GIS-based modeling of drought and historical population change on the Canadian Prairies

Journal of Historical Geography 36 (2010) 43–56

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GIS-based modeling of drought and historical population change on the Canadian Prairies Robert McLeman a, *, Sam Herold a, Zoran Reljic a, Mike Sawada a and Daniel McKenney b, y a b

Department of Geography, University of Ottawa, Simard Hall 031, 60 University, Ottawa, Ontario, Canada K1N 6N5 Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste. Marie, Ontario, Canada P6A 2E5

Abstract This article describes the development of a GIS-based model of historical drought and population change in western Canada, designed to support qualitative field research into drought adaptation and migration. The model combines digitized census data and recently available modeled historical climate data at a 10 km2 grid cell scale and can be used to generate maps of ‘hotspots’ where historical declines in rural populations may be associated with extended periods of heat and lack of precipitation. The results suggest a promising avenue for expanding and refining GIS-based modeling of historical human–climate interactions to support qualitative research and to potentially serve as a stepping stone toward forecasting future risk areas of drought-related migration in continental dryland areas. Ó 2009 Elsevier Ltd. All rights reserved. Keywords: Drought migration; GIS models; Prairie Provinces; Depression-era migration

Introduction Researchers have for some time now used historical cases of extreme climate conditions as learning opportunities to better understand the human impacts of climate change, how adaptation processes function, and to identify barriers to adaptation and missed opportunities.1 Extreme climatic conditions and events are known to have affected population patterns in a variety of regions in the past, and a number of authors have expressed concerns that

future impacts of climate change will lead to population displacements at unprecedented scales.2 Extreme droughts are among the climatic conditions known to have led to large-scale population movements in many parts of the world.3 One well-known example is that of the 1930s, when drought conditions struck large areas of the North American Great Plains. The combination of drought conditions, depressed commodity prices and economic recession led to the displacement and migration of hundreds of thousands of people in Canada and the US.4 In recent years, scholars have used

* Corresponding author. E-mail addresses: [email protected] (R. McLeman), [email protected] (D. McKenney). y Tel.: þ1 705 541 5569; fax: þ1 705 541 5700. 1 M. Glantz, The use of analogies in forecasting ecological and societal responses to global warming, Environment 33 (1991) 10–33; K. Duncan, The impacts of global warming in south-east Scotland: an historical analogue approach, Scottish Geographical Magazine 108 (1992) 172–178; C. Rosenzweig and D. Hillel, The Dust Bowl of the 1930s: analog of greenhouse effect in the Great Plains?, Journal of Environmental Quality 22 (1993) 9–22; R. Kates, Cautionary tales: adaptation and the global poor, Climatic Change 45 (2000) 5–17; R. McLeman, Migration out of 1930s rural Eastern Oklahoma: insights for climate change research, Great Plains Quarterly 26 (2006) 27–40. 2 N. Myers, Environmental refugees in a globally warmed world, BioScience 43 (1993) 752–761; B. Do¨o¨s, Environmental degradation, global food production, and risk for large-scale migrations, Ambio 23 (1994) 124–130; L. Brown, Troubling New Flows of Environmental Refugees, Washington DC, 2004; United Nations University, Institute for Environment and Security, As Ranks of ‘Environmental Refugees’ Swell Worldwide, Calls Grow for Better Definition, Recognition, Support, Bonn, 2005; Christian Aid, Human Tide: the Real Migration Crisis, London, 2007. 3 E. Meze-Hausken, Migration caused by climate change: how vulnerable are people in dryland areas?, Mitigation and Adaptation Strategies for Global Change 5 (2000) 379–406; R. McLeman and B. Smit, Migration as an adaptation to climate change, Climatic Change 76 (2006) 31–53. 4 J. Gray, Men against the Desert, Saskatoon, 1967; D. Worster, Dust Bowl: the Southern Plains in the 1930s, New York, 1979; J. Gregory, American Exodus: the Dust Bowl Migration and Okie Culture in California, New York, 1989; McLeman, Migration out of 1930s rural Eastern Oklahoma (note 1). 0305-7488/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.jhg.2009.04.003

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a variety of techniques to explore the relationship between drought and population change on the North American Great Plains.5 This article comes out of a multi-year research project being conducted on drought-related migration patterns in the Canadian portion of the North American Great Plains, and is intended to introduce to readers how recent developments in historical climate models permit researchers to operationalize in new ways various spatial concepts to more precisely identify specific areas or ‘hotspots’ where historical changes in population on the North American Great Plains are associated with acute drought conditions. As a demonstration, this article reports on a first-stage identification of drought-associated rural population change in the Canadian provinces of Alberta, Saskatchewan and Manitoba during the 1930s. By combining sorted, gridded historical climate model data with digitized census data in a Geographic Information System (GIS), the research team was able to generate detailed maps of Canada’s Prairie Provinces that identify with considerable precision particular areas where historical population losses and acute drought conditions during summer growing seasons coincided. These ‘hotspots’ are found in belts across southwestern Manitoba, south-central Saskatchewan and southeastern Alberta that appeared in the period 1926–1936. The GIS model described here is presently being used to help the research team target suitable locales for ongoing qualitative field investigations into the processes by which households adapt to climatic stress, household migration decision-making during times of drought, and identification of potential opportunities for adaptive capacity-building missed by governments and institutions during that period. Additionally, the model is being expanded to identify potential study locations where drought and population decrease were associated in subsequent decades, associations that may not have received similar attention from researchers because the scale of population decrease may not have been as great in terms of absolute number of migrants or geographical area affected in comparison with the 1930s. Droughts of the severity of those experienced between 1926 and 1936 have been common in the more distant past, and increases in average temperatures across the North American Great Plains as a result of climate change are expected to exacerbate the risk of future droughts and water scarcity.6 This increased risk of future drought is not limited to the North American Plains, but is expected to occur in many dryland continental areas across the globe.7 While the socio-economic systems, population patterns and conditions of agricultural production in western Canada have changed considerably since the 1930s, research elsewhere suggests that the historical analogue of 1930s droughts provides a useful case for generating new insights into of the process of adaptation, the various means and mechanisms by which rural households may

