Hasil untuk "Physical geography"

April S. Masarik, R. Conger

R. O’Caoimh, D. Sezgin, M. O'Donovan et al.

INTRODUCTION The prevalence of frailty at population level is unclear. We examined this in population-based studies, investigating sources of heterogeneity. METHODS PubMed, Embase, CINAHL and Cochrane Library databases were searched for observational population-level studies published between 1 January 1998 and 1 April 2020, including individuals aged ≥50 years, identified using any frailty measure. Prevalence estimates were extracted independently, assessed for bias and analysed using a random-effects model. RESULTS In total, 240 studies reporting 265 prevalence proportions from 62 countries and territories, representing 1,755,497 participants, were included. Pooled prevalence in studies using physical frailty measures was 12% (95% CI = 11-13%; n = 178), compared with 24% (95% CI = 22-26%; n = 71) for the deficit accumulation model (those using a frailty index, FI). For pre-frailty, this was 46% (95% CI = 45-48%; n = 147) and 49% (95% CI = 46-52%; n = 29), respectively. For physical frailty, the prevalence was higher among females, 15% (95% CI = 14-17%; n = 142), than males, 11% (95% CI = 10-12%; n = 144). For studies using a FI, the prevalence was also higher in females, 29% (95% CI = 24-35%; n = 34) versus 20% (95% CI = 16-24%; n = 34), for males. These values were similar for pre-frailty. Prevalence increased according to the minimum age at study inclusion. Analysing only data from nationally representative studies gave a frailty prevalence of 7% (95% CI = 5-9%; n = 46) for physical frailty and 24% (95% CI = 22-26%; n = 44) for FIs. CONCLUSIONS Population-level frailty prevalence varied by classification and sex. Data were heterogenous and limited, particularly from nationally representative studies making the interpretation of differences by geographic region challenging. Common methodological approaches to gathering data are required to improve the accuracy of population-level prevalence estimates. PROTOCOL REGISTRATION PROSPERO-CRD42018105431.

J. M. Kizza

J. Dufresne, M. Foujols, S. Denvil et al.

We present the global general circulation model IPSL-CM5 developed to study the long-term response of the climate system to natural and anthropogenic forcings as part of the 5th Phase of the Coupled Model Intercomparison Project (CMIP5). This model includes an interactive carbon cycle, a representation of tropospheric and stratospheric chemistry, and a comprehensive representation of aerosols. As it represents the principal dynamical, physical, and bio-geochemical processes relevant to the climate system, it may be referred to as an Earth System Model. However, the IPSL-CM5 model may be used in a multitude of configurations associated with different boundary conditions and with a range of complexities in terms of processes and interactions. This paper presents an overview of the different model components and explains how they were coupled and used to simulate historical climate changes over the past 150 years and different scenarios of future climate change. A single version of the IPSL-CM5 model (IPSL-CM5A-LR) was used to provide climate projections associated with different socio-economic scenarios, including the different Representative Concentration Pathways considered by CMIP5 and several scenarios from the Special Report on Emission Scenarios considered by CMIP3. Results suggest that the magnitude of global warming projections primarily depends on the socio-economic scenario considered, that there is potential for an aggressive mitigation policy to limit global warming to about two degrees, and that the behavior of some components of the climate system such as the Arctic sea ice and the Atlantic Meridional Overturning Circulation may change drastically by the end of the twenty-first century in the case of a no climate policy scenario. Although the magnitude of regional temperature and precipitation changes depends fairly linearly on the magnitude of the projected global warming (and thus on the scenario considered), the geographical pattern of these changes is strikingly similar for the different scenarios. The representation of atmospheric physical processes in the model is shown to strongly influence the simulated climate variability and both the magnitude and pattern of the projected climate changes.

P. Estabrooks, Rebecca E. Lee, N. Gyurcsik

K. S. Carslaw, L. A. Regayre, L. A. Regayre et al.

<p>A grand challenge in climate science is to translate advances in our fundamental understanding into reduced uncertainty in climate projections. Model uncertainty, characterized for example by the spread of simulations of future climate projections, has changed little over the past few decades despite major advances in model complexity, resolution, and the growing number of intercomparison projects and observational datasets. Here we argue that the use of perturbed parameter ensembles (PPEs) would accelerate our understanding of uncertainty in its broadest sense and help identify strategies for reducing it. We make eleven recommendations for future research priorities, drawing on existing studies that have used PPEs to guide model development and simplification, understand inter-model differences, more fully characterize the plausible spread in climate projections, define observational requirements, and to enhance our understanding of complex atmospheric processes. These studies extend across climate, weather, atmospheric chemistry, clouds, aerosols and renewable energy using process-based high-resolution models through to global-scale models. Although increases in model complexity, resolution and intercomparison projects consume most computing resources today, we argue that, in<span id="page4652"/> synergy with these efforts, PPEs are essential for fully characterizing model uncertainty and improving model reliability.</p>

Nadia Khazri, Essam Heggy, Alin Mihu-Pintilie et al.

