
==== Front
iScience
iScience
iScience
2589-0042
Elsevier

S2589-0042(24)01853-4
10.1016/j.isci.2024.110628
110628
Article
Causal discovery reveals complex patterns of drought-induced displacement
Tárraga Jose María jose.maria.tarraga@uv.es
13∗
Sevillano-Marco Eva 1
Muñoz-Marí Jordi 1
Piles María 1
Sitokonstantinou Vasileios 1
Ronco Michele 1
Miranda María Teresa 2
Cerdà Jordi 1
Camps-Valls Gustau 1
1 Image Processing Laboratory, Universitat de València, 46980 Paterna, Spain
2 Internal Displacement Monitoring Center (IDMC), 1202 Geneva, Switzerland
∗ Corresponding author jose.maria.tarraga@uv.es
3 Lead contact

02 8 2024
20 9 2024
02 8 2024
27 9 11062820 5 2024
15 6 2024
29 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

The increasing frequency and severity of droughts present a significant risk to vulnerable regions of the globe, potentially leading to substantial human displacement in extreme situations. Drought-induced displacement is a complex and multifaceted issue that can perpetuate cycles of poverty, exacerbate food and water scarcity, and reinforce socio-economic inequalities. However, our understanding of human mobility in drought scenarios is currently limited, inhibiting accurate predictions and effective policy responses. Drought-induced displacement is driven by numerous factors and identifying its key drivers, causal-effect lags, and consequential effects is often challenging, typically relying on mechanistic models and qualitative assumptions. This paper presents a novel, data-driven methodology, grounded in causal discovery, to retrieve the drivers of drought-induced displacement within Somalia from 2016 to 2023. Our model exposes the intertwined vulnerabilities and the leading times that connect drought impacts, water and food security systems along with episodes of violent conflict, emphasizing that causal mechanisms change across districts. These findings pave the way for the development of algorithms with the ability to learn from human mobility data, enhancing anticipatory action, policy formulation, and humanitarian aid.

Graphical abstract

Highlights

• Drought displacement is expected to rise

• Causal discovery reveals the complex interplay between displacement and key drivers in Somalia

• Market prices and conflict shocks are key displacement drivers, not just precipitation

• Insights for better responses to mitigate drought impacts

Geography; Human geography; Climatology; Global change.

Subject areas

geography
human geography
climatology
global change
Published: August 2, 2024
==== Body
pmcIntroduction

Increased population displacement due to extreme weather events is a pressing issue demanding concerted action. Each year, an average of 22 million people are forced to flee their homes due to hazards such as floods, storms, and droughts,1 leading to extreme vulnerability and potential humanitarian emergencies.2 In a rapidly changing climate, extreme weather events are expected to escalate in frequency and severity, and are anticipated to further magnify human displacement.3,4 Population displacement due to disasters can attain profound implications for populations as it can perpetuate cycles of poverty, intensify scarcity of food and water, and amplify socio-economic inequalities.5,6,7,8 Nonetheless, considerable debate remains regarding the individual contributions of extreme weather events and non-weather factors to such displacements.9 This lack of understanding of causes and interactions between factors also challenges accurate projections,10 the effectiveness of early-warning systems,11 reliable anticipatory action,12 and optimal resource allocation strategies.13

There is never a single reason why people move but rather an intricate tangle of environmental, socioeconomic, and political factors.14,15,16,17,18 Despite this, there is a lack of quantitative research addressing both environmental and socioeconomic causes of displacement,9 an issue that becomes particularly evident in slow-onset hazards such as drought events due to their cumulative impacts across space and time.19 In fact, the creeping nature of droughts challenges understanding their impacts, often diminishing the urgency that would otherwise trigger a timely response.

Particularly, the drylands of the Horn of Africa, which are reliant on seasonal rainfall, are expected to experience some of the most severe drought impacts globally.20 Still, alerting on dry spells’ arrival alone is not enough to anticipate famine and drought displacement,21,22,23 since other factors like agricultural production, unemployment, environmental degradation, and water and food security crises, among others, also contribute to increased vulnerability and disruption of livelihoods.24,25,26 Meanwhile, the impact of violent conflict on households in drought contexts yields various findings, which depend on both the context and the area of study.17,27,28,29,30,31 Overall, while the body of literature is growing, the interactions of displacement drivers at local level are largely undocumented or remain ambiguous due to the complexity and the lack of granular data on the subject.5,12

Causal discovery of drought displacement drivers

Most existing approaches to modeling and understanding climatic and disaster-induced mobility rely on mechanistic models, which are occasionally validated with limited data and often make strong assumptions about the underlying causal mechanisms.15 Some of which include dynamic system modeling,32 agent-based modeling,33,34 and gravity or radiation models.35,36 However, in some scenarios, these methods lack evidence on the data-generating mechanisms, especially across time.37 This issue has led to unrealistically large mass migration predictions due to model extrapolation issues.38,39 Statistical and machine learning methods have also been used to tackle the problem.40 Certainly, a more agnostic data-driven approach can enhance modeling and forecasting, being free from assumptions about variable relations and interaction terms. Bayesian networks,41 generalized models and simulation frameworks,42 time series forecasting43 and hypothesis testing through regression27,44 are some of the methods used. However, these models only capture association patterns and are thus not guaranteed to capture true causal relations between the involved drivers, impeding an accurate attribution of cause and effect relationships.45,46

Within drought contexts, drought displacement occurs when people can no longer maintain their livelihoods or experience hunger or water scarcity. Even so, the tipping point is reached depending on the coping capabilities and livelihood options of affected populations, which heavily depend on the context.26,47 To alleviate the sufficiency assumption, asserting that all relevant variables influencing the target have been included, our study adopts the framework proposed by the Internal Displacement Monitoring Centre.48 Under this framework, drought displacement results from a network of processes related to precipitation impacts, food access, livestock breeding, and conflict, which are also detailed in previous research.21,22,49,50,51 Therefore, we collected and harmonized a set of explanatory covariates from district-level sources in Somalia accounting for the relevant drivers (see Table 1). Then, we use a novel data-driven approach using weekly time series data to discover drivers of drought displacement in Somalia’s districts during years 2016–2023, where major drought crisis occurred (Figure 1).Table 1 Variables and sources used in the study, temporal, and spatial resolution

Variable	Source	Spatial resolution	Temporal resolution	
Precipitation	CHIRPS52	0.05°	Daily	
Evapotranspiration	ERA5-Land53	0.1°	Daily	
Local Market Prices	FSNAU54	District	Monthly	
Drought Displacement	UNHCR PRMN55	District	Weekly	
Somalia Districts	UNDP56	District	Static	
Livelihood Zones	FEWS NET57	Sub-national	Static	

Figure 1 Overview of the methodology proposed to understand drought internal displacement through causal discovery

Total number and percentage of new displacements attributed to Somalia’s droughts and conflicts during 2016–2023 (A). Weekly timeseries data are collected in three of the selected Somalia districts included in the study (Baidoa, Diinsoor, and Kurtunwarey) accounting for drought impacts (SPEI), socioeconomic (food, livestock, and water prices), and conflict drivers (fatalities) (B). From the preprocessed time series, we derive causal graphs per district with the PCMCI method (C).

To our knowledge, this is the first attempt to apply modern observational causal discovery methods to disaster-induced mobility studies. Causal discovery is a field of machine learning and statistics that allows the retrieval of a causal graph of variable interrelations from purely observational data.46,58,59 Using causal discovery methods based on conditional independence tests we provide empirical evidence of the factors involved in drought displacement within Somalia districts. Models contribute to understanding the situation by yielding explicit and explanatory causal graphs, causal strength of relations, and temporal lags of interactions in weeks and how they change across districts. These models address the limitations of methods that solely use associational techniques or assume graphs of complex systems whose driver interactions are not fully known. Our approach provides a new perspective on how human mobility patterns can be studied and interpreted with causal discovery and ultimately supports evidence-based anticipatory action, policy design, and humanitarian aid.

