Low-cost stage-camera system for continuous water-level monitoring in ephemeral streams

Abstract. Monitoring ephemeral and intermittent streams is a major challenge in hydrology. While direct field observations are best to detect spatial patterns of flow persistence, on site inspections are time and labor intensive and may be impractical in difficult-to-access environments. Motivated by latest advancements of digital cameras and computer vision techniques, in this work, we describe the development and application of a stage-camera system to monitor the water level in ungauged headwater streams. The system encompasses a consumer grade wildlife camera with near infrared (NIR) night vision capabilities and a white pole that serves as reference object in the collected images. Time-lapse imagery is processed through a computationally inexpensive algorithm featuring image quantization and binarization, and water level time series are filtered through a simple statistical scheme. The feasibility of the approach is demonstrated through a set of benchmark experiments performed in controlled and natural settings, characterized by an increased level of complexity. Maximum mean absolute errors between stage-camera and reference data are approximately equal to 2 cm in the worst scenario that corresponds to severe hydrometeorological conditions. Our preliminary results are encouraging and support the scalability of the stage-camera in future implementations in a wide range of natural settings.

[1]  H. J. van Meerveld,et al.  A Low-Cost, Multi-Sensor System to Monitor Temporary Stream Dynamics in Mountainous Headwater Catchments , 2019, Sensors.

[2]  Maurizio Porfiri,et al.  Measurements and Observations in the XXI century (MOXXI): innovation and multi-disciplinarity to sense the hydrological cycle , 2018 .

[3]  M. Kröhnert,et al.  AUTOMATIC WATERLINE EXTRACTION FROM SMARTPHONE IMAGES , 2016 .

[4]  Maurizio Porfiri,et al.  Orienting the camera and firing lasers to enhance large scale particle image velocimetry for streamflow monitoring , 2014 .

[5]  Piergiorgio Manciola,et al.  Water Level Measurements from Drones: A Pilot Case Study at a Dam Site , 2018 .

[6]  W. Dietrich,et al.  Drainage from the Critical Zone: Lithologic Controls on the Persistence and Spatial Extent of Wetted Channels during the Summer Dry Season , 2018, Water Resources Research.

[7]  Hernsoo Hahn,et al.  Remote Detection and Monitoring of a Water Level Using Narrow Band Channel , 2007, J. Inf. Sci. Eng..

[8]  M. Kröhnert,et al.  SEGMENTATION OF ENVIRONMENTAL TIME LAPSE IMAGE SEQUENCES FOR THE DETERMINATION OF SHORE LINES CAPTURED BY HAND-HELD SMARTPHONE CAMERAS , 2017 .

[9]  H. Elsenbeer,et al.  Distributed modeling of storm flow generation in an Amazonian rain forest catchment: Effects of model parameterization , 1999 .

[10]  Yang Shao,et al.  Modeling wet headwater stream networks across multiple flow conditions in the Appalachian Highlands , 2018, Earth Surface Processes and Landforms.

[11]  Claudio Rossi,et al.  River segmentation for flood monitoring , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[12]  P. Chaudharya,et al.  Water level prediction from social media images with a multi-task ranking approach , 2020 .

[13]  Gerhard Schoener,et al.  Time-Lapse Photography: Low-Cost, Low-Tech Alternative for Monitoring Flow Depth , 2018 .

[14]  G. Ricci,et al.  Setting Up of an Experimental Site for the Continuous Monitoring of Water Discharge, Suspended Sediment Transport and Groundwater Levels in a Mediterranean Basin. Results of One Year of Activity , 2020, Water.

[15]  Huibin Wang,et al.  Visual Measurement of Water Level under Complex Illumination Conditions , 2019, Sensors.

[16]  G. Botter,et al.  The Stream Length Duration Curve: A Tool for Characterizing the Time Variability of the Flowing Stream Length , 2020, Water resources research.

[17]  Mark S. Johnson,et al.  DOC and DIC in Flowpaths of Amazonian Headwater Catchments with Hydrologically Contrasting Soils , 2006 .

[18]  C. Leigh,et al.  Ecological research and management of intermittent rivers: An historical review and future directions , 2016 .

[19]  Claire Goulsbra,et al.  Temporary streams in a peatland catchment: pattern, timing, and controls on stream network expansion and contraction , 2014 .

[20]  Hidetomo Sakaino,et al.  Camera-Vision-Based Water Level Estimation , 2016, IEEE Sensors Journal.

[21]  Zhen Zhang,et al.  In-situ water level measurement using NIR-imaging video camera , 2019, Flow Measurement and Instrumentation.

[22]  Jemima M. Tabeart,et al.  Collection and extraction of water level information from a digital river camera image dataset , 2020, Data in brief.

[23]  F. Naef,et al.  Spatio‐temporal variability in contributions to low flows in the high Alpine Poschiavino catchment , 2018, Hydrological Processes.

[24]  S. Godsey,et al.  Discontinuous headwater stream networks with stable flowheads, Salmon River basin, Idaho , 2016 .

[25]  J. H. Sá,et al.  Connectivity of Ephemeral and Intermittent Streams in a Subtropical Atlantic Forest Headwater Catchment , 2020, Water.

[26]  Jae-Jun Kim,et al.  Development of an internet-based water-level monitoring and measuring system using CCD camera , 2007, ICMIT: Mechatronics and Information Technology.

[27]  Youngjoon Han,et al.  Embedded implementation of image-based water-level measurement system , 2011 .

[28]  Danilo Schneider,et al.  Photogrammetric water level determination using smartphone technology , 2019, The Photogrammetric Record.

[29]  Jorrit P. Mesman,et al.  High stream intermittency in an alpine fluvial network: Val Roseg, Switzerland , 2019, Limnology and Oceanography.