respond to drought-related risks (including through migration), and identification of opportunities missed by governments and policy-makers to build capacity in drought-affected areas.8 The use of GIS to explore problems in historical geography has expanded in recent years. Although it enables the analysis of historical spatial relationships in an increasing number of useful ways, a range of methodological challenges have also been identified, and the present study provides a number of examples.9 For instance, one set of challenges related to finding appropriate climatic data for use in this study. During the period 1926–36, weather data were collected at 290 locations in Canada’s three Prairie Provinces.10 Given the size of this region, the density of monitoring coverage is not especially great, and there is a considerable degree of variability in terms of the completeness, continuity and quality of data, especially at smaller stations. While monitoring stations at larger centres, such as those shown in Fig. 1, contain complete and detailed weather data, a sampling of smaller stations showed that precipitation data were often missing, and some stations showed temporary breaks in coverage. Archival research uncovered, hand-drawn maps of drought conditions made for specific periods of time and for specific purposes (e.g. Fig. 2). The combination of maps and point-source data provided useful starting points for designing the study, but do not provide a sufficiently detailed picture of climatic conditions across the entire study region for the entire period of interest, particularly in the large areas between monitoring stations. Another set of challenges related to identifying spatial units for which population data are available and which enabled comparison across time. Censuses were carried out in the study region in 1926, 1931 and 1936. These data are captured in 48 large, rural census divisions (CDs), a spatial scale much coarser than modeled climate data. Although census takers broke each CD down into smaller internal divisions (IDs) for data collection purposes, the boundaries for these IDs were regularly tinkered with from one census year to the next, meaning that direct comparisons of census data between periods cannot be done at this level of resolution. Census data for the study period were also collected at the much more local, gridbased township level, and it is possible to make direct comparisons across time at this relatively fine spatial resolution. Each spatial scale of interest presents its own data collection, management and interpretation challenges with respect to population data, some of which are described in greater detail below. Overall, then, available historical climate and population data present significant challenges in distinguishing sub-regional variability in climate and drought conditions and in associating these with population change in Canada’s Prairies. Despite the challenges, efforts to capture the relationship between drought conditions and population change with greater precision and spatial

5 G. Deane and M. Gutmann, Blowin’ down the road: investigating bilateral causality between dust storms and population in the Great Plains, Population Research and Policy Review 22 (2003) 297–331; M. Gutmann, G. Deane, N. Lauster and A. Peri, Two population-environment regimes in the Great Plains of the United States, 1930–1990, Population and Environment 27 (2005) 191–225; G. Cunfer, On the Great Plains: Agriculture and Environment, College Station, TX, 2005; McLeman, Migration out of 1930s rural Eastern Oklahoma (note 1). 6 National Assessment Synthesis Team, Climate Change Impacts on the United States: the Potential Consequences of Climate Variability and Change, US Global Change Research Program, New York, 2002; D. Sauchyn, J. Stroich and A. Beriault, A paleoclimatic context for the drought of 1999–2001 in the northern Great Plains of North America, The Geographical Journal 169 (2003) 158–167; D. Sauchyn, S. Kennedy and J. Stroich, Drought, climate change and the risk of desertification on the Canadian plains, Prairie Forum 30 (2005) 143–156; D. Schindler and W. Donahue, An impending water crisis in Canada’s western prairie provinces, Proceedings of the National Academy of Sciences 103 (2006) 7210–7216. 7 N. Adger et al., Summary for policymakers, in: Climate Change 2007: Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report, Cambridge, 2007. 8 McLeman, Migration out of 1930s rural Eastern Oklahoma (note 1); R. McLeman, D. Mayo, E. Strebeck and B. Smit, Drought adaptation in rural Eastern Oklahoma in the 1930s: lessons for climate change adaptation research, Mitigation and Adaptation Strategies for Global Change 13 (2008) 379–400. 9 I.N. Gregory and P.S. Ell, Historical GIS: Technologies, Methodologies and Scholarship, Cambridge, 2007; A.K. Knowles (Ed.), Placing History: How GIS is Changing Historical Scholarship, Redlands CA, 2008. 10 Based on historical weather records available through Environment Canada, the responsible federal agency. See http://www.climate.weatheroffice.ec.gc.ca/climateData/ canada_e.html.

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Fig. 1. Study area, showing relief, 1936 rural census division boundaries, and cities.

resolution through GIS-based models have a role to play in understanding historical human–climate interactions in the study region. There is general agreement among social scientists that climatic conditions, including drought, influenced population movements on the Prairies during the 1920s and 1930s, but views on the respective contributions of climatic and non-climatic factors to patterns of population change vary.11 This project demonstrates that the use of recently available, high-resolution climate data in conjunction with historical census data to create GIS models of historical drought and population change represents a significant development in the toolkit for researchers interested in historical climate-related population change. While the models and data developed here are particular to western Canada, the methods and potential applications are not confined to climate and population data nor to this particular study region. Overview of study area, social and climatological context The study area consists of agricultural regions in the three Canadian provinces of Alberta, Saskatchewan and Manitoba (see Fig. 1). Most of this area is the northern extension of the North American

Great Plains, a vast semi-arid grassland ecosystem that stretches south to the American state of Texas. Much of the Canadian portion, referred to generally as the Canadian Prairies, has been converted from grassland to agricultural use, a process begun by non-Aboriginal settlers in the final decades of the 19th century, escalating rapidly through the first two decades of the 20th century.12 Agricultural settlement in the region generally progressed from east to west and south to north, with towns and villages emerging at strategic locations along railways and their branch lines.13 Across large areas, grasslands were plowed and planted with grain crops, with wheat being the preferred choice. The acreage of wheat sown in Canada’s Prairies grew from fifteen thousand acres in 1876, to one and one-quarter million acres in 1896, to more than ten million acres by 1913.14 Much of this expansion in grain production has been attributed to an influx at the turn of the century of American farmers who had developed techniques and equipment tailored to dryland farming, and who were lured by 1908 amendments to the Dominion Lands Act that increased the acreage of farmland on offer to homesteaders.15 This allowed for greater settlement in the more arid areas of southern Saskatchewan and Alberta where population growth had been

11 D. Jones, Empire of Dust: Settling and Abandoning the Prairie Dry Belt, Edmonton, 1987; P. Berton, The Great Depression 1929–1939, Toronto, 1990; J. Gray, The Winter Years: the Depression on the Prairies, Calgary, 1966; J.H. Archer, Saskatchewan History, Saskatoon, 1980; J.H. Thompson, Forging the Prairie West, Toronto, 1998; B. Waiser, Saskatchewan: a New History, Calgary, 2005. 12 For greater detail, see K.H. Norrie, The national policy and the rate of Prairie settlement, in: R.D. Francis and H. Palmer, The Prairie West: Historical Readings, Edmonton, 1992. 13 Archer, Saskatchewan History (note 11); Waiser, Saskatchewan (note 11). 14 Dominion Bureau of Statistics, Agriculture, Climate and Population of the Prairie Provinces of Canada: a Statistical Atlas Showing Past Development and Present Conditions, Ottawa, 1931; Thompson, Forging the Prairie West (note 11). 15 Waiser, Saskatchewan (note 11).

