Mountainous Regions Research Articles

Spatial disparities and dynamics in the high quality agricultural development in Heilongjiang Province of China

As a key commodity and strategic reserve grain base in China, evaluating and analyzing the high-quality agricultural development (HQAD) within Heilongjiang Province is crucial for advancing national agricultural quality. We construct a multidimensional evaluation framework with 19 indicators across four dimensions: green agriculture, quality and efficiency improvement of supply, agricultural structural coordination, and shared benefits of development. Using the Entropy Weighted-TOPSIS model, Dagum’s Gini Coefficient, and the Markov chain, the study quantifies the HQAD in Heilongjiang from 2013 to 2022, examining regional disparities and development dynamics. The findings indicate that: (1) Heilongjiang’s overall HQAD has steadily improved, rising from 0.160 in 2013 to 0.396 in 2022, with an annual average growth rate of 10.595%. Agricultural structural coordination, growing at 2.160% annually, has become the primary constraint. (2) Spatial imbalances are evident. The 12 prefecture-level cities in the province are grouped into four regions based on agricultural endowments. Harbin and Qiqihar in the Songnen Plain, and Mudanjiang in the Mudanjiang Semi-mountainous Areas lead in HQAD, while cities in the Sanjiang Plain lag behind. Intra-regional disparities are narrowing in Sanjiang Plain and Xing’an Mountains Regions except the Songnen Plain (from 0.000 to 0.062 during 2013–2022), increasingly contributing to the overall disparity (from 15.717% to 30.065% during 2013–2022). Inter-regional disparities are decreasing, with their contribution declining from 80.769% in 2013 to 37.481% in 2022. (3) HQAD levels in cities exhibit low mobility, fluctuating between medium-low (57.993%) and medium-high (22.082%). Based on these findings, we recommend strengthening agricultural structural coordination, promoting coordinated HQAD tailored to local conditions, and pursing institutional breakthroughs.

Optimal distribution modeling and multifractal analysis of wind speed in the complex terrain of Sichuan Province, China

Increasing drought events have threaten electricity supply security in the predominantly hydropower-based Sichuan Province. Wind power has the potential to complement hydropower, yet its complex fluctuations required a systematic assessment. Accordingly, we evaluated maximum likelihood estimation and three goodness-of-fit tests to identify the optimal distribution model of daily wind speed records during 1961–2017 across 156 weather stations in Sichuan Province among six commonly used probability density distributions. The study further analyzed the spatiotemporal features of persistence and multifractality in wind speed records across various landform types using multifractal detrended fluctuation analysis. The principal outcomes of our study indicated that the generalized extreme value distribution served as the optimal model for fitting wind speeds in Sichuan Province, outperforming the commonly used Weibull distribution. Persistence was evident in all wind speed series as the Hurst index exceeds 0.5, with the strongest persistence in mountainous areas and the weakest in plains. Multifractality was confirmed by the non-linear dependencies of the Generalized Hurst Exponent [h(q)] and mass exponent [τ(q)] on q, as well as by the multifractal spectrum widths exceeding 0.05. Among landform types, plains exhibited the strongest multifractality, followed by plateaus, with mountains showing the weakest multifractality. Long-range correlations were identified as the primarily caused of multifractality, as indicated by narrower multifractal spectrum widths in both shuffled and surrogate series, and stronger narrowness in the shuffled series. The multifractal spectrum width of the mountain shuffle series, which slightly exceeded 0.05, further highlighted the determinative influence of long-range correlations. Considering these findings, the southwestern mountainous region emerges as the optimal area for wind farm development, given its stability (persistence) and moderate fluctuation complexity (multifractality), crucial for effective wind resource utilization in hydropower-dominated settings. Our study provides a novel approach to assessing wind resources and offers guidance for wind farm placement in complex terrain regions, supporting sustainable energy diversification in Sichuan Province.

