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style: prettier-format newly added problem packs
Whitespace pass from `pnpm format` across recently added packs. Unblocks format:check.
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‎problem-packs/disaster-resilience/climate-migration-south-asia/datasets.md‎

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# Dataset Inventory
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| Source | Grain | Status | Use |
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| ------ | ----- | ------ | --- |
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| Census migration data | District, decadal | Usable | Migration flows |
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| MODIS NDVI | 250m | Usable | Vegetation and crop condition |
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| CHIRPS rainfall | 0.05 deg | Usable | Rainfall anomalies |
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| NSS employment surveys | District, quinquennial | Usable | Economic drivers |
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| Population census | District, decadal | Usable | Origin and destination population |
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| Source | Grain | Status | Use |
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| ---------------------- | ---------------------- | ------ | --------------------------------- |
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| Census migration data | District, decadal | Usable | Migration flows |
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| MODIS NDVI | 250m | Usable | Vegetation and crop condition |
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| CHIRPS rainfall | 0.05 deg | Usable | Rainfall anomalies |
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| NSS employment surveys | District, quinquennial | Usable | Economic drivers |
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| Population census | District, decadal | Usable | Origin and destination population |
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## Required Properties
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‎problem-packs/disaster-resilience/climate-migration-south-asia/evidence.json‎

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[{"id":"world-bank-groundswell-2021","problem_id":"disaster-resilience/climate-migration-south-asia","claim":"World Bank Groundswell report projects up to 40 million internal climate migrants in South Asia by 2050 under pessimistic scenarios, primarily from agricultural areas affected by water stress and crop failure.","evidence_type":"primary-source","source":{"title":"Groundswell: Acting on Internal Climate Migration","url":"https://www.worldbank.org/en/news/infographic/2021/09/13/groundswell-acting-on-internal-climate-migration"},"source_date":"2021-09-13","access_date":"2026-06-06","method":"Reviewed Groundswell methodology, regional projections, and driver analysis.","limitations":["Projections are modeled from biophysical and demographic scenarios, not observed migration flows.","Sub-national projections have wide uncertainty ranges."],"confidence":"medium"},{"id":"rigaud-environmental-migration-review-2021","problem_id":"disaster-resilience/climate-migration-south-asia","claim":"A 2021 review found moderate but significant correlations between environmental change (rainfall deficit, temperature increase, flood frequency) and out-migration at district scale in South Asia, with economic drivers explaining the majority of migration variance.","evidence_type":"peer-reviewed-study","source":{"title":"Environmental change and migration in South Asia: a systematic review","url":"https://link.springer.com/article/10.1007/s11111-021-00378-2"},"source_date":"2021-03-15","access_date":"2026-06-06","method":"Reviewed systematic search, correlation estimates, and attribution methodology.","limitations":["Census data is decadal and cannot capture short-term displacement.","Attribution to climate versus economic drivers is inherently uncertain."],"confidence":"medium"}]
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[
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{
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"id": "world-bank-groundswell-2021",
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"problem_id": "disaster-resilience/climate-migration-south-asia",
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"claim": "World Bank Groundswell report projects up to 40 million internal climate migrants in South Asia by 2050 under pessimistic scenarios, primarily from agricultural areas affected by water stress and crop failure.",
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"evidence_type": "primary-source",
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"source": {
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"title": "Groundswell: Acting on Internal Climate Migration",
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"url": "https://www.worldbank.org/en/news/infographic/2021/09/13/groundswell-acting-on-internal-climate-migration"
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},
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"source_date": "2021-09-13",
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"access_date": "2026-06-06",
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"method": "Reviewed Groundswell methodology, regional projections, and driver analysis.",
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"limitations": [
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"Projections are modeled from biophysical and demographic scenarios, not observed migration flows.",
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"Sub-national projections have wide uncertainty ranges."
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],
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"confidence": "medium"
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},
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{
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"id": "rigaud-environmental-migration-review-2021",
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"problem_id": "disaster-resilience/climate-migration-south-asia",
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"claim": "A 2021 review found moderate but significant correlations between environmental change (rainfall deficit, temperature increase, flood frequency) and out-migration at district scale in South Asia, with economic drivers explaining the majority of migration variance.",
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"evidence_type": "peer-reviewed-study",
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"source": {
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"title": "Environmental change and migration in South Asia: a systematic review",
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"url": "https://link.springer.com/article/10.1007/s11111-021-00378-2"
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},
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"source_date": "2021-03-15",
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"access_date": "2026-06-06",
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"method": "Reviewed systematic search, correlation estimates, and attribution methodology.",
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"limitations": [
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"Census data is decadal and cannot capture short-term displacement.",
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"Attribution to climate versus economic drivers is inherently uncertain."
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],
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"confidence": "medium"
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}
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]

