HS2.2.9 | Advances in river system monitoring and modelling for a changing climate
Advances in river system monitoring and modelling for a changing climate
Co-organized by GM11
Convener: Nick Everard | Co-conveners: Almudena García-GarcíaECSECS, Alexandre Hauet, Anette EltnerECSECS, Pietro StradiottiECSECS, Alonso PizarroECSECS
| Mon, 15 Apr, 10:45–12:30 (CEST)
Room 2.17
Posters on site
| Attendance Tue, 16 Apr, 10:45–12:30 (CEST) | Display Tue, 16 Apr, 08:30–12:30
Hall A
Orals |
Mon, 10:45
Tue, 10:45
Water is our planet’s most vital resource, and the primary agent in some of the biggest hazards facing society and nature. Recent extreme heat and flood events underline the significance of water both as a threat and as an increasingly volatile resource.
The accurate and timely measurement of streamflow is therefore more critical than ever to enable the management of water for ecology, for people and industry, for flood risk management and for understanding changes to the hydrological regime. Despite this, effective monitoring networks remain scarce, under-resourced, and often under threat on a global scale. Even where they exist, observational networks are increasingly inadequate when faced with extreme conditions, and lack the precision and spatial coverage to fully represent crucial aspects of the hydrological cycle.

This session aims to tackle this problem by inviting presentations that demonstrate new and improved methods and approaches to streamflow monitoring, including:
1) Innovative methods for measuring/modelling/estimating river stream flows;
2) Real-time acquisition of hydrological variables;
3) Remote sensing and earth observation techniques for hydrological & morphological monitoring;
4) Measurement in extreme conditions associated with the changing climate;
5) Measurement of sudden-onset extreme flows associated with catastrophic events;
6) Strategies to quantify and describe hydro-morphological evolution of rivers;
7) New methods to cope with data-scarce environments;
8) Inter-comparison of innovative & classical models and approaches;
9) Evolution and refinement of existing methods;
10) Guidelines and standards for hydro-morphological streamflow monitoring;
11) Quantification of uncertainties;
12) Development of expert networks to advance methods.

Contributions are welcome with an emphasis on innovation, efficiency, operator safety, and meeting the growing challenges associated with the changing climate, and with natural and anthropogenically driven disasters such as dam failures and flash floods.

Additionally, presentations will be welcomed which explore options for greater collaboration in advancing river flow methods and which link innovative research to operational monitoring.

Orals: Mon, 15 Apr | Room 2.17

Chairpersons: Alexandre Hauet, Anette Eltner, Pietro Stradiotti
On-site presentation
Andrew Wickert, Jabari Jones, and G.-H. Crystal Ng

Rating curves translate between river stage (i.e., water level) and water discharge. They are applied ubiquitously for stream monitoring, water-resource estimation, and flood forecasting. However, they are calculated using a basic empirical power-law fit that lacks flexibility to robustly represent channel–floodplain structure or to adapt to changing hydraulic geometry or roughness. Furthermore, such empirical fits require many measurements of stage and discharge. Gathering these measurements is expensive and might not be possible if the channel and/or floodplain evolve before a sufficient range of flows may be measured.

To address this deficit with a similarly simple but physically grounded approach, we present a strategy based on Manning's equation. This "double-Manning" approach implements Manning's equation within and above the channel and a power-law relationship – analogous to a generalized Manning's equation – for flows crossing the floodplain. We demonstrate that the double-Manning equation can effectively fit field data and, in the process, accurately estimate bankfull width, bankfull depth, channel Manning's n, and Manning-style power-law parameters for floodplain-flow characteristics. For sites lacking exhaustive field data, the physical basis of the double-Manning approach enables rating-curve creation using a combination of stage–discharge data and common field measurements of the channel and floodplain. Such rating curves may be adjusted as the channel and floodplain evolve to predict how geomorphic change might affect flow depth and flood inundation.

The double-Manning approach may be run as a forward (predictive) or inverse (fit to data) model. Documented, open-source code may be acquired from GitHub (https://github.com/MNiMORPH/doublemanning) and Zenodo.

How to cite: Wickert, A., Jones, J., and Ng, G.-H. C.: Flexible and physically based stage–discharge rating curves using a "double-Manning" approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4937, https://doi.org/10.5194/egusphere-egu24-4937, 2024.

On-site presentation
Elisa Bozzolan, Simone Bizzi, Andrea Brenna, Nicola Surian, and Patrice Carbonneau

Satellite imageries are starting to become for geomorphologists a new tool to monitor medium-large river dynamics at high revisit time (weekly or daily). The Sentinel 2 mission, in particular, provides without charges a multi-spectral image of the earth surface at 10 meters resolution every 5 days (cloud cover permitting). Machine learning algorithms can then classify these images, automatically discriminating those river macro-geomorphic features, i.e. water, sediment and vegetation, that describe how a river responds to different hydrological impulses and boundary conditions. When using these tools (Sentinel 2 images + machine learning algorithm), it is important to first identify what geomorphic processes we can reliably detect, i.e. what are the applicability boundaries dictated by the spatio-temporal resolution of these images. In a dynamic, braided reach of the Sesia River (Northern Italy), we assessed how this inherent uncertainty associated with S2's spatiotemporal resolution can impact the interpretation of the active channel (a combination of sediment and water) delineation and evolutionary trajectory. The analysis demonstrates that water is ∼20% underestimated whereas sediments are ∼30% overestimated. These under- and over-underestimations are not random but a function of the mixed pixels present in each classified macro geomorphic unit. Nevertheless, the results show that these spatial errors are an order of magnitude smaller than the geomorphic changes detected in the 5 years analysed, so the derived active channel trajectory can be considered robust. Within these newly assessed applicability boundaries, in the Po River basin we started to explore in similarly dynamic river reaches new geomorphic indicators able to describe river responsiveness to seasonality and to different flood regimes.

How to cite: Bozzolan, E., Bizzi, S., Brenna, A., Surian, N., and Carbonneau, P.: Monitoring river morphology with Sentinel 2 data: limitations and opportunities across scales , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6865, https://doi.org/10.5194/egusphere-egu24-6865, 2024.

