Mongolian Journal of Geography and Geoecology
published by the of Institute of Geography and Geoecology, Mongolian Academy of Sciences. Abstracting and indexing in Google Scholar, Dimensions, and EBSCO Discovery service
Institute of Geography and Geoecology, Mongolian Academy of Sciences
Copyright on any research article in the Mongolian Journal of Geography and Geoecology is retained by the author(s). The authors grant the Mongolian Journal of Geography and Geoecology a license to publish the article and identify itself as the original publisher. Articles in the Mongolian Journal of Geography and Geoecology are Open Access articles published under a Creative Commons Attribution 4.0 International License CC BY. This license permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Copyright (c) 2025 Dashtseren Avirmed https://creativecommons.org/licenses/by/4.0
Synergistic use of PRISMA hyperspectral and Sentinel-1B SAR data for land cover classification
The aim of this study is to compare the performance of deep learning, machine learning, and advanced hyperspectral image classification methods for distinguishing land cover types in Ulaanbaatar city. The study area includes various land cover classes such as built-up areas, ger districts, forests, willows, grasslands, soil, and water, with significant statistical overlaps between the built-up areas and the ger districts. For data sources, we selected PRISMA (Hyperspectral Precursor of the Application Mission) and Sentinel-1B dual-polarization synthetic aperture radar (SAR) images. Three different band combinations were utilized to identify the mixed urban land cover classes in Mongolia's capital city. To differentiate the existing classes, we employed an artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM), assessing their performance against one another. To evaluate the accuracy of the classification results, we applied the Kappa coefficient. For all three band combinations, the SVM method demonstrated superior performance, with Kappa coefficients ranging from 0.96 to 0.98. The ANN showed the second-highest performance, with Kappa coefficients ranging from 0.83 to 0.96. In contrast, the SAM yielded the lowest performance, with Kappa coefficients between 0.67 and 0.71. Our study observed that the performance of the selected classification techniques depended on the chosen parameters and the structure of the datasets. Overall, this study highlights that the combined use of hyperspectral and microwave datasets can enhance the classification of land cover types, with the SVM approach emerging as the most reliable method for producing an accurate land cover map.
Copyright (c) 2025 Amarsaikhan Damdinsuren, Odontuya Gendaram, Damdinsuren Enkhjargal, Tsogzol Gurjav, Jargaldalai Enkhtuya, Ochirhuyag Lkhamjav https://creativecommons.org/licenses/by/4.0
Comparison of machine learning, supervised, and unsupervised algorithms for a land cover classification in northern Mongolia
The aim of this study is to compare the performance of machine learning approaches alongside supervised and unsupervised techniques to differentiate land cover classes in the northern Mongolia. The primary goal is to identify which of these methods can achieve the highest classification accuracy. For this analysis, we selected ten original spectral bands from Sentinel-2 data and utilized three different feature combinations. To differentiate among the available classes, we employed a support vector machine (SVM), a Mahalanobis distance classifier, and K-means clustering, assessing their relative effectiveness. In the three-band feature combination, K-means obtained the lowest accuracy at 70.08%, whereas SVM achieved 92.72%, ranking it as the most effective method. The Mahalanobis distance classifier closely followed with an accuracy of 90.36%. In the five-band combination, K-means improved its accuracy to 95.56%, surpassing earlier results, while the Mahalanobis distance achieved 95.07%, and SVM recorded an accuracy of 93.33%. In the analysis involving all ten bands, K-means again delivered the highest accuracy at 95.83%. The Mahalanobis distance classifier reached an accuracy of 93.93%, while SVM had an accuracy of 93.14%. In many cases, machine learning techniques can often outperform traditional methods. However, in this study, the traditional unsupervised technique surpassed both machine learning and supervised techniques in two cases. Thus, the results suggest that achieving high accuracy is not invariably attainable with machine learning or supervised image classification methods. In many instances, it depends on the selection of parameters, the data structure, and the radiometric properties of the objects of classes.
Copyright (c) 2025 Amarsaikhan Damdinsuren, Damdinsuren Enkhjargal, Odontuya Gendaram, Byambadolgor B, Tsogzol Gurjav, Sainbayar D, Boldbaatar N https://creativecommons.org/licenses/by/4.0
Research on opportunities of using GEP accounting in Mongolia: A case study of the valuation of ecosystem services in Khovd
The study could be a basis for designing and choosing appropriate economic valuation methods for ecosystem goods and services in Mongolia. Ecosystem economic valuation is an effective way to measure and understand the significance of the benefits people receive from ecosystems. Gross Ecosystem Product (GEP) is the monetary value of ecosystem services, making the findings comparable to GDP. The GEP concept seeks to employ specific indicators to quantify the economic value of all ecosystem products and services. GEP is the total value of final ecosystem goods and services supplied to human well-being in a region annually. We have developed the first GEP framework for Mongolia and conducted a case study using the market valuation method, shadow engineering method, replacement cost method, avoided cost method, conservation value method, and travel cost method. The GEP was calculated at the provincial level, and a framework was customized for the unique economic and ecological situations of Khovd Province. Our findings revealed that the total GEP in Khovd Province ranged from 110040.7 million dollars to 113650.2 million dollars in 2015 and 2020, respectively. The GEP to GDP ratio varied between 815 and 390 during this time. Among the different components of GEP, the value of ecosystem regulating services played a crucial role, accounting for 99.6% equally in both 2015 and 2020. The research results show that the distribution of GEP components in Khovd province is entirely distinct. The contribution of soil conservation services is the highest to the GEP of the selected case.
Copyright (c) 2025 Gerelsuren Gaanjuur, Enkhdul Tuuguu https://creativecommons.org/licenses/by/4.0
Environmental conditions in the mining areas of the Umnugovi province of Mongolia
As part of a collaborative project, soil and well water samples were collected from herder households located near the Oyu Tolgoi and Erdenes Tavan tolgoi deposits in Khanbogd and Tsogttsetsii soums of Umnugovi province to assess contamination levels. In 2024, field investigations included sampling soil and well water from herder households within the mining vicinity. The Oyu Tolgoi company conducts monitoring of water quality and water levels for certain wells of herder households within the mine’s influence zone. However, comprehensive studies on heavy metal contamination in soil and well water among herders in the mining area remain scarce. More than 20 groundwater samples and over 30 soil samples were collected from areas adjacent to the coal mining operations at the Erdenes Tavan Tolgoi deposit and the copper mining facility at the Oyu Tolgoi deposit. Chemical and analytical procedures were conducted under both field and laboratory conditions using standard methodologies. Metal concentrations were determined by atomic emission spectrometry. Petroleum hydrocarbon contents were measured using a Fluorat analyzer. Soil pH (pH_H2O) was determined potentiometrically. Organic carbon content (Corg) was measured using the wet oxidation method according to Tyurin. High concentrations of nickel, copper, zinc, and arsenic, exceeding sanitary and hygienic standards by several fold, were detected in soils of adjacent territories. Near coal mining enterprises, the total soil pollution index (∑c) corresponds to the “moderately dangerous” and “dangerous” pollution categories. Elevated levels of sodium, fluoride, magnesium, chloride, sulphates, and several heavy metals have been detected in groundwater used for residents’ water supply. In the vicinity of copper and coal mining enterprises, the water pollution index (WPI) is elevated. Monitoring of water resources conducted by mining enterprises in production areas also indicates contamination and depletion of groundwater. However, these enterprises do not conduct ecological assessments of other natural environmental components.
