Theoretical and Applied Climatology最新文献

{"title":"Comparison of the calculated frost event return period based on copula models under climate change: a case study of Chadegan region in Isfahan province- Iran","authors":"Elham Mazaheri, Jahangir Abedi Koupai, Manouchehr Heidarpour, Mohammad Javad Zareian, Alireza Gohari","doi":"10.1007/s00704-024-05064-9","DOIUrl":"https://doi.org/10.1007/s00704-024-05064-9","url":null,"abstract":"<p>Spring frost, is one of the important phenomena that damage agricultural production in cold areas. Predicting the occurrence of frost events can be valuable for managing and mitigating frost risk in orchards. In this study, copula models were applied to calculate the joint bivariate return period of frost event in both historical (1984–2014) and future (2023–2053) periods in Chadegan’s almond orchard. For the future period, a combination of 10 general circulation models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) under three Shared Socioeconomic Pathway scenarios (SSPs) SSP1-2.6, SSP2-4.5 and SSP5-8.8 was employed using a weighting approach. The results indicated that the Generalized Pareto (GP) and Inverse Gaussian were the best marginal distribution functions of the severity (S) and duration (D), respectively. The Frank copula best explained the relationship between severity and duration of frost event. According to the joint bivariate return period of frost event, the extreme frost occurred more frequently in the future period under three SSPs compared to the historical period. In both historical and future periods, in “AND” mode, the frost event with S ≥ 6 ̊C and D ≥ 4 ̊C days, would be more likely to return in 64.71 years and about 14 years, respectively. In \"OR\" mode, the joint bivariate return period of mentioned frost event increase slightly in future period (3 years for SSPs) compared to the historical (1.54 years). This probabilistic assessment was pointed as a strong toll for predicting the return period of frost event in Chadegan.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial variability of seasonal rainfall onset, cessation, length and rainy days in Rwanda","authors":"Joseph Ndakize Sebaziga, Bonfils Safari, Joshua Ndiwa Ngaina, Didier Ntwali","doi":"10.1007/s00704-024-05086-3","DOIUrl":"https://doi.org/10.1007/s00704-024-05086-3","url":null,"abstract":"<p>This study investigates the spatial patterns and variabilities of Seasonal Rainfall Onset Day (OD), Cessation Day (CD), Seasonal Length (SL), and Number of Rainy Days (RD) in Rwanda for the long rain season (LR) of March–April-May (MAM) and the short rain season (SR) of September–October-November–December (SOND). Data used, provided by the Rwanda Meteorology Agency, consisted of a time series of gridded rainfall and temperature from 1983 to 2021. The northern, western, and southwestern regions experience earlier OD than the remaining parts of the country, [mid-February, early March] for LR and [early September, mid-October] for SR. The entire eastern region experiences later OD ([mid-March, end March]) during LR. During SR, the central east and the southeastern regions experience later OD ([mid-October, end November]). During LR and SR, the mean SL and mean RD are highest in the northwestern and southwestern regions and lowest in the central-eastern and southeastern regions. In those regions, the mean SL and mean RD are higher during SR ([81, 116], [49, 74] days) than during LR ([77, 99], [46, 68] days). In the remaining parts of the country, they are lower during SR ([46, 81], [24, 49] days) than during LR ([55, 77], [24, 46] days). The temporal variability (coefficient of variation) is relatively high in different places. During LR, for OD ([21.5, 34] %) over the northwest, central plateau, and eastern regions, for SL ([22.5, 35] %) over the northern and eastern regions, and for RD ([24.5, 32] over the eastern region. During SR, for SL ([23, 31] %) over the southcentral and the central plateau regions, and for RD ([25.5, 38] %) over the northern, western, southern, and central plateau regions. The seasonal length and the number of rainy days are strongly dependent to rainfall intensity, but more dependent in short rains seasons. An investigation done El Nino and the Indian Ocean Dipole indicates that they may have an influence on the studied rainfall characteristics in Rwanda. Results from this study are important, as the country’s economy remains dependent on rain-fed agriculture. They will help farmers, policy and decision-making for appropriate adaptation and mitigation strategies and policies.