This study presents an analysis of long-term rainfall variability and trends in the summer monsoon region of Southeast Asia, encompassing Lao People’s Democratic Republic (Lao PDR), Thailand, Vietnam, Cambodia, and Myanmar, as well as their respective river basins. Utilizing Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) having a spatial resolution of 5 km spanning from 1981 to 2021, rainfall variability and trends were examined. Data preprocessing and geospatial analysis were conducted using R-Studio and ArcGIS software. The Mann–Kendall (MK) test and Sen’s slope estimator were employed for annual and seasonal rainfall trend analysis. Myanmar exhibited the highest average annual rainfall of 2137 mm during the study period, while Thailand had the lowest (1641 mm). Over the past four decades, the Peninsula Malaysian Basin experienced the highest average annual rainfall (2691 mm), whereas the Chao Praya Basin recorded the lowest (1311 mm). Increasing trends in rainfall were observed across all five countries and nine major river basins. Vietnam displayed the highest annual rainfall trend of 5.63 mm/year, while Lao PDR exhibited the lowest trend (3.16 mm/year). Among the river basins, the Chao Phraya Basin demonstrated the maximum annual rainfall trend (11.21 mm/year), while the Peninsula Malaysia Basin had the minimum trend (1.21 mm/year). These findings could significantly contribute to climate change monitoring in the region and can aid policymakers in sectors such as agriculture, urban planning, and disaster management.
{"title":"Long-Term Rainfall Variability and Trends for Climate Risk Management in the Summer Monsoon Region of Southeast Asia","authors":"Mahesh Edirisinghe, Niranga Alahacoon, Manjula Ranagalage, Yuji Murayama","doi":"10.1155/2023/2693008","DOIUrl":"https://doi.org/10.1155/2023/2693008","url":null,"abstract":"This study presents an analysis of long-term rainfall variability and trends in the summer monsoon region of Southeast Asia, encompassing Lao People’s Democratic Republic (Lao PDR), Thailand, Vietnam, Cambodia, and Myanmar, as well as their respective river basins. Utilizing Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) having a spatial resolution of 5 km spanning from 1981 to 2021, rainfall variability and trends were examined. Data preprocessing and geospatial analysis were conducted using R-Studio and ArcGIS software. The Mann–Kendall (MK) test and Sen’s slope estimator were employed for annual and seasonal rainfall trend analysis. Myanmar exhibited the highest average annual rainfall of 2137 mm during the study period, while Thailand had the lowest (1641 mm). Over the past four decades, the Peninsula Malaysian Basin experienced the highest average annual rainfall (2691 mm), whereas the Chao Praya Basin recorded the lowest (1311 mm). Increasing trends in rainfall were observed across all five countries and nine major river basins. Vietnam displayed the highest annual rainfall trend of 5.63 mm/year, while Lao PDR exhibited the lowest trend (3.16 mm/year). Among the river basins, the Chao Phraya Basin demonstrated the maximum annual rainfall trend (11.21 mm/year), while the Peninsula Malaysia Basin had the minimum trend (1.21 mm/year). These findings could significantly contribute to climate change monitoring in the region and can aid policymakers in sectors such as agriculture, urban planning, and disaster management.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078804","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":"Retracted: Deep Learning-Based English-Chinese Translation Research","authors":"Advances in Meteorology","doi":"10.1155/2023/9791526","DOIUrl":"https://doi.org/10.1155/2023/9791526","url":null,"abstract":"<jats:p />","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136062560","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}
Climate change is an environmental challenge for rural communities that rely heavily on rainwater-based agriculture. The main goal of this study is to investigate spatiotemporal variability and trends in rainfall and temperature in southern Ethiopia. Extreme temperature and rainfall indices were computed using the ClimPACT2 software. The detection and quantification of trends in rainfall and temperature extremes were analyzed using a nonparametric modified Mann–Kendall (MMK) test and Sen’s slope estimator. Results indicated that the mean annual rainfall has a declining trend at Boditi School and Mayokote stations with a statistically significant amount at magnitudes of 0.02 mm and 0.04 mm, respectively. The highest average monthly rainfall in the catchment was observed in the months of April, May, June, July, and August up to maximum rainfall of 117.50 mm, 177.43 mm, and 228.84 mm in Bilate Tena, Boditi, and Mayakote stations, respectively. On a seasonal scale, rainfall in Bilate Tena station was highly variable in all months, ranging from 49.54% to 126.92%, and three seasons except spring which showed moderate variation at 40.65%. In addition, the three locations over the catchment exhibited varied drought signs such as severe (1.28 < SRA < 1.65) and extreme drought (SRA > 1.65). The temperature indices, on the other hand, exhibited a warming trend over the catchment which was observed through an increased annual number of warm days (TX90p) and warm nights (TN90p) ranges from 0.274 to 6.03 and 0.274 to 3.16, respectively. The annual maximum value of the daily maximum temperature (TXx) ranges from 30.10 to 33.76°C in the three agroecological zones and showed low, medium, and high values in Dega, Woyna Dega, and Kola agroecologies, while the annual maximum value of the daily minimum temperature (TNx) ranged between 17 and 17.44°C at Dega and Kola, respectively. Therefore, based on trends in rainfall variability and persistent temperature rise, appropriate adaptation strategies should be adopted.
