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Epoxide hydrolases (EHs) catalyze the conversion of epoxides into vicinal diols. The epoxide hydrolase gene from P. chrysosporium was previously cloned and subjected to site-directed mutation to study its enzyme activity, but the results were unsatisfactory. This study used error prone PCR and DNA shuffling to construct a PchEHA mutation library. We performed mutation-site combinations on PchEHA based on enzyme activity measurement results combined with directed evolution technology. More than 15,000 mutants were randomly selected for the preliminary screening of PchEHA enzyme activity alongside 38 mutant strains with increased enzyme activity or enantioselectivity. Protein expression and purification were conducted to determine the hydrolytic activity of PchEHA, and three mutants increased their activity by more than 95% compared with that of the wt. After multiple rounds of screening and site-specific mutagenesis, we found that F3 offers the best enzyme activity and enantioselectivity; furthermore, the molecular docking results confirmed this result. Overall, this study uncovered novel mutants with potential value as industrial biocatalysts.

Streamflow change and its influencing factors are synchronous and correlated in temporal and spatial scales. The aim of this study is to develop a spatio-temporal coupling analysis framework for quantifying streamflow change and its influencing factors was established. Specifically, the Mann–Kendall test, Pettitt's test, hierarchical cluster analysis, and Ripley's L-function were jointly used to study the spatial heterogeneity of the temporal evolution of streamflow; and the Soil and Water Assessment Tool (SWAT) model was employed to quantify the impacts of climate and human activities on streamflow change. The preliminary application in the Dawen River Basin (China) case has shown that (1) the natural streamflow change in the basin during 1953–2013 is mainly affected by climate change–human activities, followed by climate change and human activities, accounting for a total area of 52.04, 24.90, and 23.06%, respectively; and (2) the vast majority of sub-basins with relatively large natural streamflow change are mainly driven by climate change (i.e., precipitation). In general, the proposed framework can effectively reflect the spatio-temporal patterns of streamflow change and its influencing factors, which can provide a theoretical basis for water resources management in the context of global change. HIGHLIGHTS Developing a spatio-temporal coupling analysis framework for quantifying streamflow and its influencing factors.; Quantifying the contribution of drivers to the streamflow change on the sub-basin scale.; Streamflow change and their spatial patterns in the DRB are mainly driven by climate change–human activities.; The vast majority of sub-basins with relatively large streamflow change are mainly driven by climate change.;

Abstract The availability of terrestrial water storage anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) supports many hydrological applications. Five TWSA products are operational and publicly available, including three based on mass concentration (mascon) solutions and two based on the synthesis of spherical harmonic coefficients (SHCs). The mascon solutions have advantages regarding the synthesis of SHCs since the basis functions are represented locally rather than globally, which allows geophysical data constraints. Alternative new solutions based on SHCs are, therefore, critical and warranted to enrich the portfolio of user-friendly TWSA data based on different algorithms. TWSA data based on novel processing protocols is presented with a spatial re-sampling of 0.25 arc-degrees covering 2002–2022. This approach parameterizes the improved point mass (IPM) and adopts the synthesized residual gravitational potential as observations. The assay indicates that the proposed Hohai University (HHU-) IPM TWSA data reliably agree with the mascon solutions. The presented HHU-IPM TWSA data set would be instrumental in regional hydrological applications, particularly enabling improved assessment of regional water budgets.

This study discovered non-monotonic variations in river flows for seven rivers originating from the Tibetan Plateau at warming levels of 1.5 °C, 2.0 °C, and 3.0 °C, which then resulted in different consequences for riparian countries

Abstract Rivers are among the most diverse, dynamic, and productive ecosystems on Earth. River flow regimes are constantly changing, but characterizing and understanding such changes have been challenging from a long-term and global perspective. By analyzing water extent variations observed from four-decade Landsat imagery, we here provide a global attribution of the recent changes in river regime to morphological dynamics (e.g., channel shifting and anabranching), expansion induced by new dams, and hydrological signals of widening and narrowing. Morphological dynamics prevailed in ~20% of the global river area. Booming reservoir constructions, mostly skewed in Asia and South America, contributed to ~32% of the river widening. The remaining hydrological signals were characterized by contrasting hotspots, including prominent river widening in alpine and pan-Arctic regions and narrowing in the arid/semi-arid continental interiors, driven by varying trends in climate forcing, cryospheric response to warming, and human water management. Our findings suggest that the recent river extent dynamics diverge based on hydroclimate and socio-economic conditions, and besides reflecting ongoing morphodynamical processes, river extent changes show close connections with external forcings, including climate change and anthropogenic interference.

Automatic extraction of surface water is of great significance to the study of the global water cycle and the dynamic management of water resources. At present, the water extraction accuracy of high-resolution multi-spectral remote sensing images has been greatly improved. But it is still affected by the shadow of the mountains and the shadow of the tall buildings inside the city. The spectral information of shadow is basically consistent with the spectral information of water, so the accuracy of any traditional index extraction of water will be questioned by users. Or the user must adjust threshold parameters many times to obtain good extraction results, which is contradictory to fast and large-area remote sensing monitoring. To solve the above problems, this paper firstly introduces the thermal infrared band at the data end for pre-treatment. Then, a lightweight neural network (EDCM) combining the most advanced lightweight image classification model and semantic segmentation model is proposed, which is specially used for fast, large area and automatic water extraction. Multi-scale training of samples using lightweight convolutional networks aims to extract multi-scale context information. The newly constructed model was tested in three highly heterogeneous scenarios, and the results showed that the trained EDCM model achieved the highest accuracy in all selected test areas, reaching more than 95.28%. The EDCM model can be used for the high-precision extraction of surface water in complex areas.

