# Wildfire Sample This page provides a complete sample response for a wildfire hazard from the Active Hazards API endpoint. The featured sample is an **actively burning PDC satellite-detected wildfire spanning the Karnali and Lumbini regions of Nepal into Uttar Pradesh, India** — 73,799 acres of largely forested terrain with 130 H3 cells currently active (out of 207 ever-active), SEVERE severity with EXPECTED urgency, peak Fire Radiative Power of 828 MW, and exposure assessment affecting 30,600 people across two countries and seven admin districts. This record exercises the full satellite incident model documented in the source wildfire pipeline: detection scoring, 24‑hour FRP rollups, land‑usage classification, trend analytics, and a 39‑polygon exposure footprint. ## Wildfire Sources The WILDFIRE hazard type aggregates two distinct source pipelines, which populate different subsets of `incident.properties`: | Source | Source IDs (examples) | Event values | Typical `incident.properties` shape | | ------------------------------------------ | ---------------------------------------------------------- | ----------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | | PDC satellite detection pipeline | `2001` | `"Potential Large and/or Intense Wildfire"` | Full incident analytics — detection scores, FRP rollups, land‑usage fractions, severity/certainty/urgency classification, trend deltas, location enrichment | | NWS / national meteorological alerts (CAP) | `1787` (EMHI), `1797` (CMA), `1798` (IMD), `1801` (NWS US) | `"Fire Weather"`, `"Pre Fire Alert"`, `"Forest Fire"` | CAP alert metadata — `senderName`, `headline`, `description`, `urgency`, `severity`, `certainty`, `status`, VTEC/identifier fields | Satellite-detected incidents use `sourceId: 2001` and set `sourceRecordId` to the internal PDC incident UUID. Alert-derived wildfires use the national provider's `sourceId` and a provider‑issued alert identifier. The featured sample below is a satellite-detected incident. ## Wildfire-Specific Properties ### Incident Identity and Lifecycle | Field | Description | | ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `incident_id` | Stable identifier of the wildfire incident (primary key in the incident processor). Matches `incident.uuid` and `sourceRecordId` for satellite-detected records. | | `version` | Monotonic version number of the incident state used for conflict resolution. | | `created_at` | Timestamp when the incident was first created in the incident processor. ISO‑8601 string. | | `last_updated_at` | Timestamp of the most recent perimeter or incident update applied to this incident. | | `last_emitted_at` | Timestamp when this incident state was last emitted downstream. | | `last_emitted_hash` | Hash of the last emitted state used to suppress duplicate downstream forwards. | | `last_seen_at` | Timestamp of the most recent meaningful hotspot evidence for the incident. | | `is_expired` | Indicates whether the incident has been expired and should no longer be treated as active. | | `current_cells_n` | Count of H3 cells currently active in the incident. Values near zero are valid when the fire has cooled below detection thresholds but the incident has not yet been expired. | | `ever_cells_n` | Count of all H3 cells that have ever been part of the incident (superset of `current_cells_n`). | ### Classification and Reasoning The incident processor derives three independent classification axes, each with a machine‑readable value and a human‑readable `*_reason` string that names the thresholds and weighted scores that drove the choice. | Field | Values observed for satellite-detected wildfires | Description | | ------------------ | ------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- | | `severity` | `MODERATE`, `SEVERE`, `EXTREME` | Derived severity classification. | | `severity_reason` | (string) | Explanation of the severity classification — typically references the 24h FRP threshold, acreage support, and dominant