Assessing methods for predicting green roof rainfall capture: A comparison between full-scale observations and four hydrologic models

Carson, Tyler; Keeley, Melissa; Marasco, Daniel E.; McGillis, Wade R.; Culligan, Patricia J.

doi:10.1080/1573062x.2015.1056742

Cited by 49 publications

(51 citation statements)

References 47 publications

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“…Substrate physical properties, such as PAW and WHC, as determined from standard laboratory tests, are increasingly being used to predict the storage capacity of green roofs prior to construction [16,24]. However, in our three substrates using PAW or WHC to calculate storage over-estimated rainfall retention by 50-100%.…”

Section: Relationship Between Substrate Water Storage and Rainfall Rementioning

confidence: 99%

“…Fassman and Simcock [14] and Stovin et al [26] also found water retention of green roof substrates was better related to PAW than WHC. Therefore, despite not being generally described for green roof substrates [24], we suggest that PAW should be routinely measured for all of the substrates to give a better indication of their storage capacity.…”

Section: Effect Of Substrate Physical Properties and Vegetation Covermentioning

confidence: 99%

“…For example, PAW is seldom measured (for exceptions see [14,18,22,23]), and is generally not supplied for commercially available substrates [24]. Yet, PAW is potentially a more important predictor of green roof hydrologic performance than WHC [14].…”

Section: Introductionmentioning

confidence: 99%

“…Although the storage capacity of components, such as drainage layers, can be determined with confidence, the maximum storage capacity of substrates in situ is difficult to predict from laboratory-based methods. Policy makers therefore need a method to accurately predict green roof performance prior to construction [16,24]. In the absence of such a method, assumptions are likely to be made about rainfall retention based on laboratory-determined substrate physical properties, such as PAW and WHC, resulting in an over-estimation of rainfall retention performance.…”

Section: Introductionmentioning

confidence: 99%

“…Modelling approaches have been proposed [26,27] to overcome this impediment, but their success depends on the accurate and realistic representations of in situ substrate water retention [24,27]. Some physical green roof hydrological models assume that the maximum rainfall retention capacity of substrates is equal to laboratory-based assessments of WHC or PAW [26,[28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Laboratory Tests of Substrate Physical Properties May Not Represent the Retention Capacity of Green Roof Substrates In Situ

Szota

Fletcher

Desbois

et al. 2017

Water

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Green roofs can be used to reduce the volume of polluted stormwater that is generated by cities. Modelling rainfall retention is critical, but green roof water balance models often rely on the physical properties of substrates. In these models, substrate water holding capacity (WHC) determines the depth of water which can be stored before runoff is generated; whereas, the permanent wilting point (PWP) limits evapotranspiration. The WHC and PWP, as well as plant available water (PAW; where PAW = WHC − PWP), as determined from laboratory tests, may not truly reflect how substrates perform on green roofs. We therefore ran a simulated rainfall experiment on green roof modules to (i) compare the rainfall retention of vegetated and non-vegetated substrates with different WHC and PAW, and (ii) relate retention to substrate storage capacity, as calculated from laboratory measures of WHC and PAW. We found that the PAW of a substrate is a better indicator of evapotranspiration and retention when compared with WHC. However, we also found that substrates always retained less water than their calculated storage capacity would suggest, most likely being due to their high permeability. Our results indicate that using laboratory-derived measures of WHC and PAW in green roof models may be over-estimating both evapotranspiration and rainfall retention.

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Section: Relationship Between Substrate Water Storage and Rainfall Rementioning

confidence: 99%

Section: Effect Of Substrate Physical Properties and Vegetation Covermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Laboratory Tests of Substrate Physical Properties May Not Represent the Retention Capacity of Green Roof Substrates In Situ

Szota

Fletcher

Desbois

et al. 2017

Water

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Investigating model performance and parameter sensitivity for runoff simulation across multiple events for a large green roof

Worthen

Kelleher

Davidson

2021

Hydrological Processes

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Green roofs are a form of green infrastructure aimed at retaining or slowing the movement of precipitation as stormwater runoff to sewer systems. To determine total runoff versus retention from green roofs, researchers and practitioners alike employ hydrologic models that are calibrated to one or more observed events. However, questions still remain regarding how event size may impact parameter sensitivity, how best to constrain initial soil moisture (ISM), and whether limited observations (i.e., a single event) can be used within a calibration-validation framework. We explored these questions by applying the storm water management model to simulate a large green roof located in Syracuse, NY. We found that model performance was very high (e.g., Nash Sutcliffe efficiency index > 0.8 and Kling-Gupta efficiency index > 0.8) for many events. We initially compared model performance across two parameterizations of ISM. For some events, we found similar performance when ISM was varied versus set to zero; for others, varying ISM yielded higher performance as well as greater water balance closure. Within a calibration-validation framework, we found that calibrating to larger events tended to produce moderate to high performance for other noncalibration events. However, very small storms were notoriously difficult to simulate, regardless of calibration event size, as these events are likely fully retained on the roof.Using regional sensitivity analysis, we confirmed that only a subset of model parameters was sensitive across 16 events. Interestingly, many parameters were sensitive regardless of event size, though some parameters were more sensitive when simulating smaller events. This emphasizes that storm size likely influences parameter sensitivity.Overall, we show that while calibrating to a single event can achieve high performance, exploring simulations across multiple events can yield important insight regarding the hydrologic performance of green roofs that can be used to guide the gathering of in situ properties and observations for refining model frameworks.

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Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review

Xiang

Horton

et al. 2020

Agricultural Water Management

200

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Assessing methods for predicting green roof rainfall capture: A comparison between full-scale observations and four hydrologic models

Cited by 49 publications

References 47 publications

Laboratory Tests of Substrate Physical Properties May Not Represent the Retention Capacity of Green Roof Substrates In Situ

Laboratory Tests of Substrate Physical Properties May Not Represent the Retention Capacity of Green Roof Substrates In Situ

Investigating model performance and parameter sensitivity for runoff simulation across multiple events for a large green roof

Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review

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