Pub Date : 2023-02-17DOI: 10.1080/20964471.2023.2171581
P. Wetchayont, C. Ekkawatpanit, Sunsern Rueangrit, Jittawat Manduang
ABSTRACT Bangkok is located in a low land area, and floods frequently occur from rainfall, river discharge, and tides. High-accuracy rainfall data are needed to achieve high-accuracy flood predictions from hydrological models. The main objective of this study is to establish a method that improves the accuracy of precipitation estimates by merging rainfall from three sources: an infrared channel from the Himawari-8 satellite, rain gauges, and ground-based radar observations. This study applied cloud classification and bias correction using rain gauges to discriminate these errors. The bias factors were interpolated using the ordinary kriging (OK) method to fill in the areas of estimated rainfall where no rain gauge was available. The results show that bias correction improved the accuracy of radar and Himawari-8 rainfall estimates before their combination. The merged algorithm was then adopted to produce hourly merged rainfall products (GSR). Compared to the initial estimated product, the GSR is significantly more accurate. The merging algorithm increases the spatial resolution and quality of rainfall estimates and is simple to use. Furthermore, these findings not only reveal the potential and limitations of the merged algorithm but also provide useful information for future retrieval algorithm enhancement.
{"title":"Improvements in rainfall estimation over Bangkok, Thailand by merging satellite, radar, and gauge rainfall datasets with the geostatistical method","authors":"P. Wetchayont, C. Ekkawatpanit, Sunsern Rueangrit, Jittawat Manduang","doi":"10.1080/20964471.2023.2171581","DOIUrl":"https://doi.org/10.1080/20964471.2023.2171581","url":null,"abstract":"ABSTRACT Bangkok is located in a low land area, and floods frequently occur from rainfall, river discharge, and tides. High-accuracy rainfall data are needed to achieve high-accuracy flood predictions from hydrological models. The main objective of this study is to establish a method that improves the accuracy of precipitation estimates by merging rainfall from three sources: an infrared channel from the Himawari-8 satellite, rain gauges, and ground-based radar observations. This study applied cloud classification and bias correction using rain gauges to discriminate these errors. The bias factors were interpolated using the ordinary kriging (OK) method to fill in the areas of estimated rainfall where no rain gauge was available. The results show that bias correction improved the accuracy of radar and Himawari-8 rainfall estimates before their combination. The merged algorithm was then adopted to produce hourly merged rainfall products (GSR). Compared to the initial estimated product, the GSR is significantly more accurate. The merging algorithm increases the spatial resolution and quality of rainfall estimates and is simple to use. Furthermore, these findings not only reveal the potential and limitations of the merged algorithm but also provide useful information for future retrieval algorithm enhancement.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83307933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1080/20964471.2023.2172823
Zhiyuan Yang, Jing Li, J. Hyyppä, J. Gong, Jingbin Liu, Banghui Yang
{"title":"A comprehensive and up-to-date web-based interactive 3D emergency response and visualization system using Cesium Digital Earth: taking landslide disaster as an example","authors":"Zhiyuan Yang, Jing Li, J. Hyyppä, J. Gong, Jingbin Liu, Banghui Yang","doi":"10.1080/20964471.2023.2172823","DOIUrl":"https://doi.org/10.1080/20964471.2023.2172823","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82375844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-13DOI: 10.1080/20964471.2023.2172820
S. Karozis, I. Klampanos, A. Sfetsos, D. Vlachogiannis
ABSTRACT Numerical Weather Prediction (NWP) simulations produce meteorological data in various spatial and temporal scales, depending on the application requirements. In the current study, a deep learning approach, based on convolutional autoencoders, is explored to effectively correct the error of the NWP simulation. An undercomplete convolutional autoencoder (CAE) is applied as part of the dynamic error correction of NWP data. This work is an attempt to improve the seasonal forecast (3–6 months ahead) data accuracy for Greece using a global reanalysis dataset (that incorporates observations, satellite imaging, etc.) of higher spatial resolution. More specifically, the publically available Meteo France Seasonal (Copernicus platform) and the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) (NOAA) datasets are utilized. In addition, external information is used as evidence transfer, concerning the time conditions (month, day, and season) and the simulation characteristics (initialization of simulation). It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting. Interestingly, the month evidence yields the best agreement indicating a seasonal dependence of the performance.
