Search Results (127)

The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, plays a vital role in both hydrological and ecological processes. The presence of AMC on leaf surfaces serves as an indicator of leaf water potential and overall ecosystem health. However, the large-scale assessment of AMC on leaf surfaces remains limited. To address this gap, we propose a leaf area index (LAI)-derived condensation potential (LCP) index to estimate potential dew yield, thereby supporting more effective land management and resource allocation. Based on psychrometric principles, we apply the nocturnal condensation potential index (NCPI), using dew point depression (ΔT = T_a − T_d) and vapor pressure deficit derived from field meteorological data. Kriging interpolation is used to estimate the spatial and temporal variations in the AMC. For management applications, we develop a management suitability score (MSS) and prioritization (MSP) framework by integrating the NCPI and the LAI. The MSS values are classified into four MSP levels—High, Moderate–High, Moderate, and Low—using the Jenks natural breaks method, with thresholds of 0.15, 0.27, and 0.37. This classification reveals cases where favorable weather conditions coincide with low ecological potential (i.e., low MSS but high MSP), indicating areas that may require active management. Additionally, a pairwise correlation analysis shows that the MSS varies significantly across different LULC types but remains relatively stable across groundwater potential zones. This suggests that the MSS is more responsive to the vegetation and micrometeorological variability inherent in LULC, underscoring its unique value for informed land use management. Overall, this study demonstrates the added value of the LAI-derived AMC modeling for monitoring spatiotemporal micrometeorological and vegetation dynamics. The MSS and MSP framework provides a scalable, data-driven approach to adaptive land use prioritization, offering valuable insights into forest health improvement and ecological water management in the face of climate change. Full article

Accurately extracting the phenology of maize, one of the three major staple crops, is crucial for assessing regional suitability under climate change, optimizing field management, predicting yield fluctuations, and ensuring food security. This study compares and validates the accuracy of various vegetation indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), solar-induced chlorophyll fluorescence (SIF), and kernel NDVI (kNDVI), in extracting the phenological phases of summer maize at the sixth leaf (V6), tasseling (VT), and maturity (R6). Additionally, explainable machine learning methods were employed to elucidate how climate and stress factors influence the phenological sequences of summer maize. The results show that compared to NDVI and EVI, SIF and kNDVI are more suitable for extracting the summer maize phenological phase. SIF achieved the highest phenological extraction precision at the V6 and R6 phases, with root mean square errors (RMSEs) of 7.86 and 8.22 days, respectively. kNDVI provided the highest extraction accuracy for the VT phase, with an RMSE of 5 days. SHapley Additive exPlanations (SHAP) analysis revealed that temperature and radiation are the primary meteorological factors influencing maize phenology in the study area. Regarding stress factors, drought and heat stress delayed phenology at the V6 and VT phases, while heat stress prior to maturity accelerated summer maize maturation. In conclusion, this study reveals the potential of emerging vegetation indices for extracting maize phenology, offering both data and theoretical support for regional crop adaptability assessments. Full article

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has demonstrated significant potential for soil moisture content (SMC) monitoring due to its high spatiotemporal resolution. However, GNSS-IR inversion experiments are notably influenced by vegetation and meteorological factors. To address these challenges, this study proposes a multi-factor SMC inversion method. Six GNSS stations from the Plate Boundary Observatory (PBO) were selected as study sites. A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. A multi-factor SMC inversion dataset was constructed, and three machine learning models were selected to develop the SMC prediction model: Support Vector Regression (SVR), suitable for small and medium-sized regression tasks; Convolutional Neural Networks (CNN), with robust feature extraction capabilities; and NRBO-XGBoost, which supports automatic optimization. The multi-factor SMC inversion method achieved remarkable results. For instance, at the P038 station, the model attained an R² of 0.98, with an RMSE of 0.0074 and an MAE of 0.0038. Experimental results indicate that the multi-factor inversion model significantly outperformed the traditional univariate model, whose R² (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R² outperforming SVR by 0.11 and CNN by 0.03. Additionally, the analysis of different surface types showed that the method achieved higher accuracy in grassland and open shrubland areas, with all models reaching R² values above 0.9. Therefore, the accuracy of the multi-factor SMC inversion model was validated, supporting the practical application of GNSS-IR technology in SMC inversion. Full article

