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This paper is an attempt to assess the impact of climate on wheat yield in the Indo-Gangetic Plain (IGP) region of India by using panel data analysis. Five IGP states namely Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal have been considered to frame a panel. The study used the data of climatic and non-climatic variables from 1990 to 2022 to achieve the objective of the study. The Im-Pesaran-Shin unit-root test was applied to check the stationarity of data. The results of the panel least square dummy variable model indicated that all the climatic variables had non significant influence. Among non-climatic variables that help increase wheat yield, fertilizer consumption and mechanization in agriculture were found to have a significant positive impact on wheat yield in the IGP region of India. [ABSTRACT FROM AUTHOR]

Calculation of the new climatological standard normals for the period 1991–2020 was a motivation to carry out the homogenization of the required climatic variables in the Spanish Meteorological Agency (AEMET). The national observation network has undergone changes along its history that often introduce non-climatic interferences to the series. On the other hand, for the calculation of various parameters and climatic indices, it is essential to have complete daily series. With this in mind, homogenization of daily series of precipitation, maximum and minimum temperatures, sunshine hours, relative humidity, station level pressure, mean wind speed, and maximum wind gust was carried out. This paper shows how the homogenization process was performed, covering the period 1975–2020 with carefully selected daily data sets from the national climatological database. The homogenization software Climatol v.4.0 was used for this process, and derived variables such as average temperature, sea level pressure, and vapor pressure were calculated from their related homogenized series. The peculiarities and issues of each variable are explored and, finally, the homogenization results were used to readily calculate the 1991–2020 climatological standard normals with the dedicated software CLINO_tool v.1.5. [ABSTRACT FROM AUTHOR]

Though the magnitude of future rainfall is important in most water resources applications, many applications require its occurrence/nonoccurrence rather than its magnitude such as in agricultural systems management, drought management systems, regulated deficit irrigation for various crops, short-term municipal water demand modeling and management, and reservoir operation. The occurrence of rainfall is a classification problem that also affects day-to-day human activities and management. However, most of the work on rainfall forecasting is for rainfall magnitude, and very few studies on rainfall occurrence forecasting have been carried out in the past. Also, few artificial intelligence and machine learning techniques have been utilized in rainfall magnitude forecasting but not any work registered so far for forecasting rainfall occurrence using these methods. The proposed novel approach in this paper integrates two machine learning methods, artificial neural network (ANN) and decision tree (DT), which are capable of making rainfall occurrence forecasting comprehensible and accurate. For this purpose, the rules have been extracted by generating a DT using the input-output data obtained from an ANN rainfall occurrence forecasting model. Daily climatic data are employed to illustrate the methodology developed in this study. The obtained results show that during training, ANN models learned a fixed set of rules for rainfall occurrence forecasting. The obtained rules are simple and can be used as a tool for rainfall occurrence forecasting. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to identifying the climate variables that influence the size of the area burned by forest wildfires. Multiple linear regression was used in combination with nonlinear variable transformations to determine relevant nonlinear forest wildfire size functions. Data from the Prague-East District of the Czech Republic was used for model derivation. Individual burned forest area was hypothesized as a function of water vapor pressure, air temperature and wind speed. Wind speed was added to enhance predictions of the size of forest wildfires, and further improvements to the utility of prediction methods were added to the regression equation. The results show that if the air temperature increases, it may contain less water and the fuel will become drier. The size of the burned area then increases. If the relative humidity in the air increases and the wind speed decreases, the size of the burned area is reduced. Our model suggests that changes in the climate factors caused by ongoing climate change could cause significant changes in the size of wildfire in forests. [ABSTRACT FROM AUTHOR]

