Termites pose a threat to water conservancy infrastructure due to their concealed, recurring, and long-term destructive behaviors. Consequently, implementing termite monitoring in critical hydrological facilities—such as reservoirs—is essential for timely prevention and control. Owing to its real-time efficiency, environmental sustainability, and overall effectiveness, the intelligent termite monitoring system has gradually emerged as a pivotal strategy for termite management. This paper systematically expounds upon the fundamental principles, overarching architecture, and key features of the current intelligent termite monitoring technology in China. Drawing on its practical application in the Suokoutan Reservoir, we detail the specific implementation procedures and examine the corresponding outcomes. Furthermore, an in-depth analysis of the system’s prospective development is presented, providing feasible references and scientific guidance for advancing termite monitoring, early warning, and control in water conservancy projects.

With global climate change and the deterioration of the ecological environment, the safety of hydraulic engineering faces severe challenges, among which soil-dwelling termite damage has become an issue that cannot be ignored. Reservoirs and embankments in China, primarily composed of earth and rocks, are often affected by soil-dwelling termites, such as Odontotermes formosanus and Macrotermes barneyi. Identifying soil-dwelling termite damage is crucial for implementing monitoring, early warning, and control strategies. This study developed an improved YOLOv8 model, named MCD-YOLOv8, for identifying traces of soil-dwelling termite activity, based on the Monte Carlo random sampling algorithm and a lightweight module. The Monte Carlo attention (MCA) module was introduced in the backbone part to generate attention maps through random sampling pooling operations, addressing cross-scale issues and improving the recognition accuracy of small targets. A lightweight module, known as dimension-aware selective integration (DASI), was added in the neck part to reduce computation time and memory consumption, enhancing detection accuracy and speed. The model was verified using a dataset of 2096 images from the termite damage survey in hydraulic engineering within Hubei Province in 2024, along with images captured by drone. The results showed that the improved YOLOv8 model outperformed four traditional or enhanced models in terms of precision and mean average precision for detecting soil-dwelling termite damage, while also exhibiting fewer parameters, reduced redundancy in detection boxes, and improved accuracy in detecting small targets. Specifically, the MCD-YOLOv8 model achieved increases in precision and mean average precision of 6.4% and 2.4%, respectively, compared to the YOLOv8 model, while simultaneously reducing the number of parameters by 105,320. The developed model is suitable for the intelligent identification of termite damage in complex environments, thereby enhancing the intelligent monitoring of termite activity and providing strong technical support for the development of termite control technologies.

Dynamic canal-system scheduling faces the fundamental challenge of determining the optimal reduction in the current period’s water allocation to reserve sufficient water for remaining periods, thereby hedging against potentially greater future water shortages. Although forecast information has been widely incorporated to address this hedging problem, its effectiveness is heavily dependent on forecast accuracy. Integrating abundant historical canal scheduling data with forecast information provides a promising pathway to improve scheduling performance, yet relevant studies remain limited. This study introduces the concept of Target Residual Lump-Sum Water Quota (TRLSWQ) for each time interval and develops a novel “Bi-level, Two-stage” (BT) model for dynamically updated canal-system scheduling that jointly leverages TRLSWQ and forecast information. The model defines clear canal scheduling rules and effectively adapts to the hierarchical structure in canal system scheduling. The model is applied to the summer–autumn irrigation scheduling of the Yongji main canal and six associated sub-canals in the Hetao Irrigation Area, Inner Mongolia, China. The results indicate that compared with the conventional model, the BT model reduces the total water shortage index of sub-canals from 40.81 to 31.44 (a decrease of 22.9%) and increases the utilization rate of the water quota from 89.3% to 92.9% (an increase of 3.9%). Furthermore, this study clarifies the mechanism of canal scheduling deviations caused by forecast errors: early-stage rainfall under-forecasting induces excessive early-stage allocation, leaving no water for later periods, whereas early-stage over-forecasting leads to withheld early allocation and unused residual lump-sum quota in later stages. The BT model effectively balances shortage risks between current and future periods and offers a practical and robust strategy for improving dynamic canal scheduling in irrigation districts.

Groundwater depth is a key control on soil salinization in arid irrigation districts, but seasonal groundwater depth thresholds for different salinization risks remain poorly constrained. Here, we examined the Yichang irrigation area of the Hetao Irrigation District using groundwater depth observations and 0–60 cm soil salinity samples collected before spring irrigation and during the crop-growing season. Indicator Kriging was used to map threshold-based probability zones for groundwater depth and soil salinity in the 0–60 cm root zone; soil salinity thresholds of 2 and 3 g kg⁻¹ represented light-or-higher and moderate-or-higher salinization, respectively, and high-probability matching rates were used to identify the corresponding critical groundwater depths. Groundwater depth showed moderate spatial variability in both seasons, whereas soil salt content showed strong spatial variability. The critical groundwater depths for light and moderate salinization were 2.6 and 2.2 m before spring irrigation and 2.2 and 1.8 m during the growing season, respectively. Soil salt content in the 0–20, 20–40, 40–60 and 0–60 cm layers decreased with increasing groundwater depth, with the 0–60 cm layer well described by an exponential response function. These findings provide a spatially explicit basis for seasonal groundwater regulation, salinization risk zoning and field-scale water–salt management in arid irrigation districts.

Groundwater depth is a key control on soil salinization in arid irrigation districts, but seasonal groundwater-depth thresholds for different salinization risks remain poorly constrained. Here, we examined the Yichang irrigation area of the Hetao Irrigation District using groundwater-depth observations and 0-60 cm soil-salinity samples collected before spring irrigation and during the crop-growing season. Indicator Kriging was used to map threshold-based probability zones for groundwater depth and soil salinity, and high-probability matching rates were used to identify critical groundwater depths for light and moderate salinization. Groundwater depth showed moderate spatial variability in both seasons, whereas soil salt content showed strong spatial variability. The critical groundwater depths for light and moderate salinization were 2.6 and 2.2 m before spring irrigation, and 2.2 and 1.8 m during the growing season, respectively. Soil salt content in the 0-20, 20-40, 40-60 and 0-60 cm layers decreased with increasing groundwater depth, with the 0-60 cm layer well described by an exponential response function. These findings provide a spatially explicit basis for seasonal groundwater regulation, salinization-risk zoning and field-scale water-salt management in arid irrigation districts.