All Earth Pub Date : 2024-07-21 DOI: 10.1080/27669645.2024.2380223

Tongchun Qin, Chao Zhang, Di Ning, Haigang Wang

引用次数: 0

Identification of nitrate sources and transformation in karst cave water using hydrochemistry and NO 3 − isotopes (δ 15 N/δ 18 O) combined with a Bayesian mixing model 利用水化学和 NO 3 - 同位素（δ 15 N/δ 18 O）结合贝叶斯混合模型确定岩溶洞穴水的硝酸盐来源和转化情况

All Earth Pub Date : 2024-05-23 DOI: 10.1080/27669645.2024.2356138

Dan Su, Zhongfa Zhou, Xiaohuan Gong, Lihui Yan, Shengjun Ding, Hui Dong, Ye Zhang, Xiaoduo Wang

引用次数: 0

Spatio-temporal study on coupling coordination between urbanization and eco-resilience in the Erhai Lake Basin 洱海流域城市化与生态恢复力耦合协调的时空研究

All Earth Pub Date : 2024-02-26 DOI: 10.1080/27669645.2024.2317053

Zhanting Deng, Yuhan Chen, Fengshan Jiang, Yangbin Zhang, Zhiqiang Xie, Yuning Zhang, Lei Zhao

引用次数: 0

Impacts of climate and land use/cover changes on the sustainability of irrigation water in West Africa: a systematic review 气候和土地利用/覆盖变化对西非灌溉水可持续性的影响：系统综述

All Earth Pub Date : 2024-01-31 DOI: 10.1080/27669645.2024.2308371

Gemechu Fufa Arfasa, E. Owusu-Sekyere, D. Doke, Justice Aygei Ampofo

引用次数: 0

Radioactivity distributions and biohazard assessment of coastal marine environments of niger-delta, Nigeria 尼日利亚尼日尔三角洲沿海海洋环境的放射性分布和生物危害评估

All Earth Pub Date : 2023-12-29 DOI: 10.1080/27669645.2023.2299109

Maxwell Omeje, M. Orosun, Godfrey U. Aimua, O. Adewoyin, Soheil Sabri, Hitler Louis, Emmanuel S. Joel, Conrad A. Omohinmin, E. Ahuekwe, P. Isibor, Mojisola R. Usikalu, I. Oha, Nuradeen N. Garba, T. V. Targema

引用次数: 0

Topographic amplification of seismic ground motion at the broad rift zone of SW Ethiopia 埃塞俄比亚西南部宽断裂带地震地面运动的地形放大效应

All Earth Pub Date : 2023-12-23 DOI: 10.1080/27669645.2023.2293282

Anteneh Legesse, T. Mammo

引用次数: 0

Development of rainfall intensity-duration-frequency curves for the Fiji Islands: integrating TRMM-3B42 and measured gauge data with future projections 斐济群岛降雨强度-持续时间-频率曲线的绘制：将 TRMM-3B42 和测量数据与未来预测相结合

All Earth Pub Date : 2023-11-15 DOI: 10.1080/27669645.2023.2278827

Sanjana De Zoysa, H. Perera, M. Gunathilake, Upaka S. Rathnayake

{"title":"Development of rainfall intensity-duration-frequency curves for the Fiji Islands: integrating TRMM-3B42 and measured gauge data with future projections","authors":"Sanjana De Zoysa, H. Perera, M. Gunathilake, Upaka S. Rathnayake","doi":"10.1080/27669645.2023.2278827","DOIUrl":"https://doi.org/10.1080/27669645.2023.2278827","url":null,"abstract":"ABSTRACT This paper explores the utilisation of gauge rainfall and satellite-based precipitation product (SPP)-TRMM3B42, to develop IDF curves for the Fiji Islands. The study compares the application of remote sensing data against rain gauge (RG) data for two main stations, Nadi and Nausori (1991 to 2020). The accuracy of SPPs is evaluated through statistical analysis, employing continuous and categorical evaluation indices. The results indicate that TRMM3B42 tends to overestimate light precipitation and underestimate heavy rainfall in low elevations when compared to rain gauge data. Rainfall intensities derived from satellite data exhibit relative changes within ± 10%. This study also performs future projections. Two greenhouse emission scenarios, Shared Socioeconomic Pathways (SSP) 2–4.5 and 5–8.5, are employed for IDF curve projection. The analysis reveals that changes in IDF curves are more pronounced for short-duration rainfall as compared to high-duration rainfall. Additionally, higher emission scenarios demonstrate greater changes compared to lower scenarios. These findings emphasise the importance of accounting for climate change and future projections in designing urban infrastructure, particularly considering potential urban expansion and human settlements. This study helps in solving design problems associated with urban runoff control and disposal where knowing the rainfall intensities of different return periods with different durations is vital.","PeriodicalId":238835,"journal":{"name":"All Earth","volume":"46 10","pages":"361 - 380"},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271057","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}

