Hydrological Factors Research Articles

Designing climate-resilient sustainable drainage system (SuDS) for mass rapid transit (MRT) development: A hydrodynamic modelling approach

The progressive industrialisation and urbanisation of urban areas have significantly altered hydrological factors such as terrain, imperviousness, and surface flow. These developments, accompanied by increased traffic congestion and carbon emissions, contribute to climate change. Consequently, implementing Mass Rapid Transit (MRT) systems is crucial for alleviating traffic congestion and reducing carbon emissions globally. However, the changes in urban hydrology following development, combined with increased rainfall due to climate change, exacerbate risks of flooding, erosion, and sedimentation for the MRT development and surrounding low-lying areas. Therefore, researching urban drainage systems under climate change conditions for MRT development is essential. This study utilised 1D/2D hydrodynamic modelling and simulation to investigate the performance of an existing MRT drainage system designed with a control-at-source concept and a new sustainable drainage system incorporating infiltration engineering and flow-retarding techniques, including porous layers and a bioecological system to mitigate urban flooding erosion and sedimentation. The results show that the new sustainable drainage system, featuring permeable pavements and bioretention facilities, effectively integrates 30% of the MRT-developed land typology without requiring additional land acquisition, which is cost-effective and practical for mitigating major flooding and reducing total suspended solids, thereby controlling erosion and sedimentation.

Hotter temperatures alter riparian plant outcomes under regulated river conditions

AbstractClimate change and river regulation alter environmental controls on riparian plant occurrence and cover worldwide. Simultaneous changes to river flow and air temperature could result in unanticipated plant responses to novel environmental conditions. Increasing temperature could alter riparian plant response to hydrology and other factors, while river regulation may exacerbate environmental stress through novel flows like those resulting from power generation. Further, plant establishment and growth may require differing conditions, which may be decoupled by novel conditions. Using a large dataset that spans a natural 5°C mean annual temperature (MAT) gradient and a Bayesian model that integrates plant occurrence and cover, we address four questions: (1) Does hotter MAT modify plant response to hydrology, substrate composition, topography, and cover of co‐occurring plant species? (2) Does the timing of hydropower tides benefit some species over others? (3) Does dam‐induced erosion hinder riparian species more than upland species? (4) Do occurrence and cover respond to different environmental variables, allowing for decoupling of life history processes? We addressed these questions with data collected along 364 km of the Colorado River downstream of Glen Canyon Dam, Arizona, United States of America. Occurrence and cover class were recorded in >10,000 plots from 2016 to 2020, along with environmental covariates that repeat across the climate gradient. For 36 species, plant occurrence and cover were modeled with respect to MAT, hydrology, substrate, topography, other plant cover, and their interactions with MAT. There were four key results. (1) Increasing MAT will not only directly influence plants but will mediate their responses to the environment, including greater dependence on stable water supplies. (2) The timing of hydropower tides shapes plant community composition. (3) Dam‐related erosion has an outsized effect on riparian species, which could lead to a loss of regionally unique plant species. (4) For all species, the most important covariates driving occurrence differed from those for cover, suggesting the potential for these life stages to be decoupled. Not only will climate change and river regulation independently alter plant distributions, interactions among hotter temperature, dam‐controlled flow patterns, and limited fine sediments will determine which species flourish or perish under future conditions.

Combining InSAR and Time-Series Clustering to Reveal Deformation Patterns of the Heifangtai Loess Terrace