The major industrial cities of Bangladesh are experiencing significant air-pollution-related problems due to the increased trend of particulate matter (PM2.5) and other pollutants. This paper aimed to investigate and understand the relationship between PM2.5 and land use and climatic variables to identify the riskiest areas and population groups using a geographic information system and regression analysis. The results show that about 41% of PM2.5 concentration (μg/m3) increased within 19 years (2002–2021) in the study area, while the highest concentration of PM2.5 was found from 2012 to 2021. The concentrations of PM2.5 were higher over barren lands, forests, croplands, and urban areas. From 2002–2021, the concentration increased by about 64%, 62.7%, 57%, and 55% (μg/m3) annually over barren lands, forests, cropland, and urban regions. The highest concentration level of PM2.5 (84 μg/m3) among other land use classes was found in urban areas in 2021. The regression analysis shows that air pressure (hPa) (r2 = −0.26), evaporation (kg m−2) (r2 = −0.01), humidity (kg m−2) (r2 = −0.22), rainfall (mm/h) (r2 = −0.20), and water vapor (kg m−2) (r2 = −0.03) were negatively correlated with PM2.5. On the other hand, air temperature (k) (r2 = 0.24), ground heat (W m−2) (r2 = 0.60), and wind speed (m s−1) (r2 = 0.34) were positively correlated with PM2.5. More than 60 Upazilas were included in the most polluted areas, with a total population of 11,260,162 in the high-risk/hotspot zone (1,948,029 aged 0–5, 485,407 aged 50–69). Governmental departments along with policymakers, stainable development practitioners, academicians, and others may use the main results of the paper for integrated air pollution mitigation and management in Bangladesh as well as in other geographical settings worldwide.

Purpose There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM10.Design/methodology/approach The procedure included daily data collection of current PM10 concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network.Findings It was observed that, over time, there was a decrease in the PM10 concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM10 of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network.Originality/value The presentation of a given PM10 concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications. [ABSTRACT FROM AUTHOR]

Weather conditions are important determinants of tourism demand. After reviewing the main contributions of previous research on the role of climatic variables in tourism demand functions, we explore different modelling alternatives to introduce temperature and rainfall in a gravity model. The dataset used comprises interregional tourism flows by Spanish residents from 2011 to 2015. We first estimate a benchmark model with both temperature and rainfall at the destination expressed in levels, and then consider some extensions to this model. In particular, special attention is paid to analyzing whether the sensitivity that tourists may have to weather factors can change across seasons. Other modelling issues examined include the relationship between climatic variables at the destination and at home, the influence of weather in previous periods (lagged values of temperature and rain), the variability of the weather variables (captured by the standard deviation of these variables), or whether the effect of temperature varies with the climatic characteristics of the region. Our empirical results confirm that spring and summer tourism in Spain is more sensitive to weather conditions, that the number of domestic overnight stays in Spain is strongly influenced by changes in the difference in temperature between tourists' home and destination regions, that the estimated parameters of lagged weather variables are higher than those corresponding to the travelling months, that temperature variability in the destination region reduces tourism demand, and that the effect of temperature on destination choice for residents in moderate-climate regions is lower than for residents in other types of regions. 天气状况是旅游需求的重要决定因素。本文在回顾了以往关于气候变量在旅游需求函数中的作用的研究成果后, 我们探索了在重力模型中引入温度和降雨的不同建模方案。使用的数据集包括2011年至2015年西班牙居民的跨地区旅游流量。我们首先用水平表示的目的地温度和降雨量来估计基准模型, 然后考虑对该模型的一些扩展。特别要注意的是, 游客对天气因素的敏感性是否会随着季节的变化而变化。研究涉及的其他建模问题包括目的地与客源地气候变量之间的关系,天气的影响在上一时期(以温度和降雨作为滞后值),天气变量的变异性(这些变量的标准差),或者温度随地区气候特征而变化的效应。实证结果证实, 西班牙春季和夏季旅游对天气状况更为敏感,国内在西班牙过夜游客量受游客客源地和目的地温差的显著影响,天气滞后变量的估计参数高于相应旅行月份的同期值,目的地地区温度变化减少了旅游需求,温度对中等气候地区居民目的地选择的影响小于其他气候类型地区居民。 [ABSTRACT FROM AUTHOR]

Water demand prediction is essential in any short or long-term management plans. For short-term prediction of water demand, climatic factors play an important role since they have direct influence on water consumption. In this paper, prediction of future daily water demand for Al-Khobar city in the Kingdom of Saudi Arabia is investigated. For this purpose, the combined technique of Artificial Neural Networks (ANNs) and time series models was constructed based on the available daily water consumption and climatic data. The paper covers the following: forecast daily water demand for Al-Khobar city, compare the performance of the ANNs [General Regression Neural Network (GRNN) model] technique to time series models in predicting water consumption, and study the ability of the combined technique (GRNN and time series) to forecast water consumption compared to the time series technique alone. Results indicate that combining time series models with ANNs model will give better prediction compared to the use of ANNs or time series models alone. [ABSTRACT FROM AUTHOR]