Copyright (c) 2025 Olga Gagarinova, Irina Belozertseva, Enkh-Amgalan Sandag, Natalia Emelianova, Gerelma Dugarova, Olga Valeeva, Ulgiichimeg Ganzorig, Bazarvaani Sarantsetseg https://creativecommons.org/licenses/by/4.0
The effect of tree species on soil organic carbon stock in Mongolian forest
Forest ecosystems in Mongolia play a critical role in storing soil organic carbon (SOC), a key component in mitigating climate change. However, little is known about how SOC stocks vary with dominant tree species in these forests. This study investigates the impact of tree species on SOC stocks in Mongolia’s boreal forests, focusing on four commonly found species: Larix sibirica, Pinus sylvestris, Pinus sibirica, and Betula platyphylla. Data were compiled from 885 soil samples across 177 soil profiles, representing diverse forest ecosystems. SOC stocks were standardized to two depth intervals (0–30 cm and 30–60 cm) using a weighted averaging method. Statistical analyses, including as ANOVA and Principal Component Analysis (PCA), were conducted to determine species-level differences and the influence of environmental factors. Results show significant variation in SOC stocks among species (F = 8.79, p < 0.001), with Betula platyphylla exhibiting the highest mean SOC stock (110.1 Mg C ha⁻¹) and Pinus sylvestris the lowest (64.2 Mg C ha⁻¹). SOC stocks generally decreased with depth across all species, though Pinus sylvestris showed higher subsurface SOC than in surface layers, likely due to deeper root systems in sandy Arenic Podzols. PCA revealed that SOC in surface layers is positively influenced by slope, temperature, and precipitation, while subsurface SOC is more closely associated with latitude and elevation. The study highlights the role of tree species and site-specific conditions in shaping SOC dynamics, emphasizing the need to consider forest composition in carbon management and climate adaptation strategies in Mongolia.
Copyright (c) 2025 Byambaa Ganbat, Batkhishig Ochirbat, Bat-Erdene Khatanbaatar, Nyamdavaa Batsaikhan https://creativecommons.org/licenses/by/4.0
Heavy metal contamination in soil surrounding a waste battery recycling facility
In Mongolia, approximately 6,250 tons or 1.6 million lead-acid batteries are discarded annually. Of these, only 19% are recycled under poor conditions and exported, while the remaining 80% are improperly disposed into the environment, contributing significantly to soil, air, and water pollution. This study was conducted to determine the levels, vertical distribution, and chemical fractions of heavy metals in soils around a battery recycling plant in Nalaikh District, Ulaanbaatar, and to assess their environmental risks. A total of 78 soil samples were collected and analyzed for eight heavy metals (Cr, Cu, Pb, Ni, Zn, Co, As, Cd) using standard methods. The mobility of metals was assessed using sequential extraction. The results revealed that, except for Pb and As, most metals were within the permissible limits of MNS 5850:2019, though slightly elevated compared to control samples. The Pb concentration in the surface soil (0–5 cm) exceeded the standard by 52.6 times and the hazardous level by 687 mg/kg. Furthermore, 79.7% of Pb was in a mobile form, indicating high potential for environmental contamination. The enrichment factor (EF) of Pb was 18.5, and the geoaccumulation index (Igeo) ranged between 0.77 and 3.45, indicating moderate to heavy pollution. These findings confirm that contamination originates mainly from human activities, particularly from poor battery recycling practices. This study provides a detailed assessment of total and mobile heavy metal fractions, highlighting the urgent need to implement appropriate remediation technologies and protective measures such as fume capture systems.
Copyright (c) 2025 Elbegzaya Gankhuyg, Batkhishig Ochirbat, Jargalbayar Ulziitumur, Enkhdul Tuuguu https://creativecommons.org/licenses/by/4.0
Healthcare characteristics in the Yakut Arctic
An analysis of the current healthcare issues in the Arctic zone of the Republic of Sakha (Yakutia) has been conducted based on materials from state statistics, data provided by the regional Ministry of Health, and a survey of managers of central district hospitals. The Yakut Arctic comprises 13 districts, and the entire territory occupying over 1.6 million km² is characterized by extremely specific and highly unfavorable ecological and socioeconomic conditions. According to statistical data, since the end of the last century the Yakut Arctic has experienced a negative demographic trend; between 1989 and 2023 the population decreased by nearly 2.5 times (from 146,000 to 65,000 inhabitants). One of the primary causes is the low standard of living, including dissatisfaction with the quality of received medical services. Accordingly, this paper devotes considerable attention to the issues of material and technical support for central district hospitals and the shortage of qualified personnel particularly, narrowly specialized physicians such as infectious disease specialists, psychiatrists specializing in addiction, ophthalmologists, neurologists, etc. The paper outlines the activities of the regional Ministry of Health in addressing several pressing issues, including the operation of mobile medical teams and medical aviation. Practical recommendations are proposed for improving healthcare provision and for attracting medical professionals to work in the remote areas of the republic, including through the provision of preferential conditions for the appointment of labor pensions. The authors emphasize that the organization of healthcare in the Yakut Arctic must be comprehensive, with mandatory support from federal programs and the involvement of the region’s fundamental economic sectors, primarily industry. The integration of new technologies, improvement of infrastructure, and development of programs for retaining and attracting healthcare workers are not only vital for the enhancement of the healthcare system in the Arctic zone of the Republic of Sakha (Yakutia) but also should contribute to reducing population outflow.
Copyright (c) 2025 Spiridon Sleptsov, Anna Romanova, Snezhana Sleptsova, Tatiana Burtseva https://creativecommons.org/licenses/by/4.0
Ecological vulnerability Assessment in the Heilongjiang-Amur River Transboundary Basin
The Heilongjiang–Amur River Basin (HARB) is an important transboundary basin in Northeast Asia, characterized by complex and diverse ecosystems and facing significant environmental pressures. Assessing ecological vulnerability in this region is of great importance. This study adopts the Exposure–Sensitivity–Adaptive capacity (ESA) framework and applies the CRITIC–AHP weighting method to evaluate the spatial pattern of ecological vulnerability (EVI) in HARB in 2020 and identify its main influencing factors. The results show that the overall ecological vulnerability of the basin is relatively high, the average EVI in 2020 was 31.0. Areas with high vulnerability are mainly concentrated in the Gobi region of Mongolia, the Northeast China Plain, and parts of southern Russia. Soil retention, net primary productivity (NPP), and habitat quality index (HQI) are the key factors contributing to the spatial variation of EVI, which explained 63%, 42% and 40% of the variation, respectively. The Mongolian part of the basin shows relatively higher ecological vulnerability, which reflects the significant influence of arid climate and fragile land resources. These findings provide a scientific basis for ecological vulnerability management and transboundary cooperation in the HARB, and offer useful reference for developing sustainable ecological strategies in other transboundary river basins.
Copyright (c) 2025 Xinyuan Wang, Hao Cheng, Li Fujia, Dashtseren Avirmed, Kirill Ganzey https://creativecommons.org/licenses/by/4.0
Soil carbon management index under different land use
This study evaluates the Carbon Management Index (CMI) across various land uses: settlement, pasture, forest, wheat cropland, abandoned cropland, and potato cropland in Jargalant soum, Central province, Mongolia. We assessed soil quality and carbon dynamics using permanganate oxidizable carbon (POXC) as an indicator of labile carbon. Soil samples from 0–15 cm and 15–30 cm depths revealed POXC ranging from 0.36 to 1.68 g kg⁻¹, highest in forest soils and lowest in abandoned cropland. The results of the study showed that at the 0-15 cm depth, CMI varied from 22.3 (settlement) and 28.4 (abandoned cropland) to 60.6 (wheat cropland), 60.9 (potato cropland), and 76 (pasture), indicating degradation in settlement and abandoned soils and rehabilitation in pasture and croplands soil. 15-30 cm depth, CMI varied from 67 (settlement) and 78 (abandoned cropland) to 131 (wheat cropland), 138 (pasture), and 198 (potato cropland), indicating degradation in settlement and abandoned soils and rehabilitation in pasture and cropland soils. POXC strongly correlated with soil organic carbon (SOC; r = 0.908, 0.841). These findings highlight POXC and CMI as sensitive tools for monitoring soil health and informing sustainable land management practices.
