A machine learning analysis of drought and rural population change on the North American Great Plains since the 1970s.

Machine learning techniques have to date not been widely used in population-environment research, but represent a promising tool for identifying relationships between environmental variables and population outcomes. They may be particularly useful for instances where the nature of the relationship is not obvious or not easily detected using other methods, or where the relationship potentially varies across spatial scales within a given study unit. Machine learning techniques may also help the researcher identify the relative strength of influence of specific variables within a larger set of interacting ones, and so provide a useful methodological approach for exploratory research. In this study, we use machine learning techniques in the form of random forest and regression tree analyses to look for possible connections between drought and rural population loss on the North American Great Plains between 1970 and 2020. In doing so, we analyzed four decades of population count data (at county-size spatial scales), monthly climate data, and Palmer Drought Severity Index scores for Canada and the USA at multiple spatial scales (regional, sub-regional, national, and county/census division levels), along with county level irrigation data. We found that in some parts of Saskatchewan and the Dakotas - particularly those areas that fall within more temperate/less arid ecological sub-regions - drought conditions in the middle years of the 1970s had a significant association with rural population losses. A similar but weaker association was identified in a small cluster of North Dakota counties in the 1990s. Our models detected few links between drought and rural population loss in other decades or in other parts of the Great Plains. Based on R-squared results, models for US portions of the Plains generally exhibited stronger drought-population loss associations than did Canadian portions, and temperate ecological sub-regions exhibited stronger associations than did more arid sub-regions. Irrigation rates showed no significant influence on population loss. This article focuses on describing the methodological steps, considerations, and benefits of employing this type of machine learning approach to investigating connections between drought and rural population change. Supplementary Information: The online version contains supplementary material available at 10.1007/s11111-022-00399-9.

Inequalities through the lens of science.

Inequalities are ubiquitous in every society on Earth, and the COVID-2019 pandemic has exposed the marginalized communities that suffer the most. A warming planet will only magnify this gap. On the eve of the 26th session of the Conference of the Parties to the UNFCCC, this Voices asks: how can science inform and address inequalities?

The forgotten drought of 1765-1768: Reconstructing and re-evaluating historical droughts in the British and Irish Isles.

Historical precipitation records are fundamental for the management of water resources, yet rainfall observations typically span 100-150 years at most, with considerable uncertainties surrounding earlier records. Here, we analyse some of the longest available precipitation records globally, for England and Wales, Scotland and Ireland. To assess the credibility of these records and extend them further back in time, we statistically reconstruct (using independent predictors) monthly precipitation series representing these regions for the period 1748-2000. By applying the Standardized Precipitation Index at 12-month accumulations (SPI-12) to the observed and our reconstructed series we re-evaluate historical meteorological droughts. We find strong agreement between observed and reconstructed drought chronologies in post-1870 records, but divergence in earlier series due to biases in early precipitation observations. Hence, the 1800s decade was less drought prone in our reconstructions relative to observations. Overall, the drought of 1834-1836 was the most intense SPI-12 event in our reconstruction for England and Wales. Newspaper accounts and documentary sources confirm the extent of impacts across England in particular. We also identify a major, "forgotten" drought in 1765-1768 that affected the British-Irish Isles. This was the most intense event in our reconstructions for Ireland and Scotland, and ranks first for accumulated deficits across all three regional series. Moreover, the 1765-1768 event was also the most extreme multi-year drought across all regional series when considering 36-month accumulations (SPI-36). Newspaper and other sources confirm the occurrence and major socio-economic impact of this drought, such as major rivers like the Shannon being fordable by foot. Our results provide new insights into historical droughts across the British Irish Isles. Given the importance of historical droughts for stress-testing the resilience of water resources, drought plans and supply systems, the forgotten drought of 1765-1768 offers perhaps the most extreme benchmark scenario in more than 250-years.

Environmental influences on African migration to Canada: focus group findings from Ottawa-Gatineau.

There is limited empirical evidence of how environmental conditions in the Global South may influence long-distance international migration to the Global North. This research note reports findings from seven focus groups held in Ottawa-Gatineau, Canada, with recent migrants from the Horn of Africa and francophone sub-Saharan Africa, where the role of environment in migration decision-making was discussed. Participants stated that those most affected by environmental challenges in their home countries lack the financial wherewithal to migrate to Canada. Participants also suggested that internal rural-urban migration patterns generated by environmental challenges in their home countries underlay socioeconomic factors that contributed to their own migration. In other words, environment is a second- or third-order contributor in a complex chain of interactions in the migrant source country that may lead to long-distance international migration by skilled and educated urbanites. These findings have informed the scope and detail of a larger, ongoing empirical study of environmental influences on immigration to Canada.

Empirical research on international environmental migration: a systematic review.

This paper presents the findings of a systematic review of scholarly publications that report empirical findings from studies of environmentally-related international migration. There exists a small, but growing accumulation of empirical studies that consider environmentally-linked migration that spans international borders. These studies provide useful evidence for scholars and policymakers in understanding how environmental factors interact with political, economic and social factors to influence migration behavior and outcomes that are specific to international movements of people, in highlighting promising future research directions, and in raising important considerations for international policymaking. Our review identifies countries of migrant origin and destination that have so far been the subject of empirical research, the environmental factors believed to have influenced these migrations, the interactions of environmental and non-environmental factors as well as the role of context in influencing migration behavior, and the types of methods used by researchers. In reporting our findings, we identify the strengths and challenges associated with the main empirical approaches, highlight significant gaps and future opportunities for empirical work, and contribute to advancing understanding of environmental influences on international migration more generally. Specifically, we propose an exploratory framework to take into account the role of context in shaping environmental migration across borders, including the dynamic and complex interactions between environmental and non-environmental factors at a range of scales.

What we learned from the Dust Bowl: lessons in science, policy, and adaptation.

This article provides a review and synthesis of scholarly knowledge of Depression-era droughts on the North American Great Plains, a time and place known colloquially as the Dust Bowl era or the Dirty Thirties. Recent events, including the 2008 financial crisis, severe droughts in the US corn belt, and the release of a popular documentary film, have spawned a resurgence in public interest in the Dust Bowl. Events of the Dust Bowl era have also proven in recent years to be of considerable interest to scholars researching phenomena related to global environmental change, including atmospheric circulation, drought modeling, land management, institutional behavior, adaptation processes, and human migration. In this review, we draw out common themes in terms of not only what natural and social scientists have learned about the Dust Bowl era itself, but also how insights gained from the study of that period are helping to enhance our understanding of climate-human relations more generally.

Migration in the context of vulnerability and adaptation to climate change: insights from analogues.

Migration is one of the variety of ways by which human populations adapt to environmental changes. The study of migration in the context of anthropogenic climate change is often approached using the concept of vulnerability and its key functional elements: exposure, system sensitivity, and adaptive capacity. This article explores the interaction of climate change and vulnerability through review of case studies of dry-season migration in the West African Sahel, hurricane-related population displacements in the Caribbean basin, winter migration of 'snowbirds' to the US Sun-belt, and 1930s drought migration on the North American Great Plains. These examples are then used as analogues for identifying general causal, temporal, and spatial dimensions of climate migration, along with potential considerations for policy-making and future research needs.