La refonte du système d'information national pour la gestion et la mise à disposition des données hydrométriques

Leleu, Isabelle; Tonnelier, Isabelle; Puechberty, Rachel; Gouin, Philippe; Viquendi, Isabelle; Cobos, Laurent; Foray, Anouck; Baillon, Martine; Ndima, Pierre-Olivier

doi:10.1051/lhb/2014004

Cited by 62 publications

(41 citation statements)

References 0 publications

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“…We used the National Land Cover Database (NLCD), available for the years 2001, 2006, and 2011 [60][61][62] The French catchments sample was selected from the Banque Hydro database (http://www. hydro.eaufrance.fr) [63], where flow measurements are available for over 4000 stations across France. The availability of hourly streamflow and precipitation data was then checked for the period 1997-2012.…”

Section: Sample Selectionmentioning

confidence: 99%

Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters

Saadi

Oudin

Ribstein

2019

Water

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This study investigated the potential of random forest (RF) algorithms for regionalizing the parameters of an hourly hydrological model. The relationships between model parameters and climate/landscape catchment descriptors were multidimensional and exhibited nonlinear features. In this case, machine-learning tools offered the option of efficiently handling such relationships using a large sample of data. The performance of the regionalized model using RF was assessed in comparison with local calibration and two benchmark regionalization approaches. Two catchment sets were considered: (1) A target pseudo-ungauged catchment set was composed of 120 urban ungauged catchments and (2) 2105 gauged American and French catchments were used for constructing the RF. By using pseudo-ungauged urban catchments, we aimed at assessing the potential of the RF to detect the specificities of the urban catchments. Results showed that RF-regionalized models allowed for slightly better streamflow simulations on ungauged sites compared with benchmark regionalization approaches. Yet, constructed RFs were weakly sensitive to the urbanization features of the catchments, which prevents their use in straightforward scenarios of the hydrological impacts of urbanization.

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Section: Sample Selectionmentioning

confidence: 99%

Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters

Saadi

Oudin

Ribstein

2019

Water

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“…We used climate data from the SAFRAN meteorological reanalysis of Météo-France (Vidal et al, 2010), which is provided on a square grid of 8 km×8 km. Discharge data were extracted from the French Hydro database (Leleu et al, 2014) at the daily time step. They were used to perform the calibration and the evaluation of the models.…”

Section: Study Area and Hydro-meteorological Datamentioning

confidence: 99%

Spatial variability of the parameters of a semi-distributed hydrological model

Lavenne¹,

Thirel²,

Andréassian³

et al. 2016

Proc. IAHS

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Abstract. Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).

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“…The French sample was selected from the Banque Hydro database, for which hourly streamflow time series were extracted using the hydro.eaufrance.fr platform (Leleu et al, 2014). Hourly precipitation depths were prepared using the COMEPHORE product provided by Météo-France (Tabary et al, 2013), available at a 1-km resolution across France.…”

Section: Sample Descriptionmentioning

confidence: 99%

Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments?

Saadi

Oudin

Ribstein

2020

Hydrological Processes

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Landscape differences induced by urbanization have prompted hydrologists to define a fuzzy boundary between rural‐ and urban‐specific hydrological models. We addressed the validity of establishing this boundary, by testing two rural models on a large sample of 175 French and United States (US) urbanized catchments, and their 175 rural neighbours. The impact of urbanization on the hydrological behaviour was checked using four metrics. Using a split‐sample test, we have compared the performances, parameter distributions, and internal fluxes of GR4H and IHACRES, two conceptual and continuous models running at the hourly time step. Both model structures are based on soil moisture accounting reservoirs (infiltration, runoff, and actual evapotranspiration) and quick flow/slow flow routing components, with no consideration of any specific feature related to urbanization. Results showed: (a) Except for the ratio of streamflow flashiness to precipitation flashiness, the range of hydrological signature metrics in rural catchments encompassed the specificities of urbanized ones. Overall, the urbanized catchments showed higher ratios of mean streamflow to mean precipitation (median values: 0.39 vs. 0.27) and streamflow flashiness to precipitation flashiness (0.13 vs. 0.03), besides lower baseflow index (0.42 vs. 0.62) and shorter characteristic response time (3 vs. 14 hr). (b) The performances of GR4H revealed no significant distinction between rural and urbanized catchments in terms of Kling–Gupta Efficiency (KGE), whereas IHACRES better simulated urbanized catchments, especially during summer. (c) With respect to differences in urbanization level, the GR4H and IHACRES parameters showed different distributions. The differences in parameters were consistent with the differences in hydrological behaviour, which is promising for a model‐based assessment of the impact of urbanization. (d) The models agreed less in reproducing the internal fluxes over the urbanized catchments than over the rural ones. These results demonstrate the flexibility of conceptual models to handle the specificities of urbanized catchments.

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La refonte du système d'information national pour la gestion et la mise à disposition des données hydrométriques

Cited by 62 publications

References 0 publications

Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters

Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters

Spatial variability of the parameters of a semi-distributed hydrological model

Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments?

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