Curative vs. preventive management of nitrogen transfers in rural areas: lessons from the case of the Orgeval watershed (Seine River basin, France)

Sampling frequency, load estimation and the disproportionate effect of storms on solute mass flux in rivers

Riverine discharge (Q) and dissolved concentrations (C) dictate solute mass export from watersheds. Commonly Q is tracked at a much higher frequency than C for most major solutes, leading to the necessity of load estimation algorithms which are often based on sparse data. The result is that the disproportionate effects of short-duration events (e.g., storms) on solute mass fluxes are poorly known. Here we use novel lab-in-the-field instrumentation to compare high temporal-resolution (∼30 min to 7 h) datasets of major ion chemistry collected over a year of continuous monitoring in three watersheds ranging over four orders of magnitude in drainage area. In these diverse settings, we quantify the errors associated with common load estimation algorithms and reduced sampling frequencies. When sample frequencies are coarsened, the mass flux of solutes which are diluted by storm events (i.e., Ca2+, Mg2+, Na+, Cl- and SO42-) are systematically overestimated, while nutrients which become mobilized by these events (K+ and NO3-) are underestimated. This is most pronounced in the largest river, and strongly tied to the increasing likelihood that storm events are missed as sampling frequencies decrease. Coarsening our high-resolution data to monthly sampling frequency yields an average overestimate of 8 % for Na+ and an average underestimate of 32.5 % for K+ across the three watersheds, illustrating clear implications for estuary and coastal water eutrophication, chemical weathering budgets, and agricultural land management practices. SYNOPSIS: A new 'lab-in-the-field' technology produces continuous high-frequency records of the full suite of major ions in rivers. These data highlight the disproportionate effect of large storms on catchment solute exports and the error associated with temporally coarse monitoring.

A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks

Farm dams are a ubiquitous limnological feature of agricultural landscapes worldwide. While their primary function is to capture and store water, they also have disproportionally large effects on biodiversity and biogeochemical cycling, with important relevance to several Sustainable Development Goals (SDGs). However, the abundance and distribution of farm dams is unknown in most parts of the world. Therefore, we used artificial intelligence and remote sensing data to address this critical global information gap. Specifically, we trained a deep learning convolutional neural network (CNN) on high-definition satellite images to detect farm dams and carry out the first continental-scale assessment on density, distribution and historical trends. We found that in Australia there are 1.765 million farm dams that occupy an area larger than Rhode Island (4678 km2) and store over 20 times more water than Sydney Harbour (10,990 GL). The State of New South Wales recorded the highest number of farm dams (654,983; 37% of the total) and Victoria the highest overall density (1.73 dams km−2). We also estimated that 202,119 farm dams (11.5%) remain omitted from any maps, especially in South Australia, Western Australia and the Northern Territory. Three decades of historical records revealed an ongoing decrease in the construction rate of farm dams, from >3% per annum before 2000, to ~1% after 2000, to <0.05% after 2010—except in the Australian Capital Territory where rates have remained relatively high. We also found systematic trends in construction design: farm dams built in 2015 are on average 50% larger in surface area and contain 66% more water than those built in 1989. To facilitate sharing information on sustainable farm dam management with authorities, scientists, managers and local communities, we developed AusDams.org—a free interactive portal to visualise and generate statistics on the physical, environmental and ecological impacts of farm dams.

Ecological Functioning of the Seine River: From Long-Term Modelling Approaches to High-Frequency Data Analysis

At the start of the PIREN-Seine program, organic pollution by the effluent of the Parisian conurbation was responsible for episodic anoxia in the lower Seine River, while nutrients from both point and diffuse sources are used to cause eutrophication, a nuisance for drinking water production from surface water and biodiversity. The implementation of the EU Water Framework Directive led to a drastic decrease of organic carbon, phosphorus and ammonium concentrations in surface waters starting in the early 2000s and to a reduction of the frequency and the amplitude of phytoplankton blooms. However, nitrate contamination from fertiliser-intensive agriculture continued to increase or at best levelled off, threatening groundwater resources and causing unbalanced nutrient ratios at the coastal zone where eutrophication still results in harmful algal blooms. High-frequency O2 data combined with models, which have been developed for 30 years, can help discriminate the contribution of auto- vs. heterotrophic metabolism in the CO2 supersaturation observed in the Seine River. Despite the impressive improvement in water quality of the Seine River, episodic crises such as summer low-flow conditions still threaten the good ecological status of both river and coastal waters. Modelling scenarios, including further wastewater treatments and structural changes in agriculture and future changes in hydrology under climate changes, provide the basis for a future vision of the ecological functioning of the Seine River network.

