a5c-ai Stormwater Management Skill
Integrated stormwater management and green infrastructure design for sustainable urban drainage.
Purpose
This skill provides comprehensive capabilities for stormwater management planning, including hydrologic analysis, green infrastructure design, BMP selection and sizing, SWMM modeling, and MS4 permit compliance analysis.
Capabilities
SWMM Modeling and Simulation
- EPA SWMM model setup and configuration
- Subcatchment delineation and parameterization
- Drainage network modeling
- Long-term continuous simulation
- Design storm analysis
- LID representation and modeling
Hydrologic Analysis
- TR-55 methodology implementation
- Rational method calculations
- SCS Curve Number determination
- Time of concentration estimation
- Unit hydrograph development
- Rainfall-runoff modeling
Green Infrastructure Sizing
- Bioretention facility design
- Permeable pavement sizing
- Rain garden design
- Green roof specifications
- Tree box filters
- Vegetated swales
Detention/Retention Pond Design
- Storage volume calculations
- Stage-storage-discharge relationships
- Outlet structure design
- Emergency spillway sizing
- Sediment forebay design
- Maintenance access planning
Water Quality BMP Selection
- Pollutant removal efficiency analysis
- BMP selection matrix
- Treatment train design
- Sizing for TSS removal
- Nutrient removal considerations
- Cost-effectiveness analysis
Pollutant Load Modeling
- Event Mean Concentration (EMC) analysis
- Annual pollutant load estimation
- Source area contribution analysis
- Loading rate calculations
- Reduction target setting
Low Impact Development Integration
- Site-level LID planning
- Watershed-scale LID analysis
- LID retrofit opportunities
- Performance monitoring design
- Adaptive management frameworks
MS4 Permit Compliance Analysis
- NPDES requirements interpretation
- MCM implementation tracking
- TMDL compliance assessment
- Monitoring program design
- Annual report preparation
Prerequisites
Installation
pip install numpy scipy pandas matplotlib
Optional Dependencies
# For SWMM integration
pip install swmm-api pyswmm
# For GIS analysis
pip install geopandas shapely
# For visualization
pip install plotly folium
Usage Patterns
Rational Method Calculations
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple
@dataclass
class CatchmentData:
"""Catchment characteristics"""
area_acres: float
runoff_coefficient: float
time_of_concentration_min: float
description: str = ""
class RationalMethod:
"""Rational method for peak runoff calculation"""
def __init__(self):
# IDF curve coefficients (example for generic location)
# Q = C * I * A, where I from IDF: I = a / (Tc + b)^c
self.idf_coefficients = {
2: {'a': 100, 'b': 10, 'c': 0.8},
5: {'a': 120, 'b': 10, 'c': 0.8},
10: {'a': 140, 'b': 10, 'c': 0.8},
25: {'a': 160, 'b': 10, 'c': 0.8},
50: {'a': 180, 'b': 10, 'c': 0.8},
100: {'a': 200, 'b': 10, 'c': 0.8}
}
def rainfall_intensity(self, tc_min: float, return_period: int) -> float:
"""Calculate rainfall intensity from IDF curve (in/hr)"""
coef = self.idf_coefficients.get(return_period, self.idf_coefficients[10])
intensity = coef['a'] / (tc_min + coef['b']) ** coef['c']
return intensity
def peak_runoff(self, catchment: CatchmentData, return_period: int) -> float:
"""Calculate peak runoff using Rational Method (cfs)"""
C = catchment.runoff_coefficient
I = self.rainfall_intensity(catchment.time_of_concentration_min, return_period)
A = catchment.area_acres
Q = C * I * A # cfs
return Q
def composite_runoff_coefficient(self, subareas: List[Tuple[float, float]]) -> float:
"""Calculate composite C for mixed land uses
subareas: list of (area, C) tuples
"""
total_area = sum(a for a, c in subareas)
weighted_c = sum(a * c for a, c in subareas) / total_area
return weighted_c
@staticmethod
def time_of_concentration_kirpich(length_ft: float, slope_pct: float) -> float:
"""Kirpich equation for Tc (minutes)"""
tc = 0.0078 * (length_ft ** 0.77) * (slope_pct ** -0.385)
return tc
# Example runoff coefficients
RUNOFF_COEFFICIENTS = {
'commercial': 0.85,
'industrial': 0.75,
'residential_high_density': 0.65,
'residential_medium_density': 0.45,
'residential_low_density': 0.35,
'parks': 0.20,
'forest': 0.15,
'impervious': 0.95,
'lawn_steep': 0.30,
'lawn_flat': 0.20
}
# Example usage
rational = RationalMethod()
# Calculate composite C for mixed use area
subareas = [
(5.0, RUNOFF_COEFFICIENTS['commercial']),
(10.0, RUNOFF_COEFFICIENTS['residential_medium_density']),
(3.0, RUNOFF_COEFFICIENTS['parks'])
]
composite_c = rational.composite_runoff_coefficient(subareas)
catchment = CatchmentData(
area_acres=18.0,
runoff_coefficient=composite_c,
time_of_concentration_min=15.0,
description="Mixed use development"
)
for rp in [2, 10, 25, 100]:
Q = rational.peak_runoff(catchment, rp)
print(f"{rp}-year storm: Q = {Q:.1f} cfs")
SCS Curve Number Method
class SCSMethod:
"""SCS Curve Number method for runoff calculation"""
def __init__(self, curve_number: float):
self.cn = curve_number
self.S = (1000 / curve_number) - 10 # Potential retention (inches)
self.Ia = 0.2 * self.S # Initial abstraction
def runoff_depth(self, rainfall_inches: float) -> float:
"""Calculate runoff depth (inches)"""
P = rainfall_inches
if P <= self.Ia:
return 0.0
Q = (P - self.Ia) ** 2 / (P - self.Ia + self.S)
return Q
def runoff_volume(self, rainfall_inches: float, area_acres: float) -> float:
"""Calculate runoff volume (acre-feet)"""
Q_inches = self.runoff_depth(rainfall_inches)
volume_ac_ft = Q_inches / 12 * area_acres
return volume_ac_ft
@staticmethod
def composite_cn(subareas: List[Tuple[float, float]]) -> float:
"""Calculate area-weighted composite CN
subareas: list of (area, CN) tuples
"""
total_area = sum(a for a, cn in subareas)
weighted_cn = sum(a * cn for a, cn in subareas) / total_area
return weighted_cn
@staticmethod
def adjust_cn_for_amc(cn_ii: float, condition: str) -> float:
"""Adjust CN for antecedent moisture condition
condition: 'dry' (AMC-I), 'normal' (AMC-II), or 'wet' (AMC-III)
"""
if condition == 'dry':
cn = cn_ii / (2.281 - 0.01281 * cn_ii)
elif condition == 'wet':
cn = cn_ii / (0.427 + 0.00573 * cn_ii)
else:
cn = cn_ii
return cn
# Standard curve numbers (AMC-II, Hydrologic Soil Group B)
CURVE_NUMBERS = {
'impervious': 98,
'commercial': 92,
'industrial': 88,
'residential_1_8_acre': 85,
'residential_1_4_acre': 80,
'residential_1_2_acre': 75,
'residential_1_acre': 68,
'open_space_good': 61,
'open_space_fair': 69,
'forest_good': 55,
'pasture_good': 61
}
