PICO
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.
PICO: Mon, 4 May, 10:45–12:30 | PICO spot 2
Precipitation and evaporation are the two fluxes coupling the atmospheric and terrestrial compartments of the hydrologic cycle. Accurate and robust observations of the spatial and temporal variability of these two fluxes over the Earth’s continents is crucial to help understand the intricacies of land surface – atmosphere interactions. Improving our understanding and our ability to quantify these interactions is not only important for scientific purposes (such as developing better earth system models) but also for societally relevant applications (such as flood and drought forecasting). Here, we demonstrate the potential and address the limitations of microwave links from cellular communication networks for estimating both precipitation and evaporation.
Previous research has shown that attenuation of microwave signals propagating through rainfall from the transmitting to the receiving antennas of microwave links can be related to the average rainfall intensity along the path between transmitter and receiver. Over the past two decades, this notion has been successfully applied to retrieve rainfall fields from existing microwave links which are part of cellular communication networks. Rain-induced signal loss due to absorption and scattering of microwave signals by raindrops, a source of “noise” for mobile network operators, has turned out to be a “signal” for hydrometeorological science and applications. The approach of using existing cellular communication infrastructure for environmental monitoring (in this case rainfall measurement) has been dubbed “opportunistic sensing”.
However, atmospheric constituents between the transmitters and receivers of microwave links do not only affect signal propagation when it rains. When it is dry, refractive index fluctuations induced by temperature and water vapor variations resulting from rising turbulent eddies in the atmospheric boundary layer between transmitters and receivers cause received signals to “scintillate”. The variance of these scintillations has been shown to be related to the structure parameter of the refractive index, which in turn can be related to sensible and latent heat fluxes across the microwave link path using Monin-Obukhov Similarity Theory (and the aid of auxiliary information). This principle is used by microwave scintillometers, commercially available instruments for observing turbulent fluxes in the atmospheric boundary layer.
Recent research results show that microwave links from cellular communication networks can, under certain conditions, also be employed as boundary layer scintillometers. Combining this notion with the previous finding that such microwave links can also be used as path-average rain gauges suggests that there is potential to use each of the roughly five million backhaul links from cellular communication networks worldwide as combined precipitation-evaporation sensors. Hence, gaining access to received signal level data from this enormous number of microwave links would allow large-scale rainfall and evaporation mapping, also for regions across the globe which are currently poorly served in terms of dedicated meteorological stations.
We present both the physical basis of this approach and empirical results from previous and ongoing measurement campaigns to discuss the potential and challenges of opportunistic sensing of two hydrologic fluxes with one single instrument: precipitation and evaporation.
How to cite: Uijlenhoet, R., Walraven, ., van der Valk, L., Coenders, M., Hut, R., Overeem, A., and Hartogensis, O.: Opportunistic Sensing of Precipitation and Evaporation Using Microwave Links From Cellular Communication Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23288, https://doi.org/10.5194/egusphere-egu26-23288, 2026.
Accurate representation of the spatial and temporal variability of precipitation is a fundamental requirement for reliable flood modelling, especially if semi-distributed/fully-distributed models are used. However, official rain gauge networks often exhibit limited spatial coverage and low density, leading to substantial uncertainty in the representation of rainfall at the sub-basin scale. Recently, opportunistic precipitation observations derived from personal weather stations (PWS) have attracted increasing attention as a potential complementary data source, offering unprecedented spatial coverage. At the same time, PWS networks are characterized by heterogeneous data quality, inconsistent maintenance, frequent outages, incomplete records, and a dynamically changing network structure. Despite the attention that PWS have gained, their applicability in hydrological modelling, especially within semi-distributed modelling frameworks, has been explored in only a limited number of studies.
This study evaluates the feasibility of PWS rainfall data for semi-distributed hydrological flood modelling and outlines the conditions under which their application is appropriate. The Lambro catchment in northern Italy is used as a case study. PWS rainfall observations obtained from the Meteonetwork platform (https://www.meteonetwork.it/, Giazzi et al., 2022) and official rainfall data provided by the Lombardy Regional Environmental Protection Agency (ARPA) are used in this study. Different PWS-based rainfall datasets are created: namely, raw PWS data (PWSraw), quality-controlled PWS data (PWSqc), and data from persistent PWS stations, implying those PWS that were active over all considered storm events (PWSqc_p), and their combinations with the ARPA observations (denoted by ARPA + PWSraw, ARPA + PWSqc, ARPA + PWSqc_p, respectively).
