Uttam V. Pawar, P. Karunathilaka, Upaka S. Rathnayake
Changes in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen’s slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally.
{"title":"Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka","authors":"Uttam V. Pawar, P. Karunathilaka, Upaka S. Rathnayake","doi":"10.1155/2022/6456761","DOIUrl":"https://doi.org/10.1155/2022/6456761","url":null,"abstract":"Changes in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen’s slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47806543","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}
Chunlei Zhao, S. Qian, Chengzhe Meng, Yufei Chang, Wenzhe Guo, Sha Wang, Yinglong Sun
As a regulator of ecological environment, Baiyangdian Wetland is in a pivotal position in constructing the blue-green space (BGS) of Xiong’an New Area in China. This study aims to reveal the spatiotemporal changes of the BGS in Baiyangdian Wetland from 2016 to 2021. It uses Google Earth Engine (GEE) to calculate NDVI and NDWI based on Sentinel-2 Satellite remote sensing data and extracts the blue-green space by a classification model driven by NDVI and NDWI. Moreover, the land-use transfer matrix and landscape pattern indices are applied for evaluating the BGS changes in the wetland. According to the results, vegetation in the wetland shows no obvious spatial transfer. From 2016 to 2020, the BGS proportion in the wetland showed a stable increase, with the blue space getting larger by 10.8%. The indicators of the Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), Contagion, and Landscape Shape Index (LSI) of the wetland decreased, suggesting a better ecological environment since the establishment of Xiong’an New Area in 2017. Based on the results, the author makes the following conclusion: the construction of BGS in Baiyangdian Wetland results in a well-organized ecological environment. The study provides a reference for building Xiong’an New Area and monitoring BGS changes in other regions.
{"title":"Blue-Green Space Changes of Baiyangdian Wetland in Xiong’an New Area, China","authors":"Chunlei Zhao, S. Qian, Chengzhe Meng, Yufei Chang, Wenzhe Guo, Sha Wang, Yinglong Sun","doi":"10.1155/2022/4873393","DOIUrl":"https://doi.org/10.1155/2022/4873393","url":null,"abstract":"As a regulator of ecological environment, Baiyangdian Wetland is in a pivotal position in constructing the blue-green space (BGS) of Xiong’an New Area in China. This study aims to reveal the spatiotemporal changes of the BGS in Baiyangdian Wetland from 2016 to 2021. It uses Google Earth Engine (GEE) to calculate NDVI and NDWI based on Sentinel-2 Satellite remote sensing data and extracts the blue-green space by a classification model driven by NDVI and NDWI. Moreover, the land-use transfer matrix and landscape pattern indices are applied for evaluating the BGS changes in the wetland. According to the results, vegetation in the wetland shows no obvious spatial transfer. From 2016 to 2020, the BGS proportion in the wetland showed a stable increase, with the blue space getting larger by 10.8%. The indicators of the Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), Contagion, and Landscape Shape Index (LSI) of the wetland decreased, suggesting a better ecological environment since the establishment of Xiong’an New Area in 2017. Based on the results, the author makes the following conclusion: the construction of BGS in Baiyangdian Wetland results in a well-organized ecological environment. The study provides a reference for building Xiong’an New Area and monitoring BGS changes in other regions.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43993148","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}
Ying Tian, Rong Yao, Enrong Zhao, Qiangqiang Yao, Xiaolong Pan
Based on conventional observation data from the China Meteorological Administration (CMA) and reanalysis data from the American National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) between 2012 and 2021, combined with the meteorological analysis, composite synthesis, and water vapor trajectory analysis, the weather circulations of typical rainstorms during the 10 years can be divided into 4 categories: Static Front Pattern (SFP), Subtropical High Edge Pattern (SHEP), Northeast Cold Vortex Pattern (NCVP), and Low-Level Vortex and Shear Pattern (LLVSP). The SHEP and SFP rainstorms have the characteristics of long duration and wide range, while the NCVP rainstorms are characterized by mobility and disaster weather accompaniment. The daily precipitation of LLVSP cases has extremity feature. The occurrence and development of rainstorms are well coordinated with the systems on lower levels. The main water vapor channel in lower layers of the SFP cases is from the South China Sea, while it is from Bohai for the NCVP cases and the Bay of Bengal for the SHEP and LLVSP cases. The main water vapor channel in middle layers is from the Bay of Bengal because of the affection of the southwest air flow. The south boundary of the MLYRB is the most important water vapor input boundary, followed by the west boundary, while the East and North boundaries are the outflow boundaries. During the rainstorms, the low-level water vapor is exuberant with low-level water vapor convergence much stronger than the high-level divergence. Among the four types of rainstorms, the NCVP cases provide the most abundant low-level water vapor convergence, resulting in the strongest short-term precipitation among the four types. Combined with water vapor transportation and convergence, the refined spatial conceptual models of the four types of rainstorms can better judge the process intensity and falling area and provide reference for disastrous weather forecast and early warning.
