基于多层感知器的南印度河流域 GRACE 陆地水储量异常重建。
Reconstruction of GRACE terrestrial water storage anomalies using Multi-Layer Perceptrons for South Indian River basins.
机构信息
Department of Civil Engineering, G. Pulla Reddy Engineering College, Kurnool, India.
Department of Civil Engineering, National Institute of Technology, Warangal, India.
出版信息
Sci Total Environ. 2023 Jan 20;857(Pt 2):159289. doi: 10.1016/j.scitotenv.2022.159289. Epub 2022 Oct 7.
The Gravity Recovery and Climate Experiment (GRACE) satellite mission began in 2002 and ended in June 2017. GRACE applications are limited in their ability to study long-term water cycle behavior because the data is limited to a short period, i.e., from 2002 to 2017. In this study, we aim to reconstruct (1960-2002) GRACE total water storage anomalies (TWSA) to obtain a continuous TWS time series from 1960 to 2016 over four river basins of South India, namely the Godavari, Krishna, Cauvery and Pennar River basins, using Multilayer Perceptrons (MLP). The Seasonal Trend Decomposition using Loess procedure (STL) method is used to decompose GRACE TWSA and forcing datasets into linear trend, interannual, seasonal, and residual parts. Only the de-seasoned (i.e., interannual and residual) components are reconstructed using the MLP method after the linear trend and seasonal components are removed. Seasonal component is added back after reconstruction of de-seasoned GRACE TWSA to obtain complete TWSA series from 1960 to 2016. The reconstructed GRACE TWSA are converted to groundwater storage anomalies (GWSA) and compared with nearly 2000 groundwater observation well networks. The results conclude that the MLP model performed well in reconstructing GRACE TWSA at basin scale across four river basins. Godavari (GRB) experienced the highest correlation (r = 0.96) between the modelled TWSA and GRACE TWSA, followed by Krishna (KRB) with r = 0.93, Cauvery (CRB) with r = 0.91, and Pennar (PCRB) with r = 0.92. The seasonal GWSA from GRACE (GWSA) correlated well with the GWSA from groundwater observation wells (GWSA) from 2003 to 2016. KRB exhibited the highest correlation (r=0.85) followed by GRB (r=0.81), PCRB (r=0.81) and CRB (r=0.78). The established MPL technique could be used to reconstruct long-term TWSA. The reconstructed TWSA data could be useful for understanding long-term trends, as well as monitoring and forecasting droughts and floods over the study regions.
重力恢复和气候实验（GRACE）卫星任务始于 2002 年，于 2017 年 6 月结束。GRACE 应用在研究长期水循环行为方面的能力有限，因为数据仅限于短时间段，即 2002 年至 2017 年。在这项研究中，我们旨在使用多层感知器（MLP）重建（1960-2002 年）GRACE 总储水异常（TWSA），以获得印度南部四个流域，即戈达瓦里、克里希纳、高韦里和彭纳尔流域，从 1960 年到 2016 年的连续 TWS 时间序列。季节性趋势分解使用洛厄尔程序（STL）方法将 GRACE TWSA 和强迫数据集分解为线性趋势、年际、季节性和残差部分。在去除线性趋势和季节性分量后，仅使用 MLP 方法重建去季节性（即年际和残差）分量。在重建去季节性 GRACE TWSA 后添加季节性分量，以获得从 1960 年到 2016 年的完整 TWSA 系列。将重建的 GRACE TWSA 转换为地下水存储异常（GWSA），并与近 2000 个地下水观测井网络进行比较。结果表明，MLP 模型在四个流域的流域尺度上很好地重建了 GRACE TWSA。戈达瓦里（GRB）在模型 TWSA 和 GRACE TWSA 之间的相关性最高（r = 0.96），其次是克里希纳（KRB），r = 0.93，高韦里（CRB），r = 0.91，彭纳尔（PCRB），r = 0.92。从 2003 年到 2016 年，GRACE 的季节性 GWSA（GWSA）与地下水观测井的 GWSA（GWSA）相关性很好。KRB 的相关性最高（r = 0.85），其次是 GRB（r = 0.81）、PCRB（r = 0.81）和 CRB（r = 0.78）。建立的 MPL 技术可用于重建长期 TWSA。重建的 TWSA 数据可用于了解研究区域的长期趋势，以及监测和预测干旱和洪水。
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