Using rainfall measures to evaluate hydrologic performance of green infrastructure systems under climate change

Cook, Lauren M.; VanBriesen, Jeanne M.; Samaras, Constantine

doi:10.1080/23789689.2019.1681819

Cited by 17 publications

(17 citation statements)

References 36 publications

(51 reference statements)

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“…Unfortunately, no matter what size of infrastructure is selected, it is likely to experience increased variability and increases in short-duration, high-intensity rain storms. Tracking performance over time for both green and gray infrastructure, and using adaptable design principles will ensure communities are prepared for many future states (Cook et al 2019). For instance, if a larger pipe size is selected, municipalities should plan to flush pipes during periods of drought, whereas smaller pipe sizes will likely need distributed green infrastructure placed upstream in order to send flows into pipes more slowly.…”

Section: Discussionmentioning

confidence: 99%

“…To create climate-corrected IDF curves, many apply MOS techniques to the continuous rainfall time series as the first step in the process, through bias correction (Cannon et al 2015;Kuo et al 2015;Kueh and Kuok 2016), weather typing (Mandal et al 2016b), or weather generators (Wilks and Wilby 1999). The extreme rainfall events extracted from these continuous series (i.e., the annual maximum or partial duration series) can also be adjusted, e.g., through quantile mapping (Srivastav et al 2014b), genetic programming (Hassanzadeh et al 2013), or historical analogs (Castellano and DeGaetano 2016). Finally, the intensity of rainfall (return level), evaluated after the GEV is fit to the extreme events, can also be adjusted, usually using a change factor that is a ratio calculated from historical and future climate model return levels (Forsee and Ahmad 2011;Zhu et al 2012;Cook et al 2017).…”

Section: Choice Of Spatial Adjustment (Mos) Techniquementioning

confidence: 99%

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The effect of modeling choices on updating intensity-duration-frequency curves and stormwater infrastructure designs for climate change

2020

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Intensity-duration-frequency (IDF) curves, commonly used in stormwater infrastructure design to represent characteristics of extreme rainfall, are gradually being updated to reflect expected changes in rainfall under climate change. The modeling choices used for updating lead to large uncertainties; however, it is unclear how much these uncertainties affect the design and cost of stormwater systems. This study investigates how the choice of spatial resolution of the regional climate model (RCM) ensemble and the spatial adjustment technique affect climatecorrected IDF curves and resulting stormwater infrastructure designs in 34 US cities for the period 2020 to 2099. In most cities, IDF values are significantly different between three spatial adjustment techniques and two RCM spatial resolutions. These differences have the potential to alter the size of stormwater systems designed using these choices and affect the results of climate impact modeling more broadly. The largest change in the engineering decision results when the design storm is selected from the upper bounds of the uncertainty distribution of the IDF curve, which changes the stormwater pipe design size by five increments in some cases, nearly doubling the cost. State and local agencies can help reduce some of this variability by setting guidelines, such as avoiding the use of the upper bound of the future uncertainty range as a design storm and instead accounting for uncertainty by tracking infrastructure performance over time and preparing for adaptation using a resilience plan.

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Section: Discussionmentioning

confidence: 99%

Section: Choice Of Spatial Adjustment (Mos) Techniquementioning

confidence: 99%

The effect of modeling choices on updating intensity-duration-frequency curves and stormwater infrastructure designs for climate change

2020

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“…Expert elicitation can sometimes be perceived as a low‐effort alternative to computationally intensive analysis, but it is also time consuming, requires careful consideration of the questions being asked, and is, by definition, subjective and influenced by human bias (Morgan, 2014). Finally, using rainfall measures as a proxy to track or predict the hydrologic performance of green infrastructure, may provide a less data and computationally intensive method under uncertainty than hydrologic simulation and on‐site sensors (Cook et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Webber

