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We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre-existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant-soil dynamics for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth system models.

Mesoscale-to-microscale coupling is an important tool for conducting turbulence-resolving multiscale simulations of realistic atmospheric flows, which are crucial for applications ranging from wind energy to wildfire spread studies. Different techniques are used to facilitate the development of realistic turbulence in the large-eddy simulation (LES) domain while minimizing computational cost. Here, we explore the impact of a simple and computationally efficient Stochastic Cell Perturbation method using momentum perturbation (SCPM-M) to accelerate turbulence generation in boundary-coupled LES simulations using the Weather Research and Forecasting model. We simulate a convective boundary layer (CBL) to characterize the production and dissipation of turbulent kinetic energy (TKE) and the variation of TKE budget terms. Furthermore, we evaluate the impact of applying momentum perturbations of three magnitudes below, up to, and above the CBL on the TKE budget terms. Momentum perturbations greatly reduce the fetch associated with turbulence generation. When applied to half the vertical extent of the boundary layer, momentum perturbations produce an adequate amount of turbulence. However, when applied above the CBL, additional structures are generated at the top of the CBL, near the inversion layer. The magnitudes of the TKE budgets produced by SCPM-M when applied at varying heights and with different perturbation amplitudes are always higher near the surface and inversion layer than those produced by No-SCPM, as are their contributions to the TKE. This study provides a better understanding of how SCPM-M reduces computational costs and how different budget terms contribute to TKE in a boundary-coupled LES simulation.

Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal resource settings. We adapted the PFA approach to geothermal resource exploration by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building a code-based ‘processing model’ to process results. A geographic information system was used to compile a range of different data types, which we refer to as elements (e.g., faults, vents, heat flow, etc.), with distinct characteristics and measures of confidence. Discontinuous discrete data (points, lines, or polygons) for each element were transformed into continuous interpretive 2D grid surfaces called evidence layers. Because different data types have varying uncertainties, most evidence layers have an accompanying confidence layer which reflects spatial variations in these uncertainties. Risk layers, as defined here, are the product of evidence and confidence layers, and are the building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal, using a weighted sum for permeability and heat, but a different approach with seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map, using a modified weighted sum, for results that reveal favorable areas for geothermal exploration. Additional maps are also presented that do not mix contributions from evidence and confidence (to allow an isolated view of evidence and confidence), as well as maps that calculate favorability using the product of components instead of a weighted sum (to highlight where all components are present). Our approach helped to identify areas of high geothermal favorability in the western and central Snake River Plain during the first phase of study and helped identify more precise local drilling targets during the second phase of work. By identifying favorable areas, this methodology can help to reduce uncertainty in geothermal energy exploration and development.

Global trends of ocean warming, deoxygenation, and acidification are not easily extrapolated to coastal environments. Local factors, including intricate hydrodynamics, high primary productivity, freshwater inputs, and pollution, can exacerbate or attenuate global trends and produce complex mosaics of physiologically stressful or favorable conditions for organisms. In the California Current System (CCS), coastal oceanographic monitoring programs document some of this complexity; however, data fragmentation and limited data availability constrain our understanding of when and where intersecting stressful temperatures, carbonate system conditions, and reduced oxygen availability manifest. Here, we undertake a large data synthesis to compile, format, and quality-control publicly available oceanographic data from the US West Coast to create an accessible database for coastal CCS climate risk mapping, available from the National Centers for Environmental Information (accession 0277984) at 10.25921/2vve-fh39 (Kennedy et al., 2023). With this synthesis, we combine publicly available observations and data contributed by the author team from synoptic oceanographic cruises, autonomous sensors, and shore samples with relevance to coastal ocean acidification and hypoxia (OAH) risk. This large-scale compilation includes 13.7 million observations from 66 sources and spans 1949 to 2020. Here, we discuss the quality and composition of the synthesized dataset, the spatial and temporal distribution of available data, and examples of potential analyses. This dataset will provide a valuable tool for scientists supporting policy- and management-relevant investigations including assessing regional and local climate risk, evaluating the efficacy and completeness of CCS monitoring efforts, and elucidating spatiotemporal scales of coastal oceanographic variability.

In successful geoinformatics education, students' active role in the learning process, e.g. through applying self-assessment, show an increasing interest but the evidence of benefits and challenges of self-assessment are sporadic. In this article, we examine the usefulness of an online self-assessment tool developed for geoinformatics education. We gathered data in two Finnish universities on five courses (n = 11–73 students/course) between 2019 and 2021. We examined 1) how the students' self-assessed knowledge and understanding in geoinformatics subject topics changed during a course, 2) how the competencies at the end of a course changed between the years in different courses, and 3) what was the perceived usefulness of the self-assessment approach among the students. The results indicate support for the implementation of self-assessment, both as a formative and summative assessment. However, it is crucial to ensure that the students understand the contents of the self-assessment subject topics. To increase students' motivation to take a self-assessment, it is crucial that the teacher actively highlights how it supports their studying and learning. As the teachers of the examined courses, we discuss the benefits and challenges of the self-assessment approach and the applied tool for the future development of geoinformatics education. [ABSTRACT FROM AUTHOR]

