Why is "Confusing HAZARD (physical phenomenon) with RISK (hazard × vulnerability × exposure)." a common mistake in Hazardous Practical Skills?

Why it happens: Pearson 4GE1 mark schemes explicitly credit the distinction — top-band answers always separate them. How to avoid it: Memorise: HAZARD = the earthquake itself. RISK = hazard × vulnerability (e.g. weak buildings) × exposure (e.g. dense population).

Why is "Using rainbow colours on choropleth maps." a common mistake in Hazardous Practical Skills?

Why it happens: Rainbow palettes are confusing — they imply non-existent thresholds. Sequential (light-dark) is the modern standard. How to avoid it: Use SEQUENTIAL colours (light yellow → dark red) for ordered data; use DIVERGING (red-white-blue) for data centred around a value.

Why is "Plotting earthquake frequency on a LINEAR Y-axis instead of LOGARITHMIC." a common mistake in Hazardous Practical Skills?

Why it happens: Frequency drops ~10× per magnitude unit — linear axes hide the small quakes entirely. Log axes reveal the Gutenberg-Richter relationship. How to avoid it: Always use LOG Y-axis for frequency-magnitude graphs. Justify your choice in the answer.

Why is "Forgetting map elements (key, title, scale, north arrow, source)." a common mistake in Hazardous Practical Skills?

Why it happens: Examiners deduct marks for incomplete maps. Pearson 4GE1 fieldwork mark schemes credit each element separately. How to avoid it: Memorise the 5 essentials: KEY + TITLE + SCALE + NORTH ARROW + SOURCE. Add date + author too.

Why is "Claiming students do DIRECT fieldwork on active hazards." a common mistake in Hazardous Practical Skills?

Why it happens: Hazard fieldwork is dangerous + impractical — Pearson tests SECONDARY data + map skills, not direct earthquake/volcano measurement. How to avoid it: Frame fieldwork as SECONDARY data analysis: hazard map interpretation, GIS overlay, choropleth mapping — NOT direct seismic monitoring.

Why is "Citing 'the internet' or 'a website' instead of specific named sources." a common mistake in Hazardous Practical Skills?

Why it happens: Pearson rewards SPECIFIC source naming (USGS, BGS, UN OCHA, JMA, GEM, GVP). How to avoid it: Memorise key sources: USGS (US Geological Survey); BGS (British Geological Survey); UN OCHA + Reliefweb; JMA (Japan Met Agency); GEM (Global Earthquake Model); GVP (Smithsonian Global Volcanism Program).

Edexcel IGCSE Geography (4GE1)

Hazardous Practical Skills

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Detailed Study Notes

Detailed notes on Hazardous environments for Edexcel IGCSE Geography, covering key concepts, explanations, examples, and exam-focused revision points.

Hazardous Practical Skills — Pearson Edexcel International GCSE Geography (4GE1) Study Notes

Practical + map skills for hazardous environments (Paper 1 Section B). Direct fieldwork on active hazards is unsafe — Pearson tests SECONDARY-data + map skills: interpreting hazard maps, GIS overlays, frequency-magnitude graphs, choropleth maps of impacts, tsunami inundation maps, volcanic hazard zoning. Key sources: USGS, BGS, UN OCHA, JMA, GEM, GVP.

At a glance

Hazard map = spatial distribution + severity of hazard.
Risk map = hazard × vulnerability × exposure.
Choropleth maps: sequential (light-dark), 4-6 intervals, never rainbow.
Frequency-magnitude graphs use LOG Y-axis (Gutenberg-Richter).
Earthquake frequency drops ~10× per unit magnitude (M5 ~1.3k/yr; M6 ~130/yr; M7 ~13/yr; M8 ~1/yr).
GIS overlay combines hazard + vulnerability + exposure to produce risk map.
Key sources: USGS, BGS, UN OCHA, JMA, GEM, GVP.
InSAR + LiDAR give mm/sub-metre accuracy for fault + landslide mapping.
Volcanic zones: Red (pyroclastic ~7km), Yellow (ash ~12km), Blue (lahar valleys).

What you’ll learn

Mapped to the Edexcel IGCSE 4GE1 syllabus (2026 onwards).

Identify + interpret hazard maps (earthquake, volcano, tsunami).
Distinguish HAZARD, VULNERABILITY, EXPOSURE + RISK.
Plot + interpret frequency-magnitude graphs using log axes.
Construct choropleth maps with correct conventions.
Use GIS overlay analysis to derive risk maps.
Cite specific secondary data sources for hazard research.

Why hazard fieldwork uses secondary data

Active hazards are too dangerous + episodic for primary fieldwork. Pearson tests SECONDARY-data + map skills.

Most physical-geography fieldwork (rivers, coasts) uses PRIMARY measurements students take themselves. Hazard fieldwork is different — for two reasons:

Safety: standing on an active volcano or fault is dangerous. Earthquakes, lahars, pyroclastic flows + tsunamis kill instantly.
Episodic timing: hazards strike unpredictably; you can't schedule fieldwork around an eruption.

So Pearson 4GE1 spec frames hazard practical skills around secondary data analysis + map work:

Interpreting hazard maps.
Reading + plotting frequency-magnitude data.
Drawing choropleth maps of impacts.
Using GIS overlays.
Volcanic hazard zoning.
Tsunami inundation maps.

Key secondary data sources.

Source	What it provides
USGS Earthquake Hazards Program	Real-time + historic earthquake catalogue, ShakeMaps, hazard maps
BGS (British Geological Survey)	UK + global seismic data
UN OCHA + Reliefweb	Post-disaster impact data (deaths, displaced, costs)
JMA (Japan Met Agency)	Real-time earthquake + tsunami data for Japan
GEM (Global Earthquake Model)	Global hazard + risk maps
GVP (Smithsonian Global Volcanism Program)	Historic + active volcano database
ESA Sentinel + JAXA ALOS-2	Satellite InSAR for ground deformation

Hazard fieldwork = secondary data + map skills (safety + timing).
Key sources: USGS, BGS, UN OCHA, JMA, GEM, GVP.
Direct primary measurement of active hazards is dangerous.

