Water quality analysis of tidal river based on satellite observation and dynamic model
GAO Wenli1,, SHEN Fang1,2,,, CHE Yue3,4 1.State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China 2.Institute of Eco-Chongming, East China Normal University, Shanghai 200062, China 3.School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China 4.Shanghai Key Lab for Urban Ecological Process and Eco-Restoration, Shanghai 200241, China
Abstract:The advantages of continuous space-time coverage by satellite observations and high time resolution by water quality and hydrodynamic models were complemented and utilized in this study to analyze the water quality change of the tidal river. Based on the in-situ data, the chlorophyll a concentration of the tidal river-Huangpu River was quantitatively retrieved by Lansat8/OLI satellite data. The root mean square error between the inversion data and the measured data was 4.82 mg·m?3, and the determination coefficient R2 was 0.68. Based on the Delft3D model, the water quality parameters of the Huangpu River Dissolved, i.e. Oxygen (DO), Ammonia Nitrogen (NH3-N) and Potassium Permanganate Index (CODMn), were simulated. Results showed that the root mean square error of the simulated water level by the hydrodynamic module was 0.22 m, and the verified root mean square errors of DO, NH3-N, and CODMn were 0.53 mg·L?1, 0.16 mg·L?1, 0.27 mg·L?1, respectively. The correlation between chlorophyll a concentration and water quality parameters of Huangpu River from August 2013 to July 2014 was further analyzed. It was found that with the increase of chlorophyll a concentration in Huangpu River, DO and NH3-N increased while CODMn decreased. Based on the correlation between chlorophyll-a concentration and water quality parameters, the DO, NH3-N, and CODMn of Huangpu River were retrieved by using Lansat8/OLI satellite data. It was found that the water quality parameter values varied greatly with seasons for tidal river. This study demonstrated the great significance to combine satellite observation with dynamic model for water quality analysis of tidal rivers. Key words:remote sensing inversion/ Delft3D model/ tidal river/ hydrodynamic/ water quality monitoring.
YANG H J, SHEN Z M, ZHANG J P, et al. Water quality characteristics along the course of the Huangpu River(China)[J]. Journal of Environmental Sciences, 2007, 19(10): 1193-1198. doi: 10.1016/S1001-0742(07)60195-8
HOOS A B, WANG S H, SCHWARZ G E. Adapting a regional water-quality model for local application: A case study for Tennessee, USA[J]. Environmental Modelling & Software, 2019, 115: 187-199.
[9]
WANG Q, LI S, JIA P, et al. A review of surface water quality models[J]. The Scientific World Journal, 2013, 2013: 231768.
[10]
CHEN Y Z, LIN W Q, ZHU J R, et al. Numerical simulation of an algal bloom in Dianshan Lake[J]. Chinese Journal of Oceanology and Limnology, 2016, 34(1): 231-244. doi: 10.1007/s00343-015-4298-0
ZHAO H J, WANG Y, YANG L L, et al. Relationship between phytoplankton and environmental factors in landscape water supplemented with reclaimed water[J]. Ecological Indicators, 2015, 58: 113-121. doi: 10.1016/j.ecolind.2015.03.033
[13]
LIU Y, GUO H C, YANG P J. Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis[J]. Ecological Modelling, 2010, 221(4): 681-688. doi: 10.1016/j.ecolmodel.2009.03.010
PORRA R J. The chequered history of the development and use of simultaneous equations for the accurate determination of chlorophylls a and b[J]. Photosynth Research, 2002, 73(1/2/3): 149-156.
[16]
VANHELLEMONT Q, RUDDICK K. Turbid wakes associated with offshore wind turbines observed with Landsat 8[J]. Remote Sensing of Environment, 2014, 145: 105-115. doi: 10.1016/j.rse.2014.01.009
[17]
GORDON H R, BROWN J W, EVANS R H. Exact Rayleigh scattering calculations for use with the Nimbus-7 Coastal Zone Color Scanner[J]. Applied Optics, 1988, 27(5): 862-871. doi: 10.1364/AO.27.000862
[18]
WANG M, SHI W. Cloud masking for ocean color data processing in the coastal regions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(11): 3105-3196.
