On dryland hillslopes, vegetation water availability is often subsidized by the redistribution of rainfall runoff from bare soil (sources) to vegetation patches (sinks). In regions where rainfall volumes are too low to support spatially continuous plant growth, such functional connectivity between bare soil and vegetated areas enables the establishment and persistence of dryland ecosystems.  Increasing the connectivity within bare soil areas can intensify runoff and increase water losses from hillslopes, disrupting this redistribution and reducing the water available to sustain ecosystem function.  Inferring functional connectivity (from bare to vegetated, or within bare areas) from structural landscape features is an attractive approach to enable rapid, scalable characterization of dryland ecosystem function from remote observations. Such inference, however, would rely on metrics of structural connectivity, which describe the contiguity of bare soil areas.  Several studies have observed non-stationarity in the relations between functional and structural connectivity metrics as rainfall conditions vary. Consequently, the suitability of using structural connectivity to provide a reliable proxy for functional connectivity remains uncertain and motivates the work here. 

Rainfall-runoff simulations across a wide range of dryland hillslopes, under varying soil and rainfall conditions, are used to establish relations between structural and functional connectivity metrics.  The model results identify that the relations vary between two hydrologic limits -- a `local' limit, in which functional connectivity is related to structural connectivity, and a ‘global’ limit, in which functional connectivity is most related to the hillslope vegetation fraction regardless of the structural connectivity of bare soil areas. The transition between these limits within the simulations depends on rainfall intensity and duration, and soil permeability. While the local limit may strengthen positive feedbacks between vegetation and water availability, the implications of these limits for dryland functioning need further exploration, particularly considering the timescale separation between storm runoff production and vegetation growth.

How to cite: Crompton, O., Katul, G., Thompson, S., and Lapides, D.: Bridging structural and functional hydrological connectivity in dryland ecosystems, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-37, https://doi.org/10.5194/egusphere-gc8-hydro-37, 2023.

The post-mining landscape of Freiberg, Saxony, Germany is characterized by soils with elevated concentrations of non-essential elements, particularly arsenic (As), cadmium (Cd) and lead (Pb). While literature on soil mineralization and factors influencing the soil-plant transfer in managed agroecosystems is extensive, information on the accumulation in native woody plant species, including soil-associated factors influencing their accumulation, is still lacking. In this study, we evaluated the concentrations of 24 elements, including As, Cd and Pb in leaves and branches of three taxa of tree species (Betula pendula, Populus tremula and Salix spec.) throughout the study area and compared the results with data on potentially plant available element concentrations in soil (sequential extraction), total element concentrations as well as with remote sensing data on surface soil moisture and water availability in the root-zone. Populus tremula and Salix spec. were identified as plant species that are suitable for bioindication of soil pollution. Leaf concentrations were substantially higher compared to branches. The concentrations in leaves largely reflected the availability of elements in soil. Concomitantly, higher soil-plant-transfer of elements correlated with remote sensing data, onsurface water accumulation and water content in the root-zone. This suggests that higher soil water contents increase the availability of the toxic elements to plants and/or impacts the translocation of elements to aboveground plant parts. The contribution of soil-associated factors and plant-associated factors to the hyperaccumulation observed remains a field for further research. Nevertheless, we could demonstrate that coupling leaf analysis with remote sensing data on soil moisture could be a promising approach in environmental assessments as well as in phytoremediation and phytomining approaches.  

How to cite: Lovynska, V., Wiche, O., Montzka, C., Syntyk, S., Sivaprasad, V., Samarska, A., and Mengen, D.: Influence of soil hydrological conditions on the accumulation of heavy metals in tree species in the post-mining landscape of Freiberg, Germany (Saxony), A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-38, https://doi.org/10.5194/egusphere-gc8-hydro-38, 2023.

