbon fluxes to drought also showed noticeable spatial heterogeneity. With an increase in annual water balance, the sensitivity first increased and then decreased. Terrestrial carbon fluxes exhibited the highest sensitivity to drought in semi-arid areas. [ABSTRACT FROM AUTHOR]

Antibiotics can inhibit or kill microorganisms, while microorganisms have evolved antibiotic resistance strategies to survive antibiotics. Zymomonas mobilis is an ideal industrial microbial chassis and can tolerate multiple antibiotics. However, the mechanisms of antibiotic resistance and genes associated with antibiotic resistance have not been fully analyzed and characterized. In this study, we investigated genes associated with antibiotic resistance using bioinformatic approaches and examined genes associated with ampicillin resistance using CRISPR/Cas12a−based genome−editing technology. Six ampicillin−resistant genes (ZMO0103, ZMO0893, ZMO1094, ZMO1650, ZMO1866, and ZMO1967) were identified, and five mutant strains ZM4∆0103, ZM4∆0893, ZM4∆1094, ZM4∆1650, and ZM4∆1866 were constructed. Additionally, a four−gene mutant ZM4∆ARs was constructed by knocking out ZMO0103, ZMO0893, ZMO1094, and ZMO1650 continuously. Cell growth, morphology, and transformation efficiency of mutant strains were examined. Our results show that the cell growth of ZM4∆0103 and ZM4∆ARs was significantly inhibited with 150 μg/mL ampicillin, and cells changed to a long filament shape from a short rod shape. Moreover, the transformation efficiencies of ZM4∆0103 and ZM4∆ARs were decreased. Our results indicate that ZMO0103 is the key to ampicillin resistance in Z. mobilis, and other ampicillin−resistant genes may have a synergetic effect with it. In summary, this study identified and characterized genes related to ampicillin resistance in Z. mobilis and laid a foundation for further study of other antibiotic resistance mechanisms. [ABSTRACT FROM AUTHOR]

Vegetation plays an important role in linking water, atmosphere, and soil. The dynamic change in vegetation is an important indicator for the regulation of the terrestrial carbon balance and climate change. This study applied trend analysis, detrended correlation analysis, and the Hierarchical Partitioning Algorithm (HPA) to GIMMS NDVI3g data, meteorological data, and natural vegetation types for the period 1983 to 2015 to analyze the temporal and spatial changes in NDVI during the growing season and its driving factors in the arid region of northwestern China. The results showed that: (1) the growing season length (GSL) was delayed, with a regional trend of 8 d/33 a, due to a significant advancement in the start of the growing season (SOS, −7 d/33 a) and an insignificant delay to the end of growing season (EOS, 2 d/33 a). (2) The regional change in NDVI was mainly driven by temperature and precipitation, contributing to variations in NDVI of forest of 36% and 15%, respectively, and in the NDVI of grassland, of 35% and 21%, respectively. In particular, changes to forested land and medium-coverage grassland (Mgra) were closely related to temperature and precipitation, respectively. (3) The spatial distribution of the mean NDVI of forest was closely related with precipitation, temperature, and solar radiation, with these meteorological variables explaining 20%, 15%, and 10% of the variation in NDVI, respectively. Precipitation and solar radiation explained 29% and 17% of the variation in the NDVI of grassland, respectively. The study reveals the spatial–temporal evolution and driving mechanism of the NDVI of natural vegetation in the arid region of Northwest China, which can provide theoretical and data support for regional vegetation restoration and conservation. [ABSTRACT FROM AUTHOR]

