Overlapping disaster and conflict events reveal gaps in displacement reporting in the Philippines

Overlapping disaster and conflict events reveal gaps in displacement reporting in the Philippines | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Overlapping disaster and conflict events reveal gaps in displacement reporting in the Philippines Sheryn See, Aaron Opdyke, Susan Banki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7043272/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Disasters and conflict increasingly overlap in driving human displacement, yet humanitarian systems have traditionally reported displacement events in silos, leaving communities overlooked. Little is known about who is left out and how reporting gaps occur. We conduct a comparative analysis of data reporting in the Philippines, examining cases of the Marawi Siege and Severe Tropical Storm Tembin (Vinta) in 2017 and Severe Tropical Storm Nalgae (Paeng) in 2022. We present disparities in the frequency, temporal and spatial coverage of complex displacement reporting. Our results provide empirical evidence of exclusion in complex displacement reporting that arises from event-based reporting frameworks that prioritise discrete, single-hazard responses that overlook politically sensitive crises. We highlight that capturing displacement data is not only an accounting exercise – it constructs the way displacement is understood. We argue for more dynamic, multi-trigger displacement tracking and for the use of integrated displacement and conflict data in advancing anticipatory action. Scientific community and society/Geography Social science/Geography Earth and environmental sciences/Natural hazards Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Natural hazards cause greater harm in areas affected by armed conflict, where inequality, poor governance, and weakened systems heighten vulnerability (Siddiqi 2018 ; Blaikie et al. 2003 ; Caso, Hilhorst, and Mena 2023). In these fragile settings, conflict and disasters frequently overlap, compounding risks and leading to severe consequences (Mena and Hilhorst 2021; L. E. R. Peters 2021 ; IDMC 2024 ). While there is growing evidence linking climate change, conflict, and displacement (K. Peters et al. 2021), the ways that our data systems are constraining our understanding of overlapping crises has received only cursory attention. Insecure regions often come with inconsistent and unreliable displacement data, and the systems for collecting data on disasters are often severely limited or entirely absent in fragile contexts (SPARC 2024 ). Consequently, while it is well understood that hazards impact conflict-affected populations, there is limited data to comprehensively profile those experiencing overlapping crises. This has resulted in the unequal visibility of communities and distribution of aid, with those affected by both conflict and disasters potentially sidelined (ALNAP 2022 ; Siddiqi 2018 ). The humanitarian system has traditionally addressed climate-induced and conflict-induced displacement as separate challenges, reporting displacement data separately according to their immediate triggers (Weerasinghe 2021 ; IDMC and NRC 2015 ; Sánchez-Mojica 2020 ; Caso, Hilhorst, and Mena 2023; K. Peters et al. 2021). Yet, empirical evidence increasingly shows that disasters and conflicts often overlap, making this binary approach problematic (Walch 2018 ). It is not uncommon for conflict-affected populations in disaster-prone countries to be overlooked in favour of those affected solely by disasters (Siddiqi 2018 ). Despite general awareness of data gaps in disaster-conflict settings (K. Peters 2019 ; SPARC 2024 ), there has been limited systematic comparison of reporting practices between complex, overlapping crises and single-event disasters. Questions persist about where data shortfalls lie, how they impede effective responses, and how a more holistic data system might be developed. This study aims to assess the reporting discrepancies to identify current limitations and inform opportunities for reform. We ask: How does humanitarian data reporting differ between complex (involving both climate and conflict) and single-event displacement (climate)? We conducted a comparative case study to analyse the difference in data reporting between a complex displacement case (involving both disaster and conflict) and single-event displacement case (involving disaster only). Examining the case of the Philippines, we selected the Marawi Siege and Severe Tropical Storm Tembin (Vinta) in 2017 as our complex displacement scenario and Severe Tropical Storm Nalgae (Paeng) in 2022 as our single-event displacement scenario. Figure 1 presents a contextual map illustrating the geographic extent of each event. Paeng represents a recent example that took place when humanitarian actors were beginning to adopt proactive approaches to disaster responses through anticipatory action (Anticipation Hub 2021 ). In recent decades, anticipatory action has emerged in humanitarian responses to address disasters proactively, acting ahead of predicted hazards to prevent or minimise impacts on lives (ALNAP 2022 ; UNOCHA 2024b ). In contrast to early warning systems that alert communities to impending hazards, anticipatory action uses impact-based forecasting, which integrates data on exposure and vulnerability to predict hazard impacts on communities at risk and then acts on that data through the implementation of action measures, such as evacuations or cash assistance (WMO 2015 ). While anticipatory action is slowly emerging as a consideration in conflict-affected settings (Wagner and Simons 2025; Kjærum and Madsen 2025 ), the ability to forecast conflict outbreaks remains limited (Schillinger et al. 2025 ). As climate hazards intensify and complex displacement becomes more frequent (K. Peters and Dupar 2020; K. Peters et al. 2020 ), there is a need to understand the role of anticipatory action in addressing overlapping crises (Jaime et al. 2024 ; Chaves-Gonzalez et al. 2022 ). Furthermore, evidence on the implementation of anticipatory action in complex crises remains limited, especially since in fragile contexts where the delivery of regular humanitarian aid has already been constrained, there is limited opportunity to introduce new forms of aid (Easton-Calabria 2025 ; Kjærum and Madsen 2025 ). Therefore, while the focus of this study is on data reporting practices, we use the Paeng case to explore how pre-emptive humanitarian interventions can minimise disaster impacts during overlapping crises. We drew on a combination of grey literature comprising reports produced by government and humanitarian organisations and semi-structured interviews to examine how displacement data were collected and reported in both cases. The Disaster Response Operations Management, Information and Communication (DROMIC) reports published by the Philippine Department of Social Welfare and Development (DSWD) serve as the primary data source for comparative analysis. Thirty-two semi-structured interviews with humanitarian practitioners were used to complement the analysis by offering context from the ground (Adams 2015 ). The combination of these data sources was intended to capture both the quantitative dimensions of displacement reporting and the qualitative insights needed to contextualise these reporting practices. 2 Results This section presents the results from our comparative analysis. We arranged our findings according to four descriptive themes: reporting structure and consistency; spatial variation of disaster internally displaced person (IDP) data in conflict-impacted areas; visibility of conflict IDPs amidst political dynamics; and the evolution from reactive to predictive data reporting. 2.1 A shift from patchy to structured reporting We compare how displacement was reported across our two cases, revealing important shifts and inconsistencies in how displacement was recorded. Figure 2 presents the number of IDPs triggered by Case 1 – Marawi Siege and Severe Tropical Storm Tembin (Vinta) and Case 2 – Severe Tropical Storm Nalgae (Paeng), based on data extracted from DROMIC reports. Each data point represents the total number of IDPs at the time of reporting. The DSWD began releasing preparedness reports for Vinta on 20 December 2017, coinciding with the storm’s entry into the Philippine Area of Responsibility (PAR). Regular situation reports followed on 22 December 2017, the day Vinta made landfall. Daily reports were issued for approximately one month following the storm’s landfall, after which updates became more sporadic, continuing intermittently for up to three months. Reporting throughout the Vinta event was marked by inconsistencies in geographical scale and resolution. While most of the reports included municipality-level data for IDPs outside evacuation centres, IDPs residing within evacuation centres were only recorded at the provincial level. Some reports presented only region-level figures, making it challenging to construct a detailed and consistent picture of displacement across affected areas. For the case of Paeng, DSWD started releasing preparedness reports on 24 October 2022, two days before the Severe Tropical Storm entered the PAR. Regular situation reports containing displacement figures followed on 26 October 2022, coinciding with the storm’s entry into PAR two days before its first landfall. This marked an earlier reporting timeline compared to Vinta. Reporting for Paeng was also both more frequent and consistent. Daily reports continued for nearly two months following the storm’s landfall, with additional updates extending up to eleven months afterwards, providing a longer temporal window to monitor protracted displacement. Reporting for Paeng was also notably more consistent than Vinta, with IDPs both inside and outside evacuation centres reported down to the municipal level in nearly every report, providing a more granular picture of the evolving displacement situation. When reviewing the documentation of IDP returns, Vinta’s reporting revealed limited and uneven documentation of returns. The earliest indication of returns was reported in the Caraga Region, citing “the remaining families who are still staying inside evacuation centres will probably return to their respective houses by today December 24, 2017”. A more definitive return estimate was officially reported on 26 December 2017 (four days after landfall) in Region XI: “More and more families have exited the Evacuation Centers as Internally Displaced families from the Municipalities of Montevista, Nabunturan, Pantukan, Compostela, and Mabini of Compostela Valley Province have returned to their homes”. At the end of Vinta’s reporting period, approximately 16% of the peak recorded IDPs were still displaced, suggesting a notable degree of protracted displacement. In the case of Paeng, there were evident staggered patterns in the displacement figures following the peak of IDPs recorded on 4 November 2022, likely reflecting the systematic and progressive return of displaced populations. Unlike Vinta, the Paeng reports were more explicit in tracking returns, with the first instance documented on 2 November 2022 (five days after the first landfall) in Region VIII (Eastern Visayas), specifically among those who had been pre-emptively evacuated. This return was recorded even before peak displacement figures appeared in the reports. By the end of the reporting period, less than 1% of IDPs (relative to the peak recorded) remained displaced. When comparing protracted displacement at the end of the reporting period, it is important to note that Paeng had a much longer reporting period (eleven months) than Vinta (three months), limiting the validity of a direct, like-for-like comparison. However, examining both events at the three-month mark after landfall still reveals notable differences, with approximately 16% of Vinta’s peak IDP population remaining displaced, compared to only 5% of Paeng’s peak IDP population at the same stage of reporting. This contrast suggests important differences in return trajectories and long-term displacement outcomes between the two events. From a data perspective, Paeng appears to have been supported by a more structured reporting system overall – one that may not only have reflected, but also contributed to, a more coordinated response. The combination of a more extended reporting period, consistent high-frequency updates, and more granular data resolution likely facilitated more accurate monitoring of displacement patterns. Although multiple exogenous factors may have influenced the lower rate of protracted displacement, improved data systems may have contributed to a more informed and coordinated response in the case of Paeng. 2.2 Disaster displacement data is less captured in post-conflict areas Spatial analysis of province-level IDP figures for Vinta revealed patterns potentially influenced by the preceding Marawi Siege. Figure 3 presents the reported IDPs per 100,000 population during Vinta, based on cumulative IDP data from the DROMIC report as of 17 January 2018. The figures include IDPs both inside and outside evacuation centres and are normalised against respective provincial population data from the 2015 census data (Philippine Statistics Authority 2016 ). The map also traces the track of Vinta and highlights geographical variation in displacement reporting across the southern Philippines. The province of Lanao del Sur (depicted in a yellow outline polygon in Fig. 3 ) – where the Marawi Siege took place just months earlier – lay directly in the path of Vinta. As one interviewee recalled: “[Marawi IDPs] were also displaced by the typhoons, particularly those IDPs who stayed in the municipalities of Madalum and Tugaya. These are municipalities in Lanao del Sur.” Despite this direct exposure, Lanao del Sur recorded the lowest reported displacement rate throughout the region, with only 98 IDPs per 100,000 population. This figure stands in sharp contrast to the reported displacement rate in adjacent provinces – Lanao del Norte (6,564), Misamis Oriental (2,149), and Bukidnon (1,407). The highest rate was observed in Davao del Norte, with 12,261 IDPs per 100,000 people. A broader examination across the southern Philippines also shows both Lanao del Sur and Maguindanao del Norte – both part of BARMM (known as ARMM) – reported lower displacement rates compared to directly adjacent provinces that experienced similar levels of storm impact. These discrepancies suggest potential underreporting in these post-conflict areas, raising questions about the visibility of displaced communities in areas where institutional capacity, access, and security may influence data collection and reporting during disasters. Our interviews with humanitarian practitioners revealed anecdotal accounts of Marawi IDPs who were affected by Vinta, though the overlapping complexities of their experiences were not captured: “In December 2017, there was also Vinta typhoon, where the displaced Marawi residents were also affected, were also displaced at the time of their displacement.” According to a local worker, humanitarian access also played a role in shaping who was seen and supported: “During the Marawi Siege then followed by Typhoon Vinta … those who took refuge in more interior municipalities and that are also conflict areas, we actually tried to reach out to those as they don't easily receive support and assistance.” With data often used to guide aid distribution, underreporting can have significant implications, resulting in humanitarian responses being deprioritised in areas that are already vulnerable and underserved. 