Importance Of Climatic Factors Research Articles

Primary Mid-Succession Carbon Fluxes in a Spontaneously Recovering Post-Mining Ecosystem

Limited research exists on the carbon sequestration potential of spontaneously developing post-coal-mining sites in the mid-stage of primary succession. Therefore, in 2023, net ecosystem exchange (NEE) was quantified in Czechia using an eddy covariance (EC) tower to assess carbon fluxes in a spontaneously developing ecosystem dominated by pioneer tree species such as willow, along with aspen and birch, growing on a wave-like microtopography. The ecosystem functioned as a strong carbon sink, with an annual NEE of −415 g C m−2 yr−1, ~39 years after coal mining. This NEE was derived by gross ecosystem exchange (GEE) of −1423 g C m−2 yr−1 and ecosystem respiration (Reco) of 1008 g C m−2 yr−1. Seasonal variation was driven by higher GEE in summer rather than by Reco. Consequently, Reco accounted for ca. 51% of GEE in summer, compared to 56% in spring. In addition, temperature was an important climatic factor in spring, whereas vapor pressure deficit (VPD) and global radiation (Rg) were more critical in summer. Overall, our results highlight the robust carbon sequestration capacity of naturally developing pioneer forests, suggesting their potential role in restoring mined areas in Central Europe and other regions without water limitations following coal mining.

Analysis of the Distribution Pattern of Asparagus in China Under Climate Change Based on a Parameter-Optimized MaxEnt Model

Asparagus (Asparagus officinalis L.) has high health and nutritional values, but the lack of scientific and rational cultivation planning has resulted in a decline in asparagus quality and yield. Important soil, climatic, anthropogenic, and topographic environmental factors influencing the distribution of asparagus cultivation were chosen for this study. The Kuenm package in the R language (v4.2.1) was employed to optimize the maximum entropy model (MaxEnt). Pearson’s correlation analysis, optimized MaxEnt, and geographic information spatial technology were then utilized to identify the main environmental factors that influence suitable habitats for asparagus in China. Potential distribution patterns, migration, and changes in trends concerning the suitability of asparagus in China under various historical and future climate scenarios were modeled and projected. Human activities and climate factors were found to be the primary environmental factors that influence the suitability distribution of asparagus cultivation in China, followed by soil and topographic factors. Historical suitable habitats covered 345.6 × 105 km2, accounting for 36% of China. These habitats are projected to expand considerably under future climatic conditions. This research offers a basis for the rational planning and sustainable development of asparagus cultivation.

Predicting the distribution of Ixodes ricinus in Europe: integrating microclimatic factors into ecological niche models.

Ixodes ricinus, commonly known as the castor bean tick and sheep tick, is a significant vector of various diseases, such as tick-borne encephalitis and Lyme borreliosis. Owing to climate change, the distribution and activity of I. ricinus are expected to increase, leading to an increase in the number of diseases transmitted by this species. Most distribution models and ecological niche models utilize macroclimate datasets such as WorldClim or CHELSA to map the distribution of disease-transmitting ticks. However, microclimatic factors are crucial for the activity and survival of small arthropods. In this study, an ecological niche modelling approach was used to assess the climatic suitability of I. ricinus using both microclimatic and macroclimatic parameters. A Mixed model was built by combining parameters from the Soiltemp (microclimate) and Wordclim (macroclimate) databases, whereas a Macroclimate model was built with the CHELSA dataset. Additionally, future suitabilities were projected via the macroclimate model under the SSP3-7.0 and SSP5-8.5 scenarios. Macroclimate and Mixed models showed similar distributions, confirming the current distribution of I. ricinus. The most important climatic factors were seasonality, annual temperature range, humidity and precipitation. Future projections suggest significant expansion in northern and eastern Europe, with notable declines in southern Europe.

