Meteorological Applications最新文献

{"title":"Connectivity of Nocturnal Cold-Air Flows for Urban Heat Island Mitigation: Introduction of the Cold-Air Trajectory Calculator KLATra","authors":"Paule Hainz,&nbsp;Meinolf Kossmann,&nbsp;Stephan Weber","doi":"10.1002/met.70080","DOIUrl":"https://doi.org/10.1002/met.70080","url":null,"abstract":"<p>Ventilation of cities by local cold-air flows is an important measure in urban heat island mitigation and climate-resilient urban planning. We introduce a cold-air connectivity analysis to identify relevant cold-air formation areas as well as urban quarters ventilated by cold-air flows. The nocturnal cold-air flow trajectories are calculated from numerical model simulations using the single-layer cold-air drainage model KLAM_21 and the newly developed trajectory calculator KLATra. The German city of Freiburg im Breisgau is chosen to demonstrate the cold-air connectivity analysis based on trajectories calculated for two 3-hourly periods during an idealised night. Hydrological catchment boundaries and land use define eight rural cold-air formation areas as starting points for forward trajectories, whereas administrative urban district boundaries and land use data are used to define five built-up quarters potentially prone to overheating as starting points for cold-air backward trajectories. A rate of connectivity is calculated from the ratio of trajectories connecting cold-air formation areas with overheated urban quarters to the total number of trajectories. The analysis reveals the potential of cold-air formation areas to ventilate single or multiple urban quarters at connectivity rates up to 82%. The connectivity analysis therefore supports identification and assessment of the relevance of specific cold-air formation areas for urban heat island mitigation and may serve as a valuable planning tool and data basis for objective decision making.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Global Products Capture Precipitation Variability in the Galápagos Islands? An Assessment Based on Climatic Time-Series Components","authors":"María Lorena Orellana-Samaniego,&nbsp;Rolando Célleri,&nbsp;Jörg Bendix,&nbsp;Nazli Turini,&nbsp;Daniela Ballari","doi":"10.1002/met.70085","DOIUrl":"https://doi.org/10.1002/met.70085","url":null,"abstract":"<p>Small islands such as the Galápagos Islands are highly vulnerable to changes in water availability, affecting ecosystems and communities. Understanding temporal precipitation variability is crucial but challenging due to limited ground-based observations. This study evaluates five global precipitation products (satellite, reanalysis and multi-source products) at a monthly scale, complementing conventional assessment against ground-based observations with the analysis of three climatic time-series components: seasonality, anomalies, and trends, which capture distinct aspects of long-term precipitation variability relevant to climate applications. The analysis focuses on Santa Cruz and San Cristóbal Islands, where long-term ground data are available, and includes a spatial comparison of global products across the entire archipelago. Results showed that reanalysis and multi-source products (ERA5-Land, MSWEP, MSWX) generally outperformed satellite-based products (CHIRPS, PERSIANN-CCS-CDR). For example, in the cool lowlands, reanalysis and multi-source products achieved correlation values between 0.81 and 0.94, bias ranging from −0.52% to −40.3%, and probability of detection between 0.76 and 0.96. These products showed high and medium agreement with ground data in precipitation seasonality, anomalies, and trend detection. In contrast, satellite-based products revealed lower correlation values between 0.52 and 0.86, a higher underestimation bias (−10.86% to −75.43%), a lower probability of detection (0.22–0.32), and only medium or no agreement with ground data in precipitation anomalies and trends, with no agreement in seasonality. All global precipitation products exhibited significant limitations in representing precipitation seasonality in the highlands. The component-based assessment complements conventional evaluation, offering deeper insight into how errors are distributed over time. This integrated approach supports a more informed selection of precipitation products for climate analysis and water resource management in data-scarce island regions like Galápagos.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mid-Latitude Versus Tropical Scales of Predictability and Their Implications for Forecasting","authors":"Richard J. Keane,&nbsp;Douglas J. Parker,&nbsp;Etienne Dunn-Sigouin,&nbsp;Erik W. Kolstad,&nbsp;John H. Marsham","doi":"10.1002/met.70055","DOIUrl":"https://doi.org/10.1002/met.70055","url":null,"abstract":"<p>Weather predictability varies between tropical and middle latitudes: rotational effects enable forecasts on moderate spatial scales up to 10 days in middle latitudes, while longer term predictions are less reliable; in contrast, tropical weather is challenging to predict at short lead times, but seasonal forecasts are more accurate due to the influence of larger-scale oscillations, such as slowly varying oceanic surface conditions. This