# Example usage
# Pre-development condition
pre_cn = SCSMethod.composite_cn([
(20, CURVE_NUMBERS['forest_good']),
(80, CURVE_NUMBERS['pasture_good'])
])
pre_scs = SCSMethod(pre_cn)
# Post-development condition
post_cn = SCSMethod.composite_cn([
(30, CURVE_NUMBERS['impervious']),
(40, CURVE_NUMBERS['residential_1_4_acre']),
(30, CURVE_NUMBERS['open_space_good'])
])
post_scs = SCSMethod(post_cn)
rainfall = 3.5 # inches (design storm)
pre_runoff = pre_scs.runoff_volume(rainfall, area_acres=100)
post_runoff = post_scs.runoff_volume(rainfall, area_acres=100)
print(f"Pre-development CN: {pre_cn:.0f}")
print(f"Post-development CN: {post_cn:.0f}")
print(f"Pre-development runoff: {pre_runoff:.2f} acre-feet")
print(f"Post-development runoff: {post_runoff:.2f} acre-feet")
print(f"Required detention: {post_runoff - pre_runoff:.2f} acre-feet")
Bioretention Sizing
class BioretentionDesign:
"""Bioretention facility design"""
def __init__(self, infiltration_rate_in_hr: float = 1.0):
self.infiltration_rate = infiltration_rate_in_hr
def size_for_water_quality(self, drainage_area_sf: float,
impervious_fraction: float,
design_rainfall_in: float = 1.0) -> Dict:
"""Size bioretention for water quality treatment"""
# Calculate water quality volume (WQv)
# Using simplified volumetric approach
Rv = 0.05 + 0.009 * (impervious_fraction * 100) # Runoff coefficient
wqv_cf = Rv * design_rainfall_in / 12 * drainage_area_sf
# Bioretention area sizing
# Based on 6" ponding depth and filter media depth
ponding_depth_ft = 0.5 # 6 inches
drain_time_hr = 24 # Maximum drain time
# Area based on infiltration during storm
storm_duration_hr = 2 # Assume 2-hour storm
infiltrated_depth = self.infiltration_rate * storm_duration_hr / 12 # feet
# Minimum surface area
min_area_sf = wqv_cf / (ponding_depth_ft + infiltrated_depth)
# Typical sizing: 5-10% of impervious area
recommended_area_sf = drainage_area_sf * impervious_fraction * 0.05
return {
'water_quality_volume_cf': wqv_cf,
'minimum_surface_area_sf': min_area_sf,
'recommended_area_sf': max(min_area_sf, recommended_area_sf),
'ponding_depth_in': 6,
'filter_media_depth_in': 24,
'drain_time_hr': ponding_depth_ft * 12 / self.infiltration_rate
}
def design_details(self, surface_area_sf: float) -> Dict:
"""Generate design details for bioretention"""
return {
'surface_area_sf': surface_area_sf,
'filter_media': {
'depth_in': 24,
'composition': '50% sand, 30% comite, 20% mulch (top)',
'permeability_in_hr': self.infiltration_rate
},
'underdrain': {
'diameter_in': 4,
'material': 'Perforated PVC',
'slope_pct': 0.5,
'gravel_depth_in': 12
},
'overflow': {
'type': 'Standpipe or weir',
'elevation': '6 inches above media surface'
},
'plants': {
'density': '1 per 3 sq ft',
'recommended': ['Switchgrass', 'Black-eyed Susan', 'Sedges']
},
'maintenance': {
'mulch_replacement': 'Annual',
'vegetation_maintenance': 'Biannual',
'sediment_removal': 'As needed, typically 5-year'
}
}
# Example usage
bio = BioretentionDesign(infiltration_rate_in_hr=1.5)
sizing = bio.size_for_water_quality(
drainage_area_sf=43560, # 1 acre
impervious_fraction=0.6,
design_rainfall_in=1.0
)
print(f"Water quality volume: {sizing['water_quality_volume_cf']:.0f} cf")
print(f"Recommended area: {sizing['recommended_area_sf']:.0f} sf")
print(f"Drain time: {sizing['drain_time_hr']:.1f} hours")