Copyright (c) 2025 Zoljargal Khavtgai, Ikhbayar Damba, Purevdorj Tserengunsen https://creativecommons.org/licenses/by/4.0
Input-output structure characteristics analysis on Mongolia
In recent years, Mongolia has introduced a number of policies to promote economic growth and international cooperation. Analyzing the evolution of Mongolia's economic structure with the help of macroeconomic data such as input-output is helpful to systematically evaluate the effectiveness of its policies. Based on the input-output table of Mongolia provided by the Eora National Input-Output Database, this paper systematically analyzes the input-output structure characteristics of Mongolia's national economy from 2015 to 2021. The research content mainly starts from the three economic links of intermediate input and intermediate use, final use and initial input, revealing the core characteristics of Mongolia in industrial linkage, resource allocation and economic structure. On this basis, the potential impact of Mongolia's participation in regional economic cooperation on its input-output structure and industrial development under the background of the implementation of the "Steppe Road" initiative is further explored. The research results help to understand the internal logic of Mongolia's economic structure and provide a reference for policymakers in promoting economic diversification and enhancing the resilience of the domestic industrial chain.
Copyright (c) 2025 Aohan Lei, Dashtseren Avirmed, Altanbagana Myagmarsuren, Fujia Li, Hao Cheng https://creativecommons.org/licenses/by/4.0
Assessment of the current condition of Mongolian road border ports
The formation and development of border ports is the result of the development of regional economy and world economy. The border port is the gateway of land opening and the hub node of international trade and social-cultural exchanges among the countries. Mongolia is a landlocked country, connected to neighboring countries by airports, road border ports, and railway border ports. As of 2024, Mongolia’s total foreign trade reached 27.3 billion USD. Of this total, 34.5% was conducted through the Zamyn-Uud border port, 30.4% through Gashuunsukhait, 9.5% through Sukhbaatar, and 8.9% through Shiveekhuren. The share of foreign trade conducted through other border port remained below 5%. Limited infrastructure and reliance on a single port contribute to elevated costs and delays, particularly during peak seasons. This logistical bottleneck hinders economic growth and raises consumer prices. Therefore, it is essential to identify, from an economic geographical perspective, the border ports that will play a key and beneficial role in the national, regional, and local socio-economic development in the short and medium term. This study aims to assess the current conditions of Mongolia’s road border ports and to identify those that play a key role in regional socio-economic development. Within the scope of the study, an assessment of the current conditions of 20 road border ports was conducted using the Multi-Criteria Analysis (MCA) method, based on 27 criteria in 9 groups. The assessment of 20 road border ports in Mongolia revealed that the Zamyn-Uud, Altanbulag, Bulgan, Tsagaannuur, Bichigt, and Ereentsav ports received the highest scores. These ports are characterized by favorable conditions such as regional cooperation, well-established basic infrastructure, and advantageous border port classifications and operational regimes. Furthermore, based on the methodology employed in this study, it is possible to evaluate railway port and airport and it is possible to identify the border ports that require prioritized development at the national level.
Copyright (c) 2025 Nandin-Erdene Amartuvshin, Altanbagana Mygmarsuren, Urantamir Gankhuyag, Tseyenkhand Punsantsogvoo https://creativecommons.org/licenses/by/4.0
Ecological security and green development on the Mongolian plateau: A China–Mongolia collaborative response to climate change
The Mongolian Plateau plays a key role in the geopolitical and ecological structure of Northeast Asia. In recent years, the combined effects of climate change and human activities have intensified ecological risks in the region, especially land degradation and sandstorms, which have had significant impacts on both China and Mongolia. This paper reviews two decades of work by the authors’ team on the Mongolian Plateau, including research on pattern characterization, mechanism analysis, and optimization strategies related to land cover, ecosystem vulnerability, ecological-economic zoning, investment environment, industrial structure, and transport infrastructure. Building on this foundation, the paper identifies four major priorities for future cooperation: risk assessment and spatiotemporal evolution pattern of ecological security risk in the Mongolian Plateau under climate change, influencing factors and driving mechanism of ecological risks in the Mongolian Plateau, China-Mongolia joint construction plan for ecological security shield in the Mongolian Plateau, and green Development Model and Typical Demonstration of Mongolian Plateau. These future cooperation efforts aim to provide essential scientific and technological support for desertification control, land degradation prevention, sandstorm risk reduction, and the protection of biodiversity and ecosystem stability on the Mongolian Plateau. In addition, this study holds important scientific value for addressing global climate change, promoting sustainable development in transboundary regions, and mitigating large-scale ecological risks.
Copyright (c) 2025 Hao Cheng, Xinyuan Wang, Fujia Li, Dashtseren Avirmed, Wenlong Li https://creativecommons.org/licenses/by/4.0
System dynamics model and input–output model in regional ecological economy analysis and international research prospects
Regional ecological-economic research focuses on balancing ecological protection, economic growth, and resource utilization to achieve sustainable development. Under global climate change and environmental pressures, its significance is increasingly recognized. The System Dynamics (SD) model and the Multi-Regional Input–Output (MRIO) model serve as key quantitative tools in this research field. SD captures dynamic feedbacks and time-lag effects in complex systems, supporting long-term policy analysis. MRIO traces resource flows and environmental impacts across regions, enabling the assessment of economic–environmental linkages. Based on various scientific research projects undertaken by the research team, we have long utilized SD and MRIO models to conduct ecological-economic studies across diverse thematic areas and spatial scales. This paper systematically reviews the empirical research conducted by the authors' team using the System Dynamics (SD) model and the Multi-Regional Input–Output (MRIO) model across various industrial sectors and spatial scales. It further explores the integration pathways of the two approaches, aiming to enrich the theoretical framework and methodological system in the field of regional ecological-economic research. Looking ahead, the research team will promote the application of SD and MRIO models in cross-border cooperation studies among China, Mongolia, and Russia, providing strong technical support for sustainable development in transboundary regions. This study holds significant theoretical importance and practical value.
Copyright (c) 2025 Fujia Li, Hao Cheng, Aohan Lei, Ruofan Zhao https://creativecommons.org/licenses/by/4.0
Sustainable development policy in Mongolia: National aspirations and local priorities for the resource regions
The institutional framework is the most important basis for the functioning of sustainable development instruments. For an intensively developing economy like Mongolia, it is important to maintain harmony between economic, social and environmental concerns. This paper provides an overview of the key features of sustainable development policy in Mongolia. Relevant documentation is reviewed, and the most important priorities are highlighted. Authors have analyzed documents such as Vision for Sustainable Development of Mongolia until 2030 and “VISION 2050”, The Mongolian Green Taxonomy. Sustainable financing instruments are highlighted as the most important aspect of the practical application of the issues under study, especially green loans and social loans, sustainability-related loans. Due to the critical importance of the mining industry for the Mongolian economy, a model territory in the form of Umnugovi aimag is selected, for which the case of the Oyu Tolgoi mine is considered. It is important to note which aspects of policy implementation are left to governmental and non-governmental organizations, and which aspects are taken up by business. Big degree of responsibility regarding ESG-policy currently falls on the private companies. A particular case study of the Oyu Tolgoi revealed how the sustainable development of the resource region is related to the indstrual actor. This particular case study is an example of the successful implementation of such dependence, although it is unlikely to be the case for every private company. Further study of this topic will not only provide concrete analytical reports, but will also contribute to the development of practical solutions to the nature-society nexus in Mongolia.
