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- Exploration of Multi-Decadal Landslide Frequency and Vegetation Recovery
Conditions Using Remote-Sensing Big Data-
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Abstract: Abstract Major landslides caused by earthquakes, typhoons, and heavy rainfall are frequently observed in subtropical areas like Taiwan, presenting considerable risks to human life and the economy. Comprehending landslide frequency and their recovery by analyzing vegetation regrowth is crucial for understanding substantial damage and ecosystem resilience. This study automates landslide detection using temporal NDVI gradient analysis with moving-window smoothing in Taiwan from 1990 to 2022, leveraging Landsat time series data, to comprehensively estimate the frequency of landslide occurrences and vegetation recovery by monitoring the changes in vegetation. Validation is conducted through an analysis of five distinct case studies when compared to the government's landslide inventory. Results show that the landslide frequency and vegetation recovery conditions have been aptly estimated in recent years. About 20% of landslides exhibit multiple remobilization, emphasizing the recurrent nature of these events. Northern Taiwan stood out as a region predominantly characterized by landslide activity in the 1990s, while Central, Southern, and Eastern Taiwan saw a surge in landslides after the 1999 Chi-Chi earthquake and the 2009 Typhoon Morakot. The relations among landslide frequency, topography, and vegetation recovery duration are explored. Landslide frequency displays a positive correlation with elevation and slope. Vegetation recovery varies based on landslide frequency, reflecting the impact of multiple landslide events on growth impediment, whereas lower elevations and gentle slopes exhibit shorter durations for recovery. More than 50% of the vegetation has been recovered to reach the pre-event level in landslides after Typhoon Morakot. This study contributes to the understanding of landslide dynamics, leveraging automated detection and analysis methods for comprehensive regional scales.
PubDate: 2024-08-07
DOI: 10.1007/s41748-024-00432-x
- Control of Anthropogenic Factors on the Dissolved Carbon Sources in the
Ramganga River, Ganga Basin, India-
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Abstract: Abstract The Ramganga River is an important tributary of the Ganga River flowing through diverse land use classes. To examine seasonal variations in dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) concentration and assess the potential impact of natural processes and human activities, we collected water samples from the Ramganga River and its tributaries during the pre-monsoon, monsoon, and post-monsoon seasons of 2014. DOC and DIC concentration, total dissolved solids (TDS), nitrate (NO3−), chloride (Cl−), and proxies like DOC/DIC ratio, percentage share of anthropogenic contribution, and percentage of pollution were evaluated using Hierarchical Cluster Analysis. The results show annual average DOC concentration in the Ramganga basin is 2.0 ± 1.2 mg/l. The DOC and DIC concentration represent a distinct seasonal variation being higher in the non-monsoon and lower in the monsoon season. The DIC/DOC ratio of 11.3, NO3−/Ca2+ and Ca2+/Cl− suggests elevated carbonate weathering, with floodplains likely acting as the dominant source of DIC flux. DOC transport is controlled by basin physiography, the river carries 3.8 times higher DOC concentrations in the floodplains than that in the mountainous region. However, high DOC concentration in the middle and lower sections of the basin indicates a strong control of anthropogenic activities. The positive linear relationship between the percentage of pollution index and DOC, percentage share of anthropogenic contribution and DOC, and Cl− and DOC suggest a significant influence of residential wastewater on the river’s DOC flux. Hierarchical cluster analysis revealed that factors like physiography, seasonal variation, tributary contributions, and the presence of the Kalagarh dam differentially influence DOC and DIC concentration across the basin. The findings shed light on the substantial impact of urbanization on carbon transportation pathways, emphasizing the need for further research to incorporate these anthropogenically driven changes into global climate models for more accurate predictions.