The electricity industry is significantly affected by weather conditions both in terms of the operation of the network infrastructure and electricity consumption. Following privatization and deregulation, the electricity industry in the U.K. has become fragmented and central planning has largely disappeared. In order to maximize profits, the margin of supply has decreased and the network is being run closer to capacity in certain areas. Careful planning is required to manage future electricity demand within the framework of this leaner electricity network. There is evidence that the climate in the U.K. is changing with a possible 3°C average annual temperature increase by 2080. This paper investigates the impact of weather variables on monthly electricity demand in England and Wales. A multiple regression model is developed to forecast monthly electricity demand based on weather variables, gross domestic product, and population growth. The average mean absolute percentage error (MAPE) for the worst model is approximately 2.60% in fitting the monthly electricity demand from 1989 to 1995 and approximately 2.69% in the forecasting over the period 1996 to 2003. This error may reflect the nonlinear dependence of demand on temperature at the hot and cold temperature extremes; however, the inclusion of degree days, enthalpy latent days, and relative humidity in the model improves the demand forecast during the summer months. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos Geograficos is the property of Cuadernos Geograficos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bosque (03048799) is the property of Facultad de Ciencias Forestales, Universidad Austral de Chile and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Severe weather events, such as heat waves, floods, pollution, and health threats, are becoming more common in metropolitan places across the world. Overcrowding, poor infrastructure, and fast, unsustainable urbanization are some of the problems that India faces, and the country is also susceptible to natural disasters. This research analyzes climatic variables affecting urban hazards in Bangalore (also known as Bengaluru) via a thorough review. Heat waves, urban floods, heat islands, and drought were identified in 156 qualifying publications using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. Contributing variables were also considered. City development and urbanization were key to changing climate and increasing urban dangers. While long-term climatic variable distribution is uneven, warming is evident. The report promotes strong urban planning techniques, comprehensive policies, more green areas, and sustainable development beyond short-term heat response programs to boost urban climate resilience. This study shows how climate, land use, and urban dangers are interconnected. Future studies may benefit by categorizing urban risk studies and identifying climatic factors. [ABSTRACT FROM AUTHOR]

The changing global climate, characterized by rising surface air temperatures, shifting precipitation patterns, and heightened occurrences of extreme weather events, is anticipated to profoundly impact the environment, economy, and society worldwide. This impact is particularly acute in African nations like Angola, where crucial sectors, such as agriculture, rely heavily on climate variability and exhibit limited adaptive capacity. Given that the majority of Angola's agriculture is rain-fed and serves as a vital source of livelihood for the populace, the country is especially vulnerable to climate change, particularly in its southern region. Climate change has caused severe damage in Angola, especially in the southern part of the country, where the worst droughts in decades have affected over 3.81 million people, resulting in food and water shortages. Between 2005 and 2017, climate-related disasters cost the country about 1.2 billion US dollars, further exacerbating the economic and social challenges faced by the population. This study presents a systematic review of the effects of climate change on agriculture in Angola, with a focus on the southern region. Employing the PRISMA2020 methodology, the review examined 431 documents from databases such as Scopus and Web Science, spanning from 1996 to 2023, with 63 meeting inclusion criteria. The review reveals a paucity of research on the short and long-term impacts of climate change on Angolan agriculture. Projections indicate a rise in temperatures and a general decrease in precipitation, with the southern region experiencing a more pronounced decline. Agricultural productivity may suffer significantly, with models suggesting a potential 7% reduction by 2050. [ABSTRACT FROM AUTHOR]

Quantifying the dryland patterns of plant carbon (C), nitrogen (N), and phosphorus (P) concentrations and their stoichiometric values along environmental gradients is crucial for understanding ecological strategies. To understand the plant adaptive strategies and ecosystem nutrient concentrations across three desert ecosystems (e.g., desert, steppe desert, and temperate desert), we compiled a dataset consisting of 1295 plant species across three desert ecosystems. We assessed the element concentrations and ratios across plant growth forms, plant organs, and soils and further analysed the leaf vs. root N, P, and N:P scaling relationships. We found that the leaf N, P, and C concentrations were significantly different only from those of certain other growth forms and in certain desert ecosystems, challenging the generality of such differences. In leaves, the C concentrations were always greater than the N and P concentrations and were greater than those in soils depending on the soil chemistry and plant physiology. Thus, the element concentrations and ratios were greater in the organs than in the soils. The values in the leaf versus the root N, P, and N:P scaling relationships differed across the three desert ecosystems; for example, αN (1.16) was greater in the desert, αP (1.10) was greater in the temperate desert ecosystem, and αN:P (2.11) was greater in the desert ecosystem. The mean annual precipitation (MAP) and mean annual temperature (MAT) did not have significant effects on the leaf elemental concentrations or ratios across the desert ecosystems. This study advances our understanding of plant growth forms and organs, which support resource-related adaptive strategies that maintain the stability of desert ecosystems via divergent element concentrations and environmental conditions. [ABSTRACT FROM AUTHOR]