Improving water resource management in the western United States is critically needed to achieve sustainability between the competing demands of water supplies for cities and towns, for agriculture, particularly irrigated regions, by industries principally for generating electricity, and for the environment (i.e., providing adequate ecosystem services). The last decade marked by historically severe droughts revealed the need for new water management policies and environmental regulations. Moreover, the impact of climate change not only has exacerbated droughts but also may be causing episodic extreme wet periods requiring a new paradigm on water management strategies for surface water reservoirs and groundwater aquifers. The Sustainable Groundwater Management Act (SGMA) is an example of developing a water management policy for sustainability of water resources in California. California produces 80% of the world’s almonds, is the 4^th largest wine producer worldwide while also providing three-quarters of the fruits and nuts in the U.S. Much of the production requires reliable water sources for irrigation.  This has motivated research into designing networks of evapotranspiration (ET) flux tower measurements in grape and tree crop systems in conjunction with developing new remote sensing tools for mapping crop water use, ET, to efficiently use and conserve water resources across the over 1.5 million acres of woody perennial crop production fields.  This acreage is largely irrigated and uses approximately 70% of freshwater resources in the region.  The two projects, GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) and T-REX (Tree-crop Remote sensing of Evapotranspiration eXperiment) have the overall goal to identify management opportunities to maximize water use efficiency in vineyard, almond and other woody perennial crops.  This presentation will describe the measurement network used for understanding the water and energy flux exchange of these complex cropping systems and in validating and refining remote sensing modeling tools from UAS and satellite platforms for estimating ET and crop water stress from plant and sub field to regional scales required for improving water management strategies of these agricultural systems.

How to cite: Kustas, W., Bambach, N., McElrone, A., Knipper, K., Torres-Rua, A., Roby, M., Alsina, M. M., Prueger, J., Alfieri, J., Castro, S., Hipps, L., McKee, L., Belfiore, O., and D'Urso, G.: Improving water resource management through the development of a flux tower network and remote sensing modeling of evapotranspiration and water stress of woody perennial crops in California, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-32, https://doi.org/10.5194/egusphere-gc8-hydro-32, 2023.

Precision agriculture is a modern approach based on farm and irrigation management to improve the efficiency in the use of water resources. Precision agriculture, therefore, maximizes crop productivity and yield through technologies that identify, analyze, and monitor variability within a field and optimize profitability, sustainability, and land protection. This study proposes a combination of approaches to monitor a suite of environmental variables with the goal of improving agricultural management. We selected the experimental vineyard of Grignanello (Tuscany, Italy), located on a mild slope at 350 m.a.s.l in the famous Chianti wine region, where extensive ecohydrological data are available. In combination with this set of ground-based observations, the Environmental Policy Integrated Climate Model (EPIC) is adopted to model key variables for crop production, including soil temperature and soil water content. Using the EPIC model, we generate three sets of simulations based on three different parameterizations (i.e., original cosine, enhanced cosine, and pseudo heat transfer). By comparing model output against ground-based measurements and UAV-based soil temperature, we assess what model set-up is more accurate and for which environmental variable of interest. Furthermore, a new set of soil temperature and soil moisture estimates is obtained by taking the mean of the three EPIC simulations. Thus, we assess the possibility to improve the performance of the single models, as shown in previous studies across the Central Valley in California. Outcomes from this work will provide a solid basis towards developing a decision guidance system for precision agriculture management. 

How to cite: Maggioni, V., Massari, C., Kandasamy, J., Xue, Y., Modanesi, S., De Santis, D., Manca di Villahermosa, F. S., Penna, D., Benettin, P., and Dionigi, M.: Monitoring Environmental Variables to Promote Precision Agriculture, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-54, https://doi.org/10.5194/egusphere-gc8-hydro-54, 2023.

Agriculture is the main source of pressure on water resources, so accurate estimates of irrigation demands play a key role in sustainable water management. The INCIPIT (INtegrated Computer modeling and monitoring for Irrigation Planning in Italy) project (funded by Italian Min. Univ. and Research) aims to address the gaps between research and practical application in monitoring irrigation water use in six Italian regions [1].

