黄河口海域风浪诱导的泥沙再悬浮数值模拟和全球海面气象参数遥感反演
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摘要
河口和海岸区域的泥沙输运与人类的活动密切相关,是人类生产生活活动中面临的重要问题之一。在河口和海岸区域,泥沙的再悬浮是一种非常重要的物理过程,再悬浮会影响水体中泥沙输运的通量、次级生产力以及污染物扩散等等。引起泥沙再悬浮的原因比较复杂,波浪通常在其中扮演中重要的角色,因为波浪能够增强底床上的湍流并增加底应力。因而研究波浪对底沙的再悬浮作用对于河口和海岸区域的泥沙输运研究有着重要的意义。
黄河是中国第二大河,以多沙闻名于世,它携带大量的泥沙至河口地区。近年来,由于自然因素和黄河中上游的水利工程,黄河入海的水沙量减少。黄河河口动力作用的减弱和水沙供应的减少,使得黄河口海域的泥沙沉积格局发生改变,部分岸线开始蚀退。在这种背景下,研究波浪对黄河口海域泥沙再悬浮的作用具有重要的现实意义。
本文将水动力模型ROMS(Regional Ocean Modeling System)、第三代波浪模型SWAN (Simulation WAve Near shore)和泥沙输运模型CSTM(Comminute Sediment Transport Model)三者耦合的模型应用于黄河口海域的泥沙输运研究。模拟了这一区域波浪、流和悬浮泥沙的变化过程。
作者对三角洲沿岸7个点有波浪作用情况和无波浪作用情况下的悬浮泥沙浓度的变化进行了比较,对底层中波浪再悬浮作用产生的悬浮泥沙占底层总悬浮泥沙的比例进行了分析。通过比较和分析得知,在平均风速为6.3 ms-1的情况下,7个点中波浪再悬浮作用产生的悬浮泥沙占底层总悬浮泥沙量比例最小为13.8%,最高为61.3%。河口附近的三个点波浪的再悬浮作用产生的悬浮泥沙占底层总悬浮泥沙的比例均超过27%。由于黄河三角洲地区全年的平均风速为5.3 ms-1,因此黄河口海域波浪诱导下的泥沙再悬浮作用非常的显著。
计算结果表明,冬季北风情况下,波浪再悬浮作用导致的悬浮泥沙的浓度高值区在孤东外海。涨潮时,高值区靠近岸边,落潮时,高值区向外海移动。在涨潮时,三角洲沿岸自神仙沟以南至清水沟老河口沙嘴处,是波浪再悬浮作用导致的悬浮泥沙的浓度高值区。而三角洲东北部,由于涨潮时流速较高,流致再悬浮作用强烈,波浪的再悬浮作用不显著。在落潮时,自清水沟老河口沙嘴处向北至三角洲东北部均是波浪再悬浮作用导致的悬浮泥沙的高值区。
气候变化与人类生活密切相关,海洋对全球气候变化的影响一直是现代科学家研究的重要问题之一。海面的潜热通量和感热通量是海气间能量交换的重要组成,在海气相互作用中扮演着重要的角色。海面的潜热通量和感热通量的计算依赖于海面风速、海面比湿度和气温等气象参量,其中海面比湿度和气温的获取较为困难。过去海面比湿度和气温的获取主要依赖于现场观测,即使有很多志愿船只参与,数据仍然比较稀少。随着海洋遥感技术的发展,多种卫星传感器被送上太空,使得长期大范围的获取海面比湿度和气温数据成为可能。本文的工作是使用AMSR-E (Advanced Microwave Scanning Radiometer for EOS)的产品数据进行了海面比湿度和气温的遥感反演。
利用包含风速的多参数回归公式,根据2003和2004年的AMSR-E产品数据和NCEP(National Center for Environmental Prediction)再分析数据,反演了日平均和月平均海面比湿度,与NCEP再分析数据相比,日平均和月平均海面比湿度均方根误差分别为1.05 g kg~(-1)和0.61 g kg~(-1)。
由于多参数回归方法存在的固有的缺点,本文引入广义可加模型方法。利用2005和2006年的AMSR-E产品数据和NCEP再分析数据,本文建立了反演瞬时和月平均海面比湿度的广义可加模型,与NCEP再分析数据比较,反演的瞬时和月平均海面比湿度均方根误差分别为1.41 g kg~(-1)和0.56 g kg~(-1)。与多参数回归方法相比较,广义可加模型方法反演的比湿度误差比多参数回归方法小。
本文使用广义可加模型方法进行了海表面空气温度的遥感反演。根据2005和2006年的AMSR-E产品数据和NCEP再分析数据,本文建立了瞬时和月平均海面气温遥感反演的广义可加模型。与NCEP再分析数据比较,广义可加模型方法反演的瞬时和月平均海面气温均方根误差分别为1.20°C和0.66°C。与多参数回归方法比较,广义可加模型方法反演的气温误差减小。
Estuary and coastal regions are such regions that the interactions between land and sea are obvious and the sediment becomes big problem for human being. In the estuary and coastal environment, sediment resuspension is an import process, which makes influence on the sediment mass flux, secondary productivity, pollution dispersal and so on. The reasons that cause sediment resuspension are very complicated. Wave, which can enhance the bed turbulence and make the bottom shear stress increased, usually plays a key role in the sediment resuspension, especially in the shallow and micro-tidal area.
Yellow River is famous for its high sediment concentration and it carries a huge amount of sediment into Bohai Sea. Recently, due to the global climate change and works on water conservancy facilities in the upstream of the river, the amount of sediment that Yellow River carried into Bohai Sea was reduced. Some deposition area near the estuary changed to be erosion area. It is very import to study the wind wave induced sediment resuspension in the Yellow River mouth.