On-site presentation
Victor Pellet and Victor Pellet

The 4DMED-Hydrolog ESA project aims at developing a high-resolution (1km) and consistent reconstruction of the Mediterranean terrestrial water cycle by using the latest Earth Observation (EO) products. We exploit here the synergy between available EOs to better estimate the terrestrial water cycle components (i.e., precipitation P, evaporation E, water storage dS and river discharge RD). The obtained, more accurate, representation of our environment is intended to feed decision support systems, in a changing climate, for a more resilient society. Among the water components, RD is strategic because it integrates many water-related processes. Unfortunately, in situ RD measurements are very sparse spatially. This paper presents a new approach for the mapping (i.e., spatially continuous estimate) of RD based on indirect EOs and a water budget balance constraint. First, satellite estimates of P, E, and dS are corrected, at the basin scale, using RD from a gauge network. Second, the water budget is balanced at the grid level using a horizontal flow direction information from topography. This approach is therefore based on satellite products and in situ measurements, without the use of any dynamical model. This methodology is used over the Po and Ebro basins. We use the new P, E, and dS data products, at high spatio-temporal resolution (1km and daily), developed in the 4DMED project. The resulting RD mapping is evaluated using a leave-one-out experiment, resulting in a mean KGE of 0.6 over the Ebro, to be compared to 0.5 for a river dynamical model such as Continuum. The spatially continuous RD is, by design, closer to the in situ measurements. Such work combining EO datasets to optimize, at high spatial resolution, to optimize our monitoring of the water cycle opens new doors for hydrology, water management, agriculture, as well as natural hazards predictions and response.


  • Pellet, Aires, Yamazaki, Zhou, Paris, A first satellite-based mapping of river discharge over the Amazon. Journal of Hydrology,  10.1016/j.jhydrol.2022.128481, 2022.
  • Pellet, Aires, Yamazaki, Satellite monitoring of the water cycle over the Amazon using upstream/downstream dependency. Part I: Methodology and initial evaluation. Water Resources Res., 57, e2020WR028647, 2021.
  • Pellet, Aires, Yamazaki, Papa, Satellite monitoring of the water cycle over the Amazon using upstream/downstream dependency. Part II: Mass-conserved reconstruction of total water storage change and river discharge. Water Resources Research, 57, e2020WR028648, 2021.
  • Pellet, Aires, Munier, Papa, Long-term estimate of the water storage change in the large Himalayan river basins from water budget closure, HESS, 5194/hess-24-3033-2020, 2020.
  • Pellet, Aires, Munier, Optimisation of satellite observations to study the water cycle over the Mediterranean region, HESS, 5194/hess-2018-319, 2019.
  • Pellet, and Aires, Analyzing the Mediterranean water cycle via satellite data integration, Pure Appl. Geophys, 10.1007/s00024-018-1912-zpp, 2018.
  • Munier, Aires, A new global method of satellite dataset merging and quality characterization constrained by the terrestrial water cycle budget, RSE, 2017
  • Munier, Aires, Schlaffer, Prigent, Papa, Maisongrande, and Pan, Combining datasets of satellite retrieved products. Part II: Evaluation on the Mississippi Basin and closure correction model, JGR, 10/2014, 10.1002/2014JD021953, 2015
  • Aires, Combining datasets of satellite retrieved products. Part I: Methodology and water budget closure, J. Hydrometeor., 10.1175/JHM-D-13-0148.1, 2014

How to cite: Pellet, V. and Pellet, V.: Satellite-based mapping of river discharge at very high spatio-temporal resolution over the Ebro and Po basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7740, https://doi.org/10.5194/egusphere-egu24-7740, 2024.

On-site presentation
Paolo Filippucci, Luca Ciabatta, Hamidreza Mosaffa, and Luca Brocca

Climate change is increasing the challenges related to extreme weather events, shifting precipitation patterns, causing water scarcity and increasing the occurrence of natural disasters. Accurate and timely precipitation data are critical for understanding and mitigating these events, as well as for informing decision-makers. Specifically, Europe climatic and physiographic features make capturing fine-scale (1 km-daily) variations crucial to improve the precision of climate models and facilitate targeted adaptation strategies in this area.

This can be achieved by using the recent remote sensing technologies, which allow to systematically monitor wide areas without the need of maintaining ground networks. In particular, for satellite precipitation estimation, both the top-down and bottom-up approaches have been exploited in recent years to obtain information related to rainfall. Both the methodologies carry advantages and limitations. Their merging, coupled with high spatial resolution ancillary information, is therefore recommended to reach the final aim of detailed and accurate precipitation data.

In this study, the rainfall data obtained from IMERG Late Run and SM2RAIN ASCAT (H SAF) are downscaled and merged over the whole Europe. The downscaling is obtained by leveraging high spatial resolution statistical information from CHELSA product, while a triple collocation technique is applied to merge the two downscaled datasets. The resulting high resolution rainfall is subsequently compared against multiple products, including coarse resolution ones such as H SAF, IMERG-LR, ERA5, EOBS, PERSIANN, CHIRP, GSMAP, and high-resolution products like EMO, INCA, SAIH, COMEPHORE, MCM, 4DMED. These comparisons, spanning ground, model and satellite data, serve to assess its capabilities in estimating precipitation over Europe.

How to cite: Filippucci, P., Ciabatta, L., Mosaffa, H., and Brocca, L.: Improving the resolution of satellite precipitation products in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16956, https://doi.org/10.5194/egusphere-egu24-16956, 2024.

On-site presentation
Christopher Denney, Gaurav Savant, Abigail Grant, Tate McAlpin, and Keaton Jones

To address the escalating challenges posed by extreme weather events and the critical importance of water management, this presentation focuses on innovative methodologies in streamflow monitoring and prediction. Specifically, we present a comprehensive study undertaken to enhance flood mitigation in the Comite and Amite River Basins of central Louisiana.

In response to the imperative need for effective flood control, a 12-mile diversion channel has been designed to redirect flow from the Comite River into the Mississippi River. Our research, commissioned by the United States Army Corps of Engineers, New Orleans District, aims to quantify the impact of design modifications on crucial flow parameters within the diversion structure. We employ advanced three-dimensional, multiphase computational fluid dynamics (CFD) modeling techniques, utilizing the open-source OpenFOAM library with the interFoam finite volume solver.

The study evaluates the alignment of the diversion channel by analyzing flow diversion, velocity profiles, and streamlines within the channel and the associated hydraulic control structure. Special emphasis is placed on understanding the dynamics of the drop structure flow, interactions with upstream drainage features, and potential sediment accumulation risks. Our model, validated through perturbations in turbulence models, boundary roughness, and grid independence studies, provides valuable insights into the performance of the diversion structure under various flow conditions. 