land cover. | | `certainty` | `LIKELY` (observed); `POSSIBLE`, `OBSERVED`, `UNLIKELY`, `UNKNOWN` also defined | Derived confidence that the detection represents an actual wildfire. | | `certainty_reason` | (string) | Explanation — typically references biome + fragmentation weighted detection scores and acreage corroboration. | | `urgency` | `IMMEDIATE`, `EXPECTED`, `FUTURE` | Derived urgency classification. | | `urgency_reason` | (string) | Explanation of the urgency classification. | For **NWS / CAP-sourced** fire weather alerts, `certainty` follows the CAP 1.2 specification with these values: * **Observed** — determined to have occurred or to be ongoing * **Likely** — probability > 50% * **Possible** — probability ≤ 50% * **Unlikely** — not expected to occur * **Unknown** — certainty cannot be determined Note that CAP-sourced values are mixed‑case (e.g. `"Possible"`, `"Likely"`) while PDC‑derived values are upper‑case (e.g. `"LIKELY"`). The top-level `severity` on the hazard record (not `incident.properties.severity`) uses the standard DisasterAWARE tiering of `ADVISORY`, `WATCH`, `WARNING` derived from the combination of the incident classification and exposure level. ### Detection Scoring Satellite-detected incidents carry a panel of detection‑score aggregates from contributing hotspot observations: | Field | Description | | ------------------------------- | ------------------------------------------------------------------------------------ | | `detection_score_max` | Maximum detection score observed across contributing members. | | `detection_score_sum` | Sum of detection scores observed across contributing members. | | `detection_score_mean` | Unweighted mean detection score across contributing members. | | `detection_score_mean_weighted` | Weighted mean detection score across contributing members. | | `detection_score_weight_sum` | Sum of weights used when computing weighted detection-score averages. | | `detection_score_n` | Count of detection-score observations contributing to the incident rollup. | | `avg_sample_count` | Average number of hotspot samples per contributing cell over the aggregation window. | ### Fire Radiative Power (FRP) FRP fields quantify radiant heat output in megawatts. Higher values indicate more intense fire activity. Most fields roll up over a 24‑hour window. | Field | Description | | -------------------- | --------------------------------------------------------------- | | `sum_frp_mw_current` | Sum of FRP in MW across cells that are currently active. | | `max_frp_mw_ever` | Maximum FRP ever observed for any cell in the incident history. | | `frp_sum_mw_24h` | Rolling 24-hour sum of FRP across the incident. | | `frp_max_mw_24h` | Rolling 24-hour maximum FRP observed in the incident. | | `frp_mw_avg_window` | Average FRP value over the configured incident scoring window. | | `frp_scale` | Scaling factor used for FRP visualization or categorization. | ### Size and Trend | Field | Description | | ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | | `total_acres` | Estimated incident size in acres derived from the incident exposure footprint. | | `trend_window_ms` | Window length in milliseconds used for incident trend calculations. | | `trend_delta_acres` | Change in estimated acres over the configured trend window. A negative value indicates the burnt‑area footprint contracted over the window. | | `trend_acres_per_hour` | Estimated hourly acreage growth rate over the configured trend window. | | `trend_delta_frp_sum_mw_24h` | Change in rolling 24-hour FRP sum over the trend window. | | `trend_delta_frp_max_mw_24h` | Change in rolling 24-hour FRP max over the trend window. | | `trend_delta_detection_score_mean_weighted` | Change in weighted mean detection score over the trend window. | | `trend_direction` | One of `UNKNOWN`, `STEADY`, `GROWING`, `SHRINKING`. | ### Land Usage and Biome The incident processor classifies the dominant land cover and biome of the affected cells. Note that the