“””ZurichFlood Impact , extends CLIMADA Impact for Zurich Urban flood risk assessment.
I Wrote this Code for Climada as a part of Urban_flood_Petal
you can download this code from my github Page in this Address: https://github.com/sepehrkiya/climada_petal_urban_flood
"""Urban flood impact module."""
import numpy as np
from climada.engine import Impact
from climada_petal_urban_flood.hazard import UrbanFloodHazard
from climada_petal_urban_flood.exposure import UrbanFloodExposure
class UrbanFloodImpact(Impact):
"""Class for urban flood impact assessment.
This class extends CLIMADA's Impact to provide specialized methods
for urban flood impact evaluation.
"""
def __init__(self):
"""Initialize UrbanFloodImpact."""
super().__init__()
def calc_impact(self, hazard, exposure, impact_func_set, save_mat=False):
"""Calculate impact from hazard, exposure and impact functions.
Extended to handle urban-specific impact calculations.
Parameters:
hazard (UrbanFloodHazard): Urban flood hazard
exposure (UrbanExposure): Urban exposure
impact_func_set (ImpactFuncSet): Impact function set
save_mat (bool): Save impact matrix
"""
# Verify the correct type of input data
if not isinstance(hazard, UrbanFloodHazard):
hazard = UrbanFloodHazard.from_hazard(hazard)
# Use the original method to calculate impacts
super().calc_impact(hazard, exposure, impact_func_set, save_mat=save_mat)
# Additional computations specific to urban flooding can be added here
return self
def affected_population(self, threshold=0.1):
"""Calculate the number of people affected by urban flood.
Parameters:
threshold (float): Flood depth threshold in meters to consider affected
Returns:
float: Number of affected people
"""
if not hasattr(self.exp, 'value'):
raise ValueError("No exposure data found")
# Assume that the population column is stored as 'pop'
if not hasattr(self.exp, 'pop'):
raise ValueError("No population data in exposure")
# Identify areas where flood depth exceeds the threshold
affected_idx = self.eai_exp > 0
# Calculate the number of affected people
affected_pop = np.sum(self.exp.pop[affected_idx])
return affected_pop
def indirect_impacts(self, infrastructure_disruption_factor=0.5, recovery_time_days=30):
"""Estimate the indirect economic impacts of urban flooding.
Parameters:
infrastructure_disruption_factor (float): Factor for infrastructure disruption
recovery_time_days (int): Estimated recovery time in days
Returns:
float: Estimated indirect economic impact
"""
# This is a simplified model to estimate indirect effects
direct_impact = self.aai_agg
# Estimate indirect effects as a percentage of direct effects, dependent on recovery time
indirect_factor = infrastructure_disruption_factor * (recovery_time_days / 365)
indirect_impact = direct_impact * indirect_factor
return indirect_impact
