Importance Of Climatic Factors Research Articles

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.

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.

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 < 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.

Global warming leads to growth increase in Pinus sylvestris in the Kazakh steppe

Forest community structure and function in climate-sensitive regions exhibit uncertain changes under the influence of global warming. Especially in northern Central Asia, the response of pine growth to climate change remains unclear. Here, we identified changes in growth trends and climate response in a total of 646 trees for the abundant species Scots pine (Pinus sylvestris) at 30 sites in the Kazakh steppe. We used basal area increment, regional curve standardization and negative exponential curve detrended chronologies to comprehensively analyze trends in pine growth, and linear mixed effects and random forest models were calculated to explore growth-climate relationships. An increase in the overall growth of Scots pine under the influence of global warming, expressed in this region by increasing temperatures and precipitation, was observed during the period 1950–2018. Spatial differences in climate response between the sub-regions were identified showing that pine growth is more influenced by summer precipitation in the eastern compared to the western and central Kazakh steppe. Warm spring temperatures and more precipitation are important common climatic factors affecting pine growth in northern Central Asia. Moreover, increased drought intensity and longer drought duration negatively impact pine growth in this region. The Pacific Decadal Oscillation and North Atlantic Oscillation were identified to be the main atmospheric circulation patterns that influence the spatial differentiation in pine growth in this steppe. Albeit pine growth in northern Central Asia shows an increasing trend currently but also in future climate scenarios, the risks and challenges posed by drought should not be underestimated.

Variation of Local Wind Fields under the Background of Climate Change and Its Impact on Algal Blooms in Lake Taihu, China

Global climate change can greatly promote the continuing expansion of algal blooms in eutrophic inland lakes. Wind fields, an important climate factor, provide an external driving force for the movement of algal blooms. Based on algal bloom satellite imageries and wind observation data from 2003 to 2022, this study explored a quantitative assessment of the variations in surface wind fields and their impacts on the algal blooms in Lake Taihu, China. The results indicate that the mean wind speed at different time scales in the Lake Taihu area presents a continuous descending tendency in recent decades, which is the probable cause for the increasing frequency and severity of algal blooms in the lake. Wind fields affect the formation, location, and severity of algal blooms in diverse and complex ways. The area and frequency of algal blooms in Lake Taihu increase with the decrease in wind speed. The 6 h mean wind speed before 12:00 LT (Local Time) on the day of the algal bloom occurrence generally follows a Gaussian distribution, with a wind speed range of (0.6 m/s, 3.4 m/s) at the 95.5% confidence level. Accordingly, the wind speeds of 0.6 m/s and 3.4 m/s are identified to be the lower and upper critical wind speed indicators suitable for the formation of algal blooms, respectively. Another meaningful finding is that the outbreak of large-scale algal blooms requires stricter wind speed conditions, with a significantly lower wind speed threshold of around 2 m/s. Our study also demonstrates that the dominant wind direction of southeast in the region may be an important cause of the continuous water-quality decline and the high frequency and severity of algal blooms in the northwest waters of the lake. These findings will contribute to further studies on the dynamic mechanism of algal blooms and provide support for water environment management and algal bloom prevention and control.

Climatic and Anthropogenic Contributions to Vegetation Changes in Guangdong Province of South China

How to distinguish the relative role of climate change and human activities in vegetation dynamics has attracted increasing attention. However, most of the current studies concentrate on arid and semiarid regions, while the relative contributions of climate change and human activities to vegetation changes remain unclear in warm-humid regions. Based on the normalized difference vegetation index (NDVI) and climatic variables (temperature, precipitation, radiation) during 2001–2020, this study used the Theil–Sen median trend analysis, partial correlation analysis, and residual trend analysis to analyze the spatiotemporal pattern of vegetation trends, the response of vegetation to climate variations, and the climatic and anthropogenic contributions to vegetation dynamics in the warm and humid Guangdong Province of south China. Results showed that the NDVI in most areas exhibited an increasing trend. Changes in climatic variables displayed different spatial variations which, however, were not significant in most areas. Vegetation responded diversely to climate change with temperature as the most important climatic factor for vegetation improvement in most areas, while precipitation was the dominant climatic factor in the southern edge region and radiation was the dominant climatic factor in the central and western regions. Vegetation in most areas was influenced by both climate change and human activities, but the contribution rate of human activities was commonly much higher than climate change. The findings of this study are expected to enhance our understanding of the relative climatic and anthropogenic contributions to vegetation changes in warm-humid regions and provide a scientific basis for future ecological policies and ecosystem management in highly urbanized regions.