PubDate: 2024-07-27
DOI: 10.1007/s41748-024-00417-w
- Behavior of Cool and Hot/Warm Air Plumes in Tokyo Revealed by Artificial
Intelligence-
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Abstract: Abstract The primary objective of this study was to visualize cool air plumes generated in and coming from urban green areas. Differences in air temperature at meteorological stations at the edges of urban green areas and an urban site in Tokyo in August, November, January, and April were acquired at a time resolution of 10 min for 3 or 6 years. By handling more than 12,800 cases, the automatic neuro-evolution of a deep learning architecture was applied to generalize and visualize the typical wind directions and speeds when cool air plumes were in effect at meteorological stations at the edges of the urban green areas. In some cases, the visualization of cool air plume was successful. For instance, the Tokyo site at the edge of a green area was typically cooler than the urban site in Itabashi when the Tokyo site was subjected to calm wind (< 3 m s−1) from the urban green area at night in August. However, in some cases in August, the Tokyo site was hotter than the Itabashi site when the Tokyo site had strong winds from every direction in the daytime, indicating that the strong winds brought heat from heat sources surrounding the urban green area. Thus, the cooling effect at the Tokyo site was at microscale or local scale, while the heating/warming effect was mesoscale. This emphasized that, especially in the daytime in summer, the cool air plumes were prone to be engulfed by greater volumes of hot air plumes from various heat sources. Mechanisms of other related findings are further discussed.
PubDate: 2024-07-24
DOI: 10.1007/s41748-024-00405-0
- Projection of Compound Wind and Precipitation Extreme Events in the
Iberian Peninsula Based on CMIP6-
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Abstract: Abstract This study aims to investigate the potential changes in the co-occurrence of strong precipitation and wind events over the Iberian Peninsula using simulations from the Coupled Model Intercomparison Project (CMIP) Phase 6 under two scenarios (SSP2 − 4.5 and SSP5 − 8.5). Projected changes indicate a significant regional variability during all seasons. In winter, the western regions are projected to experience an increase in compound events as the century progresses under both scenarios, with a significantly larger area being affected by the end of the century. In spring, summer, and autumn, a general decline in the occurrence of these events is anticipated throughout the century, accompanied by a reduction in the area affected by them. However, in the northwesternmost area (Galicia), an increase in the occurrence of compound events is expected during the spring towards the end of the century, particularly under the SSP5-8.5 scenario.
PubDate: 2024-07-20
DOI: 10.1007/s41748-024-00429-6
- Mapping of Orogenic Gold Mineralization Potential in the Kushaka Schist
Belt, Northcentral Nigeria: Insights from Point Pattern, Kernel Density,
Staged-Factor, and Fuzzy AHP Modeling Techniques-
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Abstract: Abstract The Kushaka greenschist belt is one of Nigeria's known gold mineralized belts. This study used geological information from geophysical, remote sensing, surface geology, and geochemical interpretations to produce a gold mineral potential map (GMPM) in the belt. Statistical methods that involve fractal, Fry, Poisson distribution, statistical distance distribution, cross-correlation, similarity coefficient, and univariate and bivariate kernel density estimation techniques were used to establish spatial relationships between gold expressions and geological structures. Using an orthogonal varimax matrix, this study evaluated ore-related elements associated with orogenic gold mineralization through staged-factor analysis (SFA). Critical factors such as “gold/ligand source”, “heat source”, hydrothermal alteration subsystem, geologic structures, and gold enrichment were integrated using a mineral system approach and fuzzy AHP modeling technique to produce GMPM for the study area. The statistical analyses showed that NNE–SSW and NE–SW trending geologic structures primarily control orogenic gold mineralization in the belt. SFA revealed that gold minerals are associated with rare earth elements (lithium, zircon, tin, scandium, and yttrium), manganese, and sulfide minerals (lead–zinc). Areas with high mineralization potential scores are found in faulted and hydrothermally altered metasedimentary and granite rocks. The GMPM model's accuracy using the prediction-area (P–A) rate curve and 31 known gold occurrences resulted in an 85% success rate. This study concluded that magmatic-metamorphic processes and tectonic deformation events primarily influence orogenic gold mineralization in the Kushaka schist belt, and in some other Nigeria’s schist belts. The methodology adopted in this study can be applied to other geological settings with similar characteristics to delineate orogenic gold mineralization potential zones.
