are identified as flooded by both, but of the remaining 235 (38%), 147 are identified as flooded only according to ECOSIT 5 reports and 88 only according to VFM flood detection. The communities identified only according to ECOSIT 5 reports likely represented exposure in 2021 and 2022, major years of flooding in Chad not captured in the VFM archive. The communities identified only according to the VFM archive are not surprising given the results in Table 8 showing recency bias in survey flood reports, as households may not accurately have recalled their exposure in 2019 and 2020 when surveyed in 2022, especially given the widespread flooding in 2022. An important implication is that the analysis of the impact of flood exposure on household outcomes is therefore identified off of different sets of communities according to each definition, and represents the impacts of different flood events.

## 4.2 Empirical approach

We conduct a difference-in-differences analysis to identify the impact of community flood exposure on household outcomes. Regressions take the form:

$$Y_{icrt} = \alpha + \beta Flood_c \times Post_t + \mu_i + \gamma_{rt} + \lambda X_{icrt} + \epsilon_{icrt} \quad (1)$$

where outcome Y varies across households i, communities c, provinces r, and rounds t. Post is an indicator for being observed after the first flood event. For the VFM definition, this is 2019 or 2020, and for the flood report definition, this is ECOSIT 5 only. Flood is an indicator for the flood exposure treatment: having ever been exposed during the relevant period. This is an intent to treat analysis as flood exposure is defined at the community level based on proximity to detected flooding events and therefore not all households in the treatment group will have been directly affected by flooding. This may bias estimated effects downward but avoids concern about selection in which households are exposed and will capture both direct effects of household flood damages as well as any indirect impacts from proximity to flooding. We cluster standard errors at the level of the community of residence in ECOSIT 4 since this is the level at which the flooding treatment is assigned.

As the HFPS was implemented as a short phone survey with few modules each round, we are restricted in the set of panel outcomes we can study. We focus on a subset of outcomes tracked across all survey rounds. These include whether the household is engaged in agriculture and non-farm household enterprise, whether the survey respondent did any form of work activity in the 7 days before the survey, a normalized food insecurity index composed of self-reports of different forms of food insecurity, household perceptions of absolute and relative well-being, and an indicator for whether the household reported experiencing any non-flood shock. We note that the timeframe for some of these questions differs between ECOSIT 4/5 and the HFPS, with the food security questions in particular asking about the past 12 months in ECOSIT 4 and 5 compared to the last 30 days in the HFPS, as those surveys were more frequent. As these differences are common across all households they should be captured by the round fixed effects.
The main results we present estimate Equation 1 using two-way fixed effects (TWFE). We include household fixed effects ($\mu$) to control for time-invariant household characteristics which might affect outcomes and flood exposure, and province-by-year fixed effects ($\gamma$) to control for common changes over time across broad geographic areas. Notably, this will account for local differences in the effects of the COVID-19 crisis which began after ECOSIT 4 and before the HFPS survey rounds. $X_{icrt}$ are time-varying controls at the community level which might be correlated with both flood exposure and the outcomes.

Because there are multiple flood exposure events in the VFM definition, we also estimate Equation 1 using methods from the modern difference-in-differences literature that account for staggered treatment timing (De Chaisemartin and d'Haultfoeuille, 2023). In particular, we apply the Borusyak et al. (2024) and Callaway and Sant'Anna (2021) and estimators. These methods apply a weighted approach to handle staggered timing and treatment effect heterogeneity which may lead to more accurate pre-trend estimates, though each method calculates pre-trends differently. We include the same fixed effects, controls, and clustering as in the TWFE estimators when using these alternative estimators, and use them to compare both average treatment effects and changes in treatment effects over time. In general, average estimated treatment effects are similar across methods. We apply only the TWFE estimator when defining flood exposure based on survey reports as with this definition we cannot identify specific treatment timing and for all exposed households the treatment occurred between when they were surveyed in ECOSIT rounds 4 and 5.

### 4.2.1 Identifying causal impacts of flood exposure

The effects of experiencing flooding for all estimators are identified using variation within households over time across communities with similar observable characteristics that were and were not exposed to flooding within regions and survey rounds. The key assumption for this empirical strategy to identify causal effects of flood exposure is that, conditional on control variables, outcomes in communities exposed to flooding would have evolved in the same way as unexposed communities had it not been for the flood shock. This would be the case in expectation if flood exposure were randomly assigned to communities, but while flooding realizations have an exogenous or quasi-random component linked to variation in weather patterns, flood risk is not distributed randomly over space as discussed in Subsection 2.1. In other words, flood realizations have an endogenous component which must be accounted for in the analysis.

To do this, we follow Borusyak and Hull (2023)'s method for dealing with non-random exposure to exogenous shocks. In short, we separate out the endogenous component of flood exposure by estimating the probability that a community was exposed to a flood during the relevant time period under each definition, and controlling for differences in outcomes by flood exposure probability in each round. This approach is similar conceptually to combining difference-in-differences with matching on covariates (Ham and Miratrix, 2024; Stuart et al., 2014). Including these exposure probability by round controls in the regression models means that effects of flood exposure are identified by comparing communities in the same province with the same exposure probability but
different exposure realizations, therefore isolating impacts of the weather-driven exogenous flood shock component. Exposure probabilities are estimated in a machine learning framework using lasso regression to identify relevant predictors from among annual VFM flood detection measures in each community together with ECOSIT 4 flood reports and the community characteristics included in Table 8, and then including these in a logit model to predict flood exposure probability according to both the VFM and ECOSIT 5 reports. We then only retain communities within the range of common support of estimated probabilities among exposed and non-exposed communities. We find that controlling for the estimated probability of flood exposure fully accounts for the relationship between exposure status and historical VFM-detected annual exposure from 2012-2018: we cannot reject that there is not relationship between past flood detection and either flood exposure treatment definition we use after controlling for exposure probability (Table A6, Table A7).

While controlling for flood exposure probability increases the plausibility that communities would have evolved similarly in the absence of any flooding, this assumption is not empirically testable. Still, one suggestive test is to analyze balance in baseline characteristics of households during ECOSIT 4 by community flood exposure status. We test for balance on a variety of household and household head characteristics, on reports of shock exposure, and on measures of livelihood activities and household well-being. We find significant differences in baseline characteristics of households in communities exposed to either VFM-detected floods in 2019-2020 or household-reported floods in 2019-2022 (Table A8, Table A9), consistent with Table 8 and Table 5 showing that a variety of community and household characteristics are associated with ECOSIT flood reports. However, these differences disappear after controlling for the estimated probability of flooding, and we cannot reject the null that all of the characteristics we consider together explain no significant variation in flood exposure conditional on this probability.<fnref n="22" /> Balance in baseline characteristics is not necessary to identify causal impacts of flooding in a difference-in-differences framework but showing strong baseline balance suggests that the assumption of parallel trends in outcomes in the absence of flooding seems plausible.

## 4.3 Results

Table 13 presents the results of two-way fixed effects analyses of the impacts of flooding defined based on VFM detection of flooded pixels within 1 km (column 1) and reported recall of flood shocks at the time of the ECOSIT 5 surveys (column 2). We note again that in addition to reflecting different measures of flood exposure, the two measures also capture different flood events. The flooded pixel measure only considers flood exposure in 2019 or 2020, while the flood report measure considers any exposure from 2019-2022.

Under both definitions there is no average effect of community flood exposure after the ECOSIT 4 round on whether the survey respondent is engaged in any work activity at the time of later surveys, respondent perceptions of household well-being, or household food insecurity. We emphasize that these are *intent to treat* estimates averaging over all households in affected communities, which

<footnote n="22">²²The F-test *p* values are *p* = 0.250 for the VFM-based definition and *p* = 0.156 for the report-based definition.</footnote>
are likely to be attenuated toward zero if households not directly harmed by local flooding do not experience any adverse effects. This is important in a context where just 21% of households in communities with any report of a flood shock during ECOSIT 5 reported such a shock, on average. Impacts on households directly experiencing the flood shock may be larger than the average effects at the community level that we estimate. For example, the point estimates for the food insecurity index are large but not statistically significant, and may reflect large insecurity effects for directly affected households and null effects for others in the same community. We do not estimate these direct “treatment on the treated” effects, first because we do not know which households were specifically affected by the 2019-2020 floods under the VFM definition, and second because direct flooding exposure is correlated with many household characteristics likely to affect levels and trends in the outcomes of interest (Table 5).

<table><caption>Table 13: Average impacts of community flood exposure</caption><thead><tr><th></th><th>(1)<br>Any flooded pixel<br>(SE)</th><th>(2)<br>Any flood report<br>(SE)</th></tr></thead><tbody><tr><td>Respondent did any work in last 7 days</td><td>-0.05<br>(0.03)</td><td>-0.01<br>(0.03)</td></tr><tr><td>Any HH non-farm enterprise</td><td>0.07*<br>(0.04)</td><td>0.04<br>(0.03)</td></tr><tr><td>Any HH crop or livestock activity</td><td>-0.00<br>(0.02)</td><td>0.04***<br>(0.01)</td></tr><tr><td>HH believes is worse off than neighbors</td><td>-0.03<br>(0.03)</td><td>-0.03<br>(0.02)</td></tr><tr><td>HH believes it is poor or very poor</td><td>0.02<br>(0.02)</td><td>0.01<br>(0.02)</td></tr><tr><td>Normalized HH food insecurity index</td><td>0.09<br>(0.07)</td><td>0.03<br>(0.05)</td></tr><tr><td>Household reported a non-flood shock</td><td>-0.06<br>(0.04)</td><td>0.10***<br>(0.03)</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT surveys and VFM archive. The table shows the results of separate regressions of a particular variable (shown in the rows) on the interaction of flood exposure and a post-exposure dummy, following Equation 1. Flood exposure in column (1) is based on VFM detection of any flooded pixel within 1 km of the community centroid in 2019 or 2020. In column (2) it is based on any household in a community reporting a flood shock over the 2019-2022 recall period. All specifications include household, region by round, and flood exposure probability by round fixed effects. Respondent work in the last 7 days can include any household farm or non-farm work as well as wage employment or unpaid labor, but not domestic work. Participation in crop or livestock activity is defined over the past agricultural season. Participation in non-farm enterprise is defined based on any active enterprises at the time of the survey. The food insecurity index is constructed as the sum of eight normalized variables capturing different aspects of self-reported food insecurity. Household well-being is based on the respondent's own perceptions. * *p* < 0.1, ** *p* < 0.05, *** *p* < 0.01

Despite considering only average effects of community flood exposure, we do find evidence of significant impacts on certain outcomes, though the results differ by flooding definition. Households in communities with nearby flooded pixels in 2019 or 2020 are 7 percentage points more likely to be engaged in non-farm enterprise in the post-flood surveys, or a 16% increase over the mean of 43% of households in unexposed communities. The point estimate is also positive but not statistically significant using the reported flood definition. Households in communities with any reported flood from 2019-2022 are 4 percentage points more likely to be engaged in household agriculture in the post-flood surveys, which is a 6% increase over the control mean. Reported community flood exposure also increases the probability the household reports any non-flood shock in the period after the flooding by 10 percentage points (14%). This indicates that flooding may increase vulnerability
to later shocks, though there is no significant effect and the point estimate is negative under the VFM flooding definition.

Table 14 presents estimates of impacts of flood exposure over time as well as average impacts using the Borusyak et al. (2024) estimator for the VFM-based flood definition only, as the estimates using the survey-based definition are based on only two time periods, ECOSIT 4 and 5. Consistent with balance in baseline characteristics, there are no significant differences by flooding status in the period before exposure. Average effects over all post-exposure periods (at the bottom of the table) are similar to those in Table 13; only the effect on household engagement in non-farm enterprise is statistically significant. This effect is driven partly by large and significant increases in the period during and immediately following flood exposure: in this case, the first two rounds of the HFPS. Estimated effects remain positive but are not statistically significant in subsequent rounds. This pattern indicates that engaging in non-farm enterprise work may represent a coping mechanism for households whose livelihoods are disrupted by flooding, and that this engagement does not persist for all affected households.