The study presents the advantages of, and possible uses for, Orange software for data mining in combination with processing spatial data by ArcGIS Pro software in education. To present suitability of Orange software in education, the scientific method of Physics of Notation by D. Moody is used to evaluate the Orange software's visual vocabulary. All nine principles are applied in the presented evaluation. As a result, a high level of effective cognition of the Orange visual vocabulary is proven by this method. Namely, the semantic transparency of visual vocabulary, thanks the explicit inner icons, is semantically immediate. Also, principle of dual coding is used properly by automatic text labels of graphical symbols with the opportunity to rename labels. Renaming is also a way to ensure the partial overloading of symbols found by the first principle of semiotic clarity. The principle of cognitive interaction is partially fulfilled by automatically reorganizing connector lines between symbols to reduce the crossing of lines. A high level of effective cognition is beneficial for students. The evaluation of the visual notation of Orange software is presented to inform teachers and the geoinformatics community of the highly effective cognitive aspects of Orange software. The two practical lectures of processing in Orange and ArcGIS Pro software are shown to the teachers and students of geoinformatics community as examples of machine learning tasks. They are cluster analyses carried out with the density‐based spatial clustering of applications with noise method, first for the location of cafés in Olomouc town and the second example concerns finding similar European towns based on their land use arrangement, using the neural network and following hierarchical clustering. Both examples could provide inspiration for the geoinformatics community to adopt Orange data mining software. [ABSTRACT FROM AUTHOR]

Desertification processes in arid, semi-arid, and dry sub-humid conditions have been enhanced in recent decades. The geospatial database and associated satellite data can be effectively employed for regional planning to address desertification and land degradation. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model has been used to map environmentally sensitive areas due to desertification in the Satara and Sangli districts of Maharashtra, India. This was achieved by combining Landsat-8 multispectral data, Census data, soil data, and climatic variables like temperature, rainfall, and evapotranspiration. The algorithm of MEDALUS is the geometric mean of four indicators, namely soil quality index (SQI), climate quality index (CQI), vegetation quality index (VQI), and socio-economic quality index (SEQI). The findings indicated that the majority of the study area comes under the potential category of desertification (60.32%) followed by fragile (27.87%) and critical (11.81%). Areas with a high propensity for desertification were found over the low to very low climatic quality and moderate to high soil quality including lower socio-economic quality. The lower socio-economic quality is mainly due to high to very high population density (>100 people/km2), low to moderate illiteracy rate (<16%), and low to moderate work participation rate (<50%) that incentivize unsustainable land use practices. The study provides a valuable tool for understanding and managing natural resources. It offers a detailed analysis of the environmental sensitivity of the study area, taking into account various factors like land use, vegetation cover, slope, and soil erosion potential. The developed comprehensive map of the area helps in identifying the most sensitive regions and developing appropriate conservation strategies. The information obtained from the study can be utilized to develop and implement successful measures to prevent or alleviate desertification, which is crucial for sustaining the health of ecosystems and the welfare of local residents. [ABSTRACT FROM AUTHOR]

In the context of ecological civilization construction in China, interregional ancient trees are important natural and cultural resources, and it is vital that they are effectively protected and utilized according to their spatial distribution and regional conditions in order to provide a balance between local economic development and the ecological environment. This article analyzes the spatial heterogeneity of 26,842 ancient trees and explores the underlying natural and human factors by using geoinformatics–based techniques (i.e., the nearest neighbor index, kernel density, spatial autocorrelation, and the geographically weighted regression model) in Henan Province, Central China. The research findings conclude that: (1) The interregional ancient trees show positive spatial autocorrelation, and the aggregation effect in Western Henan is the most significant. (2) The growing environment and elevation exert a more obvious influence on the spatial pattern of interregional ancient trees. (3) The main influencing factors per region are different, for example, historical culture in Western Henan, water source and elevation in Southern Henan, and the growing environment in Eastern Henan, while ancient trees in Northern Henan have a weak relationship with each factor. This research facilitates political strategy making for sustainable development and the protection of interregional ancient trees. [ABSTRACT FROM AUTHOR]

The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low-resolution (roughly 110 km) and high-resolution (roughly 25 km) configurations—as established by the High Resolution Model Intercomparison Project protocol—to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The warm season water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes with resolution to the water cycle come from reductions in precipitation and evapotranspiration. The reduction in evapotranspiration reduces the biases across nearly all of the CONUS. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack.

The 1997 New Year's flood event was the most costly in California's history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events informs practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California-focused RRM grids, with horizontal resolution refinement of 14 km down to 3.5 km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are better represented at finer horizontal resolutions resulting in better estimates of storm total precipitation and storm duration snowpack changes. Traditional time-series and causal analysis frameworks are used to examine runoff sensitivities state-wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time-series shape, than forecast lead time, 2-to-4 days prior to the 1997 flood onset.

