Localizing Hydrological Drought Early Warning Using In-Situ Groundwater Sensors

William Veness from Imperial College London published this dataset in 2022 to support the paper 'Localizing Hydrological Drought Early Warning using In-situ Groundwater Sensors'. It contains full input data and modelling scripts for a modified AquiMod groundwater model and automated calibration procedure. The data is provided for reproducibility and includes code for processing inputs and generating plots.

Use Cases

• Calibrating a modified AquiMod groundwater model based on the provided input data and automated procedure.
• Reproducing the groundwater level plots from the associated research paper using the included plotting scripts.
• Analyzing hydrological drought signals based on in-situ groundwater sensor data mentioned in the description.

Strengths

• Includes full modelling scripts for a modified AquiMod groundwater model.
• Provides code for an automated calibration procedure with an estimated runtime of 5 minutes for 10,000 runs.
• Contains data and functions required to reproduce the figures from the associated 2022 research paper.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Imperial College London

Collection Method

In-situ groundwater sensor data, processed for model input.