We applied a coupled model to the entire Bohai Sea with emphasis on the Yellow River month. This model couples with a regional ocean circulation model– ROMS (Regional Ocean Modeling System),? a third-generation wave model– SWAN (Simulation WAve Near shore), and a sediment transport model– CSTM (Comminute Sediment Transport Model). The model simulated the current, waves and sediment transport during winter season. Then the wind wave induced sediment resuspension in the area was analyzed.
Seven stations around the Yellow River delta were selected. We compared the suspended sediment concentration affected by the wave induced resuspension effect and the suspended sediment concentration without the wave induced resuspension effect in these stations. Then we calculated the percentage of the wave induced resuspended sediment in that of the bottom layer of these stations. The percentage values of stations are various from 13.8% to 61.3%. In the three stations near the Yellow River mouth, wave resuspended more than 27% bottom sediment. The mean wind speed of the period that we analyzed is 6.3 ms~(-1). The multi-year averaged wind speed in the Yellow River month is 5.3 ms~(-1). Therefore, the wind wave induced resuspension is very import to the sediment transport in this area.
Under the north wind condition, the highest concentration center of wave resuspended sediment occurred near the Gudong town. During the flood tide, the center is near the coastline. From Shenxiangou channel to the old Yellow River mouth, the concentrations of wave induced resuspended sediment are relatively high. During the ebb tide, the highest concentration center moves out of the coastline. From the old Yellow River mouth to the northeast of the Yellow River delta, the concentrations of wave induced resuspended sediment are relatively high.
The global climate change has a strong tie with human being. The ocean plays a key role in the global climate change. Latent and sensible heat fluxes are import elements in the air-sea heat balance. The calculation of latent and sensible heat fluxes usually depends on the sea surface climate parameters such like wind speed, specific humidity and air temperature. In the past, it has been necessary to rely on in situ observations to get near surface specific humidity and air temperature. In situ observations are very sparse globally, even when many volunteer ship reports are included. However, with the advancement of remote sensing technology, various earth surface properties are observed by satellite, and the observations have high spatial and temporal resolution and cover most of the earth every few days. Because of the advances in satellite observations, the derivation of sea surface specific humidity and air temperature is made possible. The main objective of the present study is to retrieve sea surface specific humidity (Qa) and air temperature (Ta) from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements.