PubDate: 2024-07-18
DOI: 10.1007/s41748-024-00427-8
- Unveiling Hydrological Shifts under Projected Climate Change in Highly
Irrigated Semi-Arid State of Telangana, India-
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Abstract: Abstract Climate change threatens water and food security, affecting livelihoods around the globe. In Telangana, India, agriculture depends on monsoon rains, which are significantly altered by climate change. Climate Change has modified the monsoon rains drastically, and hence, the agricultural practices need to be revised to adopt the impacts of climate change. We analyzed the multimodal mean of 13 GCMs that were downscaled from the CMIP6 experiment for four different scenarios viz., SSP126, SSP245, SSP370, and SSP585, to evaluate how climate change is projected to affect Telangana’s water resources. Our findings suggest that the region’s climate systems are expected to change, with precipitation increasing by 15 to 50% and temperatures by 0.3 to 2.94 °C. This will have a significant impact on water yield and crop yield in the region. Moreover, the analysis of water balance components simulated by SWAT indicates that while evapotranspiration may experience a slight increase (0–50%), the amplified precipitation levels will significantly impact water yield (16–108%). The increased rainfall is expected to result in a more significant surge in surface runoff. The effects of climate change in the area have shown significant variation in both space and time. A thorough evaluation of the findings suggests the most effective adaptation and mitigation strategies. The study offers insights for policymakers and government officials to design effective adaptation and mitigation strategies to address these pressing water-related concerns. Shifting crop types from paddy to irrigated dry crops, prioritizing rain-fed crops, conjunctive use of surface and groundwater, recycling of water and implementing water regulation, and site specific rainwater harvesting and water conservation measures can help establish water security in the region.
PubDate: 2024-07-16
DOI: 10.1007/s41748-024-00415-y
- Model Analysis of Coastal and Continental Impacts on Boundary Layer
Meteorology over West Africa-
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Abstract: Abstract Data from the Weather Research and Forecasting (WRF) model and the regional climate model (REMO) of the Climate Service Center Germany (GERICS), combined with reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to study the influence of the Atlantic Ocean and land surfaces on atmospheric boundary layer (ABL) over West Africa. The ABL is characterised by parameters such as: boundary layer height (BLH), temperature at 2-m, specific humidity at 925 hPa, surface latent and sensible heat flux and wind at 10-m. The diurnal, monthly, and seasonal variability of each parameter varies by location (i.e., land or oceanic sites). We see that in the two types of data studied (observed and simulated), the air temperature and wind speed are lower over the Atlantic Ocean than over land. In contrast, the specific humidity at 925 hPa is highest at 00 UTC, 06 UTC and 18 UTC from the Equator to 15° N, and low at 12 UTC, at 18 UTC it varies between 4 and 14 g/kg. We note that the variability of these parameters is lower in coastal areas (close to the Atlantic Ocean) than in continental areas (near the Sahara Desert). Statistical indicators such as bias and mean square error were calculated to assess the effectiveness of the model used in this study. The results show that simulated data produced good results. REMO forces with ECMWF interim reanalysis (ERA) interim simulations of ABL parameters for historical climate (1979–2017) were compared to ERA5 data using several descriptive statistics. Compared to the ERA5 reanalysis, the REMO models realistically reproduce the seasonal characteristics of 2-metre temperature, specific humidity at 925 hPa, BLH, and near-surface wind in most sub-regions of West Africa although notable biases still exist. The diurnal analysis of radiosonde on September 2, 2006 in Dakar and Niamey shows that dew temperature is lower than the ambient temperature. This explains the fact that the thickness of the ABL in Dakar is less than that observed in Niamey. The WRF model, provided by National Center for Atmospheric Research (NCAR), which is widely utilised in research and operation, also reproduces well the diurnal variation of near-surface parameter of the ABL over this region.
PubDate: 2024-07-16
DOI: 10.1007/s41748-024-00428-7
- Regional Assessment of Groundwater Contamination Risk from Crude Oil
Spillages in the Niger Delta: A Novel Application of the
Source-Pathway-Receptor Model-
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Abstract: Abstract Onshore oil spills are known for their disastrous environmental impacts and potential to cause lasting damage to underlying groundwater. The Niger Delta is particularly vulnerable to widespread spillages linked to extensive oil exploration, transportation, and theft-related incidents. This research employed a geospatial approach in formulating risk equations, based on the source-pathway-receptor (S-P-R) model using multiple openly available data sets, to assess groundwater contamination risk in the Niger Delta Region (NDR), Nigeria. To develop the overall risk equation, the study combined fourteen thematic data layers including the volume of oil spilled, type of spill, slope, elevation, proximity to spill site, pipeline, oil wells and streams, drainage density, mean annual precipitation and population density. These layers were integrated into source potency, pathway transmissivity, and receptor susceptibility. The NDR was systematically categorized into low, moderate, and high groundwater risk zones. The delineation revealed that high-risk zones predominantly span the central areas, extending from southeast to northwest, effectively encircled by regions of low to medium risk located in both the northern and southern extents of the delta. The efficacy of the risk model was corroborated by existing knowledge. Moderate to high-risk zones were found to be in about 16% of the NDR, revealing previously unknown areas of risk. This spatial configuration underscores a significant gradient in contamination risk across the NDR, with the central corridor emerging as a critical focus for groundwater protection and remediation efforts. In line with UN Sustainable Development Goal (SDG) #6, this study recommends targeted strategies to ensure clean water provision in these identified high-risk areas. By leveraging the S-P-R model within this complex and sensitive ecological area, this research both advances environmental risk assessment and sets a precedent for future large-scale environmental risk assessments utilizing open-source data.
