HS6.12 | Synthesising Remotely Sensed and In-Situ Data to Understand Hydrological Processes at Regional and Local Scales
EDI
Synthesising Remotely Sensed and In-Situ Data to Understand Hydrological Processes at Regional and Local Scales
Convener: Christina Anna Orieschnig | Co-conveners: Zheng Duan, Yonca Cavus, Hajar Choukrani, Jianzhi Dong, Junzhi Liu, Hongkai Gao
Orals
| Mon, 24 Apr, 16:15–17:55 (CEST)
 
Room 3.16/17
Posters on site
| Attendance Mon, 24 Apr, 10:45–12:30 (CEST)
 
Hall A
Orals |
Mon, 16:15
Mon, 10:45
With the proliferation and wide accessibility of remotely sensed information, data from missions such as Landsat, Sentinel, and MODIS are being increasingly used to develop a better understanding of hydrological processes on the earth’s surface. Acquiring this understanding is a crucial prerequisite to ameliorate resource management, optimise the development of infrastructure, and adjust land use practices to changing climate conditions and hazards such as floods and droughts. However, many analyses incorporate remote sensing data by default and without a thorough critical examination of their applicability and limitations. In-situ data, though often less readily available and more eclectic, provide a valuable layer of information to act as a benchmark against methods relying solely on remotely sensed data.
This session aims to highlight innovative approaches to harnessing a synthesis of remotely sensed and in-situ data to better understand processes related to hydrology at regional and local scales in a variety of environments. We welcome contributions that focus on combining remote sensing and in-situ information and critically engage with this intersection with relation to:
- Processes such as evapotranspiration, infiltration, (Monsoon) inundations
- Hydrological extremes such as floods and droughts
- Hydrological processes shaping agricultural systems
- Intersections with societal processes and synergies with socio-hydrological approaches
- Coping with a sparsity of in-situ data in poorly gauged and ungauged basins
- Developing novel methods of gathering in-situ benchmark data to combine with remotely sensed approaches
- Reviewing recent synthesised advances of RS applications in hydrology, in natural and anthropised ecosystems
- Application of remote sensing in hydrological modelling, particularly using remotely sensed water cycle components to facilitate multi-variable calibration and spatial evaluation of hydrological models.

Orals: Mon, 24 Apr | Room 3.16/17

Chairpersons: Christina Anna Orieschnig, Zheng Duan, Hajar Choukrani
16:15–16:25
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EGU23-8336
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ECS
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On-site presentation
Eva Loerke, Josie Geris, Ina Pohle, and Mark Wilkinson

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.

16:25–16:35
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EGU23-3478
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ECS
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Highlight
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On-site presentation
Loïc Gerber and Grégoire Mariéthoz

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.

16:35–16:45
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EGU23-3327
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ECS
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On-site presentation
Samantha Petch, Keith Haines, Rob King, Bo Dong, and Tristan Quaife

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.

16:45–16:55
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EGU23-9631
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On-site presentation
John Jones, Sheel Bansal, Jacob Meier, and Christopher Pearl

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.

16:55–17:05
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EGU23-11543
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ECS
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On-site presentation
Laura Fragoso-Campón, Pablo Durán-Barroso, and Elia Quirós

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.

17:05–17:15
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EGU23-14778
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Highlight
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On-site presentation
Daniel Altdorff, Ségolène Dega, Martin Schrön, Sascha Oswald, Steffen Zacharias, Peter Dietrich, Sabine Attinger, and Hendrik Paasche

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.

17:15–17:25
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EGU23-13733
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On-site presentation
Juliana Andrade Campos, Alice César Fassoni-Andrade, Olavo Correa Pedrollo, Thais Fujita, Luz Adriana Cuartas, Eeva Bruun, Jenni Attila, and Cintia Bertacchi Uvo

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.