Drought poses significant challenges to agricultural productivity and water resource sustainability in Henan Province, emphasizing the need for effective monitoring approaches. This study investigates the suitability of the TRMM 3B43V7 satellite precipitation product for drought assessment, based on monthly data from 15 meteorological stations during 1998–2019. Satellite-derived precipitation was compared with ground-based observations, and the Standardized Palmer Drought Index (SPDI) was calculated to determine the optimal monitoring timescale. Statistical metrics, including Nash–Sutcliffe Efficiency (NSE = 0.87) and Pearson correlation coefficient (PCC = 0.88), indicate high consistency between TRMM data and ground measurements. The 12-month SPDI (SPDI-12) was found to be the most effective for capturing historical drought variability. To support integrated drought management, a regionally adaptive framework is recommended, balancing agricultural demands and ecosystem stability through tailored strategies such as enhanced irrigation efficiency in humid regions and ecological restoration in arid zones. These findings provide a foundation for implementing an operational drought monitoring and response system in Henan Province. Full article

Drought is an extremely negative phenomenon that is becoming increasingly frequent in the southern part of Romania (Oltenia Plain). An insufficiency or lack of precipitation, especially in the warm season, induces a state of stress on the vegetation, damaging it prematurely and decreasing the agricultural yield. Integrating satellite observations into research inventories has practical applications for drought dynamics in plain regions and may significantly contribute to its agricultural sustainability. The aim of our study was to highlight the relationship between drought and vegetation health in the central parts of the Oltenia Plain, namely, the Băilești Plain and Nedeia Plain. We used four different indices (SPI/SPI-CDF-ISND, SPEI, NDVI, NDMI) in order to assess the occurrence of meteorological and agricultural drought and gained a wider picture regarding past and future trends. The results of this study contribute to a better understanding of vegetation health index trends and their implications for climate change. The selected indices were the most suitable for assessing drought according to the literature, and combining all of them helped us to obtain a full picture of drought’s impact on vegetation. Full article

Agricultural practices in the cold desert region of the Himalayas are frequently affected by climate-induced uncertainty in the past few decades. This research work aimed to examine the following questions: (a) Are there any significant climatic changes in the cold desert region of Himachal Himalayas? (b) How do the local farmers perceive climate change? (c) What and how indigenous and modern climate sensitive resilience measures/practices are being adapted by farmers for risk mitigation? A modified Mann–Kendall (m-MK) test and anomaly index were used to examine the changes in climatic variables over the cold desert region. Data on the observed changes in climatic variables were investigated through gridded products provided by the Indian Meteorological Department (IMD) and farmer perception, and their adaptation measures were collected by an extensive primary survey using a semi-structured questionnaire. The results indicate that farmers’ perceptions of changing rainfall, temperature, and seasons were consistent with historical climatic data. The drying water resources and crop damage were the most pressing concerns for farmers due to climate change activity. The farmers are adapting to climate change by altering their farming practices for agricultural risk management. The binary logistics regression (BLR) model was used to investigate the influence of different variables on the adopting farmer’s decision. The result revealed that various factors like landholding size, accessibility of transport, awareness of climate change, availability of water, and distance from market were responsible for choosing suitable climate resilience adaptation measures. This research contributes to recalibrating appropriate strategies across the cold desert region for designing sustainable agricultural practices. Full article