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R. McLeman et al. / Journal of Historical Geography 36 (2010) 43–56

Fig. 2. Percentage of ‘drought years’ in the Prairie Provinces (map compiled by Searle Grain Company, Winnipeg [?] circa 1937, Glenbow Archives, Calgary G3471-C886-1937-F439).

slower and where the land had previously been seen as better suited to ranching than farming. A severe winter in 1906–07 resulted in significant losses of cattle which, combined with falling prices for cattle and rising grain prices, led many farmers who had previously sought to diversify their operations to intensify wheat production.16 Northerly areas, where the plains transition to aspen parkland, and the Peace River district of northwestern Alberta, had shorter growing seasons, varying soils, and were longer distances from railways; they were consequently slower to be settled and were less densely populated than more southerly parts of the study region.17 Ranching and mixed farming predominated in the western foothills of Alberta, where much of the land was leased to cattle companies in the late 19th century, contributing to slower population growth in that area.18 By 1926, the beginning of the study period, the combined population of the three Prairie Provinces was slightly more than 2 million, with about 63% living in rural areas (Table 1). The total number of farms in the region was just under a quarter million, with roughly half located in Saskatchewan, and the others relatively evenly divided between Alberta and Manitoba. Ten years later, the population of the region had grown by more than 16%, and the number of farms by more than 20%. The proportion of the population living in rural areas remained essentially unchanged. All three Prairie Provinces experienced population growth in urban and rural areas although, there were significant intra-regional variations in population change.

16 17 18 19 20 21 22 23

Wheat was the principal cash crop on the majority of Prairie farms, followed by oats, barley and forage crops.19 More than 85% of Prairie farms were growing wheat in 1926; in Saskatchewan, the percentage exceeded 95% (Table 2). The ratio of wheat to other field crops varied. In Manitoba, one-third of cropland was in wheat; in Saskatchewan and Alberta it was two-thirds. In drier parts of the Prairies, such as Alberta CD5, the proportion of cropland devoted to wheat exceeded 80%.20 Over the study period, the area of farmland dedicated to wheat grew across the region and in each province (Table 3). With modest soil moisture requirements, a greater ability to withstand heat stress than other common cash crops, rapid growth to maturity, and a large market, it is not surprising that wheat was the crop of choice as the number of Prairie farms expanded in the first decades of the twentieth century.21 The study area has high intra-annual variability in temperature, with average annual temperature ranges exceeding 25  C (Table 4). The period between spring and autumn frosts is approximately 120 days in the southcentral part of the Prairies and diminishes to less than eighty days at the margins, although the number of frost-free days across the region appears to be increasing.22 Within the region, average summer precipitation levels are lowest in southeastern Alberta and southwestern Saskatchewan, where they are at or below the minimum growing season requirements for most commercial, rainfed agricultural crops.23 For example, at Swift Current in southwestern Saskatchewan (see Fig. 1 for location), average

S.M. Evans, The Bar U and Canadian Ranching History, Calgary, 2004. C.A. Dawson, The Settlement of the Peace River Country: a Study of a Pioneer Area, Toronto, 1934; Archer, Saskatchewan History (note 11). H. Kariel, Land use in Alberta’s foothill country, Western Geographer 7 (1997) 20–46. Census of the Prairie Provinces, 1926. See note 19. J.R. Porter and M. Gawith, Temperatures and the growth and development of wheat: a review, European Journal of Agronomy 10 (1999) 23–36. B. Bonsal, X. Zhang, L. Vincent and W. Hogg, Characteristics of daily and extreme temperatures over Canada, Journal of Climate 14 (2001) 1959–1976. See note 14.

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Table 1 Population, number of farms, Prairie Provinces, 1926–1936 Province

1926

1936

Population

Alberta Manitoba Saskatchewan Prairie Provinces

No. of farms

Total

Urban

Rural

607,599 639,056 820,738 2,067,393

233,848 278,858 242,532 755,238

373,751 360,198 578,206 1,312,155

77,130 53,251 117,781 248,162

Population

No. of farms

Total

Urban

Rural

772,782 711,216 931,547 2,415,545

286,447 310,927 280,273 877,647

400,289 486,335 651,274 1,537,898

100,358 57,774 142,391 300,523

Sources: Dominion Bureau of Statistics, Census of Prairie Provinces, 1926, Population and agriculture, Ottawa; Dominion Bureau of Statistics, Census of the Prairie Provinces, 1936, Volume I: Population and agriculture, Ottawa. Note: In Censuses from that era, communities with populations over 1000 were treated as ‘urban’.

Table 2 Percentage of farms growing wheat in 1926 % Farms growing wheat

% of total crop acreage in wheat

Alberta Manitoba Saskatchewan

86.1 68.4 95.6

67.2 33.3 69.3

Total

85.4

62.3

Source: Census of Prairie Provinces, 1926, Population and agriculture.

Table 3 Growth in wheat acreages, 1926–1936 Acres 1926

1936

Alberta Manitoba Saskatchewan

6,161,383 2,085,547 13,558,384

7,537,233 2,556,600 14,743,991

Totals for Prairie Provinces

21,805,314

24,837,824

Source: Census of Prairie Provinces, Population and Agriculture, 1926 and 1936. Note: The ‘acre’ was the standard unit of measurement during the study period and we maintain that unit throughout this article. An approximate conversion is 1 acre ¼ 0.4 hectares.

precipitation in the months of June, July and August is 60, 52 and 40 mm respectively.24 A study of wheat yields at the Swift Current agricultural experiment station over the period 1922–1952, which took into account local soil types, evaporation rates and soil moisture storage, found that wheat crops in that area were usually being grown under conditions of moisture stress, and that average summer precipitation was typically only two-thirds of optimal levels for wheat performance.25 Yields were further influenced by intra- and inter-annual variability. Extreme precipitation events, particularly summer hailstorms, are an added climate-related risk for Prairie agriculture. Taken together, these factors can result in considerable intra-regional and inter-annual variations in agricultural productivity. At Swift Current in 1930, 103 mm of precipitation fell in June (roughly 70% more than average); the following month saw only 14.5 mm, only 1/3 the monthly average.26 Some 14% of Saskatchewan farms reported crop losses due to drought that year,