Each set is compared to the ARPA rain gauge measurements, which are used a reference dataset. The evaluation is performed by comparing rainfall features at the point- and sub-basin scales, as well as through semi-distributed hydrological flood simulations by analyzing the impact of the rainfall input on simulated peak discharge and timing of its occurrence, and runoff volume at the basin outlet. The hydrological modelling with every rainfall dataset is performed by using the semi-distributed model developed by Politecnico di Milano (Cazzaniga et al., 2022).
The results demonstrate that quality-controlled and persistent PWS datasets (PWSqc and PWSqc_p), as well as their combination with ARPA observations, generally enhance hydrological model performance. This indicates that PWS data can provide added value for semi-distributed flood modelling when appropriately controlled and integrated with reference datasets from the official networks.
References
Cazzaniga, G., De Michele, C., D’Amico, M., Deidda, C., Antonio Ghezzi, A., and Nebuloni, R.: Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations: the case study of Lambro Catchment, Hydrology and Earth Sysem. Sciences, 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, 2022.
Giazzi, M., Peressutti, G., Cerri, L., Fumi, M., Riva, I. F., Chini, A., Ferrari, G., Cioni, G., Franch, G., Tartari, G., Galbiati, F., Condemi, V., and Ceppi, A.: Meteonetwork: An Open Crowdsourced Weather Data System, Atmosphere, 13, 928, https://doi.org/10.3390/atmos13060928, 2022.
https://www.arpalombardia.it/
Acknowledgments
The authors would like to thank the COST Action “OpenSense” (CA20136) for supporting collaboration opportunities among the co-authors through the STSM program.
How to cite: Kovačević, R., Ceppi, A., De Michele, C., Nebuloni, R., and Todorović, A.: Personal weather station rainfall data for semi-distributed flood modelling: Feasibility and limitations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15052, https://doi.org/10.5194/egusphere-egu26-15052, 2026.
In this study, we present the continuation of the novel robotic mechanism introduced at the EGU General Assembly Conference 2025 (I. S. Tsimpidi, 2025) for autonomous soil moisture data collection. Soil moisture is vital for irrigation, flood and drought forecasting, and hydrological studies, yet shows strong spatial and temporal variability; therefore accurate measurements are required. We conduct field experiments to improve the fully autonomous robotized procedure with AgriOne, reducing sampling time and enhancing repeatability.
As the AgriOne robot, Figure 1, enables in situ, high-precision, spatially dense data collection across the field, we conducted additional field experiments to collect soil moisture data, both autonomously and manually. The AgriOne robot autonomously executes soil moisture data-collection missions, with sampling positions defined by georeferenced waypoints. The waypoints were generated in ArcGIS software using a grid creation tool, with the centre of each grid square as the selected position. The size of each grid cell was defined as 8m * 8m. In the sequel, these waypoints feed the robotic autonomous navigation system, which combines satellite positioning and motion sensors to continuously estimate the robot’s position and plan its trajectory and the sampling points, to meet the initially planned sampling protocol. For the robotic navigation, a hierarchical control architecture generates velocity commands to guide the robot to each target location with centimeter-level positioning accuracy. Upon reaching each waypoint, the system autonomously triggers a probing mechanism to collect and log soil moisture measurements before continuing to the next mission point. Manual data collection was performed by a human carrying a handheld TEROS 12 sensor connected to a Bluetooth sensor interface for instant readings in a mobile application. The positions for taking the measurement were selected using an empirical sampling method.
Figure 1: AgriOne robot with description of its components.
The first experiment was executed successfully in mid-July in a flat field with no vegetation cover, no precipitation, relative air humidity of 52%, air temperature of 20 °C and wind speed of 2 Bft. The autonomous data collection yielded data from 69 of the 73 waypoints where the robot stopped, and the manual data collection yielded data from 50 waypoints, both covering an area of 4.800m2. The second experiment was successfully conducted in mid-October in an area with low elevations and dense grass cover. On the experimental day, precipitation was absent; air temperature was 12°C, relative air humidity was 66% and wind speed was 1 Bft. In this experiment, AgriOne autonomously collected soil moisture data from 63 of the 72 waypoints where stopped, and from 41 waypoints we collected soil moisture data manually, covering an area of 4.700 m2. The results of the conducted experiments are presented on a satellite map of the tested areas, with proportioning the points based on the soil moisture values and are shown in Figure 2.
