River Flow Monitoring Research Articles

Optimization of Space-Time image velocimetry based on deep residual learning

Space-Time Image Velocimetry (STIV) is considered to be a highly promising method for river flow measurement due to its safe and efficient features. However, the traditional STIV method is challenged to obtain stable and accurate results when disturbed by external environments such as obstacles, waves and noise. As a consequence, a residual network-based STIV method is constructed by combining STIV with deep learning. In the synthetic dataset validation, benefited from the unique residual module and the relation-aware global attention mechanism, the recognition accuracy of STI texture angle is as high as 99.9 %, which is much higher than that of the Efficient Net b3, ViT16 and VGG19 networks. In the real dataset, the proposed method shows stronger stability and lower error by compared with gradient tensor (GT) and fast Fourier transform (FFT) methods, whose overall recognition accuracy is above 90 % under all types of STI cases, angle error is within 2°, and flow velocity error is around 0.10 m/s. Furthermore, the model was validated by a one-year comparative measurement at the Qilijie hydrological station in 2022, and the results were compared with LSPIV, GT, and FFT methods. The R2 of the model to the measured values is 0.98, the RMSE is 0.17, and the MAE is 0.12 m/s, indicating that the method has sufficient accuracy and robustness for long-term stable and real-time monitoring of river flow.

Implementation of Hydrological Model for the Malino Catchment Area in South Sulawesi Province, Indonesia

In South Sulawesi, the development of irrigation may be hindered by the continuing limitations and inadequacies of hydrological data. It is well known that the rainfall monitoring station is more extensive than the river flow monitoring station. Therefore, the Malino Catchment Area was selected to illustrate the theory of four tank components. The 1st tank (tank A) has two horizontal outlets (Qa1 and Qa2) and one vertical outlet (Ia); the 2nd tank (tank B) has one horizontal outlet (Qb) and one vertical outlet (Ib); the 3rd tank (tank C) has the same conceptual structure as tanks A and B; and the 4th tank (tank D) has only one horizontal outlet (Qd). To ensure that the tank model represents vertical and horizontal flows in a watershed region, the flows (Qa1, Qa2, Qb, Qc, and Qd) are predicted to accumulate in one flow, more or less, and must equal the measured discharge (Qo) at the specified time. Rainfall and evapotranspiration data are required to calculate this model. The 264.55 km2 (25902 ha) research area has an elevation range of 400–2400 masl. The findings for land use are dominated by plantations (41.01%), forests (40.79%), rice fields (15.44%), and residential areas (0.96%). In the calibration of the tank model, R2 is evaluated at 0.560% (good) and Nash–Sutcliffe efficiency is evaluated at 0.526% (good) to ensure that the model can represent the distribution of water flow components. Additionally, the measurements for the total water flow (Qtotal) were 13702 m3/y with a total rainfall of 3996 mm/y. Furthermore, surface flow accounts for 77.26% of the total runoff water, while intermediate flow accounts for 20.25%.

An automatic ANN-based procedure for detecting optimal image sequences supporting LS-PIV applications for rivers monitoring

River flow monitoring has recently experienced rapid development due to advancements in optical methods, which are non-intrusive and enhance safety conditions for operators. Surface velocity fields are obtained recording and analyzing displacements of floating tracer materials, artificially introduced or already present on the water surface. River discharge can be assessed coupling the surface velocity fields with geometric data of a cross section. The accuracy of optical techniques is strongly affected by different environmental and hydraulic factors, and software parameterization, with tracer features that often play a prominent role. An adequate density and spatial distribution of tracer is required to ensure a complete characterization of surface velocity fields. In practical applications such conditions might occur only for a limited portion of the entire acquired images sequence. This work proposes an automatic procedure for identifying and extracting the best portion of a recorded video in terms of seeding characteristics and demonstrates how LS-PIV software performances can be enhanced through this approach. The procedure is implemented through a data-driven empirical approach based on an Artificial Neural Network, trained using data collected during an extensive measurement campaign across different rivers in Sicily (Italy). Performances are evaluated in terms of error in reproducing surface velocity profiles along specific transects, where benchmark profiles derived using an Acoustic Doppler Current Profiler are available. The procedure, also tested via numerical simulations on synthetic image sequences, outperformed an approach based on an existing metric for seeding characterization and represents a simple and useful tool for LS-PIV based applications.

