Hasil untuk "Physical geography"

Mark W. Smith, J. Carrivick, D. Quincey

R. Baumhauer

Paulo Roger Lopes Alves, German Andres Estrada-Bonilla, Antonio Marcos Miranda Silva et al.

This study explored how different sugarcane vinasses influence the structure and composition of soil bacterial communities in two tropical Oxisols with contrasting textures. In a controlled microcosm experiment with sugarcane seedlings, two concentrations of three vinasse types were applied, and bacterial communities were monitored over 10, 30, and 60 days using T-RFLP and 16S rRNA gene sequencing. Across all treatments, vinasse application led to clear changes in bacterial community structure in both soils, regardless of the time point. Certain bacterial groups, such as <i>Sphingobacteriia</i>, <i>Alphaproteobacteria</i>, and <i>Gammaproteobacteria</i>, became more abundant—likely responding to increased carbon availability, higher pH, and greater soil moisture. At the same time, other groups declined, possibly due to excess nutrients like potassium and sulfur. Notably, these shifts occurred even when standard biochemical indicators suggested no major impact, highlighting the sensitivity of microbial community-level responses. These findings point to the importance of looking beyond traditional soil quality metrics when assessing the environmental effects of organic residue applications. Incorporating microbial indicators can offer a more nuanced understanding of how practices like vinasse reuse affect soil functioning in tropical agroecosystems.

Francesca Baldacchino, Whyjay Zheng, Kunpeng Wu et al.

Glacier velocity is a sensitive indicator of mass balance and is key to understanding how glaciers respond to climate change. Monitoring glacier velocity at high temporal resolutions enables a better understanding of the drivers of glacier dynamics. Previous studies have found that the glaciers in High Mountain Asia (HMA) tend to slow down concomitant to losing mass at an accelerating rate on decadal timescales. However, few studies have explored seasonal variations in glacier velocities and have typically focused on large, fast-flowing glaciers. We select one debris-covered glacier, and four clean-ice glaciers in HMA. Sentinel-1 and -2 images are used to calculate the glacial velocities using the feature tracking module provided by the Cryosphere And Remote Sensing Toolkit (CARST). We develop a novel, regularised linear inverse model to extract the seasonally resolved glacial velocity time series (6-day intervals) with rigorous uncertainty estimates. Our results show that three of the five glaciers have strong seasonal signals in velocities, with faster velocities in spring and/or summer compared to winter. We also find an up-glacier propagation of the late spring and/or summer accelera-tions and a down-glacier propagation of the autumn accelerations. We suggest that changes in the subglacial hydrology efficiency drive the observed seasonal variations in velocities. We also highlight that icefalls may alter the glacier flow response by blocking the development of subglacial drainage channels and thus the seasonal propagation of velocities. Our methodol-ogy enables us to successfully extract seasonal signals in glaciers that flow slowly and provide a further understanding of glacier dynamics.

Jiaquan Wan, Junchao Wang, Wei Zhang et al.

Study region: The Three Gorges Reservoir Area (TGRA) Study focus: TGRA faces increasing vulnerability to extreme precipitation events driven by complex convective weather systems. Radar echo extrapolation—predicting future precipitation patterns from current radar data—is essential for early warning systems but faces significant challenges in this topographically complex region. While data-driven approaches have advanced the field, current convolutional neural network-based diffusion models struggle with the TGRA's dynamic meteorological conditions due to their reliance on translational invariance, which often fails to capture rapid weather transitions in complex terrain. New hydrogeological insights from the region: To address these limitations, we introduce RadarDiT, a Vision Transformer-based diffusion model specifically engineered for radar extrapolation in the TGRA. First, we develop a five-year radar dataset capturing diverse convective weather phenomena unique to this region. Then, leveraging this dataset, RadarDiT employs multi-layer Vision Transformers that effectively model global dependencies and complex spatial relationships, enabling accurate prediction of convective cell evolution. Our model demonstrates superior performance in maintaining strong echo and spatial coherence over longer forecast horizons. Quantitative evaluations across multiple metrics and thresholds confirm RadarDiT's enhanced skill in forecasting heavy precipitation events, with particular improvements in Critical Success Index at higher radar echo values. This work establishes a foundation for more reliable nowcasting systems in regions with complex terrain and dynamic weather patterns, directly supporting enhanced disaster preparedness and response strategies.

Qiying Yu, Wenzhong Li, Yungang Bai et al.