<table><caption>Table 14: Impacts of community flood exposure over time</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th></th><th>Respondent working</th><th>Any non-farm enterprise</th><th>Any HH agriculture</th><th>Well-being worse than neighbors</th><th>Considers HH poor</th><th>Food insecurity index</th></tr></thead><tbody><tr><td>t-1</td><td>-0.07<br>(0.07)</td><td>0.06<br>(0.08)</td><td>-0.02<br>(0.03)</td><td>0.03<br>(0.08)</td><td>0.01<br>(0.04)</td><td>0.08<br>(0.21)</td></tr><tr><td>Treatment period</td><td>0.04<br>(0.04)</td><td>0.11**<br>(0.05)</td><td>0.00<br>(0.02)</td><td>-0.07*<br>(0.04)</td><td>0.04<br>(0.03)</td><td>0.08<br>(0.10)</td></tr><tr><td>t+1</td><td>0.02<br>(0.04)</td><td>0.11**<br>(0.05)</td><td>0.00<br>(0.03)</td><td>-0.03<br>(0.04)</td><td>-0.01<br>(0.03)</td><td>0.02<br>(0.11)</td></tr><tr><td>t+2</td><td>0.02<br>(0.04)</td><td>0.08<br>(0.05)</td><td>0.00<br>(0.03)</td><td>-0.04<br>(0.04)</td><td>0.00<br>(0.04)</td><td>-0.02<br>(0.11)</td></tr><tr><td>t+3</td><td>-0.04<br>(0.04)</td><td>0.08<br>(0.05)</td><td>0.01<br>(0.02)</td><td>-0.07**<br>(0.04)</td><td>0.02<br>(0.03)</td><td>0.02<br>(0.08)</td></tr><tr><td>t+4</td><td>-0.04<br>(0.05)</td><td>0.06<br>(0.06)</td><td>0.00<br>(0.03)</td><td>-0.03<br>(0.04)</td><td>0.03<br>(0.03)</td><td>0.06<br>(0.09)</td></tr><tr><td>Observations</td><td>14267</td><td>14267</td><td>14267</td><td>13478</td><td>14171</td><td>14267</td></tr><tr><td>Average effect</td><td>-0.01</td><td>0.08</td><td>0.00</td><td>-0.05</td><td>0.02</td><td>0.03</td></tr><tr><td>(Standard error)</td><td>0.03</td><td>0.04</td><td>0.02</td><td>0.03</td><td>0.02</td><td>0.07</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT surveys and VFM archive. The table shows the results of separate regressions of a particular variable (shown in the columns) on flood exposure over time, using the Borusyak et al. (2024) staggered difference-in-differences estimator accounting for the timing of first exposure across communities. Flood exposure in column is based on VFM detection of any flooded pixel within 1 km of the community centroid in 2019 or 2020. All specifications include household, region by round, and flood exposure probability by round fixed effects. See Table 13 for outcome variable descriptions * *p* < 0.1, ** *p* < 0.05, *** *p* < 0.01

The only other significant average estimated effects of flood exposure in Table 14 are decreases in the likelihood that the respondent perceives the household as being less well-off than its neighbors. Both immediately during the period of flood exposure and a few rounds later, respondents are 7 percentage points (26%) less likely to perceive themselves as being worse off than other households in the same community. We also find a negative point estimate for the effect of the reported flood
treatment on this outcome (Table 13 column 2), which is close to being marginally statistically significant ($p = 0.120$). One explanation is that the majority of households in flooded communities are not directly affected (as shown in the flood shock reports), and therefore perceive themselves as better off in subsequent periods than the minority of households that directly experience a shock.

### 4.3.1 Heterogeneity

The differences in effects by flooding definition Table 13 suggest that impacts of flood exposure may depend on community characteristics, as the two flooding definitions identify different sets of treated communities. The VFM definition is more likely to identify flooding in urban communities than the survey-based definition, for example. Table 15 presents tests of differences in flood impacts by rural/urban location. Although differences in effects by location are not statistically significant under either treatment definition for almost all outcomes, we do observe several interesting results. First, both definitions show large and significant increases in non-farm enterprise participation after flood exposure in urban areas. This implies that opportunities to earn income from such activities may be more restricted in rural areas making it a less attractive flood coping strategy.

<table><caption>Table 15: Impacts of community flood exposure by rural/urban location</caption><thead><tr><th></th><th>(1)<br>Any flooded<br>pixel,<br>rural= 0<br>(SE)</th><th>(2)<br>Any flooded<br>pixel<br>× rural= 1<br>(SE)</th><th>(3)<br>Any flood<br>report,<br>rural= 0<br>(SE)</th><th>(4)<br>Any flood<br>report<br>× rural= 1<br>(SE)</th></tr></thead><tbody><tr><td>Respondent did any work in last 7<br>days</td><td>0.00<br>(0.04)</td><td>-0.08<br>(0.06)</td><td>-0.07*</td><td>0.11*</td></tr><tr><td>Any HH non-farm enterprise</td><td>0.08**<br>(0.04)</td><td>0.03<br>(0.08)</td><td>0.08*<br>(0.04)</td><td>-0.06<br>(0.06)</td></tr><tr><td>Any HH crop or livestock activity</td><td>-0.00<br>(0.02)</td><td>-0.00<br>(0.03)</td><td>0.03<br>(0.02)</td><td>0.03<br>(0.03)</td></tr><tr><td>HH believes is worse off than<br>neighbors</td><td>-0.03<br>(0.04)</td><td>0.03<br>(0.06)</td><td>0.00<br>(0.03)</td><td>-0.06<br>(0.04)</td></tr><tr><td>HH believes it is poor or very<br>poor</td><td>0.04<br>(0.03)</td><td>-0.04<br>(0.04)</td><td>0.00<br>(0.03)</td><td>0.01<br>(0.04)</td></tr><tr><td>Normalized HH food insecurity<br>index</td><td>0.00<br>(0.10)</td><td>0.15<br>(0.13)</td><td>0.07<br>(0.08)</td><td>-0.12<br>(0.10)</td></tr><tr><td>Household reported a non-flood<br>shock</td><td>-0.07<br>(0.06)</td><td>0.04<br>(0.08)</td><td>0.11***<br>(0.04)</td><td>-0.03<br>(0.06)</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT surveys and VFM archive. The table shows the results of separate regressions of a particular variable on the interaction of flood exposure and the community being rural. Flood exposure in columns 1 and 2 is based on VFM detection of any flooded pixel within 1 km of the community centroid in 2019 or 2020. In columns 3 and 4 it is based on any household in a community reporting a flood shock over the 2019-2022 recall period. In each set of 2 columns, the first column represents the estimated effect in urban areas, and the second column represents the differential effect in rural areas compared to urban areas. All specifications include household, region by round, and flood exposure probability by round fixed effects, fully interacted with rural location. See Table 13 for outcome variable descriptions * $p < 0.1$, ** $p < 0.05$, *** $p < 0.01$

For the other outcomes, there is more disagreement between the two flood exposure definitions. According to the survey-based definition, flood exposure tends to decrease respondent work engagement in urban areas only, with significantly more positive effects in rural areas. The VFM definition on the other hand shows reductions in work engagement in rural areas. The VFM definition suggests food insecurity is more likely to increase after rural flooding with little effects in
urban areas, which would be consistent with better market access in urban areas, but the survey-
based definition suggests the opposite. Finally, households in communities with any flood report
are significantly more likely to report experiencing another type of shock in urban areas only, while
there is not effect on shock reports in any area according to the VFM definition of exposure. These
differences imply that it is not just the locations where flood exposure is identified according to the
two definitions that drives the difference in results, but potential differences in the nature of the
flooding events that are less straightforward to understand.

We also test for heterogeneity in impacts according to several household characteristics defined
at baseline during ECOSIT 4: whether the household head is female, has a household wealth
index is above the median, reported any flood from 2015-2018, is engaged in agriculture, and is
engaged in non-farm enterprise. The results are shown in Figure A25, and in general we find limited
evidence of consistent significant differences in impacts of flood exposure by these characteristics.
Both flood exposure measures decrease the likelihood that the respondent has worked in the last 7
days among households that reported a flood shock at baseline, during the period from 2015-2018.
The difference in effects by prior flood exposure is statistically significant when considering the
survey-based flood definition.

Outside of this, the differences are either not significant or depend on the flood exposure defini-
tion. The decrease in respondent work is concentrated among less wealthy households according to
the VFM-based definition but among more wealthy households according to the survey-based defi-
nition. Increases in engagement in agricultural activity identified using the survey-based definition
are significantly larger among households with a female head and that were engaged in non-farm
enterprise at baseline, and become negative and non-significant among households that reported
any flood exposure in the period before the baseline survey. Decreases in the likelihood that the
household perceives itself as worse off than its neighbors are driven by households that reported a
flood shock before the baseline, according to the VFM-based exposure definition, and by households
that are engaged in agriculture, according to the survey-based exposure definition. Households with
a baseline flood report have significantly smaller effects of flood exposure on the probability that
they believe they are poor or very poor according to the VFM-based definition, but significantly
larger effects according to the survey-based definition. Effects of VFM-based flood exposure on
the household food insecurity index are significantly larger and only statistically significant among
households that reported a baseline flood.

## 4.4 Discussion

We estimate average effects of being in a community exposed to flooding on household outcomes in
subsequent periods. Results from defining flood exposure based on VFM detection of flooded pixels
in proximity to the community in 2019-2020 show that households exposed to flooding respond
by increasing participation in non-farm enterprise activities, particularly in urban areas. These
households are not significantly worse off over time than households in unexposed communities
that had a similar estimated probability of exposure. The largest effects of flood exposure occur
in the period when the exposure is realized and effects do not persist over the full sample period, suggesting households are generally able to recover from the shock on average.

Results from defining flood exposure based on having any household report a flood over the period between ECOSIT 4 and 5 show that households in exposed communities increase engagement in agricultural production, particularly if they have a female head or were engaged in non-farm enterprise at baseline. Community flood exposure also increases the likelihood that households report another type of shock, suggesting decreased resilience or greater vulnerability to repeated shocks.

Increased engagement in non-farm enterprise and household agriculture following flood exposure may represent household efforts to diversify their livelihood strategies following a shock to their main source of income. We do not have data on individual migration as a response to flood exposure, but find that attrition from the ECOSIT panel is not correlated with flood exposure suggesting limited effects on the likelihood that an entire household is persistently displaced or moves away.

An important consideration is that the results all represent intent to treat effects of community-level flood exposure, averaging effects across households that are directly affected by damages to their livelihood activities or possessions or harm to household members and households that are not directly affected. If indirect impacts of community flood exposure are limited, the low share of households reporting flood shocks in communities where at least one other household reports such a shock means that estimated average effects will be attenuated toward zero relative to effects of flooding on directly affected households. In this situation, positive point estimates on a food insecurity index may suggest that directly affected households are persistently more food insecure, but not sufficiently to overcome null effects on other community households.

Further research using the same data and identification strategy could explore this by using community flood exposure to instrument for households reporting a flood shock. This approach would identify local average effects of direct flood exposure, but requires the potentially strong assumption that community-level exposure only affects households through the probability that a household experiences direct flood-related damages. Estimated effects under this strategy would likely be larger than those reported here.

Another extension to this work would be to consider impacts of flood exposure additional outcome variables available only in the main ECOSIT 4 and 5 rounds but not in the HFPS such as measures of agricultural production, incomes from different sources, and household health, as well as individual-level labor supply data. Data from the ENSA surveys could also be particularly valuable for testing effects on food insecurity, though the identification strategy would be different because the ENSA data are not a panel. One approach would be to conduct a stacked cross-sectional event study, using the Callaway and Sant'Anna (2021) estimator for example, relying on different timing of when communities surveyed at different points in time are first exposed to flooding. This approach would require relying on remotely-sensed flooding treatment measures, as the surveys themselves only measure flooding in the 6 months prior to the survey.
# 5 Conclusions

The results of this report may be useful for policymakers and stakeholders working on climate change resilience, disaster response, and social protection in Chad. Understanding the geographic distribution of flood risk and occurrences may help to target resources and efforts to support adaptation and resilience to increasing flood risk. Both flood hazard and population exposure are concentrated primarily along bodies of water in Chad. Pluvial flooding also poses an important risk but it is more challenging to measure and monitor than fluvial floods, and affects a smaller share of Chad's population. The areas combining the highest mean days of annual flooding detected in the VFM archive together with higher population densities are largely all concentrated in the areas around the Logone River. These are also the areas with the highest levels of flood hazard according to Fathom. Given limited resources, investments in flood mitigation and response may therefore be most impactful in these areas.

Results emphasizing the recent and projected increases in flood exposure in Chad may motivate greater attention and investment in these areas. Flood hazard is projected to increase in the coming decades, with some areas becoming newly exposed to flood risk and others experiencing floods of greater severity. However, population growth is the main driver of projected increases in population flood exposure. These projected increases could potentially be reduced somewhat if efforts are made to prevent or mitigate flood exposure. Improved flood risk communications, investment in flood defenses, water management infrastructure, and more durable housing, and support for relocation or protection of informal settlements in areas with high flood hazard could reduce vulnerability to flooding even as flood hazard and the exposed population increase. Such policies would be most important in fast-growing parts of urban areas in proximity to rivers. Although this analysis finds limited household-level effects of recent community flood exposure using the ECOSIT surveys, the millions of displaced persons, hundreds of deaths, and hundreds of thousands of homes destroyed and cropland flooded in recent years suggest large potential economic and human benefits of flood protection policies.