Hazard maps — what they show + how to read them

Show physical likelihood of a hazard. Colour-coded zones; combine with vulnerability for risk maps.

Hazard map = WHERE + HOW STRONG a hazard is.

Examples:

Earthquake hazard map — shows peak ground acceleration (PGA) or shaking intensity expected over a future period.
Volcanic hazard map — discrete zones (pyroclastic flow, ash fall, lahar) around a volcano.
Tsunami inundation map — predicted flooding depth + extent at the coast.

Reading a hazard map.

Map elements first: title, key, scale, north arrow, source.
Identify hazard zones by colour (typically red = highest).
Identify plate boundaries + faults (fine lines).
Identify epicentres (dots, often sized by magnitude).
Combine with vulnerability (population density, building age) for RISK.

Hazard ≠ Risk.

HAZARD = physical phenomenon (the earthquake itself).
VULNERABILITY = susceptibility to harm (weak buildings, no warning).
EXPOSURE = people + assets in harm's way (dense population).
RISK = hazard × vulnerability × exposure.

Example. San Francisco + a remote desert can have the same HAZARD level (M7 likely within 50 years). But SF has high exposure (population) + vulnerability (old buildings) — higher RISK. The desert has near-zero exposure → near-zero risk.

Pearson examiner credit: top-band answers separate hazard from risk EXPLICITLY.

Hazard map shows WHERE + HOW STRONG a hazard is.
5 map elements: title, key, scale, north arrow, source.
Hazard ≠ Risk. Risk = hazard × vulnerability × exposure.
Examiner credit: separate hazard + risk explicitly.

Frequency-magnitude graphs (Gutenberg-Richter)

Earthquake frequency drops ~10× per magnitude unit. Always plot on LOG Y-axis.

The Gutenberg-Richter law.

Global earthquake statistics follow a remarkably consistent pattern:

Magnitude	Frequency (global)
M3	~130,000/year
M4	~13,000/year
M5	~1,300/year
M6	~130/year
M7	~13/year
M8	~1/year
M9	~1 per 20 years

Note: each step up in magnitude ≈ 10× FEWER quakes + ~32× MORE energy.

How to plot.

X-axis: magnitude (linear scale).
Y-axis: frequency (LOGARITHMIC scale).
Line of best fit: should be ~straight on log scale (Gutenberg-Richter).
Gradient = b-value: ~1.0 globally; differs by region.

Why log axis?

A linear axis would compress M5-M9 quakes into invisible dots near zero. A log axis spreads them evenly.

Interpreting.

A steeper line (high b-value) = relatively MORE small quakes per large one.
A flatter line (low b-value) = relatively MORE large quakes for the region.
B-values differ between subduction zones (often <1.0 — more big quakes) + mid-ocean ridges (>1.0 — more small quakes).

Practical application.

Building codes designed for a specific return period (e.g. M7 every 100 years).
Insurance pricing reflects expected frequency.
Public communication: "we expect ~13 M7 quakes globally per year".

Earthquake frequency drops ~10× per magnitude unit.
Always plot on LOG Y-axis (Gutenberg-Richter law).
Global frequencies: M5 ~1.3k/yr, M7 ~13/yr, M8 ~1/yr.
Gradient = b-value (~1.0 globally). Higher b = more small quakes.

Choropleth mapping of hazard impacts

Sequential (light-dark) colours. 4-6 intervals justified. Always add 5 map elements.

Choropleth maps shade areas (countries, regions) according to data — perfect for visualising hazard IMPACTS (deaths, displaced, costs) across regions.

Production steps.

Collect data — e.g. earthquake deaths by country in 2023 (Türkiye ~50,500; Syria ~8,500; Morocco ~2,900; Afghanistan ~1,500; Nepal ~150).
Choose class intervals — 4-6 categories. Methods:
- Equal intervals (each band same width).
- Quantiles (each band equal number of cases).
- Natural breaks (Jenks) (statistical clustering).
Choose colours:
- Sequential (light → dark) for ordered data.
- Diverging (red-white-blue) for data centred on a value (e.g. above/below average).
- NEVER rainbow — implies false thresholds.
Add map elements: KEY, TITLE, SCALE, NORTH ARROW, SOURCE.

Worked example — 2023 earthquake deaths.

Country	Deaths	Class
Türkiye	~50,500	>10,000 (dark red)
Syria	~8,500	1,001-10,000 (red)
Morocco	~2,900	1,001-10,000 (red)
Afghanistan	~1,500	1,001-10,000 (red)
Nepal	~150	101-1,000 (orange)
Most countries	0	<10 (pale yellow)

Common pitfalls.

Rainbow colours (incorrect convention).
No source attribution.
No north arrow or scale.
Too many/few class intervals.
Class intervals not justified.

Why choropleth?

Quick VISUAL comparison.
Reveals spatial patterns (e.g. all 2023 deaths clustered in Anatolian fault region).
Supports HYPOTHESIS questions (why did Türkiye's M7.8 kill 50× more than Morocco's M6.8?).

Choropleth = shade areas by data value.
Sequential (light-dark) colours; never rainbow.
4-6 class intervals, method JUSTIFIED.
Add 5 map elements: key, title, scale, north arrow, source.
Supports spatial hypothesis questions.

GIS overlay analysis

Combine multiple layers (hazard + vulnerability + exposure) into a composite risk map.

GIS (Geographic Information Systems) combines spatial data layers into composite maps. For hazards:

Layers needed.

Layer	Source	Role
Earthquake hazard	USGS ShakeMap	Hazard
Geology	National geological survey	Soft sediment amplification
Faults	USGS / national	Hazard
Slope	LiDAR / SRTM	Landslide risk
Population density	Census	Exposure
Building age	Local council	Vulnerability
Infrastructure	Local council	Vulnerability

Overlay process.

