Estimate River Discharge Research Articles

A new approach towards temporal densification of river discharge estimates by filling cloud- and stripe-gaps using Landsat and Sentinel-2 images

ABSTRACT River discharge plays a vital role in the Earth’s water cycle. Recent studies have sought to estimate river discharge using optical sensors onboard satellites, typically deriving discharge from multispectral data after excluding many images that are contaminated by cloud or affected by the scan-line-corrector (SLC) failure on Landsat 7. As such, time series of estimated discharge generally include a high proportion of missing values. To overcome these issues, we propose an easy and feasible methodology to temporally densify the time series of discharge estimates by repairing cloud-contaminated and SLC-affected images on the Google Earth Engine platform. The middle Yangtze River (MYR) was chosen as the study area to demonstrate and evaluate the performance of the methodology. First, different water indices were compared to select the most appropriate indicator for water surface retrieval along the MYR. The Pekel method shows the best performance for extracting the water surface when turbid or under thin, semi-transparent clouds. Second, the performances of the Tourian method and the Manning equation for estimating the discharge were compared when using only good-quality images, yielding Nash-Sutcliffe efficiency (Nash) coefficients of 0.69 and 0.79, respectively. Images affected by the SLC and over 50% cloud cover accounted for 41% of 532 records used from 1986 to 2019. The repaired, temporally complete time series of discharge estimates by the Manning equation corresponds to a correlation coefficient of 0.86, a Nash coefficient of 0.73, and a root mean square error of 5697 m3/s from 2000 to 2019. These results show the strong potential of this method for estimating the discharge of rivers with known cross-sectional and the morphological conditions worldwide.

Assessment of Hydrological Processes in an Ungauged Catchment in Eritrea

This study investigates the surface processes taking place in an ungauged catchment in the Foro region in Eritrea (East Africa). We focus on estimating river discharge, sediment transport, and surface runoff to characterize hydrological fluxes in the area and provide a preliminary quantification of sediment transport and erosion. In this context, an overarching objective of the research is the study of the catchment associated with the Foro Dam. The latter comprises a silted reservoir formerly employed for agricultural water supply. The main traits associated with the system behavior across the watershed are assessed for a variety of combinations of the parameters governing the hydrological model selected. A detailed sensitivity analysis is performed to quantify the effects of the hydrological parameters on the estimated results. Numerical analyses are then performed to obtain an appraisal of expected water and sediment fluxes. Outputs of interest are largely dominated by the curve number parameter.

A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation

River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization.

Evaluation of discharge measurements methods in a natural river of low or middle flow using an electromagnetic flow meter

AbstractMeasurement of river discharge should be accurate, as it is key environmental data and essential for hydrologic simulations, regardless of whether the flow is high, medium, or low. Herein, conventional methods based on velocity measurements at different depths (i.e., one‐point method, two‐point method, and three‐point method) are evaluated with a method using a surface velocity index and a method based on the entropy theory to estimate river discharge. All the methods were implemented to calculate the discharge of Agios Germanos River, located in Northern Greece, based on 2 years of measurement. Considering that the uncertainty in flow measurement is decreasing by increasing the number of velocity measurements points (EN ISO, 2007), in this work, the reference method is the conventional three‐point method. Results show that the other two conventional methods (the one‐point and the two‐point) estimate the discharge with an error of about 3%, while the error of the other two methods ranges between 11 and 14%. Nevertheless, the nonconventional methods could be considered acceptable and could be used safely to minimize time and subsequently, the cost of field measurements.

River discharge estimation in the Punatshangchu River Basin, Bhutan using an integrated flood analysis system

River discharge estimation in the Punatshangchu River Basin, Bhutan using an integrated flood analysis system

Estimation of Snow Mass Information via Assimilation of C-Band Synthetic Aperture Radar Backscatter Observations Into an Advanced Land Surface Model

This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the Noah-Multiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates. The data assimilation (DA) experiment was conducted across Western Colorado from September 2016 to August 2017. As part of the DA experiments, the impact of a rule-based update was evaluated by comparing snow water equivalent (SWE) estimates via DA (with [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> ] and without [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v2}}$</tex-math></inline-formula> ] the rule-based update) against SNOTEL SWE measurements. Results confirmed that rule-based update helped minimize SVM controllability issues, and in turn, improved the accuracy of SWE estimates relative to both open loop (OL) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v2}}$</tex-math></inline-formula> . Comparison of SWE estimates from Sentinel-1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> against SNOTEL SWE revealed that 75% of stations showed improvements in bias and correlation coefficient relative to the OL. Assimilated SWE estimates also showed statistical improvements during both the snow accumulation and snow ablation periods. However, unbiased root mean square error showed a slight increase during the snow ablation period due to the large variability in the electromagnetic response of C-band backscatter over deep and/or wet snow. Improvement of the SWE estimates also resulted in improving river discharge estimates compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements. River discharge using Sentinel-1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> improved the Nash–Sutcliffe efficiency at all available stations. These results suggest that physically constrained SVM can serve as an efficient observation operator for snow mass DA through explicit consideration of the first-order C-band scattering mechanisms over different terrestrial snow conditions.

