feat: add urban flooding pack (32 total)

Co-authored-by: CommandCodeBot <noreply@commandcode.ai>

Author: cschanhniem

docs/wiki/Agent-Guide.md

@@ -61,6 +61,7 @@ Example: [examples/agent-submission.example.json](../../examples/agent-submissio
 - [`climate-health/malaria-early-warning-africa`](../../problem-packs/climate-health/malaria-early-warning-africa/problem.md) — Malaria Early Warning Signals In Sub-Saharan Africa
 - [`disaster-resilience/cyclone-early-warning-bangladesh`](../../problem-packs/disaster-resilience/cyclone-early-warning-bangladesh/problem.md) — Cyclone Early Warning And Evacuation Signal Verification In Bangladesh
 - [`disaster-resilience/earthquake-vulnerability-nepal`](../../problem-packs/disaster-resilience/earthquake-vulnerability-nepal/problem.md) — Seismic Vulnerability Assessment And Building-Stock Risk In Nepal
+- [`disaster-resilience/urban-flooding-south-asia`](../../problem-packs/disaster-resilience/urban-flooding-south-asia/problem.md) — Urban Pluvial Flooding Risk In South Asian Megacities
 - [`education/digital-divide-school-access-global`](../../problem-packs/education/digital-divide-school-access-global/problem.md) — Digital Divide Measurement And School Internet Connectivity In Low-Income Countries
 - [`education/girls-education-sub-saharan-africa`](../../problem-packs/education/girls-education-sub-saharan-africa/problem.md) — Girls Secondary Education Enrollment And Retention Barriers In Sub-Saharan Africa
 - [`education/learning-loss-post-pandemic`](../../problem-packs/education/learning-loss-post-pandemic/problem.md) — Post-Pandemic Learning Loss Measurement And Recovery Tracking In Low-Income Countries

docs/wiki/Home.md

@@ -8,15 +8,15 @@ The bottleneck in tackling global problems is no longer generating candidate ans
 
 ## Quick Numbers
 
-- **31 active problem packs** across **11 domains**
+- **32 active problem packs** across **11 domains**
 - Structured evidence base, task map, validation criteria, and dataset inventory for each
 - Five agent role guides with explicit merge gates
 - Machine-checkable JSON schemas for every artifact type
 - GitHub Actions validate every pull request
 
 ## Start Here
 
-- [Problem Packs](Problem-Packs.md) — all 31 active problems with task maps and evidence
+- [Problem Packs](Problem-Packs.md) — all 32 active problems with task maps and evidence
 - [Agent Guide](Agent-Guide.md) — how AI agents contribute structured, verifiable work
 - [Governance](../../GOVERNANCE.md) — decision rights, acceptance gates, status flow
 - [Safety](../../SAFETY.md) — risk levels, prohibited shortcuts, burden of proof

docs/wiki/Problem-Packs.md

@@ -100,6 +100,16 @@ Build a verified workflow for assessing where cyclone track forecasts, storm sur
 
 Build a verified workflow for assessing where building-stock vulnerability, population density, and seismic hazard create the highest earthquake mortality risk in Nepal at municipal scale.
 
+## Urban Pluvial Flooding Risk In South Asian Megacities
+
+- ID: `disaster-resilience/urban-flooding-south-asia`
+- Status: `scoped`
+- Domain: `disaster-resilience`, `climate-adaptation`
+- Region: `south-asia`
+- Source: [problem-packs/disaster-resilience/urban-flooding-south-asia](../../problem-packs/disaster-resilience/urban-flooding-south-asia/problem.md)
+
+Build a verified workflow for assessing where impervious surface expansion, drainage inadequacy, and extreme rainfall create the highest pluvial flooding risk in South Asian megacities at neighborhood scale.
+
 ## Digital Divide Measurement And School Internet Connectivity In Low-Income Countries
 
 - ID: `education/digital-divide-school-access-global`

problem-packs/disaster-resilience/urban-flooding-south-asia/datasets.md

@@ -0,0 +1,17 @@
+# Dataset Inventory
+
+| Source | Grain | Status | Use |
+| ------ | ----- | ------ | --- |
+| Landsat impervious surface | 30m | Usable | Surface sealing |
+| Sentinel-1 SAR flood maps | 10m | Usable | Historical flood extent |
+| CHIRPS rainfall | 0.05 deg, daily | Usable | Extreme rainfall frequency |
+| OpenStreetMap drainage | Line | Variable | Drainage infrastructure |
+| WorldPop population | 100m | Usable | Population at risk |
+
+## Required Properties
+
+Impervious-surface methodology, drainage data currency, rainfall data, license.
+
+## Rejection Rule
+
+Rejected if impervious-surface or drainage methodology unverifiable.

problem-packs/disaster-resilience/urban-flooding-south-asia/evidence.json

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+[{"id":"ipcc-ar6-urban-flooding-2021","problem_id":"disaster-resilience/urban-flooding-south-asia","claim":"IPCC AR6 identifies South Asian cities as high-risk for pluvial flooding with impervious-surface expansion outpacing drainage capacity by 2-3x and rainfall intensity increasing with warming.","evidence_type":"primary-source","source":{"title":"IPCC AR6 WG2 Chapter 6: Cities, Settlements and Key Infrastructure","url":"https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-6/"},"source_date":"2022-02-28","access_date":"2026-06-06","method":"Reviewed urban flooding section, impervious-surface trends, and drainage-capacity analysis.","limitations":["City-scale projections are modeled, not observed for all cities.","Drainage-capacity data is inconsistently available."],"confidence":"high"},{"id":"tellman-satellite-flood-mapping-2021","problem_id":"disaster-resilience/urban-flooding-south-asia","claim":"A 2021 Nature study demonstrated that satellite imagery and cloud-penetrating SAR can map flood extent at 10m resolution globally, with validation showing 85 percent accuracy against ground observations.","evidence_type":"peer-reviewed-study","source":{"title":"Satellite imaging reveals increased proportion of population exposed to floods","url":"https://www.nature.com/articles/s41586-021-03695-w"},"source_date":"2021-08-04","access_date":"2026-06-06","method":"Reviewed flood-mapping methodology, accuracy assessment, and population-exposure analysis.","limitations":["SAR flood detection is less reliable in urban canyons and under dense vegetation.","10m resolution may miss street-level flooding."],"confidence":"high"}]

