Drylands refer to regions with an aridity index lower than 0.65, and billions of people depend on services provided by the critically important ecosystems in these areas. How ecosystem carbon exchange in global drylands (CED) occurs and how climate change affects CED are critical to the global carbon cycle. Here, we performed a comprehensive bibliometric study on the fields of annual publications, marked journals, marked institutions, marked countries, popular keywords, and their temporal evolution to understand the temporal trends of CED research over the past 30 a (1991-2020). We found that the annual scientific publications on CED research increased significantly at an average growth rate of 7.93%. Agricultural Water Management ranked first among all journals and had the most citations. The ten most productive institutions were centered on drylands in America, China, and Australia that had the largest number and most citations of publications on CED research. "Climate change" and climate-related (such as "drought", "precipitation", "temperature", and "rainfall") research were found to be the most popular study areas. Keywords were classified into five clusters, indicating the five main research focuses on CED studies: hydrological cycle, effects of climate change, carbon and water balance, productivity, and carbon-nitrogen-phosphorous coupling cycles. The temporal evolution of keywords further showed that the areas of focus on CED studies were transformed from classical pedology and agricultural research to applied ecology and then to global change ecological research over the past 30 a. In future CED studies, basic themes (such as "water", "yield", and "salinity") and motor themes (such as "climate change", "sustainability", and "remote sensing") will be the focus of research on CED. In particular, multiple integrated methods to understand climate change and ecosystem sustainability are potential new research trends and hotspots.

Dust storms in arid and desert areas affect radiation budget, air quality, visibility, enzymatic activities, agricultural products and human health. Due to increased drought and land use changes in recent years, the frequency of dust storms occurrence in Iran has been increased. This study aims to identify dust source areas in the Sistan watershed (Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia, using remote sensing (RS) and bivariate statistical models. Furthermore, this study determines the relative importance of factors controlling dust emissions using frequency ratio (FR) and weights of evidence (WOE) models and interpretability of predictive models using game theory. For this purpose, we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data. In addition, spatial maps of topographic factors affecting dust source areas including soil, lithology, slope, Normalized difference vegetation index (NDVI), geomorphology and land use were prepared. The performance of two models (WOE and FR) was evaluated using the area under curve (AUC) of the receiver operating characteristic curve. The results showed that soil, geomorphology and slope exhibited the greatest influence in the dust source areas. The 55.3% (according to FR) and 62.6% (according to WOE) of the total area were classified as high and very high potential dust sources, while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE, although they predict different fractions of dust potential classes. Based on Shapley additive explanations (SHAP), three factors, i.e., soil, slope and NDVI have the highest impact on the model's output. Overall, combination of statistic-based predictive models (or data mining models), RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions, which can be helpful for mitigation of negative effects of dust storms.

The water resources of the Nadhour-Sisseb-El Alem Basin in Tunisia exhibit semi-arid and arid climatic conditions. This induces an excessive pumping of groundwater, which creates drops in water level ranging about 1-2 m/a. Indeed, these unfavorable conditions require interventions to rationalize integrated management in decision making. The aim of this study is to determine a water recharge index (WRI), delineate the potential groundwater recharge area and estimate the potential groundwater recharge rate based on the integration of statistical models resulted from remote sensing imagery, GIS digital data (e.g., lithology, soil, runoff), measured artificial recharge data, fuzzy set theory and multi-criteria decision making (MCDM) using the analytical hierarchy process (AHP). Eight factors affecting potential groundwater recharge were determined, namely lithology, soil, slope, topography, land cover/use, runoff, drainage and lineaments. The WRI is between 1.2 and 3.1, which is classified into five classes as poor, weak, moderate, good and very good sites of potential groundwater recharge area. The very good and good classes occupied respectively 27% and 44% of the study area. The potential groundwater recharge rate was 43% of total precipitation. According to the results of the study, river beds are favorable sites for groundwater recharge.

Net primary productivity (NPP) of the vegetation in an oasis can reflect the productivity capacity of a plant community under natural environmental conditions. Owing to the extreme arid climate conditions and scarce precipitation in the arid oasis regions, groundwater plays a key role in restricting the development of the vegetation. The Qira Oasis is located on the southern margin of the Taklimakan Desert (Tarim Basin, China) that is one of the most vulnerable regions regarding vegetation growth and water scarcity in the world. Based on remote sensing images of the Qira Oasis and daily meteorological data measured by the ground stations during the period 2006-2019, this study analyzed the temporal and spatial patterns of NPP in the oasis as well as its relation with the variation of groundwater depth using a modified Carnegie Ames Stanford Approach (CASA) model. At the spatial scale, NPP of the vegetation decreased from the interior of the Qira Oasis to the margin; at the temporal scale, NPP of the vegetation in the oasis fluctuated significantly (ranging from 29.80 to 50.07 g C/(m²•month)) but generally showed an increasing trend, with the average increase rate of 0.07 g C/(m²•month). The regions with decreasing NPP occupied 64% of the total area of the oasis. During the study period, NPP of both farmland and grassland showed an increasing trend, while that of forest showed a decreasing trend. The depth of groundwater was deep in the south of the oasis and shallow in the north, showing a gradual increasing trend from south to north. Groundwater, as one of the key factors in the surface change and evolution of the arid oasis, determines the succession direction of the vegetation in the Qira Oasis. With the increase of groundwater depth, grassland coverage and vegetation NPP decreased. During the period 2008-2015, with the recovery of groundwater level, NPP values of all types of vegetation with different coverages increased. This study will provide a scientific basis for the rational utilization and sustainable management of groundwater resources in the oasis.