In conclusion, our findings underscore the importance of informed engineering decisions for fostering climate resilience in riverine regions. By providing insights into the dynamics of the diversion channel and quantifying uncertainties associated with flow parameters, this study offers actionable solutions to enhance streamflow monitoring efficiency in the face of evolving hydrological challenges.

How to cite: Denney, C., Savant, G., Grant, A., McAlpin, T., and Jones, K.: Optimizing Flood Control: A Comprehensive 3D Computationsl Fluid Dynamic Study of the Comite Diversion Channel in the Comite and Amite River Basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14090, https://doi.org/10.5194/egusphere-egu24-14090, 2024.

On-site presentation
Marek Zreda

Before we see a stream we can hear it. The discharge of that stream can be inferred from measurements of its sound. Sound pressure level is proportional to the energy of the flowing water and is related to discharge by a sound-discharge rating curve. Measurements with a hand-held sound level meter take seconds to acquire, allowing for high-resolution, long-term monitoring of stream discharge, campaign surveys, and ad hoc measurements. Sound measurements correlate well with the standard stream gauge data over the full range of discharges studied, from 0.02 m3/s to 33 m3/s. The following characteristics make the method an attractive alternative to the standard stream gauging: the instrumentation is simple and inexpensive; field deployment requires no built infrastructure; the instrument is suitable for rapid or emergency deployment; the measurements are non-invasive and non-contact, made at a distance from the stream, using a stationary or roving instrument; the acoustic response curve is linear; and the interfering sound sources are either negligibly small or easily removed.

If there is enough time, attendees will be able to create their own sound-discharge rating curve using their cell phones and the Decibel-X app to measure sound intensity. The conference room's audio equipment will provide sound clips of an actual stream along with the independently measured discharges.

How to cite: Zreda, M.: Measuring stream discharge using audible sound, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14133, https://doi.org/10.5194/egusphere-egu24-14133, 2024.

On-site presentation
Gabriel Sentlinger and Zachary Anderson

The Salt Dilution Tracer method has been used in some form for >100 years (Allen and Taylor, 1923, Østrem, G. 1964, Moore, D 2004).  Recently, the method has undergone a renaissance as techniques and equipment have been improved, facilitating lower dosing (<100g/cms) and increased accuracy.  This paper studies the impact and best practices for filtering, extrapolation, and interpolation of the breakthrough curve to reduce uncertainty, and more importantly, extend the tail of the slug injection signal if the measurement is ended early.  By extrapolating, the user can leave the field in as little half the time, while introducing only +/- 5% uncertainty.  We examine different fitting models (gamma, SCS Unit Hydrograph, χ2, etc) and fitting methods.  An online fit/fill/filter tool is presented and happiness of user is optimized.

How to cite: Sentlinger, G. and Anderson, Z.: Fitting, Filling, and Filtering of Salt Dilution Breakthrough Curves for Reduced Field Time, Increased Accuracy, and Optimized Happiness, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4685, https://doi.org/10.5194/egusphere-egu24-4685, 2024.

On-site presentation
Uncertainty Analysis in streamflow Estimation Using Chemical Gauging Technique with Multiple Instruments in High Mountain Environments.
Aldo Muñoz Sepúlveda, Daniela Muñoz, Salvador Quezada, Santiago Montserrat, and Yarko Niño
On-site presentation
Eftychia Koursari, John MacPherson, Hazel McDonald, Maggie Creed, Stuart Wallace, Hossein Zare-Behtash, Andrea Cammarano, and Kevin Worrall

Scour is a significant impact caused by climate change on infrastructure, while also being the most common cause of bridge failure worldwide. Approximately 60% of bridge collapses are a result of scour (Briaud and Hunt, 2006; Wardhana & Hadipriono, 2003).

Climate change has resulted in the increase of extreme weather events, such as wildfires and floods among others. Global warming is evident, sea levels are rising, and the frequency and magnitude of flood events is increasing. As the climate is changing, the risk of scour is expected to increase further.

Monitoring is crucial for the identification of scour taking place around a structure, its magnitude, as well as the rate of deterioration to allow owners and operators to establish when predetermined thresholds are at risk of being reached. Scour monitoring is crucial to safeguard infrastructure that could be exposed to scour action.

According to the Design Manual for Roads and Bridges BD 97/12 Standard entitled ‘The assessment of scour and other hydraulic actions at highway structures’, scour monitoring techniques can be divided in the following categories (Highways Agency, 2012):

  • Measuring the maximum scour level that has taken place;
  • Measuring scour development adjacent to a structure during high flow events;
  • Methods correlating with scour development, such as water level monitoring, flow velocity monitoring and weather warnings.

Scour monitoring techniques are mainly reactive. This study compares existing and emerging scour monitoring methods, exploring a combination of scour monitoring sensors at structures at risk of scour.  The introduction of a new, innovative sensing platform for scour monitoring is discussed, linking the new sensor package to the asset health management platform using telematics, enhancing the understanding of scour taking place through accurate visualisation. This method facilitates more proactive monitoring of scour, the collection of data necessary for the design and implementation of scour protection measures, and innovative, more accurate scour prediction.


Briaud JL and Hunt BE (2006) Bridge scour and the structural engineer. Structure Magazine, December: pp. 57–61.

Highways Agency, Transport Scotland, Welsh Government and Department for Regional Development Northern Ireland, UK (2012) Design Manual for Roads and Bridges. Highway Structures: Inspection and Maintenance. Volume 3, Section 4, Part 21. BD 97/12. The Assessment of Scour and Other Hydraulic Actions at Highway Structures. The Stationery Office, London, UK.

Wardhana K and Hadipriono FC (2003) Analysis of recent bridge failures in the United States. J. Perform. Constr. Facil. 17 (3): 144–150. https://doi.org/10.1061/(ASCE)0887-3828(2003)17:3(144)

How to cite: Koursari, E., MacPherson, J., McDonald, H., Creed, M., Wallace, S., Zare-Behtash, H., Cammarano, A., and Worrall, K.: Monitoring of infrastructure at risk of scour and other hydraulic actions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22332, https://doi.org/10.5194/egusphere-egu24-22332, 2024.

