• Introduction
• Description of major drainage areas
• Conformity to relevant nationally recognized standards

Introduction

The Standard Drainage Area Classification (SDAC) 2003 was developed to enable the production of integrated statistics by hydrographic areas. It provides a range of geographical units that are convenient for data collection and compilation, and useful for spatial analysis of environmental, economic and social statistics. A drainage area is composed of a drainage basin as well as adjacent areas, such as coasts and islands that may not drain into the outlet^{Footnote 1}. A drainage basin is an area in which all contributing surface waters share the same drainage outlet. This classification covers drainage areas and therefore applies to all of Canada including coasts and islands that may not drain into an outlet.

The Standard Drainage Area Classification (SDAC) 2003 covers all the land and interior freshwater lakes of the country. Some drainage areas straddle the Canada-United States border. This classification includes only the parts within Canada since it is used for reporting Canadian data.

There are three levels in the 2003 version of the classification - 11 major drainage areas, 164 sub-drainage areas and 974 sub-sub-drainage areas. All drainage areas, sub-drainage areas and sub-sub-drainage areas are named and have an identifying unique code. The code used is a four-character alphanumeric code. Two leading numeric digits represent the major drainage areas, the third alphabetic character represents the sub-drainage areas and a fourth alphabetic character represents the sub-sub-drainage areas.

Among the levels in this classification, the detailed sub-drainage area level is generally used for disseminating statistics.

Description of major drainage areas

Canada is divided into eleven major drainage areas:

The table displays the name (first column) and are information (second (square kilometres) and third (%) columns).

The Maritime Provinces drainage area covers New Brunswick, Prince Edward Island, Nova Scotia and part of the Gaspé Peninsula in eastern Quebec. This area drains into the Gulf of St. Lawrence and the Atlantic Ocean. Halifax, Fredericton and Charlottetown are located in this drainage area.

The St. Lawrence drainage area includes all of southern Quebec, southern Ontario and Newfoundland. Cities in this drainage area include Montréal, Toronto, Ottawa, Thunder Bay, St. John's and Corner Brook.

The Northern Quebec and Labrador drainage area covers all the area just north of the St. Lawrence drainage area. Most of the water in this drainage area drains into Hudson, James and Ungava bays. The Labrador portion of the drainage area drains directly into the Atlantic Ocean. Communities in this drainage area include Chibougamau and Kuujjuaq in Quebec and Happy Valley-Goose Bay in Labrador.

The Southwestern Hudson Bay drainage area covers a large portion of northern Ontario, a small part of western Quebec and northeastern Manitoba. Communities in this drainage area include Val-d'Or in Quebec and Kapuskasing and Timmins in Ontario.

The Nelson River drainage area covers most of the southern Prairie provinces, from the Rockies to northwestern Ontario. Major cities within this drainage area are Winnipeg, Regina, Saskatoon, Calgary and Edmonton. Water from the Nelson River drainage area ultimately drains into Hudson Bay.

The Western and Northern Hudson Bay drainage area includes parts of eastern Alberta, all of the middle portions of Saskatchewan, northwestern Manitoba, the northeastern portion of the Northwest Territories, southeastern Nunavut, the Hudson Bay islands and part of Baffin Island. The following communities are found in this drainage area: Bonnyville, Alberta; La Ronge, Saskatchewan; and Rankin Inlet, Nunavut.

The Great Slave Lake drainage area is composed of northern Alberta, northern Saskatchewan, southern Northwest Territories and a small portion of British Columbia. Cities within this drainage area include Grande Prairie and High Level in Alberta, Yellowknife and Hay River in the Northwest Territories, and Fort St. John in British Columbia.

The Pacific drainage area includes all the area that is west of the continental divide, representing about 70% of the province of British Columbia. The southwestern part of the Yukon is also part of this drainage area. Some of the communities in the eastern portion of the drainage area are Kimberley, Golden and Invermere, all in British Columbia. Cities in the western portion include Vancouver, Victoria, Abbotsford and Whistler. Terrace and Prince Rupert in British Columbia are among the northern communities in this drainage area.