The Heifangtai Loess terrace in northwest China is frequently affected by landslides due to hydrological factors, establishing it as a significant research area for loess landslides. Advanced time-series InSAR technology facilitates the retrieval of surface deformation information, thereby aiding in the monitoring of landslide deformation status. However, existing methods that analyze deformation patterns do not fully exploit the displacement time series derived from InSAR, which hampers the exploration of potentially coexisting deformation patterns within the area. This study integrates InSAR with time-series clustering methods to reveal the surface deformation patterns and their spatial distribution characteristics in Heifangtai. Initially, utilizing the Sentinel-1 ascending and descending SAR data stack from January 2020 to June 2023, we optimize the interferometric phase based on distributed scatterer characteristics to reduce noise levels and obtain higher spatial density of measurement points. Subsequently, by combining the differential interferometric datasets from both ascending and descending orbits, the multidimensional small baseline subsets technique is employed to calculate the two-dimensional deformation time series. Finally, time-series clustering methods are utilized to extract the deformation patterns present and their spatial distribution from all measurement point time series. The results indicate that the deformation of the Heifangtai is primarily distributed around the surrounding area of the platform, with subsidence deformation being more intense than horizontal deformation. The entire terrace exhibits five deformation patterns: eastward subsidence, westward subsidence, vertical subsidence, westward movement, and eastward movement. The spatial distribution of these patterns suggests that the areas beneath the platform, namely Yanguoxia Town and Dangchuan Village, may be more susceptible to landslide threats in the future. Furthermore, wavelet analysis reveals the response relationship between rainfall and various deformation patterns, further enhancing the interpretability of these patterns. These findings hold significant implications for subsequent landslide monitoring, early warning, and risk prevention.

Identifying Suitable Dam Locations in Al Dinder: Integrating GIS, Remote Sensing, and Hydrological Factors

This study evaluates the suitability of dam site locations in the Al Dinder region of Sudan using a GIS-based approach and weighted overlay analysis. Five key criteria were assessed: Stream Order, Slope, Soil Type, Precipitation, and Land Cover. Each criterion was analyzed to determine its impact on selecting optimal sites for dam construction. The results reveal that fourth-order streams offer the highest suitability due to their larger flow capacity, covering 11.4% of the area, while first-order streams, accounting for 48.9%, are less suitable. Slope analysis shows that 99.52% of the region features gentle slopes (0-5°), which are ideal for dam construction. Soil type analysis identifies Gleysols as the most favorable for dam foundations, covering 86.1% of the area. Precipitation levels, particularly in areas receiving 1200-2200 mm of rainfall, are deemed highly suitable for dam operations. The study further reveals that 96% of the land cover consists of barren land, which is advantageous for construction due to minimal land-use conflicts. A detailed cross-sectional profile analysis of six proposed dam sites identified Dam 5 as the most suitable location, offering stable terrain, a consistent cross-section, and favorable hydrological conditions. Other sites, such as Dam 1 and Dam 6, show promise but require additional engineering modifications. The study’s findings contribute valuable insights into sustainable water resource management and infrastructure development in regions with similar environmental conditions. Key recommendations include further feasibility assessments, environmental impact analyses, and consideration of the social and economic benefits of dam construction.

Structure and assembly mechanisms of the microbial community on an artificial reef surface, Fangchenggang, China

The construction of artificial reefs (ARs) is an effective way to restore habitats and increase and breed fishery resources in marine ranches. However, studies on the impacts of ARs on the structure, function, and assembly patterns of the bacterial community (BC), which is important in biogeochemical cycles, are lacking. The compositions, diversities, assembly patterns, predicted functions, and key environmental factors of the attached and free-living microbial communities in five-year ARs (O-ARs) and one-year ARs (N-ARs) in Fangchenggang, China, were analyzed via 16S rRNA gene sequencing. Proteobacteria was the dominant taxon in all the samples, with an average relative abundance of 44.48%, followed by Bacteroidetes (17.42%) and Cyanobacteria (15.19%). The composition of bacterial phyla was similar between O-ARs and N-ARs, but the relative abundance of Cyanobacteria was greater in the water column (38.56%) than on the AR surface (mean of 7.40%). The results revealed that the Shannon‒Wiener diversity indices were 5.64 and 5.45 for O-ARs and N-ARs, respectively. Principal coordinate analysis (PCoA) revealed different distributions of O-ARs and N-ARs in the microbial community. Additionally, network analysis revealed that the bacterial community was more complex and stable in O-ARs than in N-ARs, indicating that the 5-year AR presented a more diverse and stable microbial community overall. The KEGG database was used to predict that nitrogen metabolism, carbon metabolism, and membrane transport were the dominant microbial functions, accounting for 29.93% of the total functional abundances. The results of the neutral community model revealed that stochastic processes (67.2%) dominated the assembly of BCs. Interestingly, deterministic processes may be increasingly important in community aggregation over time. Moreover, a null model revealed that dispersal limitation was the most important process among the stochastic processes, accounting for 57.14% of the total. In addition, redundancy analysis (RDA) revealed that hydrological factors obviously impacted the structure and function of the microbial community. Our results showed that the construction of ARs slightly promotes local diversities in the structure and function of the microbial community, indicating it requires a longer time to enhance the diversity of the microbial community on artificial reefs.Key points• Artificial reefs facilitate the diversity and functions of the microbial community• Stochastic processes dominate the assembly of the microbial community in artificial reefs• Nitrogen and carbon metabolism dominate microbial functions in artificial reefs

Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area

IntroductionSurface deformation in the Three Gorges Reservoir area poses significant threats to infrastructure and safety due to complex geological and hydrological factors. Despite existing studies, systematic exploration of long-term deformation characteristics and their driving mechanisms remains limited. This study combines SBAS-InSAR technology and machine learning to analyze and predict surface deformation in Fengjie County, Chongqing, China, between 2020 and 2022, focusing on riverside urban ground, riverside road slopes, and ancient landslides in the reservoir area.MethodsSBAS-InSAR technology was applied to 36 Sentinel-1A images to monitor surface deformation, complemented by hydrological and meteorological data. Machine learning models—Random Forest (RF), Extremely Randomized Trees (ERT), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were evaluated using six metrics, including RMSE, R2, and SMAPE, to assess their predictive performance across diverse geological settings.ResultsDeformation rates for riverside urban ground, road slopes, and ancient landslides were −3.48 ± 2.91 mm/yr, −5.19 ± 3.62 mm/yr, and −6.02 ± 4.55 mm/yr, respectively, with ancient landslides exhibiting the most pronounced deformation. A negative correlation was observed between reservoir water level decline and subsidence, highlighting the influence of seasonal hydrological adjustments. Urbanization and infrastructure development further exacerbated deformation processes. Among the models, LSTM demonstrated superior predictive accuracy but showed overestimation trends in ancient landslide areas.DiscussionReservoir water level adjustments emerged as a critical driver of subsidence, with rapid water level declines leading to increased pore pressure and soil compression. Seasonal effects were particularly evident, with higher subsidence rates during and after the rainy season. Human activities, including urbanization and road construction, significantly intensified deformation, disrupting natural geological conditions. Progressive slope failure linked to road expansion underscored the long-term impacts of engineering activities. For ancient landslides, accelerated deformation patterns were linked to prolonged drought and reservoir-induced hydrological changes. While LSTM models showed high accuracy, their limitations in complex geological settings highlight the need for hybrid approaches combining machine learning with physical models. Future research should emphasize developing integrated frameworks for long-term risk assessment and mitigation strategies in reservoir environments.ConclusionsThis study provides new insights into the complex surface dynamics in the Three Gorges Reservoir area, emphasizing the interplay of hydrological, geological, and anthropogenic factors. The findings highlight the need for adaptive management strategies and improved predictive models to mitigate subsidence risks.

Comparative study on lake ice phenology changes and driving factors in large lakes of mid-latitude Xinjiang, China.

