| With the development of technology, there are more and more ways to monitor water quality. This means that we can get more data from different sources with different time and space scales. Meanwhile, in the unremitting efforts of many scholars, large amount of remote retrieve models of water quality parameters have been developed. However, each model could only reflect the "true value" from one level because of the natural limitation of remote sensing. To get the relatively true value by combining all of the data sources with the various models, we developed the data assimilation method for retrieving the concentration of chlorophyll a in Taihu Lake: Firstly, multi-model collaborative retrieve algorithm was established using data assimilation method to retrieve chlorophyll a concentration in Taihu Lake, in which the model error of different remote retrieval models was considered to enhance the accuracy; Secondly, the chlorophyll a concentration data assimilation system was built by integrating with the water dynamics model, observation data (buoys, platforms and other observation data) and satellite imagery data; Finally, the assimilation experiments was conducted to evaluate and forecast the chlorophyll a concentration in Taihu Lake by applying the constructed data assimilation system, taking GOCI imagery data, buoys and platform data as the observation data. In this paper, a new method is provided to overcome the deficiencies of the existing remote retrieve models and ultimately improve water quality parameters retrieval accuracy, by combining multiple data sources, multi-scale, multi-model and multi-sensor.The main conclusions of this study are as follows:(1) The multi-model collaborative retrieve algorithm based on data assimilation could effectively blend the advantages of different retrieve models and meanwhile, could effectively weight the retrieve results. Therefore, it could improve the accuracy of the single model in lower retrieve accuracy region, and then improve the overall retrieval accuracy finally. In this study, six models were selected for remote retrieving chlorophyll a concentration in Taihu Lake based on in situ measured data during2006to2009, these models are:band ratio model, three band model, four band model, Dall’Olmo model, Gitelson model and Xu model. The mean absolute percent errors (MAPE) for these models are29%,32%,35%,25%,27%and25%; root mean square errors (RMSE) are13.19μg/L,14.21μg/L,28.35μg/L,14.78μg/L,13.98μg/L and 15.71μg/L. Then,2to6models were selected to test the efficiency of the multi-model collaborative retrieve processes. The results indicates that the best value is when six retrieve models all participate in the multi-model collaborative retrieve procedure, i.e., MAPE is23.4%, and RMSE is14.58μg/L. Meanwhile, with the increased retrieve model participated, the retrieve result gets better.(2) Kalman filter algorithm based on ensemble could improve the accuracy of evaluation and prediction of chlorophyll a concentration in Taihu Lake. Thereafter, the chlorophyll a data assimilation system of Taihu Lake was built using ensemble square root kalman filter, combining with water dynamic model. In addition, the effectiveness of this method for evaluation and prediction of the concentration of chlorophyll a was validated. When virtual buoys were laid in Meiliang Bay, the evaluation accuracy had been improved by65%. When virtual buoys were laid in the center of the lake, the evaluation accuracy had been improved by57%. However, the distribution of chlorophyll a is more continuous and stable when the buoys were laid in the center of the lake. Considering the water quality monitoring requirements, recommend buoys placed around Tuoshan Mountain. Then, the evaluation and forecasting of chlorophyll a concentration experiment in Taihu Lake were conducted based on Taihu Lake chlorophyll a data assimilation system, taking GOCI imagery data, buoys and platform data as the observation data.Taking GOCI imagery data as observation data, the MAPEs of assimilation experiment and control experiment compared to the multi-model retrieve result in the whole lake were45%and125%respectively, and that of the R2were0.71and0.41, respectively; While, compared to the in situ result, the MAPEs were28%and161%, respectively, and the RMSEs were9.57μg/L and55.66μg/L respectively. Taking buoys and platform data as the observation data, the MAPE for the platform station decreased from218to27%, and the RMSE decreased from16.23μg/L to2.97μg/L, during the assimilation procedure; the MAPE for the buoy station decreased from1125%to98%, and the RMSE decreased from17.29μg/L to3.98μg/L. In the prediction procedure, the MAPE for the platform station decreased from139%to3%, and the MAPE for the buoy station decreased from2001%to468%.(3) Sensibility of the Taihu Lake chlorophyll a assimilation system to different parameters directly control the accuracy of estimate the chlorophyll a concentration distribution when using this assimilation system. We used multispectral data of Environmental Satellite1(HJ-1), obtained on21April,2009, combined with in situ data to retrieve the concentration of chlorophyll a in Taihu Lake. Take the retrieved chlorophyll a concentration of Taihu Lake as the initial background value, then combined with the data assimilation system to analyze the influence of the ensemble size, the assimilation time, the background error, the observation error and the model error on the assimilation system. The results indicate:taking the computing cost, time cost of system and the performance of assimilation system into consideration, the assimilation system performs well when the ensemble size are30-40; the assimilation system is not very sensitive to the background error; both the observation error and the model error are very sensitive for the performance of the system; different test stations have different water dynamic properties, that induces the different performance; the estimation of chlorophyll a concentration can be improved by using the data assimilation method. |
PDF Full Text Request