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling rainfall and erosivity dynamics in Odisha’s varied agro-climatic zones for sustainable soil and water conservation planning","authors":"Ch. Jyotiprava Dash, S. S. Shrimali, M. Madhu, Randhir Kumar, Partha Pratim Adhikary","doi":"10.1007/s00704-024-05089-0","DOIUrl":"https://doi.org/10.1007/s00704-024-05089-0","url":null,"abstract":"<p>Climate change leads to changes in climatic variables, with rainfall being one of them. Changes in rainfall influence rainfall erosivity and subsequently erosion rates. This study analysed rainfall data from 1901 to 2017 in Odisha, focusing on different agro-climatic zones to discern annual rainfall pattern, its spatial variation, and trend, particularly concerning the rainfall erosivity factor and its impact on soil erosion and agricultural productivity. Notably, the Eastern Ghats Highland region received the highest average annual rainfall of 1578.5 mm, while the Western Undulating Zone received the lowest (1308.4 mm). The rainfall distribution showed spatial variability largely influenced by topography, with areas experiencing orographic lifting receiving higher rainfall. The study observed significant trend in annual rainfall, noting a maximum decline of 1.2 mm yr⁻¹ in the North Western Plateau, Western Central Table Land, and Western Undulating Zone, whereas the East and South Eastern Plain, Mid Central Table Land, North Eastern Coastal Plain, North Eastern Ghats, and South Eastern Ghats exhibited a noteworthy increase in annual rainfall (0 to 3.9 mm yr⁻¹). The decline in rainfall can result in the drying up of water bodies and reduced soil water availability to crop, thereby influencing agricultural production. On the other hand, areas with increased rainfall, may face extreme events which can aggravate soil erosion and thereby loss of soil fertility. Considering the scarcity of pluviographic data in countries like India, Modified Fournier Index (MFI) may be considered as one of the useful methods to capture rainfall’s aggressiveness towards soil erosion through rainfall erosivity (R-factor). Therefore, to evaluate potential soil erosion levels, the Modified Fournier Index method was employed, revealing varying degrees of soil erosiveness across different regions. The Eastern Ghats Highlands exhibited the highest erosion potential. The R-factor, aligned with these spatial patterns, with the Eastern Ghats Highland (12,965.4 MJ mm ha⁻¹ h⁻¹ yr⁻¹) and South Eastern Ghats (12,242.3 MJ mm ha⁻¹ h⁻¹ yr⁻¹) regions displaying the highest R-factor values. Furthermore, the research identified areas prone to soil erosion by overlaying R-factor, slope, and land use maps, highlighting vulnerable regions such as Eastern Ghats Highlands, North Eastern Ghats, South Eastern Ghats, and Western Undulating Zone. This comprehensive analysis allows for informed prioritization of conservation efforts and the implementation of appropriate measures like strip cropping of finger millet with groundnut, intercropping finger millet with hedgerows of <i>Gliricidia</i> and <i>Leucaena</i>, bio-engineering measures such as earthen or stone bunds with broom grass in arable land and growing of aromatic grasses like lemon and citronella grass, construction of staggered trenches in non-ar","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unearthing India’s soil moisture anomalies: impact on agriculture and water resource strategies","authors":"Saurabh Kumar Gupta, Suraj Kumar Singh, Shruti Kanga, Pankaj Kumar, Gowhar Meraj, Dhrubajyoti Sahariah, Jatan Debnath, Kesar Chand, Bhartendu Sajan, Saurabh Singh","doi":"10.1007/s00704-024-05088-1","DOIUrl":"https://doi.org/10.1007/s00704-024-05088-1","url":null,"abstract":"<p>Soil moisture plays a critical role in agricultural productivity and water resource management, especially in a diverse and populous country like India. Understanding variations in soil moisture across different regions and seasons is essential for adapting agricultural practices and water management strategies to local conditions. This study examines changes in soil moisture levels across India, comparing contemporary data from 2023 with historical averages from 2000 to 2005 using advanced remote sensing and GIS technologies. The primary objective of this study is to identify Soil Moisture Anomalies (SMA) across India, quantify their impacts on agriculture and water resources, and provide recommendations for targeted management