Search Results (208)

Viticulture represents an important agricultural sector in Oltenia, which is one of the Romanian regions most affected by temperature increases. The main purpose of the present study was to analyze the changes in climate suitability for grapevine and wine production against this climate context in the region. Two specific bioclimatic indices were applied, namely the bioclimatic index and the oenoclimate aptitude index, both reflecting the cumulated influence of temperature, actual sunshine duration, and precipitation amounts on the grapevine during the growing season (1 April–30 September). The indices were calculated as average values for the period 1961–2020. In order to emphasize potential shifts in suitability, the mean, maximum, and minimum values were calculated for two distinct periods, 1961–1990 and 1991–2020. The results of the analysis underlined three distinct suitability changes: the area suitable for quality red wines shifting northwards (on average, about 30′ of latitude or 55.5 km), including the eastern part of the Getic Subcarpathians, which is not currently part of any winegrowing region; the emerging new areas suitable for quality white wine (the western part of the Subcarpathians); and a potentially overly hot climate developing in Southern Oltenia where grapevine varieties are currently grown. Thus, the development of adequate adaptation strategies for viticulture to climate change in the region should be considered in the near future. Full article

This study focuses on Qilu Lake to study how to mitigate the impacts of seasonal droughts and provide technical support for drought resistance decision-making in low-latitude plateau lake basins. Using the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), and the Temperature Condition Index (TCI) as bases, in this study, the applicability of the vegetation health index (VHI) within the basin is investigated, and the optimal weight distribution between the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) in the VHI is determined. The VHI is then applied to analyze the correlation between drought frequency and severity within the basin. The results indicate that the method is most effective in assessing agricultural drought in the Qilu Lake Basin when the VCI and TCI are weighted at a 4:6 ratio, optimizing the VHI’s evaluative performance. The drought limit water levels of lakes are further divided into short- and long-term drought limit water levels. The short-term drought limit water level is divided into the drought warning water level and the drought emergency water level. The drought warning water level (corresponding to moderate drought conditions, with a frequency of P = 75%) ranges from 1794.53 m to 1795.11 m, while the drought emergency water level (corresponding to extreme drought conditions, with a frequency of P = 95%) ranges from 1793.94 m to 1794.31 m. These levels are set to meet the emergency water demand during droughts in the basin. The long-term drought limit water levels are calculated by accumulating the water deficits of various sectors within the watershed under different agricultural drought conditions, based on the short-term drought limit water levels. By setting the drought limit water level using this method, as well as considering the original water regulation capacity of the lake resources, when the watershed experiences drought, the scheduling method based on this drought limit water level can better alleviate the water supply pressure on various sectors in the local area. Full article

Baseflow is a crucial component of river flow in alpine inland basins, playing an essential role in watershed ecological health and water resource management. In high-latitude cold regions, seasonal freeze-thaw processes make baseflow formation mechanisms particularly complex. However, the dominant factors affecting baseflow and their relative contributions remain unclear, limiting the accuracy of flow estimation and effective water resource management. This study employed baseflow separation techniques and statistical methods, including the Mann-Kendall test, to investigate temporal trends and abrupt changes in baseflow and the baseflow index (BFI) at multiple time scales (annual, seasonal, and monthly) from 2005 to 2012. Additionally, the timing of snowmelt and its impact on baseflow were examined. Key findings include the following: (1) Baseflow and BFI showed distinct temporal variability with non-significant upward trends across all time scales. Annual BFI ranged from 0.48 to 0.61, contributing approximately 50% of total runoff. (2) At the seasonal scale, baseflow remained relatively stable in spring, increased in autumn, and showed non-significant decreases in summer and winter. Monthly baseflow exhibited an increasing trend. (3) The snowmelt period occurred between April and May, with baseflow during this period strongly correlated with climatic factors in the following order: winter precipitation > positive accumulated temperature > winter air temperature > negative accumulated temperature. The strongest positive correlation was observed between baseflow and winter precipitation (R = 0.724), while negative correlations were found with accumulated temperatures and winter air temperature. These findings offer valuable insights for predicting water resource availability and managing flood and ice-jam risks in cold regions. Full article

