Evapotranspiration plays an inevitable role in various fields of hydrology and agriculture. Reference evapotranspiration (ET₀) is mostly applied in irrigation planning and monitoring. An accurate estimation of ET₀ contributes to decision and policymaking processes governing water resource management, efficiency, and productivity. Direct measurements of ET₀, however, are difficult to achieve, often requiring empirical methods. The Penman–Monteith FAO56 (PM-FAO56) method, for example, is still considered to be the best way of estimating ET₀ in most regions of the globe. However, it requires a large number of meteorological variables, often restricting its applicability in regions with poor or missing meteorological observations. Furthermore, the objectivity of some elements of the empirical equations often used can be highly variable from region to region. The result is a need to find an alternative, objective method that can more accurately estimate ET₀ in regions of interest. This study was conducted in the Hexi corridor, Northwest China. In it we aimed to evaluate the applicability of 32 simple empirical ET₀ models designed under different climatic conditions with different data inputs requirements. The models evaluated in this study are classified into three types of methods based on temperature, solar radiation, and mass transfer. The performance of 32 simple equations compared to the PM-FAO56 model is evaluated based on model evaluation techniques including root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS), and Nash–Sutcliffe efficiency (NSE). The results show that the World Meteorological Organization (WMO) and the Mahringer (MAHR) models perform well and are ranked as the best alternative methods to estimate daily and monthly ET₀ in the Hexi corridor. The WMO and MAHR performed well with monthly mean RMSE = 0.46 mm and 0.56 mm, PBIAS = 12.1% and −11.0%, and NSE = 0.93 and 0.93, before calibration, respectively. After calibration, both models showed significant improvements with approximately equal PBIAS of −2.5%, NSE = 0.99, and RMSE of 0.24 m. Calibration also significantly reduced the PBIAS of the Romanenko (ROM) method by 82.12% and increased the NSE by 16.7%.

To understand how the soil microbial community structure responds to vegetation restoration in alpine mining areas, this study specifically examines the grassland ecosystem in the Qianmalong mining area of the Qilian Mountains after five years of artificial restoration. High-throughput sequencing methods were employed to analyze soil bacteria and fungi microbial characteristics in diverse grassland communities. Combined with modifications in vegetation diversity as well as soil physicochemical properties, the impact of vegetation restoration on soil microbiome diversity in this alpine mining area was investigated. The findings indicated that the dominant plants were Cyperus rotundus, Carex spp., and Elymus nutans. As the extent of the grassland’s restoration increased, the number of plant species, importance values, and plant community diversity showed an increasing trend. The plant functional groups were mainly dominated by Cyperaceae, followed by Poaceae. Plant height, density, plant cover, frequency, and aboveground biomass showed an increasing trend, and soil water content (SWC) increased. While soil pH and soil electrical conductivity (EC) exhibited a declining trend, available phosphorus (AP), total phosphorus (TP), total nitrogen (TN), nitrate nitrogen (NO₃-N), soil organic carbon (SOC), and soil water content (SWC) showed an increasing trend. The dominant bacterial communities were Actinobacteriota, Proteobacteria, Acidobacteriota, Chloroflexi, Firmicutes, and Gemmatimonadota, while the dominant fungal communities were Ascomycota, Mortierellomycota, Basidiomycota, unclassified_k_Fungi, and Glomeromycota. Significant differences were detected within soil microbial community composition among different degrees of restoration grasslands, with bacteria generally dominating over fungi. SWC, TP, and TN were found to be the main soil physicochemical factors affecting the distribution of soil bacterial communities’ structure; however, SOC, TN, and NO₃-N were the primary factors influencing the soil distribution of fungal communities. The results of this study indicate that different degrees of vegetation restoration in alpine mining areas can significantly affect soil bacterial and fungal communities, and the degree of restoration has varying effects on the soil bacteria and fungi community structure in alpine mining areas.

This study examines the atmospheric water cycle dynamics in the China-Mongolia Arid Region (CMAR), a region significantly affected by aridity. By employing a combination of Empirical Orthogonal Function (EOF) analysis, ERA5 reanalysis data, and the Dynamic Recycling Model (DRM), we investigate the spatial and temporal variations in the Precipitation Recycling Ratio (PRR) and Precipitable Water Conversion Rate (PWCR) over a forty-year period (1979–2021). Our findings reveal that both PRR and PWCR are generally higher but decreasing in most subregions of CMAR, suggesting a notable contribution of local moisture to precipitation. We also identify an increasing trend in PRR across the northwestern subregions and a decreasing trend in other areas. Similarly, PWCR exhibits an increasing trend in the northwestern and southern subregions, while decreasing elsewhere, implying a decline in water vapor conversion and recycling efficiency. Furthermore, our EOF analysis uncovers distinct spatial patterns, with dominant modes accounting for significant variances in PRR and PWCR, correlating with local variations in atmospheric moisture and advective changes. These results underscore the complex interplay between regional topography, atmospheric dynamics, and the hydrological cycle in CMAR. The insights from this study are vital for formulating effective water management strategies and adapting to climate change impacts in arid regions, holding broad implications for environmental science, climate studies, and sustainable resource management. Our findings reveal distinct spatial patterns and contrasting trends in precipitation recycling and water vapor conversion across the subregions of CMAR. This heterogeneity underscores the importance of conducting analyses at finer spatial scales to avoid contradictory conclusions that can arise from topographic influences when treating CMAR as a single unit. Future studies should focus on smaller subregions to accurately capture the intricacies of the water cycle in this topographically complex arid region.

Climate extremes pose significant natural threats to socioeconomic activities. Accurate prediction of short-term climate (STC) can provide relevant departments with warnings to effectively reduce this threat. To accurately predict STC in China, this study utilizes machine learning algorithms, particularly the random forest (RF) model, to evaluate the role of both natural and anthropogenic factors. Monthly temperature and precipitation data from 160 meteorological stations spanning China, as well as natural climate factors and an economic activity index, were obtained to perform a seasonal hindcast of air temperature and precipitation observed from 1979 to 2018. Our focus was to predict the seasonal mean temperature and precipitation, specifically the summer (June, July, and August (JJA)) and winter (December, January, and February (DJF)) air temperature and precipitation anomalies using forecast factors from the preceding season. Results show that a comprehensive consideration of both natural and anthropogenic effects provides a more accurate fit to the observed climate trends compared to using only one factor. When both factors were integrated, the model scores (coefficient of determination) exceeded 0.95, close to 1.00, which is significantly higher than those of natural (0.86 for temperature, 0.85 for precipitation) or anthropogenic (0.90 for temperature and 0.50 for precipitation) factors alone. Furthermore, we also attempted to predict similar components for 2019 and 2020. The average relative error between predictions and observations was less than 10%, indicating that this integrated model’s performance exhibited a significant improvement in predicting the STC. The findings of this study underscore the importance of accounting for both natural and anthropogenic factors in predicting climate trends to inform sustainable decision-making in China.