Results

The challenge of identifying displacement drivers in drought contexts

Once the relevant drivers have been harmonized (Figure 2), the question is about what conditions and combinations of drivers in drought contexts induce displacement and to what extent; a concept which was already studied in the literature.21,27,32,44 However, in climate mobility research it is customary to attribute the relative relevance of a set of potentially explanatory drivers by measuring their association power with the target variable.15 This is typically done by looking at the (statistically significant) coefficients of a linear fit. To illustrate the limitations of such an approach, we targeted the prediction of drought displacement in the selected districts using an ordinary least squares (OLS) regression. Results of the obtained regression coefficients, indicate that associational models may not be effective in identifying the causes of drought displacement, since many well-known drivers of displacement were not captured by an OLS analysis (see Table 2 for the specified model and results in the STAR methods section).Figure 2 Harmonized and preprocessed time series for the three selected districts

(A) Baidoa, (B) Diinsoor, (C) Kurtunwarey. An association between drought IDP waves, SPEI impacts, and market price shocks can be seen at a glance during the drought event periods for the different districts. However, different dynamics are observed as different onset and IDP magnitudes are observed across time.

Table 2 Results of OLS regression for detecting causal displacement drivers for a p value <0.05 in three selected districts for all the considered variables and time-lag τ

District	R2	Variables	Lag τ	OLS coefficient	p-value	
Baidoa	0.46	Livestock prices	10	−2.3	0.03	
		Water prices	10	1.8	0.03	
Diinsoor	0.42	–	–	–	–	
Kurtunwarey	0.35	Food prices	1	3.1	0.01	
		Food prices	2	−6.7	0.04	
		Livestock prices	10	−1.4	0.02	

Table 3 Results of Akaike Information Criterion for different αPC values in the selected districts

District	αPC	0.001	0.01	0.05	0.10	0.25	0.40	
Baidoa	AIC	−499	−500	−491	−494	−491	−485	
Diinsoor	AIC	−695	−695	−695	−667	−667	−676	
Kurtunwarey	AIC	−651	−653	−664	−663	−663	−647	
Results indicate a better model for lower αPC values across all districts.

So to address multicollinearity, confounding, and interaction between variables, we employed partial correlation tests, Granger causality, and the Peter and Clark Momentary Conditional Independence (PCMCI) algorithm for the Baidoa district. In this section, we illustrate the advantages of using PCMCI in detecting drought displacement drivers. As shown in Figure 3A, partial correlation tests were able to identify a high number of statistically significant relations between the considered drivers and displacement. However, partial correlation tests do not allow for a causal interpretation of the problem, since the graph is fully connected, resulting in spurious links. Alternatively, we employed Granger causality 3[b], which assesses the ability of past values of one variable to predict future values of another, and can isolate causal links (see the STAR methods section for details on Granger causality). Nevertheless, Granger causality is limited by the high dimensionality and autocorrelation of the time series, harnessing its detection power. On the other hand, using the PCMCI method 3[c], we overcome the challenges present in the time series data and generate plausible causal graphs of drought displacement (see the STAR methods section for details on PCMCI).Figure 3 Graphs representing relationships between the drivers of drought displacement in the Baidoa district obtained through different statistical tests for a p value <0.05

The graph in (A) is obtained using partial correlation. The graph in (B) is Granger causality. Finally, the graph in (C) is built using PCMCI controlling for autocorrelation, and presenting a superior causal link detection power. (1) Arrows in the diagram point in the direction of the links between variables, (2) the color of the arrows indicates the effect correlation (a red arrow of food prices pointing displacement means that a rise in food prices causes a rise in displacement) (3) The intensity of the color indicates the score for the statistical test (the partial Pearson correlation coefficient for partial correlation (A) and MCI in Granger causality (B) and in PCMCI (C) which translates into the strength of the detected causal link.

Detecting contextual drivers of drought displacement in a multicausal system

In this section, we display the results of PCMCI applied to three main districts: Baidoa, Diinsoor, and Kurtunwaarey, which sepicted by high drought displacement numbers during 2016–2023. PCMCI retrieves unique causal graphs across these districts, alluding to distinctive causal mechanisms operating within each (we also examined additional districts, see Figures S1 and S2). Mainly, our methodology finds different links and time-lags across districts, emphasizing the need for a disaggregated approach when attributing the effects of climate or weather and conflict on human mobility. The causal graphs depict stability across a range of values of the PCMCI hyperparameters as depicted in the “quantification and statistical analysis stability of the causal graphs” section of the STAR methods. In this segment, we discuss all the found time-lags by PCMCI, but display in Figure 4 only the most relevant lag according to its causal strength (MCI score) for visualization purposes.Figure 4 Derived causal graphs for several districts in Somalia

Derived causal graph using PCMCI for (A) Baidoa, (B) Diinsoor, (C) Kurtunwarey districts in Somalia at α=0.05 and τmax=10 weeks. The causal graphs provide insight into the relationships between the drivers of drought displacement for each district. (1) Arrows in the diagram point in the direction of causal links between variables, (2) numbers indicate the most important time-lag (highest dependence score) in which variables are affecting each other, (3) the color of the arrows indicates the causal effect correlation (a red arrow of food prices pointing displacement means that a rise in food prices causes a rise in displacement) (4) the intensity of the color indicates the MCI score which translates into the strength of the causal link. (5) Node color indicates auto-MCI which is a measure of causal auto-correlation. For visualization purposes here we show the most relevant time-lag (refer to Figure S3) for additional time-lags. The experiments show that the inflation of food prices drive displacement in all districts. However, we find different relationships depending on the district. For example, the rise in water prices is found to be causally relevant in Baidoa and Kurtunwarey districts but not in Diinsoor. In addition, the decrease in livestock prices drives drought displacement in Diinsoor but not in Baidoa or Kurtunwarey. Furthermore, we found a direct link between conflict and drought displacement only in the Baidoa district.

The obtained causal graph for Baidoa, illustrated in Figure 4A, reveals direct connections of food prices, water prices, and fatalities due to violent conflict to drought displacement. The relevant time-lags in weeks (τ) disclose the underlying processes: drought displacement is primarily triggered by increases in food prices with τ=8,1 and in water prices with τ=1,9,10 weeks (See Figure S3 instead of Figure 4 for all detected timelags). Additionally, a link from fatalities to displacement was found with τ=8 weeks. These time-lags result critical for understanding the response window available to policymakers and humanitarian organizations for implementing effective interventions. For instance, a time-lag of 8 weeks, indicates a significant delay between the escalation of food prices and its impact on displacement levels, providing a valuable period for intervention before the effects fully manifest. On the other hand, shorter lags, such as τ=1 week for both food and water prices, highlight the need for rapid response mechanisms to mitigate immediate threats and prevent cascading effects. Notably, the strongest link in Baidoa was between the cost of water and displacement, underscoring water scarcity as a significant cause of displacement. This finding aligns with insights from ground experts, reflecting the critical lack of water availability in the Bay region of Somalia during drought events.

The Diinsoor causal graph shares similarities with the graph for Baidoa, with some differences; see Figure 4B. The algorithm detects links between increasing food prices (τ=1,6) and displacement, but also detects that decreases in livestock prices trigger displacement with τ=1. This result is in line with the stress destocking of livestock as a coping capacity when pastoralist and agropastoral households cannot sustain their livelihoods and need to acquire resources during droughts.49 Lastly, in the Kurtunwarey district (Figure 4C), similar to Baidoa, it was found that increase in food prices with 8,4 weeks, and water prices with τ=8,2,9 weeks drive displacement. A connection between conflict and displacement, however, was not identified. Here, we underscore the dual importance of both short and long time-frames in drought contexts since we find such causally relevant time-lags for all drought displacement drivers in these districts.

These results agree with previous reports regarding adaptation dynamics of Somali agropastoral and pastoral livelihoods to drought conditions.26,32,47,60 However, we must note that PCMCI finds no direct connection between livestock selling pressures and drought displacement in Baidoa or Kurtunwarey, despite being a crucial income source for herder communities. This suggests that livestock deaths could contribute indirectly to drought displacement in these districts by decreasing income levels, thereby affecting the acquisition of food and water, but not directly displacing populations. While this could be a plausible pathway for agropastoral households, pastoral livelihoods are often directly displaced after losing their main source of income.61 Here, a more nuanced analysis would be required to avoid potential aggregation bias among marginalized and vulnerable communities within the districts under study.

