Hasil untuk "Physical geography"

Alireza Sharifi, Bayan Alabdullah

This paper tackles a key challenge for protecting our coasts: quickly and accurately identifying different coastal landscapes from aerial photos. We present a smart AI system that combines deep learning with powerful boosting algorithms. Our method uses a pretrained neural network (ResNet18) to extract detailed visual features from high-resolution RGB images of Beaches, Rivers, and Ports, taken from the diverse AID dataset. These features are then classified by an XGBoost model, creating a robust fusion of techniques. Trained on globally sourced Google Earth imagery, the system proves highly effective across different sensors. It achieves an excellent 94.1% accuracy and F1-score, reliably distinguishing between visually similar scenes like beaches and rivers. This work demonstrates a practical and accurate tool for coastal monitoring, supporting better management of these vital ecosystems.

Seyed Vahid Razavi-Termeh, Ali Pourzangbar, Abolghasem Sadeghi-Niaraki et al.

Managing and controlling costly natural hazards such as floods has been a fundamental and essential issue for decision-makers and planners from the past to the present. Artificial intelligence (AI) has recently proven promising to improve disaster management. There is growing interest in using AI to predict and identify flood-prone areas. However, creating accurate flood susceptibility maps with AI remains a significant challenge. Therefore, the present work endeavors to cope with this gap and produce the most efficient flood susceptibility maps employing Categorical Boosting (CatBoost) algorithms and three system-based metaheuristic methods, including Augmented Artificial Ecosystem Optimization (AAEO), Germinal Center Optimization (GCO), and Water Circle Algorithm (WCA). We selected Jahrom County, Iran, to develop machine learning-based models as our case study. We used 13 flood conditioning geophysical factors as input parameters and flood occurrence (binary classification), derived from satellite imagery, as the output. Our results show that CatBoost-AAEO performs better in flood susceptibility mapping than the other combined models, CatBoost-WCA, CatBoost-GCO, and the basic CatBoost model, which are mentioned in descending order of performance. The partial Dependence Plots (PDP) approach is used to interpret the results of the developed algorithms, highlighting that low slope, low elevation, minimal vegetation cover, flat curvature, and proximity to rivers significantly impact the performance of ML models to predict flood occurrence. The findings of this research can help planners manage and prevent floods and avoid development in sensitive areas to reduce financial losses caused by floods.

Jihoon Shin, Jong‐Jin Baik

Abstract We extend the previously developed stochastic unified convection scheme (UNICON) for shallow convection to deep convection by parameterizing the impact of mesoscale organized flow on updraft properties. The extended stochastic UNICON parameterizes thermodynamic properties of updrafts at the near‐surface as a multivariate Gaussian distribution, where the variances of the distribution are the summation of variances from non‐organized turbulence and mesoscale organized flow. The distribution of updraft radius is parameterized as a power‐law distribution with a scale break which is parameterized as a linear function of the strength of mesoscale organized flow. The proposed parameterization is validated using a series of large‐eddy simulations of deep convection. The free parameters introduced in the formulation of stochastic UNICON are optimized using 10 cases of single‐column model simulations over the ocean. Stochastic UNICON with the optimized parameters significantly reduces the biases of thermodynamic profiles and surface precipitation rates simulated in the original UNICON for tropical convection cases. The simulation of the variation in anomalies of temperature and moisture associated with the Madden‐Julian oscillation is also improved. The overall improvements in simulated thermodynamic profiles are found to be due to the increased heating and drying tendencies by convective processes in stochastic UNICON. An additional simulation of an idealized deep convection case shows that stochastic UNICON produces enhanced cloud variabilities with dependency on updraft radius, indicating its ability to represent the coexistence of shallow and deep convection.

Suming Jin, Jon Dewitz, Congcong Li et al.