It is designed to meet the requirements of sustainable water policies, such as the Water Framework Directive and the MIPAAF Ministry Decree, by providing accurate measurements and estimations of irrigated areas and water volumes. The project uses the ESA Sentinel-2 (S2) satellites as a valuable source of information to map irrigated areas and estimate distributed irrigation water requirements.

This study presents the results of the IRRISAT methodology, the first Italian satellite-based irrigation advisory service [2], which was applied in the Campania region. The methodology uses a one-step approach, based on the Penman-Monteith equation, and is adjusted with canopy parameters from S2 data, to quantify irrigation water abstraction. Effective irrigated areas were assessed by using pre-existing maps, unsupervised clustering, and supervised machine learning algorithms applied to vegetation index data [3].

The results of the methodology for the irrigation seasons of 2019 and 2020 will be presented for seven Irrigation and Land Reclamation Consortiums, which vary in size, irrigation scheme, farm delivery, irrigation methods, and crop types.

[1] INCIPIT Project, www.principit2017.it

[2] Vuolo F., D’Urso G., De Michele C., Bianchi B., Cutting M.: Satellite-based irrigation advisory services: a common tool for different experiences from Europe to Australia”. Agricultural Water Management, Elsevier, vol. 147: 82-95; dx.doi.org/10.1016/j.agwat.2014.08.004 (2015).

[3] Falanga Bolognesi S., Pasolli E., Belfiore O. R., De Michele C., D’Urso G.: Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy". Remote Sensing, MDPI, vol.12, no. 8: 1275; doi.org/10.3390/rs12081275 (2020).

How to cite: D'Urso, G., Belfiore, O. R., Coppola, A., Comegna, A., Toscano, A., Baroni, G., Consoli, S., Vanella, D., Longo Minnolo, G., Ippolito, M., De Caro, D., Castagna, A., and Gandolfi, C.: Earth Observation data for the monitoring of irrigation water use in Italy: The case study of the INCIPIT project., A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-60, https://doi.org/10.5194/egusphere-gc8-hydro-60, 2023.

Groundwater resources are biodiversity hotspots, and provide crucial ecosystems services. Yet, groundwater dependent ecosystems (GDEs) are exposed to several anthropogenic threats, including climate and land use change. Tackling these threats requires improving the on-the-ground identification of GDEs at the global scale and especially in vulnerable areas such as the Mediterranean biome where water is scarce.

In order to identify the location of groundwater dependent vegetation (GDV) in the landscape and create a harmonized global map of GDV a novel multi-instrument and multi-scale approach was developed. The approach combines a geodata-based potential GDV zones-index (pGDVZ) together with high-resolution vegetative, hydrogeological and topographic remote sensing parameters.

The pGDVZ integrates global and openly available datasets to combine groundwater vegetation interaction, land use, soil characteristics and landscape wetness potential. The index is currently tested for the whole Mediterranean. Results can help to pinpoint areas of high GDVZ potential where regional high-resolution identification of GDV is necessary.

The regional GDV-mapping concept implements different criteria aiming at: 1) high vitality and wetness during dry periods (e.g., Enhanced Vegetation Index, Normalized Difference Vegetation Index, Normalized Difference Water Index), 2) low seasonal changes in vitality, 3) low interannual changes in vitality, 4) high topographic potential of water accumulation and low water table depth and 5) general topography (elevation, slope). Processing of different remote sensing data (e.g., Sentinel 1;2, Digital Elevation Models, MODIS) is performed using the Google Earth Engine. Botanical mapping as well as integration of several geodata is used for validation and calibration of derived GDV-likelihoods. Furthermore, the integration of vegetation plots extracted from sPlot, the global vegetation database introduces a novel methodology to train machine learning algorithm for classification and modelling of GDV.

After successfully testing the mapping approach at local scale in the ‘Cilento, Vallo di Diano and Alburni National Park’ in Campania (Italy), a two-step upscaling methodology is currently designed to implement the concept on regional (county) and global (biome) scale.