IntroductionConductance-photosynthesis (Gs-A) models, accompanying with light use efficiency (LUE) models for calculating carbon assimilation, are widely used for estimating canopy stomatal conductance (Gs) and transpiration (Tc) under the two-leaf (TL) scheme. However, the key parameters of photosynthetic rate sensitivity (gsu and gsh) and maximum LUE (ϵmsu and ϵmsh) are typically set to temporally constant values for sunlit and shaded leaves, respectively. This may result in Tc estimation errors, as it contradicts field observations.MethodsIn this study, the measured flux data from three temperate deciduous broadleaved forests (DBF) FLUXNET sites were adopted, and the key parameters of LUE and Ball-Berry models for sunlit and shaded leaves were calibrated within the entire growing season and each season, respectively. Then, the estimations of gross primary production (GPP) and Tc were compared between the two schemes of parameterization: (1) entire growing season-based fixed parameters (EGS) and (2) season-specific dynamic parameters (SEA).ResultsOur results show a cyclical variability of ϵmsu across the sites, with the highest value during the summer and the lowest during the spring. A similar pattern was found for gsu and gsh, which showed a decrease in summer and a slight increase in both spring and autumn. Furthermore, the SEA model (i.e., the dynamic parameterization) better simulated GPP, with a reduction in root mean square error (RMSE) of about 8.0 ± 1.1% and an improvement in correlation coefficient (r) of 3.7 ± 1.5%, relative to the EGS model. Meanwhile, the SEA scheme reduced Tc simulation errors in terms of RMSE by 3.7 ± 4.4%.DiscussionThese findings provide a greater understanding of the seasonality of plant functional traits, and help to improve simulations of seasonal carbon and water fluxes in temperate forests.

With the development of the Chinese Fengyun satellite series, Fengyun-2G (FY-2G) quantitative precipitation estimates (QPE) can provide real-time and high-quality precipitation data over East Asia. However, FY-2G QPE cannot offer precipitation information beyond the latitude band of 50°N due to the limitation of the observation coverage of the FY-2G-based satellite-borne sensor. To this end, a precipitation space reconstruction using the geographically weighted regression (GWR) coupled with a geographical differential analysis (GDA) (PSR2G) algorithm was developed, based on the land surface variables related to precipitation, including vegetational cover, land surface temperature, geographical location, and topographic characteristics. This study used the PSR2G-based reconstructed model to estimate the FY-2G QPE over Northeast China (the latitude band beyond 50°N) from December 2015 to November 2019 with a spatiotemporal resolution of 0.1°/month. The PSR2G-based reconstructed results were validated with the ground observations of 80 rain gauges, and also compared to the reconstructed results using random forest (RF) and GWR. The results show that the spatio-temporal pattern of PSR2G QPE is closer to ground observations than those of RF and GWR, which indicates that the PSR2G QPE is more competent to capture the spatio-temporal variation of rainfall over Northeast China than other two reconstruction methods. In addition, the reconstructed precipitation dataset using PSR2G has higher accuracy over study area than the FY-2G QPE below the band of 50°N. It suggested that PSR2G reconstruction precipitation strategies do not lose the precision of the original satellite precipitation data.

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various “possible future” scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China’s climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China’s moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study’s findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation.

Drought restricts the growth of alpine grassland vegetation. This study aimed to explore a new technical system to improve the drought resistance of forage grass. Qinghai cold-land Poa pratensis seedlings were used in the drought stress experiment. A combination of abscisic acid (ABA) and polyacrylamide (PAM) were used to affect the growth, leaf physiology, soil enzyme activity, and rhizosphere microbial diversity of P. pratensis. The fresh leaf weight and root surface area were significantly increased after ABA-PAM combined treatment, while root length was significantly reduced. Besides, the leaf catalase (CAT) and superoxide dismutase (SOD) enzyme activity, proline and chlorophyll content, increased after the treatment, while malondialdehyde (MDA) content decreased. The treatment also increased sucrase, urease, and alkaline protease activities in rhizosphere soil, while decreasing acid phosphatase and neutral phosphatase enzyme activities. ABA-PAM combined treatment enhanced the rhizosphere microbial community and forage drought resistance by altering the abundance of various dominant microorganisms in the rhizosphere soil. The relative abundances of Actinobacteria, Chloroflexi, and Acidobacteria decreased, while Proteobacteria, Firmicutes, and Ascomycota increased. Unlike the relative abundance of Gibberella that decreased significantly, Komagataeibacter, Lactobacillus, Pichia, and Dekkera were significantly increased. Single-factor collinearity network analysis revealed a close relationship between the different rhizosphere microbial communities of forage grass, after ABA-PAM treatment. This study implies that ABA-PAM combined treatment can improve the drought resistance of forages. Therefore, it provides a theoretical and practical basis for restoring drought-induced grassland degradation.