2.3 Who counts as displaced and when? Before the arrival of Severe Tropical Storm Vinta in December 2017, there was an existing wave of displacement from the Marawi Siege, which started seven months prior to the storm. Anomalies in the reporting of Marawi IDPs prompted closer examination of how IDP figures evolved, especially during the period of overlap with Vinta, revealing potential gaps in how concurrent displacement events are captured in the data. There was a sudden “drop” in the reported number of Marawi IDPs around July 2017 (approximately two months after the onset of the conflict event), after which the displacement figures remained stagnant. This “decline” stands out given that the Siege continued through to October 2017. This drop coincided with a shift in the collection of displacement data using the Disaster Assistance Family Access Cards (DAFAC) issued by the DSWD, which is the primary instrument used by the Philippine government for identifying and tracking beneficiaries and services after disasters (Fernandez 2019 ). While the DAFAC system is intended to standardise beneficiary tracking, access to such mechanisms is often influenced by local political dynamics. As a participant from an international organisation observed: In the Philippines … political parties or patronage politics is highly practised. If you transfer from one village to another, this host village will not necessarily provide support – sustained support, especially if you are not a registered voter in that community because you belong to the community where you previously lived. That's why you need to go back …to avail services … those displaced families that have relocated in that municipality will not be provided any support from the municipality, but they'll receive support from the national government, from the Department of Social Welfare and Development. A local practitioner reflected: “Unfortunately in the Philippine context, even relief goods have political colour … Sometimes the basis of release of these goods are on the basis of whether or not that is their political ally or that is their supporters.” As DROMIC reports prior to the sudden “drop” made no mention of DAFAC, the adoption of this system likely excluded some IDP populations from the remaining reporting period. These observations are also echoed in a report by UNOCHA ( 2017 ), which cited reports of displaced people excluded from the DAFAC registration process, further validating the risk of politically influenced exclusion in data collection. There was also a noticeable gap in the reporting of Marawi IDPs during Vinta’s impact from December 2017 to January 2018. During this time, Marawi IDP data effectively halted as reporting focused on displacement associated with Vinta. This reflects the current single-event reporting system, which tends to isolate and attribute IDPs according to specific trigger events. Although the Marawi IDPs remained displaced, attention had been shifted to the more recent displacement event. In effect, the occurrence of the second crisis rendered the earlier displacement invisible within the data. It is also plausible that some Marawi IDPs were “reclassified” under the Vinta event, given that displacement data is often linked to the most recent trigger, noted by a practitioner: “The data that are made available, usually it's really the recent triggers that this displacement is being linked to.” This labelling practice, combined with the traditionally disaggregated nature of displacement data, ends up disregarding the lingering impacts of previous conflict. It provides little insight into whether the reported figures include individuals already displaced by prior crises. As a UN staff member pointed out, despite their local experience indicating that the same populations are repeatedly affected, the lack of clear data makes it difficult to confirm: “What we haven't received yet is the breakdown of data [showing] that these are the same areas that are affected by flooding that they reported are also affected by conflict. It’s really a challenge … because it's the typical cycle, it's a repeated displacement for Maguindanao and some parts of North Cotabato … It's like an assumption because we would see the same areas, but we haven't really had the clarity yet to identify these are the same people.” As a result, IDPs’ compounded vulnerabilities and previous conflict experiences were neglected, leading to gaps in responses and a failure to fully address the complex realities of the IDPs’ situation. An international organisation staff member stressed the importance of identifying overlapping displacement events to corroborate what they had witnessed firsthand: “It would be a very strategic study if we're able to identify overlapping emergencies conflict and natural disaster, and we can actually do that in areas plagued with conflict like in the BARMM region of Mindanao. Nobody has done that. But we're aware that it's happening, especially if the conflict happens on the Monsoon season, that's already an indicator that people will be displaced due to overlapping emergencies.” As revealed in our interviews, data collection practices are further influenced by a broader tendency within the Philippines’ humanitarian system to prioritise climate-induced disasters over conflict events. A local respondent in Mindanao reflected, “Usually the humanitarian organisations – the way I'm seeing it in the Philippines – are more used to responses to humanitarian crises that are [resulting from] natural calamities, but not really on conflict.” This tendency is also reflected in funding preferences during overlapping crises, noted by another interviewee from an international organisation, “When [events] overlap, [the way displacement is labelled] is often related to the disaster … [As] humanitarian organisations, when we label it based on the disaster, we can appeal for more support..” This data collection practice appears to have been shaped by funding and administrative considerations, which may distort the data informing humanitarian decisions. 2.4 Data evolving from reactive to predictive One of the most notable improvements to Paeng’s response was the implementation of anticipatory action before the storm. Preparedness reports for Paeng included a section on “predictive analysis for humanitarian response”, a component absent from Vinta’s reporting. This analysis identified regions expected to receive at least 100mm of accumulated rainfall over the next 72 hours. The analysis also includes the estimated population exposed and the number of family food packs required in advance of the impact. Paeng also marked one of the pilot cases of anticipatory action implementation for shelter-based early interventions. Based on forecast monitoring of Paeng’s projected path, anticipated shelter damage, and available lead time, the Philippine Red Cross (PRC) mobilised staff and volunteers to distribute and install shelter strengthening kits in the Municipality of Dipaculao on 29 October 2022 (a few hours before the storm’s nearest landfall in the area). Across two barangays in Dipaculao, 78 households – which were pre-identified as high-risk – received and installed the kits, with post-disaster surveys indicating that the reinforced shelters withstood the storm conditions effectively and helped mitigate the impacts of Paeng’s strong winds (IFRC 2023 ). Unlike reactive responses triggered by observed impacts, anticipatory action uses forecasts and pre-agreed thresholds to mobilise aid before a shock occurs (Chaves-Gonzalez et al. 2022 ). Forecast-based financing (FbF) automatically releases funds when a forecast threshold is reached, guided by an early action protocol (EAP) that outlines pre-defined triggers and early actions to be taken (Thalheimer, Simperingham, and Jjemba 2022). As noted, the implementation of the early action protocol (EAP) in Dipaculao marked a significant milestone for the Philippines, being the first instance of EAP being triggered and forecast-based financing (FbF) being utilised in the country. While the number of households reached was small compared to the overall affected population, the success of this early intervention demonstrated the potential of forecast-based action in reducing disaster impacts. While both Vinta and Paeng involved government standby funds and pre-positioned relief stockpiles as part of their response measures, these actions are generally considered part of the traditional disaster risk management rather than anticipatory action. In the case of Vinta, response measures were still largely reactive, based on the existing pre-positioning of resources, and lacked forward-looking components. Paeng was differentiated by the emergence of forecast-informed early action, which consisted of interventions that were explicitly triggered by pre-defined thresholds and linked to specific early response activities. The shift toward more proactive humanitarian action reflects a reporting approach where data are not only used to document what has occurred, but to inform and trigger early interventions before impacts escalate. Essentially, our comparison of Vinta and Paeng illustrates a shift in data from being a static record of past events to a dynamic tool that can shape anticipatory decision-making. 3 Discussion Our analysis of these two case studies offers clear empirical evidence of exclusion during the reporting of complex displacement. Those who are captured in the data are often those who fall within the institutional frameworks of visibility and recognition, while others remain undocumented and overlooked. We urge a rethinking of forecast-based models and triggers within anticipatory action to fully capture and respond to these overlapping events. 3.1 Integrating disaster-conflict displacement data Overlaying data from Marawi and Vinta reveals a broader trend within humanitarian data systems: the heavy reliance on event-based frameworks and administrative considerations that are often shaped by political dynamics, such as eligibility requirements tied to voter registration or local government recognition. National displacement reporting by DSWD typically relies on data submitted by barangay and municipal offices, which are not free from biases regarding who is included and excluded (Fernandez 2019 ). The reporting gap due to the shift to DAFAC-based registration, as well as spatial gap in Vinta IDP numbers in the Province of Lanao del Sur where the Marawi Siege took place (presented in Fig. 3 ) reflects how reporting protocols, funding preferences, and political dynamics determine what gets recorded and consequently, who is recognised as displaced. As Crisp ( 1999 ) argues in his analysis of refugee statistics, data reporting in humanitarian contexts is rarely neutral; it is entangled with politics, institutional mandates, and strategic priorities. The siloed way of documenting displacement – separating data into either conflict or disaster-induced events – directly influences how displacement is documented, interpreted, and ultimately understood. As a result, we find in our analysis that individuals who experience overlapping displacement are at risk of being underreported. These issues extend beyond the technical processes of data collection but are rather rooted in epistemic practices that influence the way we construct and understand complex displacement. This fragmentation has serious practical implications for how the humanitarian sector makes decisions. To address this, the humanitarian system must move toward a more holistic displacement tracking framework to capture displacement across multiple conflict-related and climate-related triggers over extended timeframes. This is not merely a matter of improving data reporting. Instead, it requires a fundamental shift in how displacement is conceptualised and documented. While Paeng was relatively well documented, its reporting still treated the event as a standalone disaster. For instance, the BARMM was also heavily impacted by Paeng in October 2022 (OCHA 2022 ), yet reports made no reference to the region’s existing population of over 80,000 protractedly displaced individuals from the Marawi Siege (UNHCR Philippines 2022 ). The humanitarian system must also move beyond static, one-time counts of displaced populations. For instance, if during Vinta, the data had been distinguished between individuals newly displaced by the storm and those already displaced by the Marawi conflict, the resulting figures would have provided a more representative picture of displacement. During our interviews, several participants noted that populations affected by both events received limited support. More broadly, one participant reflected on the recurring challenge of overlapping disasters in the Philippines: “Because of the multiple disasters, sometimes we forget the previous disasters. So the focus will be moved to the bigger one until such time these people will just recover on their own.” There is a need for dynamic data systems that can document initial displacement, secondary or overlapping displacement, and long-term, protracted cases, all within the same reporting cycle, thus supporting more equitable resource allocation and ensuring the inclusion of those experiencing complex, overlapping displacement 3.2 Rethinking forecast-based models and triggers Anticipatory action has gained increasing traction in recent years (ALNAP 2022 ), allowing humanitarian organisations to act before a disaster strikes based on forecasts or predictive analysis (UNOCHA 2024b ; Chaves-Gonzalez et al. 2022 ). Forecast-based thresholds such as projected wind speed, accumulated rainfall over a 72-hour period, or anticipated percentage of housing damage typically serve as the standard triggers for early action. However, applying uniform thresholds across all areas assumes that communities share similar levels of exposure and vulnerability when, in reality, the impacts of disasters are unevenly distributed (Choong et al. 2025 ; Long and Duan 2025; Qiao et al. 2024 ; Soden et al. 2023 ). Communities still recovering from previous disasters, or those experiencing protracted displacement, may be far more susceptible to the same hazard than populations encountering a single event in isolation (de Ruiter and van Loon 2022 ). Moreover, while anticipatory action’s forward-looking nature aims to reduce disaster impacts pre-emptively, its forecast models and implementation are still rooted in precedent. Anticipatory action models are typically developed using historical datasets such as climate, agro-meteorological, vulnerability, exposure, damage or loss data to identify the timing, location, and severity of previous shocks (Chaves-Gonzalez et al. 2022 ). When these data are unavailable, incomplete, or lack sufficient granularity, it becomes challenging to determine what qualifies as an “out-of-the-ordinary” event and the threshold for action (OCHA Centre for Humanitarian Data 2022 ). This is especially apparent in countries affected by conflict, as data gaps such as limited observational weather stations undermine the accuracy of historical weather records and the ability to forecast future events, making the implementation of anticipatory action challenging (Easton-Calabria 2025 ; Schultz and Mankin 2019 ). At the implementation level, the latest framework for tropical cyclones in the Philippines states that the anticipatory framework “focuses on the areas frequently affected regions of Bicol, Eastern Visayas and Caraga” (UNOCHA 2024a ). While this approach provides a practical basis for targeting resources, it also means that anticipatory initiatives are biased towards areas that have been previously impacted and have sufficient historical data. As a result, new and emerging hotspots may be overlooked, particularly as climate change introduces greater unpredictability in weather patterns (IPCC 2023 ). Several participants reflected on this growing challenge in our interviews, with one noting: “It has become increasingly difficult because of climate change primarily because it does not affect the same areas anymore as it previously did.” For instance, Typhoon Rai (Odette) in 2021 was frequently cited by participants as an example of how climate change is intensifying weather patterns. As reported in UNOCHA’s Humanitarian Needs and Priorities Plan (2022b), “contrary to predictions, Rai intensified from a tropical storm to a super typhoon within hours before making landfall.” Furthermore, “while storms typically make landfall in the southern parts of Luzon or the eastern part of the Visayas, Rai (Odette) struck regions further south, which do not typically experience the brunt of typhoons.” A further blind spot lies in the absence of displacement data within forecast models. To date, there has been limited attention within anticipatory initiatives to address people who have already been displaced (Easton-Calabria et al. 2024 ). Current anticipatory action triggers in the Philippines do not account for recent or ongoing displacement, nor integrate spatial conflict footprints such as areas affected by past or ongoing armed violence or insecurity. Yet these factors are critical in anticipating where humanitarian needs are crucial. Overlaying displacement history and conflict footprints with hazard forecasts could enable more nuanced triggering mechanisms. In contexts where populations are already displaced, aid could be scaled earlier or targeted more precisely. With this, the system is not only responding to forecasted hazard intensity, but also in recognition of compounded vulnerability (Schillinger et al. 2025 ; Start Network 2023 ). 