How to cite: El-Hokayem, L., De Vita, P., and Conrad, C.: Mapping of potential groundwater dependent vegetation zones in the Mediterranean using a simple index based on global-available geodata and high-resolution remote sensing, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-61, https://doi.org/10.5194/egusphere-gc8-hydro-61, 2023.

Near-surface soil moisture is an important parameter for estimating the water balance of ecosystems, especially for the exchange of water between atmosphere and soil. In the past decades, global and continental products, e.g. the Soil Water Index (SWI) of ESA, have been developed for large-scale monitoring from passive and active microwave data. ESA SWI was developed for Europe based on the surface soil moisture product derived from Sentinel-1 C-band SAR data (1km resolution) and Metop ASCAT surface soil moisture (12.5km). However, for practical, small-scale applications on the local level, there is currently little data available. This contribution elaborates on the validation of the SWI for temperate agricultural regions at the example of the agrometeorological network of the JECAM test site DEMMIN in Northeast Germany. For this purpose, two resampling methods, a bilinear interpolation and a statistical downscaling approach using Random Forest were tested on SWI data from 2019. The statistical downscaling approach integrates Sentinel satellite data and the Topographical Wetness Index based on a 10m resolved elevation model. Both resampling methods showed similar results. Over time, the SWI significantly overestimates the in situ data before and after the crop growing season. A higher agreement is observed in the summer months. For 19 of the 29 investigated agrometeorological stations a statistically positive correlation with R>0.5 was found. The remaining stations showed little to no correlation, most likely due influences of various crops types on the remote sensing data. The results suggest a temporally limited applicability of the SWI 1km data for local assessments of soil moisture.

How to cite: Conrad, C., Schmelzer, J., Schlaak, J., and Löw, J.: Downscaling and validation of 1km ESA Soil Water Index for soil moisture monitoring on agricultural land in temperate climate conditions, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-97, https://doi.org/10.5194/egusphere-gc8-hydro-97, 2023.

Hydrology relies on the measurement of various variables, among which snow water equivalent (SWE) plays a crucial role in predicting runoff, particularly for catchments with high levels of snow accumulation. However, SWE measurements are rare and limited to point scales, making it difficult to obtain accurate spatialized estimates. While current satellite missions do not directly measure SWE, they offer valuable proxy information that can be used to reconstruct SWE. We propose using optical and radar sensors from MODIS, Sentinel-2, and Landsat missions to extract data on snow persistence on the ground and merge this multi-scale information to obtain accurate estimates of SWE. The technique involves observing snow patterns at high spatial resolutions from Landsat and Sentinel-2 missions and using this information to reconstruct a low-resolution image from MODIS. Additionally, information on the duration of melting can be obtained using Synthetic Aperture Radar (SAR) from Sentinel-1. In-situ air temperature data is used to estimate potential melting, and snow depth observations are used to determine if accumulation is occurring. The final output is daily high-resolution SWE maps. This approach has the advantage of not relying on precipitation observations, which are often uncertain in high-elevation catchments. We investigate the effectiveness of this approach in estimating peak snowmelt discharge for two monitored catchments in South Tyrol (Italy), comparing the results to those obtained using state-of-the-art hydrological models such as GEOtop and New Age. These results have the potential to significantly improve snowmelt estimation in poorly monitored basins.

How to cite: Bertoldi, G., Premier, V., Bozzoli, M., and Marin, C.: A comparison of a new remote sensing-based SWE estimation method  with physical models for improving snow melt estimation in alpine catchments, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-100, https://doi.org/10.5194/egusphere-gc8-hydro-100, 2023.

Spatio-temporal variability of throughfall (TF) in forested catchments depends on climatic forcing, forest stand parameters, and rainfall characteristics. Identifying and quantifying these controls is fundamental for a correct analysis and modelling of interception and catchment hydrological response. Despite many studies carried out at the stand and hillslope scale focused on the analysis of controls on TF variability, very little is known about the role of hillslope topography and the associated tree population characteristics in shaping throughfall spatio-temporal variability. In addition to ground-based measurements, satellite and unmanned aerial vehicle (UAV)-data can be explored to obtain a more robust assessment of the main drivers of TF variability. Therefore, this work aimed at i) identifying the dominant factors on throughfall variability in a European beech stand along a steep hillslope; and ii) quantifying forest interception at the hillslope scale and upscaling it at the small catchment scale using ground-level measurements and UAV-derived and satellite observations.