{"title":"A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data","authors":"S. Karozis, I. Klampanos, A. Sfetsos, D. Vlachogiannis","doi":"10.1080/20964471.2023.2172820","DOIUrl":"https://doi.org/10.1080/20964471.2023.2172820","url":null,"abstract":"ABSTRACT Numerical Weather Prediction (NWP) simulations produce meteorological data in various spatial and temporal scales, depending on the application requirements. In the current study, a deep learning approach, based on convolutional autoencoders, is explored to effectively correct the error of the NWP simulation. An undercomplete convolutional autoencoder (CAE) is applied as part of the dynamic error correction of NWP data. This work is an attempt to improve the seasonal forecast (3–6 months ahead) data accuracy for Greece using a global reanalysis dataset (that incorporates observations, satellite imaging, etc.) of higher spatial resolution. More specifically, the publically available Meteo France Seasonal (Copernicus platform) and the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) (NOAA) datasets are utilized. In addition, external information is used as evidence transfer, concerning the time conditions (month, day, and season) and the simulation characteristics (initialization of simulation). It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting. Interestingly, the month evidence yields the best agreement indicating a seasonal dependence of the performance.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82229391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.1080/20964471.2022.2161218
Y. Zhuang, Jingyong Zhang
ABSTRACT Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems. In this study, we focus on the major Belt and Road Initiative (BRI) regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C, 2°C, and 3°C above pre-industrial levels under the Representative Concentration Pathway (RCP) 8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848° (~5 km) globally from EnviDat. The temperature variables include the daily maximum (Tmax), minimum (Tmin) and average (Tmp) surface air temperatures, and the diurnal temperature range (DTR). We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986–2005. The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986–2005. Furthermore, we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C, 2°C, and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean. Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions, and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions. The dataset is available at https://doi.org/10.57760/sciencedb.01850.
{"title":"Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios","authors":"Y. Zhuang, Jingyong Zhang","doi":"10.1080/20964471.2022.2161218","DOIUrl":"https://doi.org/10.1080/20964471.2022.2161218","url":null,"abstract":"ABSTRACT Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems. In this study, we focus on the major Belt and Road Initiative (BRI) regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C, 2°C, and 3°C above pre-industrial levels under the Representative Concentration Pathway (RCP) 8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848° (~5 km) globally from EnviDat. The temperature variables include the daily maximum (Tmax), minimum (Tmin) and average (Tmp) surface air temperatures, and the diurnal temperature range (DTR). We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986–2005. The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986–2005. Furthermore, we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C, 2°C, and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean. Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions, and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions. The dataset is available at https://doi.org/10.57760/sciencedb.01850.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76436878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.1080/20964471.2022.2157093
Yufen Cao, Yuanhao Qu, Jinghui Ma
ABSTRACT Serious regional ozone (O3) pollution often plagues the Yangtze River Delta (YRD). The formation mechanism of these regional pollution events, including the meteorological and emission factors leading to these pollution events and how to affect the distribution of O3, still needs further research and exploration. In this study, we first define the standard of O3 regional pollution in the YRD, and then select 248 regional pollution cases from 2015 to 2020 according to the defined standard. For the pollution cases in pollution months (May and June), PCT (principal component analysis in T-mode) classification method is used to classify the ozone concentration distribution in YRD area. The regional distribution of the O3 concentrations in the YRD is divided into five types, and the overall type (Type 1) accounts for 15%, which is related to the control of YRD area by high-pressure center. Under the control of high pressure, the weather is sunny with the high temperature, and this weather condition is favorable for ozone generation and intercity transmission, causing extensive pollution. The double center type (Type 2) accounts for 8%. This type of YRD is controlled by the front of the high pressure (the high-pressure center is located in North China), and the weather in the middle and north is conducive to the generation and transmission of O3. Inland type (Type 3) accounts for 24%. The main body of this type of high pressure is located in Mongolia. The easterly wind in YRD area is conducive to the inland transmission of O3 precursors. The northern coastal type (Type 4) accounts for 44%. This type of YRD area is mainly controlled by the weak pressure field. The weather in the northern coastal area is sunny and the solar radiation for a long time is conducive to the formation of O3. The southern coastal type (Type 5) accounts for 10%, the solar radiation is strong in the southern region mainly under the influence of the post-offshore high pressure. This study provides new insights into the relationship between O3 pollution distribution types and atmospheric circulation in YRD area, and reveals the difference of potential meteorological impacts of different O3 pollution distribution types.
