Runoff and infiltration characteristics of permeable pavements Review of an intensive monitoring program

Presentation Outline  Background & Objectives  Material & Methods field measurements lab scale experiments finite element simulation  Results & Data Analysis examples: measured infiltration rates characteristics of infiltration performance  Mathematical Approach  Conclusions

Background  permeable pavements popular and effective technique to reduce stormwater runoff but also: significant runoff contribution during storm events  evaluation & modelling of stormwater runoff common models are mainly using out-dated approaches lack of specified and consistent runoff coefficients and parameter recommendations only insufficient knowledge of particular runoff performance  need of further investigations & improved modelling methods

 main objectives analysis of runoff and infiltration performance focus on minor permeable pavements compilation and generation of a sound data base development of an upgraded computational approach recommendation of runoff coefficients  partners IKT – Institute of Underground Infrastructure Ministry of the Env., North Rhine-Westphalia Research Project

Field Measurements  methods of measurement sprinkling infiltrometer and simplified tests various types of pavement on several locations (parking lots, sidewalks, …)  focus infiltration rates after several years of use major impacts on infiltration capacity  mechanical impacts, spatial & temporal variability … generation of reference values for the lab test series

 common block and interlocking pavements joints: 4|7|10 mm; opening ratios:  = 4-15%  flag pavings with small joints joints: 4 mm; opening ratios:  = 0,6%|1,2%|1,8%  porous concrete pavement with small joints joints: 4 mm; opening ratio:  = 4,5% Lab Scale Tests  scope of the lab tests over 140 single tests several minor and medium permeable pavements analysis of various boundary conditions

 methodology systematical variation of boundary condintions rain intensities: 25  1000 l/(sha) surface slope: 2,5%, 5,0%, 7,5% dry periods: 2 h, 4 h, 15 h, 24 h, >24 h imitation of clogging effects by application of fine silica flour on new pavements Lab Scale Tests  rain intensity  dry periods  surface slope  stage of clogging  base layer characteristics  layer construction  infiltrometer tests on the lab test facility  field and literature data as reference values

Lab Test Facility

 model application calibration on monitoring data of lab tests interpretation & visualization of lab tests application on further scenarios  additional rain intensities  varying base layer and sub-base characteristics  varying initial conditions (dry periods & evaporation) ... Finite Element Simulation  HYDRUS-2D detailed fe-model for computation of water, heat and solute transport in porous media

Finite Element Simulation 5 min 10 min 20 min25 min  example: changes of water content   HYDRUS-2D detailed fe-model for computation of water, heat and solute transport in porous media

Analysis Field Data  selected results of field measurements #1: infiltration rates of 4 types of permeable pavements on 1 site (comparison) #2:parking lot of interlocked pavement with gravel filled voids (3 monitoring points) #3:highly frequented parking lot (interl. pavers) #4sparsely frequented parking lot (interl. pavers) #5: sidewalk of block pavement #6: sidewalk of cobblestone pavement #7: sidewalk of flag pavement

Field Measurements #1 Type 1 Type 2 Type 3 Type 4

Field Measurements #1  parking lot of block pavement type 1 | slope: 2,5% | joints: 4mm | continuous average values block pavement with 4 mm slots * infiltration rates as continous average values

Field Measurements #2  parking lot of interlocked pavement with gravel filled voids

Field Measurements #3 higher mechanical impact lower mechanical impact  Example of a highly frequented parking lot parking lot at the campus interlocking concrete pavement 18 infiltration tests 6 different monitoring points high spatial variability of infiltration capacities (site-scale) significant mechanical impact mechanical impact heavily increases clogging

Field Measurements #3

 Example of a sparsely frequented parking lot interlocking concrete pavement 3 monitoring points 15 infiltration tests Field Measurements #4

 Example of a sparsely frequented parking lot strong impact of traffic occupancy on infiltration performance higher traffic occupancy leads to extended clogging effect no explicit impact of weather condition observed Field Measurements #4

Field Measurements #5  block pavement monitored at the campus of the university (slots of ~5 mm)

Field Measurements #6  cobblestone pavement (non-uniform joints)

Field Measurements #7  flag pavement with narrow slots

Field Measurements  monitoring results high infiltration rates at many locations partially heavily reduced infiltration capacity huge variability between the infiltration rates  at the same location (same pavement) e.g. center parking bay  tire track parking bay  at different locations (same pavement) e.g. more or less clogged pavement  between different types of pavement e.g. porous concrete  pavement with open voids

Field Measurements  assessment of monitoring results infiltration moderately decreasing over time  saturation process ??  no proof! (open question)  limited accuracy of measurements  high infiltration rates at the beginning only result of wetting process & lateral percolation?  open question! decrease of infiltration capacity due to  accumulation of fine particles on and into the joints as well as on the surface of porous pavers (clogging)  mechanical impact by cars  minor impact by soil consolidation itself  main effect: wheel ruts enhance deposit of fine particles and increases clogging  change of position of pavers (  also increase of inf. rate)