Agro-Ecotourism as a Model for Rural Development in the Mountains Region: Amedy Region as Case Study

Objective: This study aims to examine the determinants and indicators of Amedy's region competitiveness to identify the optimal model for rural development via tourism. Theoretical Framework: Rural tourism has emerged as a controversial topic within rural development. The combination of the decline of agriculture, the rise of tourism, and the need for economic diversification necessitates the creation of a new rural development model that ensures both economic diversification and environmental conservation. Method: Qualitative data was acquired from a local perspective through semi-structured interviews based on saturation theory. Thematic analysis was performed on interview transcriptions from 81 rural inhabitants to identify key themes Results and Discussion: The results confirmed that Agro-ecotourism is the best model of rural tourism devlopmnet in mountain region. The growth in local agricultural sales and increased tourist numbers are markers of economic prosperity. The region's agricultural goods and natural resources are key factors influencing rural competitiveness; nevertheless, climate change, conflicts over water and land, and poor governance have negatively affected their management. The interplay between security and accessibility obstructs rural tourism development. The study revealed direct and indirect relationships between the variables, suggesting that inadequacies in one area impact the others. Research Implications: Rural areas with similar environmental, economic, and cultural challenges can utilize the findings as an integrative model for preserving agricultural identity and natural resources. Originality/Value: The model is novel and has not been previously observed in Iraq and Kurdistan.

Habitat use of European badger in a mountainous landscape

Abstract The habitat use of European badgers Meles meles remains relatively understudied in mountainous environments, particularly in Alpine regions. In this study, we investigated the habitat use of European badgers in a study area within the Stelvio National Park (central Italian Alps) and assessed whether they exhibit different patterns compared to habitats commonly frequented by this species. We collected data from May to October between 2019 and 2023 using 45 camera traps randomly distributed over the study area, spanning elevations from 1400 to 2500 m a.s.l. We fitted a single-season, single-species occupancy model to calculate detection and occupancy probability as a function of site-specific environmental covariates. Our findings indicate that badgers primarily used areas with a high percentage of forest cover at lower elevation, favouring these habitats over higher elevation areas. Although badgers were detected at remarkably high elevations (up to 2406 m), most detections occurred below or near the treeline. This preference reflects the importance of forests, which provide abundant food resources and suitable soils for sett construction. Additionally, badgers tended to avoid north-facing slopes, possibly due to less favourable conditions such as colder temperatures. Furthermore, they showed a slight tendency to use steeper slopes (15–20 degrees) likely due to better drainage and more opportunities for sett construction, and appeared to be unaffected by human activity. These findings provide new insights into the habitat use of badgers in mountainous regions, highlighting the species’ plasticity and its ability to adapt to diverse landscapes and environmental conditions.

Correction of global elevation data SRTM to the national elevation system in the territory of Vietnam

This paper presents a five-step procedure for correcting the global elevation data Shuttle Radar Topography Mission (SRTM) to the national elevation system involving: (1) selecting GNSS/levelling points, obtaining global elevation data SRTM from https://earthexplorer.usgs.gov/ and height anomalies of the global gravity field model EGM96 from http://icgem.gfz-potsdam.de/home, corresponding to GNSS/levelling points; (2) calculating the deviation between the global elevation data SRTM and the measured height of GNSS/levelling points; (3) converting the global elevation data SRTM to national elevation; (4) checking the global elevation data SRTM after converting to national elevation; and (5) performing correction and evaluation. The procedure is carried out on the territory of Vietnam from 813 GNSS/levelling points of grades I, II and III. The results show that the accuracy before and after correction is ±4.112 m and ±3.131 m, respectively, for the territory of Vietnam, >3 m and >2 m for mountainous regions including Northwest, Northeast, North Central, South Central, Central Highlands and Southeast, where the elevation ranges from 700 to 1500 m, and from <3 m to <2 m for plain regions including Red River Delta, Southwest, where the elevation ranges from 2 to 10 m. The correction results of the global elevation data SRTM based on GNSS/levelling points indicate that the accuracy before and after correction depends on the topographical characteristics. These correction results for eight different regions in Vietnam are also consistent with research conducted in various regions of the world.

