Background: The number of dengue cases reported to WHO increased over 8-fold over the last two decades, from 505,430 cases in 2000, to over 2.4 million in 2010, and 5.2 million in 2019. The disease ...is now endemic in more than 128 countries with Asia representing ~70% of the global burden of disease. The transmission of dengue is dependent on various environmental factors and socio-demographic and economic factors. This study aimed to describe the climatic factors and vector Indices in the occurrence of dengue fever cases in Northern Kerala. Objectives: 1) To describe the epidemiological factors for occurrence of dengue cases in Northern Kerala. 2) Serological typing of Dengue confirmed cases admitted in a tertiary care Centre in Northern Kerala. Methodology: Data of dengue fever cases admitted to a tertiary care centre in Northern Kerala for a period of one year were collected from Regional Prevention of Epidemics and Infectious Diseases Cell (R-PEID CELL). Vector indices data obtained from District vector control unit. Serology details were collected from Microbiology lab of the tertiary centre and Regional Virus Research and Diagnostic Laboratory (RVRDL). Geographical mapping and analysis of occurrence of dengue cases with rainfall done. Results: Total 518 cases were admitted to the tertiary centre during January 23-October 23. 68% Male and 32% female.77.7% Cases were reported from Kozhikode district.25 % cases were belonging to age group 20-29. Admission of cases were more during October followed by August. Serological typing of sub sample showed DEN2 as the predominant serotype. DEN1 and DEN3 serotypes were also identified. Conclusion: There is increased case transmission occurred during rainy season i.e. in North East and South West Monsoon which is in accordance with district vector indices data. DEN2 was the predominant serotype followed by DEN1.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK, VSZLJ
The Ecosystem services (ESs), which play an important role in the balance of the natural ecosystem and social-economic development, are suffering from degradation caused by human activities and ...climate change. However, the manner in which the ESs respond to the land use/cover changes (LUCCs) and the climatic factors respectively remain elusive, especially in the forest-steppe ecotone, which is highly sensitive to climate change and anthroponotic activities. Based on the remote sensing data and in situ meteorological data, we comprehensively modeled and compared 4 key ESs changes caused by 3 LUCC types, land-use change fraction, and climate changes through two simple comparative experiments. Our results showed that: the Grain for the Green Project improved the mean soil conservation, carbon sequestration, and water yield but reduced the sand fixation. The cropland expansion had a positive influence on the water yield and sand fixation, but it induced a decline in soil conservation and carbon sequestration. The urbanization very likely increased the water yield and decreased soil conservation, carbon sequestration, and sand fixation. The unequal change fractions of the same land-use conversion may affect the ESs differently. The ESs changes have different responses to climate change in different landscapes due to the ecological process. The water yield could be well explained by the temperature, precipitation, radiation, and wind speed. Climate change had a stronger effect on the water yield and carbon sequestration than the land use/cover changes but sand fixation and soil conservation were more likely to be affected by LUCCs. The impact of three types of land-use changes and climate change on the ecosystem services should be considered when formulating land-use policies. This paper might aid the decision-makers in achieving ESs sustainable management and develop land-use strategies in the forest-steppe ecotone.
