bluuebunny/crossref_metadata_2025 huggingface dataset:
El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023
Authors: Zheng Fei , Li Ke-Xin
Published: March 2024

In 2023, the majority of the Earth witnessed its warmest boreal summer and autumn since 1850. Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society. We analyzed the monthly varying global mean surface temperature (GMST) in 2023 and found that the globe, the land, and the oceans in 2023 all exhibit extraordinary warming, which is distinct from any previous year in recorded history. Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics, the GMST in 2023 is predicted to be 1.41°C ± 0.07°C, which will certainly surpass that in 2016 as the warmest year since 1850, and is approaching the 1.5°C global warming threshold. Compared to 2022, the GMST in 2023 will increase by 0.24°C, with 88% of the increment contributed by the annual variability as mostly affected by El Niño. Moreover, the multidecadal variability related to the Atlantic Multidecadal Oscillation (AMO) in 2023 also provided an important warming background for sparking the GMST rise. As a result, the GMST in 2023 is projected to be 1.15°C ± 0.07°C, with only a 0.02°C increment, if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.
The Hunga Tonga-Hunga Ha'apai eruption’s impact on the 2023 Antarctic Ozone Hole
Authors: Newman Paul , Lait Leslie , Kramarova Natalya , Coy Lawrence , Santee Michelle
Published: March 2024

Continued satellite observations since the 1970s document the Antarctic ozone hole. The severity of the ozone hole stopped increasing in the 1990-2010 period, and has shown a slight improvement over the last decade, consistent with theory and model simulations. The ozone hole is projected to recover to 1980 levels in the 2060-2070 period.
The January 2022 Hunga Tonga-Hunga Ha'apai (HTHH) eruption was the most explosive volcanic eruption in the satellite era, and the water-rich plume presented an opportunity to understand the ozone hole impact from a large phreatic eruption. As shown in NASA Aura satellite Microwave Limb Sounder (MLS) data, the water vapor plume was embedded in the 2023 Antarctic vortex in early spring 2023 but was frozen out by July 2023. In this presentation, we will review the 2023 ozone hole’s evolution, and discuss the impact of HTHH. We will also show the 2023 levels in comparison to the long-term trend. In particular, we will also show how dynamical forcing controls ozone hole recovery into the summer season.
Recent variations in the vulnerability of terrestrial gross primary productivity to compound dry-hot extremes in China
Authors: Shi Wuzhi , Zhang Ke , Xie Yuebo , Liu Linxin , Forzieri Giovanni
Published: March 2024

The compound dry-hot events (CDHEs) have been one of the main reasons for the loss of terrestrial gross primary productivity (GPP) under ongoing global warming. Better quantification of patterns and drivers of GPP vulnerability to CDHEs is essential for ecological protection and identifying ecologically vulnerable areas. Although there is growing evidence of a sustained increase in the frequency and intensity of CDHEs in China over the past few decades, quantitative assessments of GPP remain lacking overall. From the perspective of a short time window, this study revealed the variability of the response of summer vegetation productivity in China to four severity levels (i.e., mild, moderate, severe, and extreme) of CDHE over the last 40 years and its associated mechanisms using copula conditional probability, sliding window algorithm and multiple ridge regression models. The main findings are: (1) the resistance of vegetation depends on land cover type and climatic conditions with lagged response times of 1.62, 1.73 and 1.82 months from June to August, respectively; (2) the average GPP vulnerability of 59.0%, 56.7%, 55.3% and 53.8% of vegetation areas tended to decrease under the changing environment, respectively, indicating that vegetation gradually adapted to CDHE disturbance; and (3) the influence of the dominant drivers in GPP sensitivity is not immutable, such as the impact of the control factors on the middle and lower reaches of the Yangtze River basin is gradually strengthened with time, which weakens the resistance of vegetation to compound conditions. These findings would contribute to further understanding of the effects of compound climate on the water and carbon cycles of terrestrial ecosystems.
Tectonic and climatic controls on terrace preservation in a large Himalayan river catchment
Authors: Weir Eliot , Clubb Fiona , Densmore Alex , Hurst Martin
Published: March 2024