On-site presentation
Matthew Perks, Seb Pitman, Rupert Bainbridge, Alejandro Diaz Moreno, and Stuart Dunning

For process geomorphologists, accurate topographic data acquired at appropriate spatio-temporal resolution is often the cornerstone of research. Recent decades have seen advances in our ability to generate highly accurate topographic data, primarily through the application of remote sensing techniques. Structure from Motion Multi View Stereo (SfM-MVS) and LiDAR have revolutionised the spatial resolution of surveys across large spatial extents. Continuing technological developments have led to commercialisation of small form LiDAR sensors that are suited to deployment on both mobile (e.g. uncrewed aerial systems), and in fixed semi-permanent installations. Whilst the former has been adopted (e.g. DJI Zenmuse L1), the potential for the latter to generate data suitable for geomorphic investigations has yet to be assessed. We address this gap here in the context of a three-month deployment where channel change is assessed in an adjusting fluvial system. We find that the small form sensors generate change detection products comparable to those generated using an industry-grade LiDAR system (Riegl VZ-4000). Areas of no geomorphic change are adequately characterised as such (mean 3D change of 0.014m compared with 0.0014m for the Riegl), with differences in median change estimates in eroding sections of between 0.01-0.03m. We illustrate that this data enables accurate characterisation of river channel adjustments through extraction of bank long-profiles, the assessment of bank retreat patterns which help elucidate failure mechanics, and for the extraction of water surface elevations. Deployment of this emerging, new technology will enable better process understanding across a variety of geomorphic systems as data can be captured in 4D with near real-time processing.

How to cite: Perks, M., Pitman, S., Bainbridge, R., Diaz Moreno, A., and Dunning, S.: An evaluation of low-cost terrestrial LiDAR sensors for assessing geomorphic change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19935, https://doi.org/10.5194/egusphere-egu24-19935, 2024.

Posters on site: Tue, 16 Apr, 10:45–12:30 | Hall A

Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 12:30
Chairpersons: Almudena García-García, Alonso Pizarro, Nick Everard
Tristan Perriaud, Thomas Morlot, and Alexandre Hauet

EDF (Électricité De France) is the world's largest electricity generator, with an installed capacity of about 130 GW. In order to safely operate the plants, optimize natural resources and fulfill ecological requirement, EDF has installed, since 1946, a sensor network dedicated to the monitoring of hydro-climatologic parameters.


In the context of non-intrusive methods for measuring flood discharge (LSPIV, SVR[1]), understanding the depth-averaged to surface velocity ratio is crucial. The depth-averaged to surface velocity ratio is here called α. This study analyzes a substantial sample of gaugings data (current meters and ADCP methods), totaling around 6,500 observations collected at various EDF sites. For current meters measurements, three methods are employed to compute α : fitting of a log- and a power-law and using the measured surface velocity. For ADCP measurements, three methods are applied to approach α : fitting of power-power, constant-no slip and 3-point-no slip law by using the Qrame[2] application.


This study aims at creating an alpha coefficient database (classified by riverbed, hydraulic radius, etc.) directly usable for non-intrusive streamflow measurements. 

[1] LSPIV (Large-Scale Particle Image Velocimetry), SVR (Surface Velocity Radar).

[2] QRame (QRevint Adcp Massive Exctraction), INRAE, 2023.

How to cite: Perriaud, T., Morlot, T., and Hauet, A.: Velocity profile and depth-averaged to surface velocity in natural streams: a review over a large sample of rivers using current meters and ADCP measurements., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22428, https://doi.org/10.5194/egusphere-egu24-22428, 2024.

Aurélien Despax, Blaise Calmel, Jérôme Le Coz, Alexandre Hauet, and David Mueller

In the last decades, Acoustic Doppler Current Profilers (ADCP) has become the most widely used tool for measuring discharge of rivers and canals. Discharge is a key information for many risk studies, structures dimensioning and even impact assessments. These values must therefore be correctly estimated. Quality assurance and quality control (QA/QC) procedures have been established to ensure that hydrological services share the best practices. Also, to ensure that ADCP tools are working properly, services has to regularly check that the equipment is properly calibrated.

The lack of references value most of the time makes the task difficult. To make sure that ADCP is properly calibrated, interlaboratory testing are frequently organized. Large-scale intercomparaisons are particularly interesting because of the diversity of models and practices but it also makes them more complicated to organize. The Sault-Brénaz intercomparaison was definitively a big one with more than 120 European participants with 16 RiverPro, 15 M9, 15 StreamPro and 12 RS5 for a total of 160 measurements with 1870 transects among 4 sessions. Due to hydrological conditions, the protocol had to be adapted. Measurements took place on small straight canal of the Rhone river with a discharge of around 2m3/s.

Following QA/QC procedures, participant had to post-process their data with the QRevInt open-source software. QRevInt provides many quality filters and computes uncertainty following OURSIN method. Then, to compute interlaboratory results, the QRame software has been used. This open-source software has been developed to apply QRevInt with default settings to a set of ADCP discharge measurements and to retrieve post-processed discharge and uncertainty results. When the dataset is actually an ADCP interlaboratory experiment, the empirical discharge uncertainty, for a given number of transects taken in the average, can be computed by application of the standard interlaboratory method.

Results show that discharge varied slightly over time, particularly between sessions. To exploit further all the discharge results, different approaches to homogenizing data were tested. This issue of varying discharge over time is a common issue for interlaboratory experiments. A generalizable solution would enable experiments in extended conditions. Also, interlaboratory experiments permit to validate uncertainty computations. The greater the number of intercomparisons and the wider the measurement conditions, the more robust uncertainty models will be.

How to cite: Despax, A., Calmel, B., Le Coz, J., Hauet, A., and Mueller, D.: An ADCP large-scale international intercomparaison: Sault-Brénaz 2023, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9754, https://doi.org/10.5194/egusphere-egu24-9754, 2024.

Khosro Morovati, Keer Zhang, and Fuqiang Tian

The transboundary Mekong River, spanning approximately 4800 km with numerous tributaries and floodplains, serves as a vital resource for power generation, fisheries, and agriculture. Despite its significance, the river's productivity faces disruption due to inadequate cooperation among riparian countries regarding data sharing, the uneven distribution of gauging stations, and data gaps for many parts of the river length. This disparity poses challenges in accurately modeling the river's natural runoff, flow characteristics, and the flooded area, navigating through mountainous and relatively flat terrains.

