Research Scholar, School of Water Resources, Indian Institute of Technology Kharagpur, West Bengal-721302, India.
Associate Professor, School of Water Resources, Indian Institute of Technology Kharagpur, West Bengal-721302, India.
J Environ Manage. 2022 Nov 15;322:116121. doi: 10.1016/j.jenvman.2022.116121. Epub 2022 Sep 5.
With the gradual declining streamflow gauging stations in many world-rivers, emphasis is given nowadays to develop remote sensing (RS)-based approaches as the next-generation hydrometry for estimating riverine ecological flow regimes (EFR). For constructing EFR based on daily-streamflow data in scantily-gauged reaches, use of RS techniques in narrow flow-width tropical rain-fed rivers is constrained with the non-availability of finer spatial satellite data at daily scale. To address these limitations, this study proposes a novel framework that integrates the enhanced spatiotemporal adaptive reflectance fusion (FUS) of the 250 m × 1-day resolution Aqua-MODIS and 30 m × 1-day resolution Landsat satellite-based remote sensing images in the near-infrared region with the machine learning algorithms. These developed frameworks are named as Artificial Neural Network-based ANNFUS, Random Forest Regression-based RFRFUS, and Support Vector Regression-based SVRFUS models, which were tested for daily-scale streamflow estimation in a typical Brahmani River Basin, India. The results reveal that by addressing the linear and nonlinear dynamism between the streamflow and satellite signals, all the developed models could simulate the streamflow very well with the Nash-Sutcliffe efficiency>0.8, Kling-Gupta efficiency>0.8, relative root mean square error (rRMSE) of 0.051-0.12, and normalized RMSE of 0.23-0.36. However, for reproducing the high, median, and low streamflow regimes, the SVRFUS model was found to be the best with the NSE>0.85 and KGE>0.8. Conclusively, the proposed approach is found to have the potential to be replicated in other world-river basins to estimate ecological flow regimes at defunct gauging stations facilitating the basin-scale aquatic environmental management.
随着世界上许多河流的流量站逐渐减少，如今人们越来越重视开发遥感（RS）技术作为下一代水文学方法，以估算河流生态流量（EFR）。在流量稀少的河段，基于日流量数据构建 EFR 时，由于无法获得更精细的逐日空间卫星数据，限制了 RS 技术在狭窄水流宽度的热带雨养河流中的应用。为了解决这些限制，本研究提出了一种新的框架，该框架将增强的时空自适应反射融合（FUS）技术与机器学习算法相结合，融合了 250m×1 天分辨率的 Aqua-MODIS 和 30m×1 天分辨率的 Landsat 卫星近红外波段的遥感图像。这些开发的框架分别命名为基于人工神经网络的 ANNFUS、基于随机森林回归的 RFRFUS 和基于支持向量回归的 SVRFUS 模型，在印度典型的布拉马普特拉河流域进行了逐日流量估算测试。结果表明，通过解决流量和卫星信号之间的线性和非线性动态关系，所有开发的模型都可以很好地模拟流量，纳什效率系数（NSE）>0.8、金格-古普塔效率系数（KGE）>0.8、相对均方根误差（rRMSE）为 0.051-0.12，归一化均方根误差（nRMSE）为 0.23-0.36。然而，为了再现高、中、低流量，SVRFUS 模型的 NSE>0.85 和 KGE>0.8，表现最佳。综上所述，该方法有望在其他世界河流流域复制，以在废弃的流量站估算生态流量，从而促进流域尺度的水环境保护。
