Ludovic Oudin

Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments

[1] Given the contradictory results from recent studies, this paper compares classical regionalization schemes of catchment model parameters over the wide range of hydroclimates found in France. To ensure the generality of the conclusions, we used two lumped rainfall-runoff models applied to daily data over a large set of 913 French catchments. Three types of approaches were considered: regionalization using regression, regionalization based on spatial proximity and regionalization based on physical similarity. This comparison shows that in France, where a dense network of gauging stations is available, spatial proximity provides the best regionalization solution. The regression approach is the least satisfactory, with results very close to those obtained using one median parameter set for the whole country. The physical similarity approach is intermediary. However, the results obtained with these three methods lag far behind those obtained by full model calibration. Our results also show that some improvement could be made by combining spatial proximity and physical similarity, and that there is still considerable room for progress in the field of ungaged catchment modeling.

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Abstract Soil moisture is a key hydrological variable in flood forecasting: it largely influences the partition of rain between runoff and infiltration and thus controls the flow at the outlet of a catchment. The methodology developed in this paper aims at improving the commonly used hydrological tools in an operational forecasting context by introducing soil moisture data into streamflow modelling. A sequential assimilation procedure, based on an extended Kalman filter, is developed and coupled with a lumped conceptual rainfall–runoff model. It updates the internal states of the model (soil and routing reservoirs) by assimilating daily soil moisture and streamflow data in order to better fit these external observations. We present in this paper the results obtained on the Serein, a Seine sub-catchment (France), during a period of about 2 years and using Time Domain Reflectivity probe soil moisture measurements from 0–10 to 0–100 cm and stream gauged data. Streamflow prediction is improved by assimilation of both soil moisture and streamflow individually and by coupled assimilation. Assimilation of soil moisture data is particularly effective during flood events while assimilation of streamflow data is more effective for low flows. Combined assimilation is therefore more adequate on the entire forecasting period. Finally, we discuss the adequacy of this methodology coupled with Remote Sensing data.

Dynamic averaging of rainfall‐runoff model simulations from complementary model parameterizations

[1]xa0In this paper, we analyze how our evaluation of the capacity of a rainfall-runoff model to represent low or high flows depends on the objective function used during the calibration process. We present a method to combine models to produce a more satisfactory streamflow simulation, on the basis of two different parameterizations of the same model. Where we previously had to choose between a more efficient simulation for either high flows or low flows (but inevitably less efficient in the other range), we show that a balanced simulation can be obtained by using a seasonal index to weigh the two simulations, providing good efficiency in both low and high flows.

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Abstract Streamflow data are essential for the calibration of continuous rainfall—runoff (RR) models. The quantity and quality of streamflow data can significantly influence parameter calibration and thus model robustness. Most existing sensitivity analysis studies on the role of streamflow data have used continuous periods to calibrate model parameters, with a minimum of one year, though ideally much longer periods are generally advised. However, in practical model applications, streamflow data series available for model calibration may be rather short or non-continuous. This study aims at assessing the sensitivity of continuous RR models to the quantity of information used during model calibration when it is randomly sampled in the observed hydrograph, i.e. using non-continuous calibration periods. This sampling provides less auto-correlated streamflow information for model calibration than continuous records. Two daily RR models with four and six free parameters were tested on a sample of 12 basins in the USA to obtain more general conclusions. The results showed that, in general, 350 calibration days sampled out of a longer data set including dry and wet conditions are sufficient to obtain robust estimates of model parameters. The more parsimonious model requires fewer calibration data to obtain stable and robust parameter values. Stable parameter values prove more difficult to reach in the driest catchments.

Locating the sources of low-pass behavior within rainfall-runoff models

[1]xa0The reasons why most rainfall-runoff models appear relatively insensitive to potential evapotranspiration (PE) inputs, compared with rainfall inputs, are investigated. To this aim, a methodology is presented providing detailed tracking of the treatment of PE input by two rainfall-runoff models. Since uncertainties affect both the structures and the inputs of rainfall-runoff models, the analysis is based on synthetic flow data. Standard synthetic streamflow series were generated using a standard PE input. Then, the PE series were corrupted successively by random and autocorrelated errors, and the propagation of these errors through the models state variables is followed. For comparison, the same methodology was applied to rainfall data. The analysis is focused on two lumped rainfall-runoff models (the GR4J model and a lumped version of TOPMODEL) over a large sample of 308 watersheds. The investigation shows that perturbation errors in the potential evapotranspiration are absorbed by the models production (soil moisture accounting) reservoir, which controls the water losses from the model. Given the slow variations in the soil moisture accounting reservoir, rainfall-runoff models behave like low-pass filters, absorbing high-frequency variations of PE inputs. In contrast, the models tested here do not smooth the rainfall perturbation.

