Data-driven analysis of nutrient inputs and transfers through nested catchments

Highlights: • Novel data-driven screening method for estimating nutrient input and delivery • Hotspot areas of nutrient input identified in the Sava River Catchment. • Relative nutrient delivery exhibits power-law decay with increasing catchment scale. • Relationship between nutrient load and runoff consistent cross-regionally • Cross-regional consistency between concentration and socio-economic conditions A data-driven screening methodology is developed for estimating nutrient input and retention-delivery in catchments with measured water discharges and nutrient concentrations along the river network. The methodology is applied to the Sava River Catchment (SRC), a major transboundary catchment in southeast Europe, with seven monitoring stations along the main river, defining seven nested catchments and seven incremental subcatchments that are analysed and compared in this study. For the relatively large nested catchments (> 40,000 km²), characteristic regional values emerge for nutrient input per unit area of around 30 T/yr/km² for dissolved inorganic nitrogen (DIN) and 2 T/yr/km² for total phosphorus (TP). For the smaller nested catchments and incremental subcatchments, corresponding values fluctuate and indicate hotspot areas with total nutrient inputs of 158 T/yr/km² for DIN and 13 T/yr/km² for TP. The delivered fraction of total nutrient input mass (termed delivery factor) and associated nutrient loads per area are scale-dependent, exhibiting power-law decay with increasing catchment area, with exponents of around 0.2–0.3 for DIN and 0.3–0.5 for TP. For the largest of the nested catchments in the SRC, the delivery factor is around 0.08 for DIN and 0.03 for TP. Overall, the nutrient data for nested catchments within the SRC show consistency with previously reported data for multiple nested catchments within the Baltic Sea Drainage Basin, identifying close nutrient relationships to driving hydro-climatic conditions (runoff for nutrient loads) and socio-economic conditions (population density and farmland share for nutrient concentrations).