{"title":"Spatiotemporal Variability and Trends in Rainfall and Temperature in South Ethiopia: Implications for Climate Change Adaptations in Rural Communities","authors":"Daniel Dalle, Yisak Gecho, Sisay Belay Bedeke","doi":"10.1155/2023/1939528","DOIUrl":"https://doi.org/10.1155/2023/1939528","url":null,"abstract":"Climate change is an environmental challenge for rural communities that rely heavily on rainwater-based agriculture. The main goal of this study is to investigate spatiotemporal variability and trends in rainfall and temperature in southern Ethiopia. Extreme temperature and rainfall indices were computed using the ClimPACT2 software. The detection and quantification of trends in rainfall and temperature extremes were analyzed using a nonparametric modified Mann–Kendall (MMK) test and Sen’s slope estimator. Results indicated that the mean annual rainfall has a declining trend at Boditi School and Mayokote stations with a statistically significant amount at magnitudes of 0.02 mm and 0.04 mm, respectively. The highest average monthly rainfall in the catchment was observed in the months of April, May, June, July, and August up to maximum rainfall of 117.50 mm, 177.43 mm, and 228.84 mm in Bilate Tena, Boditi, and Mayakote stations, respectively. On a seasonal scale, rainfall in Bilate Tena station was highly variable in all months, ranging from 49.54% to 126.92%, and three seasons except spring which showed moderate variation at 40.65%. In addition, the three locations over the catchment exhibited varied drought signs such as severe (1.28 < SRA < 1.65) and extreme drought (SRA > 1.65). The temperature indices, on the other hand, exhibited a warming trend over the catchment which was observed through an increased annual number of warm days (TX90p) and warm nights (TN90p) ranges from 0.274 to 6.03 and 0.274 to 3.16, respectively. The annual maximum value of the daily maximum temperature (TXx) ranges from 30.10 to 33.76°C in the three agroecological zones and showed low, medium, and high values in Dega, Woyna Dega, and Kola agroecologies, while the annual maximum value of the daily minimum temperature (TNx) ranged between 17 and 17.44°C at Dega and Kola, respectively. Therefore, based on trends in rainfall variability and persistent temperature rise, appropriate adaptation strategies should be adopted.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135769951","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}
Joseph Ndakize Sebaziga, Anthony Twahirwa, Jonah Kazora, Frank Rusanganwa, Mathieu Mugunga Mbati, Steven Higiro, Sandrine Guhirwa, Jean Claude Nyandwi, Jean Marry Vienney Niyitegeka
The variability, intensity, and distribution of rainfall have drawn a lot of interest globally and especially in nations where rainfed agriculture is the norm. This article uses rainfall data from the Rwanda Meteorology Agency for the years 1981 to 2021 to delineate and analyze rainfall variability and trends in the Kayonza District. The time series were grouped using the K-means clustering technique based on computed Euclidean distance, the total within-cluster sum of squares, and the elbow plot technique to determine the optimal number of clusters. The coefficient of variation measures was employed to analyze rainfall variability, while Sen’s slope and the Mann–Kendall (MK) test were used, respectively, to find trends and changes in magnitude. The results indicated four near homogeneous zones named region one to four. The dry seasons indicated higher variability compared to rainy seasons and annual rainfall total with a variability of 128–142% over the southeastern part during June to August (JJA) season, while a variability of 16–48% was observed over most of the district during both annual and rainy seasons. It was further noted that the areas in the central part of the Kayonza District indicated a significant increasing trend at a significance level of 95% and above during January to February (JF), September to December (SOND), and on annual basis, while March to May (MAM) and JJA season exhibited no significant trend. The findings of this study are essential for creating adequate mitigation strategies to lessen climate change’s effects on agriculture as well as other socioeconomic sectors.