PubDate: 2024-07-15
DOI: 10.1007/s41748-024-00416-x
- Atlantic and Mediterranean-Sourced Precipitation over the Maghreb: Trends
and Spatiotemporal Variability-
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Abstract: Abstract The Mediterranean basin is recognized as a potential focal point of global warming, marked by a rising incidence of both droughts and floods. Nevertheless, uncertainties persist regarding the precise impact of climate change on the water cycle in this region. Therefore, this study endeavors to scrutinize the recent precipitation trends and fluctuations across the Maghreb (Morocco, Algeria, and Tunisia) and their connection to both the Atlantic and Mediterranean moisture sources. To spatialize these trends and variations, we used a satellite precipitation product that we have beforehand evaluated at different time scales. Furthermore, we employed two influential teleconnection patterns: the NAO index, representing Atlantic influence, and the WeMO index for theMediterranean influenceThe statistical assesment of the satellite-based rainfall data demonstrated strong correlations with ground-based rainfall, ranging from 0.45 to 0.8. The median Percentage Bias was found to be 10%. The median Mean Absolute Error was approximately 12 mm, while the Root Mean Square Error averaged around 18 mm. Overall, all chosen criteria yielded satisfactory outcomes, providing a suitable level of confidence for conducting spatio-temporal trend analysis at the pixel level. At temporal scale, the trend results showed some upward trends in precipitation in certain areas during the months of March, April, August, September, and October. However, for the remainder of the year, the dominant trend is a decrease in precipitation across most of the North African territories. At spatial scale, the findings unveiled a decline in precipitation levels in the central and southern regions, while showcasing an increase in precipitation across the northern Maghreb. Moreover, the sphere of influence exerted by the WeMO exhibited expansion, along with a notable amplification in its modulation of precipitation patterns, particularly from September to April. Conversely, the NAO exerted a more pronounced influence during the winter months.
PubDate: 2024-07-15
DOI: 10.1007/s41748-024-00426-9
- Multi-Model Ensemble Machine Learning Approaches to Project Climatic
Scenarios in a River Basin in the Pyrenees-
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Abstract: Abstract This study employs machine learning algorithms to construct Multi Model Ensembles (MMEs) based on Regional Climate Models (RCMs) within the Esca River basin in the Pyrenees. RCMs are ranked comprehensively based on their performance in simulating precipitation (pr), minimum temperature (tmin), and maximum temperature (tmax), revealing variability across seasons and influenced by the General Circulation Model (GCM) driving each RCM. The top-ranked approach is used to determine the optimal number of RCMs for MME construction, resulting in the selection of seven RCMs. Analysis of MME results demonstrates significant improvements in precipitation on both annual and seasonal scales, while temperature-related enhancements are more subtle at the seasonal level. The effectiveness of the ML–MME technique is highlighted by its impact on hydrological representation using a Temez model, yielding outcomes comparable to climate observations and surpassing results from Simple Ensemble Means (SEMs). The methodology is extended to climate projections under the RCP8.5 scenario, generating more realistic information for precipitation, temperature, and streamflow compared to SEM, thus reducing uncertainty and aiding informed decision-making in hydrological modeling at the basin scale. This study underscores the potential of ML–MME techniques in advancing climate projection accuracy and enhancing the reliability of data for basin-scale impact analyses.