Demonstrating almost half of cotton fiber quality variation is attributed to climate change using a hybrid machine learning-enabled approach

Understanding the effects of climate change on cotton fiber quality will reduce the risks to production caused by global warming. Machine learning algorithms are effective for forecasting climate impacts on crops. However, the impact of climate change on cotton fiber quality is unclear. Hence, a hybrid machine learning-enabled approach, the Bayesian model average (BMA) method with multiple machine learning algorithms (linear regressor, SVR, RFR, GBDT, LightGBM, and XGBoost) and bootstrap resampling, was developed to explore the impact and screen the important climatic factors affecting various traits of fiber quality. On the basis of fiber quality data from 1033 test stations across Xinjiang, China, from 2016 to 2022, the explained variance for climate change in the hybrid machine learning model was as follows: 44.72 %–50.55 % for white cotton grade, 44.06 %–53.95 % for length, 51.72 %–56.81 % for micronaire, 32.70 %–49.50 % for length uniformity, and 45.66 %–53.09 % for strength in the 1000 bootstrapping samples. In addition, recursive feature elimination with cross-validation (RFECV) was used to select the optimal feature set and calculate the contribution of each feature. The variability in micronaire in the hybrid model was affected primarily by climate factors, such as the daily minimum temperature, rainfall, and wind speed, whereas the other quality traits were affected mainly by radiation-related climatic indicators. The climate during the harvest stage in October had a significant effect on cotton quality, explaining 33.0 % of the variance in white cotton grade, 32.1 % in length, and 48.3 % in fiber strength. Conversely, the climate during the boll opening and early harvest stages in September had a greater influence on micronaire and length uniformity, accounting for 21.4 % and 37.2 % of the variance, respectively. This study highlights that climate change explains nearly 50 % of the variation in fiber quality, with the influence being notably more considerable during the later stages of the cotton growth period. These findings clarify the uncertainty in the impact of climate change on cotton fiber quality considering the uncertainty of the single machine model and model errors. Equally important, this information can be valuable for farmers and growers seeking to improve fiber quality under climate change.

Xanthium strumarium L., an invasive species in the subtropics: prediction of potential distribution areas and climate adaptability in Pakistan

Invasive species such as Xanthium strumarium L., can disrupt ecosystems, reduce crop yields, and degrade pastures, leading to economic losses and jeopardizing food security and biodiversity. To address the challenges posed by invasive species such as X. strumarium, this study uses species distribution modeling (SDM) to map its potential distribution in Pakistan and assess how it might respond to climate change. This addresses the urgent need for proactive conservation and management strategies amidst escalating ecological threats. SDM forecasts a species’ potential dispersion across various geographies in both space and time by correlating known species occurrences to environmental variables. SDMs have the potential to help address the challenges posed by invasive species by predicting the future habitat suitability of species distributions and identifying the environmental factors influencing these distributions. Our study shows that seasonal temperature dependence, mean temperature of wettest quarter and total nitrogen content of soil are important climatic factors influencing habitat suitability of X. strumarium. The potential habitat of this invasive species is likely to expand beyond the areas it currently colonizes, with a notable presence in the Punjab and Khyber Pakhtunkhwa regions. These areas are particularly vulnerable due to threats to agriculture and biodiversity. Under current conditions, an estimated 21% of Pakistan’s land area is infested by X. strumarium, mainly in upper Punjab, central Punjab and Khyber Pakhtunkhwa. The range is expected to expand in most regions except Sindh. The central and northeastern parts of the country are proving to be particularly suitable habitats for X. strumarium. Effective strategies are crucial to contain the spread of X. strumarium. The MaxEnt modeling approach generates invasion risk maps by identifying potential risk zones based on a species’ climate adaptability. These maps can aid in early detection, allowing authorities to prioritize surveillance and management strategies for controlling the spread of invasive species in suitable habitats. However, further research is recommended to understand the adaptability of species to unexplored environments.

Distributional response of the rare and critically endangered Ilex nanchuanensis to climate change in East Asia

Abstract Global climate change, dominated by climate warming, is seriously affecting the balance of global ecosystems, but the risk of species extinction is particularly high in low-altitude mountain areas. To clarify the response of the endemic and critically endangered species Ilex nanchuanensis to climate change, this study used the MaxEnt model to simulate and predict the potential habitat of I. nanchuanensis during the Last Interglacial, Last Glacial Maximum, the current period, and two future periods (the 2050 s and 2070 s). The results showed that the hottest monthly minimum temperature is the most important climatic factor affecting the geographical distribution of I. nanchuanensis. Furthermore, I. nanchuanensis will be at risk of population shrinkage and extinction in the future, with the center of mass moving further northwest as concentrations of greenhouse gases increase, especially in the 2070 s, when its geographical distribution shrinks the most under the RCP6 scenario. Therefore, to actively respond to the impacts of climate change, protected areas should be established around the geographical distribution centers of species, and core, buffer, and experimental areas should be scientifically and rationally delineated for the conservation and cultivation of germplasm resources.