Field Measurements  assessment of monitoring results major impact on the infiltration capacity by joint material (particular permeability) no clear impact of weather condition observed  dry period prior to measurements  water saturation of joint and base layer material site-specific variability higher than climate-specific variability explicit infiltration capacity after several years of use not predictable (  stochastic phenomenon)

Analysis Lab Tests  tests on interlocking concrete pavement (ICP) analysis of correlation between infiltration rate and  particular rain intensity  dry period prior to rain  initial water content  surface slope  state of clogging evaluation of runoff coefficients regression analysis of infiltration rate  as a function of rain intensity & time  as a function of cumulative rain height & time

Lab Scale Tests ICP

impact of state of clogging & dry period? impact of particular slope & rain intensity? Lab Scale Tests: ICP impact of particular rain intensity?

impact of rain intensity rain intensity | dry period | test no. infiltration rate  incresing with rain intensity  in- or decreasing with rain duration  realtivly constant over time  no clear impact of dry period

impact of surface slope r = 100 l/(s  ha)

impact of surface slope r = 200 l/(s  ha) infiltration rate  slightly decreasing with surface slope  realtivly constant over time

impact of surface slope infiltration rate  slightly decreasing with surface slope  impact of clogging much higher than impact of slope

impact of clogging & dry period r = 150 l/(s  ha) 3-4 rain intervals of 30 minutes with variable dry periods in between

impact of clogging & dry period r = 150 l/(s  ha) 3-4 rain intervals of 30 minutes with variable dry periods in between

impact of clogging & dry period infiltration rate  strongly depending on state of clogging  no clear impact of dry period prior to rain event  even only marginal differences within first few minutes

impact of water content analysis of water content  intermittent irrigation  water contents at different levels

impact of water content analysis of water content  no saturation in the base layer  delayed but fastly increasing water content  during irrigation

impact of water content analysis of water content  straight after irrigation: fast decrease of   afterwards: slowly decreasing water contents

impact of water content analysis of water content  after min. infiltrated water reaches top of base layer  after min. infiltrated water reaches bottom of base layer

impact of water content analysis of water content  no significant impacts of dry- period duration on water contents  no obvious correlation between water content in the base layer and infiltration rate

water content  infiltration rate  no correlation between infiltration rate and water content below bedding layer  shorter dry periods lead to marginally lower initial infiltration rates analysis of water content * infiltration rates as continous average values

water content  infiltration rate * infiltration rates as continous average values

water content  infiltration rate

Evaluation of Runoff Coefficients considerably clogged ICPmoderately clogged ICP

Runoff Coefficients  peak runoff coefficients  PR = q runoff / r heavily depending on particular rain intensity  not a fix value! moderate rain intensities below 100 l/(sha)  moderately clogged ICP: 0    0,10  considerably clogged ICP: 0    0,40 strong increase between l/(sha)  moderately clogged ICP: 0,40    0,70  considerably clogged ICP: 0,10    0,45 values > 0,80 for extreme intensities

Regression Analysis ICP

rain duration regression curves for rain periods 5-60 min

Regression Curves ICP infiltration rate [l/(s  ha) rain intensity [l/(s  ha) Interlocking Concrete Pavement slope: 2,5% | joints: 3-4 mm new pavement

Regression Curves ICP infiltration rate [l/(s  ha) rain intensity [l/(s  ha) Interlocking Concrete Pavement slope: 2,5% | joints: 3-4 mm moderately clogged pavement

Regression Curves ICP infiltration rate [l/(s  ha) rain intensity [l/(s  ha) Interlocking Concrete Pavement slope: 2,5% | joints: 3-4 mm considerably clogged pavement

New Modelling Approach  integration of major processes –rain and slope dependent infiltration capacity –clogging effects (reduced infiltration capacity) –storage function of base layer –interactions between top layer, base layer and natural soil layer (  water transport)  development in a MATLAB/Simulink environment  calibration and test on data base

New Modelling Approach  bi-directional layer model

New Modelling Approach  bi-directional layer model wetting & depression storage as initial losses infiltration process described by infiltration module 3 different infiltration modules available  infiltration rate constant over time, rain intensity and cumulative rain height (simple model)  infiltration rate depending on rain intensity, state of clogging and time  infiltration rate depending on cumulative rain height state of clogging and time

New Modelling Approach  bi-directional layer model infiltration module accounts for state of clogging & surface slope by appropriate parameter values storage function of base layer represented by simple storage model V = (Q in – Q out )  t permeability of subgrade or soil layer described by constant K-value

New Modelling Approach  bi-directional layer model hydraulic conductivity of subgrade regulates storage process in the base layer repression of infiltration into the pavement construction in case of saturation of the base layer (due to low permeable subgrade)