Copyright (c) 2025 Elena Davydova, Konstantin Tszian https://creativecommons.org/licenses/by/4.0
Can the mining industry improve social welfare? Evidence from Mongolian provinces
The mining industry plays a crucial role in the economies of many resource-rich countries, including Mongolia. While mining is widely recognized for driving economic growth, its implications for social welfare remain debated. This study investigates the relationship between the mining sector’s contribution to gross domestic product (GDP) and the Human Development Index (HDI) across Mongolian provinces over the period 2004–2023. Using a balanced panel dataset and a two-way fixed effects regression model, we examine how changes in mining activity influence key dimensions of human development, such as education, health, and living standards. Our findings reveal a positive and statistically significant association between the mining share of GDP and provincial HDI scores. The results suggest that mining-driven economic activity not only generates fiscal revenues and employment but also indirectly fosters improvements in public services and infrastructure, thereby enhancing social welfare. We also observe that the positive effects are more pronounced in urbanized provinces with better institutional capacity, suggesting that local governance plays a mediating role. To further test the robustness of the results, we conduct several sensitivity checks, including alternative model specifications and lagged variables. The study contributes to the literature by providing empirical evidence from a developing, resource-dependent country context and underscores the potential for the mining sector to serve as a driver of inclusive development. The findings offer policy insights for balancing economic extraction with long-term welfare enhancement in mineral-rich regions.
Copyright (c) 2025 Li Chen, Hao Cheng, Fujia Li, Myagmarsuren Altanbagana, Dashtseren Avirmed https://creativecommons.org/licenses/by/4.0
Comparative analysis of the efficiency of transportation corridors in Mongolia
The development of transportation corridors is especially important for landlocked countries, as it enhances their capacity to connect internal regions with seaports, markets, and production centers. Within the framework of the Regional Development Concept of Mongolia (2024-2050), vertical and horizontal transportation corridors of transport infrastructure have been identified. For these transportation corridors, it is important to establish the development priority based on a comparative analysis of the efficiency of the regional transport network, and then determine the distribution of construction and investment. Therefore, the purpose of this study is to conduct a comparative analysis of the efficiency of the above-mentioned transportation corridors and establish the priority. The Time-Cost-Distance (TCD) method was employed to evaluate each corridor. Data was collected on the current state of transport infrastructure, as well as the time and cost associated with transportation processes along each corridor. Based on the comparative analysis, the corridors were ranked into three priority groups, with Corridor 3 in the first priority, Corridors 1 and 4 in the second priority, and Corridors 2, 5, and 6 in the third priority. Corridor 3, which is included in the first priority, is the main road corridor of Mongolia. Corridors 1 and 4, which are included in the second priority, are important for the integration of Central and Eastern Asia into regional economic cooperation and demonstrate the importance of developing them first. Corridors 2, 5, and 6, which are included in the third priority, have relatively low economic efficiency compared to other corridors, so they can be developed at a later stage.
Copyright (c) 2025 Altanbagana Myagmarsuren, Urantamir Gankhuyag, Tseyenkhand Punsantsogvoo, Nandin-Erdene Amartuvshin, Natsagsuren Bayasgalan https://creativecommons.org/licenses/by/4.0
Plastic waste collection route optimization: Bulgan province
Efficient plastic waste collection in rural Mongolia is critical to reducing environmental pollution and improving recycling outcomes. This study optimizes plastic waste transport routes in Bulgan province using the travelling salesman problem (TSP), and vehicle routing problem (VRP) algorithms and open-source GIS tools. By analyzing population data, waste generation rates, and vehicle capacity, ten optimal routes were identified for 15-ton load serving 19 key locations. Results show large disparities in route efficiency, with urban centers generating more waste per kilometer than sparsely populated areas. Recommendations include dynamic routing, vehicle load capacity, and investing in local waste compacting equipment. The findings offer practical solutions for cost-effective, sustainable rural waste management.
Copyright (c) 2025 Enkhbaatar Dembereldash, Uyanga Batbaatar, Enkhdul Tuuguu, Sanchirgarav Batzorig https://creativecommons.org/licenses/by/4.0
Seasonal variation in groundwater quality along the Кherlen river valley
In this study, 176 groundwater samples were collected along the floodplain of the Kherlen River to assess seasonal variations in groundwater quality. Sampling was conducted three times: twice during the spring and once during the autumn. The analysis included the use of a Piper diagram, a Gibbs diagram, and correlation analysis to evaluate temporal and spatial variations in groundwater hydrochemistry. The results revealed significant seasonal fluctuations in the concentrations of HCO₃⁻, Ca²⁺, Na⁺, Fe, F⁻, and NO₃⁻, with more pronounced spatial variations during the spring due to the dilution effect of precipitation. In both seasons, the majority of groundwater samples were classified under the Ca-HCO₃ hydrochemical type, while a few samples were identified as Na-HCO₃ and Mixed-HCO₃ types. The findings indicated that the groundwater in the study area mainly originated from rock-water interactions, with only a few samples linked to evaporation mechanisms. High Na⁺/(Na⁺+Ca²⁺) ratios and low Cl⁻/(Cl⁻+HCO₃⁻) ratios suggest that groundwater chemistry is primarily influenced by the weathering of carbonate and silicate minerals. Additionally, cation exchange processes, specifically the replacement of Ca²⁺ by Na⁺, were found to actively contribute to the hydrogeochemical evolution of the aquifer system. Correlation analysis further demonstrated that groundwater salinity is predominantly controlled by dissolved ions, as evidenced by strong positive correlations between EC and TDS. Significant associations among major ions, including Ca²⁺, Mg²⁺, Na⁺, and HCO₃⁻, highlight the influence of geochemical processes such as dolomite dissolution and the weathering of sodium-rich minerals. The positive correlation between nitrate and Ca²⁺/Mg²⁺, along with its negative correlation with HCO₃⁻, points to anthropogenic impacts-particularly from agricultural inputs-as significant contributors to groundwater contamination. These findings underscore the need for improved land and water management practices to safeguard groundwater quality in the region.
Copyright (c) 2025 Enkhjargal Togtokh, Chinzorig Sukhbaatar, Dalaijargal Sandag, Baymbasuren Zorigt, Gerelt-Od Dashdondog, Erdenechimeg Surenjav https://creativecommons.org/licenses/by/4.0
Assessing climate hazard risks for Mongolian herders : A county-level study in Khovd, Bayankhongor, Dundgovi provinces
Mongolian herders are particularly vulnerable to climate change, as their livelihoods depend heavily on livestock production. Understanding the effects of climate hazards such as drought and dzud is crucial for planning and implementing the appropriate responses and effective adaptive measures. In this research, risk is defined as the potential for adverse consequences for herders due to hazardous climatic conditions, and is explained through 4 components including climate hazard, exposure, vulnerability, and response. The purpose of this study is to assess climate hazard risks of herders at the county (soum) level and explore the varying contributions of risk components. Based on a literature review and data availability, 15 factors of risk were selected including temperature and precipitation change, drought and dzud frequency, pasture use index, livestock loss, ratio of seasonal pastureland, water availability, poverty, dependency ratio, education, bank savings, prepared hay and forage, barns, and index-based livestock insurance. Principal Component Analysis (PCA) was used to aggregate the factors within 4 components of risk, and the composite risk index (RI) was calculated using the principal component scores at the soum level in Bayankhongor, Khovd, and Dundgovi provinces of Mongolia. The average RI was 19.2 in Bayankhongor, 14.2 in Khovd, and 17.0 in Dundgovi province. The results highlight the spatial heterogeneity of risk, emphasizing that the northern soums of Khovd and Bayankhongor, and the northeastern soums of Dundgovi are particularly susceptible to climate-related hazards. On the other hand, better hay and forage preparation in Khovd province contributed to its lower overall risk index. Enhancing long-term preparedness measures, such as hay and forage preparation and barn availability, alongside social and financial support mechanisms for vulnerable herder households is crucial for strengthening resilience.