{"title":"The Space Conceptual Models and Water Vapor Characteristics of Typical Rainstorms during Plum Rain Season","authors":"Ying Tian, Rong Yao, Enrong Zhao, Qiangqiang Yao, Xiaolong Pan","doi":"10.1155/2022/6971110","DOIUrl":"https://doi.org/10.1155/2022/6971110","url":null,"abstract":"Based on conventional observation data from the China Meteorological Administration (CMA) and reanalysis data from the American National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) between 2012 and 2021, combined with the meteorological analysis, composite synthesis, and water vapor trajectory analysis, the weather circulations of typical rainstorms during the 10 years can be divided into 4 categories: Static Front Pattern (SFP), Subtropical High Edge Pattern (SHEP), Northeast Cold Vortex Pattern (NCVP), and Low-Level Vortex and Shear Pattern (LLVSP). The SHEP and SFP rainstorms have the characteristics of long duration and wide range, while the NCVP rainstorms are characterized by mobility and disaster weather accompaniment. The daily precipitation of LLVSP cases has extremity feature. The occurrence and development of rainstorms are well coordinated with the systems on lower levels. The main water vapor channel in lower layers of the SFP cases is from the South China Sea, while it is from Bohai for the NCVP cases and the Bay of Bengal for the SHEP and LLVSP cases. The main water vapor channel in middle layers is from the Bay of Bengal because of the affection of the southwest air flow. The south boundary of the MLYRB is the most important water vapor input boundary, followed by the west boundary, while the East and North boundaries are the outflow boundaries. During the rainstorms, the low-level water vapor is exuberant with low-level water vapor convergence much stronger than the high-level divergence. Among the four types of rainstorms, the NCVP cases provide the most abundant low-level water vapor convergence, resulting in the strongest short-term precipitation among the four types. Combined with water vapor transportation and convergence, the refined spatial conceptual models of the four types of rainstorms can better judge the process intensity and falling area and provide reference for disastrous weather forecast and early warning.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41538785","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}
Regional accuracy was examined using extreme gradient boosting (XGBoost) to improve frost prediction accuracy, and accuracy differences by region were found. When the points were divided into two groups with weather variables, Group 1 had a coastal climate with a high minimum temperature, humidity, and wind speed and Group 2 exhibited relatively inland climate characteristics. We calculated the accuracy in the two groups and found that the precision and recall scores in coastal areas (Group 1) were significantly lower than those in the inland areas (Group 2). Geographic elements (distance from the nearest coast and height) were added as variables to improve accuracy. In addition, considering the continuity of frost occurrence, the method of reflecting the frost occurrence of the previous day as a variable and the synthetic minority oversampling technique (SMOTE) pretreatment were used to increase the learning ability.