This study also demonstrates that many floods may occur in remote areas and go unreported by media or government sources. Hundreds of thousands of people live in areas with floods detected by satellite each year, and a low but non-trivial share of households report experiencing flood shocks outside the years of major flood events in Chad. These populations are primarily concentrated in areas with high flood hazard, suggesting a need for additional monitoring resources in remote but high-risk areas with vulnerable populations. Vulnerability also varies within communities suggesting a need for targeted allocation of resources. The probability that a household reports a flood shock is correlated with factors making direct flood damages more likely: engagement in agriculture and less durable dwelling materials. Adding considerations of flood vulnerability to measures of exposure may help make investments more efficient and effective.

Finally, differences in community flood detection using satellite and survey data highlight challenges in using remote sensing alone to identify flood incidence. Flood detection in the area around a community can identify areas where household flood shocks are very likely, but will also identify
areas where households are not affected and can miss many communities reporting flood shocks. This makes rapidly responding to flood events challenging and may result in an inefficient allocation of resources. Flood detection algorithms can mistakenly identify 'floods' in communities with no flood report, but may also correctly identify flooding that does not harm households if the flooding is anticipated or not severe or if households are otherwise protected. More specific measures of deviation in the presence of surface water relative to the same place at the same time of year can reduce potential false positives for flood detection.

We show that communities with flooding detected in close proximity are very likely to have households reporting flood shocks. Additional work could consider whether particular thresholds for levels of detected flooding, as well as particular distances around communities, lead to more accurate identification of communities with reported floods. A machine learning approach could consider this prediction problem systematically and allow for different thresholds and distances based on community characteristics such as rural location, distance from a river, and so on.

Failing to detect flooding in communities where households report flood shocks may be more concerning, as this would imply that these areas are less likely to receive relief. This situation also creates greater basis risk from the perspective of a threshold-based flood response policy using remotely-detected flooding. We find that the share of communities with a flood report but no detected flooding in proximity falls as we increase the area around a community in which flood detection is considered. Further work could analyze how basis risk changes with the scale of the geography considered. The tradeoff of increasing the geographic scale of flood detection is that the share of communities identified as potentially exposed with actual flood reports will fall.

Constraints in what types of flood events different types of satellite data can capture mean that some flooding will always be missed, though this can potentially be reduced by combining results from multiple satellites and algorithms. A machine learning model using multiple flood detection inputs and trained on 'ground truth' data (likely survey reports) may be most effective in improving the accuracy of flood detection. Another consideration is that inundation is not the only way that households can be harmed by heavy precipitation, which can cause direct damage as well. Such shocks require their own set of interventions.
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A Additional figures

<figure><img src="image_21.png" /><figcaption>Figure A1: Chad agroecological zones and population density</figcaption></figure>

Note: Panel A is adapted from the World Food Programme. Panel B is from authors' calculations based on data from WorldPop (2025). We cap values at 160 people per km² to better show the distribution of population density.

<figure><img src="image_22.png" /><figcaption>Figure A2: Major rivers in Chad</figcaption></figure>

Note: Figure based on 30 m resolution data from HydroSHEDS Free Flowing Rivers Network v1 (Grill et al., 2019), showing major river networks.
<figure><img src="image_23.png" /><figcaption>Figure A3: 100-year flood depths in Chad under 2020 conditions (WRI and JRC)</figcaption></figure>

Note: Authors' calculations based on data from the WRI (World Resources Institute, 2025) and JRC (Baugh et al., 2024) flood hazard models. Both panels show the depth of inundation under 100 year floods in meters (left) and highlight areas at risk of floods of particular depths (right).
<figure><img src="image_24.png" /><figcaption>B) JRC Global River Flood Hazards Maps v1</figcaption></figure>

<figure><img src="image_25.png" /></figure>

Note: Authors' calculations based on data from the WRI (World Resources Institute, 2025) and JRC (Baugh et al., 2024) flood hazard models. Both panels show the depth of inundation under 10 year floods in meters (left) and highlight areas at risk of floods of particular depths (right).
<figure><img src="image_26.png" /><figcaption>Figure A5: Global population exposure to medium flood risk</figcaption></figure>

Note: Figure from Rentschler et al. (2022). The map shows the share of the population by administrative division exposed to 100-year floods with a depth of at least 15 cm. In aggregate, 23% of the world's population is exposed to such floods, and 44% of the world's poor population exposed to such floods is located in Africa.
<figure><img src="image_27.png" /><figcaption>Figure A6: Exposure to ≥10 cm 100-year flood depths under 2020 conditions (WRI)</figcaption></figure>

Note: Authors' calculations based on data from World Resources Institute (2025). Flood exposure is defined as estimated depth of 100-year floods of at least 10 cm. Gridded population data are from Center for International Earth Science Information Network - CIESIN - Columbia University (2018).
<figure><img src="image_28.png" /><figcaption>Figure A7: Projected change in 100-year undefended flood depths in Chad under 2050 conditions (Fathom)</figcaption></figure>

Note: Authors' calculations based on data from Fathom (2022). Each panel presents data on the depth of inundation under 100 year floods in cm. The data in panels B and C are projections of changes in flood hazard under different climate change and population growth scenarios from the IPCC Sixth Assessment Report on climate change.
<figure><img src="image_29.png" /><figcaption>Figure A8: Projected changes in 100-year flood depths (WRI)
A) 2020 conditions
B) 2050 SSP2-4.5 conditions</figcaption></figure>

Note: Authors' calculations based on data from World Resources Institute (2025). We compare flood hazard under 2020 (left) compared to 2050 climate conditions projected under SSP2-4.5 in 2050 (right).

<figure><img src="image_30.png" /><figcaption>Figure A9: Population exposure to floods in 2020 v. 2100</figcaption></figure>

Note: Figure from Rogers et al. (2025). Population is per km², and flood exposure is based on at least 10 cm of inundation for a 100-year flood.
<figure><img src="image_31.png" /><figcaption>Figure A10: Projected changes in population exposure percentage for ≥10 cm 100-year floods (Rogers et al)</figcaption></figure>

Note: Authors' calculations based on data from Rogers et al. (2025). The figure compares population flood exposure percentage (relative to the total departement population) from all flood types under 2020 conditions (Panel A) compared to 2050 climate and population conditions projected under SSP2-4.5 (Panel B). Flood exposure is based on the share of the population exposed to 100-year floods causing at least 10 cm of inundation depth.

<figure><img src="image_32.png" /><figcaption>Figure A11: Reported population affected by major flood events since 2001</figcaption></figure>

Note: Authors’ calculations based on data from EM-DAT (Guha-Sapir et al., 2023). The dataset includes information on disaster type, location, timing and the total number of people affected. For years with multiple flood events recorded (2006, 2007, 2008, 2009, 2010, 2020, 2021, and 2024), we take the sum of populations exposed across different events in the year. The EM-DAT database does not record all flood events, and other sources indicate larger populations exposed to floods in certain years.
<figure><img src="image_33.png" /><figcaption>Figure A12: Yearly average water flows across stations by month</figcaption></figure>

Note: Authors' analysis based on data obtained from the Chad Direction des Ressources en Eau (DRE). The original data are station-level monthly average water flows by year. We take the average by month across all stations in each year. Figure 9 shows averages of these values across five-year periods to better illustrate changes over time.
<figure><img src="image_34.png" /><figcaption>Figure A13: Annual station-level average water flows for peak August-November period</figcaption></figure>

Note: Authors' analysis based on data obtained from the Chad Direction des Ressources en Eau (DRE). The original data are station-level monthly average water flows by year. We take the average by station for the period from August-November with the highest typical water flows, and then average these values across five-year period to better illustrate changes over time.

<figure><img src="image_35.png" /><figcaption>Figure A14: WFP bulletin on flooding incidence in Chad as of 12 Sep 2024</figcaption></figure>

Note: Figure adapted from WFP (2024c). Flood extent is based on satellite imagery from NOAA VIIRS and Floodscan.
<figure><img src="image_36.png" /><figcaption>Figure A15: Estimated population affected by flooding by province, 2022 and 2024
B) 2024 (as of 24 August)</figcaption></figure>

Note: Produced by OCHA and the Government of Chad, based on data from local authorities and partners (United Nations OCHA, 2022a, 2024a). The maps show the estimated number of people affected by flooding by province.
<figure><img src="image_37.png" /><figcaption>Figure A16: ENSA survey reports of flood incidence, 2016-2024</figcaption></figure>

Note: Authors' calculations based on data from the ENSA surveys. The data are from a nationally-representative sample of rural households about flooding experienced in the 6 months prior to the survey date. Geographic identification is not available below the admin2 level for all years, so we show the share of households reporting any flooding by departement.
<figure><img src="image_38.png" /><figcaption>Figure A17: ENSA community shares of household flood reports, 2020-2024</figcaption></figure>

Note: Authors' calculations based on data from the ENSA surveys. The data are from a nationally-representative sample of rural households about flooding experienced in the 6 months prior to the survey date. Community coordinates are available starting in 2020, so we show the share of households reporting any flooding by community.

<figure><img src="image_39.png" /><figcaption>Figure A18: DIEM survey reports of flood incidence, 2021-2024</figcaption></figure>

Note: Authors' calculations based on data from the DIEM surveys. The surveys are representative at the level of the departements selected for each survey round. Households are asked about flood experiences over the previous three months. Geographic identification is not available below the admin2 level, so we show the share of households reporting any flooding by departement. On all maps, 0.6+ indicates at least 60% of households surveyed in the departement reporting a flood shock.
<figure><img src="image_40.png" /><figcaption>Figure A19: RIMA and ECOSIT survey reports of flood incidence, 2014-2022</figcaption></figure>

Note: Authors' calculations based on data from the RIMA and ECOSIT 4 surveys. Panel A shows data from RIMA based on a nationally-representative sample of rural households about flooding experienced in the 6 months prior to the survey date in 2014. Geographic identification is not available below the admin2 level, so we show the share of households reporting any flooding by departement. Panels B and C shows data from ECOSIT based on a nationally-representative sample of all households about flooding experienced in the 3 years prior to the survey date. For each community in the survey, we map the share of households reporting any flooding, where 0.5 indicates at least 50% of households reporting a flood.
<figure><img src="image_41.png" /><figcaption>Figure A20: Days of annual flood exposure over space by year and percentage of land area with any flooding</figcaption></figure>

Note: Authors' calculations based on data from the VFM archive NOAA and George Mason University (2025). We aggregate daily flood detection maps across the year, taking the sum of days in the year with any flooding detected for each pixel. Data for 2021 and 2022 are not available.
<figure><img src="image_42.png" /><figcaption>Figure A21: Distribution of days of annual flood exposure over space by year among pixels with any flooding</figcaption></figure>

Note: Authors' calculations based on data from the VFM archive NOAA and George Mason University (2025). We aggregate daily flood detection maps across the year, taking the sum of days in the year with any flooding detected for each pixel. Data for 2021 and 2022 are not available.
<figure><img src="image_43.png" /><figcaption>Figure A22: Share of departement area exposed to any flooding by year</figcaption></figure>

Note: Authors' calculations based on data from the VFM archive, NOAA and George Mason University (2025). We define a pixel as flooded if any flooding is detected during the year, and calculate the share of pixels that are flooded in each departement by year. Data for 2021 and 2022 are not available.
<figure><img src="image_44.png" /><figcaption>Figure A23: GFM (Sentinel-1) detection of flooding around Kanem province in 2024</figcaption></figure>

Note: Authors' calculations based on data the Copernicus Emergency Management Service Global Flood Monitoring tool. The database detects flooding using Sentinel-1 SAR data which is not affected by cloud cover but has a relatively long lag between flyovers. Data at 90m resolution for the area around Kanem province were extracted for the period from July 17-September 17 2024, the peak of the flooding season. Maximum flood extent indicates the highest pixel-level flood intensity value over this period, where flood intensity is a binary value.
<figure><img src="image_45.png" /><figcaption>Figure A24: ECOSIT 4 communities and households included in the HFPS</figcaption></figure>

Note: Authors' calculations based on data from the ECOSIT 4 and HFPS surveys. A community is considered included in the panel if at least one household was surveyed in the HFPS.
<figure><img src="image_46.png" /><figcaption>Figure A25: ECOSIT heterogeneity in flood exposure impacts
A) VFM-based definition</figcaption></figure>

