Seifeddine Jomaa

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.

Influence of rock fragment coverage on soil erosion and hydrological response: Laboratory flume experiments and modeling

Two laboratory flume experiments on the effect of surface rock fragments on precipitation-driven soil erosion yields were carried out. The total sediment concentration, the concentration of seven individual size classes and the flow discharge were measured. Digital terrain models (DTMs) were generated before and after one of the experiments. The results revealed that the rock fragments protected the soils from raindrop detachment and retarded the overland flow, therefore decreasing its sediment transport capacity. Rock fragments were found to affect selectively the different size classes in a manner that changed according to the time scale. For short times, the rock fragment coverage reduced erosion of the finer particles ( 100 µm) was unaffected. At long times the rock fragment cover decreased the concentration of the individual size classes in proportion to effective rainfall intensity and the area exposed to raindrops. An area-based modification of the Hairsine and Rose (H-R) soil erosion model was employed to analyze the experimental data. The H-R model predictions agreed well with the measured sediment concentrations when high rainfall intensity and low rock fragment cover were used. Predictions were instead less accurate with low rainfall intensity and high rock fragment cover. The DTM results showed that the presence of rock fragments on the soil surface led to increased soil compaction, perhaps due to higher soil moisture content (from greater infiltration) within the rock fragment-covered flumes.

Potential of vegetated ditches to manage organic pollutants derived from agricultural runoff and domestic sewage: A case study in Sinaloa (Mexico)

This case study presents the fate of selected organic, priority and emerging pollutants along a 3.6km sector of a vegetated, agricultural ditch situated in Sinaloa (Mexico). The ditch receives runoff of agriculture and domestic wastewater from an adjacent community. During 2013, the occurrence of 38 organic pollutants (pesticides, polycyclic aromatic hydrocarbons (PAHs), artificial sweeteners and pharmaceutical residues) was monitored monthly at five selected points in the ditch water. Additionally, sediment and Typha domingensis (cattail) plants were collected in March, June, and September 2013 and investigated concerning their ability to absorb and accumulate pollutants. The concentrations of the selected pollutants in the ditch water ranged from sub ngL-1 (metolachlor, atrazine) to μgL-1 (metalaxyl, acesulfame). The metabolites endosulfan sulfate and endosulfan lactone exceeded mostly the concentration of the precursor insecticide endosulfan. Sorption on sediments was of minor relevance for accumulation of pollutants in the ditch system. Concentrations in the sediments varied seasonally and ranged from 0.2 to 12,432μgkg-1 dry weight (d.w.). T. domingensis accumulated ten of the studied pollutants mainly in roots (5-1065μgkg-1 d.w.). Overall, the monitoring results of the ditch compartments indicated that downstream the concentrations of the target pollutants decreased. Under no-flow conditions in the hot season, the ditch revealed a noticeable potential to mitigate pollutants. Among the high microbial activity in the water and the subtropical climate conditions, the ditch vegetation contributed to natural attenuation of the selected pollutants.

Hysteretic sediment fluxes in rainfall-driven soil erosion: particle size effects

A detailed laboratory study was conducted to examine the effects of particle size on hysteretic sediment transport under time-varying rainfall. A rainfall pattern composed of seven sequential stepwise varying rainfall intensities (30, 37.5, 45, 60, 45, 37.5 and 30 mm h−1), each of 20-mins duration, was applied to a 5-m × 2-m soil erosion flume. The soil in the flume was initially dried, ploughed to a depth of 20 cm and had a mechanically smoothed surface. Flow rates and sediment concentration data for seven particle size classes (  1000 µm) were measured in the flume effluent. Clockwise hysteresis loops in the sediment concentration versus discharge curves were measured for the total eroded soil and the finer particle sizes ( 1000 µm). Overall, it is found that hysteresis varies amongst particle sizes and that the predictions of the HR model are consistent with hysteretic behavior of different sediment size classes. This article is protected by copyright. All rights reserved.

The Baltic Sea as a time machine for the future coastal ocean

Science-based, multinational management of the Baltic Sea offers lessons on amelioration of highly disturbed marine ecosystems. Coastal global oceans are expected to undergo drastic changes driven by climate change and increasing anthropogenic pressures in coming decades. Predicting specific future conditions and assessing the best management strategies to maintain ecosystem integrity and sustainable resource use are difficult, because of multiple interacting pressures, uncertain projections, and a lack of test cases for management. We argue that the Baltic Sea can serve as a time machine to study consequences and mitigation of future coastal perturbations, due to its unique combination of an early history of multistressor disturbance and ecosystem deterioration and early implementation of cross-border environmental management to address these problems. The Baltic Sea also stands out in providing a strong scientific foundation and accessibility to long-term data series that provide a unique opportunity to assess the efficacy of management actions to address the breakdown of ecosystem functions. Trend reversals such as the return of top predators, recovering fish stocks, and reduced input of nutrient and harmful substances could be achieved only by implementing an international, cooperative governance structure transcending its complex multistate policy setting, with integrated management of watershed and sea. The Baltic Sea also demonstrates how rapidly progressing global pressures, particularly warming of Baltic waters and the surrounding catchment area, can offset the efficacy of current management approaches. This situation calls for management that is (i) conservative to provide a buffer against regionally unmanageable global perturbations, (ii) adaptive to react to new management challenges, and, ultimately, (iii) multisectorial and integrative to address conflicts associated with economic trade-offs.

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.