17:25–17:35
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EGU23-12314
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ECS
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On-site presentation
Henri Isidor Schauer, Stefan Schlaffer, Emanuel Bueechi, and Wouter Dorigo

Seewinkel salt pans are a unique wetland ecosystem in eastern Austria that serves as habitat for a diverse range of e.g. birds and halophilic species. Due to groundwater drainage by channels and wells, the salt pans are in an increasingly vulnerable state as they are decisively conditioned by duration and timing of water abundance. However, water gauge data are merely given for three salt pans. The dynamics of salt pans in Seewinkel, locally referred to as Salzlacken, remain insufficiently understood in the context of continuously changing seasonal and long-term hydrological, meteorological, and climatological patterns. Based on previous results on salt pan mapping and monitoring, this work advances inundation state prediction for 34 salt pans by using high-resolution remote sensing data and machine learning methods. The random forest classification models build on hydrological and meteorological predictors in 12-monthly temporal resolution, as, e.g., reduced precipitation sums during the preceding winter season affect the recharge rates of salt pans and groundwater and, as a result, drying state in summer. Four models predict summer drying state at respective four points in time, namely in March, April, May, and June of each year between 1984 and 2022. We first show that remotely sensed water extent products, retrieved from Landsat data can serve as a target variable for data-driven modelling of small-scale salt pan water-dynamics. Secondly, we show that the applied models can successfully predict summer drying state and inundation periods of individual salt pans achieving a maximum F1-score of 0.81. Finally, it is demonstrated that very similar model results can be attained without in-situ groundwater measurements. Research based on water gauge measurements with similar model-designs has been done in the context of lakes, whereas the combination of satellite-derived water extent and salt pans, especially for ecosystems of small size, remains underrepresented. As the data retrieval in this work is based on global and freely available remote sensing data, this method is transferable to comparable salt pan ecosystems in other parts of the world. 

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.

17:35–17:45
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EGU23-11313
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ECS
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On-site presentation
Alex Kobayashi, Jamil Anache, Jullian Sone, Gabriela Gesualdo, Dimaghi Schwamback, and Edson Wendland

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.

17:45–17:55
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EGU23-400
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ECS
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On-site presentation
Vamsikrishna Vema and Balasundaram Pattabiraman

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.

Posters on site: Mon, 24 Apr, 10:45–12:30 | Hall A

Chairpersons: Jianzhi Dong, Junzhi Liu, Hongkai Gao
A.99
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EGU23-332
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ECS
Soohyun Kim and Dongkyun Kim

In this study, SAR (Synthetic Aperture Radar) images are used to validate a method for extracting the waterbody from small and medium-sized streams based on the shortest plane distance (DST). The research area is the Hantangang River and Jungnangcheon in the Hangang River Basin of South Korea. To validate the water body extraction method, SAR satellite image data (Sentinel-1) and high-resolution optical satellite PlanetScope are employed concurrently. After preprocessing, the brightness distribution of the Sentinel-1 photos is equalized using histogram matching. To achieve an efficient stream extraction, a weight is applied that is the DST from the stream centerline. The optimal parameter value is obtained using the k-means method after combining this weight value with Sentinel-1's VH and VV polarizations. Depending on the resolution limit, this value allows the waterbody to be extracted with maximum accuracy from Sentinel-1 images. The waterbody extraction can be calculated using an elliptic equation based on the correlation between the VV, VH, and DST. Results show that the average accuracy is 0.45-0.75, and the average Kappa coefficient is 0.60-0.85. This study demonstrates that the DST can be used to estimate the area of a waterbody. Furthermore, the proposed method extracts the waterbody more easily and quickly than the existing method.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2003471).

How to cite: Kim, S. and Kim, D.: Waterbody Extraction from Small and Medium-Sized Streams Using Sentinel-1 Images Based on Shortest Plane Distance, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-332, https://doi.org/10.5194/egusphere-egu23-332, 2023.

A.100
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EGU23-3065
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ECS
seulchan lee, jaehwan jeong, Nurul Syahira Mohammad Harmay, and minha choi

Globally important hydroclimatic variations take place over monsoon Asia. However, sound understanding of hydrological processes is still challenging due to the unevenly distributed observation stations. This rising issue has been partially solved through land surface model (LSM) simulations, which is known as one of the most effective ways to predict hydrological states and fluxes in ungauged regions. Recent advances in remote sensing techniques produced several multi-source-based precipitation data, which serves as a major input forcing for modeling the land surface processes. In this context, this study aims to validate the precipitation estimates from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), Global Data Assimilation System (GDAS), Integrated Multi-satellite Retrievals for GPM (IMERG), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and also evaluate the LSM-simulated soil moisture (SM) and evapotranspiration (ET) through NASA Land Information System (LIS). Precipitation products are validated with ground measurements-based Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) gridded data. Spatiotemporal errors in SM and ET outputs originating from precipitation uncertainty are quantified at locations with dense ground precipitation observations. Overall, the outcomes could possibly reveal additional error sources such as land use land cover (LULC) surface dataset or model parameterizations, which is crucial for more sophisticated LSM simulations.

