Stream discharge measuring system and the method of its operation

Abstract

A stream discharge measuring system for a water canal (300) is disclosed. It comprises a Measuring Unit [MU] (100) and a Data Processing Unit [DPU] (400), optionally integrated into a single device or communicating via data and power link (450). The MU (100) comprises at least one rotating scanning LIDAR (10) for scanning a riverbed surface (210) of the water canal (300), one FMCW radar (20) for determining the precise position of the water surface (310) below the measuring unit (100), and at least one Doppler Radar (30) for scanning the water surface (310) displacement. The use of FMCW radar (20) enables the precise determination of the relative water surface (310) position to the MU (100) which enhances distinguishing, considering time-of-flight and the emission angle of the laser ray (11), laser reflections from the riverbed surface (210) which is, or which is not submerged in the water canal (300).

Description

[0001] STREAM DISCHARGE MEASURING SYSTEM AND THE METHOD OF ITS OPERATION

[0002] DESCRIPTION

[0003] Technical Field

[0004] A present disclosure relates to the system for measuring a stream discharge in natural or manmade open channels . The measuring system combines optical and radar techniques to obtain the necessary data . Therefore , the technical field is directed to measuring volume , volume flow, mass flow, or liquid level in general . Specifically, the present disclosure belongs to the technical field where LIDAR is used for riverbed mapping and radars for data refinement and measuring the actual surface stream velocity .

[0005] Technical Problem

[0006] Global warming is a significant environmental challenge with far- reaching impacts , including increased flood ris ks . Flood monitoring plays a vital role in mitigating these risks by providing essential data for early warnings , disaster preparedness , urban planning , environmental protection, and climate change adaptation . As the climate continues to change , investing in robust flood monitoring systems - such as the said disclosure - will be crucial for safeguarding human lives , protecting property, and maintaining ecological balance .

[0007] Accurate , continuous ( online ) flow rate measurement in natural and artificial open channels presents several challenges . Existing methods often utilize a combination of water level radar and surface velocity radar to measure water level and surface velocity from above the water . However, these devices require manual measurement of the river profile cross-section geometry, which must be , subsequently, entered into the instrument or data-processing-unit . This process is labor- intensive and prone to errors, especially in dynamic environments where the riverbed can change frequently due to erosion or sediment accumulation. Consequently, the need for repeated manual measurements and data entry hinders the efficiency and accuracy of flow-discharge measurements .

[0008] The main technical problem solved by the present disclosure addresses these issues. It provides a solution that reduces or eliminates the need for manual river profile measurements and records all changes in the riverbed automatically, within the initially set timeframes, with the current data, or, even using the historical data, as will be explained later.

[0009] State of the Art

[0010] Current technologies for measuring flow rate in natural and artificial open channels include devices that combine water level measurement with surface velocity measurement using radar technology. These radarbased systems usually measure water level and surface velocity from above the water surface, providing a non-contact method of data collection. However, these devices require manual and periodic measurements of the canal bed profile, which must be entered into the instrument or data processing system. This process requires human presence which causes errors or delays in the data acquisition process. Reference 1 discusses today's challenges of radar non-contact methods :

[0011] 1) Huang, Y. ; Chen, H. ; Liu, B.; Huang, K. ; Wu, Z . ; Yan, K. RADAR TECHNOLOGY FOR RIVER FLOW MONITORING: ASSESSMENT OF THE CURRENT STATUS AND FUTURE CHALLENGES. Water 2023, 15, 1904. https: / / doi.org / 10.3390 / wl5101904

[0012] According to the abstract, 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. The detailed workflow from radar data acquisition to flow calculation is disclosed in the said reference and compared with the current state-of-the-art signal sampling and its limitations, Doppler spectrum estimation, signal processing, and flow inversion. However, the cited reference fails to observe the advantage of LIDARs and radars for mapping the riverbed.