‎problem-packs/disaster-resilience/climate-migration-south-asia/evidence.md‎

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# Evidence Ledger
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### World Bank Groundswell 2021
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40 million climate migrants by 2050 in South Asia. Water stress and crop failure as drivers.
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### Rigaud Review 2021
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Moderate environmental-migration correlation. Economics explains majority of variance.
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## Evidence Quality Rule

‎problem-packs/disaster-resilience/climate-migration-south-asia/problem.json‎

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{"id":"disaster-resilience/climate-migration-south-asia","title":"Climate-Driven Internal Migration Pattern Detection In South Asia","status":"scoped","domain":["disaster-resilience","climate-adaptation"],"region":["south-asia"],"summary":"Build a verified workflow for detecting climate-driven internal migration patterns in South Asia, linking census migration data, satellite-derived environmental change, and economic indicators at sub-national scale.","why_it_matters":"The World Bank estimates climate change could force 40 million internal migrants in South Asia by 2050. But migration data comes from censuses conducted every 10 years, and attributing migration to climate versus economic drivers is methodologically contested. Satellite data can track the environmental changes people are fleeing, but linking environmental signals to verified migration flows is the missing chain.","success_metrics":[{"metric":"Attribution honesty","target":"Every migration signal distinguishes climate-correlated from climate-caused migration with explicit methodology.","validation_method":"Schema validation and reviewer checks."}],"safety_level":"high","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."}}
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{
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"id": "disaster-resilience/climate-migration-south-asia",
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"title": "Climate-Driven Internal Migration Pattern Detection In South Asia",
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"status": "scoped",
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"domain": ["disaster-resilience", "climate-adaptation"],
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"region": ["south-asia"],
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"summary": "Build a verified workflow for detecting climate-driven internal migration patterns in South Asia, linking census migration data, satellite-derived environmental change, and economic indicators at sub-national scale.",
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"why_it_matters": "The World Bank estimates climate change could force 40 million internal migrants in South Asia by 2050. But migration data comes from censuses conducted every 10 years, and attributing migration to climate versus economic drivers is methodologically contested. Satellite data can track the environmental changes people are fleeing, but linking environmental signals to verified migration flows is the missing chain.",
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"success_metrics": [
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{
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"metric": "Attribution honesty",
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"target": "Every migration signal distinguishes climate-correlated from climate-caused migration with explicit methodology.",
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"validation_method": "Schema validation and reviewer checks."
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}
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],
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"safety_level": "high",
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"canonical_files": [
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"datasets.md",
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"evidence.json",
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"evidence.md",
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"outputs.md",
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"playbooks.md",
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"problem.json",
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"problem.md",
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"task-map.md",
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"tasks.json",
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"validation.md"
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],
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"review_policy": {
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"required_reviews": 2,
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"replication_required": true,
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"merge_gate": "Schema validation, domain review, safety review, replication."
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}
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}