Plant community assembly is the outcome of long-term evolutionary events (evident as taxonomic diversity; TD) and immediate adaptive fitness (functional diversity; FD); a balance expected to shift in favour of FD in 'harsh' habitats under intense selection pressures. We compared TD and FD responses along climatic and edaphic gradients for communities of two species (Dianthus pseudocrinitus and D. polylepis) endemic to the montane steppes of the Khorassan-Kopet Dagh floristic province, NE Iran. 75 plots at 15 sites were used to relate TD and FD to environmental gradients. In general, greater TD was associated with variation in soil factors (potassium, lime, organic matter contents), whereas FD was constrained by aridity (drought adaptation). Crucially, even plant communities hosting different subspecies of D. polylepis responded differently to aridity: D. polylepis subsp. binaludensis communities included a variety of broadly stress-tolerant taxa with no clear environmental response, but TD of D. polylepis subsp. polylepis communities was directly related to precipitation, with consistently low FD reflecting a few highly specialized stress-tolerators. Integrating taxonomic and functional diversity metrics is essential to understand the communities hosting even extremely closely related taxa, which respond idiosyncratically to climate and soil gradients. [ABSTRACT FROM AUTHOR]

Pinus oocarpa is the most important conifer for resin production in Mexico, so superior resin trees were selected in the Southern Mountains of Oaxaca, Mexico. The objective was to determine the variation and differences among provenances and among trees according to the parameters of seedling emergence and the number of cotyledons, and their relationship with elevation and climatic variables. The seedling emergence of four replicates of 20 seeds from 80 trees was counted daily. For the emergence parameters, provenance contributed 42.02% to the total variance, tree 29.19% and error 28.79%. Only tree (11.71%) and error (88.29%) contributed to the total variance of the cotyledon number. The effect of provenance (p ≤ 0.0006) and tree (p ≤ 0.0001) was significant for all variables evaluated. Higher-elevation provenances and trees had higher emergence values. The emergence parameters were positively associated with tree elevation. Climatic variables related to precipitation and temperature were negatively related to the emergence parameters. The results allow for the selection of phenotypes without emergence problems to establish seed orchards. [ABSTRACT FROM AUTHOR]

The mitochondrion, which is an intracellular organelle responsible for most of the energy‐producing pathways, can have its genome targeted for climate‐driven selection. However, climate‐driven mitochondrial selection remains a sparsely studied area in reptiles. Here, we reported the complete mitochondrial genome sequence of a lacertid lizard (Takydromus intermedius) and used mitogenomes from 54 species of lacertid lizards to study their phylogenetic relationships and to identify the mitochondrial genes under positive selection by climate. The length of the complete mitochondrial genome sequence of T. intermedius was 17,713 bp, which was within the range of lengths (17,224–18,943) ever reported for Takydromus species. The arrangement of mitochondrial genes in T. intermedius was the same as in other congeneric species. The 54 lacertid species could be divided into three geographically and climatically different clades. We identified three mitochondrial genes (ATP6, ATP8, and ND3) under positive selection by climate, and found that isothermality, temperature seasonality, precipitation of wettest month, and precipitation seasonality were the most important climatic variables contributing to the gene selection. [ABSTRACT FROM AUTHOR]