To address this, we have developed an integrated modeling framework comprising a physically-based hydrological model and a hydrodynamic model. For 2500 km of the Mekong River’s mainstream, a highly accurate hydrodynamic model was developed. The produced velocity, water level, and discharge data were compared with gauging stations with continuous data records, showing high accuracy with NSE exceeding 0.93. Additionally, a point-by-point comparison of the yielded water level and discharge data by the hydrodynamic model was conducted with the low-resolution recorded data for stations lacking continuous time series data. Results indicated a high accuracy with an average NSE greater than 0.91, demonstrating the model's precision in capturing the dynamic behavior of the Mekong River.

The hydrodynamic model's results were then used to fill data gaps in stations with significant data deficiencies, allowing the production of reliable data and sufficient gauging network distribution for the entire basin. These datasets, combined with recorded gauging data, served as the calibration stations for the developed physically-based hydrological model. This calibration aimed to assess the impacts of climate change on natural runoff, encompassing not only the mainstream but also tributaries and lake floodplains of the Mekong River. Findings revealed a discernible declining trend in natural runoff within the Mekong River over the specified four-decade period.

This enhanced modeling capability is particularly crucial for accurately simulating dynamic river flows with insufficient continuous data. Our comprehensive approach contributes to a more precise understanding of the Mekong River's complex hydrological dynamics, supporting informed decision-making for sustainable resource management.

How to cite: Morovati, K., Zhang, K., and Tian, F.: Integrated Approach to Mekong River Flow Modeling: Data Gaps and Climate Trends, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1606, https://doi.org/10.5194/egusphere-egu24-1606, 2024.

Golmar Golmohammadi, Babak Razdar, Kourosh Mohammadi, Giovanna Grossi, and Saman Javadi

River flow forecasting has been the focus of many researchers for many years.  The methods evolved from simple statistical methods to highly sophisticated mathematical models.  In recent years, due to the advancement of computers and artificial algorithms, new methods have become increasingly reliable and easier to use.  One of the promising artificial intelligence methods is the Extreme Gradient Boosting (XGBoost) model.  XGBoost is a scalable, distributed gradient-boosting decision tree machine learning library.  It provides parallel tree boosting and is the leading machine learning library for regression, classification, and ranking problems.  Three different algorithms of XGBoost were used in this research and the results were compared.  These algorithms were Random Search, Grid Search, and CatBoost. The proposed models were conducted in a station located Pò River basin which is the longest river in Italy, and it flows from the Cottian Alps and ends at a delta projecting into the Adriatic Sea new Venice.  The data were divided into training and validation sets.  The statistical indicators included mean square error, Nash-Sutcliffe efficiency, and mean absolute error were calculated for each set to compare the efficiency of each algorithm.  These indicators showed that XGBoost using random search algorithm had better performance, although the other algorithms were also acceptable predictions.  In general, the XGBoost model could be used as a reliable tool to forecast the river flow at locations with enough historical data.

How to cite: Golmohammadi, G., Razdar, B., Mohammadi, K., Grossi, G., and Javadi, S.: A comprehensive method based on machine learning schemes in predicting river flow, case study: Po River, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4402, https://doi.org/10.5194/egusphere-egu24-4402, 2024.

Daniel Scherer, Christian Schwatke, Denise Dettmering, and Florian Seitz

We present the latest version of the global reach-scale “ICESat-2 River Surface Slope” (IRIS) dataset, which comprises average and extreme water surface slopes (WSS) derived from observations of the ICESat-2 satellite between October 2018 and August 2023 as a supplement to 130,283 reaches from the “SWOT Mission River Database” (SWORD). To gain full advantage of ICESat-2’s accurate and unique measurement geometry with six parallel lidar beams, the WSS is determined across pairs of beams or along individual beams, depending on the intersection angle of spacecraft orbit and river centerline. Combining both approaches maximizes spatial and temporal coverage. IRIS can be used to research river dynamics, estimate river discharge, and correct water level time series from satellite altimetry for shifting ground tracks. Additionally, we compare IRIS with observations from the recently launched SWOT mission. 

How to cite: Scherer, D., Schwatke, C., Dettmering, D., and Seitz, F.: IRIS: Global Reach-Scale River Surface Slopes from the ICESat-2 Satellite , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5805, https://doi.org/10.5194/egusphere-egu24-5805, 2024.

Xinqi Hu, Ye Tuo, Karl Broich, Fabian Merk, and Markus Disse

Rating curve relationship is vital to hydrological studies, such as flood control and other water-related decision-making processes. Traditionally, rating curve are estimated by using single- or multiple-gauging observations, which is time-consuming, costly, and lacks spatial resolution. Hydraulic models are usually a reliable method to quickly derive the stage-discharge relation for discharge estimation, especially for assessing more reliable high-flow rating relations in extrapolation beyond gauge observation. To establish such models, hydraulic parameters such as water surface elevation, bathymetry, and bed roughness are needed, but they are mostly not available in remote and inaccessible regions. Drone-borne hydrometric monitoring technologies can be deployed to address this problem.

As one of the primary objectives of the Horizon Europe UAWOS project, which is dedicated to developing an Unmanned Airborne Water Observing System for providing key hydrometric variables at high spatial resolution/coverage, and data-based products/services to enhance management and decision-making, this work centers on integrating hydraulic modeling with the unmanned airborne water observing system to establish the rating curve relationship. Water surface elevation data is derived by radar altimetry, bathymetry data by water penetrating radar and sonar, and Doppler radar for surface velocity. By utilizing the surface velocity and water surface elevation data, in conjunction with shallow-water equations, a bathymetry estimation algorithm is used to interpolate the bathymetry from the observed cross-section to the whole simulated river channel. We also come up with a method to directly retrieve the river roughness parameter from the UAV drone observation data.

As a whole, these methods collectively establish a framework that is easily to use to estimate the rating curve in remote regions. The study shows how information from high spatial resolution and coverage hydrometric variables derived by drone-borne hydrometric monitoring technologies can improve rating curve estimates from models.

How to cite: Hu, X., Tuo, Y., Broich, K., Merk, F., and Disse, M.: A Contactless Rapid Rating Curve Assessment Based On Drone-borne Measurement, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11189, https://doi.org/10.5194/egusphere-egu24-11189, 2024.

Ida Westerberg and Reinert Huseby Karlsen

Many hydrologists face the situation that they have no, or very limited, information about the uncertainty in the discharge data they are using. Data uncertainty is rarely communicated by monitoring agencies and data providers – and is often not available on request. This means that data users typically treat data as if they are error-free, whereas in reality there can be large uncertainties and errors.