Figure 2:Presentation of the SM collected data autonomously and manually in both testing areas.
Tsimpidi, I. S. (2025). Large-scale Soil Moisture Monitoring: A New Approach. EGU General Assembly Conference Abstracts, pp. EGU25-1910.
How to cite: Tsimpidi, I., Labra Caso, F., Sumathy, V., Soulis, K., and Nikolakopoulos, G.: Autonomous Robotised Repeatable Soil Moisture Sampling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5265, https://doi.org/10.5194/egusphere-egu26-5265, 2026.
For years, the SATURO from METER Group has offered simple, fast, and precise field saturated hydraulic conductivity measurements. While this instrument excels at surface measurements, taking measurements at depth has been a challenge. Measurements at depth require digging a large hole, causing disturbance and compromising the readings. The new SATURO borehole attachment allows you to take readings at depths of up to 2 meters out of the box (additional depth possible with custom cable lengths). The measurement head is compact enough to go down a 4 inch (10 cm) borehole, significantly reducing disturbance, and allowing for more accurate readings in situ.
The SATURO borehole attachment comes with everything that you need to prepare the site, and conduct the measurements, including the installation tools, and borehole auger. The attachment also works with the current SATURO control unit, if you have already purchased one.
How to cite: Weldon, S.: A novel approach to automated field saturated hydraulic conductivity measurements at depth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15282, https://doi.org/10.5194/egusphere-egu26-15282, 2026.
Deployment and upscale of traditional monitoring systems have different challenges, specially in developing countries, because of high investment costs, difficult operation and maintenance. In the last years there have been several low-cost, open-source products developed as well as initiatives with the objective of tackling down those issues.
Herein we present the setup and first leanings of the project L-DaaS “Local people for Discharge monitoring as a Service” where we proposed a scheme of using an open-source software for flow monitoring and a low-cost hardware which use standard components with a business model centred around a local enterprise in charge of the operation and maintenance. The project was executed in Zambia with a locally-driven environmental monitoring company based in the same country. Key stakeholders where the Water Resource Management Authority of Zambia and hydropower operators.
Open-source and low-cost system are not by themselves a solution, it is also needed to create a sustainable and scalable business model which fosters affordable, efficient, and locally supported water management solutions.
How to cite: Peña-Haro, S., Samboko, H. T., and Winsemius, H. C.: Towards a Low-Cost River Monitoring Setup, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19490, https://doi.org/10.5194/egusphere-egu26-19490, 2026.
Global warming has increased the amount of deadwood in forests due to wildfires, insect outbreaks, and droughts. Deadwood and fresh wood are mobilised by erosion into river systems as driftwood, forming the largest organic carbon sink due to its slow decomposition rate. Thus, event-based driftwood transport is crucial for disaster management and assessing carbon storage. Here, we applied YOLOv8 to detect driftwood using images from surveillance cameras and a drone. Three types of driftwood, i.e., instream, riverbank, and nearshore, were used from the database of Swiss, Arctic Data Center, and our own drone surveys to train the model of object detection and instance segmentation. To estimate the volume of driftwood, we compared the detected image areas with radio-frequency identification (RFID) tagged logs of known dimensions, establishing an area-to-volume conversion. Our models achieved an mAP50 of 0.96 for in-stream object detection. Applying this model to Typhoon Kong-rey in the Liwu River, we estimated an in-stream driftwood volume of 3.5×105 m3, with a carbon stock of 8.24×1010 g C, representing 0.11% of Taiwan’s annual carbon export. Furthermore, we observed that driftwood flux increases nonlinearly with river discharge. Our analysis suggests that driftwood accumulation along the outer bends of the riverbank may lead to pulsed driftwood flux. These findings highlight the significance of event-scale driftwood transport as a quantifiable component of green carbon and demonstrate the feasibility of integrating deep learning-based detection with hydrological monitoring for carbon budget assessments.
Keywords: Driftwood flux, YOLOv8, RFID, drone, green carbon
How to cite: Kong, Q.-Y., Yang, C.-J., Tsai, C.-H., and Lee, M.-Y.: Integrating deep learning detection and hydrological monitoring for driftwood flux and carbon stock estimation in a steep tropical basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4783, https://doi.org/10.5194/egusphere-egu26-4783, 2026.