تعد دراسة الخصائص البيدوجغرافية وخاصة معدلات تعرية التربة بأحواض التصريف من الموضوعات المهمة التي يجب الأخذ بها في ظل اتجاه الدولة لزيادة مساحة الأراضي المستصلحة، كما أن دراسة معدلات التعرية وفق النماذج الرياضية يمكن من خلالها تقدير درجات الخطورة التي قد تتعرض لها تلك الأحواض، والتي تؤثر على استدامة الموارد والمحافظة البيئية عليها. واعتمدت الدراسة منهج التحليل الكمي، وتقنيات الجيوانفورماتيكس مع تطبيق الأسلوب الكمي بنموذج RUSLE لتقدير فقدان التربة بسبب التعرية المائية بحوض وادي سدر، والتي تتأثر بالعديد من العوامل والعمليات، مثل: الخصائص الجيولوجية، والتضاريسية، والمناخية، والهيدرولوجية. ويمثل عامل قابلية التربة للتعرية المائية من أهم العوامل في تقدير حجم التعرية لما له من تأثير يعكس خصائص التربة ومدى قابليتها لفقدان أجزاء منها من خلال نقل الرواسب والعمليات الهيدروليكية المرتبطة بها، حيث يرتبط بنسيج ونفاذية التربة ومحتواها من المادة العضوية. وبناءً عليه تم تصنيف المناطق المعرضة لتعرية التربة المحتملة سنويًا بحوض وادي سدر، وقد ساعد التحقق الميداني من التأكد من ذلك، ورصد بعض الدلائل والظاهرات البيدوجغرافية على عمليات التعرية المائية للتربة، مثل: رواسب القاع، والتشققات الطينية، والمصاطب، وقد توصلت الدراسة إلى وضع خطة شاملة ومتكاملة للتنمية المستدامة بحوض وادي سدر وخاصة التنمية الزراعية وتنمية محاجر الرمال والزلط، واقتراح العديد من الوسائل للحماية من أخطار التعرية المائية عن طريق إنشاء السدود والخزانات والبرابخ.AbstractThe study of pedogeographic characteristics, particularly soil erosion rates in drainage basins, is one of the significant topics that must be taken into account in light of the state’s tendency to increase the area of reclaimed lands. Besides, studying erosion rates according to mathematical models can be used to estimate the degrees of danger to which these basins may be exposed, which affects the sustainability of resources and their environmental preservation. The study adopts the quantitative analysis approach and geoinformatics techniques with the application of the quantitative method using the RUSLEmodel to estimate soil loss due to water erosion in Wadi Sidr Basin, which is affected by many factors and processes such as: geological, topographic, climatic, and hydrological characteristics. The factor of soil susceptibility to water erosion represents one of the most significant factors in estimating the extent of erosion due to its impact that reflects the characteristics of the soil and the extent of its susceptibility to losing parts through the transfer of sediments and the hydraulic processes associated with them, as it is linked to the texture and permeability of the soil as well as its organic content. Accordingly, the areas exposed to potential soil erosion annually in Wadi Sidr Basin were classified. Field investigation helped confirm this, and monitored some pedogeographic evidence and phenomena of water erosion processes such as: bottom sediments, mud cracks, and terraces. The study concludes with developing a comprehensive and integrated plan for sustainable development in Wadi Sidr Basin, particularly agricultural development and the development of sand and gravel quarries, and proposing several means to protection against the dangers of water erosion by constructing dams, reservoirs and culverts.

Sectoral real estate state management is one of the complex types of management. It includes individual objects management technologies, corporate management technologies considering the interests of the industry and state management technologies considering the state interests and inter-sectoral balance. Sectoral university real estate state management is even more complex. It uses not commercial effect as for commercial enterprises, but social effect and the criterion of resource provision of the country’s industries as the main efficiency criterion. Indirect indicators rather than direct ones consider this effect. The education system is subsidized, so the direct assessment of its efficiency is conditional. Higher education organizations real estate management is one of the important components of management. Real estate management is related to spatial economics and geoinformatics. The original development of a decision support system for educational organizations real estate management has been described. The system was developed under the assignment of the Ministry of Science and Higher Education of the Russian Federation. It uses geographic information systems as an additional component. It has been shown that it is necessary to apply geodata for real estate management. The basic principles of university real estate management with the help of this system have been described. Spatial information for sectoral real estate management should be combined with environmental, social, and economic information.

Landslides are a major natural hazard, threatening communities and infrastructures worldwide. Mitigation of these hazards relies on understanding their causes and triggering processes, which critically depend on subsurface characteristics and their variations over time. In this study, we present a novel approach combining passive seismic and low-cost inclinometer monitoring methods to improve the understanding of landslide activation mechanisms and their controls. We evaluate the efficiency of this approach on a shallow, slow-moving landslide directly endangering a road bridge, a bridge that is part of an important emergency response route. Results show the value of combining the two approaches for observing and monitoring landslide hazards. Passive seismic monitoring captures the variation in soil properties (rigidity and density) over time by sensing the variations of the seismic wave velocity (dV/V and its associated correlation coefficient). At the same time, novel low-cost inclinometers are monitoring subsurface deformation (from millimetric to pluricentimetric scale) and temperature. Seismic precursors detected at the bottom sensor a few hours prior to the reactivation are followed by the reactivation of the landslide toe, releasing stresses in the top part that lead to the reactivation of the whole landslide. This reactivation occurs during an episode of heavy rainfall following a 7-month drought. Meanwhile, temperature monitoring enables us to track water infiltration and to highlight its role in the landslide mechanisms. Overall, the combination of the two monitoring methods shows promise for quantifying the sliding mechanisms of landslide reactivations and for designing landslide early warning systems.

Frost is damaging to plants when air temperature drops below their tolerance threshold. The set of mechanisms used by cold-tolerant plants to withstand freezing is called “hardening” and typically take place in autumn to protect against winter damage. The recent incorporation of a hardening scheme in the demographic vegetation model FATES opens up the possibility to investigate frost mortality to vegetation. Previously, the hardening scheme was used to improve hydraulic processes in cold-tolerant plants. In this study, we expand upon the existing hardening scheme by implementing hardiness-dependent frost mortality into CLM5.0-FATES to study the impacts of frost on vegetation in temperate and boreal sites from 1950 to 2015. Our results show that the original freezing mortality approach of FATES, where each plant type had a fixed freezing tolerance threshold—an approach common to many other dynamic vegetation models, was restricted to predicting plant type distribution. The main results emerging from the new scheme are a high autumn and spring frost mortality, especially at colder sites, and increasing mid-winter frost mortality due to global warming, especially at warmer sites. We demonstrate that the new frost scheme is a major step forward in dynamically representing vegetation in ESMs by for the first time including a level of frost tolerance that is responding to the environment and includes some level of cost (implicitly) and benefit. By linking hardening and frost mortality in a land surface model, we open new ways to explore the impact of frost events in the context of global warming.