Using the precipitation measurements obtained from 2,419 ground meteorological stations over China from 1960 to 2005 as benchmark, the performance of 21 single-mode precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were evaluated using Taylor diagrams and several statistical metrics. Based on statistical metrics, the models were ranked in terms of their ability to reproduce similar patterns in precipitation relative to the observations. Except in Southeast and Pearl river basins, research results show that all model ensemble means overestimate in the rest of the river basins, especially in Southwest and Northwest. The performance of CMIP5 models is quite different among each river basin; most models show significant overestimation in Northwest and Yellow and significant underestimations in Southeast and Pearl. The simulations are more reliable in Songhua, Liao, Yangtze, and Pearl than in other river basins according to spatial distribution and interannual variability. No individual model performs well in all the river basins both spatially and temporally. In Songhua, Liao, Yangtze, and Pearl, precipitation indices are more consistent with observations, and the spread among models is smaller. The multimodel ensemble selected from the most reasonable models indicates improved performance relative to all model ensembles. HIGHLIGHTS The performance of CMIP5 models were evaluated using the precipitation measurements obtained from meteorological stations.; The performance of CMIP5 models is quite different among each river basin.; No individual model performs well in all the river basins both spatially and temporally.; The multimodel ensemble selected from the most reasonable models indicates improved performance relative to all model ensembles.;

The terrestrial water storage anomaly (TWSA) from the previous Gravity Recovery and Climate Experiment (GRACE) covers a relatively short period (15 years) with several missing periods. This study explores the boosted regression trees (BRT) and the artificial neural network (ANN) to reconstruct the TWSA series between 1982 and 2014 over the Yangtze River basin (YRB). Both algorithms are trained with several hydro-climatic variables (e.g., precipitation, soil moisture, and temperature) and climate indices for the YRB. The results from this study show that the BRT is capable of reconstructing TWSA and shows Nash–Sutcliffe efficiency (NSE) of 0.89 and a root-mean-square error (RMSE) of 18.94 mm during the test stage, outperforming ANN in about 2.3% and 7.4%, respectively. As a step further, the reliability of this technique in reconstructing TWSA beyond the GRACE era was also evaluated. Hence, a closed-loop simulation using the artificial TWSA series over 1982–2014 under the same scenarios for the actual GRACE data shows that BRT can predict TWSA (NSE of 0.92 and RMSE of 6.93 mm). Again, the BRT outperformed the ANN by approximately 1.1% and 5.3%, respectively. This study provides a new perspective for reconstructing and filling the gaps in the GRACE–TWSA series over data-scarce regions, which is desired for hydrological drought characterization and environmental studies. BRT offers such an opportunity for the GRACE Follow-On mission to predict 11 months of missing TWSA data by relying on a limited number of predictive variables, hence being adjudged to be more economical than the ANN.

With the rapid development of China's economy and the continuous improvement of people's living standards, large-scale infrastructure constructions (LSICs) are also increasing rapidly. LSICs with impervious surfaces have increasingly resulted in replacing natural landscapes, altering surface radiation, thermal properties, and humidity in urban areas. To study the environmental thermal changes of Beijing Daxing International Airport before and after its construction and operation, four Landsat-8 images (from the year of 2014, 2017, 2019, and 2021) were used to calculate the land surface temperature (LST). Then the LST values of four images covering the study area were compared and analyzed using the urban heat island ratio index (URI). Results show that the URI value of this area increased from 0.120 of 2014 to 0.185 of 2017 after the construction of Daxing Airport, indicating that the urban surface heat island effect in this area greatly increased. Additionally, the URI value of this area increased from 0.153 of 2019 to 0.206 of 2021 after the operation of Daxing Airport, indicating that the surface urban heat island effect in this area further increased. Therefore, we infer that this effect is closely related to airport construction and operation. Afterward, the random forest classification algorithm is used to classify land types based on pixels, and then the relationship between URI and land classification types was discussed. It is found that before the construction of Daxing Airport, both dark buildings and bare land contribute significantly to the thermal environment of the airport. After the completion of Daxing Airport, the contribution model was changed to high-reflectivity buildings and bright soil. The thermal pollution generated by the airport has a greater impact on the ground objects within the range of 7.5 km, and a relatively smaller impact on the ground objects outside the range of 9.5 km. Our results can provide a valuable reference for the study of the thermal environment caused by human activities.

