표제지
목차
요약문 5
SUMMARY 6
제1장 연구개발과제의 개요 9
제1절 연구개발의 목적 및 필요성 9
1. 연구 목적 9
2. 연구의 필요성 9
제2장 국내외 기술개발 현황 10
1. 국내 기술개발 현황 10
2. 국외 기술개발 현황 10
제3장 연구개발 수행내용 및 결과 11
제1절 해양생태계 및 수산자원 변동 요인 분석 연구 11
1. 통계분석 기법을 활용한 어장변동파악 및 요인 분석 11
2. 해양ㆍ생태계 모델/위성자료를 활용한 어장변동 파악 15
3. 통계기반 생태계 수산자원 변동 예측 17
4. 부어류 수산자원 서식특성 및 회유경로 파악 21
제2절 수치모델 기반 다중 해양변동 및 생태계변동 결합 예측 시스템 구축 25
1. 가용 해양변동 수치모델 재현ㆍ예측자료 수집 및 활용 기반 구축 25
2. 국립수산과학원 고해상도 해양변동 예측 성능 고도화 기반 마련 26
3. 국립수산과학원 중ㆍ단기 해양변동 예측성능 고도화 26
4. 다중 해양변동 예측모델 및 생태계 변동 예측모델 연계 33
제3절 해양생태계 변동 기반 수산자원 예측체계 구축 33
1. 해양먹이망 기반 해양생태계 변동 예측시스템 설계 33
2. 해양생태계 변동 기반 수산자원 예측체계 개발 43
3. 해양생태계 변동 기반 수산자원 예측체계 구축 및 시험적용 53
4. 해양생태계 변동 기반 수산자원 예측체계 고도화 59
5. 해양생태계 변동 기반 수산자원 예측체계 현행화 66
제4장 목표달성도 및 관련분야에의 기여도 74
1. 목표 달성도 74
2. 대표성과 및 기여도 74
제5장 연구개발결과의 활용계획 75
1. 추가연구의 필요성 및 계획 75
2. 기대효과 및 활용계획 75
제6장 참고문헌 76
제7장 부록 79
판권기 80
Table 1. Environmental variables for statistical analysis 11
Table 2. Collected dataset of numerical models 25
Table 3. RMSE of model result 27
Table 4. RMSE of model result (2021. 4. 1.~7. 9.) 29
Table 5. RMSE of model result and bias-corrected model result 31
Table 6. Examples of ecological model utilization in other countries 34
Table 7. Comparison of variables and mechanisms of major structural models 35
Table 8. Classification of Atlantis model input file 36
Table 9. Input parameter of Atlantis model 37
Table 10. Functional group for food-web in Atlantis model 38
Table 11. Results of selection of functional groups 40
Table 12. Water quality parameters used in the Atlantis model (EPA, 1985) 42
Table 13. The main biological mechanisms of the Atlantis model 43
Table 14. Functional group in the Atlantis model 45
Table 15. Initial conditions of Chub mackerel biomass 46
Table 16. Initial conditions of anchovy biomass 46
Table 17. Input data of Ecopath model (i.e. ecosystem in the southern sea of the Korean peninsula) 47
Table 18. Trophic level (TL) of Ecopath model in the southern sea of the Korean peninsula 48
Table 19. Biomass in the Korean waters estimated by Biomass size spectrum (BSS) model in 2018 55
Table 20. Initial conditions by functional groups 56
Table 21. Ecological Parameters (DB) of function group 56
Table 22. Scenario of biomass change according to Chub mackerel closed period 58
Table 23. Comparisons of phytoㆍzooplankton biomass by size 61
Table 24. Estimated biomass using BSS model 61
Table 25. Trophic level (TL) of major commerical fish species 62
Table 26. Trophic level (TL) of major groups 62
Table 27. Scenario of Chub mackerel considering closed fishing season 63
Table 28. Scenario of Chub mackerel considering the application ration of TAC and catch ration by groups 64
Table 29. Scenario of Chub mackerel migration routes by background seawater temperature condition 65
Table 30. Biomass changes of Chub mackerel groups by scenario considering closed fishing season 65
Table 31. Biomass changes of Chub mackerel by TAC scenario 66
Table 32. Estimation results of standing stock of fishery resources using the BSS model for in the Korean waters 68
Table 33. Methods for estimating the accuracy of GPP and ZooㆍPhytoplankton biomass 70
Table 34. Results of prediction accuracy 70
Table 35. Scenario of fishing ground changes for Chub mackerel 71
Table 36. Scenario results of fishing ground changes for Chub mackerel biomass 72
Table 37. Scenario results of biomass changes by group 72
Table 38. Direction of model scenarios on major fisheries policy issues 73
Fig. 1. Prediction of Small yellow croaker catch and fishing conditions in 2019 by Stow net 12
Fig. 2. Prediction of Chub mackerel catch and fishing conditions in 2019 by Large-purse seine 12
Fig. 3. Prediction of Common squid catch and fishing conditions by Large trawl 13
Fig. 4. Prediction of Chub mackerel catch and fishing conditions by Large-perse seine 14
Fig. 5. Prediction of Small yellow croaker catch and fishing conditions by Drift gill net 14
Fig. 6. Annual mean (A), seasonal mean (B), and heatmap (C) of Chub mackerel catch by Large-purse Seine 15
Fig. 7. Sea surface temperature (SST) by satellite and Chub mackerel catch data in autumn during 2017~2022 16
Fig. 8. Timeseries of mininum, maximum and mean temperature in the Yellow Sea (A) and hotspot position in the Yellow Sea (magenta line) and the Yellow Sea... 17
Fig. 9. Comparision among the catch predition of Swimming crab with different environment conditions in the spring season of 2021 18
Fig. 10. Comparision among the catch predition of Swimming crab with different environment conditions in the autumn season of 2021 19
Fig. 11. Comparision among the catch predition of Swimming crab with different environment conditions in the spring season of 2021 20
Fig. 12. Catch prediction of Swimming crab using ensemble model (RFR, SVM, Xgboost, MLR) in the 2022 autumn season 20