{"title":"Frost Forecasting considering Geographical Characteristics","authors":"Hyo-Sook Kim, Jong-Min Kim, Sa-Heon Kim","doi":"10.1155/2022/1127628","DOIUrl":"https://doi.org/10.1155/2022/1127628","url":null,"abstract":"Regional accuracy was examined using extreme gradient boosting (XGBoost) to improve frost prediction accuracy, and accuracy differences by region were found. When the points were divided into two groups with weather variables, Group 1 had a coastal climate with a high minimum temperature, humidity, and wind speed and Group 2 exhibited relatively inland climate characteristics. We calculated the accuracy in the two groups and found that the precision and recall scores in coastal areas (Group 1) were significantly lower than those in the inland areas (Group 2). Geographic elements (distance from the nearest coast and height) were added as variables to improve accuracy. In addition, considering the continuity of frost occurrence, the method of reflecting the frost occurrence of the previous day as a variable and the synthetic minority oversampling technique (SMOTE) pretreatment were used to increase the learning ability.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48538821","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}
The variation of solar radiation has a profound effect on the surface energy balance and hydrological cycle. Although the relationship between solar radiation variation and its influencing factors has been extensively studied, they are seldom used in Xinjiang, the largest province in China. In this study, we investigated the spatial distribution and temporal variation in global radiation (Eg), water vapor content (WVC), aerosol optical depth (AOD), total cloud cover (TCC), and low-level cloud cover (LCC) in Xinjiang, northwestern China, between 1961 and 2015. The annual average Eg reported at all stations was 5126.3–6252.8 MJ·m−2 with a mean of 5672 MJ·m−2. The highest annual mean Eg of 6252.8 MJ·m−2 occurred in Hami, eastern Xinjiang, whereas the lowest annual mean Eg of 5126.3 MJ·m−2 occurred in Urumqi, northern Xinjiang. The annual Eg variation was mainly affected by WVC, AOD, TCC, and LCC. Decreases in annual, spring, summer, autumn, and winter Eg trends were recorded in Xinjiang at rates of −33.88 × 10−2, −1.92 × 10−2, −1.89 × 10−2, −3.47 × 10−2, and −3.56 × 10−2 MJ·m−2·decade−1, respectively, with decreasing ratios of 9.43%, 5.85%, 0.14%, 8%, and 20.55%, respectively. Increasing trends in annual WVC, AOD, TCC, and LCC were noted in Xinjiang at rates of 7.12 × 10−5 mm·decade−1, 2.74 × 10−6 decade−1, 8.77 × 10−5 % decade−1, and 5.73 × 10−5% decade−1, respectively. In addition, increasing trends in the annual Eg at Yining and Yanqi stations were observed. The Eg spatial distribution was complex in Xinjiang at the stations observed in this study, which were divided into six groups. Eg at group 1 showed an increasing trend associated with decreases in the WVC and TCC, whereas decreases in Eg were observed at groups 2–6, which could have been influenced by increases in AOD, TCC, and LCC.