The Yukon River drainage area contains most of the western portion of the Yukon territory. The most southerly portion of this drainage area is in British Columbia. Communities and cities in this drainage area are Whitehorse, Dawson and Old Crow in the Yukon. The Yukon River flows through central Alaska, and ultimately into the Bering Sea.

The Arctic drainage area covers most of the arctic islands and large portions of the Northwest Territories, Nunavut, Yukon and, to a lesser extent, British Columbia and Alberta. Communities within this drainage area include Inuvik in the Northwest Territories, Fort Nelson in British Columbia, and Pangnirtung, Iqaluit and Resolute in Nunavut.

Finally, the Mississippi River drainage area covers a small area in the southernmost parts of Alberta and Saskatchewan.

Conformity to relevant nationally recognized standards

In 2000, Natural Resources Canada, Environment Canada and Statistics Canada formed a partnership to produce a single national drainage area dataset at a scale of 1:1,000,000. As a result of the partnership The National Scale Frameworks Hydrology - Drainage Areas, Canada, Version 5 was completed in 2003. In the drainage area classification of this Frameworks dataset, Canada has eleven major drainage areas which are divided into 164 sub-drainage areas; the 164 sub-drainage areas are then further divided into 978 sub-sub-drainage areas. All drainage areas, sub-drainage areas and sub-sub-drainage areas are named and have an identifying code. This classification is used by Statistics Canada as the basis for the Standard Drainage Area Classification (SDAC) 2003. The Standard Drainage Area Classification (SDAC) 2003 also contains the major drainage areas, sub-drainage areas and sub-sub-drainage areas but with the following modifications:

• Some drainage areas in the Frameworks dataset straddle the Canada-United States border; the Standard Drainage Area Classification (SDAC) 2003 includes only the parts within Canada since this is used for reporting Canadian data. The SDAC 2003 excludes 4 of the sub-sub-drainage areas that are entirely outside the boundary of Canada. Therefore, the Standard Drainage Area Classification (SDAC) 2003 contains only the 974 sub-sub-drainage areas that are within Canada.
• The Great Lakes were not assigned a drainage area in the Frameworks dataset; Canadian islands in the Great Lakes are assigned a drainage area in the Standard Drainage Area Classification (SDAC) 2003 for the purposes of data reporting.

The Frameworks dataset as well as the SDAC classification cover all of Canada including islands and freshwater lakes; however, the classification does not cover marine water. Further information on the development of drainage areas is presented in the Additional information on SDAC.

A digital representation of the national scale frameworks hydrology - drainage areas is available for free on Natural Resources Canada's Geogratis website.

Status

This standard was approved as a departmental standard on February 16, 2009.

The Standard Drainage Area Classification (SDAC) is Statistics Canada's official classification of drainage areas in Canada. The SDAC provides unique numeric codes for the levels in the hierarchy of drainage areas: major drainage areas, sub-drainage areas and sub-sub-drainage areas. The three geographic areas are hierarchically related; a 4 character code is used to show this relationship. In addition to the drainage area classes, a classification variant of the sub-sub-drainage areas by drainage regions and ocean drainage areas is included. The relationship between these geographic areas is illustrated in the diagram showing the Standard Drainage Area Classification and the hierarchical structure of the classification.

Major drainage areas are presented in the major drainage areas and sub-drainage areas map of Canada.

The Standard Drainage Area Classification (SDAC) 2003 is based on Version 5 of the National Scale Frameworks Hydrology - Drainage Areas, Canada. The National Scale Frameworks Hydrology was developed by a partnership consisting of Natural Resources Canada, Environment Canada and Statistics Canada.

• Introduction
• SDAC 2003 - Classification structure

Variant of SDAC 2003

The classification variant of SDAC is a set of customized groupings that use SDAC sub-sub-drainage areas as building blocks. In Statistics Canada, variants are created and adopted in cases where the version of the classification does not fully meet specific user needs for disseminating data or for sampling in surveys. A classification variant is based on a classification version such as SDAC 2003. In a variant, the categories of the classification version are split, aggregated or regrouped to provide additions or alternatives (e.g. context-specific additions) to the standard structure of the base version.

