Deformation is a critical indicator of structural integrity, and monitoring deformation is essential for ensuring the long-term safety of dams. However, characterizing the spatial correlations among dam deformation sequences and the similarity between displacements at various measurement points poses significant challenges when using single-point measurement models. Considering the limitations inherent in conventional models for processing spatiotemporal data, this paper introduces a novel model for predicting and imputing multi-point displacement monitoring data from earth-rock dams. The model integrates a convolutional neural network (CNN) with a bidirectional long short-term memory neural network (BiLSTM) while also incorporating an attention mechanism (AM). The CNN captures the spatial features of the displacement data, while the BiLSTM extracts temporal features. The AM assigns varying weights to input features, thereby enhancing the predictive accuracy of the model. The proposed model was experimentally validated, demonstrating its robust capabilities in data prediction and the imputation of missing data. The model provides a new strategy for forecasting dam deformation and addressing issues related to incomplete data.

Xinjiang is located in the arid region of northwestern China, and agriculture accounts for an absolute share of total water use. Resource-based, engineering, structural, and managed water shortages coexist. Therefore, it is of great significance to vigorously develop water conservation technology and improve the efficiency of water transmission and distribution in canal systems. This research aims at addressing the problems of difficult manual regulation and the overall optimization of the final canal system, low-water-resource utilization efficiency, and management efficiency. Taking the branch-double two-stage canal system of Dongfeng branch canal in Mangxiang, Jinghe irrigation district, as a case study, and the rotation irrigation group and irrigation duration as decision variables, canal distribution is modeled with the goal of minimizing seepage losses. The improved grey wolf algorithm combined with particle swarm optimization is used for the first time and compared with the traditional grey wolf algorithm, genetic particle swarm optimization fusion algorithm, and northern goshawk algorithm. The results show that (1) on the basis of meeting the water discharge capacity and water demand requirements of the canal system, the diversion time of the water distribution scheme obtained by using the improved grey wolf algorithm is shortened from 11 d to 8.91 d compared with the traditional empirical water distribution scheme. (2) The improved grey wolf algorithm converges to the optimal value within 10 generations compared to the remaining methods, and the total water leakage is reduced from 16.15 × 10⁴ m³ to 11.75 × 10⁴ m³. (3) The number of gate adjustments is reduced, and the canal gates are opened and closed at the same time within each rotational irrigation group. The grey wolf algorithm improved by its combination with particle swarm has stronger optimization ability and convergence, which can better meet the requirements of efficient water resource allocation in irrigation canal systems, as well as a high application value.

Accurate analysis of soil organic carbon (SOC) under different land uses in ecologically fragile arid zones is essential for effective regulatory measures and improvement of ecological quality. This study selected the ecologically fragile Tarim River source area as an example, aiming to quantitatively assess the SOC content, storage, carbon sequestration potential, and stratification ratio (SR) of different ecological land use types. Soil depths from 0–50 cm were determined and analyzed using the K₂Cr₂O₇-H₂SO₄ oxidation method, the equivalent soil mass method and mathematical statistics. Forest, shrubland, and grassland ecological land types were included. The results show the following: (1) Both SOC content and storage showed a decrease with increasing soil depth. The total SOC content and storage sequence from high to low were natural forest, grassland, and shrubland. (2) There are variations in the SOC sequestration potential among the different ecological land types and shrubland (40.64 Mg C ha⁻¹) > grassland (37.69 Mg C ha⁻¹). (3) The SRs of the SOC in the forest were significantly greater than those in the shrubland and grassland. The different ecological land types had significant impacts on SR2, SR3, and SR4. SR2 could serve as a reliable index for assessing the impact of management practices on soil quality. The study area has a high potential for soil carbon sequestration in the future under these ecological conservation and management measures.

The phenomenon of global climate change has led to an increase in the frequency of extreme precipitation events, an acceleration in the melting of glaciers and snow cover, and an elevation of the risk of flooding. In this study, the DB-IWHR model was employed in conjunction with the MIKE 21 hydrodynamic model to develop a simulation system for the dam failure flow process of an earth and rock dam. The study concentrated on the KET reservoir, and 12 dam failure scenarios were devised based on varying design flood criteria. The impact of reservoir failures on flood-risk areas was subjected to detailed analysis, with consideration given to a range of potential failure scenarios and flood sizes. It was determined that under identical inflow frequency conditions, the higher the water level, the more rapid the breakout process and the corresponding increase in flood peak discharge. Conversely, for a given frequency of incoming water, an elevated water level results in a transient breach process, accompanied by a reduction in flood peak flow. Moreover, for a given water level, an increase in water frequency results in a reduction in breaching time, an extension of flood duration, and an increase in flood peak flow. The observed trend of flood spreading is generally north-south, and this process is highly compatible with the topographic and geomorphological features, demonstrating good adaptability.

Changes in climate and land type directly affect the transformation and utilization of regional water resources. To analyze the evolutionary characteristics and drivers of agricultural water consumption (AWC) in arid regions, the Baicheng County is selected as an example. Based on the meteorological and land use/cover data from 1990 to 2020, the Penman–Monteith model and sensitivity method were used for analysis. The results show that: (1) The water consumption of major crops during the growth period was increasing, which was caused by climate change and changes in agricultural planting structure. (2) The sensitivity of AWC to meteorological factors was as follows: mean temperature (1.56) > mean wind speed (0.6) > precipitation (−0.12) > sunshine duration (−0.06). Temperature and wind speed were the dominant factors contributing to increased water consumption in oasis agriculture. (3) The change in land type was more obvious, mainly in cultivated land and urban and rural residential land with obvious growth, while the area of water area, forestland, and grassland showed a decreasing trend. In the past 30 years, the increase in cultivated land has reached 24.32%. The increase in cultivated land area was an important reason for the increase in AWC.

Disentangling the responses of total soil organic carbon (SOC), organic carbon fractions and soil aggregate stability to various vegetation types is essential for better understanding the carbon cycling process in terrestrial ecosystems, maintaining soil quality and mitigating global warming. To study the effects of vegetation types on soil aggregates in a specific area, the desert riverbanks of arid regions were studied. We set up experiments using three typical vegetation types in the arid zone of the Tarim River Basin (TRB), including Forestland, Shrubland, and Grassland. The total SOC content in the bulk soil and different soil aggregates was determined by oxidation with K₂Cr₂O₇ and H₂SO₄, and three carbon fractions (F1, very labile; F2, inert; F3, oxidizable resistant) were classified according to the degree of oxidation using the modified Walkley-Black method. The total SOC and three carbon fractions in the soil were significantly greater in the Forestland than in the other vegetation types, and the effect was more pronounced in macro-aggregate (MA) than in the other aggregates. In the bulk soil and soil aggregates, the percentages of F1, F2 and F3 in the total SOC with mean values of 0.36%, 0.28% and 0.36%, respectively, at soil depths of 0–20 cm, indicated that stabilizing carbon is the major carbon fraction of the SOC. The stability of the SOC in the aggregates across each vegetation type was greater in the lower layer (10–20 cm) than in the topsoil layer (0–10 cm). The SOC stability and MA content were positively related to the SOC in the soil aggregates and its F2 and F3 fractions (p < 0.05). In summary, the Forestland significantly increased the SOC content and enhanced SOC stability. Conservation measures for poplar forests in vulnerable arid zones can sustainably accumulate SOC sequestration.