---
title: Canadian Disaster Database (CDD)
url: https://www.emergentmind.com/topics/canadian-disaster-database-cdd
type: topic
---

# Canadian Disaster Database (CDD)

Searching arXiv for the specified papers and closely related work on the Canadian Disaster Database.
The **Canadian Disaster Database (CDD)** is a disaster-event archive maintained by **Public Safety Canada** and used as an empirical basis for analyzing Canadian natural-disaster risk. It contains detailed records on **over 1,000 natural, technological, and conflict events** (excluding war) since **1900**, including events that occurred domestically or abroad but directly affected Canadians. In quantitative research, the CDD has been used both for **historical trend analysis** and for **probabilistic modeling** of annual loss distributions, especially for natural disasters related to **meteorological or hydrological phenomena** [2510.08594].

## 1. Scope, inclusion criteria, and data structure

The CDD records **“significant disaster events”** rather than all adverse events. Events are included if they meet one or more criteria such as **10 or more fatalities**, **100 or more people affected/injured/evacuated/homeless**, **an appeal for national or international assistance**, **historical significance**, or **major disruption such that the affected community cannot recover on its own**. The database includes **location, date, injuries, evacuations, fatalities, and approximate financial losses**. Its data are sourced from **traceable references** and are **reviewed semi-annually** [2510.08594].

These inclusion rules matter analytically. They imply that the CDD is optimized for consequential events and for national-scale situational awareness, not for a complete census of low-impact local incidents. A plausible implication is that frequency estimates derived from the CDD are estimates for the database’s “significant event” process, rather than for all physically occurring hazards.

## 2. Event taxonomy and temporal coverage

The CDD classifies natural disasters into **13 types**: **Avalanche**, **Cold Event**, **Drought**, **Flood**, **Geomagnetic Storm**, **Heat Event**, **Hurricane / Typhoon / Tropical Storm**, **Storm**, **Unspecified / Other**, **Storm Surge**, **Storms and Severe Thunderstorms**, **Tornado**, **Wildfire**, and **Winter Storm**. In the quantitative study focused on natural hazards, the major loss-producing categories are **Flood**, **Thunderstorm**, **Wildfire**, **Winter Storm**, **Tornado**, and **Storm Other**, with smaller categories such as **Avalanche**, **Cold Event**, **Drought**, **Hurricane/Typhoon/Tropical Storm**, and **Storm Surge** entering aggregation through average annual losses [2510.08594].

The study compares two collections derived from the database: a **2017 collection** covering **1900 to 2016** and a **2024 collection** covering **1900 to 2020**. For stochastic modeling of annual loss frequency and severity, the analysis begins in **1955**, because **1955 is the first year with a non-zero loss event recorded** in the CDD. The reported totals are **789 events** and **\$22B** in losses for the 2017 collection, and **899 events** and **\$34B** in losses for the 2024 collection.

The six most frequent disaster categories in the 2024 collection are **Flood**, **Thunderstorm**, **Wildfire**, **Winter Storm**, **Tornado**, and **Drought**; together they account for **86% of all recorded disasters**. The top six categories by total loss are **Flood**, **Winter Storm**, **Thunderstorm**, **Wildfire**, **Storm Other**, and **Drought**; together they account for **94% of total disaster-related losses**.

| Category | Frequency since 1900 | Total loss since 1900 |
|---|---:|---:|
| Flood | 336 events, 37% | \$10.0B, 31% |
| Thunderstorm | 141 events, 16% | \$5.9B, 17% |
| Wildfire | 113 events, 13% | \$4.9B, 15% |
| Winter Storm | 89 events, 10% | \$6.7B, 20% |
| Drought | 46 events, 5% | \$1.7B, 5% |

Within this profile, **Flood** dominates both by frequency and by cumulative loss, while **Winter Storm** is notable for high total cost despite lower frequency than flood.

## 3. Statistical conversion of the CDD into a national loss model

The CDD has been used not only as a descriptive archive but also as input to a compound-loss framework. Event counts are modeled by a **Poisson distribution**,
$$
f(k) = \frac{e^{-\lambda}\lambda^k}{k!}, \quad k=0,1,2,\dots
$$
with
$$
\lambda = \frac{\text{number of events in sample period}}{\text{number of years in that period}}.
$$
The study gives the example of floods in the 2017 collection: **150 events from 1955–2016**, yielding an estimated frequency of **\(\lambda = 2.46\)** [2510.08594].

Event severity is modeled with the **Generalized Pareto Distribution (GPD)**,
$$
G_{\xi,\beta}(y) = 1 - \left(1 + \xi \frac{y}{\beta}\right)^{-1/\xi},
$$
equivalently written in the appendix as
$$
F^{GPD}_{\xi,\beta}(x) = 1 - \left(1 + \xi \frac{x}{\beta}\right)^{-1/\xi},
$$
where the exceedance is over the threshold of **zero-dollar losses**, and where **\(\xi\)** is the **shape** parameter and **\(\beta\)** is the **scale** parameter.

Annual total loss is then written as
$$
S = \sum_{k=1}^{K} X_k,
$$
with \(K\) the random number of events in a year and \(X_k\) the individual event losses. The characteristic function is expressed as
$$
\phi_S(z) \equiv \mathbb{E}[e^{izS}] = P_K(\phi_X(z)),
$$
and, for the Poisson case,
$$
\phi_S(z)=\exp[\lambda(\phi_X(z)-1)].
$$
The annual loss distribution is computed by an **FFT (Fast Fourier Transform)** procedure that discretizes severity, transforms to the frequency domain, applies the compound-loss formula, and then recovers the cumulative distribution by inverse FFT and cumulative summation.