California has a diverse climate that ranges from desert regions with some of the hottest places in North America to coastal regions with mild climates and cold summers. The selection of the binder's performance grade (PG) in California is based on a climate map that was developed in 2005 using 1960 to 1990 data from the National Climate Data Center (NCDC) analyzed using the Enhanced Integrated Climate Model (EICM) and the Long-Term Pavement Performance (LTPPBind 2.0) software. Climate change is happening all around the world, and California is no exception. Based on observed pavement performance, there was a need to revise the PG map using updated climatic data from various sources. In this study, the PG binder map for California was updated to better predict pavement performance using the latest available weather data. Thirty years of climate data (1991-2019) were considered to update the current binder's PG map. The weather data were collected from two main sources: NCDC and Modern-Era Retrospective Analysis for Research and Applications (MERRA). It was found that pavement structure types and thermal coefficients have insignificant effects on the binder performance grade. Also, it is recommended that the binder high PG for the central valley of California be changed based on the results obtained from analysis using EICM and LTPPBind Online.

In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing.

Gábor Mezősi was born on July 18, 1952, in Szeged and spent most of his life there. He graduated as a teacher in Mathematics and Geography in 1975. With a German state scholarship, he gained professional experience and, observing their education system, reformed the Hungarian geographer training. In 1991, he won the Academy Award, and as head of department, he helped to introduce geoinformatics training. In 1993, he became Doctor of the Hungarian Academy of Sciences in geoinformatics and landscape geography. After the turn of the millennium, he played an important role as dean of the Faculty of Science of the University of Szeged. In 2021, he was awarded Professor Emeritus of the University of Szeged and in 2023 the Civil Section of the Middle Cross of the Order of Merit of Hungary. In addition to his scientific work, Gábor Mezősi did a lot to reform geographer training as well. The conversation with the Professor gives an insight into the most important stages and experiences of his life.

Mangroves are a crucial part of the coastal ecosystem; thus, precise and up-to-date monitoring is essential to guide regional policies and inform conservation strategies. This study investigates the capabilities of semi-automated remote sensing approaches within a Google Earth Engine framework for national-scale mangrove mapping in Thailand. Remote sensing data from 2018—10,000 data points acquired from Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM)—was used to train several machine learning models. The Gradient Tree Boost (GTB) proved to be the most reliable, with the least variation in validity (the lowest IQR) and the highest average Overall Accuracy of 96.75 ± 0.63 % compared to the others—96.64 ± 0.72 % for Random Forest (RF); 96.12 ± 0.80 %for Classification and Regression Trees (CART); and 95.43 ± 0.74 % for Support Vector Machines (SVM). Thus, the GTB was instrumental in mapping mangrove distribution with 10-m spatial resolution across Thailand from 2016 to 2022, the period in which the mangrove areas increased by 11 %, reflecting successful conservation efforts over the past decade. The developed framework establishes the foundation for semi-automated mangrove mapping that can be developed for other geographical contexts. [ABSTRACT FROM AUTHOR]

The Bátaapáti National Radioactive Waste Repository was created using mining methods in a granitoid formation. Changes in the receiving rock body are monitored by the seismo-acoustic measurement network that has been in operation since the cavity was designed. The present work builds on geoinformatics methods and presents automated codes written for a Geographical Information System, which filters out incorrect spatial positions from a large mass of seismoacoustic point sources. The incorrectly measured points show the greatest inaccuracy vertically. The most intense emission source points with appropriate positions can be detected in the areas between the side walls of the adjacent storage chambers and near the fracture system detected along the trench end of the chambers. Based on the distance between the point sources and the storage chambers, it was also possible to refer to the extent of the damaged/disturbed rock zone around the excavation, which means a 7-meter rock belt.

Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world's largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta. [ABSTRACT FROM AUTHOR]

Floods, as natural disasters, impose significant human and financial burdens, necessitating stringent mitigation measures. The recurrent annual incidence of floods precipitates considerable economic setbacks and tragic human casualties. In the realm of disaster management, flood susceptibility mapping has evolved into an indispensable instrument for preemptive intervention. In recent years, the amalgamation of machine learning (ML) methodologies and geographic information systems (GIS) has demonstrated remarkable promise in the realm of flood susceptibility mapping. Nonetheless, the inherent limitations of standalone ML models have constrained their predictive efficacy. Several shortcomings are evident in prior research. These include the failure to utilize contemporary ensemble approaches capable of enhancing performance and the limited exploration of diverse classifier combinations, which are instrumental in augmenting reliability. Simultaneously, there is an absence of current and up-to-date flood susceptibility maps on recent floods within the study area. Hence, this study endeavors to enhance the precision of flood susceptibility mapping, within the Haraz-Neka River basin across Mazandaran province, by harnessing an ensemble of ML models. The research methodology encompassed several pivotal phases. Initially, data about 240 flood sites were meticulously compiled. Subsequently, 70% of this dataset was allocated for training and cartographic elucidations, whereas the remaining 30%, selected at random, served to validate the resultant maps. The analytical framework incorporated a spectrum of influential parameters, encompassing Elevation, Slope, Aspect, Rainfall, land use, Vegetation Differentiation Index (NDVI), Soil Hydrology Groups, Proximity to the River, Distance from Landslides, Topographic Wetness Index (TWI), Stream Power Index (SPI), and Sediment Transport Index (STI) for spatial modeling. The results undeniably highlight the superior performance of the ensemble model compared to its individual counterparts. Validation exercises, leveraging historical flood data, prominently endorsed the AdaBoost algorithm integrated with the Decision Tree classifier as the most efficacious. Garnering an Area Under ROC curve surpassing 0.96, accompanied by an accuracy of 0.93%, a sensitivity of 0.95%, and a specificity of 0.92%, this amalgamation substantiates its prowess. The proposed framework stands poised to empower decision-makers in identifying vulnerable regions and devising efficacious flood risk mitigation strategies. [ABSTRACT FROM AUTHOR]

