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Flood risk assessment is crucial for effective disaster risk management and community resilience. However, the current research lacks strength in identifying high-risk areas, implementing flood early warning systems, prioritising risk reduction measures, and allocating resources for emergency response planning and management. This study aims to assess flood hazard in Mirzadhare, Charsadda a highly flood-prone area in Khyber Pakhtunkhwa province of Pakistan. The study used an integrated approach by employing geographical information system (GIS) and multi-criteria decision analysis (MCDA) techniques. Further, the study used multiple datasets, including rainfall, stream density, and village points to map out flood susceptibility in the study region. Data was collected from field surveys, questionnaires, and interviews, allowing for a detailed analysis of flood hazards. Selecting average precipitation, peak river flow, and historical flood frequency as indicators, the weights of the three are 0.4, 0.3, and 0.3 respectively, an indicator system for predicting flood disasters was constructed. The results categorised the study area into four hazard zones: very high, high, medium, and low, based on their susceptibility to flood hazards. The study findings reveal that more than 65% of the area, including agricultural land with other livelihood settlements, is at a very high risk of flood hazard. Over 50% of the population lives in floodplains and faces an extremely high risk of future flood events. The precision of the results may have been affected by the accuracy and completeness of the data sources utilised, such as historical flood records, precipitation data, stream network data, and stream density. This combination of methods enabled the creation of accurate, data-driven flood risk maps. The hazard map of the area serves as a valuable tool for decision-making, resource allocation, and the development of flood risk management strategies. Based on the study findings, regular updates and continuous monitoring are recommended to ensure the accuracy and relevance of the flood hazard information over time. [ABSTRACT FROM AUTHOR]

Exploring the ecological footprint and ecological carrying capacity is an effective method to evaluate the sustainable development and utilization of natural resources. Gansu Province, one of the typical arid regions in northwest China that is primarily focused on agriculture, was selected to analyze the evolution of the water ecological footprint and carrying capacity in this paper. In addition, the breadth and depth of the water footprint were combined to further evaluate the current situation of water resource utilization and management across different regions. This study can complement the research on the ecological footprint in arid areas dominated by agriculture. The results showed that (1) the agricultural water footprint was the main footprint and the key to water conservation. The overall water ecological footprint indicated a slow decline trend from 2009 to 2022 in Gansu Province. There was a gradual reduction in the agricultural water footprint and a notable increase in the ecological environment water footprint, indicating water use structures were generally becoming reasonable. (2) During the period, the eco-capacity and per capita eco-capacity exhibited similar trends in fluctuation and change, and spatial distribution was relatively dispersed due to the precipitation, total water resources, and industrial structure in the regions. (3) For prefecture-level cities, the depth of the water ecological footprint showed obvious spatial agglomeration. According to the results, the water footprint breadth of Zhangye and Longnan was larger, and the water resource flow can meet the local water demand. The water footprint depth of Lanzhou, Jinchang, Baiyin, and Jiayuguan was high, indicating the stock of water resources needs to be consumed to satisfy social production and living. The results can provide a scientific basis for the effective management and rational conservation of water resources. [ABSTRACT FROM AUTHOR]

Human activities and climate change have significantly influenced the water cycle, impacting flood risks and water security. This study centers on the Jing River Basin in the Chinese Loess Plateau, analyzing hydrological patterns and flood progression using the HEC-HMS model under changing conditions. The findings indicate that climate change substantially affects flood predictions, increasing peak flows and volumes by up to 10.9% and 11.1%, respectively. It is essential to recognize that traditional flood models may underestimate the risks posed by these changes, emphasizing the necessity for updated methods incorporating climatic and human factors. Changes in land use, such as the expansion of grasslands and forests, have reduced peak discharges and flood volumes. Consequently, the combined impacts of climate and land use changes have intensified flood frequencies, necessitating updated strategies to manage risks effectively. The dynamics of flooding are significantly impacted by changes in climate and land use, particularly in minor floods that occur frequently, highlighting the influence of climate change on flooding trends. Within the Jing River Basin, hydrological patterns have been shaped by both climatic variations and human activities, leading to an increase in extreme hydrological events and concerns regarding water security. Using the HEC-HMS model, this study examines the hydrology of the Jing River Basin, focusing on the design of storm events and analyzing various flood characteristics under different scenarios. Climate change has resulted in higher peak discharges and volume surges ranging from 6.3% to 10.9%, while shifts in land use, such as decreases in farmland and the expansion of grasslands, have caused declines ranging from 7.2% to 4.7% in peak flows and volumes. The combined effects of climate variation and land utilization have complex implications for flood patterns, with milder to moderate floods showing a more significant impact and shorter return periods facing increased consequences. These findings underscore the interconnected nature of climate change, land use, and flooding dynamics in the Jing River Basin, highlighting the need for comprehensive strategies to address these challenges and ensure sustainable water management in the region. [ABSTRACT FROM AUTHOR]

