Abstract. Understanding large-scale patterns in flow intermittence is important for effective river management. The duration and frequency of zero-flow periods are associated with the ecological characteristics of rivers and have important implications for water resources management. We used daily flow records from 628 gauging stations on rivers with minimally modified flows distributed throughout France to predict regional patterns of flow intermittence. For each station we calculated two annual times series describing flow intermittence; the frequency of zero-flow periods (consecutive days of zero flow) in each year of record (FREQ; yr −1 ), and the total number of zero-flow days in each year of record (DUR; days). These time series were used to calculate two indices for each station, the mean annual frequency of zero-flow periods (mFREQ; yr −1 ), and the mean duration of zero-flow periods (mDUR; days). Approximately 20 % of stations had recorded at least one zero-flow period in their record. Dissimilarities between pairs of gauges calculated from the annual times series (FREQ and DUR) and geographic distances were weakly correlated, indicating that there was little spatial synchronization of zero flow. A flow-regime classification for the gauging stations discriminated intermittent and perennial stations, and an intermittence classification grouped intermittent stations into three classes based on the values of mFREQ and mDUR. We used random forest (RF) models to relate the flow-regime and intermittence classifications to several environmental characteristics of the gauging station catchments. The RF model of the flow-regime classification had a cross-validated Cohen's kappa of 0.47, indicating fair performance and the intermittence classification had poor performance (cross-validated Cohen's kappa of 0.35). Both classification models identified significant environment-intermittence associations, in particular with regional-scale climate patterns and also catchment area, shape and slope. However, we suggest that the fair-to-poor performance of the classification models is because intermittence is also controlled by processes operating at scales smaller catchments, such as groundwater-table fluctuations and seepage through permeable channels. We suggest that high spatial heterogeneity in these small-scale processes partly explains the low spatial synchronization of zero flows. While 20 % of gauges were classified as intermittent, the flow-regime model predicted 39 % of all river segments to be intermittent, indicating that the gauging station network under-represents intermittent river segments in France. Predictions of regional patterns in flow intermittence provide useful information for applications including environmental flow setting, estimating assimilative capacity for contaminants, designing bio-monitoring programs and making preliminary predictions of the effects of climate change on flow intermittence.
Understanding large-scale patterns in flow intermittence is important for effective water resource management. We used daily flow records from 628 gauging stations on rivers with minimally modified flows distributed throughout France to predict regional patterns of flow intermittence. For each station we calculated two annual times-series describing flow intermittence; the frequency of zero-flow periods (consecutive days of zero-flow) in each year of record (FREQ; yr<sup>−1</sup>), and the total number of zero-flow days in each year of record (DUR; days). These time series were used to calculate two indices for each station, the mean annual frequency of zero-flow periods (mFREQ; yr<sup>−1</sup>), and the mean duration of zero-flow periods (mDUR; days). Approximately 20% of stations had recorded at least one zero-flow period. Dissimilarities between pairs of gauges calculated from the annual times-series (FREQ and DUR) and geographic distances were weakly correlated, indicating that there was little spatial synchronization of zero-flow. A flow-regime classification for the gauging stations discriminated intermittent and perennial stations, and an intermittence classification grouped intermittent stations into three classes based on the values of mFREQ and mDUR. We used Random Forest (RF) models to relate the flow-regime and intermittence classifications to several environmental characteristics of the gauging station catchments. The RF model of the flow-regime classification had a cross-validated Cohen's kappa of 0.47, indicating fair performance and the intermittence classification had poor performance (cross-validated Cohen's kappa of 0.35). Both classification models identified significant environment-intermittence associations, in particular with regional-scale climate patterns and also catchment area, shape and slope. However, we suggest that the fair-to-poor performance of the classification models is because intermittence is also controlled by processes operating at scales smaller than catchments, such as groundwater-table fluctuations and seepage through permeable channels. We suggest that high spatial heterogeneity in these small-scale processes partly explains the low spatial synchronization of zero-flows. While 20% of gauges were classified as intermittent, the flow-regime model predicted 39% of all river segments to be intermittent, indicating that the gauging station network under-represents intermittent river segments in France. Predictions of regional patterns in flow intermittence provide useful information for applications including environmental flow-setting, estimating assimilative capacity for contaminants, designing bio-monitoring programs and making preliminary estimates of the effects of climate change on flow intermittence
Abstract.The study aims at estimating flow duration curves (FDC) at ungauged sites in France and quantifying the associated uncertainties using a large dataset of 1080 FDCs. The interpolation procedure focuses here on 15 percentiles standardised by the mean annual flow, which is assumed to be known at each site. In particular, this paper discusses the impact of different catchment grouping procedures on the estimation of percentiles by regional regression models.In a first step, five parsimonious FDC parametric models are tested to approximate FDCs at gauged sites. The results show that the model based on the expansion of Empirical Orthogonal Functions (EOF) outperforms the other tested models. In the EOF model, each FDC is interpreted as a linear combination of regional amplitude functions with spatially variable weighting factors corresponding to the parameters of the model. In this approach, only one amplitude function is required to obtain a satisfactory fit with most of the observed curves. Thus, the considered model requires only two parameters to be applicable at ungauged locations.Secondly, homogeneous regions are derived according to hydrological response, on the one hand, and geological, climatic and topographic characteristics on the other hand. Hydrological similarity is assessed through two simple indicators: the concavity index (IC) representing the shape of the dimensionless FDC and the seasonality ratio (SR), which is the ratio of summer and winter median flows. These variables are used as homogeneity criteria in three different methods for grouping catchments: (i) according to an a priori classification of French Hydro-EcoRegions (HERs), (ii) by applying regression tree clustering and (iii) by using neighbourhoods obtained by canonical correlation analysis.Correspondence to: E. Sauquet (eric.sauquet@cemagref.fr) Finally, considering all the data, and subsequently for each group obtained through the tested grouping techniques, we derive regression models between physiographic and/or climatic variables and the two parameters of the EOF model. Results on percentile estimation in cross validation show that a significant benefit is obtained by defining homogeneous regions before developing regressions, particularly when grouping methods make use of hydrogeological information.