{"title":"Spatial and Temporal Analysis of Rainfall Variability and Trends for Improved Climate Risk Management in Kayonza District, Eastern Rwanda","authors":"Joseph Ndakize Sebaziga, Anthony Twahirwa, Jonah Kazora, Frank Rusanganwa, Mathieu Mugunga Mbati, Steven Higiro, Sandrine Guhirwa, Jean Claude Nyandwi, Jean Marry Vienney Niyitegeka","doi":"10.1155/2023/5372701","DOIUrl":"https://doi.org/10.1155/2023/5372701","url":null,"abstract":"The variability, intensity, and distribution of rainfall have drawn a lot of interest globally and especially in nations where rainfed agriculture is the norm. This article uses rainfall data from the Rwanda Meteorology Agency for the years 1981 to 2021 to delineate and analyze rainfall variability and trends in the Kayonza District. The time series were grouped using the K-means clustering technique based on computed Euclidean distance, the total within-cluster sum of squares, and the elbow plot technique to determine the optimal number of clusters. The coefficient of variation measures was employed to analyze rainfall variability, while Sen’s slope and the Mann–Kendall (MK) test were used, respectively, to find trends and changes in magnitude. The results indicated four near homogeneous zones named region one to four. The dry seasons indicated higher variability compared to rainy seasons and annual rainfall total with a variability of 128–142% over the southeastern part during June to August (JJA) season, while a variability of 16–48% was observed over most of the district during both annual and rainy seasons. It was further noted that the areas in the central part of the Kayonza District indicated a significant increasing trend at a significance level of 95% and above during January to February (JF), September to December (SOND), and on annual basis, while March to May (MAM) and JJA season exhibited no significant trend. The findings of this study are essential for creating adequate mitigation strategies to lessen climate change’s effects on agriculture as well as other socioeconomic sectors.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059683","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":"Retracted: Research on Tourism Resource Evaluation Based on Artificial Intelligence Neural Network Model","authors":"Advances in Meteorology","doi":"10.1155/2023/9781801","DOIUrl":"https://doi.org/10.1155/2023/9781801","url":null,"abstract":"<jats:p />","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910545","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":"Retracted: Research on the Design of Public Space in Urban Renewal Based on Multicriteria Cluster Decision-Making","authors":"Advances in Meteorology","doi":"10.1155/2023/9813087","DOIUrl":"https://doi.org/10.1155/2023/9813087","url":null,"abstract":"<jats:p />","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910720","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":"Retracted: Evaluation Model of Eco-Environmental Economic Benefit Based on the Fuzzy Algorithm","authors":"Advances in Meteorology","doi":"10.1155/2023/9878041","DOIUrl":"https://doi.org/10.1155/2023/9878041","url":null,"abstract":"<jats:p />","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910521","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":"Retracted: Detection Algorithm of Tennis Serve Mistakes Based on Feature Point Trajectory","authors":"Advances in Meteorology","doi":"10.1155/2023/9795750","DOIUrl":"https://doi.org/10.1155/2023/9795750","url":null,"abstract":"<jats:p />","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911602","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":"Retracted: Automatic Capture Processing Method of Basketball Shooting Trajectory Based on Background Elimination Technology","authors":"Advances in Meteorology","doi":"10.1155/2023/9756269","DOIUrl":"https://doi.org/10.1155/2023/9756269","url":null,"abstract":"<jats:p />","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910544","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":"Retracted: Interaction Design of Educational App Based on Collaborative Filtering Recommendation","authors":"Advances in Meteorology","doi":"10.1155/2023/9813468","DOIUrl":"https://doi.org/10.1155/2023/9813468","url":null,"abstract":"<jats:p />","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910697","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}
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