strategies. By comparing recent soil moisture data against historical averages, the study aims to highlight trends and changes that could influence future water resource planning and agricultural practices. The research utilizes data from the Famine Early Warning Systems Network’s (FEWS NET) i.e. Land Data Assimilation System (FLDAS), obtained from NASA’s data archives. The study employs a systematic approach to analyze seasonal variations in soil moisture across different Indian states. Soil moisture levels were analyzed using zonal statistics in GIS to classify regions into categories based on the degree of anomaly observed. This classification helped in understanding the spatial distribution of soil moisture during the pre-monsoon, monsoon, and post-monsoon seasons. The study found significant regional and seasonal variations in soil moisture across India. During the monsoon period, areas such as Bihar and Jharkhand consistently showed significant moisture deficits, indicative of drought conditions, affecting agricultural output and necessitating urgent water conservation measures. Conversely, regions like Punjab benefited from positive soil moisture anomalies, enhancing agricultural productivity. The pre-monsoon and post-monsoon seasons also showed variations, with some areas experiencing deficits requiring careful water management while others had surpluses that increased the risk of flooding. The analysis of SMA in India underscores the need for region-specific agricultural and water management strategies that consider significant variability in soil moisture conditions. The study highlights the importance of integrating soil moisture monitoring into national policy frameworks to enhance climate resilience and sustainable agricultural practices. Future research should focus on updating soil moisture assessments with more recent data and refining predictive models to improve the accuracy and effectiveness of water management and agricultural interventions.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projections and uncertainty analysis of socioeconomic exposure to compound dry and hot events under 1.5℃ and 2.0℃ warming levels across China","authors":"Gengxi Zhang, Hongkai Wang, Wenfei Liu, Huimin Wang","doi":"10.1007/s00704-024-05085-4","DOIUrl":"https://doi.org/10.1007/s00704-024-05085-4","url":null,"abstract":"<p>Climate change is expected to intensify compound dry and hot events (CDHEs) in China, exacerbating socioeconomic exposure to CDHEs. Based on 23 global climate models (GCMs) data from Coupled Model Intercomparison Project 6 (CMIP6), this study analyzes and projects the socioeconomic exposure to CDHEs and its influencing factors under 1.5℃ and 2.0℃ global warming levels. The results show that the frequency of CDHEs is expected to be higher under 2.0℃ warming levels than that under 1.5℃ warming levels. Population exposures to CDHEs are projected to increase by 160 × 10⁶ persons-months (about 280%) and 210 × 10⁶ persons-months (310%) under 1.5℃ and 2.0℃ warming levels, respectively. The region with the highest increase in population exposure to CDHEs is East China, followed by Central China and South China; and the regions with the smallest increase in population exposure are Tibet, Inner Mongolia, and Xinjiang. GDP exposures are expected to increase by 24 times and 20 times under 1.5 °C warming levels for SSP2-4.5 and SSP5-8.5 scenarios, while the values would be up to 38 times and 28 times under 2.0 °C warming levels. The climate effect (accounting for 80%) is the determinate factor that triggers the change of population exposure to CDHEs, followed by the interaction between the population and climate changes, while the influence of the population factor is the least. Interactive effect contributes the most to GDP exposure whereas climate change contributes the least. Across most regions of China, the warming level is the main uncertainty source, accounting for 46.1% and 70.5% of the population and GDP exposure, respectively. The results are beneficial for identifying hotspots of vulnerable regions exposed to CDHEs and provide beneficial information for conducting climate change mitigation and adaptation strategies.