How to cite: lee, S., jeong, J., Mohammad Harmay, N. S., and choi, M.: Impacts of Precipitation Forcing on Hydrological Simulation over Monsoon Asia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3065, https://doi.org/10.5194/egusphere-egu23-3065, 2023.

A.101
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EGU23-5839
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ECS
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Theresa C. van Hateren, Marco Chini, Patrick Matgen, and Adriaan J. Teuling

With the emergence of accurate high resolution remotely sensed datasets of hydrological variables, opportunities arise to study hydrological processes at an unprecedented scale and resolution. We took this opportunity to study spatiotemporal drought patterns over the country of Luxembourg. A daily 100x100 m2 soil moisture dataset based on the VanderSat technologya will be analysed in conjunction with a 6-day 60x60 m2 soil moisture dataset retrieved from Sentinel-1 data (Pulvirenti et al., 2018; van Hateren et al., 2021), and the Copernicus 1x1 km2 daily soil moisture product based on the TU Wien algorithmb. First of all, the consistency between the different products will be tested, as well as their ability to resolve small-scale variability in soil moisture. Then, the soil moisture products are compared to in situ soil moisture data, meteorological data and vegetation indices during major droughts in the last decade (2018, 2022). We will compare small scale spatial and temporal patterns of drought indices with land use, geology and elevation to see how the indices developed during these droughts and how they depend on the local landscape.

ahttps://data.public.lu/en/datasets/soil-humidity-in-luxembourg-2002-2022/

bhttps://land.copernicus.eu/global/products/ssm

Pulvirenti, L. et al., ‘A Surface Soil Moisture Mapping Service at National (Italian) Scale Based on Sentinel-1 Data’, Environmental Modelling & Software 102 (April 2018): 13–28, https://doi-org.ezproxy.library.wur.nl/10.1016/j.envsoft.2017.12.022.

van Hateren, T.C. et al., ‘Optimal Spatial Resolution of Sentinel-1 Surface Soil Moisture Evaluated Using Intensive in Situ Observations’, in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, 6311–14, https://doi-org.ezproxy.library.wur.nl/10.1109/IGARSS47720.2021.9553041.

How to cite: van Hateren, T. C., Chini, M., Matgen, P., and Teuling, A. J.: High resolution soil moisture drought monitoring over Luxembourg, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5839, https://doi.org/10.5194/egusphere-egu23-5839, 2023.

A.102
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EGU23-8115
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ECS
Babak Mohammadi, Hongkai Gao, Zijing Feng, Petter Pilesjö, and Zheng Duan

Hydrological models as common simulation tools for water resources management play a key role in improving our understanding of hydrological processes on the catchment and global scales. The reliability of hydrological simulations depends on the model structure, the quality of input data, and the calibration of model parameters. A large number of model parameters and interactions among each hydrological variable increase the complexity of the model calibration. Multi-objective model calibration is beneficial to reduce hydrological modeling uncertainty by different calibration criteria, which can lead to more realistic simulations. The FLEXG model is a conceptual glacio-hydrological model within the flexible modeling framework. The FLEXG model considers the effects of topography on the spatial distribution of temperature, precipitation, and runoff generation to have a better understanding of the impacts of landscape on hydrological processes. The FLEXG can simulate various glacio-hydrological variables such as runoff from different sources (e.g. snow and glacier melt), glacier mass balance, snow cover area, and snow water equivalent. This study aims to evaluate the influences of several calibration strategies on the FLEXG model's performance in simulating simulated runoff, snow cover area, glacier mass balance, and snow water equivalent in a glacierized catchment in Sweden. To this end, the FLEXG model was calibrated based on remotely sensed snow cover area data and compared to the traditional calibration strategy (calibrating merely against gauged streamflow data). The FLEXG model was also calibrated based on both gauged streamflow data and satellite snow cover area data as a multi-objective calibration. Glacio-hydrological simulations from the FLEXG model using different calibration strategies were evaluated with multiple metrics and at different temporal scales. Results showed that calibrating the FLEXG using only one variable (runoff or snow cover area) can provide acceptable results for only one variable, while the multi-objective calibration strategy can have acceptable simulation for both runoff and snow cover area. In addition, calibrating the FLEXG model using only snow cover area may underestimate runoff simulation.