One of the major difficulties in studying and protecting non-perennial rivers is the lack of knowledge about the occurrence of dry periods and their duration. Traditional flow measurement systems are not reliable in measuring zero or near-zero flows and do not provide any information on the presence of isolated ponds during periods of no flow. In this context, satellite observations can make a crucial contribution thanks to their global coverage and high observation frequency, especially if freely available data can be exploited. In the field of multispectral satellite data, the free datasets provided by Landsat (USGS/NASA) and Sentinel-2 (ESA) are particularly useful. Another dataset with interesting features is that of PlanetScope, but unfortunately this data is not free, although available on request for research purposes. In this study, we present an analysis of the potential and limitations of these three datasets in observing the intermittency regime of non-perennial rivers. The differences in their spatial, temporal and spectral resolution make them more or less suitable for monitoring specific rivers with given characteristics and observation requirements. It emerged that thanks to a long archive of observations (more than 40 years), Landsat is particularly useful for analyzing changes in the intermittent flow regime over time, enabling the detection of climatic trends over the standard climatological period of 30 years but due to coarse spatial resolution (30 m) it only allows observation of rivers with sufficiently wide active riverbeds (around 90 m or more). Thanks to the finer spatial resolution (10 or 20 m depending on the band), Sentinel-2 allows observation of water features greater than 6-15 m in rivers larger than approximately 30 m for an observation period that currently stands at 9 years. Thanks to the short revisit time of 5 days or less and the free availability of data, this dataset is particularly useful for continuous observations and obtaining the annual intermittency regime in a larger set of rivers of adequate size. PlanetScope provides data with spatial resolution of around 3 m and a revisit time up to 1 day. Although the spatial resolution is significantly higher than that of Sentinel-2, the ability to observe small water surfaces is not improved proportionally. In fact, we have found that this data allows for the observation of water features greater than 4-10 m in rivers larger than approximately 20 m. This is likely due to the different spectral characteristics of the acquired data. Another factor affecting performance is related to the acquisition time, which for Sentinel-2 is the same for all acquisitions of the same scene, while for PlanetScope images it is variable. This leads to inconsistency in the dataset, making it more challenging to identify water surfaces.
For all considered datasets, the “flowing,” “ponding,” and “dry” phases can be distinguished in a supervised manner using false-color images or automatically by exploiting the reflectance characteristics of water. The performances of both supervised and unsupervised classification are analyzed for different datasets and in various case studies.
How to cite: Papa, M. N., Cavallo, C., Iantorno, L., Brichetto, I., Manfreda, G., Negro, G., and Vezza, P.: Potential and limitation of Landsat, Sentinel-2 and Planet datasets in monitoring the intermittency regime of non-perennial rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13018, https://doi.org/10.5194/egusphere-egu26-13018, 2026.
Recent developments in hydrometric practices enable the measurements of continuous maximum surface velocity of flow passing at a river section and the corresponding water level using a combined surface velocity and water level sensors installed across river bridges. These measurements enable continuous discharge estimation at these river section by employing the well-studied entropy methods. However, the cost of equipping these combined radars at many gauging stations of a river may be prohibitive. But the use of standalone water level radars at many stations may not be cost-wise prohibitive. Taking into consideration of this aspect, the current study proposes a novel method of routing the upstream estimated velocity hydrograph to a desired downstream station of a river reach, which is equipped with a water level sensor, and using the routed velocity hydrograph and the water levels measured at that station, one can estimate the corresponding discharge hydrograph. The proposed study establishes the equation governing the velocity hydrograph propagation in a channel reach which is of the same form as that of the weak-diffusive wave equations governing the discharge and flow depth hydrographs propagation. The derived velocity routing equation is of the same form as the Muskingum routing equation. The parameters of the routing method are estimated using the channel and velocity characteristics of the propagating velocity hydrograph. The proposed velocity routing method is tested by routing the hypothetical velocity hydrographs arrived at by routing a given hypothetical discharge hydrograph defined by Pearson Type-III mathematical function at the inlet of 25 uniform trapezoidal channel reaches each characterised by a unique combination of bed slope and Manning’s roughness characteristics. The benchmark solutions were arrived using the HEC-RAS model. The routed velocity hydrographs enable the close reproduction of the corresponding estimated benchmark velocity hydrographs and, thus, proving the appropriateness of the proposed velocity hydrograph routing method.
How to cite: Perumal, M. and Rao, C. M.: Velocity Hydrograph Routing to Enable Discharge Estimation at a Channel Section, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13052, https://doi.org/10.5194/egusphere-egu26-13052, 2026.