‎problem-packs/disaster-resilience/climate-migration-south-asia/tasks.json‎

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[{"id":"source-inventory","problem_id":"disaster-resilience/climate-migration-south-asia","title":"Inventory migration and environmental data","status":"scoped","owner_role":"literature-scout","outcome":"Dated source inventory.","inputs":["census migration","NDVI","CHIRPS","employment surveys","population data"],"expected_artifact":"datasets.md plus evidence.json.","validation_method":"Reviewer checks sources.","reviewer_needed":"domain-reviewer","safety_risk":"medium","done_condition":"Five sources classified."},{"id":"migration-compilation","problem_id":"disaster-resilience/climate-migration-south-asia","title":"Compile sub-national migration flow data","status":"needs-triage","owner_role":"data-cleaner","outcome":"District-level migration-flow dataset with comparability documentation.","inputs":["census data","survey data","migration definitions"],"expected_artifact":"validation.md update.","validation_method":"Replicator confirms data compilation and comparability.","reviewer_needed":"replicator","safety_risk":"medium","done_condition":"Migration dataset with source documentation and replication."},{"id":"environmental-correlation","problem_id":"disaster-resilience/climate-migration-south-asia","title":"Correlate environmental change with migration flows","status":"needs-triage","owner_role":"implementation-planner","outcome":"Correlation analysis with explicit non-causal caveats.","inputs":["migration data","NDVI trends","rainfall anomalies","economic controls"],"expected_artifact":"outputs.md update.","validation_method":"Replicator reruns with alternative environmental indicators and confirms correlation robustness.","reviewer_needed":"domain-reviewer","safety_risk":"high","done_condition":"Correlation includes economic controls, non-causal caveats, and replication."},{"id":"red-team-review","problem_id":"disaster-resilience/climate-migration-south-asia","title":"Red-team misuse of climate migration claims","status":"needs-triage","owner_role":"red-team-reviewer","outcome":"Failure-mode review.","inputs":["correlation analysis","safety policy"],"expected_artifact":"SAFETY.md review.","validation_method":"Mitigations or block, replicated.","reviewer_needed":"red-team-reviewer","safety_risk":"high","done_condition":"Claims qualified or gated."},{"id":"field-reality-check","problem_id":"disaster-resilience/climate-migration-south-asia","title":"Check whether outputs match urban-planning decision needs","status":"needs-triage","owner_role":"field-reality-reviewer","outcome":"Field-facing review.","inputs":["draft outputs","urban planning context"],"expected_artifact":"playbooks.md update.","validation_method":"Field reviewer identifies decision, user, misuse risk, replicated.","reviewer_needed":"field-reality-reviewer","safety_risk":"high","done_condition":"Named user, named decision, non-use cases."}]
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[
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{
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"id": "source-inventory",
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"problem_id": "disaster-resilience/climate-migration-south-asia",
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"title": "Inventory migration and environmental data",
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"status": "scoped",
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"owner_role": "literature-scout",
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"outcome": "Dated source inventory.",
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"inputs": [
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"census migration",
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"NDVI",
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"CHIRPS",
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"employment surveys",
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"population data"
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],
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"expected_artifact": "datasets.md plus evidence.json.",
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"validation_method": "Reviewer checks sources.",
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"reviewer_needed": "domain-reviewer",
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"safety_risk": "medium",
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"done_condition": "Five sources classified."
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},
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{
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"id": "migration-compilation",
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"problem_id": "disaster-resilience/climate-migration-south-asia",
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"title": "Compile sub-national migration flow data",
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"status": "needs-triage",
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"owner_role": "data-cleaner",
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"outcome": "District-level migration-flow dataset with comparability documentation.",
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"inputs": ["census data", "survey data", "migration definitions"],
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"expected_artifact": "validation.md update.",
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"validation_method": "Replicator confirms data compilation and comparability.",
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"reviewer_needed": "replicator",
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"safety_risk": "medium",
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"done_condition": "Migration dataset with source documentation and replication."
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},
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{
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"id": "environmental-correlation",
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"problem_id": "disaster-resilience/climate-migration-south-asia",
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"title": "Correlate environmental change with migration flows",
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"status": "needs-triage",
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"owner_role": "implementation-planner",
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"outcome": "Correlation analysis with explicit non-causal caveats.",
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"inputs": ["migration data", "NDVI trends", "rainfall anomalies", "economic controls"],
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"expected_artifact": "outputs.md update.",
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"validation_method": "Replicator reruns with alternative environmental indicators and confirms correlation robustness.",
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"reviewer_needed": "domain-reviewer",
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"safety_risk": "high",
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"done_condition": "Correlation includes economic controls, non-causal caveats, and replication."
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},
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{
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"id": "red-team-review",
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"problem_id": "disaster-resilience/climate-migration-south-asia",
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"title": "Red-team misuse of climate migration claims",
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"status": "needs-triage",
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"owner_role": "red-team-reviewer",
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"outcome": "Failure-mode review.",
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"inputs": ["correlation analysis", "safety policy"],
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"expected_artifact": "SAFETY.md review.",
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"validation_method": "Mitigations or block, replicated.",
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"reviewer_needed": "red-team-reviewer",
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"safety_risk": "high",
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"done_condition": "Claims qualified or gated."
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},
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{
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"id": "field-reality-check",
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"problem_id": "disaster-resilience/climate-migration-south-asia",
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"title": "Check whether outputs match urban-planning decision needs",
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"status": "needs-triage",
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"owner_role": "field-reality-reviewer",
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"outcome": "Field-facing review.",
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"inputs": ["draft outputs", "urban planning context"],
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"expected_artifact": "playbooks.md update.",
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"validation_method": "Field reviewer identifies decision, user, misuse risk, replicated.",
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"reviewer_needed": "field-reality-reviewer",
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"safety_risk": "high",
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"done_condition": "Named user, named decision, non-use cases."
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}
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]