Investigating the urban heat island (UHI) effect and its driving factors is crucial for supporting future climate mitigation actions and human adaptation strategies. Due to the unique climatic characteristics and vulnerable ecological environment of arid and semi-arid regions, it is valuable to detect the UHI effect in cities in these regions, which have not been fully explored yet. Utilizing moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data from 2010 to 2020, this study quantified the summer, winter, and annual diurnal mean surface urban heat island intensity (SUHII) of 30 cities in the arid and semi-arid regions of northern China and comprehensively investigated the spatio-temporal patterns and drivers of UHI. The results showed that the annual mean daytime SUHII had a significant decreasing trend, and the nighttime SUHII had an increasing trend for these cities between 2010 and 2020. The nighttime SUHII was stronger than the daytime SUHII, and some cities exhibited surface urban cool island (SUCI) phenomena during daytime, especially in winter. It was also found that cities at higher latitudes experienced higher daytime SUHII throughout the year, and that it was more pronounced in winter. The driving factor analysis revealed that daytime SUHII was primarily influenced by the urban area size (UAS), total precipitation (TP), and the differences in white sky albedo (ΔWSA), enhanced vegetation index (ΔEVI), and normalized difference moisture index (ΔNDMI) between urban and suburban areas. Nighttime SUHII was mainly correlated with ΔWSA, ΔEVI, ΔNDMI, and the differences in elevation (ΔDEM) between urban and suburban areas. This indicated that the background climate was a potential driver for the spatial pattern of UHI in this region. As for the nightlight difference between urban and sub-urban areas (ΔNTL), no correlation was observed with neither daytime SUHII nor nighttime SUHII. These findings are promising in providing theoretical support and scientific guidance for formulating sustainable development strategies and mitigating the UHI effects of cities in the arid and semi-arid regions. Full article

Reference evapotranspiration (ETo) refers to the combined processes of evaporation and transpiration, which are relevant for hydrology, climate change research, and irrigation system design. The ETo is considered for different climatological studies, agriculture-focused studies, drought indices and climate change as well. From the ETo, water needs can be obtained, and along with precipitation, it is important to determine water availability and drought indices like the Standardized Precipitation Evapotranspiration Index (SPEI). Currently, there are different methods to estimate the ETo based on various climatic variables, which have been proposed for different climates and applied in different regions worldwide. The method standardized by most studies for determining the ETo is the “modified Penman–Monteith” method by the Food and Agriculture Organization (FAO). This method is versatile as it considers different climatic conditions and global latitudes. Due to limited climate data in developing countries like Mexico, alternative methods are used. The present study analyzed 40 comparative methods for determining ETo and their influence on SPEI. The best methods for the study area were chosen, including Hansen, Hargreaves and Samani, and Trajkovic, as they are the best based on the available information in Mexico. Additionally, each equation was adjusted to reduce errors and achieve closer approximations to actual ETo values to obtain the most accurate values possible. The influence on SPEI calculation indicates overestimations in temperature-based methods and underestimations in radiation and mass-transfer-based methods. The SPEI calculation showed fewer errors when using the modified HANSEN equations. In the absence of information, Allen’s temperature-based method is recommended. Full article

The Irtysh River Basin (IRB), a transboundary river basin spanning China, Kazakhstan, and Russia, has experienced significant vegetation changes driven by climate change and human activities. This study investigated the spatiotemporal dynamics of different types of vegetation in the IRB from 2001 to 2020 using the normalized difference vegetation index (NDVI) and quantified the contributions of driving forces to the evolution of vegetation. The results revealed that the end of the global warming hiatus in 2013 aggravated climate changes, leading to an abrupt shift in NDVI dynamics. This spatial shift was mainly reflected in grassland and farmland in the arid regions of northern Kazakhstan, where overall vegetation cover has improved in recent years. Precipitation and temperature were identified as the main drivers of spatial vegetation differentiation in the basin, with precipitation being more limiting in arid regions, while temperature affected non-arid regions at higher latitudes more strongly, and climate change had a greater positive effect on vegetation in non-arid regions than in arid regions. The relative contribution of climatic factors to vegetation changes decreased from 45.93% before the abrupt change to 42.65% after the abrupt change, while the contribution of other drivers, including human activities, increased from 54.07% to 57.35%. The combined effect of climate change and human activities was more significant than that of individual drivers, with human interventions such as environmental policies and ecological restoration projects having strongly contributed to the greening trend in recent years. This study highlights the need for zonal management strategies in the IRB, prioritizing sustainable forest management in non-arid zones and sustaining environmental protection projects in arid regions to support vegetation restoration and sustainable ecosystem management. Full article

