We invite submissions focused on hydrological monitoring utilizing advanced technologies, such as remote sensing, AI, machine learning, Unmanned Aerial Systems (UAS), and various camera systems, in combination with citizen science. Topics of interest include, but are not limited to:
• Disruptive and Innovative sensors and technologies in hydrology.
• Advancing opportunistic sensing strategies in hydrology.
• Automated and semi-automated methods.
• Extraction and processing of water quality and river health parameters (e.g., turbidity, plastic transport, water depth, flow velocity).
• New approaches to long-term river monitoring (e.g., citizen science, camera systems—RGB/multispectral/hyperspectral, sensors, image processing, machine learning, data fusion).
• Innovative citizen science and crowd-based methods for monitoring hydrological extremes.
• Novel strategies to enhance the detail and accuracy of observations in remote areas or specific contexts.
The goal of this session is to bring together scientists working to advance hydrological monitoring, fostering a discussion on how to scale these innovations to larger applications.
This session is co-sponsored by MOXXI, the working group on novel observational methods of the IAHS.
PICO: Wed, 30 Apr | PICO spot A
Water level information is essential for monitoring and modelling river systems. Traditional, water level monitoring is done using intrusive gauging methods, such as pressure gauges; however, these sensors might be lost during an intense flood. Furthermore, in extreme flood or droughts events, measurements may become insufficient. Camera gauges have gained attention over recent years. These techniques emerge to be a low-cost and remote sensing approach for river monitoring. Camera gauges provide a more flexible and convenient setup, with cameras installed in a safe location. Moreover, they can efficiently monitor a wide range of water level values. Additionally, image sequences can be used to estimate water surface velocity and to eventually measure river discharge. Common camera gauge setups use one camera requiring additional information, e.g., a 3D model of river reach and ground control points (GCPs). On this setup, the water area is extracted from the images, and the water surface contour is reprojected into the 3D model, with the reprojection process being supported by GCPs. However, capturing 3D models can be challenging and is sometimes not possible. Further, due to cross-section change over time, there is a need to update the 3D model to ensure precise measurement. Here, we propose to change the camera gauge paradigm by using two cameras and applying stereo-photogrammetry. Using a traditional stereo-photogrammetry approach, points from two images can be projected into a 3D space, without the need for a 3D model. In this setup, the only required additional information besides the interior camera geometry is the distance between the two cameras, e.g., the baseline. After retrieving the relative camera positions, images can be densely matched to produce high resolution point clouds of the river cross-section.
For stereo-reconstruction, one of the first steps is the matching of key points between the images. Matched points are used to retrieve the relative camera poses (position and orientation). The matching can be done using standard matching algorithms (e.g., SIFT, and SURF). However, these can fail in cases where images have low texture or when they are captured in challenging light conditions. Deep learning has gained attention as an alternative to improving stereo processing. Neural networks for the feature matching achieved state-of-the-art results, being more robust in challenging conditions. Attempts to fully replace the traditional stereo reconstruction have been made (e.g., DUSt3R and MASt3R). These approaches can be used in stereo reconstruction without any prior information; however, they were not yet evaluated for camera gauge applications.
The overall goal of our research is to produce an easy and robust stereo camera gauge setup that to flexibly estimate a 3D model of river cross-sections. Thereby, we can deliver a more robust long-term camera gauge, lowering system deployment costs and maintenance efforts, allowing for a flexible densification of the hydrological monitoring network.
How to cite: Zamboni, P. A. P., Krüger, R., and Eltner, A.: Stereo photogrammetry for river water level and cross-section update: classical and deep learning approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9375, https://doi.org/10.5194/egusphere-egu25-9375, 2025.
TEMBO Africa is a Horizon Europe project that seeks to improve in situ sensing of weather and water in sub-Saharan Africa. To ensure beyond-the-project sustainability, we are using innovative sensors to measure variables such as rainfall, bathymetry, river flow, and large-scale soil moisture. TEMBO also develops services for hydropower, agriculture, and disaster management. These services will produce societal and economical value, for which governments and companies are willing to pay. These payments, in turn, serve to maintain the observation networks. One guiding principle is that the new data gathering method should cost less than 10% of existing methods in term of total costs of ownership. This principle implicitly pays special attention to the local availability of human resources. Many monitoring projects in Africa consist of installation by experts from the Global North, followed by a short training of local technicians. This works nicely until something breaks down. In TEMBO, African universities and spin-off companies are co-developing the technologies such that any operational problems can be solved without flying in expensive foreign experts.
In this presentation, we will go through the sensor innovations and how these feed into different products and services.
How to cite: van de Giesen, N., Annor, F., Ayambila, S. N., Duah, K., Fico, T., Gatti, A., Hoes, O., Kranjac-Berisavljevic, G., Peña-Haro, S., Realini, E., Samboko, H., and Winsemius, H.: From innovative sensors to steady data streams: The TEMBO Africa project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-400, https://doi.org/10.5194/egusphere-egu25-400, 2025.
The combination of in-situ measuring systems and non-intrusive optical technologies can highly improve the monitoring of water quantity and water quality in rivers and reservoirs. This paper presents two applications about innovative camera-based and satellite-based approaches to estimate flow velocity, water level, discharge, turbidity and chlorophyll concentration. The river site of the first case study presented is equipped with a DischargeKeeper, a camera-based discharge measuring system for a continuous measurement of water level, velocity and discharge in real time, and with a Multi-Parameter System MPS for water quality measurement. The MPS measures water temperature, turbidity, oxygen concentration, oxygen saturation, electric conductivity and total suspended solids TSS. The MPS probe is connected to a data logger with data transmission module to deliver measured data in real time. The DK offers. The DK consists of a video camera, an infrared beamer for illumination, a central unit for data processing, a modem for data transfer and a power supply. In operational use the camera takes video sequences of around 5s in predefined intervals, usually ranging from a few minutes to several hours. To determine the surface flow velocity of the river a processing technique called Surface Structure Image Velocimetry (SSIV) is applied. The transmitted proof images with time stamp are very helpful for the optical verification of the measurement especially during flood events. Furthermore, the camera used can be installed at almost any position with respect to the flow, regardless of the presence of a bridge, as far as the flow is in the view of the camera with a good resolution.
Optical satellite sensors, which is the second case study of this paper, provide the opportunity to determine water constituents for whole water bodies. It is possible to derive optically active substances, which leads to good assessment of chlorophyll concentration as a proxy for algal blooms, of the water turbidity, coloured dissolved organic matter and suspended sediment. If the concentration of algae is high enough (appr. > 10 µg/l), also the occurrence of cyanobacteria can be detected. For deriving these parameters, atmospheric correction and in-water retrieval are most important processing steps. The products derived from satellite data can be aligned with the in-situ measurements acquired within DIWA which provides a complementary view on a water body. In our case we aim in combining high temporal, but punctual in-situ data with the spatial information derived from satellite data. They both contribute to the warning system for exceptional high algal blooms or occurrence of cyanobacteria. In case of river systems, the detection of a bloom that occurs upstream can already help to prepare for measures further downstream. Besides the added value that satellite data provide, limits come with reduced data availability due to cloud coverage and limits in spatial resolution for very small water bodies or very narrow river systems.
Both case studies presented showed a very good applicability of image processing technologies for measuring various hydrological and water quality parameters.
How to cite: Hansen, I., Peña-Haro, S., Luethi, B., Stelzer, K., and König, M.: Hydrological Monitoring of Rivers and Reservoirs Using Innovative Image Processing and Satellite-based Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6948, https://doi.org/10.5194/egusphere-egu25-6948, 2025.
Bathymetric surveys and underwater structure inspections are critical for ensuring the safe operation of hydraulic engineering projects. Accurate data on topographical changes and structural conditions help mitigate operational risks caused by erosion, scouring, or structural deficiencies. However, traditional manned vessels face significant limitations in shallow and complex areas, such as downstream spillways, due to accessibility and maneuverability challenges.
