To monitor aquatic habitats and understand physical and biogeochemical processes, the analysis of high-resolution spatial patterns in water temperature is of outmost importance. The spatial resolution of remotely sensed thermal infrared (TIR) data ranges from cm-scale for airborne to m- and km-scale for spaceborne observations, enabling the analysis of a variety of hydrological processes. However, while remotely sensed TIR data reflects temperatures emitted from the direct surface, the temperature of waterbodies may also vary significantly with depth. Hence, there are limits to relying purely on remotely sensed water temperature data to understand 3D water temperature patterns, leading to the need of high-resolution 3D water temperature data.

Here, we combined a novel self-build in-situ sensor system with remotely sensed TIR data to explore high-resolution, natural and anthropogenically influenced 3D spatio-temporal patterns in river water temperature. The study site involved a ~780 m long stretch of a river in the Northeast of Scotland, with a smaller section (~50m long) that is influenced by cooling water being discharged from a local distillery. We applied our new observation system to gain a better understanding on the 3D extend of a thermal plume and how this local anomaly compares to and affects the overall thermal variability within the river. Three surveys were conducted (during April-June 2021) to measure the surface water temperature of the river with an UAV based TIR camera. We additionally installed the novel in-situ sensor system to measure 3D water temperature during each survey. The surveys were planned to acquire data under contrasting ambient conditions as well as at a time when no cooling water was being discharged, allowing us to also observe spatio-temporal thermal variability under natural conditions. While the acquired TIR datasets give an overall view of the thermal variability at the surface, subsets of the TIR datasets were merged with the corresponding data from the in-situ sensor system to spatially interpolate high resolution 3D water temperature of the area influenced by the thermal plume. The results show that (I) the combination of remote sensing and sensor system can detect pattern in 3D in high spatial resolution, (II) surface temperatures and their spatial patterns differ from temperatures and their spatial patterns at greater depth and (III) at this site, local anomalies due to cooling water releases do not alter the overall thermal variability within the river.

The combination of the novel sensor system with remotely sensed TIR data has the potential to be used to observe a broad range of hydrological processes in natural and artificial aquatic environments and to contribute to the understanding of overall energy budgets, infiltration, limnology, groundwater surface water exchange or similar processes.

How to cite: Loerke, E., Geris, J., Pohle, I., and Wilkinson, M.: Combining a novel in-situ sensor system with remotely sensed thermal infrared data to analyse spatial water temperature patterns in 3D, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8336, https://doi.org/10.5194/egusphere-egu23-8336, 2023.

High-density gauging station networks and complete hydrological time-series are needed to adequately model and manage water resources and assess the effects of climate change on hydrological processes. In data scarce regions however, remote sensing data has proven to be a viable alternative, but before the year 2000 satellite records often contain gaps or are not available at all.

We propose to create synthetic images of precipitation, temperature, evapotranspiration, and terrestrial water storage products to complete and extend past data availability to pre-satellite periods. Ideally, the synthetic images should be indistinguishable from real satellite acquisitions. The approach used is based on the relation between meteorological predictors and available satellite images, and the hypothesis that, under similar meteorological conditions, patterns of a particular process may be repeated over the years. Using ERA5 reanalysis data as meteorological predictor, a K-Nearest Neighbor algorithm associated with a process-specific similarity metric is applied to create synthetic images of the different satellite products.

The approach is tested on the Volta River Basin in West Africa, where water resources for millions of people are critically stressed by the effects of climate change. For calibration and validation, the synthetic images are fed to a spatially-distributed hydrological model (the mesoscale Hydrologic Model mHM). Their quality is assessed by their capacity to reproduce historical streamflow time series. This test phase allows improving the generation technique to obtain synthetic imagery that can be considered a reasonable approximate of unobserved processes consistent with the available climate data, and which will help improve modelling accuracy.

Keywords: Remote sensing, Climate reanalysis, Satellite time series, Hydrological modelling

How to cite: Gerber, L. and Mariéthoz, G.: Synthetic hydrological data consistent with climate reanalysis to enable long-term hydrological modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3478, https://doi.org/10.5194/egusphere-egu23-3478, 2023.