Soil salinization is a global ecological and environmental problem, which is particularly serious in arid areas. The formation process of soil salinity is complex, and the interactive effects of natural causes and anthropogenic activities on soil salinization are elusive. Therefore, we propose an automated machine learning framework for predicting soil salt content (SSC), which can search for the optimal model without human intervention. At the same time, post hoc interpretation methods and graph theory knowledge are introduced to visualize the nonlinear interactions of variables related to SSC. The proposed method shows robust and adaptive performance in two typical arid regions (Central Asia and Xinjiang Province in western China) under different environmental conditions. The optimal algorithms for the Central Asia and Xinjiang regions are Extremely Randomized Trees (ET) and eXtreme Gradient Boosting (XGBoost), respectively. Moreover, precipitation and minimum air temperature are important feature variables for salt-affected soils in Central Asia and Xinjiang, and their strongest interaction effects are latitude and normalized difference water index. In both study areas, meteorological factors exhibit the greatest effect on SSC, and demonstrate strong spatiotemporal interactions. Soil salinization intensifies with long-term climate warming. Regions with severe SSC variation are mainly distributed around the irrigation water source and in low-terrain basins. From 1950 to 2100, the regional mean SSC (g/kg) varies by +20.94% and +64.76% under extreme scenarios in Central Asia and Xinjiang, respectively. In conclusion, our study provides a novel automated approach for interaction analysis of driving factors on soil salinization in drylands. Full article

Utilizing daily data gathered from 63 meteorological stations across Sichuan Province between 1970 and 2022, this study investigates the spatial and temporal shifts in extreme precipitation patterns, alongside the connections between changes in extreme precipitation indices (EPIs) and the underlying drivers, such as geographic characteristics and atmospheric circulation influences, within the region. The response of precipitation to these factors was examined through various methods, including linear trend analysis, the Mann–Kendall test, cumulative anomaly analysis, the Pettitt test, R/S analysis, Pearson correlation analysis, and wavelet transformation. The findings revealed that (1) Sichuan Province’s EPIs generally show an upward trend, with the simple daily intensity index (SDII) demonstrating the most pronounced increase. Notably, the escalation in precipitation indices was more substantial during the summer months compared to other seasons. (2) The magnitude of extreme precipitation variations showed a rising pattern in the plateau regions of western and northern Sichuan, whereas a decline was observed in the central and southeastern basin areas. (3) The number of days with precipitation exceeding 5 mm (R5mm), 10 mm (R10mm), and 20 mm (R20mm) all exhibited a significant change point in 2012, surpassing the 95% significance threshold. The future projections for EPIs, excluding consecutive dry days (CDDs), align with historical trends and suggest a continuing possibility of an upward shift. (4) Most precipitation indices, with the exception of CDDs, demonstrated a robust positive correlation with longitude and a negative correlation with both latitude and elevation. Except for the duration indicators (CDDs, CWDs), EPIs generally showed a gradual decrease with increasing altitude. (5) Atmospheric circulation patterns were found to have a substantial impact on extreme precipitation events in Sichuan Province, with the precipitation indices showing the strongest associations with the Atlantic Multidecadal Oscillation (AMO), the Sea Surface Temperature of the East Central Tropical Pacific (Niño 3.4), and the South China Sea Summer Monsoon Index (SCSSMI). Rising global temperatures and changes in subtropical high pressure in the western Pacific may be deeper factors contributing to changes in extreme precipitation. These insights enhance the understanding and forecasting of extreme precipitation events in the region. Full article