Fig. 13. seasonal variation of Yellowtail catch data from 2012 to 2018 21
Fig. 14. Estimated migration routes (○) and its confidene interval (dark gray: 50%, light gray: 95%) for Yellowtail released in December 2018 22
Fig. 15. Yellowtail ecological characteristics of (A) and depth seawater temperature (B) observed by PSAT 23
Fig. 16. Comparisons of migration route expriments among the using SST (A), SST considering mixed layer depth (B), numerical model result (C) 23
Fig. 17. Tracking algorithm of fish migration routes (step 3 was improved) 24
Fig. 18. Tracking of migration routes by swimming speed of 0.5m/s (A) and 1m/s (B) 24
Fig. 19. Model domain components of bathymetry (A), masking (B), grid (C) 26
Fig. 20. Comparison of SST between the observation (red) and model result (black) 27
Fig. 21. Comparison of climatological mean SST between the observation (red) and model result (black) 28
Fig. 22. Comparison of wind field in the grid resolution of the 3km (A) and 1/10° (B). (2021. 5. 26.) 29
Fig. 23. Comparison of SST between the observation (red) and model result (blue) 29
Fig. 24. Initial condition of sea surface temperature (A) and salinity (B) by the seasonal forecast model 30
Fig. 25. Comparison among the observation, model result and bias-corrected model result 30
Fig. 26. Mid-range ocean prediction model result (2021.5.) of SST (A), SSS (B), SSH (C), and EKE (D) 31
Fig. 27. Comparison of Common squid habitat of model result in May 2020 (A), prediction in May 2021 (B), and model result in May 2021(C) using mid-range... 32
Fig. 28. Distribution of habitat suitability index (HSI) by Chub mackerel habitat model in 2021 (A) and 2022 (B) 32
Fig. 29. Chub mackerel catch and fishing ground by Large-purse Seine in 2021 (A) and 2022 (B) 32
Fig. 30. Polygon generating process of bathmetry (A), ocean current (B), catch (C), open boundary (D) 39
Fig. 31. Flow chart of water quality and ecology calculation of Atlantis 41
Fig. 32. Biogeochemical cycle module simulation results of Chl-a (A) and DON (B) 43
Fig. 33. Schematic diagram of energy flow and mechanism of Atlantis 44
Fig. 34. Schematic diagram of spwning, recruitment, maturation, and aging of Atlantis 44
Fig. 35. Schematic diagram of migration and horizontal distribution of Atlantis 44
Fig. 36. Food-web structure between prey and predator 46
Fig. 37. Food-web structure of Ecopath model in the southern sea of the Korean peninsula 48
Fig. 38. GUI main page of Atlantis model 49
Fig. 39. GUI page of NPP and BSS model 49
Fig. 40. Comparison of observation and primary productivity algorithm result 50
Fig. 41. Estimates of phytoplankton biomass by size in 2018 51
Fig. 42. Parameter sensitivity analysis results of random sampling result (A) and biomass ration (B) using maximum growth rate and mortality 51
Fig. 43. Probability (red circle) of phytoplankton biomass change (blue circle) by uncertainty analysis (blue arrow) 52
Fig. 44. Biomass change rate by biological functional group 53
Fig. 45. Contents of GUI-based fishery resource fluctuation multi-prediction system 54
Fig. 46. Horizontal distribution of annual mean NPP (A, B) and GPP (C) 55
Fig. 47. Comparisons of observation (temperature, Chl-a, PP, Zooplankton) and model results 57
Fig. 48. Model validation of Species Abundance Distribution (SAD) (A) and Trophic level (TL) (B) 57
Fig. 49. Biomass change rates of major commercial species by scenario 58
Fig. 50. GUI platform for the biomass of Atlantis model result 59
Fig. 51. Comparisons of MODIS-Aqua and VIIRS-SNPP. Chl-a (A), SST (B), PAR (C), Zeu (D), and horizontal distribution and primary production (E, F) 60
Fig. 52. Comparisons of primary production by MODIS-Aqua (A) and VIIRS-SNPP (B) 60
Fig. 53. Comparisons of Chl-a in box #14 (A) and #15 (B) 62
Fig. 54. Comparisons of biomass between observation and model result. primary production (A, B), phytoplankton (C, D), and zooplankton (E, F) biomass 63
Fig. 55. Total (A), spwaning (C), and reproduction (C) bomass changes of Chub mackerel 64
Fig. 56. Timeseries of Chub mackerel biomass by TAC scenario. T1 (A), T2 (B), T3 (C), and T4 (D) 64
Fig. 57. Distribution of mackerel migration routes by background seawater temperature condition 65
Fig. 58. GUI design and model display 67
Fig. 59. Species rank abundance curves of initial biomass by functional groups in the Model of the 4th year (red dots) and in the Model of the current year (blue dots) 69
Fig. 60. Expression of trophic levels of Chub mackerel in the East Sea and the South Sea 69
Fig. 61. Scenario results of Chub mackerel biomass changes by age (A), and fish species (B) 71
Fig. 62. Scenario results of Chub mackerel biomass changes by age 71
Fig. 63. Biomass changes by individual TL (A) and averaged TL (B). Schematic diagram of TL pyramid under climate change (Nagelkerken et al 2020) (C) 72