引用次数: 0

Time to treat the climate and nature crisis as one indivisible global health emergency 是时候将气候和自然危机视为一个不可分割的全球卫生紧急事件

All Earth Pub Date : 2023-11-03 DOI: 10.1080/27669645.2023.2276497

Kamran Abbasi, Parveen Ali, Virginia Barbour, Thomas Benfield, Kirsten Bibbins-Domingo, Gregory E. Erhabor, Stephen Hancocks, Richard Horton, Laurie Laybourn-Langton, Robert Mash, Peush Sahni, Wadeia Mohammad Sharief, Paul Yonga, Chris Zielinski

引用次数: 0

Optimized Weighted Mean Temperature Model Based on Generalized Regression Neural Network 基于广义回归神经网络的加权平均温度优化模型

All Earth Pub Date : 2023-10-09 DOI: 10.1080/27669645.2023.2262127

Junyu Li, Mingyun Hu, Lilong Liu, Chaolong Yao, Liangke Huang, Tengxu Zhang, Lv Zhou, Fade Chen

{"title":"Optimized Weighted Mean Temperature Model Based on Generalized Regression Neural Network","authors":"Junyu Li, Mingyun Hu, Lilong Liu, Chaolong Yao, Liangke Huang, Tengxu Zhang, Lv Zhou, Fade Chen","doi":"10.1080/27669645.2023.2262127","DOIUrl":"https://doi.org/10.1080/27669645.2023.2262127","url":null,"abstract":"The weighted mean temperature (Tm) is a key parameter to calculate the Global Navigation Satellite System (GNSS)-based precipitable water vapor (PWV). Data fusion provides a solution to depict the characteristics of Tm in detail. However, multi-source heterogeneity, unequal accuracies, and even serious system deviation may lead to unreliable and inconsistent accuracies in the fusion results. We utilize generalized regression neural network (GRNN) to establish an optimized model for the Tm from the stratified numerical weather forecasting products of the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_MESO) of China and the Tm from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data around China from 2016 to 2017. Then an example fusion using the radiosonde (RS) Tm and the optimized Tm is carried out. The results confirm the systematic deviations between GRAPES/ERA5 Tm and RS Tm. After optimization, the bias of GRAPES and ERA5 Tm is almost eliminated, and the root mean squared error (RMSE) decreased by 21.1% and 18.7%, respectively. Compared to RS Tm, the fusion results based on the optimized Tm have good consistencies and unbiased accuracies, and can merge more detailed spatial features than that of a single data source.","PeriodicalId":238835,"journal":{"name":"All Earth","volume":"325 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135043924","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}

引用次数: 0

Future prediction of existing glacial lake’s size in the Himalaya by Markov model and glacial surface topography 基于马尔可夫模型和冰川表面地形的喜马拉雅现有冰湖规模未来预测

All Earth Pub Date : 2023-09-07 DOI: 10.1080/27669645.2023.2256034

L. Mohanty, Vishal Pateswary, Sabyasachi Maiti, Debasis D Mohanty

{"title":"Future prediction of existing glacial lake’s size in the Himalaya by Markov model and glacial surface topography","authors":"L. Mohanty, Vishal Pateswary, Sabyasachi Maiti, Debasis D Mohanty","doi":"10.1080/27669645.2023.2256034","DOIUrl":"https://doi.org/10.1080/27669645.2023.2256034","url":null,"abstract":"ABSTRACT Glacial lakes in the Himalayas are increasing in size and number at an alarming rate, with the majority of lakes concentrated in the eastern and central parts of the mountain range. This research focuses on the future prediction of regional-scale glacial lake size in the entire Himalayan mountain range to avoid future glacial lake outburst flood (GLOF). Here, we used Landsat TM and OLI images, Google Earth imageries, Cartosat DEM, and SRTM DEM, and used some techniques: average lake size change rate, Markov model and glacial surface topography to extract glacial lake size change and future prediction of lake size. A total of 9675 individual glacial lakes were noticed during the study period (1990–2020), out of which the lakes present constantly throughout the study period (with size > 0.01 km2) were added in this study.1This study used two methods (i.e., Markov model and Glacier surface topography) to predict future lake size and identified 191 and 94 critical lakes that are most at risk of enlargement in Markov and glacial surface topography, respectively. Moreover, 443 critical lakes in Himalaya is distributed as 227 in the eastern Himalayas, 195 in the central Himalayas, and 21 in the western Himalayas. The findings suggest that the number and size of glacial lakes will continue to increase, posing an even greater risk of GLOFs in the future.","PeriodicalId":238835,"journal":{"name":"All Earth","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114081771","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}

引用次数: 1

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