Theoretical and Applied Climatology最新文献

The intensification of water resource usage, correlated with changes in land use and cover as well as climate variability, has led to significant alterations in the hydrological cycle, impacting water availability in basins. This study assesses hydrological trends in the section of the Janeiro River basin located in the Brazilian savannah (Cerrado biome), focusing on the influence of anthropogenic activities and climate variability between 1985 and 2017. Using precipitation, actual evapotranspiration, streamflow, land use and cover, and water use authorization data, we applied statistical tests (Mann–Kendall, Sen's slope, Pettitt, and RHO Spearman) to identify trends, abrupt changes, and correlations. The results show a decreasing trend in average and minimum flows, with reductions of 30 to 40%, respectively, compared to the historical series average, not attributable to significant changes in precipitation but rather to an expansion of agricultural areas and an intensification of water consumption for irrigation. There was a reduction from 76.5% to the sum of Natural and Forest Formation areas and an increase of 71.1% in Agricultural areas. The correlation between land use changes and streamflows suggests that the conversion of natural vegetation into agricultural lands is directly associated with the decline in water availability. This study highlights the need for sustainable planning and management of water resources, considering the seasonality of water availability and agricultural demands, to mitigate the negative impacts on the hydrological cycle and ensure water sustainability in the Brazilian savannah region.

Southern Thailand has experienced significant shifts in precipitation patterns in recent years, exerting substantial impacts on regional water resources and infrastructure systems. This study aims to elucidate these changes and underlying factors based on daily precipitation observations from Nakhon Si Thammarat Province spanning 1980 to 2022. Additionally, data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is utilized to investigate projected changes in precipitation for 2015–2100 relative to the historical period (1980–2014), employing a comprehensive analysis considering two emissions scenarios (SSP245 and SSP585) across six models. Various precipitation indices are selected to assess trends and statistical significance using the Mann-Kendall test. Both observed and climate model data indicate an increasing precipitation trend in Southern Thailand, with a reduced association with the El Niño-Southern Oscillation (ENSO) under warming conditions. Extreme precipitation indices also exhibit an increasing trend, with total precipitation and the 95th percentile of daily precipitation (R95p) revealing very wet conditions in recent years, projected to continue increasing. Contrastingly, the number of dry days is also mounting, suggesting that both dry and wet extremes will impact Southern Thailand under a warmer climate. The findings from this study provide an early indication of future precipitation and extreme event scenarios, which can inform the development of measures to mitigate climate change-related hazards in the region.

Climate change significantly impacts water cycle processes and water resource management in the upper Yellow River (UYR), China. Understanding the influence of meteorological factors on low-flow changes is crucial, but the optimal number of antecedent days and the specific contributions of different factors remain unclear. In this study, we use a structural equation model and path analysis to dissect the direct and indirect effects of selected meteorological factors (daily precipitation, P; average temperature, AT; average wind velocity, AWV; average relative humidity, ARH; and total radiation, TR) on four low-flow indices in the UYR. We employ data from 1958 to 2017, collected from six meteorological stations and eight hydrological stations above Lanzhou hydrological station. Our findings reveal that: (1) meteorological factors have varying direct and indirect impacts on low-flow changes at corresponding and cumulative scales. For instance, at the corresponding scale, P, AT, AWV, ARH, and TR have direct impacts of 42%, 54%, 74%, 79%, and 59%, respectively. At the cumulative scale, these values change to 67%, 59%, 67%, 64%, and 60%, respectively. (2) Cumulative effects of meteorological factors enhance the significance and goodness of fit of the analysis model, decreasing residual path coefficients and elevating the contribution of independent variables to the model. (3) The dominant components of meteorological factors affecting low-flow changes differ between corresponding and cumulative scales, explaining the variations in direct and indirect impacts. These insights are valuable for sustainable water resource management in drought-prone regions with water scarcity.

The sustainable management of forestry activities, together with changes in vegetation due to deforestation or degradation, contributes to sediment risk and increases the risk of surface runoff. Changes in meteorological criteria, such as precipitation and temperature, as a result of global climate change are also significant factors affecting sediment risk. In this study, sediment risk was predicted spatially and temporally for 65 provinces in Turkey using criteria related to average forest road construction rates and average wood harvesting rates for the period between 2017 and 2021, as well as climate change models (GFDL-ESM2M, HadGEM2-ES, and MPI-ESM-MR) and their scenarios (RCP 4.5 and RCP 8.5) for five-year periods between 2022 and 2096. In addition, changes in sediment risk in the short and long terms—that is, trends—were determined in spatially and temporally. Entropy-based WASPAS and fuzzy clustering analysis were used together to determine sediment risk in this context. The results show that, in terms of sediment risk, criteria related to forestry activities had a higher weight than criteria related to climate change when looking at the overall criterion weights. In addition, it was generally observed that the contribution of the average precipitation criterion to sediment risk increased in weight over five-year periods in the context of climate change models and scenarios. Regarding climate change models and scenarios, it was found that provinces consistently in the highest risk category (R1) over five-year periods were mainly located in the Black Sea and Marmara regions. In addition, provinces showing an increase or decrease in sediment risk trends between two consecutive five-year periods were mostly found in the Black Sea and Mediterranean regions. When evaluating the 15-year time intervals, differences in sediment risk trends were found between the geographical regions. In conclusion, the study results indicate that, regionally, Turkey’s northern regions, especially the Black Sea and Marmara regions, as well as the southern Mediterranean and western Aegean regions, will become increasingly vulnerable to sediment risk over time owing to the impact of climate change.