Terraces are important archives of past environmental conditions, recording variations in both climate and tectonics on thousand-to-million-year timescales. For example, fill terraces in large mountain ranges may contain repeated deposits from large scale debris flow or landslide events, or may deform in response to gradients in tectonic uplift rate. However, our knowledge of the geographical extent of Himalayan terraces is currently very limited. This hinders our understanding of the spatial and temporal patterns of extreme hazard events such as large landslides, major earthquakes, and glacial lake outburst floods. Our limited understanding of Himalayan terraces may be a consequence of low preservation potential due to erosional processes within a rapidly uplifting mountain range. Even a comprehensive assessment of terraces in an area may not provide a complete archive of depositional processes as terraces can be destroyed or modified. Alternatively, terraces may be present, but difficult to recognise in the field or to manually identify from aerial photographs or satellite imagery. An automatic method for identifying river floodplains and terraces has recently been developed (Clubb et al, 2017). Using this method, we identify terraces at a catchment scale for the first time within the Gandaki catchment of central western Nepal using the 12m TanDEM-X digital elevation model. We explore the spatial pattern of terraces along the long profiles of each major river within the catchment by calculating the total terrace area adjacent to the channel. We then attempt to link terrace preservation to tectonic drivers by analysing the relationship between terrace exposures and channel steepness, knickpoints and major structural boundaries along the river profile. Coupling an analysis of spatial patterns in terrace preservation with the shape of terrace profiles downstream compared with the modern channel allows for investigation into whether terrace preservation is controlled by long term tectonic forces or stochastic high magnitude flooding events. We find that terrace preservation within the Gandaki catchment is largely tectonically controlled, with terraces mostly preserved directly upstream of major tectonic structures such as the Main Frontal Thrust, the Main Boundary Thrust and within the Thakkhola-Mustang Graben. However, we link a pattern of preserved terraces directly south of the Main Central Thrust to the stochastic occurrence of high magnitude debris flow events. These highly elevated terraces decrease in downstream elevation compared with the modern channel, are unconstrained by downstream tectonic structures and source from the steep topography of the High Himalaya. Our work demonstrates the potential of automated terrace extraction techniques for understanding controls on sediment storage and dynamics across actively uplifting mountain ranges.
New age constraints on the Early Oligocene flora diversity and surface uplift of the southeastern margin of the Tibetan Plateau
Authors: Sun Xilin , Ding Wenna , Kuiper Klaudia , Tian Yuntao , Zhang Zengjie , Guo Rujun , Wijbrans Jan
Published: March 2024

Sedimentary basins in the southeastern margin of the Tibetan Plateau yield valuable geophysical and fossil records, documenting the paleoenvironmental and biodiversity changes in the Tibetan Plateau and the Hengduan Mountains. Constraining the age of sediment deposition is fundamental in understanding the evolutionary history of biodiversity, climate (e.g. development of the monsoon), and regional landscape changes. Many basins in this area were originally considered as Miocene basins based on regional lithostratigraphic comparison and biostratigraphy, although this has been challenged recently for several basins (e.g. Lühe, Markma, Jianchuan, Wenshan and Relu basins). Here we report a new age for the Mouding Basin, Central Yunnan, China. We arrive at an early Oligocene (33 ± 1 Ma) instead of a Miocene age for the fossil-bearing strata of this basin. U-Pb and 40Ar/39Ar dating of a volcanic ash layer from the Mouding Basin and one from the Lühe Basin in combination with zircon Hf isotope data suggest that the early Oligocene Laojiezi eruption is a likely source for ash layers in both basins. Fossilized dicot leaves were used to reconstruct the paleoclimate and the paleo-elevation of the Mouding Basin. This paleoclimate reconstruction indicates that the paleoclimate in the Mouding Basin closely resembles that of the Lühe Basin. The paleoclimate between the two floras is more similar than their respective modern climates. During the early Oligocene, the Mouding Basin experienced colder and more humid winters with a milder monsoonal climate compared to today. Despite this, the Mouding flora exhibits high floristic similarities with its contemporary Lühe and Markam floras, as well as with the modern vegetation in southwestern China, supporting that the modernization of the biodiversity in the SE margin of the Tibetan Plateau has initiated since the Paleogene.
Examining the spatial and temporal characteristics of hydrological drought in the largest basin of the Indian Peninsula
Authors: Ghosal Meghomala , Swarnkar Somil , Kundu Soumya
Published: March 2024

The intensified warming conditions have substantially impacted the occurrence, duration, and magnitude of severe hydroclimatic events worldwide. Consequently, economic conditions have experienced considerable influence in the past decades. Droughts, in particular, are complex catastrophic events, rendering them extremely unpredictable and hard to comprehend. It is a gradual and prolonged catastrophe marked by insufficient rainfall, leading to a scarcity of water. In addition, drought is often defined as a period of reduced rainfall resulting in water shortage. It is frequently assessed by examining combinations of many factors, such as precipitation, temperature, and soil moisture. Specifically, hydrological droughts are precisely characterized as prolonged periods when water levels in rivers and streams fall below a preset threshold value. Furthermore, frequent occurrences of hydrological drought pose a significant threat to freshwater resources. Thus, identifying the spatiotemporal characteristics of preceding droughts is crucial for the effective management of future water resources. Hence, this work focuses on analyzing the spatial and temporal patterns of hydrological drought events that occurred between 1964 and 2020 in the Godavari River Basin (GRB) located in the peninsular area of India. The GRB has an area of roughly 0.3 million square kilometers, making it the biggest river basin in peninsular India. Over the last several decades, the GRB has been confronted with severe drought conditions. Therefore, the present analysis utilized the dataset of daily observed water discharge data collected at 21 gauging stations by the Central Water Commission (CWC). In addition to eliminating minor droughts and aggregating droughts, the 'Variable Threshold' concept is utilized to derive hydrological drought characteristics at various stations, including intensity, deficit, and duration. According to our findings, significant spatial and temporal variation is evident in the regional hydrological drought characteristics of the GRB. Additionally, flash drought conditions have been reported at multiple stations. The results derived from this research contribute to the advancement of knowledge regarding the spatiotemporal patterns of droughts in the GRB.
Data for Understanding Community Flood Resilience
Authors: Chapagain Dipesh , Hochrainer-Stigler Stefan , Velev Stefan , Rubenstein Naomi , Keating Adriana , Hyun Jung-Hee , Mechler Reinhard
Published: March 2024

