Normalized difference vegetation index (NDVI) is the most widely used factor in the growth status of vegetation, and improving the prediction of NDVI is crucial to the advancement of regional ...ecology. In this study, a novel NDVI forecasting model was developed by combining time series decomposition (TSD), convolutional neural networks (CNN) and long short-term memory (LSTM). Two forecasting models of climatic factors and four NDVI forecasting models were developed to validate the performance of the TSD-CNN-LSTM model and investigate the NDVI's response to climatic factors. Results indicate that the TSD-CNN-LSTM model has the best prediction performance across all series, with the RMSE, NSE and MAE of NDVI prediction being 0.0573, 0.9617 and 0.0447, respectively. Furthermore, the TP-N (Temperature & Precipitation-NDVI) model has a greater effect than the T-N (Temperature-NDVI) and P-N (Precipitation-NDVI) models, according to the climatic factors-based NDVI forecasting model. Based on the results of the correlation analysis, it can be concluded that changes in NDVI are driven by a combination of temperature and precipitation, with temperature playing the most significant role. The preceding findings serve as a helpful reference and guide for studying vegetation growth in response to climate changes.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This study examined the spatio-temporal dynamics of malaria epidemiological patterns considering environmental(vegetation, water bodies, slope, elevation) and climatic factors (rainfall, temperature ...and relative humidity) in Ondo State, Nigeria, from 2013 to 2017 using ArcGIS 10.4 and QGIS software. The factors influencing malaria were studied using a multi-criteria analysis (Analytical Hierarchical Process-AHP). The trend analysis revealed an increase in cases over time, indicating a significant increase in the occurrence of malaria in all study areas. The most important climatic variable impacting malaria transmission in the study was temperature. Nevertheless, other environmental and climatic factors causing transmission include vegetation, water bodies, slopes, elevation, rainfall, and relative humidity. With the exception of Okitipupa, the study identified high-risk locations (vulnerable areas/hot spots) in almost all of the local government areas, while Ondo East, Akure South, Akoko South West, and Owo are the most vulnerable areas. The findings reveal that the malaria incidence is high in the developed LGAs having more towns where temperature is higher due to several anthropogenesis activities, high population and increased land-use. Thus, in-depth epidemiological studies on malaria should be undertaken in Ondo State and other regions of Nigeria considering environmental factors impacting malaria incidence as this will enable one to ascertain the major factors influencing the disease, thereby taking adequate measures to curb the increase in incidence.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
1 We reviewed worldwide spatial patterns in the food habits of the brown bear Ursus arctos in relation to geographical (latitude, longitude, altitude) and environmental (temperature, snow cover depth ...and duration, precipitation, primary productivity) variables. 2 We collected data from 28 studies on brown bear diet based on faecal analysis, covering the entire geographical range of this widely distributed large carnivore. We analysed separately four data sets based on different methods of diet assessment. 3 Temperature and snow conditions were the most important factors determining the composition of brown bear diet. Populations in locations with deeper snow cover, lower temperatures and lower productivity consumed significantly more vertebrates, fewer invertebrates and less mast. Trophic diversity was positively correlated with temperature, precipitation and productivity but negatively correlated with the duration of snow cover and snow depth. Brown bear populations from temperate forest biomes had the most diverse diet. In general, environmental factors were more explicative of diet than geographical variables. 4 Dietary spatial patterns were best revealed by the relative biomass and energy content methods of diet analysis, whereas the frequency of occurrence and relative biomass methods were most appropriate for investigating variation in trophic diversity. 5 Spatial variation in brown bear diet is the result of environmental conditions, especially climatic factors, which affect the nutritional and energetic requirements of brown bears as well as the local availability of food. The trade‐off between food availability on the one hand, and nutritional and energetic requirements on the other hand, determines brown bear foraging decisions. In hibernating species such as the brown bear, winter severity seems to play a role in determining foraging strategies. Large‐scale reviews of food habits should be based on several measures of diet composition, with special attention to those methods reflecting the energetic value of food.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
This article presents an ecological research carried out during 8 years on the population of Parectopa robiniella living in the Northern area of the Arges county inhabited by black locusts. This ...article presents information regarding the type of distribution, variation, density, the effects of the attacks, the effects of parasitoids, correlations between the dynamics of the population and climatic factors. Besides the species we studied on black locust leaves, we also identified the Phyllonorycter robiniella and Obolodiplosis robiniae species which allowed us to calculate the niche overlap.