PubDate: 2024-07-09
DOI: 10.1007/s41748-024-00408-x
- Effects of Irrigation Water Quality on Soil Physico-chemical Proprieties:
Case Study in North-West of Tunisia-
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Abstract: Abstract Poor-quality water containing a high concentration of soluble salts is used to irrigate cropland worldwide, especially in arid and semi-arid regions, the limited rainfall is insufficient to wash away salts from the root zone, which builds up salt in the soil and affects soil properties, causing secondary soil salinization. This risk makes it necessary to monitor the variation of salinization in the irrigated perimeters through physico-chemical analyses. In Tunisia, soils affected by salts occupy an area of 1.5 million hectares, about 10% of the country’s total area. Our study aims to make a diagnosis of irrigation water quality, investigate its effect on the physicochemical properties of soil, and determine the soil salinity states of two irrigated agricultural perimeters. The two study areas located in Laaroussa (2723ha) and Gaafour (1642 ha), belonging to the governorate of Siliana in the northwest of Tunisia, Laaroussa perimeter is irrigated by groundwater pumped from “Krib” aquifer and Gaafour area irrigated from Siliana river. Understanding the Dynamics of secondary salinization required a pedological and physico-chemical study of the 0–30 cm layer of the soil, which 20 samples were taken from Laaroussa perimeter and 11 samples from Gaafour perimeter. The soils of two study areas are very salty to extremely salty with an electrical conductivity (EC) equal to 8.46 mS/cm at Laaroussa perimeter and 17.57 mS/cm at Gaafour perimeter, the pH of the two soils is alkaline with a value equal to 8.32, having an impact on the soil organic matter (SOM), which plays an important role in the physical (stabilization of soil structure), chemical (buffering and pH changes) and biological (microbial activity) regulation of soil properties. The chemical analysis results of irrigation water show that groundwater (Krib aquifer) used for irrigation belongs to a class of very high salinity and low risk of alkalinization with high sodium contents and surface water (Siliana river) belongs to a class of high salinity and low risk of alkalinization. The expansion of irrigated agriculture is of enormous importance to feeding the world's burgeoning population. However, without appropriate management, this expansion can lead to environmental problems of soil salinization induced by irrigation, which is the case in our study area and in any country with an arid and semi-arid climate, so appropriate management of Soil salinization is imperative to achieve most sustainable development goals.
PubDate: 2024-07-09
DOI: 10.1007/s41748-024-00422-z
- Uncertainties Assessment of Regional Aerosol Classification Schemes in
South America-
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Abstract: Abstract In the realm of aerosol classification, South America poses distinctive challenges due to data constraints and the varied methods used, often without consideration of their uncertainties or suitability for the specific problem at hand. This study delves into the impact of uncertainties in aerosol optical properties on widely employed classification methods in the continent. Employing partial derivatives, we propagated uncertainties within 2-D threshold-based classification schemes across 30 sites within the AERONET network. The analysis examines the interplay between total and aerosol-type-specific potential misclassification rates. Our findings underscore pronounced uncertainties in complex schemes, and those reliant on the Angstrom exponent. Additionally, differentiation between maritime and continental aerosols poses significant challenges across all methods, with a potential uncertainty of 63%. The finest performance is particularly evident in tropical dry climates and rainforest environments, where top-performing methods achieve an average uncertainty rate of 21%. In contrast, regions characterized by the mountain grasslands & scrublands biome and coastal locations present formidable challenges, resulting in 76% averaged misclassification. Notably, the fine mode fraction-based scheme excels in coastal and island environments, while aerosol relative optical depth emerges as a valuable metric for classification in mountainous, rainforest, rural, and urban areas. The implications of this study are significant for aerosol classification applications, offering a replicable methodology capable of reducing uncertainties. We recommend further measurements on the continent, as well as the use of our insights to select the most suitable classification method depending on the study site and aerosol conditions.
PubDate: 2024-07-05
DOI: 10.1007/s41748-024-00423-y
- From Risk to Resilience: Analyzing Key Success Factors in Malaysian Water
Risk Management-
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Abstract: Abstract The study examines critical success factors (SF) while developing water risk management (WRM) by Malaysian water operators (WO). Seven critical factors of success were proposed, namely, regulatory authorities, organizational competence (OC), economics, water assets (WA), resources of human beings, and public as well as water contracts (WC). With the help of the theory of perceived risk, legitimacy theory, and stakeholder theory the proposed critical SF was created. To examine the proposed critical SF, a cross-sectional survey was utilized. The adopted sampling technique was stratified random sampling. A total of 13 WOs were involved in this research. 400 management personnel of WO were selected randomly. Thus, each water operator received 31 questionnaires (400/13). The results showed that regulatory agencies (RA), OC, financial, human resources (HR), WCs, and the public influence the success of WRM significantly. The results of the investigation discovered workplace, personal as well as theoretical best practices for implementing WRM in Malaysian WO and strategies to grow the quality of water services.