Enhancing resilience and reducing disaster risks are major societal challenges. Hence, it is crucial to understand resilience at the community level, as the impact of disasters and the potential for resilient development are particularly high at this scale. Key for understanding community resilience is the systematic assessment and measurement of resilience. The Flood Resilience Measurement for Communities (FRMC) framework offers a holistic approach to measure community flood resilience, facilitating the identification of interventions to strengthen resilience. This paper discusses empirically measured flood resilience data from 292 communities across 20 developing countries worldwide using the FRMC framework. Furthermore, it will provide examples of how such data can serve as a valuable resource for understanding the typology of community flood resilience across the world, and for identifying targeted interventions that address the unique challenges faced by different communities.
The MANABAS COAST project: Mainstreaming Nature-Based Solutions through coastal systems
Authors: de Vries Jurre , van Well Lisa , Sorensen Per , Kannekens Frances , Lodder Quirijn , Gersonius Berry , Boersema Matthijs
Published: March 2024

The North Sea region is facing the effects of climate change, including sea level rise, more severe storm surges and heat stress along our coasts. These effects can have a detrimental impact on coastal zones. If coastal management does not adapt to these changes, the coasts will become more vulnerable to flooding, erosion, biodiversity loss, and higher life cycle cost of traditional grey infrastructure. Therefore, adaptive coastal management is needed to protect the coast and deliver a sustainable future for the North Sea region. 
 
Aware of these growing pressures, coastal authorities from Sweden, Denmark, Germany, the Netherlands, Belgium and France are working together in the MANABAS COAST project to set the stage for widescale implementation of Nature Based Solutions (NBS) along the North Sea coast. The project is co-funded by the Interreg North Sea (NS) Programme and builds upon the previous Interreg NS projects Building with Nature and C5A. MANABAS COAST runs from 2022 to 2027.
 
MANABAS Coast focuses on breaking the pilot paradox by learning in different natural and socio-economic settings how we can implement NBS on different scales. We do this by implementing fifteen study pilots covering the coasts from the north of France up to southern Sweden. We use the six enablers identified by the public-private partnership EcoShape, which are crucial in mainstreaming NBS.  In our pilots, we evaluate and learn from these enablers with respect to mainstreaming NBS. The enablers are: 1 Technology and system knowledge, 2 Multi-stakeholder approach, 3 Adaptive management, maintenance and monitoring, 4 Institutional embedding, 5 Business case, and 6 Capacity building. This learning-by-doing approach provides us with a wealth of information which is used to develop an accessible and evidence-based framework for widescale implementation of NBS in coastal areas to protect biodiversity and society.
 
This presentation highlights our wide range of NBS study pilots and focuses on the differences in perceptions of the benefits and limitations across countries and regions. Each setting is unique in the readiness of the NBS technology and especially in the societal acceptance and institutional embedding of NBS. The site-specific contexts highlight different barriers and enablers for mainstreaming when viewing NBS pilot through the lens of the enablers. However, also several common themes become apparent. An example of such a theme is the interplay, and sometimes friction, between flood risk management and nature legislation.
Arctic springtime temperature and energy flux interannual variability are driven by 1 to 2 week frequency atmospheric events
Authors: Grysko Raleigh , Kim Jin-Soo , Schaepman-Strub Gabriela
Published: March 2024

The Arctic is experiencing amplified climate warming, decreasing sea ice extent, increasingly earlier springtime snowmelt, and a related increase in fire activity. The transition from cold to warm season in the Arctic strongly varies between years, but our understanding of temperature and surface energy budget changes over the springtime is limited. Here we investigate intraseasonal variability of Arctic springtime temperature extremes, the subsequent surface energy budget component response, and their interannual trends over 40 years (1981 – 2020) across the terrestrial Arctic (above 60° N) using ERA5-Land reanalysis data. We found the central and western Siberian regions to have the highest interannual variability in spring temperature anomaly among all Arctic regions during the 40-year period. Also in this region, we discovered the strength increased for heat extremes and decreased for cold extremes when comparing the first and the last 20 years of our study. Peaks in composited extreme temperature and surface energy budget anomalies were observed to occur concurrently, indicating temperature extremes are not driven by surface energy budget components. Lastly, by utilizing power spectrum analyses, we identified the primary driver of temperature anomaly interannual variability to be operating at a 1 to 2 week frequency. Based on our findings and observations in the recent literature, we hypothesize that the observed interannual variability in springtime temperature can be attributed to increased Arctic sea ice decline and an increase in the frequency and strength of associated atmospheric blocking events.
Effect of salinity on the abundance and distributions of Glycerol dialkyl glycerol tetraethers and 3-hydroxy fatty acids in terrestrial aquatic interface 
Authors: Kumar Vishal , Samantaray Santrupta , Sanyal Prasanta
Published: March 2024

