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Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10 latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.

Sustainable energy development plays a prominent role in energy planning to maintain natural resources and mitigate the usage of fossil fuels. The atmospheric factor is one of the main influencing factors that changed the electricity consumption’s behavior due to global warming. In this study, the recurrent neural network (RNN) models were developed to examine the effects of 66 climate variables, collected from the European Center for Medium-Range Weather Forecast (ECMWF) ERA5 reanalysis, on power demand in Cambodia. The statistically significant climate variables were filtered by considering the cross-correlation between power demand and each climate variable. Moreover, the wide range of feedback delays was computed from the power demand dataset and was defined using the 95% confidence intervals. The comparison between a nonlinear autoregressive neural network with exogenous inputs (NARX) using historical power demand with the correlated climate variables and a nonlinear autoregressive neural network (NAR) using only historical power demand dataset was made. The various benchmarked models were evaluated and compared for their performances using statistical indices such as normalized root-mean-square error (NMSE) and coefficient of determination (R2). The results showed the NARX model could perform better than the NAR model for predicting electricity demand time-series.

The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the most serious global public health crises. A collection and analysis of studies related to the association between COVID-19 (coronavirus disease 2019) transmission and meteorological factors, such as humidity, is vital and indispensable for disease prevention and control. A comprehensive literature search using various databases, including Web of Science, PubMed, and Chinese National Knowledge Infrastructure, was systematically performed to identify eligible studies from Dec 2019 to Feb 1, 2021. We also established six criteria to screen the literature to obtain high-quality literature with consistent research purposes. This systematic review included a total of 62 publications. The study period ranged from 1 to 8 months, with 6 papers considering incubation, and the lag effect of climate factors on COVID-19 activity being taken into account in 22 studies. After quality assessment, no study was found to have a high risk of bias, 30 studies were scored as having moderate risks of bias, and 32 studies were classified as having low risks of bias. The certainty of evidence was also graded as being low. When considering the existing scientific evidence, higher temperatures may slow the progression of the COVID-19 epidemic. However, during the course of the epidemic, these climate variables alone could not account for most of the variability. Therefore, countries should focus more on health policies while also taking into account the influence of weather. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-15929-5.

In this article we draw on an interdisciplinary study on drinking water quality in Maputo, the capital of Mozambique, to examine the nature, scale, and politics of waterborne diseases. We show how water contamination and related diseases are discursively framed as household risks, thereby concealing the politics of uneven exposure to contaminated water and placing the burden of being healthy on individuals. In contrast, we propose that uneven geographies of waterborne diseases are best understood as the product of Maputo’s urban metabolism, in which attempts at being sanitary and healthy are caught up in relations of power, class, and variegated citizenship. Waterborne diseases are the result of complex and fragmented circulations and intersections of (waste)waters, generated by uneven urban development, heterogeneous infrastructure configurations, and everyday practices to cope with basic service deficits, in conjunction with increasing climatic variability. The latrine—from which ultimately contamination and diseases spread—is an outcome of these processes, rather than the site to be blamed. This article also advances an interdisciplinary framework for analyzing urban metabolism and deepening its explanatory potential. It serves as a demonstration of how interdisciplinary approaches might be taken forward to generate new readings of more-than-human metabolic processes at distinct temporal and spatial scales.

By conserving natural resources and reducing the consumption of fossil fuels, sustainable energy development plays a crucial role in energy planning. Specifically, demand-side planning must be researched and anticipated based on electricity consumption at the grounded level. Due to the global warming crisis, atmospheric conditions are among the most influential components that have altered electricity consumption patterns. In this study, 66 climate variables from the ERA5 reanalysis and the observed power demand at four grid substations (GSs) in Cambodia were examined using recurrent neural networks (RNNs). Using the cross-correlation function between power demand and each climate variable, statistically significant climate variables were sorted out. In addition, a wide range of feedback delays (FDs) was generated from the data on power demand and defined using 95% confidence intervals. The combination of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique with a nonlinear autoregressive neural network with exogenous inputs (NARX) and a nonlinear autoregressive neural network (NAR) produced a hybrid electricity forecasting model. The data were decomposed into the intrinsic mode functions (IMFs) and were then used as inputs in optimized NARX and NAR models. The performance of the various benchmarked models was analyzed and compared using mainly statistical indicators such as the normalized root mean square error (NMSE) and the coefficient of determination (R2). The hybrid models perform exceptionally well in predicting electricity demand, and the ICEEMDAN-NARX hybrid model with correlated climate variables performs the best among the tested experiments as a useful prediction tool.