4 Methods This section details our case selection criteria, case description, data collection and analysis. We combined grey literature comprising reports produced by government and humanitarian organisations and semi-structured interviews to examine how displacement data was collected and reported in both cases. Our comparative analysis relied on cross-case synthesis following Yin’s (2009) analytic approach. We first examined each case independently using time-series plots, a spatial map, and qualitative interview insights to identify within-case patterns, before moving to cross-case comparison and synthesis. Ethics approval for this research was granted by the Human Ethics Research Committee at the University of [redacted for peer review]. 4.1 Case selection criteria We employed a case study selection process guided by pre-defined criteria. Our criteria sought to ensure a robust comparison between complex displacement scenarios (where disaster-induced and conflict-induced displacement overlap) and single-event displacement scenarios (displacement induced by disaster) in the Philippines. We aimed to identify one historical case representative of each scenario. The guiding selection criteria included: 1) Displacement scenarios – One case must have displacement induced by overlapping conflict events and hazards, while the other case must have displacement induced by hazards only. 2) Hazard intensity – The cases should involve hazards of similar intensity, as indicated by metrics such as the “peak intensity within the Philippine Area of Responsibility (PAR)” as reported by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA). 3) Extent of damage – The hazard of each case should involve comparable levels of infrastructure damage. 4) Displacement figures – The number of displaced individuals should be comparable across cases. 5) Scale of humanitarian responses – The cases should have received similar levels of humanitarian intervention (e.g., both cases triggering international assistance or neither) to control for differences in aid provision. 6) Data availability – The cases should have sufficient and reliable data on humanitarian responses from sources such as Disaster Response Operations Management, Information and Communication (DROMIC) reports, the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), and the International Federation of Red Cross and Red Crescent Societies (IFRC) to ensure a robust and evidence-based analysis. We selected the Marawi Siege and Severe Tropical Storm Tembin (Vinta) as our complex displacement scenario and Severe Tropical Storm Nalgae (Paeng) as our single-event displacement scenario. The case of the Marawi Siege and Vinta in December 2017 is relevant as the storm occurred just two months after the end of the conflict event. It illustrates how communities displaced by conflict can be subsequently affected by natural hazards, with the storm directly impacting communities already displaced by the conflict, further straining humanitarian response efforts (Fernandez 2019). Examining this case offers insights into the challenges of responding to the overlap of disaster-induced and conflict-induced displacement and the complexities of data reporting in such contexts. Severe Tropical Storm Nalgae (Paeng) in October 2022 represents a more recent example of a single-event displacement scenario. It occurred at a time when humanitarian responses were beginning to adapt more proactive approaches, particularly through the implementation of anticipatory action. As climate-related hazards intensify, cases of complex displacement are likely to become more frequent (K. Peters and Dupar 2020; K. Peters et al. 2020). It is becoming pressing to understand the potential of anticipatory actions in addressing overlapping crises (Jaime et al. 2024; Chaves-Gonzalez et al. 2022). To the best of our knowledge, no complex displacement case in the Philippines has coincided with the implementation of anticipatory action. Therefore, while the primary focus of this comparative analysis is on differences in data reporting, Paeng also offers a useful contrast to illustrate the evolution from a reactive response during Vinta to a more proactive approach in Paeng, where the system was equipped – at least in theory – with tools to anticipate and act before impact. With that, we explore how pre-emptive humanitarian interventions can minimise disaster impacts during overlapping crises. Table 1 provides a summary of both case studies, while Figure 1 presents a contextual map illustrating the geographic extent of each event. We acknowledge that these two real-world cases may not have identical characteristics regarding reported housing damage, displacement numbers, and aid allocations. However, the differences between both cases are analytically relevant as they reflect variation in data availability, reporting systems, and institutional capacities at the time of occurrence. We consider Vinta and Paeng sufficiently comparable along key dimensions, and that the contrasts that emerge between the cases offer valuable insights into how the quality of data and reporting influence humanitarian responses. Table 1: Summary of selected case studies Criteria Case 1: Marawi Siege and Severe Tropical Storm Tembin (Vinta) Case 2: Severe Tropical Storm Nalgae (Paeng) Date of occurrence Marawi Siege (May - October 2017) Vinta (December 2017) October 2022 Displacement scenario Complex displacement (Vinta struck two months after the end of the Marawi Siege, impacting the same conflict-affected area) Single-event displacement Hazard intensity (peak storm intensity within the Philippine Area of Responsibility) 2 120 km/h 100 km/h Extent of damage Vinta: 9,580 damaged houses 77,742 damaged houses Displacement figures (cumulative displacement) Marawi Siege: 527,704 displaced persons 3 Vinta: 436,586 displaced persons 3,030,198 displaced persons Size of humanitarian response Marawi Siege: 764,585,081 PHP (13,757,477 USD) Vinta: 48,096,500 PHP (865,419 USD) 592,592,581 PHP (10,662,749 USD) 4.2 Description of cases 4.2.1 Case 1: Marawi Siege and Severe Tropical Storm Tembin (Vinta) On 23 May 2017, just three days before the beginning of Ramadan, conflict erupted in Marawi City, the capital of Lanao del Sur province in the southern Philippines (Dizon 2017; Maitem 2017). The Philippine military launched operations against local extremist groups, triggering intense fighting that lasted for 5 months ( Amnesty International 2017; Bueza 2017). By the time a ceasefire was declared on 23 October 2017, the once-vibrant city had transformed into a landscape of ruins, leaving a profound psychological toll on those being displaced, who were primarily ethnic and religious minorities (Wapano and Dagalangit-Pundato 2024; Veloso 2022). DSWD reported that approximately half a million people were displaced during the Siege (DSWD 2017). Many people fled to neighbouring cities and municipalities in Northern Mindanao and the Autonomous Region of Muslim Mindanao (ARMM), now known as the Bangsamoro Autonomous Region of Muslim Mindanao (BARMM) (Veloso 2022). In 2023, an estimated 71% of the country’s conflict-induced internally displaced persons (IDPs) still trace their displacement back to the 2017 conflict (IDMC 2024). Just two months after the end of the siege, on 22 December 2017, Tropical Storm Tembin (Vinta) hit Mindanao (IFRC 2019), affecting areas hosting the Marawi Siege IDPs (Fernandez 2019). The storm brought heavy rainfall that triggered widespread flooding and landslides (IFRC 2019), damaging evacuation centres that were sheltering IDPs of the Marawi Siege (UNHCR 2017). It is estimated that a total of 436,586 persons were displaced (DSWD 2018a) and a total of 9,580 houses were damaged by Severe Tropical Storm Tembin (Vinta) (DSWD 2018b). During the disaster response, the coordination and delivery of aid to consecutive crises were made complex given the presence of violence, as humanitarian organisations faced security-related challenges due to the presence of non-state armed groups during Tembin (Vinta) (IFRC 2019; Fernandez 2019). 4.2.2 Case 2: Severe Tropical Storm Nalgae (Paeng) On 28 October 2022, Severe Tropical Storm Nalgae (Paeng) made its first landfall in the Philippines (PAGASA 2022). The storm brought intense rains to the country, resulting in massive flooding and rain-induced landslides in Luzon, Visayas, and Mindanao (UNOCHA 2022a). At the height of the disaster, displacement figures peaked at over 1.1 million people, with cumulative numbers eventually recording over 3 million people (DSWD 2023). Paeng occurred at a time when anticipatory action mechanisms were gaining traction in the Philippines. Since 2019, the country has served as a pilot site for FbF and EAPs for typhoons and floods, led by the Philippine Red Cross (PRC) (Anticipation Hub 2021). Informed by forecast thresholds, these systems are designed to activate funding and pre-planned actions such as evacuation of livestock, early harvesting of crops, and shelter strengthening, enabling early intervention ahead of a disaster’s impact (ibid). Two days prior to Paeng’s entry into the PAR, the Philippines Department for Social Welfare and Development (DSWD) began releasing preparedness reports mapping predicted populations requiring assistance. Around the same time, early warning indicators also reached the threshold for the Philippine Red Cross (PRC) to activate the EAP for typhoons in several municipalities in the Aurora Province, including Casiguran, Dinangulan, Dilasag, and Dipaculao (IFRC 2023). PRC mobilised staff and volunteers to distribute and install shelter strengthening kits for 78 targeted households in the Municipality of Dipaculao. On 31 October 2022, Paeng weakened and exited the Philippine Area of Responsibility (PAR) (DSWD 2023). Despite these early actions, the scale of disruption from Paeng was still significant across the country, damaging 77,742 houses (DSWD 2023). Paeng marked the first instance of EAP being triggered and FbF being utilised in the country (IFRC 2023), seen as a crucial step in enhancing disaster preparedness. 4.3 Data collection We drew on a combination of grey literature comprising reports produced by government and humanitarian organisations and semi-structured interviews to examine how displacement data was collected and reported in both cases. While often an overlooked source of knowledge in peer-reviewed studies, grey literature is a critical source in the humanitarian and non-profit sector, offering insights drawn from years of operational experience (Davidson 2017; Lawrence 2017). Meanwhile, semi-structured interviews with humanitarian practitioners were used to complement the analysis by helping interpret reported patterns and offering context from the ground (Adams 2015). The combination of these data sources was intended to capture both the quantitative dimensions of displacement reporting and the qualitative insights needed to contextualise these reporting practices. Figure 4 presents the methodological framework for the comparative analysis. 4.3.1 Displacement, meteorological, and response reporting data We reviewed three sources of grey literature: 1) DSWD DROMIC Situation Reports served as the primary source of displacement data. These reports are published by the Philippine Department of Social Welfare and Development (DSWD) during disasters to track the evolving situation. They provide numbers of internally displaced persons (IDPs) both inside and outside evacuation centres, with figures typically disaggregated by administrative boundaries (region, province, municipality). The reports are updated daily during the emergency phase and are publicly available. 2) PAGASA Summary Meteorological Reports were used as supplementary references to understand the meteorological timeline of Vinta and Paeng. These reports include detailed summaries of each storm’s progression, documenting key moments in a storm’s lifecycle, including its entry into the PAR, the timing and location of landfall(s), peak intensity within the PAR, and its eventual exit. Aligning these timelines with DROMIC reporting allowed us to assess the timing and responsiveness of reporting relative to the progression of the storms. 3) IFRC and PRC Reports provided supplementary information on the implementation of anticipatory action in the case of Paeng. As leading actors in disaster response in the Philippines, the International Federation of Red Cross and Red Crescent Societies (IFRC) and Philippine Red Cross (PRC) produced reports detailing early action protocols, shelter-based interventions, and the targeting of beneficiaries. These documents provided context for the operational rollout of anticipatory action. 4.3.2 Interview data Participants were selected based on two criteria: (1) a previous or current affiliation with a humanitarian organisation, government body, or local university in the Philippines; and (2) a minimum of three years’ experience in displacement response in the Philippines. Our selection included both international and local humanitarian organisations as well as academics from Mindanao-based universities who engaged in peacebuilding activities and provided valuable insights into the deeply rooted interplay of climate and conflict. We prioritised individuals with at least three years of experience as we considered this level of experience necessary for providing informed perspectives on displacement trends and organisational practices. We recruited 32 participants from United Nations organisations (9), Red Cross societies (5), bilateral organisations (3), non-governmental organisations (11), national government (2), and local universities (2), consisting of approximately 60% women and 40% men. We ensured diversity of perspectives and experiences by including organisations of varying scales, size, and mandate. Participants were identified through staff directories on humanitarian organisation websites, LinkedIn searches, and snowball sampling. Recruitment concluded once we achieved balance across organisational types and reached thematic saturation, where no substantially new insights emerged (Guest, Bunce, and Johnson 2006). Interviews were conducted both in-person (14) and online (18). Before commencing, participants were provided with an information statement, and written consent was obtained. During interviews, we explored participants’ observations on the intersection of climate-induced and conflict-induced displacement, prompting questions such as “Could you share a bit about your observations of seeing climate and conflict overlap and creating displacement?” For those who indicated direct experience of responding during the Marawi Siege and Severe Tropical Storm Vinta, follow-up questions were asked for event-specific insights: “For the case of Marawi Siege and Typhoon Vinta, can you tell me a bit more about what happened and what humanitarian organisations were doing at that time?” We also posed reflective and forward-looking questions such as “What do you think are the gaps that humanitarian organisations have yet to fill when they’re addressing overlapping displacement crises?” and “Do you see any opportunities for responses to disaster displacement and conflict displacement to overlap and learn from the other?” Interviews were semi-structured and supplemented with follow-up ‘why’ or ‘how’ questions to explore participants’ perspectives in greater depth (Adams 2015). This flexible format allowed participants to steer the conversation towards themes relevant to their experience. At the end of the interview, we asked closing remarks such as “Is there anything else you want to comment on or any questions you have for us?” , inviting them to share anything that came to mind beyond the scope of the interview questions. 