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Accurate and reliable streamflow monitoring data are urgently needed for many new locations to tackle the on-going climate emergency, where we now see increasingly severe impacts on society from extreme flows. Yet, traditional river monitoring methods depend on empirical rating-curve methods for which it typically takes many years or decades to obtain reliable data, in particular for extreme flows. This gap between increasing needs and current monitoring capabilities calls for new methods to be developed.Drones provide an unprecedented ability to measure both the physical and hydraulic characteristics of a river in an efficient manner. Topography, water surface slope, surface water velocity and even bathymetry can be derived from drone images and drone lidar data. We exploited this potential by incorporating drone data into the framework for Rating curve Uncertainty estimation using Hydraulic Modelling (RUHM). The RUHM framework combines a one-dimensional hydraulic model with Bayesian inference and together with drone data it allows us to efficiently estimate a reliable rating curve and its associated uncertainty based on as few as three gaugings.We present our results from applying RUHM to Swedish gauging stations where we model rating curves and streamflow based on drone data. We primarily used low-cost camera drones to collect both the input (DEM, vegetation, bathymetry) and calibration data (water surface slope, surface velocity) for the hydraulic model, but also tested the capabilities of drone lidar data. Our aim was to estimate reliable rating curves with RUHM based only on data from the drone flights. We assessed the uncertainty in the drone-derived model input and calibration data compared to traditional fieldwork techniques, as well as their impact on the RUHM-modelled rating curves and streamflow results.We find that careful planning of when to fly the drone is important for obtaining good-quality model input and calibration data. Using a combination of drone camera and drone lidar data we were able to obtain all the data needed for RUHM from the drone flights. Extreme low and high flows were reliably modelled with RUHM with constrained uncertainty based on as few as three low and middle flow gaugings, without the need for gauging extreme flows. We conclude that using RUHM with drone data is an efficient and promising alternative to traditional streamflow monitoring methods, being much less time-consuming and costly, as well as involving fewer risks to field staff.

This work presents trend analyses performed on time series. It follows the work done by Giuntoli et al. (2013) on trend analysis of low flows and their relationship with large-scale climate variability in France. A R package was implemented to allow the extraction of variables on time series and performing statistical trend analysis on the extracted variables. Detected trend can be summarised on maps. The methodology uses the Mann-Kendall statistical test to assess the significance of linear trend. Regional consistency can also be checked.The methodology used was performed on 207 French daily streamflow over the period 1968-2020. Three period subsets are studied: 1968-2000, 1968-2010 and 1968-2020 to compare trend results and assess the variability over time.Results confirm a North-South geographical split in temporal trends for droughts. They also show the increase of the severity of droughts over the last decades in Southern France.Results also suggests a similar North-South geographical split for high and medium flows. They show that temporal trends are decreasing over the last decades in the Southern part of France for both high and medium flows. However, in the North part of France, results shows less significant trends in streamflow time series.Giuntoli, I., Renard, B., Vidal J.-P., and Bard, A. (2013). Low flows in France and their relationship to large-scale climate indices, Journal of Hydrology, 482, 105-118, http://dx.doi.org/10.1016/j.jhydrol.2012.12.038.

Climate change, together with other natural and anthropogenic drivers lead to changes in streamflow patterns that are now occurring with increasing frequency. At the same time traditional streamflow monitoring methods are time-consuming and costly so that it typically takes many years of significant field efforts to establish reliable streamflow data for a new location or for stations with major temporal changes to the stage—discharge relation. To provide timely and reliable streamflow data to tackle these changes to the hydrological regime and their impacts on society’s water management requires new cost-effective monitoring methods that can rapidly produce data with low uncertainty. Hydraulically modelled rating curves are a promising alternative to traditional power-law methods as they need much fewer calibration gaugings, but they are associated with additional uncertainty sources in the hydraulic knowledge and these need to be assessed.We present the Rating curve Uncertainty estimation using Hydraulic Modelling (RUHM) framework which was developed to rapidly estimate rating curves and their uncertainty. The RUHM framework combines a one-dimensional hydraulic model with Bayesian inference to incorporate information from both hydraulic knowledge and the calibration gauging data. In this study we compare RUHM and the Bayesian power-law method BaRatin in application to a Swedish site using nine different gauging strategies associated with different costs. We compare results for the two methods in terms of accuracy, cost and time required for establishing rating curves. We found that rating curves with low uncertainty could be modelled with fewer gaugings for RUHM compared to BaRatin. As few as three gaugings were needed with RUHM if these gaugings covered low and medium flows, whereas high flow gaugings were not necessary. This makes the RUHM method both cost effective and time efficient as low and medium flows occur more frequently than high flows. When using all gaugings (i.e., a high-cost gauging strategy), the uncertainty for RUHM and BaRatin was similar. The results for this Swedish site show that hydraulic rating curve uncertainty estimation is a promising tool for quickly estimating rating curves and their uncertainties. Finally, we discuss the potential of using RUHM together with drone-derived data to make field efforts even more efficient.

