Drinking water utilities might be interested in examining many different disaster scenarios. They could be acute incidents like power outages and earthquakes or they could be long term issues like persistent pipe leaks, population fluctuation, and changes to supply and demand. The following section describes disaster scenarios that can be modeled in WNTR. The example disaster_scenarios.py demonstrates methods to define disaster scenarios.
Earthquakes can be some of the most sudden and impactful disasters that a water systems experiences. An earthquake can cause lasting damage to the system that could take weeks, if not months, to fully repair. Earthquakes can cause damage to pipes, tanks, pumps, and other infrastructure. Additionally, earthquakes can cause power outages and fires.
WNTR includes methods
to add leaks to pipes and tanks,
shut off power to pumps,
and change demands for fire conditions, as described in the sections below.
Earthquake class includes methods
to compute peak ground acceleration, peak ground velocity, and repair rate based on the earthquake
location and magnitude.
Alternatively, external earthquake models or databases (e.g., ShakeMap [WWQP06]) can be used to compute earthquake properties and
those properties can be loaded into Python for analysis in WNTR.
When simulating the effects of an earthquake, fragility curves are commonly used to define the probability that a component is damaged with respect to peak ground acceleration, peak ground velocity, or repair rate. The American Lifelines Alliance report [ALA01] includes seismic fragility curves for water system components. See Stochastic simulation for more information on fragility curves.
Since properties like peak ground acceleration, peak ground velocity, and repair rate are a function of the distance to the epicenter, node coordinates in the water network model must be in units of meters. Since some network models use other units for node coordinates, WNTR includes a method to change the coordinate scale. To change the node coordinate scale by a factor of 1000, for example, use the following code:
The following code can be used to compute peak ground acceleration, peak ground velocity, and repair rate:
epicenter = (32000,15000) # x,y location magnitude = 6.5 # Richter scale depth = 10000 # m, shallow depth earthquake = wntr.scenario.Earthquake(epicenter, magnitude, depth) distance = earthquake.distance_to_epicenter(wn, element_type=wntr.network.Pipe) pga = earthquake.pga_attenuation_model(distance) pgv = earthquake.pgv_attenuation_model(distance) repair_rate = earthquake.repair_rate_model(pgv)
Pipe breaks or leaks¶
Pipes are susceptible to leaks. Leaks can be caused by aging infrastructure, the freeze/thaw process, increased demand, or pressure changes. This type of damage is especially common in older cities where distribution systems were constructed from outdated materials like cast iron and even wood.
WNTR includes methods to add leaks to junctions and tanks. Leaks can be added to a pipe by splitting the pipe and adding a junction. To add a leak to a specific pipe:
wn.split_pipe('123', '123_B', '123_leak_node') leak_node = wn.get_node('123_leak_node') leak_node.add_leak(wn, area=0.05, start_time=2*3600, end_time=12*3600)
add_leak adds time controls to a junction which includes the start and stop time for the leak.
Power outages can be small and brief, or they can also span over several days and effect whole regions as seen in the 2003 Northeast Blackout. While the Northeast Blackout was an extreme case, a 2012 Lawrence Berkeley National Laboratory study [ELLT12] showed the frequency and duration of power outages are increasing by a rate of two percent annually. In water distribution systems, a power outage can cause pump stations to shut down and result in reduced water pressure. This can lead to shortages in some areas of the system. Typically, no lasting damage in the system is associated with power outages.
WNTR can be used to simulate power outages by changing the pump status from ON to OFF and defining the duration of the outage. To model the impact of a power outage on a specific pump:
wn.add_pump_outage('335', 5*3600, 10*3600)
add_pump_outage adds time controls to a pump to start and stop a power outage.
When simulating power outages, consider placing check bypasses around pumps
and check valves next to reservoirs.
WNTR can be used to simulate damage caused to system components due to fire and/or to simulate water usage due to fighting fires. To fight fires, additional water is drawn from the system. Fire codes vary by state. Minimum required fire flow and duration are generally based on building area and purpose. While small residential fires might require 1500 gallons/minute for 2 hours, large commercial spaces might require 8000 gallons/minute for 4 hours [ICC12]. This additional demand can have a large impact on water pressure in the system.
WNTR can be used to simulate fire fighting conditions in the system. WNTR simulates fire fighting conditions by specifying the demand, time, and duration of fire fighting. Pressure dependent demand simulation is recommended in cases where fire fighting might impact expected demand. To model the impact of fire conditions at a specific node:
fire_flow_demand = 0.252 # 4000 gal/min = 0.252 m3/s fire_start = 10*3600 fire_end = 14*3600 fire_flow_pattern = wntr.network.elements.Pattern.BinaryPattern('fire_flow', step_size=wn.options.time.pattern_timestep, start_time=fire_start, end_time=fire_end, duration=wn.options.time.duration) wn.add_pattern('fire_flow', fire_flow_pattern) node = wn.get_node('197') node.demand_timeseries_list.append( (fire_flow_demand, fire_flow_pattern, 'Fire flow'))
Environmental change is a long term problem for water distribution systems. Changes in the environment could lead to reduced water availability, damage from weather incidents, or even damage from subsidence. For example, severe drought in California has forced lawmakers to reduce the state’s water usage by 25 percent. Environmental change also leads to sea level rise which can inundate distribution systems. This is especially prevalent in cities built on unstable soils like New Orleans and Washington, DC which are experiencing land subsidence.
WNTR can be used to simulate the effects of environmental change on the water distribution system by changing supply and demand, adding disruptive conditions (i.e., power outages, pipe leaks) caused by severe weather, or by adding pipe leaks caused by subsidence. Power outages and pipe leaks are described above. Changes to supply and demand can be simple (i.e., changing all nodes by a certain percent), or complex (i.e., using external data or correlated statistical methods). To model simple changes in supply and demand:
for reservoir_name, reservoir in wn.reservoirs(): reservoir.head_timeseries.base_value = reservoir.head_timeseries.base_value*0.9 for junction_name, junction in wn.junctions(): for demand in junction.demand_timeseries_list: demand.base_value = demand.base_value*1.15
Water distribution systems are vulnerable to contamination by a variety of chemical, microbial, or radiological substances. During disasters, contamination can enter the system through reservoirs, tanks, and at other access points within the distribution system. Long term environmental change can lead to degradation of water sources. Contamination can be difficult to detect and is very expensive to clean up. Recent incidents, including the Elk River chemical spill and Flint lead contamination, highlight the need to minimize human health and economic impacts.
WNTR simulates contamination incidents by introducing contaminants into the distribution system and allowing them to propagate through the system. The example water_quality_simulation.py includes steps to define and simulate contamination incidents.
Future versions of WNTR will be able to simulate changes in source water quality due to disruptions.
Other disaster scenarios¶
Drinking water systems are also susceptible to other natural disasters including floods, droughts, hurricanes, tornadoes, extreme winter storms, and wind events. WNTR can be used to simulate these events by combining the disaster models already described above. For example, tornadoes might cause power outages, pipe breaks, other damage to infrastructure, and fires. Floods might cause power outages, changes to source water (because of treatment failures), and pipe breaks.