The development of unmanned surface vehicles (USVs) offers an efficient and precise alternative for surveying and inspection in shallow water environments. This study utilized the Huawei-3 USV to conduct a bathymetric survey and underwater structure inspection in the shallow downstream area of the spillway at the Wangfuzhou Hydropower Station, Hubei Province, China.
The survey employed the Huawei-3 USV, equipped with high-precision echo sounders and RTK systems, to collect bathymetric and structural data. Water surface elevation data were acquired using RTK measurements, with water levels observed five times before and after the survey to establish a reference elevation. In areas less than 2 meters deep, RTK was also used to directly measure the bottom elevation. The USV combined its draft depth and transducer depth with RTK-derived water surface elevations to calculate the bottom elevation. Satellite imagery was used for pre-planning survey lines, which were aligned parallel to the downstream protective apron, spaced 5 meters apart, ensuring a point spacing of approximately 2 meters. In complex or nearshore areas, manual control was applied to densify survey lines. Data processing involved converting depth to elevation, noise filtering, and generating CAD and 3D models.
The results revealed significant scouring near the downstream protective apron, forming a scour pit with an area of 2,897.2 m², a minimum elevation of 70.26 m, and a proximity of 6.87 m to the reinforced apron edge. The overall underwater topography of the reinforced apron section closely matched the design, with a minimum measured elevation of 70.937 m, differing by only 6.3 cm from the designed elevation of 71 m, indicating stability. However, a portion of the 73 m design elevation zone showed scouring depths up to 25 cm, with an average depth of 12.5 cm. No significant deepening of scour was observed between 2022 and 2024.
The findings demonstrate that USV-based bathymetric systems are highly applicable in shallow water environments, achieving data accuracy that meets regulatory standards. These systems effectively identify scour pits and structural changes, providing reliable data support for ensuring the safe operation of hydraulic engineering projects. Moreover, the method shows significant potential for application in other shallow, complex water environments in the future.
How to cite: Gan, X., Wan, P., Li, J., and Ding, B.: Bathymetric Survey and Underwater Structure Inspection for Hydraulic Engineering in Shallow Waters Using Unmanned Surface Vehicles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2786, https://doi.org/10.5194/egusphere-egu25-2786, 2025.
Flow velocity and water surface elevation (WSE) are fundamental for understanding hydraulic phenomena in river engineering. Although underwater flow properties are not directly observable, these two parameters encapsulate the hydraulic properties governing river flow, as described by the conservation of mass and momentum equations. This information enables the understanding of actual hydraulics and facilitates the creation of digital twins, even during large scale flood events.
To measure flow velocity from UAV imagery, we developed a novel, reference-free image analysis method based on image conversion. This method eliminates the need for physical reference points, addressing practical challenges in field deployments. It leverages readily available camera information, including position (x, y, z) and orientation (pitch, roll, yaw). Complementary WSE data, obtainable from various sources, completes the required input. This allows accurate conversion of video pixel data to surface coordinates, enabling velocity measurements at any point within the river flow. Particle image velocimetry (PIV) is then applied to the converted images to derive the velocity field.
For WSE determination, we explored three approaches: Light Detection and Ranging (LiDAR), Structure from Motion (SfM), and edge-based downscaling of SfM. LiDAR data, while valuable and easy to observing, exhibits lower point density on the water surface compared to the surrounding non-water areas, depending on water surface conditions. However, even sparse LiDAR data in the mid-channel provides crucial hydraulic information. For SfM, we employed multiple UAVs capturing images at appropriate timing to resolve temporal WSE changes. As a downscaling approach using a single UAV, WSE data extracted solely from the riverbank can also be utilized.
We have begun accumulating observations of large-scale flow phenomena. Our results reveal cellular secondary currents and flow patterns over bedforms. Observations of cellular secondary currents show boiling-type phenomena occurring on the order of seconds, and more persistent cellular structures when averaging the flow field over one minute. From an engineering perspective, although these events are infrequent, they can significantly impact float-based discharge measurements when they occur. Observations of flow over bedforms show spatial variations in velocity and WSE along the flow direction, exhibiting wave-like patterns. The out-of-phase relationship between these wave patterns suggests they are associated with micro-bedforms, indicating active sediment transport. Furthermore, this understanding of sediment hydraulics can be used to estimate water depth.
How to cite: yorozuya, A. and kudo, S.: Flow structures in actual rivers obtained by areal measurement of flow velocity and water surface elevation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16904, https://doi.org/10.5194/egusphere-egu25-16904, 2025.
Image-based techniques have gained significant attention for monitoring natural and artificial rivers, thanks to their many advantages over traditional methods. These non-intrusive and highly-versatile optical approaches provide accurate flow discharge measurements, even in challenging conditions like flood events, while ensuring the safety of both operators and equipment. However, the accuracy of optical measurements is affected by several factors, including environmental conditions, river flow characteristics, field acquisition protocols, and the parameterization of the processing software.
Image-based techniques follow a three-phase workflow: (i) seeding, (ii) recording, and (iii) processing. Seeding introduces natural or artificial tracers onto the water surface to detect motion. Recording captures video sequences from stationary or mobile platforms (e.g., UASs – Unmanned Aerial Systems). Processing extracts the surface velocity field and flow metrics. The latter phase is divided into three sub-steps: pre-processing, surface velocity evaluation, and post-processing. Pre-processing includes stabilization, orthorectification, and graphical enhancement; surface velocity evaluation uses correlation-based or similar algorithms to track tracers across frames; finally, post-processing refines velocity data by filtering noise, interpolating missing data, and extracting relevant metrics.
Among the steps of optical techniques, graphical enhancement is particularly critical. By increasing the contrast between tracers and the background, it enhances the ability of software algorithms to accurately track motion, thereby reducing errors. However, an inadequate parametric setup of the processing software can also result in the estimation of biased velocities. To investigate these interdependencies, this study conducted a comprehensive sensitivity analysis, evaluating the combined effects of graphical enhancement techniques and processing parameters on the performance of image-based analyses. The analysis compares traditional algorithms with more innovative approaches, including colorspace transformation, and assesses the impact of varying processing parameters under different operational conditions. A dataset of videos acquired from UAS platforms and fixed stations during discharge measurement campaigns on Sicilian rivers, in Italy, was used. The videos were analyzed using PIVlab and SSIMS-Flow software, and the results were benchmarked against ADCP measurements.
The findings reveal that both the choice of graphical enhancement methods and the optimization of key software parameters significantly affect the accuracy of velocity and discharge estimates. The study also provides valuable insights into selecting the most appropriate enhancement techniques and configuring processing parameters, tailored to specific field conditions and operational requirements, further demonstrating the potential of image-based methods for hydraulic monitoring.
How to cite: Alongi, F., Robert Ljubičić, R., Pumo, D., Dal Sasso, S. F., and Noto, L. V.: Optimizing Image-Based Techniques for River Monitoring: Insights into Graphical Enhancement and Parameter Sensitivity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18462, https://doi.org/10.5194/egusphere-egu25-18462, 2025.
Herein we present an analysis of the performance of the Image Wave Velocimetry Estimation (IWaVE), a python library for image-based river discharge calculations. IWaVE simultaneously performs a 2D velocimetry analysis and calculates the stream depth through 2D Fourier transform, exploiting the sensitivity of water wave dynamics to flow conditions. Unlike existing velocimetry approaches such as Particle Image Velocimetry (PIV), Particle Tracking Velocimetry (PTV) or Space-Time Image Velocimetry (STIV), the uniqueness of this approach lies in: 1) velocities that are advective of nature can be distinguished from other wave forms such as wind waves. This makes the approach particularly useful in estuaries or river stretches affected strongly by wind, or in shallow streams in the presence of standing waves. 2) The velocity is estimated based on the physical behavior of the water surface, accounting for the speed of propagation of waves and ripples relative to the main flow. This makes the approach more robust than traditional methods when there are no visible tracers. 3) If the depth is not known, it can be estimated along with the optimization of x and y-directional velocity. Depth estimations are reliable only in fast and shallow flows, where wave dynamics are significantly affected by the finite depth.