24

while only 6% of Alberta farms did so.27 On the other hand, 10% of Alberta farms that year reported crop losses attributable to hail or excess rain, but only 6% of farms in Saskatchewan did so. The popularity of wheat was reinforced by high market demand driven by the First World War (Fig. 3). At that time, wheat was planted in late April or early May and was ready for harvest by midAugust,28 a system that remains popular today despite increased rates of autumn plantings (known as ‘winter wheat’) in recent decades. While generally suited for dryland farming, spring wheat cultivars of the types commonly used during the 1930s were susceptible to failure when soil moisture levels were low during the periods of flowering and heading, which occur approximately 40 and 60 days after planting.29 Reduced levels of soil moisture during the period after heading and before ripening are less likely to kill the plant but may reduce the yield of grain. Wheat plant flowering and heading typically occurs in June in the study area, meaning that precipitation and temperature levels were particularly critical during that month.30 Overall precipitation during the summer is typically less than potential evapotransporation, and July and August rainfall may not penetrate the soil to any great depth. Consequently, wheat crops rely heavily on stored soil moisture to reach maturity as the summer progresses. When extreme heat conditions occur during the summer months, and are not offset by above-average levels of precipitation, yields will decline. If a hot, dry season is followed by a winter of below-average precipitation, the following year’s crop may begin with inadequate soil moisture and an elevated risk of failure; in other words, a succession of dry years may have a self-reinforcing effect on the potential for failure of future crops. The Prairie socio-economic system of the mid-1920s engaged a large proportion of the population in livelihoods that were tied closely to the wheat crop, which on many farms was being grown under climatic conditions that hovered near the minimum threshold for success. In the second half of the 1920s parts of the Canadian Prairies began to experience below-average levels of annual precipitation and above-average summer temperatures.31 Worsening weather conditions for wheat production in many areas coincided with a steady decline in market prices for wheat (Fig. 3). Although the population numbers, the number of farms and the acreage planted to wheat rose across the Prairie Provinces

Environment Canada, Canadian Climate Data Online, http://www.climate.weatheroffice.ec.gc.ca/climateData/monthlydata_e.html (accessed at January 2009). W.J. Staple and J.J. Lehane, Weather conditions influencing wheat yields in tanks and field plots, Canadian Journal of Agricultural Science 34 (1954) 552–564. 26 See note 24. 27 Dominion Bureau of Statistics, Seventh Census of Canada, Vol. II, 1931, cxxviii. 28 G.W. Robertson, Wheat yields for 50 years at Swift Current, Saskatchewan in relation to weather, Canadian Journal of Plant Science 54 (1974) 625–650. 29 E. de Jong and D. Cameron, Efficiency of water use by agriculture for dryland crop production, in: Prairie Production Symposium: Soil and Land Resources, Saskatoon, 1980. 30 Robertson, Wheat yields for 50 years at Swift Current, Saskatchewan in relation to weather (note 28). 31 Some parts of the study area experienced drought conditions even earlier, including southeastern Alberta, and parts of southwestern Saskatchewan, where dry conditions emerged up to a decade earlier. See discussion of Alberta CD 1 and 2 in later sections of the present article, as well as Jones, Empire of Dust (note 11); G.P. Marchildon, S. Kulshreshtha, E. Wheaton and D. Sauchyn, Drought and institutional adaptation in the Great Plains of Alberta and Saskatchewan, 1914–1939, Natural Hazards 45 (2008) 391–411; C. McManus, Happyland: the Agricultural Crisis in Saskatchewan’s Drybelt, 1917–1927, M.A. thesis, University of Saskatchewan, 2005. 25

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250

as a whole there were significant decreases in population in many drought-stricken areas between 1926 and 1936. Historical accounts identify and describe Prairie population changes during the droughts with various degrees of detail,32 and with recent developments in climate modeling and spatial analyses with Geographic Information System (GIS) technologies we are able to map hotspots of drought-related population change with greater spatial acuity.

Methodology – overview While Fig. 1 shows the entire area of Canada’s Prairie Provinces, subsequent figures for the region exclude large but lightly populated census divisions (CDs) that fall within the Boreal forest spanning the northern regions of each province, where agriculture was not common during the study period. We focus instead on population change in CDs within the agricultural regions of the Prairie grassland ecosystem and bordering transitional areas (often referred to as aspen parkland). The southern border of the study area is the Canada-US border, the easternmost one the provincial boundary between Manitoba and Ontario. The western border is the provincial boundary between Alberta and British Columbia. It should be noted that CDs along the edges of the study area, such as the easternmost CD in Manitoba, westernmost CDs in Alberta and several of the northern CDs in each province may contain large areas that were not used for agriculture or for cropland. Alberta CD9, for example, is increasingly mountainous east to west. Within such areas, temperature and precipitation, as well as human settlement and land-use patterns, differ from those in CDs that fall primarily within Prairie environments. To identify areas where high levels of population decline coincided with drought conditions, we used a set of GIS capabilities that are available in most commercial software packages. Population data from the Canada census for the years 1926, 1931 and 1936 for the study area were digitized and entered into the GIS database in vector layers. From historical spatial climate models we generated a gridded dataset comprised of modeled historical temperature and precipitation attributes identified by the Agricultural Experimental Station in Swift Current Saskatchewan as being critical climatic determinants of wheat crop performance in the Prairie region during the study period, and these were entered into the GIS as

100 50

1938

1936

1934

1932

1930

0 1928

Source: Environment Canada, Canadian Climate Norms 1971–2000, accessed at http://www.climate.weatheroffice.ec.gc.ca/climate_normals/index_e.html, 6 December 2007.

