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Soil and water characteristics in micro basins with different land uses/land cover (LULC) can influence riparian vegetation diversity, stream water quality, and benthic diatom diversity. We analyzed 18 streams in the upper part of the La Antigua River basin, México, surrounded by cloud forests, livestock pastures, and coffee plantations. Concentrations of P, C, and N were elevated in the humus of forested streams compared to other land uses. In contrast, cations, ammonium, and total suspended solids (TSS) of water streams were higher in pastures and coffee plantations. These results indicate that LULC affects stream chemistry differently across land uses. Vegetation richness was highest (86–133 spp.) in forest streams and lowest in pastures (46–102), whereas pasture streams had the greatest richness of diatoms (9–24), likely due to higher light and temperatures. Some soil and water characteristics correlated with both true diversity and taxonomic diversity; soil carbon exchange capacity (CEC) correlated with vegetation diversity (r = 0.60), while water temperature correlated negatively (r = − 0.68). Diatom diversity was related to soil aluminum (r = − 0.59), magnesium (r = 0.57), water phosphorus (r = 0.88), and chlorophyll (r = 0.75). These findings suggest that land use affects riparian vegetation, while physical and chemical changes influence diatom diversity in stream water and soil. The lack of correlation between vegetation and diatom diversity indicates that one cannot predict the other. This research is an essential first step in understanding how land use changes impact vegetation and diatom diversity in mountain landscapes, providing valuable insights for environmental monitoring and conservation efforts in tropical cloud forests. [ABSTRACT FROM AUTHOR]

Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies. [ABSTRACT FROM AUTHOR]

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future. [ABSTRACT FROM AUTHOR]

Monitoring the land use/land cover (LU/LC) changes that have occurred with rapid population growth and urbanization since the Industrial Revolution is important for the optimal configuration of landscape patterns and ensuring the sustainability of ecological functions. Spatiotemporal dynamic pattern of LU/LC change using high-resolution land use data is an indicator to evaluate the landscape ecological risk through landscape pattern index analysis. In this study, the landscape ecological risk index (LERi) based on LU/LC change was calculated using remote sensing images of Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) Rdata of a Gediz Mainstream Sub-basin in Turkiye between 1992 and 2022, and the spatial distribution regularity of LERi values was determined with spatial statistical analysis. According to the results, it was determined that the LERi values of the study area changed by 45% in 30 years. The highest change is in the very high-risk class, with an increase of 10.96%, and the least change occurred in the very low-risk class, with a decrease of 1.29%. According to the obtained statistical analysis results, it was determined that the global spatial autocorrelation values analyzed at different grain levels showed positive autocorrelation for both years and that the LERi values tended to have strong spatial clustering. As a result, it is emphasized that strict control measures should be taken for areas showing High-High (HH) autocorrelation type located in the southeast and north-southwest line of the study area at the local level, and ecological restoration applications should be given priority in these areas. [ABSTRACT FROM AUTHOR]

The Lower Kosi Basin (LKB) in North Bihar is highly prone to floods and is influenced by upstream hydrology. A flood susceptibility index has been modelled by integrating eleven flood conditioning parameters (precipitation, elevation, slope, drainage density, distance from the river, ruggedness index, topographic wetness index, stream power index, curvature, normalized difference vegetation index, land use and land cover) derived from the satellite data, using a weighted linear summation model. The study uses Sentinel-1 synthetic aperture radar data to estimate flood frequency over a temporal scale of 2016–2020. The flood frequency was used to validate the flood susceptibility derived using multi-criteria decision making methods combined with geographical information system (MCDM-GIS). The study shows that ~ 66% of the area in LKB is susceptible to high to moderate flooding while the remaining ~ 34% is falls in the low flooding category. 15.24% of the area has high frequency (> 3 flood occurrences) of the flood, 9.66% has moderate (2 flood occurrences) and 9.72% of the area faced one-time flood during five years of period (2016–2020). The accuracy of MCDM-GIS derived flood susceptibility map was assessed using area under curve, confusion matrix, precision, recall, F1 score, weighted F1 score and overall accuracy. [ABSTRACT FROM AUTHOR]

Combining various viewpoints to produce coherent and cohesive results requires decision fusion. These methodologies are essential for synthesizing data from multiple sensors in remote sensing classification in order to make conclusive decisions. Using fully polarimetric Synthetic Aperture Radar (PolSAR) imagery, our study combines the benefits of both approaches for detection by extracting Pauli's and Krogager's decomposition components. The Local Pattern Differences (LPD) method was employed on every decomposition component for pixel-level texture feature extraction. These extracted features were utilized to train three independent classifiers. Ultimately, these findings were handled as independent decisions for each land cover type and were fused together using a decision fusion rule to produce complete and enhanced classification results. As part of our approach, after a thorough examination, the most appropriate classifiers and decision rules were exploited, as well as the mathematical foundations required for effective decision fusion. Incorporating qualitative and quantitative information into the decision fusion process ensures robust and reliable classification results. The innovation of our approach lies in the dual use of decomposition methods and the application of a simple but effective decision fusion strategy. [ABSTRACT FROM AUTHOR]

The evolution of land use/land cover (LULC) patterns significantly influences the dynamics of carbon storage (CS) in terrestrial ecosystems. In response to future environmental changes, however, most studies fail to synthesize the effects of policy pathways and evolving core driving factors on LULC projections. This article presents a systematic framework to assess the dynamic response of the terrestrial ecosystem CS to future LULC changes. After investigating spatiotemporal characteristics and driving forces, policy effects and future core driving factors are integrated into the improved Markov–future land use simulation model to project LULC across diverse scenarios. Then the Integrated Valuation of Ecosystem Service and Tradeoff model is coupled to explore CS dynamics with LULC changes. This framework was applied to the Weihe River Basin. The finding reveals that the overall proportion of cultivated land, forestland and grassland is above 85% and is significantly influenced by policy effects. Precipitation, temperature, population density and gross domestic product are core driving factors of LULC changes. Equal-interval projection is a viable approach to mitigate policy impacts by avoiding error propagation while coupling future core driving factors to improve LULC projection accuracy. Ecological protection should be emphasized in the future. The rate of increase in CS is 1.25 and 1.63 times higher than the historical trend and economic development scenario, respectively, which alleviates carbon loss from the expansion of built-up land. This research provides a valuable reference for future insight and optimization of ecological conservation strategies. [ABSTRACT FROM AUTHOR]

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland. [ABSTRACT FROM AUTHOR]

This study investigates land cover change and carbon potential in mangrove ecosystems within the social forestry area of Indramayu Regency. The research aims to assess land cover changes from 2014 to 2020 and estimate the carbon potential stored in mangrove ecosystems. Field surveys and satellite images analyze land cover change patterns and quantify mangrove carbon storage potential. The research findings reveal that aquaculture land cover dominates the study area (reaching 90%). The study did not find significant changes in land cover within the social forestry area. Only minor changes were noted, with mangroves converting to aquaculture and vice versa. The carbon potential is obtained from biomass calculated based on tree diameter within the research area. According to the calculations, it was found that the three research areas have different potentials due to varying tree diameters and densities. The carbon potential from the permitted areas of Karya Wana Tiris, Babadan Lestari, and Hijau Mandiri are 24.54 tons, 18.33 tons, and 24.87 tons. The highest carbon potential occurred in 2020 (2,419.69 tons), while the lowest was in 2017 (1,414.06 tons). [ABSTRACT FROM AUTHOR]

The Indonesian government established Social Forestry (SF) Program in 2015 to overcome inequality in land tenure, overcome agrarian conflicts and improve the welfare of communities around the forests. Pesawaran Regency does not yet have SF location guidelines in the Indicative and Social Forestry Areas Map. In fact, Pesawaran Regency has a forest area potential of 28% and has a Human Development Index (HDI) score below the provincial HDI average. This study aims to (1) analyze land use and cover changes in forest areas in 2015 and 2020, and (2) determine land potential for SF. SPOT image is used to analyze land cover with visual interpretation. Land suitability evaluation uses the Multi Criteria Evaluation (MCE) method based on Geographic Information Systems (GIS). The parameters used in this study were land/use cover, soil type, altitude, slope, rainfall, distance from rivers, distance from settlements, and distance from roads The results show that almost half of the forest area in 2020 is in the form of mixed gardens (49.63%), forests (24%), and coffee/cocoa plantations (22.2%). The potential land use changes are from forests to mixed gardens, coffee/cocoa plantation and mines. The parameters most influencing the land suitability for SF are land cover, slope, and soil type. Based on land suitability and land availability analysis for SF, it was found that 66% of the research area had potential for SF. [ABSTRACT FROM AUTHOR]

