Recommendations for Bank and Bluff Erosion Monitoring Using Citizen Science in Walkerton, Ontario

The Saugeen River flows directly through downtown Walkerton, Ontario and it is cutting into its valley across from Riverbend Park. The river has encroached into the valley walls, resulting in consistent and considerable bank and bluff erosion over the last few decades. This study explores the ways in which the erosion across from Riverbend Park could be better understood and monitored using citizen science – a suite of tools and methods that rely on non-scientists and local citizens to produce scientific data. Because bank and bluff erosion in general is complex and variable in time and space, citizen science methods could be critical for provide observation and reports for qualitative assessment of erosion. In addition, image based methods such as 3D photogrammetry have been used to produce high-resolution quantitative information that could allow for better understanding of the specific process, rates, patterns, and locations of erosion in Walkerton. The success of citizen science is often dependent on the presence of motivated citizens, which may be more likely to exist in Walkerton due to its infamous history of a water-related E. coli outbreak. The Walkerton bluff is also the location of protected bird species, with further complicates efforts to abate erosion yet could be another focus of citizen science. The results of this study are a series of discussions and recommendations for possible monitoring and erosional assessment programs using citizen science methods

Assessment of the soil loss caused by riverbank erosion in Serbia

Riverbank erosion and lateral channel migration are important geomorphological processes which cause various landscape, socio-economic, and environmental consequences. Although those processes are present on the territory of Serbia, there is no available data about the soil loss caused by riverbank erosion for the entire country. In this study, the spatial and temporal dynamics of the riverbank erosion for the largest internal rivers in Serbia (Velika Morava, Zapadna Morava, Južna Morava, Pek, Mlava, Veliki Timok, Kolubara) was assessed using remote sensing and GIS. The aim of this paper is to determine the total and average soil loss over large-scale periods (1923-2020), comparing data from the available sources (aerial photographs, satellite images, and different scale paper maps). Results indicated that lateral migration caused significant problems through land loss (approximately 2,561 ha), especially arable land, and land use changes in river basins, but also economic loss due to the reduction of agricultural production. Total and average soil loss was calculated for five most representative meanders on all studied rivers, and on the basis of the obtained values, certain regularities about further development and dynamics of riverbank movement are presented. A better understanding of river channel migration in this area will be of a great importance for practical issues such as predicting channel migration rates for river engineering and planning purposes, soil and water management and land use changes, environment protection

INVESTIGATION INTO ITADORI KNOTWEED AS A CONTROL OF BANK EROSION IN NEW HAMPSHIRE RIVERS

As floods increase in frequency and magnitude throughout New England, research on controls of erosion is necessary to help manage riverbank erosion and its implications on river system health and safety of infrastructure situated along the bank. Reynoutria japonica (Itadori knotweed) is an invasive species spreading throughout New Hampshire rivers which is suspected to cause riverbank erosion due to its unique root structure and winter die-back. To examine the impact of knotweed on riverbank erosion, paired knotweed and native species vegetation patches were selected as study sites along the Sugar and Lamprey Rivers, New Hampshire. Study sites were monitored over the course of a year using bank pins, remote sensing (LiDAR and Structure from Motion), and hydraulic modeling. Banks colonized by knotweed experienced 6.8 cm more erosion on average than similar banks with native vegetation. No statistically significant difference was recorded between modeled applied shear stress or estimated critical shear stress values between paired vegetation patches, apart from Sugar Site 6. Overall, the results of the bank erosion monitoring, estimated critical shear stress, and modeled applied shear stress results show that the only significant differences across paired vegetation patches were the presence of Itadori knotweed and an increase in erosion measured at knotweed patches. To minimize the impacts on river ecosystems by knotweed and damage to infrastructure by erosion, river corridor management should consider efforts to remove knotweed from river systems

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

Exploring accuracy of statistical characterization of gravel beds using Kinect device in the laboratory scale

Mountainous river beds-generally consist of gravel particles that the precise description of such bed is not only important hydraulically, but also it has great environmental significance. The accurate estimation of bed roughness gives us valuable information to make reliable hydraulic models of flow in river with rigorous bed form. This study discuses about the accuracy of the Kinect device in determining the digital elevation model (DEM) of the gravel-bed. In this regard, the DEMs of the two beds include-hemispheres and two beds with artificial gravel beds have been -used and their statistical characteristics have been analyzed. The results show that while the error in the area among the particles is quite high, themethod can accurately conduct these in general. The comparison of the bed elevation histograms shows that although the artificial gravel beds histograms have higher accuracy compared to the histograms of the beds with hemispheres form, the gravel bed with distance elevations histogram shows the best fit among the four explored beds. Furthermore, exploring the statistical characteristics of these four beds shows that the Kinect device is able to obtain reasonable error rate in statistical parameters except the skewness quantity which has the highest rate of relative error. The variogram analysis of artificial gravel beds emphasis that the Kinect and scanner variograms reasonably close to each other and the longitudinal and transversal particles length scales are exactlythe same. According to the results of this investigation, application ofthe Kinect device in statistical analysis of the gravel beds can be suggested