Ocean drainage areas and drainage regions are presented in the ocean drainage areas and drainage regions map of Canada.

• Introduction
• Drainage Regions - Variant of SDAC 2003

Purpose

To familiarize the participants with Indirect Sampling and the Generalised Weight Share Method; apply these methods for surveying difficult to reach populations.

Benefits to participant

Participants will benefit from a thorough description of Indirect Sampling, together with its related weighting method: the Generalised Weight Share Method. The content is of current interest: we are more and more interested in producing statistics for populations for which there is no sampling frame, or where the development of a frame would be too expensive.

The emphasis will be put on Indirect sampling, which is a generalisation of well-known sampling methods for populations difficult to reach: Network Sampling, Adaptive Cluster Sampling and Snowball Sampling.

The course will involve the study of real problems to solve, in order to facilitate the understanding of the basic notions. Thus, by having discussions with the participants, and with the professor as a moderator (and a motivator!), the basic notions will become clearer for solving real needs in sampling. For the most complicated notions, teaching will be done in a classical way, with references to current surveys.

Target population

Employees who develop and implement complex sampling plans for surveying populations difficult to reach, either for the social or business sectors.

Course outline

Indirect Sampling, The Generalised Weight Share Method (GWSM), Properties of the GWSM, Other generalisations of the GWSM, Fair Share Method, Ernst's (1989) contribution, Network Sampling, Adaptive Cluster Sampling, ‘Snowball’ Sampling

Prerequisite

Advanced knowledge of mathematical statistics and basic knowledge in sampling theory.

Duration

1 day

Related Courses

STC0413 Statistical Sampling Theory

Purpose

To provide an overview of record linkage, focusing mainly on probabilistic linkage

Benefits to participants

Participants will explore one-file and two-file linkages including planning, preprocessing, weighted comparison rules, iterative linkage development, mapping for business logic and batch mode. They will also learn about the Statistics Canada linkage context. The participants will also do a short project using G-Link interspersed with lecture content.

Target population

Professionals involved or about to be involved in record linkage activities.

Course outline

• Overview of record linkage
• Definitions and concepts related to probabilistic linkage
• Probabilistic record linkage steps and theory (including practical tutorial)
• Current challenges in record linkage research
• Processes and policy around record linkage at Statistics Canada

Prerequisite

A solid understanding of functions and probability will be helpful.

Delivery type: Virtual instructor-led

Duration: 8 days (2hrs per session)

Contact:
If you have questions or to register to the course, contact us at statcan.msmdsstatstraining-msmsdformationstats.statcan@statcan.gc.ca.

Purpose

Most of the data published by statistical agencies consist of time series, which is of figures measuring the evolution of socio-economic variables through time. In modern economies, time series data assist governments, businesses and socio-economic actors in their decision making. Based on the movements recorded in time series, governments initiate policies designed to curb unemployment, inflation, etc.; corporations accelerate or slow down the production of goods and services; unions closely monitor the labour market situation to negotiate appropriate wages. Even consumers, more or less systematically, use time series to decide whether the time is right to purchase a house, an automobile, whether to look for a job, etc. Thus, a good understanding of time series translates into better decision-making by everyone and into increased prosperity.

Benefits to participants

This course will enable the participants to recognize, understand and interpret the components present in time series: the trend-cycle, the seasonality, the trading-day effect, the Easter effect, and the irregular. They will get to know different types of outliers and the graphical representation of data.

Target population

The course targets a broad audience: professional and semi-professional social scientists and statisticians, authors, and editors of publications. The content of the course is relatively non-technical but provides notions critical to the understanding of time series.

