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The occurrence of toxic metals in the aquatic environment is as caused by a variety of contaminations which makes difficulty in the concentration prediction. In this study, conventional methods of back-propagation neural network (BPNN) and nonlinear autoregressive network with exogenous inputs (NARX) were applied as benchmark models. Explanatory variables of Fe, pH, electrical conductivity, water temperature, river flow, nitrate nitrogen, and dissolved oxygen were used as different input combinations to forecast the long-term concentrations of As, Pb, and Zn. The wavelet transformation was applied to decompose the time series data, and then was integrated with conventional methods (as WNN and WNARX). The modelling performances of the hybrid models of WNN and WNARX were compared with the conventional models. All the given models were trained, validated, and tested by an 18-year data set and demonstrated based on the simulation results of a 2-year data set. Results revealed that the given models showed general good performances for the long-term prediction of the toxic metals of As, Pb, and Zn. The wavelet transform could enhance the long-term concentration predictions. However, it is not necessarily useful for each metal prediction. Therefore, different models with different inputs should be used for different metals predictions to achieve the best predictions.

Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.

Combating desertification is vital for arresting land degradation and ensuring sustainable development of the global ecological environment. This study has analyzed the current desertification status and determined its control needs based on the difference between potential normalized difference vegetation index (PNDVI) and actual normalized difference vegetation index (ANDVI) in the Hotan desertoasis. The MaxEnt model, combined with the distribution point data of natural vegetation with long-term stable normalized difference vegetation index (NDVI) and 24 environmental factors was used to predict the PNDVI spatial distribution of different vegetation coverage grades and compared it with ANDVI. Excluding the areas of intense human activity such as arable land, the simulation results show that PNDVI with high, medium, and low vegetation cover was mainly distributed in the southwest and southeast of Hotan Oasis, in the midstream and downstream of Kalakash River and Yulong Kashi River, and the desert or Gobi area outside the oasis, respectively. The distribution of PNDVI with high, medium, and low vegetation cover accounted for 6.80%, 7.26%, and 9.17% of Hotan oasis, respectively. The comparison between ANDVI and PNDVI shows that 18.04% (ANDVI < PNDVI, about 3900 km 2 ) of the study area is still suffering from desertification, which is mainly distributed in the desert-oasis ecotone in Hotan. The findings of this study implied that PNDVI could be used to assess the desertification status and endorsement of desertification control measures in vulnerable ecosystems. Hence, PNDVI can strengthen the desertification combating efforts at regional and global scales and may serve as a reference point for the policymakers and scientific community towards sustainable land development.

Identifying suitable habitats for endangered species is critical in order to promote their recovery. However, conventional species distribution models (SDMs) need large amounts of labeled sample data to learn the relationship between species and environmental conditions, and are difficult to fully detangle the role of the environment in the distribution of the endangered species, which are very sparsely distributed and have environmental heterogeneity. This study’s first innovation used the semi-supervised model to accurately simulate the suitable habitats for endangered species with a small sample size. The model performance was compared with three conventional SDMs, namely Maxent, the generalized linear model, and a support vector machine. Applying the model to the endangered species Populus euphratica (P. euphratica) in the lower Tarim River basin (TRB), Northwest China. The results showed that the semi-supervised model exhibited better performance than conventional SDMs with an accuracy of 85% when only using 443 P. euphratica samples. All models developed using smaller sample sizes exhibit worse performance in the prediction of habitat suitability areas for endangered species while the semi-supervised model is still excellent. The results showed that the suitable habitat for P. euphratica is mainly near the river channel of the lower TRB, accounting for 13.49% of the study area. The lower Tarim River still has enormous land potential for the restoration of endangered P. euphratica . The model developed here can be used to evaluate a suitable habitat for endangered species with only a small sample size, and provide a basis for the conservation of endangered species.

