{"title":"ESTIMATION OF PARTICULATE MATTER LESS THAN 10 MICRONS VOLUME THROUGH VARIOUS FORMATS OF SPATIAL INTERPOLATION METHODS","authors":"Jumpol Itsarawisut, T. Laosuwan","doi":"10.21163/gt_2022.172.03","DOIUrl":"https://doi.org/10.21163/gt_2022.172.03","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42542864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aditya Saputra, A. Sigit, Y. Priyana, Andy Muhammad Abror, A. Sari, Oky Nursetiyani
{"title":"A LOW-COST DRONE MAPPING AND SIMPLE PARTICIPATORY GIS TO SUPPORT THE URBAN FLOOD MODELLING","authors":"Aditya Saputra, A. Sigit, Y. Priyana, Andy Muhammad Abror, A. Sari, Oky Nursetiyani","doi":"10.21163/gt_2022.172.04","DOIUrl":"https://doi.org/10.21163/gt_2022.172.04","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43266707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fitriana Nur Indah Sari, D. Sugianto, Kunarso Kunarso
{"title":"EARLY WARNING SYSTEM BASED ON HISTORICAL COASTAL FLOOD EVENTS IN SEMARANG CITY, INDONESIA","authors":"Fitriana Nur Indah Sari, D. Sugianto, Kunarso Kunarso","doi":"10.21163/gt_2022.172.02","DOIUrl":"https://doi.org/10.21163/gt_2022.172.02","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48470525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Prasanchum, Nutthasit Tumma, Worapong Lohpaisankrit
: Due to global climate change, drought phenomena are becoming longer. As a result, water availability is insufficient to meet demand for agriculture during the cultivation seasons and many areas have affected by drought. These can cause difficulties in water resources management. The main objectives of this study were to establish the spatial distribution of drought phenomena at sub-basin scales in the Lam Khan Chu and Lam Nam Chi Part II watersheds, which is the major headwater of the Chi River Basin, Thailand. The Soil and Water Assessment Tool (SWAT) model was used to synthesize streamflow data at the sub-basins of the watersheds. To estimate drought severity, the Streamflow Drought Index (SDI) was computed from the time series of the synthesized streamflow data during 2008 to 2019. The simulated streamflow of the SWAT model was acceptable based on the R 2 , Nash-Sutcliffe Efficiency (NSE) and Percent bias (PBIAS) reaching good levels in both calibration and verification. According to the SDI calculation, the most severe drought in the study period was in the year 2016. During January and April of 2016, the affected areas of about 3%, 30%, 14%, 30 and 23% of the study area were identified as none, mild, moderate, severe and extreme droughts, respectively. The integration of the hydrological model and drought index to monitor drought severity in space and time will be a useful tool for assessing drought severity. Moreover, the results can support managers for water resources management.
：由于全球气候变化，干旱现象越来越长。因此，水资源不足以满足耕种季节的农业需求，许多地区受到干旱的影响。这些可能会给水资源管理带来困难。本研究的主要目的是建立Lam Khan Chu和Lam Nam Chi第二部分流域的次流域尺度干旱现象的空间分布，该流域是泰国池河流域的主要源头。土壤和水评估工具（SWAT）模型用于综合流域子流域的流量数据。为了估计干旱严重程度，径流干旱指数（SDI）是根据2008年至2019年期间合成径流数据的时间序列计算得出的。基于R2、Nash-Sutcliffe效率（NSE）和百分比偏差（PBIAS）在校准和验证中均达到良好水平，SWAT模型的模拟流量是可接受的。根据SDI的计算，研究期间最严重的干旱发生在2016年。2016年1月和4月，研究区域约3%、30%、14%、30%和23%的受影响区域分别被确定为无干旱、轻度、中度、重度和极端干旱。将水文模型和干旱指数结合起来，在空间和时间上监测干旱的严重程度，将是评估干旱严重程度的有用工具。此外，研究结果可以支持管理人员进行水资源管理。
{"title":"ESTABLISHING SPATIAL DISTRIBUTIONS OF DROUGHT PHENOMENA ON CULTIVATION SEASONS USING THE SWAT MODEL","authors":"H. Prasanchum, Nutthasit Tumma, Worapong Lohpaisankrit","doi":"10.21163/gt_2022.172.01","DOIUrl":"https://doi.org/10.21163/gt_2022.172.01","url":null,"abstract":": Due to global climate change, drought phenomena are becoming longer. As a result, water availability is insufficient to meet demand for agriculture during the cultivation seasons and many areas have affected by drought. These can cause difficulties in water resources management. The main objectives of this study were to establish the spatial distribution of drought phenomena at sub-basin scales in the Lam Khan Chu and Lam Nam Chi Part II watersheds, which is the major headwater of the Chi River Basin, Thailand. The Soil and Water Assessment Tool (SWAT) model was used to synthesize streamflow data at the sub-basins of the watersheds. To estimate drought severity, the Streamflow Drought Index (SDI) was computed from the time series of the synthesized streamflow data during 2008 to 2019. The simulated streamflow of the SWAT model was acceptable based on the R 2 , Nash-Sutcliffe Efficiency (NSE) and Percent bias (PBIAS) reaching good levels in both calibration and verification. According