The crux of the problem is spatial heterogeneity. Conventional evapotranspiration models were developed for agricultural fields and large-scale hydrological budgets, where vegetation is comparatively uniform. A bioretention basin is nothing of the sort. Within a few meters, plant height can jump from bare ground to four-meter shrubs, buildings and highway walls cast shifting shadows, and soil moisture swings dramatically between the flooded basin floor and its drier flanks. Because evapotranspiration, or ET, is governed by leaf area, solar radiation, vapor pressure deficit, and soil water availability, this patchiness propagates directly into uncertainty. Reported estimates of how much stormwater ET removes from green infrastructure range from nearly zero to almost 90 percent, a spread so wide it borders on useless for design purposes.

To pin down what is actually happening, the team spent a growing season measuring ET directly at eleven fixed plots within the Philadelphia basin using the closed dynamic chamber method. Transparent chambers were sealed over collars set into the soil, and a laser-based gas analyzer recorded water vapor accumulation once per second, allowing the researchers to calculate fluxes from the rate of buildup. Measurements were taken five to six times per day across seven dates from June through October, deliberately spanning dry spells and post-storm conditions. After discarding about 17 percent of records for quality reasons, 302 usable flux measurements remained, each tied to a specific combination of plant stature, topographic position, and microclimate.

The spatial context came from an unusual pairing of technologies. Terrestrial LiDAR scanning produced a bare-earth digital elevation model of the basin, while drone-based photogrammetry yielded a digital surface model of everything above it; subtracting the two mapped vegetation height across the site. To capture shading from the highway wall, nearby buildings, and a billboard, the researchers used a regional aerial LiDAR dataset and the Area Solar Radiation calculator in ArcGIS to compute shade coefficients for every pixel on every day of the study. These coefficients, typically between 0.75 and 0.95, scaled the measured solar radiation to reflect how much light actually reached each patch of ground. The basin was then segmented into 967 roughly plant-sized polygons using an image-clustering algorithm, and all environmental variables were aggregated to that scale.

With these ingredients, the team built a statistical model of ET that explicitly incorporated plant height, topographic position, shade, vapor pressure deficit, solar radiation, and soil moisture, complete with interaction terms and random effects for plot and date. The model explained most of the observed variation, with fixed effects alone accounting for 65 percent of variance. Its coefficients told a coherent physiological story: ET rose strongly with solar radiation and vapor pressure deficit, was lower on the drier upper flanks of the basin, and increased with plant height, reflecting greater leaf area per unit ground. Interestingly, the effect of radiation weakened in wetter soils, likely because near-saturated conditions restrict both evaporation and plant transpiration, while the effect of moisture on ET differed between the basin floor and its slopes.

Scaling the model across the whole basin revealed daily ET ranging from zero to about 6 millimeters per day, peaking in early and midsummer and declining to roughly 1 millimeter per day by mid-autumn. The clearest spatial pattern was elevated ET beneath the tallest vegetation, particularly a cluster of shrubs hugging the highway wall. Just as telling was what the sensitivity analysis showed. Setting all plant heights to zero reduced season-wide ET by 41 percent, while assigning every segment a uniform 100-centimeter height inflated it by 60 percent; even using the basin’s mean height of 36 centimeters everywhere depressed the total by 13 percent. Shade mattered too: removing all structural shading boosted seasonal ET by 11 percent, while assuming only a quarter of incoming radiation reached the basin cut it nearly in half. Topographic position, by contrast, had little effect on the basin-wide total.

How did the conventional models fare? The team ran six widely used equations, including the two Penman-Monteith variants endorsed by the American Society of Civil Engineers and the FAO, Priestley-Taylor, Hargreaves-Samani, Matt-Shuttleworth, and Granger-Gray. Most overpredicted ET on average. The Penman-Monteith, Priestley-Taylor, and Hargreaves-Samani models consistently overestimated relative to the empirical estimates, while Matt-Shuttleworth and Granger-Gray, which were less responsive to atmospheric conditions, overpredicted at the low end of the range and underpredicted at the high end. Digging into the discrepancies, the researchers found that differences between Penman-Monteith and empirical estimates grew systematically with wind speed, explaining up to 39 percent of the bias for the ASCE variant, whereas accounting for soil-water limitation made little difference; water stress was infrequent during the study period.