The different morphotectonic parameters have been analyzed for estimating tectono-geomorphic anomaly index (TGAI). The analysis of river long cross-profile and hypsometric curve has been integrated with the TGAI and epicenter distribution for monitoring tectono-geomorphic anomaly in Lower Tista sub-basin, India. Higher value of TGAI is observed in Rathong chhu, Rel chhu, Upper rangpo chhu, and Great Rangit micro-basin region which shows anomaly of basin tilt, stream segmentation, compressed meander, and topographic curvature. Number of earthquake is much higher (17) between 2000 and 2022 when compared with the status before 2000 in this region. The foothill region is associated with moderate TGAI, which shows the anomaly of resistance difference in soft sedimentary rock, stream migration, upliftment, drainage deflection, and rejuvenation. In moderate region, number of earthquake is 22 between 2000 and 2022 which is twice than the past (11). The signature of tectono-geomorphic anomaly suggests that the activation of thrust fault and resultant few earthquake indicating the strain of the exiting thrust continuously building in the sub-surface, which have not released as expected. As a result, moderate and low TGAI region have not produced any major earthquake at present and may expect severe seismic hazard in future. [ABSTRACT FROM AUTHOR]

Geospatial technologies enable accurate and detailed documentation of cultural heritage sites. They allow for the creation of digital representations of these sites that can be shared with interested audiences. Given the above, this paper presents the possibility of using archival data to reconstruct the topography of two German labor camps, Dyhernfurth I and II, which operated during World War II. To perform this task, multi-source archival data was obtained and interpreted. These data came from various sources: archives, historical institutes, and museums. The results of the study were presented to the staff of the Gross-Rosen Museum, who anticipated the possibility of using the proposed tools in the management of other labor camps. The proposed methodology can be replicated at other locations and easily implemented by other martyrdom museums involved in the preservation of cultural heritage. [ABSTRACT FROM AUTHOR]

Mass movements in the form of rock, debris, and earth are affecting mountainous areas worldwide. The present research is dedicated to identifying areas at risk of landslides along National Highway 37, a vital corridor that links the states of Manipur and Assam. To create a comprehensive database of regions impacted by landslides, a combination of on-site field surveys, satellite imagery, and Google Earth was employed. Categorization of the road stretch polygon has been done into five vulnerable zones based on “frequency ratio, Shannon’s entropy, and weight of evidence” models. The AUC and landslide density methods have been utilised in the process of validating these models. The overall maximum accuracy of 86.95% has been observed for frequency ratio model which is more than weight of evidence (84.98%) and Shannon’s entropy (80.47%) models. The existing model based on information value method was 0.81 showing 81% overall prediction rate. This map will contribute for upcoming developments to facilitate public in terms of safe transportation and infrastructure. [ABSTRACT FROM AUTHOR]

Landslides are among the most critical geo-environmental disasters for both humans and the environment. This study intended to create a landslide susceptibility map (LSM) for the Akre District of Kurdistan Region, Northern Iraq. In this paper, 15 landslide causative criteria including elevation, slope, curvature, aspect, topographic wetness index, topographic roughness index, steam power index, lithology, lineament density, soil types, land use/cover, normalized difference vegetation index, distance to roads, rainfall, and distance to streams were analysed. For this purpose, frequency ratio (FR), analytic hierarchy process (AHP), and ensemble FR-AHP models based on geoinformatics techniques were applied. The LSMs were then assigned into five categories based on their susceptibility levels: very low, low, medium, high, and very high. The results showed that the high and very high landslide susceptibility categories for the FR, AHP, and hybrid FR-AHP models were 27.75% (732,01 km2), 28.44% (750,36 km2), and 28.84% (761 km2), respectively. The results showed that the majority of historical landslide incidents occurred in mountainous terrain in the northern parts of the study area, which are classified as high and very high susceptibility zones. The predicted rate curves for the FR, AHP, and hybrid FR-AHP models had areas under curve of the receiver operating characteristic curve (AUC-ROC) values of 93.4%, 89.3%, and 93.8%, respectively, which indicate that the ensemble FR-AHP model provides more reliable and accurate results for LSM. The LSM generated via the hybrid FR-AHP model can be utilised by local authorities, managers, and decision-makers in further land use/cove planning to mitigate the devastating influences of landslides in the area. [ABSTRACT FROM AUTHOR]

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

Abstract. This paper presents version 1.0 of the World Atlas ofLast Interglacial Shorelines (WALIS), a global database of sea-level proxiesand samples dated to marine isotope stage 5 (∼ 80 to 130 ka).The database includes a series of datasets compiled in the framework of aspecial issue published in this journal (https://essd.copernicus.org/articles/special_issue1055.html, last access: 15 December 2022). This paper collates the individual contributions (archivedin a Zenodo community at https://zenodo.org/communities/walis_database/, last access: 15 December 2022) into anopen-access, standalone database (Rovere et al., 2022,https://doi.org/10.5281/zenodo.7348242). The release of WALIS 1.0 includescomplete documentation and scripts to download, analyze, and visualize thedata (https://alerovere.github.io/WALIS/, last access: 15 December 2022). The databasecontains 4545 sea-level proxies (e.g., marine terraces or fossil beachdeposits), 4110 dated samples (e.g., corals dated with U-series), and 280other time constraints (e.g., biostratigraphic constraints or tephralayers) interconnected with several tables containing accessory data andmetadata. By creating a centralized database of sea-level proxy data for theLast Interglacial, the WALIS database will be a valuable resource to thebroader paleoclimate community to facilitate data–model integration andintercomparisons, assessments of sea-level reconstructions between differentstudies and different regions, as well as comparisons between past sea-levelhistory and other paleoclimate proxy data.

