Addis Abeba and the Akaki River corridor, belg rains. Dense urban settlements along the river exposed to flash flooding. 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 Addis Abeba / Akaki 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? Addis Abeba / Akaki 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 Addis Abeba / Akaki, Ethiopia? Which mechanism fits Addis Abeba / Akaki — 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? Addis Abeba and the Akaki River corridor, belg rains. Dense urban settlements along the river exposed to flash flooding. 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. The Akaki River and its tributaries (Kebena, Bantyiketu, Kechene) drain a small, highly urbanised catchment inside Addis Abeba. How does this shape the flood threat the analyst is watching for? Small urban catchments respond to a storm on the timescale of hours, not weeks.
Urban flash flooding: an intense convective storm over the city can push the Akaki and its tributaries over their banks within hours Slow riverine flooding that takes a month to develop from distant headwaters Coastal flooding driven by tides Flooding here can only result from dam releases upstream BN purpose: Why short-lead tail-risk evidence, not the ensemble mean, carries the warning signal in a fast-responding urban catchment.
Q10. Which combination of factors makes the Akaki corridor's settlements vulnerable when heavy rain is forecast over Addis Abeba? Consider what has happened to the hills, the surfaces and the floodplain over recent decades.
Deforested surrounding hills, expanding impervious surfaces, informal housing on the floodplain and an ageing stormwater system of limited capacity Strict enforcement keeps all housing far from the river, so vulnerability is minimal The catchment is heavily forested, so runoff is fully absorbed Vulnerability stems only from earthquake-damaged buildings BN purpose: Exposure and vulnerability context: why the same rainfall evidence implies higher risk in this admin-1 than in a less encroached catchment.
Q11. Your current risk estimate — before the model runs? Addis Abeba / Akaki 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.