We analyzed 2 videos recorded from a drone on a site in the Netherlands over a tidal channel in Zeeland at Waterdunen - Breskens. One of the videos has strong winds, which creates waves moving upstream. ADCP measurements for both videos are available. The videos were taken at different moments during different tidal conditions, they were processed using IWaVE, a LSPIV and a STIV methods. The results show that the LSPIV, STIV and IWaVE are in good agreement with the ADCP measurements for the case where there is no wind. However when there is wind the LSPIV and STIV methods fail to obtain the correct surface velocity, while the velocity calculated with IWaVE is in good accordance with the ADCP.
How to cite: Peña-Haro, S., Dolcetti, G., and Winsemius, H.: Performance of the Image Wave Velocimetry Estimation for physics-based non-contact discharge measurement in rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3849, https://doi.org/10.5194/egusphere-egu25-3849, 2025.
Discharge estimation at a river site depends on local hydraulic conditions identified by recording water levels. In fact, stage monitoring is straightforward and relatively inexpensive compared with the cost necessary to carry out flow velocity measurements which are, however, limited to low flows and constrained by the accessibility of the site. In this context, the mean flow velocity is hard to estimate for high flow, affecting de-facto the reliability of discharge assessment for extreme events. On the other hand, the surface flow velocity can be easily monitored by using radar sensors allowing to achieve a good estimate of discharge by exploiting the entropy theory applied to rivers hydraulic (Chiu,1987). The growing interest towards the use of no-contact methods to estimate discharge (Tauro et al., 2018) in field applications has shown that the cross-track velocity distribution can be inferred with sufficient accuracy using the surface velocities, usurf, sampled using Surface Velocity Radars (SVR) (Fulton and Ostrowski, 2008; Moramarco et al., 2017, Alimenti et al. 2020), the quantitative imaging techniques as LSPIV (Fujita et al., 1998) or PTV (Tauro et al., 2019). In this context, overall the velocity-area method is applied to estimate the mean flow velocity starting from the depth-averaged velocity, uvert, which is inferred through the velocity index, k=uvert/usurf.. For many river gage sites configurations, k has been set to 0.85. However, considering k refers to a monotonous velocity profile, not taking account of dip phenomena, the application may fail in estimating the depth-averaged velocity (Moramarco et al., 2017; Koussis et al., 2022, Pumo et al., 2025). Based on that, this work proposes a new entropy-based approach to estimate the depth-averaged velocity starting from the measured surface velocity retrieved by conventional and/or no-contact measurements. The approach exploits the dependence of the entropy parameter M with the hydraulic and geometric characteristics of channel (Moramarco and Dingmann, 2017), allowing to derive formulations on Manning’s roughness, shear velocity and water surface slope. Based on these features, the entropy-based method by using the measured surface velocity and the geometry of the river site is able to turn usurf into uvert considering for each usurf an index which depends on the local water surface slope. The application to river sites along the Tiber River, Po River and Amazon River has shown the effectiveness of the approach in estimating the depth-averaged velocities with a fair accuracy along all verticals. Therefore, the method well lends itself to be integrated in the field of no-contact streamflow measurements.
How to cite: Moramarco, T.: Entropy-based depth-averaged velocity assessment from surface flow velocity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19742, https://doi.org/10.5194/egusphere-egu25-19742, 2025.
With the increase in urbanisation and climate change around the globe, there is an increased risk of surface water flooding. Although extreme flood events are commonly discussed, smaller, more frequent flood events also cause significant disruptions that impact human life and put financial stress on authorities. The majority of urban flooding is due to drainage failure. For effective surface water management, it is important to assess the effectiveness of existing surface drainage assets and accordingly plan asset maintenance or retrofitting of new drainage assets (both traditional and nature-based solutions). The surface drains usually fail because they are either not positioned where the surface water accumulates or are blocked and not maintained to meet the standards. Microtopography significantly influences the surface water flow movement, flow path, flow direction, and velocity, and consequently, dictates the areas of water accumulation. Thefore, this study explores a novel approach to evaluate storm drain inlet positions using high-resolution topographic indices maps derived from Unmanned Aerial System (UAS) imagery. The Topographic Wetness Index (TWI) and Topographic Control Index (TCI) were employed to identify drains misaligned with surface water pathways and pinpoint critical drains in the sink points of the topography, respectively.
Storm drain inlets were classified as functional or non-functional based on their intersection with the flow path defined by the optimal TWI threshold. The optimal threshold was determined to be the 90th percentile at a value of 6.19 based on the spatial similarity of the delineated runoff-contributing flow path with the 1 in 100 year surface water flood map produced by the Environment Agency. The validation of the classification of storm drains effectiveness based on TWI using field data yielded an overall accuracy of 53 %, 75% precision, and an F1 score of 62%, indicating a moderate success of TWI in identifying functional drains. Although validation with LIDAR data showed a slight improvement in accuracy and precision, the results generally demonstrated that TWI has a strong capability to correctly identify functional drains; however, it is slightly more challenging to identify nonfunctional drains.
A comparison of the UAS-derived TCI map with the LIDAR-derived TCI map demonstrated a 90% match in the identified sink areas and a high accuracy of 93% in identifying critical drains in the sink areas. The results suggest that the combined use of TWI and TCI offers a promising approach for assessing storm drain effectiveness, based on its position and guiding authorities in identifying areas with drainage deficits and preparing targeted drainage maintenance strategies. The findings of this research provide valuable insights for urban planners and decision-makers to not only optimise the placement and maintenance of storm drain inlets but also highlight the potential for alternative nature-based low-impact development (LID) solutions in locations where traditional drainage is found to be inefficient. This would ultimately enhance the resilience of urban areas to surface-water flooding.
How to cite: Ramachandran, R., Rivas Casado, M., Bajon Fernandez, Y., and Truckell, I.: Enhancing Urban Resilience to Surface Water Flooding: A Novel Approach Using UAS-Derived Topographic Indices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4525, https://doi.org/10.5194/egusphere-egu25-4525, 2025.
The growing prevalence of urban floods necessitates the development of cost-effective and scalable monitoring solutions. Traditional water-level sensors are often prohibitively expensive for widespread deployment. Moreover, existing image-based methods frequently encounter limitations in generalizability, particularly the difficulty of harmonizing selected reference features in large-scale quantitative measurements. To address this research gap, we present a novel method that utilizes traffic camera imagery to provide a lightweight solution for quantitatively monitoring urban flood inundation depths with high spatial and temporal resolution. Specifically, the waterline in flood images is recognized by a neural network and localized using world coordinates calibrated by common road markings, allowing for accurate inundation depth measurement based solely on the imagery. This method eliminates the need for costly point cloud data collection or pre-calibrated measurement objectives in urban settings. Additionally, this method enables the simultaneous collection of waterlogging depths from multiple reference objectives within the same image, yielding more robust measurements. This innovative approach paves the way for cost-effective, high-resolution, and reliable quantitative monitoring of urban flood inundation depths, ultimately providing crucial data support for emergency responses and long-term flood mitigation strategies.
How to cite: Qin, J. and Ping, S.: A Scalable and Lightweight Urban Flood Monitoring Solution Utilizing Only Traffic Camera Images, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10710, https://doi.org/10.5194/egusphere-egu25-10710, 2025.
