Although numerous models have been employed to address the issue of landslide susceptibility at regional scale, few have incorporated landslide typology into a model application. Thus, the aim of the present study is to perform landslide susceptibility zonation taking landslide classification into account using a data-driven model. The specific objective is to answer the question: how to select reasonable influencing factors for different types of landslides so that the accuracy of susceptibility assessment can be improved? The Qilihe District in Lanzhou City of northwestern China was undertaken as the test area, and a total of 12 influencing factors were set as the predictive variables. An inventory map containing 227 landslides was created first, which was divided into shallow landslides and debris flows based on the geological features, distribution, and formation mechanisms. A weighted frequency ratio model was proposed to calculate the landslide susceptibility. The weights of influencing factors were calculated by the integrated model of logistic regression and fuzzy analytical hierarchy process, whereas the rating among the classes within each factor was obtained by a frequency ratio algorithm. The landslide susceptibility index of each cell was subsequently calculated in GIS environment to create landslide susceptibility maps of different types of landslide. The analysis and assessment process were separately performed for each type of landslide, and the final landslide susceptibility map for the entire region was produced by combining them. The results showed that 73.3% of landslide pixels were classified into “very high” or “high” susceptibility zones, while “very low” or “low” susceptibility zones covered only 3.6% of landslide pixels. The accuracy of the model represented by receiver operating characteristic curve was satisfactory, with a success rate of 70.4%. When the landslide typology was not considered, the accuracy of resulted maps decreased by 1.5~5.4%.

The surface water quality issue in arid regions is becoming increasingly severe and has become a significant challenge for global environmental protection and water resource management. By continuously collecting surface water samples (2000~2024) and utilizing hydrochemical and principal component analysis, the changes in the chemical composition of surface water and its water quality pollution characteristics are examined in the Shiyang River Basin. The surface water anion concentrations are characterized by HCO₃⁻ > SO₄²⁻ > Cl⁻, with average concentrations of 214.11 mg/L, 117.31 mg/L, and 21.61 mg/L, respectively. The cation concentrations follow the trend of Ca²⁺ > Mg²⁺ > Na⁺ > K⁺, with average concentrations of 56.22 mg/L, 33.75 mg/L, 22.91 mg/L, and 5.33 mg/L, respectively. The dominant water types are Ca-HCO₃ and Ca (Mg)-HCO₃ in the mountainous area and in the plains, respectively. The weathering of carbonates and silicates is the main controlling factor for the evolution process of surface water. Strong evaporation leads to significant differences in ion concentrations, which is manifested as low in mountainous areas and high in plain areas. In addition, the surface water quality in the plains is worse than that of the mountainous areas. The main pollution indicators include DO, CODM_n, COD, BOD₅, NH₄⁺-N, TP, TN, and fecal coliforms. The surface water quality of Hongyashan Reservoir and Caiqi has improved significantly, reflecting the impact of the water diversion project. The results of this study are of great significance for improving water resource management and ensuring the sustainability of the ecological environment in arid regions.

Considering the possibility of increasing water supply in China in the short term and the long-term threat posed by shrinking glaciers, this paper studied the spatiotemporal evolution of runoff in typical arid areas and the influence of hydrometeorological elements on runoff, aiming to clarify the hydrological cycle law and provide a basis for adjusting water resource management strategies to cope with future uncertain changes. Based on hydrological data from 1956 to 2020, the spatial and temporal variation in runoff were discussed by means of wavelet analysis, MK test, RS analysis, and spatial interpolation. The influencing factors of runoff evolution in the Shule River Basin were determined. The results showed that the runoff in the Shule River Basin showed an increasing trend in the past 60 years. Five hydrological stations (Changmabao Station, Panjiazhuang Station, Shuangtabao Reservoir, Dangchengwan Reservoir, and Danghe Reservoir) were selected as the research objects. Among them, the runoff of Changmabao Station increased the most, which was 1.202 × 10⁸ m³/10 a. Future projections suggest a continued rise in runoff, particularly at Shuangtabao Reservoir. The runoff exhibited positive persistence and varying degrees of mutation, with most mutations occurring in the early 21st century. The runoff in the basin has a periodicity of multiple time scales (there are 2–3 main cycles), and the main cycle of annual runoff is concentrated in 58 years. This comprehensive analysis provides valuable insights for the sustainable management of water resources in inland river basins amidst changing environmental conditions. The spatial variation in runoff in summer and autumn and the whole year showed a significant southeast to northwest decreasing pattern. During the study period, accelerated glacier melting caused by rising temperatures had the most significant impact on runoff change (p < 0.01), and the upstream of the study area also complied with this rule (temperature contribution rate [25.96%] > precipitation contribution rate [23.91%]). The contribution of temperature and precipitation changes caused by human activities in the middle stream to runoff was relatively large, which showed that the contribution rate of temperature in Guazhou Station to runoff was 34.23% and the contribution rate of precipitation in Dangchengwan to runoff was 60.27%. The research results provide a scientific basis for the rational and efficient utilization of water resources in the arid area of Northwest China.

Water shortages are serious in northwest China due to the level of social and economic development, engineering, resource shortages, and other factors being restricted, so the conflict between supply and demand for water resources is prominent in different regions and different water use sectors. Therefore, Pingliang City was selected as the research object in this study. The membership function was introduced, and an interactive algorithm to correct model parameters based on the fairness constraint was derived. An uncertain multi-objective fuzzy programming model was also established. The results show that the optimal allocation of water will be 38,051.9~40,740 × 10⁴ m³ and 39,938.4~41,317.5 × 10⁴ m³ under a normal year (p = 50%) and a dry year (p = 75%) in 2025, respectively, and the corresponding water shortage rates will be 4.2% and 6.7%. In 2035, the optimal water allocation will be 45,644.1~49,245.9 × 10⁴ m³ and 46,442.4~50,044.2 × 10⁴ m³ and the water shortage rates will be 7.0% and 7.0%, respectively. The proportion of groundwater supply will decrease by 8.8% and 13.8% in 2025 and 2035 after the optimal allocation, the proportion of surface water supply will increase by 9.6% and 12.2%, and the proportion of reclaimed water will increase by −0.78% and 2.1%, respectively. The results can provide a technical reference for the development and utilization of water resources in other cities and similar areas in semi-arid regions.