The diversity and biogeography of bacterial communities in lake sediments across different climate zones

Bacteria are diverse and play important roles in biogeochemical cycling of aquatic ecosystems, but the global distribution patterns of bacterial communities in lake sediments across different climate zones are still obscure. Here we integrated the high-throughput sequencing data of 750 sediment samples from published literature to investigate the distribution of bacterial communities in different climate zones and the potential driving mechanisms. The obtained results indicated that the diversity and richness of bacterial community were notably higher in temperate and cold zones than those in other climate zones. In addition, the bacterial community composition varied significantly in different climate zones, which further led to changes in bacterial functional groups. Specifically, the relative abundance of nitrogen cycling functional groups in polar zones was notably higher compared to other climate zones. Regression analysis revealed that climate (mean annual precipitation, MAP; and mean annual temperature, MAT), vegetation, and geography together determined the diversity pattern of sediment bacterial community on a global scale. The results of partial least squares path modeling further demonstrated that climate was the most significant factor affecting the composition and diversity of bacterial communities, and MAP was the most important climate factor affecting the composition of bacteria community (R2 = 0.443, P < 0.001). It is worth noting that a strong positive correlation was observed between the abundance of the dominant bacterial group uncultured_f_Anaerolineaceae and the normalized difference vegetation index (NDVI; P < 0.001), suggesting that vegetation could affect bacterial community diversity by influencing dominant bacterial taxa. This study enhances our understanding of the global diversity patterns and biogeography of sediment bacteria.

Temperature differentially regulates estuarine microbial N2O production along a salinity gradient

Nitrous oxide (N2O) is atmospheric trace gas that contributes to climate change and affects stratospheric and ground-level ozone concentrations. Ammonia oxidizers and denitrifiers contribute to N2O emissions in estuarine waters. However, as an important climate factor, how temperature regulates microbial N2O production in estuarine water remains unclear. Here, we have employed stable isotope labeling techniques to demonstrate that the N2O production in estuarine waters exhibited differential thermal response patterns between nearshore and offshore regions. The optimal temperatures (Topt) for N2O production rates (N2OR) were higher at nearshore than offshore sites. 15N-labeled nitrite (15NO2-) experiments revealed that at the nearshore sites dominated by ammonia-oxidizing bacteria (AOB), the thermal tolerance of 15N-N2OR increases with increasing salinity, suggesting that N2O production by AOB-driven nitrifier denitrification may be co-regulated by temperature and salinity. Metatranscriptomic and metagenomic analyses of enriched water samples revealed that the denitrification pathway of AOB is the primary source of N2O, while clade II N2O-reducers dominated N2O consumption. Temperature regulated the expression patterns of nitrite reductase (nirK) and nitrous oxide reductase (nosZ) genes from different sources, thereby influencing N2O emissions in the system. Our findings contribute to understanding the sources of N2O in estuarine waters and their response to global warming.

Potential distribution prediction of Pomacea canaliculata in China based on the Biomod2.

Pomacea canaliculata is a globally significant invasive species that poses substantial threats to agricultural production, ecosystems, and human health in China. To evaluate its habitat suitability in China, we collected and collated 741 county-level occurrence points of P. canaliculata in China through database query, literature search and news reports, and obtained the five most important climatic factors by variance inflation factor and Pearson correlation coefficient from 19 bioclimatic factors. We investigated the potential suitable distribution areas of P. canaliculata under current and future climate scenarios using the Biomod2 ensemble model, and identified the primary environmental variables influencing their distributions. The results showed that under the current climate, the suitable habitat areas were concentrated in southern provinces or municipalities, such as Zhejiang, Shanghai, Fujian, Jiangxi, Guangdong, Guangxi, Hainan, and Yunnan. Under different climate scenarios in the future, the suitable habitat area would show large-scale expansion in the southern provinces or municipalities, and spread to the northern region. The most two important environmental factors affecting the distribution of suitable habitat areas were precipitation of wettest month and mean temperature of wettest quarter. A combination model to predict the potential distribution of P. canaliculata under current and future climate conditions would help manage the risk of its invasion and spread, and provide a reference for relevant regions and departments to take active measures in advance to prevent the spread, monitor and mitigate its invasion.