We have aimed to improve the understanding of regional water and energy budgets in large catchments from observations, focusing on the period 2002-2013. To do this we have utilised new available satellite data from the Gravity Recovery and Climate Experiment (GRACE).  Despite recent improvements in remote sensing capabilities, we still see inconsistencies amongst datasets. Observed surface energy fluxes from CERES and FluxCOM indicate unrealistic increases/decreases in surface energy storage over different catchments. We also see imbalances in the water budget, suggesting inaccuracies in the measurements. In order to assess these imbalances, we introduce a flux-inferred surface storage (FIS) for both water and energy, based on integrating the flux observations. This exposes mismatches in seasonal water storage as well as important interannual variability. We have produced optimised estimates for each component of the terrestrial water and energy budgets based on observations and their relative uncertainties. Our new optimisation approach ensures that flux estimates are consistent with total water storage changes from GRACE on short (monthly) and longer timescales, while also balancing a coupled long term energy budget. Flux adjustments remain small and are evaluated using a chi squared test. By using multiple data products, the optimisation reduces formal uncertainties on the budget variables. When compared with results from previous literature, our estimates show good agreement with GRACE variability and trends on account of the multiple timescale constraints imposed during the optimisation. We next aim to extend our approach to include carbon budgets alongside the water and energy budgets to produce a truly coupled Earth system cycling analysis, with applications such as testing Earth and climate circulation models.

How to cite: Petch, S., Haines, K., King, R., Dong, B., and Quaife, T.: Water and Energy budgets on short and long timescales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3327, https://doi.org/10.5194/egusphere-egu23-3327, 2023.

NASA created the Observational Products for End-Users from Remote Sensing Analysis (OPERA) project to develop satellite-based analysis ready data products for resource management, environmental protection, and science. The OPERA product that is focused on inland surface water detection, Dynamic Surface Water Extent (DSWx), will be produced using data from both optical and synthetic aperture RADAR systems. The first DSWx product release (DSWx-HLS) relies on Harmonized Landsat Sentinel-2 (HLS) data to yield a median observation frequency of 3 days at the equator with near-global coverage. Subsequent DSWx releases will be based on inputs from Sentinel-1, Surface Water Ocean Topography (SWOT), and NASA-ISRO Synthetic Aperture Radar (NISAR).

DSWx accuracy in monitoring open water bodies is estimated through comparison with coincident, higher spatial resolution satellite imagery for locations around the globe. DSWx product suite algorithms also target the detection of mixtures of water and vegetation at input data subpixel scale as well as water under vegetation. The accurate assessment of algorithm performance given these especially challenging targets requires the development and analysis of databases that have as a foundation, data collected in the field.

A DSWx predecessor (USGS DSWE) and provisional DSWx data have been combined with in situ data on river discharge, aquatic species occurrence, water quality, and wetland processes to test and develop product utility. At sites spread across the US, low-cost sensors have been employed to record surface inundation. Trail cameras adapted for scientific research are providing useful information on weather, vegetation, and water conditions. Imagery from multiple high-resolution remote sensing instruments, including uncrewed aerial systems and commercial satellites, as well as sensors on-board the International Space Station, are being periodically collected. During intensive field campaigns, vegetation structure is being measured at each site. The imagery and in situ data are combined to improve DSWx development, uncertainty assessment, and application.

How to cite: Jones, J., Bansal, S., Meier, J., and Pearl, C.: Combining OPERA Dynamic Surface Water Extent (DSWx) with in situ measurements to improve product development and application, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9631, https://doi.org/10.5194/egusphere-egu23-9631, 2023.

Water resources management is difficult due to the uncertainties of the parameters controlling the hydrological response and, this uncertainty is even greater in ungauged basins where parameters are generally defined by regionalisation approaches. Among the available methods, one of the most used is the regression-based approach, which relates the most appropriate parameter values to catchment properties, such as physical properties, topographic, land use, soil and geological data. This approach assumes that the hydrological response depends on the catchment attributes and the hydrological response in catchments with similar characteristics is meant to be similar, and traditionally, these properties are derived from cartographic data sources. Since the spectral response of the territory depends on these attributes, this study uses remote sensing techniques to characterise the spectral response and apply it to the regionalisation of hydrological parameters using a machine learning approach with Random Forest.

The study area is a Mediterranean environment in Spain and corresponds to eighteen gauged watersheds in the region of Extremadura, in which we find two bioclimatic variants: a wetter and a drier one. In this study the algorithm is tested in two scenarios for regionalisation, the new approach using the spectral signature of the catchments and the results are compared with the traditional approach using the physical properties from data provided by the European Soil Data Centre. The spectral response of the catchments is studied using images from the Sentinel-1 (S1) and Sentinel-2 (S2) missions of the Copernicus Program of the European Commission. S1 is a synthetic aperture radar (SAR) sensor (C-band ) and S2 is a multispectral sensor working in the visible, near-infrared and shortwave infrared bands. In addition, several spectral indices and texture metrics derived from the grey-level cooccurrence matrix are also used for a better characterization of the watersheds.

The results perform well in both scenarios showing almost the same goodness of fit and the efficiency depends on the climatic environment. In this sense, the prediction in the wetter catchments exhibits better performance than the driest variant.  Specifically in the latter, the spectral regionalisation outperformed the physical scenario. The new spectral approach shows promising results, especially considering the advantage of having continuous coverage of Sentinel data worldwide, which offers new possibilities in areas where no mapping information is available.