Zymomonas mobilis is a native ethanologen with the unique anaerobic Entner Doudoroff pathway. In this study, a heterologous poly-3-hydroxybutyrate (PHB) pathway was engineered into Z. mobilis. Genetic strategies were applied to improve PHB production including overexpression of the phaCAB operon by using a strong promoter and/or increasing its copy numbers, cofactor balance by overexpressing the endogenous genes for NADPH balance, and competition of acetaldehyde for acetyl-CoA generation through the ethanol utilization pathway (EUP). PHB contents in recombinant strains were enhanced gradually. The highest PHB content of 37.68 ± 2.56% (dry cell weight, DCW) was achieved in the ZMPtN2-EUP strain, which contains copies of the phaCAB operon in both the chromosome and plasmid, an extra copy of the native zwf gene for cofactor balance, and genes for the EUP. The PHB production in ZMPtN2-EUP was further improved by optimizing the C/N ratio, and the PHB content was increased to 50.30 ± 3.39% (DCW) at a C/N ratio of 20/2 (20 g L−1 glucose and 2 g L−1 yeast extract). ZMPtN2-EUP was then empowered with the self-flocculating phenotype using a native CRISPR-Cas genome editing system to generate ZMPt-FloN2-EUP for biomass recovery through gravity sedimentation. The PHB content of 40.57 ± 0.60% (DCW) in ZMPt-FloN2-EUP with self-flocculation capability was slightly higher than that of ZMPtN2-EUP, which further increased to 74.03 ± 1.81% (DCW) at a C/N ratio of 20/2. Moreover, ZMPtN2-EUP was able to be used as a microbial biocatalyst for continuous co-production of high-titer ethanol and PHB without affecting ethanol percentage productivity. Our study thus demonstrated that the strategy to combine genetic and process engineering is effective to develop non-model microorganisms for heterologous production of bio-based chemicals, and the capability of Z. mobilis for continuous co-production of high-titer ethanol and PHB exhibited that Z. mobilis is a promising chassis for economic biochemical production. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is the largest flux in the global terrestrial carbon cycle. Drought has significantly impacted global terrestrial GPP in recent decades, and has been projected to occur with increasing frequency and intensity. However, the drought risk of global terrestrial GPP has not been well investigated. In this study, global terrestrial GPP during 1981–2016 was simulated with the process‐based Boreal Ecosystem Productivity Simulator model. Then, the drought risk of GPP was quantified as the product of drought probability and reduction of GPP caused by drought, which was determined using the standardized precipitation evapotranspiration index. During the study period, the drought risk of GPP was high in the southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. It was low at some high latitudes of the Northern Hemisphere and in part of tropical South America, where terrestrial GPP increased slightly in drought years. The drought risk of terrestrial GPP was greater during 2000–2016 than during 1981–1999 in 21 out of 24 climatic zones. The global mean drought risk of GPP increased from 13.6 g C m−2 yr−1 during 1981–1999 to 19.3 g C m−2 yr−1 during 2000–2016. The increase in drought risk of GPP was mainly caused by the increase in drought vulnerability. Simulation experiments indicated that the drought vulnerability of GPP was mainly induced by climatic variability. This study advances our understanding on the impact of drought on GPP over the globe. Plain Language Summary: Drought generally affects gross primary productivity (GPP) of terrestrial ecosystems. However, the drought risk of global terrestrial GPP has not been well investigated. We assessed the drought risk of GPP based on the simulation by a process‐based model for the years 1981–2016. The drought risk of GPP was quantified in terms of the chances of drought occurring and the anticipated reduction of GPP that would be caused by the drought. The results were variable across the world, being serious in the southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. The drought risk of GPP at some high latitudes of the Northern Hemisphere and in part of tropical South America was not found to be problematic. In those areas, GPP increased slightly in drought years in comparison with that in normal years. The drought risk of terrestrial GPP was greater during 2000–2016 than during 1981–1999 in 21 out of 24 climatic zones. The global mean drought risk of GPP increased from 13.6 g C m−2 yr−1 during 1981–1999 to 19.3 g C m−2 yr−1 during 2000–2016. The increase in drought risk of GPP was mainly caused by climatic variability. Key Points: Drought vulnerability and risk of reductions in global terrestrial gross primary productivity (GPP) were assessed using a remote‐sensing, process‐based modelHigh risk located in southeastern United States, southern Europe, most of South America, central and eastern Africa, and eastern AustraliaThe risk of reductions in global GPP was greater in 2000–2016 than in 1981–1999, mainly owing to the increase in drought vulnerability [ABSTRACT FROM AUTHOR]