{"title":"Variation in Surface Solar Radiation and the Influencing Factors in Xinjiang, Northwestern China","authors":"Lili Jin, Zhenjie Li, Qing He, Alim Abbas","doi":"10.1155/2022/1999997","DOIUrl":"https://doi.org/10.1155/2022/1999997","url":null,"abstract":"The variation of solar radiation has a profound effect on the surface energy balance and hydrological cycle. Although the relationship between solar radiation variation and its influencing factors has been extensively studied, they are seldom used in Xinjiang, the largest province in China. In this study, we investigated the spatial distribution and temporal variation in global radiation (Eg), water vapor content (WVC), aerosol optical depth (AOD), total cloud cover (TCC), and low-level cloud cover (LCC) in Xinjiang, northwestern China, between 1961 and 2015. The annual average Eg reported at all stations was 5126.3–6252.8 MJ·m−2 with a mean of 5672 MJ·m−2. The highest annual mean Eg of 6252.8 MJ·m−2 occurred in Hami, eastern Xinjiang, whereas the lowest annual mean Eg of 5126.3 MJ·m−2 occurred in Urumqi, northern Xinjiang. The annual Eg variation was mainly affected by WVC, AOD, TCC, and LCC. Decreases in annual, spring, summer, autumn, and winter Eg trends were recorded in Xinjiang at rates of −33.88 × 10−2, −1.92 × 10−2, −1.89 × 10−2, −3.47 × 10−2, and −3.56 × 10−2 MJ·m−2·decade−1, respectively, with decreasing ratios of 9.43%, 5.85%, 0.14%, 8%, and 20.55%, respectively. Increasing trends in annual WVC, AOD, TCC, and LCC were noted in Xinjiang at rates of 7.12 × 10−5 mm·decade−1, 2.74 × 10−6 decade−1, 8.77 × 10−5 % decade−1, and 5.73 × 10−5% decade−1, respectively. In addition, increasing trends in the annual Eg at Yining and Yanqi stations were observed. The Eg spatial distribution was complex in Xinjiang at the stations observed in this study, which were divided into six groups. Eg at group 1 showed an increasing trend associated with decreases in the WVC and TCC, whereas decreases in Eg were observed at groups 2–6, which could have been influenced by increases in AOD, TCC, and LCC.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42473864","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}
Yaulande Alotse Douanla, O. Mamadou, André Dembélé, Djidjoho Renaud Roméo Koukoui, Fifamè Edwige Akpoly, A. Lenouo
The comparative analysis of the intra- and interannual dynamics between the Direct Normal Irradiation (DNI) under clear sky conditions and five aerosol types (Dust, Sea Salt, Black Carbon, Organic Carbon, and Sulfate) is the purpose of this study. To achieve this aim, we used fifteen-year DNI and aerosols data downloaded at 3-hour time intervals in nine defined zones throughout Cameroon. The wavelet transform is a powerful tool for studying local variability of amplitudes in a temporal dataset and constitutes our principal tool. The results show unequal distribution of aerosol types according to zones, but the Desert Dusts (DU) and Organic Carbon (OM) aerosols have been found as dominant particles in the studied region. The wavelet coherence analysis between DNI and each aerosol type reveals three bands of periodicity: ∼ 4-month band, 8–16-month band, and sometimes after-32-month band, with the most important frequency at 8–16-month band period. However, the intensity of coherence across bands varies with respect to aerosol type as well as each of the nine climate zones. A significant anticorrelation relationship was obtained between DNI and each type of aerosol, emphasizing that the presence of such atmospheric particles could dampen the renewable energy utilized by power systems. Also, the analysis shows that scattering aerosols such as Sulfate and Sea Salt (SU and SS, respectively) lead DNI in phase while absorbing aerosols such as Organic Carbon, Black Carbon, and Dust (OM, BC, and DU, respectively) give phase lag with DNI.
{"title":"Wavelet Analysis of the Interconnection between Atmospheric Aerosol Types and Direct Irradiation over Cameroon","authors":"Yaulande Alotse Douanla, O. Mamadou, André Dembélé, Djidjoho Renaud Roméo Koukoui, Fifamè Edwige Akpoly, A. Lenouo","doi":"10.1155/2022/1030330","DOIUrl":"https://doi.org/10.1155/2022/1030330","url":null,"abstract":"The comparative analysis of the intra- and interannual dynamics between the Direct Normal Irradiation (DNI) under clear sky conditions and five aerosol types (Dust, Sea Salt, Black Carbon, Organic Carbon, and Sulfate) is the purpose of this study. To achieve this aim, we used fifteen-year DNI and aerosols data downloaded at 3-hour time intervals in nine defined zones throughout Cameroon. The wavelet transform is a powerful tool for studying local variability of amplitudes in a temporal dataset and constitutes our principal tool. The results