Land degradation is one of the critical ecological issue in the Aral Sea Basin (ASB). This study investigates land degradation in ASB during 1982–2015 using the Normalized Difference Vegetation Index (NDVI) as a proxy. The residual trend (RESTREND) for temperature, precipitation, soil moisture adjusted NDVI has been applied to identify the land degradation in ASB and quantify the contribution of climate factors such as temperature and rainfall. In addition, a binary logistic regression model is adopted to assess the contributions of land transition, socio-economic, and topographical conditions on land degradation. Based on RESTREND, the relative contribution of precipitation (30.2%), soil moisture (23%), and temperature (11.4%) indicates that precipitation is one of the main driving factors of land degradation. The results further revealed that 36.5% of ASB is degraded, which is mostly concentrated in the lower part of the ASB. In contrast, 33.2% of ASB depicts land improvement, especially in the upper part of the basin. According to the land transition assessment, 66.6% of the water area and 11.68% of forest converted to barren land and shrubland during the study period, respectively. The binary logistic regression model demonstrated water and forest area transitions into shrubland and barren land as the major contributors of contemporary land degradation in ASB. Cropland recorded a net increment by 2.69% of its initial area, and the abandoned cropland converted to shrubland and barren lands that negatively impacted land cover change. This in-depth analysis of land degradation can assist in designing pragmatic policy interventions for implementing land restoration plans in the area.

Resumo Atualmente as pessoas têm relacionado problemas de saúde às mudanças climáticas de forma direta ou indireta, ocasionando o aumento da prevalência de certas doenças. O trabalho mostra como as variáveis meteorológicas influenciam na prevalência das doenças transmitidas pelo mosquito Aedes aegypti. Foram analisados dados coletados Sistemas de Monitoramento Diário de Agravos (SIMDA) da Secretaria de Saúde do Município de Fortaleza e os dados meteorológicos foram coletados do site Instituto Nacionais de Meteorologia (INMET) referente ao período de Janeiro de 2013 a Dezembro de 2017. Para verificar as influências dos elementos climáticos no desencadeamento da Dengue, Chikungunya e Zika o presente trabalho buscou relacionar as variáveis climáticas com as doenças por estações do ano e por semestre. Abstract People today have directly or indirectly related health problems to climate change, causing an increase in the prevalence of certain diseases. The work shows how the meteorological variables influence the prevalence of diseases transmitted by the Aedes aegypti mosquito. Data were collected from the Daily Health Monitoring Systems (SIMDA) of the Health Department of the Municipality of Fortaleza and the meteorological data were collected from the National Institute of Meteorology (INMET) website for the period from January 2013 to December 2017. To verify the influence of the climatic elements in the triggering of Dengue, Chikungunya and Zika the present work sought to relate the climatic variables to the diseases by seasons and by semester.

The overgrowth of macrophytes is a recurrent problem within reservoirs of urbanized and industrialized areas, a condition triggered by the damming of rivers and other human activities. Although the occurrence of aquatic plants in waterbodies has been widely monitored using remote sensing, the influence of climate variables on macrophyte spatiotemporal dynamics is rarely considered in studies developed for medium scales to long periods of time. We hypothesize that the spatial dispersion of macrophytes has its natural rhythms influenced by climate fluctuations, and, as such, its effects on the heterogeneous spatial distribution of this vegetation should be considered in the monitoring of water bodies. A eutrophic reservoir is selected for study, which uses the Normalized Difference Vegetation Index (NDVI) as a proxy for macrophytes. Landsat’s NDVI long-term time series are constructed and matched with the Climate Variable (CV) from the National Oceanic and Atmospheric Administration (NOAA) to assess the spatiotemporal dynamics of aquatic plants and their associated climate triggers. The NDVI and CV time series and their seasonal and trend components are correlated for the entire reservoir, compartments, and segmented areas of the water body. Granger-causality of these climate variables show that they contribute to describe and predict the spatial dispersion of macrophytes.

Accurate (spatio-temporal) estimation of the crop yield relation to climate variables is essential in the densely populated Indus, Ganges, and Brahmaputra (IGB) river basins of South Asia for devising appropriate adaptation strategies to ensure regional food and water security. This study examines wheat (Triticum aestivum) and rice (Oryza sativa) crop yields’ sensitivity to primary climate variables (i.e., temperature and precipitation) and related changes in irrigation water demand at different spatial (i.e., province/state, districts and grid cell) and temporal (i.e., seasonal and crop growth phase) scales. To estimate the climate driven variations in crop yields, observed and modelled data applying the Lund-Potsdam-Jena managed Land (LPJmL) model are used for six selected study sites in the IGB river basins over the period 1981–2010. Our statistical analysis underscores the importance of impacts assessments at higher spatio-temporal scales. Our grid cell (aggregated over study sites) scale analysis shows that 27–72% variations in wheat and 17–55% in rice crop yields are linked with temperature variations at a significance level of p < 0.001. In the absence of irrigation application, up to 39% variations in wheat and up to 75% variations in rice crop yields are associated with precipitation changes in all study sites. Whereas, observed crop yields show weak correlations with temperature at a coarser resolution, i.e., up to 4% at province and up to 31% at district scales. Crop yields also showed stronger sensitivity to climate variables at higher temporal scale (i.e., vegetative and reproductive phases) having statistically strong negative relationship with temperature and positive with precipitation during the reproductive phase. Similarly, crop phase-specific variations in climate variables have considerable impacts (i.e., quantity and timing) on irrigation water demand. For improved crop water planning, we suggest integrated climate impact assessments at higher spatio-temporal scales which can help to devise appropriate adaptation strategies for sustaining future food demand.

