• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
The Chesapeake Bay Program Modeling System: Overview and Recommendations for Future Development.切萨皮克湾项目建模系统:概述与未来发展建议
Ecol Modell. 2021 Sep 15;465:1-109635. doi: 10.1016/j.ecolmodel.2021.109635.
2
Valuing Ecological Improvements in the Chesapeake Bay and the Importance of Ancillary Benefits.评估切萨皮克湾的生态改善及附带效益的重要性。
J Benefit Cost Anal. 2018 Spring;9(1):1-26. doi: 10.1017/bca.2017.9. Epub 2017 Jun 15.
3
Metolachlor metabolite (MESA) reveals agricultural nitrate-N fate and transport in Choptank River watershed.甲草胺代谢物(MESA)揭示了切萨皮克湾流域农业硝酸盐氮的归宿和运移。
Sci Total Environ. 2014 Mar 1;473-474:473-82. doi: 10.1016/j.scitotenv.2013.12.017. Epub 2014 Jan 2.
4
Nutrient Improvements in Chesapeake Bay: Direct Effect of Load Reductions and Implications for Coastal Management.切萨皮克湾的营养物改善:削减负荷的直接影响及对沿海管理的启示。
Environ Sci Technol. 2022 Jan 4;56(1):260-270. doi: 10.1021/acs.est.1c05388. Epub 2021 Dec 21.
5
Chesapeake Bay nitrogen fluxes derived from a land-estuarine ocean biogeochemical modeling system: Model description, evaluation, and nitrogen budgets.基于陆地 - 河口 - 海洋生物地球化学建模系统得出的切萨皮克湾氮通量:模型描述、评估及氮收支
J Geophys Res Biogeosci. 2015 Aug;120(8):1666-1695. doi: 10.1002/2015JG002931. Epub 2015 Aug 28.
6
Major point and nonpoint sources of nutrient pollution to surface water have declined throughout the Chesapeake Bay watershed.整个切萨皮克湾流域向地表水排放营养物污染物的主要点源和非点源都已减少。
Environ Res Commun. 2022 May 6;4(4):1-11. doi: 10.1088/2515-7620/ac5db6.
7
Societal benefits of floodplains in the Chesapeake Bay and Delaware River watersheds: Sediment, nutrient, and flood regulation ecosystem services.切萨皮克湾和特拉华河流域洪泛区的社会效益:沉积物、养分及洪水调节生态系统服务
J Environ Manage. 2023 Nov 1;345:118747. doi: 10.1016/j.jenvman.2023.118747. Epub 2023 Aug 19.
8
Development of a multimetric water quality Indicator for tracking progress towards the achievement of Chesapeake Bay water quality standards.开发多指标水质指标,以跟踪切萨皮克湾水质标准实现的进展。
Environ Monit Assess. 2020 Jan 6;192(2):94. doi: 10.1007/s10661-019-7969-z.
9
Planktonic eukaryotes in the Chesapeake Bay: contrasting responses of abundant and rare taxa to estuarine gradients.切萨皮克湾浮游真核生物:丰富和稀有分类单元对河口梯度的对比响应。
Microbiol Spectr. 2024 May 2;12(5):e0404823. doi: 10.1128/spectrum.04048-23. Epub 2024 Apr 12.
10
Assessment of best management practices for improvement of dissolved oxygen in Chesapeake Bay estuary.切萨皮克湾河口溶解氧改善的最佳管理实践评估。
Water Sci Technol. 2001;44(7):173-80.

引用本文的文献

1
Coastal generalized ecosystem model (CGEM) 1.0: Flexible model formulations for simulating complex biogeochemical processes in aquatic ecosystems.沿海广义生态系统模型(CGEM)1.0:用于模拟水生生态系统中复杂生物地球化学过程的灵活模型公式
Ecol Modell. 2024 Oct 1;496. doi: 10.1016/j.ecolmodel.2024.110831.
2
Long-term regional trends of nitrogen and sulfur deposition in the United States from 2002 to 2017.2002年至2017年美国氮和硫沉降的长期区域趋势。
Atmos Chem Phys. 2022 Sep 30;22(19):12749-12767. doi: 10.5194/acp-22-12749-2022.
3
Response of hypoxia to future climate change is sensitive to methodological assumptions.缺氧对未来气候变化的响应对方法学假设很敏感。
Sci Rep. 2024 Jul 30;14(1):17544. doi: 10.1038/s41598-024-68329-3.