The monitoring of small headwater catchment represents a major issue in hydrology, especially in remote areas, where gathering real-time hydrological data is often prohibitive due to the limited availability of power and connectivity. However, recent advances in non-contact computer vision and informatic technology offer an opportunity to fill such technical gap. In this regard, we designed and developed the MagicHydroBox prototype (MHB), and all-in-one camera and processing unit system aimed at monitoring the water level in small headwater rivers. The tasks performed by MHB include image collection, image processing, storage and transmission of the processed data. Since the MHB is equipped with NIR (NearInfrared) leds and camera, the image collection can be carried out both during the day and the night period. The image processing takes place directly in the MHB, to guarantee the onsite analysis, it is based on the Otsu’s segmentation method to identify a properly placed target within the images, and results in the direct estimation of water depth. Finally, we built in the MHB the possibility to transmit the processed data both through Gprs (mobile data) and LoRaWan (a long-range, low-power system). The MHB is also equipped with a GUI that allows the user to set and calibrate the instrument. We carried out preliminary field tests to evaluate the effectiveness of the MHB in providing an accurate measure of the target and transmitting the processed data. The preliminary results highlight the potential of the MHB to estimate the water level, especially in NIR images, and to provide a real-time hydrological monitoring where Internet signal is available. The main innovation of the MHB is represented by the fact that it automated a series of tasks that were instead manually performed in previous works. The concentration of all the necessary tasks within the MHB simplify the data acquisition, the processing and the management providing an useful tool where frequent maintenance or monitoring surveys are not possible. Moreover, the MHB is promising for future implementation of algorithms to measure surface velocimetry and discharge.
How to cite: Noto, S., Durighetto, N., Tauro, F., Apollonio, C., Petroselli, A., and Grimaldi, S.: Hydrological monitoring in small catchments: the MagicHydroBox, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16334, https://doi.org/10.5194/egusphere-egu25-16334, 2025.
Urban flooding, intensified by climate change, presents significant risks to public safety and infrastructure, necessitating effective early warning systems. These events are categorized into fluvial and pluvial flooding, with the latter becoming increasingly challenging to predict due to its localized nature and short lead times.
In this work we develop a novel low-cost device based on Internet of Things (IoT) useful for urban flooding monitoring. The proposed sensor leverages advances in open-source technology, using ESP32 development boards, to create an accessible and cost-effective solution based on ultrasonic and reed-switch mechanisms. The system features innovative design principles, including a 3D-printed structure, low power consumption, and reliable connectivity through LoRaWAN and MQTT protocols used as potential early warning system.
The system’s primary objective is to detect storm drain overflow caused by intense rainfall, triggering timely alerts to mitigate flood impacts. Functional requirements emphasize ease of installation, durability, and cost-effectiveness, enabling widespread adoption in diverse urban contexts. The sensor design incorporates a float mechanism, reed switch, and microcontroller housed in a compact, water-resistant case.
Preliminary testing demonstrated the system's ability to detect water level changes and transmit alerts efficiently. Further work includes refining the design to minimize false positives and enhance system reliability under various environmental conditions. This development represents a significant step toward scalable, low-cost flood monitoring systems, contributing to global efforts in urban flood risk management.
How to cite: Cancelliere, A., Buonacera, G., Palazzolo, N., Campisano, A., Gullotta, A., and Peres, D. J.: Development of a low-cost IoT system for monitoring storm drain overflow during urban flooding , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11952, https://doi.org/10.5194/egusphere-egu25-11952, 2025.
Flash floods in Mediterranean regions pose significant threats to lives, infrastructures, and economies. Recent episodes of extreme rainfall in one such region led to devastating flash floods, resulting in loss of life, destruction of homes, and widespread disruption of transportation networks. Therefore, there is a critical need for advanced methods to monitor and analyze the flood dynamics, especially in urban areas. This study investigates the use of two advanced image-based techniques, Fudaa-LSPIV (Coz et al., 2014) and SSISM-Flow (Ljubičić et al., 2024) for surface velocity and discharge estimation of urban flash floods. The research used videos or images of historical urban flood events and estimated the surface velocity. To analyze the urban floods, Matera, a city of southern Italy, was selected as case study. Matera was chosen because its historical city center, the “Sassi”, was affected by extreme rainfall events in the last few years, e.g. 2014, 2018, 2019, and 2023. Five extreme past flood events occurred on 3 Aug 2018, 12 Nov 2019, 2 Jun 2023, and 2 & 21 July 2024 were recorded for estimation of surface velocity. Fudaa-LSPIV works according to the Particle Image Velocimetry (PIV) principles, while SSISM-Flow is a user-friendly and Python-based innovative tool with OpenCV integration for precise surface velocity filed extraction. These methods involve steps such as image stabilization, camera calibration, orthorectifications, and velocity calculation. Both techniques were evaluated based on their accuracy, performance, and application to overcome the limitations of analyzing the surface flow of urban floods. This study is innovative in comparing methods to estimate surface velocity of real-time flash floods in urban areas. Using these techniques, the surface velocities were estimated along key transects, and results were cross-validated using the Float Time method as benchmark. The outcomes of both approaches turned out to be consistent with benchmark data, confirming their reliability in monitoring urban floods. This comprehensive flow analysis provided insights for calibrating flood models and enhanced risk management. This study introduced a novel application of these techniques in real-time urban flood monitoring. Furthermore, it contributes to the development of an early warning system, enhances management strategies, and mitigates flood risks in vulnerable areas.
Reference
Ljubičić, R., et al., 2024. SSIMS-flow: image velocimetry workbench for open-channel flow rate estimation. Environ. Model. Softw. 173, 105938.
Coz, Jérôme Le, wt al., 2014. Image-Based Velocity and Discharge Measurements in Field and Laboratory River Engineering Studies Using the Free Fudaa-LSPIV Software. In Proc.of the Inter. Conf. on Fluvial Hydraulics, River Flow, 1961–67.
Acknowledgments
This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.
How to cite: Albano, R., Asif, M., Dal Sasso, S., and Sole, A.: Using citizens recorded videos to estimate water surface velocity and dischargefor urban flash flood monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9477, https://doi.org/10.5194/egusphere-egu25-9477, 2025.
Digital twin, as a virtual representation of physical systems, is increasingly recognised as a core component of timely and accurate water management, particularly for interconnected and rapidly changing systems. Digital twin supports the simultaneous monitoring, simulation, and optimisation of real-world operations by integrating multiple data sources, including in-situ measurements, remote sensing and modelling data. By enabling a detailed characterisation of catchment functioning and its ecological boundary conditions, a digital twin facilitates equitable water allocation across sectors and supports timely and evidence-based decision-making.
Developing a digital twin requires extensive datasets, robust scientific evidence, and a clear grasp of ecological boundaries, reflecting the interconnected nature of multi-sectoral decision-making. The Bode River Basin, one of the best-monitored catchments in central Europe, serves as a showcase for designing and implementing a digital twin system for multi-sectoral and sustainable water management at catchment scale. The recent prolonged droughts (2017–2021) and their impacts on various water bodies offer a real-world “experiment” of extreme climate scenarios, highlighting the vulnerabilities and risks within the catchment and illustrating the complex trade-offs inherent in water resource management.
This study integrates long-term, high-resolution monitoring strategies with coupled surface water, groundwater, and water quality models into a unified framework that addresses both quantitative and qualitative aspects of water systems. Such a comprehensive approach enables forecasting climate change impacts and optimising water resource allocation across sectors. Overall, this work demonstrates the potential of digital twins to advance sustainable water resource management under changing climatic conditions.
Acknowledgment
This work was supported by the OurMED PRIMA Program project funded by the European Union’s Horizon 2020 research and innovation under grant agreement No. 2222.
How to cite: Rouhani, A., Mate Marin, A., Moya Diez, A., Gómez-Hernández, J. J., Rode, M., and Jomaa, S.: Transforming water resources management at river basin scale with digital twin technology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17076, https://doi.org/10.5194/egusphere-egu25-17076, 2025.