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•The influence of LUCC and climate changes on ESs were quantified and compared.•The unequal change fractions of the same land-use conversion may affect the ESs differently.•The WY and CS were mainly influenced by the climate change, while SF and SC were mainly influenced by the LUCC.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The objective of this study is to assess the gully head-cut erosion susceptibility and identify gully erosion prone areas in the Meimand watershed, Iran. In recent years, this study area has been ...greatly influenced by several head-cut gullies due to unusual climatic factors and human induced activity. The present study is therefore intended to address this issue by developing head-cut gully erosion prediction maps using boosting ensemble machine learning algorithms, namely Boosted Tree (BT), Boosted Generalized Linear Models (BGLM), Boosted Regression Tree (BRT), Extreme Gradient Boosting (XGB), and Deep Boost (DB). Initially, we produced a gully erosion inventory map using a variety of resources, including published reports, Google Earth images, and field records of the Global Positioning System (GPS). Subsequently, we distributed this information randomly and choose 70% (102) of the test gullies and the remaining 30% (43) for validation. The methodology was designed using morphometric and thematic determinants, including 14 head-cut gully erosion conditioning features. We have also investigated the following: (a) Multi-collinearity analysis to determine the linearity of the independent variables, (b) Predictive capability of piping models using train and test dataset and (c) Variables importance affecting head-cut gully erosion. The study reveals that altitude, land use, distances from road and soil characteristics influenced the method with the greatest impact on head-cut gully erosion susceptibility. We presented five head-cut gully erosion susceptibility maps and investigated their predictive accuracy through area under curve (AUC). The AUC test reveals that the DB machine learning method demonstrated significantly higher accuracy (AUC = 0.95) than the BT (AUC = 0.93), BGLM (AUC = 0.91), BRT (AUC = 0.94) and XGB (AUC = 0.92) approaches. The predicted head-cut gully erosion susceptibility maps can be used by policy makers and local authorities for soil conservation and to prevent threats to human activities.
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•The gully erosion susceptibility map created at a regional scale using GIS.•BT, BGLM, BRT, XGB, and DB models used for gully erosion modelling.•The proposed models present >90% prediction accuracy.•Predicted maps can be used by policy makers for in-situ soil conservation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A large specimen dataset captures the biogeographic variation in Lithocarpus spp.•Two fruit types of Lithocarpus exhibit a physical and chemical defense trade-off.•Traits of two fruit types exhibit ...distinctive correlation with climatic factors.•Vegetative and fruit traits exhibit different association with climatic factors.•Local co-occurrence of two-fruit-type species reflects diversification of Lithocarpus.
Stone oaks (genus Lithocarpus, Fagaceae) are common canopy trees in the tropical and subtropical forests across China and Southeast Asia, which exhibit both great species diversity and interspecific variation in fruit morphology represented by two major fruit types. Acorn (AC) fruits of Lithocarpus are similar to oak (Quercus) acorns, and enclosed receptacle (ER) fruits generally have larger seeds enclosed by thick lignified fruit husks. It is therefore worth examining the adaptation of stone oaks to a wide range of ecosystems and climatic conditions by interspecific functional differentiation for understanding their diversification. By applying the herbarium-specimen database (20,516 specimens) and records from the Flora of China, we examined the variation in both reproductive (seed, fruit husk and cupule size) and vegetative (leaf length, leaf width and maximum tree height) traits among 91 species, in relation to climatic factors (mean annual temperature and wetness annual precipitation minus annual potential evapotranspiration). We found that even though species representing both fruit types co-occurred over Southern China, they exhibited different correlations with climatic factors: three reproductive components of AC fruits were all positively related to both temperature and wetness, whereas only husk volume of ER fruits representing mechanical protection to seed increased with temperature. In vegetative traits, leaf length and tree height of ER-type species were both negatively related with excessive wetness. Our results suggested that vegetative traits of stone oaks were independent of fruit types and fruit morphological variations. Overall, the distinctive correlation between interspecific reproductive and vegetative traits with climatic factors could be important in the diversification of the stone oaks in the tropical and subtropical evergreen broad-leaved forests.
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IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Predicting electricity demand at city level is vital for stakeholders. There are various data-driven methods in electricity consumption prediction, but their applicability to forecast monthly ...electricity demand has yet to be systematically explored. This study examines several widely used data-driven methods, namely multiple linear regression (MLR), machine learning (ML) method including support vector machine (SVM) and random forest (RF), and deep learning (DL) method including long short-term memory network (LSTM) and LSTM-gated recurrent unit (GRU), for long-term energy prediction using climatic and historical electricity datasets in Singapore (2005–2019) and Hong Kong (1975–2019). In Singapore, ML outperforms other methods in terms of statistical criteria and time series plots, and SVM provides the best accuracy with mean absolute percentile error (MAPE) of 2.55 %, while MLR exhibits the worst accuracy with MAPE of 3.1 %. In Hong Kong, DL surpasses ML in terms of statistical criteria, time series stability, and generalization ability. MLR achieves the best overall prediction accuracy with R2 up to 0.95 but shows poor ability for predicting peak and low electricity consumption, while LSTM exhibits the smallest bias in these months. RF shows strong overfitting issues in both cities. Overall, SVM and LSTM are recommended for small and large datasets, respectively.