behaviour has been demonstrated in previous studies, but has yet to be focused on in detail, despite its importance to the development of forecasting systems in Tropical regions. This study systematically evaluates precipitation in weather prediction models across both regions using the fractions skill score, evaluating performance at progressively longer lead times and averaging scales, and compares the results with an evaluation based on upper air error kinetic energy. The results confirm that the prediction systems perform better on smaller scales and shorter lead times at middle latitudes and on larger scales and longer lead times at tropical latitudes. A “crossover” in performance is seen at forecast lead times of 5–7 days, a result that appears to be consistent across a range of model resolutions, and occurs both when specifically comparing European and African domains and when comparing whole latitude bands. This differential pattern of model skill even occurs for machine learning-based forecast models, suggesting that it is a fundamental property of the atmosphere rather than an effect of the construction of currently used operational forecasting systems. These findings highlight the need for different forecasting methodologies in tropical regions to address the lack of short-term predictability and leverage long-term statistical predictability.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Tropical Cyclones Across Global Basins: Dynamics, Tracking Algorithms, Forecasting, and Emerging Scientometric Research Trends”","authors":"","doi":"10.1002/met.70081","DOIUrl":"https://doi.org/10.1002/met.70081","url":null,"abstract":"<p>Singh, V., G. Tiwari, A. Singh, et al. 2025. “Tropical Cyclones Across Global Basins: Dynamics, Tracking Algorithms, Forecasting, and Emerging Scientometric Research Trends.” <i>Meteorological Applications</i> 32, no. 3: e70067. 10.1002/met.70067.</p><p>The abbreviation “(ARB)” should be added after “Arabian Sea” in the Abstract section. The revised sentence should read as follows:</p><p>We apologize for these errors.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking the Potential of Dynamical Models for Drought Forecasting in Iran: Insights From Multi-Model Ensemble Analysis","authors":"Zahra Eslami,&nbsp;Amin Shirvani,&nbsp;Francesco Granata","doi":"10.1002/met.70082","DOIUrl":"https://doi.org/10.1002/met.70082","url":null,"abstract":"<p>This paper assesses dynamical models to construct monthly (January through December for lead times of 0.5–2.5 months) and seasonal (January–March [JFM], April–June [AMJ], July–September [JAS], and October–December [OND] for lead times of 1.5–3.5 months) forecasting of drought based on the standardized precipitation evapotranspiration index (SPEI) over Iran. The air temperature (minimum, maximum, and mean) and precipitation data, as the components of SPEI, are forecasted using six North American Multi-Model Ensemble (NMME) and European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS51 as well as their ensemble multi-model mean (MMM) for a common period from 1991 to 2021. These forecast data are interpolated to stations using inverse distance weighting, and then the SPEI is computed for each model. The observed SPEI is calculated for 67 synoptic stations across Iran. The SPEI forecast skill of the MMM surpasses that of individual models. Additionally, MMM demonstrates improved forecast skill during wet and cold months (November–March) compared to dry and warm months (June–September). There is a statistically significant Pearson correlation coefficient between observed and forecast JFM SPEI in most areas of the study area for lead times of 1.5, 2.5, and 3.5 months at a 5% significance level. Moreover, the SPEI forecast is significant in most areas for JFM, AMJ, and OND for the 1.5-month lead time. The canonical correlation analysis is employed to investigate the relationship between observed global sea surface temperature anomalies (SSTA) and seasonal SPEI to achieve insights into the source of drought predictability in Iran, as well as how the skill of the MMM forecasts is affected by SSTA. The spatial pattern root mean square error of the MMM forecasts and SSTA is similar. The canonical correlation coefficient between SSTA and observed SPEI is stronger than in JFM, indicating that MMM exhibits promising potential for SPEI forecasts.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional Land Surface Conditions Developed Using the High-Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region","authors":"Buri Vinodhkumar,&nbsp;Krishna Kishore Osuri,&nbsp;A. P. Dimri,&nbsp;Sandipan Mukherjee,&nbsp;Sami G. Al-Ghamdi,&nbsp;Dev Niyogi","doi":"10.1002/met.70072","DOIUrl":"https://doi.org/10.1002/met.70072","url":null,"abstract":"<p>The Uttarakhand state of India has been witnessing spatiotemporal variations in heavy rainfall, posing landslides, avalanches, and risks to livelihood and infrastructure. The complex terrain (ranging 250–~7500 m) and weather in this part of the Himalayan region pose difficulties in maintaining land surface observations, thus creating uncertainties in surface energy and hydrological processes. The present study