Arid regions such as Oman are increasingly susceptible to severe flash floods driven by climate change and rapid urbanization. Accurate water level measurements are vital for flood preparedness and the development of early warning systems required to mitigate severe socio-economic impacts, including substantial property damage and the recurring loss of life. Beyond disaster mitigation, recording runoff is essential for sustainable water management and for enhancing the understanding of hydrological processes in small-scale, ephemeral catchments that remain largely ungauged. However, traditional water level monitoring via pressure gauges or radar sensors is often hindered by high infrastructure costs, physical vulnerability to high-flow events, and the changing morphology of wadi channels. To address these limitations, we present a robust photogrammetric workflow integrated into a low-cost, Raspberry Pi-based optical monitoring system for measuring water level, surface velocities and discharge assessment.
The workflow relies on the synergy between a single fixed low-cost camera and high-resolution Digital Terrain Models (DTMs) generated through UAV-based Structure-from-Motion (SfM-MVS). To convert 2D image measurements into 3D object space, both the camera and the DTM must be referenced in a shared coordinate system. Traditionally, this is established using permanent Ground Control Points (GCPs) measured with RTK GNSS; however, establishing and maintaining such markers in adverse wadi conditions is logistically challenging and the physical markers are prone to being lost during flood events. We address this by employing the GIRAFFE (Geospatial Image Registration And reFErencing) workflow. This approach replaces physical markers by performing an image-to-geometry registration that aligns the real 2D camera view with a synthetic image rendered from the UAV-based 3D pointcloud. Using the AI-based LightGlue matching algorithm, the system automatically identifies homologous points between the views to create 2D–3D correspondences. These correspondences function as pseudo-control points, allowing for the precise determination of the camera’s 3D pose and orientation via spatial resection.
For the hydrological monitoring, the workflow further employs two AI-driven stages:
Water Level Estimation: Convolutional Neural Networks (CNNs) segment the water area in time-lapse images. The resulting waterlines are projected into 3D space and intersected with the DTM to derive accurate water levels.
Discharge Assessment: Surface flow velocities are measured using the PIPs++ (Persistent Independent Particle tracker) technique. Unlike traditional frame-by-frame methods, PIPs++ tracks particles across multiple time steps jointly, providing enhanced temporal smoothness and robustness against illumination changes or partial occlusions. Based on these surface velocities, the mean velocity is determined and combined with the wetted cross-section from the DTM to estimate total discharge.
Initial results from deployments in Wadi Al-Hawasinah, Oman, demonstrate that this solar-powered, remote system successfully captures ephemeral flow events. By leveraging GIRAFFE for automated localization and PIPs++ for robust surface velocity estimation, this workflow provides a scalable and cost-effective solution for enhancing flood early warning systems in complex, ungauged terrains.
How to cite: Krüger, R., Zamboni, P., Grundmann, J., Al-Rawas, G., and Eltner, A.: AI-Driven Photogrammetric Workflow for Low-Cost Wadi Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19511, https://doi.org/10.5194/egusphere-egu26-19511, 2026.
The double whammy of increasing human water consumption and the effects of warming world is poised to further shrink large lakes, especially in arid and semi-arid regions. Further, reservoir sedimentation remains a critical challenge to global water security, causing a progressive loss in storage capacity and disrupting the ecological balance of downstream river systems. The motivation of the present study is based on the fact that at present reservoir managers are dependent upon expensive hydrographic surveys to locate the sedimentation impacted area, which cannot be conducted as and when required due to financial constraints. Further, the recent floods in the Indian state of Punjab (August 2025) were related to intense rainfall, sudden sediment inflow, resulting in drastic reduction in the storage capacity of the reservoirs.
This study presents a comprehensive geospatial framework on assessment of elevation-area-capacity relationships of Ukai reservoir. The method uses multi-temporal optical and SAR satellite imageries (Landsat-9, Sentinel-2 and Sentinel-1) and corresponding altimetry water level data to delineate the water spread areas (contours) at varying elevations. Two different time-periods (historical, 2008-2010 and 2021-2022) were compared to assess the change in contours. The results of sedimentation assessment clearly show an expansion in water boundary extent in recent years compared to that in the past decade. Additionally, the findings reveal that over the last decade Ukai reservoir’s live storage capacity has significantly declined by ~200 MCM, indicating ~20 MCM annual sedimentation rate. The spatial analysis distinctly maps the geographical areas of sediment accumulation or erosion and show that the sediment change is not uniform.