Note: Authors' calculations based on data from the ECOSIT surveys and VFM archive. The figures show the results of separate regressions of a particular variable on the interaction of flood exposure and various household characteristics indicated in the legend. Each panel uses a different flood exposure definition. Each household characteristic is a binary variable and we show the estimated impact in households where the value is 0 and where the value is 1, where the latter is the sum of the effect where the value is 0 and the differential effect when the value is 1. All specifications include household, region by round, and flood exposure probability by round fixed effects, fully interacted with the particular household characteristics. See Table 13 for outcome variable descriptions
B Additional tables

<table><caption>Table A1: Comparison of selected flood hazard databases</caption><thead><tr><th>Database</th><th>Flooding types</th><th>Data resolution</th><th>Model</th><th>Notes</th></tr></thead><tbody><tr><td>Rogers et al (2025)</td><td>Coastal/Lake, Fluvial, Pluvial</td><td>90m</td><td>Separate models by flood type</td><td>Public data aggregated to ADM2 level, DEM based on many public sources</td></tr><tr><td>World Resources Institute Aqueduct v2</td><td>Coastal/Lake, Fluvial</td><td>1km</td><td>GLOFRIS</td><td>Open source available in GEE, focuses on large rivers</td></tr><tr><td>JRC Global River Flood Hazard Maps v1</td><td>Fluvial</td><td>1km</td><td>LISFLOOD</td><td>Open source available in GEE, focuses on large rivers</td></tr><tr><td>Fathom</td><td>Coastal/Lake, Fluvial, Pluvial</td><td>30m</td><td>Bates et al 2010, Neal et al 2021</td><td>Private/paywalled, improved DEM (FABDEM+)</td></tr><tr><td>JBA Global Resilience</td><td>Coastal/Lake, Fluvial, Pluvial</td><td>30m</td><td>Unclear</td><td>Private/paywalled, bare-earth Digital Terrain Model</td></tr></tbody></table>

Note: DEM means Digital Elevation Model. A review or more detailed considerations of specific flood hazard models used in these databases is beyond the scope of this report. Baugh et al. (2024), Fathom (2022), Rogers et al. (2025), and World Resources Institute (2025), provide more detail. There is limited public information on the methods used in the JBA Group models.
<table><caption>Table A2: Summary of main historical flooding databases</caption><thead><tr><td>Database</td><td>Source</td><td>Flood detection inputs</td><td>Years of coverage</td><td>Spatial resolution</td><td>Temporal resolution</td><td>Accessibility</td><td>Update frequency</td></tr></thead><tbody><tr><td>VIIRS Flood Mapping (VFM), https://noaa-jpss.s3.amazonaws.com/index.html#JPSS Blended Products/</td><td>NOAA, George Mason University, University of Wisconsin</td><td>VIIRS satellite imagery</td><td>2012-present</td><td>375 m</td><td>1/5 day composites</td><td>Publicly available archive</td><td>Near-real time (daily)</td></tr><tr><td>GloFAS Global Flood Monitoring (GFM), https://global-flood.emergency.copernicus.eu/technical-information/glofas-gfm/</td><td>Copernicus Emergency Management Service</td><td>Sentinel-1 satellite Synthetic Aperture Radar</td><td>2021 - present</td><td>20 m</td><td>6-12 days (each Sentinel-1 pass, varies by location)</td><td>Publicly available after registering in web portal; data may be accessed in portal or through API but there are restrictions on the duration and geographic extent of data that can be accessed at once</td><td>Near-real time (daily)</td></tr><tr><td>Global Flood Monitoring System (GFMS), https://flood.umd.edu/</td><td>NASA, University of Maryland</td><td>Satellite-based precipitation (TRMM/GPM) and hydrological</td><td>2013 - present</td><td>12 km</td><td>Daily</td><td>Publicly available archive</td><td>Near-real time (daily)</td></tr></tbody></table>
<table><caption>Table A2 (continued)</caption><thead><tr><td>Database</td><td>Source</td><td>Flood detection inputs</td><td>Years of coverage</td><td>Spatial resolution</td><td>Temporal resolution</td><td>Accessibility</td><td>Update frequency</td></tr></thead><tbody><tr><td>Near Real-Time (NRT) Global Flood Product, https://www.earthdata.nasa.gov/data/instruments/viirs/near-real-time-data/nrt-global-flood-products</td><td>NASA Earth-Data</td><td>MODIS imagery (Terra/Aqua satellites); moving toward VIIRS satellite imagery</td><td>2011 - 2022 (legacy product); 2021 - present (current)</td><td>250 m</td><td>1/2/3 day composites</td><td>Only data from the last 8 days are available for download through the online database. Archives are no longer available online but specific data may be requested.</td><td>Near-real time (daily)</td></tr><tr><td>African Flood and Drought Monitor, https://hydrology.soton.ac.uk/apps/afdm/</td><td>Princeton Climate Institute and University of Southampton</td><td>Combination of ground water gauges, satellite rainfall estimates, and hydrological models</td><td>2008-present</td><td>5 km across Africa</td><td>Daily</td><td>Data for the past month viewable in online interface but not downloadable.</td><td>Near-real time (daily)</td></tr><tr><td>Automated Disaster Analysis and Mapping (ADAM) Floods, https://gis.wfp.org/adam/</td><td>World Food Programme</td><td>VIIRS, MODIS, and Sentinel-1 satellites, Floodscan (SFED and MFED 90m)</td><td>Unclear; post 2018 - present</td><td>Unclear, likely at least 375 m</td><td>Unclear</td><td>Current flood data viewable in online portable but archives not accessible</td><td>Near-real time (daily)</td></tr><tr><td>FloodScan, https://aer.powerserve.net/weather-risk-management/floodscan-near-real-time/near-real-time-historical-flood-mapping/index.html</td><td>Atmospheric and Environmental Research</td><td>Multiple passive satellite microwave scanners and imagers</td><td>1998 - present</td><td>90 m</td><td>Daily</td><td>Interactive web interface is publicly available for viewing but downloads only available for a fee</td><td>Near-real time (daily)</td></tr></tbody></table>
<table><caption>Table A2 (continued)</caption><thead><tr><td>Database</td><td>Source</td><td>Flood detection inputs</td><td>Years of coverage</td><td>Spatial resolution</td><td>Temporal resolution</td><td>Accessibility</td><td>Update frequency</td></tr></thead><tbody><tr><td>EM-DAT International Disaster Database, https://www.emdat.be/</td><td>Centre for Research on the Epidemiology of Disasters (CRED)</td><td>Government reports, UN agencies, NGOs, insurance agencies, research institutes, news reports</td><td>1900 - present</td><td>Country-level reporting (uses national administrative unit for location)</td><td>Event-based; provides start/end dates for individual events</td><td>Publicly available spreadsheet download</td><td>Varies by event (post-event compilation); often days to weeks post-event</td></tr><tr><td>Twitter Global Flood Monitor, https://www.globalfloodmonitor.org/</td><td>de Bruijn et al. (2019)</td><td>Twitter text mining in 11 languages to geolocate flood mentions</td><td>2014 - 2023</td><td>Country and administrative level based on tweet location aggregation</td><td>Event based; identifies dates of tweets for a given event</td><td>Publicly available spreadsheet download</td><td>No longer updated</td></tr><tr><td>DesInventar Sendai Hazardous Events Database, https://www.desinventar.net/</td><td>United Nations Office for Disaster Risk Reduction (UNDRR)</td><td>Official government and local reports, disaster agency records, news reports</td><td>1970s - present but varies by country</td><td>Subnational; granular administrative units within each country for selected countries (not global)</td><td>Event-based; provides start/end dates for individual events</td><td>Publicly available country-level event databases</td><td>Typically weeks to months after events (entered by local authorities)</td></tr></tbody></table>
<table><caption>Table A2 (continued)</caption><thead><tr><td>Database</td><td>Source</td><td>Flood detection inputs</td><td>Years of coverage</td><td>Spatial resolution</td><td>Temporal resolution</td><td>Accessibility</td><td>Update frequency</td></tr></thead><tbody><tr><td>Dartmouth Flood Observatory (DFO), https://floodobservatory.colorado.edu/</td><td>University of Colorado, IN-STAAR, CS-DMS</td><td>News and government reports supplemented by remote sensing-based detection (e.g., MODIS)</td><td>1985 - 2022</td><td>Event polygons covering affected areas (often whole river basins or admin regions)</td><td>Event-based; provides start/end dates for individual events</td><td>Publicly available spreadsheet and shapefile downloads</td><td>Days to weeks after flood (dependent on detection & reports); appears to no longer be updated</td></tr><tr><td>Global Flood Database, https://global-flood-database.servatory.cloudtostreet.ai/</td><td>Floodbase, Dartmouth Flood Observatory (DFO)</td><td>MODIS imagery (Terra/Aqua satellites) and DFO Archive</td><td>2000 - 2018</td><td>250 m for flood events in DFO Archive</td><td>Event-based; provides start date and duration of flood in days for individual events</td><td>Publicly available on Google Earth Engine (GEE)</td><td>No longer updated</td></tr></tbody></table>
<table><caption>Table A3: Correlates of ECOSIT 4 community flood reports, by year</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Rural location</td><td>-0.132**<br>(0.063)</td><td>-0.133**<br>(0.063)</td><td>-0.130**<br>(0.063)</td><td>-0.131**<br>(0.062)</td><td>-0.133**<br>(0.063)</td><td>-0.133**<br>(0.063)</td><td>-0.130**<br>(0.063)</td><td>-0.132**<br>(0.062)</td></tr><tr><td>Share of HHs engaged in agriculture</td><td>0.029<br>(0.132)</td><td>0.031<br>(0.132)</td><td>0.018<br>(0.132)</td><td>0.003<br>(0.132)</td><td>0.034<br>(0.132)</td><td>0.033<br>(0.133)</td><td>0.016<br>(0.132)</td><td>0.009<br>(0.131)</td></tr><tr><td>Share of HHs with cement/metal roof</td><td>-0.065<br>(0.115)</td><td>-0.063<br>(0.115)</td><td>-0.073<br>(0.115)</td><td>-0.091<br>(0.115)</td><td>-0.061<br>(0.115)</td><td>-0.061<br>(0.115)</td><td>-0.073<br>(0.115)</td><td>-0.097<br>(0.115)</td></tr><tr><td>Share of HHs with cement/tile floor</td><td>-0.237<br>(0.154)</td><td>-0.234<br>(0.154)</td><td>-0.225<br>(0.154)</td><td>-0.235<br>(0.153)</td><td>-0.233<br>(0.154)</td><td>-0.234<br>(0.154)</td><td>-0.229<br>(0.154)</td><td>-0.244<br>(0.153)</td></tr><tr><td>Log total flooded pixels in year</td><td>0.007<br>(0.013)</td><td>0.002<br>(0.013)</td><td>0.019<br>(0.012)</td><td>0.031***<br>(0.011)</td><td></td><td></td><td></td><td></td></tr><tr><td>&gt;=10% of surrounding pixels flooded in year</td><td></td><td></td><td></td><td></td><td>-0.006<br>(0.057)</td><td>-0.002<br>(0.055)</td><td>0.100*<br>(0.057)</td><td>0.156***<br>(0.052)</td></tr><tr><td>Observations</td><td>625</td><td>625</td><td>625</td><td>625</td><td>625</td><td>625</td><td>625</td><td>625</td></tr><tr><td>Year of flood detection</td><td>2015</td><td>2016</td><td>2017</td><td>2018</td><td>2015</td><td>2016</td><td>2017</td><td>2018</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT 4 surveys and the VFM archive. The table shows the results of separate regressions where the outcome variable is a dummy for whether any household in the community reported experiencing a flood shock from 2015-2018. Flood incidence data from the VFM archive are linked to the ECOSIT data based on community centroids. On average there are 22 pixels within 1 km. * *p* < 0.1, ** *p* < 0.05, *** *p* < 0.01
<table><caption>Table A4: Average estimated flood exposure 2012-2024 by department</caption><thead><tr><th>Departement</th><th>Avg total pop. exposed (1000s)</th><th>Avg. share of pop. exposed</th></tr></thead><tbody><tr><td>Abdi</td><td>0.55</td><td>0.003</td></tr><tr><td>Aboudeia</td><td>9.98</td><td>0.121</td></tr><tr><td>Abtouyour</td><td>2.88</td><td>0.012</td></tr><tr><td>Amdjarass</td><td>0.22</td><td>0.001</td></tr><tr><td>Assoungha</td><td>0.48</td><td>0.001</td></tr><tr><td>Baguirmi</td><td>11.12</td><td>0.040</td></tr><tr><td>Bahar Signaka</td><td>6.51</td><td>0.046</td></tr><tr><td>Bahr Azoum</td><td>49.75</td><td>0.201</td></tr><tr><td>Bahr Koh</td><td>51.75</td><td>0.122</td></tr><tr><td>Barh El Gazel Nord</td><td>0.00</td><td>0.000</td></tr><tr><td>Barh El Gazel Ouest</td><td>0.27</td><td>0.005</td></tr><tr><td>Barh El Gazel Sud</td><td>0.82</td><td>0.003</td></tr><tr><td>Barh Sara</td><td>15.22</td><td>0.051</td></tr><tr><td>Batha Est</td><td>16.05</td><td>0.064</td></tr><tr><td>Batha Ouest</td><td>10.79</td><td>0.040</td></tr><tr><td>Biltine</td><td>0.45</td><td>0.001</td></tr><tr><td>Borkou</td><td>0.00</td><td>0.000</td></tr><tr><td>Borkou Yala</td><td>0.00</td><td>0.000</td></tr><tr><td>Chari</td><td>31.64</td><td>0.191</td></tr><tr><td>Dababa</td><td>5.94</td><td>0.019</td></tr><tr><td>Dagana</td><td>13.82</td><td>0.054</td></tr><tr><td>Dar Tama</td><td>0.01</td><td>0.000</td></tr><tr><td>Djode</td><td>2.67</td><td>0.016</td></tr><tr><td>Djourf Al Ahmar</td><td>4.55</td><td>0.037</td></tr><tr><td>Fada</td><td>0.97</td><td>0.012</td></tr><tr><td>Fitri</td><td>25.88</td><td>0.168</td></tr><tr><td>Fouli</td><td>5.55</td><td>0.057</td></tr><tr><td>Grande Sido</td><td>3.78</td><td>0.026</td></tr><tr><td>Gueni</td><td>1.50</td><td>0.014</td></tr><tr><td>Guéra</td><td>3.35</td><td>0.013</td></tr><tr><td>Haraze Al Biar</td><td>22.10</td><td>0.114</td></tr><tr><td>Haraze Mangueigne</td><td>6.71</td><td>0.087</td></tr><tr><td>Kanem</td><td>0.00</td><td>0.000</td></tr><tr><td>Kaya</td><td>23.62</td><td>0.146</td></tr><tr><td>Kimiti</td><td>17.69</td><td>0.038</td></tr><tr><td>Kobé</td><td>0.24</td><td>0.001</td></tr><tr><td>Kouh Est</td><td>15.79</td><td>0.106</td></tr><tr><td>Kouh Ouest</td><td>2.92</td><td>0.034</td></tr><tr><td>La Nya Pendé</td><td>5.17</td><td>0.033</td></tr><tr><td>La Kabbia</td><td>42.44</td><td>0.136</td></tr><tr><td>La Nya</td><td>24.58</td><td>0.115</td></tr><tr><td>La Pendé</td><td>46.41</td><td>0.198</td></tr><tr><td>Lac Iro</td><td>38.98</td><td>0.159</td></tr><tr><td>Lac Léré</td><td>22.92</td><td>0.094</td></tr><tr><td>Lac Wey</td><td>26.02</td><td>0.069</td></tr><tr><td>Loug Chari</td><td>37.32</td><td>0.147</td></tr><tr><td>Mamdi</td><td>22.13</td><td>0.251</td></tr><tr><td>Mandoul Occidental</td><td>15.01</td><td>0.072</td></tr><tr><td>Mandoul Oriental</td><td>7.62</td><td>0.022</td></tr><tr><td>Mangalmé</td><td>2.66</td><td>0.020</td></tr><tr><td>Mayo Binder</td><td>0.73</td><td>0.011</td></tr><tr><td>Mayo Boneye</td><td>102.01</td><td>0.308</td></tr><tr><td>Mayo Dallah</td><td>3.33</td><td>0.007</td></tr><tr><td>Mayo Lemyé</td><td>24.90</td><td>0.232</td></tr><tr><td>Megri</td><td>0.03</td><td>0.000</td></tr><tr><td>Mont Illi</td><td>47.47</td><td>0.152</td></tr><tr><td>Monts de Lam</td><td>24.00</td><td>0.063</td></tr><tr><td>Mourtcha</td><td>0.00</td><td>0.000</td></tr><tr><td>N'Djamena</td><td>163.07</td><td>0.112</td></tr><tr><td>Ngourkosso</td><td>20.14</td><td>0.088</td></tr><tr><td>Nord Kanem</td><td>0.01</td><td>0.000</td></tr><tr><td>Quara</td><td>1.29</td><td>0.002</td></tr><tr><td>Tandjilé Centre</td><td>21.02</td><td>0.273</td></tr><tr><td>Tandjilé Est</td><td>45.56</td><td>0.140</td></tr><tr><td>Tandjilé Ouest</td><td>76.61</td><td>0.175</td></tr><tr><td>Tibesti Est</td><td>0.51</td><td>0.016</td></tr><tr><td>Tibesti Ouest</td><td>0.04</td><td>0.001</td></tr><tr><td>Wadi Bissam</td><td>0.04</td><td>0.000</td></tr><tr><td>Wadi Hawar</td><td>1.23</td><td>0.015</td></tr><tr><td>Wayi</td><td>21.93</td><td>0.081</td></tr></tbody></table>

