南海北部海洋生态模型的参数分析及遗传算法优化

doi:10.11978/2019054

摘要/Abstract

摘要：

海洋生态系统动力学模型是研究海洋生态环境的重要手段。随着模型的发展, 生态参数取值不确定性增加, 对模型结果的影响逐渐增大, 因此模型参数优化显得尤为重要。本研究在南海北部应用一维物理-生态耦合模型, 通过对模型生态参数进行敏感性分析, 获取关键生态参数, 利用遗传算法对参数进行优化。结果表明, 模型中的敏感参数主要集中于浮游植物生长和浮游动物生长、摄食和死亡以及碎屑沉降等过程。针对以上参数利用遗传算法优化, 发现仅加入表层卫星数据, 模型表层和垂向模拟误差分别降低27.80%和21.40%; 加入垂向观测数据, 表层和垂向模拟误差分别降低14.90%和32.70%。遗传算法应用于海洋生态模型的关键参数优化研究, 所获取的参数对模型有明显的改善效果, 提高了耦合模型对生态系统的模拟精度, 为参数优化在三维模型中的应用提供了依据。

关键词: 南海北部, 海洋生态模型, 遗传算法, 参数优化

Abstract:

Marine ecosystem dynamics model is an important means to study marine ecological environment. As the model complexity increases, the number and uncertainty of biological parameters increase, which has a great impact on model results; therefore, optimization of model parameters is particularly important. In this paper, a one-dimensional physical-biological model is applied to the northern South China Sea, and the key biological parameters obtained through sensitivity analysis are optimized by using genetic algorithm. The results show that the sensitive parameters in the model are related to phytoplankton growth, zooplankton growth, feeding and death, and detritus sinking. Based on the genetic algorithm optimization of the above-mentioned parameters, we find that the surface and vertical simulation errors of the model are reduced by 27.80% and 21.40%, respectively, by using only surface satellite data; the surface and vertical simulation errors are reduced by 14.90% and 32.70%, respectively, by adding observed profile data. The success of applying genetic algorithm in the one-dimensional model provides the basis for its further application in three-dimensional marine ecosystem models.

Key words: northern South China Sea, marine ecosystem model, genetic algorithm, parameter optimization

中图分类号:

P731

舒婵, 耿兵绪, 房巍巍, 修鹏. 南海北部海洋生态模型的参数分析及遗传算法优化[J]. 热带海洋学报, 2020, 39(2): 98-106.

Chan SHU, Bingxu GENG, Weiwei FANG, Peng XIU. Parameter analysis and optimization using genetic algorithm in a marine ecosystem model of the northern South China Sea[J]. Journal of Tropical Oceanography, 2020, 39(2): 98-106.

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参数	描述/(单位)	取值	敏感度
akz2	Z2捕食半饱和常数/(mmol_N·m^-3)	0.25	-16.55
reg2	Z2铵盐排出速率/d^-1	0.05	-2.00
Chl2cs2_m	S2叶绿素同碳比值最大值/(mg_Chl·mg_C^-1)	6.50×10^-2	-1.59
beta2	Z2最大捕食速率/d^-1	0.53	1.11
bgamma2	Z2捕食效率	0.75	1.11
bgamma1	Z1捕食效率	0.75	1.04
beta1	Z1最大捕食速率/d^-1	0.80	1.01
Chl2cs1_m	S1叶绿素同碳比值最大值/(mg_Chl mg_C^-1)	6.50×10^-2	0.99
wsd	碎屑下沉速率/(m·d^-1)	10.00	-0.84
aknh4s1	S1吸收铵盐半饱和常数/(mmol_N·m^-3)	0.05	0.71
akz1	Z1捕食半饱和常数/(mmol_N·m^-3)	0.50	0.64
reg1	Z1铵盐排出速率/d^-1	0.10	0.49
bgamma5	碎屑衰减速率/d^-1	0.05	0.45
gmaxs1	S1最大特征生长速率/d^-1	2.00	0.37
gmaxs2	S2最大特征生长速率/d^-1	3.00	0.27
amaxs1	S1 P-I曲线初始斜率/(m²·W^-1·d^-1)	2.50×10^-2	-0.26
akox	氧化作用半饱和常数/(mmol_O·m^-3)	30.00	0.23
ak2	浮游植物特征光衰减速率/(m²·mmol^-1)	0.03	0.22
akno3s1	S1吸收硝酸盐半饱和常数/(mmol_N·m^-3)	1.00	-0.22
akno3s2	S2吸收硝酸盐半饱和常数/(mmol_N·m^-3)	2.00	0.21
pis2	S2铵盐抑制系数	3.00	0.14
bgamma4	S2死亡率/d^-1	0.01	0.18
wsp	S2下沉速率/(m·d^-1)	1.00	0.13
bgamma0	Z2特征死亡率/d^-1	0.08	-0.13
aksio4s2	S2吸收硅酸盐半饱和常数/(mmol·m^-3)	4.00	0.13
parsats1/s2	S1/S2光合有效辐射饱和初始参数/(W·m^-2)	40.00	0.13
akpo4s1/s2	S1/S2吸收硝酸盐半饱和常数/(mmol·m^-3)	0.08	0.13
akco2s1/s2	S1/S2吸收二氧化碳半饱和常数/(mmol·m^-3)	500.00	0.13
wsdsi	碎屑硅下沉速率/(m·d^-1)	60.00	0.13
bgamma7	硝化作用速率/d^-1	0.08	0.10
pis1	S1铵盐抑制系数	1.50	0.08
bgamma6	浮游植物凝聚速率/d^-1	0.05	0.05
bgamma3	S1死亡率/d^-1	0.01	0.02

参数	bgamma2	akz2	beta2	bgamma1	Chl2cs1_m	wsd	reg2	amaxs1	gmaxs1
r²	0.11	0.09	0.06	0.04	0.03	0.02	0.02	0.01	0.01
下限	0.37	0.12	0.25	0.37	0.03	5.00	0.02	0.01	1.00
上限	1.00	0.50	1.00	1.00	0.08	20.00	0.10	0.05	4.00

参数	bgamma2	akz2	beta2	bgamma1	Chl2cs1_m	wsd	reg2	amaxs1	gmaxs1
控制模型取值	0.75	0.25	0.53	0.75	6.50×10^-2	10.00	0.05	2.50×10^-2	2.00
实验一值	0.85	0.30	0.59	0.74	0.05	10.95	0.06	2.20×10^-2	3.28
变化率	1.14	1.22	1.12	0.99	0.75	1.09	1.20	0.88	1.64
实验二值	0.86	0.28	0.49	0.78	0.05	12.07	0.06	2.40×10^-2	3.44
变化率	1.15	1.14	0.93	1.04	0.84	1.21	1.20	0.96	1.72

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