demonstrates the value of the high-resolution land data assimilation system (HRLDAS) integrated at 2 km grid spacing from 2011 to 2021 over Uttarakhand and validated against in situ, satellite, and reanalyzes products. Diurnal variation of sensible heat flux (SHF), and latent heat flux (LHF) are closer to the in situ observations (−35 to 64 Wm⁻²) than the global and regional analysis (−125 to 129 Wm⁻² and −40 to 172 Wm⁻²) during monsoon season. The HRLDAS soil moisture (SM) is overestimated against in situ and exhibited less error against European Space Agency Climate Change Initiative (ESACCI) (0.02 m³ m⁻³ with 30%) and Cyclone Global Navigation Satellite System (CYGNSS) (−0.02 m³ m⁻³ error with 21%). The HRLDAS performs better for soil temperature (ST) with high correlation and less bias (0.94°C and −0.34°C) than the GLDAS (0.83°C and −0.61°C) and IMDAA (0.86°C and 2.2°C), when verified against in situ observations. The spatial distribution of HRLDAS shows maximum ST in the southern parts and minimum ST in the northern parts of the Uttarakhand region and is consistent with the GLDAS and IMDAA during monsoon. HRLDAS shows lesser biases in net radiation (12 Wm⁻²), SHF (−10 Wm⁻²), and LHF (9.7 Wm⁻²) compared to GLDAS (25, −17, 10.3 Wm⁻²), and IMDAA (38, −11, 16 Wm⁻²), respectively. Besides the performance, the HRLDAS products represent better spatial heterogeneity than the coarser global and regional analysis and are useful to initialize numerical models.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Wind Characteristics Over Different Terrains for Wind Turbine Deployment","authors":"Rohan Kumar,&nbsp;Anna Rutgersson,&nbsp;Muhammad Asim,&nbsp;Ashish Routray","doi":"10.1002/met.70079","DOIUrl":"https://doi.org/10.1002/met.70079","url":null,"abstract":"<p>Understanding how complex orography influences lower atmospheric winds is essential for accurately characterizing wind conditions, especially in regions considered for wind energy development. Complex terrain alters flow dynamics through mechanisms such as wind channeling, flow separation, and the formation of turbulent eddies and mountain waves, all of which significantly affect near-surface wind speed and direction. High-resolution numerical weather prediction (NWP) models, particularly the weather research and forecasting (WRF) model, have demonstrated substantial improvements in simulating these effects when fine-scale terrain and land surface datasets are employed, outperforming simulations based on coarse-resolution inputs. In this study, the WRF model is benchmarked for the first time using climate reanalysis data for the Askervein Hill campaign—a canonical field study of wind conditions over varying terrain. Multiple model configurations, including vertical and horizontal grid setups and ERA and NCEP/NCAR reanalysis input data, are evaluated to identify optimal settings for flat and complex terrain. Results show that while changes in vertical resolution have limited impact, finer horizontal resolution significantly improves predictions, particularly in complex orographic settings, with ERA data consistently outperforming NCEP/NCAR in all configurations. The model captures velocity profiles on flat terrain with RMSE within 2.5% (10–348 m heights) and turbulence intensity with RMSEs under 3%. Over complex terrain, near-surface flow is not adequately resolved, and the model overpredicts turbulence, which corresponds to an underprediction of the wind profile. However, the model performance improves significantly at wind turbine operational heights, with prediction errors reducing to below 2.4%. This discrepancy can be attributed to model limitations in resolving terrain-induced wind shear and stability gradients, to which the WRF model is particularly sensitive. These findings underscore the critical role of high-resolution terrain and land surface representation in improving WRF model performance for wind energy applications, highlighting the need for careful treatment of model physics, boundary conditions, and domain design to ensure accurate yet computationally efficient simulations.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Merging Weather Surveillance Radar Precipitation Estimates From Different Sources: A Quality-Index Approach","authors":"David R. L. Dufton,&nbsp;Tamora D. James,&nbsp;Mark Whitling,&nbsp;Ryan R. Neely III","doi":"10.1002/met.70070","DOIUrl":"https://doi.org/10.1002/met.70070","url":null,"abstract":"<p>Weather surveillance radar (WSR) provide distributed quantitative precipitation estimates (QPEs) of great value to the modelling, understanding and management of many hydro-meteorological processes. To obtain these observations over regional or larger scale domains it is necessary to composite data from multiple WSRs. These composites are often produced operationally by national or international meteorological agencies yet valuable data from ad-hoc sources such as research groups and local-level WSR operators are not included in these products. This study presents a methodology for incorporating data from a research radar deployment (the National Centre for Atmospheric Science mobile X-band weather radar, NXPol-1) into a national scale