Recent studies have revealed a rise in extreme heat events worldwide, while extreme cold has reduced. It is highly likely that human-induced climate forcing will double the risk of exceptionally severe heat waves by the end of the century. Although extreme heat is expected to have more significant socioeconomic impacts than cold extremes, the latter contributes to a wide range of adverse effects on the environment, various economic sectors and human health. The present research aims to evaluate the contemporary spatio-temporal variations of extreme cold events in Southeastern Europe through the intensity–duration cold spell model developed for quantitative assessment of cold weather in Bulgaria. We defined and analyzed the suitability of three indicators, based on minimum temperature thresholds, for evaluating the severity of extreme cold in the period 1961–2020 across the Köppen–Geiger climate zones, using daily temperature data from 70 selected meteorological stations. All indicators show a statistically significant decreasing trend for the Cfb and Dfb climate zones. The proposed intensity–duration model demonstrated good spatio-temporal conformity with the Excess Cold Factor (ECF) severity index in classifying and estimating the severity of extreme cold events on a yearly basis. Full article

The comprehension of seasonal patterns of evapotranspiration (ET), as well as the interactive response to environmental factors, holds paramount importance for illuminating the intricate interaction within the carbon–water cycle of desert riparian forest ecosystems. Nonetheless, the driving mechanism behind ET changes is complex, and different components show significant differences in response to the same factor. Moreover, water resources are scarce in the region, and sustainable water resources management in arid regions usually aims to maximize transpiration (T) and minimize evaporation (E); therefore, reasonable calculation of ET components is urgent to effectively assess water resources consumption and improve water use efficiency. This discussion assessed the suitability and reliability of different methods for partitioning ET within the desert oasis in Northwestern China, calculated water use efficiency (WUE), and explored the differences in the response patterns of ET, transpiration (T), and WUE to environmental elements of constructive Populus euphratica forests in this region during the growing season. Continuous measurements of meteorological, soil, and vegetation factors were collected from 2014 to 2021 to facilitate this investigation. This study demonstrated that the underlying water use efficiency (uWUE) method effectively partitions ET into vegetation T and soil evaporation (E). Seasonal variations in ET and T were predominantly driven by temperature (Ta), radiation (Rn), soil moisture, and leaf area index (LAI). In addition, the exchange of water and carbon across different scales was governed by distinct regulatory mechanisms, where canopy-level WUE (WUEc) primarily depended on climatic conditions, while ecosystem-level WUE (WUEe) was more strongly influenced by vegetation structural characteristics. This study provided valuable insights into the ET characteristics, influencing factors, and water–carbon consumption mechanisms of desert vegetation in arid regions, and the conclusions of the discussion may provide theoretical insights for policymakers and ecosystem managers interested in preserving the ecological balance of arid regions. Full article

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity. Full article

The objective of this research is to determine the meteorological drought index and the effective rainfall model that exhibit the highest correlation with the yield of rainfed wheat in the Karkheh watershed. Additionally, using spatial statistics analysis, the trend and status of drought in various parts of the watershed will be identified. This will allow for the determination of suitable areas for rainfed wheat cultivation in the near future. In this research, meteorological drought monitoring was conducted using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) drought indices at 34 synoptic stations of the Karkheh watershed in southwestern Iran from 1981 to 2016. Effective precipitation (EPCP) was calculated using four methods, namely the United States bureau of reclamation method (USBR), the simplified version of soil conservation service of the United States department of agriculture method (USDA-SCS simplified), the food and agriculture organization of the United Nations method (FAO), and the CROPWAT version of the USDA-SCS method (USDA-SCS CROPWAT). The correlation between SPI and SPEI and between wheat yield and these indices was performed using Pearson’s correlation coefficient (R) to select the best index for assessing the effects of meteorological droughts on the agricultural sector. The analysis of the drought features showed that the SPEI reported milder and longer droughts in most synoptic stations. The spatial pattern of drought in the Karkheh watershed was determined using spatial statistics methods, and global Moran’s I statistic showed that the drought in the Karkheh watershed had spatial autocorrelation and a clustered pattern with a 99% confidence level. The results of global and local Moran’s I and the Getis-Ord statistic (Gi*) showed that milder droughts prevailed in the northern clusters and more severe droughts in the southern clusters. Also, the correlation between wheat yield and the SPI and SPEI was positive in all stations except for the stations of Kermanshah province and Aqa Jan Bolaghi, which is due to the existence of numerous dams and springs in these areas. Considering the impact of temperature (Tmean) on meteorological drought, it is suggested that in addition to the SPI, the SPEI should also be used to predict droughts in the Karkheh watershed. Full article