Lipid biomarkers like Glycerol dialkyl glycerol tetraethers (GDGTs) and 3-hydroxy fatty acids (3-OH FAs) are valuable proxies used in paleoenvironmental studies. Although temperature and pH are essential parameters responsible for the variations in abundance and isomeric distribution patterns of GDGTs and 3-OH FAs, the effect of other factors is still not fully investigated, especially in salt-stress environments. While the impact of salinity on GDGTs is not fully understood, the effects on 3-OH FAs have not been previously explored. In this study, we investigated the sensitivity of both proxies and their response to environmental factors (temperature, pH, and salinity) in the Pulicat Lagoon, India, where salinity ranges from 60 ppt (hypersaline) to 32 ppt (brackish). Interestingly, conventional temperature indicators like the Methylation index of brGDGTs (MBT) and the Isomers ratio of brGDGTs (IR) correlated more with water salinity than with lake water temperature. In contrast, the RAN15 index in 3-OH FA showed a stronger temperature response than the RAN17 index. pH-related indices like the Cyclization ratio of branched tetraethers (CBT and CBT’) in GDGTs correlated negatively with sediment pH, while certain 3-OH FA indices such as ratio of all Iso and Ante-isomers to all Normal isomers (RIAN), ratio of all Iso-isomers to all Normal isomers (RIN), Branched index and Branching ratio correlated significantly with salinity. This disparity in response patterns between GDGTs and 3-OH FAs could be due to bacterial diversity and their salinity adaptation mechanisms. Salinity may alter bacterial membrane lipid structure, affecting branching or methylation in fatty acids to maintain cytoplasmic lipid fluidity and permeability. This adaptation is reflected in the temperature indices of GDGTs and pH indices of 3-OH FAs. We propose these proxies as potential salinity indicators, but caution is advised when using them for paleo-temperature and pH reconstruction in salt-stress environments.
 
Ambient Seismic noise-based Groundwater Monitoring: A Comprehensive Overview
Authors: Voisin Christophe , Garambois Stéphane , Gaubert-Bastide Thomas , Bordes Clarisse , Brito Daniel , Danquigny Charles , Massonnat Gérard , Lesueur Jean-Louis
Published: March 2024

Groundwater plays a critical role in sustaining ecosystems, agriculture, and human activities. Effective groundwater monitoring is essential for assessing aquifer health and managing water resources. This abstract provides a comprehensive overview of the emerging approach of utilizing ambient seismic noise for groundwater monitoring.
Ambient seismic noise, generated by natural and anthropogenic sources, has proven to be a valuable source of information for characterizing subsurface properties. This technique leverages the continuous and ubiquitous nature of seismic signals, providing a non-invasive and cost-effective means to monitor changes in groundwater levels and properties.
The methodology involves analyzing the variations in seismic noise patterns collected by a network of sensors deployed in proximity to the groundwater monitoring area. Changes in subsurface conditions, such as water table fluctuations or alterations in aquifer properties, induce measurable variations in seismic velocity and attenuation. By employing advanced signal processing techniques, researchers can extract valuable information about groundwater dynamics, recharge rates, and aquifer characteristics.
This abstract discusses key advantages of ambient seismic noise-based groundwater monitoring, including its ability to offer real-time data, continuous monitoring capabilities, and the potential for early detection of groundwater-related anomalies. Furthermore, the method's non-intrusive nature minimizes environmental impact and reduces the need for extensive drilling or intrusive measurements.
The challenges and limitations associated with this approach are also addressed, highlighting the need for further research to optimize data processing techniques, improve resolution, and enhance the method's applicability across diverse geological settings.
As the field of ambient seismic noise-based groundwater monitoring continues to evolve, this abstract underscores its potential as a valuable tool for enhancing our understanding of subsurface hydrological processes and advancing sustainable water resource management practices. The integration of ambient seismic noise into existing groundwater monitoring frameworks holds promise for revolutionizing how we assess and manage this vital component of the Earth's water cycle.
Water Vapor Budget Evaluation in Atmospheric Rivers Associated with Heavy Rainfall Events in the Southern Andes
Authors: Mudiar Dipjyoti , Rondanelli Roberto , A. Valenzuela Raul , D Garreaud René
Published: March 2024