Copyright (c) 2025 Saruul Galtbayar, Altanbagana Myagmarsuren, Urantamir Gankhuyag, Otgonkhuu Tsedev-Ish, Dong Suocheng, Masato Shinoda, Munkhjargal Erdenebadrakh https://creativecommons.org/licenses/by/4.0
Adapting to the new normal: Challenges and strategies for landlocked Asian mountainous countries in the face of extreme climate events
The aim of this investigation is to assess the compounded vulnerabilities of landlocked developing countries (LLDCs) in association with global warming, due to their geographic isolation, limited infrastructure, and socio-economic fragility. Employing a literature review, policy analysis, and comparative case studies, the paper evaluates specific climate impacts and adaptation strategies of Nepal, Bhutan, and Mongolia. Key findings indicate severe hydrological fragility resulting from glacier melt and permafrost degradation, transportation disruptions affecting economic stability, and degradation of essential ecosystem services vital for rural livelihoods. Each country exhibits unique adaptive approaches; Nepal emphasizes ecosystem-based adaptation and integration of local traditional knowledge, Bhutan prioritizes risk assessment and resilient hydropower development supported by innovative financing, and Mongolia focuses on climate-smart herding practices coupled with advanced early warning systems. Conclusions highlight the urgent need for tailored international support, increased regional cooperation, and integrated climate policy frameworks. Practical recommendations include expanding remote sensing technologies for cryosphere monitoring, establishing basin-level councils for coordinated water resource management, scaling community-based adaptation programs utilizing traditional ecological knowledge, transitioning to sustainable tourism models, and advocating for LLDCs issues within international climate negotiations. Addressing these structural vulnerabilities is essential for sustainable development, resilience-building, and regional stability in mountainous LLDCs.
Copyright (c) 2025 Anumandal Orgil, Bashudev Neupane, Munkhdulam Otgonbayar, Batjargal Zamba, Dulguun Damdin-Od, Sudeep Thakuri https://creativecommons.org/licenses/by/4.0
Estimating the spatial distribution of urban heat islands in six central districts of Ulaanbaatar city using machine learning algorithm
Urban Heat Islands (UHIs) represent a growing environmental challenge in rapidly urbanizing cities, particularly in extreme climate regions such as Ulaanbaatar, Mongolia. This study aims to model and analyze the spatial distribution of UHIs across six central districts of Ulaanbaatar using Landsat 5 and Landsat 8 satellite imagery. Land Surface Temperature (LST) was derived from Landsat 8, and its relationship with six spectral indices the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare Soil index (BI), Normalized Differences Water Index (NDWI), and Normalized Building Soil Index (NDBSI) was assessed. Two ensemble machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGB), were employed to model UHI patterns. RF performed better in 1994 (a coefficients of determination (R²) = 0.72, a root-mean-square error (RMSE) = 1.89°C), while XGB showed superior performance in 2024 (R2 = 0.75, RMSE = 1.97°C). BI and NDBSI were the most influential contributors to UHI intensity, whereas vegetated areas (NDVI, MNDVI) had consistent cooling effects. The spatial modeling revealed a clear intensification of UHI effects over time, particularly in built-up and bare land zones. This study is significant as it applies ML techniques to long-term satellite data in a cold-climate, data-limited urban setting. By providing a rare longitudinal perspective, it contributes to understanding UHI dynamics in Mongolia and demonstrates the potential of open-access remote sensing data combined with ML for urban climate assessments. The findings offer valuable insight for urban planners, emphasizing the critical role of green infrastructure in mitigating thermal stress and informing climate-resilient development strategies in rapidly growing cities.
Copyright (c) 2025 Byambadolgor Batdorj, Amarsaikhan Damdinsuren, Sainbayar Dalantai, Munkh-Erdene Altangerel https://creativecommons.org/licenses/by/4.0
Topoclimatic drivers of land cover change in the Mongol Altai Mountains using random forest and eXtreme gradient boosting algorithms
Investigating land cover change in mountainous regions is crucial for sustainable land use planning and enhancing climate adaptation strategies. This study examines the spatial and temporal patterns of land cover dynamics in the Mongol Altai Mountains over a 30-year period, and identifies the key climatic and topographic factors, collectively referred topoclimatic drivers, that influencing these changes. Landsat satellite imagery with a 30 m resolution was processed using the Random Forest (RF) algorithm in R to classify land cover types. A change detection matrix was employed to quantify transitions between land cover types, and the results were visualized using a Sankey diagram. To assess the impact of environmental variables, eXtreme Gradient Boosting (XGBoost) algorithm was applied and model interpretability was enhanced using SHAP (Shapley Additive exPlanations) values. The classification demonstrated high accuracy, with overall accuracy values of 92.5% for 1990 and 93.7% for 2020. Over the study period, notable declines were observed in grasslands (−20.08%), tree-covered areas (−22.84%), and glaciers (−43.01%), while shrublands (26.82%) and artificial surfaces (36.28%) experienced substantial increases. XGBoost analysis identified vapor pressure, elevation, and wind speed as the most significant environmental factors driving land cover change. These factors demonstrated complex, non-linear relationships, highlighting the heterogeneous nature of high mountain ecosystems. The findings provide valuable insights for formulating climate-responsive land management and conservation strategies in mountainous regions.
Copyright (c) 2025 Batnyam Tseveengerel, Purevsuren Munkhtur, Renchinmyadag Tovuudorj https://creativecommons.org/licenses/by/4.0
Knowledge-based geoecological mapping for sustainable land management in Khuvsgul region
Geoecological mapping plays a critical role in advancing sustainable land management by enabling the integration of geological, geomorphological, and ecological dynamics. This study elaborated a geoecological map by incorporating detailed geomorphological and ecological information using a knowledge-based mapping approach. The methodology combines rule-based logic, expert interpretation, and the integration of various spatial datasets. Geological relationships, including stratigraphic sequences, lithological associations, and fault structures were encoded within a rule-based framework to ensure spatial and conceptual coherence. Expert knowledge, we derived from pre-existing geological and geomorphological data and supported by conceptual models, guided manual digitization and semi-automated interpretation processes. Multi-source datasets, including satellite imagery, digital elevation models (DEMs), and seismological layers, were integrated using domain-specific reasoning strategies. Geoecological units were delineated by considering the combined cumulative effects of three primary driving forces: (1) endogenic factors, stemming from deep-seated geodynamic processes such as tectonics and seismic activity; (2) exogenic factors, related to surface processes such as weathering, erosion, and mass movement; and (3) technogenic factors, resulting from human-induced changes including land use alterations, infrastructure development, and resource exploitation. The final geoecological map offers a robust analytical framework for understanding landscape evolution, assessing environmental vulnerability, and supporting evidence-based decision-making in land-use planning and natural resource management.
Copyright (c) 2025 Boldbaatar Natsagdorj, Bayartungalag Batsaikhan, Amarsaikhan Damdinsuren, Khaliuna Sukhbat https://creativecommons.org/licenses/by/4.0
Human impacts on pasture degradation (A case study of the Dornod, Sukhbaatar, Khentii provinces)
As Mongolia transitioned to a market economy, rapid population and economic growth have spurred human settlements and significant urbanisation. Over the last two decades, Mongolia’s population has increased by 1 million and livestock numbers have soared by over 30 million. This growth, coupled with the expansion of urban areas and road networks, has intensified pressure on land resources, leading to degradation. Notably, pastureland constitutes over 80 percent of Mongolia's total land area. This study aims to identify and map the human impacts contributing to pasture degradation in Mongolia’s eastern regions, i.e., Dornod, Sukhbaatar, and Khentii provinces. We developed a map that illustrates these human impacts by considering eight factors rooted in recent research and the particular land use characteristics of Mongolia. These factors include the proximity of wells, rivers and lakes, winter and spring camps, road networks, settlement areas, mining areas, cropland, and livestock density. To assess the human impacts, we produced individual maps for each of the eight selected factors, culminating in a comprehensive assessment map. Our findings reveal that 12.6 percent of the eastern region, equivalent to 3,607 thousand hectares, remains unaltered by human activities. In contrast, 62 percent of the region (17,742 thousand hectares) experiences low impact, 24.2 percent (6,935 thousand hectares) encounters moderate impact, and 1.2 percent (337 thousand hectares) is highly affected by human activities.