Import layers into GIS (e.g. QGIS, ArcGIS).
Geo-reference all layers to same coordinate system (e.g. WGS84).
Weight layers by importance (e.g. building age 30%, soft sediment 20%, population 25%, infrastructure 25%).
Combine (multiplication or weighted sum) → composite risk score per grid cell.
Visualise as colour-coded risk map (green → red).

Uses.

Retrofitting priorities: rank schools + hospitals by risk score.
Evacuation planning: identify high-risk zones needing routes + assembly points.
Insurance pricing: differentiate premiums by risk score.
Land-use planning: restrict new construction in highest-risk zones.

Real-world examples.

Istanbul Master Plan uses GIS overlay to prioritise retrofitting + emergency services.
Tokyo Hazard Map integrates 6+ layers; updated annually.
California USGS ShakeMap produces post-quake risk maps within minutes.

Limitations.

Output only as good as input data.
Weighting choices are subjective.
Risk maps must be UPDATED as buildings, population change.

GIS overlays: hazard + geology + faults + slope + population + building age + infrastructure.
Weight layers (e.g. age 30%, sediment 20%, population 25%, infrastructure 25%).
Output: composite risk map for retrofitting + evacuation + insurance + land-use.
Examples: Istanbul, Tokyo, USGS ShakeMap.

Volcanic + tsunami hazard mapping

Volcanic = discrete colour zones (Red/Yellow/Blue). Tsunami = modelled inundation depth + extent.

Volcanic hazard maps.

Different process types need different zoning.

Pyroclastic flow zone (Red) — usually within ~5-10 km of vent; immediate evacuation required.
Ash fall zone (Yellow) — wider, often 10-30 km depending on wind; can affect aviation across continents.
Lahar zone (Blue) — follows valleys radiating from volcano; can travel >50 km.
Lava flow paths — modelled by slope.

Example: Vesuvius (Italy).

Red Zone (~7 km from crater): immediate evacuation in eruption; ~600,000 residents at risk.
Yellow Zone (~7-12 km): ash fall risk; ~1 million residents.
Blue Zone: lahar pathways along valleys to coast.

Map production:

Historic deposit mapping (field work for past eruptions).
Eruption modelling for different VEI scenarios.
Combine with wind direction probabilities.
Add evacuation routes + assembly points.

Tsunami inundation maps.

Show predicted flooding depth + extent for tsunami scenarios.

Production:

Bathymetry (seabed depth) — high resolution.
Coastal topography — LiDAR ground elevation (~10 cm accuracy).
Source modelling — earthquake magnitude, fault rupture, sub-marine landslide.
Wave propagation modelling — software like MOST (NOAA), COMCOT.
Inundation modelling — wave run-up height + extent inland.
Output — map showing depth (m) at each location for multiple scenarios.

Use:

Building codes (ground floor unusable in inundation zone).
Evacuation route planning (high-ground destinations).
Public signage (tsunami evacuation routes marked blue + white).
Insurance pricing.

Examples: Japan Tōhoku coast after 2011; Pacific Northwest USA (Cascadia subduction); Mediterranean tsunami modelling (Italy, Greece).

Volcanic zones: Red (pyroclastic ~7km) / Yellow (ash ~12km) / Blue (lahar valleys).
Vesuvius Red Zone: ~600k residents at risk.
Tsunami maps use bathymetry + LiDAR + wave models (MOST, COMCOT).
Inform codes, evacuation routes, signage, insurance.

Evaluation — usefulness + limits of hazard mapping

Maps inform planning + warnings + insurance — but they don't enforce themselves. Türkiye 2023 proved that.

Strengths of hazard mapping.

Land-use planning: avoid building in high-risk zones (e.g. Japan tsunami inundation lines).
Building code design: PSHA (Probabilistic Seismic Hazard Analysis) underpins seismic codes.
Insurance pricing: Chile's ~80% penetration relies on hazard maps.
Early warning: J-ALERT + Pacific Tsunami Warning Centre use hazard data for real-time alerts.
Public communication: visual maps translate technical risk for non-specialists.

Limitations.

Data uncertainty: long return periods (500-1000+ years for megathrust events); paleoseismic data sparse.
Maps don't enforce themselves: Türkiye had hazard maps before 2023 Kahramanmaraş — corruption + amnesties killed ~60k+.
Static vs dynamic: maps must be UPDATED as population + building stock change.
Cultural acceptance: communities may resist relocation even with hazard map evidence.
Cost: dense seismic networks + GIS expertise expensive — out of reach for many developing countries.

Top-band synthesis.

"Hazard maps are NECESSARY but NOT SUFFICIENT to reduce disaster impacts. They guide planning, codes + insurance — but only deliver lives saved when paired with enforcement, governance + public engagement."

Strengths: land-use planning, codes, insurance, early warning, communication.
Limits: data uncertainty, no self-enforcement, static, cultural resistance, cost.
Top-band line: hazard maps are NECESSARY but NOT SUFFICIENT.
Türkiye 2023 proves: codes + maps without enforcement = catastrophe.

Quick recap

Hazard fieldwork = secondary data + map skills (active hazards too dangerous for primary).
Key sources: USGS, BGS, UN OCHA, JMA, GEM, GVP.
Hazard ≠ Risk: Risk = hazard × vulnerability × exposure.
Frequency-magnitude: always LOG Y-axis (Gutenberg-Richter).
Choropleth: sequential (light-dark) colours, 4-6 intervals, never rainbow.
GIS overlay combines hazard + vulnerability + exposure to produce risk map.
Volcanic zones: Red/Yellow/Blue.
Tsunami maps: bathymetry + LiDAR + wave models (MOST, COMCOT).
Hazard maps are NECESSARY but NOT SUFFICIENT — need enforcement + governance.