International audience; Establishing streamflow time series in unstable rivers is challenged by changes in the stage-discharge relation after floods. Then, the field hydrologist must develop a new stage-discharge rating curve using new calibration data but also using some information from previous calibration data and previous rating curves. The process includes a large amount of informal expert knowledge and hydraulic assumptions seldom made explicit. This paper develops a stage-period-discharge (SPD) model based on the physical interpretation of changes in the stage-discharge relation across a series of stability periods defined by known dates and times. Using simple hydraulic equations, the user provides prior knowledge on the controls, their static and varying parameters, and their possible changes. As a single model is used for all the periods, the estimation of all rating curves can be performed in one go: All gaugings hence provide information to estimate the static parameters and the varying parameters for the relevant periods. Bayesian inference is used, providing a natural way to include prior knowledge and to quantify uncertainty. The generality and some key properties of the method are demonstrated through application to two hydrometric stations, differing in hydraulic configuration and in number and type of changes. Specific experiments demonstrate the ability of the SPD model to transfer information across periods. Consequently, rating curves are more precisely estimated than by separately estimating them for each period. The SPD model provides a hydraulically based, transparent, and user-friendly approach to replace manual shift corrections traditionally applied in operational practice, with a quantification of uncertainties.

Streamflow estimation from rain events is a delicate exercise. Watersheds are complex natural systems and their response to rainfall events is influenced by many factors. Hydrological rainfall-runoff modelling is traditionally used to understand those factors by predicting discharges from precipitation data. These models are simplified conceptualisations and thus still struggle when facing some particular processes linked to the catchment. Among those processes, the tide influence on river discharges is rarely accounted for in hydrological modelling when estimating streamflow series at river mouth areas. Instead, estimated streamflow series are sometimes corrected by coefficients to account for the tide effect.In this presentation, we explored a semi-distributed hydrological model by adapting it to account for tidal-influence in the river mouth area. This model uses observed spatio-temporal rainfall and potential evapotranspiration databases to predict streamflow at gauged and ungauged locations within the catchment. The hydrological model is calibrated using streamflow observations and priors on parameter values to calibrate each model parameters of each sub-catchments. A drift procedure in the calibration process is used to ensure continuity in parameter values between upstream and downstream successive sub-catchments.This novel approach was applied to a tidal-affected catchment: the Adour’s catchment in southern France. Estimated results were compared to simulations without accounting for the tidal influence. Results from the new hydrological model were improved at tidal-affected locations of the catchment. They also show similar estimations in tidal-unaffected part of the catchment.

Establishing reliable rating curves and thereby reliable streamflow monitoring records is fundamental to much of hydrological science and water management practice. Cost-effective methods that enable rapid rating curve estimation with low uncertainty are needed given diminishing monitoring resources and increasing human-driven changes to the water cycle. Traditional power-law rating curves rely on numerous gaugings to estimate rating curves and their associated uncertainty. Hydraulically-modelled rating curves are a promising alternative to power-law methods as they rely on fewer gaugings, but they are associated with additional uncertainty sources in the hydraulic knowledge (bed slope, roughness, topography and vegetation), which need to be assessed.Our aim with this study was to compare power-law and hydraulic-model based methods for estimating rating curves and their uncertainty. We focused on assessing their accuracy as well as the costs and time required for establishing rating curves. We compared the Rating curve Uncertainty estimation using Hydraulic Modelling (RUHM) framework with the Bayesian power-law method BaRatin. The RUHM framework combines a one-dimensional hydraulic model with Bayesian inference to incorporate information from both hydraulic knowledge and the calibration gauging data. We applied both methods to the 584 km2 River Röån station in Sweden under nine different gauging strategies associated with different costs. The gauging strategies differed in the number and flow magnitude of the gaugings used as well as the probability of observing the gauged flows.We found that rating curves with low uncertainty could be modelled with fewer gaugings using the RUHM framework compared to BaRatin. As few as three gaugings were needed for RUHM if these gaugings covered low and medium flows, making the estimation both cost effective and time efficient. When using all the gaugings available (i.e. a high-cost gauging strategy), the uncertainty for RUHM and BaRatin was similar at the Röån station. Furthermore, we found that BaRatin needed gaugings with lower probability of occurrence (i.e. covering a larger part of the flow range) than needed when using hydraulic modelling (low and middle flow gaugings with high probability of occurrence gave good results). The results for this Swedish site show that hydraulic rating curve uncertainty estimation is a promising tool for quickly estimating rating curves and their uncertainties. In particular, it is useful for previously ungauged or remote sites, or at stations where there have been major temporal changes to the stage–discharge relation.