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes. [ABSTRACT FROM AUTHOR]

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The increase in flood events observed in West African countries, and often in specific river basins, can be influenced by several factors, including anthropogenic land use and land-cover changes. However, the potential contribution of land cover changes to flood events still needs to be explored, especially in West Africa. Here, the fully coupled atmosphere-hydrology WRF-Hydro system, which comprises an atmospheric model and additionally incorporates the surface, subsurface, overland flow, and channel routing, is used to investigate the potential impact of a land cover change scenario on flood events in the Senegal River basin. The simulation was performed from 2010 to 2020, with a calibration period spanning from 2011 to 2012 and a validation period from 2013 to 2020. Several skill scores, including Nash-Sutcliffe Efficiency (NSE), BIAS, and Kling-Gupta Efficiency (KGE), were utilized to assess the calibration and validation performances. Additionally, two planetary boundary layer schemes (PBL5 and PBL7) were used to determine their associated uncertainty. Our results show that the best calibration results (NSE = 0.70; KGE = 0.83; PBIAS = -7% and BE = 0.67) in the Senegal River basin are obtained with PBL5 when the calibration is performed with a SLOPE parameter 0.03. A similar good performance was also obtained for the validation with NSE = 0.74, KGE = 0.84, and PBIAS = -8%. Likewise, our findings indicate that converting savanna to woody savannas can elevate water resources, with a 2% rise in precipitation and a 4% increase in runoff. This transition also correlates with an increase in moderate flood events (3500-4000 m3/s), a decrease in severe floods (4000-5000 m3/s), and their associated occurrence of extreme floods (>5000 m3/s) in the Senegal River basin. [ABSTRACT FROM AUTHOR]

The relationship between urban heat island (UHI) and land use/land cover (LULC), and local climate zone (LCZ) is apparent and takes rising attention in the current literature. This study aimed to investigate the relationship between meteorological data collected from 30 stations between 2016 and 2022 and Istanbul's LULCs and LCZs. Several notable findings were uncovered, providing light on the UHI phenomenon and its consequences for the city's characteristics. The stations in urban areas (typology A) had higher temperatures than stations in rural/suburban (typology B) and forested landscape (typology C). Those yearly values were ∼1°C for monthly mean temperatures and ∼1.5°C for monthly minimum temperatures. Moreover, urban areas possessed +4 and +2 hot days (35°C and above) for typologies B and C, respectively. Another remarkable result was that stations situated close to water surfaces exhibited a lower tendency to exceed temperatures of 35°C. Furthermore, built-type LCZs wind velocity achieved a lower value than land cover type, and humidity in typology A was 5% and 10% less than the typologies B and C, respectively. Consequently, the southern part of Istanbul emerged as the most vulnerable location to the UHI phenomena, suffering greater temperatures. [ABSTRACT FROM AUTHOR]

The garden-city model, characterised by sprawling urban expansion and extensive open spaces, features green and water surfaces alongside substantial road networks, large parking lots and unshaded lands. This diverse landscape contributes to uneven air temperature distribution within the city. Our study investigated urban heat island (UHI) patterns in various urban environments within Putrajaya, focusing on rural, business, residential and near-water areas. Atmospheric data were collected over a year. Putrajaya exhibits two distinct UHI patterns: cool-day-hot-night in residential zones and hot-noon-temperate-elsewhere in impermeable areas. Business areas display alternating patterns based on seasonal climatic conditions and anthropogenic factors. The efficacy of artificial water bodies as a UHI countermeasure in garden cities depends on the size of the water body and the nature of the surrounding land cover. Shaded and green areas exhibit UHI intensities limited to 3°C, while impermeable areas, regardless of proximity to water surfaces, experience UHI up to 6°C. Overall, the garden-city model tends to restrict UHI variations within −1 to 3°C compared to −2 to 4°C in nearby Kuala Lumpur. [ABSTRACT FROM AUTHOR]

The study aimed to determine how changes in land cover and surface water are being made using stratified objectoriented analysis based on the interpretation of remote sensing images. It is the first step toward managing the region’s annual land-use inventories projects. The study used Sentinel-2 images from 2019 through 2021 to delineate the changing urban land cover in the Ninh Kieu District, Can Tho City, Viet Nam. The study used QGIS software to interpret the images and eCognition software to classify the objects based on the NDBI, NDVI, and NDWI indices. The interpretation results were checked for the accuracy, and the land cover was changed over the years. The results show that urban land cover changes with the increase of urban land and the decrease of vegetation land used for urban land, while water surface area inwards decreased from 2019 to 2020 but increased in 2021. Maps of the current state of the urban land covers in the study area were delineated. The interpretation results contribute to the preliminary method by using satellite images for the annual land use inventory project in the region, even though some difficulties still exist and need to be modified. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Tropical peatlands play a crucial role in the global ecosystem. Thesedistinctive ecosystems are frequently regarded as peripheral areas that are not suitable for agricultural purposes. Peatlands serve as efficient carbon storage systems in a consistent climate, however, they are vulnerable to alterations. This study focuses on analyzing the dynamic of peatland use land cover changes, its driver and impact on green house gas emission, and rehabilitating degraded peatlands in Jambi province, Indonesia, specifically in the Bram Itam and Londerang peat forest reserves. This study was depicted into 3 objectives consisted of 1) Utilize spatial analysis to examine the dynamics of peatland use change and the trajectory of peatland use, as well as to identify the drivers behind these changes; 2) describe the effects of altering peatland utilization; and 3) describe seedling performance planted on the peatland forest of Jambi province, as Tanjung Jabung Barat and Tanjung Jabung Timur. METHODS: Land-use and land-cover change analysis was carried out utilizing various map resources. Cellular Automata-Markov is employed to forecast forthcoming land cover alterations by evaluating the likelihood of land cover transitions throughout a given period. Planting trials using native peatland tree species were conducted at 2 locations with 2 different rehabilitation models following social typology of the sites. FINDINGS: Key findings indicate that peatland conversion to non forest use significantly increases greenhouse gas emissions, while rehabilitation with native tree species shows promising results in carbon sequestration. Peatland use land cover alteration in anthropogenically disturbed peat areas significantly contributes to emissions through peat decomposition and fires. Biophysical analysis reveals variations in peat depth, water table levels, and precipitation between these sites. The two locations exhibit notable disparities in terms of peat soil attributes, such as potential of hydrogen, nutrient composition, and exchangeable cations. This study examines the growth performance of several native tree species that were planted on degraded peatlands. Native peatland tree species like Shorea balangeran, Ilex cymosa, and Fagraea crenulata show promising survival and growth rates. Areca nut (Areca catechu), a non native peatland species, was planted for non-timber forest product purposes. This study delivers significant insights for policymakers looking to strike a balance between economic priorities and environmental conservation. CONCLUSION: Land use change that results in the degradation of peatlands must be avoided because it will be a source of greenhouse gas emissionsThe selection of suitable indigenous tree species holds significant importance in the restoration of peatlands, taking into account ecological, social, and economic considerations. The findings support potential for wet agroforestry and paludiculture to restore peatlands, mitigate carbón dioxide emissions, and enhance livelihoods while emphasizing the need for inclusive governance and technical capacity. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Efforts to enhance carbon stocks and boost carbon absorption potential are essential for climate change mitigation. Peatland ecosystems, known for their high organic content, are particularly vulnerable to environmental management. The study aimed to examine the alterations in land use and land cover that occurred between 1998 and 2022, spanning a 24-year duration. Additionally, it sought to assess the associated variations in carbon stocks within the designated Kepau Jaya specific purpose forest area. The area under investigation encompasses a peatland ecosystem that has experienced substantial changes in land cover and land use. This study investigated the fluctuations in carbon stock caused by these alterations and provides valuable perspectives on the potential of agroforestry systems to promote a wider range of land uses. Additionally, it highlights their role in ecosystem restoration initiatives and the better management of forest peatland regions. METHODS: A spatial analysis was conducted on Landsat 5 and 8 satellite images by using shapefile data stored within the Google Earth Engine platform. Data analysis was carried out using Classification and Regression Tree, a decision tree algorithm used in machine learning for guided classification. Furthermore, purposive sampling was utilized to gather socioeconomic data, followed by the implementation of a benefit-cost analysis. FINDINGS: The results revealed significant changes in the land cover within the Kepau Jaya specific purpose forest area over a 24-year period, with forested areas and open areas decreasing by 23.15 hectares per year and 16.94 hectares per year respectively, and oil palm plantation areas expanding by 40.10 hectares per year. From 1998 to 2022, there has been a consistent annual decline in carbon stocks, resulting in a reduction of 1,933.11 tons of carbon per year. The changes in land use and cover are closely linked to this decline. In an effort to increase plant species diversity in the area and support the gradual transition away from monoculture, a participatory agroforestry scheme was implemented by intercropping Coffea liberica and Shorea balangeran between oil palm rows in a 2-hectare oil palm plantation block within the agroforestry demonstration plot. According to measurements taken at breast height, the aboveground biomass of these species was measured, leading to projected estimates of carbon stocks in Kepau Jaya specific purpose forest area reaching 19,455 tonnes of carbon by the year 2030, with Coffea liberica contributing 4,148 tonnes carbon and Shorea balangeran contributing 15,307 tonnes carbon. CONCLUSION: The study area experienced a substantial reduction in forest cover, whereas the extent of oil palm areas expanded significantly. The findings underscore the need for proactive measures to strengthen the governance of specific-purpose forest areas through community empowerment and the establishment of demonstration plots to promote agroforestry development. The results of this study provide insights for long-term forest rehabilitation strategies aimed at fostering sustainable forest management that yields environmental and socio-economic benefits in the long run. [ABSTRACT FROM AUTHOR]