Memorise this

Verbatim phrases and definitions Edexcel mark schemes credit.

Key sources: USGS + BGS + UN OCHA + JMA + GEM + GVP.
5 map elements: KEY, TITLE, SCALE, NORTH ARROW, SOURCE.
Earthquake frequency: M5 ~1.3k/yr, M6 ~130/yr, M7 ~13/yr, M8 ~1/yr (drops ~10× per unit).
Risk = hazard × vulnerability × exposure.
Sequential (light-dark) colours, never rainbow.
Vesuvius zones: Red ~7km (pyroclastic, ~600k residents), Yellow ~12km (ash, ~1m), Blue (lahar valleys).
Tsunami models: MOST (NOAA), COMCOT; LiDAR ~10cm accuracy.
Türkiye 2023 = hazard maps existed but corruption + amnesties killed ~60k+.

How it’s examined

Paper 1 Section B (Fieldwork) — short-answer questions on map elements, defining hazard maps, naming sources.
Paper 1 Section B — 4-6 mark questions on producing a hazard/risk map, choropleth map, or frequency-magnitude graph.
Paper 1 Section B — 6-10 mark evaluation of hazard mapping techniques or comparing methods.
Mark schemes credit naming MAP ELEMENTS (key, scale, source), specific sources (USGS, BGS, JMA), specific techniques (LiDAR, InSAR, MOST).
Top-band answers always distinguish HAZARD from RISK + reference enforcement gaps.
Examiner complaint: vague sourcing ('a website') and missing map elements lose easy marks.

Sources: Pearson Edexcel International GCSE Geography (4GE1) specification, Issue 4, February 2026 — Paper 1 Section B fieldwork.; USGS Earthquake Hazards Program + ShakeMap documentation.; British Geological Survey (BGS).; UN OCHA + Reliefweb reports.; Global Earthquake Model (GEM).; Smithsonian Global Volcanism Program (GVP).; Pearson 4GE1 past papers + mark schemes (2019-2024 series).; Pearson 4GE1 Examiner Reports.. Last reviewed 2026-06-02.

Take this whole topic with you

Step-by-step worked examples — Hazardous Practical Skills

Step-by-step solutions to past-paper-style questions on hazardous practical skills, written exactly the way a tutor would explain them at the board.

