DOI: 10.3897/jbgs.e166425

Authors: Fiorella La Matta Romero, Vanessa Arevalo-Seijas, David Valqui-Peña, Moya MacDonald, Jemma Wadham, Raul Loayza-Muro

Abstract: The retreat of tropical glaciers in the Peruvian Andes, particularly in the Cordillera Blanca, has significantly altered hydrogeological and geochemical processes in mountain water-sheds. This study investigates the influence of glacier change-driven acid rock drainage (ARD) upon benthic macroinvertebrate communities in 19 glacier-fed streams of the Santa River watershed over two consecutive dry and wet seasons (2019–2020). The findings reveal that ARD driven by glacier melt and sulphide oxidation has led to increased metal concentrations (e.g., Fe, Mn, Al, Pb) and pH reductions (of 2–3 in some sites), creating a “toxic or treat” scenario for aquatic biodiversity. Statistical analyses, including principal component analysis (PCA), principal coordinate analysis (PCoA), and canonical correspondence analysis (CCA), indicate significant correlations between physical and chemical changes and macroinvertebrate assemblages. Collector-gatherers (e.g., Chironomidae, Baetidae) were dominant in sites impacted by ARD, while sensitive functional feeding groups, such as scrapers and shredders, declined under high metal stress. Seasonal variations also affected taxonomic richness, with greater abundance observed during the dry season. These results highlight the cascading effects of climate-induced glacier loss on freshwater ecosystems and provide critical insights into the ecological consequences of ongoing environmental changes in high-altitude Andean rivers.

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DOI: 10.3897/jbgs.e169740

Authors: Hrachya Astsatryan, Rita Abrahamyan, Artur Gevorgyan, Hasmik Panyan, Furtado Kalli

Abstract: Droughts cause danger to human health and socioeconomic development worldwide. The traditional station-based analysis of droughts has limitations. The most relevant is the insufficient spatial resolution of the observations, particularly over mountain topography. This study evaluates the performance of two satellite precipitation products—the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Climate Prediction Center Morphing Method (CMORPH)—for monitoring meteorological droughts in mountainous environments, using the Armenian Highlands as a case study. We focused on a drought event in June 2021, which was the hottest and driest month in Armenia in nearly nine decades. The performance of gridded global precipitation products was evaluated against in-situ observations for June 2021. Statistical evaluation using the Pearson correlation coefficient, root mean square error, mean absolute error, mean bias, and standard deviation has been analyzed. Results indicate that both products have challenges in accurately estimating the Standardized Precipitation Index (SPI) under severe drought conditions. However, IMERG’s drought detection aligned more closely with in-situ observations than CMORPH’s, which tended to underestimate drought severity. In addition to precipitation-based indices, Landsat-8 and Sentinel-2 vegetation and moisture indices (NDVI, NDMI, NDWI) were evaluated, yielding complementary data regarding the impact of drought on the environment. We found a correlation between low SPI values and stressed vegetation (low NDVI/NDMI), validating the ecological impact of the meteorological drought. Outcomes discuss the merits and disadvantages of satellite precipitation records over mountainous regions and advise operational drought monitoring and early warning systems within data-limit-ed topographically complex areas worldwide.