PICO: Thu, 1 May | PICO spot A
Personal Weather Stations (PWS) have gained attention in recent years as a potential complement to operational meteorological networks, which are often sparse and may not adequately capture localized rain events, especially in areas with complex orography. PWS, on the other hand, can improve the spatial resolution of rainfall data due to their affordability and, thus, widespread distribution. However, their effectiveness and reliability depend on overcoming certain challenges. PWS often lack adherence to World Meteorological Organization standards, as they may not be properly placed nor regularly maintained, and there are no standardised approaches for data quality check. Frequent gaps in the series (mainly due to data transmission issues), and a constantly changing network layout further limit reliability and consistency of PWS data for hydrological modelling. Therefore, the application of PWS rain data for hydrological modelling is still in its infancy.
This research focuses on evaluating PWS rainfall data for hydrological modelling in the peri-urban Lambro catchment in northern Italy, by comparing characteristics of hourly rainfall data obtained from the MeteoNetwork (Giazzi et al., 2022; https://doi.org/10.3390/atmos13060928, 2022) to those of the rain gauge data obtained from the Regional Agency for the Protection of the Environment of Lombardy (ARPA). This study focuses on the characteristics of the subcatchment-averaged rainfall series are compared. The rain depths in each of the 15 subcatchments are calculated by using the inverse-distance weighting method with the power of 2, and with increasing maximum distance between the station and the centroid of a subcatchment (10km, 25km and 50km). The two subcatchment-averaged rainfall series are compared in terms of (1) accumulated rain depth, (2) maximum rainfall intensity, and (3) timing of the peak rainfall intensity during a rain event.
Our results indicate that, compared to ARPA rainfall data, PWS data can both underestimate and overestimate rainfall values with similar frequency. Specifically, the magnitude of error in rain depths ranges from -44% to +56% across the subcatchments, and this range does not change significantly with increasing maximum distance. With the maximum distance of 10 km, in eight out of 15 subcatchments the absolute value of the error is smaller than 15%, while the median value amounts to 1.9%, and decreases to -17% and -19% with increasing maximum distance. The errors in maximum rainfall intensity are slightly larger, ranging from -67% to 76%, when compared to the official ARPA gauges with the maximum distance of 10 km. The median error amounts to 15.5%, -26% and -30% for the three maximum distance values. Concerning the timing of peak intensity, there are no discrepancies between the two datasets, and PWS data can be considered accurate in this regard. However, large errors in rain depths and intensities suggest that PWS rain data alone cannot be expected to yield accurate outputs in hydrological simulations. This conclusion will be tested by running a hydrological model with these datasets.
Acknowledgement
This research is part of the work within the COST Action “Opportunistic Precipitation Sensing Network” (OpenSense, CA20136)
How to cite: Kovačević, R., Todorović, A., De Michele, C., Nebuloni, R., and Ceppi, A.: Evaluating the Potential of Personal Weather Stations (PWS) for Semi-distributed Hydrological Modelling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18603, https://doi.org/10.5194/egusphere-egu25-18603, 2025.
Extreme weather events, including sudden and intense rainfall, have become increasingly frequent due to the growing impact of climate change. This rapid influx of water often carries a variety of pollutants, including nutrients, heavy metals, and microbial contaminants, significantly modifying the physicochemical and microbiological characteristics of urban streams. This study aims to evaluate the effects of rainfall events on the physicochemical and microbiological properties of the Tócó Stream, focusing on changes in key water quality parameters and microbial dynamics. Two sampling points were selected to represent different environmental areas: one site was located in a near-natural area, and the other was situated in an industrial zone, surrounded by facilities and a highway connecting road. Measurements were conducted both before and after the rainfall event. On-site measurements were performed included precipitation (mm), water level, dissolved oxygen content, and water temperature, while water samples were collected for laboratory analysis. The collected samples were tested for pH and electrical conductivity (EC) as well as for nutrient-concentrations of NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, K⁺, SO₄²⁻, chemical oxygen demand (COD) and biological oxygen demand (BOD5) were also determined from the samples. In case of microbiological parameters, total coliforms, yeasts, and total plate count were determined. Our results revealed differences between the two sampling sites and the pre- and post-rainfall conditions. At the industrial site the nutrient contents have decreased due to the rainfall, while at the near natural site we did not determine such change in connection with these elements. The same trend were detected in the case of EC as well. The microbiological analysis of the water samples clearly showed that while both total bacterial count and total coliform count showed an increasing trend after the rainfall at the first site, this trend was much less pronounced at the site reflecting the natural state. Our objecitve was to study the influence of sudden rainfall events, for the reason that these effects remain understudied, particularly in terms of their short- and long-term impacts on water quality and microbial properties.
The research presented in the article was carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00008 project.
How to cite: Kun, S., Boczonádi, I., Nagy, P. T., Szabó, A., Tóth, F. A., Fehér, Z. Z., Magyar, T., Madar, L. A., Szűcs, I., Tamás, J., and Nagy, A.: Impact of intense rainfall event on the physicochemical and microbiological characteristics of an urban stream, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19738, https://doi.org/10.5194/egusphere-egu25-19738, 2025.
Soil moisture is a critical factor for understanding the interactions and feedback between the atmosphere and Earth's surface, particularly through energy and water cycles. It also plays a key role in land climate and hydrological processes. Recent advancements in autonomous robotic applications for precision agriculture have introduced significant solutions, particularly in remote sensing. Currently, these platforms enable autonomous soil parameter measurement and on-site data collection, which is essential for resource optimization and data-driven agricultural decision-making. However, challenges persist, especially in real-time soil moisture monitoring—a key focus for improving irrigation efficiency, water use, and crop yields. Soil moisture measurement in-situ techniques include the accurate oven-drying method and soil moisture sensors, while satellite remote sensing uses optical, thermal, and microwave imaging to estimate surface soil moisture from a broader perspective. However, fully autonomous robotised sampling procedures for optimising the process, increasing repeatability and overall accuracy, as well as increasing the reachability of the sampling of remote areas, are still not utilized.
Soil moisture measurement in-situ techniques include the accurate oven-drying method and soil moisture sensors, while satellite remote sensing uses optical, thermal, and microwave imaging to estimate surface soil moisture from a broader perspective. However, fully autonomous robotised sampling procedures for optimising the process, increasing repeatability and overall accuracy, as well as increasing the reachability of the sampling of remote areas, are still not utilized.
Measuring soil moisture presents a significant challenge due to its reliance on human labour, which is required to cover extensive areas for sensor measurements manually. Additionally, soil moisture measurements at a specific point vary with time and environmental conditions, making these values unstable. While satellites offer a potential solution to some of these issues, their accuracy is affected by environmental factors such as cloud cover and dense vegetation, while they only describe the upper soil layer. Moreover, ground measurements of surface soil moisture are still necessary for calibrating and training the satellite systems. To address these challenges, we propose an adaptable in situ method for automating soil moisture measurements.
Our approach introduces AgriOne, an autonomous soil moisture measurement robot leveraging a surface-aware data collection framework to achieve precise and efficient soil moisture assessments, thereby minimizing reliance on permanent sensors and reducing associated costs and labour. The hardware of AgriOne consists of a UGV Husky A200 from Clearpath Robotics loaded with the soil moisture sensor TEROS12 from Meter Group. The sensor is mounted on a linear actuator probe, as shown in the figure.
To evaluate the proposed approach, we conducted two field experiments in different locations under different weather and soil conditions. The experiments were successful in both cases, and we collected 70 and 66 measurements, respectively, of surface soil moisture. For the first experiment, we covered an area of 380m2 in 57 minutes, and for the second experiment, we covered an area of 800m2 in 2,5 hours. The results showed proof of concept because of the workability of the AgriOne robot and the reliability of the data collection framework.
How to cite: Tsimpidi, I., Soulis, K., and Nikolakopoulos, G.: Large-scale Soil Moisture Monitoring: A New Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1910, https://doi.org/10.5194/egusphere-egu25-1910, 2025.