show unequal distribution of aerosol types according to zones, but the Desert Dusts (DU) and Organic Carbon (OM) aerosols have been found as dominant particles in the studied region. The wavelet coherence analysis between DNI and each aerosol type reveals three bands of periodicity: \u0000 \u0000 ∼\u0000 \u0000 4-month band, 8–16-month band, and sometimes after-32-month band, with the most important frequency at 8–16-month band period. However, the intensity of coherence across bands varies with respect to aerosol type as well as each of the nine climate zones. A significant anticorrelation relationship was obtained between DNI and each type of aerosol, emphasizing that the presence of such atmospheric particles could dampen the renewable energy utilized by power systems. Also, the analysis shows that scattering aerosols such as Sulfate and Sea Salt (SU and SS, respectively) lead DNI in phase while absorbing aerosols such as Organic Carbon, Black Carbon, and Dust (OM, BC, and DU, respectively) give phase lag with DNI.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45949025","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 zone classification promotes our understanding of the climate and provides a framework for analyzing a range of environmental and socioeconomic data and phenomena. The Köppen–Geiger classification system is the most widely used climate classification scheme. In this study, we compared the climate zones objectively defined using data-driven methods with Köppen–Geiger rule-based classification. Cluster analysis was used to objectively delineate the world’s climatic regions. We applied three clustering algorithms—k-means, ISODATA, and unsupervised random forest classification—to a dataset comprising 10 climatic variables and elevation; we then compared the obtained results with those from the Köppen–Geiger classification system. Results from both the systems were similar for some climatic regions, especially extreme temperature ones such as the tropics, deserts, and polar regions. Data-driven classification identified novel climatic regions that the Köppen–Geiger classification could not. Refinements to the Köppen–Geiger classification, such as precipitation-based subdivisions to existing Köppen–Geiger climate classes like tropical rainforest (Af) and warm summer continental (Dfb), have been suggested based on clustering results. Climatic regions objectively defined by data-driven methods can further the current understanding of climate divisions. On the other hand, rule-based systems, such as the Köppen–Geiger classification, have an advantage in characterizing individual climates. In conclusion, these two approaches can complement each other to form a more objective climate classification system, wherein finer details can be provided by data-driven classification and supported by the intuitive structure of rule-based classification.
{"title":"Data-Driven versus Köppen–Geiger Systems of Climate Classification","authors":"Vajira Lasantha, T. Oki, Daisuke Tokuda","doi":"10.1155/2022/3581299","DOIUrl":"https://doi.org/10.1155/2022/3581299","url":null,"abstract":"Climate zone classification promotes our understanding of the climate and provides a framework for analyzing a range of environmental and socioeconomic data and phenomena. The Köppen–Geiger classification system is the most widely used climate classification scheme. In this study, we compared the climate zones objectively defined using data-driven methods with Köppen–Geiger rule-based classification. Cluster analysis was used to objectively delineate the world’s climatic regions. We applied three clustering algorithms—k-means, ISODATA, and unsupervised random forest classification—to a dataset comprising 10 climatic variables and elevation; we then compared the obtained results with those from the Köppen–Geiger classification system. Results from both the systems were similar for some climatic regions, especially extreme temperature ones such as the tropics, deserts, and polar regions. Data-driven classification identified novel climatic regions that the Köppen–Geiger classification could not. Refinements to the Köppen–Geiger classification, such as precipitation-based subdivisions to existing Köppen–Geiger climate classes like tropical rainforest (Af) and warm summer continental (Dfb), have been suggested based on clustering results. Climatic regions objectively defined by data-driven methods can further the current understanding of climate divisions. On the other hand, rule-based systems, such as the Köppen–Geiger classification, have an advantage in characterizing individual climates. In conclusion, these two approaches can complement each other to form a more objective climate classification system, wherein finer details can be provided by data-driven classification and supported by the intuitive structure of rule-based classification.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42967435","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}