This study aimed to determine the relationship between the abundance of phytoplankton and zooplankton, climatic variables, and water quality parameters in a tilapia broodfish pond. Water quality parameters were daily collected while plankton abundance was monthly recorded. Daily climatic data were obtained from the local Meteorological Department of Bangladesh. Throughout the study period, fluctuations were detected in both climatic and water quality parameters. Phytoplankton abundance showed annual variations, with the highest value of 10x105/L recorded in June and the lowest value of 3.5x105/L in December. Similarly, zooplankton exhibited seasonal fluctuations, with the highest value of 10x105/L in October and the lowest value of 2.3x105/L in January. Among the phytoplankton composition, Chlorophyceae accounted for 52% of the total, followed by Bacillariophyceae, Cyanophyceae, and Euglenophyceae. On the other hand, Rotifera constituted 29% of the total zooplankton, followed by Cladocera, Copepoda, and Protozoa. The fluctuations in phytoplankton and zooplankton abundance were influenced by both climatic factors and water quality parameters. The canonical correlations between the pairs of canonical variates were estimated at 1.000 and 0.883, with significance probabilities of 0.054 and 0.631, respectively. The initial canonical function showed a strong correlation of 1.00 (100%) between climatic variables, water quality parameters, and plankton abundance. In addition, this study revealed a negative relationship between plankton abundance and factors, such as air temperature, rainfall, water temperature, pH, and ammonia levels, while a positive correlation was observed with dissolved oxygen (DO) levels. The second canonical function showed a significant correlation of 0.883 (88.3%) between climatic variables, water quality parameters, and plankton abundance. In this context, phytoplankton abundance exhibited a negative correlation with dissolved oxygen and solar intensity, while showing an opposite relationship with water transparency. Similarly, zooplankton abundance showed a positive relationship with water transparency, but an opposite relation with dissolved oxygen and solar intensity. [ABSTRACT FROM AUTHOR]

This paper is an attempt to assess the impact of climate on wheat yield in the Indo-Gangetic Plain (IGP) region of India by using panel data analysis. Five IGP states namely Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal have been considered to frame a panel. The study used the data of climatic and non-climatic variables from 1990 to 2022 to achieve the objective of the study. The Im-Pesaran-Shin unit-root test was applied to check the stationarity of data. The results of the panel least square dummy variable model indicated that all the climatic variables had non significant influence. Among non-climatic variables that help increase wheat yield, fertilizer consumption and mechanization in agriculture were found to have a significant positive impact on wheat yield in the IGP region of India.

Estimation of evapotranspiration (ET) for mountain ecosystem is of absolute importance since it serves as an important component in balancing the hydrologic cycle. The present study evaluates the performance of original and location specific calibrated Hargreaves equation (HARG) with the estimates of Food and Agricultural Organization (FAO) Penman Monteith (PM) method for higher altitudes in East Sikkim, India. The results show that the uncalibrated HARG model underestimates ET by 0.35 mm day whereas the results are significantly improved by regional calibration of the model. In addition, this paper also presents the variability in the trajectory associated with the climatic variables with the changing climate in the study site. Nonparametric Mann-Kendall (MK) test was used to investigate and understand the mean monthly trend of eight climatic parameters including reference evapotranspiration ( ET) for the period of 1985 - 2009. Trend of ET was estimated for the calculations done by FAO PM equation. The outcomes of the trend analysis show significant increasing ( p ≤ 0.05) trend represented by higher Z-values, through MK test, for net radiation ( Rn), maximum temperature ( T) and minimum temperature ( T), especially in the first months of the year. Whereas, significant (0.01 ≥ p ≤ 0.05) decreasing trend in vapor pressure deficit (VPD) and precipitation ( P) is observed throughout the year. Declining trend in sunshine duration, VPD and ET is found in spring (March - May) and monsoon (June - November) season. The result displays significant (0.01≤ p ≤ 0.05) decreasing ET trend between (June - December) except in July, exhibiting the positive relation with VPD followed by sunshine duration at the station. Overall, the study emphasizes the importance of trend analysis of ET and other climatic variables for efficient planning and managing the agricultural practices, in identifying the changes in the meteorological parameters and to accurately assess the hydrologic water balance of the hilly regions. [ABSTRACT FROM AUTHOR]