As the number of humans living in cities has grown, interest in the value of community gardens to provide agricultural products has increased. However, neighborhoods with different land cover patterns and socioeconomic characteristics often differ in their ecological attributes, leading to potential differences in biodiversity-mediated ecosystem services (e.g., pollination and pest control). Here we ask, how do impervious surface and socioeconomic features of the urban matrix around community gardens impact arthropod biodiversity and pollination and pest control services? We collected arthropods (insects, arachnids, myriapods, and isopods) across community gardens in Boulder Co., CO, and used experimental jalapeño pepper plants as a sentinel crop to measure herbivory damage and pollination services. We categorized arthropods into functional guilds to see how impervious surface and neighborhood wealth in the urban matrix surrounding a site impacts the abundance of three focal groups – pollinators, herbivorous pests, and predators. We also looked at how bee Hill-Simpson diversity responded to these variables. Through structural equation modeling, we found that fruit size increased as bee biodiversity increased, and bee biodiversity and overall pollinator abundance were negatively related to neighborhood wealth. Additionally, pollinator abundance was lower in gardens surrounded by higher amounts of impervious surfaces. Neighborhood wealth and impervious surfaces were positively correlated with herbivore and predator abundances, but these abundances had no relationship with herbivory damage in our plants. This research shows that reducing the amounts of impervious surface in the urban matrix can help increase bee biodiversity and abundance and improve pollination services in urban community gardens. [ABSTRACT FROM AUTHOR]

Peri-urban landscapes are transitional areas between urban and rural areas that are constantly changing. They are characterised by land use mixes and land cover changes, leading to significant changes in wildlife habitats. These changes, combined with an increasing anthropogenic presence, turn peri-urban landscapes into arenas of intensified human-wildlife interactions. In many scientifically documented cases, scientists are focusing on negative interactions. Furthermore, research about appropriate policy instruments for managing human-wildlife interactions is rare. This study focused on case studies and their characteristics from around the world on positive, neutral, or negative interactions between humans and wildlife at local level. In addition, influencing factors of human-wildlife interactions and policy instruments for managing human-wildlife interactions in peri-urban landscapes were addressed. A survey was conducted with an international group of scientists and practitioners working in this field. The results showed that various species were involved in human-wildlife interactions in peri-urban landscapes worldwide, with mammals as being the most common taxon. Contemporary changes in land use and land cover could be identified as a significant factor for increasing human-wildlife interactions in peri-urban landscapes. It can be highlighted that a policy mix consisting mainly of social and cultural instruments in combination with legal and regulatory instruments could be most suitable to address this situation. [ABSTRACT FROM AUTHOR]

This study aims to investigate the impact of spatio-temporal dynamics of urban sprawl on habitats in Istanbul, using the CORINE 1990 and 2018 land cover datasets. In this context, by means of geographic information system (GIS) tools and techniques, land cover maps are created for land cover change analysis and visualization, the Shannon's entropy method is performed for measuring urban growth and sprawl, and the landscape metrics method is applied for assessing habitat loss and fragmentation. Results indicate that the rate of urban sprawl in the area increased by 0.1230 as per the Shannon's entropy index over the 28-year period. Similarly, the Shannon Diversity Index and the Shannon Evenness Index rose from 1.74 to 1.85 and from 0.68 to 0.70, respectively, indicating an increase in urbanization within the area. Moreover, an analysis of patch numbers reveals that habitat fragmentation increased in shrub and/or herbaceous vegetation associations (72.55%), heterogeneous agricultural areas (45.11%), arable lands (42.5%), forests (36.13%) and pastures (15.05%), due to urbanization. Habitat fragmentation has had a detrimental effect on the local biodiversity. While 15 flora species were identified as vulnerable, 13 as endangered and 9 as critically endangered, 19 fauna species were identified as vulnerable and 5 as endangered. This study highlights that the natural habitats and biodiversity of Istanbul will suffer further decline due to urbanization unless sustainable urban planning and management policies are put into practice. It is essential to have controlled urban development to preserve the ecosystem's carrying capacity, and urbanization decisions must consider this requirement. [ABSTRACT FROM AUTHOR]

Groundwater quality is a serious concern in alluvial aquifers undergoing vast agricultural transformation around the globe. Mehsana district of Gujarat is an example of agricultural landscape having most of the seasonal crops and vegetables. Groundwater samples were collected in monsoon 2017 and pre-monsoon 2018 to analyze major ions and isotopes to derive the hydrochemistry. The hydrochemical facies reveal Na+–Cl−, Na+–HCO3− and Ca2+–HCO3− type water suggesting complex hydrochemical reaction taking place in the region. The scatter plot of Ca2+ + Mg2+ versus HCO3− + SO42− and Na+ versus Cl− shows the ion exchange and silicate weathering along with intermixing of saline water. The presence of high sodium, alkaline pH and bicarbonate promotes the release of fluoride from minerals in groundwater which is high during monsoon. The fluoride concentration increases progressively along the Sabarmati River. The spatial variation map of nitrate in the district shows both point and non-point sources of NO3− contamination. This evidence will support the strong contribution of agricultural activities, as shown by land use that long-term flood irrigation practices have enhanced this impact. The deep aquifers are suitable for drinking purposes as compared to shallow aquifers which are affected with NO3− pollution due to the leaching process during the pre-monsoon season. The agriculture indices show contaminated groundwater for irrigation in more than 50% of wells. The composition of isotopes varied between (δ18O − 2.08 to − 4.41‰ and δD − 19.19 to − 32.28‰). The groundwater trendline was found below the local meteoric water line which indicates evaporation due to semiarid climate before infiltration. [ABSTRACT FROM AUTHOR]

Determining groundwater quality status and hydro-geochemistry of an aquifer is crucial to ensure cleaner and safer water for human consumption. Groundwater sustainability issues are getting attraction due to global warming and complexities in land utilization. These concerns increased the challenge of gaining an appropriate comprehension of anthropogenic activities and natural processes, as well as how they influence the quality of surface water and groundwater systems. In the present investigation, 48 groundwater samples were collected and analyzed from the Panchkula district of Haryana, located in the Upper Ghaggar River Basin, to explore the sources and driving factors responsible for hydro-geochemistry of the aquifers falling under the natural and anthropogenically intervened areas. Groundwater sampling was carried out in the premonsoon (PRM) as well as postmonsoon (POM) seasons to examine the spatio-seasonal variations in its quality. The results of the study revealed that reverse ion exchange processes are mainly controlling the aquifer geochemistry in areas occupied by natural vegetation, while the nitrate and chloride content of groundwater was the result of anthropogenic activities. The mechanisms governing groundwater quality and the related hydro-geochemical processes were assessed using binary plots of different cations and anions, along with the Gibbs plot and chloro-alkaline indices (CAI). Saturation indices (SI) indicated that groundwater was over-saturated with respect to dolomite, calcite, and aragonite, while 68.75% and 50% of samples indicated under-saturation with respect to anhydride and gypsum, respectively. Principal component analysis (PCA) and Pearson's correlation coefficients were also considered to affirm the processes taking place to control the aquifer chemistry. Groundwater in the majority of the area was found suitable for drinking purposes except in the locations where high nitrate concentrations were recorded, thus suggesting the use of water treatment facilities for such areas. [ABSTRACT FROM AUTHOR]