4.4 Data analysis 4.4.1 Time-series and spatial analysis of displacement To analyse patterns in displacement reporting, we constructed time-series plots that will allow us to observe reporting frequency, identify inconsistencies, and detect gaps in displacement data throughout the reporting period. We extracted key variables from the DROMIC reports for each event, including the report number, report release date, and the total number of IDPs (inside and outside evacuation centres). The data were extracted and processed using Python (see Appendix C-H) and plotted chronologically. For Case 1, the Marawi Siege and Vinta displacement figures were plotted on a unified timeline to examine how reporting practices responded to overlapping conflict and climate events. This allowed for identifying potential gaps, delays, or discontinuities in reporting during concurrent displacement. For Case 2, a separate timeline was created for Severe Tropical Storm Paeng using a matching temporal resolution to enable comparison with Vinta. To evaluate the responsiveness of humanitarian reporting relative to storm development, report dates of Vinta and Paeng were cross-referenced with meteorological information from respective PAGASA reports (PAGASA 2019; 2022). Specifically, we examined the timing of preparedness reports relative to the storm’s entry into PAR, the continuity of situation reporting following landfall, and the document of returns relative to the landfall dates. To examine spatial patterns in reporting for the complex displacement scenario (Case 1), we mapped province-level IDP figures for Vinta, with particular attention to how the preceding Marawi Siege may have influenced reporting in affected areas. We extracted cumulative IDP data from the DROMIC report dated 17 January 2018 – the final DROMIC report (2018a) containing cumulative figures by province. We combined IDP counts inside and outside evacuation centres to determine total displacement per province. As the provinces have varying population sizes, we normalised the IDP numbers by calculating the proportion of IDPs relative to each province’s population, using the 2015 population census data from the Philippine Statistics Authority (2016) – the most recent census available prior to the 2017 events. These figures were then standardised and expressed as reported IDPs per 100,000 population, allowing for more meaningful comparison across regions. Using Quantum Geographic Information System (QGIS), we mapped the calculated IDP proportions by provinces onto administrative boundaries (NAMRIA 2023) to assess spatial variation in displacement reporting. In addition, we overlaid the track position of Vinta, as recorded in the PAGASA report (2019), to contextualise the storm’s trajectory relative to areas impacted by the Marawi Siege. This spatial mapping enabled the identification of reporting disparities during Vinta in post-conflict areas, offering insights into how reporting responded spatially to complex displacement scenarios. 4.4.2 Interview qualitative analysis Interview transcripts were analysed thematically using NVivo, guided by a grounded theory approach that allowed patterns and concepts to emerge from the data (Noble and Mitchell 2016; Flick 2018). We created initial codes such as “complex displacement”, “climate vs conflict response”, and “examples of overlap responses” during the first round of coding. As new insights emerged, additional themes were created while existing codes were refined. This process was carried out iteratively, allowing the merging of overlapping codes and continuous adjustment to ensure thematic consistency across datasets (Nowell et al. 2017; King 2004). 4.4.3 Cross-case comparative analysis and synthesis We employed cross-case synthesis as the primary strategy for our comparative analysis, following the analytic approach outlined by Yin (2009). We began by analysing each case independently to identify within-case patterns, drawing upon both time-series plots and qualitative interview data. Rather than relying on direct variable comparisons, we adopted a qualitative lens to synthesise findings and retain the holistic integrity of each case (Yin 2009). We examined each event’s reporting trajectory by analysing key dimensions such as the frequency and duration of reporting, the scale and resolution of displacement data, and the alignment of reporting timelines with meteorological benchmarks. These within-case analyses explored “how” displacement data was reported. Interview data were used to supplement some of the “why” questions and provide contextual insights. After constructing a detailed narrative for each case, we then proceeded to cross-case comparison. For the complex displacement scenario (Case 1), we also analysed how displacement was represented (or underrepresented) through time-series plots and a spatial map of displacement data. We considered how existing conflict, security issues, and institutional presence may have shaped the reporting process. These findings were then compared against the single-event displacement scenario (Case 2) to examine whether similar patterns were present. In Case 2, we focused on the role of anticipatory action, particularly how predictive data was reported and when preparedness reports were released. Comparing this with Case 1, which occurred five years earlier in the absence of anticipatory action, allowed us to explore how institutional preparedness and data practices may have evolved over time. Following Yin’s (2009) guidance, we sought to “think upward conceptually” by examining broader themes emerging from the synthesis. This included identifying divergent patterns, such as how reporting practices differ in complex versus single-event displacement scenarios, and how humanitarian responses evolve from reactive to proactive approaches. This cross-case synthesis enabled a more grounded and forward-looking discussion on how humanitarian systems can be adapted to better anticipate and respond to the realities of complex displacement. 4.5 Limitations We recognise several limitations in our analysis. Firstly, we rely on displacement data directly from DROMIC reports. However, reporting during the emergency and response phase often experiences delays and regional inconsistencies, so both displacement and return data may lag or vary in quality across provinces and municipalities. Secondly, our study is grounded primarily in documented data and reports, which means we capture only one dimension of humanitarian response. Exogenous factors such as institutional capacity, funding availability, and media attention likely influenced reporting practices but lie beyond our data-centric scope. Furthermore, there is a five-year gap between Vinta (2017) and Paeng (2022), during which broader humanitarian system reforms and technological advances may account for some differences we observe, irrespective of the overlapping occurrence of disaster and conflict events. Finally, our reliance on grey literature and interviews may introduce biases based on source availability and participant recall of their memory. Despite these constraints, the patterns we identify, such as reporting frequency and resolution, spatial underrepresentation, and the emergence of anticipatory data, still offer a reliable basis for recommendations to strengthen reporting in complex displacement contexts. Declarations Competing interests The authors declare no competing interests. Author Contribution Conceptualisation: S.S., A.O., S.B.; Data curation: S.S.; Formal analysis: S.S.; Funding acquisition: A.O., S.B.; Investigation: S.S.; A.O., S.B.; Methodology: S.S.; A.O.; S.B.; Supervision: A.O., S.B.; Validation: S.S., A.O., S.B.; Visualisation: S.S.; Writing – original draft: S.S.; Writing – review & editing: A.O., S.B. Acknowledgement We would like to thank the Sydney Environment Institute (SEI) for supporting this research through a collaborative grant. 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HUMANITARIAN NEEDS AND PRIORITIES REVISION. https://reliefweb.int/report/philippines/philippines-super-typhoon-rai-odette-humanitarian-needs-and-priorities-revision . UNOCHA. 2024a. ‘Anticipatory Action Philippines (September 2024 Update) - Philippines | ReliefWeb’. Manual and Guideline. https://reliefweb.int/report/philippines/anticipatory-action-philippines-september-2024-update . UNOCHA. 2024b. ‘Briefing Note: Anticipatory Action – An Innovative Tool at the Intersection of Disaster Risk Reduction and Humanitarian Response’. https://www.undrr.org/media/101447/download#:~:text=AA%20is%20 understood%20as%20acting,fast%20release%20of%20anticipatory%20finance. Veloso, Diana Therese M. 2022. ‘Safety and Security Issues, Gender-Based Violence and Militarization in the Time of Armed Conflict: The Experiences of Internally Displaced People From Marawi City’. Frontiers in Human Dynamics 3 (July). https://doi.org/10.3389/fhumd.2021.703193 . Wagner, Marie, and Beth Simons. 2025. ‘Anticipatory Action (AA) in Complex Settings: WAHAFA Guidelines on Navigating AA, Conflict and Displacement’. Germany: Welthungerhilfe Anticipatory Humanitarian Action Facility (WAHAFA). https://www.welthungerhilfe.org/news/publications/detail?tx_cart_product%5Bproduct%5D=2258&cHash=acb73a 6e6aa075642cab7b589ecbd046. Walch, Colin. 2018. ‘Disaster Risk Reduction amidst Armed Conflict: Informal Institutions, Rebel Groups, and Wartime Political Orders’. Disasters 42 (S2): S239–64. https://doi.org/10.1111/disa.12309 . Wapano, Mary Rachelle R., and Soraimie P Dagalangit-Pundato. 2024. ‘From Siege to Survival: Exploring the Multi-Faceted Traumas of Meranao Internally Displaced Persons’. IOER INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL 6 (1): 121–33. https://doi.org/10.54476/ioer-imrj/663097 . Weerasinghe, Sanjula. 2021. ‘Bridging the Divide in Approaches to Conflict and Disaster Displacement: Norms, Institutions and Coordination in Afghanistan, Colombia, Niger, the Philippines and Somalia’. UNHCR and IOM. WMO. 2015. ‘WMO Guidelines on Multi-Hazard Impact-Based Forecast and Warning Services’. WMO-No. 1150. World Meteorological Organization (WMO). https://library.wmo.int/records/item/54669-wmo-guidelines-on-multi-hazard-impact-based-forecast-and-warning-services?offset= . Yin, Robert K. 2009. Case Study Research: Design and Methods . SAGE. Footnotes This map displays displacement data for the island of Mindanao only. IDP figures reflect cumulative totals reported by DSWD DROMIC as of 17 January 2018. Population data are based on the 2015 Philippine census published by the Philippine Statistic Authority (PSA). Administrative boundaries were downloaded from the Humanitarian Data Exchange. The map uses the ESRI Gray (light) basemap sourced from ArcGIS Online. The duration of wind speeds is reported as 10-minute maximum sustained wind speeds. There are inconsistencies in the cumulative displacement numbers reported during the Marawi Siege. The figure cited here reflects the highest cumulative displacement number throughout the reporting period, extracted from the DSWD DROMIC Report #71 dated 20 July 2017. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7043272","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483100834,"identity":"65f7b12e-00cf-42f7-a2a3-48204e77304a","order_by":0,"name":"Sheryn See","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Sheryn","middleName":"","lastName":"See","suffix":""},{"id":483100835,"identity":"c96cb565-aa0d-481c-aaa5-a340aa62ecfb","order_by":1,"name":"Aaron Opdyke","email":"data:image/png;base64,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","orcid":"","institution":"The University of Sydney","correspondingAuthor":true,"prefix":"","firstName":"Aaron","middleName":"","lastName":"Opdyke","suffix":""},{"id":483100836,"identity":"4a889807-0dac-470a-89aa-99beab90304f","order_by":2,"name":"Susan Banki","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Banki","suffix":""}],"badges":[],"createdAt":"2025-07-04 05:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7043272/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7043272/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86830314,"identity":"5f7de845-42bf-42d2-abee-e7ad4d7a19f9","added_by":"auto","created_at":"2025-07-16 05:54:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1565223,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical context of Case 1: Marawi Siege (May – October 2017) and Severe Tropical Storm (STS) Tembin (Vinta) (December 2017) and Case 2: Severe Tropical Storm (STS) Nalgae (Paeng) (October 2022)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7043272/v1/512b94dabda40201c59450c8.png"},{"id":86830735,"identity":"4193c169-01ae-4e95-88ba-629ca37a4c6b","added_by":"auto","created_at":"2025-07-16 06:02:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2217470,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of displacement data from Case 1: Marawi Siege (May – Oct 2017) and Severe Tropical Storm Tembin (Vinta) (Dec 2017) and Case 2: Severe Tropical Storm Nalgae (Paeng) (Oct 2022)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7043272/v1/0abf927b4e96d6b9793a523c.png"},{"id":86830312,"identity":"cf7ab90c-44c1-4ff2-8079-c689aed53ca1","added_by":"auto","created_at":"2025-07-16 05:54:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1688678,"visible":true,"origin":"","legend":"\u003cp\u003eReported IDPs per 100,000 population across provinces during Severe Tropical Storm Tembin (Vinta) (December 2017)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7043272/v1/81ad5430dd0a177bb403e380.png"},{"id":86830317,"identity":"0afd2a6b-835e-4638-bbe3-68821ca35629","added_by":"auto","created_at":"2025-07-16 05:54:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":338097,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological framework of comparative analysis\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7043272/v1/aa3d4c4c94af1d8e177fdaf6.png"},{"id":89311628,"identity":"b830b0f2-1bb4-44f5-9004-f3eb83b10ccd","added_by":"auto","created_at":"2025-08-18 16:11:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6898413,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7043272/v1/8951cdd3-67c0-41f4-8bd9-c19af84b6674.pdf"},{"id":86830311,"identity":"e78fd191-9d45-48b4-a46a-3206852c3291","added_by":"auto","created_at":"2025-07-16 05:54:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":102839,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7043272/v1/0117829d96593f48b5ef70d3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Overlapping disaster and conflict events reveal gaps in displacement reporting in the Philippines","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eNatural hazards cause greater harm in areas affected by armed conflict, where inequality, poor governance, and weakened systems heighten vulnerability (Siddiqi \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Blaikie et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Caso, Hilhorst, and Mena 2023). In these fragile settings, conflict and disasters frequently overlap, compounding risks and leading to severe consequences (Mena and Hilhorst 2021; L. E. R. Peters \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; IDMC \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While there is growing evidence linking climate change, conflict, and displacement (K. Peters et al. 2021), the ways that our data systems are constraining our understanding of overlapping crises has received only cursory attention. Insecure regions often come with inconsistent and unreliable displacement data, and the systems for collecting data on disasters are often severely limited or entirely absent in fragile contexts (SPARC \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, while it is well understood that hazards impact conflict-affected populations, there is limited data to comprehensively profile those experiencing overlapping crises. This has resulted in the unequal visibility of communities and distribution of aid, with those affected by both conflict and disasters potentially sidelined (ALNAP \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Siddiqi \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe humanitarian system has traditionally addressed climate-induced and conflict-induced displacement as separate challenges, reporting displacement data separately according to their immediate triggers (Weerasinghe \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; IDMC and NRC \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; S\u0026aacute;nchez-Mojica \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Caso, Hilhorst, and Mena 2023; K. Peters et al. 2021). Yet, empirical evidence increasingly shows that disasters and conflicts often overlap, making this binary approach problematic (Walch \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is not uncommon for conflict-affected populations in disaster-prone countries to be overlooked in favour of those affected solely by disasters (Siddiqi \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite general awareness of data gaps in disaster-conflict settings (K. Peters \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; SPARC \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), there has been limited systematic comparison of reporting practices between complex, overlapping crises and single-event disasters. Questions persist about where data shortfalls lie, how they impede effective responses, and how a more holistic data system might be developed. This study aims to assess the reporting discrepancies to identify current limitations and inform opportunities for reform. We ask:\u003c/p\u003e\u003cp\u003e\u003cem\u003eHow does humanitarian data reporting differ between complex (involving both climate and conflict) and single-event displacement (climate)?