Improving Discharge Data for Water Resources Management—Hydraulic Modelling as a Tool for Rapid Rating Curve Estimation; Stockholm, Sweden, 8 November 2018

Stage-fall-discharge (SFD) rating curves are traditionally used to compute streamflow records at sites where the energy slope of the flow is variable due to variable backwater effects. We introduce a model with hydraulically interpretable parameters for estimating SFD rating curves and their uncertainties. Conventional power functions for channel and section controls are used. The transition to a backwater-affected channel control is computed based on a continuity condition, solved either analytically or numerically. The practical use of the method is demonstrated with two real twin-gauge stations, the Rh\^one River at Valence, France, and the Guthusbekken stream at station 0003$\cdot$0033, Norway. Those stations are typical of a channel control and a section control, respectively, when backwater-unaffected conditions apply. The performance of the method is investigated through sensitivity analysis to prior information on controls and to observations (i.e. available gaugings) for the station of Valence. These analyses suggest that precisely identifying SFD rating curves requires adapted gauging strategy and/or informative priors. The Madeira River, one of the largest tributaries of the Amazon, provides a challenging case typical of large, flat, tropical river networks where bed roughness can also be variable in addition to slope. In this case, the difference in staff gauge reference levels must be estimated as another uncertain parameter of the SFD model. The proposed Bayesian method is a valuable alternative solution to the graphical and empirical techniques still proposed in hydrometry guidance and standards. This article is protected by copyright. All rights reserved.

River Flow 2018: 9th International Conference on Fluvial Hydraulics, Lyon, FRA, 05-/09/2018 - 08/09/2018; International audience; The stage-discharge measurements and rating curves accumulated over decades at hydrometric stations are a valuable source of information on the long-term evolution of river bed levels. However, the methodology to extract meaningful geomorphic information from such hydrometric data is not straightforward. We introduce an original method to estimate the parameters of successive rating curves by Bayesian analysis in sequence. These parameters reflect the physical properties of the channel features that control the stage-discharge relation: low-flow riffles, main channel, floodway (bars), floodplain, etc. The dates of rating changes are assumed to be known in existing hydrometric records. The uncertainty interval of each parameter is estimated, assuming, however, that no rating change has been ignored by the station manager. It is thus possible to clearly distinguish overall trends of the channel bed level from the local evolution of riffles and to evaluate whether the observed temporal changes are significant compared to the estimation uncertainties.

International audience; Streamflow time series are commonly derived from stage-discharge rating curves, but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast, uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time-varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.

International audience; An original hydraulics-based Bayesian method is introduced for developing stage-fall-discharge (SFD) rating curves used at twin-gauge stations and estimating their uncertainties. A channel control with variable energy slope due to variable backwater is considered in combination with a backwater -unaffected control, usually another channel control. Succession between backwater -affected and backwater-unaffected controls is solved based on the continuity of the stage-discharge relation. The difference between the reference levels at the two stations is estimated as another uncertain parameter of the SFD model. Results at one typical twin-gauge station affected by the backwater of a run-of-the-river dam are presented. The accuracy and uncertainty of predicted discharges appear to be acceptable even when the uncertainty of the transition between backwater- affected and backwater-unaffected controls is high; Une méthode bayésienne à base hydraulique est introduite pour le développement des courbes de tarage hauteur -dénivelée-débit (SFD) utilisées pour les stations à double échelle et l’estimation de leurs incertitudes. Un contrôle par chenal avec une pente d’énergie variable due à un remous variable est considéré en combinaison avec un contrôle non influencé par le remous, habituellement un autre contrôle par chenal. La succession entre le contrôle influencé par le remous variable et le contrôle non influencé est résolue par continuité de la relation hauteur -débit. La différence entre les niveaux de référence des zéros des deux échelles limnimétriques est estimée comme un autre paramètre incertain du modèle SFD. On présente les résultats d’une station à double échelle typique d’une influence par le remous variable d’un barrage. L’exactitude et l’incertitude des débits estimés semblent acceptables, même lorsque l’incertitude sur la hauteur de transition entre les deux contrôles influencé et non influencé par le remous est élevée.