Regarding drought-conflict dynamics, PCMCI identified a link from conflict directed to displacement caused by drought in Baidoa, a district reported to have inter-clan conflicts arising from disputes over pasture and water.62 Such violence could potentially exacerbate the situation for households already impacted by drought, thereby escalating insecurity and triggering displacement.63,64 It must be noted that the link is found with τ=8, which indicates a long-term influence of conflict on displacement. Discussions with local stakeholders revealed that violent conflicts have frequently led to the destruction of water infrastructure in Baidoa. This underscores the need for a deeper exploration of the effects of these isolated incidents which may not be captured by our conflict data. Moreover, we did not find these links for Diinsoor or Kurtunwarey district, underscoring the need for an in-depth study to explore the drought-conflict dynamics across various districts. This corroborates emerging literature, which increasingly advocates for situation-specific understandings of the interactions between climate change and conflict.9,30,65

Finally, our causal diagrams depict food prices as a persistent driver of drought displacement across districts. Notably, 74% of surveyed households at site arrival within these districts reported food as their primary need following displacement. Previous research has sought to understand how precipitation deficits can predict food insecurity in the Horn of Africa,22,66 and here our methodology retrieves an indirect, rather than direct, link between SPEI and displacement across all examined districts. This indicates that while precipitation is indeed a critical indicator of food security crises in East Africa,20 it may fail to predict food insecurity and drought-induced displacement. This limitation prevails not only under complex weather patterns but also due to the significant role of other factors influencing food insecurity.67 The harmonized time series data (Figure 2) displays this case. Despite record SPEI values reported in 2019, there were notably smaller food price increases compared to other drought periods, such as 2017 or 2022, where the combination of food prices, water prices, and SPEI resulted in substantial drought-induced displacements across districts. It’s important to underscore that this approach uncovered data-driven patterns without necessitating any prior assumptions, and its effectiveness could be enhanced by including more data over time. This provides empirical evidence to understanding drought crisis, underlining that drought displacement arises from a complex interplay of environmental, socioeconomic, and political factors.9,68 Comprehending these interactions is vital for decision-makers to identify relevant vulnerabilities in their processes to catalyze more effective responses.67,69,70

Discussion

The forced movements of populations due to weather-related hazards affect millions of people worldwide, especially those who are already vulnerable. In a changing climate, it’s crucial to have efficient preparedness and response strategies for displacement events caused by droughts, aiming to minimize their adverse effects on affected communities.5,50 However, accurately measuring the impact of droughts on displacement faces obstacles due to challenges in data aggregation and the complexity of identifying multiple causes, which traditional parametric models struggle to address.14,71,72

Additionally, studying the temporal dynamics of human mobility poses a more intricate challenge than identifying spatial patterns.37 To address these complexities, we propose a new set of data-driven methods to understand the relationships between drought impacts, socio-economic factors, conflict, and displacement patterns using observational data. We presented a displacement time series dataset at the district level in Somalia covering the period from 2016 to 2023 considering relevant drought displacement drivers. Subsequently, by leveraging the data structure through causal discovery, we obtained causal graphs, which suggest that displacement is contextually driven by the compound effects of market price shocks and violent conflicts. These findings underscore that precipitation deficits do not directly drive drought-induced mobility in Somalia, but act as indirect drivers, i.e., they impact food and water security processes that ultimately drive mobility. These results align with earlier studies indicating the crucial interconnection between the impact of environmental stressors and the socioeconomic conditions prevailing in the affected region.8,73,74,75,76,77,78

In conclusion, our research emphasizes the complex interplay of compounded vulnerabilities during droughts related to water and food security systems, coupled with episodes of violent conflict, as key drivers of displacement in Somalia. By explicitly illustrating the causal graph underpinning each model and identifying the temporal lags influencing each variable, we present causally informed models that extend beyond the limitations of current modeling approaches, thus enriching hypotheses and qualitative analyses during drought crises. While our study primarily focuses on unveiling data-driven relationships in a drought-affected context within a particular region, causal theory equips us with additional tools to assess impacts or to forecast interventions in other disaster settings. Hence, our methodology offers a pathway for deploying algorithms to extract insights from human mobility data, ultimately benefiting proactive measures, policy making, and humanitarian aid.

Limitations of the study

To advance the field of human mobility research, future studies should integrate high-resolution data alongside the underlying causal mechanisms to explore the connections among climate, weather, conflict, and human mobility. Our study focuses on identifying the primary drivers of displacement in response to drought hazards.48 By concentrating on external drivers (e.g., precipitation deficits, conflict, and food accessibility) within the causal graph, we can control for potential confounding factors. Nonetheless, the reliability of our causal models depends on the quality of the training data, and drawing causal inferences is contingent upon our current assumptions (e.g., faithfulness and sufficiency assumptions). In this study, causal sufficiency is assumed only for displacement variables, implying that depicted associations between SPEI, market prices, and fatalities may be influenced by confounding factors rather than direct causality. Furthermore, we did not investigate long-term or structural characteristics (e.g., socioeconomic status, land degradation, policy changes, etc.) that contribute to variations in vulnerability, resilience, and exposure dimensions of households. Consequently, further research should infer the responsible factors for differences in displacement levels across districts and potential spatial spill-over effects. Here, invariant causal prediction (ICP) methods aimed at identifying causal relationships from observational data across different environments or experimental conditions could be further explored.79 Departing from causal graphs, future research should also assess cause-effect estimation metrics, properly ranking the influence of drivers triggering drought displacement. Future work should also address the nonlinear impacts of droughts on populations such as legacy effects of droughts and recognize the importance of multi-hazard events, such as floods, which contribute to an elevated vulnerability toward drought displacement. This multifaceted approach will provide a more nuanced understanding of the intricacies of human mobility in drought contexts.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER	
Deposited data	
	
Harmonized Drought displacement data	This paper	https://github.com/IPL-UV/Causal4Migrations	
	
Software and algorithms	
	
Tigramite package	Runge et al.1	https://github.com/jakobrunge/tigramite	
Causal discovery pipeline	This paper	https://github.com/IPL-UV/Causal4Migrations	
Data cube builder	This paper	https://github.com/IPL-UV/Causal4Migrations	
	
Other	
	
Resource website	This paper	https://github.com/IPL-UV/Causal4Migrations	

Resource availability

Lead contact

Further information and requests for resources should be directed to the lead contact, José María Tárraga (Jose.Maria.Tarraga@uv.es).

Materials availability

No new unique reagents were generated in this study.

Data and code availability

Code snippets, data and demos can be found in Causal4migrations. We used the PCMCI code provided in the Tigramite toolbox.

Method details

Understanding complex systems through causal discovery

Causal discovery aims to identify causal relationships between variables in complex systems using only observed data instead of experimental interventions. This field is particularly important in many fields of science, where controlled experiments are difficult, impossible, costly, or unethical,45,46,80 such as in drought mobility or food insecurity studies. Within a causal framework, a variable X is the cause of another variable Y if intervening on X necessarily affects the variable Y, but conversely, intervening on Y leaves X intact. Causality is pivotal not only for a better academic understanding of processes in science but also for more robust models and forecasts and for attributing the right causes of events. Many fields of science and engineering are using causal inference/discovery methods, from Earth and climate sciences,59,81 neurosciences,82 social sciences,83 health and epidemiology,84 or economics.85

While associational (regression) models may be proficient at predicting “What” happens, they are not well-suited to answering most of the causal questions in the form of “why?” and “what if?”, which are crucial for decision-making during drought conditions. Causal inquiries involve utilizing an underlying causal mechanism and include questions like “Does X cause Y?” (causal discovery), “What is the overall effect of food prices on displacement?” (cause-effect estimation), “How should water trucking or financial aid be distributed to maximize a specific outcome?” (intervention analysis), and predict the effect of interventions with questions such as “What would have occurred if we had taken action X rather than Y?” (counterfactual analysis). These determinations frequently rely on expert knowledge and qualitative evaluation in challenging circumstances. The emerging field of causal inference and discovery, while still in its infancy, has the potential to assist this group of inquiries.

Various techniques have been developed in the field of observational causal discovery over the last decades, including graphical models, Bayesian networks, structural equation modeling, and counterfactual inference.46 Many methods are available to perform causal discovery from observational time series,86 including Granger causality (GC)87 and convergent-cross mapping (CCM).88 In this work, we will rely on a bagged version of the Peter-Clark (PC) Momentary Conditional Independence (PCMCI) algorithm,89 which presents several advantages over other methods.

Discovering causal networks using PCMCI

In this work we rely on the PCMCI algorithm to obtain causal graphs of drought displacement. PCMCI originated in the field of climate sciences but has been successfully used in many fields of science and engineering and mainly adapts the PC algorithm to the time series case. Unlike GC and CCM, PCMCI can cope with issues like strong autocorrelation and highly interdependent time series, such as in our case. Moreover, PCMCI has been widely used and validated under different settings and generally provides more detection power to deal with time series causal discovery.89 In our case, the market, conflict, weather, and displacement time series Xti are treated as nodes in a directed acyclic graph (DAG). Then, PCMCI tries to identify if causal links Xt−τi→Xtj exist up to a τmax and the strength of the relation, if any. PCMCI can estimate such links by exploiting optimal conditional independence tests on Xtj given the past of the variables and can also identify indirect and common cause links.