The practical payoff came from testing three correction strategies. Applying shade coefficients to the solar radiation inputs improved agreement for the Penman-Monteith and Priestley-Taylor models. Multiplicative landscape coefficients, analogous to crop coefficients in agriculture and calculated here at roughly 0.5 to 1.0, also helped those same models. But the strongest results came from additive corrections: simply subtracting a regression-derived offset from Penman-Monteith and Priestley-Taylor estimates raised concordance correlation coefficients to about 0.88 or 0.89, a dramatic improvement. The caveat is that additive correction can produce negative daily ET values when uncorrected estimates are small, so it must be used with care. None of the corrections improved the already-decent Matt-Shuttleworth and Granger-Gray estimates, and the Hargreaves-Samani model remained the poorest performer regardless of adjustment, a notable finding given that some reviews have recommended it for its minimal data requirements.

The implications ripple outward from one Philadelphia basin. For designers of similar systems, the message is that Penman-Monteith and Priestley-Taylor, once corrected, can credibly represent ET in water balances used for regulatory compliance and volume-reduction credit, rather than being ignored or trusted blindly. The study also suggests a design lever: because plant height and shade dominate basin-scale ET, landscape architects can deliberately position larger plants in unshaded locations to boost atmospheric water removal, provided the coarse, fast-draining soil media can supply enough moisture between storms. Deep-rooted shrubs and trees may be particularly valuable here, drawing water from a broader depth range during dry spells.

The authors are candid about limitations. Their empirical model was calibrated on a single basin, and ET from the tallest plants, which covered only about 10 percent of the area but exerted outsized influence, required extrapolation beyond the height of the flux chambers. Microclimatic variables other than solar radiation were assumed uniform across the basin, and lacking inflow data, the team could not compute ET’s share of total stormwater removal. Still, the basin contains many features common to bioretention design everywhere, and the growing availability of high-resolution LiDAR, drone imagery, and weather data makes locally calibrated corrections increasingly feasible. As cities pour billions into green stormwater infrastructure to combat sewer overflows and climate-driven downpours, this study delivers a refreshingly actionable insight: the atmosphere’s share of the water budget is measurable, modelable, and, with a few well-chosen coefficients, finally predictable.

Subject of Research: Spatial heterogeneity in evapotranspiration estimates for urban bioretention basins

Article Title: Incorporating spatial heterogeneity into evapotranspiration estimates for bioretention basins

Article References: Caplan, J. S., Bouda, M., Salisbury, A. B., Alonzo, M., Nyquist, J. E., Toran, L., & Eisenman, S. W. (2026). Incorporating spatial heterogeneity into evapotranspiration estimates for bioretention basins. Hydrology and Earth System Sciences, 30(18), 6075-6093. https://doi.org/10.5194/hess-30-6075-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-6075-2026

Keywords: evapotranspiration, bioretention basins, green stormwater infrastructure, Penman-Monteith, spatial heterogeneity, urban hydrology, flux chambers, LiDAR, shade coefficients, landscape coefficients, Philadelphia, stormwater management

The research team, led by Koorosh Azizi of Tennessee State University and The University of Texas at Austin, together with Margaret Garcia of Arizona State University, Dev Niyogi and R. Patrick Bixler of The University of Texas at Austin, set out to explain why policy support for green stormwater infrastructure has not translated into the collective impact that planners hoped for. Individual household decisions matter, but the hydrological benefits of decentralized stormwater control only materialize at scale, when large fractions of a watershed’s impervious surface are treated. That means the crucial question for cities is not whether a few enthusiastic early adopters will install a rain garden, but whether adoption will diffuse through neighborhoods in a self-sustaining wave.

To answer it, the team constructed one of the most empirically grounded simulations of green infrastructure uptake to date. The model represents individual households as agents, each endowed with socio-demographic attributes drawn from census data, parcel attributes from municipal GIS records, and behavioral traits calibrated against a de-identified household survey. The agents form social networks, exchange influence through peer effects, and update internal psychological states, including awareness of green stormwater practices, personal norms, attitudes toward adoption, and social capital, the web of trust and reciprocity that shapes how neighbors persuade one another. Adoption occurs when a household’s intention crosses an individual threshold, a formulation rooted in established behavioral theory, including the theory of planned behavior and social-cognitive frameworks of observational learning.