Copyright (c) 2025 Narantsatsral Tseren, Danzanchadav Ganbat, Urtnasan Mandakh, Telmen Purevsuren, Ikhbayar Tsevelmaa https://creativecommons.org/licenses/by/4.0
Addressing desertification to achieve sustainable herding and grasslands
The present review paper outlines a series of efforts to identify and solve desertification and wind erosion issues with the aim of creating sustainable drylands. These efforts incorporate the following three key projects under the Japan-Mongolia partnership that were funded by the Japan Society for the Promotion of Science (JSPS): “Integrating Dryland Disaster Science,” “Mongolian Herding Vision 2050 for Adaptation to Climate and Social Changes,” and “Regional Assessment of Desertification Using Bioaerosols: A Novel Method.” Regional focus is placed on the Eurasian grasslands, specifically Mongolia. This paper highlights the importance of (1) understanding the global history of desertification as a consequence of agriculture and climate change on a ten-thousand-year time scale, (2) assessing the irreversible nature of desertification using satellite remote sensing on a continental scale, and (3) monitoring the real-time change in wind erosion and soil fertility using bioaerosols on a regional scale. The combination of research from the above-mentioned spatial and temporal scales will help elucidate the dynamics of desertification, strengthening desertification risk governance with the goal of sustainable land management, and enhancing early warnings and early actions to avoid exceeding an irreversible threshold.
Copyright (c) 2025 Masato Shinoda https://creativecommons.org/licenses/by/4.0
Interrelated factors influencing the human development potential in the Central region of Mongolia /For example Umnugovi province/
This article examines the main factors of human potential formation. There is a gradual increase in the human development index in the Umnugovi region. This is due to the influence of various factors that are closely interrelated and have varying degrees of manifestation at the regional level. To assess the degree of influence of factors on human development, a selection of indicators characterizing the situation in the region was made. All indicators are aggregated into four groups of factors and given a total value in the range from zero to one, which allows for a quantitative assessment of the factors. This makes it possible to assess the degree of influence of a particular factor on human potential development, as well as to identify the main problems, the solution of which will contribute to further improvement of human development.
Copyright (c) 2025 Ayauli Yerlan, Enkh-Amgalan Sandag, Olga Valeeva https://creativecommons.org/licenses/by/4.0
Extending NDVI time series in Mongolia using spatial correlation analysis between AVHRR-GIMMS and MODIS TERRA data
Accurate and long-term monitoring of vegetation dynamics is critical for understanding how ecosystems respond to climate change and human-induced pressures, especially in environmentally sensitive regions like Mongolia. This study examined the relationship between the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the third-generation Global Inventory Modeling and Mapping Studies (GIMMS) dataset to generate an extended NDVI time series for Mongolia. Spatial correlation analysis was performed for the growing season (April to September) from 2002 to 2015, with MODIS NDVI resampled to the 8 km resolution of the GIMMS dataset to ensure comparability. The results revealed a strong correlation between the two datasets, with the coefficient of determination (R²) ranging from 0.776 to 0.905 and root mean square error (RMSE) values between 0.034 and 0.070. Higher agreement was observed in steppe and forest-steppe regions, while reduced consistency in mountainous and arid areas highlighted challenges in monitoring vegetation in complex or sparsely vegetated environments. The application of the Partial Least Squares Regression (PLSR) model demonstrated that MODIS NDVI could be reliably reconstructed from GIMMS NDVI values. This integration enables the extension of MODIS-like NDVI back to the 1980s, capitalizing on the longer temporal coverage of the GIMMS dataset for comprehensive vegetation monitoring and trend analysis. Overall, the study supports the combined use of MODIS and GIMMS NDVI for robust, long-term ecological assessments and provides a solid foundation for future research focused on cross-sensor harmonization using advanced statistical and machine learning approaches.
Copyright (c) 2025 Munkhdulam Otgonbayar, Davaagatan Tuyagerel, Renchinmyadag Tovuudorj, Odbaatar Enkhjargal https://creativecommons.org/licenses/by/4.0
Recent CO₂ and water vapor fluxes at Udleg forest research station, northern Mongolia Baseline data for climate–ecosystem studies
This study investigated seasonal and interannual patterns of net ecosystem exchange (NEE) and evapotranspiration (ET) obtained at a larch-dominated forest in northern Mongolia between 2010–2012 and 2022–2024. Seasonal and interannual variability in both fluxes has been observed; years with earlier leaf-out and higher early-season leaf area index (LAI) resulted in higher CO₂ uptake, while wetter summers promoted higher ET. Bayesian regression analysis revealed a shift in the relative influence of environmental drivers in the two periods. The soil water effect is remarkable in summer ET variation in previous relatively drier years. In contrast, surface soil temperature and solar radiation have become more influential than soil water in recent years. LAI emerged as a significant driver of NEE only in the latter period, suggesting improved photosynthetic responsiveness under favorable water conditions. These results indicate the sensitivity of larch ecosystems to decadal scale climate variability and suggest the importance of phenological dynamics and soil–plant–atmosphere interactions. Although these changes were likely within the natural variability range, they indicate the need for sustained monitoring. Long-term, interdisciplinary studies will be essential to understand the ecosystem's water–carbon coupling in the forest–steppe transition zone.
Copyright (c) 2025 Ayumi Kotani, Temuujin Khurelbaatar, Dashtseren Avirmed, Baatarbileg Nachin, Shin Miyazaki, Mamoru Ishikawa, Tetsuya Hiyama https://creativecommons.org/licenses/by/4.0
Seasonal water yield modelling of the Baidrag River Basin
Water yield plays a critical role in sustaining ecological balance and supporting ecosystem services, yet it exhibits significant spatial and seasonal variability. Estimating water yield in data-scarce regions remains challenging, emphasizing the importance of model-based approaches. This study evaluated the performance of the Integrated Valuation of Ecosystem Services and Tradeoffs - Seasonal Water Yield Model (InVEST-SWYM) for simulating seasonal water yield in the Baidrag River Basin, Mongolia, over the period 2000–2020. The model was applied using key input parameters, including monthly precipitation, evapotranspiration, a digital elevation model, land use and land cover data, and soil characteristics derived from satellite imagery and primary sources. Key outputs included monthly and annual quickflow (QF), baseflow (B), and actual evapotranspiration (AET). Results revealed increasing trends in precipitation (180.9–253.7 mm/year) and quickflow (15.15-21.77 mm/year), with peak runoff in July. AET increased from 163.4 mm to 230.14 mm, while potential evapotranspiration (PET) declined from 1314.9 mm to 1139.6 mm. Baseflow remained low (0.1–4 mm), with higher values in northern and north-eastern zones. Quickflow showed strong seasonality and was spatially concentrated in the northern and western sub-basins. These patterns were interpreted to reflect the combined influence of precipitation distribution, topographic gradients, and land cover characteristics, based on visual analysis of spatial model outputs. The results highlight reduced flows during winter due to frozen ground and elevated summer flows linked to precipitation peaks. The seasonal quickflow estimation was validated by comparing the predicted results with observed streamflow data from the Baidrag-Baidrag gauging station for the years 2000 and 2020. To assess statistical correlation and reliability, Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) were calculated. NSE values were 0.77 and 0.79 for 2000 and 2020, respectively, with PBIAS values of 25.64 and –23.64. The model tended to overestimate streamflow during spring snowmelt (May) and underestimate it during the summer rainfall season, likely due to bias in CHIRPS precipitation data.