In PCMCI, causal diagrams are interpreted based on the weight and directionality of their edges. These edges represent the magnitude and direction of the partial correlation coefficient between two variables after accounting for all the variables in the system (named as the MCI score). The color of the corresponding arrow can either be blue or red, signifying a positive or negative partial correlation score respectively. For example, a red arrow from food prices to displacement indicates that an increase in food prices drives displacement with its corresponding time-lag. The time-lag represents the leading time in which changes in a variable drives another. Please, refer to the following sections for details about our application of PCMCI to drought displacement timeseries and for a detailed description of the PCMCI algorithm.

In evaluating the stability and robustness of causal graphs, our approach involved utilizing bagging within the PCMCI framework by bootstrapping the time series data, while preserving temporal dependence. Subsequently, we employed majority voting to identify the most frequently occurring causal links (refer to the next section for detailed methodology). This procedure ensures that the identified causal relationships remain consistent across multiple subsamples of the time-series data. Furthermore, we applied the bagged PCMCI method across a range of αPC values (the main hyperparameter in PCMCI), demonstrating near-complete link stability for drought displacement drivers in the selected districts (see Figures S4–S6). The following results section presents graphs for αPC=0.05, based on the Akaike Information Criterion (AIC) findings (see the quantification and statistical analysis for detailed statistical validation). Additionally, we selected τmax=10 as the maximum time-lag for our models and investigated the sensitivity of the PCMCI algorithm with respect to the τmax parameter. Our analysis revealed that both shorter and longer time lags are crucial for accurately analyzing drought displacement events. However, increasing τmax significantly raises the dimensionality of the network, which in turn may reduce the algorithm’s detection capabilities (see Figure S7). For further details, see the quantification and statistical analysis section in STAR methods.

In this work, we apply PCMCI to three main districts: Baidoa, Diinsoor and Kurtunwaarey. These districts were chosen considering the high number of displaced populations, and their historical significance in studies of migration patterns due to climatic extremes in Somalia. For instance, the Bay region has been particularly vulnerable to drought impacts. Additionally, the selection criteria included the higher quality and longer time span of timeseries data available. Subsequently, we also examined additional districts (see Figures S1 and S2).

Application of PCMCI to drought displacement timeseries

Here, we used a bagged version of PCMCI to improve the accuracy and stability of the causal graphs. We conducted 100 bootstrapped sampling runs and aggregated the most frequent links by majority voting as proposed in similar studies.90,91 Two free parameters need to be selected in PCMCI: the maximum time-lag τmax of the interactions and the αPC parameter, which acts as a regularizer by setting the p-value threshold for the significance tests in the PC algorithm. Our causal graphs are generated using a partial correlation independence test (i.e., Pearson correlation between the residuals of linear regressions) by imposing different αPC values for the PC stage and a p-value of <0.05 for the MCI stage. Additionally, we set a maximum time-lag τmax=10 based on the rationale that 10 weeks is sufficient to capture the significant impacts of key factors like rainfall deficits, market prices, and conflict on displacement events. As τmax increases, we observe a denser network of causal links, indicating that both immediate and delayed effects are important in understanding the full scope of drivers behind drought displacement (please refer to the quantification and statistical analysis section of the STAR methods for details on the choice of τmax for the Baidoa district). We also block SPEI links by imposing that none of the considered socioeconomic processes can cause precipitation. This assumption follows previous studies showing that prior knowledge can improve the accuracy and computational complexity of constrained causal discovery algorithms.92,93 Finally, missing values from the displacement time series are masked from the PC search so no low displacement event is misrepresented. The result is an estimated causal network with causal links, strengths, and causal lags that can help understand the complex drought displacement situation in a given district.

The methodology follows the standard assumptions of causal discovery: (1) Causal Sufficiency assumption. An important point to observe in our causal discovery approach is that all possible driving variables of drought displacement should enter the analysis to account for confounding effects. This means that the dataset should cover the primary push factors of displacement in the context of a drought hazard in Somalia under the considered time frame, mainly drought impacts, food accessibility and conflict.48 While we assume causal sufficiency for displacement, we do not make the same assumption for the other variables in the graph. Therefore, the connections between SPEI, market prices, and fatalities in the graph are not direct causal relationships and could be due to confounding factors. (2) Faithfulness assumption requires that all observed conditional independencies must arise from the causal graphical structure. Hence, the joint probability distribution between the variables must describe the causal relationships. (3) We assume no contemporaneous causal effects, and (4) we assume causal stationarity in the time series, meaning that the causal mechanisms of drought displacement must not change over time.

Details on the PCMCI algorithm for causal discovery

The PCMCI method has been used extensively in Earth sciences for similar tasks.59,89 Let us fix the notation. We assume that the nodes in a (time series) graph represent the variables at different time-lags, and a causal link Xt−τi→Xtj exists if Xt−τi is not conditionally independent of Xtj given the past of all variables, which are formally defined by . Here, the symbol denoting the absence of a (conditional) independence, the vertical bar | meaning “conditional on”, and Xt−⧵{Xt−τi} denoting the past of all N variables up to a maximum time-lag τmax excluding Xt−τi.

Causal discovery theory45 tells us that the parents P of a variable Xtj are a sufficient conditioning set that allows establishing conditional independence (Causal Markov property80). Causal discovery algorithms, such as the PC algorithm, allow us to detect these parents and can be flexibly implemented with different kinds of conditional independence tests and variables that are discrete or continuous and univariate or multivariate. However, as shown in theoretical discussion and numerical experiments,89 the PC algorithm cannot be directly used for the time series case, particularly since autocorrelation can lead to high false favorable rates.

Hence, the proposed procedure adapts the PC algorithm to the highly interdependent time series case. The PCMCI algorithm consists of two stages: (1) PC condition selection to identify relevant conditions Pˆ(Xtj) for all included time series variables j=1,…,N, and (2) the momentary conditional independence (MCI) test to test whether Xt−τi→Xtj using .

The two stages in PCMCI serve the following purposes: PC removes irrelevant conditions for each of the N variables by iterative independence testing. A significance level αPC in the tests lets PC adaptively converge to typically only a few relevant conditions that include the causal parents P with high probability. The main free parameter of PCMCI is this significance level for αPC in PC1 that can be chosen based on model selection criteria such as the Akaike Information Criterion (AIC), cross-validation or discuss all models across a wide set of αPC values. Then, the MCI test identifies spurious links by conditioning only on the set of parents of a variable Xtj. As an example, for testing Xt−21→Xt3, the conditions Pˆ(Xt3) are sufficient to establish conditional independence (Markov property), that is, to identify indirect and common cause links. On the other hand, the additional condition on the parents Pˆ(Xt−21) accounts for autocorrelation leading to correctly controlled false positive rates at the expected level, in contrast to conditioning on the whole past of all processes as in FullCI. FullCI can be seen as a generalization of Granger causality89 and directly tests the link-defining conditional independence by setting αPC=1, meaning no conditions are removed from the PC algorithm. The high dimensionality of including Nτmax−1 conditions on the one hand, and the reduced effect size due to conditioning on Xt−11 and Xt−12, on the other, leads to a potentially drastically reduced detection power (see Figure 3 in the main manuscript).

Bagged-PCMCI: Method, parameter selection, tuning

Similarly to what has been proposed in literature, bootstrap aggregation in graphical model learning can improve stability, detection power and correct false positive links.90,91 Here we use a bagged version of PCMCI to improve robustness and stability of the retrieved graphs. In this procedure, we generated 100 bootstrap replicates using the tigramite library which facilitates bootstrapping times eries data while maintaining temporal lag dependencies. We then run PCMCI independently on each replicate, ultimately producing 100 bootstraped graphs. Subsequently, these 100 graphs were aggregated into an output graph via edge-level majority voting.

The selected independence test in the PCMCI algorithms is partial correlation and retrieves links with p<0.05 in the MCI stage. Moreover, by imposing different αPC values, we can ensure the found links’ stability to assess causal relationships between variables (). We set a maximum time-lag τmax=10 weeks, assuming that two months is enough to measure the effects of drought-prone conditions ultimately leading to crop failure in the agricultural season on displacements. We assume there are no contemporaneous effects, that is, causal effects with lag 0. SPEI links have been intentionally excluded from consideration as none of the evaluated variables could feasibly provoke precipitatory deficits. Also, any missing displacement values in the PRMN dataset were masked, following the proposal by Kretschmer et al. (2016).94 Rather than assume no displacement when the data are missing, it was deemed sensible to mask such data points, ensuring that low displacement magnitude data points are not biased.