Samaras

2022

Earth's Future

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Decision making under deep uncertainty (DMDU) approaches are well‐suited for making decisions about infrastructure to manage flooding exacerbated by climate change. One important system for climate resilience and flood management is green infrastructure, which refers to a network of natural and semi‐engineered areas that provides ecosystem functions. Green infrastructure is often characterized as a low‐regret strategy with multiple co‐benefits under uncertainty. These attributes enable green infrastructure to be an important adaptation strategy under DMDU frameworks for flood management. However, DMDU analyses that include green infrastructure are still relatively limited, perhaps due to computational or modeling complexity and other barriers. This paper identifies and reviews publications in the flood management literature that use DMDU frameworks and refer to green infrastructure adaptation strategies, in order to identify trends and inform future research. The reviewed publications are categorized according to a variety of performance metrics, climate change scenarios, DMDU metrics, and hydrologic modeling techniques, and represent several adaptation strategies applied to case studies on five continents using a range of data sources and assumptions. This paper highlights a number of solutions that can be employed to facilitate additional research at the intersection of these fields. Primary among these is the transparent documentation and use of open source models, methods, and data. Future research should also focus on communication among different stakeholders, particularly in ensuring definitions, assumptions, and data requirements are clear. These partnerships can facilitate effective application of robust strategies such as green infrastructure for urban adaptation to the effects of climate change.

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“…This is also evident from Figure 4, the dotted lines of the 100-and 50-year are closer to the upper bound (2070-2099 timeframe) compared to the 2-and 10-year return periods. Previous studies have also shown that GI has lower performances for larger rainfall events (Cook et al, 2019;Vaillancourt et al, 2019), since the GI soil and media may reach saturation, hence providing less storage capacity. Webber et al (2020) also found the trend of diminishing effectiveness of GI in reducing flood peak as events become more intense, i.e., associated with a lower probability of occurrence.…”

Section: Gi Mitigation Impact-cso Total Volume Comparison Between W/ and W/o Ppmentioning

confidence: 99%

The Impacts of Climate Change and Porous Pavements on Combined Sewer Overflows: A Case Study of the City of Buffalo, New York, USA

et al. 2021

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Combined sewer overflows (CSOs) release pollutants collected in urban runoff into local waterways, impacting both aquatic life and human health. The impact of climate change on precipitation may result in an increase in the frequency and magnitude of heavy precipitation events, with a corresponding increase in CSO discharges. The installation of Green Infrastructure (GI) such as Porous Pavements (PP) is a resilient approach to mitigate CSO events. However, an understanding of the impact of climate change on CSO events and the effectiveness of GI practices is crucial for designing sustainable urban stormwater management systems. Using the Storm Water Management Model (SWMM), the performance of PP as a CSO abatement strategy was studied for the city of Buffalo, New York, USA. This paper used the Intensity-Duration-Frequency (IDF) curves for current (1970–1999) and future (2070–2099) design rainfall scenarios, with four rainfall durations (1, 6, 12, and 24 hours) and four return periods (2, 10, 50, and 100 years). The simulation results show that (1) current 100-year events generate CSO volumes similar to predicted 50-year events; (2) CSO volumes could increase by 11–73% in 2070–2099 compared to 1970–1999 when no GI intervention is performed; and (3) the installation of PP can reduce 2–31% of future CSO volume. This case study demonstrates the regional CSO challenges posed by climate change and supports the use of GI as a mitigation strategy.

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Using rainfall measures to evaluate hydrologic performance of green infrastructure systems under climate change

Abstract: Samaras (2019): Using rainfall measures to evaluate hydrologic performance of green infrastructure systems under climate change, Sustainable and Resilient Infrastructure,

Cited by 17 publications

References 36 publications

The effect of modeling choices on updating intensity-duration-frequency curves and stormwater infrastructure designs for climate change

The effect of modeling choices on updating intensity-duration-frequency curves and stormwater infrastructure designs for climate change

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

The Impacts of Climate Change and Porous Pavements on Combined Sewer Overflows: A Case Study of the City of Buffalo, New York, USA

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