In Egypt, planning practice is physical and rooted in design principles, resulting in plans that seldom consider the impact of land use and land cover changes on the environment. Most used planning models are static. The paper offers urban metabolism and dynamic modeling as an alternative mode for decision making. It models population growth and expanded economic activities that characterize rapid urbanization and induce changes in land use affecting various urban economic sectors using the Greater Cairo Region (GCR) as a case study. Urban transportation contributes to climate change. The authors used secondary data and satellite images to detect land use and cover changes since 2005. After building a stock-flow diagram to describe the system's structure, they used dynamic modeling and GIS to assess the impact of transportation on air quality. Results indicate that the population of GCR will range from 33.348 to 40.334 million in 2050. The emissions from transportation will be around 52,521.2 Gg, which is beyond the ability of the region's ecosystems to store carbon. These findings shed doubt about the ability of GCR to reach carbon neutrality and, therefore, call for strict measures to control emitting GHGs and change in planning practices and education. [ABSTRACT FROM AUTHOR]

The study of changes in land use and land cover (LULC) is helpful in the understanding of change and management of environmental sustainability. As a result, the central Telangana districts are the focus of this study since they are under stress from both natural and human-caused problems. The examination of LULC variations and predictions for the region between 2007–2015 and 2021–2030 employed Landsat OLI datasets, TerrSet, and geographic information system (GIS) tools. The LULC image is produced using a Landsat dataset and classified using a support vector machine (SVM). Then, consecutively to project future LULC change, LULC maps were constructed using the CA Markov model. The four stages included were change analysis, transition possibility, change forecasting, and model validation. It is found that the vegetation and the arid landscape are stressed and accumulating. The total accuracy was above 87 percent, and the kappa statistic measurement was above 85 percent with a three-year target. The study has found using the Markov chain land change modeler that Medchal–Malkajgiri district urban settlements will grow by 46, 37, and 26% from 2021–2030, 2030–2050, and 2050–2100, respectively. On other hand, the Warangal (Hanamkonda) observed 39, 45, and 30% between 2021–2030, 2030–2050, and 2050–2100, respectively, and Rangareddy districts observed 60, 24, and 12% between 2021–2030, 2030–2050, and 2050–2100, respectively. Given that urban areas are especially susceptible to flash flooding, this research will offer policymakers advice and a framework on behalf of planning city growth and managing the available resources judiciously with utmost planning. [ABSTRACT FROM AUTHOR]

Rising population and urban expansion have ushered drastic transformations in land use patterns, which can considerably affect soil components and processes. Peri-urban regions at the rural-urban interface are of specific concern because of their dynamic and complex land use pattern. Here, we investigated the spatial variability in soil physicochemical parameters of the surface soil (0–30 cm) in peri-urban landscape of Ghaziabad district, located in the upper Gangetic plain of Uttar Pradesh, India. A total of 45 sites belonging to five different land use classes namely agriculture, park, residential, industrial and bare land were sampled from December 2019 to January 2020. Descriptive analysis of data showed wide variability in the soil properties, with chemical properties being more variable than physical properties. We observed significant differences in soil properties across land use types and their heterogeneous distribution across the three sites. Industrial land use with low vegetation cover was distinctly differentiated from other anthropogenic land use and land cover by high bulk density. Residential land use with high nutrient input from domestic waste and physical disturbance from construction activities was separated from other overlapping land use types by high water-holding capacity and calcium (Ca) concentration. Overall, the results of the study suggest that multifaceted anthropogenic disturbances including modification of parent material, extensive fertilization and irrigation, transportation and waste dumping entail significant changes in soil characteristics and may have serious implications on soil structure and function. Such regional and local studies would help in developing scientifically informed practical measures to improve and maintain the soil quality. Hence, further detailed research exploring the impact of anthropogenic land uses on soil characteristics is needed for management and preservation of the soil mantle. [ABSTRACT FROM AUTHOR]

Satellite data are increasingly used to provide information to support forest monitoring and reporting at varying levels of detail and for a range of attributes and spatial extents. Forests are dynamic environments and benefit from regular assessments to capture status and changes both locally and over large areas. Satellite data can provide products relevant to forest science and management on a regular basis (e.g. annually) for land cover, disturbance (i.e. date, extent, severity, and type), forest recovery (e.g. quantification of return of trees following disturbance), and forest structure (e.g. volume, biomass, canopy cover, stand height), with products generated over large areas in a systematic, transparent, and repeatable fashion. While pixel-based outcomes are typical based upon satellite data inputs, many end users continue to require polygon-based forest inventory information. To meet this information need and have a spatial context for forest inventory attributes such as tree species assemblages, we present a new work-flow to produce a novel spatially explicit, stand-level satellite-based forest inventory (SBFI) in Canada applying image segmentation approaches to generate spatially unique forest stands (polygons), which are the fundamental spatial unit of management-level inventories. Thus, SBFI offers spatial context to aggregate and generalize other pixel-based forest data sets. Canada has developed a National Terrestrial Ecosystem Monitoring System (NTEMS) that utilizes medium spatial resolution imagery, chiefly from Landsat, to annually characterize Canada's forests at a pixel level from 1984 until present. These NTEMS datasets are used to populate SBFI polygons with information regarding status (e.g. current land cover type, dominant tree species, or total biomass) as well as information on dynamics (e.g. has this polygon been subject to change, when, by what, and if so, how is the forest recovering). Here, we outline the information drivers for forest monitoring, present a set of products aimed at meeting these information needs, and follow to demonstrate the SBFI concept over the 650-Mha extent of Canada's forest-dominated ecosystems. In so doing, the entirety of Canada's forest ecosystems (managed and unmanaged) were mapped using the same data, attributes, and temporal representation. Moreover, the use of polygons allows for the generation of attributes such as tree species composition, and total biomass and wood volume in a stand-scale format familiar to landscape managers and suitable for strategic planning. The data, methods, and outcomes presented here are portable to other regions and input data sources, and the national SBFI outcomes for Canada are available via open access. [ABSTRACT FROM AUTHOR]

The rate of population growth in Sukabumi Regency continues to grow, along with the increasing need for food. This population growth, combined with the constant changes in land cover can reduce the productivity of environment in providing natural capital for food availability. Therefore, this study aimed to examine the condition of ecosystem service provision for a decade in Sukabumi Regency due to changes in land cover. In general, the efficient use of remote sensing method and geographic information systems to monitor ecosystem services had received widespread recognition. Following this scenario, the current study used geospatial analysis with dasymetric method which was integrated with supply and demand formulas for ecosystem services provision, food availability, and remote sensing. Geographic information system was also used for land cover interpretation data. The results showed that three districts in Sukabumi Regency, namely Cicurug, Cibadak, and Cicantayan, had "exceeded" condition when the environmental condition already passed the threshold or were unable to support population's needs. Meanwhile, the other districts have "not exceeded" condition, when the environmental conditions were still able to fulfill the needs of population. Finally, the changes in agricultural land cover had a significant influence on the condition of ecosystem services. [ABSTRACT FROM AUTHOR]

River systems act as conveyor belts carrying sediments in huge quantities from upstream to downstream regions of the basins. Suspended sediment (SS) is a vital element in a river system due to its importance in the planning, design, and management of hydraulic structures. Abrupt variations in sediment dynamics have been observed recently, attributing these to both changes in climate and different anthropogenic actions. A proper understanding of the dominance and impact of these anthropogenic and natural factors on sediment dynamics will help to maintain the sediment equilibrium of any basin. In this study, we present a comprehensive analysis of different changed regimes of suspended-sediment concentration (SSC) and quantify the dominant factors during such changed regimes in Indian river basins. Declining trends are evident in the SSC profiles of most of the gauge stations (∼80%) and can be attributed to increasing anthropogenic activities, especially the construction of water retaining structures throughout India. On average, more than a 50% reduction in sediment quantity was recorded at most gauge stations over the past three decades. It is apparent that natural factors such as discharge and rainfall still are the most dominant factors; but quite interestingly, their relative importance is decreasing after each change point, whereas the influence from anthropogenic factors such as changes in the cropland and grassland are increasing. Large river basins show more influence of anthropogenic activities on sediment dynamics when compared with other smaller basins. Also, changes in land use land cover (LULC) have increasing impact on upstream/midstream parts of the river basins. It is evident that the completion/commissioning of water retaining structures brings abrupt changes in the sediment dynamics, but once the system stabilizes, other natural and anthropogenic factors take over as dominant factors until the next structure comes up and changes the system dynamics again. [ABSTRACT FROM AUTHOR]