The changes in lake ice phenology (LIP) can intuitively reflect the climate evolution in the regions where lakes are located, serving as an important indicator of climate change. The Tianshan Mountains, situated at the southern edge of freezing lakes in the Northern Hemisphere, are a crucial water resource base in Xinjiang and support significant ecosystems closely related to human activities. In the context of intensified climate change, this study focuses on the geographical location, altitude, and water quality differences among large lake groups in the mid-latitude region of Xinjiang, aiming to explore the characteristics of LIP changes in these lakes and their responses to driving factors, thereby providing a basis for effective environmental management and protection. This research conducts a comparative analysis of the LIP changes and driving factors of three large lakes-Sayram Lake (SL), Bosten Lake (BL), and Ebnur Lake (EL)-using multi-source remote sensing data to reveal the response and adaptation mechanisms of lakes under global warming. It effectively captures the time series variations of ice formation and melting, as well as the common responses to environmental and climatic factors. The results indicate that SL has experienced significant climate change effects, with earlier freezing times and accelerated melting speeds; In contrast, EL and BL have shown relatively minor changes, suggesting that geographical and hydrological factors may buffer the impacts of climate. The study finds that all three lakes are jointly influenced by environmental factors such as temperature, wind speed, and precipitation; however, due to differences in altitude, lake surface area, and water transparency, their responses to these climatic factors vary significantly. For instance, SL's high altitude gives water transparency a dominant role in LIP, while BL's larger surface area enhances the impact of precipitation and thermal capacity on the melting process. This indicates that, despite facing similar climate pressures, local environmental conditions can lead to different trends in ice phenology changes. This study offers a novel and efficient monitoring method for LIP, providing valuable insights for future LIP research and water resource management.

Comparative assessment of Watershed Hydrological Health (WHH) using multi-criteria decision-making approach based on PSR framework.

The health of a watershed is a crucial aspect that involves evaluating ecological, hydrological, and human factors to determine its overall well-being and functionality. Hydrological components are key parts of a watershed. Therefore, the present study aims to assess Watershed Hydrological Health (WHH) using the Pressure-State-Response (PSR) framework and to compare WHH priorities derived from Multi-Criteria Decision Making (MCDM) approaches in the Gorganroud Watershed, Iran. To do this, with a problem-oriented approach, 16 hydrological criteria were conceptualized in three indices of Pressure (P), State (S), and Response (R) (PSR). In the next step, each of the mentioned criteria was quantified in 16 sub-watersheds of the Gorganroud Watershed. Finally, WHH was calculated based on the PSR framework. According to the criteria Importance Through Intercriteria Correlation (CRITIC) method, the criteria were then weighted, and WHH was calculated using three MCDM methods. To compare the results, the results of the PSR framework were used as the basis and compared with three MCDM methods. The results showed that WHH values in the Gorganroud Watershed ranged from 0.31 to 0.67 in S15 and S9, respectively. Overall, the weighted average WHH in the watershed was 0.52, indicating moderate conditions. Weighting results showed that criteria C4 and C14 had the highest and lowest weights, respectively. WHH priorities based on MCDM methods showed that in the ELimination Et Choice Translating REality (ELECTRE) and Simple Additive Weighting (SAW) methods, sub-watershed S9 had the highest WHH priority, while in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, sub-watershed S7 achieved the highest priority. Among these, the ELECTRE and SAW methods performed better in determining WHH priorities and are recommended for future research.

Spatial-temporal variation of the ecosystem services value (ESV) in the Yellow River Delta wetland and its response to land use/land cover changes (lu/lc)

The assessment of wetland ecosystem services value can provide theoretical basis for regional social and economic development, ecological environment protection and other planning and measures. Based on the factors of hydrology, meteorology, society, economy, resources, environment and culture, the value of wetland ecosystem services in the Yellow River Delta was evaluated by the revised equivalent factor method. The results showed that from 2005 to 2020, the area of tidal flat wetland decreased by 54.35%, and aquaculture increased by 206.91%. The total value of wetland ecosystem services increased from 28.03 billion CNY to 35.77 billion CNY, an increase of 27.63%, showing an N-shaped change of "increase - decrease - increase" on the whole. The ecosystem service value per unit area was more in coastal areas and less in inland areas. From 2005 to 2020, the ecosystem service sensitivity coefficient of the Yellow River Delta wetland was all less than 1. Under the premise of rational development and utilization, it is predicted that the value of ecological services will continue to increase in the future.