Note: Authors' calculations based on data from the VFM archive NOAA and George Mason University (2025) and WorldPop (2025). The table shows the mean total annual population (in 1000s) exposed to floods detected in the VFM data across 2012-2024 by department, along with the mean share of department population exposed. Note that data for 2021 and 2022 are not available in the VFM archive.
<table><caption>Table A5: Baseline characteristics of ECOSIT panel households</caption><thead><tr><th></th><th>Mean</th><th>SD</th><th>Min</th><th>25<sup>th</sup></th><th>50<sup>th</sup></th><th>75<sup>th</sup></th><th>Max</th><th>N</th></tr></thead><tbody><tr><td colspan="9"><em>Household characteristics</em></td></tr><tr><td>Rural location</td><td>0.55</td><td>0.50</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Household size</td><td>5.63</td><td>3.05</td><td>1.0</td><td>4.0</td><td>5.0</td><td>7.0</td><td>40.0</td><td>6223</td></tr><tr><td>Household owns its dwelling</td><td>0.78</td><td>0.41</td><td>0.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Roof is cement or metal</td><td>0.44</td><td>0.50</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Floor is tile or cement</td><td>0.12</td><td>0.32</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Main source of light is electricity</td><td>0.16</td><td>0.37</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Household has toilet or latrine</td><td>0.29</td><td>0.45</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Normalized HH wealth index</td><td>-0.03</td><td>0.99</td><td>-0.9</td><td>-0.6</td><td>-0.3</td><td>0.2</td><td>5.7</td><td>6223</td></tr><tr><td>Household is below national poverty line</td><td>0.29</td><td>0.46</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td colspan="9"><em>Household head characteristics</em></td></tr><tr><td>Household head age</td><td>43.62</td><td>14.60</td><td>15.0</td><td>32.0</td><td>41.0</td><td>54.0</td><td>106.0</td><td>6223</td></tr><tr><td>Household head is female</td><td>0.23</td><td>0.42</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Household head is Muslim</td><td>0.64</td><td>0.48</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Household head is literate</td><td>0.47</td><td>0.50</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Household head completed primary school</td><td>0.33</td><td>0.47</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Any 2019-2020 flooded pixel within 1 km</td><td>0.37</td><td>0.48</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td colspan="9"><em>Baseline household shock exposure</em></td></tr><tr><td>Any flood shock in 3 years before baseline</td><td>0.10</td><td>0.30</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Any drought shock in 3 years before baseline</td><td>0.19</td><td>0.39</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Any other agricultural shock in 3 years before baseline</td><td>0.21</td><td>0.41</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Any income/job shock in 3 years before baseline</td><td>0.22</td><td>0.41</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Any conflict shock in 3 years before baseline</td><td>0.09</td><td>0.29</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Any shock to HH member in 3 years before baseline</td><td>0.51</td><td>0.50</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td colspan="9"><em>Household activity and well-being outcomes</em></td></tr><tr><td>Any HH crop or livestock activity</td><td>0.71</td><td>0.45</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Any HH non-farm enterprise</td><td>0.45</td><td>0.50</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Respondent did any work in last 7 days</td><td>0.72</td><td>0.40</td><td>0.0</td><td>0.5</td><td>1.0</td><td>1.0</td><td>1.0</td><td>6223</td></tr><tr><td>Received any transfer or assistance</td><td>0.24</td><td>0.43</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>6223</td></tr><tr><td>Normalized HH food insecurity index</td><td>0.00</td><td>0.97</td><td>-2.2</td><td>-0.5</td><td>-0.1</td><td>0.4</td><td>5.9</td><td>6223</td></tr><tr><td>HH believes is worse off than neighbors</td><td>0.30</td><td>0.46</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>6043</td></tr><tr><td>HH believes it is poor or very poor</td><td>0.79</td><td>0.41</td><td>0.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>6190</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT 4 surveys. Panel households are those that were successfully surveyed again in either the HFPS or in ECOSIT 5. The wealth index is constructed as the sum of normalized variables related to housing quality and household assets. The food insecurity index is constructed as the sum of eight normalized variables capturing different aspects of self-reported food insecurity. Household poverty is based on estimated annual per capita consumption. Household well-being is based on the respondent's own perceptions.
<table><caption>Table A6: Balance in historical VFM-detected community flooding by 2019-2020 VFM-based exposure</caption><thead><tr><th rowspan="2"></th><th colspan="2">No flood propensity control</th><th colspan="2">With flood propensity control</th></tr><tr><th>Non-flooded<br>HH Mean<br>(SD)</th><th>Flooded<br>difference<br>(SE)</th><th>Non-flooded<br>HH Mean<br>(SD)</th><th>Flooded<br>difference<br>(SE)</th></tr></thead><tbody><tr><td>Count of flooded pixels in 2012,<br>1km buffer</td><td>0.10<br>(0.82)</td><td>4.52***<br>(0.41)</td><td>0.23<br>(1.45)</td><td>0.39*<br>(0.23)</td></tr><tr><td>Count of flooded pixels in 2013,<br>1km buffer</td><td>0.04<br>(0.32)</td><td>3.61***<br>(0.32)</td><td>0.08<br>(0.39)</td><td>-0.02<br>(0.07)</td></tr><tr><td>Count of flooded pixels in 2014,<br>1km buffer</td><td>0.01<br>(0.10)</td><td>3.61***<br>(0.33)</td><td>0.02<br>(0.14)</td><td>-0.01<br>(0.02)</td></tr><tr><td>Count of flooded pixels in 2015,<br>1km buffer</td><td>0.34<br>(1.76)</td><td>2.79***<br>(0.34)</td><td>0.33<br>(1.62)</td><td>-0.34<br>(0.25)</td></tr><tr><td>Count of flooded pixels in 2016,<br>1km buffer</td><td>0.17<br>(1.03)</td><td>3.01***<br>(0.29)</td><td>0.23<br>(1.10)</td><td>-0.03<br>(0.19)</td></tr><tr><td>Count of flooded pixels in 2017,<br>1km buffer</td><td>0.08<br>(0.54)</td><td>3.45***<br>(0.33)</td><td>0.16<br>(0.79)</td><td>0.24*<br>(0.15)</td></tr><tr><td>Count of flooded pixels in 2018,<br>1km buffer</td><td>0.25<br>(1.18)</td><td>4.51***<br>(0.37)</td><td>0.37<br>(1.27)</td><td>-0.05<br>(0.20)</td></tr><tr><td>Observations</td><td colspan="2">616</td><td colspan="2">479</td></tr><tr><td>Test of joint significance</td><td colspan="2">F = 29.18<br>p < 0.001</td><td colspan="2">F = 1.70<br>p = 0.107</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT surveys and VFM archive. The table shows the results of separate regressions of a particular variable on flood exposure. Flood exposure is based on VFM detection of any flooded pixel within 1 km of the community centroid in 2019 or 2020. We include results for the joint test of significance of all the variables in explaining flood exposure. * *p* < 0.1, ** *p* < 0.05, *** *p* < 0.01