Study Area: The Tailan River Basin in the Aksu region and the Yulong Kashi River in the Hotan River Basin of Xinjiang are located at respective geographical coordinates of 80°21'44'' to 81°10'14'' E, 40°41'41'' to 42°15'13'' N, and 77.25° to 81.75° E, 34.75° to 36.25° N. Study Focus: To tackle the complexity of runoff prediction in high-altitude cold regions, alongside the limitations of existing machine learning approaches, where nonlinear relationships, long-term dependencies, and sparse observational data pose significant challenges, previous models have consistently struggled to account for these issues. In response, we propose a hybrid runoff prediction model that combines Dung Beetle Optimization (DBO)'s optimization capabilities, Temporal Convolutional Networks (TCN)’s proficiency in extracting local temporal features, and the Transformer’s ability to capture long-term dependencies. In addition, the Bootstrap method is employed to merge point prediction outcomes for interval runoff forecasting, providing robust uncertainty estimates to address data limitations in these regions. New Hydrological Insights for the Region: The DBO-TCN-Transformer model consistently attains a Nash-Sutcliffe Efficiency (NSE) above 0.81, showcasing enhanced performance over traditional models. Across various forecast periods, the model’s NSE values are 6.9–26.9 % higher than those of the TCN and Transformer models, offering more reliable short-term and long-term predictions. Furthermore, the Bootstrap algorithm’s probabilistic approach provides valuable insights into forecast uncertainty, a crucial feature for managing water resources and mitigating flood risks in high-altitude cold regions with complex hydrological dynamics.

Mega L. Syamsuddin, Subiyanto Subiyanto, Tonny Bratasena et al.

The Makassar Strait is one of the Indonesian Throughflow (ITF) branches that transports warm water masses from the Pacific Ocean to the Indian Ocean. These water masses have a significant impact on oceanographic parameters, which in turn affects the skipjack tuna distribution. Satellite-derived oceanographic factors from January 2015 to December 2020 included sea surface temperature (SST), chlorophyll-a, salinity, sea surface height (SSH), surface current, and surface wind are used to predict the potential habitat of skipjack tuna (Katsuwonus pelamis) in the Makassar Strait using the maximum entropy (MaxEnt) model. The SSH was the most important oceanographic variable affecting the skipjack tuna catch, contributing 49.7% to the model gain. An increasing skipjack tuna catch was observed within the following oceanographic variable ranges: 0.48–0.58 m of SSH, 34–35 ppt of salinity, 0.1–1.2 m/s of surface current, 29–30 °C of SST, 5–6 m/s of surface wind, and 0.1–0.5 mg/l of chlorophyll-a concentrations.

Marcos Plischuk, Rocío García Mancuso, Bárbara Desántolo

Yingying Xu, Qiying Yu, Chengshuai Liu et al.

Study region: Typical basin in humid areas in the Huaihe River Study focus: Accurate flood forecasting is essential for making timely decisions regarding flood control and disaster reduction. The theory of watershed runoff generation and convergence serves as a crucial foundation for flood forecasting, while the calculation of runoff is necessary to simulate flood discharge. Identifying watershed runoff generation mechanisms has been a challenging task, particularly under complex underlying surface conditions. To improve the accuracy of flood simulation, this study examines the underlying surface information in the watershed, such as particle composition and content, soil bulk density, geological slope, land use, and other spatial attributes, aiming to analyze the mechanisms of runoff generation. In the study of sub-watersheds, various combinations of runoff generation mechanisms are identified to determine the patterns of runoff. Subsequently, a semi-distributed hydrological model is developed, which incorporates both saturation-excess and infiltration-excess runoff, utilizing the information obtained from the underlying surface. The model is validated using rainfall-runoff data from 14 events at the Xiagushan watershed. New hydrological insights for the region: The analysis of the fundamental physical conditions of the underlying surface of the watershed revealed that 69.70% of the area is prone to saturation-excess runoff, with an additional 30.30% of the area being susceptible to infiltration-excess runoff. The model considers the spatial distribution of runoff patterns by incorporating complex underlying surface information and demonstrates high accuracy in simulating flood events (NSE= 0.87, Epeak = 12.08%, Wpeak = 13.16%, Tpeak = 0.14 h, R2 = 0.90). The model is straightforward, practical, and exhibits promising potential in terms of timeliness and applicability, thus lending itself well to further application in other watersheds, contributing to the scientific foundation of flood warning and forecasting efforts.

Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud

Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer vision to predict living conditions in areas with limited data, but recent studies increasingly focus on causal analysis. Despite this shift, the use of EO-ML methods for causal inference lacks thorough documentation, and best practices are still developing. Through a comprehensive scoping review, we catalog the current literature on EO-ML methods in causal analysis. We synthesize five principal approaches to incorporating EO data in causal workflows: (1) outcome imputation for downstream causal analysis, (2) EO image deconfounding, (3) EO-based treatment effect heterogeneity, (4) EO-based transportability analysis, and (5) image-informed causal discovery. Building on these findings, we provide a detailed protocol guiding researchers in integrating EO data into causal analysis -- covering data requirements, computer vision model selection, and evaluation metrics. While our focus centers on health and living conditions outcomes, our protocol is adaptable to other sustainable development domains utilizing EO data.

Antonio Alfieri, Fraser Binns

In a recent note F. Lin showed that if a rational homology sphere $Y$ admits a taut foliation then the Heegaard Floer module $HF^-(Y)$ contains a copy of $\mathbf{F}[U]/U$ as a summand (arXiv:2309.01222). This implies that either the $L$-space conjecture is false or that Heegaard Floer homology satisfies a geography restriction. We verify that Lin's geography restriction holds for a wide class of rational homology spheres. Indeed, we show that the Heegaard Floer module $HF^-(Y)$ may satisfy a stronger geography restriction.

K. Anderson, S. Tooth, Daehyun Kim et al.

This editorial reports on a horizon scan exercise that was undertaken to identify new frontier topics, new or emerging themes/concepts, or new philosophical questions of relevance to physical geography. Researchers with broad geographical and disciplinary scope, all of whom were members of the journal's editorial board or editorial advisory board, were invited to join a horizon scan panel. The horizon scan Chair canvassed panel members for ideas, resulting in an initial 32 independently proposed topics. Similar topics were merged by the Chair, and panel members were then invited to vote on and score anonymously the remaining 28 topics, bearing in mind the perceived importance/relevance and novelty for the discipline. The final ranking and sifting phase produced a list of 12 topics, which were categorised as being either new study areas or new epistemological frameworks for physical geography. In this editorial, we outline these 12 topics, some of which have been inspired by developments outside of the discipline, but we identify how potentially fertile contributions could be added by physical geographers. We discuss how new studies of extreme event geographies, the impacts of compound stressors, cross-system pollution and toxicity, the geomorphological basis of zoonoses, ancient environmental DNA, the projection and visualisation of landscape futures, and planetary sciences can all benefit from additional physical geography perspectives. We then consider the ways in which physical geographers may engage further with new approaches in personalised and internet-of-things monitoring, artificial intelligence, innovative technologies for teaching physical geography, the study of human-climate impacts, and the raising of the profile of physical geography thinking alongside other knowledge forms. We encourage more physical geographers to apply their unique skillsets and ways of thinking to these topics. The journal will welcome new submissions, or proposals for special issues, that address these topics from physical geographers and their colleagues.

Song Gao

This paper examines the recent advances and applications of AI in human geography especially the use of machine (deep) learning, including place representation and modeling, spatial analysis and predictive mapping, and urban planning and design. AI technologies have enabled deeper insights into complex human-environment interactions, contributing to more effective scientific exploration, understanding of social dynamics, and spatial decision-making. Furthermore, human geography offers crucial contributions to AI, particularly in context-aware model development, human-centered design, biases and ethical considerations, and data privacy. The synergy beween AI and human geography is essential for addressing global challenges like disaster resilience, poverty, and equitable resource access. This interdisciplinary collaboration between AI and geography will help advance the development of GeoAI and promise a better and sustainable world for all.

Shruti Aggarwal, Anu Kumari, Satyabrata Adhikari

Entanglement detection is an important problem in quantum information theory because quantum entanglement is a key resource in quantum information processing. Realignment criteria is a powerful tool for detection of entangled states in bipartite and multipartite quantum system. It is an important criteria for entanglement detection because it works well; not only for negative partial transpose entangled states (NPTES) but also for positive partial transpose entangled states (PPTES). Since the matrix corresponding to realignment map is indefinite so the experimental implementation of the map is an obscure task. In this work, firstly, we have approximated the realignment map to a positive map using the method of structural physical approximation (SPA) and then we have shown that the structural physical approximation of realignment map (SPA-R) is completely positive. Positivity of the constructed map is characterized using moments which can be physically measured. Next, we develop a separability criterion based on our SPA-R map in the form of an inequality and have shown that the developed criterion not only detect NPTES but also PPTES. We have provided some examples to support the results obtained. Moreover, we have analysed the error that may occur because of approximating the realignment map.