1Reading a hazard map
Getting started• AO4, method
Question
Describe how to use a HAZARD MAP to identify earthquake risk zones. [4]
Step-by-step solution
1. Step 1
  Identify map elements (1 mark): locate the KEY/LEGEND showing colour-coded hazard zones (typically red = highest, yellow = moderate, green = lowest). Identify the SCALE (e.g. 1:1,000,000) + ORIENTATION (north arrow).
2. Step 2
  Identify plate boundaries (1 mark): trace fault lines + plate margins shown on the map (e.g. San Andreas, Mid-Atlantic Ridge, Japan Trench). Mark these in pencil.
3. Step 3
  Identify epicentres (1 mark): plot historic earthquake epicentres (often shown as dots, sized by magnitude). Count density to identify clustering.
4. Step 4
  Combine with vulnerability data (1 mark): overlay population density, building age, infrastructure data to produce a RISK map (hazard × vulnerability × exposure).
Answer
Identify map key + scale; trace plate boundaries + faults; plot historic epicentres (sized by magnitude); overlay vulnerability data (population, building age) to produce a risk map.
Examiner tip
Pearson 4GE1 mark schemes credit naming MAP ELEMENTS (key, scale, orientation) + recognising the difference between hazard (physical) and risk (hazard × vulnerability × exposure).
2Plotting earthquake frequency vs magnitude
Getting started• AO4, graph
Question
Describe how to plot a FREQUENCY-MAGNITUDE graph for earthquakes. [4]
Step-by-step solution
1. Step 1
  Collect data (1 mark): use USGS or BGS catalogue to record number of earthquakes at each magnitude band (M2, M3, M4 ... M9) over a fixed time period (e.g. 1 year, 10 years globally).
2. Step 2
  Set up axes (1 mark): X-axis = magnitude (linear); Y-axis = frequency (LOGARITHMIC scale — because frequency drops ~10× per unit of magnitude).
3. Step 3
  Plot + draw line of best fit (1 mark): plot each data point; draw a straight line of best fit (Gutenberg-Richter law gives ~linear decline on log scale).
4. Step 4
  Interpret (1 mark): the gradient (b-value, ~1.0 globally) tells you the relative frequency of small vs large quakes. A higher b-value = MORE small quakes per large one.
Answer
Collect frequency data from USGS/BGS by magnitude band; X-axis = magnitude, Y-axis = log frequency; plot + draw line of best fit; interpret using Gutenberg-Richter b-value (~1.0 globally).
Examiner tip
The LOG Y-axis is essential — frequency drops ~10× per unit magnitude (M5 ~1,300/year globally; M6 ~130/year; M7 ~13/year; M8 ~1/year).
3Choropleth mapping hazard impacts
Building confidence• AO4, map
Question
Describe how to draw a CHOROPLETH MAP showing earthquake deaths by country in 2023. [4]
Step-by-step solution
1. Step 1
  Collect data (1 mark): record earthquake deaths by country for 2023 from USGS or UN OCHA (e.g. Türkiye ~50,500; Syria ~8,500; Morocco ~2,900; Afghanistan ~1,500; Nepal ~150).
2. Step 2
  Choose class intervals (1 mark): pick 4-6 intervals (e.g. <10; 10-100; 101-1,000; 1,001-10,000; >10,000). Use natural breaks or equal intervals.
3. Step 3
  Assign colours (1 mark): use a SEQUENTIAL colour scheme — light to dark (e.g. pale yellow → dark red). Darker = higher value. Avoid rainbow — sequential is clearer.
4. Step 4
  Add map elements (1 mark): KEY/legend showing intervals + colours, TITLE, SCALE, NORTH ARROW, SOURCE attribution.
Answer
Collect death data by country (e.g. Türkiye ~50.5k); pick 4-6 class intervals; use sequential light-to-dark colours; add key, title, scale, north arrow, source.
Examiner tip
Pearson rewards choosing class intervals JUSTIFIED + sequential (not rainbow) colours. Don't forget map elements.
4GIS overlay analysis
Building confidence• AO4, GIS
Question
Explain how to use GIS OVERLAY ANALYSIS to assess earthquake RISK in a city. [4]
Step-by-step solution
1. Step 1
  Identify hazard layer (1 mark): import seismic hazard data (shaking intensity by location) from USGS ShakeMap or national seismic agency. Geology layer (rock vs soft sediment) added.
2. Step 2
  Add vulnerability layers (1 mark): population density, building age (pre/post code), infrastructure (hospitals, schools, gas pipes).
3. Step 3
  Combine layers (1 mark): use GIS overlay to multiply hazard × vulnerability. Areas of HIGH hazard AND HIGH vulnerability = HIGH risk.
4. Step 4
  Output + decision (1 mark): produce a risk map; use to prioritise retrofitting + evacuation planning + insurance pricing.
Answer
Hazard layer (USGS ShakeMap + geology) + vulnerability layers (population, building age, infrastructure) → multiplied via GIS overlay → risk map → use for retrofitting + planning.
Examiner tip
Pearson 4GE1 rewards explicitly distinguishing HAZARD, VULNERABILITY + RISK + naming GIS layers.
5Volcanic hazard zone mapping
Stretch• AO4, method
Question
Describe how a VOLCANIC HAZARD MAP is produced for a high-risk volcano (e.g. Vesuvius). [6]
Step-by-step solution
1. Step 1
  Historic eruption record (1 mark): compile past eruption data — magnitude (VEI), eruption type, deposits. Vesuvius: Plinian eruptions ~AD79 (Pompeii) + 1631 + 1944.
2. Step 2
  Field mapping of deposits (1 mark): map past pyroclastic flow extents, ash fall thickness, lava flow paths.
3. Step 3
  Model future scenarios (1 mark): use volcanic eruption models to predict hazard footprints for different magnitudes + wind directions.
4. Step 4
  Zone the map (1 mark): Red Zone (within ~7km of crater — pyroclastic flow risk, immediate evacuation), Yellow Zone (~7-12 km — ash fall risk), Blue Zone (lahar risk along valleys).
5. Step 5
  Add evacuation routes (1 mark): mark roads, assembly points, shelter locations on map.
6. Step 6
  Communicate (1 mark): distribute to local populations + emergency planners; use for land-use planning (e.g. no new schools in Red Zone).
Answer
Historic record (VEI, eruption type); field map past deposits; model future scenarios; zone map (Red ~7km / Yellow ~12km / Blue lahar valleys); add evacuation routes; distribute to public + planners.
Examiner tip
Naming Vesuvius's Red/Yellow/Blue zone system + historical eruptions = top-band specificity.
6Tsunami inundation mapping
Stretch• AO4, method
Question
Describe how a TSUNAMI INUNDATION MAP is produced. [6]
Step-by-step solution
1. Step 1
  Bathymetric + topographic data (1 mark): high-resolution seabed bathymetry + coastal topography (LiDAR ground elevation data accurate to ~10 cm).
2. Step 2
  Source modelling (1 mark): select possible earthquake/landslide source magnitudes (e.g. M9.0 megathrust); model rupture characteristics.
3. Step 3
  Wave propagation modelling (1 mark): use computer models (e.g. MOST, COMCOT) to simulate tsunami wave generation + propagation across the ocean.
4. Step 4
  Inundation modelling (1 mark): model wave behaviour at the coast — run-up height, inundation distance inland, flow velocity.
5. Step 5
  Map output (1 mark): produce inundation map showing depth + extent for different scenarios; mark high-ground evacuation zones.
6. Step 6
  Use (1 mark): inform building codes (e.g. ground floor unusable in inundation zone), evacuation route planning, public warning signage.
Answer
Bathymetry + LiDAR topography; source modelling (e.g. M9.0); wave propagation modelling (MOST/COMCOT); inundation modelling (run-up + extent); map output by depth/extent; inform codes + evacuation + signage.
Examiner tip
Naming modelling tools (MOST, COMCOT) + LiDAR resolution = top-band technical specificity.

Model Answers — Hazardous Practical Skills

High-scoring sample answers for hazardous practical skills on the Cambridge IGCSE paper, with examiner-style notes mapping each response to the mark scheme and assessment objectives.

Question 1
2 marks
[EASY] Define the term 'HAZARD MAP'. [2]
Model answer
A hazard map is a map that shows the spatial distribution and severity of a natural hazard — for example, areas at risk of earthquakes (by shaking intensity), volcanic eruptions (by zone), or tsunamis (by inundation depth) [1 mark]. It is used to inform land-use planning, building codes, evacuation routes and insurance pricing [1 mark].
Why this scores
Mark scheme: 1 mark for defining hazard map (spatial + severity of hazard); 1 mark for giving a use (land-use, codes, evacuation, insurance).
Question 2
3 marks
[EASY] State THREE secondary data sources you could use to research earthquake hazards. [3]
Model answer
USGS Earthquake Hazards Program — provides real-time + historic earthquake catalogues, ShakeMaps + hazard maps [1 mark].

British Geological Survey (BGS) — UK + global seismic data; hazard reports [1 mark].

UN OCHA / Reliefweb — provides post-disaster impact data (deaths, displaced, damage costs) for major events [1 mark].
Why this scores
Mark scheme: 1 mark each for any 3 named sources from: USGS, BGS, UN OCHA, Reliefweb, JMA (Japan Meteorological Agency), GEM (Global Earthquake Model), GVP (Global Volcanism Program), etc.
Question 3
4 marks
[MEDIUM] Describe how you would plot a graph showing earthquake FREQUENCY versus MAGNITUDE for the world. [4]
Model answer
Step 1 — collect data. Use the USGS Earthquake Hazards Program catalogue to record the number of earthquakes occurring globally in each magnitude band (M2, M3, M4 ... M9) over a fixed period — for example, 1 year [1 mark].