Climate dynamics and the effect of topography on snow cover variation in the Indus-Ganges-Brahmaputra river basins

The river basins of the Indus-Ganges-Brahmaputra (IGB) are characterized by rapid population growth, increased water demand, and rapid snow/ice melting, all of which combine to make these catchments especially vulnerable to climate change. Here, we present a comprehensive dataset (2003−2020) for precipitation, temperature and snow cover area (SCA) to investigate the spatio-temporal variability of important climate factors in relation to elevation and across three major sub-basins of the IGB. Improved MODIS (Moderate Resolution Imaging Spectro-radiometer) Terra and Aqua snow cover data are used to estimate regional snow cover, while Climate Hazards Group InfraRed Precipitation with Station Data version 2.0 (CHIRPS-V2.0) is deployed for precipitation, and MODIS Land Surface Temperature (LST) data are used to estimate temperature. The analysis reveals significant changes in precipitation, LST and snow cover throughout the IGB region together with a strong degree of spatial coherence between SCA and regional surface topography (R2 = 0.82).Maximum changes were observed in the Brahmaputra basin at mean annual precipitation (13.9 mm/year) and reduced SCA (155.4 km2/year) over the period studied. Similar trends are also evident, albeit with relatively lower absolute values in the other two basins, whereby the Indus basin evidences a significant reduction in annual SCA, averaging 82.2 km2/year with increased precipitation (3.12 mm/year) and LST. This investigation further highlights the relative influence of climate change factors and regional human activities in determining high-elevation changes in SCA. Most of the middle elevation regions of the Ganges experienced a significant reduction in SCA, with human impact contributing 82% of the observed variation, while climate factors are more prominent in the Indus and Brahmaputra basins. Understanding the dynamics of snow cover and its regional influencing factors is crucial to the more accurate evaluation of water resources and to defining appropriate mitigation and adaptation strategies for the coming decades.

Predicting the Environmental Suitability and Identifying Climate and Sociodemographic Correlates of Guinea Worm (Dracunculus medinensis) in Chad.

A comprehensive understanding of the spatial distribution and correlates of infection are key for the planning of disease control programs and assessing the feasibility of elimination and/or eradication. In this work, we used species distribution modeling to predict the environmental suitability of the Guinea worm (Dracunculus medinensis) and identify important climatic and sociodemographic risk factors. Using Guinea worm surveillance data collected by the Chad Guinea Worm Eradication Program (CGWEP) from 2010 to 2022 in combination with remotely sensed climate and sociodemographic correlates of infection within an ensemble machine learning framework, we mapped the environmental suitability of Guinea worm infection in Chad. The same analytical framework was also used to ascertain the contribution and influence of the identified climatic risk factors. Spatial distribution maps showed predominant clustering around the southern regions and along the Chari River. We also identified areas predicted to be environmentally suitable for infection. Of note are districts near the western border with Cameroon and southeastern border with Central African Republic. Key environmental correlates of infection as identified by the model were proximity to permanent rivers and inland lakes, farmlands, land surface temperature, and precipitation. This work provides a comprehensive model of the spatial distribution of Guinea worm infections in Chad 2010-2022 and sheds light on potential environmental correlates of infection. As the CGWEP moves toward elimination, the methods and results in this study will inform surveillance activities and help optimize the allocation of intervention resources.