Integrated management for sustainable cropping systems: Looking beyond the greenhouse balance at the field scale

Cover crops (CC) promote the accumulation of soil organic carbon (SOC), which provides multiple benefits to agro-ecosystems. However, additional nitrogen (N) inputs into the soil could offset the CO2 mitigation potential due to increasing N2 O emissions. Integrated management approaches use organic and synthetic fertilizers to maximize yields while minimizing impacts by crop sequencing adapted to local conditions. The goal of this work was to test whether integrated management, centered on CC adoption, has the potential to maximize SOC stocks without increasing the soil greenhouse gas (GHG) net flux and other agro-environmental impacts such as nitrate leaching. To this purpose, we ran the DayCent bio-geochemistry model on 8,554 soil sampling locations across the European Union. We found that soil N2 O emissions could be limited with simple crop sequencing rules, such as switching from leguminous to grass CC when the GHG flux was positive (source). Additional reductions of synthetic fertilizers applications are possible through better accounting for N available in green manures and from mineralization of soil reservoirs while maintaining cash crop yields. Therefore, our results suggest that a CC integrated management approach can maximize the agro-environmental performance of cropping systems while reducing environmental trade-offs.

Long-term changes in greenhouse gas emissions from French agriculture and livestock (1852-2014): From traditional agriculture to conventional intensive systems

France was a traditionally agricultural country until the first half of the 20th century. Today, it is the first European cereal producer, with cereal crops accounting for 40% of the agricultural surface area used, and is also a major country for livestock breeding with 25% of the European cattle livestock. This major socioecological transition, with rapid intensification and specialisation in an open global market, has been accompanied by deep environmental changes. To explore the changes in agricultural GHG emissions over the long term (1852-2014), we analysed the emission factors of N₂O from field experiments covering major land uses, in a gradient of fertilisation and within a range of temperature and rainfall, and used CH₄ emission coefficients for livestock categories, in terms of enteric and manure management, considering the historical changes in animal excretion rates. We also estimated indirect CO₂ emissions, rarely accounted for in agricultural emissions, using coefficients found in the literature for the dominant energy consumption items (fertiliser production, field work and machinery, and feed import). From GHG emissions of ~30,000 ktons CO₂ Eq yr^-1 in 1852, reaching 54,000 ktons CO₂ Eq yr^-1 in 1955, emissions more than doubled during the 'Glorious thirties' (1950-1980), and peaked around 120,000 ktons CO₂ Eq yr^-1 in the early 2000s. For the 2010-2014 period, French agriculture GHG emissions stabilised at ~114,000 ktons CO₂ Eq yr^-1, distributed into 49% methane (CH₄), 22% carbon dioxide (CO₂) and 29% nitrous oxide (N₂O). A regional approach through 33 regions in France shows a diversity of agriculture reflecting the hydro-ecoregion distribution and the agricultural specialisation of local areas. Exploring contrasting scenarios at the 2040 horizon suggests that only deep changes in the structure of the agro-food system would double the reduction of GHG emissions by the agricultural sector.

How can water quality be improved when the urban waste water directive has been fulfilled? A case study of the Lot river (France)