Drier August and colder September slow down the delaying trend of leaf senescence in herbaceous plants on the Qinghai–Tibetan Plateau

Plant phenological shifts on the Qinghai–Tibetan Plateau (QTP) have gained considerable attention over the last few decades. However, temporal changes in plant autumn phenology and the main driving factors remain uncertain. Most previous studies used satellite-derived phenological transition dates and climatic statistics during the preseason, which have relatively large uncertainties and may mask some important climate change characteristics at the intra-annual scale, thus affecting exploration of the underlying phenological change causes. This study collected 1685 phenological records at 27 ground stations on the QTP during 1983–2017. Temporal change trends and break points in leaf senescence date (LSD) of 23 herbaceous species were assessed using least squares regression, a meta-analysis procedure, and the Pettitt test. The main drivers and causes were investigated through correlation analysis and contribution calculation based on LSD observations and monthly climatic data. Results showed that, LSD of QTP herbaceous plants was significantly delayed at a rate of 4.45 days/decade during 1983–2017. Break points were concentrated during 1999–2003, with an overall mean in 2001. After 2001, the delay trend in LSD decreased, falling from 5.26 days/decade to 2.54 days/decade. Air temperature and precipitation were the most important climatic factors that showed closer and more extensive correlations with LSD and greater contributions to the inter-annual variations in LSD. August and September were the most critical period during which climatic factors had higher contributions to the LSD shifts. However, August was drier, with precipitation significantly decreasing and temperature increasing, and September was colder after 2001. Therefore, the declining trend in LSD may be attributed to the drier August and colder September. This study has not only provided reliable field evidence on temporal changes in autumn phenology on the QTP, but has also provided valuable insights into autumn phenological modelling and regional carbon cycling in alpine regions.

A Bayesian network model to disentangle the effects of stand and climate factors on tree mortality of Chinese fir plantations

Tree mortality is a complex process that not only be affected by the various factors, such as stand and climate factors, but also the various long-term effects of the factors to each other. In this study, based on the long-term spacing trials of Chinese fir in four regions of southern China, a Bayesian network was used to model tree mortality in response to stand and climate factors, as well as comparing this approach with logistic regression and random forest method. The results showed that the Bayesian network method had the highest accuracy in predicting tree mortality. In addition, the Bayesian network approach could find the dependency in the relationship between data and provide a theoretical framework for modeling uncertainty by using probabilistic calculus and underlying graph structure. Sensitivity analysis showed relative diameter was the most important factor, and temperature was the most important climate factor. Furthermore, climate factors not only directly affected tree mortality, but also indirectly affected tree mortality through affecting relative diameter, stand density and Gini coefficient. We also found that stand competition, structural heterogeneity and age affected tree mortality under climate change, and a moderate level of competition condition and stand structure heterogeneity weakened the negative impact of climate factors on tree mortality. Old trees were more sensitive to climate change than young trees, especially under extreme climate conditions. Besides, we found that tree mortality was negatively correlated with moderate annual precipitation, winter mean minimum temperature, and stand structure (Gini), and low age, but positively correlated with low relative diameter, high density and age. The results will provide adaptive options for effective forest management of Chinese fir plantations under the backdrop of global climate change in the future.

Long-term spatiotemporal analysis of the climate related impact on the transmission rate of COVID-19.

BackgroundThe association between weather conditions and the spread of COVID-19 was demonstrated by previous studies but focused on specific countries or investigated shorter periods of duration limiting the interpretation of the results. AimTo make an international comprehensive insight into the association between the weather conditions and the spread of COVID-19 by spanning many regions in the Northern and Southern hemispheres over a period of two years for the COVID-19 Outbreak. MethodsThe data were analyzed by using statistical description, linear and multiple regressions, and the Spearman rank correlation test. Daily and weekly COVID-19 cases, the average temperatures, Wind Speed, the amount of precipitation as well as the relative humidity rates were collected from Irbid, Jordan as the main location of analyses, as well as comparison cities and countries in both hemispheres. Resultswe found that certain climate variables are significant factors in determining the transmission rate of COVID-19 worldwide. Where, The temperature in the northern hemisphere regions was the most important climate factor that affects the increase in the transmission rate of COVID-19 (Northern Hemisphere rs = −0.65; Irbid rs = −0.74995; P < 0.001), While in southern hemisphere, the climate factor that affects the increase in the transmission rate of COVID-19 was the humidity (rs = 0.55; P < 0.01), In addition, we found the negligible and oscillated effect of wind speed on the transmission rate of COVID-19 worldwide. Moreover, we found that in Irbid 82% of COVID-19 cases were in the fall and winter seasons, while in summer the percentage of COVID-19 cases didn't exceed 3% during the total study period. ConclusionThis study can help develop international strategies and policies against COVID-19-related pandemic peaks, especially during the colder seasons in the Northern Hemisphere regions from the first month of fall to the last month of winter.