Radar Technology for River Flow Monitoring: Assessment of the Current Status and Future Challenges

As an emerging non-contact method of flow monitoring, radar technology compensates for the shortcomings of traditional methods in terms of the efficiency, timeliness, and difficulty in monitoring high floods, and can provide accurate measurement results, making it one of the most promising flow monitoring methods in the future. This paper describes in detail the workflow from radar data acquisition to flow calculation; compares and analyzes the current state-of-the-art signal sampling and its limitations, Doppler spectrum estimation, signal processing and flow inversion; points out the challenges that these technologies may face in the future; and provides corresponding solutions in order to improve the real-time and accuracy of hydrometric as well as promote the development of non-contact flow monitoring technology.

Testing the theoretical principles of citizen science in monitoring stream water levels through photo-trap frames

IntroductionMonitoring water levels of ephemeral streams is a difficult yet important task in hydrology, especially when studying minor river flows in remote areas. The installation of flow gauging stations on upstream tributaries is impacted by the lack of economic resources, by accessibility problems and unstable morphological conditions of riverbeds avoiding the implementation of distributed observation networks at large scales. This major challenge in hydrology may be addressed by eventually adopting image-analysis approaches that constitute an effective parsimonious river flow monitoring method, but the demonstration of such techniques is still an open research topic.MethodologyThis study focuses on the testing of a novel technique that employs a white pole “staff gauge” to be photographed using a phototrap (i.e., named stage-cam which is a high-speed camera trigger system). This technology shows to be particularly efficient for observing flood events that represent the most difficult scenario for streamflow monitoring. Furthermore, the testing of this innovative hydrological data-gathering method is performed by adopting citizen science and participatory image analysis to assess the value and effectiveness of non-expert volunteers to operationalize this novel method. Citizen engagement may be essential for supporting distributed flow monitoring supporting large scale image analysis algorithm calibration associated to a continuous series of phototrap images. The Montecalvello watershed, located near Rome, is selected for this pilot case study.ResultsResults of the conducted tests, involving the University of Tuscia student community, are presented toward the demonstration of the effectiveness of citizen science to collect valid quantitative hydrological observations, which may correlate consistently with expert estimates. To better interpret results, the authors consider mean absolute error (MAE) and mean absolute relative error (MARE) as synthetic indices to determine the uncertainties associated to voluntary observations. Low margins of error return positive feedback on the adopted methodology.DiscussionThis research promotes the use of participatory approaches for addressing an actual hydrological monitoring challenge. In addition, it fosters increased citizen knowledge and awareness of the importance and value of hydrological monitoring of small ungauged river basins.

Hydro-Climate Variability and Trend Analysis in the Jemma Sub-Basin, Upper Blue Nile River, Ethiopia

Understanding hydro-climate variability in areas where communities are strongly dependent on subsistence natural resource-based economies at finer spatial resolution can have substantial benefits for effective agricultural water management. This study investigated the hydro-climate variability and trend of the Jemma sub-basin, in the Upper Blue Nile (Abbay) basin, using Mann–Kendall test, Sen’s slope estimator, and Standardized Precipitation Index (SPI). Climate data from 11 weather stations inside the basin and two major streams were used for the statistical analysis. The climate data were also correlated with the ENSO phenomenon to explain drivers of the variability. The results show that the sub-basin has been experiencing normal to moderate variability in the annual and Kiremt season rainfalls, but high variability and declining trend for 73% of the minor (Belg) season rainfall, negatively affecting the planting of short-cycle crops that account for about 20% of crop production in the study area. Generally, strong El Nińo (SST anomaly >1) has been correlated to a substantial decline in the Belg season rainfall. Stream-flow variability has also been found to be very high (CV > 30%) in both river flow monitoring stations. Subsequently, ensuring agricultural water security for short-cycle crop production seems to be a risky and daunting task unless supplemented with groundwater conjunctive use or water harvesting.