Landscape Pattern Changes and Ecological Vulnerability Assessment in Mountainous Regions: A Multi-Scale Analysis of Heishui County, Southwest China

Against the backdrop of intensifying global climate change, the impact of land use pattern changes on ecosystem vulnerability has garnered increasing attention. However, systematic studies concerning the ecological vulnerability of mountainous regions remains inadequate, with relevant policies primarily remaining at the macro-regulation level and lacking specific guidance measures. Taking Heishui County in southwest China as a case study, this research innovatively combines landscape pattern with the sensitivity–pressure–resilience (SPR) model to systematically analyze land use spatiotemporal evolution characteristics, ecological vulnerability and spatial differentiation patterns, and their driving mechanisms across multiple scales, including county, township, and land use types. The findings reveal that the region’s ecological vulnerability exhibits a spatial distribution pattern of “high in the southeast and low in the northwest”, with a radiating decrease from the centers of Longba and Weigu towns. The high degree of farmland landscape fragmentation is identified as a crucial factor contributing to its heightened ecological vulnerability. Geological disasters, human activities, meteorological conditions, and topographical features are the primary driving factors affecting ecological vulnerability intensity. Furthermore, land use pattern changes, characterized by landscape patch fragmentation, lack of dominant patch types, and decreased landscape diversity have further intensified regional ecological vulnerability. This research holds significant theoretical and practical implications for guiding ecological environmental governance in mountainous regions, enhancing ecological resilience, and promoting regional sustainable development.

Many winners, few losers: stable bird populations on an Afrotropical mountain amidst climate change

Organisms in mountainous areas are frequently exposed to climatic extremes and are among the most vulnerable to climate change. Long-term studies on birds along elevational gradients, which are vital in understanding species dynamics, are rare in tropical mountains, which limits the ability to understand their population trends in the face of climate change. We modelled local abundances of understorey bird species (N=18) over a 13-year period (2011–2023) in Mt. Kasigau, Kenya, using mist netting data collected along an elevational gradient. Our models show relatively stable bird abundances in the study period. However, we found two distinct population crashes that affected most species in 2015 and 2022, suggesting that changes in local dynamics may lead to heavy declines of bird populations in mountainous regions. Most species had stable local abundances in the study period, but parametric bootstrapping revealed a declining trend for a few species, including an endemic, threatened species. We highlight the importance of mountainous regions in maintaining relatively stable populations in the face of global environmental transformation such as posed by climate change, and the dynamism of bird species populations across relatively small spatial-temporal variations. While mountain ecosystems are viewed as potential refugia for biodiversity in the face of a warming climate, further studies are needed to understand the drivers of short and long-term declines in bird populations at higher elevations, especially in tropical Africa.

Scale effects of spatial patterns of ecosystem services in the Tarim River Basin in Central Asia.

Determining the effects of different scales on the geographical distribution of ecosystem services and validating the underlying driving mechanisms have been considered to be the keys to the sustainable management of ecosystem services. Our goal was to look at the Tarim River Basin's sub-watershed and county level trade-offs, synergistic linkages, and driving forces among habitat quality, soil conservation, and carbon storage. Variations in ecosystem services in the scenarios of natural land use and farmland preservation in 2030 under the influence of precipitation. The findings indicate the following: (1) The northern mountainous region has the majority of the high-value ecosystem service areas. Woodland contains highly valuable carbon reserves. (2) Correlations between different ecosystem services can vary across scale. The strength of synergistic relationship in sub-basins is higher than the county scale. Ecosystem services exhibit both synergistic and trade-off relationships in space on the two scales, with low-low agglomeration being the primary type of synergistic association. (3) Precipitation is the driver that affects ecosystem services the most. At the sub-basin level, precipitation and habitat quality are traded off with carbon storage. (4) By 2030, agricultural conservation scenarios and ecosystem services in natural scenarios will work in concert. Furthermore, synergistic are ecosystem services and precipitation.

Transformer-based travel time estimation method for plateau and mountainous environments.

Travel time estimation (TTE) is a critical function in intelligent driving systems. Current research and applications related to TTE primarily focus on urban environments. The objective of this study is to develop TTE methods that are applicable to wilderness areas characterized by plateau and mountainous topography. We selected Transformer, which has greater robustness in capturing long-distance dependencies than LSTM, to develop a Transformer-based model. The model simultaneously integrates positional encoding and multi-head self-attention mechanisms with the objective of enhancing the accuracy of travel time predictions based on a substantial number of trajectory points in wilderness settings. A meta-learning strategy was employed to improve the model's generalization ability, thereby ensuring its applicability for accurate travel time estimation across a range of challenging environments. Two datasets were constructed based on measurements from two selected areas in eligible plateau and mountainous regions of western China. For each dataset, two categories of features were defined: terrain-weather features and spatio-temporal features. These categories were established in accordance with the influence of seven specific features on traffic conditions in both urban and wilderness areas. Experiments were conducted on both datasets utilizing terrain-weather features. When evaluated alongside the five models that are most commonly utilized in urban settings, the mean absolute percentage error (MAPE) of our model exhibited a 14.89% improvement in plateau environments and a 12.20% improvement in mountainous environments in comparison with the most effective model, namely MetaTTE-GRU. These findings substantiate the assertion that the proposed model is an effective means of estimating travel times in complex environments, and that it exhibits superior accuracy compared to existing LSTM-based estimation models.