Trend Analysis of MODIS Land Surface Temperature and Land Cover in Central Italy

Land Surface Temperature (LST) is an important climate factor for understanding the relationship between the land surface and atmosphere. Furthermore, LST is linked to soil moisture and evapotranspiration, which can potentially alter the severity and regime of wildfires, landslide-triggering precipitation thresholds, and others. In this paper, the monthly daytime and nighttime LST products of Moderate Resolution Imaging Spectroradiometer (MODIS) are employed for the period 2000–2023 in order to find areas that have been cooling or warming in a region of great interest in Central Italy, due to its complex geological and geomorphological settings and its recent seismic sequences and landslide events. The annual MODIS land cover images for 2001–2022 are also utilized to investigate the interconnection between LST and land cover change. The results of the non-parametric Mann–Kendall trend test and its associated Sen’s slope reveal a significant nighttime warming trend in the region, particularly in July, linked to forest and woodland expansion. Grasslands toward the coastline with low elevation (less than 500 m a.s.l.) have experienced significant heat waves during the summer, with an LST of more than 35 °C. A significant negative correlation between the elevation and LST is observed for each calendar month. In particular, the daytime and nighttime LST have more than 80% correlation with elevation during winter and summer, respectively. In addition, nighttime warming and gradual drainage are noticed in Lake Campotosto. The results of this study could be useful for wildfire and landslide susceptibility analyses and hazard management.

Discounting Water for Optimal Carbon Gain as a Basis of Stomatal Closure

AbstractThe exchange of carbon dioxide and water vapor between terrestrial ecosystems and the atmosphere is regulated by stomata (small pores in the leaves of plants). Unsurprisingly, environmental factors controlling the opening and closure of stomata has been sought as early as 1800. One approach, popularized in the early 1970s, is a stomatal optimization framework. This framework is based on the hypothesis that plants optimize carbon gain subject to water loss or water availability constraints. This constraint optimization problem was solved in various forms assuming instantaneous adjustments of stomatal aperture to maximize a reward function with no future foresight or legacy effects. Holtzman et al. (2024, https://doi.org/10.1029/2023av001113) offers a novel approach that can diagnose the effective timescale over which the reward function maximization must be time‐integrated. The developed method thus optimizes an integrated carbon gain function but adjusted by a discount factor subject to water availability in the root zone. The discount factor considers how the plant values carbon gain to save water and its timescale can be inferred from observations because the model is analytically tractable. The results suggest that the most important climate factor that determines this discount timescale is multi‐annual mean of the longest dry period during the growing season. The findings highlight how local climate traits influence the spatial variation in ecosystem‐level water use strategies. This sets the stage for expanding such a framework to cases where multiple constraints act in concert while operating at distinct time scales.

Precision agriculture for wine production: A machine learning approach to link weather conditions and wine quality

The agricultural sector, in particular viticulture, is highly susceptible to variations in the environment, crop conditions, and operational factors. Effectively managing these variables in the field necessitates observation, measurement, and responsive actions. Leveraging new technologies within the realm of precision agriculture, vineyards can enhance their long-term efficiency, productivity, and profitability. In our work we propose a novel analysis of the impact of pedoclimatic factors on wine, with a case study focusing on the Denomination of Controlled and Guaranteed Origin Chianti Classico (DOCG), a prime wine-producing region located in Tuscany, between the provinces of Siena and Florence. We first collected a novel dataset, where geographic information as well as wine quality information were collected, using publicly available sources. Using such geographic information retrieved and an unsupervised machine learning approach, we conducted an in-depth examination of pedoclimatic and production data. To collect the whole set of possibly relevant features, we first assessed the region's morphological attributes, including altitude, exposure, and slopes, while pinpointing individual wineries. Subsequently we then calculated crucial viticultural indices such as the Winkler, Huglin, Fregoni, and Freshness Index by utilizing daily temperature records from Chianti Classico, and we further related them to an assessment of wine quality. In addition to this, we designed and distributed a survey conducted among a sample of wineries situated in the Chianti Classico area, obtaining valuable insights into local data. The primary goal of this study is to elucidate the interrelationships between various parameters associated with the region, considering influential factors such as the environment, viticulture, and field operations that significantly impact wine production. By doing so, wineries could potentially unlock the full potential of their resources. In fact, through the unsupervised and correlation analysis we could elucidate the relationships existing between the pedoclimatic parameters of the region, considering the most important factors such as viticulture and field operations, and relate them to wine quality as for instance using the survey data collected. This study represents an unprecedent in the literature, and it could pave the path for future studies focusing on the importance of climatic factors into production and quality of wines.

