Discharge of the river Rhine from multi-sensor data from empirical and physical methods

River discharge is a key variable to quantify the water cycle and its flux.&#160; This study focuses on the river Rhine, of width between 200 and 500 meters.&#160;River discharge is evaluated in this paper from the Sentinel-3 altimeter water level using various approches, which are the empirical rating curve method, the semi-empirical Bjerklie method and the physically-based method based on hydraulic equations.The Sentinel-3 GPOD ESA products from the SAMOSA+ retracker perform better than the standard Copernicus products that use the OCOG and ocean retrackers. Root-mean-square errors (RMSEs) between altimetry and in-situ stations are between 0.10 m and 0.30 m at 10 of the 17 virtual tide gauge locations.&#160;The empirical rating curve method applied to the altimetric water level and in-situ discharge provides estimates of the water discharge with accuracy of 3-7% (expressed as RMSE normalized with the mean of the discharge).The performance of the semi-empirical Bjerklie method and of the physically-based Manning algorithm to estimate the river discharge is assessed from water surface slope, elevation and top width data for different part of the river and flow conditions. Firstly, daily synthetic water surface slopes and elevations are generated from selected in-situ gauges and mean top river widths. Secondly the input to the discharge algorithm comes from the 1D-hydrodynamic model Sobek. Various choises for reach lengths and for number of observed time-series are considered. Different time sampling are used to study the effect of the repeat cycle of nadir altimeter and SWOT missions. The effect of the priori information on the accuracy of the flow water discharge is investigated.

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Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

10.5194/egusphere-egu2020-2650 ◽

2020 ◽

Author(s):

Daniel Scherer ◽

Christian Schwatke ◽

Denise Dettmering

Keyword(s):

Remote Sensing ◽

Time Series ◽

Water Level ◽

River Discharge ◽

Water Surface ◽

Satellite Altimetry ◽

Remote Sensing Data ◽

Sensing Data

Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM&#8217;s &#8220;Database of Hydrological Time series of Inland Waters&#8221; (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.

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Analytical approach for determining the mean water level profile in an estuary with substantial fresh water discharge

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1177-2016 ◽

2016 ◽

Vol 20 (3) ◽

pp. 1177-1195 ◽

Cited By ~ 16

Author(s):

Huayang Cai ◽

Hubert H. G. Savenije ◽

Chenjuan Jiang ◽

Lili Zhao ◽

Qingshu Yang

Keyword(s):

Water Level ◽

Fresh Water ◽

River Discharge ◽

River Flow ◽

Water Discharge ◽

Value Theory ◽

Velocity Amplitude ◽

Mean Water Level ◽

The Mean ◽

Fresh Water Discharge

Abstract. The mean water level in estuaries rises in the landward direction due to a combination of the density gradient, the tidal asymmetry, and the backwater effect. This phenomenon is more prominent under an increase of the fresh water discharge, which strongly intensifies both the tidal asymmetry and the backwater effect. However, the interactions between tide and river flow and their individual contributions to the rise of the mean water level along the estuary are not yet completely understood. In this study, we adopt an analytical approach to describe the tidal wave propagation under the influence of substantial fresh water discharge, where the analytical solutions are obtained by solving a set of four implicit equations for the tidal damping, the velocity amplitude, the wave celerity, and the phase lag. The analytical model is used to quantify the contributions made by tide, river, and tide–river interaction to the water level slope along the estuary, which sheds new light on the generation of backwater due to tide–river interaction. Subsequently, the method is applied to the Yangtze estuary under a wide range of river discharge conditions where the influence of both tidal amplitude and fresh water discharge on the longitudinal variation of the mean tidal water level is explored. Analytical model results show that in the tide-dominated region the mean water level is mainly controlled by the tide–river interaction, while it is primarily determined by the river flow in the river-dominated region, which is in agreement with previous studies. Interestingly, we demonstrate that the effect of the tide alone is most important in the transitional zone, where the ratio of velocity amplitude to river flow velocity approaches unity. This has to do with the fact that the contribution of tidal flow, river flow, and tide–river interaction to the residual water level slope are all proportional to the square of the velocity scale. Finally, we show that, in combination with extreme-value theory (e.g. generalized extreme-value theory), the method may be used to obtain a first-order estimation of the frequency of extreme water levels relevant for water management and flood control. By presenting these analytical relations, we provide direct insight into the interaction between tide and river flow, which will be useful for the study of other estuaries that experience substantial river discharge in a tidal region.