Multi-timescale-based precipitation concentration dynamics and their asymmetric impacts on dry and wet conditions in a changing environment

Precipitation is one of the most important climatic factors affecting the natural environment and hydrological processes. The excessive concentration of precipitation at different time scales in spatial and temporal distribution could increase the possibility of floods or droughts and their disaster-causing extent in a changing environment. Nevertheless, the dynamics of precipitation concentration and its specific effects on dry and wet conditions remain unknown. To this end, the precipitation concentration indexes of different time scales was adopted to characterize the non-uniform distribution of precipitation within the year in this study, and the spatial and temporal variation characteristics of precipitation concentration was examined. The impacts of precipitation concentration dynamics on dry and wet conditions were then quantified based on the conditional probability of dry and wet conditions when unusual concentration of precipitation events occur under different scenarios. The Weihe River basin, the largest tributary of the Yellow River in China, was selected as a case study. The results show that: (1) daily concentration index (CID), monthly concentration index (CIM), and precipitation concentration degree (PCD) are higher in the north part of the basin and show an insignificant increase trend, the precipitation distribution in the region is extremely non-uniform, and the risk of floods and droughts is increasing; (2) in the same scenario, CIM has the strongest impact on dry conditions, followed by CID and finally PCD, PCD has the strongest impact on wet conditions, followed by CID and finally CIM; (3) under different scenarios, as CID, CIM and PCD increase, the probability of dry conditions gradually decreases, while the probability of wet conditions gradually increases; (4) precipitation concentration shows an asymmetric impact on drought and wet conditions. In general, this study provides new insights into the precipitation concentration dynamics and their asymmetric impacts on dry and wet conditions, which is highly relevant for drought and flood prevention.

Quantifying impact of climatic and anthropogenic factors on runoff in the source region of Alpine River in northeast Qinghai-Tibetan Plateau

Study regionThe Datong River source region in the Northeast Qinghai-Tibetan Plateau Study focusClimate change and anthropogenic factors are the most important factors influencing runoff. Land use/land cover (LULC) is an important anthropogenic factor that has a significant impact on runoff changes. Quantifying runoff variation characteristics and determining the effect of changes in climate and LULC on runoff is important for water resource management and application. The mathematical statistics analysis and soil and water assessment tool (SWAT) were combined to perform a comprehensive analysis of runoff based on meteorological and hydrological data from the Datong River source area, and the effect of changes in climate and LULC on runoff was quantitatively calculated. New hydrological insights for the regionThe results indicate that under the warm and humid climate, there was a slight change in the inter-annual runoff, while the distribution of runoff within the year was uneven due to the control of climate factors. Climate exhibited a positive driving effect on runoff, while LULC reduced the runoff and contributed 125% and − 25%, respectively, to the change in the runoff. Rainfall is the most important climatic factor influencing runoff change. It is recommended that the flood tragedy induced by short periods of high rainfall be taken into account during the process of water resource management in the basin.

Elevational changes in climate response of Pinus nigra pallasiana tree-rings on the Crimean Peninsula

Climate change is most evident on the periphery of species distribution ranges. Using four tree-ring chronologies, we identified the most important climatic factors influencing the radial growth of black pine growing along an elevational transect on the eastern slope of Mount Ai-Petri (Crimean Peninsula), at the northernmost part of its range. The relationship between tree-ring width and climate was determined using response function analysis. Results indicate an increase in correlation between radial growth and hydrothermal data along the transect: from a near absence of any correlation at the lowest elevation to r = 0.6 at the top. This change in response was not only caused by differences in climatic variables, but also related to topography, soil, and bedrock features. The currently ongoing aridization may lead to a decrease in the stability and persistence of black pine stands only in the upper parts of Mount Ai-Petri.