The experimental activities were carried out in the upper part of the Re della Pietra catchment, Tuscany Apennines, Central Italy. The hillslope is roughly 110 m long and 60 m wide, has a mean slope of 30°, and is dominantly covered by European beech trees. The TF experimental plot consists of 126 throughfall collectors divided in two square grids of 144 m² with 49 collectors at the bottom and the top of the hillslope, and a transect of 28 collectors from the bottom to the top grid. TF was manually measured from the collectors approximately monthly and compared with gross precipitation. Moreover, five automatic gauges were installed along the hillslope to increase the temporal resolution. Topographic surveys were conducted to measure the main physiological characteristics (diameter, height and age) of the trees in the TF plot. Leaf Area Index (LAI) was estimated using a ceptometer above each sampler in four dates in the dormant and in the growing season.

The 40 manual measurements revealed a large spatial and temporal variability of the TF/precipitation ratio (mean 68%, standard deviation 37%). In the growing period, the TF/precipitation ratio showed higher spatial variability compared to the dormant season (66±29% and 85±31%, respectively), suggesting that the crown expansion can be an important control on TF variability. Moreover, TF was consistently lower in the bottom plot, characterized by larger tree size compared to the top grid indicating a control by trees size. Event-scale data from the gutter gauges show a rainfall intensity control on TF but showed no correlation between TF and hillslope position.

To corroborate the preliminary observations on crown and tree size, in early March 2023 a UAV survey will be conducted to determine the crown architecture and lateral expansion, and relate this parameters to the observed TF. Furthermore, LAI ground measurements will be compared with LAI data derived from Sentinel-2 data to establish a relation between ground measurements and satellite observations. This relationship will be then use to upscale LAI and its possible associate control on TF variability from the hillslope to the small (0.3 km²) catchment scale.

How to cite: Verdone, M., Llorens, P., Massari, C., van Meerveld, I., and Penna, D.: Identifying controls on throughfall variability at the hillslope scale through satellite data and UAV-assisted techniques, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-69, https://doi.org/10.5194/egusphere-gc8-hydro-69, 2023.

Most of the climate scenarios forecast increased water scarcity in semi-arid and arid areas, such as Hungary. Since only 2% of Hungary’s agricultural land is irrigated where mostly outdated irrigation technology is applied, there is a huge need for act to enhance advanced irrigation. The general aim of the present research was to develop the basis of variable rate irrigation for a water-saving precision sprinkler irrigation system on an maize site (85 ha) located in the reference area of the Tisza River Basin. There are limited available water resources at the site, therefore alternative water sources utilization system was set up for irrigation to adapt to climate change and reduce fertilizers. As the alternative water resource for irrigation, inland excess water, treated wastewater, and biogas fermentation sludge are collected in a water reservoir with a capacity of 114,000 m³. For proper irrigation scheduling, heterogeneity of topography, hydrological, soil and crop conditions have to be explored and monitored. For this reason, UAV-based surveys were carried out with high spatial and temporal resolution by which DEM, DSM, multispectral vegetation data was conducted both on irrigated and non-irrigated parts of a maize field in Hungary this site. Supplementing these data with physically based modelling of the soil and crop status, and the water balance surveying is tested to use for accurate irrigation scheduling.