River obstructions are a subject of global concern due to their impact on river connectivity and aquatic ecosystems. However, detecting and quantifying these structures, especially small and undocumented ones, remains a major challenge due to limitations in existing data sets and detection methods. This study focuses on improving the global detection of river obstructions and revealing their spatial distribution patterns. We developed a deep‐learning‐based detection framework combined with manual validation, resulting in the Deep Learning‐Global River Obstructions Database, which comprises 50,061 river obstructions identified globally. This represents a 64% increase over previous estimates, which were based solely on manual identification. Spatial analyses reveal strong correlations between obstruction density and factors such as Gross Domestic Product, agricultural expansion, urbanization, and river morphology. By enhancing the precision and comprehensiveness of river obstruction data, our open‐source data set provides a solid foundation for accurate assessment of global river connectivity, basin‐to‐continental‐scale hydrological modeling, and impact assessments.

The human‐environmental system in drylands is centered on oases. Despite its extent and socio‐ecological importance, understanding the dynamic changes of global oases and their human and environmental driving forces is imperative for sustainable development in drylands under global warming. Nevertheless, the dynamic changes of global oases and how they respond to the evolving environment are not well established. In this study, three criteria were summarized (i.e., existing in dryland climates, surrounded or partially surrounded by desert terrain, having a reliable source of freshwater and forming landscape units with higher vegetation coverage/productivity). A global oasis distribution map from 1995 to 2020 was generated using European Space Agency Climate Change Initiative Land Cover and GIMMS‐3G+ data (overall accuracy within a 95% confidence interval is 0.85 ± 0.01) based on overlay analysis and visual interpretation. In addition, we used geographic and temporal weighted regression methods to evaluate the potential macro‐level elements affecting both global and local oasis growth. The result showed that the global oases area in 2020 occupied an area of 191.91 Mha, and most oases existed in Asia (77.3%). The global oases area has significantly increased from 1995 to 2020 (+8.65 Mha). However, about 13.43 Mha of the global oases are desertified, indicating a high risk of desertification. Water resources, contributing 51.36% to the total driver's contribution, are key to the global oasis expansion. In the context of climate (climate variability and climate change), this research highlights the need for improved holistic water resource management for long‐term global oasis growth, particularly in developing countries where the oases' development is threatened by water scarcity and desertification. Plain Language Summary In recent decades, global oases have exhibited an expanding trend. Under the backdrop of climate (climate variability and climate change), water resources profoundly influence this trend. We comprehensively assessed the distribution, dynamic changes, and factors driving the expansion of global oases. The research findings indicate that from 1995 to 2020, the global oasis area significantly increased by 8.65 Mha. However, approximately 13.43 Mha of global oases degraded into deserts, highlighting the extremely high risk of desertification. The abundance of water resources, accounting for 51.36% of the overall effect, emerges as the key determinant in global oasis growth. These findings emphasize the need for improved global comprehensive water resource management, particularly in developing countries' oasis regions, to address water scarcity and achieve the sustainable development of the oasis. Key Points Global oases changed significantly from 1995 to 2020, yet desertification risk remains severe Water resources comprise 51.36% of all drivers and are the key to the world oasis's growth In developing countries oases, climate (climate variability and climate change) will increase the demand for water resources

Interest in the use of enhanced-efficiency nitrogen (N) fertilizers (EENFs) has increased in recent years due to their potential to increase crop yield and reduce environmental N loss. Drip-fertigation is widely used for crop production in arid regions to improve water and nutrient use efficiency whereas the effectiveness of EENFs with drip irrigation remains unclear. A field experiment was conducted in 2015 and 2016 to examine the effects of EENFs on yield, N use and quality of cotton ( Gossypium hirsutum ) grown under drip-fertigation in arid NW China. Treatments included an unfertilized control and application of 240 kg N ha −1 by polymer-coated urea (ESN), urea alone, or urea plus urease (NBPT) and nitrification (DCD) inhibitors. ESN was all banded in the plant row at planting, whereas urea was applied with 20% N banded at planting and 80% N by six fertigation events over the growing season. Results showed there was generally no treatment effect on seed and lint yield, N concentration or allocations, N recovery efficiency and fiber quality index of cotton. A lack of treatment effect could be due to N supplied with drip-fertigation better synthesized with crop N needs and the relatively high soil native NO 3 − availability, which hindered the effect of polymer-coated urea and double inhibitors. These results highlight the challenge of the employment of EENFs products for drip-fertigation system in arid area. Further research is required to define the field conditions under which the agronomic efficiency of EENFs products may be achieved in accordance with weather conditions.

