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Twomey AJ, Nunez K, Carr JA, Crooks S, Friess DA, Glamore W, Orr M, Reef R, Rogers K, Waltham NJ, Lovelock CE. Planning hydrological restoration of coastal wetlands: Key model considerations and solutions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:169881. [PMID: 38190895 DOI: 10.1016/j.scitotenv.2024.169881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/22/2023] [Accepted: 01/01/2024] [Indexed: 01/10/2024]
Abstract
The hydrological restoration of coastal wetlands is an emerging approach for mitigating and adapting to climate change and enhancing ecosystem services such as improved water quality and biodiversity. This paper synthesises current knowledge on selecting appropriate modelling approaches for hydrological restoration projects. The selection of a modelling approach is based on project-specific factors, such as costs, risks, and uncertainties, and aligns with the overall project objectives. We provide guidance on model selection, emphasising the use of simpler and less expensive modelling approaches when appropriate, and identifying situations when models may not be required for project managers to make informed decisions. This paper recognises and supports the widespread use of hydrological restoration in coastal wetlands by bridging the gap between hydrological science and restoration practices. It underscores the significance of project objectives, budget, and available data and offers decision-making frameworks, such as decision trees, to aid in matching modelling methods with specific project outcomes.
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Affiliation(s)
- Alice J Twomey
- School of the Environment, The University of Queensland, St. Lucia, QLD 4072, Australia.
| | - Karinna Nunez
- Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA 23062, USA
| | - Joel A Carr
- U.S. Geological Survey, Eastern Ecological Science Center, USA
| | - Steve Crooks
- Silvestrum Climate Associates, LLC, Sausalito, CA 94165, USA
| | - Daniel A Friess
- Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA 70118, USA
| | - William Glamore
- Water Research Laboratory, School of Civil and Environmental Engineering, UNSW, Sydney, NSW, Australia
| | - Michelle Orr
- Silvestrum Climate Associates, LLC, Sausalito, CA 94165, USA; Environmental Science Associates, 575 Market Street, Suite 3700, San Francisco, CA 94105, USA
| | - Ruth Reef
- School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia
| | - Kerrylee Rogers
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia
| | - Nathan J Waltham
- Centre for Tropical Water and Aquatic Ecosystem Research, James Cook University, Townsville, QLD, Australia
| | - Catherine E Lovelock
- School of the Environment, The University of Queensland, St. Lucia, QLD 4072, Australia
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Rafei A, Danehkar A, Goodarzi MS. Linear programming the Ramsar convention's criterion IV (case study: Shadegan Wetland, West Asia). ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1194. [PMID: 37698676 DOI: 10.1007/s10661-023-11809-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 08/30/2023] [Indexed: 09/13/2023]
Abstract
Wetland ecosystems provide critical ecological services to both humans and wildlife. However, many wetlands around the world are facing challenges that threaten their ecological integrity and jeopardize their status as Ramsar Convention sites. The Shadegan Wetland, a Ramsar site since 1975, has been on the Montreux Record since 1993 due to changing conditions in the wetland. This study aims to utilize linear programming (LP) techniques to evaluate the status of criterion IV of the Ramsar Convention within the Shadegan Wetland. Using mathematical language and Excel software, we defined criterion IV and developed a linear model. The Lingo software was employed to verify the model by setting constraints for proxy variables (X variables). We selected constraints based on extreme climatic conditions, such as energy and water limitations, during the study period while considering the trend of each variable. By identifying effective interventions for promoting sustainable use of the wetland while preserving its ecological balance, the LP can support the efforts to re-nominate the Shadegan Wetland as a Ramsar site. Considering the critical conditions, the lowest value of Z in the studied period unravels the critical year as the target. Based on the result, 2015 with the lowest value of the Z index (- 0.36) was identified as the critical year in the entire study period starting from 2001-2019. In the critical year itself, the population of birds equals 50,000 birds, while the average population of birds over the course of the past 20 years was nearly 37,000 birds.
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Affiliation(s)
- Asma Rafei
- Department of Environment Sciences, Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | - Afshin Danehkar
- Department of Environment Sciences, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
| | - Mehdi Sheikh Goodarzi
- Department of Environment Sciences, Faculty of Natural Resources, University of Tehran, Karaj, Iran
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Liu L, Song W, Zhang Y, Han Z, Li H, Yang D, Wang Z, Huang Q. Zoning of Ecological Restoration in the Qilian Mountain Area, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182312417. [PMID: 34886142 PMCID: PMC8656991 DOI: 10.3390/ijerph182312417] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022]
Abstract
Ecosystem restoration has been widely concerned with the damage and degradation of ecosystems worldwide. Scientific and reasonable formulations of ecological restoration zoning is the basis for the formulation of an ecological restoration plan. In this study, a restoration zoning index system was proposed to comprehensively consider the ecological problems of ecosystems. The linear weighted function method was used to construct the ecological restoration index (ERI) as an important index of zoning. The research showed that: (1) the ecological restoration zones of the Qilian Mountains can be divided into eight basins, namely the headwaters of the Datong River Basin, the Danghe-Dahaerteng River Basin, the northern confluence area of the Qinghai Lake, the upper Shule River to middle Heihe River, the Oasis Agricultural Area in the northern foothills of the Qilian Mountain, the Huangshui Basin Valley, Aksay (corridor region of the western Hexi Basin), and the northeastern Tsaidam Basin; (2) the restoration index of the eight ecological restoration zones of the Qilian Mountains was between 0.34–0.8, with an average of 0.61 (the smaller the index, the more prominent the comprehensive ecological problem representing the regional mountains, rivers, forests, cultivated lands, lakes, and grasslands, and thus the greater the need to implement comprehensive ecological protection and restoration projects); and (3) the ecological problems of different ecological zones are frequently numerous, and often show the phenomenon of multiple overlapping ecological problems in the same zone.
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Affiliation(s)
- Lin Liu
- School of Land Science and Space Planning, Hebei GEO University, Shijiazhuang 050031, China;
| | - Wei Song
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Y.Z.); (Z.H.); (H.L.); (D.Y.); (Z.W.)
- Correspondence:
| | - Yanjie Zhang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Y.Z.); (Z.H.); (H.L.); (D.Y.); (Z.W.)
| | - Ze Han
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Y.Z.); (Z.H.); (H.L.); (D.Y.); (Z.W.)
| | - Han Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Y.Z.); (Z.H.); (H.L.); (D.Y.); (Z.W.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Dazhi Yang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Y.Z.); (Z.H.); (H.L.); (D.Y.); (Z.W.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Zhanyun Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Y.Z.); (Z.H.); (H.L.); (D.Y.); (Z.W.)
- Institute of Mountain Hazards and Environment, Chinese Academy of Sciences and Ministry of Water Conservancy, Chengdu 610041, China
| | - Qiang Huang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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