[0013] Another approach, the contact one, is discussed in the reference 2:

[0014] 2) Mueller, D.S. , Wagner, C.R., Rehmel, M.S., Oberg, K.A, and Rainville, Francois, 2013, MEASURING DISCHARGE WITH ACOUSTIC DOPPLER CURRENT PROFILERS FROM A MOVING BOAT (ver. 2.0, December 2013) : U.S. Geological Survey Techniques and Methods, book 3, chap . http: / / dx.doi.org / 10.3133 / tm3A22.

[0015] The above-cited technique uses acoustic Doppler current profilers (ADCPs) from a moving boat, which is now a commonly used method for measuring streamflow. The technology and methods for making ADCP-based discharge measurements are different from the technology and methods used to make traditional discharge measurements with mechanical meters. However, this contact method is accurate but has a serious drawback because it requires human presence and / or an automated vessel with sonar equipment in contact with the water surface, which is not suitable for all purposes.

[0016] The prior art has the advanced non-contact optical method discussed as well.

[0017] 3) US 11,313,678 for REMOTE MEASUREMENT OF SHALLOW DEPTHS IN SEMITRANSPARENT MEDIA, filed in the name of The Regents of the University of Colorado, US, and Astra Lite, Inc. , US.

[0018] The above-cited document reveals a novel approach to distant measuring of shallow depths, preferably from the aircraft, using the LIDAR bathymetry. It discloses a discrimination of the scattered signal polarization state , a lidar system measures a distance through semitransparent media by the reception of single or multiple scattered signals from a scattering medium . Combined and overlapped single or multiple scattered light signals from the medium can be separated by exploiting varying polarization characteristics . This removes the traditional laser and detector pulse width limitations that determine the system ' s operational bandwidth, translating relative depth measurements into the conditions of two surface timing measurements and achieving sub-pulse width resolution . The advantage of the disclosed system is that offers a low-cost , accurate , selfcalibrating , and scalable solution with a differential measurement requiring no knowledge of the lidar system' s platform vertical position . However, the proposed system is silent regarding the water mass surface velocity crucial for the estimation of the stream discharge .

[0019] 4 ) WO2023230583A2 for SYSTEM AND METHOD FOR OPTICAL REMOTE

[0020] MEASUREMENT OF WATER TURBIDITY , BATHYMETRY , AND FLOW SPEED, filed in the name of Michigan Aerospace Corporation, US .

[0021] Reference 4 discloses a remote optical sensing device that combines multiple water data products into a low-cost sensor system; relaxes durability requirements by remaining above the water surface ; and minimizes long-term operating expenses . The device measures water turbidity, suspended particle size , flow speed, surface height , and depth using lasers and cameras from above the water . Coplanar collimated laser beams are directed into the water and the beams are imaged with a camera . The beams bloom out as they propagate into the water driven by the particulate scattering phase function . By analyzing the bloom pattern, particle size can be estimated . Turbidity is estimated from the rate of attenuation into the water . When the pixels are calibrated to their corresponding distances from the laser sources , the distance to the water surface and water bottom can be inferred from the positions on the image where the beams intersect the surface and terminate on the water floor . Flow speeds at each depth can be estimated by tracking features going from one beam to another and calculating the speeds by timing the propagation delay and knowing the beam spacings .

[0022] It seems that reference 4 offers an all-in-one non-contact device capable of being mounted on the drone or the desired fixed position . The water flow value is extracted from the relative movements of the particles in the water stream in time , which opens many questions regarding the reliability of such obtained data . Furthermore , the applicant' s website is also silent regarding the actual product ability; only the turbidity data are reported, for instance in reference 5 below :

[0023] 5 ) Grantee Research Proj ect Results ; Final Report : Egret - A LIDAR Sensor for Storm Water Monitoring , the report delivered in the name of Michigan Aerospace Corporation, US , retrieved from: https : / / cfpub . epa . gov / ncer abstracts / index . cfm / fuseaction / displa y . abstractDetail / abstract id / 11225 / report / F in July 2024 .

[0024] From the above , it is possible to conclude that there is no existing technology that effectively combines the convenience of non-contact measurement of the flow rate with automatic measurement of the channel or river bed profile . This gap in technology highlights the need for an innovative solution that can provide accurate and simple continuous flow rate measurements without the need for frequent manual intervention, or the complexities associated with submerged sensors .