River Flow Measurements Utilizing UAV-Based Surface Velocimetry and Bathymetry Coupled with Sonar

Water velocity and discharge are essential parameters for monitoring water resources sustainably. Datasets acquired from Unoccupied Aerial Systems (UAS) allow for river monitoring at high spatial and temporal resolution, and may be the only alternative in areas that are difficult to access. Image or video-based methods for river flow monitoring have become very popular since they are not time-consuming or expensive in contrast to traditional methods. This study presents a non-contact methodology to estimate streamflow based on data collected from UAS. Both surface velocity and river geometry are measured directly in field conditions via the UAS while streamflow is estimated with a new technique. Specifically, surface velocity is estimated by using image-based velocimetry software while river bathymetry is measured with a floating sonar, tethered like a pendulum to the UAV. Traditional field measurements were collected along the same cross-section of the Aggitis River in Greece in order to assess the accuracy of the remotely sensed velocities, depths, and discharges. Overall, the new technique is very promising for providing accurate UAV-based streamflow results compared to the field data.

Temporal and regional variability of cumulative ecological risks of pesticides in Japanese river waters for 1990–2010

We quantitatively evaluated the cumulative ecological risks from multiple pesticides used in paddy fields in Japan. Moreover, we visualized the temporal and regional variability of those risks for 1990–2010. Considering the region-specific parameters of environmental conditions, region-specific predicted environmental concentrations were estimated at 350 river-flow monitoring sites in Japan. Then the multi-substance potentially affected fraction (msPAF) was calculated as a risk index of multiple pesticides by using the computation tool NIAES-CERAP. The median msPAF values for insecticides and herbicides decreased by 92.4% and 53.1%, respectively, from 1990 to 2010. This substantial reduction in ecological risk was attributed to the development of low-risk pesticides by manufacturers, the efforts of farmers in risk reduction, and tighter regulation by the Japanese government. In particular, the substantial reduction of the ecological risk from insecticides was largely due to the decrease in the use of organophosphorus insecticides.

PREDIKSI DEBIT ALIRAN MASUK KE TELAGA MENJER MENGGUNAKAN PERSAMAAN NERACA AIR DAN PEMODELAN HEC-HMS

The Menjer lake is the main source for Hydroelectric Power Plant of the PLTA Garung. Information about the water balance and the potential of existing water resources in the Menjer Catchment Area (DTA) is needed to obtain an efficient operating pattern, the sustainability of the Garung hydropower plant, and good management of the Menjer Lake. The purpose of this study was to estimate the inflow of three main rivers in the Menjer catchment area using HEC-HMS hydrological and water balance approach. Simulated results of the HEC-HMS model shows that the average of total the inflows of three main rivers to the Menjer lake in 2017, 2018 and 2019 during rainy season are 0.954 m3/s, 0.944 m3/s, and 1.017 m3/s, and during dry season are 0.820 m3/s, 0.783 m3/s, and 0.80 m3/s, respectively. While the prediction results of the discharge with the equation of the water balance shows that the average of total river inflows to the Menjer lake during rainy season is 2017 is 1.628 m3/s, in 2018 it is 1.579 m3/s, and in 2019 it is 3.296 m3/s and during dry season is 1.893 m3/s in 2017, 1.176 m3/s tahun 2018, and 1.893 m3/s in 2019. These results indicate that the results of discharge modeling with HEC-HMS are smaller than those predicted by the water balance equation. The study concluded that HEC-HMS could be used to predict daily inflows. However, further calibration and validation need to be carried out by recommending installing a river flow monitoring station at each river outlet.Keywords: water balance HEC-HMS, inflow prediction

Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales

Image-based approaches for surface velocity estimations are becoming increasingly popular because of the increasing need for low-cost river flow monitoring methods. In this context, seeding characteristics and dynamics along the video footage represent one of the key variables influencing image velocimetry results. Recent studies highlight the need to identify parameter settings based on local flow conditions and environmental factors apriori, making the use of image velocimetry approaches hard to automatise for continuous monitoring. The seeding distribution index (SDI) – recently introduced by the authors – identifies the best frame window length of a video to analyse, reducing the computational loads and improving image velocimetry performance. In this work, we propose a method based on an average SDI time series threshold with noise filtering. This method was tested on three case studies in Italy and validated on one in the UK, where a relatively high number of measurements was available. Following this method, we observed an error reduction of 20–39% with respect to the analysis of the full video. This beneficial effect appears even more evident when the optimisation is applied at sub-sector scales, in cases where the SDI shows a marked variability along the cross-section. Finally, an empirical parameter τ was proposed, calibrated, and validated for practical uses to define the SDI threshold. τ showed relatively stable values in the different contexts where it has been applied. Application of the seeding index to image-based velocimetry for surface flow velocity estimates is likely to enhance measurement accuracy in future studies.