Multi-Target Firefighting Task Planning Strategy for Multiple UAVs Under Dynamic Forest Fire Environment

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios.

Spatial variability of near‐surface air temperature in the Copernicus Arctic regional reanalysis

AbstractThis study investigates spatial variability of near‐surface air temperature at scales of tens of kilometres in the European Arctic, using the Copernicus Arctic Regional Reanalysis (CARRA) data from 1991 to 2022. It examines factors influencing this variability, compares model‐derived variability with observed subgrid variability, and evaluates its impact on model verification. Key findings reveal that spatial variability is strongly influenced by geographical features and seasonal changes. The highest seasonal spatial variability is found during winter, particularly near coasts, mountainous regions, and sea ice edges where persistent temperature gradients drive baseline variability. In contrast, in inland Scandinavia, where climatic temperature gradients are less pronounced, situation‐dependent variability predominates. The study highlights these distinct patterns of variability in Ny‐Ålesund, Svalbard, and Sodankylä, Finland. In Sodankylä, high variability is found under cold and stable conditions, with variability peaking above the surface due to spatial differences in temperature inversion development. The observed spatial variability largely aligns with the modeled CARRA data, particularly in terms of the relationship between baseline and situation‐dependent variability. Furthermore, considerable subgrid variability observed in Sodankylä accounts for a substantial portion of the difference between the model grid box value and observed point value. These findings emphasize the importance of considering spatial variability in weather forecasting, model verification, model intercomparisons, and process studies.

Fire Severity Outperforms Remote Sensing Indices in Exploring Post-Fire Vegetation Recovery Dynamics in Complex Plateau Mountainous Regions

Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with Random Forest (RF) demonstrating superior accuracy (OA = 0.90) for fire severity classification compared to Support Vector Machine (SVM) OA of 0.88, Classification and Regression Tree(CART) OA o f0.85, and Naive Bayes(NB) OA of 0.78. Using RF, we generated annual fire severity maps alongside the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR) from 2005 to 2020. Key findings include the following: (1) fire severity classification outperformed traditional remote sensing indices in characterizing vegetation recovery; (2) distinct recovery trajectories emerged across severity levels, with moderate areas recovering in 7 years, severe areas transitioning within 2 years, and low severity areas peaking at 2 years post-fire; (3) southern mountainous regions exhibited 1–2 years faster recovery than northern areas. These insights advance understanding of post-fire ecosystem dynamics in complex terrains and support more effective recovery strategies.

Control Design for Electronic Voltage Stabilizer

Vietnam's electrical infrastructure has undergone notable advancements in recent times. Nevertheless, concerns pertaining to under-voltage, over-voltage, and voltage fluctuations persist in rural, mountainous, and industrial regions. This results in the inefficient operation of electrical equipment, disruptions in industrial production processes, data loss, and equipment damage. This paper puts forth a novel control design methodology for electronic voltage stabilizers, with the objective of mitigating the detrimental effects of grid voltage fluctuations on electrical apparatus. A compact and cost-effective AC-AC converter is proposed as a means of regulating the compensation voltage. The paper puts forth the use of the state-space averaging method for system modeling and proposes the combination of feedback and feed-forward controllers to achieve high accuracy and rapid response times. Furthermore, the moving average method is proposed for measuring the Root Mean Square (RMS) voltage, which significantly enhances the control response speed and accuracy. A 10 kVA single-phase electronic voltage stabilizer was constructed and tested in a laboratory setting under a range of grid voltage conditions, from 150V to 290V, and with varying loads. The results demonstrate that the electronic voltage stabilizer is effective in maintaining the load voltage within the desired range. The maximum response time recorded was found to be half a cycle of the grid voltage in the simulation and one cycle in the experiments, which is a significantly faster response time than that of similar designs. Furthermore, the low total harmonic distortion provides additional confirmation of the effectiveness of the designed electronic voltage stabilizer.