In the surveys we used a DJI Matrice 300 RTK UAV drone equipped with a Zenmuse L1 LiDAR payload with integrated RGB Surveying Solution and a DJI Zenmuse H20T thermal camera, which measures in the spectral range from 8000 to 14,000 nm. A DJI Mavic 2 Zoom drone equipped with a Sentera Double 4K sensor that can calculate NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red-Edge Index) by filtering out red and NIR wavelengths was used for the multispectral research. These data enables the monitoring of crop height and biomass and the assessment of the thermal properties and the photosynthetic activity of the crop, respectively. A crucial work phase is the data management of these remotely sensed data by which we gain point-cloud and raster from semi-raw formats and photogrammetry analysis can commence. For validation, the results of field measurements for crop height, general status and chlorophyll content were applied by virtue of the high spatial resolution provided by the sensors. Based on the results, the considerable relation was found between the field and RS based data to survey the surface, the height and health status of the maize, which contributes to the mapping of a proper vegetation patterns fostering variable rate irrigation prescription maps.

The abstract was funded by European Union’s Horizon 2020 “WATERAGRI Water retention and nutrient recycling in soils and steams for improved agricultural production” research and innovation programme under Grant Agreement No. 858375. This research was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences

How to cite: Nagy, A., Buday-Bódi, E., Szabó, A., Fehér, Z. Z., and Tamás, J.: Assessment of UAV-based LiDAR and photogrammetry data in crop morphology monitoring for advanced irrigation, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-103, https://doi.org/10.5194/egusphere-gc8-hydro-103, 2023.

Traditional water quality monitoring in River Systems is both labor intensive and expensive. However, in order to better understand the different phenomena occurring in river systems, it is vital to have robust data available. Satellite observations have been successful in monitoring different environmental systems, but generally, current available spatial resolutions and cloud cover in inland waters limit the monitoring of rivers. Recent developments in the use of Unmanned Aerial Systems (UAS) highlighted the potential to address this gap in environmental monitoring. 

In this study, UAS and image processing techniques were utilized to gather an overview of the water quality variability, specific to turbidity level and pollutant transport, along the Sarno River, which is the most polluted river in Europe, and the river pollution has long been subject to disputes between many sectors. Preliminary findings highlighted the potential of image processing and allowed to identify the  variability in river water quality along the main river by adopting a sampling protocol in several points of the Sarno River. While there were few observations of plastic in river banks, organic transport was mostly observed and interestingly, there is a water quality spatial mixing in the river mouth, which is difficult to observe using traditional in situ point measurements. This study only covers the initial phase of the river monitoring activities. 

Keywords: UAS river monitoring, sediment transport, image processing, spectral indices, remote sensing, drones, water quality assessment

References:

Miglino, D., Jomaa, S., Rode, M., Isgro, F., & Manfreda, S. (2022). Monitoring Water Turbidity Using Remote Sensing Techniques. Environmental Sciences Proceedings, 21(1), 63.

How to cite: Saddi, K. C., Miglino, D., Jomaa, S., Rode, M., Isgro, F., and Manfreda, S.: Utilizing Unmanned Aerial Systems in River Water Quality Variability Monitoring, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-106, https://doi.org/10.5194/egusphere-gc8-hydro-106, 2023.

Non-perennial rivers are characterized by periods with dry bed or chains of isolated ponds. Given the extremely high biodiversity and various ecosystem services, these environments require careful management. The main obstacle to the implementation of correct management practices is related to the lack of information about the duration and frequency of zero-flow periods, that are the primary determinants of ecosystem processes in this kind of streams.

In many cases, the presence of non-perennial reaches within the river network is unknown. Given the high extension of the network of non-perennial rivers and their strong spatial inhomogeneity, traditional gauging systems are not adequate to provide measures with adequate spatial coverage. Moreover, point measures cannot capture the space-pattern of presence/absence of water. On the other hand, field surveys of water patterns have a limited temporal resolution and therefore lack in capturing the regime’s time-patterns. In this context, satellite data can make a key contribution thanks to the possibility of monitoring large areas with high temporal resolutions. Their use for monitoring the regime of non-perennial rivers has so far been limited by the availability of images with adequate resolution and accessible costs.