<table><caption>Table A7: Balance in historical VFM-detected community flooding by 2019-2022 report-based exposure</caption><thead><tr><th rowspan="2"></th><th colspan="2">No flood propensity control</th><th colspan="2">With flood propensity control</th></tr><tr><th>Non-flooded<br>HH Mean<br>(SD)</th><th>Flooded<br>difference<br>(SE)</th><th>Non-flooded<br>HH Mean<br>(SD)</th><th>Flooded<br>difference<br>(SE)</th></tr></thead><tbody><tr><td>Count of flooded pixels in 2012,<br>1km buffer</td><td>1.13<br>(3.26)</td><td>1.43***<br>(0.37)</td><td>1.56<br>(3.99)</td><td>-0.43<br>(0.32)</td></tr><tr><td>Count of flooded pixels in 2013,<br>1km buffer</td><td>0.81<br>(2.32)</td><td>1.27***<br>(0.29)</td><td>1.20<br>(2.95)</td><td>-0.18<br>(0.24)</td></tr><tr><td>Count of flooded pixels in 2014,<br>1km buffer</td><td>0.72<br>(2.24)</td><td>1.38***<br>(0.29)</td><td>1.15<br>(2.99)</td><td>-0.14<br>(0.25)</td></tr><tr><td>Count of flooded pixels in 2015,<br>1km buffer</td><td>0.89<br>(2.70)</td><td>1.08***<br>(0.30)</td><td>1.20<br>(3.19)</td><td>-0.29<br>(0.27)</td></tr><tr><td>Count of flooded pixels in 2016,<br>1km buffer</td><td>0.81<br>(2.26)</td><td>1.05***<br>(0.26)</td><td>1.11<br>(2.61)</td><td>-0.15<br>(0.21)</td></tr><tr><td>Count of flooded pixels in 2017,<br>1km buffer</td><td>0.70<br>(2.13)</td><td>1.45***<br>(0.29)</td><td>1.18<br>(2.96)</td><td>-0.04<br>(0.23)</td></tr><tr><td>Count of flooded pixels in 2018,<br>1km buffer</td><td>1.05<br>(2.65)</td><td>1.91***<br>(0.34)</td><td>1.72<br>(3.70)</td><td>-0.04<br>(0.28)</td></tr><tr><td>Observations</td><td colspan="2">616</td><td colspan="2">607</td></tr><tr><td>Test of joint significance</td><td colspan="2">F = 8.71<br>p < 0.001</td><td colspan="2">F = 0.89<br>p = 0.513</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT surveys and VFM archive. The table shows the results of separate regressions of a particular variable on flood exposure. Flood exposure is based on any household in a community reporting a flood shock over the 2019-2022 recall period. We include results for the joint test of significance of all the variables in explaining flood exposure. * *p* < 0.1, ** *p* < 0.05, *** *p* < 0.01
<table><caption>Table A8: Baseline balance by VFM-detected community flood exposure</caption><thead><tr><th rowspan="2"></th><th colspan="2">No flood propensity control</th><th colspan="2">With flood propensity control</th></tr><tr><th>Non-flooded<br>HH Mean<br>(SD)</th><th>Flooded<br>difference<br>(SE)</th><th>Non-flooded<br>HH Mean<br>(SD)</th><th>Flooded<br>difference<br>(SE)</th></tr></thead><tbody><tr><td colspan="5"><em>Household characteristics</em></td></tr><tr><td>Rural location</td><td>0.61<br>(0.49)</td><td>-0.15***<br>(0.04)</td><td>0.58<br>(0.49)</td><td>0.05<br>(0.06)</td></tr><tr><td>Household size</td><td>5.54<br>(2.97)</td><td>0.22**<br>(0.10)</td><td>5.64<br>(3.07)</td><td>0.14<br>(0.16)</td></tr><tr><td>Household owns its dwelling</td><td>0.82<br>(0.38)</td><td>-0.12***<br>(0.02)</td><td>0.80<br>(0.40)</td><td>0.01<br>(0.04)</td></tr><tr><td>Roof is cement or metal</td><td>0.36<br>(0.48)</td><td>0.19***<br>(0.03)</td><td>0.40<br>(0.49)</td><td>-0.03<br>(0.05)</td></tr><tr><td>Floor is tile or cement</td><td>0.08<br>(0.28)</td><td>0.10***<br>(0.02)</td><td>0.10<br>(0.30)</td><td>-0.01<br>(0.03)</td></tr><tr><td>Main source of light is electricity</td><td>0.14<br>(0.35)</td><td>0.05**<br>(0.02)</td><td>0.16<br>(0.36)</td><td>-0.04<br>(0.04)</td></tr><tr><td>Household has toilet or latrine</td><td>0.26<br>(0.44)</td><td>0.09***<br>(0.03)</td><td>0.28<br>(0.45)</td><td>-0.06<br>(0.05)</td></tr><tr><td>Normalized HH wealth index</td><td>-0.13<br>(0.91)</td><td>0.27***<br>(0.06)</td><td>-0.06<br>(0.97)</td><td>-0.06<br>(0.12)</td></tr><tr><td>Household is below national poverty line</td><td>0.31<br>(0.46)</td><td>-0.04*<br>(0.02)</td><td>0.30<br>(0.46)</td><td>0.02<br>(0.03)</td></tr><tr><td colspan="5"><em>Household head characteristics</em></td></tr><tr><td>Household head age</td><td>43.76<br>(14.80)</td><td>-0.37<br>(0.43)</td><td>43.86<br>(14.74)</td><td>0.22<br>(0.60)</td></tr><tr><td>Household head is female</td><td>0.23<br>(0.42)</td><td>-0.01<br>(0.01)</td><td>0.23<br>(0.42)</td><td>-0.03<br>(0.02)</td></tr><tr><td>Household head is Muslim</td><td>0.70<br>(0.46)</td><td>-0.15***<br>(0.04)</td><td>0.69<br>(0.46)</td><td>0.02<br>(0.06)</td></tr><tr><td>Household head is literate</td><td>0.44<br>(0.50)</td><td>0.09***<br>(0.02)</td><td>0.46<br>(0.50)</td><td>-0.02<br>(0.04)</td></tr><tr><td>Household head completed primary school</td><td>0.27<br>(0.44)</td><td>0.14***<br>(0.02)</td><td>0.29<br>(0.46)</td><td>0.01<br>(0.04)</td></tr><tr><td colspan="5"><em>Baseline household shock exposure</em></td></tr><tr><td>Any flood shock in 3 years before baseline</td><td>0.08<br>(0.27)</td><td>0.07***<br>(0.01)</td><td>0.09<br>(0.28)</td><td>-0.00<br>(0.02)</td></tr><tr><td>Any drought shock in 3 years before baseline</td><td>0.20<br>(0.40)</td><td>-0.03<br>(0.02)</td><td>0.19<br>(0.39)</td><td>0.02<br>(0.03)</td></tr><tr><td>Any other agricultural shock in 3 years before baseline</td><td>0.23<br>(0.42)</td><td>-0.04**<br>(0.02)</td><td>0.21<br>(0.41)</td><td>-0.02<br>(0.02)</td></tr><tr><td>Any income/job shock in 3 years before baseline</td><td>0.20<br>(0.40)</td><td>0.04***<br>(0.02)</td><td>0.22<br>(0.41)</td><td>0.01<br>(0.03)</td></tr><tr><td>Any conflict shock in 3 years before baseline</td><td>0.10<br>(0.29)</td><td>-0.02<br>(0.01)</td><td>0.10<br>(0.30)</td><td>0.02<br>(0.03)</td></tr><tr><td>Any shock to HH member in 3 years before baseline</td><td>0.49<br>(0.50)</td><td>0.04**<br>(0.02)</td><td>0.50<br>(0.50)</td><td>-0.01<br>(0.03)</td></tr><tr><td colspan="5"><em>HH activity and well-being outcomes</em></td></tr><tr><td>Any HH crop or livestock activity</td><td>0.75<br>(0.43)</td><td>-0.11***<br>(0.03)</td><td>0.73<br>(0.45)</td><td>0.03<br>(0.05)</td></tr><tr><td>Any HH non-farm enterprise</td><td>0.44<br>(0.50)</td><td>0.02<br>(0.02)</td><td>0.44<br>(0.50)</td><td>-0.06<br>(0.04)</td></tr><tr><td>Respondent did any work in last 7 days</td><td>0.72<br>(0.41)</td><td>0.01<br>(0.02)</td><td>0.72<br>(0.41)</td><td>0.02<br>(0.03)</td></tr><tr><td>Received any transfer or assistance</td><td>0.25<br>(0.44)</td><td>-0.03*<br>(0.02)</td><td>0.25<br>(0.43)</td><td>-0.03<br>(0.02)</td></tr><tr><td>Normalized HH food insecurity index</td><td>-0.11<br>(0.93)</td><td>0.31***<br>(0.06)</td><td>-0.04<br>(0.97)</td><td>-0.05<br>(0.10)</td></tr><tr><td>HH believes is worse off than neighbors</td><td>0.28<br>(0.45)</td><td>0.06***<br>(0.02)</td><td>0.29<br>(0.45)</td><td>0.02<br>(0.03)</td></tr><tr><td>HH believes it is poor or very poor</td><td>0.80<br>(0.40)</td><td>-0.03**<br>(0.02)</td><td>0.80<br>(0.40)</td><td>0.02<br>(0.02)</td></tr><tr><td>Observations</td><td colspan="2">6018</td><td colspan="2">4704</td></tr><tr><td>Test of joint significance</td><td colspan="2">F = 6.69<br>p &lt; 0.001</td><td colspan="2">F = 1.18<br>p = 0.250</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT surveys and VFM archive. The table shows the results of separate regressions of a particular variable on flood exposure. Flood exposure is based on VFM detection of any flooded pixel within 1 km of the community centroid in 2019 or 2020. See Table A5 for additional outcome variable descriptions. We include results for the joint test of significance of all the variables in explaining flood exposure. * p < 0.1, ** p < 0.05, *** p < 0.01
<table><caption>Table A9: Baseline balance by household-reported community flood exposure</caption><thead><tr><th rowspan="2"></th><th colspan="2">No flood propensity control</th><th colspan="2">With flood propensity control</th></tr><tr><th>Non-flooded<br>HH Mean<br>(SD)</th><th>Flooded<br>difference<br>(SE)</th><th>Non-flooded<br>HH Mean<br>(SD)</th><th>Flooded<br>difference<br>(SE)</th></tr></thead><tbody><tr><td colspan="5"><em>Household characteristics</em></td></tr><tr><td>Rural location</td><td>0.58<br>(0.49)</td><td>-0.06<br>(0.04)</td><td>0.55<br>(0.50)</td><td>0.03<br>(0.04)</td></tr><tr><td>Household size</td><td>5.57<br>(2.88)</td><td>0.12<br>(0.10)</td><td>5.62<br>(3.05)</td><td>0.01<br>(0.11)</td></tr><tr><td>Household owns its dwelling</td><td>0.82<br>(0.38)</td><td>-0.08***<br>(0.02)</td><td>0.78<br>(0.41)</td><td>-0.02<br>(0.02)</td></tr><tr><td>Roof is cement or metal</td><td>0.39<br>(0.49)</td><td>0.11***<br>(0.03)</td><td>0.44<br>(0.50)</td><td>-0.02<br>(0.03)</td></tr><tr><td>Floor is tile or cement</td><td>0.10<br>(0.30)</td><td>0.04**<br>(0.02)</td><td>0.12<br>(0.33)</td><td>-0.01<br>(0.02)</td></tr><tr><td>Main source of light is electricity</td><td>0.13<br>(0.34)</td><td>0.05**<br>(0.02)</td><td>0.16<br>(0.37)</td><td>0.01<br>(0.02)</td></tr><tr><td>Household has toilet or latrine</td><td>0.26<br>(0.44)</td><td>0.07**<br>(0.03)</td><td>0.30<br>(0.46)</td><td>-0.01<br>(0.03)</td></tr><tr><td>Normalized HH wealth index</td><td>-0.11<br>(0.90)</td><td>0.19***<br>(0.06)</td><td>-0.02<br>(0.99)</td><td>-0.00<br>(0.06)</td></tr><tr><td>Household is below national poverty line</td><td>0.30<br>(0.46)</td><td>-0.01<br>(0.02)</td><td>0.29<br>(0.45)</td><td>0.02<br>(0.02)</td></tr><tr><td colspan="5"><em>Household head characteristics</em></td></tr><tr><td>Household head age</td><td>43.95<br>(15.01)</td><td>-0.68<br>(0.42)</td><td>43.65<br>(14.62)</td><td>-0.50<br>(0.46)</td></tr><tr><td>Household head is female</td><td>0.23<br>(0.42)</td><td>0.00<br>(0.01)</td><td>0.23<br>(0.42)</td><td>0.00<br>(0.01)</td></tr><tr><td>Household head is Muslim</td><td>0.70<br>(0.46)</td><td>-0.12***<br>(0.04)</td><td>0.65<br>(0.48)</td><td>-0.06<br>(0.04)</td></tr><tr><td>Household head is literate</td><td>0.44<br>(0.50)</td><td>0.07***<br>(0.02)</td><td>0.47<br>(0.50)</td><td>0.01<br>(0.02)</td></tr><tr><td>Household head completed primary school</td><td>0.27<br>(0.44)</td><td>0.11***<br>(0.02)</td><td>0.32<br>(0.47)</td><td>0.05*<br>(0.03)</td></tr><tr><td colspan="5"><em>Baseline household shock exposure</em></td></tr><tr><td>Any flood shock in 3 years before baseline</td><td>0.08<br>(0.27)</td><td>0.05***<br>(0.01)</td><td>0.10<br>(0.30)</td><td>-0.01<br>(0.01)</td></tr><tr><td>Any drought shock in 3 years before baseline</td><td>0.21<br>(0.41)</td><td>-0.05**<br>(0.02)</td><td>0.19<br>(0.39)</td><td>-0.03<br>(0.02)</td></tr><tr><td>Any other agricultural shock in 3 years before baseline</td><td>0.23<br>(0.42)</td><td>-0.04**<br>(0.02)</td><td>0.21<br>(0.41)</td><td>-0.02<br>(0.02)</td></tr><tr><td>Any income/job shock in 3 years before baseline</td><td>0.20<br>(0.40)</td><td>0.04**<br>(0.01)</td><td>0.22<br>(0.41)</td><td>0.01<br>(0.02)</td></tr><tr><td>Any conflict shock in 3 years before baseline</td><td>0.08<br>(0.27)</td><td>0.02<br>(0.01)</td><td>0.09<br>(0.28)</td><td>0.03**<br>(0.02)</td></tr><tr><td>Any shock to HH member in 3 years before baseline</td><td>0.48<br>(0.50)</td><td>0.06***<br>(0.02)</td><td>0.51<br>(0.50)</td><td>0.04**<br>(0.02)</td></tr><tr><td colspan="5"><em>HH activity and well-being outcomes</em></td></tr><tr><td>Any HH crop or livestock activity</td><td>0.74<br>(0.44)</td><td>-0.06**<br>(0.03)</td><td>0.71<br>(0.45)</td><td>0.01<br>(0.03)</td></tr><tr><td>Any HH non-farm enterprise</td><td>0.42<br>(0.49)</td><td>0.05**<br>(0.02)</td><td>0.45<br>(0.50)</td><td>0.01<br>(0.02)</td></tr><tr><td>Respondent did any work in last 7 days</td><td>0.73<br>(0.40)</td><td>-0.01<br>(0.02)</td><td>0.72<br>(0.41)</td><td>0.00<br>(0.02)</td></tr><tr><td>Received any transfer or assistance</td><td>0.27<br>(0.44)</td><td>-0.04***<br>(0.02)</td><td>0.25<br>(0.43)</td><td>-0.04***<br>(0.02)</td></tr><tr><td>Normalized HH food insecurity index</td><td>-0.10<br>(0.96)</td><td>0.23***<br>(0.05)</td><td>0.01<br>(0.98)</td><td>0.04<br>(0.06)</td></tr><tr><td>HH believes is worse off than neighbors</td><td>0.27<br>(0.44)</td><td>0.06***<br>(0.02)</td><td>0.30<br>(0.46)</td><td>0.04**<br>(0.02)</td></tr><tr><td>HH believes it is poor or very poor</td><td>0.80<br>(0.40)</td><td>-0.02<br>(0.01)</td><td>0.79<br>(0.41)</td><td>-0.00<br>(0.02)</td></tr><tr><td>Observations</td><td colspan="2">6018</td><td colspan="2">5931</td></tr><tr><td>Test of joint significance</td><td colspan="2">F = 3.05<br>p < 0.001</td><td colspan="2">F = 1.28<br>p = 0.156</td></tr></tbody></table>