An Analysis of River Channel Change Over Time in New England Rivers

Analyzing river channel change can be important for the development and protection of infrastructure located within floodplains and on the riverbanks. Historical aerial images were delineated and evaluated to estimate river channel change in the Ammonoosuc River in New Hampshire and the Dog River in Vermont. Previous field assessments were connected to the change estimates to determine factors correlated to river channel change. Flood frequency was also assessed for the rivers to determine if flooding impacts river channel change, with a specific focus on flooding caused by Hurricane Irene. The river channel change resulting from Hurricane Irene was significantly higher than other periods, hinting at the episodic nature of river channel change. This methodology provides a mechanism for planners to monitor river channel change cheaply with easily obtainable data

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application's objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models' principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

On the use of smartphones as novel photogrammetric water gauging instruments: Developing tools for crowdsourcing water levels

The term global climate change is omnipresent since the beginning of the last decade. Changes in the global climate are associated with an increase in heavy rainfalls that can cause nearly unpredictable flash floods. Consequently, spatio-temporally high-resolution monitoring of rivers becomes increasingly important. Water gauging stations continuously and precisely measure water levels. However, they are rather expensive in purchase and maintenance and are preferably installed at water bodies relevant for water management. Small-scale catchments remain often ungauged. In order to increase the data density of hydrometric monitoring networks and thus to improve the prediction quality of flood events, new, flexible and cost-effective water level measurement technologies are required. They should be oriented towards the accuracy requirements of conventional measurement systems and facilitate the observation of water levels at virtually any time, even at the smallest rivers. A possible solution is the development of a photogrammetric smartphone application (app) for crowdsourcing water levels, which merely requires voluntary users to take pictures of a river section to determine the water level. Today’s smartphones integrate high-resolution cameras, a variety of sensors, powerful processors, and mass storage. However, they are designed for the mass market and use low-cost hardware that cannot comply with the quality of geodetic measurement technology. In order to investigate the potential for mobile measurement applications, research was conducted on the smartphone as a photogrammetric measurement instrument as part of the doctoral project. The studies deal with the geometric stability of smartphone cameras regarding device-internal temperature changes and with the accuracy potential of rotation parameters measured with smartphone sensors. The results show a high, temperature-related variability of the interior orientation parameters, which is why the calibration of the camera should be carried out during the immediate measurement. The results of the sensor investigations show considerable inaccuracies when measuring rotation parameters, especially the compass angle (errors up to 90° were observed). The same applies to position parameters measured by global navigation satellite system (GNSS) receivers built into smartphones. According to the literature, positional accuracies of about 5 m are possible in best conditions. Otherwise, errors of several 10 m are to be expected. As a result, direct georeferencing of image measurements using current smartphone technology should be discouraged. In consideration of the results, the water gauging app Open Water Levels (OWL) was developed, whose methodological development and implementation constituted the core of the thesis project. OWL enables the flexible measurement of water levels via crowdsourcing without requiring additional equipment or being limited to specific river sections. Data acquisition and processing take place directly in the field, so that the water level information is immediately available. In practice, the user captures a short time-lapse sequence of a river bank with OWL, which is used to calculate a spatio-temporal texture that enables the detection of the water line. In order to translate the image measurement into 3D object space, a synthetic, photo-realistic image of the situation is created from existing 3D data of the river section to be investigated. Necessary approximations of the image orientation parameters are measured by smartphone sensors and GNSS. The assignment of camera image and synthetic image allows for the determination of the interior and exterior orientation parameters by means of space resection and finally the transfer of the image-measured 2D water line into the 3D object space to derive the prevalent water level in the reference system of the 3D data. In comparison with conventionally measured water levels, OWL reveals an accuracy potential of 2 cm on average, provided that synthetic image and camera image exhibit consistent image contents and that the water line can be reliably detected. In the present dissertation, related geometric and radiometric problems are comprehensively discussed. Furthermore, possible solutions, based on advancing developments in smartphone