details = bio.design_details(sizing['recommended_area_sf'])
print(f"\nFilter media depth: {details['filter_media']['depth_in']} inches")
Pollutant Load Analysis
class PollutantLoading:
"""Stormwater pollutant load estimation"""
# Event Mean Concentrations (mg/L) by land use
EMC = {
'residential': {'TSS': 101, 'TP': 0.38, 'TN': 2.5, 'Zn': 0.14},
'commercial': {'TSS': 69, 'TP': 0.22, 'TN': 2.2, 'Zn': 0.22},
'industrial': {'TSS': 85, 'TP': 0.26, 'TN': 2.0, 'Zn': 0.32},
'highway': {'TSS': 142, 'TP': 0.34, 'TN': 3.2, 'Zn': 0.35},
'open_space': {'TSS': 40, 'TP': 0.10, 'TN': 1.0, 'Zn': 0.05}
}
# BMP removal efficiencies (%)
BMP_REMOVAL = {
'bioretention': {'TSS': 85, 'TP': 60, 'TN': 50, 'Zn': 80},
'wet_pond': {'TSS': 80, 'TP': 50, 'TN': 30, 'Zn': 60},
'dry_pond': {'TSS': 60, 'TP': 20, 'TN': 15, 'Zn': 30},
'grass_swale': {'TSS': 70, 'TP': 25, 'TN': 20, 'Zn': 40},
'perm_pavement': {'TSS': 85, 'TP': 60, 'TN': 50, 'Zn': 70},
'sand_filter': {'TSS': 85, 'TP': 50, 'TN': 35, 'Zn': 80}
}
def annual_load(self, area_acres: float, land_use: str,
annual_runoff_in: float, pollutant: str) -> float:
"""Calculate annual pollutant load (lbs/year)"""
emc = self.EMC.get(land_use, self.EMC['residential']).get(pollutant, 0)
# Load = EMC * Volume
# Volume (L) = runoff (in) * area (acres) * 43560 sf/ac * 0.0254 m/in * 1000 L/m3
volume_l = annual_runoff_in * 0.0254 * area_acres * 43560 * 0.0929 * 1000
# Load in mg, convert to lbs
load_mg = emc * volume_l
load_lbs = load_mg / 453592
return load_lbs
def load_reduction(self, load_lbs: float, bmp_type: str,
pollutant: str) -> Dict:
"""Calculate load reduction from BMP"""
efficiency = self.BMP_REMOVAL.get(bmp_type, {}).get(pollutant, 0) / 100
removed = load_lbs * efficiency
remaining = load_lbs - removed
return {
'initial_load_lbs': load_lbs,
'removal_efficiency_pct': efficiency * 100,
'load_removed_lbs': removed,
'remaining_load_lbs': remaining
}
# Example usage
loading = PollutantLoading()
# Calculate loads for a 50-acre commercial development
area = 50 # acres
annual_runoff = 30 # inches
land_use = 'commercial'
for pollutant in ['TSS', 'TP', 'TN']:
load = loading.annual_load(area, land_use, annual_runoff, pollutant)
reduction = loading.load_reduction(load, 'bioretention', pollutant)
print(f"{pollutant}: {load:.1f} lbs/yr -> {reduction['remaining_load_lbs']:.1f} lbs/yr "
f"({reduction['removal_efficiency_pct']:.0f}% removal)")
Usage Guidelines
When to Use This Skill
- Stormwater management plan development
- Green infrastructure design
- BMP selection and sizing
- MS4 permit compliance analysis
- Watershed planning and TMDL implementation
Best Practices
- Match design storm to local requirements
- Consider treatment train approaches for multiple benefits
- Plan for maintenance access in design
- Verify infiltration rates with field testing
- Include pre-treatment for high pollutant areas
- Monitor performance for adaptive management
Process Integration
- WW-004: Stormwater Management Planning (all phases)
Dependencies
- numpy, scipy: Numerical calculations
- pandas: Data analysis
- pyswmm: SWMM model interaction (optional)
References
- EPA SWMM Reference Manual
- ASCE Manual of Practice No. 77
- State-specific stormwater design manuals
- NCHRP Report 565 BMP Selection