Note: Authors' calculations based on data from the ECOSIT surveys and VFM archive. The table shows the results of separate regressions of a particular variable on flood exposure. Flood exposure is based on any household in a community reporting a flood shock over the 2019-2022 recall period. See Table A5 for additional outcome variable descriptions. We include results for the joint test of significance of all the variables in explaining flood exposure. * p < 0.1, ** p < 0.05, *** p < 0.01
C Flood management stakeholders in Chad

High levels of flood risk and population exposure across the vast country of Chad present a major challenge for flood monitoring, prevention, management, and response. Flood management in Chad involves a diverse range of stakeholders, including governmental agencies, non-governmental organizations (NGOs), and international actors playing different roles (Government of Chad, 2014, 2023b, 2024). We present information on a selection of key stakeholders in Chad in Table A10, summarizing their roles and primary activities.

Governmental agencies primarily focus on policy development, disaster preparedness, infrastruc-
ture planning, and emergency response coordination (ACAPS, 2024; Government of Chad, 2022,
2023a, 2024). Flood prevention activities undertaken by government ministries in collaboration
with local authorities include the reinforcement of dikes and riverbanks along the Chari and Lo-
gone rivers, the deployment of heavy machinery for flood control infrastructure in N'Djamena, and
the strengthening of pumping stations to prevent urban flooding (Government of Chad, 2023a).
These investments and activities are helpful but will likely provide little protection against the
threat of increasingly severe 100-year floods.

Among key government stakeholders, the Ministry of Social Action, National Solidarity and Humanitarian Affairs (MASAH)'s Direction de la Solidarité Nationale coordinates humanitarian aid and emergency flood relief, and is the primary actor responsible for responding to most flood events. The National Meteorology Agency (ANAM) is responsible for weather forecasting to anticipate flooding risk and support early warning systems. In response to increasing flood risk, in 2022 the Strategic Committee for Flood Management and Prevention was formed by government decree. This committee is responsible for integrating various ministerial departments, United Nations agencies, international NGOs, and local disaster management actors. Its work is structured around three specialized sub-committees focusing on flood prevention and management, humanitarian response and post-flood recovery, and health and sanitation for affected populations (Government of Chad, 2024). However, this committee is primarily active only in case of major flood disasters, and does not coordinate responses to most flood events.

To strengthen institutional response capacity, the government has expanded the roles of key
ministries such as MASAH, the Ministry of Land Management, Housing, and Urban Planning
(MATHU), the Ministry of Public Health, and the Ministry of Water and Energy. These min-
istries, in collaboration with local authorities and the Office of Military Engineering and Produc-
tion (OGEMIP), have undertaken various flood prevention activities, including the reinforcement
of dikes and riverbanks along the Chari and Logone rivers, the deployment of heavy machinery for
flood control infrastructure in N'Djamena, and the strengthening of pumping stations to prevent
urban flooding (Government of Chad, 2023a). MASAH remains the central government actor in
flood response.

Non-governmental organizations (NGOs) and international organizations are essential in both
immediate disaster response and long-term resilience-building (ACAPS, 2024; Government of Chad,
2023a; United Nations OCHA, 2022a, 2024a). NGOs often fill gaps where government resources
are limited, particularly in remote or highly vulnerable areas. Their work is crucial not only in emergency interventions but also in risk reduction strategies, capacity-building programs, and advocating for sustainable flood management policies (IFRC, 2022, 2023, 2024). For example, the Food and Agriculture Organization (FAO) of the United Nations has conducted post-flood agricultural damage assessments in the most affected provinces, namely Logone Occidental, Logone Oriental, Mandoul, Mayo-Kebbi Est, Moyen Chari, and Tandjilé, to evaluate the long-term impact on food security and recommend appropriate interventions since 2022s floods (FAO, 2023a, 2023b, 2023d, 2024a). The Chadian Red Cross, supported by the IFRC, plays a key role in supporting flood response efforts across all 23 provinces (IFRC, 2022, 2023, 2024).

International and national organizations have strengthened their coordination mechanisms to enhance disaster preparedness, emergency response, and long-term resilience-building. The United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), the FAO, the World Food Programme (WFP), the United Nations Children's Fund (UNICEF), the United Nations High Commissioner for Refugees (UNHCR), and the World Health Organization (WHO) have played an important role in mobilizing humanitarian aid, coordinating flood response logistics, and ensuring food security, health services, and water sanitation for affected populations (UNICEF, 2024; United Nations OCHA, 2022a, 2022b, 2024a, 2025; WFP, 2024a; WHO, 2022). In particular, UNOCHA has enhanced data-sharing and response coordination between humanitarian actors and government agencies (United Nations OCHA, 2022a, 2024a, 2025).

Local community organizations and civil society groups play an essential role in engaging affected populations, disseminating information, and supporting response efforts at the grassroots level. These groups often act as intermediaries between large organizations and local communities, ensuring that aid and flood mitigation efforts are appropriately targeted.

Despite increased attention and funding and efforts to organize around flood preparedness and response, critical vulnerabilities persist, limiting the effectiveness of flood response efforts. The absence of a legally binding multi-sectoral contingency plan, over-reliance on external humanitarian aid, and weak enforcement of urban planning policies continue to hinder long-term disaster resilience in Chad (Government of Chad, 2024; MASSAH, 2025). Communication and technological challenges are another constraint. For example, the April-May 2024 seasonal forecast bulletins from ANAM had predicted above-average rainfall and rising water levels in the Chari and Logone river basins, which ultimately resulted in widespread flooding (IRD, 2024), but there was limited capacity to disseminate and act on these forecasts to mitigate flood risk and exposure. The General Directorate of Water Resources (DRE) has expanded hydrological observation networks, but many water-level monitoring stations remain non-functional, highlighting the urgent need for technological upgrades, including automated water level sensors (Government of Chad, 2023b, 2024). There are also challenges in identifying flood incidence locally to help target relief efforts.

Despite increased attention and funding and efforts to organize around flood preparedness and response, critical vulnerabilities persist, limiting the effectiveness of flood response efforts. The absence of a legally binding multi-sectoral contingency plan, over-reliance on external humanitar-
ian aid, and weak enforcement of urban planning policies continue to hinder long-term disaster
resilience in Chad (Government of Chad, 2024; MASSAH, 2025). Communication and technologi-
cal challenges are another constraint. For example, the April-May 2024 seasonal forecast bulletins
from ANAM had predicted above-average rainfall and rising water levels in the Chari and Logone
river basins, which ultimately resulted in widespread flooding (IRD, 2024), but there was limited
capacity to disseminate and act on these forecasts to mitigate flood risk and exposure. The General
Directorate of Water Resources (DRE) has expanded hydrological observation networks, but many
water-level monitoring stations remain non-functional, highlighting the urgent need for technolog-
ical upgrades, including automated water level sensors (Government of Chad, 2023b, 2024). There
are also challenges in identifying flood incidence locally to help target relief efforts.