A new multivariate regression formula for retrieving sea surface specific humidity from remote sensing data from AMSR-E is proposed. Daily and monthly specific humidity data from the National Center for Environmental Prediction (NCEP) reanalysis dataset and data of sea surface temperature, atmospheric total water vapor, and wind speed from AMSR-E oceanographic products were used to derive the regression coefficients of the formula and validate the formula. The root mean square (rms) error for daily retrieved Qa over the global oceans is 1.05 g kg~(-1), and the rms error for monthly retrieved Qa is 0.61 g kg~(-1).
To overcome some disadvantage of multivariate regression method, a new method, Generalized Additive Models (GAMS), is proposed to derive instantaneous and monthly mean sea surface specific humidity. Instantaneous and monthly specific humidity data from the NCEP reanalysis dataset and AMSR-E oceanographic products are used for training the retrieval model and validating it. The rms error for instantaneous retrieved Qa over the global oceans is 1.41 g kg~(-1), and the rms error for monthly retrieved Qa is 0.56 g kg~(-1). Compared to the multivariate regression method, the rms of GAMs method retrieved Qa is smaller.
The GAMs is also applied to retrieve the instantaneous and monthly mean sea surface air temperature. Instantaneous and monthly specific humidity data from the NCEP reanalysis dataset and AMSR-E oceanographic products are used to train the retrieval model and validate it. The rms error for instantaneous retrieved Ta over the global oceans is 1.20°C, and the rms error for monthly retrieved Ta is 0.66°C.
黄河是中国第二大河,以多沙闻名于世,它携带大量的泥沙至河口地区。近年来,由于自然因素和黄河中上游的水利工程,黄河入海的水沙量减少。黄河河口动力作用的减弱和水沙供应的减少,使得黄河口海域的泥沙沉积格局发生改变,部分岸线开始蚀退。在这种背景下,研究波浪对黄河口海域泥沙再悬浮的作用具有重要的现实意义。
本文将水动力模型ROMS(Regional Ocean Modeling System)、第三代波浪模型SWAN (Simulation WAve Near shore)和泥沙输运模型CSTM(Comminute Sediment Transport Model)三者耦合的模型应用于黄河口海域的泥沙输运研究。模拟了这一区域波浪、流和悬浮泥沙的变化过程。
作者对三角洲沿岸7个点有波浪作用情况和无波浪作用情况下的悬浮泥沙浓度的变化进行了比较,对底层中波浪再悬浮作用产生的悬浮泥沙占底层总悬浮泥沙的比例进行了分析。通过比较和分析得知,在平均风速为6.3 ms-1的情况下,7个点中波浪再悬浮作用产生的悬浮泥沙占底层总悬浮泥沙量比例最小为13.8%,最高为61.3%。河口附近的三个点波浪的再悬浮作用产生的悬浮泥沙占底层总悬浮泥沙的比例均超过27%。由于黄河三角洲地区全年的平均风速为5.3 ms-1,因此黄河口海域波浪诱导下的泥沙再悬浮作用非常的显著。
计算结果表明,冬季北风情况下,波浪再悬浮作用导致的悬浮泥沙的浓度高值区在孤东外海。涨潮时,高值区靠近岸边,落潮时,高值区向外海移动。在涨潮时,三角洲沿岸自神仙沟以南至清水沟老河口沙嘴处,是波浪再悬浮作用导致的悬浮泥沙的浓度高值区。而三角洲东北部,由于涨潮时流速较高,流致再悬浮作用强烈,波浪的再悬浮作用不显著。在落潮时,自清水沟老河口沙嘴处向北至三角洲东北部均是波浪再悬浮作用导致的悬浮泥沙的高值区。
气候变化与人类生活密切相关,海洋对全球气候变化的影响一直是现代科学家研究的重要问题之一。海面的潜热通量和感热通量是海气间能量交换的重要组成,在海气相互作用中扮演着重要的角色。海面的潜热通量和感热通量的计算依赖于海面风速、海面比湿度和气温等气象参量,其中海面比湿度和气温的获取较为困难。过去海面比湿度和气温的获取主要依赖于现场观测,即使有很多志愿船只参与,数据仍然比较稀少。随着海洋遥感技术的发展,多种卫星传感器被送上太空,使得长期大范围的获取海面比湿度和气温数据成为可能。本文的工作是使用AMSR-E (Advanced Microwave Scanning Radiometer for EOS)的产品数据进行了海面比湿度和气温的遥感反演。
