Quantifying heterogeneity in ecohydrological partitioning in urban green spaces through the integration of empirical and modelling approaches.

Urban green spaces (UGS) can help mitigate hydrological impacts of urbanisation and climate change through precipitation infiltration, evapotranspiration and groundwater recharge. However, there is a need to understand how precipitation is partitioned by contrasting vegetation types in order to target UGS management for specific ecosystem services. We monitored, over one growing season, hydrometeorology, soil moisture, sapflux and isotopic variability of soil water under contrasting vegetation (evergreen shrub, evergreen conifer, grassland, larger and smaller deciduous trees), focussed around a 150-m transect of UGS in northern Scotland. We further used the data to develop a one-dimensional model, calibrated to soil moisture observations (KGE's generally > 0.65), to estimate evapotranspiration and groundwater recharge. Our results evidenced clear inter-site differences, with grassland soils experiencing rapid drying at the start of summer, resulting in more fractionated soil water isotopes. Contrastingly, the larger deciduous site saw gradual drying, whilst deeper sandy upslope soils beneath the evergreen shrub drained rapidly. Soils beneath the denser canopied evergreen conifer were overall least responsive to precipitation. Modelled ecohydrological fluxes showed similar diversity, with median evapotranspiration estimates increasing in the order grassland (193 mm) < evergreen shrub (214 mm) < larger deciduous tree (224 mm) < evergreen conifer tree (265 mm). The evergreen shrub had similar estimated median transpiration totals as the larger deciduous tree (155 mm and 128 mm, respectively), though timing of water uptake was different. Median groundwater recharge was greatest beneath grassland (232 mm) and lowest beneath the evergreen conifer (128 mm). The study showed how integrating observational data and simple modelling can quantify heterogeneities in ecohydrological partitioning and help guide UGS management.

An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 1: Background and model description.

A new Model for the Agent-based simulation of Faecal Indicator Organisms (MAFIO) is developed that attempts to overcome limitations in existing faecal indicator organism (FIO) models arising from coarse spatial discretisations and poorly-constrained hydrological processes. MAFIO is a spatially-distributed, process-based model presently designed to simulate the fate and transport of agents representing FIOs shed by livestock at the sub-field scale in small (<10 km2) agricultural catchments. Specifically, FIO loading, die-off, detachment, surface routing, seepage and channel routing are modelled on a regular spatial grid. Central to MAFIO is that hydrological transfer mechanisms are simulated based on a hydrological environment generated by an external model for which it is possible to robustly determine the accuracy of simulated catchment hydrological functioning. The spatially-distributed, tracer-aided ecohydrological model EcH2O-iso is highlighted as a possible hydrological environment generator. The present paper provides a rationale for and description of MAFIO, whilst a companion paper applies the model in a small agricultural catchment in Scotland to provide a proof-of-concept.

An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 2: Application to a small agricultural catchment.

The new Model for the Agent-based simulation of Faecal Indicator Organisms (MAFIO) is applied to a small (0.42 km2) Scottish agricultural catchment to simulate the dynamics of E. coli arising from sheep and cattle farming, in order to provide a proof-of-concept. The hydrological environment for MAFIO was simulated by the "best" ensemble run of the tracer-aided ecohydrological model EcH2O-iso, obtained through multi-criteria calibration to stream discharge (MAE: 1.37 L s-1) and spatially-distributed stable isotope data (MAE: 1.14-3.02‰) for the period April-December 2017. MAFIO was then applied for the period June-August for which twice-weekly E. coli loads were quantified at up to three sites along the stream. Performance in simulating these data suggested the model has skill in capturing the transfer of faecal indicator organisms (FIOs) from livestock to streams via the processes of direct deposition, transport in overland flow and seepage from areas of degraded soil. Furthermore, its agent-based structure allowed source areas, transfer mechanisms and host animals contributing FIOs to the stream to be quantified. Such information is likely to have substantial value in the context of designing and spatially-targeting mitigation measures against impaired microbial water quality. This study also revealed, however, that avenues exist for improving process conceptualisation in MAFIO (e.g. to include FIO contributions from wildlife) and highlighted the need to quantitatively assess how uncertainty in the spatial extent of surface flow paths in the simulated hydrological environment may affect FIO simulations. Despite the consequent status of MAFIO as a research-level model, its encouraging performance in this proof-of-concept study suggests the model has significant potential for eventual incorporation into decision support frameworks.

Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment.

An 11year dataset of concentrations of E. coli at 10 spatially-distributed sites in a mixed land-use catchment in NE Scotland (52km2) revealed that concentrations were not clearly associated with flow or season. The lack of a clear flow-concentration relationship may have been due to greater water fluxes from less-contaminated headwaters during high flows diluting downstream concentrations, the importance of persistent point sources of E. coli both anthropogenic and agricultural, and possibly the temporal resolution of the dataset. Point sources and year-round grazing of livestock probably obscured clear seasonality in concentrations. Multiple linear regression models identified potential for contamination by anthropogenic point sources as a significant predictor of long-term spatial patterns of low, average and high concentrations of E. coli. Neither arable nor pasture land was significant, even when accounting for hydrological connectivity with a topographic-index method. However, this may have reflected coarse-scale land-cover data inadequately representing "point sources" of agricultural contamination (e.g. direct defecation of livestock into the stream) and temporal changes in availability of E. coli from diffuse sources. Spatial-stream-network models (SSNMs) were applied in a novel context, and had value in making more robust catchment-scale predictions of concentrations of E. coli with estimates of uncertainty, and in enabling identification of potential "hot spots" of faecal contamination. Successfully managing faecal contamination of surface waters is vital for safeguarding public health. Our finding that concentrations of E. coli could not clearly be associated with flow or season may suggest that management strategies should not necessarily target only high flow events or summer when faecal contamination risk is often assumed to be greatest. Furthermore, we identified SSNMs as valuable tools for identifying possible "hot spots" of contamination which could be targeted for management, and for highlighting areas where additional monitoring could help better constrain predictions relating to faecal contamination.