We simulate the introduction of shared, automated, and electric vehicles (SAEVs) providing on-demand shuttles service in a large-scale transport digital twin of the San Francisco Bay Area region (California, USA) based on transit supply and demand data, and using the mesoscopic agent-based Behavior, Energy, Autonomy, and Mobility beta software (BEAM) developed at the Lawrence Berkeley National Laboratory (LBNL). The main goal of this study is to test the operations of this novel mobility service integrated with existing fixed-route public transportation service in a mesoscopic simulation of a real case scenario, while testing the BEAM beta software capabilities. In particular, we test the introduction of fleets of on-demand vehicles bound to operate within circular catchment areas centered on high-frequency transit stops, with the purpose of extending the reach of fixed-route transit by providing an alternative first- and last-mile connection at high-frequency public transport stations. Results show that on-demand automated shuttles represent the best solution for some users, increasing the overall transit ridership by 3%, and replacing mostly ride-hail trips, especially those connecting to transit stops, but also some walking trips. This type of service has the potential to reduce overall vehicle miles traveled (VMT), increase transit accessibility, and save energy, but future research is needed to optimize this type of service and make it more attractive to travelers.

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly.

The recent development of the TOUGH3 code allows for a faster and more reliable fluid flow simulator. At the same time, new versions of FLAC3D are released periodically, allowing for new features and faster execution. In this paper, we present the first implementation of the coupling between TOUGH3 and FLAC3Dv6/7, maintaining parallel computing capabilities for the coupled fluid flow and geomechanical codes. We compare the newly developed version with analytical solutions and with the previous approach, and provide some performance analysis on different meshes and varying the number of running processors. Finally, we present two case studies related to fault reactivation during CO2 sequestration and nuclear waste disposal. The use of parallel computing allows for meshes with a larger number of elements, and hence more detailed understanding of thermo-hydro-mechanical processes occurring at depth.

We introduce two new drought stress algorithms designed to simulate isoprene emission with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) model. The two approaches include the representation of the impact of drought on isoprene emission with a simple empirical approach for offline MEGAN applications and a more process-based approach for online MEGAN in Community Land Model (CLM) simulations. The two versions differ in their implementation of leaf-temperature impacts of mild drought. For the online version of MEGAN that is coupled to CLM, the impact of drought on leaf temperature is simulated directly and the calculated leaf temperature is considered for the estimation of isoprene emission. For the offline version, we apply an empirical algorithm derived from whole-canopy flux measurements for simulating the impact of drought ranging from mild to severe stage. In addition, the offline approach adopts the ratio (f PET) of actual evapotranspiration to potential evapotranspiration to quantify the severity of drought instead of using soil moisture. We applied the two algorithms in the CLM-CAM-chem (the Community Atmosphere Model with Chemistry) model to simulate the impact of drought on isoprene emission and found that drought can decrease isoprene emission globally by 11% in 2012. We further compared the formaldehyde (HCHO) vertical column density simulated by CAM-chem to satellite HCHO observations. We found that the proposed drought algorithm can improve the match with the HCHO observations during droughts, but the performance of the drought algorithm is limited by the capacity of the model to capture the severity of drought.

Nonthaburi, a suburban province adjacent to the Bangkok Metropolis, has experienced a reduction in green spaces due to urban expansion. This study quantified Nonthaburi’s green space through visual interpretation of land use and land cover (LULC) using THEOS and Sentinel-2. Areas of green space were extracted using remote sensing indices and pixel-based classification based on THEOS. The extracted green area was then integrated with the existing LULC patterns to align with the green space characteristic established by Thailand’s Office of Natural Resource and Environmental Policy and Planning. This includes public services, functional utility, median strips, community economics, fallow, and natural green space. The analysis of green space management and planning utilized the Urban Green Space Index (UGSI), Per Capita Green Space (PCGS), and accessibility to public green space. The results revealed that Nonthaburi comprises a green space area of 465.29 km2 or 73.06%, exhibiting a higher prevalence within its western region while displaying a relatively lower extent in the urban zone adjacent to the Bangkok Metropolis. The per capita green space is 367.71 m2 but decreases to 255.82 m2 when accounting for the latent population, meaning it still meets the World Health Organization (WHO) criteria. Currently, only six parks (single and clusters) meet the criteria for public green space. Additionally, both fallow and median strip green spaces (at road interchanges) need to be considered for their potential use in new public service. Furthermore, very high-resolution imagery from unmanned aerial vehicles (UAVs) should be used for green space planning by the organization.

The use of Geographic Information Systems (GIS) and remote sensing technologies for the development of water quality management programs and for post-implementation assessments has increased dramatically in the past decade. This increase in adoption has been made more accessible through the interfaces of many popular software tools used in the regulation and assessment of water quality. Customized applications of these tools will increase, as ease of access and affordability of directly monitored and remotely sensed datasets improve over time. Concurrently, there is a need for inclusive participatory engagement with stakeholders to achieve solutions to current watershed management challenges. This paper explores the potential of these GIS and remote sensing datasets, tools, models, and immersive engagement technologies from other domains, for improving public participation and stakeholder engagement throughout the watershed planning process. To do so, an initial review is presented about the use of GIS and remote sensing in watershed management and its role in impairment identification, model development, and planning and implementation. Then, ways in which GIS and remote sensing can be integrated with stakeholder engagement through (1) leveraging GIS and remote sensing datasets, and (2) stakeholder engagement approaches including outreach and education, modeler-led development, and stakeholder-led involvement and feedback, are discussed. Finally, future perspectives on the potential for transforming public participation and stakeholder engagement in the watershed management process through applications of GIS and remote sensing are presented.