The escalating adversities of climate change increasingly jeopardise agriculture in coastal Indian states like Odisha. The significance of the agriculture sector for the state necessitates effectively mitigating the adverse climatic impacts. Strengthening the resilience of agriculture has been widely acknowledged as one of the most effective strategies for mitigating negative climatic impacts. Framing and implementing essential resilience-enhancing measures depends on a comprehensive preliminary assessment of existing resilience. This study estimates agricultural resilience to climate change in Odisha by constructing district-level composite indicators. The Principal Component Analysis and Analytic Hierarchy Process are employed to assign weights to a multidimensional set of indicators and aggregate them into composite indicators. In addition, the Cluster Analysis is employed to identify heterogeneity among the districts in terms of their agricultural resilience. The study finds that the coastal districts in the state have the lowest agricultural resilience, which may be attributed to the higher vulnerability of these districts to a number of climatic risks. The composite indicators further highlight the need for region-specific interventions. Similarly, the interplay of multiple social and environmental factors is found to influence resilience, underscoring crucial implications for public decision-making.

Drought is one of the significant natural disasters that has a profound impact on human societies, particularly in arid places such as Balochistan, Pakistan. Geographic information system and remote sensing has played a major role in predicting the effect of drought events and mitigate. Therefore, the purpose of this study was firstly to evaluate the spatiotemporal patterns of drought in Balochistan, Pakistan, utilizing MODIS based satellite data and validate the PMD stations data with CHIRPS data. Secondly the objective of this research to quantify the influence of drought on vegetation anomalies and comparison between droughts patterns with vegetation response. Drought conditions in Balochistan by integrating remote sensing (RS) drought indices (RSDI).RSDI was calculated through Hargreaves method using monthly data. The following remaining indices were the main focus of the study i.e., Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), Temperature Vegetation Dryness Index (TVDI), and Precipitation Condition Index (PCI). These indices offered differing perspectives, emphasizing the value of a comprehensive strategy. Approximately 60% of the area was significantly affected by drought conditions, with SPEI values for the period being less than -1.5.SPEI and TVDI performed better in identifying droughts. TVDI values ranged from 0.63 to 0.88, indicating agricultural dryness. For instance, the East experienced a severe drought between 2001 and 2022, according to SPEI. Significant drought events occurred in 2001, 2004, 2009, 2014, and 2022, allowing comparative analysis. TVDI proved more effective than VCI in predicting drought. RDI and TVDI localized drought better than PCI. SPEI, RDI, and TVDI contributed significantly to understanding drought (73.63%, 74.15%, and 72.30% respectively). Considering diverse indices is vital for long-term drought mitigation strategies. RDI, especially valuable with limited temperature data, aids in understanding drought dynamics. This analysis aids in predicting future droughts and mitigating agricultural losses in Balochistan, informing decision-making and adaptive measures.

This study analyzes the atmospheric and oceanic mechanisms that influenced rainfall distribution in Ceará, Brazil during the 2018 rainy season and the impacts of the observed rainfall distribution on agriculture in the region. Special attention is given to the month of March, when precipitation was below the climatological average. Precipitation, wind, omega, specific humidity, outgoing longwave radiation, and oceanic indices from the Pacific and Atlantic data were used in the analyses. The Multivariate Real-Time Madden–Julian Oscillation (MJO) Index was employed to assess the influence of the MJO on precipitation in March 2018. The results indicate that subseasonal variability, through the MJO in phases 4, 5, and 6, played a crucial role in suppressing convective activity. Additionally, a delayed austral summer pattern was found, with the South American Convergence Zone (SACZ) positioned further north and a quasi-stationary trough at altitude. These two atmospheric factors inhibited the more intense rainfall activity in the Intertropical Convergence Zone (ITCZ). In the ocean analyses, Sea Surface Temperature (SST) anomalies indicated the presence of La Niña in the equatorial Pacific, although it was in transition from cooling to warming. This, in addition to the neutrality of the interhemispheric gradient of the Tropical Atlantic SST anomalies, may have also contributed to negative precipitation anomalies and influenced the MJO displacement. MJO phases associated with suppression contributed to economic losses for the agricultural sector.