\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe conducted a comparative case study to analyse the difference in data reporting between a complex displacement case (involving both disaster and conflict) and single-event displacement case (involving disaster only). Examining the case of the Philippines, we selected the \u003cem\u003eMarawi Siege and Severe Tropical Storm Tembin (Vinta)\u003c/em\u003e in 2017 as our complex displacement scenario and \u003cem\u003eSevere Tropical Storm Nalgae (Paeng)\u003c/em\u003e in 2022 as our single-event displacement scenario. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a contextual map illustrating the geographic extent of each event.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePaeng represents a recent example that took place when humanitarian actors were beginning to adopt proactive approaches to disaster responses through anticipatory action (Anticipation Hub \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In recent decades, anticipatory action has emerged in humanitarian responses to address disasters proactively, acting ahead of predicted hazards to prevent or minimise impacts on lives (ALNAP \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; UNOCHA \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). In contrast to early warning systems that alert communities to impending hazards, anticipatory action uses impact-based forecasting, which integrates data on exposure and vulnerability to predict hazard impacts on communities at risk and then acts on that data through the implementation of action measures, such as evacuations or cash assistance (WMO \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While anticipatory action is slowly emerging as a consideration in conflict-affected settings (Wagner and Simons 2025; Kj\u0026aelig;rum and Madsen \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the ability to forecast conflict outbreaks remains limited (Schillinger et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As climate hazards intensify and complex displacement becomes more frequent (K. Peters and Dupar 2020; K. Peters et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), there is a need to understand the role of anticipatory action in addressing overlapping crises (Jaime et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chaves-Gonzalez et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, evidence on the implementation of anticipatory action in complex crises remains limited, especially since in fragile contexts where the delivery of regular humanitarian aid has already been constrained, there is limited opportunity to introduce new forms of aid (Easton-Calabria \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kj\u0026aelig;rum and Madsen \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, while the focus of this study is on data reporting practices, we use the Paeng case to explore how pre-emptive humanitarian interventions can minimise disaster impacts during overlapping crises.\u003c/p\u003e\u003cp\u003eWe drew on a combination of grey literature comprising reports produced by government and humanitarian organisations and semi-structured interviews to examine how displacement data were collected and reported in both cases. The Disaster Response Operations Management, Information and Communication (DROMIC) reports published by the Philippine Department of Social Welfare and Development (DSWD) serve as the primary data source for comparative analysis. Thirty-two semi-structured interviews with humanitarian practitioners were used to complement the analysis by offering context from the ground (Adams \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The combination of these data sources was intended to capture both the quantitative dimensions of displacement reporting and the qualitative insights needed to contextualise these reporting practices.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003eThis section presents the results from our comparative analysis. We arranged our findings according to four descriptive themes: reporting structure and consistency; spatial variation of disaster internally displaced person (IDP) data in conflict-impacted areas; visibility of conflict IDPs amidst political dynamics; and the evolution from reactive to predictive data reporting.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 A shift from patchy to structured reporting\u003c/h2\u003e\u003cp\u003eWe compare how displacement was reported across our two cases, revealing important shifts and inconsistencies in how displacement was recorded. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the number of IDPs triggered by Case 1 \u0026ndash; Marawi Siege and Severe Tropical Storm Tembin (Vinta) and Case 2 \u0026ndash; Severe Tropical Storm Nalgae (Paeng), based on data extracted from DROMIC reports. Each data point represents the total number of IDPs at the time of reporting.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe DSWD began releasing preparedness reports for Vinta on 20 December 2017, coinciding with the storm\u0026rsquo;s entry into the Philippine Area of Responsibility (PAR). Regular situation reports followed on 22 December 2017, the day Vinta made landfall. Daily reports were issued for approximately one month following the storm\u0026rsquo;s landfall, after which updates became more sporadic, continuing intermittently for up to three months. Reporting throughout the Vinta event was marked by inconsistencies in geographical scale and resolution. While most of the reports included municipality-level data for IDPs outside evacuation centres, IDPs residing within evacuation centres were only recorded at the provincial level. Some reports presented only region-level figures, making it challenging to construct a detailed and consistent picture of displacement across affected areas.\u003c/p\u003e\u003cp\u003eFor the case of Paeng, DSWD started releasing preparedness reports on 24 October 2022, two days before the Severe Tropical Storm entered the PAR. Regular situation reports containing displacement figures followed on 26 October 2022, coinciding with the storm\u0026rsquo;s entry into PAR two days before its first landfall. This marked an earlier reporting timeline compared to Vinta. Reporting for Paeng was also both more frequent and consistent. Daily reports continued for nearly two months following the storm\u0026rsquo;s landfall, with additional updates extending up to eleven months afterwards, providing a longer temporal window to monitor protracted displacement. Reporting for Paeng was also notably more consistent than Vinta, with IDPs both inside and outside evacuation centres reported down to the municipal level in nearly every report, providing a more granular picture of the evolving displacement situation.\u003c/p\u003e\u003cp\u003eWhen reviewing the documentation of IDP returns, Vinta\u0026rsquo;s reporting revealed limited and uneven documentation of returns. The earliest indication of returns was reported in the Caraga Region, citing \u003cem\u003e\u0026ldquo;the remaining families who are still staying inside evacuation centres will probably return to their respective houses by today December 24, 2017\u0026rdquo;.\u003c/em\u003e A more definitive return estimate was officially reported on 26 December 2017 (four days after landfall) in Region XI: \u003cem\u003e\u0026ldquo;More and more families have exited the Evacuation Centers as Internally Displaced families from the Municipalities of Montevista, Nabunturan, Pantukan, Compostela, and Mabini of Compostela Valley Province have returned to their homes\u0026rdquo;.\u003c/em\u003e At the end of Vinta\u0026rsquo;s reporting period, approximately 16% of the peak recorded IDPs were still displaced, suggesting a notable degree of protracted displacement.\u003c/p\u003e\u003cp\u003eIn the case of Paeng, there were evident staggered patterns in the displacement figures following the peak of IDPs recorded on 4 November 2022, likely reflecting the systematic and progressive return of displaced populations. Unlike Vinta, the Paeng reports were more explicit in tracking returns, with the first instance documented on 2 November 2022 (five days after the first landfall) in Region VIII (Eastern Visayas), specifically among those who had been pre-emptively evacuated. This return was recorded even before peak displacement figures appeared in the reports. By the end of the reporting period, less than 1% of IDPs (relative to the peak recorded) remained displaced.\u003c/p\u003e\u003cp\u003eWhen comparing protracted displacement at the end of the reporting period, it is important to note that Paeng had a much longer reporting period (eleven months) than Vinta (three months), limiting the validity of a direct, like-for-like comparison. However, examining both events at the three-month mark after landfall still reveals notable differences, with approximately 16% of Vinta\u0026rsquo;s peak IDP population remaining displaced, compared to only 5% of Paeng\u0026rsquo;s peak IDP population at the same stage of reporting. This contrast suggests important differences in return trajectories and long-term displacement outcomes between the two events. From a data perspective, Paeng appears to have been supported by a more structured reporting system overall \u0026ndash; one that may not only have reflected, but also contributed to, a more coordinated response. The combination of a more extended reporting period, consistent high-frequency updates, and more granular data resolution likely facilitated more accurate monitoring of displacement patterns. Although multiple exogenous factors may have influenced the lower rate of protracted displacement, improved data systems may have contributed to a more informed and coordinated response in the case of Paeng.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Disaster displacement data is less captured in post-conflict areas\u003c/h2\u003e\u003cp\u003eSpatial analysis of province-level IDP figures for Vinta revealed patterns potentially influenced by the preceding Marawi Siege. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the reported IDPs per 100,000 population during Vinta, based on cumulative IDP data from the DROMIC report as of 17 January 2018. The figures include IDPs both inside and outside evacuation centres and are normalised against respective provincial population data from the 2015 census data (Philippine Statistics Authority \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The map also traces the track of Vinta and highlights geographical variation in displacement reporting across the southern Philippines.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe province of Lanao del Sur (depicted in a yellow outline polygon in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) \u0026ndash; where the Marawi Siege took place just months earlier \u0026ndash; lay directly in the path of Vinta. As one interviewee recalled: \u003cem\u003e\u0026ldquo;[Marawi IDPs] were also displaced by the typhoons, particularly those IDPs who stayed in the municipalities of Madalum and Tugaya. These are municipalities in Lanao del Sur.\u0026rdquo;\u003c/em\u003e Despite this direct exposure, Lanao del Sur recorded the lowest reported displacement rate throughout the region, with only 98 IDPs per 100,000 population. This figure stands in sharp contrast to the reported displacement rate in adjacent provinces \u0026ndash; Lanao del Norte (6,564), Misamis Oriental (2,149), and Bukidnon (1,407). The highest rate was observed in Davao del Norte, with 12,261 IDPs per 100,000 people.\u003c/p\u003e\u003cp\u003eA broader examination across the southern Philippines also shows both Lanao del Sur and Maguindanao del Norte \u0026ndash; both part of BARMM (known as ARMM) \u0026ndash; reported lower displacement rates compared to directly adjacent provinces that experienced similar levels of storm impact. These discrepancies suggest potential underreporting in these post-conflict areas, raising questions about the visibility of displaced communities in areas where institutional capacity, access, and security may influence data collection and reporting during disasters. Our interviews with humanitarian practitioners revealed anecdotal accounts of Marawi IDPs who were affected by Vinta, though the overlapping complexities of their experiences were not captured: \u003cem\u003e\u0026ldquo;In December 2017, there was also Vinta typhoon, where the displaced Marawi residents were also affected, were also displaced at the time of their displacement.\u0026rdquo;\u003c/em\u003e According to a local worker, humanitarian access also played a role in shaping who was seen and supported: \u003cem\u003e\u0026ldquo;During the Marawi Siege then followed by Typhoon Vinta \u0026hellip; those who took refuge in more interior municipalities and that are also conflict areas, we actually tried to reach out to those as they don't easily receive support and assistance.\u0026rdquo;\u003c/em\u003e With data often used to guide aid distribution, underreporting can have significant implications, resulting in humanitarian responses being deprioritised in areas that are already vulnerable and underserved.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Who counts as displaced and when?\u003c/h2\u003e\u003cp\u003eBefore the arrival of Severe Tropical Storm Vinta in December 2017, there was an existing wave of displacement from the Marawi Siege, which started seven months prior to the storm. Anomalies in the reporting of Marawi IDPs prompted closer examination of how IDP figures evolved, especially during the period of overlap with Vinta, revealing potential gaps in how concurrent displacement events are captured in the data.\u003c/p\u003e\u003cp\u003eThere was a sudden \u0026ldquo;drop\u0026rdquo; in the reported number of Marawi IDPs around July 2017 (approximately two months after the onset of the conflict event), after which the displacement figures remained stagnant. This \u0026ldquo;decline\u0026rdquo; stands out given that the Siege continued through to October 2017. This drop coincided with a shift in the collection of displacement data using the Disaster Assistance Family Access Cards (DAFAC) issued by the DSWD, which is the primary instrument used by the Philippine government for identifying and tracking beneficiaries and services after disasters (Fernandez \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While the DAFAC system is intended to standardise beneficiary tracking, access to such mechanisms is often influenced by local political dynamics. As a participant from an international organisation observed:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn the Philippines \u0026hellip; political parties or patronage politics is highly practised. If you transfer from one village to another, this host village will not necessarily provide support \u0026ndash; sustained support, especially if you are not a registered voter in that community because you belong to the community where you previously lived. That's why you need to go back \u0026hellip;to avail services \u0026hellip; those displaced families that have relocated in that municipality will not be provided any support from the municipality, but they'll receive support from the national government, from the Department of Social Welfare and Development.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA local practitioner reflected: \u003cem\u003e\u0026ldquo;Unfortunately in the Philippine context, even relief goods have political colour \u0026hellip; Sometimes the basis of release of these goods are on the basis of whether or not that is their political ally or that is their supporters.\u0026rdquo;\u003c/em\u003e As DROMIC reports prior to the sudden \u0026ldquo;drop\u0026rdquo; made no mention of DAFAC, the adoption of this system likely excluded some IDP populations from the remaining reporting period. These observations are also echoed in a report by UNOCHA (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which cited reports of displaced people excluded from the DAFAC registration process, further validating the risk of politically influenced exclusion in data collection.\u003c/p\u003e\u003cp\u003eThere was also a noticeable gap in the reporting of Marawi IDPs during Vinta\u0026rsquo;s impact from December 2017 to January 2018. During this time, Marawi IDP data effectively halted as reporting focused on displacement associated with Vinta. This reflects the current single-event reporting system, which tends to isolate and attribute IDPs according to specific trigger events. Although the Marawi IDPs remained displaced, attention had been shifted to the more recent displacement event. In effect, the occurrence of the second crisis rendered the earlier displacement invisible within the data.