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DOI: 10.3897/jbgs.e165458

Authors: Nomin Enkhtamir, Gabor Pirisi

Abstract: This study examines the spatial and economic dimensions of 247 small rural towns across Mongolia between 2015 and 2023, focusing on the interplay between traditional mainstay economic base types, livestock herding, sown area, enterprise activity (including mining, business, etc.), and changes in total population and age structure. We track demographic outcomes as changes in total population and the working-age share; component processes (births, deaths, migration) are not decomposed. Nonetheless, the spatial patterns we observe near transport corridors and mining towns are consistent with net in-migration. Drawing on economic base theory and functional rurality, the research uses standardized spatial datasets to classify settlements and track population structure in relation to economic specialization and infrastructure access. The findings reveal that livestock remains the most widespread economic base, particularly in central and western Mongolia, though increasingly vulnerable to environmental shocks. Enterprise activity has expanded significantly, especially in towns with access to rail and road networks, contributing to labor retention and demographic growth. Sown area intensity has remained concentrated in traditional grain-producing regions, with only modest expansion observed in some central and western provinces. Towns dependent on a single economic base, especially livestock or aging mining, experienced working age population decline, while towns with diversified or infrastructure-linked economies showed demographic resilience. These results contribute to the understanding of rural transformation in Mongolia and offer insights for targeted, decentralized development planning. Findings highlight the need for coordinated investment in diversified local economies to support demographic sustainability in small rural towns.

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DOI: 10.3897/jbgs.e162641

Authors: Elena Todorova, Miglena Zhiyanski

Abstract: This study suggests an understanding, based on the local perspectives, of the underlying reasons why the municipality of Velingrad, a mountainous region with abundant natural resources and promising tourism sector does not retain its population and harness its endogenous potential to develop sustainably. Q methodology is employed to better comprehend how people collectively perceive certain problems and their solutions. The results lead us to three well-defined perspectives that highlight high-disagreement areas where dialogue and negotiation are most needed, especially around tourism’s role, environmental trade-offs, and benefit-sharing mechanisms. The study revealed deep distrust in the governing system and a general sense of political and ecological alienation. The future of mountain villages is not perceived as a collective responsibility, but rather as a challenge they are expected to manage on their own. As a result of these insights, the study suggests changing the course of local development and reframing the management model into a more open community-based platform that integrates local voices and energy by involving them not only in framing the policies but also in the implementation and monitoring process. This study uses Q methodology to explore how local stakeholders perceive the factors influencing the socio-ecological framework that guides local management decisions.

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DOI: 10.3897/jbgs.e164548

Authors: Youssef Lassiane, Farid El Wahidi, Hassan Ait Naceur, Hoda Benazun

Abstract: The Skoura oasis, located in the Ouarzazate region of southern Morocco, represents a fragile agro-ecosystem increasingly affected by land degradation processes. This study aims to analyze the spatio-temporal dynamics of desertification in the oasis from 1984 to 2024, in light of climate variability and anthropogenic pressures. An integrated approach combining remote sensing data and environmental indicators is adopted to characterize changes in vegetation and soil conditions. High-resolution satellite imagery from Pléiades 2023 and time series data from the Landsat (5, 7, 8) and Sentinel-2 missions are processed using object-based image analysis and segmentation techniques. Three key indicators are employed: the Modified Soil Adjusted Vegetation Index (MSAVI), surface albedo, and the Sand Fraction Index (SFI). These indicators are integrated to construct a Desertification Monitoring Index (DMI) within the Google Earth Engine platform. Results reveal that in 1984, 24.3% of the oasis area was already classified as highly desertified, particularly in the eastern, southern, and central zones. A slight improvement was ob-served by 1996, with the desertified surface decreasing to 8.6 %. However, a renewed intensification occurred between 1996 and 2010, especially in areas dominated by date palms and olive groves. From 2010 to 2024, desertification progressed further, marked by significant vegetation loss. The findings highlight the persistence and aggravation of land degradation over four decades. The study demonstrates the value of integrated remote sensing approaches for monitoring desertification and supports the need for adaptive strategies to ensure the sustainable management of oasis ecosystems.