本文引用的文献

1
The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation.社区多尺度空气质量(CMAQ)模型5.3版和5.3.1版:系统更新与评估
Geosci Model Dev. 2021 May 20;14:2867-2897. doi: 10.5194/gmd-14-2867-2021.
2
Predicting species distribution: offering more than simple habitat models.预测物种分布:提供的不仅仅是简单的栖息地模型。
Ecol Lett. 2005 Sep;8(9):993-1009. doi: 10.1111/j.1461-0248.2005.00792.x. Epub 2005 Jun 23.
3
Monthly Patterns of Ammonia Over the Contiguous United States at 2-km Resolution.美国本土2公里分辨率下氨的月度分布模式。
Geophys Res Lett. 2021 Mar 8;48(5). doi: 10.1029/2020gl090579.
4
From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?从水文学气象学到河流水质:深度学习模型能否在大陆尺度上预测溶解氧?
Environ Sci Technol. 2021 Feb 16;55(4):2357-2368. doi: 10.1021/acs.est.0c06783. Epub 2021 Feb 3.
5
Effects of reduced shoreline erosion on Chesapeake Bay water clarity.减少海岸线侵蚀对切萨皮克湾水质清澈度的影响。
Sci Total Environ. 2021 May 15;769:145157. doi: 10.1016/j.scitotenv.2021.145157. Epub 2021 Jan 14.
6
Predicting algal blooms: Are we overlooking groundwater?预测藻类水华:我们是否忽视了地下水?
Sci Total Environ. 2021 May 15;769:144442. doi: 10.1016/j.scitotenv.2020.144442. Epub 2021 Jan 6.
7
Seabed Resuspension in the Chesapeake Bay: Implications for Biogeochemical Cycling and Hypoxia.切萨皮克湾的海床再悬浮:对生物地球化学循环和缺氧的影响
Estuaries Coast. 2021;44(1):103-122. doi: 10.1007/s12237-020-00763-8. Epub 2020 Jun 9.
8
Factors driving nutrient trends in streams of the Chesapeake Bay watershed.驱动切萨皮克湾流域溪流中营养物趋势的因素。
J Environ Qual. 2020 Jul;49(4):812-834. doi: 10.1002/jeq2.20101. Epub 2020 Jun 24.
9
The Shallow and Deep Hypothesis: Subsurface Vertical Chemical Contrasts Shape Nitrate Export Patterns from Different Land Uses.浅层和深层假说:地下垂直化学对比塑造了不同土地利用类型的硝酸盐输出模式。
Environ Sci Technol. 2020 Oct 6;54(19):11915-11928. doi: 10.1021/acs.est.0c01340. Epub 2020 Sep 4.
10
Discerning effects of warming, sea level rise and nutrient management on long-term hypoxia trends in Chesapeake Bay.识别变暖、海平面上升和养分管理对切萨皮克湾长期缺氧趋势的影响。
Sci Total Environ. 2020 Oct 1;737:139717. doi: 10.1016/j.scitotenv.2020.139717. Epub 2020 May 26.

切萨皮克湾项目建模系统:概述与未来发展建议

The Chesapeake Bay Program Modeling System: Overview and Recommendations for Future Development.

作者信息

Hood Raleigh R, Shenk Gary W, Dixon Rachel L, Smith Sean M C, Ball William P, Bash Jesse O, Batiuk Rich, Boomer Kathy, Brady Damian C, Cerco Carl, Claggett Peter, de Mutsert Kim, Easton Zachary M, Elmore Andrew J, Friedrichs Marjorie A M, Harris Lora A, Ihde Thomas F, Lacher Iara, Li Li, Linker Lewis C, Miller Andrew, Moriarty Julia, Noe Gregory B, Onyullo George, Rose Kenneth, Skalak Katie, Tian Richard, Veith Tamie L, Wainger Lisa, Weller Donald, Zhang Yinglong Joseph

机构信息

Horn Point Laboratory, University of Maryland Center for Environmental Science, P.O. Box 775, Cambridge, MD 21613, USA.

USGS Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA.

出版信息

Ecol Modell. 2021 Sep 15;465:1-109635. doi: 10.1016/j.ecolmodel.2021.109635.

DOI:10.1016/j.ecolmodel.2021.109635
PMID:34675451
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8525429/
Abstract

The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

摘要

切萨皮克湾是美国大陆最大、生产力最高且生物多样性最丰富的河口湾,为具有文化和经济重要性的物种提供了关键栖息地和自然资源。人口增长以及相关的开发活动和农业集约化带来的压力,导致过多的营养物质和沉积物进入该海湾,对海湾生态系统的健康及其提供的经济服务产生了负面影响。切萨皮克湾项目(CBP)是一个独特的项目,于1983年正式成立,是一个多方利益相关者的合作伙伴关系,旨在指导和促进切萨皮克湾及其流域的恢复。自成立以来,CBP合作伙伴关系一直在开发、更新和应用一个由流域、空气流域和河口模型组成的复杂关联建模系统,作为一种规划工具,为战略管理决策和海湾恢复工作提供信息。本文介绍了2017年CBP建模系统以及美国国家海洋和大气管理局切萨皮克湾办公室开发的更高营养级模型,同时还介绍了2018年一次研讨会提出的具体建议,该研讨会旨在为未来的模型开发提供信息。建议强调需要以更高分辨率模拟流域输入、条件、过程和实践,以提供更好的信息来指导地方营养物质和沉积物管理计划。对流域地貌与河口子区域之间的连通性、河口水动力、流域和河口水质、河口-流域社会生态系统以及生物资源进行更明确和广泛的建模,对于拓宽和改进对目标营养物质和沉积物负荷减少的响应特征至关重要。最后,强调了在各辖区管理人员、科学家、建模人员、支持人员和利益相关者社区之间保持有效合作的价值和重要性。一个开放、协作和透明的过程是迄今为止取得成功的关键要素,并且在CBP合作伙伴关系推进建模系统改进以帮助利益相关者发展新知识、改进管理策略并更好地传达成果时,至关重要。