150

1926

373 412 483 386 334 388 350 349 513

1924

18.4 16.2 15.9 18 19.5 18.8 18.2 18.1 19.5

1922

July

18 8.9 13.5 7.8 10.2 16 17 12.4 17.8

1920

January

200

1918

Brandon Calgary Edmonton Lethbridge Medicine Hat Regina Saskatoon Swift Current Winnipeg

Average annual precipitation (mm)

Average daily temperature ( C)

1914

Location

price (cents/bushel)

Table 4 Climate normals, selected locations on Canadian Prairies (see Fig. 1)

1916

48

Fig. 3. Average wholesale wheat prices in Canada, inter-war years. Source: Statistics Canada, Historical Statistics of Canada, Series M228-238, Wholesale market prices for selected agricultural products, 1867 to 1974, online at http://www.statcan.ca/english/ freepub/11-516-XIE/sectionm/M228_238.csv last accessed January 2009.

raster layers.28 Maps showing the combined climate model data and population data were then generated. Selection and treatment of population data The Government of Canada conducted its first national census in 1871. Beginning in 1921, the national census has been repeated every fifth year. For this project, the relevant census years were 1926, 1931 and 1936. While historical reports suggest that drought may have continued to influence population patterns in the study region for several years after 1936,33 the 1941 census was not used because it was taken two years after Canada entered the Second World War, and the mobilization of the population and economy for war had a significant influence on population trends in the study area. Census data for the study period are not available in electronic form. The study team therefore had to digitize the geographic boundaries of census divisions (CD) by manually scanning a hard copy map from a 1926 census volume.34 The CD boundaries for the period of interest were established in 1922 and continued to be used throughout the 1926–1936 census years. To create a geographically accurate spatial data file of the CDs, the scanned map was georeferenced using registration points from a digital dataset of current CD boundaries obtained from Statistics Canada, the government department that now administers the census. Past and current census divisions share several common intersections; using shared boundary intersections as a reference for conflation, unshared boundaries for the 1926 census were georeferenced. The base scale of the scanned map was determined to be 1:250,000. We therefore assumed positional errors of at least 125 m in addition to those from the registration process. However, these errors do not significantly affect the analysis as they are orders of magnitude smaller than the spatial resolution of the climate data and enumeration units. The total population for each census division was then entered manually into the database for each census year and subsequently validated through manual inspection by a thirdparty. Because this study was targeted at identifying rural population change, the populations for CDs had to be adjusted prior to

32 B. Broadfoot, Ten Lost Years, Toronto, 1973; Archer, Saskatchewan History (note 11); Waiser, Saskatchewan (note 11); Gray, The Winter Years (note 11); Marchildon, Kulshreshtha, Wheaton and Sauchyn, Drought and institutional adaptation in the Great Plains of Alberta and Saskatchewan, 1914–1939 (note 31). 33 Gray, The Winter Years (note 11); Jones 1987; Waiser, Saskatchewan (note 11). 34 Source map ¼ insert map accompanying Census of Prairie Provinces 1926: Population and Agriculture.

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Fig. 4. Population change by census division (a) 1926–1936 and (b) 1931–1936.

entry by removing population counts for urban centres, which were defined in the census as being settlements of more than 1000 people. Population and other data for each CD were reported in census volumes at three levels of aggregation: the CD as a whole; internal divisions (or ‘IDs’); and at the sub-township level. Fig. 4a, b displays CDs across the study region according to the rate of population change for the entire study period and for the second half, from

1931 to 1936. It was during this latter period that large numbers of CDs experienced population losses that have been associated with drought conditions by authors previously cited in this article. The CDs experiencing population losses between 1931 and 1936 form a contiguous block from south-central Alberta through to the rural areas surrounding the city of Winnipeg.35 Alberta CD5 had the highest reported rate of population decline, at slightly more than 15%.

35 Note that the cities of Winnipeg, St. Boniface and Dauphin, which are all situated within Manitoba CD6, experienced population increase from 1931 to 1936. When their population figures are deducted, the remainder of the CD (i.e. the non-urban areas) experienced a population decrease over the study period. This CD is therefore coded as experiencing a population decline, given the emphasis on rural population change in this study.

50

R. McLeman et al. / Journal of Historical Geography 36 (2010) 43–56

Fig. 5. Transformation of internal divisions for data comparison for Alberta Census Division 5.

The large size of prairie CDs can mask population changes at smaller spatial scales. The next finest spatial resolution of population data available in the census is the ‘internal division’ or ID level. IDs generally corresponded to the original township survey grid for the study region, but from one census year to the next IDs were often aggregated or disaggregated by census takers. Consequently, population data can be retrieved at the ID level, but comparisons of population statistics across time are not possible without making significant adjustments through aggregation and elimination to arrive at comparable spatial units. As an example, in 1926 population statistics for Alberta CD5 were reported for the CD as a whole, as well as for 23 IDs, 3 towns and 9 villages. In the 1931 Census, the growing town of Drumheller, situated along a river that serves as the partial boundary between CD5 and CD6, was reclassified as a city and simultaneously moved from the CD5 population count to CD6. A new ID was created within CD5 to count population in the area near Drumheller, which was done by reducing the size of two previous IDs. In the 1936 Census, the number of IDs reported within CD5 was reduced to 18 through the combination of several IDs in the southwest and northcentral portions of the CD. As well, one ID in the southeast was split in two along an east–west line that did not align with previous ID boundaries. The steps required to create comparable spatial units to show population change within CD5 at the ID level are shown in Fig. 5. While Fig. 5 suggests that population in Alberta CD5 increased in the southwest part of the CD during the study period and decreased in northern and eastern areas, this is somewhat misleading. Census data within each CD are also reported for the study period at still smaller spatial units, based on the Prairie township grid. When the study region was first surveyed, the Dominion Lands Survey system (DLS) established boundaries that matched meridians of latitude and longitude.36 Beginning near Winnipeg, principal meridians of longitude were established for survey purposes at approximately 4 apart. The 49th parallel of latitude (the Canada-US border) was used as the southern boundary and the 60th parallel the northern boundary under the DLS. The area within the survey was subdivided into townships six miles square, and

36

then further subdivided into thirty-six sections. Population data collected during the 1926–1936 period were reported for nine foursection blocks within each township. As is shown in later figures (after climate data have been added), when population change is displayed for Alberta CD5 at the sub-township level the areas experiencing the highest rates of population loss during the study period tend to be distributed along an axis running from northeast to southwest, a trend masked at the ID or higher levels of aggregation. Selection and treatment of climate data Models of historical climate conditions for the study area used in this project were developed by McKenney et al.37 These models made use of historical weather station records of monthly precipitation, and maximum and minimum temperatures across North America for the period 1901–2000. Thin plate smoothing splines, as implemented in ANUSPLIN, were used to develop elevationdependent, spatially continuous surfaces and subsequently resolved at approximately 10 km2 resolution.38 Withheld data tests and standard diagnostics associated with the methods suggest errors are generally greatest with precipitation-related variables and over mountainous and coastal surfaces; error rates are considerably lower over smoother land surfaces such as the Prairies. ANUSPLIN-based models tend to have difficulty representing climate data across sharp spatial gradients, given the high degree of topoclimatic variability, complex boundary layers and strong temperature gradients over small linear distances.39 In the mountainous and coastal environments of Alaska, Yukon and British Columbia, studies have found data derived from Parameter-Elevation Regression on Independent Slopes Model (PRISM) techniques are less error-prone than ANUSPLIN-based climate model data.40–42 The ANUSPLIN-derived data used here have error ranges for grid cells over surfaces on the Canadian Prairies in the range of 1–1.5  C for temperature and 20–40% for precipitation in the 1920s and 1930s.43 Average error rates for modeled climate datasets tend to be high early in the century, given the relatively few monitoring stations from which to draw records; error rates tend to decline in