Investigating the variation in and key factors influencing the yield of super hybrid rice cultivated at different altitudes but within the same latitude provides valuable insights for further improvements in super hybrid rice grain yields. Field and pot experiments were conducted using four rice varieties at the following two altitudinal locations in Sichuan Province, China: Hanyuan (high, 1000 m) and Luxian (low, 300 m). The results indicated that Hanyuan achieved an average grain yield of 13.89 t ha⁻¹ in paddy fields, with yields being from 63.6% to 94.2% higher than those at Luxian in the field experiments and from 10.8% to 68.0% higher in the pot experiments. The grain yield was consistently higher in the soil from Hanyuan compared to that from Luxian at the same sites. In the field experiments, the grain yield was influenced by location (L), plant density (P), and variety (V), but there were no significant interactions between these factors. In the pot experiments, the grain yield was significantly impacted by L, soil (S), and the interaction between L and S. Climatic factors, which varied with the altitude of the planting site, played a crucial role in achieving optimal yields of the super hybrid rice. Hanyuan exhibited more cumulative solar radiation with a longer growth duration and lower temperatures and higher soil fertility compared to Luxian. The higher grain yield observed at Hanyuan was linked to increases in panicle numbers, spikelets per panicle, grain filling, pre- and post-heading biomass production, and the harvest index. The variations in biomass production between Hanyuan and Luxian were largely due to differences in pre- and post-heading crop growth rates (CGRs) and pre-heading radiation use efficiency (RUE), which were influenced by differences in the maximum and minimum temperatures and cumulative solar radiation. This study indicated that the differences in the grain yield of super hybrid rice across various ecological sites are primarily influenced by altitude and soil fertility, and further enhancement of the grain yield can be achieved by concurrently increasing biomass production before and after heading through improvements in pre- and post-heading CGR. Full article

Recent advancements in texture-based metrics have improved the representation of landscape heterogeneity, yet global-scale analyses of the relationship between vegetation homogeneity and land surface temperature (LST) remain limited. This study addresses this gap by examining the correlation between Enhanced Vegetation Index (EVI)-derived texture metrics and LST worldwide. We used texture-based metrics from the EVI to assess landscape homogeneity, with LST data from the 2015 MODIS MOD11A1 V6.1 product at a 1 km spatial resolution. Correlation analyses and nonlinear regression models were applied to explore how EVI homogeneity relates to LST across latitudes. Our findings reveal a significant positive correlation between EVI homogeneity and LST, with the strongest association in the Northern Hemisphere (R² = 49.3%), followed by a moderate relationship in the Southern Hemisphere (R² = 21.1%). In tropical regions (−10° to 10° latitudes), the association is weaker but still significant (R² = 15.1%). The distribution of EVI homogeneity follows a Gaussian curve, peaking in mid-latitudes (from −35° to −15° in the Southern Hemisphere and from 15° to 35° in the Northern Hemisphere), while tropical regions exhibit consistently low homogeneity with minimal variation. Our results indicate that regions with higher EVI homogeneity, representing less fragmented vegetation, tend to experience higher LST, whereas areas with more fragmented vegetation (lower homogeneity) exhibit cooler temperatures. Our findings offer valuable insights into the role of vegetation structure in regulating surface temperature across diverse ecosystems. The study highlights the potential for texture-based metrics to enhance environmental monitoring, contributing to improved climate adaptation strategies and sustainable land management practices globally. Full article

The northern Indian Ocean is located in a typical monsoon region that is also influenced by climate events such as the Indian Ocean Dipole (IOD), which makes Sthenoteuthis oualaniensis habitat highly susceptible to changes in climate and marine environmental conditions. This study established a suitability index (SI) model and used the arithmetic average method to construct a comprehensive habitat suitability index (HSI) model based on S. oualaniensis production statistics in the northern Indian Ocean from 2017 to 2019. Variations in the suitability of S. oualaniensis habitat during different IOD events were then analyzed. The results indicate that the model performed best when year, month, latitude, longitude, sea surface temperature (SST), wind speed (WS), and photosynthetically active radiation (PAR) variables were included in the generalized additive model (GAM). SST, WS, and PAR were identified as the most important key environmental factors. The HSI model showed that the most suitable habitat during a positive IOD event was smaller than during a negative IOD event and that the suitable habitat’s center was located west of the positive IOD event and east of the negative IOD event. There was a significant inverse relationship between the area, suitable for habitation, and the north–south shift in the latitudinal gravity center and the Dipole modal index (DMI). The results indicate significant differences in the habitat of S. oualaniensis in the northern Indian Ocean during different IOD events, as well as differences in suitable habitat ranges and the spatial distribution of the species. Full article