Atmospheric rivers (AR) are transient, narrow, and elongated channels in the atmosphere through which poleward transport of water vapor takes place from the tropics. In central-southern Chile, ARs contribute about 50% of the annual precipitation. ARs also produce extreme rainfall events, which may occasionally result in compound events. The water vapor associated with an AR originates from evaporation from the ocean, local mass convergence, and direct poleward moisture transport from the tropics. One way to understand the sources of moisture along an AR is the analysis of column integrated water vapor (IWV) budget, as suggested in previous studies. In this study, we have evaluated moisture and heat budgets for 50 land-falling zonal AR between 1980 and 2023 that caused heavy to extreme rainfall in the Southern Andes. The ARs are identified based on a global AR detection algorithm included in the ARTMIP (Guan and Waliser, 2018). The budget terms for all the ARs are calculated using the ERA5 reanalysis datasets along a backward trajectory obtained from the HYSPLIT trajectory model at 6-hour intervals. The budget analysis suggests that while mass convergence dominates the vertically integrated water vapor transport (IVT), the advection of moisture is significantly enhanced near the coast and the landfalling region. It is shown that the enhanced IWV bands from the tropics and subtropics can merge along the AR channel to enhance moisture convergence. The convergence of the tropical IWV bands occurs in a region between a stationary subtropical anticyclone and a midlatitude trough which migrate towards the continent. This region of convergence further acts as a source of moisture and heat that ends up being transported to the subtropical Andes.  Over the land-falling regions, the IVT convergence and precipitation rate remain tightly associated (R2 =0.79), suggesting stronger IVT convergence (intense AR) may produce heavy rainfall events in Chile. It is also shown that synergistic action between moisture and heat enhances IVT convergence along an AR. An enhanced IVT convergence along AR channels produces intense AR. These intense AR can produce extreme rainfall events when they encounter coastal mountains and the Andes through the orographic precipitation enhancement mechanisms.
Soft and hard clustering of geoelectrical data for detection of leachate accumulation zones in municipal solid waste landfills
Authors: Melegari Davide , De Donno Giorgio , Piegari Ester
Published: March 2024

Uncontrolled accumulation of leachate in municipal solid waste landfills can represent a potential hazard in municipal solid waste (MSW) landfills, since it is a liquid with a high pollutant content that can contaminate aquifers. Electrical resistivity tomography (ERT) in combination with induced polarization (IP) method can be highly effective for detecting areas with presence of leachate, given the electrical properties of leachate (highly conductive and chargeable) compared to the unsaturated waste mass and the surroundings areas (covering and bottom layers). However, the geoelectrical reconstruction expressed in terms of individual geophysical quantities (resistivity and chargeability), leaves room for ambiguities in such a complex scenario that involves several resistive-conductive transitions.
In this study, we use a machine learning-based approach to integrate ERT and IP tomographic data for improving the accuracy in the detection of leachate accumulation zones. After geophysical data inversion, we perform the cluster analyses on the inverted models (resistivity, chargeability and normalized chargeability) by using both hard (K-Means) and soft (Fuzzy C-Means) clustering algorithms. We finally achieve a comprehensive integrated model of the landfill with also an assessment of the reliability of the reconstruction through computation of validation indices and, only for soft clustering, of the uncertainty by means of the membership function.
We apply the proposed procedure to synthetic examples and to a real-world investigation of a MSW landfill located in Central Italy. We firstly carry out synthetic examples to test the efficiency of hard and soft clustering in imaging leachate accumulation zones, by developing multi-layer models simulating a typical MSW geometry. Then, the results of the application of the proposed approach to the real-world example show an improvement of the accuracy in the detection of leachate accumulation zones as well as an assessment of the uncertainty both in the lateral and vertical directions. Therefore, this approach can be an effective and fast tool in real cases where leachate pollution detection is required at short time intervals for waste management.
Use of Ground-Based Interferometric Radars (GB-InRa) as remote, real-time landslide early-warning systems
Authors: Pettinari Alessandro
Published: March 2024

Ageing of existing infrastructure, man-made intervention and increasing effects of climate change are expected to intensify the pressure on Critical Infrastructure, including natural environment, thus requiring more effective and accurate monitoring campaigns aimed to provide a continuous real-time alerting in case of potential failures. To date, most monitoring projects are still based on the deployment of several contact sensors, each providing a single point measurement and requiring access to the asset to be monitored with non-negligible impacts on safety. Ground-based Interferometric Radars (GB-InRa) provide remote sensing capability allowing to monitor wide areas  in their entirety and without the need to place any contact sensors; thanks to its intrinsic characteristics, Radar Interferometry can be a fundamental asset to guarantee a 24/7 monitoring of critical infrastructures, granting sub-millimetric displacement accuracy, identification of failure precursors and automatic alarming generation capability. This article is aimed to provide a series of case studies in which InRa technology proved its effectiveness both before and after slope failure.
Using Deep Learning to simulate the urban heat island over Paris
Authors: Johannsen Frederico , Soares Pedro M. M. , Langendijk Gaby S.
Published: March 2024

Half of the global population lives in cities and this percentage is expected to increase throughout the 21st century. Therefore, we must learn how climate and climate change affect cities to understand how to properly devise adaptation and mitigation policies. Spatial resolutions of physically-based models are often either too coarse or too expensive to run to obtain high-resolution and high-quality simulations of the urban environment. Deep Learning is a promising computationally efficient technology that has recently been successfully implemented in various applications in Earth Sciences. Here, we present an application of Convolutional Neural Networks (CNNs) to simulate 2-meter temperature (T2m) and Land Surface Temperature (LST) over the city of Paris for the 2004-2022 period. Subsequently, we analyse the CNNs quality in simulating the urban heat island (UHI) over Paris. DL presents substantial improvements over ERA5 (the benchmark of this study) in simulating LST, T2m, and both surface and air UHIs. This study supports the potential of DL as a technology to help improve the simulation of urban climate (namely, temperature extremes).
 
