As an important indicator for assessing regional water resources, the study of the spatiotemporal evolution and driving factors of water resources carrying capacity (WRCC) is essential for achieving sustainable water resource utilization. This study focuses on Yulin City, a typical arid region located on the Loess Plateau in northwestern China. By constructing an evaluation index system for regional WRCC and combining an improved fuzzy comprehensive evaluation model with the TOPSIS evaluation model, a comprehensive WRCC evaluation model is established. Additionally, Geodetector is used to explore the main driving factors behind the evolution of regional WRCC. This multidimensional analytical framework aims to deeply analyze the dynamic evolution trends of WRCC and the driving mechanisms of different factors in its spatiotemporal changes. The results indicate that (1) from 2011 to 2020, the overall WRCC of Yulin City showed a trend of positive improvement, with Shenmu, Yuyang, and Fugu areas performing the best, and by 2020, more than half of the counties had achieved Grade 3 or above; (2) the spatial variability of WRCC in Yulin City was more significant than its temporal changes; and (3) in terms of driving mechanisms, the northern six counties gradually shifted from traditional economic-driven factors to ecological and environmental drivers, whereas the southern six counties remained constrained by economic factors. Overall, water resource factors remain the primary driving force for the socio-economic development and environmental sustainability of the entire Yulin City. The study provides valuable information for water resource allocation and differentiated management in arid regions.

(1) Background: Geological surveying is undergoing a digital transformation process towards the adoption of intelligent methods in China. Cognitive intelligence methods, such as those based on knowledge graphs and machine reading, have made progress in many domains and also provide a technical basis for quality detection in unstructured lithographic description texts. (2) Methods: First, the named entities and the relations of the domain-specific knowledge graph of petrography were defined based on the petrographic theory. Second, research was carried out based on a manually annotated corpus of petrographic description. The extraction of N-ary and single-entity overlapping relations and the separation of complex entities are key steps in this process. Third, a petrographic knowledge graph was formulated based on prior knowledge. Finally, the consistency between knowledge triples extracted from the corpus and the petrographic knowledge graph was calculated. The 1:50,000 sheet of Fengxiangyi located in the Dabie orogenic belt was selected for the empirical research. (3) Results: Using machine reading and the knowledge graph, petrographic knowledge can be extracted and the knowledge consistency calculation can quickly detect description errors about textures, structures and mineral components in petrographic description. (4) Conclusions: The proposed framework can be used to realise the intelligent inspection of petrographic knowledge with complex entities and relations and to improve the quality of petrographic description texts effectively.

China’s Yangtze and Yellow River Basins exhibit divergent drought patterns, yet the underlying mechanisms driving these differences remain underexplored. This study compares their drought characteristics from 1961 to 2022 using the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, and Palmer Drought Severity Index, and identifies their drivers through attribution models and interpretable machine learning. Our results reveal two distinct paradigms: the Yangtze Basin is characterized by high-frequency, over 14% in all seasons, short-duration droughts, reflecting a rapid hydrological response, while the Yellow River Basin experiences low-frequency, long-duration events indicative of strong soil moisture memory. Quantitative attribution demonstrates that atmospheric evaporative demand (VPD) plays a significantly greater role in the Yellow River Basin, contributing over 20% to soil drought, far exceeding its 14.4% contribution in the Yangtze Basin. Furthermore, their large-scale drivers differ fundamentally: the Yangtze Basin responds primarily to the Atlantic Multidecadal Oscillation (AMO) and Arctic Oscillation (AO), whereas the Yellow River Basin is mainly influenced by solar activity and the El Niño-Southern Oscillation (ENSO). These findings reveal that Yangtze drought is primarily driven by precipitation deficits, while Yellow River drought is a composite phenomenon amplified by evaporative demand. This distinction underscores the need for basin-specific water management strategies.

The Anhui reach of the Huaihe River Basin features low-lying terrain, dense river networks, and high flood risk spatial heterogeneity, restricting regional socio-ecological sustainability. Traditional flood risk assessments suffer from incomplete indicators, subjective grading, and one-sided outputs when adopting single empirical or unsupervised clustering models. Based on disaster system theory, this study constructed a 14-indicator evaluation system covering hazard susceptibility, environmental sensitivity, and hazard-bearing vulnerability. Subjective-objective weights from Analytic Hierarchy Process (AHP) and entropy weight were fused via game theory equilibrium to build the Game Theory-AHP-Entropy Weight Coupled FCE Model (GAE-FCE Model) for comprehensive flood risk quantification. Meanwhile, principal component analysis (PCA) was utilized for indicator dimensionality reduction, and two unsupervised clustering models, namely, K-Means and Gaussian Mixture Model (GMM), were developed. Confusion matrix, Adjusted Rand Index, and correlation analysis were used to compare four model outputs. Individual models display weak correlation due to distinct risk characterization mechanisms; the ensemble scheme compensates single-model defects, achieving AUC = 0.9161, Recall = 0.9231, and F1 = 0.8889 under optimal weights, and a multi-model coupling framework was further proposed to obtain integrated flood risk zoning. Results show that five dominant factors (Distance to rivers, Topographic Roughness, NDVI, Distance to lakes, and Population Density) contribute over 59.6% of total weight. GAE-FCE reflects inundation characteristics yet lacks natural-factor comprehensiveness; clustering methods capture natural background differentiation but ignore socio-economic vulnerability, whereas ensemble coupling offsets individual model defects. High-risk zones are concentrated along major rivers, and low-risk areas mainly distribute in southern mountainous regions. This multi-model framework provides scientific support for flood prevention and territorial spatial optimization, advancing sustainable disaster risk reduction for the Huaihe River Basin and similar plain basins.

Increasing evidence indicates that microbial metabolism and mineral transformations within low-permeability clay layers, together with solute exchange between clay layers and adjacent aquifers, can jointly regulate arsenic (As) release, immobilization, and remobilization. However, how coupled C, N, S, and Fe cycling in clay layers regulates As oxidation state, solid–liquid partitioning, and transport, and how these effects extend across clay–aquifer interfaces, has not been critical narratively synthesized. This review focuses on subsurface clay layers and summarizes their hydrogeochemical properties, microbial communities, and vertical redox zonation. It examines how microbially mediated C–N–S–Fe processes influence As release, transformation, and immobilization, and further discusses the export of reactive constituents from clay layers, biogeochemical reactions at clay–aquifer interfaces, As enrichment in adjacent aquifers, and the effects of groundwater pumping on As transport. Available studies suggest that As behavior is condition-dependent and reflects interactions among microbial metabolism, electron-donor and electron-acceptor availability, mineral transformations, and interfacial transport, which together determine whether As is retained, released, or remobilized. Management implications and future research priorities are also considered. The resulting cross-scale framework links biogeochemical processes within clay layers with clay–aquifer exchange and aquifer responses, providing a basis for long-term groundwater As risk assessment, monitoring, and management.