Temporal and spatial changes in non-point source pollution, driven by significant alterations in land use due to increased human activity, have considerably affected the quality of groundwater, surface water, and soil environments in the region. This study examines the Weihe River basin in greater detail, an area heavily impacted by human activity. The study developed the River Section Potential Pollution Index (R-PPI) model using the Potential Non-Point Source Pollution Index (PNPI) model in order to investigate the dynamic changes in River Section Potential Pollution (R-PP) over a 31-year period and its associated risks, especially those related to land use and land cover change (LUCC). The predominant land uses in the Weihe River Basin are cropland, grassland, and forest, making up around 97% of the basin's total area. The Weihe River Basin underwent a number of soil and water conservation initiatives between 1990 and 2020, which significantly decreased the potential pollution risk in the river segment. The research separated the R-PP risk values in the area into five different categories using a quantile classification technique. According to the data, there is a polarization of R-PP risk in the area, with downstream parts especially having an increased risk of pollution in river segments impacted by human activity. On the other hand, river segments in the middle and upper reaches of the basin showed a discernible decline in possible pollution risk throughout the study period. The Weihe River Basin's rapid urbanization and land degradation are to blame for the current increase in R-PP risk. The substantial influence of LUCC on the dynamic variations in R-PP risk in the Weihe River Basin is highlighted by this study. Additionally, it offers crucial information for upcoming conservation initiatives and urban planning guidelines meant to enhance the area's ecological well-being and environmental standards. [ABSTRACT FROM AUTHOR]

Wetlands serve as crucial ecological buffers, significantly influencing temperature reduction, carbon storage, regional climate regulation, and urban wastewater treatment. To elucidate the relationship between wetland landscape patterns and ecological environment, and to accurately assess lake ecosystems, this study proposes a semi-supervised classification method based on RSEI and K-Means. By integrating landscape pattern indices, the Remote Sensing Ecological Index (RSEI), and disturbance proximity, a comprehensive evaluation of the ecological quality of the Dianchi wetlands was conducted. The results indicate that the RSEI-K-Means method, with K set to 50, achieved overall accuracies (OAs) and Kappa values of 0.91 and 0.88, surpassing the SVM's 0.85 and 0.80. This method effectively combines ecological and landscape indices without relying on extensive training samples, enhancing accuracy and speed in wetland information extraction and addressing the challenges of spatial heterogeneity. This study reveals that from 2007 to 2009, and 2013 to 2015, landscape patterns were significantly influenced by the rapid expansion of Kunming city, exacerbating wetland fragmentation. Notably, significant ecological quality changes were observed in 2009 and 2013, with gradual recovery post-2013 due to strengthened environmental protection policies. The RSEI disturbance proximity analysis indicated that the affected areas were primarily concentrated in regions of high human activity, confirming the method's high sensitivity and effectiveness. This study can help in wetland ecosystem research and management. [ABSTRACT FROM AUTHOR]

The Qinling region in central China, known as the 'Dragon Vein of China', is a vital ecological barrier facing significant soil erosion challenges. This study aims to enhance soil erosion management and analyse the spatiotemporal changes of soil erosion in the northern foothills of the Qinling Mountains. We collected data on precipitation, terrain, land use types, and soil in the designated region. Using GIS technology and the Revised Universal Soil Loss Equation (RUSLE) model, we created a detailed soil erosion map and analysed its evolution from 2018 to 2022. Results show a significant reduction in soil erosion in 2020–2021 despite a general upward trend in other years. Innovation includes integrating remote sensing with RUSLE for high-precision mapping and introducing a hierarchical approach for erosion risk assessment. The study found erosion peaks in summer and autumn, with higher levels in the southern parts compared to the northern parts. Influential factors include climate variables, human activities, soil, and vegetation types. The average soil erosion modulus in 2023 is 233.515 t/(km2·a), with total soil erosion of 85,233.046 t/a, mainly concentrated in the valley and mountain basin areas. This research provides a theoretical basis for improving the natural environment and implementing comprehensive soil and water conservation measures in the Qinling region, offering a model for similar ecological regions globally. [ABSTRACT FROM AUTHOR]