We discuss an algorithm for the autoregression (AR) model as a typical time-series model. By analyzing the structure of the AR model, we highlight the shortcomings of traditional algorithms for model parameter estimation and propose an approach to overcome the shortcomings of traditional solutions for the AR model. The errors-in-variables (EIV) model is used to solve an existing problem. There is an obvious difference between the AR model and a traditional model. For the AR model, one observation datum appears repeatedly; hence, the residual of one observation datum is not unique. Furthermore, in theory, the optimal estimation of model parameters cannot be obtained by current solutions. Based on an analysis of the AR model, we focus on how to obtain the optimal estimation when the observation data of the AR model are contaminated by outliers. The median function is used to establish a modified solution for the AR model based on the institute of geodesy & geophysics, Chinese academy of sciences (IGG) weight function by comparison with current algorithms for the robust estimation of the EIV model. We propose an iterative algorithm based on median function. Finally, we apply two numerical instances to compare the proposed algorithm with traditional algorithms and draw conclusions from results of the instances. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Chlorella was introduced into MFC for Cd(II) removal in the biocathode. • Simultaneous high power generation and metal removal efficiencies can be achieved. • NF-rGO enhanced power generation (36.4 mW m−2) and Cd(II) removal (115 g m−2). • NF-rGO improve the maximum tolerable concentrations of Cd(II) for algal biocathode. • Cadmium were removed mainly through hydroxide precipitation and biosorption. Abstract Microalgae ( Chlorella sp. QB-102) was introduced into microbial fuel cell (MFC) as the cathode for Cd(II) removal using nickel foam/graphene (NF/rGO) as electrodes. It was found that graphene enhanced the power generation and decreased the internal resistance in the algal-cathode MFC. The start-up time was shortened and the maximum power density obtained by using NF/rGO (36.4 mW m−2) was eight times higher against bare NF. Graphene further improve the maximum tolerable concentrations of Cd(II) in the algal-cathode, which estimated to be 50 ppm. Using NF/rGO electrode, the algal-cathode MFC achieved a high Cd(II) removal efficiency of almost 95% and a maximum adsorption amount of 115 g m−2. It was found that hydroxide precipitation and biosorption are the major mechanisms for the Cd removal in the biocathode self-sustained MFC. The results suggested that photoautographic MFC is feasible and effective for heavy metal removal in the algal-cathode and graphene modification in the electrodes is recommended. [ABSTRACT FROM AUTHOR]

Temperature and light intensity are important environmental factors influencing microalgae for biodiesel production. The aim of present work was to study the effects of temperature (15 °C, 25 °C, and 35 °C) and its combination with high light intensity (HL, 400 μmol photon m −2  s −1 ) on lipid production of Monoraphidium dybowskii Y2 which was isolated from desert. The results demonstrated that algal growth was only inhibited at 15 °C. Promoted lipid content and decreased Fv/Fm were observed in 15 °C and 35 °C. Cellular carbohydrate, protein conversion and membrane lipid (MGDG, DGDG and SQDG) remodeling contributes for lipid accumulation. Stress combined temperatures with HL are benefit for lipid production, especially desired neutral lipid productivity all exceed 40 mg L −1  d −1 . Fatty acids compositions of C16:0 and C18:1 were further promoted under 15 °C or 35 °C combined with HL. Thus, M. dybowskii Y2 will well-adapted to outdoors cultivation for biodiesel production. [ABSTRACT FROM AUTHOR]