V2 API surfaces these fields without the `land_` prefix that appears in the backing incident schema (so spreadsheet field `land_dominant_class` maps to API property `dominant_class`, `land_biome_class` → `biome_class`, `land_fraction_*` → `fraction_*`, and so on). | Field | Description | | --------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `dominant_class` | Dominant land-cover class. Values observed: `TREE_COVER`, `GRASSLAND`, `CROPLAND`, `BUILT_UP`, `BARE_SPARSE`, `PERMANENT_WATER`. | | `biome_class` | Dominant biome classification. Values observed: `Tropical Forest`, `Temperate Forest`, `Savanna Grassland`, `Mediterranean Shrubland`, `Desert`, `Mangrove`, `Unknown`. | | `fraction_forest`, `fraction_grass`, `fraction_cropland`, `fraction_urban`, `fraction_bare`, `fraction_water`, `fraction_wetland` | Land‑cover fractions for the incident footprint. Sum to approximately 1.0. | | `land_usage_weight` | Weight applied to the aggregated land-usage summary. | | `road_pixel_count` | Count of road pixels intersecting the incident land-usage summary. | | `road_pixel_fraction` | Fraction of the incident land-usage summary classified as roads. | | `is_urban_area` | Whether the incident land-usage summary indicates an urban area. | | `touches_urban` | Whether any contributing incident area intersects urban land cover. | | `fraction_urban_agg` | Incident-level aggregate urban fraction across contributing members. | | `wui_is_interface` | Whether the land-usage summary indicates wildland-urban interface. | | `wui_frac_interface` | Wildland-urban interface fraction for the incident land-usage summary. | | `touches_wui` | Whether any contributing incident area intersects the wildland-urban interface. | | `wui_frac_interface_agg` | Incident-level aggregate WUI fraction across contributing members. | ### Location Enrichment The V2 API enriches the raw incident record with human-readable place names. These fields are not present in the backing wildfire-incident schema — they are added during V2 enrichment from the incident's geometry. | Field | Description | | ------------------- | ------------------------------------------------------------------------------------------------ | | `locationName` | Human-readable location string (also mirrored to the top-level hazard `name` and `description`). | | `hazardDescription` | Long-form narrative describing the detection, severity, and land context. | | `country_codes` | ISO 3166-1 alpha-3 country codes, comma-separated. | | `country_names` | Country names, comma-separated. | | `regions` | Admin level 1 (state/province) names, comma-separated. | | `boundary_names` | Finer-grained admin boundary names (admin level 2 / district), comma-separated. | | `event` | Event category label (e.g. `"Potential Large and/or Intense Wildfire"`). | ### Features The `features` property is `null` for satellite-detected wildfires. When an NWS or other national‑alert source emits a wildfire record with a published hazard zone, `features` carries a GeoJSON `FeatureCollection` of those zone polygons. Fire perimeter polygons (e.g. NIFC IRWIN perimeters) are delivered via a separate perimeter pipeline and are not included in the active hazard record's `features` property. ### Exposure Areas `exposureAreas` is a GeoJSON `FeatureCollection` whose features carry `properties.exposureLevel` (1, 2, or 3). Satellite-detected wildfires use a **single exposure level** derived from the active hotspot footprint: * **Level 1**: Minor damage expected * **Level 2**: Moderate damage expected * **Level 3**: Severe damage expected The geometry is typically a `MultiPolygon` — a single incident can span many disjoint burnt patches. The featured sample's exposure geometry is a `MultiPolygon` of 39 disjoint polygons reflecting the spatial distribution of active detection cells across the incident footprint. ### Exposure Assessment The `exposureAssessment` object provides detailed population and infrastructure exposure data for the