Functional Multi‐Scale Integration of Agricultural Nitrogen‐Budgets Into Catchment Water Quality Modeling

Abstract

Agricultural nitrogen (N) pollution has become the major pressure on inland and coastal aquatic ecosystems (Reusch et al., 2018;Van Meter et al., 2018). Meanwhile, agricultural intensification remains one of the main strategies to satisfy increasing global food demand (Chukalla et al., 2020;Schils et al., 2018). Overarching policies for sustainable development are increasingly calling for better reconciliation of agricultural interests with environment protection needs and further development of cost-effective and targeted measures, especially under changing climatic conditions (EEA, 2020a(EEA, , 2020bReusch et al., 2018).In agricultural research, budget methods are commonly used to assess nitrogen use efficiencies and potential environmental impact (Cassman et al., 2002). The latter is often indicated by N surplus (input minus output) and assessed based on readily available data from the farm gate to national scales (Eurostat, 2013). The budgeting methods provide a clear evidence base of complex relationships between agricultural activities and environmental responses and therefore are particularly favorable for communicating to policy makers (Cherry et al., 2008;Zhang et al., 2020). However, in addition to potential uncertainty embedded in data acquisition (Oenema et al., 2003), it is well recognized that budget calculations only provide estimates of potential N losses, and their credibility Abstract Diffuse Nitrogen pollution from agriculture maintains high pressures on groundwater and aquatic ecosystems. Further mitigation requires targeted measures that reconcile agricultural interests in environmental protection. However, the agriculture-related processes of catchment N modeling remain poorly defined due to discipline-specific data and knowledge gaps. Using field-experimental data, crop N uptake responses to fertilizer management were parsimoniously conceptualized and integrated into a catchment diffuse-N model. The improved catchment modeling further facilitated integration with agricultural budget-based assessments. The integrated analysis in a mesoscale catchment disentangled contrasting agri-environment functional mechanisms in typically flashy chemodynamic and transport-limited chemostatic export regimes. Moreover, the former was actively responsive to interannual climatic variability and agricultural practices; the latter exhibited drought-induced enhancement of N enrichment, which could likely be mitigated through reduced fertilization. This interdisciplinary integration of data and methods provided an insightful evidence base for multi-sector targeted measures, especially under cumulative impacts of changing climate and fertilizer-use intensities.Plain Language Summary Due to intensive nitrogen fertilizer use in agriculture over recent decades, excessive nitrogen has largely accumulated in soil and groundwater systems and is gradually being released to surface waters, polluting aquatic environments. Considering the changing climate and growing food demand, future environmental mitigation should purs...

Upscaling Tracer‐Aided Ecohydrological Modeling to Larger Catchments: Implications for Process Representation and Heterogeneity in Landscape Organization

“…Here, we defined the drainage areas above the MEIS gauging station as the upland areas (184 km 2 , dominated by cambisols overlaying the shallow schist/claystone bedrock) and the rest as the lowland areas (272 km 2 , dominated by chernozems developed on the loess deposits). In line with recent European‐wide changing climate, the catchment has experienced severe droughts in 2018–2019 (Yang, Rode, et al., 2022; Yang et al., 2021), exhibiting lower precipitation and higher air temperature than averages (Figure S1 in Supporting Information ).…”

“…In the Selke uplands, the high‐elevation headwaters exhibited much higher precipitation and annual runoff than the leeward‐positioned central areas. The thin soils and shallow bedrock have resulted in a relatively flashy flow regime, with rapid replenishment of soil water storage deficits (Dupas et al., 2017; Yang, Rode, et al., 2022). Coupled with the spatially varying precipitation, the simulated total runoff and contributions of surface overland flow exhibited significant spatial variability (Figure 6b).…”

“…Labels at the end of vegetation-and soil-type dependent parameters corresponded to abbreviations in Figures 1c and 1d, respectively. The full sensitivity ranking results were provided in the associated repository (Yang et al, 2022).…”