Author
Listed:
- Hathaichanok Kumgumpol
(Laboratory of Fisheries Oceanography, Fisheries College, Ocean University of China, Qingdao 266003, China
Estuarine and Coastal Dynamics Modeling Laboratory, Faculty of Fisheries, Kasetsart University, Bangkok 10900, Thailand)
- Yang Liu
(Laboratory of Fisheries Oceanography, Fisheries College, Ocean University of China, Qingdao 266003, China
The Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China, Qingdao 266100, China)
- Tanuspong Pokavanich
(Estuarine and Coastal Dynamics Modeling Laboratory, Faculty of Fisheries, Kasetsart University, Bangkok 10900, Thailand)
- Irene D. Alabia
(Arctic Research Center, Hokkaido University, Sapporo 001-0021, Japan)
- Zixu Yin
(Laboratory of Fisheries Oceanography, Fisheries College, Ocean University of China, Qingdao 266003, China)
- Sei-Ichi Saitoh
(Arctic Research Center, Hokkaido University, Sapporo 001-0021, Japan)
- Yongjun Tian
(Laboratory of Fisheries Oceanography, Fisheries College, Ocean University of China, Qingdao 266003, China
The Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China, Qingdao 266100, China)
Abstract
The green mussel ( Perna viridis ) is one of the most commercially-important cultured species along the coast of Thailand. In this study, a suitable aquaculture site-selection model (SASSM) was developed to identify the most suitable areas in the inner part of the Gulf of Thailand (InnerGoT) for green mussel culture. Satellite-derived chlorophyll-a (Chl-a) and hydrodynamic model outputs for sea surface temperature (SST), salinity, maximum water current (MWC), and bathymetry between 2018 and 2019 were used as input to the SASSM. The results show that suitability scores in mussel aquaculture areas were lowest (1–3) during the Southwest (SW) monsoon, rainy season (July–August), and highest (6–7) during the Northeast (NE) monsoon, cold season (November–December). Moderate suitability scores (4–5) were obtained during the monsoon transition from the NE monsoon to the SW monsoon, summer (April–May). The study area was further divided into three zones: the western, central, and eastern regions. The western and eastern parts showed high suitability scores (5–7) while the central zone exhibited low suitability scores (2–4). The model results show a similar pattern to the actual mussel production in the study area. Seasonal events (i.e., flood and dry seasons) were incorporated into the model to examine the seasonal effects on the suitable mussel aquaculture areas. The suitability scores during the SW monsoon in 2018 were more sensitive to changes in SST and salinity relative to 2019. The higher freshwater discharge and lower temperature in 2018 relative to 2019 resulted in the accrual of suitable aquaculture areas. This pattern is consistent with the productions of the green mussel, where higher production was recorded in 2018 (2002.5 t) than in 2019 (410.8 t). However, correlations among atmospheric (air temperature, rainfall, and wind) and oceanographic factors (SST and MWC) were significant in the western and central regions, suggesting that the suitability of green mussel aquaculture in these regions is vulnerable to environmental disturbances. Thus, the SASSM can be a powerful tool in providing useful information on spatial management for marine aquaculture in environmentally-dynamic coastal systems.
Suggested Citation
Hathaichanok Kumgumpol & Yang Liu & Tanuspong Pokavanich & Irene D. Alabia & Zixu Yin & Sei-Ichi Saitoh & Yongjun Tian, 2020.
"Environmental Habitat Mapping of Green Mussel: A GIS-Based Approach for Sustainable Aquaculture in the Inner Gulf of Thailand,"
Sustainability, MDPI, vol. 12(24), pages 1-16, December.
Handle:
RePEc:gam:jsusta:v:12:y:2020:i:24:p:10643-:d:465134
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10643-:d:465134. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.