However, the absence of metadata and ‘hard’ information about data uncertainty does not mean that there is no information about the data uncertainty. Instead, we can use other types of ‘soft’ information to understand the likelihood that discharge data in a particular location are uncertain. For example, if high flows are of short duration (i.e., a few hours) and the rainfall-runoff lag time is short, it is practically quite difficult to manage to gauge high flows, leading to likely extrapolation of stage–discharge rating curves and large high flow uncertainty. A second example is if a river is ice-covered during the winter season, then most of the winter water-level time series is subjectively estimated, leading to substantial uncertainty in winter low flows. Such soft information about data uncertainty is well known by field hydrologists and data uncertainty experts but is not as commonly known in the wider hydrological community. In this presentation we focus on uncertainty in discharge data calculated from stage–discharge rating curves and aim to share – and to encourage sharing – of soft information about data uncertainty sources, to promote more informed decisions on data uncertainty in hydrological studies.

We summarize the soft information about discharge data uncertainty as a perceptual model of uncertainty. Our perceptual model divides the soft information into three categories: station characteristics, climate and flow regime, and catchment characteristics. For each category we present and describe different types of soft information, the uncertainty sources and impacts they can inform us about, and sources for each soft information type (e.g., photos, satellite images, land use). We find that soft information can inform us about three main types of uncertainty sources: uncertainty related to the hydraulic control, uncertainty related to incomplete gauging of the full flow range, and uncertainty due to measurement error.

Our generalised perceptual model can be seen as a smorgasbord of information about uncertainty sources, where the soft information can be considered as relevant to a particular dataset and can inform us if high or low data uncertainty is likely. We believe that a key benefit of the type of generalized perceptual model of uncertainty we present is to facilitate dialogue on, and understanding of, possible sources of observational uncertainties and their impacts.  We encourage others to complement our perceptual model of discharge data uncertainty based on experience from different regions and for other discharge monitoring techniques such as index-velocity stations or drone/camera-based methods.

How to cite: Westerberg, I. and Huseby Karlsen, R.: Sharing perceptual models of uncertainty – on the use of soft information about discharge data uncertainty, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16356, https://doi.org/10.5194/egusphere-egu24-16356, 2024.

Matěj Orság, Milan Fischer, Almudena García-García, Jian Peng, Luis Samaniego, and Miroslav Trnka

Evapotranspiration (ET) is one of the main environmental variables for the study of land-atmosphere interactions due to its interconnection with the energy and water balance at the land surface. Despite the dedicated effort of the remote sensing community to estimate the magnitude of ET at global scales, the uncertainties and differences between products are still very large, especially when comparing ET products with different spatial resolutions. Here, we designed a round-robin experiment to determine the product or products most suitable for future integration in hydrological modeling. The evaluation is performed using eddy covariance measurements as reference and point-scale downscaling (PSD) benchmarking criteria to identify the added value of the high-resolution products. The eddy covariance measurements of latent and sensible heat fluxes are known to not close the surface energy budget. Therefore, the use of eddy covariance measurements as a reference could have important consequences for the later use of ET products in assimilation approaches. Therefore, two main strategies to deal with the energy balance closure problem are considered here. Firstly, we considered three energy balance closure scenarios – (i) assigning the energy balance residuum to sensible heat flux; (ii) distributing the residuum to both turbulent energy fluxes by preserving their ratio, i.e. Bowen ratio; (iii) assigning the entire residuum to latent heat flux. While the first case has no impact on ET, the two remaining ones lead to an increase in ET. Secondly, the use of the triple collocation method, which does not require a reference dataset, will be explored to complement these results. Despite these efforts to identify the best ET product for the integration of satellite ET products in hydrological models, we cannot conclude that the products reaching the best metrics in this evaluation will be the products adding more value to the assimilation approach. Therefore, further experiments should be designed to test if the products selected in the round-robin exercise are indeed improving the performance of hydrological models or on the contrary other ET products are more suitable for assimilation approaches. We acknowledge support from AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation (CZ.02.01.01/00/22_008/0004635).

How to cite: Orság, M., Fischer, M., García-García, A., Peng, J., Samaniego, L., and Trnka, M.: Evaluation of remote sensing actual evapotranspiration products for hydrological modeling applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19308, https://doi.org/10.5194/egusphere-egu24-19308, 2024.

Jens Grundmann, Xabier Blanch, André Kutscher, Ralf Hedel, and Anette Eltner

Coping with natural disasters such as floods places special demands on the emergency units. From the point of view of command-and-control operators, observations of watercourses are desirable in the event of flooding in order to obtain an accurate picture of the situation. Optical measurement methods using cameras offer thereby advantages as they do not require water contact and hence can be used safely. Therefore, the project "KIWA: Artificial Intelligence (AI) for Flood Warning" (http://kiwa.hydro.tu-dresden.de/) is developing AI-based tools for the robust quantification of water levels, flow velocities and flow rates from surveillance cameras.

In this article, we present the workflow for an exclusive optical measurement of time series of water level and discharge from single images and short video sequences. The basis is a high-precision (i.e., at centimetre level), georeferenced 3D terrain model of the measurement site including the riverbed. The terrain model is created using the structure-from-motion (SfM) technique and georeferenced via ground control points (GCPs) measured with a multiband GNSS receiver. To determine the water level, the water area in the single images is automatically segmented using AI based on convolutional neural networks (CNNs) and then intersected with the terrain model. Changes of the camera geometry influence the measurement accuracy during long-term observations. Therefore, the GCPs are automatically detected in the individual images with an adapted AI-based keypoint detector to frequently update the estimated camera orientation. To estimate the discharge, the water surface flow velocity is determined using short video sequences and applying the particle tracking (PTV) method, whereby the segmented water area narrows down the search area for the particle detection. Afterwards, the "OptiQ" modelling approach is used to derive the discharge times series based on the PTV measurements. Thereby, data filtering and error correction methods are used to achieve continuous time series. 

The methods were developed at three different measuring gauges, whose cameras record single images and videos every 15 minutes over several months. The accuracy of the water level measurement is in the centimetre range, even at night with the support of infrared emitters. Depending on the water level, there are deviations in the flow rate, which average less than 10%.

How to cite: Grundmann, J., Blanch, X., Kutscher, A., Hedel, R., and Eltner, A.: Towards a comprehensive optical workflow for monitoring and estimation of water levels and discharge in watercourses, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12507, https://doi.org/10.5194/egusphere-egu24-12507, 2024.