Leveraging Remote Sensing for Exploring Climatic and Hydro‐Geomorphic Linkages to Flooding: A Case of Bagmati River in Central Nepal

ABSTRACTRivers are dynamic systems that evolve in response to different geologic, hydrologic, climatic, and anthropogenic factors. This study utilized over three decades of remote sensing imagery to quantify river planform dynamics of the Bagmati River in central Nepal. It maps the spatiotemporal changes in the active river channel extent and lateral migration rates from 1990 to 2022, revealing an alarming decline in total river channel area by about 65% as compared to the historic extent. Lateral migration rates of the river channel varied significantly along the reach ranging between 2 and 40 m per year (m/yr). Intra‐annual analysis of the erosion and accretion areas from river masks before and after the monsoon season revealed a stronger correlation between peak annual flow and accretion area in the study reach. Further exploration of a flood event in July 2019 using Sentinel‐1 radar imagery revealed that the inundated area followed the historic channel extent, and multi‐day precipitation event had a major contribution to triggering floods in the Bagmati. This study highlights the invaluable role of satellite remote sensing in studying river planform dynamics, which helps devise informed approaches for flood mitigation, erosion control, and myriads of other crucial endeavours to safeguard the population in the vicinity.

Attribution Identification of Runoff Changes Based on the Budyko Elasticity Coefficient Method: A Case Study of the Middle and Upper Reaches of the Jinghe River in the Yellow River Basin

The impacts of climate change and human activities on water resources are a complex and integrated process and a key factor for effective water resource management in semi-arid regions, especially in relation to the Jinghe River basin (JRB), a major tributary of the Yellow River basin. The Sen’s slope estimator and the Mann–Kendall test (M–K test) are implemented to examine the spatial and temporal trends of the hydrological factors, while the elasticity coefficient method based on Budyko’s theory of hydrothermal coupling is employed to quantify the degree of runoff response to the various influencing factors, from 1971 to 2020. The results reveal that the runoff at Pingliang (PL), Jingchuan (JC), and Yangjiaping (YJP) hydrological stations shows an obvious and gradual decreasing trend during the study period, with a sudden change in about 1986, while precipitation shows a fluctuating and increasing trend alongside a potential evapotranspiration-induced fluctuating and decreasing trend. Compared to the previous period, a change of −29%, in relative terms, in the runoff at the YJP hydrological station is observed. The interaction of human activities and climate change in the watershed contributes to the sharp decrease in runoff, with precipitation, potential evapotranspiration, and human activities accounting for −14.3%, −15.1%, and 70.6% of the causes of the change in runoff, respectively. Human activities (e.g., construction of water conservancy projects), precipitation, and potential evapotranspiration are the main factors contributing to the change in runoff.

Эколого-географическая характеристика озера Пелёнкино (Ростовская область) по материалам ретроспективных и современных исследований

The physical and geographical characteristics and geological structure of Lake Pelenkino are described. During the analysis of the results of the satellite survey, it was found that the area, length and width of the water surface of the reservoir, as well as the length of the lake's shoreline, the elongation index, and the compactness of the lake vary significantly both by year and by season. It has been suggested that the water surface area and the chemical composition of the water are influenced by both the amount of precipitation and wind-driven effects in the Sea of Azov. The chemical composition of water and mud solution is prone to accumulation of an equivalent amount of Mg2+ relative to Ca2+ over time, which may be due, along with the influence of a hydrological factor, to a change in the physical and chemical equilibrium in the water-mud system. According to the description of the well cores, deposits up to a depth of about 15-35 cm are represented by silt (mud), which first turns into steel-gray clay of a semi-liquid consistency, and then to the bottom of the layer into denser clay. The physical and chemical parameters of the mud of Lake Pelenkino allow us to classify them as continental, silt, mineral, medium mineralized, slightly alkaline, slightly sulfide peloids. Lithological control was found for a set of parameters (pH, Eh, CH4, Ssulfide), which manifests itself in their synchronous change during the transition from silts to the bedrock sediments of the reservoir bed – steel-gray clays. The content of gross mercury in the mud and sediments of the lake does not exceed the threshold of the Low range of Exposure (EQL), equal to 0.15 micrograms/g of dry weight. The bottom sediments of the rivers of the Pelenkino Lake basin and its mud themselves are under anthropogenic influence, which primarily forms a low level of perfringence titer values and its virulence. The use of lake mud for therapeutic purposes without prior preparation is not recommended.