problem-packs/disaster-resilience/urban-flooding-south-asia/evidence.md

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+# Evidence Ledger
+
+### IPCC AR6 2022
+South Asian cities high-risk. Impervious expansion 2-3x drainage capacity.
+
+### Tellman et al. 2021 Nature
+Satellite SAR flood mapping at 10m. 85% accuracy. Street-level flooding may be missed.
+
+## Evidence Quality Rule
+
+Evidence only when source, method, limitations, confidence explicit.

problem-packs/disaster-resilience/urban-flooding-south-asia/outputs.md

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+# Outputs
+
+None yet. Candidate types: evidence inventory, impervious-surface map, drainage-capacity analysis, flood-risk map, red-team review, field-reality review.
+
+## Publication Rule
+
+Outputs must state they are not drainage-infrastructure or land-use regulation recommendations.

problem-packs/disaster-resilience/urban-flooding-south-asia/playbooks.md

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+# Playbooks
+
+## Analyst Playbook
+
+Start with `problem.json`. Add evidence before claims. Mark operational inferences reviewed/replicated/blocked.
+
+## Reviewer Playbook
+
+Ask: What flood-risk claim? What impervious and drainage data? What method? What makes the risk map wrong? Who could misuse it to justify eviction in flood-prone informal settlements?
+
+## Field-Reality Playbook
+
+Ask whether a municipal engineer would change drainage-upgrade priorities based on this map. Flood risk in informal settlements is a drainage, land-tenure, and poverty problem — maps alone cannot capture all three.

problem-packs/disaster-resilience/urban-flooding-south-asia/problem.json

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+{"id":"disaster-resilience/urban-flooding-south-asia","title":"Urban Pluvial Flooding Risk In South Asian Megacities","status":"scoped","domain":["disaster-resilience","climate-adaptation"],"region":["south-asia"],"summary":"Build a verified workflow for assessing where impervious surface expansion, drainage inadequacy, and extreme rainfall create the highest pluvial flooding risk in South Asian megacities at neighborhood scale.","why_it_matters":"South Asian megacities experience increasingly frequent pluvial flooding as impervious surfaces replace permeable ground and drainage systems fail to keep pace. The 2022 Pakistan floods and repeated Mumbai, Dhaka, and Chennai flooding events demonstrate that drainage infrastructure and land-use data are not connected in a way that enables neighborhood-scale risk mapping.","success_metrics":[{"metric":"Data integration","target":"Every flooding risk estimate links impervious-surface, drainage, and rainfall data with methodology.","validation_method":"Schema validation and reviewer checks."}],"safety_level":"medium","canonical_files":["datasets.md","evidence.json","evidence.md","outputs.md","playbooks.md","problem.json","problem.md","task-map.md","tasks.json","validation.md"],"review_policy":{"required_reviews":2,"replication_required":true,"merge_gate":"Schema validation, domain review, safety review, replication."}}

problem-packs/disaster-resilience/urban-flooding-south-asia/problem.md

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+# Urban Pluvial Flooding Risk In South Asian Megacities
+
+## Problem Statement
+
+Mumbai floods. Dhaka floods. Chennai floods. South Asian megacities flood with increasing frequency not from rivers overflowing but from rain falling on impervious surfaces with nowhere to go. Drainage maps are outdated or nonexistent. Satellite-derived impervious-surface maps exist at 30m resolution from Landsat, and CHIRPS provides rainfall data. What's missing is connecting these layers to identify which neighborhoods flood because of drainage inadequacy versus impervious-surface coverage.
+
+> Which neighborhoods in South Asian megacities face the highest evidence-backed pluvial flooding risk from impervious-surface-drainage mismatch?
+
+## Known Facts
+
+- Verified fact: IPCC AR6 identifies South Asian cities as high-risk for pluvial flooding, with impervious-surface expansion outpacing drainage capacity by 2-3x in most megacities.
+- Verified fact: Landsat-derived impervious-surface maps at 30m resolution can track surface-sealing over time but have not been systematically combined with drainage-infrastructure data for flood-risk mapping.
+- Verified fact: The 2022 Pakistan floods affected 33 million people and caused $30 billion in damages, with urban flooding from overwhelmed drainage as a major component.
+- Verified fact: CHIRPS and GPM-IMERG satellite rainfall data at sub-daily resolution exist but lack the temporal precision needed for sub-hourly flash-flood prediction.
+
+## Uncertain Areas
+
+Whether drainage-infrastructure data exists at sufficient detail for neighborhood-risk mapping. How informal-settlement drainage practices affect flood risk. Whether risk maps change drainage investment or land-use decisions.
+
+## Initial Scope
+
+Evidence inventory, impervious-surface mapping, drainage-capacity analysis, rainfall-extreme analysis, risk mapping, red-team review, field-reality review.
+
+## Done Condition For V0
+
+One reviewed and replicated neighborhood-scale pluvial flooding risk analysis for at least two South Asian megacities.