Phenological events are key indicators for the assessment of climate change impacts on ecosystems. Most previous studies have focused on identifying the timing of phenological events, such as flowering, leaf-out, leaf-fall, etc. In this study, we explored the characteristics of the green chromatic coordinate (GCC) values of the evergreen broadleaf tree (Quercus acuta Thunb.), which is a widely used index that serves as a proxy for the seasonal and physiological responses of trees. Additionally, we estimated their relationship with meteorological variables using time series models, including time series decomposition and a seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX). Our results showed that the GCC values and the meteorological variables, which were collected at daily intervals, exhibited a strong autocorrelation and seasonality. This suggests that time series analysis methods are more suitable than ordinary least squares (OLS) regression methods for the fulfillment of statistical assumptions. The time series analysis results highlighted a strong association between precipitation and GCC variation in evergreen broadleaf trees, particularly during the dry season. These results improve our understanding of the response of plant phenology to climate change. Full article

The comfort level of air temperature in a region is one of the influencing factors that affect tourists’ choice of tourism purpose. As a national red cultural mecca, the study of air temperature in Jiangxi Province can provide an important scientific reference for the development of tourism and the dissemination of red culture. Temperature is one of the most important indicators for climate comfort studies. Thus, in this paper, the average air temperature in Jiangxi Province in 2018 was studied. Three interpolation methods of Kriging interpolation, the inverse distance weight method, and the spline function method were used to spatially interpolate the data from 26 weather stations to obtain the spatial distribution map of air temperature for comparative study. At the same time, the method of cross-validation was adopted, and the average error and the root-mean-square error were quoted as the evaluation indexes for accuracy assessment. The conclusions of this paper are as follows: (1) the ME of IDW and spline method can reach 0.02–1.82 °C and the RMSE can reach 1.22–2.72 °C; (2) Kriging interpolation improves the RMSE by 27% and 55% compared to IDW and spline function methods, respectively; (3) considering the relatively sparse distribution of meteorological stations in Jiangxi Province, Kriging interpolation can avoid the extreme value phenomenon due to the influence of distance by reasonably choosing the shape and size associated with the surface space in the process of solving. Moreover, the results of this experimental study show that the accuracy of the kriging interpolation method is higher, so this method is more suitable for the spatial interpolation of the temperature in Jiangxi Province. In conclusion, this study provides a reference for the study of temperature comfort in Jiangxi Province. Full article

