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Although now over 100 years old, the classification of climate originally formulated by Wladimir Köppen and modified by his collaborators and successors, is still in widespread use. It is widely used in teaching school and undergraduate courses on climate. It is also still in regular use by researchers across a range of disciplines as a basis for c...
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In the ocean, heat waves are vital climatic extremes that can destroy the ecosystem together with ensuing socioeconomic consequences. Marine heat waves (MHW) recently attracted public interest, as well as scientific researchers, which motivates us to analyze the current heat wave events over the Red Sea and its surrounding sea region (Gulf of Aden)...
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... Dominant soil types are Haplic and Stagnic Luvisols (IUSS Working Group WRB, 2022) indicating a wide range of soil textures from clay loams and silty clay loams to soils with a sandier texture (Gehrt et al., 2021;LBEG, 2020). According to Köppen & Geiger the study area is in the transition from temperate oceanic (Cfb) to temperate continental climate (Dfb) (Peel et al., 2007). The mean annual temperature is 9.8 ± 0.7 • C with a mean annual precipitation of 701 ± 103 mm for the period from 2008 to 2023 (weather station Alfeld, DWD, 2023). ...
Soil loss due to crop harvesting (SLCH) is a globally occurring and underestimated process that promotes soil degradation. Despite its negative effects on soil functionality and fertility SLCH has received comparatively little scientific attention to date. In Europe, sugar beets hold particular significance due to high production rates, while research in commercial mechanized farming of sugar beets is lacking. The aim of this study is to measure SLCH for sugar beets including nutrient and SOC losses using typical state of the art harvesters and compare that values to estimated SLCH provided by sugar beet factories. In addition, we tried to identify crop and soil variables that influence SLCH. Therefore, sugar beets and soil samples were collected for 14 sampling sites over a three-year period in Northern Germany to measure SLCH dependent on different crop characteristics, soil properties and weather conditions. The results indicate that SLCH is 0.064 kg per kg harvested sugar beet (SLCHspec) on the average, which corresponds to a loss of 5.7 Mg ha-1 harvest-1 (SLCHcrop). These numbers are higher than former comparable studies but also of about 83.3% higher than SLCH estimated by sugar beet factories. Additionally, amounts of SLCH considerably varied between years and fields, but also within fields. The most influential variables on SLCH are soil water content (SWC) and clay content, and we also observed that soil properties impact SLCH differently in relation to SWC. Moreover, we estimated that SLCH of sugar beets can lead to significant SOC and nutrient losses, latter resulting in direct costs for farmers of 18-34.4 € ha-1 harvest-1. The results confirm the importance of considering SLCH for soil degradation analyses and estimations and the need for models which spatially assess SLCH from field to global scales. This is important to explore soil conservation measures and strategies to reduce ongoing soil degradation especially in highly mechanized agriculture.
... It is bounded above by the Atakora Mountains, and forms a large Delta in the South at Cotonou, and discharges into the Gulf of Guinea at an average rate of 170 m 3 /s. According to the Koppen climate classification, the Ouémé River Basin falls in the Tropical Savanna climate zone [16]. Its largest tributaries are the Okpara River on the left and the Zou River on the right sides (Fig. 1). ...
The rapid increase in population and urban development are exacerbating the transformation of
natural environments into unnatural forms. While detailed assessment of the environment is
beneficial for efficient ecosystem system management, it can also be time and resourcesconsuming. This study aimed to map and quantify the spatio-temporal changes in land use and
land cover (LULC) using the Ou´em´e River Basin as a case study. The supervised classification in
Google Earth Engine (GEE) cloud-computing platform was employed to distinguish Landsat images for 1986, 2000, 2015 and 2023 into forest areas, settlements/bare lands, savanna areas
(woodlands), agricultural lands and water bodies. Analysis of the LULC changes revealed that
savanna areas and woodlands which were predominant in the basin in 1986 have steadily
declined by 24 % in area in 2023. Forest areas have diminished by 4.3 % at an annual rate of 4 %.
Agricultural lands have however grown exponentially by 28 % since 1986, with a more rapid
increase between 2015 and 2023 at an annual rate of 3.7 %, driven by rising food demand due to
population growth within and around the basin. Settlements and bare areas tripled in area,
reflecting a similar trend to Benin’s urban population growth. Accuracy statistics of the LULC
classification showed overall accuracy and kappa statistic values above 90 % and 86 %, respectively, indicating the admirable performance and reliability of the Simple Composite Landsat
algorithm for image composition, and the Random Forest Classifier for LULC classification
approach applied in this study. The approach also demonstrates the robustness and potential of
LULC mapping in large and complex ecosystems using the GEE cloud-based remote sensing tool,
which is underutilized in the study area. Overall, the LULC trends provide beneficial insights
useful to policy-makers and any other stakeholders involved in sustainable ecosystem management planning in the basin.