{"title":"Classification of ozone pollution and analysis of meteorological factors in the Yangtze River Delta","authors":"Yufen Cao, Yuanhao Qu, Jinghui Ma","doi":"10.1080/20964471.2022.2157093","DOIUrl":"https://doi.org/10.1080/20964471.2022.2157093","url":null,"abstract":"ABSTRACT Serious regional ozone (O3) pollution often plagues the Yangtze River Delta (YRD). The formation mechanism of these regional pollution events, including the meteorological and emission factors leading to these pollution events and how to affect the distribution of O3, still needs further research and exploration. In this study, we first define the standard of O3 regional pollution in the YRD, and then select 248 regional pollution cases from 2015 to 2020 according to the defined standard. For the pollution cases in pollution months (May and June), PCT (principal component analysis in T-mode) classification method is used to classify the ozone concentration distribution in YRD area. The regional distribution of the O3 concentrations in the YRD is divided into five types, and the overall type (Type 1) accounts for 15%, which is related to the control of YRD area by high-pressure center. Under the control of high pressure, the weather is sunny with the high temperature, and this weather condition is favorable for ozone generation and intercity transmission, causing extensive pollution. The double center type (Type 2) accounts for 8%. This type of YRD is controlled by the front of the high pressure (the high-pressure center is located in North China), and the weather in the middle and north is conducive to the generation and transmission of O3. Inland type (Type 3) accounts for 24%. The main body of this type of high pressure is located in Mongolia. The easterly wind in YRD area is conducive to the inland transmission of O3 precursors. The northern coastal type (Type 4) accounts for 44%. This type of YRD area is mainly controlled by the weak pressure field. The weather in the northern coastal area is sunny and the solar radiation for a long time is conducive to the formation of O3. The southern coastal type (Type 5) accounts for 10%, the solar radiation is strong in the southern region mainly under the influence of the post-offshore high pressure. This study provides new insights into the relationship between O3 pollution distribution types and atmospheric circulation in YRD area, and reveals the difference of potential meteorological impacts of different O3 pollution distribution types.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78277337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-03DOI: 10.1080/20964471.2022.2148331
Rongrong Zhang, Virgílio A. Bento, Junyu Qi, Feng Xu, Jianjun Wu, Jianxiu Qiu, Jianwei Li, Wei Shui, Qianfeng Wang
ABSTRACT Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), traditionally derived at a monthly scale, are widely used drought indices. To overcome temporal-resolution limitations, we have previously developed and published a well-validated daily SPI/SPEI in situ dataset. Although having a high temporal resolution, this in situ dataset presents low spatial resolution due to the scarcity of stations. Therefore, based on the China Meteorological Forcing Dataset, which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset, we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over China. It spans from 1979 to 2018, with a spatial resolution of 0.1° × 0.1°, a temporal resolution of 1-day, and the timescales of 30-, 90-, and 360-days. Results show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/SPEI. The correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest, with linear correlation, Nash-Sutcliffe coefficient, and normalized root mean square error of 0.98, 0.97, and 0.04, respectively. The daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/SPEI. Our improved SPI/SPEI shows high accuracy and credibility, presenting enhanced results when compared to the monthly SPI/SPEI. The total data volume is up to 150 GB, compressed to 91 GB in Network Common Data Form (NetCDF). It can be available from Figshare (https://doi.org/10.6084/m9.figshare.c.5823533) and ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103).
