墨累达令盆地两个月最大水深:使用指南

D. Penton, J. Teng, C. Ticehurst, S. Marvanek, A. Freebairn, J. Vaze, Fathaha Khanam, A. Sengupta
{"title":"墨累达令盆地两个月最大水深:使用指南","authors":"D. Penton, J. Teng, C. Ticehurst, S. Marvanek, A. Freebairn, J. Vaze, Fathaha Khanam, A. Sengupta","doi":"10.36334/modsim.2023.penton","DOIUrl":null,"url":null,"abstract":": The recently released two monthly maximum water depth maps by Teng et al., (2023) provide opportunities for scientists to examine the relationship between hydrological and ecological processes. The depth maps provide a consistent spatial estimate of flood water depth across the Murray–Darling Basin (MDB) over the past 35 years. The product is available from CSIRO’s Data Access Portal at (https://doi.org/10.25919/c5ab-h019) and through web portal (https://map.csiro.easi-eo.solutions/). The dataset including its validation against hydrodynamic models is described in Penton et al. (2023). This abstract provides guidance on how best to use the product to undertake further analysis. We recommend a four-step process to systematically account for the product’s accuracy. First, researchers should confirm with local sources (using web portal) that major floods in the region of interest are visible in the product (were cloud-free during acquisition). Second, most analyses will require around 20 two-monthly images so the model errors converge to a known statistical distribution (e.g. a Laplace or Cauchy distribution). Given enough images, it is then possible to remove the product bias by increasing the flood depth by 0.34 m (the median error estimated in the benchmark set). Third, when estimating the water depths for locations with permanent water storages (especially reservoirs) use a local data-source to infill. For example, infill with depths calculated from observed levels in large reservoirs using bathymetry, which are usually available from the reservoir operator (the bathymetry may need to be digitised). Finally, we recommend calculating the sensitivity of the results and conclusions to scaled depth inputs.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Two-monthly maximum water depth for the Murray�Darling Basin: Usage guidance\",\"authors\":\"D. Penton, J. Teng, C. Ticehurst, S. Marvanek, A. Freebairn, J. Vaze, Fathaha Khanam, A. Sengupta\",\"doi\":\"10.36334/modsim.2023.penton\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": The recently released two monthly maximum water depth maps by Teng et al., (2023) provide opportunities for scientists to examine the relationship between hydrological and ecological processes. The depth maps provide a consistent spatial estimate of flood water depth across the Murray–Darling Basin (MDB) over the past 35 years. The product is available from CSIRO’s Data Access Portal at (https://doi.org/10.25919/c5ab-h019) and through web portal (https://map.csiro.easi-eo.solutions/). The dataset including its validation against hydrodynamic models is described in Penton et al. (2023). This abstract provides guidance on how best to use the product to undertake further analysis. We recommend a four-step process to systematically account for the product’s accuracy. First, researchers should confirm with local sources (using web portal) that major floods in the region of interest are visible in the product (were cloud-free during acquisition). Second, most analyses will require around 20 two-monthly images so the model errors converge to a known statistical distribution (e.g. a Laplace or Cauchy distribution). Given enough images, it is then possible to remove the product bias by increasing the flood depth by 0.34 m (the median error estimated in the benchmark set). Third, when estimating the water depths for locations with permanent water storages (especially reservoirs) use a local data-source to infill. For example, infill with depths calculated from observed levels in large reservoirs using bathymetry, which are usually available from the reservoir operator (the bathymetry may need to be digitised). Finally, we recommend calculating the sensitivity of the results and conclusions to scaled depth inputs.\",\"PeriodicalId\":390064,\"journal\":{\"name\":\"MODSIM2023, 25th International Congress on Modelling and Simulation.\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MODSIM2023, 25th International Congress on Modelling and Simulation.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36334/modsim.2023.penton\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MODSIM2023, 25th International Congress on Modelling and Simulation.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36334/modsim.2023.penton","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

Teng等人(2023)最近发布的两个月最大水深图为科学家研究水文和生态过程之间的关系提供了机会。深度图提供了过去35年来整个墨累-达令盆地(MDB)洪水深度的一致空间估计。该产品可从CSIRO的数据访问门户网站(https://doi.org/10.25919/c5ab-h019)和web门户网站(https://map.csiro.easi-eo.solutions/)获得。该数据集包括其对水动力模型的验证,见Penton等人(2023)。该摘要提供了如何最好地使用产品进行进一步分析的指导。我们建议采用四步流程来系统地考虑产品的准确性。首先,研究人员应该与当地资源(使用门户网站)确认,感兴趣地区的主要洪水在产品中是可见的(在获取期间没有云计算)。其次,大多数分析将需要大约20个两个月的图像,以便模型误差收敛到已知的统计分布(例如拉普拉斯或柯西分布)。给定足够的图像,然后可以通过将洪水深度增加0.34 m(基准集中估计的中位数误差)来消除产品偏差。第三,在估计有永久储水(特别是水库)的地点的水深时,使用当地的数据源进行填充。例如,利用测深法根据大型油藏的观测水平计算深度进行充填,这些数据通常可以从油藏运营商那里获得(测深法可能需要数字化)。最后,我们建议计算结果和结论对缩放深度输入的敏感性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-monthly maximum water depth for the Murray�Darling Basin: Usage guidance
: The recently released two monthly maximum water depth maps by Teng et al., (2023) provide opportunities for scientists to examine the relationship between hydrological and ecological processes. The depth maps provide a consistent spatial estimate of flood water depth across the Murray–Darling Basin (MDB) over the past 35 years. The product is available from CSIRO’s Data Access Portal at (https://doi.org/10.25919/c5ab-h019) and through web portal (https://map.csiro.easi-eo.solutions/). The dataset including its validation against hydrodynamic models is described in Penton et al. (2023). This abstract provides guidance on how best to use the product to undertake further analysis. We recommend a four-step process to systematically account for the product’s accuracy. First, researchers should confirm with local sources (using web portal) that major floods in the region of interest are visible in the product (were cloud-free during acquisition). Second, most analyses will require around 20 two-monthly images so the model errors converge to a known statistical distribution (e.g. a Laplace or Cauchy distribution). Given enough images, it is then possible to remove the product bias by increasing the flood depth by 0.34 m (the median error estimated in the benchmark set). Third, when estimating the water depths for locations with permanent water storages (especially reservoirs) use a local data-source to infill. For example, infill with depths calculated from observed levels in large reservoirs using bathymetry, which are usually available from the reservoir operator (the bathymetry may need to be digitised). Finally, we recommend calculating the sensitivity of the results and conclusions to scaled depth inputs.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信