performance of stormwater collection systems Erle Kristvik*, Tone - - PowerPoint PPT Presentation

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performance of stormwater collection systems Erle Kristvik*, Tone - - PowerPoint PPT Presentation

Nov 22, 2022 825 likes β€’1.01k views

Seasonal variations in climate and the performance of stormwater collection systems Erle Kristvik*, Tone M. Muthanna* *Department of Civil and Environmental Engineering, NTNU B ringing IN novation to on GO ing Water Management

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Seasonal variations in climate and the performance of stormwater collection systems

Erle Kristvik*, Tone M. Muthanna* *Department of Civil and Environmental Engineering, NTNU

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Providing practical knowledge and tools to end users, water managers and decision- makers to better cope with all climate projections, including droughts and floods.

www.projectbingo.eu

Bringing INnovation to onGOing Water Management

Funded by Horizon 2020 Coordinated by LNEC - Portugal

WP2 Climate predictions and downscaling WP3 Analysis of the water cycle WP4 Impacts of extreme weather events WP5 Risk treatment and adaptation strategies WP6 Excellence and actionable research WP7 Dissemination and exploitation WP1 Coordination

The BINGO project has received funding from the European Union's Horizon 2020 Research and Innovation programme, under the Grant Agreement number 641739.

BINGO Research Sites

Norway Bergen The Netherlands The Veluwe Germany Wupper River Basin Portugal Tagus Spain Badalona Cyprus Troodos Mountains

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Study site

  • DamsgΓ₯rd
  • Residential area in the city of Bergen,

Norway

  • Prioritized transition of area
  • Pronounced topography
  • Runoff from mountainous area entering

residential area

  • Combined sewer system
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Blue-green and open solutions

  • Conflicting policies
  • Retention capacity varies with differences in temperature and precipitation
  • Across locations
  • Between seasons
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Application of climate data

Kleiven et. al (2017)*

*Kleiven, G. H., Kristvik, E., Muthanna, T. M., Lohne, J. (2017). Local Climate Projections And Their Implications For Raingardens In Bergen. Poster presented at the Embrace the water (ETW) conference, Gothenborg, Sweden, 2017.

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Climate indices

Name Index for Equation Explanation PRCPTOT Total precipitation in wet- days π‘„π‘†π·π‘„π‘ˆπ‘ƒπ‘ˆ

π‘˜ = ෍ π‘†π‘†π‘—π‘˜

RRij is the daily precipitation amount on day i in period j Rx1day Maximum 1-day PRCP 𝑆𝑦1π‘’π‘π‘§π‘˜ = max(π‘†π‘†π‘—π‘˜) RRij is the daily precipitation amount on day i in period j Rx5day Maximum consecutive 5-day PRCP 𝑆𝑦5π‘’π‘π‘§π‘˜ = max(π‘†π‘†π‘™π‘˜) RRkj is the precipitation amount for the 5-day interval ending k, period j R95pTOT Annual total PRCP when RR>95p 𝑆95π‘žπ‘˜ = ෍

π‘₯=1 𝑋

𝑆𝑆π‘₯π‘˜ π‘₯β„Žπ‘“π‘ π‘“ 𝑆𝑆π‘₯π‘˜ = 𝑆𝑆π‘₯π‘œ95 RRwj is the daily precipitation amount

  • n a wet day w in period j, and RRwn95

is the 95th percentile of precipitation on wet days in the 1961-1990 period R99pTOT Annual total PRCP when RR>99p 𝑆99π‘žπ‘˜ = ෍

π‘₯=1 𝑋

𝑆𝑆π‘₯π‘˜ π‘₯β„Žπ‘“π‘ π‘“ 𝑆𝑆π‘₯π‘˜ = 𝑆𝑆π‘₯π‘œ99 RRwj is the daily precipitation amount

  • n a wet day w in period j, and RRwn99

is the 99th percentile of precipitation on wet days in the 1961-1990 period

http://etccdi.pacificclimate.org/list_27_indices.shtml (Karl et al. 1999; Peterson Folland, C., Gruza, G., Hogg, W., Mokssit, A., Plummer, N. 2001)

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Observed seasonality

  • Rx1day
  • Rx5day
  • R20mm

DJF = Winter JJA = Summer MAM = Spring SON = Fall

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Observed seasonality

Rx1day Q90 Q99 Rtot Mu Fw DJF 80.1 19 43.2 1093.6 11.9 0.54 MAM 67.1 12.8 36.2 763.5 9.0 0.45 JJA 71.8 16.2 41.9 847.1 11.0 0.45 SON 121.2 27 55.5 1371.4 14.6 0.62 ANNUAL 121.2 19 44.9 3210.2 12.0 0.52 Mu = wet-day mean Fw = wet-day frequency

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Observed seasonality

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Long-term projections

  • R package β€˜esd’
  • Methodology proposed by Benestad and

Mezghani (2015)*

  • Empirical-statistical statistical downscaling of:

– Wet-day mean, Mu – Wet-day frequency, Fw

  • Predictor: Temperature / saturation

evaporation pressure

  • Calibration with data from NCEP/NCAR

reanalysis

  • Projections of 19 GCMs

*Benestad, R. E. and Mezghani, A. (2015) β€˜On downscaling probabilities for heavy 24-hour precipitation events at seasonal-to-decadal scales’, Tellus, Series A: Dynamic Meteorology and Oceanography, 67(1), pp. 1–20. doi: 10.3402/tellusa.v67.25954.

Parameters of the exponential distribution

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Results

  • Best fit
  • btained for

the wet-day mean, Mu Mu Fw

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Results

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Projected seasonality

DJF MAM JJA SON Mu Fw

1 Present: 1961-1990 2 Future: 2071-2100

Present1 Future2

11.9 9.0 11.0 11.2 11.8

+8% +24% +11% +7%

14,6 13,2 15,8 0.54 0.45 0.45 0.62 0.52 0.47 0.48 0.58

  • 6%

+7% +4%

  • 4%
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Projected seasonality

67 80 121 72 Rx1Day Q99

1 Present: 1961-1990 2 Future: 2071-2100

43 36 42 56 61 52 54 73

+43% +41% +30% +31% DJF MAM JJA SON Present1 Future2

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Discussion

  • Climate indices as

indicators for stormwater

  • Assumption: exponential

distribution

  • Downscaling of Mu and Fw
  • What is β€˜good enough’?
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Further work

  • Projections for temperature
  • Implications for blue-green infrastructure

Thank you!