Late blight is one of the main biological stresses limiting the potato yield; however, the biochemical mechanisms underlying the infection process of Phytophthora infestans remain unrevealed. In this study, the late blight-resistant potato cultivar Ziyun No.1 (R) and the susceptible cultivar Favorita (S) were inoculated with P. infestans. Untargeted metabolomics was used to study the changes of metabolites in the compatible and incompatible interactions of the two cultivars and the pathogen at 0, 48, and 96 h postinoculation (hpi). A total of 819 metabolites were identified, and the metabolic differences mainly emerged after 48 hpi. There were 198 and 115 differentially expressed metabolites (DEMs) in the compatible and incompatible interactions. These included 147 and 100 upregulated metabolites during the compatible and incompatible interactions, respectively. Among them, 73 metabolites were identified as the P. infestans-responsive DEMs. Furthermore, the comparisons between the two cultivars identified 57 resistance-related metabolites. Resistant potato cultivar had higher levels of salicylic acid and several upstream phenylpropanoid biosynthesis metabolites, triterpenoids, and hydroxycinnamic acids and their derivatives, such as sakuranetin, ferulic acid, ganoderic acid Mi, lucidenic acid D2, and caffeoylmalic acid. These metabolites play crucial roles in cell wall thickening and have antibacterial and antifungal activities. This study reports the time-course metabolomic responses of potatoes to P. infestans. The findings reveal the responses involved in the compatible and incompatible interactions of potatoes and P. infestans.

One of the important tasks of the Chinese geostationary and meteorological satellite Fengyun-2 (FY2) series is to provide quantitative precipitation estimates (QPE) with high spatiotemporal resolutions for East Asia. To analyze the monitoring capabilities of FY2-based QPEs in extreme rainfall events, this study comprehensively evaluated and compared the performances of FY-2G and FY-2H QPEs for the “7.20” rainstorm in Henan province, China from 17 July 2021 to 22 July 2021. Three continuous metrics and three categorical metrics were adopted to assess the accuracies of FY-2G and FY-2H QPEs, referenced by gauge observations from 116 meteorological stations. The results show that the FY-2G QPE has lower BIAS (−9.64% for FY-2G, −46.22% for FY-2H) and RMSE (5.83 mm/h for FY-2G, 8.4 mm/h for FY-2H) and higher CC (0.57 for FY-2G, 0.24 for FY-2H) than FY-2H QPE in this rainstorm event. Moreover, the FY-2G QPE is not only more consistent with the ground reference with respect to the rainfall amount, but also has higher detecting capability in the “7.20” rainstorm event when compared with the FY-2H QPE. The FY-2G QPE presented a higher capability to correctly capture the precipitation event for the “7.20” rainstorm because of higher POD (probability of detection) and CSI (critical success index) relative to FY-2H QPE, especially in complex topography. From the spatial distribution of precipitation amount, the FY-2G QPE captured the rainstorm center of extreme precipitation more accurately relative to the latest FY-2H product. On the other hand, the previous generation of FY-2G QPE was closer to the continuous rainfall process and precipitation duration with ground observations than the latest FY-2H QPE. Therefore, the precipitation retrieval algorithm of FY-2H QPE still had room to improve. It is necessary to introduce error correction algorithms, especially in complex topography for rainstorm events.

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data.

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) provides new-generation satellite precipitation datasets with high spatio-temporal resolution and accuracy, which is widely applied in hydrology and meteorology. However, most examinations of the IMERG were conducted on daily, monthly, and annual scales, and inadequate research focused on the sub-daily scale. Thus, this study set up four sub-daily scales (1 h, 3 h, 12 h, and 24 h at 0.1° spatial resolution) and four spatial scales (0.1°, 0.25°, 0.5°, and 1° at 1 h temporal resolution) to finely evaluate the performance of IMERG products in the summer seasons from 2014 to 2019 over mainland China. The precipitation amount (PA), frequency (PF), and intensity (PI) were adopted to assess the performance of the IMERG referenced by the ground-based precipitation product of the China Meteorological Administration (CMA). The results show that the IMERG can capture the spatial patterns of precipitation characters over mainland China, but the PA and PI are overestimated and the PF is underestimated, and the evaluation results are highly sensitive to the different temporal and spatial resolutions. Compared with fine spatio-temporal scales, the performance of the IMERG is significantly improved when scaled up to coarser scales. Moreover, the IMERG shows a better performance of PA and PI in larger regions and during longer periods. This study provided a reference for the application of IMERG products in different spatial and temporal scales.

Abstract Bacillus subtilis S1-4, isolated from chicken feather could efficiently degrade feathers by secreting several extracellular proteases. In order to get insight into the individual protease involved in keratin hydrolysis, a keratinase designed as BsKER71 was cloned and expressed in Bacillus subtilis WB600. In silico analysis revealed that BsKER71 protein contained a mature protein of 36.1 kDa. Further, purified BsKER71 could hydrolyze a variety of natural proteins, such as fibrous protein, collagen protein, casein, keratin and bovine serum albumin. In addition, this keratinase exhibited high enzyme activity in a wide range of pH and optimal pH of 10.0 and 9.0 in the hydrolysis of casein and keratin, respectively. Similarly, the optimal temperature was 55 °C and 50 °C for the hydrolysis of above two substrates, respectively. The hydrolytic activity was significantly inhibited by phenylmethanesulfonyl fluoride (PMSF), indicating the presence of serine residue in the active site. Moreover, ethylenediaminetetraacetic acid (EDTA) and phenanthroline moderately inhibited the hydrolytic activity. The catalytic activity was stimulated by Mg2+ and Ca2+, but greatly inhibited by Cu2+. Furthermore, several chemicals exhibited different effects on the hydrolysis of casein and keratin by BsKER71. These results provided a better understanding of BsKER71 from feather degrading bacterium B. subtilis S1-4.