Details of the DLS were retrieved from the Alberta Land Surveyors Association website http://www.alsa.ab.ca/GeneralInfo/township.htm (last accessed 9 March 2009). D. McKenney, J. Pedlar, P. Papadopol and M. Hutchinson, The development of 1901–2000 historical monthly climate models for Canada and the United States, Agricultural and Forest Meteorology 138 (2006) 69–81. 38 See ANUSPLIN Version 4.3, Centre for Resource and Environmental Studies, Australian National University, http://cres.anu.edu.au/outputs/anusplin.php. 39 C. Daly, Guidelines for assessing the suitability of spatial climate data sets, International Journal of Climatology 26 (2006) 707–721. 40 Simpson, J. J., Hufford, G. L., Daly, C., Berg, J. S., & Fleming, M. D. (2005). Comparing maps of mean monthly surface temperature and precipitation for Alaska and adjacent areas of Canada produced by two different methods, Arctic, 58(2), 137–191. 41 PRISM-derived historical climate model datasets were not readily available for use in this study, and so it was not possible to perform a comparison of the error performance rates of these two historical climate model data alternatives that are among the best currently available. In any event, because error rates of PRISM-derived model data tend to be sensitive to the number and spatial concentration of monitoring sources, it cannot be said from existing literature that access to PRISM-based data would necessarily have enhanced the accuracy of the maps generated in this study. 42 D.T. Price, D.W. McKenney, I.A. Nalder, M.F. Hutchinson and J. Kesteven, A comparison of two statistical methods for spatial interpolation of Canadian monthly mean data, Agricultural and Forest Meteorology 101 (2000) 81–94. 43 McKenney, Pedlar, Papadopol and Hutchinson, The development of 1901–2000 historical monthly climate models for Canada and the United States (note 37). 37

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Fig. 6. Model of data manipulation to create final climate model in GIS.

concert with the expanding network of available monitoring stations. Given the actual intra-annual average temperature range of 25  C or more in the study region, the temperature error range of the model data was not seen to be significant. While the potential error range is greater for precipitation, the amount of precipitation received in the study area during the growing season tends to be small, meaning the error would be commensurately small for much of the year in absolute terms. It should be also noted that the error rates of the historical climate data used here compare favourably with those of downscaled general circulation models on which other regional climate studies are often based.44 Given the importance of wheat in the regional economy during the study period, Robertson’s 1974 study of wheat performance at Swift Current from 1922 to 1972 was used as a guide for selecting specific climatic variables.45 Robertson identified a range of climatic factors that influence wheat yields, the most significant of which can be categorized into three groups: precipitation received in the months of May and June; pre-planting-season precipitation; and June and July maximum temperatures. The category of May– June precipitation is the most influential of the three according to Robertson’s factorial yield models, but the relative degree of influence in comparison with the other two groups of factors is fairly close. In testing we found that applying a relative weighting factor to each set of climatic factors had only a slightly perceptible effect on the output. The cartographic model in Fig. 6 summarizes the steps taken within our GIS to transform the monthly climate data into a final climate model that identifies the likelihood of drought conditions across our study area. Monthly precipitation and mean maximum monthly temperature data for the period 1926–1936, in the form of 10 km2 ASCII grids georeferenced to the North American Datum of 1983 were imported into a commercially available GIS. Within the

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GIS, using local cell-based grid arithmetic, total precipitation for May–June periods and mean maximum temperature for June–July periods were calculated for each cell for each year. Precipitation for each December–April period was summed for each cell location across all years and used to represent pre-planting precipitation.46 These steps produced 11 May–June precipitation grids, 11 June–July temperature grids, and 11 pre-season precipitation grids for the years 1926–1936 inclusive. The next step was to determine a common classification schema for each precipitation dataset and for the June–July temperatures. In this way individual years would be made comparable via the creation of a common scale. First, all grids were clipped to the extent of the study region census divisions (CDs). Determining the class break points required creating three datasets, one containing all cell observations across all years for pre-planting precipitation, one for all years for May–June precipitation and one for all years of June–July temperature predictions. Each dataset was created by concatenating the 11 grids for each year (for each dataset) within the R language for statistical computing,47 resulting in three R onedimensional matrices, where the number of observations in each was equal to eleven times the number of grid cells in the study region. For each of the three datasets we generated empirical cumulative frequency (ECF) functions and graphed these, with the curve divided into ten percentiles (deciles) to determine the classification break values for both sets of precipitation variable and for June–July temperature.48 A decile-based system identifies at each threshold the number of observations less than or equal to the class value suitable for mapping the extremes and makes comparisons across the study period straightforward. By way of example, Fig. 7 illustrates the ECF for the May–June precipitation period, and displays the reclassification break values that were then applied to each grid for each year within our GIS.

S. Kotlarski et al., Regional climate model simulations as input for hydrological applications: evaluation of uncertainties, Advances in Geosciences 5 (2005) 119–125. Robertson, Wheat yields for 50 years at Swift Current, Saskatchewan in relation to weather (note 28). 46 Much precipitation received in winter months is received as snow and upon melting in the spring is available to crops at planting time. According to Robertson, precipitation received up to 21 months prior to planting can influence in wheat yields in a given year, although the relative contribution of precipitation to yield decays with the time elapsed. For the purposes of this study, we found it was not essential to capture the entire 21-month window of potential pre-season contribution directly, such as through an algorithm mimicking Robertson’s factorial models. When we tested the model using alternative weighting factors for climatic variables we found relatively little responsiveness in the final outputs. 47 See http://www.r-project.org. 48 The choice of ten was found during the analysis process to be appropriate given the spatial resolution of the raw climatic model data being used, and enables an intuitive comparison of the output in terms of percentiles. 45

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Fig. 7. Empirical cumulative distribution frequency for May–June precipitation.