In this work, the preindustrial control (piControl) and Historical experiments results from climatic Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are analyzed for their ability to predict the El Niño–Southern Oscillation (ENSO). Using the principal component method, it is shown that the Global Atmospheric Oscillation (GAO), of which the ENSO is an element, is the main mode of interannual variability of planetary anomalies of surface air temperature (SAT) and atmospheric sea level pressure (SLP) in the ensemble of 50 CMIP6 models. It turns out that the CMIP6 ensemble of models reproduces the planetary structure of the GAO and its west–east dynamics with a period of approximately 3.7 years. The models showed that the GAO combines ENSO teleconnections with the tropics of the Indian and Atlantic Oceans, and with temperate and high latitudes. To predict strong El Niño and La Niña events, we used a predictor index (PGAO) obtained earlier from observation data and reanalyses. The predictive ability of the PGAO is based on the west–east propagation of planetary structures of SAT and SLP anomalies characteristic of the GAO. Those CMIP6 models have been found that reproduce well the west–east spread of the GAO, with El Niño and La Niña being phases of this process. Thanks to this, these events can be predicted with approximately a year’s lead time, thereby overcoming the so-called spring predictability barrier (SPB) of the ENSO. Thus, the influence of global anomalies of SAT and SLP on the ENSO is shown, taking into account that it may increase the reliability of the early forecast of El Niño and La Niña events. Full article

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R² > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions. Full article

Agroclimatic indicators help convey information about climate variability and change in terms that are meaningful to the agricultural sector. This study evaluated climate projections for Angola, particularly for provinces with more significant agricultural potential. To this end, 15 predefined agroclimatic indicators in 2041–2070 and 2071–2099, under the anthropogenic forcing scenarios RCP4.5 and RCP8.5, were compared with the historical period 1981–2010 as a baseline. We selected two climate scenarios and two temporal horizons to obtain a comprehensive view of the potential impacts of climate change in Angola. Data were extracted within the geographic window of longitudes 10–24° E and latitudes 4–18° S and from five general circulation models (GCM), namely MIROC-ESM-CHEM, HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, and NorESM1-M. The set averages of agroclimatic indicators and their differences between historical and future periods are discussed in relation to the likely implications for agriculture in Angola. The results show significant increases in average daily maximum (2–3 °C) and minimum (2–3 °C) temperatures in Angola. For the future, a generally significant reduction in precipitation (and its associated indicators) is expected in all areas of Angola, with the southwest region (Namibe and Huíla) recording the most pronounced decrease, up to 300 mm. At the same time, the maximum number of consecutive dry days will increase across the country, especially in the Northeast. A widespread increase in temperatures is expected, leading to hot and dry conditions in Angola that could lead to more frequent, intense, and prolonged extreme events, such as tropical nights, the maximum number of consecutive summer days, hot and rainy days, and warm period duration index periods. These changes can seriously affect agriculture, water resources, and ecosystems in Angola, thereby requiring adaptation strategies to reduce risks and adverse effects while ensuring the sustainability of the country’s natural resources and guaranteeing its food security. Full article

The northern permafrost regions are increasingly experiencing frequent and intense extreme events, with a rise in the occurrence of compound extreme events. Many climate-related hazards in these areas are driven by such compound events, significantly affecting the stability and functionality of vegetation ecosystems. However, the cumulative and lagged effects of compound extreme events on vegetation remain unclear, which may lead to an underestimation of their actual impacts. This study provides a comprehensive analysis of the spatiotemporal variations in compound extreme events and the vegetation response to these events in the northern permafrost regions from 1982 to 2022. The primary focus of this study is on examining the cumulative and lagged effects of compound extreme climate events on the Kernel Normalized Difference Vegetation Index (kNDVI) during the growing seasons. The results indicate that in high-latitude regions, the frequency of extreme high temperature–precipitation compound events and high temperature–drought compound events have increased in 58.0% and 67.0% of the areas, respectively. Conversely, the frequency of extreme low temperature–drought compound events and extreme low temperature–precipitation compound events has decreased in 70.6% and 57.2% of the areas, with the high temperature–drought compound events showing the fastest increase. The temporal effects of compound extreme events on kNDVI vary with vegetation type; they produce more cumulative and lagged effects compared with single extreme high-temperature events and fewer effects compared with single extreme precipitation events, with compound events significantly affecting forest and grassland ecosystems. Notably, extreme high temperature–precipitation compound events exhibit the strongest cumulative and lagged effects on vegetation, while extreme low temperature–drought compound events influence wetland and shrubland areas within the same month. This study underscores the importance of a multivariable perspective in understanding vegetation dynamics in permafrost regions. Full article