Handling unstructured and missing data (UMD) remains a significant challenge in environmental monitoring and precision agriculture. This study focuses on the imputation of UMD in soil matric potential (SMP) datasets, a critical parameter in assessing soil water availability and managing irrigation systems. Missing data can distort trends, complicate analysis, and hinder decision-making in critical areas such as water management and precision irrigation. Using Extreme Learning Machine (ELM) and Time Series Models with Exogenous Inputs (TSMX), the research reconstructs missing SMP records by integrating adjacent sensor datasets and explanatory environmental variables. This approach demonstrates the potential of advanced data-driven techniques to enhance the reliability of agricultural and hydrological datasets. The dataset encompasses hourly SMP measurements and explanatory variables, including meteorological inputs such as relative humidity, air temperature, and soil properties, collected across multiple sensors in a precision agriculture setup. Exploratory analysis revealed variations in data structure, including non-stationary trends and significant statistical differences between training and testing datasets. These insights guided the selection of inputs and model configurations, emphasizing the importance of autocorrelation analysis in determining the most significant predictors. The ELM model exhibited superior performance in imputing missing SMP values, achieving an R-value of 0.992, RMSE of 0.164 cm, and NSE of 0.983 using five key inputs. This robustness highlights ELM's capability to generalize across diverse input combinations effectively. Additionally, TSMX has also been explored for its potential to leverage temporal dependencies and explanatory variables for consistent imputation. The incorporation of adjacent sensor data in modeling efforts underscores the importance of spatial and temporal relationships in enhancing accuracy, particularly in heterogeneous environmental conditions. This research underscores the critical role of input selection and model tuning in addressing UMD in SMP datasets. The findings demonstrate the complementary strengths of ELM and TSMX, offering practical insights for improving data reliability in precision irrigation and environmental monitoring. Future studies could explore integrating additional explanatory variables and employing advanced machine learning architectures to optimize imputation performance under varying environmental conditions further.
Keywords: Missing Data Imputation; Soil Matric Potential; Extreme Learning Machine; Time Series Models; Exogenous Inputs; Precision Agriculture; Environmental Monitoring.
How to cite: Zeynoddin, M., Gumiere, S. J., and Bonakdari, H.: Bridging Data Gaps in Soil Matric Potential for Enhanced Water Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14585, https://doi.org/10.5194/egusphere-egu25-14585, 2025.
The growing demand for high-resolution hydrological data necessitates innovative, scalable, and cost-effective monitoring solutions. This study presents the development of a low-cost soil moisture sensor station designed to address these challenges by leveraging advancements in open-source hardware and software.
The sensor station employs modified versions of commercially available capacitive soil moisture sensors, selected after a thorough review of existing technologies and preliminary evaluations to balance affordability and robustness. Built around the Raspberry Pi Pico microcontroller, the station features modular MicroPython programming, combined with a real-time clock (RTC) and an SD card module for robust data logging. Reconfiguration is streamlined through a JSON-based setup, avoiding the need for firmware modifications.
A custom-designed power supply unit, powered by a Li-Poly battery recharged using a 5W solar panel, ensures long-term operation. The station employs power-saving sleep modes during dormant periods, enabling continuous logging at intervals as low as 15 minutes even under suboptimal sunlight conditions in continental Europe, as per conservative estimates. Housed in a 3D-printed enclosure, the main control unit integrates ports for connecting up to three capacitive soil moisture sensors (3.3/5 V Analogue Out) at various depths, a (DHT 11) temperature and relative humidity sensor, and a UART interface for real-time access to runtime logs.
The affordability of the proposed design potentially allows for the deployment of multiple stations for the cost of a single commercially available system. This scalability is particularly critical for applications requiring dense sensor networks, such as watershed-scale studies, hydrological forecasting, or localized climate impact assessments. While acknowledging that the precision and robustness of such systems may not fully match commercial counterparts, this trade-off is expected to be offset by their adaptability and wide applicability in aforementioned cases.
Advancements in monitoring and communication technologies brought about by the "Industry 4.0" phenomena have been instrumental in enabling the design and development of this sensor station. By harnessing these innovations, the study demonstrates how innovative, cost-efficient technologies can be adapted for hydrological monitoring applications. This work wishes to not only showcase the potential of such advancements to bridge the technological and economic barriers in environmental monitoring but also wishes to highlight their role in addressing the growing gap between the demand for hydrological data and its availability.
This study aspires to facilitate and encourage further translation of advancements in monitoring and communication technologies from the "Industry 4.0" era into hydrological monitoring systems in the hope that such advancements could help democratize access to hydrological monitoring technologies, potentially addressing critical data gaps, and in-turn enabling better-informed water management and research practices.
How to cite: Kootanoor Sheshadrivasan, V. and Langhammer, J.: Development of a Low-Cost Soil Moisture Sensor Station for Hydrological Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6748, https://doi.org/10.5194/egusphere-egu25-6748, 2025.
Eutrophication is a significant environmental concern, which is often monitored through Chlorophyll-a (Chla) concentrations in inland and coastal waters. While traditional in-situ measurement methods are accurate, these are time-intensive, labor-demanding, and limited in spatial and temporal resolution. In recent years, remote sensing and machine learning approaches have emerged as promising alternatives for environmental monitoring, although their effectiveness is limited by challenges such as constrained in-situ data availability, the variability of water characteristics, and difficulties in transferring models across regions. Existing global models prioritize data quantity over quality, often lacking in comprehensive analysis of relationships between water quality parameters and remote sensing bands and indices. This study aimed to enhance global Chla prediction accuracy by improving data quality and identifying key predictive features using Earth Observation (EO) data. Two feature groups were examined: Group 1 (reflectance values from single bands and band ratio indices) and Group 2 (reflectance values from single bands combined with mathematical transformations of multiple bands). Machine learning models, including Random Forest (RF), Least Squares Boosting (LSBoost), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), were assessed for overall performance, cross-validation accuracy, and transferability to external datasets. Among tested models with their own dataset, GPR achieved the highest overall accuracy (R² = 0.95, RMSE = 2.82 µg/L with Group 2 features), while SVR exhibited the weakest performance. For transfer validation using data from external lakes, RF (R² = 0.73, RMSE = 12.39 µg/L) and LSBoost demonstrated the greatest transferability. Spatial-temporal predictions of Chla over 2023–2024 successfully captured seasonal trends by revealing reliable and consistent patterns of Chla distribution. The present study highlights the potential of the proposed framework for global Chla monitoring in inland waters, also, emphasizing the potential in areas outside the training dataset.
Keywords: global chla monitoring, transferability, remote sensing, machine learning
How to cite: Moe, A. C., Saddi, K. C., Zhuang, R., Miglino, D., Saavedra Navarro, J., and Manfreda, S.: Global Framework for Chlorophyll-a Monitoring in Inland Lakes: Integrating Remote Sensing, Machine Learning, and Databases - Achievements and Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12047, https://doi.org/10.5194/egusphere-egu25-12047, 2025.
Dissolved oxygen (DO) levels are crucial for aquatic life, especially under climate change, making continuous monitoring essential for effective lake management. However, local measurements are often costly and time-intensive, whether collected through field campaigns or permanent gauges. This study investigates the feasibility of using remote sensing techniques, coupled with machine learning; to track and estimate DO in a shallow eutrophic lake. Because DO cannot be directly measured with optical sensors, we first identify optically sensitive parameters—chlorophyll-a (Chl-a), temperature, and water depth—that correlate statistically with ground-measured DO. A two-step pipeline is then introduced: (1) estimating water level changes, Chl-a, and surface temperature from satellite data, and (2) predicting DO based on these derived parameters.
Model development starts with developing three separate models to estimate Chl-a (Sentinel-2), water level changes (Sentinel-1), and lake surface temperature (MODIS), using the Google Earth Engine Python API for data processing and analysis. Subsequently, both remotely sensed parameters and local measurements are used to train a DO prediction model. The training procedure explores 16 machine learning frameworks with hyperparameter tuning, using a 70%–15%–15% time-series split for training, validation, and testing, implemented in scikit-learn and Optuna. Search stopped with the model with R² values of 0.89 and 0.64 and mean absolute errors of 0.81 mg/L and 1.29 mg/L for locally measured and predicted test datasets, respectively. These results highlight the potential of combining remote sensing-derived parameters with machine learning to estimate DO, an otherwise non-optically measurable parameter.