Copyright (c) 2025 Purevsuren Munkhtur, Batnyam Tseveengerel, Bayanjargal Bumtsend, Odbaatar Enkhjargal https://creativecommons.org/licenses/by/4.0
Factors influencing surface water changes in the Khar-Us Lake basin, western Mongolia
This study investigates the spatiotemporal variability in the surface areas of Khar-Us, Khar, and Durgun Lakes in western Mongolia between 1992 and 2022 using Landsat imagery and climatic datasets. The Normalized Difference Water Index (NDWI) was employed to extract lake extents, and trend analyses such as the Mann–Kendall test, Sen’s Slope Estimator, and Innovative Trend Analysis Method were used to examine the influence of climate variables - precipitation and air temperature. Results show a statistically significant decline in lake surface area, particularly for Khar-Us Lake, which decreased by approximately 0.43 km² annually. Over the past 30 years, a strong climatic influence has been observed, as indicated by the Mann Kendall (MK) trend (Z = -2 to -6). Air temperature has exhibited a gradual warming MK trend (Z = 0.68), whereas precipitation has shown a declining trend (Z = -0.781). Climatic factors, including reduced precipitation and increasing temperatures, were identified as the dominant drivers. While surface changes in Khar and Durgun Lakes between 2006 and 2010 appear temporally aligned with the operational period of the Durgun Hydropower Plant (HPP), the current study does not include hydrological or operational data to quantitatively confirm causation. Hence, the potential influence of anthropogenic factors is discussed as a hypothesis requiring further research. These findings contribute to understanding the impacts of climatic variability on lake ecosystems in semi-arid regions.
Copyright (c) 2025 Tsolmonbayar Galbadrakh, Yanjindulam Ganbold, Li Dingjun, Altanbold Enkhbold https://creativecommons.org/licenses/by/4.0
Monitoring and estimation of aufeis area dynamic changes using remote sensing and machine learning in Erdenebulgan soum of Khuvsgul province in Mongolia
Aufeis, or icings, are seasonal ice formations that occur when groundwater or surface water repeatedly freezes, forming layered ice sheets in cold climates. Monitoring aufeis dynamics is essential for understanding regional hydrology, permafrost processes, and the impacts of climate variability. This study analyzes the interannual variability of aufeis extent from 2018 to 2025 in Erdenebulgan soum, Khuvsgul province, Mongolia, using a combination of Sentinel-2 satellite imagery, UAV Phantom 4 RTK drone orthophotos, and a Random Forest (RF) machine learning classification model. Twelve input variables-including spectral bands, vegetation and snow indices, and topographic features-were tested for their contribution to classification accuracy. Feature importance analysis indicated that the Maximum Difference Ice Index (MDII), Topographic Position Index (TPI), and Sentinel-2 Band 12 were the most influential predictors. Aufeis area showed significant temporal variation during the study period, increasing from 149 ha in 2018 to a peak of 235 ha in 2022, then declining sharply to 105 ha by 2025. This decline may reflect changing precipitation, groundwater discharge, or warming trends affecting ice formation. The RF model was validated using high-resolution UAV data collected in 2025. The aufeis extent derived from Sentinel-2 imagery (32.47 ha) closely matched the UAV-based measurement (32.78 ha), demonstrating the method's high spatial accuracy. These results confirm that combining Sentinel-2 data with machine learning and UAV validation is an effective approach for long-term aufeis monitoring in mountainous, permafrost-affected regions.
Copyright (c) 2025 Temuujin Khurelbaatar, Dashtseren Avirmed, Maralmaa Ariunbold, Bilguun Tsogoo, Undrakhbayar Enkhjargal https://creativecommons.org/licenses/by/4.0
Spatiotemporal assessment of water quality and geochemical evolution of Ugii Lake
Ugii Lake, a shallow freshwater body in Mongolia’s semi-arid steppe zone, plays an important ecological and socioeconomic role. However, its water quality is increasingly influenced by both natural variability and human-induced pressures. This study investigates the seasonal and spatial dynamics of water chemistry in Ugii Lake by analyzing samples collected during three key periods November 2023 (late autumn), June 2024 (summer), and January 2025 (winter). A total of 24 samples were analyzed for major ions, nitrogen compounds, and supporting physicochemical indicators. To understand the processes shaping the lake’s hydrochemistry, we employed a combination of Piper diagram, ion ratio-based mixing, scatter plots, and multivariate statistical tools, including hierarchical cluster analysis (HCA) and decision tree modeling. The findings revealed that Ugii Lake water predominantly belongs to the Ca–Mg–HCO₃ type, consistent with carbonate and silicate mineral weathering. Notably, fluoride concentrations in several samples exceeded WHO drinking water guidelines, indicating possible natural geogenic enrichment. Ammonium levels were also elevated in certain areas, suggesting localized organic input or anthropogenic sources. Cluster analysis grouped water samples primarily by season, highlighting strong temporal patterns in water chemistry rather than spatial ones. Meanwhile, the decision tree analysis identified magnesium, fluoride, and ammonium as key predictors of total nitrogen concentrations, underscoring their geochemical and possibly anthropogenic origin.
Copyright (c) 2025 Uuriintuya Gantsetseg, Odsuren Batdelger, Narangerel Serdyanjiv, Batnyam Tseveengerel, Odbaatar Enkhjargal, Purevsuren Munkhtur, Davaagatan Tuyagerel, Chao Han, Cheng Liu, Zhaode Wang https://creativecommons.org/licenses/by/4.0
Prospects for restoring ecosystems in the Pasvik River basin reconstruction of climate change, biodiversity, history of nature management and conservation practices
The paper is devoted to discussing the effectiveness of measures to restore degraded lands and preserve biodiversity in the north-west of the Murmansk Region in the Pechenga Municipal District (Russian Federation). The purpose of the study was to study climate change, biodiversity, the history of nature management and environmental practices in the basin of the Paz River. Published and archived data from climatic, soil-geobotanical, archaeological, and historical studies were collected and systematized, as well as their own field geoecological studies (2021-2024). The use of reconstruction and forecasting methods made it possible to recreate an objective picture of climatic changes in the basin of the Paz River over the past 9.5 thousand years and to identify a multiple change of vegetation (from Arctic tundra to forest) after the melting of the glacier. The study of archaeological and historical materials allowed us to identify five stages of economic development of the territory, which led to a reduction in biodiversity and land degradation (with a maximum in the 70s of the XX century at the peak of the development of copper-nickel production). Three main factors hindering the restoration of degraded lands in the Paz River basin have been identified: large areas of man-made wastelands with a high content of heavy metals, long-range irradiation of microparticles of pollutants, and low rates of natural restoration of Arctic ecosystems. To accelerate the processes of natural restoration, it is proposed to replace expensive methods of reclamation of man-made wastelands with the creation of a set of conditions for the formation of biological soil crusts (communities of cryptogamous organisms).