How to cite: Mohammadi, B., Gao, H., Feng, Z., Pilesjö, P., and Duan, Z.: A multi-objective approach for calibrating the FLEXG model to improve glacio-hydrological modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8115, https://doi.org/10.5194/egusphere-egu23-8115, 2023.

A.103
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EGU23-10483
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ECS
André Luiz de Campos, Reinaldo Bomfim da Silveira, José Eduardo Gonçalves, Nathalli Rogiski da Silva, Leandro Ávila Rangel, Camila Freitas, Cassia Silmara Aver Paranhos, and Fernando Mainardi Fan

In rainfall-runoff modeling, the main input variable is precipitation, and the understanding of its temporal and spatial variation is the key for good hydrological simulation results. Conventionally, the precipitated volumes are measured by rain gauges, which are representative of its surroundings and, consequently, it is necessary to apply extrapolation techniques to obtain data in ungauged regions. However, classical techniques are based on mathematical interpolation and do not consider the physical evidence for the occurrence of precipitation. Remote sensing represents a valuable alternative to hydrological modeling due to its wide coverage, and from observations by meteorological satellites and radars, quantitative precipitation estimation is possible. In this sense, the integrated use of data from rain gauges and remote sensing has the potential to improve the accuracy of hydrological simulations. This study aims to evaluate the performance of a hydrological model in the Colider River basin (Brazil), when calibrated with a global product that provides precipitation data based on rain gauges observations, satellite and weather radar. The model used was the MGB-IPH and the data source of precipitation was MSWEP (Multi-Source Weighted-Ensemble Precipitation). Two different calibrations were performed: the first, considering only the precipitation data from rain gauges; the second, considering the precipitation estimated by the product. The comparison between the rain datasets indicates that MSWEP tends to overestimate the precipitation in most cases, except during periods of considerable drought, when it underestimates. Nevertheless, the results in the hydrological simulation were satisfactory, with the model calibrated with MSWEP presenting equivalente or slightly better performance metrics than the one with conventional data. This is an indication that the continuous development of remote sensing products can be the key to increase the reliability of tools that comprise hydrological modeling, such as forecasting hydrological events, climatic hazards and also commercialization of electric energy.

Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian National Electricity Regulatory Agency (ANEEL) under its Research and Development Project PD 6491-0503/2018 – “Previsão Hidroclimática com Abrangência no Sistema Interligado Nacional de Energia Elétrica” developed by the Paraná State electric company (COPEL GeT), the Meteorological System of Paraná (SIMEPAR) and the RHAMA Consulting company.

How to cite: de Campos, A. L., da Silveira, R. B., Gonçalves, J. E., da Silva, N. R., Rangel, L. Á., Freitas, C., Paranhos, C. S. A., and Fan, F. M.: Evaluation of a global precipitation product in the hydrological modeling of a river in the Amazon basin., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10483, https://doi.org/10.5194/egusphere-egu23-10483, 2023.

A.104
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EGU23-11055
Junzhi Liu

The management and conservation of lakes should be conducted in the context of catchments because lakes collect water and materials from their upstream catchments. Thus, the datasets of catchment-level characteristics are essential for limnology studies. Remote sensing is an important data source for the characterization of lake-catchments. Leveraging remote sensing data, we constructed the first dataset of lake-catchment characteristics for 1525 lakes with areas from 0.2 to 4503 km2 on the TP. Considering that large lakes block the transport of materials from upstream to downstream, lake catchments are delineated in two ways: the full catchment, which refers to the full upstream-contributing area of each lake, and the inter-lake catchments, which are obtained by excluding the contributing areas of upstream lakes larger than 0.2 km2 from the full catchment. There are six categories (i.e., lake body, topography, climate, land cover/use, soil and geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the time series of 16 hydrological and meteorological variables are extracted, which can be used to drive or validate lumped hydrological models and machine learning models for hydrological simulation. The dataset contains fundamental information for analyzing the impact of catchment-level characteristics on lake properties, which on the one hand, can deepen our understanding of the drivers of lake environment change, and on the other hand can be used to predict the water and sediment properties in unsampled lakes based on limited samples. This provides exciting opportunities for lake studies in a spatially explicit context and promotes the development of landscape limnology on the TP. The details of this dataset can be found in our paper published in Earth Syst. Sci. Data (https://doi.org/10.5194/essd-14-3791-2022).

