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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Numerical Simulation and Forecasting of Water Level for Qinghai Lake Using Multi-Altimeter Data Between 2002 and 2012
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Numerical Simulation and Forecasting of Water Level for Qinghai Lake Using Multi-Altimeter Data Between 2002 and 2012

机译：2002-2012年基于多高度数据的青海湖水位数值模拟与预报。

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摘要

Satellite radar altimetry has effectively been used for monitoring the water level change in recent years. In this study, Qinghai Lake was taken as an example to simulate and forecast water level using the multi-altimeter data from Envisat/RA-2, Cryosat-2/Siral, and Jason-1/Poseidon-2. First, using the robust least square method and system bias correction algorithms, abnormal water levels and the system bias were eliminated, and an accurate lake-level time series was obtained. Then, singular spectrum analysis (SSA) algorithms were used to extract the effective fluctuation signal from the accurate lake-level time series, and the accuracy of the altimetry data was improved. Based on an analysis of SSA algorithms’ characteristics, comparison of the SSA-extracted fluctuation signal, and in-situ gauge measurements of Qinghai Lake, the accurate lake-level time series was affected by white noise of zero-mean and 0.5-m variance and colored noise of 0.2202–0.2473-m mean and 0.252–0.2800-m root-mean-square difference. After eliminating the white noise, the accuracy of the altimeter data reached the decimeter level in inland lake monitoring. Next, the SSA-extracted fluctuation signal was decomposed into linear composition, periodic components, and a residual component, and a combined linear-periodic-residual model was established using simple regression, a trigonometric function, and autoregressive-moving-average models. Using the model, the water level change of Qinghai Lake was simulated and forecasted to 2 years, with its accuracy reaching the decimeter level. The experiences of this study can provide an effective reference for the other lakes.
机译：近年来，卫星雷达测高已被有效地用于监测水位变化。本研究以青海湖为例，利用Envisat / RA-2，Cryosat-2 / Siral和Jason-1 / Poseidon-2的多高度数据对水位进行模拟和预测。首先，使用鲁棒最小二乘法和系统偏差校正算法，消除了异常水位和系统偏差，并获得了准确的湖面时间序列。然后，使用奇异频谱分析（SSA）算法从准确的湖面时间序列中提取有效波动信号，从而提高了测高数据的准确性。在分析SSA算法的特征，比较SSA提取的波动信号以及对青海湖进行实地测量的基础上，准确的湖面时间序列受到零均值和0.5m方差的白噪声的影响。彩色噪声的平均值为0.2202-0.2473-m，均方根差为0.252-0.2800-m。消除白噪声后，内陆湖泊监测中的高度计数据精度达到了分米级。接下来，将SSA提取的波动信号分解为线性成分，周期成分和残差成分，并使用简单回归，三角函数和自回归移动平均模型建立组合的线性周期-残差模型。利用该模型对青海湖水位变化进行了模拟，并预测了2年的时间，其精度达到了分米级。这项研究的经验可以为其他湖泊提供有效的参考。

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