How to cite: Fragoso-Campón, L., Durán-Barroso, P., and Quirós, E.: Regression-based regionalisation of hydrological parameters using catchment’s spectral signature, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11543, https://doi.org/10.5194/egusphere-egu23-11543, 2023.

Information about soil water content (SWC) in adequate spatial and temporal resolution is highly desired for a variety of scientific and practical applications. Cosmic-Ray Neutron Sensing (CRNS) has become an established method for passive SWC data collection, providing SWC information over several hectares, either by stationary CRNS sensors (local continuous measurements) or by mobile CRNS roving (expanding the footprint on certain field campaign days). Recent approaches of automatic rail-based CRNS roving (Rail-CRNS) allowed to expand the monitored areas further up to the kilometer scale in high temporal resolution. While a pilot study on Rail-CRNS provided promising results along the railway track, currently in daily resolution, it also raised the question of how transferable these SWC data are for areas not directly adjacent to the footprints along the railway. In this study, we have tested the performance of SWC regionalization by probabilistic predictions based on Rail-CRNS derived SWC data. A Monte Carlo approach was applied in regression random forest, using static (e.g. topographical indices, soil properties) and dynamic (precipitation) predictors and quantified their impact on the prediction accuracy. Using daily SWC values from a ~ 9 km long railway at the Harz mountain, Germany, recorded by the Rail-CRNS between September 2021 and July 2022, we predicted the daily spatial SWC variation for an area of ~ 85 km² and a period of 300 days on a 250 x 250 m grid. The resulting maps of gravimetric soil moisture showed realistic pattern for both, spatial and temporal SWC variation. The maps resolved spatial variation as related to land cover, seasonal SWC dynamics and individual responses of single areas to wetting and drying periods. As the demonstrated data represented the outcome of a relatively narrow area as given by the limited training Rail-CRNS data, the extension of the proposed approach by expanding the railway networks, by future technical improvements and by the automatization of the workflow has the clear potential to offer near real time SWC products for the large scale (> 100 km). 

How to cite: Altdorff, D., Dega, S., Schrön, M., Oswald, S., Zacharias, S., Dietrich, P., Attinger, S., and Paasche, H.: Probabilistic regionalization of soil moisture data measured by Rail-based cosmic ray neutron sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14778, https://doi.org/10.5194/egusphere-egu23-14778, 2023.

The Pantanal is the largest tropical wetland on earth, covering an area of 158000 km² between Brazil (~70%), Bolivia (~20%) and Paraguay (~10%). The regular flood pulse of this region produces unique ecological and geomorphological processes in the floodplains. Due to the extensive areas with flat topography, the water velocity of the rivers gets reduced, and large sediment deposition processes begin to take place in the Pantanal floodplain. Despite its unique characteristics and great environmental importance, the rivers from this region have very scarce in situ monitoring of suspended sediment concentration (SSC), with approximately one gauge per 8000 km², and experiences low collection frequency (average of four measurements per year). Therefore, the characterization of sediment dynamics in this region remains challenging, and the spatial-temporal variation of suspended sediments in the Pantanal rivers is still poorly understood.

Remote sensing techniques offer enormous advantages by providing cost-effective systematic observations of large water systems, allowing spatial-temporal mapping of wild areas such as the Pantanal. The suspended matter in water bodies increases the reflectance in the green, red, and near-infrared (NIR) bands, i.e., the backscatter radiation increases as the SSC in water increases. Therefore, reflectance from the visible bands and NIR band can be used as proxies of SSC in water bodies.

The focus of this study is to assess the spatial-temporal variations of SSC in rivers that drain to and through the Pantanal wetland, by using surface reflectance (SR) from satellite images and artificial neural network (ANN)-based models. We used atmospherically corrected SR from Sentinel-2, Landsat 8, and Landsat 9 (bands of blue, green, red and NIR) as input variables, and in situ data on SSC from 23 gauges along the Pantanal rivers as output variables in the models.

Through this methodology, we expect to obtain time series of SSC estimated by the ANN-based model and reflectance data from satellite images for different parts of the Pantanal hydrographic basin.

The resulting time series allows us to:

• Characterize the spatial variations of suspended sediments along different rivers in the Pantanal wetland.
• Identify the main drivers of these spatial variations by comparing these differences with land use, vegetation cover, topography, and types of soil within the drainage watershed of the rivers.
• Characterize the influence of the seasonal hydrological regime on SSC transport.
• Identify the influence of anthropic activities on the amount of SSC transported to the wetland.

How to cite: Campos, J. A., Fassoni-Andrade, A. C., Pedrollo, O. C., Fujita, T., Cuartas, L. A., Bruun, E., Attila, J., and Uvo, C. B.: Mapping suspended sediment dynamics in the Pantanal wetland using remote sensing and ANN-based models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13733, https://doi.org/10.5194/egusphere-egu23-13733, 2023.