For the aggregate national distribution across event types, the study does not assume perfect independence. Instead, it reports **positive Spearman correlations** among the six major event types, with the highest around **60%**, and uses a **Normal copula** to model dependence. The procedure simulates marginal annual losses for each event type, maps them through the copula, sums the corresponding annual losses, and repeats this **10,000 times** to estimate the aggregate national loss distribution.

## 4. Empirical findings on frequency, severity, and tail risk

The principal empirical conclusion is that there is **no strong evidence that any specific disaster type became more frequent nationwide over the past seven years**. This is supported by comparisons of event counts in the 2017 and 2024 collections, by **Poisson frequency estimates**, and by **Wald tests**, which are reported as **not significant** [2510.08594].

By contrast, the study identifies a severity shift. The clearest increases are for **Thunderstorm** and **Wildfire**, which have caused **greater financial losses in recent years**, while other categories are described as relatively stable. The parameter estimates reported for the GPD also support this emphasis, especially through larger scale parameters and heavier large-loss behavior in the 2024 collection for these categories.

The national aggregate distribution is interpreted as having become **more heavy-tailed**. The 2024 collection shows a **lower main body** of losses at the **25th**, **50th**, and **75th** percentiles, but **much higher tail losses** at the **90th**, **99th**, and **99.9th** percentiles. This means that ordinary years may look somewhat less costly, while the risk of catastrophic national losses is materially higher.

| Percentile (\$MM) | 2017 | 2024 |
|---|---:|---:|
| 25% | 108 | 78 |
| 50% | 196 | 164 |
| 75% | 478 | 432 |
| 90% | 1,012 | 1,173 |
| 99% | 4,312 | 5,711 |
| 99.9% | 6,038 | 10,460 |

The study explicitly interprets this pattern as evidence that Canada may be becoming **warmer** and **wetter**. The stated reasoning is that **Wildfire losses are increasing**, consistent with warming and more intense fire conditions; **storm-related losses are rising**, consistent with wetter and stormier conditions; **Storm Other** becoming more frequent supports a wetter climate signal; and **Drought** has declined in relative importance, further supporting a shift away from dryness.

An additional modeling implication concerns low-frequency categories. For some event types, the annual loss distribution begins with a high probability at **zero loss**, because such events occur only once every few years. The study gives the example that if **\(\lambda = 0.3\)**, the probability of zero losses in a year is about **70%**.

## 5. Relation to disaster monitoring and recovery analysis

The CDD is fundamentally an event-cataloguing resource, but its analytical relevance extends to post-disaster measurement and recovery modeling. A separate study using **call detail records (CDRs)** on the **2010 earthquake in Haiti**, the **2015 Gorkha earthquake in Nepal**, and **Hurricane Matthew in Haiti in 2016** frames disaster analysis in terms of **resettlement curves**, **decay curves**, and **recovery rates**, and explicitly connects such work to the broader problem of documenting displacement and recovery through disaster monitoring and post-disaster analysis [1908.02381].

That CDR-based study characterizes the fraction of internally displaced persons who remain disrupted each week after a disaster and fits the resulting decay with a **double-exponential model**,
$$
f(t) = \alpha_{1}\exp(-\beta_{1}t) + \alpha_{2}\exp(-\beta_{2}t),
$$
interpreted as a mixture of **fast-recovering** and **slow-recovering** groups. It reports that **half the original number of displaced persons** had resettled within **four to five weeks** across the three disasters, and argues that such methods can assist **disaster monitoring**, **measurement of resilience / recovery**, **comparison of recovery rates across disasters**, and **estimation of how many people still require support at a given time**.

This connection is significant for understanding the place of the CDD in disaster research. The CDD records event timing, location, and impacts; CDR-based studies measure post-event human mobility and resettlement. Taken together, they represent complementary layers of disaster evidence: one centered on event cataloguing and financial consequences, the other on behavioral recovery trajectories. A plausible implication is that disaster databases and mobility analytics can be integrated in future comparative work on post-disaster recovery.

## 6. Acronym ambiguity and distinction from the “Comprehensive Disaster Dataset”

In recent literature, the acronym **CDD** is not unique. A 2026 computer-vision paper uses **CDD** to denote the **Comprehensive Disaster Dataset**, an aerial image benchmark containing **13,316 images** across **five classes**: **infrastructure damages, fire, water disasters, human damages, and normal** [2603.19496].

That image dataset is unrelated to the **Canadian Disaster Database** maintained by Public Safety Canada. Its purpose is embedded image classification under resource constraints, and it is used to evaluate **VeloxNet**, a lightweight CNN with **gMLP blocks** and a **Spatial Gating Unit**. The paper reports **6,737 training**, **1,542 validation**, and **5,037 testing** images, with classes heavily imbalanced toward **normal**, and gives a headline result of **77.46% weighted F1** on that benchmark for VeloxNet.

The acronym overlap can create ambiguity in interdisciplinary searches, particularly when disaster research spans hazard databases, mobility analysis, and computer vision. The Canadian Disaster Database is an event archive and risk-modeling substrate; the Comprehensive Disaster Dataset is an aerial imagery benchmark. Treating them as interchangeable would conflate fundamentally different data modalities, sampling frames, and inferential targets.

Source: https://www.emergentmind.com/topics/canadian-disaster-database-cdd