SITUATION REPORT Bayesian Flood Risk Model — Colombia (Country-Scale)
SITUATION REPORT
Bayesian Flood Risk Model — Colombia (Country-Scale)
72-Hour Forecast Horizon | Hazard-Dominant Mode | Robust Run
1. Executive Summary
The Capybara Geek Center’s Bayesian Flood Risk Model, run in hazard-dominant mode with a 72-hour forecast window over the full Colombian territory, projects severe and geographically extensive flood exposure. The model estimates approximately 61 million people (46.4% of Colombia’s population of ~131.5 million) fall within zones of expected flood exposure over the forecast period. Cropland emerges as the most critically exposed asset class, with 81.7% of mapped cropland area (376 ha of 461 ha at the model’s aggregation scale) under expected exposure. Electric grid infrastructure shows 44.4% exposure (393 km of 885 km mapped), and urban areas register 39.3% expected exposure.
These results must be interpreted in the context of Colombia’s ongoing flood emergency. Since late January 2026, persistent heavy rainfall — linked to La Niña-like conditions and an atypical Caribbean cold front — has caused catastrophic flooding across at least 22 departments. Soils remain saturated in much of the country, which is reflected in the model’s posterior weighting through elevated forecast rainfall likelihoods fusing with already high prior flood densities.
The spatial distribution of posterior risk (visible in the map layer) concentrates most heavily in the Andean valleys, the Caribbean lowlands, the Pacific coast (particularly Chocó and Nariño), and the Magdalena-Cauca river system. These align with known flood corridors and with areas where NOAA/CPC’s current weather hazards outlook (2–8 April 2026) forecasts 100–200 mm of additional rainfall over already saturated soils.
2. Model Output Summary
The table below reproduces the posterior-weighted expected exposure statistics from the model run:
3. Spatial Analysis
The map layer shows posterior flood risk concentrated in several distinct corridors:
Caribbean Lowlands (Córdoba, Sucre, Bolívar, Magdalena): The densest continuous exposure zone, consistent with ongoing humanitarian emergency since February 2026. The Sinú, San Jorge, and lower Magdalena basins show near-total coverage.
Andean Valleys (Cauca, Huila, Tolima, Antioquia): High-density exposure in the Cauca and Magdalena river valleys. The Cali–Popayán corridor and Medellín’s metropolitan area show substantial risk patches.
Pacific Coast (Chocó, Nariño): Broad exposure along the Atrato and Patía basins, aggravated by orographic rainfall enhancement.
Eastern Llanos fringe: Moderate exposure along the Arauca–Casanare piedmont, though less dense than the western corridors.
Amazon transition zone: Scattered exposure in Caquetá and Putumayo, lower density but covering remote and underserved communities.
4. Contextual Evidence (JANUS-Coded)
The following evidence segments provide contextual grounding for the model output, coded under the JANUS analytic framework:
5. Key Findings and Implications
Compounding risk: The model’s hazard-dominant mode amplifies the forecast rainfall signal in areas with prior flood history. Given that soils across much of western and northern Colombia have been saturated since late January (S01, S02, S09), the 72-hour forecast rainfall of 100–200 mm compounds on already critical baseline conditions rather than representing an isolated event.
Agricultural exposure dominance: The 81.7% cropland exposure is the highest proportional figure across all asset classes. This is consistent with ground-truth reports of massive agricultural losses in Córdoba and the broader Caribbean lowlands (S04). The April timing is especially damaging as it coincides with the onset of the primera planting season in many Andean and Caribbean departments.
Infrastructure vulnerability: Electric grid exposure at 44.4% is notable and, combined with the 39.3% urban area exposure, indicates material cascading-risk potential. Power outages in flood-affected zones amplify humanitarian impacts through loss of water pumping, communications, and cold-chain for medical supplies.
Data gaps: Roads, oil and gas sites, wind farms, and solar farms returned N/A, indicating missing or incomplete asset layers at the country scale. Roads data in particular would be critical for response logistics assessment and should be prioritized for integration.
Population exposure interpretation: The 46.4% population exposure (61 million) is an expected-value estimate across probabilistic posterior risk surfaces, not a deterministic count of flood-affected people. It indicates broad geographic coincidence between populated areas and elevated posterior risk. Actual direct flood impact will be substantially lower but geographically diffuse.
6. Limitations and Caveats
Model outputs are posterior-weighted expected values, not deterministic forecasts. Actual flood extent will differ.
The hazard-dominant mode intentionally amplifies the hazard signal over exposure weighting; results may overstate risk in low-vulnerability areas with high rainfall forecast.
Asset layers (cropland, urban, population) operate at aggregated scales and may not capture fine-grained local variation.
Contextual evidence (Section 4) spans January–April 2026; the most recent ground-truth reports date from late March. Conditions may have evolved since publication.
IDEAM bulletins could not be directly accessed due to domain-level restrictions; IDEAM-sourced information is cited through secondary sources.
Produced by: Capybara Geek Center | Alejandro Feged-Rivadeneira, Ph.D.
Framework: JANUS Analytic Framework for AI-Assisted Humanitarian Intelligence (Feged Rivadeneira, 2025)
Platform: Google Earth Engine | Data sources: GFS forecast, WorldPop, ESA WorldCover, OpenStreetMap, VIIRS, Global Power Plant Database
Distribution: Unrestricted
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