Abstract: Recently, severe droughts that occurred in North America are likely to have impacted its terrestrial carbon sink. However, process‐based understanding of how meteorological conditions prior to the onset of drought, for instance warm or cold springs, affect drought‐induced carbon cycle effects remains scarce. Here we assess and compare the response of terrestrial carbon fluxes to summer droughts in 2011 and 2012 characterized by contrasting spring conditions. The analysis is based on a comprehensive ensemble of carbon cycle models, including FLUXCOM, TRENDY v5, SiBCASA, CarbonTracker Europe, and CarbonTracker, and emerging Earth observations. In 2011, large reductions of net ecosystem production (NEP; −0.24 ± 0.17 Pg C/year) are due to decreased gross primary production (−0.17 ± 0.18 Pg C/year) and slightly increased ecosystem respiration (+0.07 ± 0.17 Pg C/year). Conversely, in 2012, NEP reductions (−0.17 ± 0.25 Pg C/year) are attributed to a larger increase of ecosystem respiration (+0.48 ± 0.27 Pg C/year) than gross primary production (+0.31 ± 0.29 Pg C/year), induced predominantly by an extra warmer spring prior to summer drought. Two temperate ecoregions crops/agriculture and the grass/shrubs contribute largest to these reductions and also dominate the interannual variations of NEP during 2007–2014. Moreover, the warming spring compensated largely the negative carbon anomaly due to summer drought, consistent with earlier studies; however, the compensation occurred only in some specific ecoregions. Overall, our analysis offers a refined view on recent carbon cycle variability and extremes in North America. It corroborates earlier results but also highlights differences with respect to ecoregion‐specific carbon cycle responses to drought and heat. [ABSTRACT FROM AUTHOR]

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties. [ABSTRACT FROM AUTHOR]

Background: Compared with other general energy crops, microalgae are more compatible with desert conditions. In addition, microalgae cultivated in desert regions can be used to develop biodiesel. Therefore, screening oil-rich microalgae, and researching the algae growth, CO2 fixation and oil yield in desert areas not only effectively utilize the idle desertification lands and other resources, but also reduce CO2 emission. Results: Monoraphidium dybowskii LB50 can be efficiently cultured in the desert area using light resources, and lipid yield can be effectively improved using two-stage induction and semi-continuous culture modes in open raceway ponds (ORPs). Lipid content (LC) and lipid productivity (LP) were increased by 20% under two-stage industrial salt induction, whereas biomass productivity (BP) increased by 80% to enhance LP under semi-continuous mode in 5 m2 ORPs. After 3 years of operation, M. dybowskii LB50 was successfully and stably cultivated under semi-continuous mode for a month during five cycles of repeated culture in a 200 m2 ORP in the desert area. This culture mode reduced the supply of the original species. The BP and CO2 fixation rate were maintained at 18 and 33 g m−2 day−1, respectively. Moreover, LC decreased only during the fifth cycle of repeated culture. Evaporation occurred at 0.9–1.8 L m−2 day−1, which corresponded to 6.5–13% of evaporation loss rate. Semi-continuous and two-stage salt induction culture modes can reduce energy consumption and increase energy balance through the energy consumption analysis of life cycle. Conclusion: This study demonstrates the feasibility of combining biodiesel production and CO2 fixation using microalgae grown as feedstock under culture modes with ORPs by using the resources in the desert area. The understanding of evaporation loss and the sustainability of semi-continuous culture render this approach practically viable. The novel strategy may be a promising alternative to existing technology for CO2 emission reduction and biofuel production. [ABSTRACT FROM AUTHOR]

Pretreatment is the key step to overcome the recalcitrance of lignocellulosic biomass making sugars available for subsequent enzymatic hydrolysis and microbial fermentation. During the process of pretreatment and enzymatic hydrolysis as well as fermentation, various toxic compounds may be generated with strong inhibition on cell growth and the metabolic capacity of fermenting strains. Zymomonas mobilis is a natural ethanologenic bacterium with many desirable industrial characteristics, but it can also be severely affected by lignocellulosic hydrolysate inhibitors. In this review, analytical methods to identify and quantify potential inhibitory compounds generated during lignocellulose pretreatment and enzymatic hydrolysis were discussed. The effect of hydrolysate inhibitors on Z. mobilis was also summarized as well as corresponding approaches especially the high-throughput ones for the evaluation. Then the strategies to enhance inhibitor tolerance of Z. mobilis were presented, which include both forward and reverse genetics approaches such as classical and novel mutagenesis approaches, adaptive laboratory evolution, as well as genetic and metabolic engineering. Moreover, this review provided perspectives and guidelines for future developments of robust strains for efficient bioethanol or biochemical production from lignocellulosic materials. [ABSTRACT FROM AUTHOR]