The Cerambycidae family (Insecta: Coleoptera) has approximately 38 thousand species. In Brazil, more than 4,300 species and 1,050 genera are registered, and despite the ecological and agricultural importance of this family, no study has yet been done in the Cerrado of the Distrito Federal (Brazil). The objective of this study was to evaluate the richness and abundance of Cerambycidae in the Cerrado area using two types of fruits (banana and pineapple), fermented with sugarcane juice, as bait and to verify whether the richness is influenced by climate variables. The work was carried out in an area of the cerrado sensu stricto at Água Limpa Farm in the Distrito Federal. Beetles were collected weekly using 40 bait traps with two types of fruits that remained in the field for 12 uninterrupted months (November 2013 to October 2014). The traps were installed 1.50 m above the ground level, distributed in four 80 m transects, and spaced 20 m apart. A total of 1,599 individuals, belonging to 13 genera and different 19 species, were collected. The main species were as follows: Oxymerus basalis (Dalman, 1823) representing 78.3%, Retrachydes thoracicus thoracicus (Olivier, 1790) representing 9.9%, and Chydarteres bicolor (Fabricius, 1787) representing 4.5% of the total specimens collected. There was a significant difference in richness and abundance of Cerambycidae among the baits evaluated, with the pineapple bait presenting the highest values. The greatest number of individuals and species occurred soon after the first rains, especially in November. Temporal variation was confirmed through Rayleigh’s uniformity test, following the seasonality of the Cerrado, with the greatest number of individuals and species found in the rainy season. Temperature and humidity influenced the richness of cerambycid beetles. This is the first work carried out with pineapple fermented with sugarcane juice as bait to capture Cerambycidae, and this type of bait proved to be efficient for the collection of insects, comparable in efficiency to the synthetic baits that are normally used. All species collected were new distribution records for the Distrito Federal (Brazil). Resumo: A família Cerambycidae (Insecta: Coleoptera) possui aproximadamente 38 mil espécies. No Brasil são registradas mais de 4.300 espécies e 1.050 gêneros e, apesar da importância ecológica e agrícola desta família, ainda não foram realizados trabalhos no Cerrado do Distrito Federal. O objetivo deste trabalho foi avaliar a riqueza e abundância de Cerambycidae utilizando dois tipos de frutas (banana e abacaxi) fermentadas com caldo de cana em área de cerrado e verificar se a riqueza é influenciada pelas variáveis climáticas. O trabalho foi realizado em uma área de cerrado sensu stricto na Fazenda Água Limpa (FAL) em Brasília, Distrito Federal. A coleta de adultos de Cerambycidae foi realizada semanalmente usando 40 armadilhas iscadas com os dois tipos de frutas que ficaram em campo por 12 meses ininterruptos (novembro de 2013 a outubro de 2014). As armadilhas foram instaladas a 1,50 m do nível do solo, distribuídas em quatro transectos de 80 m e espaçadas 20 m entre si. Foram coletados 1.599 indivíduos, 13 gêneros e 19 espécies. As principais espécies foram Oxymerus basalis (Dalman, 1823) com 78,3% do total de espécimes coletado, Retrachydes thoracicus thoracicus (Olivier, 1790) com 9,9% e Chydarteres bicolor (Fabricius, 1787) com 4,5%. Houve diferença significativa na riqueza e abundância de Cerambycidae entre as iscas avaliadas, sendo a isca de abacaxi a que apresentou os maiores valores. O maior número de indivíduos e de espécies ocorre logo após as primeiras chuvas, principalmente no mês de novembro. A variação temporal foi confirmada através do teste de uniformidade de Rayleigh que acompanha a sazonalidade do Cerrado, com maior número de indivíduos e de espécies na estação chuvosa. A temperatura e umidade são as variáveis climáticas que influenciaram a riqueza de cerambicídeos. Este é o primeiro trabalho realizado com abacaxi fermentado com caldo de cana para captura de Cerambycidae, e este tipo de isca se mostrou eficiente para a coleta dos insetos, podendo ser comparado com as iscas sintéticas que normalmente são utilizadas. Todas as espécies coletadas são novos registros de distribuição para o Distrito Federal.

A dataset consisting of 30 modern pollen surface samples from the basic plant communities in the Kamchia River downstream region was created in order to obtain reliable modern pollen analogues for palaeoclimate reconstructions using the Modern Analog Technique (MAT). Pollen percentage values were calculated on a sum of 42 pollen taxa for each pollen sample. Climatological data for each site, including average annual temperature, average temperature of the warm and cold half–year and average annual precipitation were taken from the nearest meteorological station and were corrected with an altitudinal coefficient of temperature variation. Statistical analysis was used to reveal the relationship between individual pollen types and climate variables in order to create a GIS-based modern calibrated climate-pollen dataset for 2019 year.