Study Region: Coastal aquifers at river outlets in North Africa are facing rapid degradation due to increasing aridity. Of particular interest is how these degradations extend to vulnerable lagoonal systems in semi-arid areas that are suffering from recent, accentuated climate fluctuations. Among these, the Medjerda River and Ghar El Melh Lagoon exhibit notable hydroclimate complexity, which is representative of several lagoonal systems in the southern Mediterranean Basin that remain poorly quantified. Study Focus: To address this deficiency, we utilize GALDIT-AHP statistical methods to assess the susceptibility of coastal aquifers to seawater intrusion under decadal hydroclimatic fluctuations from 1985 to 2023. GALDIT combines aquifer hydraulic conductivity, groundwater levels, proximity to the coastline, seawater intrusion, and aquifer thickness with piezometric data and historical records spanning 1997–2022, encompassing hydrogeological, hydrological, and geospatial parameters during wet and dry seasons. New Hydrological Insights for the Region: Our results indicate that aquifer vulnerability in these lagoonal systems is primarily governed by anthropogenic extraction, rather than the river basin’s seasonal hydraulic conditions. The correlation between GALDIT-AHP vulnerability index and seawater intrusion is well established. Vulnerability is most pronounced during the dry season, with hotspots concentrated along lagoon coastlines and in highly urbanized areas. Our findings underscore the need to reinforce sustainable groundwater management practices in the lagoonal systems of the southern Mediterranean Basin, thereby mitigating the growing and potentially irreversible seawater intrusion associated with increasing hydroclimatic fluctuations in coastal watersheds.

Zhiqin Gui, Huiqin Ma, Jingcheng Zhang et al.

Yellow rust (Puccinia striiformis f. sp. Tritici, YR) and Fusarium head blight (Fusarium graminearum, FHB) are two major wheat diseases. These two diseases frequently pose concurrent risks to grain security, particularly in high-yielding wheat regions of eastern China. Accurate regional-scale discrimination of wheat YR and FHB is essential for developing effective green and intelligent disease management strategies. While satellite remote sensing shows potential for regional crop disease monitoring, conventional machine learning modeling approaches widely employed often fail to exploit the spectral-spatial information inherent in imagery. Meanwhile, the scarcity of ground-based disease survey samples limits the application of emerging sample-driven deep learning methods. This study evaluated the effectiveness of 27 sample-feature-algorithm combinatorial modeling strategies for discriminating regional-scale wheat YR and FHB using Sentinel-2 imagery. We augmented disease samples using a stepwise approach that combines marking diseased field vector boundaries with sliding window segmentation (SWS), horizontal-vertical flipping (HVF), and multi-angle rotation (MAR). Recursive feature elimination with cross-validation (RFECV) was employed to optimize spectral and textural features, yielding in two distinct feature sets: disease-sensitive spectral features (SFs) and spectral-textural combined features (STCFs). The original spectral bands (OSBs) served as a third feature set. These sample sets and feature sets were input into several fundamentally distinct algorithms to construct wheat YR and FHB discrimination models. These include three commonly used machine learning (ML) methods, namely, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Additionally, include two deep learning methods, namely, the two-dimensional convolutional neural network (2D-CNN) and the spectral-spatial attention network (SSAN). The results indicated that three ML algorithms exhibited stable performance across all three feature sets under SWS-based sample augmentation. SVM yielded the best overall accuracy, but texture features provided only limited improvement over the SVM model compared with RF and XGBoost. The OSBs outperformed SFs and STCFs in 2D-CNN and SSAN modeling, achieving an overall accuracy (OA) comparable to that of SVM under SWS + HVF + MAR-based sample augmentation. Specifically, the SWS + HVF + MAR-OSBs-SSAN model demonstrated superior performance metrics. This model achieved an average accuracy of 81.8 %, a Kappa coefficient of 0.704, a G-means of 0.892, and an F1-score of 81.1 %. These accuracy results surpassed those of the SWS-STCFS-SVM model, even though the latter achieved the highest OA of 82.8 %. Sample augmentation yielded limited gains in modeling for the 2D-CNN but demonstrated more significant gains for the SSAN. Overall, the STCFs-based SVM modeling strategy remains preferable under sample constraints, whereas the OSBs-based SSAN modeling strategy is more competitive with further sample augmentation. Our findings contribute valuable insights towards improved regional-scale crop biological stress discrimination.