technology and image processing as well as the increasing availability of 3D reference data, are presented in the synthesis of the work. The app Open Water Levels, which is currently available as a beta version and has been tested on selected devices, provides a basis, which, with continuous further development, aims to achieve a final release for crowdsourcing water levels towards the establishment of new and the expansion of existing monitoring networks.Der Begriff des globalen Klimawandels ist seit Beginn des letzten Jahrzehnts allgegenwärtig. Die Veränderung des Weltklimas ist mit einer Zunahme von Starkregenereignissen verbunden, die nahezu unvorhersehbare Sturzfluten verursachen können. Folglich gewinnt die raumzeitlich hochaufgelöste Überwachung von Fließgewässern zunehmend an Bedeutung. Pegelmessstationen erfassen kontinuierlich und präzise Wasserstände, sind jedoch in Anschaffung und Wartung sehr teuer und werden vorzugsweise an wasserwirtschaftlich-relevanten Gewässern installiert. Kleinere Gewässer bleiben häufig unbeobachtet. Um die Datendichte hydrometrischer Messnetze zu erhöhen und somit die Vorhersagequalität von Hochwasserereignissen zu verbessern, sind neue, kostengünstige und flexibel einsetzbare Wasserstandsmesstechnologien erforderlich. Diese sollten sich an den Genauigkeitsanforderungen konventioneller Messsysteme orientieren und die Beobachtung von Wasserständen zu praktisch jedem Zeitpunkt, selbst an den kleinsten Flüssen, ermöglichen. Ein Lösungsvorschlag ist die Entwicklung einer photogrammetrischen Smartphone-Anwendung (App) zum Crowdsourcing von Wasserständen mit welcher freiwillige Nutzer lediglich Bilder eines Flussabschnitts aufnehmen müssen, um daraus den Wasserstand zu bestimmen. Heutige Smartphones integrieren hochauflösende Kameras, eine Vielzahl von Sensoren, leistungsfähige Prozessoren und Massenspeicher. Sie sind jedoch für den Massenmarkt konzipiert und verwenden kostengünstige Hardware, die nicht der Qualität geodätischer Messtechnik entsprechen kann. Um das Einsatzpotential in mobilen Messanwendungen zu eruieren, sind Untersuchungen zum Smartphone als photogrammetrisches Messinstrument im Rahmen des Promotionsprojekts durchgeführt worden. Die Studien befassen sich mit der geometrischen Stabilität von Smartphone-Kameras bezüglich geräteinterner Temperaturänderungen und mit dem Genauigkeitspotential von mit Smartphone-Sensoren gemessenen Rotationsparametern. Die Ergebnisse zeigen eine starke, temperaturbedingte Variabilität der inneren Orientierungsparameter, weshalb die Kalibrierung der Kamera zum unmittelbaren Messzeitpunkt erfolgen sollte. Die Ergebnisse der Sensoruntersuchungen zeigen große Ungenauigkeiten bei der Messung der Rotationsparameter, insbesondere des Kompasswinkels (Fehler von bis zu 90° festgestellt). Selbiges gilt auch für Positionsparameter, gemessen durch in Smartphones eingebaute Empfänger für Signale globaler Navigationssatellitensysteme (GNSS). Wie aus der Literatur zu entnehmen ist, lassen sich unter besten Bedingungen Lagegenauigkeiten von etwa 5 m erreichen. Abseits davon sind Fehler von mehreren 10 m zu erwarten. Infolgedessen ist von einer direkten Georeferenzierung von Bildmessungen mittels aktueller Smartphone-Technologie abzusehen. Unter Berücksichtigung der gewonnenen Erkenntnisse wurde die Pegel-App Open Water Levels (OWL) entwickelt, deren methodische Entwicklung und Implementierung den Kern der Arbeit bildete. OWL ermöglicht die flexible Messung von Wasserständen via Crowdsourcing, ohne dabei zusätzliche Ausrüstung zu verlangen oder auf spezifische Flussabschnitte beschränkt zu sein. Datenaufnahme und Verarbeitung erfolgen direkt im Feld, so dass die Pegelinformationen sofort verfügbar sind. Praktisch nimmt der Anwender mit OWL eine kurze Zeitraffersequenz eines Flussufers auf, die zur Berechnung einer Raum-Zeit-Textur dient und die Erkennung der Wasserlinie ermöglicht. Zur Übersetzung der Bildmessung in den 3D-Objektraum wird aus vorhandenen 3D-Daten des zu untersuchenden Flussabschnittes ein synthetisches, photorealistisches Abbild der Aufnahmesituation erstellt. Erforderliche Näherungen der Bildorientierungsparameter werden von Smartphone-Sensoren und GNSS gemessen. Die Zuordnung von Kamerabild und synthetischem Bild erlaubt die Bestimmung der inneren und äußeren Orientierungsparameter mittels räumlichen Rückwärtsschnitt. Nach Rekonstruktion der Aufnahmesituation lässt sich die im Bild gemessene 2D-Wasserlinie in den 3D-Objektraum projizieren und der vorherrschende Wasserstand im Referenzsystem der 3D-Daten ableiten. Im Soll-Ist-Vergleich mit konventionell gemessenen Pegeldaten zeigt OWL ein erreichbares Genauigkeitspotential von durchschnittlich 2 cm, insofern synthetisches und reales Kamerabild einen möglichst konsistenten Bildinhalt aufweisen und die Wasserlinie zuverlässig detektiert werden kann. In der vorliegenden Dissertation werden damit verbundene geometrische und radiometrische Probleme ausführlich diskutiert sowie Lösungsansätze, auf der Basis fortschreitender Entwicklungen von Smartphone-Technologie und Bildverarbeitung sowie der zunehmenden Verfügbarkeit von 3D-Referenzdaten, in der Synthese der Arbeit vorgestellt. Mit der gegenwärtig als Betaversion vorliegenden und auf ausgewählten Geräten getesteten App Open Water Levels wurde eine Basis geschaffen, die mit kontinuierlicher Weiterentwicklung eine finale Freigabe für das Crowdsourcing von Wasserständen und damit den Aufbau neuer und die Erweiterung bestehender Monitoring-Netzwerke anstrebt

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world