Forecasting the values of essential climate variables like land surface temperature and soil moisture can play a paramount role in understanding and predicting the impact of climate change. This work concerns the development of a deep learning model for analyzing and predicting spatial time series, considering both satellite derived and model-based data assimilation processes. To that end, we propose the Embedded Temporal Convolutional Network (E-TCN) architecture, which integrates three different networks, namely an encoder network, a temporal convolutional network, and a decoder network. The model accepts as input satellite or assimilation model derived values, such as land surface temperature and soil moisture, with monthly periodicity, going back more than fifteen years. We use our model and compare its results with the state-of-the-art model for spatiotemporal data, the ConvLSTM model. To quantify performance, we explore different cases of spatial resolution, spatial region extension, number of training examples and prediction windows, among others. The proposed approach achieves better performance in terms of prediction accuracy, while using a smaller number of parameters compared to the ConvLSTM model. Although we focus on two specific environmental variables, the method can be readily applied to other variables of interest.

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020-1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future.

Aim We use cluster analysis to delimit climatically and functionally distinct mammalian faunal clusters. These entities form regional species pools and are relevant to community assembly processes. Similar clusters can be differentiated in the fossil record, offering the potential for use as palaeoenvironmental proxies. Location North America within W178 degrees, W14 degrees, N83 degrees, N7 degrees and Europe within W32 degrees, E35 degrees, N80 degrees, N35 degrees. Major taxa studied 575 and 124 land mammal species from North America and Europe. Methods K-means clustering was used to subdivide North America and Europe into distinct faunas ranging in number from 3 (largest scale) to 21 (smallest scale). Each set of faunas was tested for significant differences in climate (mean annual precipitation, mean annual temperature) and functional traits (body mass, locomotion and diet). Results In North America, climatic differentiation exists at the scale where mammals are divided into 11 or fewer distinct faunas and, in Europe, at the scale where there are five or fewer faunas. Functional trait differentiation in body mass occurs at a larger spatial scale in North America (8 distinct faunas), but locomotor differentiation is present at all spatial scales, and dietary differentiation is not present at any scale. No significant differentiation in any functional trait at any scale was found in Europe. Main conclusions Faunal clusters can be constructed at any spatial scale, but clusters are climatically and functionally meaningful only at larger scales. Climatic (and environmental) differences and their associated functional trait specialisations are likely to be barriers to large-scale mixing. We argue, therefore, that functionally and climatically distinct faunal clusters are the entities that form regional species pools for community assembly processes. In North America, there are eight such mammal pools, but only one in Europe. Since the functional traits in our study are observable in the fossil record, functional trait analysis can potentially be used to diagnose climatically distinct regions in the past.

Adult males of many migratory species arrive at breeding grounds before females and young. In a 34-year study of the masked shrike, Lanius nubicus, an age- and sex-specific pattern of spring arrival was distinguished. Adult males arrived first followed by juvenile males and adult females, whereas juvenile females appeared last. We hypothesized that these differences in migratory strategies would be reflected in a differential response to climate conditions at the wintering grounds. Testing correlations between spring arrival time and winter climate conditions provided strong support to our hypothesis. Adult males’ arrival time exhibited high associations with climate conditions in early spring, upon migratory take-off, whereas juvenile males responded mostly to conditions in November, upon autumn arrival in Africa. Adult females responded to both parameters in autumn and early spring, whereas young females’ arrival correlated only with a few variables in autumn. GLM models of median spring arrival day for all categories but the young females were highly statistically significant with adjusted R-squared values of 0.81–0.93. The emerging pattern of different associations between timing of spring migration and climate conditions at the wintering grounds sheds new light on existing evolutionary theories regarding age- and sex-specific migratory strategies.

In recent years, Guangxi has become one of the most severely affected provinces by epidemics of avian cholera in China. To date, the major determinant climatic factors of the disease in the region have remained largely unknown, making it difficult to effectively target countermeasures for avian cholera surveillance and control. This study aimed to quantify the relationship between climatic variables and cases of avian cholera in subtropical areas of China. Data relating to local meteorological variables and notified cases of avian cholera were supplied by the relevant authorities between January 2006 and December 2015. Spearman correlation, co-linearity statistics and cross-correlation analysis were applied to the data, controlling for co-linearity and lag effects. A time-series Poisson regression analysis was conducted to examine the degree of correlation between the climate variables and avian cholera transmission. The results indicated that monthly mean temperature, relative humidity, rainfall and the multivariate El Niño Southern Oscillation Index, with 2-3 months lag, were correlated with avian cholera incidence. The final model had good predictive ability for the occurrence of avian cholera. Overall, the findings indicate that climate variability plays an important role in avian cholera transmission in Guangxi province. Adoption of the model presented in this study could usefully inform avian cholera surveillance strategies, making them significantly simpler and more effective. The model could also serve as a decision support tool for veterinary professionals and health authorities.

A dataset consisting of 63 modern pollen surface samples from the basic plant communities along the North–South transect of the Bulgarian Black Sea Coast and typical agrocenoses was created in order to obtain reliable modern pollen analogues for palaeoclimate reconstructions using the Modern Analog Technique (MAT). Pollen percentage values were calculated on a sum of 43 pollen taxa for each pollen sample. Climatological data for each site, including average annual temperature, average temperature of the warm and cold half–year and average annual precipitation were taken from the nearest meteorological station and were corrected with an altitudinal coefficient of temperature variation. Statistical analysis was used to reveal the relationship between individual pollen types and climate variables in order to create a GIS-based modern calibrated climate-pollen dataset for 2017 year.