\u003c/p\u003e\u003cp\u003eIt is also plausible that some Marawi IDPs were \u0026ldquo;reclassified\u0026rdquo; under the Vinta event, given that displacement data is often linked to the most recent trigger, noted by a practitioner: \u003cem\u003e\u0026ldquo;The data that are made available, usually it's really the recent triggers that this displacement is being linked to.\u0026rdquo;\u003c/em\u003e This labelling practice, combined with the traditionally disaggregated nature of displacement data, ends up disregarding the lingering impacts of previous conflict. It provides little insight into whether the reported figures include individuals already displaced by prior crises. As a UN staff member pointed out, despite their local experience indicating that the same populations are repeatedly affected, the lack of clear data makes it difficult to confirm: \u003cem\u003e\u0026ldquo;What we haven't received yet is the breakdown of data [showing] that these are the same areas that are affected by flooding that they reported are also affected by conflict. It\u0026rsquo;s really a challenge \u0026hellip; because it's the typical cycle, it's a repeated displacement for Maguindanao and some parts of North Cotabato \u0026hellip; It's like an assumption because we would see the same areas, but we haven't really had the clarity yet to identify these are the same people.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs a result, IDPs\u0026rsquo; compounded vulnerabilities and previous conflict experiences were neglected, leading to gaps in responses and a failure to fully address the complex realities of the IDPs\u0026rsquo; situation. An international organisation staff member stressed the importance of identifying overlapping displacement events to corroborate what they had witnessed firsthand: \u003cem\u003e\u0026ldquo;It would be a very strategic study if we're able to identify overlapping emergencies conflict and natural disaster, and we can actually do that in areas plagued with conflict like in the BARMM region of Mindanao. Nobody has done that. But we're aware that it's happening, especially if the conflict happens on the Monsoon season, that's already an indicator that people will be displaced due to overlapping emergencies.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs revealed in our interviews, data collection practices are further influenced by a broader tendency within the Philippines\u0026rsquo; humanitarian system to prioritise climate-induced disasters over conflict events. A local respondent in Mindanao reflected, \u003cem\u003e\u0026ldquo;Usually the humanitarian organisations \u0026ndash; the way I'm seeing it in the Philippines \u0026ndash; are more used to responses to humanitarian crises that are [resulting from] natural calamities, but not really on conflict.\u0026rdquo;\u003c/em\u003e This tendency is also reflected in funding preferences during overlapping crises, noted by another interviewee from an international organisation, \u003cem\u003e\u0026ldquo;When [events] overlap, [the way displacement is labelled] is often related to the disaster \u0026hellip; [As] humanitarian organisations, when we label it based on the disaster, we can appeal for more support..\u0026rdquo;\u003c/em\u003e This data collection practice appears to have been shaped by funding and administrative considerations, which may distort the data informing humanitarian decisions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data evolving from reactive to predictive\u003c/h2\u003e\u003cp\u003eOne of the most notable improvements to Paeng\u0026rsquo;s response was the implementation of anticipatory action before the storm. Preparedness reports for Paeng included a section on \u0026ldquo;predictive analysis for humanitarian response\u0026rdquo;, a component absent from Vinta\u0026rsquo;s reporting. This analysis identified regions expected to receive at least 100mm of accumulated rainfall over the next 72 hours. The analysis also includes the estimated population exposed and the number of family food packs required in advance of the impact.\u003c/p\u003e\u003cp\u003ePaeng also marked one of the pilot cases of anticipatory action implementation for shelter-based early interventions. Based on forecast monitoring of Paeng\u0026rsquo;s projected path, anticipated shelter damage, and available lead time, the Philippine Red Cross (PRC) mobilised staff and volunteers to distribute and install shelter strengthening kits in the Municipality of Dipaculao on 29 October 2022 (a few hours before the storm\u0026rsquo;s nearest landfall in the area). Across two barangays in Dipaculao, 78 households \u0026ndash; which were pre-identified as high-risk \u0026ndash; received and installed the kits, with post-disaster surveys indicating that the reinforced shelters withstood the storm conditions effectively and helped mitigate the impacts of Paeng\u0026rsquo;s strong winds (IFRC \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnlike reactive responses triggered by observed impacts, anticipatory action uses forecasts and pre-agreed thresholds to mobilise aid before a shock occurs (Chaves-Gonzalez et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Forecast-based financing (FbF) automatically releases funds when a forecast threshold is reached, guided by an early action protocol (EAP) that outlines pre-defined triggers and early actions to be taken (Thalheimer, Simperingham, and Jjemba 2022). As noted, the implementation of the early action protocol (EAP) in Dipaculao marked a significant milestone for the Philippines, being the first instance of EAP being triggered and forecast-based financing (FbF) being utilised in the country. While the number of households reached was small compared to the overall affected population, the success of this early intervention demonstrated the potential of forecast-based action in reducing disaster impacts.\u003c/p\u003e\u003cp\u003eWhile both Vinta and Paeng involved government standby funds and pre-positioned relief stockpiles as part of their response measures, these actions are generally considered part of the traditional disaster risk management rather than anticipatory action. In the case of Vinta, response measures were still largely reactive, based on the existing pre-positioning of resources, and lacked forward-looking components. Paeng was differentiated by the emergence of forecast-informed early action, which consisted of interventions that were explicitly triggered by pre-defined thresholds and linked to specific early response activities. The shift toward more proactive humanitarian action reflects a reporting approach where data are not only used to document what has occurred, but to inform and trigger early interventions before impacts escalate. Essentially, our comparison of Vinta and Paeng illustrates a shift in data from being a static record of past events to a dynamic tool that can shape anticipatory decision-making.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eOur analysis of these two case studies offers clear empirical evidence of exclusion during the reporting of complex displacement. Those who are captured in the data are often those who fall within the institutional frameworks of visibility and recognition, while others remain undocumented and overlooked. We urge a rethinking of forecast-based models and triggers within anticipatory action to fully capture and respond to these overlapping events.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Integrating disaster-conflict displacement data\u003c/h2\u003e\u003cp\u003eOverlaying data from Marawi and Vinta reveals a broader trend within humanitarian data systems: the heavy reliance on event-based frameworks and administrative considerations that are often shaped by political dynamics, such as eligibility requirements tied to voter registration or local government recognition. National displacement reporting by DSWD typically relies on data submitted by barangay and municipal offices, which are not free from biases regarding who is included and excluded (Fernandez \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The reporting gap due to the shift to DAFAC-based registration, as well as spatial gap in Vinta IDP numbers in the Province of Lanao del Sur where the Marawi Siege took place (presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reflects how reporting protocols, funding preferences, and political dynamics determine what gets recorded and consequently, who is recognised as displaced. As Crisp (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) argues in his analysis of refugee statistics, data reporting in humanitarian contexts is rarely neutral; it is entangled with politics, institutional mandates, and strategic priorities.\u003c/p\u003e\u003cp\u003eThe siloed way of documenting displacement \u0026ndash; separating data into either conflict or disaster-induced events \u0026ndash; directly influences how displacement is documented, interpreted, and ultimately understood. As a result, we find in our analysis that individuals who experience overlapping displacement are at risk of being underreported. These issues extend beyond the technical processes of data collection but are rather rooted in epistemic practices that influence the way we construct and understand complex displacement. This fragmentation has serious practical implications for how the humanitarian sector makes decisions.\u003c/p\u003e\u003cp\u003eTo address this, the humanitarian system must move toward a more holistic displacement tracking framework to capture displacement across multiple conflict-related and climate-related triggers over extended timeframes. This is not merely a matter of improving data reporting. Instead, it requires a fundamental shift in how displacement is conceptualised and documented. While Paeng was relatively well documented, its reporting still treated the event as a standalone disaster. For instance, the BARMM was also heavily impacted by Paeng in October 2022 (OCHA \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), yet reports made no reference to the region\u0026rsquo;s existing population of over 80,000 protractedly displaced individuals from the Marawi Siege (UNHCR Philippines \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe humanitarian system must also move beyond static, one-time counts of displaced populations. For instance, if during Vinta, the data had been distinguished between individuals newly displaced by the storm and those already displaced by the Marawi conflict, the resulting figures would have provided a more representative picture of displacement. During our interviews, several participants noted that populations affected by both events received limited support. More broadly, one participant reflected on the recurring challenge of overlapping disasters in the Philippines: \u003cem\u003e\u0026ldquo;Because of the multiple disasters, sometimes we forget the previous disasters. So the focus will be moved to the bigger one until such time these people will just recover on their own.\u0026rdquo;\u003c/em\u003e There is a need for dynamic data systems that can document initial displacement, secondary or overlapping displacement, and long-term, protracted cases, all within the same reporting cycle, thus supporting more equitable resource allocation and ensuring the inclusion of those experiencing complex, overlapping displacement\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Rethinking forecast-based models and triggers\u003c/h2\u003e\u003cp\u003eAnticipatory action has gained increasing traction in recent years (ALNAP \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), allowing humanitarian organisations to act before a disaster strikes based on forecasts or predictive analysis (UNOCHA \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Chaves-Gonzalez et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Forecast-based thresholds such as projected wind speed, accumulated rainfall over a 72-hour period, or anticipated percentage of housing damage typically serve as the standard triggers for early action. However, applying uniform thresholds across all areas assumes that communities share similar levels of exposure and vulnerability when, in reality, the impacts of disasters are unevenly distributed (Choong et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Long and Duan 2025; Qiao et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Soden et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Communities still recovering from previous disasters, or those experiencing protracted displacement, may be far more susceptible to the same hazard than populations encountering a single event in isolation (de Ruiter and van Loon \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, while anticipatory action\u0026rsquo;s forward-looking nature aims to reduce disaster impacts pre-emptively, its forecast models and implementation are still rooted in precedent. Anticipatory action models are typically developed using historical datasets such as climate, agro-meteorological, vulnerability, exposure, damage or loss data to identify the timing, location, and severity of previous shocks (Chaves-Gonzalez et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When these data are unavailable, incomplete, or lack sufficient granularity, it becomes challenging to determine what qualifies as an \u0026ldquo;out-of-the-ordinary\u0026rdquo; event and the threshold for action (OCHA Centre for Humanitarian Data \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is especially apparent in countries affected by conflict, as data gaps such as limited observational weather stations undermine the accuracy of historical weather records and the ability to forecast future events, making the implementation of anticipatory action challenging (Easton-Calabria \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Schultz and Mankin \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At the implementation level, the latest framework for tropical cyclones in the Philippines states that the anticipatory framework \u0026ldquo;focuses on the areas frequently affected regions of Bicol, Eastern Visayas and Caraga\u0026rdquo; (UNOCHA \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile this approach provides a practical basis for targeting resources, it also means that anticipatory initiatives are biased towards areas that have been previously impacted and have sufficient historical data. As a result, new and emerging hotspots may be overlooked, particularly as climate change introduces greater unpredictability in weather patterns (IPCC \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several participants reflected on this growing challenge in our interviews, with one noting: \u003cem\u003e\u0026ldquo;It has become increasingly difficult because of climate change primarily because it does not affect the same areas anymore as it previously did.\u0026rdquo;\u003c/em\u003e For instance, Typhoon Rai (Odette) in 2021 was frequently cited by participants as an example of how climate change is intensifying weather patterns. As reported in UNOCHA\u0026rsquo;s Humanitarian Needs and Priorities Plan (2022b), \u0026ldquo;contrary to predictions, Rai intensified from a tropical storm to a super typhoon within hours before making landfall.\u0026rdquo; Furthermore, \u0026ldquo;while storms typically make landfall in the southern parts of Luzon or the eastern part of the Visayas, Rai (Odette) struck regions further south, which do not typically experience the brunt of typhoons.\u0026rdquo;\u003c/p\u003e\u003cp\u003eA further blind spot lies in the absence of displacement data within forecast models. To date, there has been limited attention within anticipatory initiatives to address people who have already been displaced (Easton-Calabria et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Current anticipatory action triggers in the Philippines do not account for recent or ongoing displacement, nor integrate spatial conflict footprints such as areas affected by past or ongoing armed violence or insecurity. Yet these factors are critical in anticipating where humanitarian needs are crucial. Overlaying displacement history and conflict footprints with hazard forecasts could enable more nuanced triggering mechanisms. In contexts where populations are already displaced, aid could be scaled earlier or targeted more precisely. With this, the system is not only responding to forecasted hazard intensity, but also in recognition of compounded vulnerability (Schillinger et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Start Network \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Methods","content":"\u003cp\u003eThis section details our case selection criteria, case description, data collection and analysis. We combined grey literature comprising reports produced by government and humanitarian organisations and semi-structured interviews to examine how displacement data was collected and reported in both cases. Our comparative analysis relied on cross-case synthesis following Yin\u0026rsquo;s\u0026nbsp;(2009)\u0026nbsp;analytic approach. We first examined each case independently using time-series plots, a spatial map, and qualitative interview insights to identify within-case patterns, before moving to cross-case comparison and synthesis. Ethics approval for this research was granted by the Human Ethics Research Committee at the University of [redacted for peer review].