标准化降水指数(SPI)和标准化降水蒸散指数(SPEI)是目前广泛应用的干旱指标，传统上以月为尺度推导。为了克服时间分辨率的限制，我们之前开发并发布了一个经过良好验证的每日SPI/SPEI原位数据集。虽然具有较高的时间分辨率，但由于站点的稀缺，该原位数据集的空间分辨率较低。基于中国气象强迫数据集(由1000多个地面观测点和多个遥感栅格气象数据集组成)，首次构建了中国地区高时空分辨率的日SPI/SPEI栅格数据集。时间跨度为1979 ~ 2018年，空间分辨率为0.1°× 0.1°，时间分辨率为1天，时间尺度为30天、90天和360天。结果表明，日SPI/SPEI探测的干旱事件特征空间分布与月SPI/SPEI基本一致。12个月量表日值与SPI/SPEI月值相关性最强，呈线性相关，Nash-Sutcliffe系数和标准化均方根误差分别为0.98、0.97和0.04。日SPI/SPEI比月SPI/SPEI对突发性干旱更为敏感。我们改进的SPI/SPEI具有较高的准确性和可信度，与每月SPI/SPEI相比，结果有所增强。数据总量高达150gb, NetCDF (Network Common data Form)格式压缩为91gb。可以从Figshare (https://doi.org/10.6084/m9.figshare.c.5823533)和ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103)获得。
{"title":"The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018","authors":"Rongrong Zhang, Virgílio A. Bento, Junyu Qi, Feng Xu, Jianjun Wu, Jianxiu Qiu, Jianwei Li, Wei Shui, Qianfeng Wang","doi":"10.1080/20964471.2022.2148331","DOIUrl":"https://doi.org/10.1080/20964471.2022.2148331","url":null,"abstract":"ABSTRACT Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), traditionally derived at a monthly scale, are widely used drought indices. To overcome temporal-resolution limitations, we have previously developed and published a well-validated daily SPI/SPEI in situ dataset. Although having a high temporal resolution, this in situ dataset presents low spatial resolution due to the scarcity of stations. Therefore, based on the China Meteorological Forcing Dataset, which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset, we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over China. It spans from 1979 to 2018, with a spatial resolution of 0.1° × 0.1°, a temporal resolution of 1-day, and the timescales of 30-, 90-, and 360-days. Results show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/SPEI. The correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest, with linear correlation, Nash-Sutcliffe coefficient, and normalized root mean square error of 0.98, 0.97, and 0.04, respectively. The daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/SPEI. Our improved SPI/SPEI shows high accuracy and credibility, presenting enhanced results when compared to the monthly SPI/SPEI. The total data volume is up to 150 GB, compressed to 91 GB in Network Common Data Form (NetCDF). It can be available from Figshare (https://doi.org/10.6084/m9.figshare.c.5823533) and ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103).","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85566454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/20964471.2022.2163130
Siqin Wang, Ruomei Wang, Xiao Huang, Zhenlong Li, S. Bao
ABSTRACT COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers’ origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.