How to cite: Ghosh Natoo, N., Kumar Gupta, P., and Ramchandra Nikam, B.: Rapid Sedimentation Impact Assessment of Ukai Reservoir using Geospatial methodology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16174, https://doi.org/10.5194/egusphere-egu26-16174, 2026.
Within river deployments that combine Acoustic Doppler Current Profiler (ADCP) hydrodynamics with high-frequency water quality sensing now offer unprecedented detail regarding instream physical processes, chemical mixing, and nutrient transformations, but a key barrier is translating complex, high-volume datasets into process-based interpretations. Here, we present the Reactivity Index – Hydrodynamic Index (RI - H) workflow, an approach that combines standard hydrodynamic and water-quality sensor data into diagnostic behavioural classes describing hydrochemical behaviours, demonstrated at the Kennet-Thames confluence (Reading, UK).
The workflow was developed and tested using a remote-controlled moving-boat platform (ArcBoat) equipped with a SonTek M9 (ADCP) and a YSI EXO2 multiparameter sonde. We collected simultaneous near-surface measurements of velocity, nutrients (NH₄⁺-N, NO₃⁻-N), fluorescent dissolved organic matter (fDOM), turbidity, and specific conductivity across a quasi-synoptic transect design (upstream controls, repeated cross-sections, and diagonal transects) on three days. The study reach was segregated into 14 spatial zones to monitor the chemical and physical changes from upstream end-members (of the Rivers Kennet and Thames), how the end-members interact and evolve downstream of the confluence, and the shift in chemical and physical behaviour under different hydrological conditions.
RI and H were derived from solute concentrations and flow velocities. Plotting the two indices enabled each observation to be classified into one of five process-based behavioural categories (e.g., Retentive, Reactive, Low-energy depletion, Attenuating, and Conservative). Across three campaigns (including a rainfall-impacted survey), zonal contrasts in RI were consistently strong (Kruskal–Wallis ε² = 0.28–0.81; p < 0.0001), identifying zones with distinct behavioural signatures (nitrate reaction or ammonium retention zones). Extending the same logic to turbidity yielded complementary particulate-transport classes (Local Input, Advective Input, Sediment Deposition, Advective Dilution and Conservative mixing), demonstrating that the workflow was applicable for solutes and particulates.
Our high-frequency transect sampling captured the hydrological and biogeochemical response to sequential rainfall events on 26 February 2025. Following morning rainfall, we identified a pollutant pulse characterised by elevated NH4+ and fDOM, indicative of sewage or wastewater influence in the River Kennet, which diluted progressively downstream. A late-afternoon high-intensity rain and hail event triggered a distinct second wave, marked by a sharp spike in nitrate NO3- and turbidity, characteristic of surface run-off. The rapid succession of these pulses reveals differing pollutant sources and pathways activated under varying rainfall intensities. Statistically strong spatial contrasts in reactivity persisted even during this dynamic event (Kruskal-Wallis ε² > 0.28 - 0.76 for all solutes). This outcome demonstrates the workflow can resolve within-event functional shifts, translating sensor data into a real-time diagnostic of a river's response to rainfall. The RI – H framework provides a standardised approach by enabling event-scale diagnosis of solute and sediment behaviour that cannot be resolved by fixed or point-based monitoring alone. By classifying how rivers transport and process materials in space and time using deployable sensors, the workflow offers a diagnostic, process-informed water quality assessments relevant to better understanding pollutant dispersal, chemical transformations and biota in fluvial systems.
How to cite: Pesso, C., Rameshwaran, P., Wade, A. J., and Everard, N.: Resolving Sequential Storm-Driven Pollutant Pulses using Novel Hydrochemical Measurements within a Reactivity-Hydrodynamic workflow., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7732, https://doi.org/10.5194/egusphere-egu26-7732, 2026.
Rivers play a key role in the transport and retention of floating debris, including plastics. Reliable and scalable monitoring of riverine plastic accumulation is essential for identifying hotspots, understanding debris movement, and supporting mitigation strategies. However, conventional in situ monitoring approaches are often labor-intensive, spatially limited, and difficult to deploy consistently across large or remote river systems. This study presents a semi-automated, image-based monitoring framework that integrates satellite remote sensing and machine learning to detect and map riverine plastic accumulation hotspots at a global scale.