Step 2 — set up axes. Plot magnitude on the X-axis (linear scale) and frequency on the Y-axis using a LOGARITHMIC SCALE — because frequency drops approximately 10× for each unit increase in magnitude (e.g. ~1,300 M5/year, ~130 M6/year, ~13 M7/year, ~1 M8/year globally) [1 mark].

Step 3 — plot points + line of best fit. Plot each data point as a cross or dot. Draw a straight line of best fit — the Gutenberg-Richter law states that this should be approximately linear on a log scale [1 mark].

Step 4 — interpret. The gradient of the line is the b-value, typically ~1.0 globally. A higher b-value means relatively more small earthquakes; a lower b-value means relatively more large earthquakes for a region. This helps identify high-risk seismic areas [1 mark].

Add a TITLE, AXES LABELS (with units), KEY (if multiple regions), and SOURCE attribution to complete the graph.
Why this scores
Mark scheme: 1 mark for data source (USGS/BGS) + magnitude bands; 1 mark for axes (X linear magnitude; Y log frequency); 1 mark for plot + line of best fit + Gutenberg-Richter; 1 mark for interpreting using b-value.
Question 4
4 marks
[MEDIUM] Describe how to draw a CHOROPLETH MAP showing earthquake deaths by country in 2023. [4]
Model answer
Step 1 — collect data. Record total earthquake deaths by country in 2023 from USGS or UN OCHA — for example, Türkiye ~50,500; Syria ~8,500; Morocco ~2,900; Afghanistan ~1,500; Nepal ~150 [1 mark].

Step 2 — choose class intervals. Select 4–6 intervals that distribute countries across categories — for example: <10; 10–100; 101–1,000; 1,001–10,000; >10,000. Use natural breaks or equal intervals; justify the choice [1 mark].

Step 3 — choose colours. Use a SEQUENTIAL colour scheme — light to dark (e.g. pale yellow → dark red). Darker = higher death count. Avoid rainbow palettes — sequential schemes are clearer for readers [1 mark].

Step 4 — add map elements. Include a KEY showing each interval + colour; TITLE ('Earthquake deaths by country, 2023'); SCALE BAR; NORTH ARROW; SOURCE attribution (USGS/UN OCHA); date [1 mark].

The completed map allows quick visual comparison of which countries suffered most in 2023 and supports questions about WHY (e.g. why Türkiye's M7.8 killed 50× more than Morocco's M6.8 — building enforcement).
Why this scores
Mark scheme: 1 mark for collecting data with figures; 1 mark for choosing + justifying class intervals; 1 mark for sequential (not rainbow) colours; 1 mark for map elements (key, title, scale, north, source).
Question 5
6 marks
[HARD] Explain how GIS OVERLAY ANALYSIS can be used to assess earthquake RISK in a major city. [6]
Model answer
Step 1 — define the goal. The aim is to combine HAZARD (physical likelihood of strong shaking) with VULNERABILITY + EXPOSURE (people + buildings at risk) to produce a RISK map [1 mark].

Step 2 — import hazard layers. Bring in seismic hazard data — USGS ShakeMap for expected shaking intensity, geological maps showing rock vs soft sediment (soft sediment amplifies shaking 2-5×), fault line locations, slope data for landslide risk [1 mark].

Step 3 — import vulnerability + exposure layers. Add population density (census data), building age (pre/post code adoption — pre-1981 in Japan, pre-2000 in Türkiye), infrastructure (hospitals, schools, gas mains, power lines), critical facilities [1 mark].

Step 4 — overlay + weighting. Combine layers using GIS — each layer can be weighted by importance (e.g. building age might weight 30%, soft sediment 20%, population 25%, infrastructure 25%). Output is a composite risk score for each grid cell [1 mark].

Step 5 — produce risk map. Display the result as a colour-coded map (green = low risk → red = high risk). Identify priority zones for action [1 mark].

Step 6 — use for decision-making. The risk map guides RETROFITTING (priority for old hospitals on soft sediment), EVACUATION PLANNING (where to position emergency centres), INSURANCE PRICING (high-risk zones pay more premium), and LAND-USE PLANNING (restrict new schools in high-risk zones). Istanbul + Tokyo + Los Angeles all use this approach [1 mark].

GIS overlay turns disparate datasets into actionable decisions — it is the modern foundation of seismic risk management.
Why this scores
Mark scheme: 1 mark for defining hazard × vulnerability × exposure → risk; 1 mark for hazard layers (ShakeMap, geology, faults, slope); 1 mark for vulnerability/exposure layers (population, building age, infrastructure); 1 mark for overlay + weighting; 1 mark for producing risk map output; 1 mark for use cases (retrofitting, evacuation, insurance, land-use).
Question 6
6 marks
[HARD] Evaluate the usefulness + limitations of HAZARD MAPS in reducing disaster impacts. [6]
Model answer
Hazard maps are central to modern disaster risk management — but their usefulness depends on accurate data, public access, and enforcement.

Strengths.

Hazard maps direct land-use planning. Japan's tsunami inundation maps prevent housing development below the 14-m line in some Tōhoku coastal areas; California's USGS hazard maps inform school + hospital siting [1 mark].

They support insurance pricing + early-warning systems. Chile uses national hazard maps to price earthquake insurance (~80% penetration); Japan's J-ALERT system uses real-time hazard data for mass alerts [1 mark].

They visualise risk clearly to non-specialists. Choropleth, hazard zoning + GIS overlays translate technical seismic data into maps anyone can understand — supporting public engagement, school education + media communication of disaster risk [1 mark].