\u003c/p\u003e\n\u003ch2 id=\"_Toc198807423\"\u003e4.1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Case selection criteria\u003c/h2\u003e\n\u003cp\u003eWe employed a case study selection process guided by pre-defined criteria. Our criteria sought to ensure a robust comparison between complex displacement scenarios (where disaster-induced and conflict-induced displacement overlap) and single-event displacement scenarios (displacement induced by disaster) in the Philippines. We aimed to identify one historical case representative of each scenario. The guiding selection criteria included:\u003c/p\u003e\n\u003cp\u003e1) \u003cstrong\u003eDisplacement scenarios\u003c/strong\u003e \u0026ndash; One case must have displacement induced by overlapping conflict events and hazards, while the other case must have displacement induced by hazards only.\u003c/p\u003e\n\u003cp\u003e2) \u003cstrong\u003eHazard intensity\u003c/strong\u003e \u0026ndash; The cases should involve hazards of similar intensity, as indicated by metrics such as the \u0026ldquo;peak intensity within the Philippine Area of Responsibility (PAR)\u0026rdquo; as reported by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA).\u003c/p\u003e\n\u003cp\u003e3) \u003cstrong\u003eExtent of damage\u003c/strong\u003e \u0026ndash; The hazard of each case should involve comparable levels of infrastructure damage.\u003c/p\u003e\n\u003cp\u003e4) \u003cstrong\u003eDisplacement figures\u003c/strong\u003e \u0026ndash; The number of displaced individuals should be comparable across cases.\u003c/p\u003e\n\u003cp\u003e5) \u003cstrong\u003eScale of humanitarian responses\u003c/strong\u003e \u0026ndash; The cases should have received similar levels of humanitarian intervention (e.g., both cases triggering international assistance or neither) to control for differences in aid provision.\u003c/p\u003e\n\u003cp\u003e6) \u003cstrong\u003eData availability\u003c/strong\u003e \u0026ndash; The cases should have sufficient and reliable data on humanitarian responses from sources such as Disaster Response Operations Management, Information and Communication (DROMIC) reports, the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), and the International Federation of Red Cross and Red Crescent Societies (IFRC) to ensure a robust and evidence-based analysis.\u003c/p\u003e\n\u003cp\u003eWe selected the Marawi Siege and Severe Tropical Storm Tembin (Vinta) as our complex displacement scenario and Severe Tropical Storm Nalgae (Paeng) as our single-event displacement scenario. The case of the Marawi Siege and Vinta in December 2017 is relevant as the storm occurred just two months after the end of the conflict event. It illustrates how communities displaced by conflict can be subsequently affected by natural hazards, with the storm directly impacting communities already displaced by the conflict, further straining humanitarian response efforts\u0026nbsp;(Fernandez 2019). Examining this case offers insights into the challenges of responding to the overlap of disaster-induced and conflict-induced displacement and the complexities of data reporting in such contexts.\u003c/p\u003e\n\u003cp\u003eSevere Tropical Storm Nalgae (Paeng) in October 2022 represents a more recent example of a single-event displacement scenario. It occurred at a time when humanitarian responses were beginning to adapt more proactive approaches, particularly through the implementation of anticipatory action. \u0026nbsp;As climate-related hazards intensify, cases of complex displacement are likely to become more frequent (K. Peters and Dupar 2020; K. Peters et al. 2020). It is becoming pressing to understand the potential of anticipatory actions in addressing overlapping crises (Jaime et al. 2024; Chaves-Gonzalez et al. 2022). To the best of our knowledge, no complex displacement case in the Philippines has coincided with the implementation of anticipatory action. Therefore, while the primary focus of this comparative analysis is on differences in data reporting, Paeng also offers a useful contrast to illustrate the evolution from a reactive response during Vinta to a more proactive approach in Paeng, where the system was equipped \u0026ndash; at least in theory \u0026ndash; with tools to anticipate and act before impact. With that, we explore how pre-emptive humanitarian interventions can minimise disaster impacts during overlapping crises.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 provides a summary of both case studies, while Figure 1 presents a contextual map illustrating the geographic extent of each event. We acknowledge that these two real-world cases may not have identical characteristics regarding reported housing damage, displacement numbers, and aid allocations. However, the differences between both cases are analytically relevant as they reflect variation in data availability, reporting systems, and institutional capacities at the time of occurrence. We consider Vinta and Paeng sufficiently comparable along key dimensions, and that the contrasts that emerge between the cases offer valuable insights into how the quality of data and reporting influence humanitarian responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Summary of selected case studies\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase 1: Marawi Siege and Severe Tropical Storm Tembin (Vinta)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase 2: Severe Tropical Storm Nalgae (Paeng)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDate of occurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3333%;\"\u003e\n \u003cp\u003eMarawi Siege (May - October 2017)\u003c/p\u003e\n \u003cp\u003eVinta (December 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1667%;\"\u003e\n \u003cp\u003eOctober 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisplacement scenario\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3333%;\"\u003e\n \u003cp\u003eComplex displacement\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Vinta struck two months after the end of the Marawi Siege, impacting the same conflict-affected area)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1667%;\"\u003e\n \u003cp\u003eSingle-event displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard intensity (peak storm intensity within the Philippine Area of Responsibility)\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3333%;\"\u003e\n \u003cp\u003e120 km/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1667%;\"\u003e\n \u003cp\u003e100 km/h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtent of\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003edamage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3333%;\"\u003e\n \u003cp\u003e\u003cu\u003eVinta:\u003c/u\u003e 9,580 damaged houses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1667%;\"\u003e\n \u003cp\u003e77,742 damaged houses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisplacement figures (cumulative displacement)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3333%;\"\u003e\n \u003cp\u003e\u003cu\u003eMarawi Siege:\u003c/u\u003e 527,704 displaced persons\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cu\u003eVinta:\u003c/u\u003e 436,586 displaced persons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1667%;\"\u003e\n \u003cp\u003e3,030,198 displaced persons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize of humanitarian response\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.3333%;\"\u003e\n \u003cp\u003e\u003cu\u003eMarawi Siege:\u003c/u\u003e 764,585,081 PHP (13,757,477 USD)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cu\u003eVinta:\u003c/u\u003e 48,096,500 PHP\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(865,419 USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.1667%;\"\u003e\n \u003cp\u003e592,592,581 PHP\u003c/p\u003e\n \u003cp\u003e(10,662,749 USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2 id=\"_Toc198807424\"\u003e4.2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Description of cases\u003c/h2\u003e\n\u003ch3\u003e4.2.1 \u0026nbsp; \u0026nbsp; Case 1: Marawi Siege and Severe Tropical Storm Tembin (Vinta)\u003c/h3\u003e\n\u003cp\u003eOn 23 May 2017, just three days before the beginning of Ramadan, conflict erupted in Marawi City, the capital of Lanao del Sur province in the southern Philippines\u0026nbsp;(Dizon 2017; Maitem 2017). The Philippine military launched operations against local extremist groups, triggering intense fighting that lasted for 5 months\u0026nbsp;(\u003cem\u003eAmnesty International\u003c/em\u003e 2017; Bueza 2017). By the time a ceasefire was declared on 23 October 2017, the once-vibrant city had transformed into a landscape of ruins, leaving a profound psychological toll on those being displaced, who were primarily ethnic and religious minorities (Wapano and Dagalangit-Pundato 2024; Veloso 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDSWD reported that approximately half a million people were displaced during the Siege\u0026nbsp;(DSWD 2017). Many people fled to neighbouring cities and municipalities in Northern Mindanao and the Autonomous Region of Muslim Mindanao (ARMM), now known as the Bangsamoro Autonomous Region of Muslim Mindanao (BARMM)\u0026nbsp;(Veloso 2022). In 2023, an estimated 71% of the country\u0026rsquo;s conflict-induced internally displaced persons (IDPs) still trace their displacement back to the 2017 conflict\u0026nbsp;(IDMC 2024).\u003c/p\u003e\n\u003cp\u003eJust two months after the end of the siege, on 22 December 2017, Tropical Storm Tembin (Vinta) hit Mindanao (IFRC 2019), affecting areas hosting the Marawi Siege IDPs (Fernandez 2019). The storm brought heavy rainfall that triggered widespread flooding and landslides (IFRC 2019), damaging evacuation centres that were sheltering IDPs of the Marawi Siege (UNHCR 2017). It is estimated that a total of 436,586 persons were displaced (DSWD 2018a) and a total of 9,580 houses were damaged by Severe Tropical Storm Tembin (Vinta) (DSWD 2018b). During the disaster response, the coordination and delivery of aid to consecutive crises were made complex given the presence of violence, as humanitarian organisations faced security-related challenges due to the presence of non-state armed groups during Tembin (Vinta) (IFRC 2019; Fernandez 2019).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e4.2.2 \u0026nbsp; \u0026nbsp; Case 2: Severe Tropical Storm Nalgae (Paeng)\u003c/h3\u003e\n\u003cp\u003eOn 28 October 2022, Severe Tropical Storm Nalgae (Paeng) made its first landfall in the Philippines (PAGASA 2022). The storm brought intense rains to the country, resulting in massive flooding and rain-induced landslides in Luzon, Visayas, and Mindanao (UNOCHA 2022a). At the height of the disaster, displacement figures peaked at over 1.1 million people, with cumulative numbers eventually recording over 3 million people (DSWD 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePaeng occurred at a time when anticipatory action mechanisms were gaining traction in the Philippines. Since 2019, the country has served as a pilot site for FbF and EAPs for typhoons and floods, led by the Philippine Red Cross (PRC)\u0026nbsp;(Anticipation Hub 2021). Informed by forecast thresholds, these systems are designed to activate funding and pre-planned actions such as evacuation of livestock, early harvesting of crops, and shelter strengthening, enabling early intervention ahead of a disaster\u0026rsquo;s impact (ibid).\u003c/p\u003e\n\u003cp\u003eTwo days prior to Paeng\u0026rsquo;s entry into the PAR, the Philippines Department for Social Welfare and Development (DSWD) began releasing preparedness reports mapping predicted populations requiring assistance. Around the same time, early warning indicators also reached the threshold for the Philippine Red Cross (PRC) to activate the EAP for typhoons in several municipalities in the Aurora Province, including Casiguran, Dinangulan, Dilasag, and Dipaculao\u0026nbsp;(IFRC 2023). PRC mobilised staff and volunteers to distribute and install shelter strengthening kits for 78 targeted households in the Municipality of Dipaculao.\u003c/p\u003e\n\u003cp\u003eOn 31 October 2022, Paeng weakened and exited the Philippine Area of Responsibility (PAR)\u0026nbsp;(DSWD 2023). Despite these early actions, the scale of disruption from Paeng was still significant across the country, damaging 77,742 houses\u0026nbsp;(DSWD 2023). Paeng marked the first instance of EAP being triggered and FbF being utilised in the country\u0026nbsp;(IFRC 2023), seen as a crucial step in enhancing disaster preparedness.\u003c/p\u003e\n\u003ch2 id=\"_Toc198807425\"\u003e4.3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Data collection\u003c/h2\u003e\n\u003cp\u003eWe drew on a combination of grey literature comprising reports produced by government and humanitarian organisations and semi-structured interviews to examine how displacement data was collected and reported in both cases. While often an overlooked source of knowledge in peer-reviewed studies, grey literature is a critical source in the humanitarian and non-profit sector, offering insights drawn from years of operational experience (Davidson 2017; Lawrence 2017). Meanwhile, semi-structured interviews with humanitarian practitioners were used to complement the analysis by helping interpret reported patterns and offering context from the ground (Adams 2015). The combination of these data sources was intended to capture both the quantitative dimensions of displacement reporting and the qualitative insights needed to contextualise these reporting practices. Figure 4 presents the methodological framework for the comparative analysis.\u003c/p\u003e\n\u003ch3\u003e4.3.1 \u0026nbsp; \u0026nbsp; Displacement, meteorological, and response reporting data\u003c/h3\u003e\n\u003cp\u003eWe reviewed three sources of grey literature:\u003c/p\u003e\n\u003cp\u003e1) \u003cstrong\u003eDSWD DROMIC Situation Reports\u003c/strong\u003e served as the primary source of displacement data. These reports are published by the Philippine Department of Social Welfare and Development (DSWD) during disasters to track the evolving situation. They provide numbers of internally displaced persons (IDPs) both inside and outside evacuation centres, with figures typically disaggregated by administrative boundaries (region, province, municipality). The reports are updated daily during the emergency phase and are publicly available.\u003c/p\u003e\n\u003cp\u003e2) \u003cstrong\u003ePAGASA Summary Meteorological Reports\u003c/strong\u003e were used as supplementary references to understand the meteorological timeline of Vinta and Paeng. These reports include detailed summaries of each storm\u0026rsquo;s progression, documenting key moments in a storm\u0026rsquo;s lifecycle, including its entry into the PAR, the timing and location of landfall(s), peak intensity within the PAR, and its eventual exit. Aligning these timelines with DROMIC reporting allowed us to assess the timing and responsiveness of reporting relative to the progression of the storms.\u003c/p\u003e\n\u003cp\u003e3) \u003cstrong\u003eIFRC and PRC Reports\u003c/strong\u003e provided supplementary information on the implementation of anticipatory action in the case of Paeng. As leading actors in disaster response in the Philippines, the International Federation of Red Cross and Red Crescent Societies (IFRC) and Philippine Red Cross (PRC) produced reports detailing early action protocols, shelter-based interventions, and the targeting of beneficiaries. These documents provided context for the operational rollout of anticipatory action.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e4.3.2 \u0026nbsp; \u0026nbsp; Interview data\u003c/h3\u003e\n\u003cp\u003eParticipants were selected based on two criteria: (1) a previous or current affiliation with a humanitarian organisation, government body, or local university in the Philippines; and (2) a minimum of three years\u0026rsquo; experience in displacement response in the Philippines. Our selection included both international and local humanitarian organisations as well as academics from Mindanao-based universities who engaged in peacebuilding activities and provided valuable insights into the deeply rooted interplay of climate and conflict. We prioritised individuals with at least three years of experience as we considered this level of experience necessary for providing informed perspectives on displacement trends and organisational practices.