Rémi Dupas

Disentangling the influence of hydroclimatic patterns and agricultural management on river nitrate dynamics from sub-hourly to decadal time scales

Despite extensive efforts to reduce nitrate transfer in agricultural areas, limited response is often observed in the nitrate concentration in rivers. To investigate the reasons for this limited response, nitrate dynamics in a 100km(2) agricultural catchment in eastern Germany was analysed from sub-hourly to decadal time-scales. Sub-hourly analysis of storm event dynamics during a typical hydrological year (2005-2006) was performed to identify periods of the year with high leaching risk and to link the latter to agricultural management practices in the catchment. Dynamic Harmonic Regression analysis of a 32-year (1982-2014) record of nitrate and discharge revealed that i) the long-term trend in nitrate concentration was closely related to that in discharge, suggesting that large-scale weather and climate patterns were masking the effect of improved nitrogen management on nitrate trends; ii) a persistent seasonal pattern with winter concentration maxima and summer minima could be observed, which was interpreted in terms of a dynamic nitrate concentration profile in the soil and subsoil; and iii) the catchment progressively changed from chemodynamic to more chemostatic behaviour over the three decades of study, which is a sign of long-term homogenisation of nitrate concentrations distribution over depth. This study shows that detailed physical understanding of nitrate dynamics across time scales can be obtained only through combined analysis of long-term records and high-resolution sensor data. Hence, a joint effort is advocated between environmental authorities, who usually perform long-term monitoring, and scientific programmes, which usually perform high-resolution monitoring.

The role of mobilisation and delivery processes on contrasting dissolved nitrogen and phosphorus exports in groundwater fed catchments

Diffuse transfer of nitrogen (N) and phosphorus (P) in agricultural catchments is controlled by the mobilisation of sources and their delivery to receiving waters. While plot scale experiments have focused on mobilisation processes, many catchment scale studies have hitherto concentrated on the controls of dominant flow pathways on nutrient delivery. To place mobilisation and delivery at a catchment scale, this study investigated their relative influence on contrasting nitrate-N and soluble P concentrations and N:P ratios in two shallow groundwater fed catchments with different land use (grassland and arable) on the Atlantic seaboard of Europe. Detailed datasets of N and P inputs, concentrations in shallow groundwater and concentrations in receiving streams were analysed over a five year period (October 2010-September 2015). Results showed that nitrate-N and soluble P concentrations in shallow groundwater give a good indication of stream concentrations, which suggests a dominant control of mobilisation processes on stream exports. Near-stream attenuation of nitrate-N (-30%), likely through denitrification and dilution, and enrichment in soluble P (+100%), through soil-groundwater interactions, were similar in both catchments. The soil, climate and land use controls on mobilisation were also investigated. Results showed that grassland tended to limit nitrate-N leaching as compared to arable land, but grassland could also contribute to increased P solubilisation. In the context of land use change in these groundwater fed systems, the risk of pollution swapping between N and P must be carefully considered, particularly for interactions of land use with soil chemistry and climate.

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

We tested an empirical modelling approach using relatively low-cost continuous records of turbidity and discharge as proxies to estimate phosphorus (P) concentrations at a sub-hourly time step for estimating loads. The method takes into account non-linearity 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 sub-hourly 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 modelling 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 non-linear 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 non-continuous P time series when using continuous proxies, and this could potentially be very useful for completing time-series datasets in high-frequency surveys, even over extended periods.

Geographical modeling of exposure risk to cyanobacteria for epidemiological purposes

The cyanobacteria-derived neurotoxin β-methylamino-L-alanine (BMAA) represents a plausible environmental trigger for amyotrophic lateral sclerosis (ALS), a debilitating and fatal neuromuscular disease. With the eutrophication of water bodies, cyanobacterial blooms and their toxins are becoming increasingly prevalent in France, especially in the Brittany region. Cyanobacteria are monitored at only a few recreational sites, preventing an estimation of exposure of the human population. By contrast, phosphorus, a limiting nutrient for cyanobacterial growth and thus considered a good proxy for cyanobacteria exposure, is monitored in many but not all surface water bodies. Our goal was to develop a geographic exposure indicator that could be used in epidemiological research. We considered the total phosphorus (TP) concentration (mg/L) of samples collected between October 2007 and September 2012 at 179 monitoring stations distributed throughout the Brittany region. Using readily available spatial data, we computed environmental descriptors at the watershed level with a Geographic Information System. Then, these descriptors were introduced into a backward stepwise linear regression model to predict the median TP concentration in unmonitored surface water bodies. TP concentrations in surface water follow an increasing gradient from West to East and inland to coast. The empirical concentration model included five predictor variables with a fair coefficient of determination (R(2) = 0.51). The specific total runoff and the watershed slope correlated negatively with the TP concentrations (p = 0.01 and p< 10(-9), respectively), whereas positive associations were found for the proportion of built-up area, the upstream presence of sewage treatment plants, and the algae volume as indicated by the Landsat red/green reflectance ratio (p < 0.01, p < 10(-6) and p < 0.01, respectively). Complementing the monitoring networks, this geographical modeling can help estimate TP concentrations at the watershed level, delivering a proxy for cyanobacteria exposure that can be used along with other risk factors in further ALS epidemiologic case-control studies.

