A novel thermophilic Gram staining positive strain Rx1 was isolated from hot springs in Baoshan of Yunnan Province, China. The strain was characterized as a hemicellulose-decomposing obligate anaerobe bacterium that is rod-shaped (diameter: 0.5–0.7 μm; length: 2.0–6.7 μm), spore-forming, and motile. Its growth temperature range is 38–68 °C (optimum 50–55 °C) and pH range is 4.5–8.0 (optimum 7.0). The maximum tolerance concentration of NaCl was 3 %. Rx1 converted thiosulfate to elemental sulfur and reduced sulfite to hydrogen sulfide. The bacterium grew by utilizing xylan and starch, as well as a wide range of monosaccharide and polysaccharides, including glucose and xylose. The main products of fermentation were ethanol, lactate, acetate, CO2, and H2. The maximum xylanase activity in the culture supernatant after 30 h of incubation at 55 °C was 16.2 U/ml. Rx1 DNA G + C content was 36 mol %. 16S rRNA gene sequence analysis indicated that strain Rx1 belonged to the genus Thermoanaerobacterium of the family ‘Thermoanaerobacteriaceae’ (Firmicutes), with Thermoanaerobacterium aciditolerans 761–119 (99.2 % 16S rRNA gene sequence similarity) being its closest relative. DNA–DNA hybridization between Rx1 and T. aciditolerans 761–119 showed 36 % relatedness. Based on its physiological and biochemical tests and DNA–DNA hybridization analyses, the isolate is considered to represent a novel species in the genus Thermoanaerobacterium, for which the name Thermoanaerobacterium calidifontis sp. nov. is proposed, with the type strain is Rx1 (=JCM 18270 = CCTCC M 2011109).Electronic supplementary materialThe online version of this article (doi:10.1007/s00203-013-0895-5) contains supplementary material, which is available to authorized users.

The scarcity of freshwater resources is a global concern that is exacerbated by an increasing global population and climate change induced by global warming. To address this issue, the largest water-consuming sector has taken a series of measures termed as drip irrigation schemes. The primary purposes of drip irrigation are to reduce water scarcity near the root zone, reduce evaporation, and decrease water use. The application scope of drip irrigation is getting wider and wider, with the number of papers related to drip irrigation increasing year by year from 1990 to 2022. This study reviews crops planted in China that had been irrigated by drip irrigation equipment. The effects of drip irrigation technology on crop growth, physiology, quality, yield, and water use efficiency are summarized. This paper also provides an overview of drip irrigation technology on crop root development and nitrogen uptake. Through a global meta-analysis, it is found that in the case of water shortage, drip irrigation can save water and ensure crop yield compared to flooding irrigation, border irrigation, furrow irrigation, sprinkler irrigation, and micro-sprinkler irrigation. When the drip irrigation amount is more (100–120%), drip irrigation significantly increases crop yields by 28.92%, 14.55%, 8.03%, 2.32%, and 5.17% relative to flooding irrigation, border irrigation, furrow irrigation, sprinkler irrigation, and micro-sprinkler irrigation, respectively. When water resources are sufficient, increasing the amount of drip irrigation also improves crop yield. Moreover, the researchers found that drip irrigation can reduce fertilizer leaching and soil salinity. However, more studies should be conducted in the future to enrich the research on drip irrigation. In conclusion, drip irrigation technology is effective in improving crop growth, water use efficiency, and reducing water scarcity while decreasing fertilizer leaching and soil salinity, making it an ideal solution to the issue of freshwater resource scarcity globally.

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Reference Crop evapotranspiration (ET0) datasets based on reanalysis products can make up for the time discontinuity and the spatial insufficiency of surface meteorological platform data, which is of great significance for water resources planning and irrigation system formulation. However, a rigorous evaluation must be conducted to verify if reanalysis products have application values. This study first evaluated the ability of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) dataset for officially estimating ET0 (the local meteorological station data is used as the reference dataset). The results suggest that the temperature data of CLDAS have high accuracy in all regions except the Qinghai Tibet Plateau (QTP) region. In contrast, the global solar radiation data accuracy is fair, and the relative humidity and wind speed data quality are poor. The overall accuracy of ET0 is acceptable other than QTP, but there are also less than 15% (103) of stations with significant errors. In terms of seasons, the error is largest in summer and smallest in winter. Additionally, there are inter-annual differences in the ET0 of this data set. Overall, the CLDAS dataset is expected to have good applicability in the Inner Mongolia Grassland area for estimating ET0, Northeast Taiwan, the Semi Northern Temperate zone, the Humid and Semi Humid warm Temperate zone, and the subtropical region. However, there are certain risks in other regions. In addition, of all seasons, summer and spring have the slightest bias, followed by autumn and winter. From 2017 to 2020, bias in 2019 and 2020 are the smallest, and the areas with large deviation are south of climate zone 3, the coastal area of climate zone 6, and the boundary area of climate zone 7.

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The accurate calculation of reference evapotranspiration (ET0) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET0 when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (Rs) data, the machine learning methods obtain better results in their estimation of ET0. However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U2) data, using RHCLD and U2CLD from the CLDAS to estimate ET0 and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET0 using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone.

This study aims to assess the accuracy of the crop reference evapotranspiration (ET0 CLDAS, ET0 ERA5) estimated by CLDAS, ERA5 reanalysis products, as well as the quality of reanalysis weather variables required to calculate PM-ET0, and to achieve the application of these reanalysis products to locations where weather data quality are low or (and) weather variables are missing. For this purpose, the applicability of surface meteorological elements such as daily maximum and minimum air temperatures, relative air humidity, 2m wind speed, and shortwave radiation from the ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and the second-generation China Meteorological Administration Land Data Assimilation System (CLDASV2.0) datasets are evaluated in China by comparison with local observations from 689 stations reported by the Chinese Meteorological Administration (CMA). Statistical statistics including percent bias (PBias), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are used to check the accuracy. The results show the highest correlation between reanalysis temperature and station observations, with a mean R2 of 0.96,0.90 for CLDAS reanalysis maximum and minimum air temperatures and 0.87,0.84 for ERA5. For the reanalysis of estimated solar radiation and relative humidity, an overestimation trend is shown for Rs, but to a lesser degree, an underestimation trend is shown for RH. Unlike the previous reanalysis variables, the reanalysis wind speed shows a lower accuracy, and average R2 = 0.25 (R2 = 0.18) for CLDAS reanalysis (ERA5 reanalysis) and site observations. In addition, the accuracy of ET0 estimated by the two reanalysis products is acceptable in China, but the spatial and temporal consistency between CLDAS estimates and site observations is higher, with mean RMSE, R2 of 0.91,0.82 for ET0 CLDAS and 1.42, 0.70 for ET0 ERA5, respectively, and the performance of describing the boundary details of the study area is better since CLDAS reanalysis products integrate terrain adjustment, the elevation of target location, wind speed, and other factors are taken into account.