M. Qin, Liting Zhang, Shiquan Wan, Yuan Yue, Qiong Wu, Lu Xia
The spatiotemporal characteristics of dry-wet trends were identified and assessed, and the dominant meteorological factors were identified for the climate of Jiangsu province in humid southeastern China for the period 1960–2019. We conducted the research using data for the entire Jiangsu province as well as three major regions in Jiangsu (Huaibei, Jianghuai, and Sunan) with different regional climates. The results showed that decreased precipitation and relative humidity in spring and autumn over the study period were mainly responsible for the dry trends of the climates of all three regions and the entire province. Precipitation had a greater influence in spring and relative humidity in autumn. Decreases in sunshine hours and wind speed were responsible for the summer wet trends of the climates of Huaibei and Jianghuai and the entire province. However, precipitation increased significantly in the summer and was responsible for the increasing wet trend in Sunan. Significantly increased precipitation in winter was primarily responsible for the increasing wetness in Jianghuai and Sunan and the entire province in that season. However, the wet trend in northern Huaibei in winter was mainly caused by the decrease in wind speed over the study period. For the growing season and annually, the positive effects of changes in wind speed, sunshine hours, and precipitation led to increased humidity index in Jianghuai, Sunan, and the entire province. Precipitation showed a decreasing trend that countered the positive effects of decreases in wind speed and sunshine hours, which resulted in a slight decrease in the humidity index in Huaibei for both the growing season and annually. Sensitivity analysis indicated that the humidity index was positively sensitive to precipitation and relative humidity and negatively sensitive to air temperature, wind speed, and sunshine hours in Jiangsu province during 1960–2019. Overall, the humidity index in this region of southeastern China was most sensitive to changes in precipitation followed, in order of sensitivity, by sunshine hours, air temperature, wind speed, and relative humidity. Our findings provide a theoretical basis for adjusting irrigation programs and efficient utilization of water resources at the regional scale in humid southeastern China.
{"title":"Spatiotemporal Climate Variation and Analysis of Dry-Wet Trends for 1960–2019 in Jiangsu Province, Southeastern China","authors":"M. Qin, Liting Zhang, Shiquan Wan, Yuan Yue, Qiong Wu, Lu Xia","doi":"10.1155/2022/9183882","DOIUrl":"https://doi.org/10.1155/2022/9183882","url":null,"abstract":"The spatiotemporal characteristics of dry-wet trends were identified and assessed, and the dominant meteorological factors were identified for the climate of Jiangsu province in humid southeastern China for the period 1960–2019. We conducted the research using data for the entire Jiangsu province as well as three major regions in Jiangsu (Huaibei, Jianghuai, and Sunan) with different regional climates. The results showed that decreased precipitation and relative humidity in spring and autumn over the study period were mainly responsible for the dry trends of the climates of all three regions and the entire province. Precipitation had a greater influence in spring and relative humidity in autumn. Decreases in sunshine hours and wind speed were responsible for the summer wet trends of the climates of Huaibei and Jianghuai and the entire province. However, precipitation increased significantly in the summer and was responsible for the increasing wet trend in Sunan. Significantly increased precipitation in winter was primarily responsible for the increasing wetness in Jianghuai and Sunan and the entire province in that season. However, the wet trend in northern Huaibei in winter was mainly caused by the decrease in wind speed over the study period. For the growing season and annually, the positive effects of changes in wind speed, sunshine hours, and precipitation led to increased humidity index in Jianghuai, Sunan, and the entire province. Precipitation showed a decreasing trend that countered the positive effects of decreases in wind speed and sunshine hours, which resulted in a slight decrease in the humidity index in Huaibei for both the growing season and annually. Sensitivity analysis indicated that the humidity index was positively sensitive to precipitation and relative humidity and negatively sensitive to air temperature, wind speed, and sunshine hours in Jiangsu province during 1960–2019. Overall, the humidity index in this region of southeastern China was most sensitive to changes in precipitation followed, in order of sensitivity, by sunshine hours, air temperature, wind speed, and relative humidity. Our findings provide a theoretical basis for adjusting irrigation programs and efficient utilization of water resources at the regional scale in humid southeastern China.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46277392","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}