The habitat suitability for pollinators is an important factor for biodiversity and crop-based ecosystem services. Most flowering plants, including wild plants, rely on pollination ecosystem services for fructification and reproduction. Suitable nesting sites and accessible floral food resources are crucial to the abundance of pollinator insects. Therefore, the suitability for pollinators and the pollination service itself are influenced by the type of land use, the composition of the land cover and structures in the landscape. One way to estimate pollinator habitat suitability is to use computer models such as ESTIMAP and InVEST. Both models calculate the habitat suitability based on spatial land use data and their suitability as nesting and feeding habitats. Besides the decision for a model, the selected spatial dataset also has important effects on the modelling results. In Germany, a large number of Land Use and Land Cover (LULC) datasets is available, such as the European CORINE Land Cover, CORINE Urban Atlas, Biotope types, ATKIS or Open Street Map. These datasets differ in terms of spatial and thematic resolutions, LULC types, and abundance of structural elements, which are crucial input factors for modelling with ESTIMAP and InVEST. We applied and tested both models on the basis of two different datasets in the study area Hannover region in central Germany. A literature-based estimation and expert-based questionnaire determined the biophysical properties required for modelling with ESTIMAP and InVEST. The differences between the results based on two different spatial datasets and the differences between the models were estimated and the results that can be obtained by using freely available data were investigated and compared with the results based on non-publicly available data. The comparison of the results shows that the proportion of near-natural structures in the landscape are a decisive factor for modelling results. The comparison of the models shows that ESTIMAP estimates a higher influence of small structures in the landscape than InVEST, resulting in a higher pollinator habitat suitability. The median similarity index of the two models is between 0.68 to 0.93 for the highly detailed biotope type dataset and 0.40 to 0.79 for the less detailed Corine dataset. The results provide a guidance on how to choose the appropriate model and data to assess pollination ecosystem services. [ABSTRACT FROM AUTHOR]

Dryland precipitation depends on upwind and local moisture sources via moisture recycling. How upwind moisture variations affect interannual variations of downwind precipitation and vegetation in China's drylands remains unclear. We used high‐resolution moisture tracking data sets and found terrestrial moisture (93%) was the dominant moisture source for China's drylands, especially from drylands themselves (46%). In most dryland grids, we observed strong correlations between precipitation and upwind moisture sources from 2003 to 2022 (median r = 0.55), with a more significant effect in drier areas. These demonstrated the upwind moisture control on interannual variations of dryland precipitation, in which internal moisture from drylands exceeds the influence of external terrestrial sources. The upwind moisture variations, especially the recycled moisture of drylands, propagate to influence downwind vegetation greenness in precipitation‐sensitive dryland areas. Our findings revealed that upwind moisture variations induced by climate or land‐cover changes have important implications for water and food security in China's drylands. Plain Language Summary: Moisture recycling refers to the hydrological cycling in which winds transport evapotranspiration water from upwind source regions and generate precipitation in downwind regions. Precipitation in China's drylands depends on moisture from their upwind source regions, mainly Eurasia and the drylands themselves. Therefore, variations in evaporated water from upwind source regions may affect precipitation in China's drylands and vegetation activities that require water. This study used moisture tracking data sets and found the crucial influence of upwind moisture on dryland precipitation across years. The year‐by‐year changes in upwind moisture, especially from drylands themselves, influence precipitation and vegetation activities in dryland areas where vegetation growth is sensitive to water. The moisture changes due to climate or land cover changes in the upwind source regions could affect water and food security in China's drylands. Key Points: Upwind moisture source regions and downwind China's drylands are tele‐connected through moisture recyclingUpwind moisture variations induce interannual variations in dryland precipitation and vegetation greennessUpwind moisture variations have implications for water and food security in China's drylands [ABSTRACT FROM AUTHOR]

The present work was carried out on River Yamuna passing through Delhi-National Capital Region (Delhi-NCR) of India for a stretch of about 125 km to assess the fluvial characteristics in the highly human-dominated region for a period of 200 years with the help of historical maps, topographical maps and satellite data with integration of geographic information system (GIS) environment. Erosion, deposition and channel stability data were analyzed for 1977–1986, 1986–1996, 1996–2006, 2006–2016, and 2016–2022 period. Digital Shoreline Analysis System (DSAS) application was utilized to quantify river channel movement with average channel migration of 22.8 m/year. Westward migration is 4149.2 m maximum in length and eastward migration is about 4083.8 m. The river has migrated a total of 32.26 km2 of area during 1955-2022. The findings indicate that various human activities, such as engineering structures, sand mining, embankments, urbanization, land use/land cover changes, and canal networks, have significantly impacted the river. The DSAS application was also used to predict the position of river channel centerline in future for 2032 and 2042 with channel length of 132.5 and 141.6 km respectively. [ABSTRACT FROM AUTHOR]

Understanding the effects of human disturbance on the bedload transport regime of anthropised rivers is a topic of growing importance, as such information is of interest for adequate river diagnosis, correct implementation of restoration measures and appropriate design of post‐action monitoring programs. However, such assessments are complex, especially in sites where multiple factors simultaneously influence the bedload transport regime, so that it is difficult to establish simple causal relationships between human disturbances and changes in the sediment transport regime, notably on bedload. To overcome this, there is a need for rigorous hypothesis‐driven approaches to assess the isolated effects of each driver. With this in mind, we have characterised the dynamics of bedload transport in the Upper Garonne (Central Pyrenees, Spain‐France), a river impacted by sediment retention, flow diversion and mining that influence its morphological conditions and transport regime. We assessed the effects of (1) surface grain size distribution, (2) river morphology, (3) sediment supply and (4) flow diversion on the bedload transport regime. Four sites with different degrees of river anthropisation were selected. After defining hypotheses on the most likely bedload transport conditions for each site, we proposed a set of discriminating criteria to test these hypotheses, based on temporal within‐site and spatial between‐site comparisons of coarse particle tracking measurements over four years. The results of this research showed that the hydrosedimentary regime of the Garonne is controlled by a complex combination of drivers such as valley physiography, which exerts a first‐order control on differences in reach‐scale bedforms and bedload dynamics; and human disturbances which contribute to a reduction in sediment supply through changes in land cover and hydropower dams, or to changes in hydrology (i.e., flow competence) due to water diversion and abstraction. [ABSTRACT FROM AUTHOR]

Reliable information plays a pivotal role in sustainable urban planning. With advancements in computer technology, geoinformatics tools enable accurate identification of land use and land cover (LULC) in both spatial and temporal dimensions. Given the need for precise information to enhance decision-making, it is imperative to assess the performance and reliability of classification algorithms in detecting LULC changes. While research on the application of machine learning algorithms in LULC evaluation is widespread in many countries, it remains limited in Zambia and Sri Lanka. Hence, we aimed to assess the reliability and performance of support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms for detecting changes in land use and land cover taking Lusaka and Colombo City as the study area from 1995 to 2023 using Landsat Thematic Mapper (TM), and Operational Land Imager (OLI). The results reveal that the RF and ANN models exhibited superior performance, both achieving Mean Overall Accuracy (MOA) of 96% for Colombo and 96% and 94% for Lusaka, respectively. Meanwhile, the SVM model yielded Overall Accuracy (OA) ranging between 77% and 94% for the years 1995 and 2023. Further, RF algorithm notably produced slightly higher OA and kappa coefficients, ranging between 0.92 and 0.97, when compared to both the ANN and SVM models, across both study areas. A predominant land use change was observed as the expansion of vegetation by 11,990 ha (60.4%), primarily through the conversion of 1,926 ha of bare lands into vegetation in Lusaka during 1995-2005. However, a noteworthy shift was observed as built-up areas experienced significant growth from 2005 to 2023, with a total increase of 25,110 ha (71%). However, despite the conversion of vegetation to built-up areas during the entire period from 1995 to 2023, there was still a net gain of over 11,000 ha (53.4%) in vegetation cover. In case of Colombo, built-up areas expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha (62.3%) during concerned period. LULC simulation also indicated a 160-ha expansion of built-up areas during the 2023-2035 period in Lusaka. Likewise, Colombo saw a rise in built-up areas by 337 ha within the same period. Overall, the RF algorithm outperformed the ANN and SVM algorithms. Additionally, the prediction and simulation results indicate an upward trend in built-up areas in both scenarios. The resultant land cover maps provide a crucial baseline that will be invaluable for urban planning and policy development agencies in both countries. [ABSTRACT FROM AUTHOR]