利用包含风速的多参数回归公式,根据2003和2004年的AMSR-E产品数据和NCEP(National Center for Environmental Prediction)再分析数据,反演了日平均和月平均海面比湿度,与NCEP再分析数据相比,日平均和月平均海面比湿度均方根误差分别为1.05 g kg~(-1)和0.61 g kg~(-1)。
由于多参数回归方法存在的固有的缺点,本文引入广义可加模型方法。利用2005和2006年的AMSR-E产品数据和NCEP再分析数据,本文建立了反演瞬时和月平均海面比湿度的广义可加模型,与NCEP再分析数据比较,反演的瞬时和月平均海面比湿度均方根误差分别为1.41 g kg~(-1)和0.56 g kg~(-1)。与多参数回归方法相比较,广义可加模型方法反演的比湿度误差比多参数回归方法小。
本文使用广义可加模型方法进行了海表面空气温度的遥感反演。根据2005和2006年的AMSR-E产品数据和NCEP再分析数据,本文建立了瞬时和月平均海面气温遥感反演的广义可加模型。与NCEP再分析数据比较,广义可加模型方法反演的瞬时和月平均海面气温均方根误差分别为1.20°C和0.66°C。与多参数回归方法比较,广义可加模型方法反演的气温误差减小。
Estuary and coastal regions are such regions that the interactions between land and sea are obvious and the sediment becomes big problem for human being. In the estuary and coastal environment, sediment resuspension is an import process, which makes influence on the sediment mass flux, secondary productivity, pollution dispersal and so on. The reasons that cause sediment resuspension are very complicated. Wave, which can enhance the bed turbulence and make the bottom shear stress increased, usually plays a key role in the sediment resuspension, especially in the shallow and micro-tidal area.
Yellow River is famous for its high sediment concentration and it carries a huge amount of sediment into Bohai Sea. Recently, due to the global climate change and works on water conservancy facilities in the upstream of the river, the amount of sediment that Yellow River carried into Bohai Sea was reduced. Some deposition area near the estuary changed to be erosion area. It is very import to study the wind wave induced sediment resuspension in the Yellow River mouth.
We applied a coupled model to the entire Bohai Sea with emphasis on the Yellow River month. This model couples with a regional ocean circulation model– ROMS (Regional Ocean Modeling System),? a third-generation wave model– SWAN (Simulation WAve Near shore), and a sediment transport model– CSTM (Comminute Sediment Transport Model). The model simulated the current, waves and sediment transport during winter season. Then the wind wave induced sediment resuspension in the area was analyzed.
Seven stations around the Yellow River delta were selected. We compared the suspended sediment concentration affected by the wave induced resuspension effect and the suspended sediment concentration without the wave induced resuspension effect in these stations. Then we calculated the percentage of the wave induced resuspended sediment in that of the bottom layer of these stations. The percentage values of stations are various from 13.8% to 61.3%. In the three stations near the Yellow River mouth, wave resuspended more than 27% bottom sediment. The mean wind speed of the period that we analyzed is 6.3 ms~(-1). The multi-year averaged wind speed in the Yellow River month is 5.3 ms~(-1). Therefore, the wind wave induced resuspension is very import to the sediment transport in this area.
