The analysis of concentration‐discharge (C‐Q) relationships from low‐frequency observations is commonly used to assess solute sources, mobilization, and reactive transport processes at the catchment ...scale. High‐frequency concentration measurements are increasingly available and offer additional insights into event‐scale export dynamics. However, only few studies have integrated inter‐annual and event‐scale C‐Q relationships. Here, we analyze high‐frequency measurements of specific conductance (EC), nitrate (NO3‐N) concentrations and spectral absorbance at 254 nm (SAC254, as a proxy for dissolved organic carbon) over a two year period for four neighboring catchments in Germany ranging from more pristine forested to agriculturally managed settings. We apply an integrated method that adds a hysteresis term to the established power law C‐Q model so that concentration intercept, C‐Q slope and hysteresis can be characterized simultaneously. We found that inter‐event variability in C‐Q hysteresis and slope were most pronounced for SAC254 in all catchments and for NO3‐N in forested catchments. SAC254 and NO3‐N event responses in the smallest forested catchment were closely coupled and explainable by antecedent conditions that hint to a common near‐stream source. In contrast, the event‐scale C‐Q patterns of EC in all catchments and of NO3‐N in the agricultural catchment without buffer zones around streams were less variable and similar to the inter‐annual C‐Q relationship indicating a homogeneity of mobilization processes over time. Event‐scale C‐Q analysis thus added key insights into catchment functioning whenever the inter‐annual C‐Q relationship contrasted with event‐scale responses. Analyzing long‐term and event‐scale behavior in one coherent framework helps to disentangle these scattered C‐Q patterns.
Key Points
We compare event‐scale and inter‐annual concentration‐discharge relationships in four adjoined catchments with contrasting land use
The variability of event‐scale C‐Q relationships was shaped by land use and antecedent conditions for biogeochemically reactive but not for geogenic solutes
For biogeochemically reactive solutes, event‐scale C‐Q patterns can contrast the inter‐annual pattern obtained from all observations
•Storms stream flow, turbidity, NO3 and DOC concentrations and groundwater levels were described.•A set of functional descriptors was proposed to identify and interpret storm patterns.•Groundwater ...dynamics control seasonality of storm responses via sources connectivity.
The response of stream chemistry to storm is of major interest for understanding the export of dissolved and particulate species from catchments. The related challenge is the identification of active hydrological flow paths during these events and of the sources of chemical elements for which these events are hot moments of exports. An original four-year data set that combines high frequency records of stream flow, turbidity, nitrate and dissolved organic carbon concentrations, and piezometric levels was used to characterize storm responses in a headwater agricultural catchment. The data set was used to test to which extend the shallow groundwater was impacting the variability of storm responses. A total of 177 events were described using a set of quantitative and functional descriptors related to precipitation, stream and groundwater pre-event status and event dynamics, and to the relative dynamics between water quality parameters and flow via hysteresis indices. This approach led to identify different types of response for each water quality parameter which occurrence can be quantified and related to the seasonal functioning of the catchment. This study demonstrates that high-frequency records of water quality are precious tools to study/unique in their ability to emphasize the variability of catchment storm responses.
Agriculture affects the biogeochemical cycles of carbon, nitrogen, and phosphorus, leading to a deterioration of surface water quality. The increasing magnitude of climate change raises questions ...about potential additional or mitigating effects of climate change on this deterioration. One way to understand these potential effects is to cross‐analyze the dynamics of nutrient concentrations and hydroclimatic variables at multiple time scales. Here, we used a 16‐year data set, from a 5 km2 agricultural watershed in France with a temperate oceanic climate, that contains a daily record of nutrient concentrations and hydroclimatic variables from 2002–2017. We calculated Mann‐Kendall and Theil‐Sen tests, Fourier transforms, and daily hydroclimatic distributions associated with extreme stream concentrations to investigate long‐term trends, seasonal dynamics and their interannual variations, and the daily time scale, respectively. Dynamics of dissolved organic carbon (DOC) and nitrate (NO3) concentrations displayed opposite patterns at the three temporal scales, while soluble reactive phosphorus concentrations showed decoupled dynamics, related more to extreme hydrological events. Climate and past agricultural changes seem to have a synergetic effect that leads to long‐term NO3 decrease and DOC increase. Precipitation and, to a greater extent, watershed wetness controlled seasonal and event‐driven dynamics.