[30]  T. Datry,et al.  Intermittent Rivers and Ephemeral Streams: A Unique Biome With Important Contributions to Biodiversity and Ecosystem Services , 2020, Encyclopedia of the World's Biomes.

[31]  Amy J. Burgin,et al.  Zero or not? Causes and consequences of zero‐flow stream gage readings , 2020, WIREs. Water.

[32]  James W. Kirchner,et al.  Dynamic, discontinuous stream networks: hydrologically driven variations in active drainage density, flowing channels and stream order , 2014 .

[33]  A. Ward,et al.  Simulation of dynamic expansion, contraction, and connectivity in a mountain stream network , 2018 .

[34]  Simon Etter,et al.  Virtual Staff Gauges for Crowd-Based Stream Level Observations , 2019, Front. Earth Sci..

[35]  John B. Lindsay,et al.  Characterizing ephemeral streams in a southern Ontario watershed using electrical resistance sensors , 2015 .

[36]  Liu Xinwu.  This is How the Discussion Started , 1981 .

[37]  M. Rumor.  Environmental Monitoring and Assessment , 2011 .

[38]  Walter K. Dodds,et al.  Fundamental spatial and temporal disconnections in the hydrology of an intermittent prairie headwater network , 2015 .

[39]  Diana Spieler,et al.  Automatic Image‐Based Water Stage Measurement for Long‐Term Observations in Ungauged Catchments , 2018, Water Resources Research.

[40]  M. Camporese,et al.  Intraseasonal Drainage Network Dynamics in a Headwater Catchment of the Italian Alps , 2020, Water Resources Research.

[41]  Jim Constantz,et al.  Heat as a tracer to determine streambed water exchanges , 2008 .

[42]  Daniel R. Fuka,et al.  Technical Note: Proposing a Low-Tech, Affordable, Accurate Stream Stage Monitoring System , 2012 .

[43]  Brian L. McGlynn,et al.  Ephemeral and intermittent runoff generation processes in a low relief, highly weathered catchment , 2017 .

[44]  Salvatore Manfreda,et al.  An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems , 2020, Remote. Sens..

[45]  Alessio Maria Braccini,et al.  Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges , 2020, Hydrological Sciences Journal.

[46]  Stephen B. Shaw,et al.  Investigating the linkage between streamflow recession rates and channel network contraction in a mesoscale catchment in New York state , 2016 .

[47]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[48]  P. J. Wigington,et al.  Stream network expansion: a riparian water quality factor , 2005 .

[49]  K. Kaiser,et al.  Probability of Streamflow Permanence Model (PROSPER): A spatially continuous model of annual streamflow permanence throughout the Pacific Northwest , 2019, Journal of Hydrology X.

[50]  Syed Azer Reza,et al.  Agile lensing-based non-contact liquid level optical sensor for extreme environments , 2010 .

[51]  M. Nones,et al.  Time-Lapse Photography of the Edge-of-Water Line Displacements of a Sandbar as a Proxy of Riverine Morphodynamics , 2018 .

[52]  Kenneth W. Chapman,et al.  Source and magnitude of error in an inexpensive image-based water level measurement system , 2013 .

[53]  Han-Chung Yang,et al.  Applying image recording and identification for measuring water stages to prevent flood hazards , 2014, Natural Hazards.

[54]  Ming-Der Yang,et al.  Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information , 2020, Remote. Sens..

[55]  G. Botter,et al.  Hydrological controls on river network connectivity , 2019, Royal Society Open Science.

[56]  B. McGlynn,et al.  Temporally Variable Stream Width and Surface Area Distributions in a Headwater Catchment , 2019, Water Resources Research.

[57]  Stefano Mattoccia,et al.  Enabling Image-Based Streamflow Monitoring at the Edge , 2020, Remote. Sens..

[58]  Benjamin L Turner,et al.  Connectivity of overland flow by drainage network expansion in a rain forest catchment , 2014 .

[59]  Yoichi Takagi,et al.  Development of a noncontact liquid level measuring system using image processing , 1998 .

[60]  Peter Ashmore,et al.  Technical note: Stage and water width measurement of a mountain stream using a simple time-lapse camera , 2017 .

[61]  Kirk Martinez,et al.  Image analysis techniques to estimate river discharge using time-lapse cameras in remote locations , 2015, Comput. Geosci..

[62]  D. Montgomery,et al.  Channel and Perennial Flow Initiation in Headwater Streams: Management Implications of Variability in Source-Area Size , 2007, Environmental management.

[63]  D. Hannah,et al.  Mediterranean intermittent rivers and ephemeral streams: Challenges in monitoring complexity , 2019, Ecohydrology.

[64]  Xin Li,et al.  Real-time water level monitoring using live cameras and computer vision techniques , 2020, Comput. Geosci..

[65]  C. Delacourt,et al.  Photogrammetric discharge monitoring of small tropical mountain rivers: A case study at Rivière des Pluies, Réunion Island , 2016 .

[66]  Rajeev Sharma,et al.  Noncontact level sensing technique using computer vision , 2002, IEEE Trans. Instrum. Meas..

[67]  D. Chandler,et al.  Combining observations of channel network contraction and spatial discharge variation to inform spatial controls on baseflow in Birch Creek, Catskill Mountains, USA , 2017 .

[68]  C. Robinson,et al.  Effects of an experimental increase in flow intermittency on an alpine stream , 2020, Hydrobiologia.

[69]  Matthew T Perks,et al.  Technical Note: advances in flash flood monitoring using unmanned aerial vehicles (UAVs). , 2016 .

[70]  Kenneth W. Chapman,et al.  Camera-based Water Stage and Discharge Prediction with Machine Learning , 2021 .

[71]  Yi-Chun Lin,et al.  Automatic water-level detection using single-camera images with varied poses , 2018, Measurement.