Trend analysis of hydrometeorological data is vital for proper water resources planning and management. This paper examines the trends of the hydrometeorological data in Gilgel Gibe catchment and whether the trends are significant. Daily rainfall, temperature, and streamflow data of the stations in/around (nearby) the catchment (7 stations for rainfall, 4 stations for temperatures, and 6 stations for streamflow) for a period longer than 25 years were collected and then analyzed to detect the variability and the changes in trend. Prior to conducting trend tests, the missed data were filled, and their inconsistencies were also adjusted. The nonparametric Mann-Kendall test along with Sen’s slope technique was employed to detect monotonic trends in the data series. The results showed that, on average, the rainfall exhibits an insignificant increasing tendency. It was also observed that there is, in general, an increasing trend in temperature (both maximum and minimum) in the study area. The analysis of the stream flows indicated that only one station (Bulbul Nr. Serbo) showed a positive slope at a 5% significance level. Two stations (Aweitu Nr. Babu and Gibe Nr. Seka) showed a slightly increasing trend, whereas the remaining 3 stations (Gibe Nr. Assendabo, Aweitu at Jimma, and Kitto Nr. Jimma) indicated an insignificant decreasing trend. The streamflow of the catchment generally shows a tiny decreasing tendency (0.007% per year) at its outlet. However, the results in general specified statistically insignificant trend changes of the hydrometrological data of the study catchment.
水文气象数据的趋势分析对于合理规划和管理水资源至关重要。本文探讨了吉尔吉尔集水区水文气象资料的变化趋势及其是否显著。收集集水区内外(附近)站点(7个降雨量站点、4个温度站点、6个流量站点)25年以上的日降雨量、气温和流量数据，分析其变率和趋势变化。在进行趋势测试之前，对缺失的数据进行了填充，并对其不一致性进行了调整。采用非参数Mann-Kendall检验和Sen斜率技术来检测数据序列的单调趋势。结果表明，平均而言，降雨量呈不显著的增加趋势。还观察到，总的来说，研究地区的温度(最高和最低温度)都有上升的趋势。径流分析表明，只有一个站点(Bulbul Nr. Serbo)在5%的显著水平上呈现正斜率。2个站点(Aweitu Nr. Babu和Gibe Nr. Seka)呈轻微上升趋势，其余3个站点(Gibe Nr. Assendabo、Aweitu at Jimma和Kitto Nr. Jimma)呈不显著下降趋势。汇水口的流量总体上呈每年0.007%的微小下降趋势。但总体而言，研究流域水文气象资料的趋势变化在统计上不显著。
{"title":"Trend Analysis of Hydrometeorological Data of Gilgel Gibe Catchment, Ethiopia","authors":"Zeinu Ahmed Rabba","doi":"10.1155/2022/7485270","DOIUrl":"https://doi.org/10.1155/2022/7485270","url":null,"abstract":"Trend analysis of hydrometeorological data is vital for proper water resources planning and management. This paper examines the trends of the hydrometeorological data in Gilgel Gibe catchment and whether the trends are significant. Daily rainfall, temperature, and streamflow data of the stations in/around (nearby) the catchment (7 stations for rainfall, 4 stations for temperatures, and 6 stations for streamflow) for a period longer than 25 years were collected and then analyzed to detect the variability and the changes in trend. Prior to conducting trend tests, the missed data were filled, and their inconsistencies were also adjusted. The nonparametric Mann-Kendall test along with Sen’s slope technique was employed to detect monotonic trends in the data series. The results showed that, on average, the rainfall exhibits an insignificant increasing tendency. It was also observed that there is, in general, an increasing trend in temperature (both maximum and minimum) in the study area. The analysis of the stream flows indicated that only one station (Bulbul Nr. Serbo) showed a positive slope at a 5% significance level. Two stations (Aweitu Nr. Babu and Gibe Nr. Seka) showed a slightly increasing trend, whereas the remaining 3 stations (Gibe Nr. Assendabo, Aweitu at Jimma, and Kitto Nr. Jimma) indicated an insignificant decreasing trend. The streamflow of the catchment generally shows a tiny decreasing tendency (0.007% per year) at its outlet. However, the results in general specified statistically insignificant trend changes of the hydrometrological data of the study catchment.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43479088","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}