{"title":"A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data","authors":"Siqin Wang, Ruomei Wang, Xiao Huang, Zhenlong Li, S. Bao","doi":"10.1080/20964471.2022.2163130","DOIUrl":"https://doi.org/10.1080/20964471.2022.2163130","url":null,"abstract":"ABSTRACT COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers’ origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88790868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/20964471.2022.2160156
Homa Masoumi, S. Shirowzhan, Paria Eskandarpour, C. Pettit
ABSTRACT The emerging field of City Digital Twins has advanced in recent years with the help of digital infrastructure and technologies connected to the Internet of Things (IoT). However, the evolution of this field has been so fast that a gap has opened in relation to systematic reviews of the relevant literature and the maturation of City Digital Twins on an urban scale. Our work bridges this gap by highlighting maturity in the field. We conducted a systematic literature review with bibliometric and content analysis of 41 selected papers published in Web of Science and Scopus databases, covering five areas: data types and sources, case studies, applied technologies and methods, maturity spectrum, and applications. Based on maturity indicators, the majority of the reviewed studies (90%) were at initial to medium stages of maturity (up to element 3), most of them focused on 3D modelling, monitoring and visualisation. However, digital twins cannot be limited to 3D models, monitoring and visualisation, for they can be developed to include two-directional interactions between humans and computers. Such a high level of maturity, which was not found in the reviewed studies, requires advanced technologies and methods such as cloud computing, artificial intelligence, BIM and GIS. We also found that further studies are essential if the field is to handle the complex urban challenges of multidisciplinary digital twins . While City Digital Twins extend by definition beyond mere 3D city modelling, some studies involving 3D city models still refer to their subjects as City Digital Twins. Among the research gaps we identified, we’d like to highlight the need for near-real-time data analytics algorithms, which could furnish City Digital Twins with big data insights. Other opportunities include public participation capabilities to increase social collaboration, integrating BIM and GIS technologies and improving storage and computation infrastructure.
近年来，随着数字基础设施和与物联网(IoT)相关的技术的发展，城市数字孪生(City Digital Twins)这一新兴领域得到了发展。然而，这一领域的发展如此之快，以至于在对相关文献的系统回顾和城市规模的城市数字孪生的成熟方面出现了空白。我们的工作通过突出该领域的成熟度来弥合这一差距。我们对Web of Science和Scopus数据库中发表的41篇论文进行了系统的文献综述和内容分析，涵盖了数据类型和来源、案例研究、应用技术和方法、成熟度谱和应用五个方面。根据成熟度指标，大多数审查的研究(90%)处于成熟度的初始到中期阶段(直到元素3)，其中大多数集中在3D建模，监测和可视化。然而，数字双胞胎不能局限于3D模型、监控和可视化，因为它们可以发展到包括人与计算机之间的双向交互。如此高的成熟度，在所审查的研究中没有发现，需要先进的技术和方法，如云计算、人工智能、BIM和GIS。我们还发现，如果该领域要处理多学科数字孪生的复杂城市挑战，进一步的研究是必不可少的。虽然城市数字双胞胎的定义超越了单纯的3D城市建模，但一些涉及3D城市模型的研究仍然将其研究对象称为城市数字双胞胎。在我们发现的研究差距中，我们想强调对近实时数据分析算法的需求，这可以为城市数字双胞胎提供大数据见解。其他机会包括公众参与能力，以增加社会协作，集成BIM和GIS技术，改善存储和计算基础设施。
{"title":"City Digital Twins: their maturity level and differentiation from 3D city models","authors":"Homa Masoumi, S. Shirowzhan, Paria Eskandarpour, C. Pettit","doi":"10.1080/20964471.2022.2160156","DOIUrl":"https://doi.org/10.1080/20964471.2022.2160156","url":null,"abstract":"ABSTRACT The emerging field of City Digital Twins has advanced in recent years with the help of digital infrastructure and technologies connected to the Internet of Things (IoT). However, the evolution of this field has been so fast that a gap has opened in relation to systematic reviews of the relevant literature and the maturation of City Digital Twins on an urban scale. Our work bridges this gap by highlighting maturity in the field. We conducted a systematic literature review with bibliometric and content analysis of 41 selected papers published in Web of Science and Scopus databases, covering five areas: data types and sources, case studies, applied technologies and methods, maturity spectrum, and applications. Based on maturity indicators, the majority of the reviewed studies (90%) were at initial to medium stages of maturity (up to element 3), most of them focused on 3D modelling, monitoring and visualisation. However, digital twins cannot be limited to 3D models, monitoring and visualisation, for they can be developed to include two-directional interactions between humans and computers. Such a high level of maturity, which was not found in the reviewed studies, requires advanced technologies and methods such as cloud computing, artificial intelligence, BIM and GIS. We also found that further studies are essential if the field is to handle the complex urban challenges of multidisciplinary digital twins . While City Digital Twins extend by definition beyond mere 3D city modelling, some studies involving 3D city models still refer to their subjects as City Digital Twins. Among the research gaps we identified, we’d like to highlight the need for near-real-time data analytics algorithms, which could furnish City Digital Twins with big data insights. Other opportunities include public participation capabilities to increase social collaboration, integrating BIM and GIS technologies and improving storage and computation infrastructure.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77214454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1080/20964471.2022.2146632
M. Jiang, L. Jia, M. Menenti, Yelong Zeng
ABSTRACT The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.