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A Sediment Rating-curve Method to Determine Sediment Discharge for Rainy Season in Micro-scale Watersheds

Indonesian Journal of Agricultural Research ◽

10.32734/injar.v2i1.857 ◽

2019 ◽

Vol 2 (1) ◽

pp. 21-27 ◽

Cited By ~ 1

Author(s):

Atikah Sitorus ◽

Edi Susanto

Keyword(s):

River Discharge ◽

Secondary Data ◽

Sediment Discharge ◽

Climate Data ◽

Rating Curve ◽

Concentration Data ◽

Discharge Data ◽

Sediment Rating Curve ◽

Curve Method ◽

Sediment Data

This research was carried out to overcome the problem of the lack of sediment data available in several watersheds in North Sumatra, the lack of available sediment data is caused by the requirement of a large amount of time, cost and risk to obtain such data. Purpose of this study was to obtain the equation of sediment rating curve. The sediment rating curve is an equation that connects the river discharge with sediment discharge, so that to obtain the sediment discharge, it is enough to use the river discharge data. This research used the descriptive method using the primary (sediment discharge and concentration data) and secondary data (climate data). Result of the study obtained the equation of the sediment rating curve of Qs = 14.115 Q2.2736 and the value of R2 of 0.711. The sediment discharge obtained has exceeded the limit set by the Ministry of Forestry regarding the criteria for determination of watersheds.

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Comparative Analysis of GEDI’s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies

Remote Sensing ◽

10.3390/rs14020340 ◽

2022 ◽

Vol 14 (2) ◽

pp. 340

Author(s):

Ibrahim Fayad ◽

Nicolas Baghdadi ◽

Frédéric Frappart

Keyword(s):

Water Level ◽

Water Surface ◽

Strong Dependence ◽

Ecosystem Dynamics ◽

Estimation Accuracy ◽

In Situ Data ◽

Gauge Station ◽

Water Level Variations ◽

Lower Variability

Spaceborne LiDAR altimetry has been demonstrated to be an essential source of data for the estimation and monitoring of inland water level variations. In this study, water level estimates from the Global Ecosystem Dynamics Investigation (GEDI) were validated against in situ gauge station records over Lake Geneva for the period between April 2019 and September 2020. The performances of the first and second releases (V1 and V2, respectively) of the GEDI data products were compared, and the effects on the accuracy of the instrumental and environmental factors were analyzed in order to discern the most accurate GEDI acquisitions. The respective influences of five parameters were analyzed in this study: (1) the signal-over-noise ratio (SNR); (2) the width of the water surface peak within the waveform (gwidth); (3) the amplitude of the water surface peak within the waveform (A); (4) the viewing angle of GEDI (VA); and (5) the acquiring beam. Results indicated that all these factors, except the acquiring beam, had an effect on the accuracy of GEDI elevations. Nonetheless, using VA as a filtering criterion was demonstrated to be the best compromise between retained shot count and water level estimation accuracy. Indeed, by choosing the shots with a VA ≤ 3.5°, 74.6% of the shots (after an initial filter) were retained with accuracies similar to choosing A > 400 (46.2% retained shots), SNR > 15 dB (63.3% retained shots), or gwidth < 10 bins (46.5% of retained shots). Finally, the comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant, but higher, bias and fewer deviations to the in situ data than V1. Indeed, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r = 0.66) and 42.8 cm with V1 (r = 0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA ≤ 3.5°). Regarding the bias, a higher value was observed with V2, but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number and strong dependence on GEDI dates.

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AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-327-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 327-334 ◽

Cited By ~ 1

Author(s):

Paulo Henrique Costa ◽

Eric Oliveira Pereira ◽

Philippe Maillard

Keyword(s):

Water Level ◽

Water Surface ◽

Satellite Altimetry ◽

Water Levels ◽

Small Lakes ◽

Water Surface Area ◽

Volume Fluctuations ◽

Calm Water ◽

Gauging Station

Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of in situ gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often “contaminated” by the surrounding land. To try to solve this, we created a “lake” retracker that rejects waveforms that cannot be attributed to “calm water” and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby in situ gauging station. Although the “lake” retracker worked well with calm water, the presence of waves and other factors was such that the standard “ice1” retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.