With strong technology improvement, especially GNSS, coordinate transformation has become an essential tool in everyday practice. For this reason, it is beneficial to have a good understanding of the mathematical process of coordinate transformation, as well as the method for calculating transformation parameters. The purpose of this article is to provide a detailed explanation of the coordinate transformation procedure and the calculation of transformation parameters for seven transformation models: 3-parameter, 5-parameter standard and abridged, 7-parameter, 8-parameter, 9-parameter, and 12-parameter. For each transformation model, a basic transformation equation, a procedure for calculating transformation parameters, inverse expression for reversible transformation, as well as characteristics of each model are provided. As an addition, for each model, a numerical example of calculating transformation parameters for the territory of the Republic of Croatia is provided. Additionally, for the example of the 7-parameter transformation, importance of reversible transformation, rotation convention, as well as the form of the rotation matrix used during transformation are demonstrated. [ABSTRACT FROM AUTHOR]

Making paleoecological data accessible, usable, and useful to a global and diverse community of researchers, educators, and students across Earth sciences is an essential yet challenging task. Multiple data access and discovery tools must co-evolve with ever-changing user needs through an iterative, open-ecosystem software development approach. We employ a user-centered design study to evaluate one such ecosystem, the Neotoma Paleoecology Database, whose mission is to advance understanding of global-change processes by providing an expertcurated data resource. Neotoma contains over 11 million observations from 30,000 + datasets across 20,000 + sites, representing proxies of environmental and ecological conditions of the past. Neotoma comprises three interrelated software components -- an interactive web mapping application, statistical programming package, and API -- that provide different levels of functionality to different audiences. Although development efforts of these software components have involved the user community, Neotoma lacks a systematic evaluation process, which is important for informing the gap between perceived potential and actual utility. We address this knowledge gap using scenario-based design exercises and a usability/utility assessment. Major contributions include actionable insights for enhancing Neotoma's user experience and a user-centered design model that can be adapted to advance the development of other Earth and environmental science software ecosystems. [ABSTRACT FROM AUTHOR]

A multi-disciplinary method has been applied for the subsurface mapping of a diesel (refined petroleum product) spillage in the Apapa area of Lagos, Nigeria. The methods include geological, geophysical, hydrogeological, and geoinformatics approaches. Apapa is located within the eastern sector of the Dahomey Basin, Nigeria. Twelve profiles were probed, adopting a 2D Wenner configuration. Three 3D resistivity images were produced from the 2D profiles using ZONDRES 3D and SURFER 16. Additionally, six boreholes were drilled for lithological information and the determination of groundwater flow directions of the area. The study area is characterized by loose, fine to coarse grained sand underlain by peat. The sands were observed to be partly contaminated by diesel (spilled diesel). The 3D resistivity images revealed that the area is composed of 3 geoelectric layers; an organic debris layer (<15 Ωm), overlain by a clean sandy layer (15-54 Ωm), as well as a contaminated sand layer (>55 Ωm). The results showed that higher resistivity values were obtained at shallow depths ≤3 m which depicts the diesel contaminated sand. The 3D model also showed that the magnitude of contamination decreases from the southern part (close to the source of contamination) towards the north of the area. These results are in tandem with the principal flow direction of water which is from south to north, confirming that the pollution source is from the southern part of the area. The combination of the resistivity, groundwater flow direction, and lithological information, therefore, unraveled the source, direction, and extent of contamination in the study area for a successful remediation plan. [ABSTRACT FROM AUTHOR]

A significant amount of energy is released from sub-surface during an earthquake which is caused by seismotectonic activity. The occurrences of earthquake and its distribution pattern are necessary to correlate with concentrated and deviated energy in the sub-surface for better understanding the seismotectonic activity. Hence, the main aim of the present research is to develop hierarchical sub-surface energy anomaly index and its significant on seismotectonic activity by geospatial analysis of the seismic cluster zone and active tectonic signature through geoinformatics for Lower Tista sub-basin, India. In the present research, sub-surface energy has been quantified and threshold energy has been computed to estimate sub-surface energy anomaly in various hierarchical order. Also, the hierarchical sub-surface energy anomaly index has been computed and compared with the seismic cluster zone, seismotectonic activity, and geological formation. The analysis shows that the north and central part of the study region along Rathong chhu, Kalet Khola and Great rangit micro basin denotes as HSEAI Class V which represents low intensity earthquake with clustered concentration and includes in least to weak shaking anomaly zone. This zone is located in the fringe of greater and lesser Himalayan sequence along Main central thrust and inside Ramgarh thrust of lesser Himalayan sequence. It suggests higher energy concentrated and released through micro earthquakes along the active thrust with less energy anomaly. The southern part of the study region in quaternary surface adjacent to Himalayan foothills along Lish, Gish, Chel, and Dharala micro basin denotes as HSEAI Class II which are not resemble with any recent earthquakes and lies on least intensity sub-surface energy anomaly zone. This region intersects the frontal thrust and adjoins with Gish fault and Tista lineament which is presently not in active even though higher energy is concentrated in the sub surface. It suggests the energy anomaly is less even though energy accumulated in the sub-surface which signifies the energy concentrated in the sub-surface but not yet released. It predicts the energy may release with the displacement of thrust in future with higher magnitudes. [ABSTRACT FROM AUTHOR]