Granger causality

Granger causality is a statistical hypothesis test for determining whether one time series is useful in forecasting another.87 Granger causality has been widely applied in economics, neuroscience, and various fields of engineering. It is particularly useful in time series analysis when the goal is to understand the interaction and the directional influence between two or more variables over time. Consider two stationary time series, X and Y. The basic idea of Granger causality is to see if past values of X contain information that helps predict Y above and beyond the information contained in past values of Y alone. A simple bivariate autoregressive model can be used to test for Granger causality from X to Y:(1) Yt=α+∑i=1nβiYt−i+∑i=1nγiXt−i+ϵt

where Yt is the current value of the Y series, Yt−i are the lagged values of Y, Xt−i are the lagged values of X, α is a constant, βi and γi are coefficients, and ϵt is the error term. The null hypothesis for the Granger causality test is that the coefficients of the lagged values of X (γi) are all zero. In other words, X does not Granger-cause Y. This hypothesis is tested using an F-test. While Granger causality is a powerful tool for time series analysis, it has limitations in high autocorrelated and multidimensional settings.89 In this work we use an efficient multivariate version of Granger causality provided in the Tigramite package.

Quantification and statistical analysis

Linear regression results in the three selected districts

In climate mobility statistical research it is common to attribute the relevance of a set of drivers by looking at the (statistically significant) coefficients of a linear fit. To illustrate the limitations of such approach to detect causal features, we used linear regression models in selected districts using an ordinary least squares (OLS) regression. The specified model islog(IDPt)=β0+∑i=1v∑τ=1τmaxβτiXt−τi+ε,

where β0 is the intercept term, βi are the coefficients of the following v=5 variables Xi, i=1,…,5: the semesterly SPEI, food prices, water prices, livestock prices and fatalities, up to a temporal lag of τmax=10 weeks, and ϵ is the error term. Table 2 indicates that associational models may not be effective in identifying the causes of drought displacement. In our OLS test, statistical significance (under p-values ¡ 0.05) was only found for a few links in the selected districts, omitting well-known causal drivers of drought displacement. These results are shown in Table 2.

Robustness and stability of obtained graphs

We conducted additional tests with varying values of the αPC parameter (0.001, 0.01, 0.05, 0.1, 0.25, 0.4) to evaluate the stability of the causal graphs produced by the PC and MCI phases of PCMCI. Our findings indicate that for Baidoa and Kurtunwarey, links between the identified drivers and drought displacement are consistent and robust across all considered αPC values. One exception is the incorrect detection of the link direction between food prices and drought displacement in Kurtunwarey. However, in Diinsoor, a link between livestock prices and displacement in Diinsoor was not observed for higher αPC values. To establish confidence in this potential association with livestock, we conducted further analysis via linear regression with the obtained causal parents of drought-related displacements from PCMCI. Utilizing the Akaike information criterion as recommended by,59 we determined that the best models were given for lower αPC values across the selected districts (See Table 3).

Robustness of PCMCI for τmax values

The selection of the maximum time delay, τmax, must be aligned with the expected maximum physical time lag in the complex system under study. In the primary section we analyzed the impact of various drivers on drought displacement over a maximum time lag of 10 weeks, selected to capture the effects of rainfall deficits during Somalia’s crop season. This section explores the impact of variations in τmax on the causal graphs within the Baidoa district. Our results show that an increase in τmax leads to a more dense network of causal connections between drivers and drought displacement, with the network stabilizing at higher τmax values. This finding emphasizes the importance of considering both shorter and longer time lags for a thorough understanding of displacement dynamics in drought conditions. Importantly, the connection to fatalities becomes evident when τmax exceeds 6 weeks, underscoring the delayed impact of conflict on displacement. Using PCMCI, a larger choice of τmax is desired to account for dependencies between variables for longer time-frames. However, extending τmax beyond 10 weeks poses challenges for the PCMCI algorithm, as the number of conditional independence tests required grows exponentially with τmax, potentially affecting its ability to accurately detect causal relationships,89 as illustrated in Figure S7, where it incorrectly identifies the directionality from Food Prices to Drought IDP.

Data sources

Displacement drivers

Quality data are critical in displacement studies,95,96 and particularly at sub-national levels historical records of drought displacement drivers are scarce.97 Despite this, we identified the United Nations High Commissioner for Refugees (UNHCR) Protection and Return Monitoring Network (PRMN) time series data in Somalia as suitable for causal discovery.55 This network compiles weekly records of displacement flows, capturing data on Internally Displaced Persons (IDP), their arrival dates, points of origin, and reasons for displacement. Our final dataset encompasses 74 districts, and the most extensively sampled are represented by around 400 data points. These districts exhibit variability not only in terms of data quality, but also on the onset and magnitude of displacement waves registered (see Figure 2), underscoring the presence of contrasting causal mechanisms between regions.

In this study, we assessed drought impacts using a Standardized Precipitation and Evaporation Index (SPEI).98 We computed a semesterly window for the SPEI to encompass both the dry and wet seasons, which are crucial for the rain-dependent livelihoods of Somali households.61 This decision was also informed by our observation that expanding the SPEI window size resulted in a higher correlation with drought-induced displacement, indicating the importance of the accumulated effects of rainfall deficits. To track food and water insecurity trends in Somalia, we used monthly prices of common commodities in Somali households, livestock and water drums prices in local markets.54 We condensed the different commodities and livestock prices into two distinct indexes, namely “Food prices” and “Livestock prices”99,100 (see Figure S8). Lastly, the degree of conflict is represented by the number of district-level fatalities101 following previous studies.27 To more accurately capture the trends and reduce the noise in displacement and conflict, we applied a four-month moving sum to smooth the drought IDP and fatalities time-series. Consequently, all findings from this analysis should be interpreted as monthly trends in drought displacement.

Data collection

Displacement data in Somalia was facilitated by the PRMN.55 The network collects weekly data on displaced populations in focal points such as IDP settlements, recording the origin and destination areas and one main reason for displacement as reported by households (e.g., flood, drought or conflict). Here, we focus on the years 2016−2023. To explore the factors that drive households to abandon their homes during droughts, we isolated on drought as the reported cause of displacement. Thanks to the database’s weekly sampling resolution, high spatial disaggregation and a stated reason for displacement, these data are suitable for detecting contextual push factors from drought displacement trends, enabling the tracking of the environmental and societal changes during drought events, unlike other human mobility panel databases.97 To characterize drought impacts, we calculated a semesterly SPEI index using high-resolution Earth observation variables, namely precipitation by the Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)52 and potential evapotranspiration data from ERA5-Land53 as input variables. To track food insecurity trends in Somalia, we used monthly market prices of livestock, staple food, and water from the Food Security and Nutrition Analysis Unit (FSNAU).54 These prices are monitored in local markets, and we use them to measure the capability of households to access food and deal with price shocks during rainfall deficits.22 Rural households are significantly affected by this, as they are forced to buy food when they cannot produce it, and when savings run out, they face famine and displacement.49 In addition, households may spend much of their wages on buying water during dry seasons,61 and in situations of scarce water availability, prices may become unaffordable, leading to displacement. Moreover, livestock price shocks caused by pasture depletion are also expected due to selling pressures to obtain resources for relocation or to the decline in livestock quality.102 When the number of livestock heads in a pastoralist household is reduced, their livelihood may become unsustainable and lead to displacement. Finally, we covered the conflict dimension using the data from the Armed Conflict Location & Event Data Project (ACLED).101 ACLED data include geolocated events every hour, reporting fatalities resulting from an event, which we use to measure the conflict’s intensity.27,103 Table 1 provides a summary of the variables and data sources used in this study.

Aggregation of displacement and conflict data

All data were aggregated at the district level (administrative division level 2) in Somalia, as humanitarians often report situations at this level. We aggregated them at the district level using boundary files provided by the United Nations Development Programme (UNDP).56 Other potential spatial aggregations, such as livelihood zone aggregation57 or regional aggregation (administrative division 1),43 could also be considered. Figure 2 illustrates the aggregated time series of three selected districts, each affected by drought-induced displacement to varying degrees. The differing profiles of the time series in the three districts suggest that displacement results from unique blends of factors and interactions, necessitating a district-level analysis to accommodate potential aggregation biases.