affected area. **Top-level fields:** * `population` — Object containing total exposed population broken down by age group: * `total` — Total exposed population * `total0_14` — Population aged 0-14 * `total15_64` — Population aged 15-64 * `total65_Plus` — Population aged 65 and older * `households` — Number of households * `vulnerable` — Estimated vulnerable population * `capital` — Object containing infrastructure exposure: * `total` — Total infrastructure replacement value in USD * `school` — Number of exposed schools * `hospital` — Number of exposed hospitals * `totalByCountry` — Array of country-level breakdowns, each containing `population`, `capital`, `country` (ISO 3166-1 alpha-3), `admin0`, `admin1`, `admin2`, and humanitarian needs estimates (`foodNeeds`, `waterNeeds`, `wasteNeeds`, `shelterNeeds` with corresponding unit fields) * `totalByAdmin` — Array of admin-level breakdowns with the same structure * `exposureLevels` — Array of per-level exposure data, each containing `level`, `exposureDescription`, and a `data` object with the same `population`, `capital`, `totalByCountry`, and `totalByAdmin` structure * Top-level humanitarian needs: `foodNeeds`, `waterNeeds`, `wasteNeeds`, `shelterNeeds` with corresponding unit fields * Metadata: `hazardUuid`, `hazardType`, `version`, `timestamp`, `exposureDescription` ### Exposure Assessment Units The `exposureAssessment` object uses units that vary by country: **United States:** * `foodNeedsUnit`: MRE (Meals Ready-to-Eat) * `waterNeedsUnit`: gal (gallons) * `wasteNeedsUnit`: 25 gal (25-gallon units) * `shelterNeedsUnit`: sq feet (square feet) **All other countries (including the featured Nepal / India sample):** * `foodNeedsUnit`: CAL (calories) * `waterNeedsUnit`: liter (liters) * `wasteNeedsUnit`: 100 liter (100-liter units) * `shelterNeedsUnit`: sq meters (square meters) The top-level humanitarian needs fields use the metric units (CAL, liter, 100 liter, sq meters) regardless of the country mix. ## Source Schema Coverage The backing wildfire incident pipeline persists three related tables: **wildfire-incident** (72 fields — the incident-level rollup), **display-hotspot-stats** (34 fields — per-cell hotspot aggregates), and **wildfire-perimeter** (47 fields — individual perimeter events). The V2 WILDFIRE hazard API surfaces a curated projection of the **wildfire-incident** table as `incident.properties`, with some fields renamed (the `land_` prefix is dropped) and several enrichment fields added. ### Wildfire Incident (`wildfire-incident`) — 72 fields | Category | In API | Excluded / Storage-only | | ------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Identity and lifecycle | `incident_id`, `version`, `created_at`, `last_updated_at`, `last_emitted_at`, `last_emitted_hash`, `last_seen_at`, `is_expired` | `merged_into_incident_id` (populated only when an incident is merged into a survivor) | | Cell membership | `current_cells_n`, `ever_cells_n` (counts only) | `member_perimeter_ids`, `current_cells`, `ever_cells`, `current_cells_compacted`, `ever_cells_compacted` (raw arrays retained in storage only) | | Classification | `severity`, `severity_reason`, `certainty`, `certainty_reason`, `urgency`, `urgency_reason` | — | | Detection scoring | `detection_score_max`, `detection_score_sum`, `detection_score_weight_sum`, `detection_score_mean`, `detection_score_mean_weighted`, `detection_score_n`, `avg_sample_count` | — | | FRP | `sum_frp_mw_current`, `max_frp_mw_ever`, `frp_sum_mw_24h`, `frp_max_mw_24h`, `frp_mw_avg_window`, `frp_scale` | `frp_per_current_cell`, `current_to_ever_ratio` | | Size and trend | `total_acres`, `trend_window_ms`, `trend_delta_acres`, `trend_acres_per_hour`, `trend_delta_frp_sum_mw_24h`, `trend_delta_frp_max_mw_24h`, `trend_delta_detection_score_mean_weighted`, `trend_direction` | — | | Land usage (renamed: `land_*` → no