Biogeochemical models simulate soil nitrogen (N) turnover and are often used to assess N losses through denitrification. Though models simulate a complete N budget, only specific N pools/fluxes (i.e. N2O, NO3-, NH3, NOx) are usually published, because the full budget cannot be validated with measured data. Field studies rarely include full N balances, especially N2 fluxes, which are difficult to quantify. Limiting publication of modeling results based on available field data is a missed opportunity to improve the understanding of modeled processes. We suggest that the modeler community support publication of all simulated N pools and processes in future studies.

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.

Soil-gas diffusivity plays a fundamental role on diffusion-controlled migration of climate impact gases from different terrestrial ecosystems including managed pasture systems. Soil-gas diffusivity has a strong bearing on soil type/texture and soil structure (e.g., density) and typically shows a depth-dependent behavior in subsurface. This study investigated the gas diffusivity in soils sampled from a managed pasture site at Ambewela, Sri Lanka at 0-5 cm depth range along a downgrading transect. The soils were pre-characterized for particle-size distribution, organic matter content, dry density and particle density. Soil-gas diffusivity was measured using one-chamber diffusion apparatus using N2 and O2 as experimental gases. The measured diffusivity, together with selected intact and repacked soil data from literature, were tested against the existing predictive gas diffusivity models. We used a generalized descriptive parametric two-region model to represent bimodal/two-region behaviour of selected soils which was able to statistically outperform the predictive models for both intact and repacked soils and hence demonstrated its applicability to better characterize site-specific greenhouse gas emissions with useful implications for pasture management.