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A methodological approach for filling the gap in extreme daily temperature data: an application in the Calabria region (Southern Italy)","authors":"Emanuele Barca, Ilaria Guagliardi, Tommaso Caloiero","doi":"10.1007/s00704-024-05079-2","DOIUrl":"https://doi.org/10.1007/s00704-024-05079-2","url":null,"abstract":"<p>Regional studies are crucial for monitoring and managing the impacts of extreme climatic events. This phenomenon is particularly important in some areas, such as the Mediterranean region, which has been identified as one of the most responsive regions to climate change. In this regard, the analysis of large space-time sets of climatic data can provide potentially valuable information, although the datasets are commonly affected by the issue of missing data. This approach can significantly reduce the reliability of inferences derived from space-time data analysis. Consequently, the selection of an effective missing data recovery method is crucial since a poor dataset reconstruction could lead to misleading the decision makers’ judgments. In the present paper, a methodology that can enhance the confidence of the statistical analysis performed on the reconstructed data is presented. The basic assumption of the proposed methodology is that missing data within certain percentages cannot significantly change the shape or parameters of the complete data distribution. Therefore, by applying several missing data recovery methods whose reconstructed dataset better overlaps the original dataset, larger confidence is needed. After the gap filling procedure, the temporal tendencies of the annual daily minimum temperature (T &lt; 0 °C) were analysed in the Calabria region (southern Italy) by applying a test for trend detection to 8 temperature series over a 30-year period (1990–2019). The results showed that there was a constant reduction in the duration of frosty days, indicating the reliability of the effect of climate change.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a combined drought indicator against crop yield estimations and simulations over the Argentine Humid Pampas","authors":"Spennemann Pablo C., Gustavo Naumann, Mercedes Peretti, Carmelo Cammalleri, Mercedes Salvia, Alessio Bocco, Maria Elena Fernández Long, Martin D. Maas, Hyunglok Kim, Manh-Hung Le, John D. Bolten, Andrea Toreti, Venkataraman Lakshmi","doi":"10.1007/s00704-024-05073-8","DOIUrl":"https://doi.org/10.1007/s00704-024-05073-8","url":null,"abstract":"<p>Droughts pose serious threats to the agricultural sector, especially in rainfed-dominated agricultural regions like those in Argentina’s Humid Pampas. This region was recently impacted by slow-evolving and long-lasting droughts as well as by flash droughts, resulting in losses reaching thousands of millions of US dollars. Improvements of drought early warning systems are essential, particularly given the projected increase in drought frequency and severity over southern South America. The spatial and temporal relationship between precipitation deficits, soil moisture and vegetation health anomalies are crucial for better understanding and representation of the agricultural droughts and their impacts. In this context, the Combined Drought Indicator (CDI) considers the causal and time-lagged relationship of these three variables. The study’s objective is twofold: (1) Analyze the time-lagged response between precipitation deficits, soil moisture and satellite fAPAR anomalies; and (2) Evaluate the CDI’s capability to characterize the severity of drought events on the Humid Pampas against agricultural yield estimations and simulations, as well as agricultural emergency declarations. The correlation among the variables shows strong spatial variability. The highest Pearson correlation values (<i>r</i> &gt; 0.42) are observed over parts of the Humid Pampas for time lags of 0, 10, and 20 days between the variables. Although the CDI has limitations, such as its coarse spatial resolution and monthly temporal resolution of precipitation data, it effectively tracks the progression of major drought events in the region. The CDI’s performance aligns well with estimations and simulations of soybean and corn yields, as well as official declarations of agricultural emergencies. Insights from this study also provide a basis for discussing potential improvements to the CDI. This study highlights the global and regional significance of evaluating and enhancing the CDI for effective drought monitoring, emphasizing the role of collaborative efforts for future advancements in drought early warning systems.