Yerko Torres-Nova

Roughly speaking, the problem of geography asks for the existence of varieties of general type after we fix some invariants. In dimension $1$, where we fix the genus, the geography question is trivial, but already in dimension $2$, it becomes a hard problem in general. In higher dimensions, this problem is essentially wide open. In this paper, we focus on geography in dimension $3$. We generalize the techniques which compare the geography of surfaces with the geography of arrangements of curves via asymptotic constructions. In dimension $2$ this involves resolutions of cyclic quotient singularities and a certain asymptotic behavior of the associated Dedekind sums and continued fractions. We discuss the general situation with emphasis on dimension $3$, analyzing the singularities and various resolutions that show up, and proving results about the asymptotic behavior of the invariants we fix.

Yin Ching Lai

Hong Kong' senior geography curriculum has included GIS since the early 2000s. However, GIS in secondary schools does not play a significant role in Hong Kong secondary geography education. Analyzing GIS benefits by literature review, it is believed that GIS should be included in both the senior and junior geography curriculum. Moreover, the literature review indicates that without clear instruction from the Hong Kong Education Bureau (EDB), low preparedness of Hong Kong geography teachers, and unsupportive attitudes from academia and textbook publishers, GIS cannot be implemented in secondary schools of Hong Kong. Therefore, suggestions are made for the EDB, geography teachers, academia and textbook publishers to facilitate GIS involvement in senior and junior geography curriculums. The EDB can develop clear guidelines for teachers, academia and textbook publishers' references, and offer student-centered GIS educational courses for teachers. It is important for teachers to be prepared for advanced GIS technology and to even learn along with students. Academics and textbook publishers can provide free GIS maps targeted at Hong Kong' junior and senior geography curriculums. Although the report provides brief information towards the GIS implementation in Hong Kong geography education, it can inspire new ideas from other scholars to facilitate the usage of GIS in Hong Kong secondary school geography teaching.

Matteo Straccamore, Vittorio Loreto, Pietro Gravino

Cities and metropolitan areas are major drivers of creativity and innovation in all possible sectors: scientific, technological, social, artistic, etc. The critical concentration and proximity of diverse mindsets and opportunities, supported by efficient infrastructures, enable new technologies and ideas to emerge, thrive, and trigger further innovation. Though this pattern seems well established, geography's role in the emergence and diffusion of new technologies still needs to be clarified. An additional important question concerns the identification of the innovation pathways of metropolitan areas. Here, we explore the factors that influence the spread of technology among metropolitan areas worldwide and how geography and political borders impact this process. Our evidence suggests that political geography has been highly important for the diffusion of innovation till around two decades ago, slowly declining afterwards in favour of a more global innovation ecosystem. Further, the visualisation of the evolution of countries and metropolitan areas in a 2d space of competitiveness and diversification reveals the existence of two main innovation pathways, discriminating between different strategies towards progress. Our work provides insights for policymakers seeking to promote economic growth and technological advancement through tailored investments in prioritarian innovation areas.

E. Sharp, G. Brierley, J. Salmond et al.

This paper aims to foster an explicit geoethical orientation in physical geography. Using examples from Aotearoa New Zealand, we approach the work of physical geography with a set of ethical coordinates derived from our research, arguing that they allow for greater sensitivity in considering what is more-than-human in our research relationships. Working with these ethical co-ordinates lays the political groundwork for thinking and doing physical geography differently in the pursuit of less exploitative social and ecological relations. Our proposition offers new potentials for the practice of geography more generally: opportunities for enactive research encounters, those that perform generative change for a decolonised, post-productivist, physical geography.

B. Hariyanto, Ita Mz, Wiwik Su et al.

This research is a development research that goals to provide knowledge and understanding about how to make 4D model learning media to implementing in Geography Field Work. The output of this research is the acquisition of mastery knowledge from students on how to use the Physical Geography Field Work Lecture Manual as a 4D model of learning media. The Physical Geography Field Work Lecture Manual in this study was compiled and developed based on the 4-D Thiagarajan model which consists of four stages: define, design, develop, and disseminate. The stages in this research are planning,data/information collection, design, construction, testing and analysis of results. This research on the development of the Physical Geography Field Work Lecture Guidebook, and provides benefits to students in an effort to improve their creativity, ability and field study skills so that in the future the research output in the form of this Physical Geography Field Work Lecture Manual is useful in supporting the learning and teaching process in the UNESA Geography Education Department.