Using the May–June precipitation as an example, for each year every precipitation grid cell that ranked in the lowest ten percentile was assigned a score of one. Cell values falling between the 10th–20th percentiles were assigned a score of two, and so forth. The inverse was done for temperature grid cells (i.e. each temperature cell that ranked in the highest ten percent of all temperature cells across the region was assigned a value of one, and so forth). Thus, the value of one in either the reclassified temperature or precipitation grids can represent the extreme conditions (90th percentile of temperature or 10th percentile for precipitation) that when coincident likely lead to drought conditions at a local level. This process provided a relative ordinal reclassification of the 11 annual grids for each precipitation and temperature variables. A final grid for each of the three variables representing the entire 11 year period of 1926–36 was derived in our GIS using basic map algebra operations. Theoretically, cell values in the final grid for each variable could range from a low of 11 (consecutive rankings of 1) to a high of 110 (consecutive rankings of 10), with low values representing cumulative years of relatively low precipitation and high temperatures. The final climate model was created by simply adding the three final grids together in the GIS using grid arithmetic (see Fig. 6). In the final climate model, grid values range from a low of 92 to a high of 311. Cells with the lowest values are those where hot and dry growing season conditions for wheat were most frequent during the study period. The results of our treatment of the climate data are shown in Fig. 8a–c. Fig. 8a shows that pre-season precipitation for the study period tended to be lowest along a large north–south belt running through the central portion of the study region, and highest along the eastern and western margins. Fig. 8b shows that precipitation during the critical May–June period for wheat flowering and heading was lowest in an area that overlaps roughly the southern two-thirds of the region where pre-season precipitation was lowest. Finally, Fig. 8c shows that summer temperatures were typically highest in roughly the same areas as where May–June precipitation was lowest. In other words, the three most critical

climatic variables for successful wheat production during that era were all at their least favourable in an area running from southeastern Alberta to southwestern Manitoba. The final step in the modeling and mapping exercise was to combine this climatic information with the census data to identify potential spatial associations between climatic conditions and population change. Results and discussion When the climate and population datasets are combined, there is a considerable degree of visual correlation between areas that were repeatedly hot and dry throughout the study period and CDs that experienced population loss (Fig. 9a, b). The number of CDs experiencing population losses increased as the number of hot and dry years began to accumulate during the 1931–36 period, forming the contiguous pattern noted previously. The association between the climate model data and population change is not perfect, however; we can see that the two southernmost CDs in Alberta (CD1 and CD2) did not experience population decline during the study period, despite falling largely within the area determined to be very hot and dry during the study period. At smaller scales we see evidence that population losses were not random, but appear to follow particular spatial trends within CDs. Using Alberta CD5 as an example, we can see that while most townships experienced population loss during the study period, those townships that experienced the highest losses were situated along a band that runs generally from northeast to southwest (Fig. 10). The northwestern parts of this CD, where climatic conditions were relatively most favourable, had low rates of population loss and/or experienced population increases over the study period, suggesting a possible association between climate and population trends. However, the southeastern townships of CD5, which are represented as being very hot and dry during the study period, do not fall within the band of high population losses. To uncover potential reasons why the climate–population change association does not always hold in the maps generated here requires going beyond the data that were used in creating

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Fig. 8. (a) December–April precipitation, 1926–1936, (b) May–June precipitation, 1926–1936 and (c) June–July maximum temperatures, 1926–1936.

the model. The model provides information only for the snapshot in time that was 1926–1936. As noted previously, southeastern Alberta and southwestern Saskatchewan had experienced severe drought conditions in the decade prior to the study period we

selected. This region included much of Alberta CD1 and CD2, as well as southeastern townships in CD5.49 The Saskatchewan and Alberta governments responded differently to droughts. Beginning in the 1920s the Alberta government began implementing

49 Jones, Empire of Dust (note 11); Marchildon, Kulshreshtha, Wheaton and Sauchyn, Drought and institutional adaptation in the Great Plains of Alberta and Saskatchewan, 1914–1939 (note 31).

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Fig. 9. Cumulatively hottest and driest grid cells and rural census divisions with negative population change (a) 1926–1936 and (b) 1931–1936.

programs and policies to actively discourage settlement in drought-stricken southeastern areas, promoted ranching instead of cultivation, and offered free rail freight to families wishing to relocate to northwestern Alberta.50 The province of Saskatchewan took a different approach, and with the assistance of the federal government implemented programs designed to encourage farmers to stay on the land; only toward the end of the 1930s did it begin providing freight assistance to families seeking to relocate northward. These antecedent institutional measures may help explain why population levels in southernmost Alberta did not drop during the study period, and why population levels had already experienced a drought-related decrease before 1926 (Fig. 11). It is worth reflecting further on the spatial specificity of the maps and our treatment of climatic data to characterize drought conditions. Had we simply superimposed point-source mean average annual temperature and mean total precipitation data onto maps that showed which census districts lost population during the 1930s, we might possibly have obtained some very general and crude visual association between the two. However, in

a dryland agricultural system such as this one, which was (and remains) heavily weighted toward production of one particular cash crop, and during a period when irrigation was not widely practiced, average trends in temperature and precipitation are less relevant than their timing relative to the growth cycle of the crop plants in question. Precipitation in the final third of the calendar year contributes to soil moisture potentially available the following year, but its immediate benefits to Prairie spring wheat producers are limited and in some cases may interfere with harvesting activities. Yet, this precipitation has a strong influence on measures of average annual precipitation. Similarly, a brief period of extremely hot and dry conditions in summer that does not stand out in annualized data may have a severe, negative impact on wheat plants. The crop research therefore highlights a need to seek out areas of year-over-year conditions of high temperatures and precipitation deficits in specific temporal periods rather than use average climate data. It should be noted, however, that not all farms were exclusively devoted to growing wheat during the study period, and that the proportion of wheat to other agricultural products varied

50 Institutional response efforts described in this paragraph are taken from Marchildon, Kulshreshtha, Wheaton and Sauchyn, Drought and institutional adaptation in the Great Plains of Alberta and Saskatchewan, 1914–1939 (note 31).