Silver nanoparticles (AgNPs) were synthesized with wheat straw biomass at room temperature using light irradiation. The reaction conditions were optimized, including the light intensity, biomass concentration, NaCl addition, and reaction time. The silver nanoparticles fabricated at the optimum conditions (light intensity 60,000 lx, biomass concentration 2 mg/mL, and reaction time 90 min) were characterized by UV-Vis spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and zeta potential. The TEM results showed that the silver nanoparticles were mostly spherical, with an average diameter of 17.2 nm. The zeta potential of AgNPs reached -21.6 mV. The XRD spectra showed that the AgNPs were highly crystalline, with four characteristic peaks. The FTIR of nanoparticles implied that alcohols and proteins may have a vital role in the formation and stability of AgNPs. The silver nanoparticles synthesized by wheat straw biomass revealed antimicrobial activity against Escherichia coli and Bacillus subtilis strains.

In the so-called point-mass modeling, surface densities are represented by point masses, providing only an approximated solution of the surface integral for the gravitational potential. Here, we propose a refinement for the point-mass modeling based on Taylor series expansion in which the zeroth-order approximation is equivalent to the point-mass solution. Simulations show that adding higher-order terms neglected in the point-mass modeling reduces the error of inverted mass changes of up to 90% on global and Antarctica scales. The method provides an alternative to the processing of the Level-2 data from the Gravity Recovery and Climate Experiment (GRACE) mission. While the evaluation of the surface densities based on improved point-mass modeling using ITSG-Grace2018 Level-2 data as observations reveals noise level of approximately 5.77 mm, this figure is 5.02, 6.05, and 5.81 mm for Center for Space Research (CSR), Goddard Space Flight Center (GSFC), and Jet Propulsion Laboratory (JPL) mascon solutions, respectively. Statistical tests demonstrate that the four solutions are not significant different (95% confidence) over Antarctica Ice Sheet (AIS), despite the slight differences seen in the noises. Therefore, the estimated noise level for the four solutions indicates the quality of GRACE mass changes over AIS. Overall, AIS shows a mass loss of −7.58 mm/year during 2003–2015 based on the improved point-mass solution, which agrees with the values derived from mascon solutions.

Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.

As a tipping bucket rain gauge, the HOBO Data Logging Rain Gauge RG3-M (RG3-M) has been widely used for the field precipitation observation owing to its superiority of independent power supply by a small portable battery. To quantify the measurement accuracy of the RG3-M gauge, a standard Manual Gauge (MG) and eight other models of tipping bucket rain gauges were installed at the Chuzhou hydrological experiment station of China. In this study, we first compared and investigated the accumulated mounts of 18 rainfall events of two RG3-M gauges benchmarked by the standard MG. Then, five typical rainfall events were chosen to further analyse the observed accuracy of the RG3-M gauge for different rainfall intensities at hourly temporal scale. Finally, the impacts of wind speed and rainfall intensity on the precipitation measurements of the RG3-M gauge were preliminarily explored. Results indicate that the RG3-M gauge measurement generally underestimates rainfall approximately −4% against the standard MG observation, but the maximum deviation even reaches −12.87%. In terms of the hourly rainfall process, the reliable measurement scope of the RG3-M gauge is ranging from 1.5 to 3 mm/h; however, it should be noted that the underestimation is rather significant at the higher rainfall rates (>6 mm/h). Last, it was found that rainfall intensity is a nonnegligible factor for influencing the measurement of the RG3-M gauge. But the windy effect seems to be insignificant in our experiments, which might be attributed to the similar exposure of the compared gauges.

ABSTRACT Phytoene synthase (PSY) is the rate-limiting enzyme for carotenoid biosynthesis. To date, several studies focused on PSY genes in the context of abiotic stress responses. In this study, two phytoene synthase encoding genes, IbPSY1 and IbPSY2, were identified from a published transcriptome and bioinformatic analysis showed that they shared conserved domains with phytoene synthases from other plants. The IbPSY1 gene was cloned and carefully characterized. Digital gene expression profiling (DGE) showed that the highest transcription level of IbPSY1 was in young leaves, and the lowest level was in stems. In vivo expression levels of IbPSY1 under abiotic stress were observed to be highest in stems at day 11. Over-expression of IbPSY1 in Escherichia coli and yeast cells endowed the cells with better growth under salt and drought stress than the control cells. This study demonstrated that IbPSY1 not only played an important role in vivo, but also in E. coli and yeast to improve tolerance to salinity and drought stress. Thus, IbPSY1 may be aid in the development of transgenic plants with enhanced stress tolerance.