Acknowledgements: This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020-IDL. Frederico Johannsen was supported by FCT with the doctoral grant UI/BD/151498/2021. Pedro M. M. Soares was supported by a grant through the project “ELABORAÇÃO DA ESTRATÉGIA REGIONAL DE ADAPTAÇÃO ÀS ALTERAÇÕES CLIMÁTICAS DO ALENTEJO – ERAACA”.
Importance of discrete fracture network structural properties on incipient karst genesis
Authors: Aliouache Mohammed , Jourde Hervé
Published: March 2024

Even though speleology allows to map the geometry of different cave systems, the majority of these systems remain unreachable underground. It has been observed that karst cavities often develop along features such as fractures and bedding planes in limestone reservoirs. In this study, we use a reactive transport model that simulate dissolution processes in fracture networks to investigate the effect of discrete fracture network structural properties on incipient karst network under different boundary conditions (constant head versus constant rate condition, diffuse versus concentrated recharge condition). We focused on single fracture (with/without cementation) and ladder like fracture networks which are common on limestone. Results showed that fracture networks properties have a considerable effect on the final geometry of dissolved wormholes. For instance, the presence of cement on a single fracture results on more tortuous and curvilinear wormholes. Moreover, anisotropy of the fracture network leads into the formation of anisotropic incipient karst geometries. Also, the type of fracture intersections plays a role in the final incipient karst conduit geometry and topology. Finally, the type of BC remains a major factor that governs dissolution processes.
A COMPARATIVE STUDY OF SATELLITE IMAGE RESOLUTIONS FOR DETECTING PEST DAMAGE IN SUNFLOWER FIELDS
Authors: Ercan B. S. , Maden B. , Kara S. , Sunar F. , Aysal T. , Ozkaya N. , Saglam O.
Published: March 2024

Abstract. The diversity of sensors in remote sensing allows for faster and easier detection of changes and issues across different scales, in contrast to conventional ground-based systems. One of the most important distinguishing features among these sensors is their varying resolutions, contributing to the versatility of remote sensing technologies across diverse environmental applications. In this study, the effectiveness of PlanetScope and Sentinel-2 satellite images with different image resolutions in detecting damage caused by a harmful insect (beet webworm moth - Loxostege sticticalis) in sunflower fields in Lüleburgaz district of Kırklareli in the Trace region was evaluated. Damage rates in sunflower fields were analyzed using various spectral indices (Enhanced Vegetation Index and Chlorophyll Index Green) and spectral transformation (Tasseled Cap Greenness) in conjunction with in situ data. Based on the spectral analysis, the satellite image dated 26 July, which showed the most severe damage, was used in the damage assessment analysis. The damaged areas were compared by classifying both satellite images with the Random Forest algorithm. According to the results of the classification accuracy assessment, PlanetScope satellite imagery showed the highest accuracy, with 90% overall accuracy and 84% Kappa statistics, making it a more suitable sensor choice for agricultural applications.
”CITYJSON2RDF” A CONVERTER FOR PRODUCING 3D CITY KNOWLEDGE GRAPHS
Authors: Akın A. T. , Cömert Ç.
Published: March 2024

Abstract. The increasing prevalence of 3D city models (3DCMs) in various applications, such as mixed reality and navigation, highlights the need for efficient data exchange. CityGML serves as a standard model for this purpose, encompassing geometric and semantic information in 3DCM data. To enhance interoperability, a compatible transfer mechanism is essential. This study introduces a conversion tool that transforms CityGML data into RDF, a knowledge graph (KG) format. Utilizing semantic web technologies, this conversion ensures the data’s seamless integration across applications. The RDF model facilitates linking to open ontologies, promoting data circulation without loss. The tool employs CityJSON encoding for its mappable JSON structure, enabling straight-forward conversion to RDF using Python components. While existing XML to RDF tools exist, this tool distinguishes itself by addressing accessibility and user intervention challenges. Linking is established by matching subject classes with relevant ontology definitions, a process dependent on developers’ understanding of the CityGML data model. The tool, accessible through URL-2, is still in development, offering a promising solution for achieving effective 3DCM data interoperability.
SUITABILITY ANALYSIS FOR URBAN BICYCLE TOURISM OF ILOILO CITY USING GIS
Authors: Fargas Jr. D. C. , Gregorio A. D. , Endaya M. D.
Published: March 2024

