Khartoum and the Nile / Blue Nile confluence, heavy seasonal rains and high river levels (Aug-Sep 2019). Riverine and urban neighbourhoods exposed to flooding and house collapse. 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 Khartoum 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? Khartoum 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 Khartoum, Sudan? Which mechanism fits Khartoum — 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? Khartoum and the Nile / Blue Nile confluence, heavy seasonal rains and high river levels (Aug-Sep 2019). Riverine and urban neighbourhoods exposed to flooding and house collapse. 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 August in Khartoum, at the Blue Nile / White Nile confluence. The kiremt season on the Ethiopian Plateau has been running above average. Why does rain falling hundreds of kilometres upstream matter for the duty analyst here? The Blue Nile's flood wave is generated outside Sudan.
Above-average kiremt rain on the Ethiopian Plateau swells the Blue Nile and Atbara, so river levels at Khartoum can keep rising even on locally dry days Upstream rain is irrelevant because rivers lose all their water before reaching Khartoum Only rainfall inside Khartoum state can affect the Nile's level Upstream rain lowers the river by recharging aquifers BN purpose: Why antecedent and upstream-catchment evidence keeps the riverine risk elevated even when the local forecast looks moderate.
Q10. Beyond the rivers themselves, which exposure characteristic makes Khartoum's neighbourhoods vulnerable during a heavy-rain spell? Think about what sustained rain does to traditional building materials on flat clay plains.
Widespread mud-brick housing weakens when saturated, and low-lying districts near the confluence flood from both river rise and poorly drained local downpours Housing is uniformly reinforced concrete on elevated ground Only riverside farmland is exposed; residential areas are out of reach The city's storm drainage is designed for far larger events than any forecast BN purpose: Exposure context: combined riverine and pluvial pathways mean moderate evidence on either channel still implies significant risk to housing.
Q11. Your current risk estimate — before the model runs? Khartoum 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.