composite (the UK Met Office British Isles gridded composite) using a quality-index. Firstly a quality-index is developed for NXPol-1 using an intuitive, multi-factor approach. The quality-index is then cross-referenced with the existing quality-index for the national composite, to allow production of a dynamically merged two source WSR QPE. The method developed is then evaluated using surface precipitation measurements from an extensive rain gauge network. Merging QPE from the two sources using a quality-index improves the accuracy of WSR QPE when compared to either individual data source, showing it is possible to combine ad-hoc WSR data with national products dynamically such that precipitation estimation is improved. Improving local QPE using additional radar deployments will benefit flood forecasting accuracy and local incident response, particularly when that data is used to enhance existing coverage.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Surface Energy Balance of a Himalayan Mature Pine (Pinus roxburghii) Ecosystem During Drought Stress Conditions","authors":"Sandipan Mukherjee,&nbsp;Priyanka Lohani,&nbsp;Krishna K. Osuri,&nbsp;Rajiv Pandey,&nbsp;A. P. Dimri","doi":"10.1002/met.70063","DOIUrl":"https://doi.org/10.1002/met.70063","url":null,"abstract":"<p>This study presents the energy balance dynamics of a mature Pine (<i>Pinus roxburghii</i>) ecosystem of the Indian Himalaya using multiple year (March 2020 to December 2022) eddy covariance-based measurements. Efforts are made to quantify the inter-annual dynamics of surface energy balance at seasonal and annual time scales. The impact of drought conditions, induced by soil moisture and vapor pressure deficit, on energy partitioning of the ecosystem is quantified using Bowen ratio (<i>β</i>) and evaporative fraction (EF). The energy balance closure is assessed for three seasons (i.e., pre-monsoon, monsoon, and post-monsoon) of each observation year. We find that the closure fraction (CF) of the site is more than 80% on an annual scale. Higher CF is observed during pre-monsoon (⁓80%) and monsoon (⁓90%) seasons due to the onset and duration of the growing season. The available energy partitioned into latent heat flux is larger than the sensible heat flux for the ecosystem, signifying that evapotranspiration is one of the dominant components of water and energy budgets. The evaporative cooling at the site takes place during the monsoon season through higher EF; however, the Pine ecosystems sustained the dry pre-monsoon season with higher <i>β</i> values. We find that the soil moisture-induced drought at the site resulted in higher partitioning of the available energy to sensible heat flux, effectively promoting the drought stress condition. However, it is to be noted that a better comprehension could be made for Pine forest behavior under environmental stress if such studies are further replicated.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Spatial Variability and Temporal Trends in the Extreme Rainfall of Kagera Sub-Basin, Tanzania","authors":"Nickson Tibangayuka,&nbsp;Deogratias M. M. Mulungu,&nbsp;Fides Izdori","doi":"10.1002/met.70076","DOIUrl":"https://doi.org/10.1002/met.70076","url":null,"abstract":"<p>Understanding the temporal and spatial variability of rainfall extremes is essential for developing effective adaptation strategies and making informed decisions in water resource management, agriculture, and infrastructure development. This study examines the spatial variability and temporal trends of extreme rainfall events in the Kagera sub-basin, using nine climate indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) and the Standardized Precipitation Index (SPI). The Sen's slope estimator was used to quantify the magnitude of the trend, whereas the Mann-Kendall (MK) test was applied to evaluate its statistical significance at a significance level of <i>α</i> = 0.1. The findings revealed significant trends in the rainfall regime across both annual and seasonal time scales. Annually, consecutive dry days (CDD) showed predominantly negative trends, ranging from −0.24 to −0.1 days/year, whereas consecutive wet days (CWD) generally exhibited positive trends, ranging from 0.16 to 1.0 days/year. Both heavy and very heavy rainfall events, as well as the highest 1- and 5-day rainfall totals, displayed increasing trends, especially in the eastern and central regions of the sub-basin. Seasonally, the results show a decreasing trend in consecutive dry days (CDD) ranging from −0.3 to −0.03 days/year, whereas CWD exhibit an increasing trend, ranging between 0.01 and 0.65 days/year. Both heavy and very heavy rainfall events also exhibited a predominant upward trend. The SPI revealed that the sub-basin experienced periods of severe and extreme drought, particularly between 1991 and 2005. However, there is a notable shift towards wetter conditions, as evidenced by predominantly increasing trends in the 3-, 6-, and 12-month SPI. These findings provide critical insights for developing adaptation strategies to address socio-environmental challenges which are often exacerbated by extreme rainfall events.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}