The National Land Cover Database (NLCD) 2016 products show that, between 2001 and 2016, nearly half of the land cover change in the conterminous United States (CONUS) involved forested areas. To ensure the quality of NLCD land cover and land cover change products, it is important to accurately detect the location and time of forest disturbance. We designed a comprehensive strategy to integrate a continuous time series forest change detection method and a discrete 2-date forest change detection method to produce the NLCD 1986–2019 forest disturbance product, which shows the most recent forest disturbance date between the years 1986 and 2019 for every 2- to 3-year interval. This method, the Time-Series method Using Normalized Spectral Distance (NSD) index (TSUN), uses NSD to detect multi-date forest land cover changes and was shown to be easily extended to a new date even when new images were processed in a different way than previous date images. The discrete 2-date method uses the Multi-Index Integrated Change Analysis (MIICA) method to detect changes between 2-date images. A method based on confidence and object grouping was designed to combine the multiple MIICA outputs to improve change detection accuracy. Finally, an aggregation scheme was implemented to combine the TSUN output, the integrated MIICA results, and ancillary data to produce the NLCD 2019 forest disturbance 1986–2019 product. The initial accuracy assessments from 1,600 samples over 4 Landsat path/rows show that the producer’s and user’s accuracies of the 2001–2019 forest disturbance map are 76% and 74%, respectively. The final CONUS-wide forest disturbance product is provided at https://www.mrlc.gov/nlcd-2019-science-research-products.

Omid Memarian Sorkhabi, Jamal Asgari

Study region: The Kabudarahang Plain and the Razan-Qahavand Plain. Study focus: Improper use of water resources has reduced groundwater levels and created land subsidence (LS) in many plains of Iran. The aim and innovation of this research are to study multi-sensor observations for LS and groundwater depletion and explore the relationships of the involved variables with high confidence. The gravity recovery and climate experiment (GRACE) observations can be used to evaluate water storage changes at the Earth’s surface. GRACE has stripe errors, leakage and various noises that multilevel 3D wavelet decomposition (M3WD) has been suggested to mitigate noises and downscale for small scale. This study has investigated the interferometric synthetic-aperture radar (InSAR) of Sentinel-1 images from October 2014 to September 2019, the GRACE data from March 2002 to July 2016, and groundwater hydrograph (GH) from 2014 to 2020. New hydrological insight for the region: The maximum LS rate, obtained from small baseline subset-differential of InSAR is 20 mm/year at the Kabudarahang Plain (KP) and 30 mm/year at Razan-Qahavand Plain (RQP). The groundwater storage variations (ΔGW) have a decreasing trend of 78.45 ± 0.2 million cubic meters/year. The GH for the KP and RQP shows a downward trend of 3.25 and 1.81 m/year, respectively. Based on the outcomes, the M3WD can increase the correlation of ΔGW with other sensors by 15 %. Also, validation between sensors with normalized cross-correlation has remarkable compatibility. The multi-sensor study of ΔGW and LS revealed various dimensions with high reliability and can facilitate the water resource management.

Yongqing Li, Weimin Huang, Xinrong Lyu et al.

Reanalysis wind fields are obtained by correcting the numerically forecasted wind fields based on observation data (i.e., either remote sensing or in-situ observations, or both). Although they are more accurate than forecasted wind fields, reanalysis wind fields tend to have time latencies because they can only be released after the observations are obtained. In order to produce accurate estimates of wind fields in a more timely manner, we develop an adversarial learning approach to correcting forecasted wind fields to be close to reanalysis wind fields. The adversarial learning approach is conducted by an adversarial ConvLSTM network (ACLN) framework that consists of a corrector and a discriminator. The corrector aims at comprehensively capturing both spatial and temporal characteristics of a sequence of forecasted wind fields and producing a corrected forecast wind field for the final field in the sequence. The discriminator tries to distinguish corrected forecast wind field from its corresponding reanalysis wind field. The training of ACLN is alternate between the corrector and the discriminator in an adversarial fashion. The adversarial training mechanism enhances the corrector’s representational power. Additionally, the corrector exploits a residual learning architecture that effectively learns the differences between forecasted wind fields and the corresponding reanalysis wind fields. In this scenario, the well trained corrector requires neither reanalysis wind fields nor observations such that it can correct forecasted wind fields in a timely manner. Furthermore, corrected forecast wind fields are employed for oil spill drift prediction. Extensive experiments validate the effectiveness of the proposed ACLN framework in forecasted wind field correction along with oil spill drift prediction. Compared with ECMWF numerical forecasts, the ACLN achieves an average reduction of 6.2%, 6.9%, and 10.6% in RMSE, MAE, and MAPE, respectively. Compared with a basic drift prediction method, the ACLN based prediction method reduces the error by about 5000 m in the Sanchi oil spill accident. The source codes are available at https://github.com/liyongqingupc/ACLN-WindFieldCorrection, providing a baseline for correcting forecasted wind fields.

Sajjad Hussain, Shujing Qin, Wajid Nasim et al.

Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops (cotton, rice, wheat, and sugarcane) in the District Vehari, Pakistan, from 1984 to 2020 using remote sensing (RS) technology. The crop identification was pre-processed in ArcGIS software based on Landsat images. After pre-processing, supervised classification was used, which explains the maximum likelihood classification (MLC) to identify the vegetation changes. Our results showed that in the study area cultivated areas under wheat and cotton decreased by almost 5.4% and 9.1% from 1984 to 2020, respectively. Vegetated areas have maximum values of NDVI (>0.4), and built-up areas showed fewer NDVI values (0 to 0.2) in the District Vehari. During the Rabi season, the temperature was increased from 19.93 °C to 21.17 °C. The average temperature was calculated at 34.28 °C to 35.54 °C during the Kharif season in the District Vehari. Our results showed that temperature negatively affects sugarcane, rice, and cotton crops during the Rabi season, and precipitation positively affects sugarcane, rice, and cotton crops during the Kharif season in the study area. Accurate and timely assessment of crop estimation and relation to climate change can give very useful information for decision-makers, governments, and planners in formulating policies regarding crop management and improving agriculture yields.

Vamshi Karanam, Mahdi Motagh, Shagun Garg et al.

The subsidence in coal mines induced by surface and subsurface fires leading to roof collapse, infrastructure loss, and loss of lives is a prominent concern. In the study, satellite imagery from thermal and microwave remote sensing data is used to deduce the effect of coal fires on subsidence in the Jharia Coalfields, India. The Thermal Infrared data acquired from the Landsat-8 (band 10) is used to derive the temperature anomaly maps. Persistent Scatterer Interferometry analysis was performed on sixty Sentinel-1, C-band images, the results are corrected for atmospheric error using Generic Atmospheric Correction Online Service for InSAR (GACOS) atmospheric modelling data and decomposed into vertical displacement values to quantify subsidence. A zone-wise analysis of the hazard patterns in the coalfields was carried out. Coal fire maps, subsidence velocity maps, and land cover maps were integrated to investigate the impact of the hazards on the mines and their surroundings. Maximum subsidence of approximately 20 cm/yr. and temperature anomaly of up to 25 °C has been observed. The findings exhibit a strong positive correlation between the subsidence velocity and temperature anomaly in the study area. Kusunda, Keshalpur, and Bararee collieries are identified as the most critically affected zones. The subsidence phenomenon in some collieries is extending towards the settlements and transportation networks and needs urgent intervention.

Mostafa Dastorani, Vahid Safarianzengir, Bromand Salahi

Introduction: The present study investigated one of these types of disease (skin cancer) and its relationship with climatic parameters. The aim of this study was to investigate the relationship between climate change and skin cancer in Ardabil province. Materials and Methods: This descriptive correlational study was conducted to investigate the effect of six climatic parameters (frost, sunny hours, minimum mean humidity, maximum absolute temperature, minimum absolute temperature, and mean temperature) on skin cancer in Ardabil province in a 3-year statistical period (2012-2014). The data were analyzed using the Spearman correlation relationship in SPSS version 24 software, also Minitab version 16 software was used for linear interpolation. Results: According to the findings, the highest correlation (more than 95%) of skin cancer in three cities of Parsabad, Khalkhal, and Ardabil with the climatic parameter was related to minimum absolute temperature. However, in Khalkhal station in three years of study, sunny hours had the highest correlation and the lowest correlation was related to glacial climate parameter in all four cities. It can be said that the factors of sunny hours and maximum temperature have an effect on the incidence of skin cancer, and the minimum absolute temperature increases the exacerbation of this type of disease. Conclusion: According to the results of statistical correlation and the effects of climatic parameters on skin cancer, it can be concluded that climate parameters are one of the effective factors in skin cancer.

Tian Hu, Albert I.J.M. van Dijk, Luigi J. Renzullo et al.