Precipitation concentration is a key climatic factor in the hydrologic system. The purpose of this study was to explore the spatiotemporal variation in precipitation concentration and its possible ...relationship with large‐scale atmospheric circulation and land use types. Based on daily precipitation recorded at 254 stations during 1961–2019 throughout the Haihe River basin (HRB), linear slope and Manner–Kendall trend analysis were used to analyse the spatial variations and trends of the annual daily precipitation concentration index (CI). The Pearson correlation coefficient and cross wavelet analysis were used to evaluate potential correlations between CI and eight climatic factors. Meteorological stations were classified into three land use types: urban type (UT), farmland type (FT), and natural type (NT) using hierarchical clustering, and the impacts of land use types on CI variations and trends were investigated. Annual CI in HRB showed a significant downward trend with a linear rate of −0.0058/decade, and about 210 of the 254 stations had a downtrend, in which 42 stations were at the significance level of 0.05 but did not reach the significance level of 0.01, and 52 stations are at the 0.01 significance level. The East Asian monsoon index (EASMI), South Asian monsoon index (SASMI), South China Sea monsoon index (SCSMI), and Southern Oscillation Index (SOI) were positively correlated with CI, whereas the Pacific Decadal Oscillation (PDO), Multivariate ENSO index (MEI), and Western Pacific index (WP) were negatively correlated. The Sunspot index (SS) had a significant resonance period of 9–14‐year with CI. EASMI and ENSO events were the dominant factors driving CI trends. The negative CI trends of UT were more significant than those of FT and NT, with linear slope gaps of −0.0041/decade (UT versus FT) and −0.0064/decade (UT vs. NT), respectively.
We investigated trends in daily precipitation concentration, and its potential correlation with climatic factors, as well as effects of land use types on it. The results indicate that EASMI and ENSO events were the dominated factors driving CI trends. The negative CI trends of urban type were more significant than those of farmland type and natural type.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Forecasting water demand based on past consumption patterns is one of the main methods of planning the water supplies in urban areas, especially where water shortage and multiple droughts occur. ...Water consumption is affected by different environmental factors of which one may refer to climatic factors. In this purpose, two models were designed to predict water consumption with regard to the role of climatic factors. In the pre-processing stage, the outlier data were identified and smoothed in a box diagram, then the relationship between climatic factors and water consumption was investigated through Pearson’s correlation coefficient. In the analysis stage, two innovative hybrid methods known as Univariate-LSTM (UV-LSTM) and Multivariate-LSTM (MV-LSTM) were adopted to predict the monthly water consumption via two methods, including the Long Short-Term Memory (LSTM) network and the correlation coefficient results. The monthly water consumption by the urban users and the climatic factors in Yazd, Iran, from 2011 to 2020 were used as the data to test the proposed models. The results showed that the average temperature had the greatest positive effect on the water consumption, so it was considered as an input variable in the MV-LSTM model. Also, comparing the predictive performances of the two models via the root mean squared error showed that the MV-LSTM model outperforms the UV-LSTM.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
For durum wheat quality assessment the commonly used parameters are protein content, yellow pigment content, hectoliter mass, grain vitreousness, 1000-grain weight and sodium dodecyl sulphate ...sedimentation. For wheat processing quality, in this study the Mixolab, instrument of a newer generation was used. Mixolab has been largely used for a rapid assessment of the Triticum aestivum quality but there is no a lot of data about durum wheat quality assesment. Therefore, the aim of this work was to test its potential in the quality characterization of fourteen durum wheat breeding lines grown during two production years with different climate conditions. The obtained results showed significant differences in starch-amylase complex part of Mixolab curve between two studied years. Mixolab parameters C3, C4 and C5 were in line with Falling Number values and amylolytic activity of samples. Samples from 2013 production year with higher precipitation sum had lower values of C3, C4 and C5 parameters as well as Falling Number values and higher amylolytic activity. On contrary, protein part of Mixolab curves expressed differences in dependence of genotype. In comparison to the standard parameters of protein and starch quality of durum wheat, Mixolab provides more complete information in a shorter time frame.