Flash flood dynamics in the foothills of the NW Himalayas: insights into hydrological and morphological controls.

The Himalayas experiences several cloudburst events due to its varied physiographical, geomorphological, and geological conditions and high rainfall. Uttarakhand is one of the Indian states circumscribed by the Himalayan ranges and has experienced a rise in the number of cloudburst catastrophes in the last few decades. These events cause substantial loss of life and property; however, very few studies have characterized these unpredictable cloudburst-induced flash floods in different regions of Uttarakhand. This study examines the geological and hydrological factors associated with the Raipur-Kumalda cloudburst event that occurred from 20 to 21 August 2022 in the Dehradun district of Uttarakhand. The resulting flash flood caused significant damage to roads, bridges, and settlements across the valley. The study aims to understand the geological and geomorphological controls of the event by analyzing the peak discharge and various flood parameters. The basin geomorphometry and rainfall intensity of the region reveal poorly developed drainage networks with low drainage density, steep slopes, rapid peak flows, a sharp peak hydrograph, and intense, concentrated rainfall, all of which worsen the impact of the flood. Various flood indices, including the rising curve gradient (K), flood magnitude ratio (M), and flood response time (TP), indicate a discharge 50 to 100 times higher during the event compared to the average monsoonal discharge. This study also discusses the role of mountain topography, climate, regional geology, and irreversible land use-land cover (LULC) changes associated with urbanization in intensifying the destruction.

Characteristics and Impacts of Pollution and Remediation on Riverine Greenhouse Gas Emissions: A Review

Rivers are not only a vital part of the Earth’s water cycle but also sources and sinks for greenhouse gases (GHGs), exerting a significant influence on the global carbon budget. Rapid urbanization and intense human activities lead to water pollution and river habitat degradation, thereby affecting riverine greenhouse gas (GHG) emissions indirectly. Artificial management and restoration measures taken for rivers further increase the uncertainty of GHG emissions from rivers. In the context of carbon neutrality goals, research on GHG emissions from rivers has gradually become a hot topic. However, there is a scarcity of collective and comparative studies on the spatiotemporal patterns and mechanisms of riverine GHG emissions, especially a lack of summaries exploring the impacts of pollution and restoration on GHG emissions from rivers. This work systematically reviews recent studies concerning the emissions of CO2, CH4, and N2O from rivers, with a particular focus on the characteristics and driving factors. Results have shown that riverine GHG emissions exhibit significant spatiotemporal heterogeneity. Besides hydrological factors such as wind speed, flow velocity, rainfall, and water level, large amounts of pollutants entering rivers strongly affect the production and emission of GHGs, since nutrients, organic matter, heavy metals, microplastics, and antibiotics can alter the biogeochemical processes in river ecosystems. Remediation measures can reduce water pollution levels, but some measures may further increase the emission of GHGs from rivers. This work emphasizes the need for conducting in-depth research on the synergies between treating river pollution and reducing riverine GHG emissions. It also proposes to reinforce the monitoring of GHGs and construct emission databases of rivers for sustainable watershed management.

Hydrological drought vulnerability assessment in Ethiopia using multi-criteria decision-making

ABSTRACT Drought significantly impacts the livelihoods of communities over extensive regions, with Ethiopia being particularly prone to frequent and persistent occurrences. Hydrological drought analysis necessitates spatially distributed data, a challenge faced in Ethiopian studies. We employed remote sensing and analytical hierarchy process (AHP) to assign weightage values to nine hydrological drought-influencing factors, namely land use and land cover, soil texture, population density, elevation, drainage density, aridity index, slope, rainfall departure, and water storage deficit index. Our results identify spatial diversity of hydrological drought vulnerability, with six categories of extreme and very high vulnerability areas covering over 22% of the country from January to June, primarily in the northeast and southeast lowlands. The majority of the country falls within high and medium vulnerability areas, emphasizing the prevalence of hydrological drought, particularly from June to October, while some southwest regions experience low vulnerability. Overall, a substantial portion of the country is susceptible to severe hydrological drought throughout most months.