Abstract. Urban bicycle tourism refers to the recreational and enjoyable use of bicycles in navigating urban cities like Iloilo City. The city has already established an 11-kilometer cycling infrastructure within its boundaries. It is imperative to maximize its use to combat the negative impacts of rapid urbanization and promote safe and sustainable transportation. This paper shows the use of Geographic Information Systems (GIS) to assess the bike lanes network's suitability and accessibility for building a bike-sharing system that also caters to drop-off and ride-on points at each station. Deletion of duplicates and cleaning of road network data and tourist attraction point data was performed prior to any analysis. Service areas were generated to determine the number of tourist attractions catered by each potential bike share station – defined by main road intersections. Point distance analysis was performed to reduce the number of road intersections to just thirty-nine (39). Bicycle route maps and itineraries were produced to visualize the optimal routes tourists can use for exploring the city based on preferred sites category. From here, Route Analysis was conducted to create four (4) itineraries based on tourism categories: Top 10 Most Rated Tourist Attractions, Food and Dining, Museums and Churches, and Shopping and Leisure. The output maps and itineraries can be presented to the City Tourism Office of Iloilo City as a decision support tool for its programs and policies regarding bike lanes.
RESEARCH ON CONSTRUCTION OF NATURAL RESOURCES THREE-DIMENSIONAL SPATIO-TEMPORAL DATABASE SYSTEM
Authors: Gao Y. , Liu J. , Liu J. , Zhang Y. , Che J. , Zhai Z. , Zhao B. , Li H.
Published: March 2024

Abstract. The construction of an efficient and practical database management system based on large-scale three-dimensional spatiotemporal databases presents a novel challenge in the field of natural resource information technology. This research, with the objective of supporting the contemporary era of unified natural resource management and building upon the foundation of a national-level three-dimensional spatiotemporal natural resource database, proposes a comprehensive framework for a distributed architecture database management system. Subsequently, the research outlines the system's components, encompassing a service publishing system, online application system, professional management system, and operational supervision system, and designs core functionalities for the integrated management of natural resource elements within a three-dimensional spatiotemporal context. Building upon this framework, the research utilizes cloud-based database software and hardware environments, along with distributed technology pathways, to establish a national-level three-dimensional spatiotemporal database management system for natural resources. Furthermore, it conducts typical applications in the field of natural resources. The results demonstrate that the database management system effectively achieves the integration, representation, and analytical application of large-scale natural resource survey and monitoring data within a three-dimensional spatiotemporal context. It plays a crucial supportive role in the development of a national-level three-dimensional spatiotemporal natural resource database and holds valuable insights for the construction of provincial-level database management systems and other related systems in various domains.
CROP HEIGHT ESTIMATION OF WHEAT USING SENTINEL-1 SATELLITE IMAGERY: PRELIMINARY RESULTS
Authors: Narin O. G. , Bayik C. , Sekertekin A. , Madenoglu S. , Pinar M. O. , Abdikan S. , Balik Sanli F.
Published: March 2024

Abstract. Wheat is one of the primary crop productions for half of the global production. It is the most exported cereal, which reached up to 40% according to the records of the Food and Agriculture Organization of the United Nations (FAO) in 2020. The relationship between the crop parameters and remote sensing tools has become essential for the temporal monitoring and estimations of crop growth. In this study, we investigate the relationship between crop height and Synthetic Aperture Radar (SAR) backscatter. Plant height was collected twice, in the early (13 April 2023) and late stages (21 June 2023) of wheat, for a total of 70 samples. Sentinel-1 SAR data was also attempted to be synchronized with ground measurements. For this purpose, 8 images were obtained, 4 in both ascending (8 and 15 April, 16 and 26 June) and descending (9 and 14 April, 20 and 25 June) directions. The basic steps of image pre-processing, calibration, filtering, and topography correction, were applied to each image. By evaluating the correlation coefficients between plant height and images, data with low correlations were excluded and the first three data, dated 8 April and 19 June, were used for plant height estimation. For prediction purposes, Linear Regression (LR) and Random Forest (RF) methods were evaluated with three different training data sets. The highest correlation and minimum error value were achieved by VH polarization with random forest (r = 0.868, RMSE = 5.377 cm) in the early stage and LR (r = 0.616, RMSE = 14.451 cm) in the late stage. In general, higher training (80%) and lower test data (20%) produced better results.
GIS-BASED REAL-TIME GAS LEAK DETECTION SYSTEM AT BASKENT NATURAL GAS DISTRIBUTION COMPANY
Authors: Ozen S. , Seker D. Z.
Published: March 2024

Abstract. Natural gas has been used for heating and production purposes in Turkey since 1987, and every passing year, the natural gas pipeline network continues to expand with investments. The distribution of natural gas within urban areas is achieved through a complex network structure formed by the joining of Polyethylene (PE) and Steel (ST) pipes using various connection elements. This complex structure is susceptible to deformation over time due to various reasons such as corrosion in steel lines, manufacturing errors in connection elements in PE lines, and other factors. As a result, natural gas leaks occasionally occur in these pipelines. The leakage control process in natural gas pipelines is carried out using specially designed equipment and vehicles equipped for this purpose. However, while these systems may be sufficient for collecting the necessary data for leak detection, the complex structure of the distribution network and the necessary data for leak detection, the complex structure of the distribution network and the necessity of performing this process with multiple teams bring about many challenges in terms of tracking, management, and reliability of the operation. At this point, the advancement of GIS (Geographic Information System), mobile, and communication technologies has made it possible to overcome these challenges. In this study, a real-time GIS-supported leak detection system developed using these technologies for Başkent Natural Gas Distrubition Inc. (Başkentgaz), which provides natural gas distribution services in Ankara, has been described. The study discusses the benefits brought by this system to the natural gas leak detection process.
MAPPING AND ASSESSMENT OF THE SPATIAL AND TEMPORAL DISTRIBUTION OF TURBIDITY IN LAKE BUHI FROM SENTINEL-2 IMAGES USING GEOGRAPHICALLY WEIGHTED REGRESSION AND NORMALIZED DIFFERENCE TURBIDITY INDEX
Authors: Amaro K. A. N. , Cortez L. M. G. , Medina J. M. , Blanco A. C.
Published: March 2024