A better understanding of climate change impact on dry/wet conditions is crucial for agricultural planning and the use of renewable energy, in terms of sustainable development and preservation of natural resources for future generations. The objective of this study was to investigate the impact of climate change on temporal fluctuations of dry/wet conditions in Serbia on multiple temporal scales through multifractal analysis of the standardized precipitation evapotranspiration index (SPEI). We used the well-known method of multifractal detrended fluctuation analysis (MFDFA), which is suitable for the analysis of scaling properties of nonstationary temporal series. The complexity of the underlying stochastic process was evaluated through the parameters of the multifractal spectrum: position of maximum ${\alpha }_0$ (persistence), spectrum width $W$ (degree of multifractality) and skew parameter $r$ dominance of large/small fluctuations). MFDFA was applied on SPEI time series for the accumulation time scale of 1, 3, 6 and 12 months that were calculated using the high-resolution meteorological gridded dataset E-OBS for the period from 1961 to 2020. The impact of climate change was investigated by comparing two standard climatic periods (1961–1990 and 1991–2020). We found that all the SPEI series show multifractal properties with the dominant contribution of small fluctuations. The short and medium dry/wet conditions described by SPEI-1, SPEI-3, and SPEI-6 are persistent ($0.5<{\alpha }_0<1$); stronger persistence is found at higher accumulation time scales, while the SPEI-12 time series is antipersistent ($0<{\alpha }_0-1<0.5$). The degree of multifractality increases from SPEI-1 to SPEI-6 and decreases for SPEI-12. In the second period, the SPEI-1, SPEI-3, and SPEI-6 series become more persistent with weaker multifractality, indicating that short and medium dry/wet conditions (which are related to soil moisture and crop stress) become easier to predict, while SPEI-12 changed toward a more random regime and stronger multifractality in the eastern and central parts of the country, indicating that long-term dry/wet conditions (related to streamflow, reservoir levels, and groundwater levels) become more difficult for modeling and prediction. These results indicate that the complexity of dry/wet conditions, in this case described by the multifractal properties of the SPEI temporal series, is affected by climate change. Full article

Desert ecosystems, particularly in arid regions like the Tengger Desert, are highly sensitive to both anthropogenic activities and climate change, making the monitoring and evaluation of ecological quality critical for sustainable management and restoration efforts. This study analyses the spatiotemporal evolution of ecological quality in the Tengger Desert from 2001 to 2021 using the Remote Sensing Ecological Index (RSEI), incorporating meteorological factors (temperature, precipitation, wind speed), topographical factors (elevation, slope, relief) and anthropogenic indices (land use and land cover). The mean RSEI fluctuated between 0.1542 and 0.2906, indicating poor ecological quality, with a peak in 2008 attributed to national ecological projects. Despite initial improvements, overall ecological quality declined at a rate of 0.0008 a⁻¹ from 2008 to 2021. Spatially, degradation was most pronounced in the central and southern areas. Due to sand-binding engineering in the Tengger Desert in 2008 and the mountain climate suitable for vegetation growth, improvements occurred in the northeast and southwest. Moran’s I and Hurst index analyses revealed significant spatial clustering of ecological quality and persistence of degradation trends, with over 49.53% of the area projected to experience further deterioration. Geodetector analysis identified land use and land use cover as the most influential factors on RSEI, especially in combination with wind speed, temperature, and precipitation, underscoring the role of both human activities and climate. The study highlights the need for sustained ecological management, particularly in areas showing continuous degradation, to prevent further ecological deterioration. Full article

Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments to implement incentives to augment their installed capacity of wind turbines. Alongside augmenting the installed capacity of wind turbines, identifying suitable locations for their installation is crucial for optimizing turbine performance. This study aims to evaluate potential sites for wind power plant installation via a GIS, a mapping technique. The Analytic Hierarchy Process (AHP) was employed to assess the locations, including both quantitative and qualitative aspects that significantly impact the wind farm suitability map. Utilizing the GIS methodology, all datasets were examined through height and raster transformations of land surface temperature, plant density index, air pressure, humidity, wind speed, air temperature, land cover, solar radiation, aspect, slope, and topographical characteristics, resulting in the creation of a wind farm map. The correlation between the five-year meteorological data and environmental parameters (wind direction, daily wind speed, daily maximum and minimum air temperatures, daily relative humidity, daily average air temperature, solar radiation duration, daily cloud cover, air humidity, and air pressure) influencing the wind power plant in Iğdır province, including Iğdır Airport, Karakoyunlu, Aralık, and Tuzluca districts, was analyzed. If wind energy towers are installed at 1 km intervals across an area of roughly 858,180 hectares in Igdir province, an estimated 858,180 GWh of wind energy can be generated. The GIS-derived wind power plant map indicates that the installation sites for wind power plants are located in regions susceptible to wind erosion. Full article