Upscaling Nitrogen Removal Capacity from Local Hotspots to Low Stream Orders’ Drainage Basins

Abstract

Denitrification is the main process removing nitrate in river drainage basins and buffer input from agricultural land and limits aquatic ecosystem pollution. However, the identification of denitrification hotspots (for example, riparian zones), their role in a landscape context and the evolution of their overall removal capacity at the drainage basin scale are still challenging. The main approaches used (that is, mass balance method, denitrification proxies, and potential wetted areas) suffer from methodological drawbacks. We review these approaches and the key frameworks that have been proposed to date to formalize the understanding of the mechanisms driving denitrification: (i) Diffusion versus advection pathways of nitrate transfer, (ii) the biogeochemical hotspot, and (iii) the Damkö hler ratio. Based on these frameworks, we propose to use high-resolution mapping of catchment topography and landscape pattern to define both potential denitrification sites and the dynamic hydrologic modeling at a similar spatial scale (<10 km 2 ). It would allow the quantification of cumulative denitrification activity at the small catchment scale, using spatially distributed Damkö hler and Peclet numbers and biogeochemical proxies. Integration of existing frameworks with new tools and methods offers the potential for significant breakthroughs in the quantification and modeling of denitrification in small drainage basins. This can provide a basis for improved protection and restoration of surface water and groundwater quality.

Enhanced hyporheic exchange flow around woody debris does not increase nitrate reduction in a sandy streambed

“…Low-order (headwater) streams are important in the downstream transport of diffuse nitrate (Alexander et al 2007). However, rivers are not inert pipelines, on the contrary, they are actually biogeochemical hot spots (Benstead and Leigh 2012;McClain et al 2003;Pinay et al 2015), which also play a major role in the transformation of reactive nitrogen to less ecologically active forms (Bernhardt et al 2005;Bernot and Dodds 2005). Across large catchments, nitrate attenuation is inversely correlated with stream-order, due to the relatively large surface area to volume ratios in smaller streams which aids surface water contact with the hypoxic bed (Alexander et al 2000;Peterson et al 2001).…”

Benthic and hyporheic macroinvertebrate distribution within the heads and tails of riffles during baseflow conditions

“…Consequently, the dynamic pattern of vertical hydraulic exchange exerts a strong influence on physical and chemical conditions such as temperature, oxygen concentrations and the residence time of water (Olsen & Townsend, 2003;Krause et al, 2011b). Surface water downwelling into subsurface sediments typically delivers oxygen and inputs of organic matter to hyporheic habitats, promoting aerobic microbial processes (Findlay et al, 1993;Pinay et al, 2015). In contrast, oxygen poor, nutrientrich water often enters the stream at upwelling locations (Grimm et al, 2007).…”

Constitution of a catchment virtual observatory for sharing flow and transport models outputs

“…We propose that the comparison of travel time from catchments from differing biomes could lead to a more systematic understanding of catchment behaviour and general theory. Because biogeochemistry is tightly linked to residence time, a better understanding of the travel time distributions will improve the assessment of nutrient removal capacity (Pinay et al, 2015), climate change impacts on stream chemistry (Abbott et al, 2015;Goode, 1996;McGuire et al, 2005) and exposure time (Ginn, 1999;Frei et al, this issue;Oldham et al, 2013).…”

“…These hydrological parameters influence catchment biogeochemistry (Ocampo et al, 2006;Oldham et al, 2013;Pinay et al, 2015;Tetzlaff et al, 2007), further increasing their value as 4 indicators and predictors of catchment-scale water quality and chemistry. Because these parameters are of great general interest they feature prominently in the inputs and outputs of many models (e.g.…”

“…The mean transit time and whole stream residence time distribution are powerful metrics of catchment functioning, providing synoptic hydrological information such as water renewal time, heterogeneity of flowpaths, and overall water volume (Godsey et al, 2010;Hrachowitz et al, 2010; Marçais et al, 2015;McGuire and McDonnell, 2006;Van der Velde et al, 2012) . These hydrological parameters influence catchment biogeochemistry (Ocampo et al, 2006;Oldham et al, 2013;Pinay et al, 2015;Tetzlaff et al, 2007), further increasing their value as …”