prefix) | `dominant_class`, `biome_class`, `fraction_forest`, `fraction_grass`, `fraction_cropland`, `fraction_urban`, `fraction_bare`, `fraction_water`, `fraction_wetland`, `is_urban_area`, `road_pixel_count`, `road_pixel_fraction`, `wui_is_interface`, `wui_frac_interface`, `land_usage_weight`, `touches_urban`, `touches_wui`, `fraction_urban_agg`, `wui_frac_interface_agg` | — | | Hotspot confidence counts | — | `reading_count_24h`, `high_confidence_count_24h`, `nominal_confidence_count_24h` (available on the hotspot-stats table) | | Geometry | `alertGeometry`, `exposureAreas`, top-level `longitude` / `latitude` | `center_point`, `exposure_geometry` (backing PostGIS columns) | | Spatial coherence | — | `spatially_incoherent`, `spatial_cluster_count`, `spatial_largest_cluster_fraction`, `spatial_second_cluster_fraction`, `spatial_max_distance_hops` (populated only when the footprint fails coherence checks) | **V2 enrichment fields added beyond the incident schema:** `locationName`, `hazardDescription`, `event`, `country_codes`, `country_names`, `regions`, `boundary_names`. ### Display Hotspot Stats (`display-hotspot-stats`) — 34 fields Per-H3-cell hotspot aggregates used for map display. **Not surfaced at the hazard-record level** — individual hotspot points are available through the hotspot endpoints, not through the WILDFIRE hazard API. The incident-level rollups (`detection_score_*`, `frp_*_24h`, `avg_sample_count`) in `incident.properties` are derived from these per-cell stats. ### Wildfire Perimeter (`wildfire-perimeter`) — 47 fields Individual perimeter events (the components that merge into an incident). **Not surfaced at the hazard-record level** for satellite-detected wildfires — `features` is `null` on all 152 satellite-detected records in the observed dataset. Official perimeter polygons (e.g. NIFC IRWIN) arrive through a separate perimeter pipeline keyed off `perimeter_id`. ## Sample Response ```json { "name": [ { "locale": "en", "value": "Wildfire - Karnali and Lumbini regions, Nepal and India" } ], "description": [ { "locale": "en", "value": "A Potential Large and/or Intense Wildfire has been detected for April 23, 2026 10:26:53 UTC through April 25, 2026 12:08:31 UTC for Surkhet, Banke, Dang, Shravasti, Bardiya, Salyan, Balrampur.\n\nIt is estimated that 30,600 people, 7,010 households, and $164 Million (USD) of infrastructure* are within the affected area(s). Exposure estimates will be refined as additional data from official sources becomes available.\n\nEvent Summary*:\nEarlier on April 23, 2026, a wildfire was first identified in a largely forested area spanning the Karnali and Lumbini regions along the Nepal–India border. Detections are considered likely based on consistent recent signals and acreage corroboration, and confidence takes local land and ecosystem conditions into account. The incident is classified as severe and is producing an intense heat signature across a large forested landscape, though recent activity direction is unclear. Because it may require attention soon and the direction of activity is uncertain, conditions warrant close monitoring.\n\n*The cost represents the total replacement value of the infrastructure." } ], "severity": "ADVISORY", "category": "EVENT", "incident": { "uuid": "ff7c5cc7-07ec-4db8-9f1b-005572477209", "type": "WILDFIRE", "sourceId": 2001, "sourceRecordId": "ff7c5cc7-07ec-4db8-9f1b-005572477209", "properties": { "incident_id": "ff7c5cc7-07ec-4db8-9f1b-005572477209", "version": 17, "event": "Potential Large and/or Intense Wildfire", "locationName": "Surkhet, Banke, Dang, Shravasti, Bardiya, Salyan, Balrampur", "hazardDescription": "Earlier on April 23, 2026, a wildfire was first identified in a largely forested area spanning the Karnali and Lumbini regions along the Nepal–India border. Detections are considered likely based on consistent recent signals and acreage corroboration, and confidence takes local land and ecosystem conditions into account. The incident is classified as severe and is producing an intense heat signature across a large forested landscape, though recent activity direction