Western Rwanda, long-rains. Steep terrain prone to flash floods and landslides; hillside communities exposed. You are the DOC duty analyst as the forecast window opens.
Act I quiz — build the evidence inventory Every answer becomes an input to the risk model: evidence → classification → confidence → belief update → decision. Not scored.
Q1. The rainfall outlook for Western Province comes from the ECMWF ensemble (many parallel model runs, updated daily). First, the basics: what is a DETERMINISTIC forecast? A single model run giving one outcome — no range of possibilities Many model runs combined into probabilities A forecast that is always correct An observation of what has already happened BN purpose: Forecast literacy — why a single number is not enough for risk decisions.
Q2. And what is an ENSEMBLE prediction system (EPS)? The same model run many times from slightly different starting conditions, giving a spread of outcomes Several forecasters voting on the most likely outcome One model run at a higher resolution An average of past observed seasons BN purpose: Why forecasts are SOFT evidence in the BN — the spread is the confidence.
Q3. What is the strongest evidence currently available? On your desk this round: 7-day antecedent rainfall (IMERG) · Ensemble-mean exceedance prob (ECMWF P_heavy)
Forecast Observation Community report Historical analogue BN purpose: Determines which BN node receives the highest weight.
Q4. Classify that evidence. Check the tags on the evidence cards: 7-day antecedent rainfall (IMERG) · Ensemble-mean exceedance prob (ECMWF P_heavy)
Hard Soft Virtual BN purpose: Determines confidence weighting — hard = observation (one-hot), soft = probability vector, virtual = likelihood message.
Q5. How reliable is this evidence? Western Province is a TAIL-risk case — the ensemble mean can look benign while a few members carry the danger; weigh the tail, not the average. Reliability sets how hard the BN leans on it.
Very Low Low Moderate High Very High BN purpose: Evidence likelihood weighting.
Q6. What hazard condition does this evidence support in Western Province, Rwanda? Which mechanism fits Western Province — slow river rise, sudden flash runoff, or urban-drainage overload? The mechanism decides which impacts follow.
Heavy rainfall River flooding Flash flooding No hazard BN purpose: Updates the hazard node.
Q7. Which impact pathway is most likely here? Western Rwanda, long-rains. Steep terrain prone to flash floods and landslides; hillside communities exposed. You are the DOC duty analyst as the forecast window opens.
Population displacement Road disruption Crop loss Water contamination BN purpose: Updates the impact node.
Q8. What evidence would you request next? Still off the table this round: Ensemble-max / 2-yr RP (pixel p95) · Spatial coverage of heavy-rain pixels · Yesterday's posterior carried forward (DBN, alpha=0.6) · Observed rainfall / river response. Which would most reduce your uncertainty before you decide?
Satellite observations Gauge observations Hydrological model Field assessment Exposure information BN purpose: Real DOC operations seek more evidence — watch which cards the Act II rounds reveal.
Q9. Western Rwanda sits on the steep Congo-Nile divide escarpment above Lake Kivu, with cultivated hillsides draining through small, short tributary catchments. Which flood mechanism dominates here? Think about how fast a small, steep catchment turns rainfall into runoff.
Slow riverine flooding that builds over weeks on a broad floodplain Flash floods, debris flows and landslides with very short catchment response times Coastal storm-surge inundation Urban drainage backup in a flat paved city centre BN purpose: Why short catchment response times make the forecast tail, not the observed river level, the actionable signal here.
Q10. It is late April in the long rains (Itumba) and soils on the western hillsides are already wet from weeks of rain. Why does this antecedent moisture matter for the NEXT heavy-rain forecast? The first evidence card you see is 7-day antecedent rainfall.
It does not matter; only the new forecast total counts Wet soils absorb more of the next storm, lowering risk Saturated slopes convert more of the next storm to runoff and are primed for landslides, so even a moderate forecast is dangerous Antecedent rain only matters for drought monitoring BN purpose: Why the antecedent_rainfall node raises flood risk before the forecast nodes fire.
Q11. Your current risk estimate — before the model runs? Western Province is a TAIL-risk case — the ensemble mean can look benign while a few members carry the danger; weigh the tail, not the average. No model has spoken yet — commit your own read so Act III can compare it to the engine.
Low Moderate High Very High BN purpose: Your pre-BN estimate. Act III compares it against the engine risk indication.
Q12. Recommended DOC status — commit now, before seeing the BN output. The ensemble mean may look benign but the tail signal is rising ahead of onset. What DOC level do you set, and why? A 'Watch + re-check + note the tail uncertainty' is a strong answer.
Monitor Watch Warning Emergency Coordination BN purpose: Your pre-BN decision. Act III compares it against the CRMA state.
Q13. The risk model you are about to work with was built from expert rules, not calibrated against decades of events. How much should you trust it? No right answer — your stance is revisited in Act III against what the model actually did, and what history recorded.
Fully — it computes probabilities Not at all — rules are subjective As a transparent colleague — consistent and inspectable, but encoded judgment BN purpose: Hold this question. Act III returns to it.