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Daily River Discharge Estimation Using Multi-Mission Radar Altimetry Data and Ensemble Learning Regression in the Lower Mekong River Basin

Remote Sensing ◽

10.3390/rs11222684 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2684 ◽

Cited By ~ 3

Author(s):

Kim ◽

Lee ◽

Chang ◽

Bui ◽

Jayasinghe ◽

...

Keyword(s):

River Basin ◽

Ensemble Learning ◽

River Discharge ◽

Water Levels ◽

Mekong River ◽

Altimetry Data ◽

Rating Curve ◽

Mekong River Basin ◽

Radar Altimetry

Estimating river discharge (Q) is critical for ecosystems and water resource management. Traditionally, estimating Q has depended on a single rating curve or the Manning equation. In contrast to the single rating curve, several rating curves at different locations have been linearly combined in an ensemble learning regression method to estimate Q (ELQ) at the Brazzaville gauge station in the central Congo River in a previous study. In this study, we further tested the proposed ELQ and apply it to the Lower Mekong River Basin (LMRB) with three locations: Stung Treng, Kratie, and Tan Chau. Two major advancements for estimating Q with ELQ are presented. First, ELQ successfully estimated Q at Tan Chau, downstream of Kratie, where hydrodynamic complexities exist. Since the hydrologic characteristics downstream of Kratie are extremely diverse and complex in time and space, most previous studies have estimated Q only upstream from Kratie with hydrologic models and statistical methods. Second, we estimated Q over the LMRB using ELQ with water levels (H) obtained from two radar altimetry missions, Envisat and Jason-2, which made it possible to estimate Q seamlessly from 2003 to 2016. Owing to ELQ with multi-mission radar altimetry data, we have overcome the problems of a single rating curve: Locations for estimating Q have to be close to virtual stations, e.g., a few tens of kilometers, because the performance of the single rating curve degrades as the distance between the location of Q estimation and a virtual station increases. Therefore, most previous studies had not used Jason-2 data whose cross-track interval is about 315 km at the equator. On the contrary, several H obtained from Jason-2 altimetry were used in this study regardless of distances from in-situ Q stations since the ELQ method compensates for degradation in the performance for Q estimation due to the poor rating curve with virtual stations away from in-situ Q stations. In general, the ELQ-estimated Q (QELQ) showed more accurate results compared to those obtained from a single rating curve. In the case of Tan Chau, the root mean square error (RMSE) of QELQ decreased by 1504/1338 m3/s using Envisat-derived H for the training/validation datasets. We successfully applied ELQ to the LMRB, which is one of the most complex basins to estimate Q with multi-mission radar altimetry data. Furthermore, our method can be used to obtain finer temporal resolution and enhance the performance of Q estimation with the current altimetry missions, such as Sentinel-3A/B and Jason-3.

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The potential of satellite radar altimetry in flood forecasting: concept and implementation for the Niger-Benue river basin

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-370-223-2015 ◽

2015 ◽

Vol 370 ◽

pp. 223-227 ◽

Cited By ~ 4

Author(s):

R. Pandey ◽

G. Amarnath

Keyword(s):