A comparative study of intelligent prediction models for landslide susceptibility: random forest and support vector machine

Colluvial landslides widely developed in mountainous and hilly areas have the characteristics of mass occurrence and sudden occurrence. How to reveal the spatial distribution rules of potential landslides quickly and accurately is of great significance for landslide warning and prevention in the study area. Landslide susceptibility prediction (LSP) modeling provides an effective way to reveal the spatial distribution of regional landslides, however, it is difficult to accurately divide slope units and select prediction models in the processes of LSP modeling. To solve these problems, this paper takes the widely developed colluvial landslides in Dingnan County, Jiangxi Province, China as the research object. Firstly, the multi-scale segmentation (MSS) algorithm is used to divide Dingnan County into 100,000 slope units, to improve the efficiency and accuracy of slope unit division. Secondly, 18 environmental factors with abundant types and clear meanings, including topography, lithology and hydrological environment factors, were selected as input variables of LSP models. Then, a widely representative Support Vector Machine (SVM) and Random Forest (RF) models were selected to explore the difference characteristics of various machine learning models in predicting landslide susceptibility. Finally, the comprehensive evaluation method is proposed to compare the accuracy of various slope unit-based machine learning methods for LSP. The results show that the MSS algorithm can divide slope units in Dingnan County efficiently and accurately. The RF model (AUC = 0.896) has a higher LSP accuracy than that of the SVM model (AUC = 0.871), and the landslide susceptibility indexes (LSI) predicted by the RF model have a smaller mean value and a larger standard deviation than those of the SVM model. Conclusively, the overall performance of RF model in predicting landslide susceptibility is higher than that of SVM model.

Investigation of the Origin of Elevated Amounts of Iron and Manganese in a Dam Reservoir

On the outskirts of the Pinios dam reservoir (Ilia Regional Unit, Greece), a water treatment plant serves the water supply needs of the surrounding municipalities, in which high concentrations of Fe and Mn, before treatment, have been observed. The main purpose of this research was to investigate the mechanism of increased iron (Fe) and manganese (Mn) levels in the reservoir of the Pinios dam, which impacts its water treatment plant operation. A wide range of hydrochemical and sedimentological analyses were conducted over a hydrological year, focusing on the spatial and temporal distribution of Fe and Mn in both water and sediment samples across the established research monitoring stations. Sediment samples from the reservoir’s bottom revealed predominantly fine-grained material, rich in total organic carbon, with elevated Mn and Fe oxide levels. Significant seasonal variations in Fe and Mn levels were also discovered, with higher Mn levels observed in the anoxic bottom waters of the reservoir during the dry season, attributed to the reduced conditions favoring Mn oxide dissolution over Fe. Conversely, during the wet season, a homogenization of metal concentrations throughout the water column was observed due to increased oxygenation and freshwater inflow. These outcomes were confirmed by the hydrochemical analysis, indicating that the redox conditions, pH, and temperature, as well as the presence of organic matter, significantly influence the mobility and bioavailability of these metals in the reservoir. The findings of this study clarify that the high concentration of Fe and Mn can be linked to the mineral composition of the upstream Neogene and flysch formations in the study area. As these formations are affected by geological weathering, they tend to enrich the streams, through soil erosion and runoff processes, with metals like Fe and Mn, which are eventually transported into the dam reservoir. This study highlights the critical influence of lithological, sedimentological, and hydrological factors on the redox conditions and seasonal stratification that govern the behavior of Fe and Mn concentrations and mobility in dam reservoirs. These findings are critical for informing water resource management practices and dam infrastructure operators and developing effective environmental conservation strategies in similar cases.