The purpose of the current study was to investigate the eco-physiological responses, in terms of growth and C:N:P stoichiometry of plants cultured from dimorphic seeds of a single-cell C₄ annual Suaeda aralocaspica (Bunge) Freitag and Schütze under elevated CO₂. A climatic chamber experiment was conducted to examine the effects of ambient (720 μg/L) and CO₂-enriched (1440 μg/L) treatments on these responses in S. aralocaspica at vegetative and reproductive stages in 2012. Result showed that elevated CO₂ significantly increased shoot dry weight, but decreased N:P ratio at both growth stages. Plants grown from dimorphic seeds did not exhibit significant differences in growth and C:N:P stoichiometric characteristics. The transition from vegetation to reproductive stage significantly increased shoot:root ratio, N and P contents, but decreased C:N, C:P and N:P ratios, and did not affect shoot dry weight. Moreover, our results indicate that the changes in N:P and C:N ratios between ambient and elevated CO₂ are mainly caused by the decrease of N content under elevated CO₂. These results provide an insight into nutritional metabolism of single-cell C₄ plants under climate change.

West Junggar is a key area for understanding intra-oceanic plate subduction and the final closure of the Junggar Ocean. Knowledge of the Carboniferous tectonic evolution of the Junggar Ocean region is required for understanding the tectonic framework and accretionary processes in West Junggar, Central Asian Orogenic Belt. A series of Early Carboniferous volcanic and intrusive rocks, namely, basaltic andesite, andesite, dacite, and diorite, occur in the Mayile area of southern West Junggar, northwestern China. Our new LA-ICPMS zircon U-Pb geochronological data reveal that diorite intruded at 334 (±1) Ma, and that basaltic andesite was erupted at 334 (±4) Ma. These intrusive and volcanic rocks are calc-alkaline, display moderate MgO (1.62%-4.18%) contents and Mg# values (40-59), and low Cr (14.5×10^-6-47.2×10^-6) and Ni (7.5×10^-6-34.6×10^-6) contents, and are characterized by enrichment in light rare-earth elements and large-ion lithophile elements and depletion in heavy rare-earth elements and high-field-strength elements, meaning that they belong to typical subduction-zone island-arc magma. The samples show low initial ⁸⁷Sr/⁸⁶Sr ratios (range of 0.703649-0.705008), positive ε_Nd(t) values (range of 4.8-6.2 and mean of 5.4), and young T_DM Nd model ages ranging from 1016 to 616 Ma, indicating a magmatic origin from depleted mantle involving partial melting of 10%-25% garnet and spinel lherzolite. Combining our results with those of previous studies, we suggest that these rocks were formed as a result of northwestward subduction of the Junggar oceanic plate, which caused partial melting of sub-arc mantle. We conclude that intra-oceanic arc magmatism was extensive in West Junggar during the Early Carboniferous.

With the increase of exploration depth, it is more and more difficult to find Au deposits. Due to the limitation of time and cost, traditional geological exploration methods are becoming increasingly difficult to be effectively applied. Thus, new methods and ideas are urgently needed. This study assessed the feasibility and effectiveness of using hyperspectral technology to prospect for hidden Au deposits. For this purpose, 48 plant (Seriphidium terrae-albae) and soil (aeolian gravel desert soil) samples were first collected along a sampling line that traverses an Au mineralization alteration zone (Aketasi mining region in an arid region of China) and were used to obtain soil Au contents by a chemical analysis method and the reflectance spectra of plants obtained with an Analytical Spectral Device (ASD) FieldSpec3 spectrometer. Then, the corresponding relationship between the soil Au content anomaly and concealed Au deposits was investigated. Additionally, the characteristic bands were selected from plant spectra using four different methods, namely, genetic algorithm (GA), stepwise regression analysis (STE), competitive adaptive reweighted sampling (CARS), and correlation coefficient method (CC), and were then input into the partial least squares (PLS) method to construct a model for estimating the soil Au content. Finally, the quantitative relationship between the soil Au content and the 15 different plant transformation spectra was established using the PLS method. The results were compared with those of a model based on the full spectrum. The results obtained in this study indicate that the location of concealed Au deposits can be predicted based on soil geochemical anomaly information, and it is feasible and effective to use the full plant spectrum and PLS method to estimate the Au content in the soil. The cross-validated coefficient of determination (R²) and the ratio of the performance to deviation (RPD) between the predicted value and the measured value reached the maximum of 0.8218 and 2.37, respectively, with a minimum value of 6.56 μg/kg for the root-mean-squared error (RMSE) in the full spectrum model. However, in the process of modeling, it is crucial to select the appropriate transformation spectrum as the input parameter for the PLS method. Compared with the GA, STE, and CC methods, CARS was the superior characteristic band screening method based on the accuracy and complexity of the model. When modeling with characteristic bands, the highest accuracy, R² of 0.8016, RMSE of 7.07 μg/kg, and RPD of 2.20 were obtained when 56 characteristic bands were selected from the transformed spectra (1/lnR)' (where it represents the first derivative of the reciprocal of the logarithmic spectrum) of sampled plants using the CARS method and were input into the PLS method to construct an inversion model of the Au content in the soil. Thus, characteristic bands can replace the full spectrum when constructing a model for estimating the soil Au content. Finally, this study proposes a method of using plant spectra to find concealed Au deposits, which may have promising application prospects because of its simplicity and rapidity.