Upper Nile, South Sudan. Nile/Sobat riverine and flood-recession communities under prolonged inundation. 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 Upper Nile 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? Upper Nile 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 Upper Nile, South Sudan? Which mechanism fits Upper Nile — 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? Upper Nile, South Sudan. Nile/Sobat riverine and flood-recession communities under prolonged inundation. 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. It is October 2019 and the western Indian Ocean is running much warmer than the east, with the positive Indian Ocean Dipole index at record strength. What does this imply for the October-December rains over South Sudan and its upstream catchments? A strong +IOD redirects moisture toward East Africa during the short-rains season.
Substantially enhanced rainfall is likely over East Africa and the catchments feeding the White Nile, Sobat and Pibor rivers A positive IOD suppresses East African rainfall, so a dry season is expected The IOD only affects Australia and has no bearing on the Nile basin The IOD matters only for temperatures, not rainfall BN purpose: Climate-driver context that justifies giving the hazard evidence a higher prior across the whole OND window.
Q10. Upper Nile's communities sit on the flat floodplains of the White Nile and Sobat. Why does antecedent (already-fallen) rain make the next forecast pulse especially dangerous here? Think about where the water goes once the floodplain is already wet.
The terrain is extremely flat and drains slowly, so once soils and channels are full each new rainfall or upstream pulse spreads the inundation wider and keeps it there for weeks Antecedent rain is irrelevant because floodwater drains away within a day Earlier rain hardens the ground, making later floods less likely Only rain falling directly on a village matters; upstream river levels can be ignored BN purpose: Why antecedent-rainfall evidence keeps the risk posterior elevated: saturation converts moderate forecasts into high impact.
Q11. Your current risk estimate — before the model runs? Upper Nile 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.