The methodology integrates high spatial resolution imagery for precise manual annotation of accumulation areas and multispectral Sentinel-2 data for classification of litter hotspots using Random Forests in Google Earth Engine. The workflow combines the most influential spectral bands with targeted spectral indices, including NDVI, PI, FDI, and SI13, to enhance class separability between plastic, water, and vegetation.
The methodology is evaluated across three highly polluted river systems in Indonesia, Guatemala, and Ghana. These sites represent a wide range of hydrological and environmental conditions, including floating vegetation, canopy shading, and narrow urban channels affected by pixel mixing. Results demonstrate high within-river classification performance, with overall accuracies up to 99.5% on independent sections of the same river, and robust cross-river generalization when spectral indices are incorporated, achieving plastic F1-scores up to 79%.
In addition to image classification, the workflow supports multi-temporal analysis to generate hotspot frequency maps, enabling the identification of persistent plastic accumulation zones linked to river morphology and infrastructure. Feature-importance analysis highlights the relevance of specific spectral bands and indices across different environmental conditions and supports the development of reduced, generalizable models.
To facilitate reproducibility and large-scale application, the methodology is operationalized in an open-access Google Earth Engine application that enables users to apply the trained model to rivers worldwide using Sentinel-2 imagery. The proposed framework contributes to the advancement of environmental monitoring and provides a foundation for future developments toward global, long-term assessment of river plastic dynamics.
More information: https://doi.org/10.1016/j.isci.2025.114570
How to cite: Pérez-García, Á., Amanda, G., López, J. Fco., Russwurm, M., and van Emmerik, T. H. M.: Identifying River Plastic Hotspots from Space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13155, https://doi.org/10.5194/egusphere-egu26-13155, 2026.
Planetary boundaries for many legacy and emerging contaminants are exceeded. Moving beyond the “safe operating space” for handling these pollutants means increased risks of tipping points which may irreversibly change the functioning of ecosystems and the services they provide, resulting in severe environmental and public health impacts.
In particular the monitoring and prediction of the strongly nonlinear behaviour of many contaminants, including pollution hotspots (locations) and hot moments (events) that disproportionally affecting catchment water quality when a significant proportion of the contaminant load is mobilised withing river catchments and transported to the river network and then further downstream, remains a significant challenge for state of the art water quality monitoring.
We here present the SMARTWATER environmental sensing platform, integrating sensor technology, network and data science innovations with and mathematical modelling with stakeholder catchment knowledge to we diagnose, understand, predict, and manage the emergence and evolution of water pollution hotspots and hot moments. We highlight how innovations in fluorescence and UV absorbance optical sensing technologies can be utilised for instance to track the drivers of extreme hypoxia events through urban and rural observatories and how the combination of easy to sense water quality proxies widely dispersed across the catchment can help optimising high-utility observational networks with regards to the placements of multi-sensor platforms as well as guiding their operation. Deploying data-science approaches including hysteresis and flushing indexes across a range of low- to higher monitoring locations revealed not only divergences in the sources and their mobilisation of different pollutant types (nutrients, DOM, metals) but also differences in their downstream evolution and spatial footprints through complex (and managed) river networks. Integrating information of the different behaviours of pollutants and functional markers such as tryptophan-like fluorescence and Chlorophyll a helped to identify pollutant specific activated source areas and mobilisation mechanisms, supporting also the development of automated event-triggered in-situ sampling solutions for analysis of emerging pollutants (including microplastics) and microbial analyses that are currently not possible to sense in-situ. Integrating this information highlights drastic differences in the contaminant specific emergence of pollution hotspots and hot moments including their large-scale footprint and longer-term relevance for catchment water pollution.
How to cite: Krause, S. and the SmartWater Team: Smart sensor networks for tracking the evolution of water pollution hotspots and hot moments through river networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7650, https://doi.org/10.5194/egusphere-egu26-7650, 2026.
Proximal remote sensing represents an effective approach for water quality monitoring, enabling the estimation of turbidity and suspended sediment concentration (SSC) through spectral indices, such as red–green band ratios. Low-cost RGB cameras are widely adopted for this purpose, however, their measurements are strongly affected by variations in illumination, shadows, surface glint, and ambient environmental conditions, which can compromise data consistency and reliability. Extending the spectral coverage into the near-infrared (NIR) domain has the potential to enhance sensitivity to suspended sediments and reduce the influence of variable lighting conditions. Although hyperspectral sensors remain costly and impractical for routine monitoring, the analysis of hyperspectral data provides valuable insights into the most informative wavelengths and supports the targeted integration of RGB imagery with selected NIR bands for future field applications.