Spaceborne River Discharge From a Nonparametric Stochastic Quantile Mapping Function

AbstractThe number of active gauges with open‐data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques for estimating river discharge from space, developing a rating curve between the ground‐based discharge and spaceborne river water level or width is the most straightforward one. However, this does not always lead to successful results, since the river section morphology often cannot simply be modeled by a limited number of parameters. Moreover, such methods do not deliver a proper estimation of the discharge's uncertainty as a result of the mismodeling and also the coarse assumptions made for the uncertainty of inputs. Here, we propose a nonparametric model for estimating river discharge and its uncertainty from spaceborne river width measurements. The model employs a stochastic quantile mapping scheme by, iteratively: (a) generating realizations of river discharge and width time series using Monte Carlo simulation, (b) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, and (c) adjusting the measurement uncertainties according to the point cloud scatter. We validate our method over 14 different river reaches along the Niger, Congo, Po Rivers, and several river reaches in the Mississippi river basin. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodeling. Moreover, the proposed algorithm delivers a meaningful uncertainty for the estimated discharge and allows us to calibrate the error bars of in situ discharge measurements.

Recent changes to Arctic river discharge

Arctic rivers drain ~15% of the global land surface and significantly influence local communities and economies, freshwater and marine ecosystems, and global climate. However, trusted and public knowledge of pan-Arctic rivers is inadequate, especially for small rivers and across Eurasia, inhibiting understanding of the Arctic response to climate change. Here, we calculate daily streamflow in 486,493 pan-Arctic river reaches from 1984-2018 by assimilating 9.18 million river discharge estimates made from 155,710 satellite images into hydrologic model simulations. We reveal larger and more heterogenous total water export (3-17% greater) and water export acceleration (factor of 1.2-3.3 larger) than previously reported, with substantial differences across basins, ecoregions, stream orders, human regulation, and permafrost regimes. We also find significant changes in the spring freshet and summer stream intermittency. Ultimately, our results represent an updated, publicly available, and more accurate daily understanding of Arctic rivers uniquely enabled by recent advances in hydrologic modeling and remote sensing.

RODEO: An algorithm and Google Earth Engine application for river discharge retrieval from Landsat

Satellite remote sensing of river discharge (RSQ) algorithms provide a useful source of observations to supplement river gauge records. RSQ algorithms have existed for over a decade yet their widespread use has been impeded by a lack of operational usability and quantitative characterization of uncertainty. Here we present RODEO, an algorithm for estimating river discharge using Landsat observations in near-real time. RODEO is validated with 456 gauges (median Kling-Gupta efficiency = 0.3) and uses a novel quantile rating curve technique that pairs Landsat river widths with discharge estimates from a global hydrologic model. RODEO also characterizes the uncertainty of RSQ estimates (estimated root-mean-square error = +7%), enabling RSQ retrievals to be used for data assimilation into hydrologic models. With the goal of expanding the RSQ user base, the RODEO algorithm is implemented as a freely available, off-the-shelf cloud-based Google Earth Engine application that provides RSQ estimates across North America from 1984-present.

Long-term mean river discharge estimation with multi-source grid-based global datasets

Long-term mean river discharge estimation with multi-source grid-based global datasets

Estimation of Long-Term River Discharge and Its Changes in Ungauged Watersheds in Pamir Plateau

The Pamir Plateau is an extremely important water resource area for over 60 million people in Central Asia. With the increasingly significant response of water resources to climate change, timely hydrological predictions for the future supply are necessary. In the plateau, accessing and monitoring the glaciers and their melt outflow are challenging due to the harsh geographic environments. Unmanned aerial vehicles (UAVs) combined with remote sensing technologies offer great potential for providing information to improve water resources management and decision-making. In this study, we integrated UAV and satellite remote sensing data, and applied a water balance model to estimate monthly and annual river discharges for the ten river sections in the Eastern Pamir Plateau, China from 1999 to 2020. We found that the glacier area in the controlled basins of these sections has decreased by approximately 63% from 1999 to 2020. Basins with smaller glacier areas are more sensitive to climate change. The ten river sections are characterized by decreasing trends in monthly river discharge, with an average reduction of −21.05%. The annual variation of total runoff and glacial meltwater discharge is consistent with the monthly variation of discharge, and the average discharge from glacier meltwater accounts for 83% of the total runoff. We conclude that the overall decreasing trend of discharge is closely related to the recession of glaciers. Under the background of climate warming in the region, glaciers are no longer sufficient to support the increase in river discharge, which has passed its peak value and shows a decreasing trend.