Arctic permafrost thaw holds the potential to drastically alter the Earth's surface in Northern high latitudes. We utilize high‐resolution large eddy simulations to investigate the impact of the changing surfaces onto the neutrally stratified atmospheric boundary layer (ABL). A stochastic surface model based on Gaussian Random Fields modeling typical permafrost landscapes is established in terms of two land cover classes: grass land and open water bodies, which exhibit different surface roughness length and surface sensible heat flux. A set of experiments is conducted where two parameters, the lake areal fraction and the surface correlation length, are varied to study the sensitivity of the boundary layer with respect to surface heterogeneity. Our key findings from the simulations are the following: The lake areal fraction has a substantial impact on the aggregated sensible heat flux at the blending height where surface heterogeneities become horizontally homogenized. The larger the lake areal fraction, the smaller the sensible heat flux. This result gives rise to a potential feedback mechanism. When the Arctic dries due to climate heating, the interaction with the ABL may accelerate permafrost thaw. Furthermore, the blending height shows significant dependency on the correlation length of the surface features. A longer surface correlation length causes an increased blending height. This finding is of relevance for land surface models concerned with Arctic permafrost as they usually do not consider a heterogeneity metric comparable to the surface correlation length. Plain Language Summary: The study explores how thawing permafrost (frozen soil) could change the Earth's surface in the Arctic region. The researchers used advanced simulations to study how different types of surfaces, like grassland and lakes, affect the atmosphere above. They found that the proportion of lakes in the area has a big impact on the heat exchange with the atmosphere, suggesting a potential feedback mechanism: as the Arctic dries due to climate change, it might speed up permafrost thaw. Additionally, the study highlights that the arrangement of surface features matters, which is important for models predicting permafrost changes in the Arctic. Key Points: Large eddy simulations with the EULAG research model of the atmospheric boundary layer over stochastic lake landscapesLake areal fraction has significant impact on land‐atmosphere energy exchangeLonger surface correlation length causes higher blending altitude [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) are regarded as important precursors for ozone and secondary organic aerosol, mainly from vegetation emissions. In the context of the expanding trend of vegetation greening, the development of high‐precision vegetation data and accurate BVOC emission estimates are essential to develop effective air pollution control measures. In this study, by integrating the multi‐source vegetation cover data, we established a high‐resolution vegetation distribution (HRVD) data set to develop a high spatio‐temporal resolution emission inventory and investigated the impact of different land cover data sets on emission simulation and impact of land cover change on BVOC emissions during 2001–2020. The annual total BVOC emissions in China for 2020 was 15.66 Tg, which were mainly from trees. The emissions simulated by CNLUCC and MODIS data sets were 1.53% and 1.72% higher than those simulated by HRVD data sets, respectively. The spatial distribution of emission differences was consistent with that of land cover differences. The simulated BVOC emissions by the HRVD data set had the best accuracy as they improved the bias between modeling and observation from 69.06% to 65.35% and decreased the underprediction of observations by a factor of 2.13 compared with simulation by MEGAN default vegetation data. The annual BVOC emissions caused by changing vegetation distribution and LAIv (LAI of vegetation covered surfaces) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAIv was the main driver of emission variations. The total OH reactivity of the resulted BVOC emissions increased at a rate of 1.59 s−1 yr−1, with isoprene contributed the most. Plain Language Summary: Biogenic Volatile organic compounds (BVOCs) are the key precursors of fine particulate matter and ozone, that mainly from vegetation emissions. To help to develop effective air pollution control measures in the context of expanding vegetation coverage for realizing carbon neutralization in China, it is urgent to develop highly precise vegetation data and accurately estimate BVOC emission. A high‐resolution vegetation distribution data set was established through integrating multi‐source vegetation cover data. Using it, the simulated annual BVOC emission in China was 15.66 Tg and mainly emitted from trees. Emissions from varied growth forms had different compound compositions. The BVOC emission simulated using the high‐resolution vegetation distribution data set we developed had better accuracy than that using the single vegetation databases. The annual BVOC emissions caused by changing vegetation cover and leaf area index (LAI) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAI was the main driver of BVOC emission variations. The interannual variation and its spatial pattern of the OH loss rates of BVOCs during 2001–2020 were consistent with that of BVOC emissions, especially isoprene. Key Points: Annual total BVOC emissions in China for 2020 was 15.66 Tg and emissions from varied growth forms had different compositionsBVOC emission inventory simulated by the developed high‐resolution vegetation distribution (HRVD) data set had better accuracyBVOC emission enhanced at a rate of 72.06 Gg yr−1 during 2001–2020 caused by land cover change, mainly driven by changing leaf area index [ABSTRACT FROM AUTHOR]

Introduction: Groundwater demand has been considerably heightened due to rapid urban growth, specifically in arid areas that rely primarily on groundwater. This study aims to utilize remote sensing and aeromagnetic data, combined with the Analytic Hierarchy Process (AHP) based GIS, to evaluate potential groundwater zones in the Sohag area, Egypt. Methods: Nine thematic layers, including soil moisture, rainfall, lithology, normalized difference vegetation index (NDVI), drainage density, lineament density, slope, and land use/land cover, were developed using various remote sensing datasets. Besides the remote sensing-derived thematic layers, a geophysics-derived thematic layer represented by the RTP aeromagnetic map was included. The aeromagnetic data were analyzed and interpreted to outline the subsurface structure affecting groundwater storage and flow. Also, the aeromagnetic data analysis helps estimate the basement depth that constitutes the Nubian Aquifer's base and identifies regions with considerable thick sedimentary deposits and significant water reserves. Results and discussion: The groundwater potentiality map was consistent with production wells in the area, and sites for drilling new wells were predicted, especially in the Nile Valley around the Tahta, El-Hamimia, and west Sohag cities. The most promising sites are clustered along the Nile Valley, and the study area's northwestern and northeastern parts. The results indicate that the predominant magnetic structural trends are NW-SE, NE-SW, N-S, and E-W, which contribute to the formation of a series of subsurface horsts (H) and grabens (G). Three main basins (A, B, and C) were identified as the most profound areas. These basins represent the most promising areas for groundwater accumulation, making them attractive for future hydrogeological exploration. This integrated approach strongly offers a powerful and effective tool to assist in developing an appropriate plan to manage groundwater in arid regions. [ABSTRACT FROM AUTHOR]

The direction and magnitude of soil organic carbon stock (SOCS) change following land use and land cover change (LULCC) are highly uncertain, largely due to the lack of relevant global soil data. Great efforts have been made to build SOCS database at regional, national and even sub-continental scales following LULCC; however, a comprehensive and open-access global database has not yet been developed, hindering a deep understanding of LULCC impact on SOCS dynamics. In this study, we introduce a new global SOCS database for LULCC, compiled from 639 articles documented in the Web of Science through the end of 2023. Targeting five major land uses (cropland, grasslands, forest, plantation, and savanna), this database – named the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) – include 1,206 sites with 5,982 records at various sampling depths. The database will enable users to assess the global impact of LULCC on SOCS dynamics and identify the factors that control SOCS changes for specific types of LULCC. The GSOCS-LULCC database is freely available from the Zenodo platform at https://doi.org/10.5281/zenodo.11183819 (Chen et al., 2024). [ABSTRACT FROM AUTHOR]