The article discusses the impact of artificial intelligence (AI) on computer vision, natural language processing, and geoinformatics. It highlights the advancements in tasks such as text classification, named entity recognition, machine translation, and machine reading comprehension. The article also explores the use of logical reasoning and graph structures to enhance AI models' performance in natural language processing tasks. Additionally, it discusses the application of AI in text classification, image classification, medical image processing, and sports analysis. The article concludes by emphasizing the ongoing research and development in AI and its potential for solving complex problems in various industries. [Extracted from the article]

Using of Geo-Informatics and Google Earth Engine (GEE) technology provide the smart system to access the land information that is very important to achieve the country's goals on Climate change. The purpose of this research were to develop the land use change monitoring system by integration of Geo-Informatics and the GEE technology and to create the spatial GHG emissions and removals datasets in Chiang-Mai. Four periods (2019-2021) of Landsat-8 and Sentinel satellite imagery were used to interpret land use and land use changes. Main types of land use change based on amount of biomass were analyzed: forest land, cropland, grassland, wetland, settlement and others land. Cropland are divided into 4 sub-groups base on amount of biomass: rice fields, rubber, oil palm and other agriculture. Then the datasets of land use change were used to assess the amount of greenhouse gas emissions and reduction using the calculation method referring to the IPCC Good Practice Guidance for LULUCF (GPC) and 2006 IPCC Guidelines for National Greenhouse Gas Inventory. The results of the study are summarized as follows: Chiang Mai Province abundant with forest areas accounted for 69.9 % of the total area. There is a national forest national park and many wildlife sanctuaries. According to the interpretation of satellite images, it was found that Chiang Mai has continued to expand urban areas and agricultural areas. Most of the land has been transformed from abandoned grasslands. It wasn't modified from the forest area as in the past. This is due to the measures of forest preservation and reforestation. As a result, GHG emissions are reduced. The analysis results show that when deducting the emission value from the amount of greenhouse gas storage, Chiang Mai Province has GHG storage of -967,349.16 tCO2 -1,308,790.00 tCO2 -1,277,804.35 tCO2 in 2019, 2020 and 2021 respectively. The results of this study are used as supporting information for the project to develop an approach to reduce GHG at the area base. This is another step forward in using geospatial technology to assess and manage carbon. This makes it possible to show the amount of GHG in the form of a map. Including the ability to identify emissions or reservoirs of GHG as well. The developed GHG monitoring system will benefit government agencies or the private sector to formulate more efficiently targets for reduction implementing projects. [ABSTRACT FROM AUTHOR]

Over the last three decades, Tehsil Mardan has gone through an incredible expansion of its built-up layer. This study explored the land use land cover changes in Tehsil Mardan from 1990 to 2021 along with population dynamics by applying geographic information systems and remote sensing techniques. Landsat satellite images for the years 1990, 1995, 2000, 2010, 2015, and 2021 were used for land use land cover classification. A maximum likelihood supervised algorithm and confusion matrix were applied for classification and accuracy assessment, respectively. The classification results outlined that there has been a substantial increase in the built-up layer from 37 km2 to 188 km2 and a significant decrease in bare land class from 437 km2 to 252 km2 from 1990 to 2021. The classification proces's overall accuracy ranged from 87.42% to 98.30%, and the Kappa Coefficient ranged from 0.82 to 0.97. Population dynamics were also studied in the present study, and it was found that the total population of Tehsil Mardan was 502,435, 864,017, and 1,403,002 in 1981, 1998, and 2017, respectively, and its population was further forecasted based on historical trends until 2027. Statistical analysis revealed a strong positive correlation (0.98) between the built-up layer and population and a significant negative correlation (−0.91) between population and bare land. Based on the findings of this study, policymakers should be able to better plan future land use and account for associated factors while keeping environmental threats and opportunities in mind. [ABSTRACT FROM AUTHOR]

We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model (MLM) with entrainment and detrainment, an Ekman MLM, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting three-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.

Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non-linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed-forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus-like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large-scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub-grid-scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties.

The deep ocean releases large amounts of old, pre-industrial carbon dioxide (CO2) to the atmosphere through upwelling in the Southern Ocean, which counters the marine carbon uptake occurring elsewhere. This Southern Ocean CO2 release is relevant to the global climate because its changes could alter atmospheric CO2 levels on long time scales, and also affects the present-day potential of the Southern Ocean to take up anthropogenic CO2. Here, year-round profiling float measurements show that this CO2 release arises from a zonal band of upwelling waters between the Subantarctic Front and wintertime sea-ice edge. This band of high CO2 subsurface water coincides with the outcropping of the 27.8 kg m-3 isoneutral density surface that characterizes Indo-Pacific Deep Water (IPDW). It has a potential partial pressure of CO2 exceeding current atmospheric CO2 levels (∆PCO2) by 175 ± 32 μatm. Ship-based measurements reveal that IPDW exhibits a distinct ∆PCO2 maximum in the ocean, which is set by remineralization of organic carbon and originates from the northern Pacific and Indian Ocean basins. Below this IPDW layer, the carbon content increases downwards, whereas ∆PCO2 decreases. Most of this vertical ∆PCO2 decline results from decreasing temperatures and increasing alkalinity due to an increased fraction of calcium carbonate dissolution. These two factors limit the CO2 outgassing from the high-carbon content deep waters on more southerly surface outcrops. Our results imply that the response of Southern Ocean CO2 fluxes to possible future changes in upwelling are sensitive to the subsurface carbon chemistry set by the vertical remineralization and dissolution profiles.