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Open AccessArticle

Discharge Estimates for Ungauged Rivers Flowing over Complex High-Mountainous Regions based Solely on Remote Sensing-Derived Datasets

1
Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, and CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Arba Minch Water Technology Institute, Faculty of Meteorology and Hydrology, Arba Minch University, P.O. Box 21, Arba Minch, Ethiopia
4
Department of Earth System Science, Tsinghua University, Beijing 100084, China
5
Regional Climate Group, Department of Earth Sciences, University of Gothenburg, P.O. Box 460, S-405 30 Gothenburg, Sweden
6
Information Center, Ministry of Water Resources, Beijing 100053, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1064; https://doi.org/10.3390/rs12071064
Received: 18 February 2020 / Revised: 23 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
Reliable information about river discharge plays a key role in sustainably managing water resources and better understanding of hydrological systems. Therefore, river discharge estimation using remote sensing techniques is an ongoing research goal, especially in small, headwater catchments which are mostly ungauged due to environmental or financial limitations. Here, a novel method for river discharge estimation based entirely on remote sensing-derived parameters is presented. The model inputs include average river width, estimated from Landsat imagery by using the modified normalized difference water index (MNDWI) approach; average depth and velocity, based on empirical equations with inputs from remote sensing; channel slope from a high resolution shuttle radar topography mission digital elevation model (SRTM DEM); and channel roughness coefficient via further analysis and classification of Landsat images with support of previously published values. The discharge of the Lhasa River was then estimated based on these derived parameters and by using either the Manning equation (Model 1) or Bjerklie equation (Model 2). In general, both of the two models tend to overestimate discharge at moderate and high flows, and underestimate discharge at low flows. The overall performances of both models at the Lhasa gauge were satisfactory: comparisons with the observations yielded Nash–Sutcliffe efficiency coefficient (NSE) and R2 values ≥ 0.886. Both models also performed well at the upper gauge (Tanggya) of the Lhasa River (NSE ≥ 0.950) indicating the transferability of the methodology to river cross-sections with different morphologies, thus demonstrating the potential to quantify streamflow entirely from remote sensing data in poorly-gauged or ungauged rivers on the Tibetan Plateau. View Full-Text
Keywords: Landsat images; MNDWI; SRTM DEM; streamflow; Lhasa River basin; Ungauged rivers Landsat images; MNDWI; SRTM DEM; streamflow; Lhasa River basin; Ungauged rivers
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MDPI and ACS Style

Kebede, M.G.; Wang, L.; Yang, K.; Chen, D.; Li, X.; Zeng, T.; Hu, Z. Discharge Estimates for Ungauged Rivers Flowing over Complex High-Mountainous Regions based Solely on Remote Sensing-Derived Datasets. Remote Sens. 2020, 12, 1064.

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