The study aims at estimating flow duration curves (FDC) at ungauged sites in France and quantifying the associated uncertainties using a large dataset of 1080 FDCs. The interpolation procedure focuses here on 15 percentiles standardised by the mean annual flow, which is supposed to be known at each site. In particular, this paper discusses the relevance of different catchments grouping procedures on percentiles estimation by regional regression models. <br><br> First, five parsimonious FDC parametric models were tested to approximate FDCs at gauged sites. The results show that the model based on Empirical Orthogonal Functions (EOF) expansion outperforms the other ones. In this model each FDC is interpreted as a linear combination of regional amplitude functions with weights – the parameters of the model – varying in space. Here, only one amplitude function was found sufficient to fit well most of the observed curves. Thus the considered model requires only two parameters to be estimated at ungauged locations. <br><br> Second, homogeneous regions were derived according to hydrological response on one hand, and geological, climatic and topographic characteristics on the other hand. Hydrological similarity was assessed through two simple indicators: the concavity index (<i>IC</i>) that represents the shape of the standardized FDC and the seasonality ratio (<i>SR</i>) which is the ratio of summer and winter median flows. These variables were used as homogeneity criteria in three different methods for grouping catchments: (i) according to their membership in one of an a priori French classification into Hydro-Eco-Regions (HERs), (ii) by applying a regression tree clustering and (iii) by using hydrological neighbourhood obtained by canonical correlation analysis. <br><br> Finally, regression models between physiographic and/or climatic variables and the two parameters of the EOF model were derived considering all the data and thereafter for each group obtained through the tested grouping techniques. Results on percentiles estimation in cross validation show a significant benefit to form homogeneous regions before developing regressions, particularly when grouping methods use hydrogeological information
Cet article propose une méthode permettant la valorisation des données de jaugeage disponibles localement, en un point du réseau hydrographique (un « site cible »), en vue de l'estimation du débit mensuel minimum de période de retour 5 ans QMNA5. La méthodologie mise en place au travers de validations croisées s'appuie sur l'ajustement d'une relation linéaire entre couples des logarithmes des débits jaugés au site cible et observés simultanément au droit d'une station hydrométrique voisine (un « site d'appui ») pour laquelle une valeur du QMNA5 est connue et fiable. La relation est alors appliquée pour estimer le QMNA5 au site d'intérêt à partir du QMNA5 connu au site d'appui. Les erreurs d'estimation associées sont quantifiées au travers de formulations empiriques intégrant les caractéristiques des campagnes de mesure (nombre et fréquence des jaugeages) et la qualité de la relation entre les deux sites (coefficient de corrélation). Les résultats obtenus en termes de performance montrent qu'au delà de 20 jaugeages, les gains n'évoluent plus significativement et qu'un jaugeage est plus informatif si les campagnes sont espacées dans le temps (une fréquence de trois jaugeages par saison d'étiage est recommandée). Le protocole enfin peut s'accommoder de suivis irréguliers et être étendu à d'autres caractéristiques d'étiage. Cette méthode peut conduire à des estimations plus précises qu'une estimation basée sur des techniques d'interpolation-même sophistiquées-dès lors qu'un nombre de jaugeages est atteint. Mots-clés : campagne de jaugeage, prédétermination de débits de basses eaux Using spot gauging data to estimate the annual minimum monthly flow with a return period of 5 years ABSTRACT.-The annual minimum monthly flow with a return period of 5 years QMNA5 at a poorly gauged site is commonly used for water quality and quantity management in France. A method using spot gauging data to estimate this low flow statistic at poorly gauged sites is presented. The estimate for QMNA5 at the partial record site is derived from the value of QMNA5 at one nearby-gauged site with long-term and high quality records. The relationship between the logarithm of these values is supposed to be well approximated by a linear regression fitted to the logarithm of concurrent flows observed at the two sites. A delete-one cross-validation analysis was performed (i) to assess sensitivity of the data collection strategy, allowing useful recommendations for operational service in charge of river flow monitoring and (ii) to derive bias and standard error models as function of the correlation coefficient between synchronous flows, the total number and the frequency of spot gauging data. The results show that increasing the number of spot gauging data leads to a significant increase in the model performance until approximately 20 gauging data; the gain becomes limited afterwards. Moreover, gauging several times the same year does not significantly improve estimates, probably because of the intra-annual dependence of low-flow data. Three measurement...
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