Limitations.

Maps depend on accurate, up-to-date data. Earthquake recurrence intervals are decades to millennia — many faults remain undiscovered; megathrust quakes occur every 500-1000 years (Cascadia, USA — last major event 1700, next overdue). Mapping these is inherently uncertain [1 mark].

Maps don't enforce themselves. Türkiye had comprehensive hazard maps before the 2023 Kahramanmaraş quake — but corruption + 'construction amnesties' meant non-compliant buildings were built in highest-risk zones. ~60,000+ died. Maps must be paired with enforcement to save lives [1 mark].

Conclusion. Hazard maps are essential — but they are NECESSARY, not SUFFICIENT. They guide planning, insurance + warnings, but only deliver lives saved when paired with enforced codes + public engagement. Top-band investment combines mapping + governance + insurance [1 mark].
Why this scores
Mark scheme: 1 mark for land-use planning use; 1 mark for insurance + warning use; 1 mark for public engagement benefit; 1 mark for data uncertainty limitation; 1 mark for enforcement limitation with case study; 1 mark for clear evaluative conclusion.
Question 7
8 marks
[CHALLENGE] Compare the techniques used to produce VOLCANIC hazard maps and EARTHQUAKE hazard maps. [8]
Model answer
Both volcanic + earthquake hazard maps use similar data principles but differ in resolution, certainty + zoning systems.

Volcanic hazard maps (e.g. Vesuvius, Mt St Helens, Mt Pinatubo).

Data: historic eruption record (VEI, eruption type, deposit thickness); field mapping of past pyroclastic flow, ash + lahar deposits; magma chemistry analysis; real-time monitoring (seismometers, tiltmeters, gas sensors).

Modelling: eruption hazard models simulate ash dispersal, pyroclastic flow run-out, lahar pathways by valley.

Output zones: e.g. Vesuvius's Red Zone (~7 km — pyroclastic flow risk), Yellow Zone (~12 km — ash fall), Blue Zone (lahar pathways along valleys).

Certainty: medium — eruption interval often hundreds of years; precursors usually detectable [3 marks].

Earthquake hazard maps (e.g. ShakeMap, USGS National Seismic Hazard Maps).

Data: historic earthquake catalogue (instrumental + paleoseismic); fault mapping (GPS, InSAR satellite, LiDAR); ground motion records.

Modelling: probabilistic seismic hazard analysis (PSHA) — estimates shaking probability over 50-100 years; ground motion models account for distance, soil type, magnitude.

Output zones: typically continuous gradient maps (peak ground acceleration) rather than discrete zones.

Certainty: lower — earthquake recurrence intervals can be 500-1000+ years; precursors rare; timing impossible to predict [3 marks].

Comparison.

Both use historic data + modelling + GIS.

Volcanic maps have DISCRETE zones (red/yellow/blue); earthquake maps tend to use CONTINUOUS gradients.

Volcanic eruptions usually have PRECURSORS (swelling, gas, micro-earthquakes); earthquakes do NOT.

Volcanic maps support evacuation planning; earthquake maps support code design + retrofit prioritisation.

Both must be paired with vulnerability data (population, buildings) to become RISK maps [2 marks].
Why this scores
Mark scheme: 3 marks for volcanic mapping techniques + example; 3 marks for earthquake mapping techniques + example; 2 marks for comparative analysis (zones vs gradients; precursors; uses).
Question 8
10 marks
[EXTENDED] 'Remote sensing + GIS have replaced the need for traditional fieldwork in studying natural hazards.' To what extent do you agree? [10]
Model answer
Remote sensing + GIS have transformed hazard research — but they complement rather than replace fieldwork. The strongest hazard science combines both.

The case FOR replacement.

Remote sensing covers vast areas instantly. InSAR satellites (Sentinel-1, ALOS-2) measure ground deformation across entire fault systems to millimetre accuracy. LiDAR maps fault traces + landslide-prone slopes at sub-metre resolution. GPS networks (Japan's GEONET — ~1,300 stations) record continuous plate motion. These cannot be done by fieldwork at scale [2 marks].

GIS turns data into decisions. GIS overlay analysis combines hazard, vulnerability + exposure into risk maps. ShakeMap integrates real-time seismometer data with population + building data within minutes of an earthquake — guiding emergency response. Fieldwork could never match this speed or scale [2 marks].

Real-time monitoring saves lives. Earthquake Early Warning Systems use real-time seismometer networks to alert millions in seconds. Tsunami warning systems use satellite + buoy data (DART buoys) to predict wave arrival. These are TECH-DEPENDENT [2 marks].

The case AGAINST replacement.

Fieldwork validates remote data. Paleoseismic trenches — digging across fault lines — reveal earthquake recurrence intervals that satellites cannot. The 1700 Cascadia megathrust earthquake was identified largely from buried marsh sediments + Japanese tsunami records — not satellites. Ground-truth fieldwork is essential to calibrate remote-sensing models [2 marks].

Fieldwork captures human experience + vulnerability. Surveys, interviews + community mapping reveal lived experience of hazard — which buildings residents fear, which evacuation routes are blocked by traffic, which households lack insurance. Satellites cannot measure social vulnerability — but field surveys can. Bangladesh's cyclone preparedness was built on community-based fieldwork [1 mark].

Volcanic + lahar mapping still need field deposits. Volcanic hazard zones (Vesuvius's Red/Yellow/Blue) are built from FIELD MAPPING of past pyroclastic deposits + lahar paths. Remote sensing detects modern deformation + gas; field geology reveals long-term eruption history [1 mark].