Under the north wind condition, the highest concentration center of wave resuspended sediment occurred near the Gudong town. During the flood tide, the center is near the coastline. From Shenxiangou channel to the old Yellow River mouth, the concentrations of wave induced resuspended sediment are relatively high. During the ebb tide, the highest concentration center moves out of the coastline. From the old Yellow River mouth to the northeast of the Yellow River delta, the concentrations of wave induced resuspended sediment are relatively high.
The global climate change has a strong tie with human being. The ocean plays a key role in the global climate change. Latent and sensible heat fluxes are import elements in the air-sea heat balance. The calculation of latent and sensible heat fluxes usually depends on the sea surface climate parameters such like wind speed, specific humidity and air temperature. In the past, it has been necessary to rely on in situ observations to get near surface specific humidity and air temperature. In situ observations are very sparse globally, even when many volunteer ship reports are included. However, with the advancement of remote sensing technology, various earth surface properties are observed by satellite, and the observations have high spatial and temporal resolution and cover most of the earth every few days. Because of the advances in satellite observations, the derivation of sea surface specific humidity and air temperature is made possible. The main objective of the present study is to retrieve sea surface specific humidity (Qa) and air temperature (Ta) from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements.
A new multivariate regression formula for retrieving sea surface specific humidity from remote sensing data from AMSR-E is proposed. Daily and monthly specific humidity data from the National Center for Environmental Prediction (NCEP) reanalysis dataset and data of sea surface temperature, atmospheric total water vapor, and wind speed from AMSR-E oceanographic products were used to derive the regression coefficients of the formula and validate the formula. The root mean square (rms) error for daily retrieved Qa over the global oceans is 1.05 g kg~(-1), and the rms error for monthly retrieved Qa is 0.61 g kg~(-1).
To overcome some disadvantage of multivariate regression method, a new method, Generalized Additive Models (GAMS), is proposed to derive instantaneous and monthly mean sea surface specific humidity. Instantaneous and monthly specific humidity data from the NCEP reanalysis dataset and AMSR-E oceanographic products are used for training the retrieval model and validating it. The rms error for instantaneous retrieved Qa over the global oceans is 1.41 g kg~(-1), and the rms error for monthly retrieved Qa is 0.56 g kg~(-1). Compared to the multivariate regression method, the rms of GAMs method retrieved Qa is smaller.
The GAMs is also applied to retrieve the instantaneous and monthly mean sea surface air temperature. Instantaneous and monthly specific humidity data from the NCEP reanalysis dataset and AMSR-E oceanographic products are used to train the retrieval model and validate it. The rms error for instantaneous retrieved Ta over the global oceans is 1.20°C, and the rms error for monthly retrieved Ta is 0.66°C.
引文
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[40] Umlauf, L., H. Burchard. A generic length-scale equation for geophysical turbulence models. Journal of Marine Research, 2003, 61:235-265.
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[44]王殿志,张庆河,时钟.?渤海湾风浪场的数值模拟.?海洋通报,2004,23(5):11‐17?
[45]李燕,薄兆海.?SWAN模式对黄渤海海域浪高的模拟能力试验.海洋预报,2005,22(3):75‐82?
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[44]王殿志,张庆河,时钟.?渤海湾风浪场的数值模拟.?海洋通报,2004,23(5):11‐17?
[45]李燕,薄兆海.?SWAN模式对黄渤海海域浪高的模拟能力试验.海洋预报,2005,22(3):75‐82?
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[55] Grant, W.D., Madsen, O.S.. Movable bed roughness in unsteady oscillatory flow. Journal Geophysical Research, 1982, 87 (C1), 469-481.
