Reservoirs, as critical nodes in regional water management, play an increasingly important role in drought mitigation. This study aims to optimize the drought-limited water level in the reservoir group and propose an evaluation method for selecting the optimal regulation scheme during drought periods. The reservoir water supply module within the Water Allocation and Simulation (WAS) model was enhanced to optimize the drought-limited water level of the reservoir group. A comprehensive adaptation index (CAI) was developed to quantitatively evaluate the effectiveness of water resource regulation under various drought scenarios. This methodology was applied to large and medium-sized reservoirs in the central Yunnan Province, China. The results show that the optimized drought-limited water level significantly improved the water supply performance of the reservoir group during drought years. Specifically, the optimized drought-limited water level notably reduced severe water shortage for water users in the long series and typical drought years, effectively mitigating the impacts of drought. Additionally, the most suitable water resource regulation strategies for different drought scenarios were identified. These research findings can provide technical references for reservoir management departments and drought operations authorities to formulate drought-limited level for the reservoir group and implement regional drought early warning and defense decision-making.

The encroachment of buildings into the waters of rivers and lakes can lead to increased safety hazards, but current semantic segmentation algorithms have difficulty accurately segmenting buildings in such environments. The specular reflection of the water and boats with similar features to the buildings in the environment can greatly affect the performance of the algorithm. Effectively eliminating their influence on the model and further improving the segmentation accuracy of buildings near water will be of great help to the management of river and lake waters. To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. First, we designed a residual dynamic short-cut down-sampling (RDSC) module to minimize the interference of complex building shapes and building scale differences on the segmentation results; second, we reduced the semantic and resolution gaps between multi-scale features using a multi-channel cross fusion transformer module (MCCT); finally, a double-feature channel-wise fusion attention (DCF) was designed to improve the model’s ability to depict building edge details and to reduce the influence of similar features on the model. Additionally, an HRI Building dataset was constructed, comprising water-edge buildings situated in a riverine and lacustrine regulatory context. This dataset encompasses a plethora of water-edge building sample scenarios, offering a comprehensive representation of the subject matter. The experimental results indicated that the statistical metrics achieved by RDAU-Net using the HRI and WHU Building datasets are better than those of others, and that it can effectively solve the building segmentation problems in the management of river and lake waters.

The global output of organic solid residues (e.g., crop straw) is substantial, creating an urgent sustainability need for low-impact pathways that avoid open burning or disposal while recovering renewable energy. Dry anaerobic digestion (AD) offers a water-saving, high-solids valorization route for straw-rich substrates, but its deployment is often constrained by acidification that suppresses methanogenesis, reducing reliability and limiting practical adoption. Here, at laboratory scale, we formulated a co-digestion substrate dominated by wheat straw (50%) with swine manure and household organic waste, and evaluated whether co-supplementation of trace metals (Fe, Ni, Co) can enhance process stability and energy recovery, thereby strengthening the sustainability of high-solids straw treatment. System performance was assessed by pH, biogas production, volatile fatty acids (VFAs), functional genes, and microbial community profiles to elucidate micronutrient effects and microbial responses. Micronutrient addition stabilized pH (minimum 6.5) and enhanced biogas output. Specific yields in the supplemented digester were 260.64 ± 11.83 mL g⁻¹ TS and 319.89 ± 14.27 mL g⁻¹ VS, compared with 220.31 ± 9.45 mL g⁻¹ TS and 270.33 ± 11.72 mL g⁻¹ VS in the control; cumulative gas production was higher by 18.33%. Community analyses showed marked enrichment of Methanosarcina, increasing from 7.28% on day 10 to 44.00% on day 30. Molecular ecological network analysis indicated a transition from a sparse, fragmented configuration to a highly connected, centralized one: the number of nodes decreased from 74 to 70; the number of edges increased from 46 to 223 (a 4.85-fold rise); network density increased from 0.0170 to 0.0923; mean degree increased from 1.24 to 6.37; the number of modules declined from 39 to 5; and the proportion of positive versus negative links shifted from 85%/15% to 70%/30%, evidencing stronger interspecies coupling and functional robustness. Consistently, methyl-coenzyme reductase subunit A gene copy numbers were about 1.60-fold higher on day 30 and about 1.51-fold higher on day 50 than in the control. Overall, Fe-Ni-Co co-supplementation enhances methane potential and suppresses acidification in straw-rich dry anaerobic digestion, providing a low-input and practical strategy to stabilize high-solids systems. By improving microbial robustness, this approach enables efficient renewable energy recovery with reduced water demand and lower risk of process failure, thereby supporting scalable straw valorization and advancing circular bioeconomy pathways for agricultural and organic solid residues.