Justin Cranshaw, Eran Toch, Jason I. Hong et al.

Dhais Peña-Angulo, Yves Tramblay, Sergio M. Vicente-Serrano et al.

Study Region: Sub-Saharan Africa, a region highly vulnerable to climate variability, faces significant challenges from hydrological droughts due to their widespread socio-economic and environmental impacts. Study Focus: This study investigates the spatiotemporal evolution of hydrological droughts and their links to meteorological droughts across Sub-Saharan Africa. Using the African Database of Hydrometric Indices (ADHI), we analyze streamflow data from 1466 gauging stations spanning 1951–2018 to detect trends in drought characteristics. New Hydrological Insight: A major shift in hydrological drought patterns occurred in the 1980s, with increased drought duration and severity during 1951–1980, followed by a general decrease from 1981 onward. Spatially, southern Africa experienced more frequent but shorter and less severe droughts, whereas central and eastern regions saw fewer but more intense and prolonged events. These spatial contrasts reflect differences in climate and basin characteristics. Although meteorological drought indices (SPI, SPEI) broadly align with hydrological drought trends, local factors introduce important variability. These findings enhance our understanding of drought dynamics in the region, with implications for water management, food security, and climate adaptation strategies.

Tatenda Dzurume, Roshanak Darvishzadeh, Timothy Dube et al.

Fall Armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a significant risk to global food and income security by attacking various crops, particularly maize. Early detection and management of FAW infestation are crucial for mitigating its impact on crop yields. This study investigated the effect of FAW infestation on the spectral signature of maize fields and classified infestation severity in Bangladesh using Sentinel-2 satellite imagery and Random Forest (RF) classification. Field observations on FAW infestation severity (none, moderate, and severe), collected by the Bangladesh Department of Agricultural Extension during 2019 and 2020, were used to train the RF classifier. Six thousand nine hundred ninety-eight observations were collected from 579 maize fields through weekly scouting. The Kruskal-Wallis test and Dunn’s post-hoc test were applied to identify the most significant spectral bands (P < 0.05) for detecting FAW incidence and severity across different maize growth stages. The results demonstrated that the spectral reflectance from Sentinel-2 bands varied significantly among different classes of FAW infestation, with noticeable differences observed during the early developmental stages of maize (vegetative growth stages 3 to 8). RF identified nine spectral bands and two spectral vegetation indices as important for FAW infestation discrimination. The RF classifier was evaluated using five-fold cross-validation, achieving an overall accuracy between 74 % and 84 %. The independent test set’s accuracy ranged from 72 % to 82 %. The mean multiclass AUC ranged from 0.83 to 0.95. Moreover, the results demonstrated the feasibility of detecting the severity of FAW infestation using temporal Sentinel-2 data and machine learning techniques. These findings underscore the potential of remote sensing and machine learning techniques for effectively monitoring and managing crop pests. The study provides valuable insights for classifying FAW infestation using high-resolution multitemporal data.

Jui-Tien Tsai, Yen-Yu Chiu, Su-Chin Chen

Study region: Eighteen main rivers in Taiwan. Study focus: This study focused on the analysis of total stream power (TSP) and specific stream power (SSP) along river longitudinal profiles, which are critical indicators of river dynamics. A new two-parameter regression model is proposed, addressing inaccuracies in traditional models and providing a more-precise representation of river profiles. By incorporating concavity, drainage area distribution, discharge, and river width relationships, the model identifies the locations of the TSP and SSP peaks. The study employs log (river slope, S) – log (drainage area, A) plots to evaluate the spatial variability of these metrics under diverse geomorphological and hydrological conditions. New hydrological insights for the region: The model was used to categorize 18 rivers in Taiwan into three groups based on river source elevation and drainage area–flow length exponents. Key findings indicate that lower source elevations correspond to increased upstream drainage-area distribution and greater concavity. This highlights the interplay between intrinsic watershed characteristics and external hydrological forces in shaping TSP and SSP distributions. These insights provide a basis for improved river management, sediment transport predictions, and conservation efforts.

Ivan Kalynych, Mykola Karabiniuk, Mariia Nychvyd et al.