[Objective] Taking Jingzhou City as an example, this paper discussed the relationship between urban expansion and farmland loss and its impact on economic growth. [Method] Using a 30-meter annual land cover dataset in China, the spatial and temporal patterns and regional differences of urban expansion and farmland loss during 1990-2019 were analyzed. The environmental Kuznets curve (EKC) hypothesis was introduced to test the relationship between urban expansion and economic growth through regression analysis of provincial panel data, and the future urban land expansion (FULE) dataset was used to predict farmland pressure under future scenarios. [Result] Urban expansion was the main cause of farmland loss, accounting for 76% of newly built urban land and 28% of farmland loss. Cities above the county level were more inclined to use farmland for construction, while cities below the county level had more farmland converted to other uses. There was an inverted U-shaped relationship between urban expansion and farmland loss, indicating that the loss of farmland increased first and then decreased with economic growth, and began to decouple after reaching an inflection point in 2012. Some coastal and large cities had entered a stage of reduced farmland loss, while some inland and small and medium-sized cities were still in a stage of increased farmland loss. It was estimated that by 2050, 0.3-0.4 million hectares of arable land in Jingzhou will be occupied by urban land, and third-and fourth-tier cities will suffer the most losses. [Conclusion] It was recommended to strengthen land planning and management, control urban expansion, optimize land use structure, improve land use efficiency, protect arable land resources, promote regional coordinated development, and achieve a balance between economy and environment. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LULCC) have profoundly altered land surface properties and ecosystem functions, including carbon and water production. While mapping these changes from local to global scales has become more achievable due to advancements in earth observations and remote sensing, linking land cover changes to ecosystem functions remains challenging, especially at regional scale. Our study attempts to fill this gap by employing a computationally efficient method and two types of widely used high-resolution satellite images. We first investigated the contribution of landscape composition to ecosystem function by examining how land cover and proportion affected gross primary production (GPP) and evapotranspiration (ET) at six macro-landscapes in Mongolia and Kazakhstan. We hypothesized that both ecosystem and landscape GPP and ET are disproportionate to their composition and, therefore, changes in land cover will have asymmetrical influences on landscape functions. We leveraged a computational-friendly linear downscaling approach to align the coarse spatial resolution of MODIS (500 m) with a fine-grain and localized land cover map developed from Landsat (30 m) for six provinces in countries where intensive LULCC occurred in recent decades. By establishing two metrics—function to composition ratio (F/C) and function to changes in composition change (ΔF/ΔC)—we tested our hypothesis and evaluated the impact of land cover change on ecosystem functions within and among the landscapes. Our results show three major themes. (1) The five land cover types have signature downscaled ET and GPP that appears to vary between the two countries as well as within each country. (2) F/C of ET and GPP of forests is statistically greater than 1 (i.e., over-contributing), whereas F/C of grasslands and croplands is close to or slightly less than 1 (i.e., under-contribution). F/C of barrens is clearly lower than 1 but greater than zero. Specifically, a unit of forest generates 1.085 unit of ET and 1.123 unit of GPP, a unit of grassland generates 0.993 unit of ET and GPP, and a unit of cropland produces 0.987 unit of ET and 0.983 unit of GPP. The divergent F/C values among the land cover classes support the hypothesis that landscape function is disproportionate to its composition. (3) ΔET/ΔC and ΔGPP/ΔC of forests and croplands showed negative values, while grasslands and barrens showed positive values, indicating that converting a unit of forest to other land cover leads to a decrease in ET and GPP, while converting units of grassland or barren to other land cover classes will result in increased ET and GPP. This linear downscaling approach for calculating F/C and ΔF/ΔC is labor-saving and cost-effective for rapid assessment on the impact of land use land cover change on ecosystem functions. [ABSTRACT FROM AUTHOR]

We introduce CAMELS-INDIA (Catchment Attributes and MEteorology for Large-sample Studies – India), the hydrometeorological time series, and catchment attributes for 472 catchments in Peninsular India. Peninsular India covers 15 intrastate river basins defined by the Central Water Commission (CWC), where river flow and water level datasets are available for several gauge stations through the open-source India Water Resources Information System (India-WRIS). However, many of these gauge stations lack reliable metadata, and data are not in an analysis-ready format for large-sample hydrological studies. Therefore, we utilized 472 gauge stations and their catchment boundaries, characterized as stations with reliable metadata, from the 'Geospatial dataset for Hydrologic analyses in India (GHI)' (Goteti, 2023). For each of these catchments, the CAMELS-INDIA provides a catchment mean time series of meteorological forcings for 41 years (1980–2020) and around 211 catchment attributes representing hydroclimatic and land cover characteristics extracted from multiple data sources (including ground-based observations, remote sensing-based products, and reanalyses datasets). The CAMELS-INDIA follows the same standards of the previously developed CAMELS datasets for the USA, Chile, Brazil, Great Britain, Australia, Switzerland, Germany, and Denmark to facilitate comparisons with catchments of those countries and inclusion in global hydrological studies. Notably, the CAMELS-INDIA includes available observed streamflow and catchment mean time series of 19 meteorological forcings, including precipitation, maximum, minimum, and average temperature, long-wave and short-wave radiation flux, U and V-components of wind, relative humidity, evaporation rates from canopy and soil surface, actual and potential evapotranspiration, and soil moisture of four layers (covering depth up to 3 m below ground) for detailed hydrometeorological studies. We also derived catchment attributes representing human influences, including the number of dams and their utilization, total volume contents of dams in catchments, population density, and increase in urban and agricultural land covers to facilitate studies to understand human influences on catchment hydrology. Furthermore, the dataset includes predicted streamflow time series from a regionally trained Long-Short Term Memory (LSTM)-based hydrological model, which can fill gaps in observed streamflow data or serve as a benchmark for testing and developing new hydrological models. We envision that CAMELS-INDIA will provide a strong foundation for a community-led effort toward gaining new hydrological insights from hydrologically distinct Indian catchments and solving pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unraveling regional-scale hydrologic functioning, and climate change impact assessment of catchments across India. The CAMELS-INDIA dataset is available at https://doi.org/10.5281/zenodo.13221214 (Mangukiya et al., 2024). [ABSTRACT FROM AUTHOR]

Introduction: The selection of an optimal scale or granularity in landscape analysis is pivotal for uncovering inherent patterns and changes driven by processes. Variations in spatial resolution can significantly alter the proportions and distributions of various landscape types, thereby impacting the assessment of landscape patterns. Despite its importance, the scale factor is frequently neglected in studies focusing on long-term landscape dynamics. Methods: Bridging this gap, we utilized remote sensing imagery data from 1986 to 2020 for Lushui City, integrating remote sensing (RS) and geographic information system (GIS) technologies to generate land cover maps. Our focus centered on investigating the sensitivity of landscape pattern indices within the 30-1000m scale. Combining the first scale domain with an information loss assessment model, we identified the optimal granularity for the analysis, conducting a detailed spatiotemporal examination of landscape pattern from 1986 to 2020 using the index analysis method. Results and discussion: The results show that: (1) The dominance of forests in Lushui City, yet reveal a significant increase in construction land area over the study period, primarily driven by the conversion of forest and grassland. (2) Among the 10 examined indices, four (PD, ED, TE, and LSI) demonstrated predictable responses to changes in granularity, while three (PAFEAC, COHESION, AI) exhibited unpredictable stepwise reactions. Three indices (LPI, SHDI, PLAND) displayed minimal regularity to granularity changes. (3) The optimal long-term landscape analysis granularity for Lushui was identified as 100 m. (4) Before 1996, the city's landscape exhibited characteristics of aggregation, good connectivity, and minimal anthropogenic disturbance. However, post-1996, the landscape experienced disruptions, leading to an overall increase in fragmentation. The expansion of cultivated land and construction land due to urbanization has intensified landscape fragmentation. However, policies such as converting cropland to forest and planned ecological civilization initiatives have restored forest coverage and improved landscape cohesion and connectivity in Lushui City. This research offers vital insights for ecological planning and resource management in alpine valley watershed cities, deepening our grasp of landscape pattern evolution. [ABSTRACT FROM AUTHOR]

Satellite image time series (SITS) segmentation is crucial for many applications, like environmental monitoring, land cover mapping, and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine-grained annotation. We propose S4, a new self-supervised pretraining approach that significantly reduces the requirement for labeled training data by utilizing two key insights of satellite imagery: (a) Satellites capture images in different parts of the spectrum, such as radio frequencies and visible frequencies. (b) Satellite imagery is geo-registered, allowing for fine-grained spatial alignment. We use these insights to formulate pretraining tasks in S4. To the best of our knowledge, S4 is the first multimodal and temporal approach for SITS segmentation. S4's novelty stems from leveraging multiple properties required for SITS self-supervision: (1) multiple modalities, (2) temporal information, and (3) pixel-level feature extraction. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially aligned, multimodal, and geographic-specific SITS that serves as representative pretraining data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data. Through a series of extensive comparisons and ablation studies, we demonstrate S4's ability as an effective feature extractor for downstream semantic segmentation. [ABSTRACT FROM AUTHOR]

As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m3/m3) and the highest coefficient of determination (R2, above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices. [ABSTRACT FROM AUTHOR]