[0025] Summary of the Invention

[0026] A present disclosure relates to a stream discharge measuring system for a water canal . It comprises a Measuring Unit [MU] and a Data Processing Unit [ DPU] , which are optionally integrated into a single device .

[0027] The MU comprises at least one Rotating Scanning Light Detection and Ranging [ RS-LIDAR] for scanning a riverbed and the riverbed surface of the said water canal , one Frequency Modulated Continuous Wave Radar [ FMCWR] for determining the precise position water surface below the MU, at least one Doppler Radar [ DR] for scanning the water surface displacement . It has fastening means , and optionally a communication means for communication of the said MU with the distant DPU via a dedicated connection .

[0028] The DPU is configured to receive the MU data in the form of RS-LIDAR data , FMCWR data , DR data , and user input received via a communication interface . DPU is capable of storing the received data and inputs and executing a computer program for performing calculations for estimating the real-time water flow data for the said water canal and communicating via the communication interface to the user .

[0029] In the preferred embodiment , the RS-LIDAR operates in the infrared ( IR) regime , and the MU comprises only one RS-LIDAR which is situated approximately in the middle of the riverbed and, preferably, fastened via the fastening means to the bridge . In the preferred embodiment , the DPU is situated on the shore and connected, via the dedicated connection to distant MU, where the said connection is a wired data link and the power link for the said MU .

[0030] In one variant of the disclosure , the DPU is powered by solar energy .

[0031] A method of operating the stream discharge measuring system for a selected water canal is disclosed as well . In the preferred embodiment , the method comprises repeating steps A-E :

[0032] A . the DPU acquires , and stores RS-LIDAR data obtained from one or more RS-LIDARS , FMCWR data, and DR data in the predetermined time intervals ,

[0033] B . the DPU uses newly recorded data from step A, from the measured FMCWR data the water surface distance from the MU is uniquely determined and used for distinguishing which laser rays , emitted from the RS-LIDAR via selected emitting angle and reflected in recorded time-of-f light [TOF] back to the RS-LIDAR, are reflected from the riverbed surface situated out of the water canal and the riverbed surface submerged into the water canal , where the TOF values obtained from submerged riverbed surface later is corrected by the law of refraction, finally converting all RS-LIDAR data to the raw riverbed surface in and out the water,

[0034] C . the DPU uses the raw riverbed surface data , and obtains a water cross-section, eventually this data is corrected by the received user' s input ,

[0035] D . the DPU may optionally use historical data , preferably the data when the water canal was low in the riverbed, and compares the riverbed surface calculations performed before and the riverbed surface calculation in step C to generate an updated cross-section of the water canal model and store it for use , supervised by the user input if necessary, and

[0036] E . the DPU uses a water canal cross-section from step D, DR data regarding the displacement velocity, and a hydrodynamic model to calculate water flow data which is transmitted to the user via the communication interface .

[0037] In the preferred embodiment , distinguishing in step B is performed in a way that the TOF is analyzed . If TOF is shorter than expected for the direct water surface reflection for the selected angle , the reflection occurred from the riverbed surface situated out of the water canal or the floating obj ect . If the TOF is longer than expected for the direct water surface reflection, the reflection occurred from the riverbed surface submerged in the water canal or the underwater floating obj ect

[0038] Finally, the hydrodynamic model in step E takes into account the velocity distribution within the water canal ( 300 ) cross-section to improve the accuracy of the stream discharge measuring system. Description of Figures

[0039] Figure 1 depicts the measuring unit (MU) with only one RS-LIDAR and two radars , FMCWR and Doppler . Figure 2 is the side view of the MU .

[0040] Figure 3 shows the relative positions of used RS-LIDAR and radars .

[0041] Figure 4 depicts the MU position over the water and the Data Processing Unit ( DPU) situated ashore .

[0042] Figure 5 shows a riverbed measuring data obtained by the disclosed method, corrected by the disclosed method, and later compared with the manual measurement .

[0043] Figure 6 shows a typical river or canal model for easy calculation of the river / canal discharge .