An outbreak of the novel COVID-19 virus occurred during February 2020 onwards in almost all the European countries, including Spain. This study covers the correlation found between weather variables (Maximum Temperature, Minimum Temperature, Mean Temperature, Atmospheric Pressure, Daily Rainfall, Daily Sun hours) and the coronavirus propagation in Spain. A strong relationship is found when correlating the virus spread to the mean temperature, minimum temperature, and atmospheric pressure in different Spanish provinces. In this analysis we have used the ratio of the PCR COVID-19 positives with respect to the population size. A linear regression model using the mean temperature is implemented. Moreover, an analysis of variance is used to confirm the influence of mean temperature on the spread of virus. As a second measurement of the COVID-19 outbreak we have used the results of the antibodies tests carried out in Spain that provide an estimation of the heard immunity achieved. Based on this analysis, an estimation of the asymptomatic population is performed. All these results exhibit significant correlation with weather variables. The most affected provinces were Soria, Segovia and Ciudad Real, which are the coldest. On the opposite side, places such as Southern Spain, the Baleares, and Canary Islands showed a lower rate of spread. This might be related to the warmer climate and the insularity of these islands. Besides, the coastal influence and the daily sun hours might also influence the lower rates in the east and west regions in Spain. This analysis provides a deeper insight of the influence of weather variables onto the COVID-19 spread in Spain.

Warming of the Earth climate system is unequivocal. That climate changes affect the stability of natural and engineered slopes and have consequences on landslides, is also undisputable. Less clear is the type, extent, magnitude and direction of the changes in the stability conditions, and on the location, abundance, activity and frequency of landslides in response to the projected climate changes. Climate and landslides act at only partially overlapping spatial and temporal scales, complicating the evaluation of the climate impacts on landslides. We review the literature on landslide-climate studies, and find a bias in their geographical distribution, with large parts of the world not investigated. We recommend to fill the gap with new studies in Asia, South America, and Africa. We examine advantages and limits of the approaches adopted to evaluate the effects of climate variations on landslides, including prospective modelling and retrospective methods that use landslide and climate records. We consider changes in temperature, precipitation, wind and weather systems, and their direct and indirect effects on the stability of single slopes, and we use a probabilistic landslide hazard model to appraise regional landslide changes. Our review indicates that the modelling results of landslide-climate studies depend more on the emission scenarios, the Global Circulation Models, and the methods to downscale the climate variables, than on the description of the variables controlling slope processes. We advocate for constructing ensembles of projections based on a range of emissions scenarios, and to use carefully results from worst-case scenarios that may over/under-estimate landslide hazards and risk. We further advocate that uncertainties in the landslide projections must be quantified and communicated to decision makers and the public. We perform a preliminary global assessment of the future landslide impact, and we present a global map of the projected impact of climate change on landslide activity and abundance. Where global warming is expected to increase the frequency and intensity of severe rainfall events, a primary trigger of rapid-moving landslides that cause many landslide fatalities, we predict an increase in the number of people exposed to landslide risk. Finally, we give recommendations for landslide adaptation and risk reduction strategies in the framework of a warming climate.

Study region: Changes in reference evapotranspiration (ETo) may have important consequences for agricultural suitability in the central region of Argentina. Annual ETo variation was assessed, in terms of both territory and time, for the 7 decades between 1941 and 2010, analyzing the behavior of the 4 atmospheric variables which determine it: temperature, vapor pressure, wind speed and cloud cover. Study focus: The influence of each variable on ETo was evaluated from a multiple regression model and a simple correlation analysis, using climate data from the observation network, and repeating this analysis using interpolated variables. In this grid scheme, linear relationships were determined between ETo and the different key atmospheric variables, plus precipitation (PP), and the t test was applied to establish the statistically significant sectors (P 91%) presents a non-significant variation of ETo over time, with a mostly non-significant change of each driving variable, regarding both its relationship with ETo and its own trend of change. The beneficial change in agricultural suitability reported for this water-limited region was found to be produced almost exclusively by increasing PP. Keywords: Reference evapotranspiration, Climate change, Climate variables, Precipitation

In this study, we aimed at analyzing the associations between transmission of and deaths caused by SARS-CoV-2 and meteorological variables, such as average temperature, minimum temperature, maximum temperature, and precipitation. Two outcome measures were considered, with the first aiming to study SARS-CoV-2 infections and the second aiming to study COVID-19 mortality. Daily data as well as data on SARS-CoV-2 infections and COVID-19 mortality obtained between December 1, 2019 and March 28, 2020 were collected from weather stations around the world. The country's population density and time of exposure to the disease were used as control variables. Finally, a month dummy variable was added. Daily data by country were analyzed using the panel data model. An increase in the average daily temperature by one degree Fahrenheit reduced the number of cases by approximately 6.4 cases/day. There was a negative correlation between the average temperature per country and the number of cases of SARS-CoV-2 infections. This association remained strong even with the incorporation of additional variables and controls (maximum temperature, average temperature, minimum temperature, and precipitation) and fixed country effects. There was a positive correlation between precipitation and SARS-CoV-2 transmission. Countries with higher rainfall measurements showed an increase in disease transmission. For each average inch/day, there was an increase of 56.01 cases/day. COVID-19 mortality showed no significant association with temperature., Graphical abstract Unlabelled Image, Highlights • First study covering all countries affected by COVID-19 verifying the association with climate variables. • Negative association between country average temperature and COVID-19 infections. • Positive associations between country precipitation and COVID-19 infections. • No association between deaths and country temperature or precipitation.