•Regression, machine and deep learning methods were used to predict electricity consumption.•Temperature is the most explanatory climatic factor in Singapore, while it is cooling degree days in Hong Kong.•Machine learning performs best on small datasets, and support vector machine is the most suitable model.•Deep learning shows overall superiority over machine learning on larger datasets, and long-short term memory is recommended.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Background: It is unknown if COVID-19 will exhibit seasonal pattern as other diseases e.g., seasonal influenza. Similarly, some environmental factors (e.g., temperature, humidity) have been shown to ...be associated with transmission of SARS-CoV and MERS-CoV, but global data on their association with COVID-19 are scarce. Objective: To examine the association between climatic factors and COVID-19. Methods: We used multilevel mixed-effects (two-level random-intercepts) negative binomial regression models to examine the association between 7- and 14-day-lagged temperature, humidity (relative and absolute), wind speed and UV index and COVID-19 cases, adjusting for Gross Domestic Products, Global Health Security Index, cloud cover (%), precipitation (mm), sea-level air-pressure (mb), and daytime length. The effects estimates are reported as adjusted rate ratio (aRR) and their corresponding 95% confidence interval (CI). Results: Data from 206 countries/regions (until April 20, 2020) with ≥100 reported cases showed no association between COVID-19 cases and 7-day-lagged temperature, relative humidity, UV index, and wind speed, after adjusting for potential confounders, but a positive association with 14-day-lagged temperature and a negative association with 14-day-lagged wind speed. Compared to an absolute humidity of <5 g/m3, an absolute humidity of 5–10 g/m3 was associated with a 23% (95% CI: 6–42%) higher rate of COVID-19 cases, while absolute humidity >10 g/m3 did not have a significant effect. These findings were robust in the 14-day-lagged analysis. Conclusion: Our results of higher COVID-19 cases (through April 20) at absolute humidity of 5–10 g/m3 may be suggestive of a ‘sweet point’ for viral transmission, however only controlled laboratory experiments can decisively prove it.
•A global study from 206 countries/regions to examine the effects of climatic factors on COVID-19.•We adjusted for a range of climatic and structural factors in the analysis.•No association with 7-day lagged climatic variables and COVID-19.•Positive association between COVID-19 and 14-day lagged temperature.•Negative association between COVID-19 and 14-day lagged wind speed.•Consistently higher rate of COVID-19 cases in absolute humidity of 5–10 g/m3.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Bark stripped trees were studied from the point of extent of damage, production characteristics and radial growth in relation to climatic factors.•The stand volume of heavily damaged trees was ...50–71% lower compared to healthy trees.•The vertical stem decay reached maximally up to 4.5–6.0 m (mean 1.9–3.1 m) with the mean speed of vertical spreading of 5.7–9.6 cm yr−1. Decayed wood accounted for 30–39% of the stem volume.