It is important to note, that we aimed to explain the short-term conditions that instigated drought displacement in each district. However, these displacements are noted upon arrival, potentially introducing a time-lag due to travel duration. This could influence the identified lags in the causal graphs. In response, we calculated the median arrival and departure dates from the Population Tracking Initiative (PMT) dataset (obtained through NDA from iDMC) to approximate the travel time to each region. We discovered that the median travel time for most districts is τ=0. This finding is corroborated by the observation that most recorded IDPs remain within their district of origin, moving from nearby rural to urban areas, to adjacent districts or the capital, and longer travel times are rare and not associated with high IDP numbers.

A critical step in our pipeline involved addressing missing values within the IDP time series. The PRMN methodology provides data on general trends and patterns of movements, along with key displacement events,104 yet some districts are systematically sampled. For instance, Baidoa’s IDP data have 18 out of 409 points as missing values or reporting zero displacement. Similarly, in Diinsoor, 21 out of 406 points report missing or zero displacement. However, in Kurtunwarey, 182 out of 407 points indicate missing or zero displacement. While the drought IDP time series display comparable patterns of high magnitude displacement waves for low values of SPEI across time (Figure 2), to account for potential unrecorded events, we applied a mask to points with zero displacement in the PCMCI algorithm. The conflict variable also had to be treated wherein we imputed zero values for missing points in the fatalities time series. This may introduce uncertainties, particularly in areas where reporting sources and media penetration are not accessible (i.e., remote rural areas or insecure areas). For this reason, we smoothed displacement (drought and conflict IDP) and Violent Conflict times series employing a 4-window moving sum (monthly window) to account for possible temporal uncertainties in the data.

Aggregation of earth observation and market prices data

To calculate the SPEI drought indicator, we extracted mean daily precipitation and potential evaporation values for 2000−2023, and employed the xclim python package.105 Finally, we spatially averaged the SPEI across all pixels within a district. We then computed a semesterly SPEI index for several reasons. Firstly, the correlation with drought displacement increases with the extension of the SPEI window, indicating an accumulated period significantly affect populations. Secondly, a six-month window effectively encompasses both the dry and wet seasons in Somalia, ensuring a comprehensive assessment of hydrological drought impacts. Lastly, selecting a semesterly window for SPEI prevents overfitting, achieving a balance between capturing seasonal variations and avoiding an excessively large window size, which could diminish the precision of drought impact assessments.

For market prices data, given that a district may host several markets, we chose the local market with the fewest missing data values from each district. We also considered several factors during the preprocessing of market prices. For example, the Somali Shilling exhibits a consistent pattern of devaluation over time. Therefore, we converted market prices to USD using district-specific SOS/USD exchange rate data in local markets. Moreover, we computed a linear detrending of prices across all districts to account for USD inflation. This conversion allows effective tracking of price shocks resulting from commodity shortages or the distressed sale of livestock. Subsequently, we sought to build a Consumer Minimum Basket Index (CMB), which accounts for changes in prices in a basic basket of goods for a Somali household, as detailed in.99 The methodology involves weighing prices based on household purchase data obtained via previously conducted FSNAU field surveys. The formula employed is:CMB=∑n=18Wn·Prn∑Wn,

where the index n represents a commodity (referenced in Figure S8, Prn indicates the price of the commodity, and Wn signifies its corresponding weight in the basket. We named this variable “Food Prices”. Following a similar method, we also compiled an index of livestock prices, which encompasses cattle, goats, and camels. In this instance, the weights were derived from the capital contribution of each type of livestock in South-Central Somalia, as shown in.100

Regarding temporal aggregation of prices, interpolation was required to reconcile the monthly time frame resolution with the weekly time resolution derived from displacement data. We used quadratic interpolation for an approximation of market price dynamics compared to linear interpolation. Although this method is subject to uncertainty and limitations, it does not hinder the objective of identifying the extreme fluctuations in market prices that influence food security and livestock trade. A potential downside may be the impact on determining the causal direction of links between time series due to the lack of precise time resolution.

Supplemental information

Document S1. Figures S1–S8

Acknowledgments

The authors thank IDMC, Ivana Hajzmanova, and Sylvain Ponsere for facilitating access to data and humanitarian actors in the country. The authors want to thank Albert Abou and Tessa Richardson from the IDP Working Group for the IDP data provided, and Rogerio Bonifacio, Oscar Caccavale, Joseph Hooker, and Giancarlo Pini from the World Food Program for their helpful comments on market price preprocessing. The authors also thank Abdoulaye Diallo and Abdulkadir Gure from the WASH cluster Somalia and Paolo Paron from FAO SWALIM for their insights and reporting about the water availability situation and data collection in Somalia. Funding: This work has received funding from the 10.13039/501100007601 European Union’s Horizon 2020 Research and Innovation Project “DeepCube: Explainable AI pipelines for big Copernicus data” (grant agreement no 101004188 ). GCV would like to acknowledge the support from the 10.13039/501100000781 European Research Council (ERC) under the ERC Synergy Grant USMILE (grant agreement 855187 ) and the Fundación BBVA with the project “Causal inference in the human-biosphere coupled system (SCALE)”.

Author contributions

J.M.T.: conceptualization, methodology, software, data curation, formal analysis, validation, visualization, writing. E.S-.M.: conceptualization, project administration, writing – review and editing. J.M-.M.: software, writing. M.P.: conceptualization, data curation, writing – review and editing, supervision, funding acquisition. V.S.: writing – review and editing. M.R.: methodology, writing – review and editing. M.T.M.: data curation, writing – review and editing. J.C.: visualization. G.C-.V.: conceptualization, methodology, supervision, writing, editing, funding acquisition.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2024.110628.
==== Refs
References