Evaluating net primary productivity dynamics and their response to land-use change in the loess plateau after the 'Grain for Green' program

Assessing net primary productivity (NPP) dynamics and the contribution of land-use change (LUC) to NPP can help guide scientific policy to better restore and control the ecological environment. Since 1999, the “Green for Grain” Program (GGP) has strongly affected the spatial and temporal pattern of NPP on the Loess Plateau (LP); however, the multifaceted impact of phased vegetation engineering measures on NPP dynamics remains unclear. In this study, the Carnegie-Ames-Stanford Approach (CASA) model was used to simulate NPP dynamics and quantify the relative contributions of LUC and climate change (CC) to NPP under two different scenarios. The results showed that the average NPP on the LP increased from 240.7 gC·m−2 to 422.5 gC·m−2 from 2001 to 2020, with 67.43% of the areas showing a significant increasing trend. LUC was the main contributor to NPP increases during the study period, and precipitation was the most important climatic factor affecting NPP dynamics. The cumulative amount of NPP change caused by LUC (ΔNPPLUC) showed a fluctuating growth trend (from 46.23 gC·m−2 to 127.25 gC·m−2), with a higher growth rate in period ΙΙ (2010–2020) than in period Ι (2001–2010), which may be related to the accumulation of vegetation biomass and the delayed effect of the GGP on NPP. The contribution rate of LUC to increased NPP in periods Ι and ΙΙ was 101.2% and 51.2%, respectively. Regarding the transformation mode, the transformation of grassland to forest had the greatest influence on ΔNPPLUC. Regarding land-use type, the increased efficiency of NPP was improved in cropland, grassland, and forest. This study provides a scientific basis for the scientific management and development of vegetation engineering measures and regional sustainable development.

Calculation of Kinetic Fractionation of Open‐Water Evaporation Over an Upland Reservoir

AbstractBased on the fact that many water reservoirs built in headwater catchments receive no water input during dry season, we modified the Craig‐Gordon model and proposed an approach to calculate kinetic fractionation factors in open‐water evaporation taking into account isotopic compositions of precipitation and of lake water, lake volume, and land‐based meteorological data. The approach is centered around the determination of lake water isotope enrichment rate over time during dry periods. We then applied this approach for calculating kinetic fractionation factors, turbulence index, and open‐water evaporation for a small reservoir located in a sub‐tropical monsoonal climate. We found that the kinetic fractionation factors, εK,H and εK,O are, respectively 3.11‰ ± 0.41‰ and 2.50‰ ± 2.15‰, corresponding to the turbulence index of 0.43 ± 0.19. Evaporation rates in open‐water estimated by our approach is 1.55 times of the land‐based evaporation gauged in the nearby meteorological station. Wind speed, represented by turbulence index, is proved as an important climatic factor influencing kinetic fractionation. This study confirms that wind and surface area factors, which have been neglected in several isotope mass balance models, should be included into calculation process.

Analyzing temperature, humidity, and precipitation trends in six regions of Thailand using innovative trend analysis

The change of temperature and weather parameters is a major concern affecting sustainable development and impacting various sectors, such as agriculture, tourism, and industry. Changing weather patterns and their impact on water resources are important climatic factors that society is facing. In Thailand, climatological features such as ambient temperature, relative humidity, and precipitation play a substantial role in affecting extreme weather events, which cause damage to the economy, agriculture, tourism, and livelihood of people. To investigate recent serious changes in annual trends of temperature, relative humidity, and precipitation in Thailand, this study used the Mann–Kendall (MK) test and innovative trend analysis (ITA) methods. The MK test showed that all six regions had an upward trend in temperature and humidity index (humidex, how hot the weather feels to the average person), while relative humidity and precipitation showed both upward and downward trends across different regions. The ITA method further confirmed the upward trend in temperature and humidex and showed that most data points fell above the 1:1 line. However, the upward trend in most variables was not significant at the 5% level. The southern and eastern regions showed a significant upward trend in relative humidity and humidex at a 5% level of significance according to the MK test. The output of this study can help in the understanding of weather variations and predict future situations and can be used for adaptation strategies.

Responses of Yield and Photosynthetic Characteristics of Rice to Climate Resources under Different Crop Rotation Patterns and Planting Methods.

