# Overview ✏️ {.unnumbered}

::: {.callout-tip}
## Learning objectives

- Define and conceptualize risk within the climate context (hazard, exposure, vulnerability)
- Understand basic risk terminology (risk = probability × consequence)
- Identify how different stakeholders perceive and prioritize climate risks
- Recognize and analyze multi-hazard events (e.g., hurricane + storm surge + inland flooding)
- Understand how cascading failures in interconnected systems (power grids, supply chains) amplify risk
- Apply systems thinking approaches to complex risk interactions
:::

## Example Management Decisions

Climate risk management involves making decisions across multiple scales and contexts. Consider these examples:

### Update land use / zoning regulations?

![Boomtown development [{{< fa camera-retro >}}: @satija_boomtown:2016](../../_assets/img/boomtown.png)

### How to size stormwater infrastructure?

![Buffalo drainage infrastructure [{{< fa camera-retro >}}: Doss-Gollin](../../_assets/img/doss-gollin-buffalo-drainage.jpeg)

### How high to elevate a house for proactive flood protection?

### Require weatherization of privately owned electricity infrastructure?

![Infrastructure weatherization [{{< fa camera-retro >}}: Jonathan Cutrer / Flickr](../../_assets/img/cutrer-uri.jpg)

## Key Mathematical Concept

The most important concept is:
$$
\mathbb{E}\left[ f(x) \right] \neq f \left( \mathbb{E}[x] \right)
$$
where $x$ is some random variable and $f$ is some function.
In our context, this means that if we want to know the expected value of a risk-related variable, like damage, we should not just take the expected value of the hazard and then apply a damage function to that.
Extending the concept further, the damage caused by a hazard that scores in the 99th percentile of a chosen intensity measure may not be the 99th percentile of the damage distribution (a commonly used, but wrong, assumption).

## Climate risks to complex systems

### Interconnected and interdependent infrastructure systems

Many of the same principles we've discussed apply to complex systems, though analyzing these systems presents unique challenges. 
Climate risks to complex systems are particularly challenging because of cascading failures and interdependencies.

### Case study: 2021 Texas Freeze

The 2021 Texas freeze provides an excellent example of how climate hazards interact with complex infrastructure systems to create cascading failures.

#### What happened?

Moving forward, we'll focus largely on decision frameworks for analyzing climate risks.
We'll generally use *simple systems* for interpretability!
However, many of the same principles apply to *complex systems*.

The 2021 Texas freeze demonstrates cascading failures in interconnected and interdependent infrastructure systems.

#### Were these temperatures unprecedented?

1. Use observations only
1. Look globally and regionally
1. Past temperatures were lower / comparable

How else can we explore hazard?

The question of whether the 2021 temperatures were unprecedented requires careful analysis.
Using observations alone and looking both globally and regionally, past temperatures were actually lower or comparable to what was observed.

#### Demand for heating

For each grid cell and each hour, we can calculate the difference between observed temperature and "comfortable" temperature, then take the average weighted by population to get **inferred demand for heating**.

The key question becomes: What is the **return period** of temperatures observed during the 2021 Texas freeze as a function of the defined **duration** of the event?

#### Some pitfalls

1. The past (1950-2022) may not be a good guide to the future
1. Temperature is not the only factor that influences demand for heating

#### Electricity supply

Key points from the 2021 Texas freeze:

1. Gas production dropped ~50% 
1. Outages: 30GW of electricity
1. Inadequate winterization of electricity and gas systems
1. Financial repercussions
1. Primary culprit for outages was problems in electricity production from natural gas

#### All major fuel sources underperformed

The crisis was not limited to a single energy source - all major fuel sources experienced significant underperformance during the extreme cold event.

#### Cascading risks

The natural gas supply chain failures occurred in multiple areas:

1. Oil & gas wells
2. Gathering lines
3. Equipment malfunction at power plants
4. Outage cut power to compressor stations that moved gas

Plus other long-term issues like limited storage in TX.

#### Analysis challenges

If we wanted to model the natural gas ➡️ electricity supply chain, we would need to know (at minimum!): 

1. Locations of all wells, separators, gathering lines, compressors, power stations
1. Weatherization / vulnerability to cold of all
1. This is very hard!

Climate risk to complex systems is hard!
Hence, attempts at standardized design rules / standards.

### Policy recommendations

Based on analysis of the 2021 Texas freeze:

1. Regulation and price incentives
1. Plan for winter peaks, not summer ones!
1. Weatherization
1. Demand response
1. Regional transmission

Grid resilience in an electrified, renewable-powered world is possible but non-trivial to ensure.

### Summary: 2021 Texas Cold Snap

#### What happened:
1. Cold temperatures: demand for heating 📈
1. Cold temperatures: supply of electricity 📉
    1. Complex supply chain!
1. Joint effects: cascading failures

#### How to analyze:
1. Observations/reanalysis: not very many extremes
1. ESMs: bigger sample size, but biased
    - Many pitfalls to bias correction
1. Fragility of individual components ➡️ interdependence ➡️ system performance

### Climate risks to electricity systems

Climate risks to electricity systems involve:

1. Outages from severe weather
1. At long time scales, technological and demographic drivers of demand dominate
1. At shorter time scales, interannual variability critically important