Power forecast of hydro–wind–photovoltaic hybrid system dual-driven by physics and data

Short-term power forecast is an important way to guide operation of renewable energy stations and hybrid energy system (HES). The current studies focus on power forecast of single renewable energy station. However, the universality and applicability of power forecast model for HES is not clear. This study proposes a physics and data dual-driven day-ahead power forecast model for hydro–wind–photovoltaic HES. The WRF model and Xinanjiang model are used to drive meteorological and hydrological forecasts respectively. The hybrid variational mode decomposition - principal component analysis method is applied to further extract the features hidden in the meteorology or hydrology factors. The long short-term memory network is used to drive power forecast. China’s Guandi hydro–wind–PV HES is considered as a case study. Results show that the forecast root mean square error of dual-driven model decreases by 4.2% ~ 12.0% compared to single-driven model.

The Response of the Annual Rotation Width of Tea Trees to Climate Change in the Brown Mountains of Yunnan Province

Yunnan is located in the southwestern part of China, with rich tea tree germplasm resources and diversified geomorphological and climatic features, which help us to carry out research related to tea tree chronology and provide scientific and effective support information for enriching the database of tree rings in western Yunnan. This study took the Brown Mountain tea tree in Xishuangbanna as the research object, collected tea tree sample cores through tree growth cone sampling, measured the width of the annual rings, cross-dated them, and established a chronology of the width of the annual rings of the tea tree. The R language was used to analyze the response function of the tea tree’s annual ring chronology with the climatic factors of the study site, discussed the relationship between the radial growth of the tea tree in subtropical regions and climatic factors, and determined the main factors that affected the radial growth of the tea tree. The results of the study showed that the chronology of the tea tree’s whorl width spanned 70 years (1954–2023), with an average annual growth rate of 1.283 mm/year; the average sensitivity was 0.514, which indicated that the chronology contained richer climatic information. The representativeness of the sample group of the whorl width index (EPS) was 0.716, indicating that the consistency of the growth inter-annual variations was better among the different trees. The radial growth was correlated with climatic factors such as temperature and moisture; the radial growth of the tea tree was usually more sensitive to moisture availability, limited by hydrological and climatic factors throughout the rainy season of the year, and positively correlated with the temperature in summer and autumn. In terms of the stability of the radial growth of the tea tree in relation to the climatic response, the growth of the tea tree in the study area may have benefited from future warming of the climate and reduction in precipitation.

Equity Assessment of Groundwater Vulnerability and Risk in Drinking Water Supplies in Arid Regions

Groundwater is a vital drinking water source, especially in arid regions, sustaining both urban and rural populations. Its quality is influenced by natural (hydrogeological) and human-driven (demographic, policy) factors, which may pose significant public health risks, especially for communities relying on unregulated water supplies. This study addresses critical gaps by examining groundwater vulnerability and contamination disparities, emphasizing their implications for public health and equitable resource management. It analyzes the impact of socio-hydrogeological factors on arsenic and nitrate levels in groundwater-supplied systems in Arizona, U.S. Methods include spatial analysis, ANOVA, multivariate regression, and cluster analysis. Significant disparities in arsenic and nitrate contamination, including exceedances of regulatory limits, were observed across supply types, aquifer characteristics, jurisdictional oversights, and groundwater management areas. Domestic wells and community water systems showed distinct contamination risks. Groundwater vulnerability was influenced by geological differences (karst vs. alluvial aquifers) and regulatory oversight, with Tribal and State systems facing unique challenges and resource needs. Socioeconomic disparities were evident, with minority communities, institutional facilities, rural areas, and specific housing types disproportionately exposed to higher contaminant levels. These findings unveil the intersection of race, socioeconomic status, and public health risks, offering an adaptable framework for addressing similar groundwater challenges in arid and semi-arid regions globally. This study is innovative in its focus on policy distinctions between private and regulated wells, karst and alluvial aquifers, and State and Tribal jurisdictions. It emphasizes the need for targeted vulnerability assessments and remediation strategies that integrate geological, hydrological, and regulatory factors to address risk disparities in vulnerable communities. These environmental inequities underscore the urgent need for stronger regulations and strategic resource allocation to support marginalized communities. The study recommends enhancing monitoring protocols, prioritizing resource distribution, and implementing targeted policy interventions to ensure equitable and sustainable access to safe drinking water in arid regions.