Spatiotemporal characteristics of future precipitation variability in the Tianshan Mountain region of China

Spatiotemporal characteristics of future precipitation variability in the Tianshan Mountain region of China

An integrative conservation and management strategy based on biological and cultural diversity assessment: A case study of Miaoling mountainous region, China

An integrative conservation and management strategy based on biological and cultural diversity assessment: A case study of Miaoling mountainous region, China

High-resolution assessment of climate change impacts on the surface energy and water balance in the glaciated Naryn River basin, Central Asia.

Mountain regions of Central Asia are experiencing strong influences from climate change, with significant reductions in snow cover and glacial reserves. A comprehensive assessment of the potential consequences under the worst-case climate scenario is vital for adaptation measures throughout the region. Water balance analysis in the Naryn River basin was conducted for the baseline period of 1981-2000 including potential changes under the worst-case SSP5-8.5 scenario for 2077-2096 by combining high-resolution (5km) regional climate projections with fully distributed glacio-hydrological (1km) modeling. Results showed that with the complete degradation of glaciers and increase in evapotranspiration, the overall runoff will decrease by 16%, and in the upper basins, the reduction will exceed 40%. The maximum snow water equivalent (SWE) is projected to decrease by 17%, and the seasonal peak of SWE will occur one month earlier. The transition from snow to rain will significantly affect lower regions, increasing extremes in peak runoff and causing 10-year recurrence interval events to occur every 3-4 years. Moreover, extreme runoff in high mountainous areas will increase due to intensified snowmelt and increased rainfall extremes. Additionally, a gradient of surface soil temperature change of 0.1°C per 100m elevation gain was observed, suggesting a potential snow-albedo feedback effect that could further amplify the warming, especially at higher altitudes. This study provides a robust analytical framework to assess the complex responses of mountain ecosystems to the impacts of climate change, with the potential of widespread application for addressing the challenges facing these critical regions.

Variations in body size and reproductive mode of oribatid mites along an altitudinal gradient in a temperate mountain region

Variations in body size and reproductive mode of oribatid mites along an altitudinal gradient in a temperate mountain region

Improving the understanding of rainfall-runoff processes: Temporal dynamic of event runoff response in Loess Plateau, China.

Enhancing the understanding of the rainfall-runoff temporal dynamics in semi-arid and semi-humid regions is crucial for flood disaster mitigation. Loess Plateau is a unique environment within semi-arid and semi-humid regions, characterized by its deep loess soil, prevalent short-duration intense rainfall, and changes in underlying surface conditions. In this research, 25 catchments from the Loess Plateau were chosen to examine the temporal variations in event runoff responses across different time scales. The study analyzed event rainfall characteristics, catchment wetness conditions, and runoff responses to identify the factors influencing event-to-event variability, seasonal patterns, and long-term shifts in runoff behavior. The results show that the event runoff response exhibits distinct patterns across different ecological regions of Loess Plateau. In the Loess Hilly and Gully region, the event runoff response is primarily influenced by rainfall intensity characteristics and occurs due to infiltration excess. In the River Valley Plain and Earth-rocky Mountainous regions characterized by a humid climate, event runoff response is influenced by pre-event soil moisture and shows a strong correlation with rainfall volume. In the Loess Hilly and Gully region, catchments experience a noticeable transition between saturated and unsaturated states over long-term changes, driven by the combined impacts of land use and climate changes. The study provides insights into the mechanisms behind runoff generation at the catchment scale and offers valuable information for flood forecasting in semi-arid and semi-humid regions.

Optimizing rain-gauge network design for enhanced estimate rainfall in ungauged plain and mountainous regions

Optimizing rain-gauge network design for enhanced estimate rainfall in ungauged plain and mountainous regions

Evaluation and comparison of separated precipitation types from multi-sources data in the Chinese Tianshan mountainous region

Evaluation and comparison of separated precipitation types from multi-sources data in the Chinese Tianshan mountainous region