High-resolution oasis maps are imperative for understanding ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline, there are no high-precision datasets related to oases in the world to date. To fill this gap, detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel-2 was conducted in summer 2020, and for the first time, a high-precision dataset of China’s oases (abbreviation HDCO) with a resolution of 1 meter was constructed. HDCO comprises 1,466 oases with a total area of 277,375.56 km 2 . The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. In addition, information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis was added to the attribute table of the dataset. This dataset enables researchers to quantitatively monitor location and area of oases, fosters exploration of the relationship between oases and human under climate change and urbanization.

Camelina sativa L. is an oilseed crop with wide nutritional and industrial applications. Because of favorable agronomic characteristics of C . sativa in a water-limiting environment interest in its production has increased worldwide. In this study the effect of different irrigation regimes (I 0 = three irrigations, I 1 = two irrigations, I 2 = one irrigation and I 3 = one irrigation) on physio-biochemical responses and seed yield attributes of two C . sativa genotypes was explored under semi-arid conditions. Results indicated that maximum physio-biochemical activity, seed yield and oil contents appeared in genotype 7126 with three irrigations (I 0 ). In contrast water deficit stress created by withholding irrigation (I 1 , I 2 and I 3 ) at different growth stages significantly reduced the physio-biochemical activity as well as yield responses in both C . sativa genotypes. Nonetheless the highest reduction in physio-biochemical and yield attributes were observed in genotype 8046 when irrigation was skipped at vegetative and flowering stages of crop (I 3 ). In genotypic comparison, C . sativa genotype 7126 performed better than 8046 under all I 1 , I 2 and I 3 irrigation treatments. Because 7126 exhibited better maintenance of tissue water content, leaf gas exchange traits and chlorophyll pigment production, resulting in better seed yield and oil production. Findings of this study suggest that to achieve maximum yield potential in camelina three irrigations are needed under semi-arid conditions, however application of two irrigations one at flowering and second at silique development stage can ensure an economic seed yield and oil contents. Furthermore, genotype 7126 should be adopted for cultivation under water limited arid and semi-arid regions due to its better adaptability.

In many arid and semiarid areas, dry lake beds (saline playa) represent a tremendous source of unconsolidated salt-rich sediments that are available for aeolian transport. Severe salt-dust storms caused by the erosion of such landforms have become very harmful natural phenomena. In this study, sample analysis and field erosion monitoring of Ebinur Lake was conducted to investigate the salt content, chemical composition, and wind erosion intensity of surface salt-rich sediments. The effects of salt-dust rising from the playa on the growth and physiological health of plants were also evaluated in this study through a leaf dustfall test. The results indicate that water-soluble salts assemble densely on the dry lake bed surface. At a depth of 0–2 cm, the highest salt contents can exceed 40%, with sulfate and chloride being the main anions present and Na + , Ca 2+ , and Mg 2+ being the primary cations. The annual wind erosion rate ranged from 0.48 to 5.6 cm in the northwest portion of the lake and from 0.24 to 0.96 cm in the southeast portion. Salt-dust storms caused by wind erosion of saline playa sediments seriously influenced the normal absorption of minerals by plant leaves. Under the influence of salt-dust storms, plant leaves absorb more Na + , but far less K + .