1 IDMC Global Report on Internal Displacement 2022 https://www.internal-displacement.org/publications/2022-global-report-on-internal-displacement-grid/
2 United Nations High Commissioner for Refugees (UNCHR) Climate change is the defining crisis of our time and it particularly impacts the displaced 2020 https://www.unhcr.org/uk/news/stories/climate-change-defining-crisis-our-time-and-it-particularly-impacts-displaced
3 United Nations Network on Migration Migration and Global Environmental Change: Future Challenges and Opportunities 2011 https://migrationnetwork.un.org/resources/migration-and-global-environmental-change-future-challenges-and-opportunities
4 Clement V. Rigaud K.K. de Sherbibin A. Jones B. Adamo S. Schewe J. Sadiq N. Shabahat E.Groundswell Part 2 : Acting on Internal Climate Migration 2021 World Bank http://hdl.handle.net/10986/36248
5 Desai B. Bresch D.N. Cazabat C. Hochrainer-Stigler S. Mechler R. Ponserre S. Schewe J. Addressing the human cost in a changing climate Science 372 2021 1284 1287 34140380
6 Jayawardhan S. Vulnerability and climate change induced human displacement Consilience 2017 103 142
7 Tucker J. Daoud M. Oates N. Few R. Conway D. Mtisi S. Matheson S. Social vulnerability in three high-poverty climate change hot spots: What does the climate change literature tell us? Reg. Environ. Change 15 2015 783 800
8 Winsemius H.C. Jongman B. Veldkamp T.I. Hallegatte S. Bangalore M. Ward P.J. Disaster risk, climate change, and poverty: Assessing the global exposure of poor people to floods and droughts Environ. Dev. Econ. 23 2018 328 348
9 Cottier F. Flahaux M.L. Ribot J. Seager R. Ssekajja G. Framing the frame: Cause and effect in climate-related migration World Dev. 158 2022 106016
10 Schewe J. Gosling S.N. Reyer C. Zhao F. Ciais P. Elliott J. Francois L. Huber V. Lotze H.K. Seneviratne S.I. State-of-the-art global models underestimate impacts from climate extremes Nat. Commun. 10 2019 1005 30824763
11 Lentz E.C. Maxwell D. How do information problems constrain anticipating, mitigating, and responding to crises? Int. J. Disaster Risk Reduct. 81 2022 103242
12 Thalheimer L. Simperingham E. Jjemba E.W. The role of anticipatory humanitarian action to reduce disaster displacement Environ. Res. Lett. 17 2022 014043
13 Hoegh-Guldberg O. Jacob D. Taylor M. Bindi M. Brown S. Camilloni I. Diedhiou A. Djalante R. Ebi K.L. Engelbrecht F. Impacts of 1.5ºC Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. 2018 2018
14 Hunter L.M. Luna J.K. Norton R.M. Environmental dimensions of migration Annu. Rev. Sociol. 41 2015 377 397 29861536
15 Hoffmann R. Šedová B. Vinke K. Improving the evidence base: A methodological review of the quantitative climate migration literature Global Environ. Change 71 2021 102367
16 Black R. Adger W.N. Arnell N.W. Dercon S. Geddes A. Thomas D. The effect of environmental change on human migration Global Environ. Change 21 2011 S3 S11
17 Abel G.J. Brottrager M. Crespo Cuaresma J. Muttarak R. Climate, conflict and forced migration Glob. Environ. Change 54 2019 239 249
18 Altschul J.H. Kintigh K.W. Aldenderfer M. Alonzi E. Armit I. Barceló J.A. Beekman C.S. Bickle P. Bird D.W. Ingram S.E. To understand how migrations affect human securities, look to the past Proc. Natl. Acad. Sci. USA 117 2020 20342 20345 32759208
19 Boult V.L. Black E. Saado Abdillahi H. Bailey M. Harris C. Kilavi M. Kniveton D. MacLeod D. Mwangi E. Otieno G. Towards drought impact-based forecasting in a multi-hazard context Climate Risk Management 35 2022 100402
20 Funk C. Harrison L. Shukla S. Pomposi C. Galu G. Korecha D. Husak G. Magadzire T. Davenport F. Hillbruner C. Examining the role of unusually warm indo-pacific sea surface temperatures in recent african droughts Q. J. R. Meteorol. Soc. 144 2018 360 383
21 Owain E.L. Maslin M.A. Assessing the relative contribution of economic, political and environmental factors on past conflict and the displacement of people in east africa Palgrave Commun. 4 2018 47
22 Coughlan de Pere E. van Aalst M. Choularton R. van den Hurk B. Mason S. Nissan H. Schwager S. From rain to famine: assessing the utility of rainfall observations and seasonal forecasts to anticipate food insecurity in East Africa Food Secur 11 2019 57 68
23 Feeny E. From Early Warning to Early Action in Somalia: What Can We Learn to Support Early Action to Mitigate Humanitarian Crises? 2017 Oxfam https://policy-practice.oxfam.org/resources/from-early-warning-to-early-action-in-somalia-what-can-we-learn-to-support-earl-620345/
24 Shyrokaya A. Pappenberger F. Pechlivanidis I. Messori G. Khatami S. Mazzoleni M. Di Baldassarre G. Advances and gaps in the science and practice of impact-based forecasting of droughts WIREs Water 11 2024 e1698
25 IFPRI Global Food Policy Report Chapter 5: Integrating Displaced Communities into Food Systems https://www.ifpri.org/cdmref/p15738coll2/id/133648/filename/133859.pdf 2020
26 Hermans K. Garbe L. Droughts, livelihoods, and human migration in northern ethiopia Reg. Environ. Change 19 2019 1101 1111
27 Thalheimer L. Schwarz M. Pretis F. Large weather and conflict effects on internal displacement in somalia with little evidence of feedback onto conflict SSRN J. 79 2023
28 Selby J. Dahi O.S. Fröhlich C. Hulme M. Climate change and the Syrian civil war revisited Polit. Geogr. 60 2017 232 244
29 Kamta F.N. Schilling J. Scheffran J. Insecurity, resource scarcity, and migration to camps of internally displaced persons in northeast nigeria Sustainability 12 2020 6830
30 Seter H. Theisen O.M. Schilling J. All about water and land? resource-related conflicts in east and west africa revisited Geojournal 83 2018 169 187
31 Linke M. Witmer F.D. O’Loughlin J. McCabe J.T. The consequences of relocating in response to drought: Human mobility and conflict in contemporary kenya Environ. Res. Lett. 13 2018 094014
32 Ginnetti J. Franck T. Assessing drought displacement risk for Kenyan, Ethiopian and Somali pastoralists 2014 https://www.internal-displacement.org/publications/assessing-drought-displacement-risk-for-kenyan-ethiopian-and-somali-pastoralists;
33 Thober J. Schwarz N. Hermans K. Agent-based modeling of environment-migration linkages: a review Ecol. Soc. 23 2018
34 Nelson E.L. Khan S.A. Thorve S. Greenough P.G. Modeling pastoralist movement in response to environmental variables and conflict in Somaliland: Combining agent-based modeling and geospatial data PLoS One 15 2020 e0244185
35 Rigaud K. de Sherbinin A. Jones B. Bergmann J. Clement V. Ober K. Schewe J. Adamo McCusker B. Heuser S. Groundswell: Preparing for Internal Climate Migration 2018 World Bank Washington, DC
36 Isaacman S. Frias-Martinez V. Frias-Martinez E. Modeling human migration patterns during drought conditions in La Guajira, Colombia 2018 10.1145/3209811.3209861
37 Beyer R.M. Schewe J. Lotze-Campen H. Gravity models do not explain, and cannot predict, international migration dynamics Humanit. Soc. Sci. Commun. 9 2022 56
38 Gemenne F. Why the numbers don’t add up: a review of estimates and predictions of people displaced by environmental changes Glob. Environ. Change 21 2011 S41 S49
39 Boas I. Farbotko C. Adams H. Sterly H. Bush S. van der Geest K. Wiegel H. Ashraf H. Baldwin A. Bettini G. Climate migration myths Nat. Clim. Chang. 9 2019 901 903
40 Ronco M. Tárraga J.M. Muñoz J. Piles M. Marco E.S. Wang Q. Espinosa M.T.M. Ponserre S. Camps-Valls G. Exploring interactions between socioeconomic context and natural hazards on human population displacement Nat. Commun. 14 2023 8004 38049446
41 Drees L. Liehr S. Using Bayesian belief networks to analyse social-ecological conditions for migration in the Sahel Glob. Environ. Change 35 2015 323 339
42 Suleimenova D. Bell D. Groen D. A generalized simulation development approach for predicting refugee destinations Sci. Rep. 7 2017 13377 29042598
43 UNCHR Project Jetson https://jetson.unhcr.org/ 2019
44 Mueller V. Gray C. Kosec K. Heat stress increases long-term human migration in rural Pakistan Nat. Clim. Chang. 4 2014 182 185 25132865
45 Pearl J. Causality: Models, Reasoning, and Inference 19 2000 41 61 Cambridge University Press
46 Peters J. Janzing D. Schölkopf B. Elements of Causal Inference: Foundations and Learning Algorithms 2017 The MIT Press 33 39
47 UNDRR GAR Special Report on Drought https://www.undrr.org/media/49386/download 2021
48 IDMC Monitoring Methodology: For displacement associated with drought https://www.internal-displacement.org/sites/default/files/publications/documents/202001-Drought%20displacement.pdf 2020
49 McPeak J.G. Little P.D. Doss C.R. Risk and Social Change in an African Rural Economy: Livelihoods in Pastoralist Communities 2011 Routledge London
50 Thalheimer L. Williams D.S. van der Geest K. Otto F.E.L. Advancing the evidence base of future warming impacts on human mobility in african drylands Earth’s Future 9 2021 e2020EF001958