Federico Di Paolo, Matteo Dall'Amico, Pietro Stradiotti, and Luis Samaniego

In Europe, the majority of the precipitation during winter falls as snow over 1.000 m altitude, and remains stored in the snowpack until the melting season, when it returns in the hydrological cycle and is partly used for irrigation and power generation. Snow cover estimation is then one of the main indicators necessary to evaluate water budget and plan water management, predict possible drought conditions, and drive operational flood prediction. 

The use of Earth Observation (EO) for Snow Cover Area (SCA) estimation has been improved during the last decade thanks to high resolution satellites such as the ESA Sentinels, having a pixel resolution of 10 m. Furthermore, diverse processing techniques, nowadays mature, are used by the different data providers to retrieve SCA and Fractional Snow Cover (FSC) maps from EO data.

The scope of our work is an intercomparison of different medium- to high-resolution EO-retrieved SCA/FSC maps over Europe; we use as a benchmark a vast dataset of in situ data coming from different sources and harmonized by Matiu et al. (2021). 

Regarding the dataset, SCA or FSC maps retrieved from multispectral Sentinel-2 and Landsat-8 images are considered, together with gap-filled maps evaluated integrating Sentinel-1 (Synthetic Aperture Radar) and/or Sentinel-3 (multispectral). A unique dataset of Sentinel-1-retrieved snow depth maps is also used in the exercise. Finally, for a continuity with a previous project on EO snow products, medium-resolution MODIS-retrieved SCA images have been added to our dataset.

The results can be used to correctly interpret the accuracy of the EO datasets as well as the processing methodologies. From the comparison it can be evaluated the possibility of merging the different dataset in order to enhance the temporal resolution to a sub-weekly effective revisit time.

How to cite: Di Paolo, F., Dall'Amico, M., Stradiotti, P., and Samaniego, L.: A Round Robin Exercise for an intercomparison of Snow Cover Area maps retrieved from Earth Observation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17533, https://doi.org/10.5194/egusphere-egu24-17533, 2024.

Pietro Stradiotti, Wouter Dorigo, and Luis Samaniego

Soil moisture (SM) is a fundamental hydrological variable for understanding processes in the land-atmosphere, biological, or geophysical domains and an output of many hydrological or land surface models. It is peculiar in that its variability reflects distinct hydrological processes moving from the field scale, where local topography plays a role, to the regional scale, where meteorological forcing is the main control. Correctly representing this variety of processes is a complex modeling task often alleviated by integrating information from well-established Earth Observation (EO) systems, which produce SM data with near global coverage at coarse (10-25 km) resolution. Still, the increasing need for fine scale (1 km, 1 day) simulations of the water cycle is to be met by EO data of similarly high resolution. 

High resolution satellite-based SM data is now available from several sources. 1km datasets are multiplying following simultaneous efforts to retrieve SM from backscatter measurements of the Sentinel-1 mission with various inversion models. At the same time, physical or statistical relationships are leveraged to down-scale coarse resolution products by ingesting data from distinct observational sources, coming from the mentioned Sentinel-1 or the optical domain. However, while products of the first type are confronted with the limited sensitivity of C-band microwave to SM and reduced spatial and temporal availability, down-scaled products might retain much of the original signal and fail the fine-scale process representation. The question of which of these resources can preferably be integrated to reliably improve high-resolution modelling is therefore an open one. 

In this study we perform a round robin (i.e., inter-comparative) assessment of the most prominent high-resolution SM products in the EO landscape. While adapting validation and error characterization techniques and tools (e.g., the Quality Assurance for SM service) that are routinely used at the coarse scale, we address the partial lack of 1km scale reference measurements through the application of an emerging high resolution validation framework. Such a framework demonstrates that metrics for high resolution benchmarking can be reliably retrieved with only sparse, point-scale measurements. The first results suggest that the true spatial SM heterogeneity might explain a minimum noise tradeoff between coarse- and high-resolution EO products. This work is a fundamental step to assess the current state-of-art in EO and its maturity for integration in high-resolution water cycle modelling.

How to cite: Stradiotti, P., Dorigo, W., and Samaniego, L.: Exploring the relative scale of uncertainty in high-resolution soil moisture remote sensing products towards model integration , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15519, https://doi.org/10.5194/egusphere-egu24-15519, 2024.

Hyper-resolution Land Surface Modeling and Earth Observation: Bridging Data Gaps for Improved Plant-Soil-Water Representation in Earth System Models
Noemi Vergopolan, Sergey Malyshev, Maxwell Pike, Nathaniel Chaney, and Elena Shevlikova
Stefano Ferraris, Alessio Gentile, Davide Gisolo, Davide Canone, Stefano Bechis, Brendan Heery, Biddoccu Marcella, Giorgio Capello, Gerrit Maaschwitz, Alexander Myagkov, Enrico Gazzola, and Luca Stevanato

Water and energy balances have been monitored at a scale which is comparable with remote sensing one in three North-West Italy sites. One step has been to evaluate the performance of a land surface model, in this work the Community Land Model. The measurements taken at the horizontal hundreds meters scale are also compared with vertical profiles of local sensors of soil moisture.

At the grassland mountain site (2600 m asl) the eddy covariance data are taken from 6 years, while the 25 m high mast eddy covariance in the forest from 3 years. The scintillometer and cosmic ray in the vineyard have been installed from one year.

The main result is to have different land cover monitored at about 1 km scale, and to see that the uncalibrated simulations with CLM are following quite well the data in most cases. Also the comparison of cosmic ray and point soil moisture time series will be discussed. The future work will be the comparison with satellite data.

This work is a part of the project NODES which has received unding from the MUR-M4C2 1.5 of PNRR grant agreement no. ECS00000036

How to cite: Ferraris, S., Gentile, A., Gisolo, D., Canone, D., Bechis, S., Heery, B., Marcella, B., Capello, G., Maaschwitz, G., Myagkov, A., Gazzola, E., and Stevanato, L.: Eddy covariance, scintillometer, and cosmic ray 1 km scale measurements at three sites (grassland, forest, and vineyard) in North-West Italy compared with CLM simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19632, https://doi.org/10.5194/egusphere-egu24-19632, 2024.