The impact of one or two missing passive microwave (PMW) input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA) products were reproduced and re-released by National Aeronautics and Space Administration (NASA) Goddard Space Flight Center because the Advanced Microwave Sounding Unit-B (AMSU-B) and the Special Sensor Microwave Imager-Sounder-F16 (SSMIS-F16) input data were unintentionally disregarded in the prior retrieval. Thus, this study investigates the sensitivity of TMPA algorithm results to missing PMW sensors by intercomparing the “early” and “late” Version-7 TMPA real-time (TMPA-RT) precipitation estimates (i.e., without and with AMSU-B, SSMIS-F16 sensors) with an independent high-density gauge network of 200 tipping-bucket rain gauges over the Chinese Jinghe river basin (45,421 km2). The retrieval counts and retrieval frequency of various PMW and Infrared (IR) sensors incorporated into the TMPA system were also analyzed to identify and diagnose the impacts of sensor availability on the TMPA-RT retrieval accuracy. Results show that the incorporation of AMSU-B and SSMIS-F16 has substantially reduced systematic errors. The improvement exhibits rather strong seasonal and topographic dependencies. Our analyses suggest that one or two single PMW sensors might play a key role in affecting the end product of current combined microwave-infrared precipitation estimates. This finding supports algorithm developers’ current endeavor in spatiotemporally incorporating as many PMW sensors as possible in the multi-satellite precipitation retrieval system called Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG). This study also recommends users of satellite precipitation products to switch to the newest Version-7 TMPA datasets and the forthcoming IMERG products whenever they become available.

In order to investigate the physiological effects of exogenous γ-aminobutyric acid (GABA) on drought tolerance in white clover (Trifolium repens), GABA shunt, polyamines (PAs), and proline (Pro) metabolism were examined after plants pretreated with or without GABA (8 mM) and then exposed to water or 15% PEG-induced drought stress in growth chamber. In this study, exogenous application of GABA effectively alleviated drought-induced damage in leaves, as reflected by significantly higher relative water content, lower electrolyte leakage, lipid peroxidation, and leaf wilt. Exogenous GABA further promoted drought-induced increases in GABA transaminase and alpha ketone glutarate dehydrogenase activities, but inhibited glutamate decarboxylase activity under both control and drought conditions, resulting in an increase in endogenous glutamate (Glu) and GABA content. Besides, exogenous GABA could well accelerated PAs synthesis and suppressed PAs catabolism, which lead to the extremely enhanced different types of PAs content (free Put and Spd, insoluble bound Spd and Spm, soluble conjugated Spd and Spm, and total Put, Spd and Spm) under drought stress. In addition, exogenous GABA application further activated drought-induced Δ1-pyrroline-5-carboxylate synthetase and proline dehydrogenase activities, but suppressed drought-facilitated ornithine -δ-amino transferase activities, leading to a higher Pro accumulation and metabolism in GABA-pretreated plants in the middle and last period of drought. The results suggested that increased endogenous GABA by exogenous GABA treatment could improve drought tolerance of white clover associated with a positive regulation in the GABA-shunt, PAs and Pro metabolism.

The near-real-time satellite-derived precipitation estimates are attractive for a wide range of applications like extreme precipitation monitoring and natural hazard warning. Recently, a gauge-adjusted near-real-time GSMaP precipitation estimate (GSMaP_Gauge_NRT) was produced to improve the quality of the original GSMaP_NRT. In this study, efforts were taken to investigate and validate the performance of the GSMaP_Gauge_NRT using gauge observations over Mainland China. The analyses indicated that GSMaP_NRT generally overestimated the gauge precipitation in China. After calibration, the GSMaP_Gauge_NRT effectively reduced this bias and was more consistent with gauge observations. Results also showed that the correction scheme of GSMaP_Gauge_NRT mainly acted on hit events and could hardly make up the miss events of the satellite precipitation estimates. Finally, we extended the evaluation to the global scale for a broader view of GSMaP_Gauge_NRT. The global comparisons exhibited that the GSMaP_Gauge_NRT was in good agreement with the GSMaP_Gauge product. In conclusion, the GSMaP_Gauge_NRT had better performance than the GSMaP_NRT and was a more reliable near-real-time satellite precipitation product.

Airborne light detection and ranging (lidar) has become a powerful support for acquiring geospatial data in numerous geospatial applications and analyses. However, the process of extracting ground points accurately and effectively from raw point clouds remains a big challenge. This study presents an improved top-hat filter with a sloped brim to enhance the robustness of ground point extraction for complex objects and terrains. The top-hat transformation is executed and the elevation change intensity of the transitions between the obtained top-hats and outer brims is inspected to suppress the omission error caused by protruding terrain features. Finally, the nonground objects of complex structures, such as multilayer buildings, are identified by the brim filter that is extended outward. The performance of the proposed filter in various environments is evaluated using diverse datasets with difficult cases. The comparison of the proposed filter with the commercial software Terrasolid TerraScan and other popular filtering algorithms demonstrates the applicability and effectiveness of this filter. Experimental results show that the proposed filter has great promise in terms of its application in various types of landscapes. Abrupt terrain features with dramatic elevation changes are well preserved, and diverse objects with complicated shapes are effectively removed. This filter has minimal omission and commission error oscillation for different test areas and thus demonstrates a stable and reliable performance in diverse landscapes. In addition, the proposed algorithm has high computational efficiency because of its simple and efficient data structure and implementation.