[0044] Figure 7 represents the decision flowchart of the disclosed method, by using the cross-section model into the account .

[0045] Detailed Description of the Disclosure

[0046] As explained in the beginning , the main technical problem solved by the present disclosure is to reduce or eliminate the need for manual river profile measurements and to record all changes in the riverbed automatically, which allows a completely automated non-contact process of stream discharge measuring . The preferred embodiment of a stream discharge measuring system will be depicted in the following paragraphs . The stream discharge measuring system for a water canal ( 300 ) , which comprises a Measuring Unit [MU] ( 100 ) and a Data Processing Unit [DPU] ( 400 ) , optionally integrated into a single device is depicted in Figure 4 . Each functional part will be explained in detail .

[0047] Measuring Unit [MU] The measuring unit (100) , separated from DPU (400) , is depicted in Figures 1-3. It comprises at least one rotating scanning LIDAR (10) for scanning a riverbed surface (210) of the water canal (300) , one FMCW radar (20) , for determining the precise position of the water surface (310) below the measuring unit (100) , and at least one Doppler Radar (30) for scanning the water surface (310) displacement.

[0048] The Rotating Scanning Light Detection and Ranging [RS-LIDAR] (10) is a device common in the art. It uses rotation means for emitting and receiving the laser ray (11) at the predetermined angle, and to measure the time-of-f light (TOF) of the reflected beam. More regarding the TOF LIDARs can be found in reference 6:

[0049] 6) J Ma, S L Zhuo, L Qiu, Y Z Gao, Y F Wu, M Zhong, R Bai, M Sun, and P Y Chiang, A REVIEW OF TOF-BASED LIDAR, J. Semicond. , 2024, 45 (10) , 101201. https: / / doi.org / 10.1088 / 1674-4926 / 24040015

[0050] In the present disclosure, the following RS-LIDAR is used:

[0051] 7) SLAMTEC RPLIDAR A3 Triangulation LIDAR Indoor and Outdoor, retrieved from the WEB site: https: / / www.slamtec.ai / product / slamtec-rplidar-a3 / in June 2024.

[0052] The role of the used RS-LIDAR is to scan the riverbed surface (210) out of the water canal (300) and below the water surface (310) to extract the said surface (210) contour via the method that is explained later. The MU (100) can be equipped with one or more RS-LIDARs (10) , however, in practice it was shown that only one RS-LIDAR is sufficient for the said task.

[0053] It is well known in the art that various light wavelengths propagate differently across the water. The water penetration depth varies with wavelengths, under the same nominal LASER emitted power. In reference 8 it is possible to find more about the said topic: 8) Liu, Q.rLiu, D., Zhu, X. , Zhou, Y. , Le, C. , Mao, Z . , ... Liu, C. (2020) . Optimum wavelength of spaceborne oceanic lidar in penetration depth. Journal of Quantitative Spectroscopy and Radiative Transfer, 256, 107310. doi: 10.1016 / j . j qsrt .2020.10731

[0054] Most LIDARs, operating in water, use blue-green light with wavelengths 450-520 nm due to its ability to penetrate deepest. However, such lasers attract public attention and are usually vandalized in a very short period. In the preferred embodiment, infrared (IR) laser light is used despite a modest penetration compared to blue-green light, compensated with a higher power of the used laser source . During the operation, the RS-LIDAR (10) produces a set of raw data (401) further processed via the DPU (400) .

[0055] The role of the Frequency Modulated Continuous Wave Radar (FMCWR) (20) is to measure the distance d from the ME (100) to the water surface (310) , as depicted in Figure 4. The working principle of FMCWR is well described in reference 9 and the references it cites:

[0056] 9) Klugmann, D. (2016) . FMCW radar in the digital age. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . doi : 10.1109 / igarss .2016.7730922

[0057] The FMCWR (20) is situated perpendicular to the water surface (310) , which acts like a mirror for the radar wavelengths, due to the high electrical conductivity of the water surface. During the operation, the FMCWR (20) produces a set of raw data (402) further processed via the DPU (400) . The FMCWR (20) used in the preferred embodiment is disclosed in reference 10:

[0058] 10) GEOLUX LX80 Radar Level Sensor, retrieved from the WEB site: https: / / www.geolux-radars.com / lx80 in June 2024.