Simone Noto, Nicola Durighetto, Flavia Tauro, Salvatore Grimaldi, and Gianluca Botter

Non-perennial streams are those streams that periodically cease to flow in at least one point along their network. The research community well recognizes the importance of such watercourses for they have a global prevalence and provide diverse hydrological functions and ecosystem services. The spatiotemporal pattern of the active drainage network is anything but simple, sometimes showing a very complex pattern. Non-perennial streams, in fact, are often located in heterogeneous environments, in which the combination of climate, morphology, land cover, soil, substrate, and anthropic factors could play a role in the observed drying and wetting patterns. This work combined two techniques, with different spatiotemporal resolutions, to characterize the spatiotemporal extent of the stream network in a 3.7 km2 Mediterranean catchment of central Italy. The hydrological status of a set of nodes of the network was derived for the period 2020-2022 from sporadic visual surveys and, most importantly, through the analysis of sub-hourly images collected by 21 cameras distributed along a set of strategic nodes of the network. The latter technique is particularly promising to reconstruct the hydrological dynamics taking place in the target cross-section, as the temporal evolution of the underlying hydrological conditions (wet vs. dry), the water stage, and the corresponding discharge can be inferred from the automatic or manual analysis of the acquired images. The available experimental data  was combined exploiting the hierarchical principle, that postulates the existence of a Bayesian chain based on the local persistency of the nodes that dictates their drying/wetting order during stream retraction/contraction cycles. The results highlighted the complexity of the network dynamics in the study area: while the number of wet nodes decreased during the dry season and increased during the wet season, the local persistency of the nodes showed a highly heterogeneous and non-monotonic pattern, resulting in a dynamically disconnected network. The approach allowed the reconstruction of the entire river network and represented a useful tool to estimate the extent of its wet portion, even in case part of the network could not be inspected. This work represents a novel approach to reconstruct the extension of the wet portion of the stream network in difficult-to-access environments, where traditional techniques might be inadequate.

How to cite: Noto, S., Durighetto, N., Tauro, F., Grimaldi, S., and Botter, G.: Characterizing the space-time evolution of wet channels in a non-perennial Mediterranean catchment exploiting a network of camera traps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16913, https://doi.org/10.5194/egusphere-egu24-16913, 2024.

Jiaqing Wang, Jianshi Zhao, and Quanjun Wang

When predicting future long-term runoff using hydrological models, the large uncertainty associated with general circulation models (GCMs) pose significant limitations. Additionally, current accurate long-term runoff predictions are restricted to specific locations with gauge stations, hindering basin-wide water resource planning and management. To address these challenges, this study proposes a hybrid Hydrological model, Empirical Orthogonal Function analysis, Gaussian Process Regression (HEG) model, which demonstrates higher accuracy in daily runoff prediction across the entire basin compared to the traditional multi-model ensemble mean method, with KGE improved by 0.09~0.11, and NSE improved by 0.08~0.32). Moreover, to enhance the estimation of future extreme flood risks which are of great concern of the public but are often predicted with high uncertainty, the model incorporates uncertainty interval information into prediction and is called HEGU model. Evaluations conducted in the topographically and climatically diverse Brahmaputra River Basin confirm the effectiveness of the HEGU model. The relative error of peak discharge (REPD) is reduced to an average of ~46% of that obtained through the ensemble mean method, while the correlation coefficient (CC) for flood volume estimation during the monsoon period increases from -0.054 to 0.645. Furthermore, the HEGU model demonstrates the potential to improve overall runoff prediction accuracy across the basin when the data quality of extremely few grids in the high-fidelity dataset is enhanced. The enhancement can be achieved through the incorporation of additional runoff gauge stations, remote sensing data, and other data augmentation techniques. These findings underscore the practical significance of the HEGU model, indicating its high effectiveness and applicability in real-world future hydrological projection and water resource management scenarios.

How to cite: Wang, J., Zhao, J., and Wang, Q.: A Long-term Spatial Runoff and Flood Prediction Method in Higher Accuracy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1225, https://doi.org/10.5194/egusphere-egu24-1225, 2024.

River channel confluence routing based on steep slope
(withdrawn after no-show)
Zhongye Xia
Khaoula Ait naceur, El mahdi El khalki, Abdessamad Hadri, Oumar Jaffar, Luca Brocca, Mohamed El mehdi Saidi, Yves Tramblay, and Abdelghani Chehbouni

Hydrological modeling is critical for effective water resources management, especially in developing countries such as Morocco where data are scarce. This study aims to improve daily river discharge predictions in 26 Moroccan catchments from 1993 to 2019. It evaluates the GR4J and MISDc models, focusing on optimizing their performances using four optimization techniques: Particle Swarm Optimization (PSO), the Nelder-Mead simplex algorithm (FMIN), Simulated Annealing (SA), and the Genetic Algorithm (GA). The two hydrological models are coupled with six calibration methods to provide the different ranges of uncertainties and to assess their consistency across diverse datasets. The methods include the split-sample or half-half method, the odd/even year method, as well as the calibration on a longer period than validation and vice versa. In addition, the Kling-Gupta Efficiency (KGE) and the relative bias were used as performance criterions. Due to the high elevation of some catchments studied and to the important amount of the snowmelt contribution in the river discharge at their outlets, a snow module incorporation was necessary to assess whether snowmelt impacts runoff or not. The outcomes demonstrate that all algorithms were able to successfully calibrate the GR4J and MISDc models (-0.26<median KGE< 0.34). However, FMIN and PSO demonstrated greater consistency in their performance across all calibration methods and proved to be the most computationally efficient algorithms, making them the best choices in situations requiring both time effectiveness and performance. Despite its slower speed, GA's robustness makes it a viable option under less time-sensitive conditions. The relative bias metric indicates that for the GR4J model, the FMIN, PSO, and GA had comparable and balanced performance, while SA showed greater variability. For the MISDc model, FMIN showed a tendency to slightly underestimate the discharge, while GA and PSO showed higher biases in some cases. In addition, MISDc significantly outperformed GR4J in simulating runoff across all catchments, making it a suitable choice for our region. The integration of a snow module in both models enhanced their performance in some larger pluvio-nival catchments, illustrating the complexity of snow dynamics in hydrological modeling and the need for high resolution data as well as ground measurements.

Keywords: River discharge prediction, GR4J, MISDc, Moroccan catchments, Optimization methods, Data scarcity.

How to cite: Ait naceur, K., El khalki, E. M., Hadri, A., Jaffar, O., Brocca, L., Saidi, M. E. M., Tramblay, Y., and Chehbouni, A.:  Daily Streamflow Simulations Improvement in Data Scarce Watersheds using different Optimization Techniques and Calibration Methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4407, https://doi.org/10.5194/egusphere-egu24-4407, 2024.