This approach offers a cost-effective alternative for modeling continuous temporal variations in DO and supports comprehensive temporal assessments of DO concentrations in shallow eutrophic lakes. Ultimately, this framework shows promise for broader applications and generalizations, thereby contributing to the effective monitoring of non-optical water quality parameters and advancing sustainable aquatic ecosystem management.
How to cite: Ünalan, U. B., Yüzügüllü, O., and Aksoy, A.: Prediction of Temporal Dissolved Oxygen Concentrations in a Lake Using Remote Sensing and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9460, https://doi.org/10.5194/egusphere-egu25-9460, 2025.
The primary objective of groundwater analysis is to determine the direction and velocity of water flow, which are essential for effective groundwater resource management and contaminant investigation. Conventional methods of evaluating groundwater flow direction, such as using solute or thermal tracers, require the installation of multiple observation wells and are typically laborious, expensive, and time-consuming. Furthermore, the uneven distribution of observation wells and the heterogeneity of aquifers often lead to inaccurate estimations of groundwater flow velocity and direction. Accordingly, this study proposes a novel approach: the thermal vector distributed temperature sensor (TV-DTS) method, combined with a heated line source, to overcome these challenges. The TV-DTS apparatus consists of a single heated fiber and four sensing fibers. The heated fiber functions as the heat source, while the sensing fibers are used to measure temperature changes. These measurements are then used to determine the direction and velocity of water flow by the analytical solution derived from the heat transfer with a heated line source. This method employs only a single-well heating test to estimate both the direction and velocity of groundwater flow, eliminating the need for multiple wells and significantly reducing the time and financial resources. Besides, the TV-DTS has several advantages, such as the ability to provide continuous spatial-temporal temperature data, ensuring reliable and high-resolution monitoring. Two groundwater contamination sites in northern and southern Taiwan have be selected to demonstrate the effectiveness of TV-DTS. The preliminary results showed that at the northern site, the flow direction was predominantly northeast to southwest, with velocities ranging from 0.25 - 0.34 m/day at different depths. In contrast, at the southern site, the flow direction was mainly toward west with higher velocities of 0.1 – 8.0 m/day. The estimated directions and velocities from both sites aligned with previous studies; however, uncertainties were higher at the southern site due to greater velocities observed. This method provides a high-resolution, cost-effective approach for hydrogeological investigations and contaminated sites assessment, serving as a valuable reference for the future investigation and evaluation of hydrogeological characterization.
Keywords: groundwater flow direction, groundwater flow velocity, heat transfer, distributed temperature sensors, borehole, uncertainty, contamination
How to cite: Liu, C. H. and Chiu, Y. C.: Utilizing distributed temperature sensors in a single well with a heating line source to simultaneously estimate the direction and velocity of groundwater flow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3004, https://doi.org/10.5194/egusphere-egu25-3004, 2025.
The use of digital cameras in river monitoring activities can increase our knowledge of water quality status, solving the cost and spatial and temporal data resolution limitations of the existing techniques. The challenge of image-based procedures using camera systems is the proper red, green, and blue (RGB) bands signal interpretation and processing. The actual water upwelling light that reaches the camera lens is the sum of various reflectance components of the suspended particles, the riverbed background and the water itself. One component could prevail over the others, depending on the variability of hydrological (water level, flow velocity, etc.) and environmental (suspended solids concentration, floating pollutants, etc.) characteristics of the river. The effect of water level and turbidity concentration on the riverbed component of the total water upwelling light can be substantial, especially for shallow water. As a result, the riverbed reflectance component, if neglected, can significantly affect the evaluation of the water reflectance, and hence, water turbidity.
In our field campaign, a synthetic turbidity event was recreated by adding a natural clay tracer into the river, and we monitored it using a camera system. Two turbidimeters were installed within the river section to validate the results. Moreover, a submerged panel was fixed directly on the riverbed. This choice was prompted by the shallow water conditions during the experiment, where the riverbed reflectance significantly contributed to the total upwelling light captured by the camera, particularly under low turbidity levels. We defined a clear water condition in which the panel was fully visible, where turbidity level was considered equal to zero. As turbidity increased and the panel visibility decreased, we applied an image-based procedure to assess the actual river turbidity level. In addition, we applied a pixel-by-pixel mean of the camera frames every 2 minutes, for minimizing the signal distortions due to the effect of ripples, sun glare and shadows within the analyzed region of interest of the river surface. These methodological steps allowed us to properly decompose the image into different reflectance components, and to enhance long-term monitoring practices that are subject to a wide range of environmental and hydrological variability.
This study focuses on implementing camera systems in real-world settings, supporting existing river monitoring techniques with early warning networks, and developing innovative solutions for water resource management.
Keywords: camera system, river monitoring, turbidity, image processing, remote sensing, water quality
How to cite: Miglino, D., Jomaa, S., Saddi, K. C., Moe, A. C., Rode, M., and Manfreda, S.: The Role of Riverbed Background Reflectance in Long Term Turbidity Monitoring Using Camera Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17492, https://doi.org/10.5194/egusphere-egu25-17492, 2025.
The ability to record high-resolution data for extended periods using affordable systems can improve the study of hydrological and environmental processes. Unlike commercial alternatives, publicly available open-source sensors can be implemented at a significantly lower cost, allowing higher spatiotemporal resolution and continuous, real-time monitoring. In this presentation, I will outline the fundamental principles, advantages, and challenges of using open-source, self-assembly hardware for hydrological related monitoring using two novel systems. The first system is an incubation chamber system composed of O₂, CO₂, CH₄ low-cost sensors for monitoring gas fluxes from sludge samples, specifically tested on wetland samples under different temperature, oxygen, and light conditions. The second system consists of a portable photoreactor/spectrophotometer driven by Raspberry Pi and Arduino UNO microcontrollers. Validation tests of the photoreactor system were performed in one preliminary design for Rhodamine B dye photodegradation, in which the spectral module was constituted by seven arrays of high-power LED of different wavelengths (UVC and VIS), bismuth ferrite (BiFeO₃) catalyst, and hydrogen peroxide. Results showed significant dye degradation (39.7%) at high chamber temperature (45 °C). The performance of this system is improved in a new design, which includes an exchangeable light module, sampling system, and a spectrophotometer for real-time monitoring of the photocatalytic process in water. Complete technical guides on design, assembly, and installation are provided for both systems, aiming to promote their reproducibility and application for new microbial activity studies and laboratory water treatment applications.
How to cite: Orozco, D.: New open-source, self-assembly tools to study microbial activity and water treatment applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5229, https://doi.org/10.5194/egusphere-egu25-5229, 2025.