Regression Infiltration Rate

 mathematical formulation of infiltration rate f(r,t) = A  ln(r) – B  f max as function of particular rain intensity r parameter A, B and f max for each pavement and time step Parameter A rain duration D [min] Parameter A Parameter b rain duration D [min] Parameter B Parameter f max rain duration D [min] Parameter f max

Regression Model Parameters regression of model parameters by exponential function  time-based formulation with pavement-specific parameter values  1 parameter set for each state of clogging Parameter A rain duration D [min] Parameter A Parameter B rain duration D [min] Parameter B Parameter f max rain duration D [min] Parameter f max

Correlation Model Parameters Correlation Parameter A and B Correlation of Parameters A & B Parameter A Parameter B Correlation of Parameters A & B moderately clogged pavement Correlation of Parameters A & B considerably clogged pavement Parameter A Parameter B

Correlation Model Parameters Correlation f max and A Correlation of Parameters A & f max Parameter f max Parameter A Correlation of Parameters A & f max new pavement Parameter f max Parameter A Correlation of Parameters A & f max moderately clogged pavement Parameter f max Parameter a

Infiltration Model  simplification of the infiltration model by correlation of single regression parameters f(r,t) = A  ln(r) – B  f max with f max =  2 + ( 1 -  2 )  e -(t - 0,5) A =  1  e    f max B =  1  A –  2  time-based formulation of infiltration process  7 pavement-specific parameters ( 1,  2, ,  1,  2,  1,  2 )

Alternative Algorithm infiltration rate [l/(s  ha) cumulative rain height [l/(s  ha) infiltration rate [l/(s  ha)  infiltration rate as function of cumulative rainfall

Alternative Algorithm infiltration rate [l/(s  ha) cumulative rain height [l/(s  ha) Correlation of infiltration rate and cumulative rain height interlocking concrete pavers slope: 2,5% | considerably clogged lack of data in the range of mm rainfall

Alternative Algorithm infiltration rate [l/(s  ha) cumulative rain height [l/(s  ha) infiltration rate [l/(s  ha) Correlation of infiltration rate and cumulative rain height interlocking concrete pavers slope: 2,5% | considerably clogged

Alternative Algorithm  formulation of a correlation with cumulative rain amount f min  f(h N,t) =  1  ln(h N ) +  2  f max Parameter  1,  2, f min and f max for each pavement, grade of clogging and time step with  2 = C 1 e -C 2 D + C 3 D - C 4

Analysis Lab Tests: FP  tests on concrete flagstone pavement 3 types of common flagstone pavement (FP)  size: 30x30 cm, 40x40 cm and 50x50 cm  opening ratios: 1,8%, 1,2%, 0,6% new & considerably clogged pavement surface slope: 2,5% various rain intensities additional tests on pavement on low permeable subgrade

Analysis Lab Tests: FP

impact of rain intensity

impact of rain height

impact of opening ratio

impact of low permeable subgrade

ambiguous monitoring data: strong decrease of infiltration rate probably caused by proceeding accumulation of washed-off silica flour on joint material (ongoing clogging); clogging not completed until end of 2 rd irrigation interval impact of low permeable subgrade

Runoff Coefficients

 peak runoff coefficients  PR = q runoff / r depending on particular rain intensity  not a fix value! depending on particular state of clogging example:r = 100 l/(sha)  PR = 0,20  0,70 (huge variation!)

Assessment Flagstone Pavement  impacts on infiltration rate rain intensity as well as opening ratio have only for new pavements a significant impact (infiltration rates of 70 – 140 l/(sha)) for clogged flag pavements the infiltration rate is more or less independent of rain intensity and cumulative rain height (~ 30 l/(sha)) for clogged flag pavements with small joints the infiltration rate is also independent of the ratio of openings or the diameter of the joints state of clogging is the most governing impact on the infiltration capacity (30 – 140 l/(sha))

Assessment Flagstone Pavement  impacts on infiltration rate for pavement with a base of lower permeable material no strictly “negative” impact on infiltration rate was observed in a singular test even higher infiltration rates were measured (but poor and ambiguous data) storage function of base layer (with high storage capacity of ~ 5-25 mm) infiltration rate = k f -value of subgrade in case of saturation developed models applicable even though constant infiltration rates seem to be appropriate

 minor impacts on infiltration capacity surface slope (for more permeable pavements) water content of the base layer subgrade characteristics evaporation & dry period Overall Conclusions  extreme local variability  major impacts on infiltration capacity state of clogging (  traffic) particular rain intensity size/fraction of openings slope (for low permeability) infiltration capacity of joint material

Summary & Outlook  broad and reliable data base –field and lab experiments + fe-simulation –quantification of major processes, interactions and characteristic values  evaluation & modelling of stormwater runoff –mean and peak runoff coefficients –advanced modelling algorithm  outlook –additional field measurements & simulations –compilation & analysis of a data pool (stochastic aspects)