Establishing reliable streamflow time series is essential for hydrological studies and water-related decisions, but it can be both time-consuming and costly since streamflow is typically calculated ...

Complex rating curves, with stage and additional variables as inputs are necessary to establish streamflow records at sites where the stage-discharge relation is non-unique. Within the same Bayesian framework, hydraulically-based methods are introduced and tested to develop complex rating curves and estimate their uncertainties: stage-gradient-discharge (SGD) models to address hysteresis due to transient flow, stage-fall-discharge (SFD) models to address variable backwater at twin gauge stations, stage-period-discharge (SPD) model to address net rating changes due to bed evolution. Each model was applied to contrasting hydrometric stations and evaluated through sensitivity analyses. For each of the three sources of non-uniqueness in the stage-discharge relation, the proposed Bayesian methods provide not only quantitative uncertainty analysis but also efficient solutions to recurrent problems with the traditional procedures for complex ratings., Les courbes de tarage complexes, qui prennent en entrée la hauteur d'eau et des variables supplémentaires, sont nécessaires pour établir les chroniques de débit des cours d'eau là où la relation hauteur-débit n'est pas univoque. Dans le même cadre bayésien, des méthodes à base hydraulique sont proposées et testées pour construire les courbes de tarage complexes et estimer leurs incertitudes : des modèles hauteur-gradient-débit (SGD) pour résoudre l'hystérésis due aux écoulements transitoires, des modèles hauteur-dénivelée-pente (SFD) pour résoudre le remous variable aux stations à double échelle, le modèle hauteur-période-débit (SPD) pour résoudre les détarages nets dus aux évolutions du lit. Chaque modèle a été appliqué à des stations hydrométriques variées et évalué grâce à des analyses de sensibilité. Pour chacune des trois sources de non-univocité de la relation hauteur-débit, les méthodes bayésiennes proposées fournissent non seulement une analyse d'incertitude quantitative mais aussi des solutions efficaces à des problèmes récurrents que posent les procédures traditionnelles pour les courbes de tarage complexes.

Establishing a reliable stage-discharge (SD) rating curve for calculating discharge at a hydrological gauging station normally takes years of data collection. Estimation of high flows is particularly difficult as they occur rarely and are often difficult to gauge in practice. At a minimum, hydraulicallymodelled rating curves could be derived with as few as two concurrent SD and water-surface slope measurements at different flow conditions. This means that a reliable rating curve can, potentially, be developed much faster via hydraulic modelling than using a traditional rating curve approach based on numerous stage-discharge gaugings. In this study, we use an uncertainty framework based on Bayesian inference and hydraulic modelling for developing SD rating curves and estimating their uncertainties. The framework incorporates information from both the hydraulic configuration (bed slope, roughness, vegetation) using hydraulic modelling and the information available in the SD observation data (gaugings). Discharge time series are estimated by propagating stage records through the posterior rating curve results. Here we apply this novel framework to a Swedish hydrometric station, accounting for uncertainties in the gaugings and the parameters of the hydraulic model. The aim of this study was to assess the impact of using only three gaugings for calibrating the hydraulic model on resultant uncertainty estimations within our framework. The results were compared to prior knowledge, discharge measurements and official discharge estimations and showed the potential of hydraulically-modelled rating curves for assessing uncertainty at high and medium flows, while uncertainty at low flows remained high. Uncertainty results estimated using only three gaugings for the studied site were smaller than ±15% for medium and high flows and reduced the prior uncertainty by a factor of ten on average and were estimated with only 3 gaugings.