Understanding hydroclimatic variability and trend for the past four decades in the Upper Tekeze River basin is significant for future sustainable water resource management as it indicates regime shifts in hydrology. Despite its importance for improved and sustainable water allocation for water supply-demand and food security, varying patterns of streamflow and their association with climate change are not well understood in the basin. The main objective of this study was to characterize, quantify, and validate the variability and trends of hydroclimatic variables in the Upper Tekeze River basin at Ghba subbasin using graphical and statistical methods for homogeneous stations for the time period from 1953 to 2017, not uniform at all stations. The rainfall, temperature, and streamflow trends and their relationships were evaluated using the regression method, Mann–Kendall (MK) test, Spearman’s rho (SR) test, Sen’s slope, and correlation analysis. The analysis focused on rainfall, temperature, and streamflow collected from 11 climate and six hydrostations. For simplicity to discuss the interannual and temporal variability the stations were categorized into two clusters according to their record length, category 1 (1983–2017) and category 2 (1953–2017). About 73% and 27% of the rainfall stations exhibited normal to moderate annual rainfall variability. The MK and SR test showed that most of the significant trends in annual rainfall were no change except in one station decreasing and the test also showed no significant change in temperature except in three stations showed an increasing trend. Overall, streamflow trends and change point timings were found to be consistent among the stations and all have shown a decreasing trend. Changes in streamflow without significant change in rainfall suggest factors other than rainfall drive the change. Most likely the observed changes in streamflow regimes could be due to changes in catchment characteristics of the subbasin. These research results offer critical signals on the characteristics, variability and trend of rainfall, temperature, and streamflow necessary to design improved and sustainable water allocation strategies.
{"title":"Hydroclimatic Variability, Characterization, and Long Term Spacio-Temporal Trend Analysis of the Ghba River Subbasin, Ethiopia","authors":"Mehari Gebreyohannes Hiben, Admasu Gebeyehu Awoke, Abraha Adugna Ashenafi","doi":"10.1155/2022/3594641","DOIUrl":"https://doi.org/10.1155/2022/3594641","url":null,"abstract":"Understanding hydroclimatic variability and trend for the past four decades in the Upper Tekeze River basin is significant for future sustainable water resource management as it indicates regime shifts in hydrology. Despite its importance for improved and sustainable water allocation for water supply-demand and food security, varying patterns of streamflow and their association with climate change are not well understood in the basin. The main objective of this study was to characterize, quantify, and validate the variability and trends of hydroclimatic variables in the Upper Tekeze River basin at Ghba subbasin using graphical and statistical methods for homogeneous stations for the time period from 1953 to 2017, not uniform at all stations. The rainfall, temperature, and streamflow trends and their relationships were evaluated using the regression method, Mann–Kendall (MK) test, Spearman’s rho (SR) test, Sen’s slope, and correlation analysis. The analysis focused on rainfall, temperature, and streamflow collected from 11 climate and six hydrostations. For simplicity to discuss the interannual and temporal variability the stations were categorized into two clusters according to their record length, category 1 (1983–2017) and category 2 (1953–2017). About 73% and 27% of the rainfall stations exhibited normal to moderate annual rainfall variability. The MK and SR test showed that most of the significant trends in annual rainfall were no change except in one station decreasing and the test also showed no significant change in temperature except in three stations showed an increasing trend. Overall, streamflow trends and change point timings were found to be consistent among the stations and all have shown a decreasing trend. Changes in streamflow without significant change in rainfall suggest factors other than rainfall drive the change. Most likely the observed changes in streamflow regimes could be due to changes in catchment characteristics of the subbasin. These research results offer critical signals on the characteristics, variability and trend of rainfall, temperature, and streamflow necessary to design improved and sustainable water allocation strategies.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44431796","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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