📊 Week 6: Rainfall Graph Analysis - Waikato Region

Unit 10: Kai, Culture and Climate — Surviving Scarcity
Analyse rainfall data for the Waikato region and connect it to food production and scarcity.

🔢 Numeracy Integration: This activity uses data analysis, graph reading, and statistical thinking. It connects to Mathematics Level 4: Statistics and Probability.

📈 Graph 1: Annual Rainfall - Waikato Region

Draw or paste a rainfall graph here
Source: NIWA Annual Climate Summary 2024

[Graph Space]
Label: X-axis (Years), Y-axis (Rainfall in mm), Title, Data points

2024 Data from NIWA:

  • Dargaville (Northland): Driest year on record
  • Whitianga (Coromandel): Driest year on record
  • Bay of Plenty & Coromandel: Below normal (50-79% of annual normal)
  • Waikato region: Check NIWA data for specific locations

Source: NIWA Annual Climate Summary 2024

📊 2024 Climate Data from NIWA

Key Findings from NIWA Annual Climate Summary 2024:

  • 2024 was New Zealand's 10th-warmest year on record (0.51°C above average)
  • Dargaville and Whitianga: Driest year on record
  • Lumsden (Southland): Wettest year since records began (1982)
  • Drought conditions: Northland, Taranaki, Manawatū-Whanganui, Wairarapa, Wellington, Marlborough, Tasman, and Nelson
  • Four state of emergency declarations: Westland (Jan & Nov), Wairoa (June), Dunedin & Clutha (October)

Source: NIWA Annual Climate Summary 2024

1. Basic Observations

What is the average annual rainfall for the Waikato region? (Research from NIWA data)


According to NIWA 2024 data, was rainfall above, below, or near normal for your region?


Is there a trend? (Is rainfall increasing, decreasing, or staying the same over time?)


2. Patterns & Extremes (Using 2024 Data)

2024 had extreme events. Which regions had very high rainfall (flood risk)?


Which regions had very low rainfall (drought risk) in 2024?


How do these extremes connect to food production and scarcity?


3. Connection to Food

How does high rainfall (flooding) affect food production in the Waikato?



How does low rainfall (drought) affect food production?



What does this mean for food scarcity?



📈 Graph 2: 2024 Monthly Rainfall Anomalies - Aotearoa New Zealand

NIWA 2024 Monthly Rainfall Anomalies Map
Source: NIWA Annual Climate Summary 2024

2024 Monthly Rainfall Anomalies for New Zealand showing below normal (brown), normal (beige), and above normal (teal) rainfall patterns across 12 months

Legend: Brown/Orange = Below normal • Beige = Normal • Teal/Blue = Above normal

📊 Analysis Questions:

  • Which months had the most widespread below normal rainfall? What regions were affected?
  • Which months had above normal rainfall? Where did this occur?
  • What pattern do you notice between the North Island and South Island?
  • How does this connect to the state of emergency declarations (Westland, Wairoa, Dunedin & Clutha)?

[Space for your analysis notes]
Record your observations about the monthly patterns

🤔 Final Analysis: Connecting 2024 Data to Food Scarcity

Based on NIWA's 2024 Annual Climate Summary and your graph analysis, answer:

  1. 2024 had both extreme droughts AND floods. How did this affect food production?
    Think about: Dargaville (driest year), Lumsden (wettest year), state of emergency declarations


  2. NIWA reports that 8 of New Zealand's 10 warmest years have occurred since 2013. What does this trend mean for food production and scarcity in the future?


  3. How does the 2024 data connect to our unit's Big Question: "What Will We Eat Tomorrow?"
    Consider: What happens to food when there's too much rain? Too little? How do farmers adapt?


💡 Extension:
  • Compare your region's 2024 data with other regions (e.g., Dargaville vs. Lumsden)
  • Research how the 2024 drought affected specific crops or farms in your area
  • Investigate how farmers adapted to the extreme weather in 2024
  • Download the full NIWA Annual Climate Summary 2024 PDF for more detailed data

Curriculum alignment

  • Statistics — Knowledge: - Categorical data can be visualised through dot plots and bar graphs. - Paired categorical variables can be visualised through a stacked bar graph or a clustered bar graph. -…
  • Statistics — Practices: - Planning and collecting data in order to respond to a statistical question (e.g. Are our feet the same length?) - Calculating the mean, median, and mode for numerical data -…
  • Statistics — Knowledge: - The response to a statistical question includes findings that are summarised and interpreted in context and using evidence. - The tapering sides of a data visualisation are …
  • Statistics — Knowledge: - A variable is an attribute or measurement of the people or objects being studied.A categorical variable classifies objects or individuals into groups.Discrete numerical vari…

📋 Teacher Planning Snapshot

Ngā Whāinga Ako — Learning Intentions

Students will engage with this resource to develop statistical investigation skills — planning inquiries, collecting and analysing data, interpreting distributions, and communicating findings. Tūhuratanga (investigation) is framed as a tool for understanding our communities and environment in Aotearoa New Zealand.

Ngā Paearu Angitū — Success Criteria

  • ✅ Students can identify an investigative question, collect relevant data, and display it clearly.
  • ✅ Students can interpret statistical findings and discuss what they might mean for a real-world community or environmental context.

Differentiation & Inclusion

Scaffold support: Provide structured investigation frameworks (PPDAC cycle templates) for entry-level access. Offer partially completed data tables for students who need additional support. Extend capable learners by asking them to critique a statistical claim from a news article, or to design their own community data investigation.

ELL / ESOL: Pre-teach statistical vocabulary (median, mode, range, distribution, sample, population). Pair visual representations (graphs, tables) with plain-language explanations. Allow students to discuss statistical ideas orally before writing. Encourage use of home language for initial sensemaking.

Inclusion: Statistical investigation offers natural differentiation — all students can engage with the same real-world question at different levels of mathematical complexity. Neurodiverse learners benefit from structured, step-by-step investigation processes. Use collaborative group investigation formats that distribute roles (data collector, recorder, analyst, presenter).

Mātauranga Māori lens: Tūhuratanga — the practice of careful investigation — resonates deeply with mātauranga Māori. The maramataka is a sophisticated data system: tracking environmental patterns, seasonal cycles, and ecological indicators over generations. Iwi environmental monitoring — counting kaimoana populations, tracking water quality, observing bird migrations — is applied statistical thinking. Framing statistics within community and environmental inquiry connects data to mana whenua responsibilities.

Prior knowledge: Students should have basic familiarity with data displays (bar graphs, dot plots). No prior statistical investigation experience required — the PPDAC inquiry cycle provides accessible scaffolding for first-time investigators.

Curriculum alignment

  • Statistics — Statistical Investigation: Plan and conduct investigations using the statistical enquiry cycle — determining appropriate variables and data collection methods; gathering, sorting, and displaying multivariate category, measurement, and time-series data to detect patterns, variations, relationships, and trends; comparing distributions visually; communicating findings, using appropriate display.
  • Statistics — Probability: Investigate situations that involve elements of chance by comparing experimental distributions with expectations from models of the possible outcomes, acknowledging uncertainty.