The Surface Energy Balance Algorithm for Land (SEBAL) is widely used to estimate actual evapotranspiration (ETa). One major limitation of the SEBAL model is the subjectiveness in selecting extreme cold/hot pixels. In the present study, the SEBAL model is modified by determining the extreme cold/hot status, based on the theoretical trapezoidal relationship between land surface temperature (Ts) and Enhanced Vegetation Index (EVI), which is established for each pixel. In this way, the dependence of SEBAL model on the existence of extreme cold/hot status and the subjectiveness in selecting cold/hot pixels with SEBAL model are eliminated. The performance of the classical SEBAL model and the modified version, T-SEBAL, are compared for estimating ETa for a semi-arid catchment, and the result showed that the accuracy of ETa estimation is improved by the T-SEBAL model compared with the classical SEBAL model.

A bacterial strain, Streptomyces albogriseolus LBX-2, was isolated from a soil sample in Chengdu, China. S. albogriseolus LBX-2 is an aerobic and Gram-positive microorganism that is capable of using the polyethylene as the sole carbon source. Results of scanning electron microscopy and tensile tests indicated that S. albogriseolus LBX-2 could cause the damages to polyethylene (PE). Suspension culture of LBX-2 resulted in the weight loss in the PE powder over a 15-day period. The bacterial growth curve assay clearly demonstrated the utilization of n-hexadecane and n-octadecane for the strain LBX-2. Phylogenetic analysis showed that it was grouped in the same clade as S. albogriseolus belonging to Streptomyces. The complete genome of strain LBX-2 consists of a chromosome of 7,210,477 bp and a linear plasmid of 336,677 bp. Compared with other strains of Streptomyces, the genome size of S. albogriseolus LBX-2 was smaller than the average but its guanine and cytosine content (72.47%) was higher than the others. The Non-Redundant Protein Database (NR), Kyoto Encyclopedia of Genes and Genomes (KEGG), SwissProt, Gene Ontology (GO) and Clusters of Orthologous Groups (COG) annotations provided information on the specific functions of encoded proteins. A total of 21 monooxygenase and 22 dioxygenase genes were found in its genome. Synteny comparison with the genome of Streptomyces coelicolor A3(2) revealed a low overall genetic diversity between them. This study provides valuable information to reveal the underlying mechanisms on PE degradation by S. albogriseolus LBX-2.

Few studies have used crustal displacements sensed by the Global Positioning System (GPS) to assess the terrestrial water storage (TWS), which causes loadings. Furthermore, no study has investigated the feasibility of using GPS to image TWS over South America (SA), which contains the world’s driest (Atacama Desert) and wettest (Amazon Basin) regions. This work presents a resolution analysis of an inversion of GPS data over SA. Firstly, synthetic experiments were used to verify the spatial resolutions of GPS-imaged TWS and examine the resolving accuracies of the inversion based on checkerboard tests and closed-loop simulations using “TWS„ from the Noah-driven Global Land Data Assimilation System (GLDAS-Noah). Secondly, observed radial displacements were used to image daily TWS. The inverted results of TWS at a resolution of 300 km present negligible errors, as shown by synthetic experiments involving 397 GPS stations across SA. However, as a result of missing daily observations, the actual daily number of available stations varied from 60–353, and only 6% of the daily GPS-imaged TWS agree with GLDAS-Noah TWS, which indicates a root-mean-squared error (RMSE) of less than 100 kg/m 2 . Nevertheless, the inversion shows agreement that is better than 0.50 and 61.58 kg/m 2 in terms of the correlation coefficient (Pearson) and RMSE, respectively, albeit at each GPS site.

Satellite precipitation products provide alternative precipitation data in mountain areas. This study aimed to assess the performance of the latest Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) version 5 (IMERG V5) and Global Satellite Mapping of Precipitation version 7 (GSMaP V7) products and their hydrological utilities over the Tibetan Plateau (TP). Here, two IMERG Final Run products (uncalibrated IMERG (IMERG-UC) and gauge-calibrated IMERG (IMEEG-C)) and two GSMaP products (GSMaP Moving Vector with Kalman Filter (GSMaP-MVK) and gauge-adjusted GSMaP (GSMaP-Gauge)) were evaluated from April 2014 to March 2017. Results show that all four satellite precipitation products could generally capture the spatial patterns of precipitation over the TP. The two gauge-adjusted products were more consistent with the ground measurements than the satellite-only products in terms of statistical assessment. For hydrological simulation, IMERG-UC and GSMaP-MVK showed unsatisfactory performance for hydrological utility, while GSMaP-Gauge demonstrated comparable performance with gauge reference data, suggesting that GSMaP-Gauge can be selected for hydrological application in the TP. Our study also indicates that accurately measuring light rainfall and winter snow is still a challenging task for the current satellite precipitation retrievals.