Although oat cultivation around the Mediterranean basin is steadily increasing, its yield in these regions lags far behind those of Northern Europe. This results mainly from the poor adaptation of current oat cultivars to Mediterranean environments. Local landraces may act as reservoirs of favorable traits that could contribute to increase oat resilience in this region. To aid selection of suitable agro-climate adapted genotypes we integrated genome-wide association approaches with analysis of field assessed phenotypes of genetic variants and of the weight of associated markers across different environmental variables. Association models accounting for oat population structure were applied on either arithmetic means or best linear unbiased prediction (BLUPs) to ensure robust identification of associations with the agronomic traits evaluated. The meta-analysis of the six joint environments (mega-environment) identified several markers associated with several agronomic traits and crown rust severity. Five of these associated markers were located within expressed genes. These associations were only mildly influenced by climatic variables indicating that these markers are good candidates to improve the genetic potential of oat under Mediterranean conditions. The models also highlighted several marker-trait associations, strongly affected by particular climatic variables including high rain pre- or post-heading dates and high temperatures, revealing strong potential for oat adaptation to specific agro-climatic conditions. These results will contribute to increase oat resilience for particular climatic conditions and facilitate breeding for plant adaptation to a wider range of climatic conditions in the current scenario of climate change., This work was funded by the Spanish MINECO (AGL2016-78965AGR), supported by FEDER funds and by the regional government through the AGR-253 group. FJCC is the holder of a FPI fellowship from the Spanish Ministry of Economy and competitiveness. NR is the holder of a Ramon y Cajal postdoctoral position from MINECO. CH and TL were supported by BBSRC BB/CSP1730/1.

Създаден е набор от данни, състоящ се от 63 съвременни поленови проби от основните растителни съобщества по трансекта Север-Юг на българското Черноморие. Процентните стойности на полена са изчислени на базата на участието на 43 поленови таксона за всяка поленова проба. Климатологичните данни за всеки обект, включително средната годишна температура, средната температура на топлото и студеното полугодие и средните годишни валежи, са взети от най-близката метеорологична станция и са коригирани с коефициент на вариация на температурата. Използван е статистически анализ за разкриване на връзките между отделните видове поленови типове и климатичните променливи.

The aim of the present paper is to present a GIS-based model for analysis of modern pollen-climate relationship in order to obtain reliable modern pollen analogues for palaeoclimate reconstructions using the Modern Analog Technique (MAT). The percentage representations of 63 pollen taxa in 59 surface pollen samples collected in the basic plant communities along the north-south transect of the Bulgarian Black Sea Coast were correlated with modern climate characteristics. Average annual temperature, average temperature of the warm and cold half-year and average annual precipitation was taken from the nearest meteorological station or was corrected with an altitudinal coefficient of temperature variation. Statistical analysis was used to reveal the relationships between individual pollen types and climate variables. All calibration data were integrated in GIS environment with base maps and digital elevation models.

This paper aims to reveal current changes (recent decades) in regional climatic variables like water temperature (WT), the duration of the ice-free period (ICE-FREE) and the precipitation rate (P), as exemplified by Petrozavodsk Bay (Lake Onega, European Russia), and to analyse their relationships with the global climatic indices NAO, AO and structural characteristics of biota (chlorophyll a concentration (Chl a), phytoplankton and zoobenthos abundance/biomass) in the lake ecosystem, which lies within the Baltic Sea catchment area. Spearman’s rank correlations yielded significant (p

The aim of this paper was to study the inorganic constituents of snow and snowmelt surface runoff in a case study of the city of Brest and to indicate components that could pose a threat to the environment. Samples of snow and snowmelt runoff were analysed for the following parameters: total suspended solids, pH, the contents of nitrate, phosphate and ammonium ions, and of heavy metals. The concentrations of most of these pollutants were higher in the snowmelt runoff than in snow. The concentrations of pollutants in the snowmelt surface runoff exceeded the levels established by national regulations (maximum permissible concentrations).

In this paper we apply time-series analysis to study the association between dengue incidence and climate variables such as temperature, humidity and precipitation while taking into account the delayed effects and further discussing the differences between using short term data (each year) and long term data from 2000 to 2010. Time-lag correlation method is employed to determine the time delay between incidence of dengue and climate variables. The result is not only consistent with previous results of other researchers, it also provides new findings. After analyzing the data in each year (short term time series data) and 10-year data (long term time series data), We find that using the short term data and the long term data can provide complementary insights into the relationship between dengue incidence and climate variables.

