Incorporating spatial heterogeneity into evapotranspiration estimates for bioretention basins

Green stormwater infrastructure (GSI) systems such as 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 chamber flux 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 at a single location, and (3) a set of six conventional ET models that did not account for spatial heterogeneity. We further evaluated three methods of adjusting conventionally-modeled ET estimates to better align with those from the modeling approach based on chamber flux data. Our empirically-based model indicated that basin-scale, daily ET ranged from 0–6 mm d −1 , with temporal variation depending 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 more closely matched empirically based estimates for the sensor location than basin-scale estimates. Most conventional models overpredicted ET on average, though three models (Granger-Gray, Hargreaves-Samani, and Matt-Shuttleworth) were less sensitive to variation in atmospheric conditions and thus overpredicted ET at the low to middle part of the range but underpredicted ET at the upper end of the range. The muted response to atmospheric conditions limited the ability of additive or multiplicative adjustments (i.e., landscape coefficients) to improve agreement. In contrast, additive and multiplicative adjustments, as well as corrections accounting for shade, substantially improved agreement for the three models more sensitive to atmospheric conditions (Penman-Monteith ASCE, Penman-Monteith FAO, and Priestley-Taylor), with the strongest agreement resulting from additive adjustment of Penman-Monteith-derived estimates. Further analyses indicated that discrepancies between Penman-Monteith and empirical estimates could be attributed, in part, to differences in their treatment of wind speed, whereas accounting for soil-water limitation had little effect on these discrepancies. 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.