Infrastructure limitations compound Chad’s vulnerability to flooding (ND-GAIN, 2025). Plan-
ning and funding for flood defenses, urban water management infrastructure, river flow manage-
ment, erosion control and prevention, and durable and resilient housing across many parts of the
country are a significant challenge.
<table><caption>Table A10: Main stakeholders involved in flood management</caption><thead><tr><td>Organization</td><td>Roles / Missions</td><td>Activities / Responsibilities</td><td>Outputs</td></tr></thead><tbody><tr><td>Chad Strategic Committee for Flood Management and Prevention</td><td>Oversees the planning, coordination, and implementation of flood-related prevention and response efforts in Chad.</td><td>Coordinates humanitarian interventions, maps flood-prone areas, evaluates relevant legislation, drafts contingency plans, mobilizes resources, and develops communication strategies to enhance preparedness and response.</td><td>The committee generates flood risk maps, contingency plans, emergency response reports, and updates on funding mobilization.</td></tr><tr><td>Chad Ministry of Social Action, National Solidarity and Humanitarian Affairs (MASAH)</td><td>Coordination of flood response and relief (Direction de la Solidarité Nationale).</td><td>Deployment of emergency shelters, food aid, and medical assistance for flood victims.</td><td>Continuous relief activities, especially during flood crises.</td></tr><tr><td>Chad Ministry of Spatial Planning, Housing and Urban Planning (MATHU)</td><td>Urban planning and development of flood mitigation infrastructures.</td><td>Construction of flood control barriers, risk mapping, and urban drainage improvements.</td><td>Urban development plans, risk mapping reports (annually or as needed).</td></tr><tr><td>Chad Ministry of Public Health</td><td>Manages health-related crises and responses during floods.</td><td>Strengthens health facilities, deploys emergency medical teams, monitors epidemiological risks, provides psychosocial support.</td><td>Epidemiological reports, emergency health response updates (regularly and post-flood events).</td></tr><tr><td>Chad Ministry of Water and Energy (MEE)</td><td>Oversees water resource management and flood-related hydrological monitoring.</td><td>Collects and analyzes hydrological data, manages flood forecasting models, oversees emergency water resource distribution.</td><td>Issues hydrological reports (regular updates during flood seasons)</td></tr><tr><td>Chad ANAM (National Meteorology Agency)</td><td>Responsible for monitoring, analyzing, and forecasting weather and hydrological conditions to anticipate flood risks in Chad.</td><td>Collects and analyzes meteorological data, publishes climate and hydrological forecasts, and alerts authorities and the public about risks related to rainfall and flooding.</td><td>Regularly publishes meteorological and hydrological bulletins, including seasonal forecasts (such as the April-May 2022 forecasts for that year's floods) and post-season analyses to assess the impact of rainfall and water flow.</td></tr></tbody></table>
<table><caption>Table A10 (continued)</caption><thead><tr><td>Organization</td><td>Roles / Missions</td><td>Activities / Responsibilities</td><td>Outputs</td></tr></thead><tbody><tr><td>Chad General Directorate of Water Resources (DRE)</td><td>Coordinates the hydrological monitoring network and water data management.</td><td>Aggregates flood-related data, provides forecasts, works with hydrological models.</td><td>Issues periodic reports on river levels and water management.</td></tr><tr><td>Chad Provincial and Local Authorities (Governors, Prefects, Mayors)</td><td>Oversee and implement flood response measures at the local level.</td><td>Coordinate emergency response within provinces, support crisis committees, facilitate logistics for evacuations, mobilize local resources, and oversee disaster risk reduction actions.</td><td>Local emergency reports, coordination meetings, and crisis updates (as needed during flood events)</td></tr><tr><td>Chad Office of Military Engineering and Production (OGEMIP)</td><td>Coordinates and implements technical and infrastructural interventions for flood prevention and management in Chad.</td><td>Development of resettlement sites, construction of drainage and flood protection structures, installation of shut-off valves, maintenance of canals, provision of temporary shelters, solar electrification, and water supply infrastructure.</td><td>Reports on infrastructure progress (as needed), monitoring of maintenance and operational activities (regularly), post-flood intervention assessments (after flood events).</td></tr><tr><td>OCHA (United Nations Office for the Coordination of Humanitarian Affairs)</td><td>Facilitates coordination between governmental bodies and humanitarian organizations.</td><td>Mobilizes international funding, coordinates humanitarian interventions, supports needs assessments.</td><td>Humanitarian updates, funding reports, disaster response bulletins (frequent)</td></tr><tr><td>FAO (Food and Agriculture Organization)</td><td>Monitors flood impact on agriculture and food security, provides technical assistance, and supports resilience-building efforts.</td><td>Uses satellite imagery for flood assessments, supports early warning systems, provides emergency aid to farmers, implements food security measures.</td><td>Produces flood monitoring reports (during the rainy season), agricultural impact assessments (post-flood events), early warning bulletins (periodic based on forecasts), and livelihood/food security reports (as needed in response to crises).</td></tr></tbody></table>
<table><caption>Table A10 (continued)</caption><thead><tr><td>Organization</td><td>Roles / Missions</td><td>Activities / Responsibilities</td><td>Outputs</td></tr></thead><tbody><tr><td>WFP (World Food Programme)</td><td>Provision of emergency food assistance, logistics, and nutritional support.</td><td>Rapid deployment of emergency food supplies and logistical support to affected populations.</td><td>Food security assessments, distribution reports (monthly and post-floods)</td></tr><tr><td>UNICEF, WHO, UNHCR</td><td>Medical assistance, child protection, and refugee response coordination.</td><td>Health interventions, vaccination campaigns, and emergency response for vulnerable groups.</td><td>Regular, particularly for health and child protection during crises</td></tr><tr><td>Chadian Red Cross</td><td>Emergency response, first aid, and humanitarian relief for disaster-affected populations.</td><td>Emergency evacuation, first aid and psychosocial support, distribution of emergency shelter and essential household items, cash assistance for affected households, water purification and hygiene promotion, community health interventions, disaster risk reduction training.</td><td>Needs assessments, disaster response reports (regularly during crises), post-flood evaluations, and operational updates (periodic)</td></tr><tr><td>Institute of Research for Development (IRD) & University of N'Djamena (Laboratory of Hydro-Geosciences and Reservoirs)</td><td>Institute of Research for Development (IRD) & University of N'Djamena (Laboratory of Hydro-Geosciences and Reservoirs).</td><td>Develops predictive flood models, monitors river levels, analyzes hydrological data, collaborates on early warning systems.</td><td>Publishes technical and operational reports (weekly and during flood crises).</td></tr></tbody></table>
D Measuring flooding by satellite

Earth observation satellites have been consistently collecting data on the Earth's surface since the 1960s and hundreds of satellites are currently in orbit. Satellites with publicly-available data that have commonly been used to detect flooding include Landsat-8, MODIS (Aqua and Terra), VIIRS, and Sentinel-1 and -2 (see Schumann et al. (2018) for a recent review). Table A11 summarizes information on the data collected by these satellites, along with two others less commonly used, ALSO PALSAR and SMAP.<fnref n="23" />

Each satellite carries sensors that measure signals emitted from Earth. Optical/multispectral or microwave sensors passively record signals. Optical sensors all high spatial and spectral detail but are limited by clouds and sunlight. Microwave sensors penetrate clouds but have a much coarser resolution. Active sensors, notably synthetic aperture radar (SAR), collect data by emitting energy and measuring the backscatter. SAR works in all weather and in both day and night, but is typically collected at lower frequency. All of the satellites listed above carry optical/MS sensors except for Sentinel-1 which carries SAR sensors.

Both optical and radar can be used to detect the presence of water. For example, the Normalized Difference Water Index (NDWI) based on optical data leverages differences in how surface water reflects and absorbs different portions of the electromagnetic spectrum. This index is commonly used as an indicator of water or moist surfaces. Algorithms for radar data leverage differences in the backscatter for radar signals bouncing off of water relative to other features of the Earth's surface. Special attention must be paid in this algorithm to account for other features which could have similar signals as water, such as cloud or terrain shadow.

The identification of surface water is key for the detection of floods using satellite data. If water is identified in pixels where there is not typically any surface water (on average or in a given time period), this is an indication of a potential flood event. Different flood detection algorithms take different approaches to determining whether water detected at a particular place and time constitutes a flood. One common method is applying a 'permanent water mask' which identifies all pixels where surface water is expected, based either on geospatial representations of water bodies or on historical patterns in satellite data. All areas with surface water outside of the permanent water mask could be considered flooded. An alternative or additional method involves comparisons of water cover in the same location at different points in time, with a larger variation indicating a greater likelihood of flooding. Flood detection algorithms also make a variety of adjustments to account for measurement issues such as dropping areas with steep slopes (based on a digital elevation map), interpolating over time to overcome issues related to cloud cover or gaps between satellite flyovers, and more.

Some remote sensing approaches rely on precipitation data rather than the detection of surface water. Microwave and radar sensors can be used to estimate precipitation patterns, which can be

<footnote n="23">²³A number of other satellites are also used in flood mapping but these are the most common. Commercial satellites such as Planetscope (Planet), WorldView (Maxar), and ICEYE offer very high resolution and high frequency data but these data must be purchased.</footnote>
combined with hydrological models to predict flooding. The accuracy of these methods depends on the local reliability of hydrological model inputs, which can be limited in many lower-income countries.

Sentinel-1 SAR data is increasingly being preferred for flood mapping due to the advantages of SAR over optical data, but the longer revisit period (6-12 days) is an important limitation. MODIS and VIIRS satellite imagery are well-suited for large-area flood mapping due to their frequent (near daily) revisit periods, but are constrained by their inability to see through clouds. Sentinel-2 has the same constraint but offers higher resolution imagery, though with a longer revisit period. The most advanced current methods for identifying flooding combine Sentinel-1 SAR data with imagery from another satellite, often incorporating machine learning methods to leverage the different advantages of each source, but no publicly-available flooding database uses such methods, which are very computationally demanding.<fnref n="24" />

Flood detection by satellite has two main advantages. First, satellite data allows for high-frequency data collection across the world at a high spatial resolution. Second, satellite-based measures are objective and based on a specific algorithm, meaning they are not subject to reporting errors. But there are also several important limitations to satellite-based flood detection.

For one, the frequency of satellite data collection, covering each point on Earth every 1-16 days for the main satellites in Table A11, still implies that fast-moving floods are likely to be missed. This has particularly important implications for non-fluvial floods, as pluvial floodwaters flow towards low ground and are unlikely to stay stagnant for very long except on very flat ground with very limited soil absorption capacity and large amounts of water. Another limitation is that cloud cover prevents optical sensors from capturing floods caused by heavy precipitation; detection of these floods requires that inundation remains detectable until the next cloud-free data the satellite passes overhead. Microwave and radar satellite data resolve this issues but are not available at high temporal frequency. Finally, although the identification of floods based on algorithms using data may be objective, it may still be biased based on how the algorithm is developed. For example, algorithms that do not account for seasonal fluctuations in water cover may misclassify some such fluctuations as floods and in general flood detection algorithms are very sensitive to the choice of reference periods for ‘non-flooding’ conditions. Indeed, maps of flooding incidence for a given time and place can vary substantially across different flood monitoring databases.

For these reasons, satellite-based sources should not be considered as definitive measures of flooding incidence, particularly at very specific points in time and space. At a more aggregate level, however, they are very useful in identifying and monitoring areas where floods are determined to be very likely to be occurring.

<footnote n="24">²⁴Patel (2025) develops such a method which also incorporates survey data from Bangladesh as a ground-truth flood measure for tuning the flood detection algorithm.</footnote>
<table><caption>Table A11: Summary of main satellites used in flood detection</caption><thead><tr><td>Satellite</td><td>Source</td><td>Sensor Type</td><td>Spatial Resolution</td><td>Revisit Period</td><td>Spectral Bands</td><td>Swath Width</td><td>Years of availability</td></tr></thead><tbody><tr><td>Landsat 7</td><td>NASA/USGS</td><td>Optical + Infrared</td><td>15 m (panchromatic), 30 m (MS), 60 m (thermal)</td><td>16 days</td><td>8 bands</td><td>185 km</td><td>1999-2022</td></tr><tr><td>Landsat 8</td><td>NASA/USGS</td><td>Optical + Infrared</td><td>15 m (panchromatic), 30 m (MS), 100 m (thermal)</td><td>16 days</td><td>11 bands + 1 panchromatic</td><td>185 km</td><td>2013-</td></tr><tr><td>MODIS (Aqua and Terra)</td><td>NASA</td><td>Optical + Infrared</td><td>250 m (Red/NIR), 500 m, 1000 m</td><td>1-2 days</td><td>36 bands</td><td>2330 km</td><td>1999- (Terra), 2002- (Aqua)</td></tr><tr><td>Sentinel-1</td><td>ESA</td><td>Synthetic Aperture Radar (SAR)</td><td>10 m</td><td>6-12 days (region-dependent)</td><td>C-band SAR</td><td>250 km (varies by mode)</td><td>2014- (S1A), 2016-2021 (S1B)</td></tr><tr><td>Sentinel-2</td><td>ESA</td><td>Optical + Infrared</td><td>10 m (visible/NIR), 20m, 60m</td><td>5 days</td><td>13 bands</td><td>290 km</td><td>2015-</td></tr><tr><td>VIIRS</td><td>NASA/NOAA</td><td>Optical + Infrared</td><td>375 m (I bands), 750 m (M bands)</td><td>1 day</td><td>22 bands</td><td>3000 km</td><td>2011- (Suomi NPP), 2017- (NOAA-20)</td></tr><tr><td>ALOS PALSAR</td><td>JAXA</td><td>Synthetic Aperture Radar (SAR)</td><td>10-100 m (depends on mode)</td><td>~46 days (ALOS-1); 14 days (ALOS-2, partial access)</td><td>L-band SAR</td><td>70-350 km</td><td>2006-2011 (ALOS-1), 2014- (ALOS-2)</td></tr><tr><td>SMAP</td><td>NASA</td><td>Passive Microwave Sensor</td><td>9-36 km (passive); 3 km (active, until 2015)</td><td>2-3 days</td><td>L-band radiometer</td><td>1000 km</td><td>2015-present</td></tr></tbody></table>
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## Acknowledgements

The SASPP is a multi-donor trust fund managed by the World Bank that supports the strengthening of adaptive social protection systems in the Sahel (Burkina Faso, Chad, Mali, Mauritania, Niger, and Senegal) to enhance the resilience of poor and vulnerable households and communities to the impacts of climate change. The program is supported by Denmark, France, Germany, and the United Kingdom.

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