We investigated the relationship of stem diameter to tree, site and stand characteristics for six major tree species (trembling aspen, white birch, balsam fir, lodgepole pine, black spruce, and white spruce) in Alberta (Canada) with data from Alberta Sustainable Resource Development Permanent Sample Plots. Using non-linear mixed effects modeling techniques, we developed models to estimate diameter at breast height using height, crown and stand attributes. Mixed effects models (with plot as subject) using height, crown area, and basal area of the larger trees explained on average 95% of the variation in diameter at breast height across the six species with a root mean square error of 2.0 cm (13.4% of mean diameter). Fixed effects models (without plot as subject) including the Natural Sub-Region (NSR) information explained on average 90% of the variation in diameter at breast height across the six species with a root mean square error equal to 2.8 cm (17.9% of mean diameter). Selected climate variables provided similar results to models with NSR information. The inclusion of nutrient regime and moisture regime did not significantly improve the predictive ability of these models.

Leaf stoichiometry (nitrogen (N), phosphorus (P) and N:P ratio) is not only important for studying nutrient composition in forests, but also reflects plant biochemical adaptation to geographic and climate conditions. However, patterns of leaf stoichiometry and controlling factors are still unclear for most species. In this study, we determined leaf N and P stoichiometry and their relationship with soil properties, geographic and climate variables for Cyclocarya paliurus based on a nation-wide dataset from 30 natural populations in China. The mean values of N and P concentrations and N:P ratios were 9.57 mg g&minus, 1, 0.91 mg g&minus, 1 and 10.51, respectively, indicating that both leaf N and P concentrations in C. paliurus forests were lower than those of China and the global flora, and almost all populations were limited in N concentration. We found significant differences in leaf N and P concentrations and N:P ratios among the sampled C. paliurus populations. However, there were no significant correlations between soil properties (including organic C, total N and P concentrations) and leaf stoichiometry. The pattern of variation in leaf N concentration across the populations was positively correlated with latitude (24.46&deg, N&ndash, 32.42&deg, N), but negatively correlated with mean annual temperature (MAT), meanwhile, leaf N concentration and N:P ratios were negatively correlated with mean temperature in January (MTmin) and mean annual frost-free period (MAF). Together, these results suggested that temperature-physiological stoichiometry with a latitudinal trend hold true at both global and regional levels. In addition, the relationships between leaf stoichiometry and climate variables provided information on how leaf stoichiometry of this species may respond to climate change.

Wheat yields have increased in Chile in the last thirty years due in part to incentives, increased input use and improved varieties. Region-specific price elasticities of perhectare yields and planted hectares are estimated using region-level data for the period 1975-2007. Price-yield and price-hectare elasticities are similar, although slightly lower than those obtained by Morales and Foster (2002) in all regions, ranging between 0.33 and 0.73 for yields and between 0 and 0.43 for hectares. Values of price elasticity for total production range between 0.38 and 1.12. For two of the regions studied, climate variables were available and their inclusion results in a reduction in of the elasticity of total production with respect to price. The results suggest that the inclusion of climate variables should be considered when estimating producer responses to prices and when projecting the likely impacts of forecasted climate changes on producer decisions. Los rendimientos de trigo han aumentado en Chile en los últimos 30 años, llegando a obtener rendimientos promedio del orden de 4,5 ton ha4 al año 2007. Este aumento en rendimientos se debe en parte al progreso tecnológico. Se realizan estimaciones de elasticidades precio-superficie y elasticidades precio-rendimiento, actualizando un modelo planteado por Morales y Foster (2002). El trabajo incorpora variables climáticas a modelos de rendimiento de trigo en dos regiones para las cuáles había datos disponibles con el propósito de comparar los resultados sobre la elasticidad total en los modelos con y sin variables climáticas. Las elasticidades para el periodo actualizado se reducen en relación a las obtenidas por Morales y Foster (2002). Al incorporar datos climáticos la elasticidad precio-rendimiento se reduce en magnitud y en nivel de significancia estadística. Este resultado demuestra que es posible sobreestimar la elasticidad precio-rendimiento si no se incluyen las variables climáticas. La investigación a futuro debe tomar en cuenta esta consideración y se debe ser cuidadoso con las estimaciones de la sensibilidad de la superficie sembrada y de rendimiento frente a los precios del trigo en Chile.

Photosynthetic capacity, determined by light harvesting and carboxylation reactions, is a key plant trait that determines the rate of photosynthesis; however, in Earth System Models (ESMs) at a reference temperature, it is either a fixed value for a given plant functional type or derived from a linear function of leaf nitrogen content. In this study, we conducted a comprehensive analysis that considered correlations of environmental factors with photosynthetic capacity as determined by maximum carboxylation (V(cm)) rate scaled to 25 degrees C (i.e., V(c),25; μmol CO2 x m(-2)x s(-1)) and maximum electron transport rate (J(max)) scaled to 25 degrees C (i.e., J25; μmol electron x m(-2) x s(-1)) at the global scale. Our results showed that the percentage of variation in observed V(c),25 and J25 explained jointly by the environmental factors (i.e., day length, radiation, temperature, and humidity) were 2-2.5 times and 6-9 times of that explained by area-based leaf nitrogen content, respectively. Environmental factors influenced photosynthetic capacity mainly through photosynthetic nitrogen use efficiency, rather than through leaf nitrogen content. The combination of leaf nitrogen content and environmental factors was able to explain -56% and -66% of the variation in V(c),25 and J25 at the global scale, respectively. Our analyses suggest that model projections of plant photosynthetic capacity and hence land-atmosphere exchange under changing climatic conditions could be substantially improved if environmental factors are incorporated into algorithms used to parameterize photosynthetic capacity in ESMs.