In this laboratory study, proximal hyperspectral sensing was employed to investigate SSC under both artificial and ambient illumination conditions, using two sediment types with contrasting optical properties (yellowish soil and white China clay). The experiments assess the influence of illumination conditions and sediment characteristics on spectral signatures, and compare the performance of reflectance information derived from the RGB and NIR spectral ranges. The results offer initial insights into sediment–reflectance interactions and contribute to the development of more robust and cost-effective proximal remote sensing strategies for water quality monitoring in real-world environments.
Keywords: Proximal remote sensing; hyperspectral data; suspended sediment concentration; laboratory experiments
How to cite: Moe, A. C., Miglino, D., Zhuang, R., Saddi, K. C., Viscido, L., Methaprayun, M., Shareen, N., Budrach, T., Mapiam, P. P., Bogaard, T., and Manfreda, S.: Assessment of Optical and Near-Infrared Proximal Remote Sensing for Suspended Sediment Concentration Estimation under Artificial and Ambient Illumination, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16213, https://doi.org/10.5194/egusphere-egu26-16213, 2026.
Granitic hard rock aquifers dominate much of semi-arid peninsular India and are characterized by highly heterogeneous, fracture-controlled flow systems with limited storage. In these settings, conventional indicators such as static water levels and standard well drawdown equations provide poor insight into actual aquifer stress and pumping sustainability. This study presents an integrated hydrological monitoring approach that combines flow meters, borewell camera scans, and continuous camera-based observations to directly understand aquifer behaviour under pumping. We deploy non-invasive flow measurement to quantify real-time abstraction, alongside step-drawdown tests and downhole camera surveys to identify active fracture zones, their depth-wise contribution to yield, and their dynamic response during sustained pumping. Continuous camera scans during pumping cycles enable direct visualization of drawdown, fracture inflows, and the rapid transition from borehole storage to fracture-limited supply, revealing why prolonged pumping from deeper depths often leads to high energy use with marginal water gains. By linking pumping rates, energy consumption, and observed subsurface flow processes, the study demonstrates how mismatches between pump capacity and fracture-controlled yields drive inefficiency and accelerated aquifer stress. The results highlight the value of image-based and sensor-driven monitoring for developing context-specific indicators of groundwater stress and for identifying optimal pumping regimes in hard rock aquifers. This integrated methodology offers a scalable pathway to improve hydrological understanding, support adaptive groundwater management, and inform incentive-based interventions aimed at conserving both water and energy in data-scarce, remote settings.
How to cite: N r, L., Vikram Jain, A., Jain, A., Misquitta, K., Grewal, V., and Srinivasan, V.: Understanding Pumping Dynamics in Granitic Hard Rock Aquifers Using Integrated Flow Metering and Camera-Based Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21112, https://doi.org/10.5194/egusphere-egu26-21112, 2026.
Autonomous camera-based systems combined with image velocimetry analyses, enables operational river flow measurements in rapidly responding rivers. The open-source OpenRiverCam (ORC) software stack supports edge and cloud video processing, time-series generation, and rating-curve development, enabling fully operational, scalable non-contact water level and discharge estimation with relatively affordable camera systems.
Despite these advances, image calibration remains a major bottleneck for broad uptake, as it typically requires high-precision surveying of non-collinear ground control points to constrain the camera's pose. This process is often complex and relies on instruments that are not readily available to many users.
We investigate a photogrammetry-based alternative workflow for camera pose estimation for possible integration in ORC: during camera installation, users collect a set of smartphone photographs from multiple viewpoints near the camera location. A photogrammetric reconstruction using these photos together with a sample video from the installed camera, jointly estimates the camera pose and lens parameters. The resulting camera pose is then used to orthorectify videos in operational data collection. Using controlled experiments and field experiments in New Zealand, Zambia and The Netherlands, we assess here (i) the accuracy of reconstructed 3D coordinates compared to traditional calibration, (ii) methods to robustly constrain the horizontal plane, (iii) the number of photographs required, and (iv) the influence of GPS accuracy on the solution.
This approach aims to significantly simplify calibration workflows and lower the barrier to deploying camera-based river monitoring systems.
How to cite: Winsemius, H., Samboko, H., Peña-Haro, S., Mather, S., and Biggs, H.: Calibration of oblique image-based monitoring systems using photogrammetric principles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12962, https://doi.org/10.5194/egusphere-egu26-12962, 2026.