The daily reference evapotranspiration (ET0) is generally demanded in the application of agriculture, meteorology, hydrology and other fields. The calculation of daily ET0 based on the data of different time resolutions is always a simplification because the change of ET0 is a continuous process in the time scale. It is valuable to discuss the influence of the simplification with different time resolutions on the accuracy of ET0 estimation. In this paper, based on the observed data of Shouxian National Climate Observatory from 2007 to 2013, the daily average values were calculated using the data with the resolution of 1 minute as the true values. The effects of the 7 different time resolutions (including 10 min, 20 min, 30 min, 40 min, 60 min, 4 times per day and 3 times per day) on the estimation of daily air temperature, wind speed, solar radiation, relative humidity and daily, monthly, yearly reference evapotranspiration (ET0) were analyzed by the error comparison. Results showed that the absolute values of the errors for ET0 and 4 climatic variables increased with the lower time resolution. Wind speed was most sensitive to the change in the time resolution, followed by solar radiation. The mean absolute relative errors (MAPE) of wind speed were 1.35%, 2.20%, 2.79%, 3.54%, 4.48%, 16.01% and 24.29% for the time resolution of 10 min, 20 min, 30 min, 40 min, 60 min, 4 times per day and 3 times per day, respectively. The change in the time resolution showed less influence on daily air temperature and relative humidity than the other 2 factors. The MAPE values of daily ET0 were 0.53%, 1.01%, 1.38%, 1.72%, 2.46%, 4.72% and 6.14% respectively for the 7 time resolutions, indicating that the accuracies of ET0 estimation for 3 and 4 times per day were significantly lower than the other 5 time resolutions. Over 95% of the absolute errors for daily ET0 with the time resolution from 10 to 40 min were in the range of -0.20-0.20 mm/d. These errors were so small and concentrated that the meteorological data with these 4 time resolutions were suitable for daily ET0 estimation. The mean bias errors (MBE) were almost equal to 0 for the time resolutions from 10 to 60 min, and the total deviation degree was very low. The MBE value for 4 times per day was -0.01 and the estimated ET0 was smaller than the true value, while the MBE value for 3 times per day was 0.02 and the estimated ET0 was larger. The change in the time resolution of the solar radiation led to the largest attribution to the error of estimated ET0, followed by wind speed, because these 2 climatic factors were more sensitive to the change in the time resolution and they were the key factors to the radiation item and dynamic item of ET0 respectively. The absolute values of the errors of monthly and yearly ET0 were significantly less than that of daily ET0. The MAPE values of monthly ET0 were 0.13%, 0.21%, 0.27%, 0.40%, 0.50%, 1.18% and 1.48% respectively for the 7 time resolutions. The absolute values of the relative error of yearly ET0 were mostly less than 0.50%. Our study demonstrated that the change in the time resolution of meteorological variables showed less impact on the estimation of the long-term integrated ET0 and it was important to improve observation time resolution for increasing the ET0 estimation accuracy. [ABSTRACT FROM AUTHOR]

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Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using the proposed model are found to be closely matched with the actual electricity demands highlighting the fact that the prediction errors are well within the acceptable limits. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0), an essential variable used to estimate crop evapotranspiration, is expected to change significantly under climate change. Detecting and attributing the change trend in ET0 to underlying drivers is therefore important to the adoption of agricultural water management under climate change. In this study, we focus on a typical agricultural region of the Fenwei Plain in northern China and use the Mann–Kendall test and contribution rate to detect the change and trend in ET0 at annual and seasonal scales and determine the major contribution factors to ET0 change for the baseline period (1985–2015) and the future period (2030–2060) based on high-resolution gridded data and climatic data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results indicate that the annual ET0 of the Fenwei Plain showed a significant decreasing trend in the baseline period but insignificant and significant increasing trends in the future period under the SSP245 and SSP585 scenarios, respectively. The annual ET0 of the plain under the SSP245 and SSP585 scenarios increase by 4.6% and 3.0%, respectively, compared to the baseline period. The change and trend in ET0 between the four seasons are different in the baseline and future periods. Winter and autumn show clear increases in ET0. VPD is the major contribution factor to the ET0 change in the plain. The change in ET0 is mainly driven by the climatic variables that change the most rather than by the climatic variables that are the most sensitive to the ET0 change. The change and trend in ET0 in the plain showed clear spatial differences, especially between the eastern and western area of the plain. To adapt to the impact of climate change on ET0, the irrigation schedule of the crops cultivated in the plain, the cropping system and management of the irrigation district in the plain need to be adjusted according to the change characteristics of spatial and temporal ET0 in the future. These results contribute to understanding the impacts of climate change on evapotranspiration in the study region and provide spatial and temporal references for adaptation in managing agricultural water use and crop cultivation under climate change. [ABSTRACT FROM AUTHOR]