\u003c/p\u003e\n\u003cp\u003eWe recruited 32 participants from United Nations organisations (9), Red Cross societies (5), bilateral organisations (3), non-governmental organisations (11), national government (2), and local universities (2), consisting of approximately 60% women and 40% men. We ensured diversity of perspectives and experiences by including organisations of varying scales, size, and mandate. Participants were identified through staff directories on humanitarian organisation websites, LinkedIn searches, and snowball sampling. Recruitment concluded once we achieved balance across organisational types and reached thematic saturation, where no substantially new insights emerged (Guest, Bunce, and Johnson 2006). Interviews were conducted both in-person (14) and online (18). Before commencing, participants were provided with an information statement, and written consent was obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring interviews, we explored participants\u0026rsquo; observations on the intersection of climate-induced and conflict-induced displacement, prompting questions such as \u003cem\u003e\u0026ldquo;Could you share a bit about your observations of seeing climate and conflict overlap and creating displacement?\u0026rdquo;\u003c/em\u003e For those who indicated direct experience of responding during the Marawi Siege and Severe Tropical Storm Vinta, follow-up questions were asked for event-specific insights: \u003cem\u003e\u0026ldquo;For the case of Marawi Siege and Typhoon Vinta, can you tell me a bit more about what happened and what humanitarian organisations were doing at that time?\u0026rdquo;\u003c/em\u003e We also posed reflective and forward-looking questions such as \u003cem\u003e\u0026ldquo;What do you think are the gaps that humanitarian organisations have yet to fill when they\u0026rsquo;re addressing overlapping displacement crises?\u0026rdquo;\u0026nbsp;\u003c/em\u003eand \u003cem\u003e\u0026ldquo;Do you see any opportunities for responses to disaster displacement and conflict displacement to overlap and learn from the other?\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInterviews were semi-structured and supplemented with follow-up \u0026lsquo;why\u0026rsquo; or \u0026lsquo;how\u0026rsquo; questions to explore participants\u0026rsquo; perspectives in greater depth (Adams 2015). This flexible format allowed participants to steer the conversation towards themes relevant to their experience. At the end of the interview, we asked closing remarks such as \u003cem\u003e\u0026ldquo;Is there anything else you want to comment on or any questions you have for us?\u0026rdquo;\u003c/em\u003e, inviting them to share anything that came to mind beyond the scope of the interview questions.\u003c/p\u003e\n\u003ch2 id=\"_Toc198807426\"\u003e4.4 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Data analysis\u003c/h2\u003e\n\u003ch3\u003e4.4.1 \u0026nbsp; \u0026nbsp; Time-series and spatial analysis of displacement\u003c/h3\u003e\n\u003cp\u003eTo analyse patterns in displacement reporting, we constructed time-series plots that will allow us to observe reporting frequency, identify inconsistencies, and detect gaps in displacement data throughout the reporting period. We extracted key variables from the DROMIC reports for each event, including the report number, report release date, and the total number of IDPs (inside and outside evacuation centres). The data were extracted and processed using Python (see Appendix C-H) and plotted chronologically. For Case 1, the Marawi Siege and Vinta displacement figures were plotted on a unified timeline to examine how reporting practices responded to overlapping conflict and climate events. This allowed for identifying potential gaps, delays, or discontinuities in reporting during concurrent displacement. For Case 2, a separate timeline was created for Severe Tropical Storm Paeng using a matching temporal resolution to enable comparison with Vinta. To evaluate the responsiveness of humanitarian reporting relative to storm development, report dates of Vinta and Paeng were cross-referenced with meteorological information from respective PAGASA reports\u0026nbsp;(PAGASA 2019; 2022). Specifically, we examined the timing of preparedness reports relative to the storm\u0026rsquo;s entry into PAR, the continuity of situation reporting following landfall, and the document of returns relative to the landfall dates.\u003c/p\u003e\n\u003cp\u003eTo examine spatial patterns in reporting for the complex displacement scenario (Case 1), we mapped province-level IDP figures for Vinta, with particular attention to how the preceding Marawi Siege may have influenced reporting in affected areas. We extracted cumulative IDP data from the DROMIC report dated 17 January 2018 \u0026ndash; the final DROMIC report\u0026nbsp;(2018a)\u0026nbsp;containing cumulative figures by province. We combined IDP counts inside and outside evacuation centres to determine total displacement per province. As the provinces have varying population sizes, we normalised the IDP numbers by calculating the proportion of IDPs relative to each province\u0026rsquo;s population, using the 2015 population census data from the Philippine Statistics Authority\u0026nbsp;(2016)\u0026nbsp;\u0026ndash; the most recent census available prior to the 2017 events. These figures were then standardised and expressed as reported IDPs per 100,000 population, allowing for more meaningful comparison across regions.\u003c/p\u003e\n\u003cp\u003eUsing Quantum Geographic Information System (QGIS), we mapped the calculated IDP proportions by provinces onto administrative boundaries\u0026nbsp;(NAMRIA 2023)\u0026nbsp;to assess spatial variation in displacement reporting. In addition, we overlaid the track position of Vinta, as recorded in the PAGASA report\u0026nbsp;(2019), to contextualise the storm\u0026rsquo;s trajectory relative to areas impacted by the Marawi Siege. This spatial mapping enabled the identification of reporting disparities during Vinta in post-conflict areas, offering insights into how reporting responded spatially to complex displacement scenarios.\u003c/p\u003e\n\u003ch3\u003e4.4.2 \u0026nbsp; \u0026nbsp; Interview qualitative analysis\u003c/h3\u003e\n\u003cp\u003eInterview transcripts were analysed thematically using NVivo, guided by a grounded theory approach that allowed patterns and concepts to emerge from the data\u0026nbsp;(Noble and Mitchell 2016; Flick 2018). We created initial codes such as \u0026ldquo;complex displacement\u0026rdquo;, \u0026ldquo;climate vs conflict response\u0026rdquo;, and \u0026ldquo;examples of overlap responses\u0026rdquo; during the first round of coding. As new insights emerged, additional themes were created while existing codes were refined. This process was carried out iteratively, allowing the merging of overlapping codes and continuous adjustment to ensure thematic consistency across datasets\u0026nbsp;(Nowell et al. 2017; King 2004).\u003c/p\u003e\n\u003ch3\u003e4.4.3 \u0026nbsp; \u0026nbsp; Cross-case comparative analysis and synthesis\u003c/h3\u003e\n\u003cp\u003eWe employed cross-case synthesis as the primary strategy for our comparative analysis, following the analytic approach outlined by Yin\u0026nbsp;(2009). We began by analysing each case independently to identify within-case patterns, drawing upon both time-series plots and qualitative interview data. Rather than relying on direct variable comparisons, we adopted a qualitative lens to synthesise findings and retain the holistic integrity of each case\u0026nbsp;(Yin 2009). We examined each event\u0026rsquo;s reporting trajectory by analysing key dimensions such as the frequency and duration of reporting, the scale and resolution of displacement data, and the alignment of reporting timelines with meteorological benchmarks. These within-case analyses explored \u0026ldquo;how\u0026rdquo; displacement data was reported. Interview data were used to supplement some of the \u0026ldquo;why\u0026rdquo; questions and provide contextual insights. After constructing a detailed narrative for each case, we then proceeded to cross-case comparison.\u003c/p\u003e\n\u003cp\u003eFor the complex displacement scenario (Case 1), we also analysed how displacement was represented (or underrepresented) through time-series plots and a spatial map of displacement data. We considered how existing conflict, security issues, and institutional presence may have shaped the reporting process. These findings were then compared against the single-event displacement scenario (Case 2) to examine whether similar patterns were present. In Case 2, we focused on the role of anticipatory action, particularly how predictive data was reported and when preparedness reports were released. Comparing this with Case 1, which occurred five years earlier in the absence of anticipatory action, allowed us to explore how institutional preparedness and data practices may have evolved over time.\u003c/p\u003e\n\u003cp\u003eFollowing Yin\u0026rsquo;s\u0026nbsp;(2009)\u0026nbsp;guidance, we sought to \u0026ldquo;think upward conceptually\u0026rdquo; by examining broader themes emerging from the synthesis. This included identifying divergent patterns, such as how reporting practices differ in complex versus single-event displacement scenarios, and how humanitarian responses evolve from reactive to proactive approaches. This cross-case synthesis enabled a more grounded and forward-looking discussion on how humanitarian systems can be adapted to better anticipate and respond to the realities of complex displacement.\u003c/p\u003e\n\u003ch2\u003e4.5 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Limitations\u003c/h2\u003e\n\u003cp\u003eWe recognise several limitations in our analysis. Firstly, we rely on displacement data directly from DROMIC reports. However, reporting during the emergency and response phase often experiences delays and regional inconsistencies, so both displacement and return data may lag or vary in quality across provinces and municipalities. Secondly, our study is grounded primarily in documented data and reports, which means we capture only one dimension of humanitarian response. Exogenous factors such as institutional capacity, funding availability, and media attention likely influenced reporting practices but lie beyond our data-centric scope. Furthermore, there is a five-year gap between Vinta (2017) and Paeng (2022), during which broader humanitarian system reforms and technological advances may account for some differences we observe, irrespective of the overlapping occurrence of disaster and conflict events. Finally, our reliance on grey literature and interviews may introduce biases based on source availability and participant recall of their memory. Despite these constraints, the patterns we identify, such as reporting frequency and resolution, spatial underrepresentation, and the emergence of anticipatory data, still offer a reliable basis for recommendations to strengthen reporting in complex displacement contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualisation: S.S., A.O., S.B.; Data curation: S.S.; Formal analysis: S.S.; Funding acquisition: A.O., S.B.; Investigation: S.S.; A.O., S.B.; Methodology: S.S.; A.O.; S.B.; Supervision: A.O., S.B.; Validation: S.S., A.O., S.B.; Visualisation: S.S.; Writing \u0026ndash; original draft: S.S.; Writing \u0026ndash; review \u0026amp; editing: A.O., S.B.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the Sydney Environment Institute (SEI) for supporting this research through a collaborative grant. We also extend our gratitude to the participants who generously shared their time, insights, and experiences during the interviews. Their contributions were invaluable to this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the supplementary information files.\u003c/p\u003e\u003cp\u003eAudio recordings or transcripts of interviews will not be shared to protect the privacy of participants and the sensitivity of their statements regarding personal stories and experiences. However, notes summarising the discussions can be provided upon reasonable request. All displacement figures analysed are provided in the Supplementary Material.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Python code used to extract displacement numbers from DSWD DROMIC is provided in the Supplementary Material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams, William C. 2015. \u0026lsquo;Conducting Semi-Structured Interviews\u0026rsquo;. In \u003cem\u003eHandbook of Practical Program Evaluation\u003c/em\u003e, 492\u0026ndash;505. 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UNHCR and IOM.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWMO. 2015. \u0026lsquo;WMO Guidelines on Multi-Hazard Impact-Based Forecast and Warning Services\u0026rsquo;. WMO-No. 1150. World Meteorological Organization (WMO). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://library.wmo.int/records/item/54669-wmo-guidelines-on-multi-hazard-impact-based-forecast-and-warning-services?offset=\u003c/span\u003e\u003cspan address=\"https://library.wmo.int/records/item/54669-wmo-guidelines-on-multi-hazard-impact-based-forecast-and-warning-services?offset=\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin, Robert K. 2009. \u003cem\u003eCase Study Research: Design and Methods\u003c/em\u003e. SAGE.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e This map displays displacement data for the island of Mindanao only. IDP figures reflect cumulative totals reported by DSWD DROMIC as of 17 January 2018. Population data are based on the 2015 Philippine census published by the Philippine Statistic Authority (PSA). Administrative boundaries were downloaded from the Humanitarian Data Exchange. The map uses the ESRI Gray (light) basemap sourced from ArcGIS Online.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The duration of wind speeds is reported as 10-minute maximum sustained wind speeds.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e There are inconsistencies in the cumulative displacement numbers reported during the Marawi Siege. The figure cited here reflects the highest cumulative displacement number throughout the reporting period, extracted from the DSWD DROMIC Report #71 dated 20 July 2017.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7043272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7043272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDisasters and conflict increasingly overlap in driving human displacement, yet humanitarian systems have traditionally reported displacement events in silos, leaving communities overlooked. Little is known about who is left out and how reporting gaps occur. We conduct a comparative analysis of data reporting in the Philippines, examining cases of the Marawi Siege and Severe Tropical Storm Tembin (Vinta) in 2017 and Severe Tropical Storm Nalgae (Paeng) in 2022. We present disparities in the frequency, temporal and spatial coverage of complex displacement reporting. Our results provide empirical evidence of exclusion in complex displacement reporting that arises from event-based reporting frameworks that prioritise discrete, single-hazard responses that overlook politically sensitive crises. We highlight that capturing displacement data is not only an accounting exercise \u0026ndash; it constructs the way displacement is understood. We argue for more dynamic, multi-trigger displacement tracking and for the use of integrated displacement and conflict data in advancing anticipatory action.\u003c/p\u003e","manuscriptTitle":"Overlapping disaster and conflict events reveal gaps in displacement reporting in the Philippines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 05:54:23","doi":"10.21203/rs.3.rs-7043272/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55102522-4114-4017-adea-127b5cd5ce97","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51296649,"name":"Scientific community and society/Geography"},{"id":51296650,"name":"Social science/Geography"},{"id":51296651,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-08-18T16:10:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-16 05:54:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7043272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7043272","identity":"rs-7043272","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}