Course outline

The course examines in depth the components of time series:

• the trend, which reflects the long-term evolution of the variable of interest,
• the business cycle, which reflects current conditions, e.g., prosperity, recession,
• seasonality, which originates from climatic and institutional factors and tends to recur year after year in a predictable manner,
• the trading-day variations caused by the different relative importance of days of the week, and other calendar variations caused by changes in the dates of holidays, e.g., Easter.

The course also stresses the meaning and limitations of same-month (from year to year) and of month-to-month comparisons, in the presence of seasonality and other time series components. This course assumes that the components of time series are known and does not cover the estimation of the components. That is done in a more technical and specialized course on seasonal adjustment.

Other Related Courses

The course is a desirable prerequisite for other courses, namely STC0434 Seasonal Adjustment with the X-12-ARIMA Method.

Delivery type: Virtual instructor-led

Duration: 3 half-days

Contact:
If you have questions or to register to the course, contact us at statcan.timeseriessupportsoutienenserieschronologiques.statcan@statcan.gc.ca.

Purpose

This course is on the X-12-ARIMA seasonal adjustment method to estimate the trend-cycle, seasonal, holiday, trading-day and irregular components of a time series. The course includes theory and demonstrations of the seasonal adjustment softwares. The purpose of this course is:

• to understand the components of time series;
• to get familiar with the options and statistical methods used in X-12-ARIMA;
• to learn how to assess the quality of the seasonal adjustment results;
• to become familiar with the X-12-ARIMA software and/or the interface to run it;
• to become familiar with X13graphjava tool to produce specialized graphs related to the time series components.

Benefits to participants

Upon completion of the course, the participants will be more familiar with many options in the X-12-ARIMA program and therefore will be able to better assess the quality of a seasonal adjustment. They will see through demonstrations how to apply the methods to select options for the X-12-ARIMA method and assess the obtained results. The course is theoretical and technical.

Target population

This course is intended for employees involved or interested in the production and analysis of seasonally adjusted series.

Course outline

The course examines

• the components of time series (summary);
• the calculations done in the method, such as moving averages and treatment of extreme observations;
• the choice of the decomposition model;
• the ARIMA forecasting as part of the X-12-ARIMA method;
• the estimation of calendar effects such as the Easter and trading day effects and the treatment of outliers by ARIMA regression;
• the direct versus the indirect seasonal adjustment;
• the tests used to assess the results of the seasonal adjustment;
• the overall strategy and criteria which should be used to do seasonal adjustment;
• and how to do all of the above specifically with the X-12-ARIMA program.

Other Related Courses

The course is specialized and requires basic statistical knowledge. The course STC0431. The components of Time Series should be taken first.

Delivery type: Virtual instructor-led

Duration: 3 half-days

Contact:
If you have questions or to register to the course, contact us at statcan.timeseriessupportsoutienenserieschronologiques.statcan@statcan.gc.ca

Purpose

To provide an overview of the concepts of statistical quality control (SQC).

Benefits to participants

The course will discuss the methods of statistical quality control within a broader framework of quality assurance and management. The course will define the various aspects of quality and address the issues of planning for quality as they relate to survey operations and processes. Statistical methods for quality control will be discussed. These include acceptance sampling, statistical process control and Schilling's acceptance control strategy for the efficient administration of SQC. The use and application of various quality tools such as Pareto analysis, cause & effect diagrams, flow charting, etc., for generating quality improvements will also be addressed. The course will involve the practical application of concepts through the use of case studies and group workshops each day.

Target population

Professionals who wish to have an overview of the concepts of statistical quality control (SQC).

This course is mathematical in nature and will contain some theoretical formulas and statistical concepts. People wishing for a more practical introduction to quality control should refer to the course "Quality Control Methods for Survey Operations" STC#0445.

Course outline

• Planning for Quality
• Principles of Statistical Quality Control
• Acceptance Sampling Techniques
• Statistical Process Control
• Administration of Statistical Quality Control
• Quality Improvement Methods & Tools

Prerequisite

A general knowledge of basic statistics is required.

Duration: 3 days (include group workshops each day)