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

Discharge estimation for medium-sized river using multi-temporal remote sensing data: a case study in Brazil

2020 ◽  
Vol 65 (14) ◽  
pp. 2402-2418
Author(s):  
Évelyn Márcia Pôssa ◽  
Philippe Maillard ◽  
Lília Maria de Oliveira
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Multi Temporal ◽  
Discharge Estimation

scholarly journals Prediction of Daily Streamflow Using Various Kernel Function Based Regression: A Case Study in India

2021 ◽  
Author(s):  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Ankur Srivast ◽  
Anuradha Kumari ◽  
Rawshan Ali ◽  
...  
Keyword(s):  
Water Resources ◽  
Decision Makers ◽  
Polynomial Kernel ◽  
Daily Streamflow ◽  
Testing Period ◽  
Water Users ◽  
Total Data ◽  
Daily Discharge ◽  
Discharge Estimation

Abstract River daily discharge estimation and modeling considers an important step for scheduling and planning different water resources for sustainable socio-economic development. In the current work, four techniques of Gaussian processes regression (GPR): Polynomial Kernel, Radial Basis Function Kernel, Normalized Polynomial Kernel, and PUK Kernel, were used to model the daily discharge. Hydrological-datasets containing daily-stage (m) and discharge (m3/sec) were gathered over the period from 2004-2013. The datasets were divided into two sections: (i) models training containing 70% (2004-2010) of the total data and (ii) remaining 30% (2011- 2013) were for testing. Comparing all the four developed models, our findings show that the superlative model was the PUK-Kernel model with a correlation coefficient (r) of 0.96, MAE of 36.70 m3/s, RMSE of 90.92 m3/s, RAE of 17.50 %, RRSE of 26.05 % in the training period. Whereas, it performed equally well in the testing period with r = 0.97, MAE = 44.84 m3/s, RMSE = 95.05 m3/s, RAE = 17.98 %, RRSE = 24.94 % in the testing period. Our findings can be included that GPR-PUK was more accurate and stable than other models, and can be used to help water-users, decision-makers, development-planners for managing water resources and achieving sustainable development.

scholarly journals Near-daily discharge estimation in high latitudes from Sentinel-1 and 2: A case study for the Icelandic Þjórsá river

2020 ◽  
Vol 241 ◽  
pp. 111684 ◽  
Author(s):  
Joost Brombacher ◽  
Johannes Reiche ◽  
Roel Dijksma ◽  
Adriaan J. Teuling
Keyword(s):  
High Latitudes ◽  
Daily Discharge ◽  
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 The effect on river discharge estimation by considering an interaction between land surface process and river routing process

2015 ◽  
Vol 369 ◽  
pp. 81-86 ◽  
Author(s):  
K. Yorozu ◽  
Y. Tachikawa
Keyword(s):  
Soil Water ◽  
Land Surface ◽  
River Discharge ◽  
Hydrologic Model ◽  
Flow Routing ◽  
Hydrologic Process ◽  
Daily Discharge ◽  
Subsurface Runoff ◽  
Discharge Estimation ◽  
River Routing

Abstract. There is much research assessing the impact of climate change on the hydrologic cycle. However, it has often focused on a specific hydrologic process, without considering the interaction among hydrologic processes. In this study, a distributed hydrologic model considering the interaction between flow routing and land surface processes was developed, and its effect on river discharge estimation was investigated. The model enables consideration of flow routing, irrigation withdrawal from rivers at paddy fields, crop growth depending on water and energy status, and evapotranspiration based on meteorological, soil water and vegetation status. To examine the effects of hydrologic process interaction on river discharge estimation, a developed model was applied to the Chao Phraya river basin using near surface meteorological data collected by the Japanese Meteorological Research Institute's Atmospheric General Circulation Model (MRI-AGCM3.2S) with TL959 spatial resolution as forcing data. Also, a flow routing model, which was part of the developed model, was applied independently, using surface and subsurface runoff data from the same GCM. In the results, the developed model tended to estimate a smaller river discharge than was estimated by the river routing model, because of the irrigation effect. In contrast, the annual maximum daily discharge calculated by the developed model was 24% greater than that by the flow routing model. It is assumed that surface runoff in the developed model was greater than that in the flow routing model because the soil water content was maintained at a high level through irrigation withdrawal. As for drought discharge, which is defined as the 355th largest daily discharge, the developed model gave a discharge 2.7-fold greater than the flow routing model. It seems that subsurface runoff in the developed model was greater than that in the flow routing model. The results of this study suggest that considering hydrologic interaction in a numerical model could affect both flood and drought estimation.

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 Improved Remotely Sensed Total Basin Discharge and Its Seasonal Error Characterization in the Yangtze River Basin

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3386 ◽  
Author(s):  
Chen ◽  
Fok ◽  
Ma ◽  
Tenzer
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Remotely Sensed ◽  
The Yangtze River ◽  
Remotely Sensed Data ◽  
Data Products ◽  
The Yangtze River Basin ◽  
Discharge Estimation

Total basin discharge is a critical component for the understanding of surface water exchange at the land–ocean interface. A continuous decline in the number of global hydrological stations over the past fifteen years has promoted the estimation of total basin discharge using remote sensing. Previous remotely sensed total basin discharge of the Yangtze River basin, expressed in terms of runoff, was estimated via the water balance equation, using a combination of remote sensing and modeled data products of various qualities. Nevertheless, the modeled data products are presented with large uncertainties and the seasonal error characteristics of the remotely sensed total basin discharge have rarely been investigated. In this study, we conducted total basin discharge estimation of the Yangtze River Basin, based purely on remotely sensed data. This estimation considered the period between January 2003 and December 2012 at a monthly temporal scale and was based on precipitation data collected from the Tropical Rainfall Measuring Mission (TRMM) satellite, evapotranspiration data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, and terrestrial water storage data collected from the Gravity Recovery and Climate Experiment (GRACE) satellite. A seasonal accuracy assessment was performed to detect poor performances and highlight any deficiencies in the modeled data products derived from the discharge estimation. Comparison of our estimated runoff results based purely on remotely sensed data, and the most accurate results of a previous study against the observed runoff revealed a Pearson correlation coefficient (PCC) of 0.89 and 0.74, and a root-mean-square error (RMSE) of 11.69 mm/month and 14.30 mm/month, respectively. We identified some deficiencies in capturing the maximum and the minimum of runoff rates during both summer and winter, due to an underestimation and overestimation of evapotranspiration, respectively.

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