DMAP-AI Station 4: Flagstaff, Arizona AI Interpretation Comparison

Station metadata and setup

1. Drought severity interpretation

Selected chart: Drought severity — SPI line with shaded events.

1.1 Method A: ChatGPT chart-only interpretation

The severity chart shows that drought conditions at the Flagstaff-area point are episodic rather than continuously persistent. Several isolated negative SPI years appear below the drought threshold, with the most visually prominent dry years occurring around 2002 and 2020. The chart suggests that the drought events are mostly short-lived annual events rather than long multi-year drought periods. From the chart alone, ChatGPT can identify the major drought episodes and the general timing of severe or extreme dry years, but it cannot reliably extract exact event magnitudes, minimum SPI values, or the formal event count without the underlying table.

1.2 Method B: ChatGPT interpretation using DMAP-AI structured severity request

The DMAP-AI structured request provides the event table, threshold, category method, and SPI calculation metadata. Using the SPI <= -0.99 event threshold, the Flagstaff-area analysis contains 7 drought events during 1981-2025. Each event lasts one yearly step, so the record indicates repeated isolated annual drought years rather than sustained multi-year drought persistence.

The most severe drought event is 2020, with SPI = -2.346 and magnitude = 1.356, classified as extreme drought. The second strongest event is 2002, with SPI = -2.228 and magnitude = 1.238, also classified as extreme drought. Severe drought also appears in 1989 and 2009. Because every event has duration = 1 yearly step, the structured request clarifies that the key severity message is not long persistence, but repeated isolated annual drought extremes.

1.3 Severity method comparison

2. Wavelet / periodicity interpretation

Selected chart/context: Wavelet scalogram (power vs time and period).

Note: This figure was reconstructed from the exported wavelet power matrix because the ZIP did not contain a wavelet_scalogram.png chart image.

2.1 Method A: ChatGPT chart-only interpretation

The scalogram visualization suggests that SPI variability is concentrated in several time-period bands rather than being uniformly distributed across all periods. A chart-only interpretation can describe where stronger wavelet power appears visually and may identify a mid-period band as important. However, the visual chart alone is vulnerable to overinterpretation: without numeric ranking, reliability values, or caution flags, ChatGPT may describe a visible band as a drought cycle even though wavelet power only indicates time-frequency variability.

2.2 Method B: ChatGPT interpretation using DMAP-AI structured wavelet request

The structured request identifies the highest-power and best reliable wavelet peak at approximately 6.48 yearly steps, with power = 1.149, coherence = 0.989, and reliability = 0.979. Nearby ranked bands around 5.57 and 7.39 yearly steps suggest a broader mid-period variability band of roughly 5.5-7.5 years. The structured request also identifies a 22-year band, but flags it for long-period low support, meaning it should be interpreted cautiously in a 45-year record.

The correct interpretation is that the Flagstaff-area SPI series contains a prominent mid-period variability signal, strongest near 6.5 yearly steps. This should not be converted into a deterministic statement that drought will recur every 6-7 years. The wavelet result is diagnostic of historical variability, not a forecast rule or proof of a physical cause.

2.3 Wavelet / periodicity method comparison

3. Location-level research conclusion

For the Flagstaff-area point, the severity task shows that chart-only interpretation can identify the visually obvious extreme drought years, but the structured DMAP-AI request provides the exact event table needed to distinguish severity, magnitude, and duration. The structured interpretation is especially useful for showing that all detected drought events are single-year events, so the main severity pattern is episodic annual drought rather than sustained multi-year persistence.

For the wavelet task, the structured request provides the stronger contribution. It identifies a dominant mid-period variability band near 6.5 yearly steps and gives reliability metrics and caution flags that are not available from a simple chart-only reading. This supports the paper hypothesis that DMAP-AI structured requests reduce overinterpretation by guiding ChatGPT away from unsupported cycle, forecast, or causal claims.

Appendix: Key values extracted from the Flagstaff export