Characterizing Catchment‐Scale Nitrogen Legacies and Constraining Their Uncertainties

Abstract

Since the beginning of the twentieth century, nitrogen (N) inputs to soils have increased tremendously worldwide, due to application of synthetic N fertilizers and manure in agricultural areas B. Zhang et al., 2017), and due to deposition of atmospheric N, coming mostly from fossil fuel combustion (Holland et al., 1999). These large N inputs to soils have not been balanced by N removal from soils through incorporation into plant biomass and crop harvest, resulting in large soil N surpluses in many places across the world, for

Pulling the rabbit out of the hat: Unravelling hidden nitrogen legacies in catchment‐scale water quality models

“…The explicit representation of one or several soil pools and, in particular, of immobile soil ON can enable simulation of N storage in soils over long timescales and thus of biogeochemical legacy effects of excessive N inputs (Figure 1). While this representation is comparably well established in current nutrient models, only very recently have some studies analysed the temporal dynamics of soil N storage in more detail (e.g., Ilampooranan et al, 2022; Lee et al, 2016; Sarrazin et al, 2022; Van Meter et al, 2017, 2018). Accordingly, detailed analyses of the impact of model structure and/or parameter values on the simulated build‐up of soil ON have rarely been performed (but see, e.g.…”

“…We argue that such model‐internal plausibility checks should also become a routine procedure in water quality modelling. In particular, the effect of biogeochemical legacy can become more evident and transparent only if modelling studies routinely report and quantify N build‐up in the inactive ON pool (Sarrazin et al, 2022). Hence, modelling studies should include, report and critically analyse model‐internal N fluxes and stores in addition to concentrations in the receiving waters.…”

“…The scarcity of field data certainly poses limitations to a thorough evaluation of simulated legacy stores in water quality models (Sarrazin et al, 2022). Long‐term data on the build‐up of ON in soils are scarce and often limited to a few locations and the top soil (e.g.…”

“…Hence, we need field data that are more tailored to characterization of N legacies such as (i) soil ON measurements over longer periods to quantify biogeochemical legacy; (ii) nitrate concentrations in groundwater to evaluate simulated IN in the subsurface; (iii) tracer data to constrain simulated TTDs (Benettin et al, 2017; Rodriguez et al, 2021; Visser et al, 2019); (iv) field assessments of denitrification in groundwater (Eschenbach et al, 2018); (v) detailed spatial and temporal data on N input from point and diffuse sources to facilitate quantification of N legacy from N budgets (Ehrhardt et al, 2021; Van Meter & Basu, 2017) or trajectories of N seasonality (Ebeling et al, 2021); and (vi) “soft data” such as geochemical information on denitrification potential in the soil and deeper subsurface. Regardless of the quantity and quality of these observational data, uncertainties in simulated legacies associated with model input data, structure and parameterisation need to be clearly addressed and communicated (Sarrazin et al, 2022).…”

“…They are now commonly used to investigate present‐state N concentrations and loads, and to predict their future trajectories in groundwater and surface water bodies under different management scenarios. While the ability of these models to capture observed N concentrations in streams is generally documented, little attention is paid to examining whether and how well these existing models represent N‐legacy effects (Sarrazin et al, 2022; Van Meter & Basu, 2015). Here we (1) briefly review and show if and how N legacies are represented in current water quality models; and (2) propose stronger efforts towards proper representation and assessment of legacy components in water quality modelling.…”