Bark stripping damage and the resultant stem rot to Norway spruce (Picea abies L. Karst), one of the most important tree species, poses a serious problem for forest management in Europe. Our research objective was to determine the effect of bark stripping, the subsequent rot decay and the impact of climatic factors in young (42–49 years) spruce stands. Moreover, we compared the differences between damage caused by red deer (Cervus elaphus L.) and sika deer (Cervus nippon Temminck). In all the cases studied, game damage was lower in forest stands when caused by sika deer (SD − 77.3%) compared to red deer (RD − 88.8%); 27.8% (SD) – 32.0% (RD) of stem circumference was damaged in average. Damaged trees showed higher growth variability and were more sensitive to a lack of precipitation and droughts, while air temperature had a higher effect on the growth of healthy trees. The initial game damage was observed in the 11 (SD) – 14 (RD) year of the mean tree age. The stem volume was lower by 25% (SD) – 28% (RD) in lightly damaged trees, and 50% (SD) – 71% (RD) in heavily damaged trees compared to healthy trees. The vertical stem decay reached a maximum of up to 4.5 m (SD) – 6.0 m (RD) (mean 1.9–3.1 m) with the mean speed of vertical spreading of 5.7 cm yr−1 (SD) – 9.6 (RD) cm yr−1. The mean decayed wood accounted for 30% (SD) – 39% (RD) of the stem volume. The peripheral stem damage by bark stripping and the age of the first occurrence were significant factors in predicting damaged crosscut area and vertical rot spreading in the stem. During this time of climate change, the stability of damaged spruce stands has been significantly disturbed by deer game.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this first worldwide synthesis of in situ and satellite‐derived lake data, we find that lake summer surface water temperatures rose rapidly (global mean = 0.34°C decade−1) between 1985 and 2009. ...Our analyses show that surface water warming rates are dependent on combinations of climate and local characteristics, rather than just lake location, leading to the counterintuitive result that regional consistency in lake warming is the exception, rather than the rule. The most rapidly warming lakes are widely geographically distributed, and their warming is associated with interactions among different climatic factors—from seasonally ice‐covered lakes in areas where temperature and solar radiation are increasing while cloud cover is diminishing (0.72°C decade−1) to ice‐free lakes experiencing increases in air temperature and solar radiation (0.53°C decade−1). The pervasive and rapid warming observed here signals the urgent need to incorporate climate impacts into vulnerability assessments and adaptation efforts for lakes.
Key Points
Lake surface waters are warming rapidly but are spatially heterogeneous
Ice‐covered lakes are typically warming at rates greater than air temperatures
Both geomorphic and climate factors influence lake warming rates
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The Eurasian steppe is the largest steppe region in the world and is an important part of the global grassland ecosystem. The eastern Eurasian steppe has favorable hydrothermal conditions and has the ...highest productivity and the richest biodiversity. Located in the arid and semi-arid region, the eastern Eurasian steppe has experienced large-scale grassland degradation due to dramatic climate change and intensive human activities during the past 20 years. Hence, accurate estimation of aboveground biomass (AGB, gC m−2) and belowground biomass (BGB, gC m−2) is necessary. In this study, plenty of AGB and BGB in-situ measurements were collected among dominated grassland types during summer in 2013 and 2016–2018 in the eastern Eurasian steppe. Vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS), Digital Elevation Model (DEM) and climate variables were chosen as independent variables to establish predictive models for AGB and BGB with random forest (RF). Both AGB (R2 = 0.47, MAE = 21.06 gC m−2, and RMSE = 27.52 gC m−2) and BGB (R2 = 0.44, MAE = 173.02 gC m−2, and RMSE = 244.20 gC m−2) models showed acceptable accuracy. Then the RF models were applied to generate spatially explicit AGB and BGB estimates for the study area over the last two decades (2000–2018). Both AGB and BGB showed higher values in the Greater Khingan Mountains and decreased gradually to the east and west sides. The mean values for AGB and BGB were 62.16 gC m−2 and 531.35 gC m−2, respectively. The climatic factors were much more important in controlling biomass than anthropogenic drivers, and shortage of water and raising temperature were the main limiting factor of AGB and BGB, respectively, in the peak growth season. These findings provide scientific data for the scientific management of animal husbandry and can contribute to the sustainable development of grassland ecology in the eastern Eurasian steppe.
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•Remote sensing provides an effective method for estimating the regional grassland belowground biomass.•Shortage of water and raising temperature limit the aboveground biomass and belowground biomass, respectively.•The climatic factors were much more important than anthropogenic drivers in the eastern Eurasian steppe.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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