Évolution potentielle du régime des crues de la Seine sous changement climatique

Nous avons regionalise 12 scenarios de changement climatique dorigine anthropique dans le bassin de la Seine, pour servir en entree de 5 modeles hydrologiques differents. Les scenarios hydrologiques resultants saccordent tous sur un assechement prononce des bassins etudies dici a la fin du 21eme siecle, avec une baisse des nappes et des debits en moyenne annuelle. A lechelle saisonniere, la baisse des debits se repercute davantage sur les basses eaux que sur les hautes eaux, dont la baisse est moins robuste. La reponse des valeurs extremes est plus contrastee, et les crues extremes, identifiees par les quantiles decennaux QJXA10, ne changeraient pas significativement au cours du 21eme siecle. Nos resultats suggerent aussi que la crue centennale, extrapolee selon la methode du gradex, resterait du meme ordre de grandeur quactuellement.

Seeking genericity in the selection of parameter sets: Impact on hydrological model efficiency

This paper evaluates the use of a small number of generalist parameter sets as an alternative to classical calibration. Here parameter sets are considered generalist when they yield acceptable performance on a large number of catchments. We tested the genericity of an initial collection of 10(6) parameter sets sampled in the parameter space for the four-parameter GR4J rainfall-runoff model. A short list of 27 generalist parameter sets was obtained as a good compromise between model efficiency and length of the short list. A different data set was used for an independent evaluation of a calibration procedure, in which the search for an optimum parameter set is only allowed within this short list. In validation mode, the performance obtained is inferior to that of a classical calibration, but when the amount of data available for calibration is reduced, the generalist parameter sets become progressively more competitive, with better results for calibration series shorter than 1 year. Key Points We produce a generalist list of parameter sets Short-list calibration is evaluated on an independent catchment data set With short calibration series, the generalist parameter sets give better results 10.1002/(ISSN)1944-7973

How should a rainfall‐runoff model be parameterized in an almost ungauged catchment? A methodology tested on 609 catchments

14 This paper examines catchments that are almost ungaged, i.e. catchments for which only a 15 small number of point flow measurements are available. In these catchments, hydrologists 16 may still need to simulate continuous streamflow time series using a rainfall-runoff model, 17 and the methodology presented here allows using few point measurements for model 18 parameterization. The method combines regional information (parameter sets of neighboring 19 gaged stations) and local information (contributed by the point measurements) within a 20 framework where the relative weight of each source of information is made dependent on the 21 Author-produced version of the article published in Water Resources Research (2016), vol. 52, n° 6 p. 4765-4784 The original publication is available at http://onlinelibrary.wiley.com/doi/10.1002/2015WR018549/epdf ©.

Process‐based interpretation of conceptual hydrological model performance using a multinational catchment set

Most of previous assessments of hydrologic model performance are fragmented, based on small number of catchments, different methods or time periods and do not link the results to landscape or climate characteristics. This study uses a large-sample hydrology to identify major catchment controls on daily runoff simulations. It is based on a conceptual lumped hydrological model (GR6J), a collection of 29 catchment characteristics, a multinational set of 1103 catchments located in Austria, France and Germany and four runoff model efficiency criteria. Two analyses are conducted to assess how features and criteria are linked: (i) a one-dimensional analysis based on the Kruskal-Wallis test and (ii) a multidimensional analysis based on regression trees and investigating the interplay between features. The catchment features most affecting model performance are the flashiness of precipitation and streamflow (computed as the ratio of absolute day-to-day fluctuations by the total amount in a year), the seasonality of evaporation, the catchment area and the catchment aridity. Nonflashy, nonseasonal, large and nonarid catchments show the best performance for all the tested criteria. We argue that this higher performance is due to fewer nonlinear responses (higher correlation between precipitation and streamflow) and lower input and output variability for such catchments. Finally we show that, compared to national sets, multinational sets increase results transferability because they explore a wider range of hydro-climatic conditions.

Data-set cleansing practices and hydrological regionalization: is there any valuable information among outliers?

Abstract In hydrological regionalization studies, where one attempts to transfer information from gauged (donor) stations to ungauged (target) ones, the problem of data quality and reliability is often raised. Should all the available data be used? Or should some donor stations be considered unreliable for some reason and therefore discarded? In this article, we address these questions by proposing a new method to detect potentially undesirable stations: this method to identify outliers is based on the detection of catchments which do not fit in their neighbourhood. We apply this approach to a case of simple regionalization involving reference flows and compare it with the traditional outlier detection method. As expected, different outlier definitions lead to considerably different results, and the proposed method appears to perform noticeably better than the traditional one. Citation Boldetti, G., Riffard, M., Andréassian, V. & Oudin, L. (2010) Data-set cleansing practices and hydrological regionalization: is there any valuable information among outliers? Hydrol. Sci. J. 55(6), 941–951.