scholarly journals An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

2016 ◽  
Vol 52 (6) ◽  
pp. 4527-4549 ◽  
Author(s):  
M. Durand ◽  
C. J. Gleason ◽  
P. A. Garambois ◽  
D. Bjerklie ◽  
L. C. Smith ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Discharge ◽  
Estimation Algorithms ◽  
Discharge Estimation

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

2020 ◽  
Vol 12 (7) ◽  
pp. 1064 ◽  
Author(s):  
Mulugeta Genanu Kebede ◽  
Lei Wang ◽  
Kun Yang ◽  
Deliang Chen ◽  
Xiuping Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Discharge ◽  
Equation Model ◽  
Roughness Coefficient ◽  
The Tibetan Plateau ◽  
Efficiency Coefficient ◽  
Ongoing Research ◽  
Mountainous Regions ◽  
Lhasa River ◽  
Discharge Estimation

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.

scholarly journals Benchmarking wide swath altimetry-based river discharge estimation algorithms for the Ganges river system

2016 ◽  
Vol 52 (4) ◽  
pp. 2439-2461 ◽  
Author(s):  
Matthew G. Bonnema ◽  
Safat Sikder ◽  
Faisal Hossain ◽  
Michael Durand ◽  
Colin J. Gleason ◽  
...  
Keyword(s):  
River Discharge ◽  
River System ◽  
Ganges River ◽  
Estimation Algorithms ◽  
Discharge Estimation ◽  
The Ganges ◽  
Wide Swath

Remote sensing-based river discharge estimation for a small river flowing over the high mountain regions of the Tibetan Plateau

2020 ◽  
Author(s):  
Mulugeta Genanu Kebede ◽  
Lei Wang ◽  
Xiuping Li ◽  
Zhidan Hu
Keyword(s):  
Remote Sensing ◽  
Tibetan Plateau ◽  
River Discharge ◽  
Equation Model ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Mountain Regions ◽  
Discharge Estimation ◽  
Academy Of Sciences ◽  
Beijing China

<p><strong>Remote sensing-based river discharge estimation for a small river flowing over the high mountain regions of the Tibetan Plateau </strong></p><p>Mulugeta Genanu Kebede <sup>1, 2, 3</sup>, Lei Wang<sup>1, 2*</sup>, Xiuping Li<sup>1</sup> and Zhidan Hu<sup>4</sup></p><p><sup>1 </sup>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, China</p><p><sup>2 </sup>University of Chinese Academy of Sciences, Beijing, China</p><p><sup>3 </sup>Arba Minch University, Water Technology Institute, Faculty of Meteorology and Hydrology, Arba Minch, Ethiopia</p><p><sup>4 </sup>Information Center, Ministry of Water Resources, Beijing 100053, China</p><p><strong>* </strong>Correspondence to: Dr. Lei Wang, Professor</p><p>Email: [email protected];     Tel.: +86-10-8409-7107; Fax: +86-10-8409-7079</p><p><strong>ABSTRACT</strong></p><p>River discharge, as one of the most essential climate variables, plays a vital role in the water cycle. Small-scale headwater catchments including high-mountain regions of Tibetan Plateau (TP) Rivers are mostly ungauged. Satellite technology shows its potential to fill this gap with high correlation of satellite-derived effective river width and corresponding in-situ gauged discharge. This study is innovative in estimating daily river discharge using modified Manning equation (Model 1), Bjerklie equation (Model 2), and Rating curve approach (Model 3) by combining river surface hydraulic variables directly derived from remote sensing datasets with other variables indirectly derived from empirical equations, which greatly contributes to the improvement of river flow measurement information especially over small rivers of TP. We extracted the effective width from Landsat image and flow depth via hydraulic geometry approach. All the input parameters directly or indirectly derived from remote sensing were combined and substituted into the fundamental flow equations/models to estimate discharges of Lhasa River. The validation of all three models’ results against the in-situ discharge measurements shows a strong correlation (the Nash–Sutcliffe efficiency coefficient (NSE) and the coefficient of determination (R<sup>2</sup>) values ≥ 0.993), indicating the potentiality of the models in accurately estimating daily river discharges. Trends of an overestimation of discharge by Model 1 and underestimation by Model 2 are observed. The discharge estimation by using Model 3 outperforms Model 1 and Model 2 due to the uncertainties associated with estimation of input parameters in the other two models. Generally, our discharge estimation methodology performs well and shows a superior result as compared with previously developed multivariate empirical equations and its application for other places globally can be the focus of upcoming studies.    </p><p><strong>Keywords:</strong> River discharge estimation, remote sensing, effective width, hydraulic relationship, Tibetan Plateau</p>