Traditionally, the assessment of heavy metal concentrations using remote sensing technology is sample-intensive, with expensive model development. Using a mining area case study of Daxigou, China, we propose a cross-time-domain transfer learning model to monitor heavy metal pollution using samples collected from different time domains. Specifically, spectral indices derived from Landsat 8 multispectral images, terrain, and other auxiliary data correlative to soil heavy metals were prepared. A cross time-domain sample transfer learning model proposed in the paper based on the TrAdaBoost algorithm was used for the Cu content mapping in the topsoil by selective use of soil samples acquired in 2017 and 2019. We found that the proposed model accurately estimated the concentration of Cu in the topsoil of the mining area in 2019 and performed better than the traditional TrAdaBoost algorithms. The goodness of fit (R2) of the test set increased from 0.55 to 0.66; the relative prediction deviation (RPD) increased from 1.37 to 1.76; and finally, the root-mean-square deviation (RMSE), decreased from 8.33 to 7.24 mg·kg−1. The proposed model is potentially applicable to more accurate and inexpensive monitoring of heavy metals, facilitating remediation-related efforts. [ABSTRACT FROM AUTHOR]

Geological hazards are widely distributed, cause huge losses, and have always been the focus of attention for engineering and environmental geologists. Geological hazard evaluation is the basis of research and has important theoretical significance for preventing and controlling geological hazards. Therefore, geological hazard evaluation has become the focus of engineering and environmental geology. The question of how to build a universal index system model of geological hazard evaluation is an urgent problem that needs to be solved in geological hazard evaluation. Based on a large amount of previous research data, this paper takes landslide hazard as an example and systematically expounds the main problems that need to be solved in the current geological hazard evaluation from five aspects: basic concept, evaluation scope and accuracy, evaluation index system and evaluation criteria, evaluation method, and applicability of evaluation results. A landslide hazard assessment index system model is proposed, which applies to all regions, including all of the factors that may affect the formation of landslides. It is also hoped that this will be used as an example to establish various types of disaster evaluation and assessment systems. If the parameter has no value in the assessment process, it can be processed as 0. On this basis, further research is suggested from the perspectives of the geological hazard evaluation level, geological hazard evaluation theory, and method. To provide thoughts on and suggestions for geological hazard risk assessment method research, standard revision, investigation and evaluation, and risk management and control need to be considered. [ABSTRACT FROM AUTHOR]

Wang, J., Huo, A., Hu, A., Zhang, X., & Wu, Y. (March-April, 2017). Simulation for non-point source pollution based on QUAL2E in the Jinghe River, Shaanxi Province, China. Water Technology and Sciences (in Spanish), 8(2), 117-126. Water pollution in river basins is significantly influenced by point-source and non-point-source pollutants. Compared with point-source pollutants, the identification and quantification of non-point-source pollutants are critical but difficult issues in water environmental pollution studies. The Jinghe River is one of the main tributaries of the Weihe River. However, the non-point-source pollution of this river is not well understood. In order to analyze the sources of pointand non-point loads to river water, the river water quality model QUAL2E and Principal Component Analysis (PCA) & Factor Analysis (FA) were applied simultaneously to calculate the point- and non-point-source loads of ammonia nitrogen and nitrate nitrogen, respectively, in dry and wet seasons from 2002 to 2007. The results show that NO3 - -N can be associated with point-source pollution, such as domestic sewage in dry seasons, but non-point-source pollution generated by precipitation in wet seasons. NH4 +-N can be associated with point-source pollution throughout the year. The methods applied in this research provide reliable results on non-point-source pollution caused by storm runoff.

Previous scholarly investigations have mainly concentrated on examining water intake, particularly within the specific domain of coal mines. Nevertheless, the scholarly discourse lacks significant research on predicting water inflow in environments with complex multi-layer mineral distributions. The Yanlong mining area is a complex mine containing coal and bauxite. Forecasting the water inflow of bauxite deposits is crucial for designing mining drainage and formulating a mining plan in a coal-bearing rock series mining area. The water inflow on the roof and floor of bauxite was studied with various numerical simulation and analytical methods (such as the big well method). The hydrogeological conceptual and numerical model of the mining area was established by the MODFLOW module in Groundwater Modeling System (GMS (7.1)) software, and the measured groundwater level was identified and verified in the model. The results show that the model average values of R 2 , E n s , and PBIAS are 0.86, 0.81 and 2.71, respectively, indicating that the established numerical simulation model can accurately forecast water inflow into the aquifer. Taking No. XII orebody in the eastern Songshan Mining area as an example, a virtual well group consisting of 12 wells was set up, and the numerical model forecast a water inflow of 71,500 m3/d from the Taiyuan Formation aquifer in the bauxite ore roof, which was lower than the value predicted by the large well method (72,786.66 m3/d). The numerical method predicted an average water inflow of 59,000 m3/d and a maximum water inflow of 82,600 m3/d from the Majiagou Formation in the bauxite ore floor. A dependence has been established that the numerical method estimates water inflow with accuracy. Additionally, the model predicts future mining water inflow, and also provides a standard framework for estimating inflow in similar mining conditions. [ABSTRACT FROM AUTHOR]