[0059] The DR (30) is situated within the MU (100) casing to be directed toward the water stream surface (310) at a predetermined angle, in our case close to 45°, Figures 2 and 3. The working principle of DR -li

[0060] (30) is well-known in the art, see reference 9. The DR used in the preferred embodiment is one disclosed in reference 11:

[0061] 11) GEOLUX RSS-2-300W Surface Velocity Radar, retrieved from the WEB site: https: / / www.geolux-radars.com / rss2300w in June 2024.

[0062] The advanced versions of DRs have, inter alia, an angle compensation means, vibration monitoring means and similar circuitry to enhance the measuring of the water surface relative movement velocity. The velocity resolution is limited by the internal oscillator accuracy and phase noise, and is around 1 mm / s . Furthermore, reference 1 gives an excellent review of DR's usage in surface flow estimation.

[0063] Data Processing Unit

[0064] The Data Processing Unit (DPU) (400) is designed to receive the MU (100) data, i.e. , RS-LIDAR data (401) , FMCWR data (402) , DR data (403) and to run a computer program that allows storing and processing said data, with or without distant user inputs (404) received via its communication interface (460) . The mentioned communication interface (460) can be selected from one or more solutions known in the art, i.e. , 2G, 3G, 4G or 5G industrial modems, LoRa-WAN solutions (https: / / en.wikipedia.org / wiki / LoRa) , or even satellite modems.

[0065] In the preferred embodiment, DPU (400) is situated on the shore and connected, via the connection (450) , to distant MU (100) positioned on the bridge or similar structure with good optical paths towards the riverbed surface (210) , as depicted in Figure 4. In this variant, the connection (450) is preferably a wired data link and the power link for the MU (100) , where DPU (400) is preferably powered by solar energy and uses the said energy for powering MU (100) as well. Using solar panels in this industry niche is a standard.

[0066] In one variant, the DPU (400) and MU (100) can be integrated into a single device. In yet another variant, several MU (100) units can be connected to one DPU (400) via several connections (450) if necessary to optically cover the entire riverbed surface (210) .

[0067] Regardless of the number of MU (100) units, the data processing is highly similar to those with only one MU (100) unit.

[0068] Method of operation

[0069] The standard representation used for understanding the stream discharge measuring system is depicted in Figure 6, borrowed from reference 12 below:

[0070] 12) Koutalakis, P. ; Zaimes, G.N. River Flow Measurements Utilizing UAV-Based Surface Velocimetry and Bathymetry Coupled with Sonar. Hydrology 2022, 9, 148. https: / / doi.org / 10.3390 / hydrology9080148

[0071] The riverbed (200) has its surface (210) which is partially submerged in the water canal (300) , below the water surface (310) . The simplest model, used for calculating the stream discharge, is dividing the water canal (300) into segments (320) with an AREA = DEPTH x WIDTH, of the said segment (320) . Therefore, the estimated discharge of the said segment (320) is DISCHARGE = AREA x VELOCITY, where the VELOCITY is obtained from the DR (10) . Finally, the total discharge is obtained via summation over all segments (320) .

[0072] The person skilled in the art will recognize immediately that this simplified approach may be improved by using more elaborated flow models, for instance, those presented in reference 1, where the surface VELOCITY above should be replaced by MEAN VELOCITY of the segment (320) . Figure 2 in this reference depicts the data processing starting from the DR signal sampling, using the surface flow velocity solved for the velocity distribution for the given riverbed (200) geometry across the water canal (300) . A detailed explanation of the method can be found in reference 13, where the surface velocity (Vsurface) and the mean velocity of the segment (Vmean) are functionally connected through the rather simple mathematical model:

[0073] 13) Geng, 0., Ardrglroglu, M. , & Agrralioglu, N. (2015) . CALCULATION OF MEAN VELOCITY AND DISCHARGE USING WATER SURFACE VELOCITY IN SMALL STREAMS. Flow Measurement and Instrumentation, 41, 115-120. https: / / doi.Org / 10.1016 / j . flowmeasinst .2014.10.013

[0074] The more advanced methods that require significant computational power, such as Computational fluid dynamics (CFD) can be used to solve the problem of known boundary conditions - static riverbed surface (210) and the surface water velocity of the said water canal (300) for calculating the velocity of each part of the larger segment (320) of the water (300) flow.

