Spatial variation in landscape attributes can account for much of the variability in water quality compared to land use factors. Spatial variability arises from gradients in topographic, edaphic, and geologic landscape attributes that govern the four dominant processes (atmospheric, hydrological, microbially mediated redox, physical and chemical weathering) that generate, store, attenuate, and transport contaminants. This manuscript extends the application of Process Attribute Mapping (PoAM), a hydrochemically guided landscape classification system for modelling spatial variation in multiple water quality indices, using New Zealand (268,021 km²) as an example. Twelve geospatial datasets and >10,000 ground and surface water samples from 2,921 monitoring sites guided the development of 16 process-attribute gradients (PAG) within a geographic information system. Hydrochemical tracers were used to test the ability of PAG to replicate each dominant process (cross validated R2 of 0.96 to 0.54). For water quality, land use intensity was incorporated and the performance of PAG was evaluated using an independent dataset of 811 long-term surface water quality monitoring sites (R2 values for total nitrogen of 0.90 - 0.71 (median = 0.78), nitrate-nitrite nitrogen 0.83 - 0.71 (0.79), total phosphorus 0.85 - 0.63 (0.73), dissolved reactive phosphorus 0.76 - 0.57 (0.73), turbidity 0.92 - 0.48 (0.69), clarity 0.89 - 0.50 (0.62) and E. coli 0.75 - 0.59 (0.74)). The PAGs retain significant regional variation, with relative sensitivities related to variable geological and climatic histories. Numerical models or policies that do not consider landscape variation likely produce outputs or rule frameworks that may not support improved water quality.

High producing grazed pastures occupy almost one third of Aotearoa New Zealand (268,000 km2) and produce exported protein to feed more than 40 million people. Trends within farming systems include increasing rates of urea fertiliser use and greater concentrations of N via urine deposition, which enhance N2O emissions and NO3 losses. Focussing on N-sensitive lake catchments, we ask: what tools can reduce uncertainties in the sources and magnitude of N losses, quantify the potential to reduce excess N, and clarify rates of change in N budgets. Dual-isotope NO3 measurements differentiate urine and urea-derived sources (δ15N < 4 ‰) from mineralized soil organic N (δ15N of 4–8 ‰). Nitrate in streams draining the Rotorua region’s pumice soils and aquifers is dominated by urine and urea sources, compared to streams flowing from finer soils that only show these lower δ15N values when large runoff events activate surface flow paths. We have confirmed that shifts in stream water δ2H and δ18O toward the values observed in the major rainfall event coincide with elevated [NO3] and low δ15N representative of urine and urea-derived sources. A combination of δ2H and δ18O and Δ14C in dissolved inorganic carbon (DIC) largely confirmed tritium-based assessments, suggesting lag times of many decades in some aquifers, but rapid responses to recent N inputs elsewhere. In the Southland region, where tile drainage enables effective pasture growth, we explored flow responses in a drainage tile where NO3 consistently showed an imprint of denitrification (δ15N > 12 ‰). In this location following major rainfall, [NO3] remained stable but dissolved organic N concentrations increased, at times associated with stormwater δ2H and δ18O shifts. The Δ14C in DIC yielded apparent ages of several hundred years during low-flow periods, suggesting ongoing breakdown of soil organic matter releases N, which should be considered in farm and catchment N budgets. We conclude that monitoring N concentrations and multiple isotope species can resolve control points of N excess, which reveal targets for potential mitigation. Specifically, N content in clover-ryegrass pastures seasonally exceeds N demand in grazing animals, suggesting alternate species or feeds could reduce animal urinary N excretion, and therefore limit soil-derived N2O emissions and NO3 losses.