•Mixolab can successfully be used for durum wheat genotypes characterization.•Regarding the protein quality, Mixolab indicates the differences between genotypes.•Regarding the starch quality, Mixolab indicates dominant production year influence.•Mixolab provides more complete information on semolina quality than standard methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Limited attention has been given to the drivers of customer behavior that originate from less direct factors, such as weather. Weather is known to significantly alter consumers’ moods and ...consequently their behavior. Building on the theoretical alignment between weather, mood, and consumer behavior, this research examined how specific weather factors drive the valence of consumer comments. Furthermore, we explore the relationship between perceived weather, consumers’ moods and affective experience, and word-of-mouth. By analyzing secondary data from 32 restaurants belonging to a national fast-casual chain, this research demonstrates that weather factors such as rain, temperature, and barometric pressure drive consumers’ complaint behavior in restaurants. Additionally, the findings of a survey study and an experimental study indicate that mood and affective experience mediate the relationship between perceived weather and word-of-mouth.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Periglacial processes and landforms together with the presence of permafrost are among the most relevant geomorphological elements in the northern Antarctic Peninsula region. Their distribution ...affects the hydrology and has consequences for the ecosystems of the ice-free areas. In this paper a compilation of the different types of periglacial landforms and processes occurring in the South Shetland Islands is carried out and their spatial distribution is analysed. Furthermore, the relationships of the periglacial landforms with local conditions and permafrost distribution have been taken into account. A total of thirty three types of periglacial landforms were identified and considered in this work. Patterned ground and stone fields are the most common periglacial landforms, which are located within a wide altitudinal range and mainly on platforms. Field studies, aerial photograph and satellite imagery interpretation were implemented to produce detailed maps from ten areas with different geological, geomorphological and relief characteristics, including the largest and most relevant ice-free areas within the archipelago, showing the presence and spatial distribution of periglacial landforms. This work shows that the periglacial environment, primarily conditioned by the regional climatic conditions, has a great diversity in the studied region and that the distribution of the periglacial landforms is also related to local relief and geomorphological characteristics, lithology, hydrogeology, and presence of permafrost where altitude plays an important role. Periglacial phenomena are widespread above 10m a.s.l. and are especially active on slopes and platforms between 30 and 100m a.s.l. The spatial distribution of periglacial landforms helps to identify the presence of permafrost that is dominant above 25–30m a.s.l. and more than 70% of the surface is occupied by active layer-related landforms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
While global climate change is impacting biota across the world, New Zealand’s maritime climate is highly variable and relatively mild, so climate change is sometimes seen as a minimal threat to ...species and ecosystems especially in comparison to the more immediate threat of invasive species. However, climate change will alter rainfall patterns, increase the incidence and severity of extreme events, and gradually increase temperatures which will all modify terrestrial, freshwater, and marine systems. Our comprehensive review of reported climate change impacts in New Zealand indicates that most measured impacts to date are due to indirect impacts (such as exacerbation of invasive species impacts) and most are in the marine realm. Ocean acidification and marine heatwaves are particularly problematic for calcareous organisms and algae respectively. Other notable impacts include thermal squeeze in the alpine zone and impacts of drought on freshwater fish. Very small populations of rare and threatened species can be very vulnerable to extreme events (e.g. fire, floods). While the evidence for climate change impacts is sparse in some regions and for some ecosystems, we encourage ongoing monitoring to identify processes of decline that may need to be mitigated. We identify five key research needs to improve our understanding of the threat of climate change to the biodiversity of Aotearoa New Zealand.
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BFBNIB, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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