Hydrological observations are essential for understanding the complex interactions between the land surface and the atmosphere, improving water resource management, strengthening flood defense, and advancing hydrological modeling. However, the long-term maintenance of experimental basins like Fiumarella di Corleto presents significant challenges, requiring continuous updates to address environmental changes and technological advancements. This study reviews over 20 years of observations at the Fiumarella basin in Southern Italy, focusing on its evolution, challenges, and future directions. The Fiumarella basin, covering an area of 32.5 km², includes a sub-basin of 0.65 km². Since 2002, a hydrometeorological network has been monitoring key variables such as rainfall, temperature, wind, and streamflow, capturing hydrological variability across spatial and temporal scales. In 2006, 22 soil moisture probes were installed along a 60-meter transect at depths of 30 and 60 cm. Additionally, high-resolution LiDAR data and pedological studies have enhanced the understanding of the basin’s morphology and soil characteristics. The maintenance of this experimental basin has posed substantial challenges. Frequent extreme flood events have resulted in significant damage to hydrometric stations, requiring reconstruction and recalibration. Moreover, the sediment and debris accumulation in the retention basin of the sub-basin necessitated periodic clearing to maintain functionality and ensure continuous data collection. These challenges underscore the effort and adaptability required to sustain long-term monitoring in dynamic environments. Data collected from the basin have significantly contributed to hydrological science. Analyses of peak flow events and antecedent soil moisture conditions have provided insights into flood response mechanisms. Spatial and temporal variability in hydrological processes has informed the calibration and validation of semi-distributed hydrological models, enhancing their accuracy and reliability. These findings highlight the importance of integrating diverse datasets such as soil moisture, precipitation, topography, and land use—for comprehensive hydrological research. Looking ahead, planned upgrades aim to further enhance the basin’s capabilities. The installation of a meteorological radar would improve rainfall measurement precision and expand spatial coverage, thereby addressing existing data gaps. Additional hydrometric sensors and automated systems would increase the granularity and reliability of observations, supporting high-resolution analyses. These advancements will ensure that the Fiumarella basin remains a state-of-the-art research facility capable of addressing emerging challenges in hydrology and climate science.
This abstract is part of the project NODES which has received funding from the MUR-M4C2 1.5 of PNRR funded by the European Union - NextGenerationEU (Grant agreement no. ECS00000036).
The present research has been carried out within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan -NRRP, Mission 4, Component 2, Investment 1.3 - D.D. 1243 2/8/2022, PE0000005).
How to cite: Aung, H. H., Onorati, B., Fiorentino, M., Dal Sasso, S. F., Sileo, B., Pizzolla, T., Manfreda, S., and Margiotta, M. R.: Long-Term Evolution and Challenges of Hydrological Observations at the Fiumarella Basin in Southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20109, https://doi.org/10.5194/egusphere-egu25-20109, 2025.
Soil Aquifer Treatment (SAT) is a widely adopted managed aquifer recharge technique that employs natural soil filtration processes to improve the quality of secondary treated wastewater. As treated wastewater percolates through the unsaturated zone, complex interactions occur between dissolved organic matter (DOM) and the soil matrix, leading to the transformation or retention of organic contaminants. Understanding the fate of DOM within SAT systems is essential for optimizing water quality outcomes and ensuring the sustainability of water reuse practices.
Fluorescent dissolved organic matter (fDOM) has emerged as an effective tracer for characterizing DOM dynamics in water systems. By utilizing excitation-emission matrices (EEMs) in conjunction with parallel factor analysis (PARAFAC), fDOM allows for the identification of distinct molecular fractions, their origins (such as microbial or terrestrial), and their reactivity within SAT environments. However, the mechanisms that govern the retention and transformation of specific fDOM fractions during soil passage remain inadequately understood.
In this study, we employed advanced fluorescence spectroscopy to monitor the behaviour of fDOM molecules in a full-scale SAT basin recharging treated wastewater. By integrating EEM-PARAFAC analysis with in-situ water sampling along the vertical profile of the soil, we uncovered complex and varied transformations in DOM as treated wastewater permeated through the soil. Shifts in fluorescence signals indicated a dynamic interplay of processes affecting DOM fractions, including changes in composition and reactivity throughout the infiltration pathway. These patterns illuminate the evolving interactions between organic matter and the soil environment, influenced by biotic and abiotic factors.
This research underscores the potential of fluorescence-based monitoring tools to provide high-resolution, molecular-level insights into DOM dynamics in SAT systems. Such advancements can enhance the design and operation of SAT basins for improved water quality management and resource sustainability.
How to cite: Adler, O., Nakonechny, F., and Arye, G.: The fate of fluorescent dissolved organic matter molecules in recharged secondary treated wastewater within soil aquifer treatment (SAT) basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5916, https://doi.org/10.5194/egusphere-egu25-5916, 2025.
Evaporation from the water surface is among the main water losses from natural and artificial lakes and ponds. Air temperature (Ta), wind speed (va), relative humidity (RH), atmospheric pressure (pa), surface water temperature (Tw) and radiation (R) are among the physical controls of this process. In recent years, water temperature data have increasingly become available so that the question arises, if the measurement of radiation (which, in turn, affects water temperature) may still be required.
The method employed in this study is modelling of daily evaporation by means of artificial neural networks (ANNs) of the multilayer perceptron type (backpropagation, one hidden layer), using varying sets of input variables. Evaporation data from a white Class A pan (Qiu et al., 2022) served as target (50 patterns of daily averages). A logistic activation function was used. Data records were divided 2:1 into training and testing sets, resp.
Data were scaled to the interval between 0.1 and 0.9, and for each run (105 epochs) the root mean square error (RMSE) of the scaled output was computed.
Learning rate (η), momentum (α) and number of hidden nodes were subject to optimization for three different sets of input variables. ANN runs of series S1 comprised Ta, va, RH, pa, Tw and incoming solar radiation (R) as inputs (6 in total). Series S2 and S3 were subsets of S1, with S2 using Ta, va, RH, pa and Tw as inputs. For the input data of Series S3, water temperature Tw was replaced by radiation R.
The neural networks achieved a fair representation of the evaporation data. Optimization yielded a minimum RMSE for Series S1 of 0.0514 and 0.0669 for training and testing, resp. (6 hidden nodes, η=0.009 and α=0.0).
Using the same input variables with the exception of the incoming radiation (in total, therefore, 5 inputs) S2 reached a minimum training RMSE of 0.0557 and a minimum testing RMSE of 0.0887 (5 hidden nodes, η=0.012 and α=0.0).
Series S3 with the 5 inputs Ta, va, RH, pa and R (with water temperature left out), finally achieved an RMSE of 0.0545 for training and 0.0775 for testing, resp. (6 hidden nodes, η=0.006 and α=0.2).
Comparison of Series S2 and S3 shows that, in the case of the data set studied here, the ANNs including incoming radiation among their input variables (but excluding water temperature) outperformed those explicitly accounting for water temperature in lieu of radiation. Using both radiation and water temperature as inputs (S1) resulted in a notable improvement of the ANN output as compared to the runs with either of these variables not accounted for explicitly.
References
Qiu, G. Y., Gao, H., Yan, C., Wang, B., Luo, J., & Chen, Z. (2022): An improved approach for estimating pan evaporation using a new aerodynamic mechanism model. Water Resources Research, 58, e2020WR027870. https://doi.org/10.1029/2020WR027870.
How to cite: Schmid, B.: On the relative importance of water temperature versus radiation for ANN-based pan evaporation modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2944, https://doi.org/10.5194/egusphere-egu25-2944, 2025.
Precise estimation of hydraulic conductivity (K) in porous media is vital for advancing hydrological and subsurface flow investigations. Groundwater experts have increasingly adopted neural computing approaches to indirectly determine K in porous media, offering a more efficient alternative to conventional methods. The research focuses on developing the Feed-Forward neural network (FFNN) and Kohonen Self-organizing maps (KSOM) models to compute the K using easily measurable porous media parameters i.e., grain-size, uniformity coefficient, and porosity. The observed data were split into 70% and 30% for the development and validation phase, respectively. The developed model's performance was examined via statistical indicators, including root mean square error (RMSE), determination coefficient (R²), and mean bias error (MBE). The findings suggest that the FFNN model significantly outperforms the KSOM model in estimating the K value, with the KSOM model achieving only moderate accuracy. During the validation phase, the FFNN model shows a stronger correlation with the measured values, yielding RMSE, R², and MBE values of 0.016, 0.94, and 0.006, while the KSOM model returns values of 0.024, 0.91, and -0.004 respectively. The FFNN model's superior predictive ability makes it a reliable tool for accurate K estimation in aquifers.
How to cite: Chandel, A. and Shankar, V.: Hydraulic conductivity estimation in Porous Media: Insights from Neural computing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1828, https://doi.org/10.5194/egusphere-egu25-1828, 2025.