How to cite: Liu, J.: Characterization of lake-catchments leveraging remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11055, https://doi.org/10.5194/egusphere-egu23-11055, 2023.

A.105
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EGU23-369
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ECS
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Aman Chandel and Deepak Swami

The conventional methods used for determination of soil moisture and electrical conductivity are tedious and laborious. It leads to the imperative need to use soil moisture and electrical conductivity sensor and logging to collect the real time data set. These devices detect the change in saturation and salinity levels of the soil. This data has huge application in the precision agriculture, optimised irrigation, soil moisture monitoring and fertilizer application etc. However, the optimal number of sensors and the associated error curtailment is of great significance but cumbersome. Therefore, this study proposes a benchmarking approach to identify the optimum number of sensors required for field scale operations based on feedback from sensor performance under varying range of working conditions such as saturation percentage, salinity and temperature. The experiments were conducted in controlled temperature conditions varying from 2 to 45˚C. The sensor arrays from minimum of three to nine were grouped to collect moisture, salinity and temperature data and associated error. Overlaying the full-scale error band and 95% confidence interval produced by the sensitivity analysis used in determining the outliers. Analysing the sensitivity plots for various sensor combinations suggested seven sensors as the optimum number to minimise the error. Further, these sensors were deployed in gridded heterogenous medium tank for continuous datalogging to study the variation in salinity.

How to cite: Chandel, A. and Swami, D.: Determination of optimal number of Soil moisture and electrical conductivity sensors deployment in field, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-369, https://doi.org/10.5194/egusphere-egu23-369, 2023.

A.106
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EGU23-12695
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ECS
Harriette Adhiambo Okal, Peter Molnar, Darcy Molnar, Sukhmani Mantel, Denis Hughes, and Jane Tanner

Successful application of hydrological models requires data to assess the validity, as well as the inherent uncertainty, of the outputs, most importantly streamflow. In parts of Sub-Saharan Africa (SSA), such data is often lacking. Therefore, it is frequently challenging to find the necessary resources for setting up a robust hydrological model and hydrological monitoring platforms. In data-scarce regions within SSA, ground data required to model and make water resources decisions are not always available and therefore, some form of alternative data sources and simplified modelling approaches are required. In recent years, satellite and climate reanalysis data have been intensely explored for watershed modelling in poorly gauged regions with variables such as precipitation, evapotranspiration, soil moisture, runoff etc. Very good potential is provided by the ERA5-Land dataset which is considered one of the best freely available global products for hydrology given its 0.1° x 0.1° spatial resolution and an hourly to monthly temporal resolution spanning from 1950 till present. Here, ERA-5 Land input on a monthly resolution was assessed in the Berg River Basin, South Africa using the Modified PITMAN model. Total precipitation, runoff, and potential evapotranspiration for each of the basin’s 12 quaternary catchments were retrieved using the Google Earth Engine platform for a study period of 40 years (1981-2021). A validation period of 20 years (1985-2005) was used corresponding to the freely available streamflow data. The assimilation of ERA5-Land precipitation data showed satisfactory results across the basin with the best results in the upstream catchment (G10A) with a 0.634 coefficient of efficiency and 0.404 KGE during the initial run. However, runoff for the downstream catchments (G10K) gave positive biases in high-flow months. This paper gives a detailed analysis of the performance of remotely sensed datasets (ERA5-Land) on catchments with varying climatic, land use and cover, water use, and geomorphological characteristics, therefore, offering a valuable reference for its applications in understanding hydrological processes in different river basins across SSA.

How to cite: Okal, H. A., Molnar, P., Molnar, D., Mantel, S., Hughes, D., and Tanner, J.: ERA5-Land Data: New Possibilities in Hydrological Modelling and Water Resources Assessment in the Data-Scarce Regions of Sub-Saharan Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12695, https://doi.org/10.5194/egusphere-egu23-12695, 2023.