to the SDI calculation, the most severe drought in the study period was in the year 2016. During January and April of 2016, the affected areas of about 3%, 30%, 14%, 30 and 23% of the study area were identified as none, mild, moderate, severe and extreme droughts, respectively. The integration of the hydrological model and drought index to monitor drought severity in space and time will be a useful tool for assessing drought severity. Moreover, the results can support managers for water resources management.","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49073120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"3D MODELLING OF BULDINGS AND URBAN AREAS USING GRASSHOPPER AND RHINOCERSOS","authors":"D. Costantino, Arianna Grimaldi, M. Pepe","doi":"10.21163/gt_2022.171.13","DOIUrl":"https://doi.org/10.21163/gt_2022.171.13","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42441526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Maulida, A. Wirasatriya, D. Ismunarti, A. Puryajati
Marine heatwave (MHW) is an extreme phenomenon of warm sea surface temperature anomaly that has a destructive impact on the marine ecosystem and organisms. This phenomenon increases every year in duration, frequency, and area due to the global warming. Almost all of the world's oceans have experienced MHW, including in the seas of the southern Java-Nusa Tenggara. The lack of detailed research in this area motivated us to analyze MHW drivers. MHW was identified with the 99th percentile method and duration of ≥ 5 days as a threshold by using blended product of SST data. In 2013, an MHW event was identified in the seas of the southern Java-Nusa Tenggara. This MHW lasts for 37 days, from 8 June to 14 July. The maximum (mean) intensity of sea surface temperature anomaly reaches 2.60C (1.83C) above climatology with an average area of 36.53 × 10 km. Locally, we found that probably the positive downward shortwave radiation affected this anomaly. Furthermore, a remote process of the propagation of downwelling Kelvin wave during negative Indian Ocean Dipole may also affect this anomaly. The strong westerly wind along the equatorial Indian ocean forms downwelling Kelvin wave that propagates to the southern Java-Nusa Tenggara increasing SST in this area. Key-words: Marine heatwave, Extreme Sea surface temperature, Downwelling Kelvin wave, Remote Sensing, South Java-Nusa Tenggara
{"title":"PHYSICAL DRIVERS OF THE 2013 MARINE HEATWAVE IN THE SEAS OF THE SOUTHERN JAVA-NUSA TENGGARA","authors":"T. Maulida, A. Wirasatriya, D. Ismunarti, A. Puryajati","doi":"10.21163/gt_2022.171.10","DOIUrl":"https://doi.org/10.21163/gt_2022.171.10","url":null,"abstract":"Marine heatwave (MHW) is an extreme phenomenon of warm sea surface temperature anomaly that has a destructive impact on the marine ecosystem and organisms. This phenomenon increases every year in duration, frequency, and area due to the global warming. Almost all of the world's oceans have experienced MHW, including in the seas of the southern Java-Nusa Tenggara. The lack of detailed research in this area motivated us to analyze MHW drivers. MHW was identified with the 99th percentile method and duration of ≥ 5 days as a threshold by using blended product of SST data. In 2013, an MHW event was identified in the seas of the southern Java-Nusa Tenggara. This MHW lasts for 37 days, from 8 June to 14 July. The maximum (mean) intensity of sea surface temperature anomaly reaches 2.60C (1.83C) above climatology with an average area of 36.53 × 10 km. Locally, we found that probably the positive downward shortwave radiation affected this anomaly. Furthermore, a remote process of the propagation of downwelling Kelvin wave during negative Indian Ocean Dipole may also affect this anomaly. The strong westerly wind along the equatorial Indian ocean forms downwelling Kelvin wave that propagates to the southern Java-Nusa Tenggara increasing SST in this area. Key-words: Marine heatwave, Extreme Sea surface temperature, Downwelling Kelvin wave, Remote Sensing, South Java-Nusa Tenggara","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44182889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"APPLICATION OF 3S TECHNOLOGY IN DISASTER RISK RESEARCH IN THE NORTHERN MOUNTAINOUS REGION OF VIETNAM","authors":"D. Nguyen, Q. Kieu","doi":"10.21163/gt_2022.171.09","DOIUrl":"https://doi.org/10.21163/gt_2022.171.09","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48128629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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