Assessment of bottom-up satellite rainfall products on estimating river discharge and hydrologic signatures in Brazilian catchments

Satellite rainfall products are one of the most valuable tools for water resources monitoring in data-scarce regions, due to their low latency and quasi-global range. However, there are still uncertainties associated with rainfall products performance used to estimate hydrologic signatures in several regions, such as Brazil. Here, we investigate the performance of three rainfall products in estimating daily precipitation, daily river discharge, and hydrologic signatures over Brazil: the SM2RAIN-ASCAT and the GPM+SM2RAIN satellite products, and the ERA5 reanalysis product. We used a subset of 520 catchments from the Catchments Attributes for Brazil (CABra) dataset and the hydrologic modeling was carried out using the MISDc hydrologic model. Satellite-based products performed better than ERA5 for most Brazilian biomes in estimating daily precipitation when compared with ground observations used as reference. Daily river discharge was also better modeled with SM2RAIN-ASCAT and GPM+SM2RAIN. Hydrologic modeling presented low values of bias and >80% of catchments with KGE > 0.5 in calibration. Lastly, hydrologic signatures were well estimated by SM2RAIN-ASCAT and GPM+SM2RAIN, and for some biomes (Atlantic Forest, Cerrado, and Caatinga) they are better predictors than ground-based observations. We showed that there is a significant added value when using SM2RAIN-ASCAT and GPM+SM2RAIN products in tropical catchments, allowing a high-quality continuous water resources monitoring even in data-scarce regions. Besides, our findings pave the way for a better understanding of hydrologic extremes (droughts and floods) using these satellite rainfall products on multiple spatial and temporal scales.

Towards the continuous monitoring of the extreme events through satellite radar altimetry observations

Changes in the frequency and magnitude of extreme hydrological events (floods and droughts) are one of the most significance consequences of the climate change and their monitoring is therefore essential in many fields. However, the number of the global in situ discharge measurements has been declining since the eighties and the global supply of available data is often restricted in many parts of world. All these reasons have fostered in the last decades the exploitation of new technologies, such as satellite data, in the estimation of river discharge. In this study, satellite altimetry water level data are used to set-up a consistent and up-to-date river discharge dataset by means of rating curves for different sites across the world. Long continuous river discharge time series so obtained are used to perform a trend analysis on extreme hydrological events. Specifically, peak-over threshold values are extracted from the discharge time series, as proxy variables of independent extreme events, and the trend analysis is then carried out to identify changes in the magnitude of extreme events.The analysis permits to identify areas of the world prone to floods, like the West Africa, or drought so that appropriate actions for disaster risk mitigation and continuous improvement in disaster preparedness, response and recovery practices can be adopted.

Image-based river discharge estimation by merging heterogeneous data with information entropy theory

An information entropy based approach for the discharge measurements is evaluated for the gaging of the Isère river at the Grenoble university campus. Over a four month period, six discharge measurements were made using a vessel-mounted aDcp. Simultaneously, particle tracking velocimetry (PTV) from video images was used to estimate surface velocities. The surface velocities are projected along the regularly surveyed river section of the Isère-Campus gaging station. The vertical velocity profile at each stream-wise location is approximated by a 1D entropy profile. Information entropy 1D velocity vertical profile depends on two parameters which are fitted using aDcp and surface velocity measurements. The inclusion of the surface velocities reduces the dispersion of the estimated entropy parameters. The measurements show that the two parameters are linearly related with a slope that is stage dependent and thus, surface velocity dependent. From there, the information entropy theory for 1D velocity distribution offers a protocol by which surface velocities only are used to compute the discharges. The protocol is calibrated with both aDcp and surface velocity measurements. It is finally validated with several events during which only surface velocities are measured. For the high water flood event the estimated discharge falls within 2% of the one estimated with the rating curve of the gaging station.

Exploring the Factors Controlling the Error Characteristics of the Surface Water and Ocean Topography Mission Discharge Estimates

AbstractThe Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50–100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning‐Strickler equation, complementing in situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g., friction coefficient and bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT‐like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst‐case SWOT discharge accuracy.

Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed

AbstractRemote sensing has gained attention as a novel source of primary information for estimating river discharge, and the Mass‐conserved Flow Law Inversion (McFLI) approach has successfully estimated river discharge in ungauged basins solely from optical satellite data. However, McFLI currently suffers from two major drawbacks: (1) existing optical satellites lead to temporally and spatially sparse discharge estimates and (2) because of the assumptions required, McFLI cannot guarantee downstream flow continuity. Hydrological modeling has neither drawback, yet model accuracy is frequently limited by a lack of discharge observations. We therefore combine McFLI and models in a data assimilation framework applicable globally. We establish a daily “ungauged” baseline model for 28,998 reaches of the Missouri river basin forced by recently published global runoff data, which we do not calibrate. We estimate discharge via McFLI using ∼1 million width measurements made from 12,000 Landsat scenes and assimilate McFLI into the model before validating at 403 USGS gauges. Results show that assimilated discharges did not impair already accurate baseline flows and achieved median improvements of 28% normalized root mean square error, 0.50 Nash–Sutcliffe efficiency (NSE), and 0.23 Kling–Gupta efficiency where baseline performance was poor (defined as baseline negative NSE, 225/403 reaches). We ultimately improved flows at 92% of these originally poorly modeled gauges, even though Landsat images only provide McFLI discharges at 1.5% of reaches and 26% of simulated days. Our results suggest that the combination of McFLI and state‐of‐the‐art hydrology models can improve flow estimations in ungauged basins globally.

A Framework for Estimating Global‐Scale River Discharge by Assimilating Satellite Altimetry

AbstractUnderstanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the elevation and slope of surface waters at &lt;100 m resolution. Methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, most SWOT data assimilation studies have been conducted at the local scale. We developed a novel framework for estimating river discharge at the global scale by incorporating SWOT observations into the CaMa‐Flood hydrodynamic model. The local ensemble transform Kalman filter with adaptive local patches was used to assimilate SWOT observations. We tested the framework using multimodel runoff forcing and inaccurate model parameters represented by corrupted Manning's coefficient values. Assimilation of virtual SWOT observations considerably improved river discharge estimates for continental‐scale rivers at high latitudes (&gt;50°) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was related to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. Accurate global river discharge estimates were obtained (Kling‐Gupta efficiency [KGE] &gt; 0.90) in river reaches with &gt;270 accumulated overpasses per SWOT cycle when no model error was assumed. Introducing model errors decreased this accuracy (KGE ≈ 0.85). Therefore, improved hydrodynamic models are essential for maximizing SWOT information. These synthetic experiments showed where discharge estimates could be improved using SWOT observations. Further advances are needed for global‐scale data assimilation.

Assessment of Artificial Intelligence Models for Developing Single‐Value and Loop Rating Curves

Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage‐discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming‐generalized reduced gradient (MGGP‐GRG) technique for estimating river discharges for steady as well as unsteady flows. It also compares the MGGP‐GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP‐GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP‐GRG model overall performs the best in developing stage‐discharge relationships for both single‐value and loop rating curves. For instance, the hybrid MGGP‐GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence (AI) models (ANN and MGGP) performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model.

Trend and Variance of Continental Fresh Water Discharge over the Last Six Decades

Trend estimation of river discharge is an important but difficult task because discharge time series are nonlinear and nonstationary. Previous studies estimated the trend of discharge using a linear method, which is not applicable to nonstationary time series with a nonlinear trend. To overcome this problem, we used a recently developed wavelet-based method, ensemble empirical mode decomposition (EEMD), which can separate nonstationary variations from the long-term nonlinear trend. Applying EEMD to annual discharge data of the 925 world’s largest rivers from 1948–2004, we found that the global discharge decreased before 1978 and increased after 1978, which contrasts the nonsignificant trend as estimated by the linear method over the same period. Further analyses show that precipitation had a consistent and dominant influence on the interannual variation of discharge of all six continents and globally, but the influences of precipitation and surface air temperature on the trend of discharge varied regionally. We also found that the estimated trend using EEMD was very sensitive to the discharge data length. Our results demonstrated some useful applications of the EEMD method in studying regional or global discharge, and it should be adopted for studying all nonstationary hydrological time series.

An improvement of the Space-Time Image Velocimetry combined with a new denoising method for estimating river discharge

The Space-Time Image Velocimetry (STIV) is a time-averaged velocity measurement method, which takes river surface images as the analysis object, and detects the Main Orientation of Texture (MOT) in a generated Space-Time Image (STI) to obtain one-dimensional velocities on the water surface. The STIV has great potential in real-time monitoring of river flow owing to its high spatial resolution and low time complexity. However, the generated STI contains a lot of noise and interference texture, which is inevitable in practical applications. The practicality of the STIV is severely limited by the low-quality STI. To solve this problem, a denoising method based on the filtering technology is proposed and combined with different texture detection algorithms in this paper. The accuracy of this method is verified through a comparative field experiment with an impellor-style current meter. The experimental results show: (1) By using this new denoising method, the robustness and accuracy of the STIV are significantly improved no matter what kind of texture detection algorithm is adopted; (2) Among all the texture detection algorithms, the FFT-based STIV combined with the new denoising method performs best. The relative errors of the surface velocities are controlled within 6%, and the relative errors of the discharges are controlled within ±4%.