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of multi-source remote sensing soil moisture products over Punjab Pakistan during 2022–2023","authors":"Saba ul Hassan, Munawar Shah, Rasim Shahzad, Bushra Ghaffar, Bofeng Li, José Francisco de Oliveira‑Júnior, Khristina Maksudovna Vafaeva, Punyawi Jamjareegulgarn","doi":"10.1007/s00704-024-05082-7","DOIUrl":"https://doi.org/10.1007/s00704-024-05082-7","url":null,"abstract":"<p>The Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a valuable tool for terrestrial remote sensing applications, particularly in the context of land Surface Soil Moisture (SSM) detection. The high-resolution capability of GNSS-R complements traditional satellite-based active and passive missions but the product reliability and robustness evaluations are still absent due to an efficient retrieval algorithms. In this study, we addressed this lack of reliability and robustness by comprehensively assessing the SSM retrievals from CYclone Global Navigation Satellite System (CYGNSS) data with the satellite-based microwave radiometry products Soil Moisture Active Passive (SMAP) and Modern Era Retrospective-Analysis for Research and Applications (MERRA2) over Punjab in various seasons. ERA5 model-based products for the same period in 2022–2023. Our study reveals a distinct seasonal average SSM variation during autumn (0.20 cm³/cm³), followed by winter values of 0.19 cm³/cm³. Subsequently, the minimum SSM values are observed during summer (0.11 cm³/cm³) and an increase in spring to 0.13 cm³/cm³. Moreover, a strong positive linear relationship (0.74) is evident between SMAP and ERRA 5 in contrast to a low correlation (0.03) between MERRA2 and both the SMAP and ERRA 5. Additionally, SMAP demonstrates moderate and weak correlation of 0.53 and 0.03 with CYGNSS and MERRA2, respectively. The CYGNSS exhibits moderate correlations (0.46) with ERRA 5 and SMAP and a weaker association (0.14) with MERRA2. Our analysis concluded that MERRA2 (Bias = 0.20 cm³/cm³, ubRMSD = 0.25 cm³/cm³, RMSE = 0.12 cm³/cm³, SD = 0.13 cm³/cm³, MAE = 0.04 cm³/cm, <i>R</i> = 0.03) SSM product performs poorly as compared to SMAP (Bias = 0.03 cm³/cm³, ubRMSD = 0.03 cm³/cm³, RMSE = 0.04 cm³/cm³, SD = 0.05 cm³/cm³, MAE = 0.03 cm³/cm³, <i>R</i> = 0.74) and CYGNSS (Bias = -0.01 cm³/cm³, ubRMSD = 0.09 cm³/cm³, RMSE = 0.07 cm³/cm³, SD = 0.06 cm³/cm³, MAE = 0.05 cm³/cm³, <i>R</i> = 0.46) products. This study provides accurate future predictions of SSM with delineating the limitations of GNSS-R in comparison to remote sensing and model values. The findings from this study have also significant implications for the advancement of GNSS-R applications in agriculture and crop management.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141532674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends and variations of tropical cyclone precipitation contributions in the Indochina Peninsula","authors":"Thi-Ngoc-Huyen Ho, S.-Y. Simon Wang, Jin-Ho Yoon","doi":"10.1007/s00704-024-05084-5","DOIUrl":"https://doi.org/10.1007/s00704-024-05084-5","url":null,"abstract":"<p>This study conducts a comprehensive analysis of the influence of tropical cyclones on precipitation variations in Indochina, examining Vietnam, Laos, and Cambodia, while exploring their connection with evolving climatic variables. Covering a span of four decades (1979–2021) and integrating daily precipitation records with climatic datasets, the research elucidates tropical cyclone’s contributions to the annual precipitation across distinct regions, revealing percentages of 27%, 16%, and 6% in Vietnam, Laos, and Cambodia, respectively. Spatial distribution mapping highlights concentrated intensities in central Vietnam, central Laos, and southern Cambodia. Additionally, an upward trend in Vietnam’s precipitation, as a representative measure of the entire region, is observed over the study duration, while its variability exhibits marginal correlations with inter-annual and decadal-scale climatic indices. The upward trend aligns with increased precipitable water over Indochina and open oceans, increased sea surface temperatures, reinforced atmospheric low-pressure systems, and intensified westerly wind patterns post-2000. These findings underscore the complex interplay between climate variables and Indochina’s precipitation dynamics, suggesting implications for disaster management and strategies to adapt to climate change.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-stationary low flow frequency analysis under climate change","authors":"Muhammet Yılmaz, Fatih Tosunoğlu","doi":"10.1007/s00704-024-05081-8","DOIUrl":"https://doi.org/10.1007/s00704-024-05081-8","url":null,"abstract":"<p>Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}