A data set consisting of 63 modern pollen surface samples from the basic plant communities along the north–south transect of the Bulgarian Black Sea Coast was created in order to obtain reliable modern pollen analogues for palaeoclimate reconstructions using the Modern Analog Technique (MAT). Pollen percentage values were calculated on a sum of 61 pollen taxa for each pollen sample. Climatological data for each site, including average annual temperature, average temperature of the warm and cold half–year and average annual precipitation was taken from the nearest meteorological station or was corrected with an altitudinal coefficient of temperature variation. Statistical analysis was used to reveal the relationships between individual pollen types and climate variables. All calibration data were integrated in GIS environment with base maps and digital elevation models.

Climate change influences crop yield vis-à-vis crop production to a greater extent in countries like Nepal where agriculture depends largely on natural circumstances. Plausible scenarios of climate change like higher temperatures and changes in precipitation will directly affect crop yields. Therefore, this study assesses the effect of observed climate variables on yield of major food-crops in Nepal, namely rice, wheat, maize, millet, barley and potato based on regression model for historical (1978-2008) climatic data and yield data for the food-crops. The yield growth rate of all the food-crops is positive. However, the growth rate for all crops, except potato and wheat, is below population growth rate during the period. Climate variables like temperature and precipitation are the important determinants of crop yields. Trend of precipitation is neither increasing nor decreasing significantly during this period. However, temperature is increasing by 0.7 0C during the period. Climate variables show some influences on the yield of these major food-crops in Nepal. Increase in summer rain and maximum temperature has contributed positively to rice yield. Also, increase in summer rain and minimum temperature has positive impact on potato yield. However, increase in summer rain and maximum temperature adversely affected the yield of maize and millet. Increase in wheat and barley yield is contributed by current trend of winter rain and temperature. Consideration of spatial variation in similar type of study in Nepal that will be helpful in identifying the region more vulnerable to climate change in terms of crop yield is highly recommended.

The investigation of climate variables impacts on small pelagic resources has increased rapidly in recent years. An observational sea surface temperature from the Advanced Very High Resolution Radiometer (AVHRR Pathfinder V5.0) satellite and the North Atlantic Oscillation index from 1999 to 2009 were used to investigate the abundance of horse mackerel in Senegalese waters using simple linear model and generalized additive models. The results show that the variability of horse mackerel abundance seems to be controlled by the sea surface temperature through the North Atlantic driven effect. The generalized additive model seems to explain better the link between climatic terms and horse mackerel abundance than the simple linear model.

The dengue and chikungunya epidemics have major challenging problems and have become essentially a public health importance in India for the recent years, and it has been transmitted by the Aedes genus (Aedes aegypti or Ae. Albopictus) mosquito vectors. The huge chikungunya epidemic cases was reported in 213 districts especially in South India during 2006 and it happened in India, after 25 years period of breaks. The epidemic was showing good improvement of spatially declining trend, however, the dengue epidemics were reported from 24 states / union territories of India with 37070 cases and 227 deaths during 2012 and it steadily increased to become a very serious threat to the public.

Activities of lead-210 and beryllium-7 have been monitored at the Vinča Institute of Nuclear Sciences in Belgrade, Serbia. The monthly mean activities in composite aerosol samples were determined on HPGe detectors by standard gamma spectrometry. The meteorological data, consisting of the temperature, atmospheric pressure, relative humidity, sunshine hours and cloud cover data were obtained from the European Climate Assessment & Dataset and the Republic Hydrometeorological Service of Serbia. Five teleconnection indices of large scale atmospheric circulation: North Atlantic Oscillation, East Atlantic Pattern, East Atlantic/West Russia Pattern, Scandinavia Pattern, and Polar/Eurasia Pattern were obtained from the data archive of the United States National Oceanic and Atmospheric Administration's Climate Prediction Center. The first lead-210 and beryllium-7 activities measured at the Vinča Institute date back to 1985 and 1991, respectively, and their relation with the climate variables and teleconnection indices is investigated using multivariate methods of analysis. The most appropriate multivariate method of analysis of these sets of measurements is selected from a wide spectrum of multivariate methods developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package. The evaluation ranking results based on the best signal efficiency and purity, show that the Boosted Decision Trees (BDT) multivariate method is the most suitable for the variable analysis. Further multivariate analysis results give insight into the dependence of lead-210 and beryllium-7 concentrations upon the climate variables and atmospheric circulation (via the teleconnection indices) during the time of measurements. The BDT method singles out the Scandinavia Pattern index as the variable with the highest importance for both radionuclides. Amongst the climate variables, temperature shows the strongest influence on the radionuclide concentrations, while relative humidity is the lowest ranking variable. Moreover, the multivariate regression methods give a good approximation of lead-210 and beryllium-7 concentrations for all the sets of climate variables and teleconnection indices.