is unclear. Because it may require attention soon and the direction of activity is uncertain, conditions warrant close monitoring.", "country_codes": "NPL, IND", "country_names": "Nepal, India", "regions": "Karnali, Lumbini, Uttar Pradesh", "boundary_names": "Surkhet, Banke, Dang, Shravasti, Bardiya, Salyan, Balrampur", "severity": "SEVERE", "severity_reason": "legacy biome+fragmentation adjusted sumFrp>=75 fallback with acreage support indicates SEVERE (frpEff=579.86, rawSum=584.32, acreageSupport=true, biome=Tropical Forest, dominantClass=TREE_COVER, exposed=true)", "certainty": "LIKELY", "certainty_reason": "biome+fragmentation weighted max>=0.35 or meanWeighted>=0.30 (maxAdj=0.4948, meanWAdj=0.3677, biomeW=1.0, fragW=0.9962, rawMax=0.4967, rawMeanW=0.3691); raised to LIKELY by acreage corroboration (acres=29933.64, exposed=true)", "urgency": "EXPECTED", "urgency_reason": "biome+fragmentation weighted max>=0.35 or meanWeighted>=0.30 (maxAdj=0.4948, meanWAdj=0.3677, biomeW=1.0, fragW=0.9962, rawMax=0.4967, rawMeanW=0.3691); raised to LIKELY by acreage corroboration (acres=29933.64, exposed=true)", "total_acres": 73798.56, "sum_frp_mw_current": 584.32, "max_frp_mw_ever": 828.47, "frp_sum_mw_24h": 2294.32, "frp_max_mw_24h": 208.83, "frp_mw_avg_window": 563.13667, "frp_scale": 2.76739, "avg_sample_count": 54, "current_cells_n": 130, "ever_cells_n": 207, "detection_score_max": 0.49673, "detection_score_sum": 5.10498, "detection_score_mean": 0.25525, "detection_score_mean_weighted": 0.36909, "detection_score_weight_sum": 130.0, "detection_score_n": 20, "trend_window_ms": 0, "trend_delta_acres": -19940.52, "trend_acres_per_hour": 0.0, "trend_delta_frp_sum_mw_24h": 4.01, "trend_delta_frp_max_mw_24h": 0.0, "trend_delta_detection_score_mean_weighted": 0.00066, "trend_direction": "UNKNOWN", "dominant_class": "TREE_COVER", "biome_class": "Tropical Forest", "fraction_forest": 0.95218, "fraction_cropland": 0.0229, "fraction_grass": 0.014, "fraction_urban": 0.00082, "fraction_water": 0.00219, "fraction_bare": 0.00522, "fraction_wetland": 0.0, "is_urban_area": false, "road_pixel_count": 21, "road_pixel_fraction": 0.01538, "wui_is_interface": false, "wui_frac_interface": 0.0, "touches_urban": false, "touches_wui": false, "fraction_urban_agg": 0.00082, "wui_frac_interface_agg": 0.0, "land_usage_weight": 130.0, "is_expired": false, "created_at": "2026-04-23T10:26:53.912Z", "last_updated_at": "2026-04-24T12:06:26.904Z", "last_emitted_at": "2026-04-24T12:06:26.904Z", "last_seen_at": "2026-04-24T08:28:00Z", "last_emitted_hash": "0fdc5f97cf5b0138049da5ae2f20e61b59958efba3aa661e9b052d1dcb76a3b9" } }, "uuid": "ea9ca784-8da3-44fe-b13e-7faf10bab359", "comment": null, "createdAt": 1777032513, "relatedHazards": [], "longitude": 81.91337, "latitude": 28.17653, "version": 17, "alertGeometry": { "type": "FeatureCollection", "features": [ { "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [...] }, "properties": null } ] }, "features": null, "exposureAreas": { "type": "FeatureCollection", "features": [ { "type": "Feature", "geometry": { "type": "MultiPolygon", "coordinates": [...] }, "properties": { "exposureLevel": 3 } } ] }, "exposureAssessment": { "population": { "total": 30600.0, "total0_14": 8791, "total15_64": 19914, "households": 7010.0, "vulnerable": 6220.0, "total65_Plus": 1948 }, "capital": { "total": 164000000.0, "school": 2, "hospital": 0 }, "totalByCountry": [ { "population": { "total": 30400.0, "total0_14": 8728, "total15_64": 19742, "households": 6950.0, "vulnerable": 6170.0, "total65_Plus": 1930 }, "country": "NPL", "capital": { "total": 164000000.0, "school": 2, "hospital": 0 }, "admin0": "Nepal", "admin1": null, "admin2": null, "foodNeedsUnit": "CAL", "foodNeeds": 12900000.0, "waterNeedsUnit": "liter", "waterNeeds": 18500.0, "wasteNeedsUnit": "100 liter", "wasteNeeds": 617, "shelterNeedsUnit": "sq meters", "shelterNeeds": 21200.0 }, { "population": { "total": 253, "total0_14": 63, "total15_64": 172, "households": 61.7, "vulnerable": 