PubDate: 2024-07-03
DOI: 10.1007/s41748-024-00413-0
- Decreasing Relative Importance of Drawdown Areas on Waters in CO2
Emissions in Drylands-
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Abstract: Abstract Previous research has primarily focused on global annual CO2 emissions from drawdown areas. However, in drylands, which are highly vulnerable to global change, elucidating the spatial and temporal significance of drawdown areas in relation to CO2 emissions from water remains unclear. In this study, we aimed to characterize the relative importance of drawdown areas on waters (RIDW) in CO2 emissions at a spatio-temporal scale. We found that the total drawdown area of drylands was estimated to be 127,442 ± 18,474 km2 per year, which represents up to 22% of the waters area. Furthermore, the annual variation of drawdown areas (coefficient of variation: 0.64) was higher than that of waters (coefficient of variation: 0.22). Our findings suggest that the shrinking of drawdown areas in temperate lakes and reservoirs has been the main factor in the decline of the total drawdown area from 2004 to 2020. As a result, the mean total CO2 emissions from drawdown areas during 2013–2020 decreased by 18% compared with 2004–2012, particularly in North Africa and Middle Asia, which saw decreases of 35% and 34%, respectively. Meanwhile, the expansion of waters area led to a 29% increase in CO2 emissions from waters. Our research further reveals that the mean annual CO2 emissions from drawdown areas in drylands are as high as 132.3 ± 23.1 Tg C yr-1, equivalent to 61% of estimated CO2 emissions from waters, but its share of CO2 emissions of non-perennial waters shows a significant downward trend. These results have important implications for understanding the role of drawdown areas in CO2 emissions and the impact of global change on dryland ecosystems.
PubDate: 2024-07-03
DOI: 10.1007/s41748-024-00406-z
- Resilience to Air Pollution: A Novel Approach for Detecting and Predicting
Aerosol Atmospheric Rivers within Earth System Boundaries-
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Abstract: The study explores extreme aerosol transport (EAT) events using atmospheric river (ARs) dynamics to identify aerosol atmospheric rivers (AARs). This provides insight into their significance in mitigating aerosol pollution and strengthening resilience within Earth system Boundaries (ESBs). AARs are narrow and long regions with high concentrations of various aerosols, including Black Carbon (BC), Dust (DU), Organic Carbon (OC), Sea Salt (SS), and Sulphate (SU), transported over long distances. Leveraging MERRA-2 re-analysis datasets, this study detects the AARs by applying various boundary conditions and develops a Spatio-Temporal AAR Availability Prediction Model (ST-AARAPM) based on a convolutional autoencoder. The model predicts AAR availability for the next t + 5-time frames using Stochastic Gradient Descent (SGD) optimization, minimizing Mean Squared Error (MSE) loss with a Rectified Linear Unit. Model performance is evaluated using metrics such as Structural Similarity Index (SSIM), Root Mean Squared Error (RMSE), Peak Signal Noise Ratio (PSNR), and MSE. From 2015 to 2022, the study identified 128,261 AARs worldwide with at least 8 AARs present at any given time frame. However, the model evaluation indicates satisfactory results, with SSIM, PSNR, RMSE, and MSE ranging from 0.88 to 0.96, 67.60 to 78.50 dB, 0.0656 to 0.1552, and 0.0043 to 0.0247, respectively. The findings highlight the effectiveness of ST-AARAPM in forecasting AAR availability and enhancing resilience in hotspot regions with significant aerosol loading, including the Indo-Gangetic plains, Eastern China, Japan, Northern Africa, Eastern USA, and South America. The study offers a fresh approach to tackling the effects of severe aerosol pollution via AARs within ESB’s. It stresses the need for policies to curb emissions, encourage sustainable production, and embrace clean energy. It calls on vulnerable systems to shift to cleaner technologies for resilience against aerosol pollution. Graphical 
PubDate: 2024-07-03
DOI: 10.1007/s41748-024-00421-0
- Assessment of Advanced Machine and Deep Learning Approaches for Predicting
CO2 Emissions from Agricultural Lands: Insights Across Diverse
Agroclimatic Zones-