[19]
WANG M, SHI W. The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing[J]. Optics Express, 2007, 15(24): 15722-15733. doi: 10.1364/OE.15.015722
[20]
TANG R G, SHEN F, PAN Y Q, et al. Multi-source high-resolution satellite products in Yangtze Estuary: Cross-comparisons and impacts of signal-to-noise ratio and spatial resolution[J]. Optics Express, 2019, 27(5): 6426-6441. doi: 10.1364/OE.27.006426
[21]
SHANG P, SHEN F. Atmospheric correction of satellite GF-1/WFV imagery and quantitative estimation of suspended particulate matter in the Yangtze Estuary[J]. Sensors, 2016, 16(12): 1997-2013. doi: 10.3390/s16121997
[22]
IRISH J L, WHITE T E. Coastal engineering applications of high-resolution lidar bathymetry[J]. Coastal Engineering (Amsterdam), 1998, 35(1/2): 47-71.
[23]
WEHR A, LOHR U. Airborne laser scanning: An introduction and overview[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999, 54(2/3): 68-82.
[24]
QIAN Z, GARBOCZI E J, YE G, et al. Anm: A geometrical model for the composite structure of mortar and concrete using real-shape particles[J]. Materials and Structures, 2016, 49(1/2): 149-158.
HUYBRECHTS N, VILLARET C, LYARD F. Optimized predictive two-dimensional hydrodynamic model of the Gironde Estuary in France[J]. Journal of Waterway, Port, Coastal, and Ocean Engineering, 2012, 138(4): 312-322. doi: 10.1061/(ASCE)WW.1943-5460.0000129
1.State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China 2.Institute of Eco-Chongming, East China Normal University, Shanghai 200062, China 3.School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China 4.Shanghai Key Lab for Urban Ecological Process and Eco-Restoration, Shanghai 200241, China Received Date: 2020-11-02 Accepted Date: 2021-03-31 Available Online: 2021-09-15 Keywords:remote sensing inversion/ Delft3D model/ tidal river/ hydrodynamic/ water quality monitoring Abstract:The advantages of continuous space-time coverage by satellite observations and high time resolution by water quality and hydrodynamic models were complemented and utilized in this study to analyze the water quality change of the tidal river. Based on the in-situ data, the chlorophyll a concentration of the tidal river-Huangpu River was quantitatively retrieved by Lansat8/OLI satellite data. The root mean square error between the inversion data and the measured data was 4.82 mg·m?3, and the determination coefficient R2 was 0.68. Based on the Delft3D model, the water quality parameters of the Huangpu River Dissolved, i.e. Oxygen (DO), Ammonia Nitrogen (NH3-N) and Potassium Permanganate Index (CODMn), were simulated. Results showed that the root mean square error of the simulated water level by the hydrodynamic module was 0.22 m, and the verified root mean square errors of DO, NH3-N, and CODMn were 0.53 mg·L?1, 0.16 mg·L?1, 0.27 mg·L?1, respectively. The correlation between chlorophyll a concentration and water quality parameters of Huangpu River from August 2013 to July 2014 was further analyzed. It was found that with the increase of chlorophyll a concentration in Huangpu River, DO and NH3-N increased while CODMn decreased. Based on the correlation between chlorophyll-a concentration and water quality parameters, the DO, NH3-N, and CODMn of Huangpu River were retrieved by using Lansat8/OLI satellite data. It was found that the water quality parameter values varied greatly with seasons for tidal river. This study demonstrated the great significance to combine satellite observation with dynamic model for water quality analysis of tidal rivers.
由于地面实测光谱是以1 nm为间隔的高光谱数据，Landsat8/OLI通道是有一定带宽的多光谱数据，因此，地面实测光谱的某一波长并不能代表整个通道。为解决这个问题，以等效波长[21-23]来代替整个通道的波长，修正公式见式(2)。 式中：$ {X}_{i} $为通道$ i $对应的等效波长；$ {g}_{i}\left(\lambda \right) $为通道$ i $的波谱响应函数；λ1和λ2为通道$ i $的上下限；Xλ代表通道$ i $对应的地面实测高光谱的遥感反射率Rrs，sr?1。
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