[0075] The vegetation in rivers or ditches can significantly affect the velocity profiles or distribution across the water (300) . The problems of unregulated rivers and unmaintained canals are described and modelled in reference 14:

[0076] 14) Tang, X. (2019) . EVALUATING TWO-LAYER MODELS FOR VELOCITY PROFILES IN OPEN-CHANNELS WITH SUBMERGED VEGETATION. Journal of Geoscience and Environment Protection, 7, 68-80. https: / / doi.org / 10.4236 / gep .2019.71006

[0077] All of the above-cited approaches are used for a water velocity estimation of an entire water segment (320) or part thereof, eventually as a function of depth, we will call a hydrodynamic model. The role of the said model is to express velocity distribution within the water canal (300) cross-section.

[0078] To describe a method of operation of the disclosed stream discharge measuring system, the setup with one MU (100) and one DPU (400) , as depicted in Figure 4 is used. The flowchart is given in Figure 7 and is divided into steps described below. STEP A. In this step, the DPU (400) acquires and stores RS-LIDAR data (401) obtained from one or more RS-LIDARS, FMCWR data (402) , and DR data (403) in the predetermined time intervals. Time intervals may vary from once a minute to once every hour, and certainly depend on the impact the monitored stream discharge has on the local hydrological situation.

[0079] STEP B. In this step, the DPU (400) uses newly recorded data from step A and processes them. From measured FMCWR data (402) the water surface (310) distance from the MU (100) is uniquely determined. This distance d is used for distinguishing laser rays (11) , emitted from the RS- LIDAR (10) , reflected from the riverbed surface (210) , which is on the shore or submerged; and to use the d-value to discard the data (401) obtained from direct reflection from the water surface (310) .

[0080] The RS-LIDAR (10) emits laser rays (11) by knowing the exact angle at which the rays (11) hit the targets below. This angle is also associated with a time-of-f light [TOE] , i.e. , the time passed for the ray (11) to reach the obstacle and to be recorded by the RS-LIDAR (10) . From the simple geometry, it is possible to precisely discard the reflected rays (11) from the water surface (310) , and all other data originating from the riverbed surface (210) , submerged or not.

[0081] Again, the simple geometry may be used to distinguish those laser rays reflected from the shore, i.e. riverbed surface that is not submerged, which has a shorter TOF than to be reflected from the water surface (310) , and those which have a significantly longer TOF then to be reflected from the water surface (310) and belongs to the reflections from the submerged riverbed surface (210) . The latter one, reflected from the submerged riverbed surface (210) , should be corrected with Snell's law of refraction, to obtain an accurate point on the submerged riverbed surface from which it is reflected, see Figure 4. Figure 5 represents a set of such not-corrected, raw estimations of the riverbed surface (210) , marked with the x sign. STEP C. This step is directed to simple data processing. The DPU (400) uses the raw riverbed surface (210) data from step B and obtains a water (300) cross-section.

[0082] Eventually, this data may be corrected by the received user's input (404) , if necessary, or if the anomalous readings are received via the communication interface for the said location due to hardware failure or other reasons. In this step, the canal / river cross-section generation process (410) is finished.

[0083] STEP D. This data processing step is dedicated to eventual corrections of the data generated in step E, for instance, due to the errors in reading that render the riverbed surface (210) with spikes or other anomalies, not generally present in the water canal (300) . It should be noted that the laser rays (10) reflections can originate from the floating objects as well as from the objects that are carried in the stream, below the water surface (310) . Repetitive measurements should discard this false reflection.