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Abstract: Abstract Prediction of carbon dioxide (CO2) emissions from agricultural soil is vital for efficient and strategic mitigating practices and achieving climate smart agriculture. This study aimed to evaluate the ability of two machine learning algorithms [gradient boosting regression (GBR), support vector regression (SVR)], and two deep learning algorithms [feedforward neural network (FNN) and convolutional neural network (CNN)] in predicting CO2 emissions from Maize fields in two agroclimatic regions i.e., continental (Debrecen-Hungary), and semi-arid (Karaj-Iran). This research developed three scenarios for predicting CO2. Each scenario is developed by a combination between input variables [i.e., soil temperature (Δ), soil moisture (θ), date of measurement (SD), soil management (SM)] [i.e., SC1: (SM + Δ + θ), SC2: (SM + Δ), SC3: (SM + θ)]. Results showed that the average CO2 emission from Debrecen was 138.78 ± 72.04 ppm (n = 36), while the average from Karaj was 478.98 ± 174.22 ppm (n = 36). Performance evaluation results of train set revealed that high prediction accuracy is achieved by GBR in SC1 with the highest R2 = 0.8778, and lowest root mean squared error (RMSE) = 72.05, followed by GBR in SC3. Overall, the performance MDLM is ranked as GBR > FNN > CNN > SVR. In testing phase, the highest prediction accuracy was achieved by FNN in SC1 with R2 = 0.918, and RMSE = 67.75, followed by FNN in SC3, and GBR in SC1 (R2 = 0.887, RMSE = 79.881). The performance of MDLM ranked as FNN > GRB > CNN > SVR. The findings of the research provide insights into agricultural management strategies, enabling stakeholders to work towards a more sustainable and climate-resilient future in agriculture.
PubDate: 2024-07-03
DOI: 10.1007/s41748-024-00424-x
- Determination of the Coseismic Displacement with PPP Wavelet Decomposition
and InSAR-
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Abstract: Abstract The 2019 northeast Ridgecrest earthquakes occurred on July 4th and July 6th with magnitudes of 6.3 and 7.1 on the Moment Magnitude Scale (Mw), respectively. Utilizing precise point positioning (PPP), coseismic displacement can be measured, which is crucial for understanding earthquake structures. Noises in PPP measurements must be mathematically modeled to mitigate these interferences and extract coseismic displacement. The PPP static method was employed to determine the coseismic displacements of ten high-rate (1 Hz) global navigation satellite system (GNSS) stations located near the earthquake epicenter. The precise orbit and clock corrections of the satellites were utilized in PPP processing, and the simulated time series were subjected to wavelet decomposition (WD). This method reconstructs the PPP signal by analyzing the time-frequency domain, effectively increasing the signal-to-noise ratio. The symlets 4 wavelet, characterized by near-symmetric, orthogonal, and biorthogonal properties, was used to determine the maximum decomposition level. The largest displacement was measured by the nearest P595 station, with the eastern component at 432.24 ± 3 mm and the northern component at -247.31 ± 2 mm. To validate the results, interferometric synthetic aperture radar (InSAR) processing was performed with two Sentinel-1 A descending images from July 4 to July 16, 2019. The significant correlation values between GNSS and InSAR displacement were 0.62 and 0.92 before and after WD, respectively, at a 95% confidence level.
PubDate: 2024-06-28
DOI: 10.1007/s41748-024-00420-1
- Evaluation of Various Deep Learning Algorithms for Landslide and Sinkhole
Detection from UAV Imagery in a Semi-arid Environment-
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Abstract: Abstract Sinkholes and landslides occur due to soil collapse in different slope types, often triggered by heavy rainfall, presenting challenges in the semi-arid Golestan province, Iran. This study primarily focuses on the detection of these phenomena. Recent advancements in unmanned aerial vehicle (UAV) image acquisition and the incorporation of deep learning (DL) algorithms have enabled the creation of semi-automated methods for highly detailed soil landform detection across large areas. In this study, we explored the efficacy of six state-of-the-art deep learning segmentation algorithms—DeepLab-v3+, Link-Net, MA-Net, PSP-Net, ResU-Net, and SQ-Net—applied to UAV-derived datasets for mapping landslides and sinkholes. Our most promising outcomes demonstrated the successful mapping of landslides with an F1-Score of 0.95% and sinkholes with an F1-Score of 89% in a challenging environment. ResUNet exhibited an outstanding Precision of 0.97 and Recall of 0.92, culminating in the highest F1-Score of 0.95, indicating the best landslide detection model. MA-Net and SQ-Net resulted in the highest F1-Score for sinkhole detection. Our study underscores the significant impact of DL segmentation algorithm selection on the accuracy of landslide and sinkhole detection tasks. By leveraging DL segmentation algorithms, the accuracy of both landslide and sinkhole detection tasks can be significantly improved, promoting better hazard management and enhancing the safety of the affected areas.