In this work, we explored the potential of medium-resolution multispectral Sentinel-2 data to identify non-perennial rivers and to assess their degree of intermittency. Examining the spectral signatures of water, sediment and vegetation covers, the bands in which these classes are most differentiated were identified. Exploiting these bands, we generated false-color image in which the pixels covered by water stand out from the background. From the false-color composite images, it was possible to identify the three distinct flowing status of non-perennial rivers: “flowing”, “ponding” and “dry” . The classification of flowing status was checked against ground truth, showing very good agreement. To enable a wider audience to identify flowing status along non-perennial rivers, we have developed and made freely available a code on the Google Earth Engine platform. For all the archive images (since 2015) we identified one of the three possible flowing status: flowing, ponding and dry bed. The obtained dataset allowed to train a random forest (RF) model able to predict the daily occurrence of a specific flowing status using as predictors spatially interpolated rainfall and air temperature data. The analysis was performed for 5 reaches of the streams Sciarapotamo, Mingardo and Lambro (Campania region, Italy), for which a RF model was calibrated. Classification RF models performed well in terms of accuracy (ranging from 82% to 97%) and true skill statistic (ranging from 0.65 to 0.95). All the studied reaches showed a no-flow condition during the observation period. Three of the five reaches resulted to have a dry bed condition each year while the other two reaches never dry up completely. With its ability to monitor the presence and absence of water in a cost-effective manner, this method has the potential to significantly improve the management and the conversation of non-perennial rivers, enabling a better understanding of their ecological status, as required by the European Water Framework Directive 2000/60/EC.

How to cite: Cavallo, C., Papa, M. N., Negro, G., Ruello, G., Gargiulo, M., and Vezza, P.: Using Sentinel-2 multispectral imagery to assess flow-intermittency in non‑perennial rivers, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-11, https://doi.org/10.5194/egusphere-gc8-hydro-11, 2023.

High-resolution monitoring of rivers is important because rivers are severely affected by climate change and both frequency and magnitude of extreme events are changing rapidly. Advanced in-situ monitoring technologies need to be combined with satellite Earth Observation (EO) to provide accurate, reliable, and spatio-temporally resolved information for effective decision support, risk assessment, investment analysis for climate change adaptation, and operational forecasting/surveillance.

Traditional hydrometric monitoring of rivers is in-situ and station-based. In-situ monitoring networks lack spatial resolution, have been declining in many regions, and data accessibility is increasingly restricted because of growing conflicts between countries over water resources allocation. To solve this problem, hydrometric monitoring using satellite earth observation needs to be combined with drone-borne hydrometric monitoring technology for validation, deployment in remote and inaccessible regions, and for reliable and accurate estimation of river discharge.

The Horizon Europe UAWOS project develops an Unmanned Airborne Water Observing System to provide key hydrometric variables (bathymetry, velocimetry, water surface elevation) at high spatial resolution/coverage, and data-based products/services to support management and decision making. UAWOS integrates airborne data streams with Copernicus water bodies and water level services for cross validation and to estimate river discharge from satellite EO data.

This contribution outlines the UAWOS work programme and reports first results of airborne surveys using (i) radar altimetry for water surface elevation mapping, (ii) water penetrating radar and sonar for bathymetric mapping and Doppler radar for surface velocity monitoring. The combination of these datasets for river discharge estimation as well as for validation and enhancement of satellite radar altimetry datasets will be discussed.

How to cite: Bauer-Gottwein, P., Olesen, D., Nielsen, K., Rietz, A., Coppo Frías, M., Dobrovolskiy, A., Kadek, A., Orlic, N., Grubesa, T., Hiller, T., Grosen, H., Nielsen, S., Tarpanelli, A., Giordan, D., Barbetta, S., Gustafsson, D., Wennerberg, D., Disse, M., Merk, F., and Romero, L. and the UAWOS project team: UAS Hydrometry – Contactless airborne measurements of water level, depth, flow velocity and discharge in rivers and streams, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-52, https://doi.org/10.5194/egusphere-gc8-hydro-52, 2023.