Daily variability of river concentrations and fluxes: indicators based on the segmentation of the rating curve

International audienceThe variability of water chemistry on a daily scale is rarely addressed due to the lack of records. Appropriate tools, such as typologies and dimensionless indicators, which permit comparisons between stations and between river materials, are missing. Such tools are developed here for daily concentrations (C), specific fluxes or yields (Y) and specific river flow (q). The data set includes 128 long-term daily records, for suspended particulate matter (SPM), total dissolved solids (TDS), dissolved and total nutrients, totalling 1236 years of records. These 86 river basins (103-106 km2) cover a wide range of environmental conditions in semi-arid and temperate regions. The segmentation--truncation of C-q rating curves into two parts at median flows (q50) generates two exponents (b50inf and b50sup) that are different for 66% of the analysed rating curves. After segmentation, the analysis of records results in the definition of nine major C-q types combining concentrating, diluting or stable patterns, showing inflexions, chevron and U shapes. SPM and TDS are preferentially distributed among a few types, while dissolved and total nutrients are more widely distributed. Four dimensionless indicators of daily variability combine median (C50, Y50), extreme (C99, Y99) and flow-weighted (C*, Y*) concentrations and yields (e.g. C99/C50, Y*/Y50). They vary over two to four orders of magnitude in the analysed records, discriminating stations and river material. A second set of four indicators of relative variability [e.g. (Y*/Y50)/(q*/q50)], takes into account the daily flow variability, as expressed by q*/q50 and q99/q50, which also vary over multiple orders of magnitude. The truncated exponent b50sup is used to describe fluxes at higher flows accounting for 75% (TDS) to 97% (SPM) of interannual fluxes. It ranges from − 0*61 to + 1*86 in the database. It can be regarded as the key amplificator (positive b50sup) or reductor (negative b50sup) of concentrations or yields variability. C50, Y50, b50sup can also be estimated in discrete surveys, which provides a new perspective for quantifying and mapping water quality variability at daily scal

La Loire, usine à carbonates

Les matières en suspension estivales du cours moyen de la Loire ont une composition chimique et minéralogique très particulière : elles sont plus riches en silice, du fait de la présence de diatomées, et plus riches en calcium lié à la présence de cristaux de calcite endogénique, que dans le cours amont. En effet, dans ce tronçon ligérien, les blooms phytoplanctoniques, nombreux et denses, perturbent l'équilibre des carbonates dissous. La calcite peut alors se former directement dans les eaux de la Loire, phénomène qui est, à ce jour, peu décrit dans les fleuves des zones tempérées

Incertitudes sur les métriques de qualité des cours d'eau (médianes et quantiles de concentrations, flux, cas des nutriments) évaluées a partir de suivis discrets Uncertainties on river water quality metrics assessement (nutrients, concentration quantiles and fluxes) based on discrete surveys

International audienceL'évaluation de la qualité des cours d'eau est réalisée à partir de deux principaux types d'indicateurs : les concentrations annuelles (moyennes arithmétiques (Cmoy), médianes (C50), quantiles supérieurs (C90), moyennes pondérées par les débits, (C*..) qui sont rapportées à une grille de qualité (SEQ-Eau) et les flux annuels ramenés généralement à la surface du bassin versant productrice. Ces indicateurs sont entachés d'une incertitude, rarement quantifiée, qui dépend d'abord de la variabilité des concentrations des différents constituants avec la saison et les débits, de la variabilité hydrologique et du nombre de mesures N/an. C'est la spécificité du programme VARIFLUX soutenu par le programme ECCO-ANR/INSU/CNRS dont les résultats sont présentés ici. River water quality assessment is commonly based on two types of metrics : i) annual concentrations (annual means, medians, upper percentiles which are compared to water quality scales and ii) annual fluxes, generally normalized to the river basin area (specific fluxes). These metrics, when determined from discrete surveys should be associated with uncertainties, rarely quantifed, which depend : i) on the concentrations variability with water discharge and/or season, ii) on the river flow variability and, ii) on the survey frequency. The VARIFLUX project is addressing these questions from a Monte-Carlo of discrete simulation surveys based on a rare set of daily records (Suspended particulate Matter, MES, total dissolved solids, TDS, nutrients) in France and in the USA

Estimation of local extreme suspended sediment concentrations in California Rivers