Conclusion. Remote sensing + GIS have revolutionised hazard research — making continental-scale, real-time monitoring possible. But they have NOT replaced fieldwork. Paleoseismic trenches, volcanic deposit mapping + community vulnerability surveys remain essential. The strongest hazard science INTEGRATES remote sensing + GIS + traditional fieldwork. To choose one over the other would weaken understanding + lose lives [no extra mark — concluding sentence].
Why this scores
Mark scheme: 2 marks for remote sensing scale (InSAR, LiDAR, GPS); 2 marks for GIS overlay + ShakeMap speed; 2 marks for real-time monitoring + EEWS/tsunami; 2 marks for paleoseismic + ground-truth fieldwork; 1 mark for social vulnerability fieldwork; 1 mark for volcanic field-mapped deposits + clear judgement.

Key Definitions and Keywords — Hazardous Practical Skills

Definitions to memorise and the exact keywords mark schemes credit for hazardous practical skills answers — sharpened from recent examiner reports for the 2026 Cambridge IGCSE sitting.

Hazard map
A map showing the spatial distribution + severity of a natural hazard (e.g. earthquake shaking intensity, volcanic zones, tsunami inundation depth).
Risk map
A map combining HAZARD × VULNERABILITY × EXPOSURE — showing where + how much harm is likely.
ShakeMap
USGS map produced within minutes of an earthquake showing the spatial distribution of ground shaking — guides emergency response.
Choropleth map
A map shaded by area (country/region) according to data values — used for visualising hazard impacts (e.g. deaths by country).
GIS (Geographic Information Systems)
Software for storing, analysing + displaying spatial data — combines multiple layers (hazard, population, buildings) to produce composite maps.
InSAR
Interferometric Synthetic Aperture Radar — satellite-based measurement of ground deformation to millimetre accuracy. Reveals strain along faults.
LiDAR
Light Detection And Ranging — laser scanning that produces high-resolution topographic maps; used for fault mapping + landslide analysis.
Gutenberg-Richter law
Statistical relationship: earthquake frequency decreases approximately 10× per unit magnitude increase. b-value (gradient) typically ~1.0 globally.
Paleoseismology
Study of past earthquakes through field evidence (trenches across faults, buried marsh sediments, displaced features). Reveals recurrence intervals.
Probabilistic Seismic Hazard Analysis (PSHA)
Method for estimating the probability of various levels of ground shaking at a site over a future time period — foundation of building code design.
Secondary data
Data collected by someone else (e.g. USGS catalogues, UN OCHA reports) — primary source for most hazard research because direct fieldwork on active hazards is dangerous.
Tsunami inundation map
Map showing predicted flooding depth + extent for tsunami scenarios — used for evacuation planning + building codes.

Common Mistakes and Misconceptions — Hazardous Practical Skills

The traps other students keep falling into on hazardous practical skills questions — taken from recent Cambridge IGCSE examiner reports and mark schemes — and how to avoid them.

✕Confusing HAZARD (physical phenomenon) with RISK (hazard × vulnerability × exposure).
Why it happens
Pearson 4GE1 mark schemes explicitly credit the distinction — top-band answers always separate them.
How to avoid it
Memorise: HAZARD = the earthquake itself. RISK = hazard × vulnerability (e.g. weak buildings) × exposure (e.g. dense population).
✕Using rainbow colours on choropleth maps.
Why it happens
Rainbow palettes are confusing — they imply non-existent thresholds. Sequential (light-dark) is the modern standard.
How to avoid it
Use SEQUENTIAL colours (light yellow → dark red) for ordered data; use DIVERGING (red-white-blue) for data centred around a value.
✕Plotting earthquake frequency on a LINEAR Y-axis instead of LOGARITHMIC.
Why it happens
Frequency drops ~10× per magnitude unit — linear axes hide the small quakes entirely. Log axes reveal the Gutenberg-Richter relationship.
How to avoid it
Always use LOG Y-axis for frequency-magnitude graphs. Justify your choice in the answer.
✕Forgetting map elements (key, title, scale, north arrow, source).
Why it happens
Examiners deduct marks for incomplete maps. Pearson 4GE1 fieldwork mark schemes credit each element separately.
How to avoid it
Memorise the 5 essentials: KEY + TITLE + SCALE + NORTH ARROW + SOURCE. Add date + author too.
✕Claiming students do DIRECT fieldwork on active hazards.
Why it happens
Hazard fieldwork is dangerous + impractical — Pearson tests SECONDARY data + map skills, not direct earthquake/volcano measurement.
How to avoid it
Frame fieldwork as SECONDARY data analysis: hazard map interpretation, GIS overlay, choropleth mapping — NOT direct seismic monitoring.
✕Citing 'the internet' or 'a website' instead of specific named sources.
Why it happens
Pearson rewards SPECIFIC source naming (USGS, BGS, UN OCHA, JMA, GEM, GVP).
How to avoid it
Memorise key sources: USGS (US Geological Survey); BGS (British Geological Survey); UN OCHA + Reliefweb; JMA (Japan Met Agency); GEM (Global Earthquake Model); GVP (Smithsonian Global Volcanism Program).

Hazardous Practical Skills

Detailed Study Notes

At a glance

What you’ll learn

Quick recap

Memorise this

How it’s examined

Take this whole topic with you

1Reading a hazard map

2Plotting earthquake frequency vs magnitude

3Choropleth mapping hazard impacts

4GIS overlay analysis

5Volcanic hazard zone mapping

6Tsunami inundation mapping

Hazard map

Risk map

ShakeMap

Choropleth map

GIS (Geographic Information Systems)

InSAR

LiDAR

Gutenberg-Richter law

Paleoseismology

Probabilistic Seismic Hazard Analysis (PSHA)

Secondary data

Tsunami inundation map

✕Confusing HAZARD (physical phenomenon) with RISK (hazard × vulnerability × exposure).

✕Using rainbow colours on choropleth maps.

✕Plotting earthquake frequency on a LINEAR Y-axis instead of LOGARITHMIC.

✕Forgetting map elements (key, title, scale, north arrow, source).

✕Claiming students do DIRECT fieldwork on active hazards.

✕Citing 'the internet' or 'a website' instead of specific named sources.