Three high-resolution satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) standard precipitation products 3B42V6 and 3B42RT and the Climate Precipitation Center's (CPC) morphing technique precipitation product (CMORPH), were evaluated against surface rain gauge observations from the Laohahe Basin in northern China. Widely used statistical validation indices and categorical statistics were adopted. The evaluations were performed at multiple time scales, ranging from daily to yearly, for the years from 2003 to 2008. The results show that all three satellite precipitation products perform very well in detecting the occurrence of precipitation events, but there are some different biases in the amount of precipitation. 3B42V6, which has a bias of 21%, fits best with the surface rain gauge observations at both daily and monthly scales, while the biases of 3B42RT and CMORPH, with values of 81% and 67%, respectively, are much higher than a normal receivable threshold. The quality of the satellite precipitation products also shows monthly and yearly variation: 3B42RT has a large positive bias in the cold season from September to April, while CMORPH has a large positive bias in the warm season from May to August, and they all attained their best values in 2006 (with 10%, 50%, and −5% biases for 3B42V6, 3B42RT, and CMORPH, respectively). Our evaluation shows that, for the Laohahe Basin, 3B42V6 has the best correspondence with the surface observations, and CMORPH performs much better than 3B42RT. The large errors of 3B42RT and CMORPH remind us of the need for new improvements to satellite precipitation retrieval algorithms or feasible bias adjusting methods.

The capability of acquiring accurate and dense three-dimensional geospatial information that covers large survey areas rapidly enables airborne light detection and ranging (LiDAR) has become a powerful technology in numerous fields of geospatial applications and analysis. LiDAR data filtering is the first and essential step for digital elevation model generation, land cover classification, and object reconstruction. The morphological filtering approaches have the advantages of simple concepts and easy implementation, which are able to filter non-ground points effectively. However, the filtering quality of morphological approaches is sensitive to the structuring elements that are the key factors for the filtering success of mathematical operations. Aiming to deal with the dependence on the selection of structuring elements, this paper proposes a novel filter of LiDAR point clouds based on geodesic transformations of mathematical morphology. In comparison to traditional morphological transformations, the geodesic transformations only use the elementary structuring element and converge after a finite number of iterations. Therefore, this algorithm makes it unnecessary to select different window sizes or determine the maximum window size, which can enhance the robustness and automation for unknown environments. Experimental results indicate that the new filtering method has promising and competitive performance for diverse landscapes, which can effectively preserve terrain details and filter non-ground points in various complicated environments.

In this study, efforts are focused on the comparison and validation of standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products—Version-7 3B42RT estimates before and after assimilation by using a Kalman filter with independent rain gauge networks located within the Jinghe basin of China. Generally, the direct comparison of TMPA precipitation estimates to 200 collocated rain gauges from 2006 to 2008 demonstrate that the spatial and temporal rainfall characteristics over the region are well captured by the assimilation estimates. Especially, results also show that using Kalman filter to assimilate TRMM-based multi-satellite real-time precipitation estimates tends to perform well over regions, where gauge network is rather sparse. Last, this study highlights that accurate detection and estimation of precipitation in the summer season by Kalman filter, particularly for nonlinear convective precipitation events, is still a challenging task for the future development of assimilation technique for improving the satellite-based precipitation accuracy.

The performance of Day-1 Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (3B42V7), was cross-evaluated using data from the best-available hourly gauge network over the Tibetan Plateau (TP). Analyses of three-hourly rainfall estimates in the warm season of 2014 reveal that IMERG shows appreciably better correlations and lower errors than 3B42V7, though with very similar spatial patterns for all assessment indicators. IMERG also appears to detect light rainfall better than 3B42V7. However, IMERG shows slightly lower POD than 3B42V7 for elevations above 4200 m. Both IMERG and 3B42V7 successfully capture the northward dynamic life cycle of the Indian monsoon reasonably well over the TP. In particular, the relatively light rain from early and end Indian monsoon moisture surge events often fails to be captured by the sparsely-distributed gauges. In spite of limited snowfall field observations, IMERG shows the potential of detecting solid precipitation, which cannot be retrieved from the 3B42V7 products.

The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25° × 0.25° resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA’s systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter.

Several frequency-based spectral similarity measures, derived from commonly-used ones, are developed for hyperspectral image classification based on the frequency domain. Since the frequency spectrum (magnitude spectrum) of the original signature for each pixel from hyperspectral data can clearly reflect the spectral features of different types of land covers, we replace the original spectral signature with its frequency spectrum for calculating the existing spectral similarity measure. The frequency spectrum is symmetrical around the direct current (DC) component; thus, we take one-half of the frequency spectrum from the DC component to the highest frequency component as the input signature. Furthermore, considering the fact that the low frequencies include most of the frequency energy, we can optimize the classification result by choosing the ratio of the frequency spectrum (from the DC component to the highest frequency component) involved in the calculation. In our paper, the frequency-based measures based on the spectral gradient angle (SAM), spectral information divergence (SID), spectral correlation mapper (SCM), Euclidean distance (ED), normalized Euclidean distance (NED) and SID × sin(SAM) (SsS) measures are called the F-SAM, F-SID, F-SCM, F-ED, F-NED and F-SsS, respectively. In the experiment, three commonly-used hyperspectral remote sensing images are employed as test data. The frequency-based measures proposed here are compared to the corresponding existing ones in terms of classification accuracy. The classification results by parameter optimization are also analyzed. The results show that, although not all frequency-based spectral similarity measures are better than the original ones, some frequency-based measures, such as the F-SsS and F-SID, exhibit a relatively better performance and have more robust applications than the other spectral similarity measures.