International audienceThe total amount of suspended sediment load carried by a stream during a year is usually transported during one or several extreme events related to high river flow and intense rainfall, leading to very high suspended sediment concentrations (SSCs). In this study quantiles of SSC derived from annual maximums and the 99th percentile of SSC series are considered to be estimated locally in a site-specific approach using regional information. Analyses of relationships between physiographic characteristics and the selected indicators were undertaken using the localities of 5-km radius draining of each sampling site. Multiple regression models were built to test the regional estimation for these indicators of suspended sediment transport. To assess the accuracy of the estimates, a Jack-Knife re-sampling procedure was used to compute the relative bias and root mean square error of the models. Results show that for the 19 stations considered in California, the extreme SSCs can be estimated with 40–60% uncertainty, depending on the presence of flow regulation in the basin. This modelling approach is likely to prove functional in other Mediterranean climate watersheds since they appear useful in California, where geologic, climatic, physiographic, and land-use conditions are highly variable

Uncertainties in assessing annual nitrate loads and concentration indicators. Part 1: Impact of sampling frequency and load estimation alogorithms

International audienceThe objectives of this study are to evaluate the uncertainty in annual nitrate loads and concentrations (such as annual average and median concentrations) as induced by infrequent sampling and by the algorithms used to compute fluxes. A total of 50 watershed-years of hourly to daily flow and concentration data gathered from nine watersheds (5 to 252 km2) in Brittany, France, were analyzed. Original (high frequency) nitrate concentration and flow data were numerically sampled to simulate common sampling frequencies. Annual fluxes and concentration indicators calculated from the simulated samples were compared to the reference values calculated from the high-frequency data. The uncertainties contributed by several algorithms used to calculate annual fluxes were also quantified. In all cases, uncertainty increased as sampling intervals increased. Results showed that all the tested algorithms that do not use continuous flow data to compute nitrate fluxes introduced considerable uncertainty. The flowƒ]weighted average concentration ratio method was found to perform best across the 50 annual datasets. Analysis of the bias values suggests that the 90th and 95th percentiles and the maximum concentration values tend to be systematically underestimated in the long term, but the load estimates (using the chosen algorithm) and the average and median concentrations were relatively unbiased. Great variability in the precision of the load estimation algorithms was observed, both between watersheds of different sizes and between years for a particular watershed. This has prevented definitive uncertainty predictions for nitrate loads and concentrations in this preliminary work, but suggests that hydrologic factors, such as the watershed hydrological reactivity, could be a key factor in predicting uncertainty levels

La Loire à l'épreuve du changement climatique

Les changements climatiques annoncés vont-ils induire une modification du cycle de l'eau et des débits dans un bassin comme la Loire, géologiquement contrasté et soumis à des climats variés (océanique, continental, cévenol) ? Nos résultats suggèrent une diminution des ressources en eau disponibles en moyennes eaux et en étiage. En revanche la dynamique et l'intensité des crues ne devraient pas varier significativement

Nonlinear empirical modeling to estimate phosphorus exports using continuous records of turbidity and discharge

Special section: Continuous nutrient sensing in research and management: applications and lessons learned across aquatic environments and watershedsInternational audienceWe tested an empirical modeling approach using relatively low‐cost continuous records of turbidity and discharge as proxies to estimate phosphorus (P) concentrations at a subhourly time step for estimating loads. The method takes into account nonlinearity and hysteresis effects during storm events, and hydrological conditions variability. High‐frequency records of total P and reactive P originating from four contrasting European agricultural catchments in terms of P loads were used to test the method. The models were calibrated on weekly grab sampling data combined with 10 storms surveyed subhourly per year (weekly+ survey) and then used to reconstruct P concentrations during all storm events for computing annual loads. For total P, results showed that this modeling approach allowed the estimation of annual loads with limited uncertainties (≈ −10% ± 15%), more reliable than estimations based on simple linear regressions using turbidity, based on interpolated weekly+ data without storm event reconstruction, or on discharge weighted calculations from weekly series or monthly series. For reactive P, load uncertainties based on the nonlinear model were similar to uncertainties based on storm event reconstruction using simple linear regression (≈ 20% ± 30%), and remained lower than uncertainties obtained without storm reconstruction on weekly or monthly series, but larger than uncertainties based on interpolated weekly+ data (≈ −15% ± 20%). These empirical models showed we could estimate reliable P exports from noncontinuous P time series when using continuous proxies, and this could potentially be very useful for completing time‐series data sets in high‐frequency surveys, even over extended periods