The Taihu Lake basin is one of the fastest-growing regions in China, where the natural environment has been seriously affected by humans. The plant area index (PAI) is an important parameter reflecting the change in vegetation growth, which plays a crucial role in studying vegetation growth and protecting the ecological environment. Advancements in remote sensing technology, complemented by machine learning techniques, have facilitated the accurate and efficient acquisition of PAI over large areas. In this study, the Taihu Lake Basin vegetation area was taken as the research object. Global Ecosystem Dynamics Investigation (GEDI) point cloud data and Landsat-8 remote sensing images were the primary information sources. MODIS land cover types were utilized to classify the vegetation into six categories. Three classical machine learning models, namely, Random Forest (RF), Support Vector Regression (SVR), and Back Propagation Neural Network (BPNN), were used to estimate the PAI in the Taihu Lake Basin. It was found that the RF model showed the best performance. The determination coefficients (R2) for grassland, evergreen forest, mixed forest, deciduous forest, farmland, and wetland were 0.71, 0.67, 0.69, 0.66, 0.65, and 0.69, respectively. Over 2000-2022, the PAI exhibited an absolute change rate of 0.035, with an overall increasing trend. The area of improved and degraded vegetation accounted for 58.33% and 41.67% of the total area, respectively. The study also revealed that PAI was positively correlated with precipitation (R = 0.64, P < 0.05) and negatively correlated with temperature (R = -0.58, P < 0.05). Different land types' effects on PAI were also analyzed, with wetland PAI having the smallest mean value and evergreen forest PAI having the most considerable mean value. This research underscores the effectiveness of integrating GEDI data and Landsat-8 imagery in PAI assessment, providing valuable insights for environmental monitoring and analysis in the Taihu Lake Basin. [ABSTRACT FROM AUTHOR]

Quantifying the attribution of climate change and human activities on runoff and suspended sediment load is crucial for formulating future watershed management measures, especially in the ecologically fragile alpine region, which is more susceptible to climate change and human activities. In this study, the temporal changes in runoff‐suspended sediment load and the potential impact factors (i.e., precipitation, potential evapotranspiration (PET), land use/land cover change (LULC) and reservoir operation) were investigated in the Lhasa River Basin (LRB) from 1956 to 2020. In addition, the contributions of those factors to the changes in runoff and suspended sediment load were quantitatively evaluated based on physically‐based Soil and Water Assessment Tools (SWAT). The results indicated that annual runoff and suspended sediment load showed an increasing but insignificant trend during 1956–2020, while annual precipitation and PET showed a significant increasing trend at a rate of 0.94 and 1.07 mm/yr, respectively. LULC mainly presented an increase in forestland area followed by a decrease in grassland and bare land area due to the implementation of ecological projects. Runoff and suspended sediment load changed abruptly at approximately 1995 and 2005 based on three abrupt change identification methods, thus, the study period was further divided into three substages: baseline period (P0, 1956–1994), dramatically increased period (P1, 1995–2004) and slightly decreased period (P2, 2005–2020). The attribution analysis showed that climate change was the dominant contributor to runoff and suspended sediment load increments during P1. LULC caused the decline in runoff and suspended sediment load in both P1 and P2, with their contribution increasing significantly from 0.40% and 4.73% in P1 to 50.24% and 51.79% in P2, respectively. Reservoir operation was the second key factor in runoff and suspended sediment load reduction in P2, contributing 42.07% and 43.72%, respectively. These findings provide the scientific foundation for reasonably allocating water resources and statistical support for the benefit evaluation of implementing ecological projects in the basin. [ABSTRACT FROM AUTHOR]

After the 2008 Wenchuan earthquake (MS 8.0), a large number of debris flow source areas formed and further affected the occurrence of debris flows. Identifying the source area that may participate in debris flow activities requires understanding its dynamically changing conditions of location and probability. Based on this, the Bayesian geomorphology method was used to analyze the variation probability in the sediment supply from coseismic landslide deposits to subsequent debris flows and to undertake a quantitative assessment as well. The segment of the Min River, stretching from the urban area of the Wenchuan County to the Yingxiu town, China, was selected as the study area. Geomorphologic characteristics, including elevation, distance to faults, seismic intensity, land cover, gradient, monthly precipitation, and lithostratigraphy, were determined as assessment factors. The method was applied and verified in three clusters of debris flow events from 2010 to 2019 in 37 debris flow catchments. The results indicate that the method is applicable for identifying the dynamic debris flow source areas in earthquake-affected regions with a high accuracy of over 87% and a refined resolution of 30 m. This study finds that the sediment supply types have changed since 2019, that is, the sediment supply from Wenchuan earthquake-induced landslide deposit zones occupied by the debris flow-prone area has dropped from an average of 95.9% in 2013 to approximately 44.7% in 2019. Still, a large number of coseismic landslide deposits exist in the debris flow basins; the probability of debris flow source areas could thus be used as an important criterion for evaluating the long-term evolution of landslides and subsequent debris flow activities in earthquake-affected regions. [ABSTRACT FROM AUTHOR]

Urban flooding problem has been exacerbated in recent times, especially in developing nations, due to haphazard changes in land use and land cover (LULC) resulting from rapid urban expansion, coupled with river encroachments and inadequately engineered river management structures. Kathmandu Valley Watershed (KVW), encompassing Kathmandu, Bhaktapur and Lalitur districts, the fastest growing cities in South Asia, is constantly growing, with a significant increase in urban areas. Due to urbanization, the watershed's water storage capacity is diminishing, while surface runoff volume and rate are accelerating. We evaluated the isolated as well as the integrated impact of multiple scenarios of LULC change and river encroachment on flood inundation characteristics in KVW. LULC prediction revealed an increase in built-up areas by 113% between 1990 and 2020, which are further projected to increase by 29% by 2050. Inundation modeling using Rainfall-Runoff Inundation (RRI) model showed that rather than the increase in inundation extent, the depth of inundation is projected to increase in future as a result of increasing urban areas. Furthermore, our research highlighted that the impact of river width encroachment had a more substantial effect on flooding compared to changes in LULC alone. Similarly, integrated impact of LULC change and river encroachment was more pronounced than the impact of change in LULC alone. The aggregate of observations leads to the conclusion that the encroachment of rivers is the predominant factor contributing to the flooding issue within the KVW. The findings of the study is anticipated to assist policymakers in effective land use planning and in proposing appropriate development initiatives concerning the river environment. [ABSTRACT FROM AUTHOR]

Coastal landslides pose significant threats to critical infrastructure in energy-dependent regions like the Assaluyeh anticline. To safeguard communities and economic stability, it is crucial to address uncertainties in coastal landslide susceptibility assessments. This necessity extends beyond scientific exploration and requires pioneering fuzzy logic methodologies, to enhance predictive accuracy. The Assaluyeh anticline is in the Persian Gulf's Zagros intercontinental region is of paramount importance, influencing Iran's energy infrastructure. To comprehend coastal landslide risk, our innovative study focuses on the Assaluyeh anticline's coastal areas. We combine 13 conditioning factors, blending traditional geological characteristics with advanced technology, including lithology, land-cover, topographic wetness index (TWI), elevation, and slope angle, using AI-driven modeling to create predictive landslide susceptibility maps. This approach surpasses conventional techniques, offering improved early warning systems and disaster management. Our research's groundbreaking aspect is its forward-looking approach, utilizing fuzzy logic to adapt to real-world conditions by embracing imprecision and uncertainty in data. We integrate historical development data, local knowledge, and forward-thinking fuzzy logic techniques to promote community-driven risk management and resilience in coastal environments. This study envisions the future, revolutionizing coastal landslide assessment and management, inspiring interdisciplinary collaboration, community engagement, and fuzzy logic-driven innovation in coastal disaster risk reduction on a global scale. [ABSTRACT FROM AUTHOR]

Featured Application: Determination the scale of hazard caused by episodic water runoff in rural areas will allow better management of the land and limit its negative effects. There has been an increase in the frequency of hazards associated with meteorological and hydrological phenomena. One of them is flash floods occurring episodically in areas of concentrated runoff—valleys without permanent drainage. In the opinion of residents and local authorities, these are potentially safe areas—they are not threatened by floods and are therefore often occupied by buildings. The importance of addressing flash floods in land use planning is essential for sustainable development and disaster risk reduction. The objective of this research was to assess the level of the hazard and to evaluate its presence in land use planning activities. This manuscript fills a research gap, as to date flash flood threats have not been analyzed for individual buildings located in catchments of dry valleys in temperate climates. More than 12,000 first-order catchments were analyzed. The study covered an upland area located in East Poland, which is characterized by high population density and dispersed rural settlement. Within the 10 municipalities, buildings located on potential episodic runoff lines were identified. Qualitative assessment was applied to ascertain the susceptibility of catchments to flash floods. Such criteria as slopes, size, shape of the catchment area, and land cover, among others, were used. Between 10 and 20% of the buildings were located on episodic runoff lines, and about 900 sub-catchments were highly or very highly susceptible to flash floods. The way to reduce the negative effects of these phenomena is to undertake proper land use planning based on knowledge of geohazards, including flash floods. However, an analysis of available planning documents shows that phenomena of this type are not completely taken into account in spatial management processes. [ABSTRACT FROM AUTHOR]