Build an Agricultural Drought Soil Moisture Trigger Simulator

Evaluate agricultural drought parametric contracts by processing ERA5 volumetric soil water layer parameters. Quantify seasonal dry spell anomalies against baseline historical quantiles to evaluate index basis risk.

Agricultural Parametric Modeler

Parse field parcel polygons and defined root-zone depth specifications.

Retrieve ERA5 volumetric soil water layer data over target growing seasons.

Calculate soil moisture deficit percentiles relative to multi-year baselines.

Synthesize simulated parametric trigger payouts and contract basis risk.

Jupyter Notebook workflow with interactive ipywidgets for setting soil moisture thresholds and viewing deficit histograms.

Download monthly averaged reanalysis soil water layers via cdsapi or load synthetic NetCDF grids representing topsoil moisture. Starter examples below are synthetic. They are not downloaded records.

Copernicus Climate Data Store client Employs cdsapi retrieval parameters for ERA5 surface variables focusing on volumetric soil water layers.

dspy, xarray, pandas. DSPy compiles structured diagnostic prompts that reason over anomalous soil moisture sequences and flag basis risk caveats. Local Llama 3 or hosted Claude model for generating qualitative risk syntheses from tabular drought indices.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Correctly integrates layer 1 and layer 2 soil moisture into an effective root zone index.

Identifies consecutive days below contract dry threshold.

Outputs contract trigger ratio without invoking actual financial disbursement.

```json [ { "input": { "parcel_id": "FIELD-IA-44", "consecutive_days_trigger": 14, "moisture_threshold": 0.18, "daily_records": [ { "date": "2023-07-01", "swvl1": 0.15 }, { "date": "2023-07-02", "swvl1": 0.14 }, { "date": "2023-07-03", "swvl1": 0.16 } ] }, "expected_output": { "parcel_id": "FIELD-IA-44", "consecutive_deficit_days": 3, "trigger_met": false, "mean_moisture": 0.15 } }, { "input": { "parcel_id": "FIELD-IA-45", "consecutive_days_trigger": 2, "moisture_threshold": 0.2, "daily_records": [ { "date": "2023-08-01", "swvl1": 0.12 }, { "date": "2023-08-02", "swvl1": 0.11 } ] }, "expected_output": { "parcel_id": "FIELD-IA-45", "consecutive_deficit_days": 2, "trigger_met": true, "mean_moisture": 0.115 } } ] ```

Emphasizes that trigger simulations are exploratory models and cannot be used as binding agricultural loss adjustments. Use mock external writes. Require human review before any real deployment.