... Under the Köppen-Geiger climate classification, Finland is therefore a cold country, without dry season, and a place which has cold summers. In the future classification cold summers are expected to change to warm summers, while other parameters stay the same [58,59]. Studies conducted in other cold locations, like Växjö [21,22] or Karlshamn [23] in Sweden, fall under class Dfb in Köppen-Geiger climate classification [58], pointing out the differences in current climatic conditions. ...
The building sector is critical in climate change mitigation by reducing its energy consumption. At the same time, warming climate requires adaptation measures from buildings, making it necessary to study the future thermal energy demand of buildings. This paper studies the impact of climate change on the thermal energy demand of buildings in a heating dominated climate of Finland, under “representative concentration pathway” (RCP) climate scenarios in near- (2030), medium- (2050) and long-term (2080) horizon. Additionally, the impact of the thermal performance level of the buildings is investigated. The thermal energy demand calculation is based on overarching standards from the European “energy performance of buildings” directive. The weather scenarios present both typical and extreme weather years, applied through morphing and future weather data from the Finnish Meteorological Institute. The results show that annual thermal energy demand in Finland in 2080 can decrease up to 74.6 kWh/m2. In addition, the decrease in heating demand will be higher than the increase in cooling demand. Cooling can become the dominant peak in long-term evaluations, especially in South Finland (Vantaa) with passive buildings under RCP8.5 climate scenario. Renovation is the most efficient action to decrease energy intensity, the earliest it is done the better.
... The climate in this department according to the Köppen-Geiger classification is "Af" (tropical climate with forest rain) (Peel et al. 2007). In this department, 36 species of mosquitoes have been reported (Ayala et al. 2020). ...
Mosquitoes (Diptera: Culicidae) pose a significant threat to public health worldwide, especially in tropical and subtropical regions, where they act as primary vectors in transmission of infectious agents. In Peru, 182 culicid species have been identified and several species of the genus Culex are known to transmit arboviruses. However, knowledge of mosquito diversity and distribution remains limited, with many studies focusing on specific regions only. Here, we describe a new morphological variation of Cx. (Culex) coronator Dyar and Knab, 1906, and report the presence of Culex (Carrollia) bonnei Dyar, 1921 in the central region of Peru, Huanuco. Specimens were obtained through larvae collections and identified through morphologic characterization, including dissection of male genitalia, and molecular analyses. In total, 17 mosquitoes were analyzed, and the genitalia of the male specimens allowed the identification of Cx. coronator and Cx. bonnei. Partial sequences of the CoxI gene corresponding to these two species were obtained (N = 10). Phylogenetic analysis revealed that the sequences of Cx. coronator grouped in a monophyletic clade with sequences ascribed to other species corresponding to the subgenus Carrollia, while Cx. bonnei specimens formed a monophyletic clade with homologous sequences from GenBank. This study underscores the importance of continued efforts to study the diversity and distribution of mosquitoes in Peru, including their potential role as vectors of human pathogens, to underpin effective disease control and prevention strategies, highlighting the importance of a complemented morphological and molecular analysis.
... Such climate zones can be provided by users. Else, the 'climetrics' R package generates them using the updated version of Köppen-Geiger climate classification (Peel et al. 2007) for both the baseline (t 1 ) and the second time period (t 2 ) using temperature (minimum, mean, and maximum) and precipitation. The Köppen-Geiger climate classification relies on annual temperature and precipitation, which are subjected to a sufficiently large time or ensemble averaging. ...
... For instance, a user can choose the mean function as the input to calculate the monthly mean climate data for the 12 months. The 'kgc' function is another auxiliary function to calculate Köppen-Geiger (Köppen 1900, Peel et al. 2007) classification of climate data in a given location and time. The Köppen-Geiger system classifies the climate zones of the world into five main classes and 30 sub-classes (Chen andChen 2013, Beck et al. 2018) and is based on threshold values (Supporting information for criteria and legend) and seasonality of monthly temperature and precipitation. ...
Climate change affects biodiversity in a variety of ways, necessitating the exploration of multiple climate dimensions using appropriate metrics. Despite the existence of several climate change metrics tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed ‘climetrics' which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are implemented, including 1) standardized local anomalies; 2) changes in probabilities of local climate extremes; 3) changes in areas of analogous climates; 4) novel climates; 5) changes in distances to analogous climates; and 6) climate change velocity. For climate change velocity, three different algorithms are implemented in the package including; 1) distanced‐based velocity (‘dVe'); 2) threshold‐based velocity (‘ve'); and 3) gradient‐based velocity (‘gVe'). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen‐Geiger (KG) climate zones. The 'climetrics' R package is integrated with the 'rts' package for efficient handling of raster time‐series data. The functions in 'climetrics' are designed to be user‐friendly, making them suitable for less‐experienced R users. Detailed descriptions in help pages and vignettes of the package facilitate further customization by advanced users. In summary, the 'climetrics' R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.