{"title":"Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region","authors":"M. Jiang, L. Jia, M. Menenti, Yelong Zeng","doi":"10.1080/20964471.2022.2146632","DOIUrl":"https://doi.org/10.1080/20964471.2022.2146632","url":null,"abstract":"ABSTRACT The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72539770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1080/20964471.2022.2136610
A. Fullhart, G. Ponce-Campos, M. Meles, Ryan P. McGehee, G. Armendariz, P. S. Oliveira, Cristiano Das Neves Almeida, J. C. de Araújo, W. Nel, D. Goodrich
ABSTRACT CLIGEN is a stochastic weather generator that creates statistically representative timeseries of daily and sub-daily point-scale weather variables from observed monthly statistics and other parameters. CLIGEN precipitation timeseries are used as climate input for various risk-assessment modelling applications as an alternative to observe long-term, high temporal resolution records. Here, we queried gridded global climate datasets (TerraClimate, ERA5, GPM-IMERG, and GLDAS) to estimate various 20-year climate statistics and obtain complete CLIGEN input parameter sets with coverage of the African and South American continents at 0.25 arc degree resolution. The estimation of CLIGEN precipitation parameters was informed by a ground-based dataset of >10,000 locations worldwide. The ground observations provided target values to fit regression models that downscale CLIGEN precipitation input parameters. Aside from precipitation parameters, CLIGEN’s parameters for temperature, solar radiation, etc. were in most cases directly calculated according to the original global datasets. Cross-validation for estimated precipitation parameters quantified errors that resulted from applying the estimation approach in a predictive fashion. Based on all training data, the RMSE was 2.23 mm for the estimated monthly average single-event accumulation and 4.70 mm/hr for monthly maximum 30-min intensity. This dataset facilitates exploration of hydrological and soil erosional hypotheses across Africa and South America.
{"title":"Gridded 20-year climate parameterization of Africa and South America for a stochastic weather generator (CLIGEN)","authors":"A. Fullhart, G. Ponce-Campos, M. Meles, Ryan P. McGehee, G. Armendariz, P. S. Oliveira, Cristiano Das Neves Almeida, J. C. de Araújo, W. Nel, D. Goodrich","doi":"10.1080/20964471.2022.2136610","DOIUrl":"https://doi.org/10.1080/20964471.2022.2136610","url":null,"abstract":"ABSTRACT CLIGEN is a stochastic weather generator that creates statistically representative timeseries of daily and sub-daily point-scale weather variables from observed monthly statistics and other parameters. CLIGEN precipitation timeseries are used as climate input for various risk-assessment modelling applications as an alternative to observe long-term, high temporal resolution records. Here, we queried gridded global climate datasets (TerraClimate, ERA5, GPM-IMERG, and GLDAS) to estimate various 20-year climate statistics and obtain complete CLIGEN input parameter sets with coverage of the African and South American continents at 0.25 arc degree resolution. The estimation of CLIGEN precipitation parameters was informed by a ground-based dataset of >10,000 locations worldwide. The ground observations provided target values to fit regression models that downscale CLIGEN precipitation input parameters. Aside from precipitation parameters, CLIGEN’s parameters for temperature, solar radiation, etc. were in most cases directly calculated according to the original global datasets. Cross-validation for estimated precipitation parameters quantified errors that resulted from applying the estimation approach in a predictive fashion. Based on all training data, the RMSE was 2.23 mm for the estimated monthly average single-event accumulation and 4.70 mm/hr for monthly maximum 30-min intensity. This dataset facilitates exploration of hydrological and soil erosional hypotheses across Africa and South America.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74323605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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