[56] Nielsen, P. Suspended sediment concentrations under waves. Coastal Engineering , 1986, 10:23-31.
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[58] Madsen, O.S.. Spectral wave–current bottom boundary layer flows. In: Coastal Engineering 1994. Proceedings of the 24th International Conference on Coastal Engineering Research Council, Kobe, Japan, pp. 384–398.
[59] Wiberg, P.L., Harris, C.K. Ripple geometry in wavedominated environments. Journal of Geophysical Research, 1994, 99 (C1):775-789.
[60] Jiang, W., T. Pohlmann, J. Sundermann et al.. A modelling study of SPM transport in the Bohai Sea. Journal of Marine Systems, 2000, 24:175-200
[61] Jiang, W., T. Pohlmann, J. Sun et al.. SPM transport in the Bohai Sea: field experiments and numerical modelling. Journal of Marine Systems, 2004, 44:175-188
[62]胡春宏,吉祖稳,王涛.黄河口海洋动力特性和泥沙输移扩散.泥沙研究,1996,4:1-10
[1]褚健婷.中国近海海气界面湍流热通量研究.硕士学位论文,2005,中科院研究生院.?
[2] Liu, W.T., K.B. Katsaros and J. A. Businger. Bluk parameterization of air-sea exchange of heat and water vapor including molecular constraints at the interface. Journal of Atmosphere Science, 1979, 36: 1722-1735.
[3] Bunker, A.F.. Computation of surface energy flux and annual air-sea interactions cycle of the North Atlantic Ocean. Monthly Weather Review, 1976, 104:1122-1140
[4] Simonot, J.Y. and Gautier, C.. Satellite estimations of surface evaporation in the Indian Ocean during the 1979 monsoon. Ocean–Air Interaction, 1989, 1:239–256
[5] Liu, W.T., Niller, P.P.. Determination of monthly mean humidity in the atmospheric surface layer over ocean from satellite data. Journal of Physical Oceanography, 1984, 14: 1451-1457.
[6] Liu, W.T.. Statistical relation between monthly mean precipitable water and surface-level humidity over global oceans. Monthly Weather Review, 1986, 14:1591-1602.
[7] Miller, D.K and K.B. Katsaros. Satellite-derived surface latent heat fluxes in a rapidly intensifying marine cyclone. Monthly Weather Review., 1992, 120: 1093-1107.
[8] Schulz, J., Schlüssel, P., and Grassl, H.. Water vapor in the atmospheric boundary layer over oceans from SSM/I measurements. International Journal of Remote Sensing, 1993, 14: 2773-2789.
[9] Schlüssel, P., Schanz, L., and Englisch, G.. Retrieval of latent heat flux and longwave irradiance at the sea surface from SSM/I and AVHRR measurments. Advances in Space Research. 1995, 16: 107-116
[10] Clayson, C.A. and J. A. Carry. Determination of surface turbulent fluxes for the tropical ocean - global atmosphere coupled ocean– atmosphere response experiment: comparison of satellite retrievals and in situ measurements. Journal of Geophysical Research, 1996, 101:28515-29528
[11] Chou, S., Atlas, R.M., Shie, C. and Ardizzone, J.. Estimate of surface humidity and latent heat fluxes over oceans form SSM/I data. Monthly Weather Review, 1995, 123: 2405-2425.
[12] Chou, S., Shie, C., Atlas, R.M., and Ardizzone, J.. Air-sea fluxes retrieved from special sensor microwave imager data. Journal of Geophysical Research, 1997, C6:12705-12726.
[13] Jones, C., Peterson, P., and Gautier, C., 1999. A new Method for deriving ocean surface specific humidity and air temperature: an artificial neural network approach. Journal of Applied Meteorology. 38, 1229-1245.
[14] Stephen J. English. Estimation of temperature and humidity profile information from microwave radiances over different surface types. Journal of applied meteorology, 1999,38:1526-1541
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