stormwater-management

# 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 ```bash pip install numpy scipy pandas matplotlib ``` ### Optional Dependencies ```bash # 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 ```python 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 ```python 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 ```python 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 ```python 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},