Abstract. Water quality monitoring is vital in ensuring the suitability of Lake Buhi for aquaculture and recreational activities. However, with the limitations of traditional monitoring methods, the Bureau of Fisheries and Aquatic Resources (BFAR) cannot comprehensively characterize the spatial and temporal water quality patterns in the lake. With the development of remote sensing methods, this study aimed to describe the spatial and temporal variability of turbidity in Lake Buhi in 2020 using Geographically Weighted Regression (GWR) and the Normalized Difference Turbidity Index (NDTI) applied to Sentinel-2 images. GWR provided accurate estimates of turbidity, with the results achieving an R2 value of 0.98 for the February image and 0.93 for October. GWR-derived turbidity was 2 used as an alternative to in situ data and then regressed with NDTI values to acquire turbidity models for dry and wet seasons. Upon validation, the best turbidity model for the dry (R2 = 0.59, RMSE = 0.60, MAE = 0.15) and wet (R2 = 0.49, RMSE = 1.22, MAE = 0.25) seasons produced acceptable results, hence, used to assess the spatial and temporal variability of turbidity in the lake throughout 2020. The analysis revealed that Lake Buhi is more turbid during the dry season than the wet season. Turbidity during the dry season is governed by its natural water flow, while it is heavily influenced by precipitation during the wet season.
MONITORING FOREST DEGRADATION OVER FOUR DECADES USING REMOTE SENSING AND MACHINE LEARNING CLASSIFICATION ALGORITHMS IN BOUSKOURA, MOROCCO
Authors: Simou M. R. , Houran N. , Benayad M. , Maanan M. , Loulad S. , Rhinane H.
Published: March 2024

Abstract. A large portion of Morocco’s forest ecosystem is being damaged by human activity and climate change, which makes it crucial for the country to monitor forest dynamics and develop measures to counter these effects. The objective of this study was to determine how the forest cover has changed over the past four decades in Bouskoura forest, Morocco. Based on Remote Sensing (RS), Geographic Information System (GIS) and machine learning algorithms. Throughout the process, Spatial data such as Landsat 5 TM, Sentinel-2B, and spectral indices, including NDVI, NDWI, NDBI, and MSAVI2 were used to train/validate Random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers. By comparing the performance of the three classifiers for all four periods, the RF method was the most effective with an overall accuracy of 0.99 and kappa coefficient of 0.99 for 1991, 2001 and 2011, and an overall accuracy of 0.99 and kappa coefficient of 0.98 for 2021 . Therefore, the RF was selected as a method of examining time variations. The results indicated that forests covered an area of 20,41 km2 in 1991 which has decreased to 18,96 km2 in 2021, a loss of 1,45 km2 (7.10%) in four decades. The highest forest loss was 2,69 km2 during 1991–2001, 2,12 km2 during 2001–2011, 1,40 km2 during 2011–2021. And the highest forest gain was found to be 3,75 km2 during 2011–2021, 0,61 km2 during 2001–2011, 0,43 km2 during 1991-2001. Recent declines in forest degradation attest to the benefits of initiatives to conserve the environment taken by the country.
3D CHANGE DETECTION FOR SEMI-AUTOMATIC UPDATE OF BUILDINGS IN 3D CITY MODELS
Authors: Tamort A. , Kharroubi A. , Hajji R. , Billen R.
Published: March 2024

Abstract. Automatic update of 3D city models has become a crucial operation in the context of urban digital twins. It commonly relies on a reconstruction from ALS point clouds. However, frequent reconstructions due to a dynamic urban environment are resource-intensive and lack change information about the scene. In this paper, we present a novel framework which aims to combine an instance change detection approach based on a distance-computing algorithm, geometric features and thresholding with a building reconstruction algorithm to ensure an efficient geometric, semantic and thematic (change labeling) update of an existing 3D city model. This approach comprises three stages spanning from data preparation to the integration of change results in the updated model. First, we prepare our input data. Next, we assess the changes between the two epochs. This process involves two stages. New and lost buildings are extracted in the first, and the changed and unchanged in the other. The results of the change detection are evaluated using standard evaluation metrics. The evaluation results are encouraging considering the various sources of errors. Finally, unchanged buildings are kept in the model, while the changed and new ones are reconstructed using Geoflow3D. The final model is semantically augmented using a change attribute. Since the 3D city model undergoes an update rather than complete reconstruction, the tracking of both geometric and semantic changes of some buildings can be made possible through a versioning system. The change information can be leveraged in multiple applications like 3D cadastre, urban inventory, urban planning…