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%.

River Flow Monitoring by Sentinel-3 OLCI and MODIS: Comparison and Combination

The monitoring of rivers by satellite is an up-to-date subject in hydrological studies as confirmed by the interest of space agencies to finance specific missions that respond to the quantification of surface water flows. We address the problem by using multi-spectral sensors, in the near-infrared (NIR) band, correlating the reflectance ratio between a dry and a wet pixel extracted from a time series of images, the C/M ratio, with five river flow-related variables: water level, river discharge, flow area, mean flow velocity and surface width. The innovative aspect of this study is the use of the Ocean and Land Colour Instrument (OLCI) on board Sentinel-3 satellites, compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) used in previous studies. Our results show that the C/M ratio from OLCI and MODIS is more correlated with the mean flow velocity than with other variables. To improve the number of observations, OLCI and MODIS products are combined into multi-mission time series. The integration provides good quality data at around daily resolution, appropriate for the analysis of the Po River investigated in this study. Finally, the combination of only MODIS products outperforms the other configurations with a frequency slightly lower (~1.8 days).

Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers

River flow monitoring is essential for many hydraulic and hydrologic applications related to water resource management and flood forecasting. Currently, unmanned aerial systems (UASs) combined with image velocimetry techniques provide a significant low-cost alternative for hydraulic monitoring, allowing the estimation of river stream flows and surface flow velocities based on video acquisitions. The accuracy of these methods tends to be sensitive to several factors, such as the presence of floating materials (transiting onto the stream surface), challenging environmental conditions, and the choice of a proper experimental setting. In most real-world cases, the seeding density is not constant during the acquisition period, so it is not unusual for the patterns generated by tracers to have non-uniform distribution. As a consequence, these patterns are not easily identifiable and are thus not trackable, especially during floods. We aimed to quantify the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) techniques under different hydrological and seeding conditions using footage acquired by UASs. With this aim, three metrics were adopted to explore the relationship between seeding density, tracer characteristics, and their spatial distribution in image velocimetry accuracy. The results demonstrate that prior knowledge of seeding characteristics in the field can help with the use of these techniques, providing a priori evaluation of the quality of the frame sequence for post-processing.

Pixel Tracking to Estimate Rivers Water Flow Elevation Using Cosmo-SkyMed Synthetic Aperture Radar Data

The lack of availability of historical and reliable river water level information is an issue that can be overcome through the exploitation of modern satellite remote sensing systems. This research has the objective of contributing in solving the information-gap problem of river flow monitoring through a synthetic aperture radar (SAR) signal processing technique that has the capability to perform water flow elevation estimation. This paper proposes the application of a new method for the design of a robust procedure to track over the time double-bounce reflections from bridges crossing rivers to measure the gap space existing between the river surface and bridges. Specifically, the difference in position between the single and double bounce is suitably measured over the time. Simulated and satellite temporal series of SAR data from COSMO-SkyMed data are compared to the ground measurements recorded for three gauges sites over the Po and Tiber Rivers, Italy. The obtained performance indices confirm the effectiveness of the method in the estimation of water level also in narrow or ungauged rivers.

Stereo System for Remote Monitoring of River Flows

In this article we present a video-based method for river flow monitoring. The proposed method aims at deriving efficient approximations of the river velocity using natural formations on the river surface. In order to overcome peculiarities of the flow, we propose to uniformly exploit all such structures that appear locally with short temporal duration. Towards this direction we explore the expanded capabilities of a stereoscopic camera layout with the dual observation fields and the potential of reverting projective deformations. By mapping to world coordinates, all spatial locations in the video reflect velocity as a uniform field, except for local flow variations. The velocity estimation is performed by computing the optical flow using a series of video frames, combining the information of the views of both cameras. The novelty of the proposed river flow estimation scheme lies on the fact that the accuracy of motion estimation is increased due to the use of the complementary views, which also enables the transition from a 2-Dimensional image-based velocity estimate to 3-Dimensional estimates. The estimated optical velocity is back-projected to the real world coordinates using the parameters extracted using the stereoscopic layout. The results on simulated and real conditions demonstrate that the proposed method is efficient in the estimation of the surface velocity and robust against locally disappearing formations, since it can compensate for a loss with other formations active in the field of view.