Climate is the most important environmental factor influencing yield during rice growth and development. To investigate the relationships between climate and yield under different crop rotation patterns and planting methods, three typical rotation patterns (vegetable-rice (V), rape-rice (R), and wheat-rice (W)) and two mechanical planting methods (mechanical transplanting (T1) and mechanical direct seeding (T2)) were established. The results showed that compared to the V rotation pattern, the average daily temperature (ADT) during the sowing to heading stage increased under both R and W rotation patterns, which significantly shortened the growth period. Thus, the effective accumulated temperature (EAT), photosynthetic capacity, effective panicle (EP), and spikelet per panicle (SP) under R and W rotation patterns significantly decreased, leading to reductions in grain yield (GY). VT2 had a higher ratio of productive tillers (RPT), relative chlorophyll content (SPAD), leaf area index (LAI), and net photosynthetic rate (Pn) than those of VT1, which significantly increased panicle dry matter accumulation (DMA), resulting in an increase in GY. Although RT2 and WT2 had a higher RPT than those of RT1 and WT1, the GY of RT1 and WT1 decreased due to the significant reductions in EAT and photosynthetic capacity. Principal component analysis (PCA) showed that the comprehensive score for different rotation patterns followed the order of V > R > T with VT2 ranking first. The structural equation model (SEM) showed that EAT and ADT were the most important climate factors affecting yield, with total effects of 0.520 and -0.446, respectively. In conclusion, mechanical direct seeding under vegetable-rice rotation pattern and mechanical transplanting under rape-rice or wheat-rice rotation pattern were the rice-planting methods that optimized the climate resources in southwest China.

Light Planograph for Insolation Energy Characteristics of Buildings and Territory of Multi-Storey Structures Modelling

This article discusses the problems associated with physical and hygienic studies aimed at determining the influence of insolation – an important climatic factor – on the light, radiation and thermal regime of buildings, and territories in urban development. The article proposes the design of an insolation device of a flatbed type, known as a Svetoplanograph‑2, which can be widely used in solving architectural, construction, urban planning, and energy problems in summer in regions with a warm climate. A method of step-by-step assessment of the insolation-light-thermal regime of buildings and territories with a complex morphological structure of buildings, which is a model of the energy characteristics of insolation, has also been developed. The use of a tablet-type device makes it possible to solve practical urban planning tasks, providing an opportunity to calculate and evaluate the quantitative and qualitative characteristics of the insolation regime in order to improve the environmental and climatic situation in urban buildings and development in the summer. The developed method of forecasting the insolation regime using a tablet-type device is the basis for modelling the energy state of insolation in a human environment in buildings and territories of cities with a hot climate.

Long‐Term Variability and Trends in Snow Depth and Cover Days Throughout Iranian Mountain Ranges

AbstractIn Iran, the mountain snow cover generally feeds major rivers and thereby largely provides water resources required for improving human lives and protecting nature. Hence, understanding historical variability and trends in mountainous snowpack water resources in Iran in response to global warming and climate change can play a critical role in the sustainable development of this country. Accordingly, this study investigated long‐term (1982–2018) snowpack climatology at 13 hydrometeorological measurement stations scattered throughout the Iranian mountain ranges, with a focus on Elburz, Azerbaijan, Zagros, and Khorasan mountainous regions. The non‐parametric Mann‐Kendall test was used to detect statistically significant (p &lt; 0.05) trends, the Pettitt test to identify possible abrupt shift years, the Pearson's correlation coefficient to measure relationships among different time series, and the partial correlation to determine the most important climate factor influencing snowpack dynamics The annual snow depth (maximum snow depth) significantly declined throughout Iranian mountain ranges during 1982–2018, with an average rate of 1.0 (3.4) cm decade−1. The annual snow cover days (SCDs) also showed significant decreasing trends, ranging from 3 to 15 days decade−1 during 1982–2018, in 69% of the stations studied. Such considerable reductions in snow depth and cover days were mainly related to the compound effects of substantial increases in temperature, sunshine, and wind speed as well as decreases in precipitation and cloudiness during the SCDs across the Iranian mountain ranges. However, precipitation was the most influential climate factor controlling snow resources throughout both the Elburz and Zagros mountains in Iran.