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Fig. 10. Sub-township divisions of Alberta CD5 experiencing greater than 50% population decline between 1931 and 1936.

across the region and from one farm to the next. There is also a degree of auto-correlation in that farms in typically dryer areas favoured wheat precisely because it was well-suited for dryland farming. By selecting climatic variables closely linked to stresses on wheat production, the maps generated from the model data as presently configured are specifically geared to identifying potential population losses associated with wheat failure. They may not provide the best means of identifying conditions that were more or less favourable for other forms of agricultural production. For example, the livelihood of a cattle rancher may be closely associated with precipitation and temperatures throughout the entire summer, not just the months selected here, because of the need for animals to have access to water on a daily basis at all times. The model user may therefore wish to use alternative manipulations of the climate data to those demonstrated here if studying climate–population interactions for areas with other land uses, something that is readily possible once the raw climate model data have been incorporated into the GIS. We have varied the model for the study period, by simply ranking grid cells by temperatures and precipitation from June to August and found the differences in map outputs to be fairly minimal – an indication of how consistently hot and dry the southern Prairies were during the study period.

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population

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By using historical climate model data and selecting those climate attributes relevant to the agro-economic system, maps such as those generated here can more clearly identify areas where population change may be associated with drought, and where the association appears strongest. This does not necessarily imply a causal relationship. Rather, it points the researcher to potential ‘hotspots’ that warrant closer field investigation to assess whether the visual association may be an actual one. For example, the models suggest that high, non-random patterns of population change occurred within Alberta CD5. On the basis of the model, Alberta CD5 was selected for field investigations beginning in 2008 to conduct a qualitative study of population changes that occurred during the 1930s, and to tease out the reasons behind those changes. Although results of the qualitative field study will not be forthcoming for some time, initial interviews with older, lifelong residents of CD5 indicate that drought conditions were indeed severest in the areas suggested by the model, and that high rates of out-migration during the 1930s from the CD should indeed be attributed to drought-related impacts on local livelihoods. However, not all households migrated away from the area despite the severe climatic conditions. The research team is presently working with long-term residents of CD5 and with individuals whose families left the area and resettled elsewhere (specifically, in northwestern Alberta’s Peace River country) to identify the factors that influenced the ability of rural households to adapt to drought through means other than migration, and those factors that led some families to undertake what was often a long and difficult migration.

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Conclusions

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Human population patterns have been influenced and modified by climatic variability, changes in prevailing climatic conditions and extreme events. For dryland areas like the Canadian Prairies, many of the physical risks associated with future anthropogenic climate change represent exacerbations of current and historical climatic conditions and extremes. The investigation of how human societies in dryland areas have been affected by and responded to past climate-related stresses presents an important opportunity for developing greater insight into the nature of the relationship

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Fig. 11. Population change, Alberta CD1, 1901–1936.

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between climatic and socio-economic processes, and for reflecting on how such relationships might play out in the future.51 Such investigations may also assist in identifying opportunities for proactive adaptive capacity-building in dryland areas and possible points of intervention to moderate negative socio-economic trajectories that emerge from climatic stresses if and when they occur. The techniques reported in this article represent just one way of taking advantage of recent developments in historical climate modeling to investigate human–climate interactions. Spatial data and climate model data have become increasingly available in recent years, and GIS technologies are ever more robust and capable of managing and manipulating the large quantities of data needed to perform the types of analyses presented here. In some jurisdictions historical census data are already available in digitized format, eliminating one of the more laborious steps in the process used here. In short, the remote modeling of human–climate interactions that was once only possible in specialized research centres using prohibitively expensive computing facilities, is becoming increasingly possible in small-scale labs and university classrooms. This article is a starting point for future investigation of droughtrelated population change on the Canadian Prairies. The maps provide compelling evidence that regions where summer drought conditions were most severe experienced significant levels of population decline. These results are consistent with historical accounts of the period, and provide relatively high-resolution identification of those areas where the relationship was likely strongest. The spatial distribution of areas experiencing population losses during that period do not appear to have been random. While the Prairie Provinces as a whole experienced population gains during the study period, the scale of population loss was considerable at particular locales within drought-stricken areas. Even so, we can identify neighbouring land divisions where the rate of population decline is high in one but notably lower in the other. To what may such differentials be attributed? Was it purely institutional reasons, as noted in the discussion of southeastern Alberta above? Or was it attributable to differences in the characteristics of neighbouring households? If so, what was it about these rural households, in terms of their social, economic and cultural attributes, that enabled them to remain in situ and cope with the droughts? To what extent did drought influence the population growth of Prairie cities and population declines in smaller towns and villages that serviced and supported the agricultural community? These and other critical questions cannot be fully explained through remote modeling techniques, and require quantitative and qualitative research gathered through systematic field investigation. One beneficial application of the technologies and methodology described here is therefore to guide researchers in selecting areas for field research in large regions such as Western Canada, where weather stations are comparatively few, and where

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intra-regional spatial variations in the severity of drought were not always well documented. Using new climate models and GIS, field researchers can identify with greater spatial precision places representing the particular types of climate–population dynamics they seek to investigate. The end goal of the qualitative field research supported by this model is to shed light on the underlying causal relationships that gave rise to drought-related population change in the study region and how these may inform research on future climate-related population change. The qualitative information being gathered will also serves to ‘ground-truth’ the model and refine the selection of input data as the project continues. For example, the climate variables that were given preference here may be better suited to one part of the study region than to others, given the variable nature of agricultural systems. The qualitative research will therefore include attempts to pinpoint more precisely the mix of climatic conditions that was most problematic for the rural populations in particular areas. This information will then be used to recalibrate the GIS model for future or later eg 1950–60 mapping projects. The research team plans to begin casting forward in time, using the described methodology to identify hotspots of drought-migration in subsequent decades, continually refining the modeling with data from the field. An eventual goal is to develop a model that can use projected population data trends and model data for future climate to forecast potential future hotspots of climate-related pressure on populations. Such a model would serve to reduce some of the extreme variability and uncertainty encountered in current projections of future climate-related population displacements. To reach such a point we must refine our ability to model past human–climate interactions and identify the particular types of information needed to generate forecasts. The relationship between changing environmental conditions and human population patterns is anything but deterministic. A variety of non-environmental processes and conditions are continually interacting and changing to influence the sensitivity of rural populations to drought and the capacity of those populations to adapt. The results described here should therefore be interpreted as a starting point for future research and development, opening up a number of potential future directions. Acknowledgements This research was funded by a standard research grant received from the Social Sciences and Humanities Research Council of Canada. Support received from the Canadian Foundation for Innovation and the Ontario Innovation Trust provided the necessary infrastructure for the GIS work described here. The researchers wish to thank Con Campbell of Agriculture Canada for sharing his expertise in prairie wheat performance and Pia Papadopol of Natural Resources Canada, Canadian Forest Service for preparation of the climate data used in this study.

Glantz, The use of analogies in forecasting ecological and societal responses to global warming (note 1).