... Given the strong overlap between vegetation types and climate zones (Rohli et al., 2015), the Köppen-Geiger climate classification, which relies solely on monthly mean temperature and precipitation, is a well-suited classification scheme to reclassify complex climate gradients into simple yet ecologically expressive ones. Introduced by Kӧppen in 1936, this classification system has garnered widespread acceptance worldwide and has undergone various updates over time (Feddema, 2005;Kottek et al., 2006;Peel et al., 2007;Thornthwaite, 1948). In contrast to earlier modifications by others, such as Trewartha, which ambiguously treated "highland" climates as a separate category, Köppen-Geiger climate classification scheme did not define the "highland" climate type. ...
... We used the algorithm developed by Peel et al. (2007) (Table S2 in Supporting Information S1) to classify the Köppen-Geiger climate zones into 30 sub-categories on a grid cell scale (0.25°× 0.25°). Each grid cell was assigned a specific class from 30 sub-categories. ...
Climate change represents a profound threat to the diversity and stability of global climate zones. However, the complex interplay between climate change and elevation in shaping climate heterogeneity is not yet fully understood. Here, we combine Shannon's diversity index (SHDI) with the Köppen‐Geiger climate classification to explore the altitudinal distributions of global climate heterogeneity; and their responses to climate change. The study reveals a distinctive pattern: SHDI, a proxy for climate heterogeneity tends to slow down or decline at lower elevations with increasing temperatures, while at higher elevations, it continues to rise due to continuing cold conditions. Examination of climate simulations, both with and without anthropogenic forcing, confirms that observed changes in climate heterogeneity are primarily attributable to anthropogenic climate change within these high‐elevation regions. This study underscores the importance of high‐elevation regions as not only custodians of diverse climate types but also potential refuges for species fleeing warmer climates.
... The experimental methodology was as identical as possible. Locations were as follows ( Figure 1): The area of Poland lies in the humid continental climate zone [19]. Its climate is also defined as transitional between a warm and rainy temperate climate and a snowy and forested boreal climate. ...
Soybean is the crop of the future, especially for countries with a high demand for food and feed protein. Therefore, soybean cultivation is moving north to countries at higher latitudes, where temperatures, photoperiodism, and rainfall distribution are not always able to meet soybean requirements. The aim of this study was to evaluate three sowing dates as a factor influencing soybean cultivars yield and seed chemical composition in agroclimatic conditions of south-western Poland. In the years 2016–2019, a field experiment was conducted in Lower Silesia region, near Wroclaw, with three sowing dates: early (mid-April), 10-day delayed (at the turn of April and May), and 20-day delayed (first half of May), and two soybean cultivars: Merlin and Lissabon. In this location, soybean sowing is recommended in mid-April, possibly at the turn of April and May. The cultivars tested differed in yield and yield component values in the years of research, but generally, the Lissabon was better suited to local conditions. Results were discussed with findings of other domestic research, to investigate the problem of the soybean sowing date in Poland. The recommended sowing date for soybean was found to vary from region to region. These differences are due to the length of the growing season in each location and the varied adaptation of cultivars to the local climatic conditions.
... The system assigns a combination of letters describing each climate zone, with the first letter indicating the major climate group and the second indicating the subtype. For example, a humid subtropical climate is classified as Cfa, where C represents temperate climates, f represents humid subtropical subtype, and A represents hot summer (Peel et al. 2007). ...
Various factors, including climate change and geographical features, contribute to the deterioration of railway infrastructures over time. The impacts of climate change have caused significant damage to critical components, particularly switch and crossing (S&C) elements in the railway network. These components are sensitive to abnormal temperatures, snow and ice, and flooding, making them susceptible to failures. The consequences of S&C failures can have a detrimental effect on the reliability and safety of the entire railway network.
It is crucial to have a reliable clustering of railway infrastructure assets based on various climate zones to make informed decisions for railway network operation and maintenance in the face of current and future climate scenarios. This study employs machine learning models to categorize S&Cs; therefore, historical maintenance data, asset registry information, inspection data, and weather data are leveraged to identify patterns and cluster failures. The analysis reveals four distinct clusters based on climatic patterns. The effectiveness of the proposed model is validated using S&C data from the Swedish railway network.