Influence of hydroclimatic variations on solute concentration dynamics in nested subtropical catchments with heterogeneous landscapes

Despite global efforts to monitor water quality in catchments worldwide, tropical and subtropical zones still lack data to study the influence of human activities and climate variations on solute dynamics. In this study, we monitored ten solutes every two weeks for six years (2010-2015) in three nested catchments (2 to30 km2), which contained heterogeneous landscapes composed of forests and agricultural land, and one small neighboring forested catchment (0.4 km2). Data analysis revealed that i) rainfall, discharge and solute concentrations displayed no clear seasonal patterns, unlike many catchments of the temperate zone; ii) solute concentrations in the agricultural area were higher than those in the forested area, but both areas displayed similar temporal patterns due to a common hydroclimatic driver; iii) all four catchments displayed a chemostatic export regime for most of the solutes, similar to catchments of the temperate zone; and iv) a positive correlation was observed between anion concentrations and ENSO (El Niño-Southern Oscillation) index. ENSO appeared to influence both hydroclimatic and anion dynamics in these subtropical catchments.

Challenges of Reducing Phosphorus Based Water Eutrophication in the Agricultural Landscapes of Northwest Europe

In this paper, we outline several recent insights for the priorities and challenges for future research for reducing phosphorus (P) based water eutrophication in the agricultural landscapes of Northwest Europe. We highlight that new research efforts best be focused on headwater catchments as they are a key influence on the initial chemistry of the larger river catchments, and here many management interventions are most effectively made. We emphasize the lack of understanding on how climate change will impact on P losses from agricultural landscapes. Particularly, the capability to disentangle current and future trends in P fluxes, due to climate change itself, from climate driven changes in agricultural management practices and P inputs. Knowing that, future climatic change trajectories for Western Europe will accelerate the release of the most bioavailable soil P. We stress the ambiguities created by the large varieties of sources and storage/transfer processes involved in P emissions in landscapes and the need to develop specific data treatment methods or tracers able to circumvent them, thereby helping catchment managers to identify the ultimate P sources that most contribute to diffuse P emissions. We point out that soil and aqueous P exist not only in various chemical forms, but also in range of less considered physical forms e. g., dissolved, nanoparticulate, colloidal and other particulates, all affected differently by climate as well as other environmental factors, and require bespoke mitigation measures. We support increased high resolution monitoring of headwater catchments, to not only help verify the effectiveness of catchments mitigation strategies, but also add data to further develop new water quality models (e.g., those include Fe-P interactions) which can deal with climate and land use change effects within an uncertainty framework. We finally conclude that there is a crucial need for more integrative research efforts to deal with our incomplete understanding of the mechanisms and processes associated with the identification of critical source areas, P mobilization, delivery and biogeochemical processing, as otherwise even highintensity and high-resolution research efforts will only reveal an incomplete picture of the full global impact of the terrestrial derived P on downstream aquatic and marine ecosystems.

Improving nitrate load estimates in an agricultural catchment using Event Response Reconstruction

Low-frequency grab sampling cannot capture fine dynamics of stream solute concentrations, which results in large uncertainties in load estimates. The recent development of high-frequency sensors has enabled monitoring solute concentrations at sub-hourly time scales. This study aimed to improve nitrate (NO3) load estimates using high-resolution records (15-min time interval) from optical sensors to capture the typical concentration response to storm events. An empirical model was developed to reconstruct NO3 concentrations during storm events in a 100-km2 agricultural catchment in Germany. Two years (Jan 2002 to Dec 2002 and Oct 2005 to Sep 2006) of high-frequency measurements of NO3 concentrations, discharge and precipitation were used. An Event Response Reconstruction (ERR) model was developed using NO3 concentration descriptor variables and predictor variables calculated from discharge and precipitation records. Fourteen events were used for calibration, and 27 events from four periods of continuous records of high-frequency measurement were used for validation. During all selected storm events, NO3 concentration decreased during flow rise and increased during the recession phase of the hydrograph. Three storm descriptor variables were used to describe these dynamics: relative change in concentration between initial and minimum NO3 concentrations (rdN), time to maximum change in NO3 concentration (TdN) and time to 50% recovery of NO3 concentration (TNrec). The ERR consisted of building linear models of discharge and precipitation to predict these three descriptors. The ERR approach greatly improved NO3 load estimates compared to linear interpolation of grab sampling data (error decreased from 10 to 1%) or flow-weighted estimation of load (error is 7%). This study demonstrated that ERR based on a few months of high-resolution data enables accurate load estimates from low-frequency NO3 data.