Problem Statement and Objective. The study aims to create a high-precision topographic framework for Lake Synevyr, one of the key internationally significant wetlands in the Carpathian region, by integrating remote sensing techniques and hydroacoustic bathymetric surveying. This approach has facilitated the development of a cartographic foundation for the implementation of long-term geoecological monitoring and the assessment of spatiotemporal changes in the lake system under increasing anthropogenic pressure and climate change. Methodology. The instrumental survey was conducted through a comprehensive mapping campaign using an unmanned aerial vehicle (UAV) equipped with a LiDAR system, photogrammetric processing of high-resolution digital imagery, and echosounding of the lakebed with GNSS referencing. The collected data were processed in specialized software environments (Terrasolid, Agisoft Metashape, Trimble HYDROpro, Digitals), enabling the generation of a digital terrain model, orthophotomap, TIN bathymetric model, and topographic plan. Study results. For the first time, a comprehensive high-accuracy instrumental mapping of Lake Synevyr has been carried out, including several key components: construction of a digital terrain model of the coastal area, creation of an orthophotomap with a ground sampling distance of 5.9 cm/pixel, and execution of bathymetric profiling with depth data obtained at an accuracy of ±0,1 m. As a result, the maximum lake depth of 19.98 m was recorded, a 3D model of the lakebed was constructed, and hypsometric profiles were developed. The acquired remote sensing and field survey data formed the basis of a high-quality 1:1 000 scale topographic map, which constitutes the core outcome of the study. Scientific novelty and practical significance. For the first time, a complex instrumental mapping of the mountainous Lake Synevyr has been implemented using an integrated approach combining LiDAR scanning, UAV-based aerial photography, and hydroacoustic bathymetric surveying with high-precision georeferencing. This enabled the acquisition of fundamentally new spatial information on the morphology of the lakebed and the creation of a multi-component geospatial data system and high-quality cartographic materials. The developed topographic plan and digital datasets provide a robust basis for further spatial analysis of natural changes and serve as a cartographic foundation for implementing long-term geoecological monitoring of Lake Synevyr. The data and cartographic products obtained enable highly accurate hydromorphological, landscape-ecological, and other scientific investigations, as well as the identification and monitoring of the dynamics of natural and anthropogenic processes. The proposed instrumental mapping methodology can be adapted for other sites in the Carpathian region, including within the framework of cross-border conservation initiatives and the systematic management of territories with special ecological status.

Antonio Martínez Cortizas, Mohamed Traoré, Olalla López-Costas et al.

Peatlands are natural reservoirs of organobromine compounds. Important advances have been made in unraveling the mechanisms involved in bromine (Br) retention in the peat but, to our knowledge, the temporal and spatial variation of the peat organic matter (OM) bromination has not been fully researched. Here, we present the study of 12 short cores (c. 30 cm, c. 150–200 years of peat accumulation) sampled from a small (c. 1 ha) area of an oceanic blanket peatland from northwestern Spain. We combine Br concentrations, spectroscopic analysis (FTIR–ATR), and structural equation statistical modelling (SEM). Our results show that Br is significantly correlated to proxies of peat aerobic decomposition, with concentrations increasing with depth in all cores (×2–10 times). Strong spatial heterogeneity was observed, with some cores showing much higher Br maximum concentrations and larger increases with depth. SEM modelling indicated that various OM functionalities contribute to Br accumulation and that their effects change with depth/age, with aromatics becoming dominant after 20–90 years. Thus, changes in organic matter molecular composition, linked to early peat diagenesis, and the geochemical conditions governing it exerted a strong control on Br accumulation in the studied peatland. Bromine wet deposition was not found to be a limiting factor.

Martin Hruška, Martin Plesch

One of key goals of contemporary physics (and, realistically, STEM) education is to develop students' science literacy and critical thinking skills. In this paper, we present the construction and use of several versions of a simple school-based digital weather station that students can use to measure fundamental physical quantities (temperature, pressure, air humidity, light intensity) as part of school activities. The weather stations were constructed at our workplace using an Arduino microcontroller, BBC micro: bit, and the school measurement system Coach. This paper proposes not only the design and related programming of the weather stations but also how students can collect, analyse, and interpret measured data, thereby learning scientific methods and developing science literacy and critical thinking. This hands-on approach also develops students' experimental skills, emphasizes the cross-curricular relationships between physics, computer science and geography, and teaches them to work with accurate data in the context of real environmental problems.