What makes the model technically distinctive is its layered experimental design. The researchers staged mechanisms incrementally, from a static baseline in which nothing changes, through layers that separately activate feasibility and affordability constraints, social capital dynamics, and behavioral channels for awareness, norms and attitudes, up to a fully coupled configuration in which gains in one psychological variable carry over into others within each simulation step. Each layer thereby isolates a candidate engine of diffusion, and the city of Austin, divided into its ten council districts, serves as the empirical laboratory. Over 120 simulated time steps, the model tracks how many households adopt two broad classes of practices: structural installations such as rain gardens and permeable surfaces, and lower-cost rain-catching measures such as barrels and cisterns.

The central finding is stark: no single factor is sufficient to trigger widespread adoption. Visibility alone, in the form of neighbors’ visible installations, generates curiosity but not commitment if residents cannot afford or physically manage the conversion. Capacity alone, even when subsidies make practices affordable, produces only scattered adoption if households never see the practice working next door or receive no social encouragement. And social capital without concrete, visible examples and feasible options can reinforce skepticism as easily as enthusiasm. Sustained diffusion emerges only when the three mechanisms operate together, creating a feedback loop in which visible adoption builds social proof, social proof lowers the perceived risk of conversion, and capacity converts intention into installed infrastructure that becomes visible in turn.

Because neighborhoods differ in their endowments of these three ingredients, their adoption trajectories diverge into qualitatively distinct patterns. Some districts exhibit rapid takeoff, with adoption accelerating once a critical mass of installations and social connections is reached. Others show delayed diffusion, where early progress is slow but momentum eventually builds as awareness and networks mature. Still others display episodic growth, alternating bursts of uptake with plateaus, or persistent stagnation, in which adoption never escapes its initial low level. The citywide average, the authors show, masks this heterogeneity almost completely, meaning that aggregate statistics can conceal neighborhoods that need entirely different interventions.

The model also reveals that the dominant psychological mechanism driving adoption shifts over time and across space. In some districts, adoption is sustained chiefly by growing awareness; in others, personal norms, attitudes or social capital take turns leading, sometimes within the same neighborhood as the diffusion process matures. This shifting dominance has practical consequences: a one-size-fits-all campaign that emphasizes environmental messaging may work in one district while doing little in another where the binding constraint is affordability or the absence of visible examples. Diagnostic tools like this model, the authors argue, can help cities identify neighborhood-specific barriers and target resources accordingly, whether through demonstration projects, cost-sharing programs, or community-network building.

The theoretical contribution is equally significant. Diffusion-of-innovations research has long recognized the roles of observability, compatibility and social networks, and studies of rooftop solar adoption have documented peer effects that cluster installations spatially. But green stormwater infrastructure poses a harder test, because it is what the authors characterize as a complex contagion: a behavior whose benefits are collective, whose adoption requires repeated social reinforcement, and whose costs fall on private parcels. By embedding behavioral dynamics, physical feasibility and social network structure in a single spatially explicit framework, the study provides a generative explanation of how those ingredients combine, and it connects the stormwater literature to broader debates about infrastructure inequality and the legacy effects of segregation on urban green space.

The implications for practice are direct. Cities seeking to accelerate green stormwater infrastructure, whether to reduce pluvial flooding, cut pollutant loads, or meet regulatory requirements, should stop asking which single lever works best and start asking whether the three conditions are jointly present. Where visibility is low, demonstration installations and open-garden events can seed social proof. Where capacity is the constraint, reverse auctions, rebates and streamlined permitting can lower the feasibility barrier, an approach consistent with earlier household incentive studies. Where social capital is thin, partnerships with community organizations may be needed to build the trust through which peer influence travels. The Austin model, whose data and code are openly available through Zenodo and GitHub, offers planners a template for testing these strategies in silico before deploying them in the watershed, a step the authors suggest could meaningfully shorten the long wait for green infrastructure to deliver its promised resilience at city scale.

Subject of Research: Agent-based modeling of the social and structural drivers of household adoption of green stormwater infrastructure in cities

Article Title: Sustaining green stormwater infrastructure adoption in cities

Article References: Sustaining green stormwater infrastructure adoption in cities. (n.d.). https://doi.org/10.1038/s44284-026-00520-1

Image Credits: AI Generated

DOI: 10.1038/s44284-026-00520-1

Keywords: green stormwater infrastructure, agent-based model, urban resilience, rain gardens, social capital, diffusion of innovations, Austin Texas, nature-based solutions, urban flooding, household adoption, stormwater management, neighborhood inequality