Monitoring agricultural drought effectively and timely is important to support drought management and food security. Effective drought monitoring requires a suite of drought indices to capture the evolution process of drought. Thermal infrared signals respond rapidly to vegetation water stress, thus being regarded useful for drought monitoring at the early stage. Several temperature-based drought indices have been developed considering the role of land surface temperature (LST) in surface energy and water balance. Here, we compared the recently proposed Temperature Rise Index (TRI) with several agricultural drought indices that also use thermal infrared observations, including Temperature Condition Index (TCI), Vegetation Health Index (VHI) and satellite-derived evapotranspiration ratio anomaly (ΔfRET) for a better understanding of these thermal infrared drought indices. To do so, we developed a new method for calculating TRI directly from the top-of-atmosphere brightness temperatures in the two split-window channels (centered around ∼11 and 12 μm) rather than from LST. TRI calculated using the Himawari-8 brightness temperatures (TRI_BT) and LST retrievals (TRI_LST), along with the other LST-based indices, were calculated for the growing season (July–October) of 2015−2019 over the Australian wheatbelt. An evaluation was conducted by spatiotemporally comparing the indices with the drought indices used by the Australian Bureau of Meteorology in the official drought reports: the Precipitation Condition Index (PCI) and the Soil Moisture Condition Index (SMCI). All the LST-based drought indices captured the wet conditions in 2016 and dry conditions in 2019 clearly. Ranking of Pearson correlations of the LST-based indices with regards to PCI and SMCI produced very similar results. TRI_BT and TRI_LST showed the best agreement with PCI and SMCI (r > 0.4). TCI and VHI presented lower consistency with PCI and SMCI compared with TRI_BT and TRI_LST. ΔfRET had weaker correlations than the other LST-based indices in this case study, possibly because of outliers affecting the scaling procedure. The capability of drought early warning for TRI was demonstrated by comparing with the monthly time series of the greenness index Vegetation Condition Index (VCI) in a case study of 2018 considering the relatively slow response of the greenness index to drought. TRI_BT and TRI_LST had a lead of one month in showing the changing dryness conditions compared with VCI. In addition, the LST-based indices were correlated with annual wheat yield. Compared to wheat yields, all LST-based indices had a peak correlation in September. TRI_BT and TRI_LST had strong peak and average correlations with wheat yield (r ≥ 0.8). We conclude that TRI has promise for agricultural drought early warning, and TRI_BT appears to be a good candidate for efficient operational drought early warning given the readily accessible inputs and simple calculation approach.

H. Beydoun, C. Hoose

Abstract We investigate the sensitivity of self‐aggregated radiative‐convective‐equilibrium cloud‐resolving model simulations to the cloud condensation nuclei (CCN) concentration. Experiments were conducted on a long (2,000‐km × 120‐km) channel domain, allowing the emergence of multiple convective clusters and dry regions of subsidence. Increasing the CCN concentration leads to increased moisture in the dry regions, increased midlevel and upper level clouds, decreased radiative cooling, and decreased precipitation. We find that these trends follow from a decrease in the strength of the self‐aggregation as measured by the moist static energy (MSE) variance. In our simulations, precipitation is correlated, both locally and in total, with the distribution of MSE anomalies. We thus quantify changes in the adiabatic/diabatic contributions to MSE anomalies (Wing & Emanuel, 2014, https://doi.org/10.1002/2013MS000269) and relate those changes to changes in precipitation. Through a simple two‐column conceptual model, we argue that the reduction in precipitation can be explained thermodynamically by the reduction in mean net radiative cooling and mechanistically by the weakening of the area‐weighted radiatively driven subsidence velocity—defined as the ratio of the total radiative cooling over the dry regions and the static stability. We interpret the system's response to increasing CCN as a thermodynamically constrained realization of an aerosol indirect effect on clouds and precipitation.

Anna Kovacs-Györi, Alina Ristea, Ronald Kolcsar et al.

Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations.

Silvina López, Guillermo Antonio Arce, Anibal Marcelo Mignone et al.

<p>Actualmente, el sistema conformado por la ciudad de Corrientes y los centros urbanos y rurales próximos de los municipios de Santa Ana, Riachuelo, San Luis del Palmar, Paso de la Patria y San Cosme, con los que mantiene una fuerte interrelación funcional y económica, evidencia un gran desequilibrio tanto en peso poblacional como en complejidad de las actividades urbanas. Esta situación evidencia un proceso de expansión dispersa de la ciudad Capital, donde la localización de las actividades productivas; la escasa conectividad; la incorporación de suelo periurbano y rural; los desarrollos residenciales en áreas peri-urbanas, han conformando una compleja sucesión de espacios de transición entre lo urbano y lo rural.<br />En este marco, el trabajo apunta a evidenciar variaciones y tendencias en la localización de la población en la capital y su área de influencia directa, relacionándolas con las formas de ocupación, los cambios de usos de suelo y el fenómeno de movilidad residencial, de manera tal que permitan visibilizar la configuración procesos de metropolización y micro regionalización, con vista a la definición de criterios para la planificación y ordenamiento territorial del espacio de estudio.<br /><br /><br /></p>

Marcela Maciel de Araújo, Claudia Zveibel Toporovski Rebelo, Tatiane Nogueira Aikawa et al.