Key Points
We analysed 16 years of daily hydroclimatic and water chemistry variables in a 5 km2 agricultural watershed
Opposite temporal patterns were observed for nitrate and DOC, independent of SRP variations, at interannual, seasonal, and event time scales
Agricultural pressures and climate drive long‐term trends, while watershed wetness controls shorter‐term variations
Protecting water quality at catchment scales is complicated by the high spatiotemporal variability in water chemistry. Consequently, determining pollutant sources requires costly monitoring ...strategies to diagnose causes and guide management solutions. However, recent studies have shown that spatial patterns in water chemistry can be persistent at catchment scales, potentially allowing identification of pollution sources and sinks with just a few sampling campaigns. Here, we tested a new method to quantify spatial persistence (SP) of water chemistry patterns with data from synoptic samplings in 22 headwater subcatchments within a 375 km2 catchment in western France (March 2018 to July 2019). This new method to quantify SP reduces dependence on long‐term metrics such as flow‐weighted concentrations, which are usually uncertain or unavailable. We applied the method to 16 ecologically relevant water quality parameters, including soluble reactive phosphorus, nitrate, and dissolved organic carbon. The results showed an average SP of 0.68 among parameters during the study period. For most parameters, SP was higher during the high‐flow winter period but lower and more variable during the low‐flow summer period. We found that the SP ultimately depended on the ratio between the temporal and spatial coefficients of variation (variance explained: 70%) rather than the temporal synchrony among subcatchments (variance explained: 4%). These results demonstrate that in these temperate catchments, synoptic sampling during the high‐flow winter period allows efficient identification of source and sink subcatchments, while more frequent samplings are needed to characterize ecological conditions at low flow.
Key Points
We found high spatial persistence of water chemistry, despite high spatiotemporal variability in water chemistry
Spatial persistence of water chemistry is primarily determined by the ratio between spatial and temporal variability
A single synoptic sampling during the high‐flow season allows efficient identification of source and sink subcatchments
Summary
High‐resolution mapping of soil phosphorus (P) concentration is necessary to identify critical source areas reliably where a large risk of transport coincides with a large potential source of ...P in agricultural landscapes. However, dense soil P data are not usually available to produce such maps and to obtain them is expensive. In this study, we modelled and mapped soil extractable P (ExtP) and total P (TP) concentrations in an intensively farmed 12‐km2 catchment in Brittany (NW France) with two different datasets to test the suitability of readily available regional or national databases for high‐resolution mapping. We used a machine learning tool (Cubist) to develop rule‐based predictive models from a calibration dataset. Covariates included pedological, geological, agricultural, terrain and geophysical‐related attributes obtained specifically in the study area (SURVEY) or derived from readily available regional or national databases (DATABASE). Even though better predictions were obtained with the SURVEY data (RMSE = 0.018 g kg−1 for ExtP and RMSE = 0.219 g kg−1 for TP), the DATABASE data produced acceptable predictions (RMSE = 0.024 g kg−1 for ExtP and RMSE = 0.253 g kg−1 for TP). The machine learning tool helped to identify key covariates that would improve the prediction of soil P when detailed data are not available. Readily available data about crop rotations could increase the accuracy of existing ExtP maps. These maps, combined with additional soil analysis for extractable Al, would improve the mapping of TP and the identification of areas with a large potential source of P.
Highlights
Modelling and mapping of soil phosphorus with the machine learning algorithm Cubist.
Comparison of regional or national databases and detailed survey data for prediction.
Models with regional and national data performed well, but some areas with large concentrations of P were not identified.
Information about crop rotation and soil extractable Al improved model performance.
Résumé
Cartographie haute‐résolution des concentrations en phosphore des sols dans les paysages agricoles en utilisant des données facilement disponibles ou issues d'enquêtes détaillées.
Une cartographie haute résolution des concentrations en phosphore (P) des sols est nécessaire pour identifier de manière fiable les zones critiques dans les paysages agricoles, c'est‐à‐dire les zones où coïncident des risques élevés liés d'une part au transport et d'autre part aux sources potentielles de P. Cependant, les bases de données issues d'un échantillonnage dense des sols, nécessaires pour produire ces cartes, sont rarement disponibles car elles présentent un coût élevé. Dans cette étude, nous avons modélisé et cartographié les concentrations en P extractible (ExtP) et P total (TP) des sols dans un bassin versant agricole intensif de 12 km2 situé en Bretagne (Nord‐Ouest de la France) en utilisant deux bases de données afin de tester l'adéquation des données régionales/nationales pour une cartographie haute résolution. Nous avons utilisé un outil d'apprentissage automatique (Cubist) pour construire des modèles de prédiction reposant sur des règles de décision dérivées d'un jeu de calibration. Les covariables utilisées décrivaient les caractéristiques pédologiques, géologiques, agricoles, topographiques et géophysiques issues d'une collecte de données spécifique dans la zone d'étude (SURVEY) ou issues de bases de données régionales et nationales déjà disponibles (DATABASE). Bien qu'une meilleure qualité de prédiction soit obtenue avec la base de données SURVEY (RMSE = 0.018 g kg−1 pour ExtP et RMSE = 0.219 g kg−1 pour TP), la base de données DATABASE a produit des prédictions acceptables (RMSE = 0.024 g kg−1 pour ExtP et RMSE = 0.253 g kg−1 pour TP). L'outil d'apprentissage automatique a permis d'identifier les covariables clés pour améliorer la prédiction des concentrations en P des sols quand un jeu de données détaillé n'est pas disponible. L'utilisation d'informations sur les rotations de culture permettrait d'améliorer la précision des cartes de ExtP, ce qui, combiné à des analyses complémentaires de l'aluminium extractible, permettrait d'établir une cartographie plus précise de TP et des zones critiques.