‎problem-packs/disaster-resilience/climate-migration-south-asia/validation.md‎

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# Validation
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| Layer | Gate |
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| ----- | ---- |
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| Structure | `pnpm validate` |
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| Evidence | Dated records |
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| Reproducibility | Rerunnable |
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| Review | Approvers |
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| Replication | Quantitative replicated |
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| Layer | Gate |
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| --------------- | ----------------------- |
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| Structure | `pnpm validate` |
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| Evidence | Dated records |
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| Reproducibility | Rerunnable |
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| Review | Approvers |
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| Replication | Quantitative replicated |
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## Baseline Requirements
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‎problem-packs/disaster-resilience/urban-flooding-south-asia/datasets.md‎

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# Dataset Inventory
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| Source | Grain | Status | Use |
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| ------ | ----- | ------ | --- |
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| Landsat impervious surface | 30m | Usable | Surface sealing |
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| Sentinel-1 SAR flood maps | 10m | Usable | Historical flood extent |
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| CHIRPS rainfall | 0.05 deg, daily | Usable | Extreme rainfall frequency |
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| OpenStreetMap drainage | Line | Variable | Drainage infrastructure |
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| WorldPop population | 100m | Usable | Population at risk |
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| Source | Grain | Status | Use |
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| -------------------------- | --------------- | -------- | -------------------------- |
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| Landsat impervious surface | 30m | Usable | Surface sealing |
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| Sentinel-1 SAR flood maps | 10m | Usable | Historical flood extent |
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| CHIRPS rainfall | 0.05 deg, daily | Usable | Extreme rainfall frequency |
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| OpenStreetMap drainage | Line | Variable | Drainage infrastructure |
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| WorldPop population | 100m | Usable | Population at risk |
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## Required Properties
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‎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"}]
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[
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{
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"id": "ipcc-ar6-urban-flooding-2021",
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"problem_id": "disaster-resilience/urban-flooding-south-asia",
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"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.",
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"evidence_type": "primary-source",
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"source": {
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"title": "IPCC AR6 WG2 Chapter 6: Cities, Settlements and Key Infrastructure",
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"url": "https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-6/"
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},
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"source_date": "2022-02-28",
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"access_date": "2026-06-06",
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"method": "Reviewed urban flooding section, impervious-surface trends, and drainage-capacity analysis.",
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"limitations": [
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"City-scale projections are modeled, not observed for all cities.",
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"Drainage-capacity data is inconsistently available."
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],
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"confidence": "high"
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},
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{
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"id": "tellman-satellite-flood-mapping-2021",
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"problem_id": "disaster-resilience/urban-flooding-south-asia",
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"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.",
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"evidence_type": "peer-reviewed-study",
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"source": {
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"title": "Satellite imaging reveals increased proportion of population exposed to floods",
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"url": "https://www.nature.com/articles/s41586-021-03695-w"
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},
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"source_date": "2021-08-04",
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"access_date": "2026-06-06",
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"method": "Reviewed flood-mapping methodology, accuracy assessment, and population-exposure analysis.",
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"limitations": [
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"SAR flood detection is less reliable in urban canyons and under dense vegetation.",
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"10m resolution may miss street-level flooding."
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],
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"confidence": "high"
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}
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]

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

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

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