51 Maystadt J.F. Ecker O. Extreme Weather and Civil War: Does Drought Fuel Conflict in Somalia through Livestock Price Shocks? Am. J. Agric. Econ. 96 2014 1157 1182
52 Funk C. Peterson P. Landsfeld M. Pedreros D. Verdin J. Shukla S. Husak G. Rowland J. Harrison L. Hoell A. Michaelsen J. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes Sci. Data 2 2015 150066 26646728
53 Muñoz-Sabater J. Dutra E. Agustí-Panareda A. Albergel C. Arduini G. Balsamo G. Boussetta S. Choulga M. Harrigan S. Hersbach H. Era5-land: A state-of-the-art global reanalysis dataset for land applications Earth Syst. Sci. Data 13 2021 4349 4383
54 FSNAU Food Security and Nutrition Analysis Unit (FSNAU) https://dashboard.fsnau.org/ 2021
55 PRMN Somalia Protection and Return Monitoring Network (PRMN) Somalia https://unhcr.github.io/dataviz-somalia-prmn/index.html 2024
56 UNDP Somalia - Subnational Administrative Boundaries https://data.humdata.org/dataset/cod-ab-som 2024
57 Famine Early Warning SYstems Network (FEWS NET) Data Center https://fews.net/ 2024
58 Nogueira A.R. Pugnana A. Ruggieri S. Pedreschi D. Gama J. Methods and tools for causal discovery and causal inference WIREs Data Min. Knowl. 12 2022 e1449
59 Runge J. Bathiany S. Bollt E. Camps-Valls G. Coumou D. Deyle E. Glymour C. Kretschmer M. Mahecha M.D. Muñoz-Marí J. Inferring causation from time series in Earth system sciences Nat. Commun. 10 2019 2553 31201306
60 IDMC No land, no water, no pasture: The urbanization of drought displacement in Somalia https://www.internal-displacement.org/sites/default/files/publications/documents/202003-somalia-slow-onset.pdf 2020
61 Brian S. Household Economy Analysis - Baseline Assessment for Building Resilience https://www.humanitarianresponse.info/sites/www.humanitarianresponse.info/files/2019/09/Oxfam-HEA-study-report.pdf 2019
62 Barrow I.H. Inter-Clan Conflicts in Somalia: When Peace Happen (Case Study Baidoa District, Bay Region) Int. J. Hum. Resour. Stud. 10 09 2020 1
63 Anderson W. Taylor C. McDermid S. Ilboudo-Nébié E. Seager R. Schlenker W. Cottier F. de Sherbinin A. Mendeloff D. Markey K. Violent conflict exacerbated drought-related food insecurity between 2009 and 2019 in sub-saharan africa Nat. Food 2 2021 603 615 37118167
64 Cattaneo C. Beine M. Fröhlich C.J. Kniveton D. Martinez-Zarzoso I. Mastrorillo M. Millock K. Piguet E. Schraven B. Human migration in the era of climate change Rev. Environ. Econ. Policy 13 2019 189 206
65 Koubi V. Climate change and conflict Annu. Rev. Polit. Sci. 22 2019 343 360
66 Krishnamurthy P.K. Choularton R.J. Kareiva P. Dealing with uncertainty in famine predictions: How complex events affect food security early warning skill in the greater horn of africa Global Food Secur. 26 2020 100374
67 Maxwell D. Khalif A. Hailey P. Checchi F. Viewpoint: Determining famine: Multi-dimensional analysis for the twenty-first century Food Pol. 92 2020 101832
68 Sen A. Poverty and Famines: An Essay on Entitlement and Deprivation 1982 Oxford university press 86 100
69 Barrett S. Steinbach D. Addison S. Assessing Vulnerabilities to Disaster Displacement: A Good Practice Review 2021 United Nations Office for Disaster Risk Reduction - Regional Office for Africa
70 Marzi S. Mysiak J. Essenfelder A.H. Pal J.S. Vernaccini L. Mistry M.N. Alfieri L. Poljansek K. Marin-Ferrer M. Vousdoukas M. Assessing future vulnerability and risk of humanitarian crises using climate change and population projections within the inform framework Global Environ. Change 71 2021 102393
71 Berlemann M. Steinhardt M.F. Climate Change, Natural Disasters, and Migration—a Survey of the Empirical Evidence CESifo Econ. Stud. 63 2017 353 385
72 Niva V. Kallio M. Muttarak R. Taka M. Varis O. Kummu M. Global migration is driven by the complex interplay between environmental and social factors Environ. Res. Lett. 16 2021 114019
73 UNDRR Global assessment report on disaster risk reduction https://www.undrr.org/publication/global-assessment-report-disaster-risk-reduction-2015 2015
74 Hoffmann R. Dimitrova A. Muttarak R. Crespo Cuaresma J. Peisker J. A meta-analysis of country-level studies on environmental change and migration Nat. Clim. Chang. 10 2020 904 912
75 Kasperson R.E. Kasperson J.X. Climate Change, Vulnerability and Social Justice 2001 Stockholm Environment Institute, Social Contours of Risk
76 Smit B. Wandel J. Adaptation, adaptive capacity and vulnerability Global Environ. Change 16 2006 282 292
77 Thomas K. Hardy R.D. Lazrus H. Mendez M. Orlove B. Rivera-Collazo I. Roberts J.T. Rockman M. Warner B.P. Winthrop R. Explaining differential vulnerability to climate change: A social science review Wiley Interdiscip. Rev. Clim. Change 10 2019 e565
78 Muttarak R. Vulnerability to climate change and adaptive capacity from a demographic perspective International Handbook of Population and Environment 10 2022 63 86
79 Peters J. Bühlmann P. Meinshausen N. Causal inference by using invariant prediction: Identification and confidence intervals J. Open Source Softw. 78 2016 947 1012
80 Spirtes P. Glymour C. Scheines R. Causation, Prediction, and Search 2nd edition 2000 MIT Press 103 127
81 Diaz E. Adsuara J.E. Martínez Á.M. Piles M. Camps-Valls G. Inferring causal relations from observational long-term carbon and water fluxes records Sci. Rep. 12 2022 1610 35102174
82 Reid A.T. Headley D.B. Mill R.D. Sanchez-Romero R. Uddin L.Q. Marinazzo D. Lurie D.J. Valdés-Sosa P.A. Hanson S.J. Biswal B.B. Advancing functional connectivity research from association to causation Nat. Neurosci. 22 2019 1751 1760 31611705
83 Marini M.M. Singer B. Causality in the social sciences Socio. Methodol. 18 1988 347 409
84 Hernán M.A. The C-word: scientific euphemisms do not improve causal inference from observational data Am. J. Public Health 108 2018 616 619 29565659
85 Hicks J. Causality in Economics 1980 Australian National University Press
86 Runge J. Gerhardus A. Varando G. Eyring V. Camps-Valls G. Causal inference for time series Nat. Rev. Earth Environ. 4 2023 487 505
87 Granger C.W.J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods Econometrica 37 1969 424 438
88 Sugihara G. May R. Ye H. Hsieh C.h. Deyle E. Fogarty M. Munch S. Detecting Causality in Complex Ecosystems Science 338 2012 496 500 22997134
89 Runge J. Nowack P. Kretschmer M. Flaxman S. Sejdinovic D. Detecting and quantifying causal associations in large nonlinear time series datasets Sci. Adv. 5 2019 eaau4996 31807692
90 Meinshausen N. Bühlmann P. Stability selection J. Roy. Stat. Soc. B 72 2010 417 473
91 Li S. Hsu L. Peng J. Wang P. Bootstrap inference for network construction with an application to a breast cancer microarray study Ann. Appl. Stat. 7 2013
92 Constantinou A.C. Guo Z. Kitson N.K. The impact of prior knowledge on causal structure learning Knowl. Inf. Syst. 65 2023 3385 3434
93 Hyttinen A. Hoyer P.O. Eberthardt F. Jarvisalo M. Discovering Cyclic Causal Models with Latent Variables: A General Sat-Based Procedure Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence 2013 301 310
94 Kretschmer M. Coumou D. Donges J.F. Runge J. Using causal effect networks to analyze different arctic drivers of midlatitude winter circulation J. Clim. 29 2016 4069 4081
95 Buhaug H. What is in a number? some reflections on disaster displacement modelling Int. Migrat. 61 2023 353 357
96 Thalheimer L. Oh W.S. An inventory tool to assess displacement data in the context of weather and climate-related events Climate Risk Management 40 2023 100509
97 IDMC Drought Displacement Modelling https://www.internal-displacement.org/publications/drought-displacement-modelling 2022
98 Vicente-Serrano S.M. Beguería S. López-Moreno J.I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index J. Clim. 23 2010 1696 1718
99 Fsnau Cost of Minimum Expenditure Basket (CMB) https://fsnau.org/downloads/FSNAU-CMB-CPI-for-Somalia.pdf 2011
100 ICPALD The Contribution of Livestock to the Somali Economy https://www.au-ibar.org/sites/default/files/2020-11/20160610_final_report_contribution_livestock_somalia_gdp_en.pdf 2015
101 Raleigh C. Linke A. Hegre H. Karlsen J. Introducing acled: An armed conflict location and event dataset: Special data feature J. Peace Res. 47 2010 651 660
102 ICRC Regional Livestock Study in the Great Horn of Africa https://www.icrc.org/en/doc/resources/documents/report/regional-livestock-study-great-horn-africa.htm 2005
103 Rubin O.D. Ihle R. Measuring Temporal Dimensions of the Intensity of Violent Political Conflict Soc Indic 132 2017 621 642
104 UNHCR Operational Data Portal (ODP) Protection and Return Monitoring Network (PRMN) - Notes on Methodology - UNHCR Somalia https://data2.unhcr.org/en/documents/details/53888 2017
105 Bourgault P. Huard D. Smith T.J. Logan T. Aoun A. Lavoie J. Dupuis É. Rondeau-Genesse G. Alegre R. Barnes C. xclim: xarray-based climate data analytics J. Open Source Softw. 8 2023 5415