55.4, "total65_Plus": 18 }, "country": "IND", "capital": { "total": 0.0, "school": 0, "hospital": 0 }, "admin0": "India", "admin1": null, "admin2": null, "foodNeedsUnit": "CAL", "foodNeeds": 116000.0, "waterNeedsUnit": "liter", "waterNeeds": 166, "wasteNeedsUnit": "100 liter", "wasteNeeds": 5.54, "shelterNeedsUnit": "sq meters", "shelterNeeds": 191 } ], "totalByAdmin": [ { "population": { "total": 30400.0, "total0_14": 8728, "total15_64": 19742, "households": 6950.0, "vulnerable": 6170.0, "total65_Plus": 1930 }, "country": "NPL", "capital": { "total": 164000000.0, "school": 2, "hospital": 0 }, "admin0": "Nepal", "admin1": null, "admin2": null, "foodNeedsUnit": "CAL", "foodNeeds": 12900000.0, "waterNeedsUnit": "liter", "waterNeeds": 18500.0, "wasteNeedsUnit": "100 liter", "wasteNeeds": 617, "shelterNeedsUnit": "sq meters", "shelterNeeds": 21200.0 }, { "population": { "total": 253, "total0_14": 63, "total15_64": 172, "households": 61.7, "vulnerable": 55.4, "total65_Plus": 18 }, "country": "IND", "capital": { "total": 0.0, "school": 0, "hospital": 0 }, "admin0": "India", "admin1": null, "admin2": null, "foodNeedsUnit": "CAL", "foodNeeds": 116000.0, "waterNeedsUnit": "liter", "waterNeeds": 166, "wasteNeedsUnit": "100 liter", "wasteNeeds": 5.54, "shelterNeedsUnit": "sq meters", "shelterNeeds": 191 } ], "exposureLevels": [ { "level": "3", "data": { "population": { "total": 30600.0, "total0_14": 8791, "total15_64": 19914, "households": 7010.0, "vulnerable": 6220.0, "total65_Plus": 1948 }, "capital": { "total": 164000000.0, "school": 2, "hospital": 0 }, "totalByCountry": [...], "totalByAdmin": [...], "foodNeedsUnit": "CAL", "foodNeeds": 13000000.0, "waterNeedsUnit": "liter", "waterNeeds": 18600.0, "wasteNeedsUnit": "100 liter", "wasteNeeds": 622, "shelterNeedsUnit": "sq meters", "shelterNeeds": 21400.0 }, "exposureDescription": "Severe Damage Expected" } ], "hazardUuid": "", "hazardType": "", "version": "", "timestamp": "1777032501519", "exposureDescription": "", "foodNeedsUnit": "CAL", "foodNeeds": 13000000.0, "waterNeedsUnit": "liter", "waterNeeds": 18600.0, "wasteNeedsUnit": "100 liter", "wasteNeeds": 622, "shelterNeedsUnit": "sq meters", "shelterNeeds": 21400.0 }, "type": "WILDFIRE", "creator": "RICHTER", "source": null, "startedAt": 1776977791, "updatedAt": 1777032501, "endedAt": 1777118911 } ``` {% hint style="info" %} Note: In this sample, coordinate arrays in `alertGeometry` and `exposureAreas` have been replaced with `[...]` for brevity. In actual API responses, these properties contain complete GeoJSON coordinate arrays — the full `alertGeometry` outer ring for this incident contains 145 points, and the `exposureAreas` MultiPolygon contains 39 disjoint polygons (the largest of which has 115 points). The `exposureLevels[0].data.totalByCountry` and `totalByAdmin` arrays have been replaced with `[...]` because they duplicate the top-level `totalByCountry` / `totalByAdmin` arrays shown above. The `certainty_reason`, `severity_reason`, and `urgency_reason` strings have been lightly truncated for readability — full responses contain the complete text. The `school` and `hospital` values in the `capital` object are counts of exposed facilities, not monetary values (2 schools and 0 hospitals exposed in this forested border region). With `current_cells_n: 130` out of `ever_cells_n: 207`, this incident is actively burning at the moment of the snapshot — roughly 63% of the H3 cells that have ever been part of the incident remain flagged as actively burning. Note that `totalByCountry` and `totalByAdmin` both contain 2 entries here because the fire crosses the Nepal–India border, but both admin breakdowns are at admin0 granularity (admin1 and admin2 are `null`) since the exposure assessment is computed at the country level for this incident. {% endhint %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://api-v2-docs.disasteraware.com/hazards/hazard-samples/wildfire-sample.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.