The Lot river, a major tributary of the downstream Garonne river, the largest river on the Northern side of the Pyrenees Mountains, was intensively studied in the 1970s. A pioneering program called "Lot Rivière Claire" provided a diagnosis of water quality at the scale of the whole watershed and proposed an ambitious program to manage nutrient pollution and eutrophication largely caused by urban wastewater releases. Later on, the implementation of European directives from 1991 to 2000 resulted in the nearly complete treatment of point sources of pollution in spite of a doubling of the basin's population. At the outlet of the Lot river, ammonium and phosphate contamination which respectively peaked to 1 mg N-NH₄ L^-1 and 0.3 mg P-PO₄ L^-1 in the 1980s returned to much lower levels in recent years (0.06 mg N-NH₄ L^-1 and 0.02 mg P-PO₄ L^-1), a reduction by a factor 15. However, during this time, nitrate contamination has regularly increased since the 1980s, from 0.5 to 1.2 mg N-NO₃ L^-1 in average, owing to the intensification of agriculture and livestock farming. Application of the Riverstrahler model allowed us to simulate the water quality of the Lot drainage network for the 2002-2014 period. We showed that, with respect to algal requirements, phosphorus and silica are well balanced, but nitrogen remains largely in excess over phosphorus and silica. This imbalance can be problematic for the ecological status of the water bodies. Using the model, for simulating various scenarios of watershed management, we showed that improvement of urban wastewater treatment would not result in any significant change in the river's water quality. Even though arable land occupies a rather limited fraction of the watershed area, only the adoption of better farming practices or more radical changes in the agro-food system could reverse the trend of increasing nitrate contamination.

A process-oriented hydro-biogeochemical model enabling simulation of gaseous carbon and nitrogen emissions and hydrologic nitrogen losses from a subtropical catchment

Quantification of nitrogen losses and net greenhouse gas (GHG) emissions from catchments is essential for evaluating the sustainability of ecosystems. However, the hydrologic processes without lateral flows hinder the application of biogeochemical models to this challenging task. To solve this issue, we developed a coupled hydrological and biogeochemical model, Catchment Nutrients Management Model - DeNitrification-DeComposition Model (CNMM-DNDC), to include both vertical and lateral mass flows. By incorporating the core biogeochemical processes (including decomposition, nitrification, denitrification and fermentation) of the DNDC into the spatially distributed hydrologic framework of the CNMM, the simulation of lateral water flows and their influences on nitrogen transportation can be realized. The CNMM-DNDC was then calibrated and validated in a small subtropical catchment belonged to Yanting station with comprehensive field observations. Except for the calibration of water flows (surface runoff and leaching water) in 2005, stream discharges of water and nitrate in 2007, the model validations of soil temperature, soil moisture, crop yield, water flows in 2006 and associated nitrate loss, fluxes of methane, ammonia, nitric oxide and nitrous oxide, and stream discharges of water and nitrate in 2008 were statistically in good agreement with the observations. Meanwhile, our initial simulation of the catchment showed scientific predictions. For instance, it revealed the following: (i) dominant ammonia volatilization among the losses of nitrogenous gases (accounting for 11-21% of the applied annual fertilizer nitrogen in croplands); (ii) hotspots of nitrate leaching near the main stream; and (iii) a net GHG sink function of the catchment. These results implicate the model's promising capability of predicting ecosystem productivity, hydrologic nitrogen loads, losses of gaseous nitrogen and emissions of GHGs, which could be used to provide strategies for establishing sustainable catchments. In addition, the model's capability would be further proved by applying in other catchments with different backgrounds.

Water management practices exacerbate nitrogen retention in Mediterranean catchments

Nitrogen (N) retention sensu lato refers to all processes preventing new reactive nitrogen brought into watersheds through agricultural or industrial activities to be exported by river systems to the sea. Although such processes protect marine systems from the threat of eutrophication and anoxia, they raise other environmental issues, including the acidification of soils, the emission of ammonia and greenhouse gases, and the pollution of aquifers. Despite these implications, the factors involved in N retention are still poorly controlled, particularly in arid and semi-arid systems. The present study evaluates the N fluxes of 38 catchments in the Iberian Peninsula with contrasting climatic characteristics (temperate and Mediterranean), land uses, and water management practices. This diversity allows addressing the contribution of physical and socioecological factors in N retention, and more specifically, exploring the relation between N retention and water regulation. We hypothesise that the extreme flow regulation implemented in the Mediterranean enhances the high N retention values associated with arid and semi-arid regions. The results show that reservoirs and irrigation channels account for >50% of the variability in N retention values, and above a certain regulation threshold, N retention peaks to values >85-90%. Future climate projections forecast a decrease in rainfall and an increase in agricultural intensification and irrigation practices in many world regions, most notably in arid and semi-arid areas. Increased water demand will likely lead to greater flow regulation, and the situation in many areas may resemble that of Iberian Mediterranean catchments. High N retention and the associated environmental risks must therefore be considered and adequately addressed.