By utilizing this clustering approach, the whole of Sweden railway network divided into 4 various groups. Utilizing this groups the development of model can associated with enhancing certainty of decision-making in railway operation and maintenance management. It provides a means to reduce uncertainty in model building, supporting robust and reliable decision-making. Additionally, this categorization supports infrastructure managers in implementing climate adaptation actions and maintenance activities management, ultimately contributing to developing a more resilient transport infrastructure.
... We hypothesized that NT would improve SOC storage, soil fertility, and soil structure relative to CT (H1), leading to an overall greater soil health than CT (H2), and that greater soil health will be linked to higher soybean yields in the Lower Mississippi River basin (H3). (Peel et al., 2007). On a mean monthly basis, there are no dry summers or winters; precipitation is evenly distributed throughout the year, the average monthly minimum temperature falls between −3˚C and +18˚C, and the maximum temperature is >22˚C. ...
No‐till (NT) alleviates adverse effects of agricultural production on environmental quality, improves soil health, and reduces greenhouse gas emissions, yet this has not been demonstrated in humid subtropical Mississippi. Research objectives were to evaluate nutrient and C dynamics and soil quality via Soil Management Assessment Framework (SMAF) following 15 years of continuous management under corn (Zea mays L.) (2008–2018) and soybean [Glycine max (L.) Merr.] (2019–2022) on a Dundee silt loam soil. After 15 years, soil organic carbon (SOC) stocks (0‐ to 30‐cm depth) under NT were 25% higher than conventional tillage (CT). Between 2020 and 2022, SOC stocks increased 3 Mg ha⁻¹ under NT and decreased 2 Mg ha⁻¹ under CT. Increased SOC retention under NT could represent an opportunity for increasing the systems’ profitability as C markets expand. Greater SOC and aggregation scores improved SMAF soil quality index (SQI) under NT (71%), compared to CT (66%), underscoring the critical role of organic matter in soil functioning in managed agrosystems. Soybean yields were not linked to SMAF SQI (p > 0.05), suggesting that crop yields are mostly affected by non‐edaphic factors (e.g., climate extremes). SMAF illustrated positive impacts of NT on SOC sequestration and soil health, which may foster its adoption as a conservation practice in the Lower Mississippi River basin. As such, NT stands as a climate‐smart management practice that allows for sustainable intensification in humid subtropical regions.
... Climate classification is the process of categorizing different areas of the Earth based on meteorological factors, such as near-surface air temperature, precipitation, humidity, wind, and radiation [1][2][3][4]. Among these climate variables, near-surface air temperature and precipitation are the most widely used for climate classification due to their impact and relatively easy data availability [2,3,5]. ...
... Climate classification is the process of categorizing different areas of the Earth based on meteorological factors, such as near-surface air temperature, precipitation, humidity, wind, and radiation [1][2][3][4]. Among these climate variables, near-surface air temperature and precipitation are the most widely used for climate classification due to their impact and relatively easy data availability [2,3,5]. This classification is crucial for understanding the climatic features of various regions and has significant implications in agriculture, ecology, industry, urban construction, species migration, and even virus spread [6,7]. ...
... The traditional Köppen-Geiger method, pioneered in 1900, is based on the knowledge of climatologists, who use statistical data of meteorological elements to manually define classification standards [2,3,[8][9][10]. However, this method, with the aim of reflecting different surface vegetation distributions, has errors in expressing actual The analysis showed that average linkage clustering was more suitable for the study, resulting in higher accuracy for the new region compared with the existing regions, thereby providing more precise climate information [30]. ...
Climate classification plays a fundamental role in understanding climatic patterns, particularly in the context of a changing climate. This study utilized hourly meteorological data from 36 major cities in China from 2011 to 2021, including 2 m temperature (T2), relative humidity (RH), and precipitation (PRE). Both original hourly sequences and daily value sequences were used as inputs, applying two non-hierarchical clustering methods (k-means and k-medoids) and four hierarchical clustering methods (ward, complete, average, and single) for clustering. The classification results were compared using two clustering evaluation indices: the silhouette coefficient and the Calinski–Harabasz index. Additionally, the clustering was compared with the Köppen–Geiger climate classification based on the maximum difference in intra-cluster variables. The results showed that the clustering method outperformed the Köppen–Geiger climate classification, with the k-medoids method achieving the best results. Our research also compared the effectiveness of climate classification using two variables (T2 and PRE) versus three variables, including the addition of hourly RH. Cluster evaluation confirmed that incorporating the original sequence of hourly T2, PRE, and RH yielded the best performance in climate classification. This suggests that considering more meteorological variables and using hourly observation data can significantly improve the accuracy and reliability of climate classification. In addition, by setting the class numbers to two, the clustering methods effectively identified climate boundaries between northern and southern China, aligning with China’s traditional geographical division along the Qinling–Huaihe River line.