Conceptual Design of a Small Hybrid Unmanned Aircraft System

UAS (Unmanned Aircraft System) technologies are today extremely required in various fields of interest, from military to civil (search and rescue, environmental surveillance and monitoring, and entertainment). Besides safety and legislative issues, the main obstacle to civilian applications of UAS systems is the short time of flight (endurance), which depends on the equipped power system (battery pack) and the flight mission (low/high speed or altitude). Long flight duration is fundamental, especially with tasks that require hovering capability (e.g., river flow monitoring, earthquakes, devastated areas, city traffic monitoring, and archeological sites inspection). This work presents the conceptual design of a Hybrid Unmanned Aircraft System (HUAS), merging a commercial off-the-shelf quadrotor and a balloon in order to obtain a good compromise between endurance and weight. The mathematical models for weights estimation and balloon static performance analysis are presented, together with experimental results in different testing scenarios and complex environments, which show 50% improvement of the flight duration.

全国350の流量観測地点を対象とした水田使用農薬の河川水中予測濃度の地域特異性の解析

全国350の流量観測地点を対象とした水田使用農薬の河川水中予測濃度の地域特異性の解析

Use of SARAL/AltiKa Observations for Modeling River Flow

In the absence of many gauging stations in the major and mighty river systems, there is a need for satellite-based observations to estimate temporal variations in the river water storage and associated water management. In this study, SARAL/AltiKa application for setting up hydraulic model (HEC-RAS) and river flow simulations over Tapi River India has been discussed. Waveform data of 40 Hz from Ka band altimeter has been used for water levels retrieval in the Tapi river. SARAL/AltiKa retrieved water levels were converted to discharge in the upstream location (track-926) using the rating curve available for the nearby gauging site and using linear spatial interpolation technique. Steady state simulations were done for various flow conditions in the upstream. Validation of river flow model was done in the downstream location (track-367) by comparing simulated and altimeter retrieved water levels (RMSE 0.67 m). Validated model was used to develop rating curve between water levels and simulated discharge for the downstream location which enables to monitor discharge variations from satellite platform in the absence of in situ observations. It has been demonstrated that SARAL/AltiKa data has potential for river flow monitoring and modeling which will feed for flood disaster forecasting, management and planning.

Integrated water resources management in an insecure river basin: a case study of Helmand River Basin, Afghanistan

The Helmand River Basin is a closed river basin in (semi)arid southern Afghanistan, Iran and Pakistan. Irrigation is by far the largest surface water use. Two of the three main rivers have large dams that regulate river flow for irrigation and hydropower. Water management in the basin faces a number of challenges, among which are insecurity, collapse of the river flow monitoring network in the Afghan part of the basin in 1980, inefficient operation and maintenance of existing water infrastructure, many drought years since 1999, reduced upstream water storage due to siltation of reservoirs, and a possible future reduction in snow storage as a result of a predicted temperature rise. The paper presents: (1) an overview of water resources and water management in the Helmand River Basin, where data are scarce and difficult to access; (2) a description of innovative methods used to generate simulated flow data for the basin, including the use of globally gridded data sets; (3) a discussion of how the reservoirs could be operated in light of irrigation and hydropower demand; and (4) a discussion on strengthening the transboundary cooperation between Afghanistan and Iran.

Reconstruction of river bed topography from free surface data using a direct numerical approach in one-dimensional shallow water flow

River bed topography is of paramount importance for the study of fluvial hydraulics, flood prediction and river flow monitoring. It is therefore important to develop fast, easy-to-implement and cost-effective methods to determine underwater river topography. This paper presents a new one-step approach for reconstructing the river bed topography from known free surface data. This problem corresponds to the inverse of the classical hydrodynamic problem where the shallow water equations provide the free surface profile for a given river bed. We show in this work that instead of treating this inverse problem in the traditional partial differential equation (PDE)-constrained optimization framework, we can conveniently rearrange the governing equations for the direct problem to obtain an explicit PDE for the inverse problem. This leads to a direct solution of the inverse problem. An interesting consequence of the analysis is that the equations governing the forward and inverse problems have a very similar form, and the same discretization technique, based on an upwind conservative numerical scheme, can be used. The proposed methodology is successfully tested on a range of benchmark problems for noisy and noiseless free surface data. It was found that this solution approach creates very little amplification of noise.