Session: 2026-05-25

Summary

Major session covering codebase infrastructure, data source improvements, and new analysis notebooks. The main analytical work extended the trigger performance analysis to both Adamawa and Benue states via a new parameterised notebook (13_model_performance.ipynb). An initial version of 14_forecast_performance.ipynb was also created, introducing skill-vs-leadtime and exceedance detection analysis for both GloFAS and Google GRRR reforecasts.

Codebase changes

Constants (`src/constants.py`)

Added Benue state PCODE constants: BENUE = "NG007", BENUE_STATE_ADM2_PCODES (5 riverine LGAs along the Benue river)
Renamed BENUE_ADM2_PCODES to ADAMAWA_ADM2_PCODES with a backwards-compatible alias
Added Google Flood Hub HYBAS gauge IDs: WUROBOKI_HYBAS and MAKURDI_HYBAS
Added STATE_CONFIG dict parameterising Adamawa and Benue state analysis by station name, LGA list, river extent, GloFAS station, Google gauge, Floodscan blob path, and trigger thresholds

Data sources

src/datasources/worldpop.py (new): WorldPop population data loading via STAC
src/datasources/hydrosheds.py (updated): HydroSheds river network loading
src/datasources/glofas.py (major additions): Extended reanalysis and reforecast loading; helper functions for both Wuroboki and Makurdi stations
src/datasources/grrr.py (major additions): Google GRRR reanalysis and reforecast loading, including HYBAS gauge lookup and streamflow extraction

Processing notebooks (new)

Data download and processing pipeline for the extended analysis:

Notebook	Purpose
`processing/download_glofas.ipynb`	Download GloFAS reanalysis for Makurdi and Wuroboki stations
`processing/download_glofas_reforecast.ipynb`	Download GloFAS reforecast ensemble
`processing/process_glofas_reanalysis.ipynb`	Process raw GloFAS reanalysis to parquet
`processing/process_glofas_reforecast.ipynb`	Process GloFAS reforecast ensemble to parquet

Analysis outputs

Trigger performance — Adamawa and Benue (`13_model_performance.ipynb`)

Notebook parameterised by state via STATE_CONFIG, reproducing the 2025 Wuroboki analysis approach for both stations:

Annual peak correlation: Side-by-side scatter plots comparing wet-season (Aug–Nov) annual discharge peaks against Floodscan annual mean SFED — one panel for GloFAS reanalysis, one for Google GRRR reanalysis. Each year is labelled, and the 5-year RP threshold is marked on both axes.

Precision, recall, F1: Performance metrics for three trigger configurations evaluated against Floodscan 5-year RP events:

Trigger	Threshold
GloFAS reanalysis	3,132 m³/s (Wuroboki)
Google GRRR reanalysis	1,195 (Wuroboki)
OR logic (either source)	—

A year-by-year table shows which years were events, which triggered, and which did not.

Forecast performance — initial version (`14_forecast_performance.ipynb`)

Initial notebook created with the following structure:

Data loading: GloFAS reanalysis + ensemble reforecast; Google GRRR reanalysis + reforecast; both accessed via STATE_CONFIG
Return period thresholds: GloFAS thresholds derived from reanalysis annual wet-season maxima; Google thresholds from the official GCS return periods dataset
Skill vs leadtime: Spearman correlation and MAPE between daily reforecast and reanalysis values, restricted to wet-season days, grouped by leadtime — separately for GloFAS (ensemble mean) and Google
Annual exceedance detection: For each year and leadtime, whether the wet-season annual max ensemble-mean reforecast exceeded the RP threshold, compared against reanalysis — reporting hit rate and false alarm rate vs leadtime per RP level

Notebook index

Notebook	Purpose
`13_model_performance.ipynb`	Annual peak correlation + Precision/Recall/F1 for GloFAS, Google, and OR trigger
`14_forecast_performance.ipynb`	Skill vs leadtime and exceedance detection — initial version

Next steps

Refine metric definitions in 14_forecast_performance.ipynb: switch to met-convention POD/FAR, implement cumulative activation logic, add probability of activation
Define trigger thresholds and leadtime windows for Benue state (Makurdi) — analogous to the Adamawa/Wuroboki work
Evaluate reforecast performance against Floodscan events directly (rather than reanalysis), disaggregated by RP level