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DOI: 10.3897/jbgs.e168308

Authors: Fiorella La Matta Romero, Todd R. Lewis, Chad Staddon

Abstract: Understanding the factors that shape household water and energy use is essential for designing targeted conservation interventions that promote both sustainability and well-being. While studies in this area often rely on traditional “frequentist” statistical methods, which can struggle to capture the complex interdependencies among demographic, behavioural, psychological, and material influences. This paper introduces Bayesian network (BN) analysis as a novel and adaptable method with useful applications in water and energy studies and a wide variety of other social sciences. The paper offers a primer on how to conduct BN analysis, including underlying logic and range of choice of software platforms, before presenting a brief worked example based on the authors’ current research into household water and energy consumption in a UK city. The paper shows how Bayesian networks can generate valuable insights from relatively small and complex datasets, capture non-linear relationships, and support scenario-based reasoning, making them well-suited for exploratory studies, “what if?” scenario-testing and policy effectiveness review. The findings contribute to a more nuanced understanding of domestic water and energy consumption and offer a practical framework that can inform the design of targeted, evidence-based interventions to encourage sustainable water and energy use in households. We argue that there is much to be gained by proliferation of this analytical approach throughout the social sciences.

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DOI: 10.3897/jbgs.e155799

Authors: Astisiasari Astisiasari, Wisyanto Wisyanto, Dian Melati, Sukristiyanti Sukristiyanti, Raditya Umbara, Yukni Arifianti, Trinugroho Trinugroho, Lian Andikasari, Taufik Ramdhani

Abstract: Landslide exposure is an adept approach to measuring the consequences of a landslide hazard on elements at risk. Landslides in the Bogor area, Province of West Java, Indonesia, have increased in number and consequences since 2015. The Bogor area also has a fairly large population that may aggravate the impact. Accordingly, this study aims to measure the landslide exposures over two substantial elements (i.e., population and land use). The actual resources for these exposed elements are available from the open-access geodata and the Google Earth Engine (GEE) platform. For the land use classification, this study employs two robust machine-learning (ML) algorithms on a GEE-based Object-based Image Analysis (OBIA), i.e., Random Forest (RF) and Gradient Tree Boosting (GTB). Moreover, the population data were retrieved from WorldPop estimates. Landslide exposures were then analyzed through an overlay between these two elements with a landslide hazard map sourced from the former study. The results show that in 2020, the Bogor area had an exposed population of 885,353 people, with Bogor Selatan District having the highest exposed population (135,475 people). Moreover, in 2021, the Bogor area had a total exposed land use reaching 347.9 km2, with built-up area having the most extensive, reaching 45.9% of the total exposed area. Here, Sukajaya District had the largest exposed land use (39.1 km2). This study is expected to reach multifaceted entities that contribute to strengthening landslide risk reduction. Through this spatial awareness of the highly exposed areas to landslides, mitigation measures can be taken accordingly.

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DOI: 10.3897/jbgs.e159722

Authors: Igor Leščešen, Pavla Pekárová, Zbyněk Bajtek

Abstract: Accurate forecasting of river discharge is critical for the sustainable management of water resources, influencing applications such as irrigation planning, flood and drought mitigation, and infrastructure development. This study investigates the application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to forecast minimum monthly discharges of the Danube River, addressing challenges posed by nonlinear and time-dependent hydrological processes. The study utilizes an extensive dataset comprising daily discharge records from ten stations across seven countries, spanning over a century. Monthly minimum discharges were computed and analyzed to identify long-term trends and seasonal patterns. The SARIMA model was selected for its proven ability to capture seasonal variations and optimize forecasting accuracy in da-ta-limited environments. Model performance was evaluated using statistical measures such as mean absolute error and root mean square error with results indicating robust predictive capabilities across the studied stations. The findings reveal significant vari-ability in discharge trends, with notable decreasing trends in minimum flows at several upstream and midstream stations, highlighting potential impacts of climate change and anthropogenic influences. In contrast, downstream stations exhibited relatively stable discharge patterns. These insights underscore the need for adaptive water manage-ment strategies to mitigate the risks associated with decreasing low flows. The study demonstrates the utility of SARIMA models in hydrological forecasting and provides a foundation for future research exploring hybrid modeling approaches incorporating climate variables. The results offer valuable inputs for policymakers and stakeholders in managing water resources under evolving climatic conditions.

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