Total phosphorus (TP) build‐up in agricultural soils represents both a threat to aquatic ecosystems and a valuable resource for future crop production, given the context of increasing food demand ...combined with the rapid depletion of the world's phosphate reserves. Therefore, it is crucially important (i) to identify the main factors controlling topsoil TP and (ii) to develop methods for mapping its spatial distribution. Multiple linear regression models were used with two distinct approaches to calculate TP and covariates linked to the P cycle. Firstly, covariates were selected from the Réseau de Mesures de la Qualité des Sols database, the French soil monitoring network, which consists of soil samples collected from 2158 sites on a 16‐km regular grid. Secondly, covariates were selected to map TP from spatially exhaustive datasets in France. The first approach explains 80% of variability in topsoil TP. The variables selected are linked to the autochthonous origin of P (parent material), to allochthonous origin (organic carbon and nitrogen contents) and to the retention capacity of soil (Al, Fe, Ca and clay contents). The predicted map obtained from the second approach provides a mean TP of 0.76 g/kg. This study demonstrates that creating national scale maps of TP, based on detailed soil sampling and many variables, is feasible and can be used to model the P cycle and P transfer processes. Such maps can be used in P erosion and transfer models over river basins, and therefore to predict P exports to surface waters.
Drying and rewetting (DRW) events in soils cause the release of molybdate-unreactive dissolved phosphorus (MUP) into soil solutions, which has been historically considered biologically-derived ...phosphorus (P) from microbial cell lysis. This unreactive P, however, could also represent P bound to soil colloids/nanoparticles, whose releases are also known to be physically stimulated during DRW events. To explore this possibility, two riparian wetland soils (A and B) with contrasting soil P speciation were subjected to three successive DRW cycles in soil columns. Leachates were successively filtrated/ultrafiltrated with 0.45 μm, 30 kDa and 5 kDa pore size membranes to separate the different colloidal/nanoparticulate fractions, in which molybdate-reactive P (MRP), total P (TP), MUP (defined as TP minus MRP), Fe, Al and dissolved organic carbon (DOC) concentrations were measured. For both soils, TP concentrations peaked at the beginning of each rewetting event then decreased rapidly upon leaching. MUP concentrations appeared similar variations as TP, but MRP concentrations remained relatively constant during leaching. Soil B showed larger TP release peaks than soil A and contained different P forms (more MUP and MRP, respectively). In both soils, colloidal/nanoparticulate P was an important fraction (up to 70%) of TP (<0.45 μm) in leachates immediately after rewetting, then this proportion decreased markedly at the end of each DRW cycle. On average, the proportion of colloidal/nanoparticulate P in TP was much higher in soil B than in soil A (45% vs. 17%). Results suggested that the colloidal/nanoparticulate carriers likely consisted of a mixture of Fe/Al oxides and organic matter, and highlighted a higher colloidal/nanoparticulate P release capacity in soil B with higher soil organic matter content and porosity. In addition, colloidal/nanoparticulate P and truly dissolved MUP had a greater response to DRW events and were exhausted more rapidly than truly dissolved MRP and smaller nanoparticulate P, suggesting potential differences in their sources and production mechanisms. Thus, this study demonstrates that colloidal/nanoparticulate P can represent an important part of the unreactive P fraction released during DRW events in soils and further highlights the importance of the colloidal/nanoparticulate P fraction in the transport and cycling of P in soils and waters.
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•Colloidal P is a large fraction of P released in riparian soil during DRW cycles.•Released colloidal P contains a combination of Fe/Al oxides and DOC.•Magnitude of colloidal-P release affected by soil OM content and porosity.•Both biotic and abiotic processes influence P release during soil DRW cycles.•The P forms released had different sources and production mechanisms in soils.
This study concerns the domain of cyclic scheduling. More precisely we consider the cyclic job shop scheduling problem with linear constraints. The main characteristic of this problem is that the ...tasks of each job are cyclic and are subjected to linear precedence constraints. First we review some approaches in the field of cyclic scheduling and present the cyclic job shop scheduling problem definition, which has an open complexity. Then we present a general approach for solving it, based on the coupling of a genetic algorithm and a scheduler. This scheduler utilises a Petri-net modelling the linear precedence constraints between cyclic tasks. The goal of this genetic algorithm is to propose an order of priority for jobs on the machines, to be used by the scheduler for solving resource conflicts. Finally a benchmark and some preliminary results of this approach are presented.
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