Incorporating spatial heterogeneity into evapotranspiration estimates for bioretention basins

Abstract. Green stormwater infrastructure (GSI) systems like bioretention basins are frequently used in urban settings to reduce the amount of stormwater runoff entering combined sewer systems, thus protecting downstream waterbodies. Retaining stormwater in GSI allows it to infiltrate into the soil or return to the atmosphere via evapotranspiration (ET). While infiltration rates can be quantified with reasonable accuracy, methods of quantifying ET typically rely on models designed for homogeneous landcover like agricultural fields; the high spatial variation in factors including vegetation, light, and soil moisture renders estimates of ET from bioretention basins highly uncertain. To assess the influence of such variation on basin-scale ET and evaluate means of correcting for it, we quantified ET for a bioretention basin in Philadelphia, USA using three approaches: (1) an empirically-based model that incorporated direct measurements of ET and accounted for heterogeneity in plant size, light conditions, and microtopography, (2) an empirical estimate of ET based on changes in soil moisture that did not account for spatial heterogeneity, and (3) a series of conventional ET models that did not account for spatial heterogeneity. We further evaluated three methods of adjusting modeled ET estimates to better align with ours. Our empirically-based model found basin-scale daily ET to range from 0–6 mm d^-1, with temporal variation dependent on weather conditions and time of year. A sensitivity analysis demonstrated that the spatial composition of plant height and shade strongly influenced basin-scale estimates. The soil moisture-based method found daily values to range from 0–4 mm d^-1, which largely agreed with the empirically-based modeling estimates for the location where sensors were placed, but underpredicted estimates of basin-scale ET. Most conventional models overpredicted ET compared to our empirically-based values on average, though three were less sensitive to variation in atmospheric conditions (Granger-Grey, Hargreaves-Samani, and Matt-Shuttleworth) and thus overpredicted ET at the low to middle part of the range but underpredicted ET at the upper end of the range. This limited the ability of additive or multiplicative adjustments to improve agreement, though adjustments were highly effective for the three conventional models more sensitive to atmospheric conditions (Penman-Monteith ASCE, Penman-Monteith FAO, and Priestly-Taylor). The strongest agreement we could achieve came from an additive adjustment to Penman-Monteith-derived values (subtracting 2.31 mm d^-1 from the ASCE formulation or 1.82 mm
d^-1 from the FAO formulation). Multiplicative adjustments (i.e., landscape coefficients) and corrections accounting for shade were also effective. Our results highlight the importance of implicitly or explicitly accounting for spatial heterogeneity when quantifying ET, especially with respect to vegetation height and shade. For basins similar to our focal basin, this can be accomplished through the provided adjustments to conventional models. Additional calibration is required otherwise, but the growing availability of required data makes this increasingly viable.