scholarly journals Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

2018 ◽  
pp. 195
Author(s):  
Pedro Perez Cutillas ◽  
Gonzalo G. Barberá ◽  
Carmelo Conesa García
Keyword(s):  
Remote Sensing ◽  
Environmental Variables ◽  
Input Data ◽  
Vegetation Index ◽  
Mineralogical Composition ◽  
Soil Erodibility ◽  
Estimation Algorithms ◽  
K Factor ◽  
Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han   mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

scholarly journals River Discharge Estimation based on Satellite Water Extent and Topography: An Application over the Amazon

2019 ◽  
Vol 20 (9) ◽  
pp. 1851-1866 ◽  
Author(s):  
Dinh Thi Lan Anh ◽  
Filipe Aires
Keyword(s):  
River Discharge ◽  
Amazon Basin ◽  
Water Cycle ◽  
Large River ◽  
Water Volume ◽  
Discharge Estimation ◽  
Three Rivers ◽  
Storage Change

Abstract River discharge (RD) estimates are necessary for many applications, including water management, flood risk, and water cycle studies. Satellite-derived long-term GIEMS-D3 surface water extent (SWE) maps and HydroSHEDS data, at 90-m resolution, are here used to estimate several hydrological quantities at a monthly time scale over a few selected locations within the Amazon basin. Two methods are first presented to derive the water level (WL): the “hypsometric curve” and the “histogram cutoff” approaches at an 18 km × 18 km resolution. The obtained WL values are interpolated over the whole water mask using a bilinear interpolation. The two methods give similar results and validation with altimetry is satisfactory, with a correlation ranging from 0.72 to 0.89 in the seven considered stations over three rivers (i.e., Wingu, Negro, and Solimoes Rivers). River width (RW) and water volume change (WVC) are also estimated. WVC is evaluated with GRACE total water storage change, and correlations range from 0.77 to 0.88. A neural network (NN) statistical model is then used to estimate the RD based on four predictors (SWE, WL, WVC, and RW) and on in situ RD measurements. Results compare well to in situ measurements with a correlation of about 0.97 for the raw data (and 0.84 for the anomalies). The presented methodologies show the potential of historical satellite data (the combination of SWE with topography) to help estimate RD. Our study focuses here on a large river in the Amazon basin at a monthly scale; additional analyses would be required for other rivers, including smaller ones, in different environments, and at higher temporal scale.

Integration of MODIS and Radar Altimetry Data for River Discharge Estimation from Space

2014 ◽  
pp. 607-610
Author(s):  
A. Tarpanelli ◽  
L. Brocca ◽  
S. Barbetta ◽  
T. Lacava ◽  
M. Faruolo ◽  
...  
Keyword(s):  
River Discharge ◽  
Altimetry Data ◽  
Radar Altimetry ◽  
Discharge Estimation

Daily discharge estimation at ungauged river sites using remote sensing

2012 ◽  
Vol 28 (3) ◽  
pp. 1043-1054 ◽  
Author(s):  
S. J. Birkinshaw ◽  
P. Moore ◽  
C.G. Kilsby ◽  
G. M. O'Donnell ◽  
A.J. Hardy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Daily Discharge ◽  
Discharge Estimation
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