[0084] In one variant, the DPU (400) may optionally use historical data, preferably the data when the water canal (300) was low in the riverbed (200) . It is possible to compare the riverbed surface (210) calculations performed in many time instances before and the riverbed surface (210) calculation in step C to generate an updated crosssection of the water canal (300) model and store it for use. Namely, the erosion of the water canals (300) in time also can be modeled, see reference 15 below:

[0085] 15) Ha j igholizadeh, M. , Melesse, A. , & Fuentes, H. (2018) . Erosion and Sediment Transport Modelling in Shallow Waters : A Review on Approaches, Models and Applications. International Journal of Environmental Research and Public Health, 15 (3) , 518. doi: 10.3390 / i j erphl5030518 and used for checking the data. This process may eventually be supervised by the user input (404) . Such corrected measurements of the riverbed surface (210) are depicted in Figure 5, marked with the sign o, and compared with the manual measurement of the same canal, depicted with . All data are expressed in meters [m] assigning the 0 depth (H) of the lowest point in the riverbed surface (210) length (L) . Step D ends with the automatic cross-section update (412) , as depicted in Figure 7.

[0086] STEP E is dedicated to producing a flow calculation (413) . The DPU (400) uses a water canal (300) cross-section from step D, DR data (403) regarding the displacement velocity, and an appropriate hydrodynamic model to calculate water flow data (414) . This water flow data (414) is communicated via the communication interface (460) back to the user; with a dedicated water management hydro-server in the middle .

[0087] Steps A-E are repeated with the desired frequency and per the water canal (300) importance, with the appropriate hydrodynamic model used to estimate the water velocity distributions .

[0088] Industrial Applicability

[0089] The proposed disclosure is highly applicable in various industrial and environmental contexts where accurate and continuous measurement of flow rate in natural and artificial open channels is crucial. This includes, but is not limited to, the following sectors:

[0090] Water Resource Management: Effective monitoring and management of water resources for irrigation, flood control, and reservoir operations .

[0091] Environmental Monitoring: Continuous assessment of water flow in natural streams and rivers to monitor ecosystem health, detect pollution, and understand hydrological cycles.

[0092] Hydroelectric Power Generation: Optimization of water flow for efficient energy production and maintenance of dam safety. Civil Engineering : Monitoring and managing water flow in urban drainage systems , canals , and other engineered water channels to prevent flooding and maintain infrastructure integrity .

[0093] Agriculture : Ensuring efficient water distribution in irrigation systems to optimize crop yield and reduce water waste .

[0094] Disaster Management : Providing real-time data for early warning systems to predict and mitigate the impacts of floods and other water-related disasters .

[0095] Navigation : Assisting in the management of navigable waterways to ensure safe and efficient transport routes .

[0096] By combining non-contact measurement of flow rate with automatic measurement of canal profile , the present disclosure reduces the need for manual intervention, minimizes maintenance challenges , and improves the accuracy and reliability of flow rate data . This enhances the ability of various industries to manage water resources more effectively and respond swiftly to changing environmental conditions . Therefore , the disclosure is industrially applicable .

[0097] Reference numbers

[0098] 10 Rotating Scanning Light Detection and Ranging ( RS-LIDAR)

[0099] 11 Laser ray

[0100] 20 Frequency Modulated Continuous Wave Radar ( FMCWR)

[0101] 30 Doppler Radar ( DR)

[0102] 90 Fastening means

[0103] 100 Measuring unit

[0104] 200 Riverbed

[0105] 210 Riverbed surface

[0106] 300 Water canal

[0107] 310 Water surface

[0108] 320 Segment

[0109] 400 Data Processing Unit ( DPU)

[0110] 401 RS-LIDAR data

[0111] 402 FMCW radar data

[0112] 403 Doppler radar data

[0113] 404 User input

[0114] 410 Chanal cross-section generation process

[0115] 411 Cross-section model

[0116] 412 Automatic Cross-section Update

[0117] 413 Flow Calculation

[0118] 414 Water Flow data

[0119] 450 Connection, data and / or power link

[0120] 460 Communication Interface d Distance , FMCW radar - Water surface v Segment ( 320 ) velocity