PubDate: 2024-06-27
DOI: 10.1007/s41748-024-00419-8
- A Multivariate Geostatistical Framework to Assess the Spatio-Temporal
Dynamics of Air Pollution and Land Surface Temperature in Bangladesh-
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Abstract: Abstract In the context of escalating urban heat dynamics, the effect of air pollutants on Land Surface Temperature (LST) is an urgent concern, especially in the Global South. These regions are experiencing rapid industrialization, leading to an increase in greenhouse gas concentrations. Although the heat-absorbing capacity of air pollutants is well-recognized, the spatiotemporal relationship between these pollutants and LST remains underexplored, particularly in densely populated and industrialized metropolitan areas. Moreover, studies examining multiple pollutants simultaneously to understand their cumulative impact on surface temperature anomalies are scarce. Our study addresses this research gap by developing a spatial–temporal framework using remote sensing data from Google Earth Engine (GEE). We assessed the levels of Nitrogen Dioxide (NO2), Carbon Monoxide (CO), Aerosol Optical Depth (AOD), Ozone (O3), Sulfur Dioxide (SO2), and Formaldehyde (HCHO) in Bangladesh. Utilizing Emerging Hotspot Analysis and Geographically Weighted Regression (GWR) and complementing these with Principal Component Analysis (PCA) to create a Pollutant Impact Index (PII), we provide a detailed understanding of pollutant's impact on LST. The results revealed a global R-squared value of 0.61 with maximum local R-squared value of 0.68. Over 30% of the areas studied exhibit high-high clusters for air pollutant coefficients, with notably alarming levels of NO2 and O3, affecting 48.53% and 54.67% of the area, respectively. The PCA underscored the significant role of these pollutants, with the first three principal components accounting for 75% of the variance. Notably, the spatial distribution of the PII across Bangladesh showed substantial regional variations. Urban areas, like Dhaka and Sylhet, exhibited much higher PII values compared to less industrialized regions. These insights highlight the need for targeted environmental strategies to mitigate the impact of air pollution on urban heat dynamics and public health. The study’s findings underscore the urgency of addressing these environmental challenges, particularly in rapidly developing areas of the Global South.
PubDate: 2024-06-23
DOI: 10.1007/s41748-024-00418-9
- Enhancing Carbon Sequestration through Afforestation: Evaluating the
Impact of Land Use and Cover Changes on Carbon Storage Dynamics-
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Abstract: Abstract Carbon sequestration is crucial for achieving net zero emissions and plays a vital role in mitigating climate change. However, changes in forest cover are having a significant impact on the amount of carbon stored in terrestrial ecosystems as forests play a crucial role in mitigating climate change by effectively storing and sequestering carbon dioxide (CO2) from the atmosphere. Assessing the impact of changes in land use and land cover (LULC) on the ability of forest ecosystems to store carbon (CS) is challenging. This study employs remote sensing techniques to examine the changes in spatiotemporal patterns of CS in Khyber Pakhtunkhwa (KPK), resulting from LULC alterations between 2002 and 2022. Using the Google Earth Engine (GEE), we identified patterns of LULC change for three distinct years; and The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was employed to assess the evolving CS patterns. The study evaluates the effectiveness of the Billion Tree Tsunami (BTTP) afforestation initiative, revealing a substantial 63% increase in forest area, indicating enhanced carbon sequestration. The carbon storage has increased from 6824 Mg/m2 to 7435 Mg/m2, representing a 9% overall increase. This can be attributed to the improved vegetation and forest cover. This research provides insights into the geographical distribution and magnitude of forestation’s impact on carbon sequestration over the past two decades. Based on the findings, this study highlights the importance of implementing initiatives like BTTP and adopting policies and management strategies that promote sustainable urbanization without jeopardizing carbon reservoirs in the country.
PubDate: 2024-06-15
DOI: 10.1007/s41748-024-00414-z
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