Thunderstorms are among the most extreme meteorological phenomena that can cause widespread destruction and loss of life. Their occurrence varies significantly across different regions and times of the year. Despite various studies on thunderstorm activity across Central Europe, direct analyses based on data from the Slovak territory still need to be made available. Given Slovakia’s diverse natural conditions, there is a need for detailed knowledge about the frequency and spatial distribution of thunderstorms in this region. To address this knowledge gap, we analysed the frequency and spatiotemporal distribution of days with thunderstorm occurrences in Slovakia between 1984 and 2023, utilising climatological data from the Slovak Hydrometeorological Institute. We limited our analysis to data of days with close thunderstorms (thunderstorms occurring within 3 km of the monitoring station). Our findings reveal a significant variation in thunderstorm occurrences across Slovakia, with peak activity in the summer, especially in June and July. However, the spatial distribution of thunderstorms differed significantly across the country, with the highest frequency observed in mountainous regions and the east-central part of Slovakia. We found significant deceasing signals of the thunderstorm activity trends during the studied period, including analyses during the colder part of the year. Furthermore, our results underscore the critical role of synoptic situations in shaping these trends, where changes in certain atmospheric patterns were closely aligned with variations in thunderstorm frequency. The interaction between these synoptic conditions and regional topography was particularly evident, reinforcing the notion that topographical and environmental complexities substantially contribute to the observed thunderstorm distribution.

Forecasting phenological events has important uses in warm Mediterranean area, where olive is one of the oldest cultivated species.Thus, continuously-recorded phenological observations for the main olive cultivar Chemlali widely spreading in warm and sub-arid area were achieved during 2005–2019 in central Tunisia. Gathered climatic and phenological data were used to: i) delineate the chill and heat accumulation periods and the thermal requirements using Partial Least Squares (PLS) approach; and to ii) develop statistical models predicting budburst and flowering dates. Results revealed significant yearly variations in budburst and flowering dates related to the climatic conditions. PLS analysis delineated two chill accumulation periods spanned from November 19th to January 12th and from the end of March to the beginning of April, respectively. Stepwise regression revealed that the best indicator of the budburst date was the mean temperature in pentad-6 of November followed by the minimum and the mean temperature during pentad-2 of February. Based on these two statistical analyses, chilling requirements seemed to be linked to the first delineated chill accumulation period. Average chilling and heat requirements of ‘Chemlali’ olive cultivar were 17 CP and 24892 GDH, respectively. A forecasting linear model was generated displaying mean absolute error of 1.6 and 2.4 days between simulated and observed budburst and start flowering dates, respectively. These proposed models will be very helpful for orchard management and the high number of independent factors determining the critical periods necessary for flowering may explain the adaptive plasticity of ‘Chemlali’ cultivar growing in sub-arid and warm areas.

Surface Air Temperature (SAT) predictions, typically generated by Global Climate Models (GCMs), carry uncertainties, particularly across different greenhouse gas emission scenarios. Machine Learning (ML) techniques can be employed to forecast long-term temperature variations, although this is a challenging endeavour with few drawbacks, such as the influence of scenarios involving greenhouse gas emissions. Therefore, the present study utilized multiple ML approaches such as Artificial Neural Networks (ANN), multiple linear regression, support vector machine and random forest, along with various daily predicted results of GCMs from Coupled Model Intercomparison Project Phase 6 as predictors and the “India Meteorological Department’s” Maximum SAT (MSAT) as the predictand, to predict daily MSAT in the months of March, April and May (MAM) over Andhra Pradesh (AP) for the period 1981–2022. The results show that ANN outperforms other ML techniques in predicting daily MSAT, with a root mean square error of 1.41, an index of agreement of 0.89 and a correlation coefficient of 0.81. The spatial distribution of hot and heat wave days indicates that the Multiple Model Mean (MMM) underestimates these occurrences, with a minimum bias of 9 and 6 days, respectively. In contrast, the ANN model exhibits much smaller biases, with a maximum underestimation of 3 hot and 2 heat wave days. These findings demonstrate that MMM does not capture the maximum temperatures well, resulting in poor predictability. Further, future temperature projections were analysed from 2023 to 2050, which display a gradual increase in mean MSAT during MAM over AP. This research demonstrates the potential of ML techniques to enhance temperature forecasting accuracy, offering valuable insights for climate modeling and adaptation. The results are crucial for stakeholders in agriculture, health, energy, water resources, socio-economic planning, and urban development, aiding in informed decision-making and improving resilience to climate change impacts.