Water Level ◽

Flood Forecasting ◽

Quantitative Relation ◽

Rating Curve ◽

Satellite Altimeter ◽

Flow Prediction ◽

Satellite Radar ◽

Downstream Flow ◽

Satellite Radar Altimetry

Abstract. Flood forecasting in the downstream part of any hydrological basin is extremely difficult due to the lack of basin-wide hydrological information in near real-time and the absence of a data-sharing treaty among the transboundary nations. The accuracy of forecasts emerging from a hydrological model could be compromised without prior knowledge of the day-to-day flow regulation at different locations upstream of the Niger and Benue rivers. Only satellite altimeter monitoring allows us to identify the actual river levels upstream that reflect the human intervention at that location. This is critical for making accurate downstream forecasts. This present study aims to demonstrate the capability of altimeter-based flood forecasting along the Niger-Benue River in Nigeria. The study includes the comparison of decadal (at every 10 days from Jason-2) or monthly (at every 35 days from Envisat/AltiKa) observations from 2002 to 2014, with historical in situ measurements from 1990 to 2012. The water level obtained from these sources shows a good correlation (0.7–0.9). After validation of hydrological parameters obtained from two sources, a quantitative relation (rating curve) of upstream water level and downstream discharge is derived. This relation is then adopted for calculation of discharge at observation points, which is used to propagate the flow downstream at a desired location using a hydraulic river model. Results from this study from Jason-2 shows a promising correlation (R2 ≥ 90% with a Nash-Sutcliffe coefficient of more than 0.70) with 5~days ahead of downstream flow prediction over the Benue stream.

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Analytical approach for predicting fresh water discharge in an estuary based on tidal water level observations

Hydrology and Earth System Sciences ◽

10.5194/hess-18-4153-2014 ◽

2014 ◽

Vol 18 (10) ◽

pp. 4153-4168 ◽

Cited By ~ 32

Author(s):

H. Cai ◽

H. H. G. Savenije ◽

C. Jiang

Keyword(s):

Water Level ◽

Fresh Water ◽

River Discharge ◽

Analytical Approach ◽

Water Discharge ◽

Tidal Wave ◽

Residual Water ◽

Tidal Water ◽

Fresh Water Discharge ◽

Residual Water Level

Abstract. As the tidal wave propagates into an estuary, the tidally averaged water level tends to rise in landward direction due to the density difference between saline and fresh water and the asymmetry of the friction. The effect of friction on the residual slope is even more remarkable when accounting for fresh water discharge. In this study, we investigate the influence of river discharge on tidal wave propagation in the Yangtze estuary with specific attention to residual water level slope. This is done by using a one-dimensional analytical model for tidal hydrodynamics accounting for the residual water level. We demonstrate the importance of the residual slope on tidal dynamics and use it to improve the prediction of the tidal propagation in estuaries (i.e. tidal damping, velocity amplitude, wave celerity and phase lag), especially when the influence of river discharge is significant. Finally, we develop a new inverse analytical approach for estimating fresh water discharge on the basis of tidal water level observations along the estuary, which can be used as a tool to obtain information on the river discharge that is otherwise difficult to measure in the tidal region.

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AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-327-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 327-334

Author(s):

Paulo Henrique Costa ◽

Eric Oliveira Pereira ◽

Philippe Maillard

Keyword(s):

Water Level ◽

Water Surface ◽

Satellite Altimetry ◽

Water Levels ◽

Small Lakes ◽

Water Surface Area ◽

Volume Fluctuations ◽

Calm Water ◽

Gauging Station

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Uncertainty in river discharge observations: a quantitative analysis

Hydrology and Earth System Sciences ◽

10.5194/hess-13-913-2009 ◽

2009 ◽

Vol 13 (6) ◽

pp. 913-921 ◽

Cited By ~ 361

Author(s):

G. Di Baldassarre ◽

A. Montanari

Keyword(s):

River Discharge ◽

River Flow ◽

Flow Data ◽

Rating Curve ◽

Po River ◽

Discharge Data ◽

Model Code ◽

Curve Method ◽

Different Sources ◽

Extrapolation Error

Abstract. This study proposes a framework for analysing and quantifying the uncertainty of river flow data. Such uncertainty is often considered to be negligible with respect to other approximations affecting hydrological studies. Actually, given that river discharge data are usually obtained by means of the so-called rating curve method, a number of different sources of error affect the derived observations. These include: errors in measurements of river stage and discharge utilised to parameterise the rating curve, interpolation and extrapolation error of the rating curve, presence of unsteady flow conditions, and seasonal variations of the state of the vegetation (i.e. roughness). This study aims at analysing these sources of uncertainty using an original methodology. The novelty of the proposed framework lies in the estimation of rating curve uncertainty, which is based on hydraulic simulations. These latter are carried out on a reach of the Po River (Italy) by means of a one-dimensional (1-D) hydraulic model code (HEC-RAS). The results of the study show that errors in river flow data are indeed far from negligible.

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