Claims

CLAIMS1. A stream discharge measuring system for a water canal (300) , which comprises a Measuring Unit [MU] (100) and a Data Processing Unit [DPU] (400) , optionally integrated into a single device; where the MU (100) comprises at least one Rotating Scanning Light Detection and Ranging [RS-LIDAR] (10) for scanning a riverbed (200) and the riverbed surface (210) of the water canal (300) , one Frequency Modulated Continuous Wave Radar [FMCWR] (20) for determining the precise position water surface (310) below the measuring unit (100) , at least one Doppler Radar [DR] (30) for scanning the water surface (310) displacement, fastening means (90) , and optionally a communication means for communication of the said MU (100) with the distant DPU (400) via a connection (450) , where the DPU (400) is configured to receive the MU (100) data in the form of RS-LIDAR data (401) , FMCWR data (402) , DR data (403) , and user's input (404) received via a communication interface (460) , said DPU (400) being capable of storing the received data and inputs and executing a computer program for performing calculations for estimating the real-time water flow data (414) for the water canal (300) , communicating via the communication interface (460) to the user .

2. The stream discharge measuring system according to claim 1, wherein the RS-LIDAR (10) operates in the infrared (IR) regime.

3. The stream discharge measuring system according to any of the preceding claims wherein the MU (100) comprises only one RS-LIDAR (10) and is situated approximately at the middle of the riverbed (200) , preferably fastened via the fastening means (90) to the bridge .

4. The stream discharge measuring system according to any of the preceding claims, wherein the DPU (400) is situated on the shoreand connected, via the connection (450) , to distant MU (100) , where the said connection (450) is a wired data link and the power link for the MU (100) .

5. The stream discharge measuring system according to any of the preceding claims, where DPU (400) is preferably powered by solar energy.

6. A method of operating the stream discharge measuring system for a selected water canal (300) according to any of the preceding claims 1-5, wherein the method comprises repeating steps A-E :A. the DPU (400) acquires and stores RS-LIDAR data (401) obtained from one or more RS-LIDARS, FMCWR data (402) , and DR data (403) in the predetermined time intervals,B. the DPU (400) uses newly recorded data from step A, from the measured FMCWR data (402) the water surface (310) distance from the MU (100) is uniquely determined and used for distinguishing which laser rays (11) , emitted from the RS- LIDAR (10) via selected emitting angle and reflected in recorded time-of-f light [TOF] back to the RS-LIDAR (10) , are reflected from the riverbed surface (210) situated out of the water canal (300) and the riverbed surface (210) submerged into the water canal (300) , where the TOF values obtained from submerged riverbed surface (210) later is corrected by the law of refraction, finally converting all RS-LIDAR data (401) to the raw riverbed surface (210) in and out the water (300) ,C. the DPU (400) uses the raw riverbed surface (210) data, and obtains a water (300) cross-section, eventually this data is corrected by the received user's input (404) ,D. the DPU (400) may optionally use historical data, preferably the data when the water canal (300) was low in the riverbed (200) , and compares the riverbed surface (210) calculations performed before and the riverbed surface (210) calculation in step C to generate an updated cross-section of the watercanal (300) model and store it for use, supervised by the user input (404) if necessary, andE. the DPU (400) uses a water canal (300) cross-section from step D, DR data (403) regarding the displacement velocity, and a hydrodynamic model to calculate water flow data (414) which is transmitted to the user via the communication interface (460) .

7. A method of operating the stream discharge measuring system for a selected water canal (300) according to claim 6, wherein the distinguishing in step B is performed in a way that if TOE is shorter than expected for the direct water surface reflection (310) , for selected angle, the reflection occurred from the riverbed surface (210) situated out of the water canal (300) or the floating object, and if the TOE is longer than expected for the direct water surface reflection (310) , the reflection occurred from the riverbed surface (210) submerged in the water canal (300) or the underwater floating object.

8. A method of operating the stream discharge measuring system for a selected water canal (300) according to claims 6 or 7, where the hydrodynamic model in step E takes into account the velocity distribution within the water canal (300) cross-section.

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