T. Arcury, W. Gesler, J. Preisser et al.

Yinqing Zhen, Qingyun Yan

The escalating water pollution in many lakes has led to more frequent occurrences of algal bloom disasters in recent decades. The severity of these disasters can be assessed through remote sensing techniques, specifically using the Normalized Difference Vegetation Index (NDVI) for measurement. However, NDVI observations using optical sensors are often affected by cloud and fog in areas with numerous water bodies, such as Taihu Lake. Sensors operating in the microwave band can effectively mitigate this issue, particularly the emerging Global Navigation Satellite System Reflectometry (GNSS-R), which offers high temporal resolution and cost-effectiveness. In this paper, we propose a new method to recover lake-surface NDVI on cloudy days, utilizing GNSS-R observables and auxiliary meteorological data in conjunction with a machine learning regression algorithm called Bagging Tree. We also examine the effective range of GNSS-R data within this application scenario. Meanwhile, the Weighted Linear Regression-Laplacian Prior Regulation Method (WLR-LPRM) image gap-filling algorithm is used as a benchmark to evaluate recovery accuracy. The regression coefficient of NDVI retrieved using the proposed method is 0.95, with a root mean square error (RMSE) of 0.021 and a mean absolute error (MAE) of 0.010. Compared to the previous work on GNSS-R algal bloom detection with overall accuracy of 0.82, this work shows significant improvement in both accuracy and utility. The recovery of lake surface NDVI provides detailed insights into algal blooms, including quantifiable metrics such as the amount and spatial distribution, which are crucial for effective monitoring and management. Additionally, the recovered image textures exhibit high clarity and closely resemble the reference NDVI images. Experimental evaluation using simulated and actual cloud blocks indicates the model’s robustness to recover NDVI under varying cloud cover conditions. In summary, this study demonstrates the capability of GNSS-R aided by supplementary data for recovering missing NDVI values on lake surfaces when optical observations are absent for the first time.

Johann Coraux, Nicolas Rougemaille

The square ice is a two-dimensional spin liquid hosting a Coulomb phase physics. When constrained under specific boundary conditions, the so-called domain-wall boundary conditions, a phase separation occurs that leads to the formation of a spin liquid confined within a disk surrounded by magnetically ordered regions. Here, we numerically characterize the ground-state properties of this spin liquid, coined the arctic square ice in reference to a phenomenon known in statistical mechanics. Our results reveal that both the vertex distributions and the magnetic correlations are inhomogeneous within the liquid region, and they exhibit a radial dependence. If these properties resemble those of the conventional square ice close to the center of the disk, they evolve continuously as the disk perimeter is approached. There, the spin liquid orders. As a result, pinch points, signaling the presence of algebraic spin correlations, coexist with magnetic Bragg peaks in the magnetic structure factor computed within the disk. The arctic square ice thus appears as an unconventional Coulomb phase sharing common features with a fragmented spin liquid, albeit on a charge-neutral vacuum.

Han Ma, Lei Zhong, Yunfei Fu et al.

Study region: The Fuhe River Basin in Jiangxi Province, China. Study focus: Global climate change and intensified human activities are making the hydrological processes at Fuhe River Basin experiencing dramatic changes. Although some studies have investigated their individual impacts on basin-scale water resources, their combined effects on hydrology have received little attention. In this study, future scenarios were constructed for three future periods, based on five global climate model outputs (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a dataset of future land use projections under three shared socioeconomic pathways and representative concentration pathways (SSP-RCPs). Then, the Soil and Water Assessment Tool (SWAT) model was used to assess the relative changes in water balance components and extreme flow frequency under these developed scenarios. Furthermore, the hydrological response assessment methodology was improved from the original multiscenario ensemble flow forecast (MESF) framework, which not only strengthens the connection between climate and land use input changes but also adds more assessment items. New hydrological insights for the region: The flow at the outlet of Fuhe River Basin is expected to increase by approximately 27.1%− 30.2%, 24.7–39.0% and 35.5%− 43.5% in the 2030 s, 2060 s and 2090 s, respectively. Water availability will increase significantly in February, August and October and decrease in November and December. To the end of 21st century, surface runoff will have more than 100% increase. Future floods and droughts will be more frequent and severe under SSP5–8.5.

Arunangshu Debnath, Robin Santra

We present a theoretical formulation for the multiphoton diffraction phenomenology in the nonrelativistic limit, suitable for interpreting high-energy x-ray diffraction measurements using synchrotron radiation sources. A hierarchy of approximations and the systematic analysis of limiting cases are presented. A convolutional representation of the diffraction signal allows classification of the physical resources contributing to the correlation signatures. The formulation is intended for developing a theoretical description capable of describing plausible absence or presence of correlation signatures in elastic and inelastic diffractive scattering. Interpreting these correlation signatures in terms of the incoming field modulated many-body electronic density correlations provides a novel perspective for structural imaging studies. More essentially, it offers a framework necessary for theoretical developments of associated reconstruction algorithms.