The paddy rice fields act as an important anthropogenic source of methane (CH 4) to the atmosphere. The study of pattern, magnitude, and environmental controls of CH 4 emissions are still insufficient due to limited measurements and understand of underlying drivers for variations of CH 4 fluxes at different temporal scales. In this study, CH 4 fluxes from a rice-wheat rotation agroecosystem in eastern China were continuously measured using the eddy covariance technique. The diurnal and seasonal variations of CH 4 flux and potential controlling factors in 2016 were analyzed using wavelet coherence, conditional Granger causality, correlation analysis and path analysis methods. CH 4 fluxes showed distinguishable diurnal variations with single peaks during 13: 00–16: 00 local time. At the diurnal timescale, gross primary productivity (GPP) regulates CH 4 fluxes after accounting for the effects of latent heat flux (LE), air temperature (TA), and soil temperature (TS) on CH 4 fluxes. LE mirrored the diurnal pattern of CH 4 fluxes when the effects of TA and TS on CH 4 fluxes were considered. Daily CH 4 fluxes exhibited large seasonal variations, with the largest daily CH 4 flux of 1191.78 mg C-CH 4 m−2 d−1 on 29 July 2016. The daily CH 4 fluxes were continuously low in the growing season of wheat, and sharply increased from very low values in late June to peaks in late July and early August, and then gradually decreased to low values at the end of the rice growing season in late November and early December. Correlation analysis and path analysis showed that seasonal variations of soil temperature, air temperature, and GPP had strong effects on daily CH 4 fluxes during pre-panicle initiation of the rice growing season, while soil temperature and leaf area index (LAI) had very strong effects on daily CH 4 fluxes during the post-panicle initiation stage. The total of CH 4 fluxes from the rice-wheat rotation agroecosystem into the atmosphere amounted to 58.08 ± 9.87 g C m−2 in 2016, and the annual net carbon (C) budget and greenhouse gas (GHG) budget were 163.50 ± 9.87 g C m−2 and 2322.53 ± 329.00 g CO 2 eq m−2, respectively. This study represents a comprehensive assessment of fluxes and drivers of CH 4 from a rice-wheat rotation agroecosystem at different timescales. Additionally, the consecutive data of CH 4 emission in this region will also useful for model calibration and validation. Unlabelled Image • CH 4 fluxes from a rice-wheat rotation agroecosystem peaked in early afternoon. • Seasonal variations of daily CH 4 fluxes can be explain by daily TA, TS, GPP, and LAI. • The rice-wheat rotation agroecosystem in eastern China acted as C and GHG sources in 2016. [ABSTRACT FROM AUTHOR]

Light precipitation events generally occur much more frequently than events of other precipitation intensities. Changes in light precipitation amounts and days (length) over time have profound implications for climate change. There is a lack of quantitative monitoring indicators or in-depth analysis techniques for atmospheric circulation mechanisms of regional light precipitation events, especially in climate transitional zones. The Huai River Eco-economic Corridor (HREC) served as a case area in this study as we assessed trends in light precipitation events and their possible atmospheric circulation mechanisms. A significant decreasing trend in the light precipitation events is observed and appears to be mainly concentrated in the lower intensities (0.1 ≤ P<2 mm/day). The decreasing trend is more obvious for consecutive than discrete light precipitation events. The mutation points for annual consecutive light precipitation events were found in 1977/1978, and averages in the later years of the observation period (1978–2014) fell significantly below those of the earlier period (1961–1977). Atmospheric circulation variables corresponding to the light precipitation days with intensity of 0.1–2 mm/day revealed that the decreasing trends in water vapor transport budgets, low cloud cover, and total column cloud liquid water may be the primary causes of decreased light precipitation events. The decreasing trends in light precipitation events were also likely attributable to increases in water vapor flux divergence, air temperature, and vertical velocity values. • Decrease in light precipitation events is mainly concentrated at lower light precipitation intensities (0.1 ≤ P<2 mm/day). • Consecutive light precipitation events decrease to greater extent than discrete light precipitation events. • Decreases in light precipitation events are affected by atmospheric circulation patterns in HREC. [ABSTRACT FROM AUTHOR]