Copyright (c) 2025 Olga Khlebosolova, Evgeniya Sosna https://creativecommons.org/licenses/by/4.0
Icings in the Selenge River basin as an indicator of climate change
The Selenga River basin, with approximately two-thirds of its area located in Mongolia and the remaining third in Russia, hosts over 29,000 icings of diverse genetic types that form annually. In this study we analyzed the interannual variability of icings in the Selenga River basin. Using multi-temporal Landsat imagery, data from meteorological stations, hydrometric stations and expedition observations, the main trends in the change of icings formation intensity were established. Over the period from 1990 to 2024, a 3.5% decrease in the area of giant icings in the Mongolian part of the basin over 10 years was revealed. In the Russian part of the catchment basin, besides the reduction in the total area of icings by 6,7% over 10 years, an increase in the number of medium, small and very small ones is observed against the background of a decrease in the number of very large icings. The established trend is related to climate change. A significant increase in air temperature in April (on average in the basin by 0.75˚ over 10 years) may be one of the reasons for the overall reduction in the area of icings. As a result of the observed warming, the thickness of seasonal freezing of rocks decreases, which leads to an increase in the transition of icing-forming groundwater outside the icing fields. Also, melting of permafrost is recorded. Thus, icings are a kind of indicator of changes in permafrost-hydrogeological conditions under the influence of global climatic processes.
Copyright (c) 2025 Vladimir Николаевич, Alexander Ayurzhanaev, Andrey Shikhov, Bator Sodnomov, Margarita Zharnikova, Temuujin Khurelbaatar, Bilguun Tsogoo, Undrakhbayar Enkhjargal, Leonid Gagarin, Bair Gurzhapov, Ariuna Badmaeva, Sergey Pyankov, Dashtseren Avirmed, Endon Garmaev https://creativecommons.org/licenses/by/4.0
Mapping of soil organic carbon content using machine learning algorithms in Bayanzurkh soum
Soil organic carbon (SOC) is the largest carbon reservoir in the terrestrial ecosystem and plays an important role in the global carbon cycle. Consequently, even a slight change in SOC content due to land use, soil management, or rates of soil erosion can considerably increase atmospheric CO2 concentrations. The main purpose of this study is to predict and map SOC content in small area by applying machine learning (ML) algorithms using field measurements and remote sensing data. We used to three different algorithms such as Random Forest (RF), Extreme Gradient Boosting (eXGB), and Gradient Boosted Regression (GBR) of ML. According to field work, 123 soil samples (0–30 cm) were collected from Bayanzurkh soum in Khuvsgul, and 26 variables were used to predict SOC content. As shown the prediction results, the GBR algorithm demonstrated the highest performance, explaining 78% of the variation in soil SOC content, with an RMSE of 42.9 g/kg and an MAE of 33.1 g/kg. The ranking of model performance in terms of prediction accuracy was GBR > eXGB > RF. Therefore, we found a strong relationship (R² = 0.94) between the predicted and measured values based on linear regression analysis. The most influential predictor variables were SILT (13.6%), CLAY (7.8%), NDVI (7.3%), and SOLAR RADIATION (6.3%). These results demonstrate that SOC content can be effectively predicted using machine learning algorithms. However, it is advisable to compare the performance of multiple algorithms and select the most suitable approach for the small area.
Copyright (c) 2025 Maralmaa Ariunbold, Saruulzaya Adiya, Purevdulam Yondonrentsen, Dawaadorj Dawaasuren https://creativecommons.org/licenses/by/4.0
Ice regime on Khuvsgul lake, Mongolia, detection by time series movement from threshold value with MODIS
It is essential that any investigation of regime changes be grounded in long-term observational measurements and data. Ice transportation on Lake Khuvsgul plays a vital role in the socio-economic life of the surrounding region. A critical factor for ensuring the safety of ice transportation is the load-bearing strength of the ice. Accordingly, this study is designed to characterize ice phenology and dynamics over the 2003–2024 period using eight-day interval datasets, with ice volume as the principal parameter. In this study, we used MODIS satellite surface‐temperature products to estimate ice‐volume parameters via the Weinberg equation. Threshold values for the ice‐volume parameter were established using Youden’s method, based on the load-bearing strength of the ice. Threshold value distinguishing young or melting ice from growing ice were established at 1089.655 m³ t⁻¹ and 1090.015 m³ t⁻¹. The onset of ice formation occurs when the ice‐surface temperature drops below 0 °C, and melting initiates once it rises above 0 °C. This underscores the exceptional purity of Lake Khuvsgul’s ice.
Copyright (c) 2025 Davaajargal Boldbaatar, Badarch Khasbaatar https://creativecommons.org/licenses/by/4.0
Changes in aufeis area formed along the Selenge River basin from 2021 to 2025
Under cold climatic conditions, when the ground surface freezes, pressurized subsurface water emerges through porous soil layers, forming ice accumulations known as aufeis. While aufeis may cause damage to infrastructure and transportation networks in populated areas, they play a crucial role in maintaining ecological balance by enhancing river flows, preserving soil moisture, and serving as freshwater reservoirs. In recent years, the rising average temperatures have led to significant reductions in permafrost and glacial formations, with corresponding decreases in aufeis coverage. This makes detailed investigation of aufeis impacts on both human systems and natural environments particularly important. This study examines spatial and temporal changes in aufeis distribution within the Selenga River basin between 2021 and 2025. The research area encompasses transboundary regions of Mongolia and Russia along the Selenga River basin. Using Sentinel-2 satellite imagery processed through Google Earth Engine, we analyzed aufeis locations and areal extent by calculating spectral indices including the Normalized Difference Snow Index (NDSI) and Normalized Difference Water Index (NDWI). The results of the study indicate that between 2021 and 2025, a total of 35,988 aufeis formations were recorded, covering a cumulative area of 317,976.25 hectares along the Selenga River basin. The year 2021 recorded the highest extent and number of aufeis formations, with 9,053 formations covering an area of 83,123.82 hectares. However, a gradual decrease in both the number and area of aufeis was observed over the subsequent years, reaching a minimum in 2025, when 4,987 formations were recorded over an area of 41,916.61 hectares. Furthermore, our study examined how climatic factors such as mean average air temperature (MAAT) and precipitation have influenced on the changes in both the area and number of aufeis formations.
Copyright (c) 2025 Undrakhbayar Enkhjargal, Bilguun Tsogoo, Temuujin Khurelbaatar, Dashtseren Avirmed https://creativecommons.org/licenses/by/4.0
Physicochemical variations and water quality conditions of the Delgermurun River basin, Khuvsgul province
River water quality is crucial as rivers support multiple uses in human needs as well as in sustaining the aquatic ecosystems. In this study, we assessed the physicochemical characteristics and water quality of the Delgermurun River Basin (DRB). We collected water samples from 16 sites along the Delgermurun River and its tributaries in June 2024. Eleven physicochemical parameters were measured onsite, and a permanganate index (CODMn) was analyzed in the laboratory. Seven heavy metals (Mn, Zn, Co, Ni, Mo, As, and Cd) were analyzed at SGS IMME Mongolia LLC, a certified laboratory. Principal component analysis (PCA) was used to explore variations in physicochemical characteristics, while water quality index (WQI) and heavy metal pollution index (HPI) were calculated to assess overall water quality of the DRB. According to the PCA, pH, turbidity, water temperature (Temp.), total dissolved solids (TDS), and elevation contributed the most to variation across the DRB. The highest values of pH, turbidity, and Temp. were found at lower sites (DELBugsei, DELG03, and DELW01), while the highest TDS was recorded at DELR02. In terms of the water quality, WQI and HPI values ranged from 0.04 to 0.1 and 5.98 to 29.20, respectively, remaining within the category of “very clean” by WQI and level of lower pollution by HPI (MNS 4586:1998). Our findings indicate that physicochemical parameters and water quality varied across the DRB, but it remains suitable for potable use. Our results can inform future water resource management, for example, identifying and protecting critical habitats, such as those for the endangered taimen (Hucho taimen), and contributing to the conservation of the DRB ecosystem.
Copyright (c) 2025 Bolortsetseg Erdenee, Gerelt-Od Dashdondog, Mendsaikhan Bud https://creativecommons.org/licenses/by/4.0