Muitos parâmetros utilizados nos modelos de intrusão de vapores provêm das características do solo e das edificações que são difíceis de caracterizar, além disso, a maioria desses modelos considera a propagação forçada do vapor para o interior das edificações. Ass im sendo, os resultados obtidos nos modelos usuais para obtenção do fator de atenuação pode muitas vezes não representar não a real situação deste risco. Nesse contexto, realizou uma pesquisa bibliográfica visando a identificação e seleção dos métodos mais adequados a serem utilizados. O método que considera o fluxo ascendente do metano, além d os resultados de subslab se mostrou mais adequado.

Leandro Diomério João dos Santos, Évio Marcos de Lima, Cláudio José Cabral et al.

O Recife iniciou seu processo de ocupação no XVI com o povoamento da unidade geomorfológica restinga. Nesse contexto, o trabalho tem como objetivo primordial o estudo do processo de ocupação do bairro do Recife Antigo, o qual tem seu sítio urbano assentado na restinga. A pesquisa vem a contribuir para o entendimento do desenvolvimento do bairro e a modificação da dinâmica natural presente na área. O trabalho foi realizado através do estudo de material histórico de Pernambuco para verificar o quanto à área foi urbanizada. Posteriormente, com a seleção de imagens da restinga ao longo dos anos, foi realizada uma relação com a morfodinâmica da unidade restinga. Nos séculos XVI e XVII a ocupação foi se intensificando a medida que o comércio aumentou e surgiu a necessidade de mais espaço para a urbanização. Assim, começaram os aterros e as construções de cunho mais intenso na restinga e nos demais séculos houve mudanças bruscas tendo a dinâmica da restinga sido alterada para de uma ilha fluvial.

Kleber Borges de Assis

Destaca o objetivo de longa data de ligar as bacias dos rios Jacuí e Ibicuí. Na época, estudos preliminares para a realização da obra estavam sendo realizados. Artigo originalmente publicado no jornal Correio do Povo.

C. Sagan, O. B. Toon, J. Pollack

Mc Ilwain

Isimar de Azevedo Santos, Julio Buchmann

The atmosphere has often been considered “chaotic” when in fact the “chaos” is a manifestation of the models that simulate it, which do not include all the physical mechanisms that exist within it. A weather prediction cannot be perfectly verified after a few days of integration due to the inherent nonlinearity of the equations of the hydrodynamic models. The innovative ideas of Lorenz led to the use of the ensemble forecast, with clear improvements in the quality of the numerical weather prediction. The present study addresses the statement that “even with perfect models and perfect observations, the ‘chaotic’ nature of the atmosphere would impose a finite limit of about two weeks to the predictability of the weather” as the atmosphere is not necessarily “chaotic”, but the models used in the simulation of atmospheric processes are. We conclude, therefore, that potential exists for developments to increase the horizon of numerical weather prediction, starting with better models and observations.

David G. Jenkins

Limnology has been greatly influenced by <em>The Lake as a Microcosm</em> (Forbes, 1887), which described a holistic focus on the internal machinations of singular, island-like aquatic ecosystems. I consider three persistent influences of <em>The Lake as a Microcosm</em>: as an organizing paradigm for the teaching of limnology relative to its practice; the idea that inland waters are like islands, and the replicability of types of inland waters. Based on inspection of recent peer-reviewed literature and 32 limnology texts, we teach limnology according to Forbes but do not practice it in that holistic context. Instead, we practice limnology as aquatic ecology. Based on novel analyses of species-area relationships for 275 inland waters and 392 islands, inland waters are more like continental habitat patches than islands; the island metaphor is poetic but not accurate. Based on a quantitative review of beta diversity (40 data sets representing 10,576 inland waters and 26 data sets representing 1529 terrestrial sites), aquatic systems are no more replicable than are terrestrial systems; a typological approach to limnology is no more justified than it is in terrestrial systems. I conclude that a former distinction between limnology and aquatic ecology no longer applies, and that we should define limnology as the ecology of inland waters. Also, we should not consider lakes and rivers as islands that represent other systems of the same type, but should consider them as open, interactive habitat patches that vary according to their geology and biogeography. I suggest modern limnology operates according to 3 paradigms, which combine to form 3 broad limnological disciplines and establish a basis for a plural, interactive view of lakes and rivers as microcosms. This model of modern limnology may help better connect it to ecology and biogeography and help limnology be even more relevant to science and society.