How to cite: Schauer, H. I., Schlaffer, S., Bueechi, E., and Dorigo, W.: Data-Driven Modelling of Steppe Wetland Variability in Eastern Austrian Seewinkel Using Satellite-Derived Water Extent and Climatological and Groundwater Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12314, https://doi.org/10.5194/egusphere-egu23-12314, 2023.

The Brazilian Cerrado ecoregion, or wooded Cerrado, is considered one of the biodiversity hotspots. Despite the region’s importance in terms of supplying the water, food, and energy demand, there have not been enough ground-based studies. Furthermore, the lack of validation due to scale incompatibility and the great site-specific heterogeneity transpires in difficulty in the validation process.

The eddy covariance method has the potential to directly measure water vapor or trace gases on an in situ scale. Their measurement directly reflects the surrounding study site; thus, each time interval has a corresponding footprint. So, each study site's heterogeneity can affect the target vegetation's representativeness.

Here, we aimed to assess how two approaches for integrating remote sensing products and in-situ data affected representativeness in the wooded Cerrado. We used the Enhanced Vegetation Index (EVI) in both approaches, which are described as follows: (i) a fixed-fetch approach of the surrounding area considering a radius of 2 km and (ii) a lagrangian footprint approach that varied by a 30-minute time interval.  We assessed their performance based on their hourly and seasonal association with canopy conductance, which was carried out using in-situ data.

Compared to the fixed-fetch technique, the EVI footprint-integrated approach has a smaller range between the lower and upper quantiles, which is indicative of better targeting of the vegetation. Furthermore, we discovered that the integrated footprint technique produced a stronger association between EVI and canopy conductance than the fixed-fetch approach throughout most seasons and examined hours. The difference is most pronounced in the winter season, reaching a gain in the correlation of almost 100%, and for the autumn and spring with consistent gains of about 30%. Our findings highlight that integrating remote sensing products with footprint analysis can significantly improve the analysis's representativeness when targeting a specific land use or land cover, hence improving understanding of complex and heterogeneous areas.

How to cite: Kobayashi, A., Anache, J., Sone, J., Gesualdo, G., Schwamback, D., and Wendland, E.: Accounting for flux footprint to enhance the representativeness between remote sensing and in-situ data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11313, https://doi.org/10.5194/egusphere-egu23-11313, 2023.

Continuous streamflow prediction in ungauged basin and the quantification of its uncertainty has been challenging over the decades. Regionalization of parameters from gauged basin has been the most promising approach adopted by the researchers. However, the improvement of hydrological model prediction in regionalization with proper alternative observed data has been constantly explored. Various researchers have used remote sensing based products such as soil moisture and evapotranspiration as variables for calibration of hydrological models. While the regionalization based approach result in higher predictive uncertainty, performance of models calibrated using remote sensing products have been found to be sub-optimal. In this context, a combined approach of regionalization and remote sensing data may result in reduced predictive uncertainty and enhanced accuracy. In this study the predictive uncertainty quantification in ungauged basin is proposed using the regression-based regionalization framework between the catchment attributes and probability distribution function (PDF) of hydrological model parameters. The PDF of the hydrological model parameters is derived using the MCMC procedure in the DREAM algorithm. The proposed approach is evaluated using the data pertaining to 12 watersheds in the MOPEX database and assuming one of the catchments as pseudo-ungauged catchment. The uncertainty quantification in regionalization for streamflow prediction analysed by average of the prediction is better performing with NSE of 0.77 in pseudo ungauged basin (Sugar Creek EdinBurgh watershed). Further, the remote sensing soil moisture from GLDAS was compared with the model simulated soil moisture analysed using NSE to sub sample the regionalization parameter space in ungauged basin. The regionalization of the reduced parameter set to assess the change in uncertainty quantification is performed and found to have same performance NSE of 0.77 in ungauged basin for streamflow prediction with reduction in average width of 0.23 mm/day in ensembles of streamflow prediction. The ensemble of the simulations has similar performance compared to the model calibrated using streamflow (NSE 0.77). The outcome of the study indicates that the calibration of hydrological model using remote sensing soil moisture product as simulating variable have improved performance the model prediction in the parameter range obtained from the regionalization framework in the ungauged basin. Thus, the integration of regionalization approach with simulation of hydrological model using remote sensing products in the ungauged basin is recommended to apply in the real time applications of water resources management.

How to cite: Vema, V. and Pattabiraman, B.: Application of Soil moisture in Regionalization framework for Predictions in Ungauged Basin and its Uncertainty quantification, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-400, https://doi.org/10.5194/egusphere-egu23-400, 2023.