Simple Summary: Colombia currently has 595,722 oil palm-cultivated hectares, but production is declining due to phytophagous insects feeding mainly on the leaves; one of them, Durrantia arcanella, is a recurring pest in the northern palm zone of Colombia, for which we do not have all the essential information. Therefore, it was proposed to determine its biology, foliar consumption rate, population fluctuation, and relationship with climatic variables and to identify its main natural enemies in the department of Cesar. The life cycle under laboratory conditions, including adult longevity, was 48.0 ± 10.1 days, the egg stage lasted 8.0 ± 0.7 days, the larva stage lasted 24.2 ± 6.2 days, the pre-pupa stage lasted 1.5 ± 0.5 days, the pupa stage lasted 7.1 ± 0.9 and the adult had a longevity of 7.2 ± 2.0 days. At the end of the larval period, it was determined that they individually consumed 8.2 ± 5.3 cm2 of leaflets. Correlation was found between D. arcanella population dynamics and climatic factors such as temperature and relative humidity, likewise with natural enemies. Durrantia arcanella is a recurring pest insect of oil palm in Colombia. Because the biology and ecology of D. arcanella are unknown, it was proposed to determine the life cycle and foliar consumption under laboratory conditions. Furthermore, through sequential sampling for two and a half years, its population fluctuation and natural enemies were determined in Agustín Codazzi and El Copey (Cesar, Colombia). Also, temperature, precipitation, and relative humidity were registered. The life cycle of D. arcanella lasted 48.0 ± 10.1 days, the egg 8.0 ± 0.7 days, larva 24.2 ± 6.2 days, pre-pupa 1.5 ± 0.5 days, pupa 7.1 ± 0.9 days, and adult 7.2 ± 2.0 days. The larvae consumed 8.2 ± 5.3 cm2 of leaflets. Correlations were found between the population fluctuation in D. arcanella and the temperature in El Copey (ρ = −0.45; p < 0.0043), relative humidity in Codazzi (ρ = 0.33; p < 0.034), and with the natural control in both locations ((ρ = 0, 61; p < 0.000044) and (ρ = 0.42; p < 0.006)). These results suggest monitoring the pest populations in the second semester of the year and show the importance of promoting native natural enemies. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an analysis of winter climate variability based on daily mean temperature and precipitation data since 1970 in the Chic-Chocs Mountain range (located in the Gaspé Peninsula, Eastern Quebec, Canada). Mountain environments are particularly sensitive to rapid climate change and are therefore good indicators of recent global warming. The main goal of this study is to demonstrate how joint probability temperature/precipitation distributions can be used to estimate winter condition changes (trends) for six meteorological stations in the study area (the altitudinal range for the stations is from 5 to 574 m). The presence and persistence of snow cover was also estimated. Previous studies have shown a lack of evidence of significant trends in snow-cover characteristics (density, depth and snow water equivalent (SWE)) from the early 1980s to the present, despite an increase in temperature over the same period. A reanalysis of these data sets in addition to the use of a combination of temperature and precipitation data categorized into four modes (warm/wet, warm/dry, cold/wet and cold/dry) was also performed. Despite this new analysis, no clear evidence of climate change could be found in the study area over the last four decades. The results revealed that patterns and trends are quite different from one station to another, but when the environment is taken into account (valley or plateau, coastal versus inland) some apparent patterns emerge. [ABSTRACT FROM AUTHOR]

The analyses presented in this paper include the concentration levels of NO and O measured during 2 successive years in twin cities (Rawalpindi and Islamabad) of Pakistan from November 2009 to March 2011. NO was determined using the passive sampling method, while ozone was determined by Model 400E ozone analyzer. The average NO and O concentration in twin cities of Pakistan was found to be 44 ± 6 and 18.2 ± 1.24 ppb, respectively. Results indicate that the concentration of NO and O show seasonal variations. Results also depict that NO and O concentration levels are high in areas of intense traffic flow and congestion. Rawalpindi has more elevated levels of NO and O as compared to the Islamabad due to the narrow roads, enclosing architecture of road network and congestion. Climatic variables also influenced the NO and O concentration, i.e., temperature is positively related with O, while negatively related with NO, relative humidity is directly related with NO and inversely related with O, whereas rainfall show negative association with both NO and O concentration. Comparing the results with WHO standards reveals that NO concentration levels at all the sampling points are above the permissible limit, while ozone concentration is still lower than the WHO standards. Thus, there is a need to take appropriate steps to control these continuously increasing levels of NO and O before they become a serious hazard for the environment and people living in those areas. [ABSTRACT FROM AUTHOR]