Sensibility analysis of transient pumping test with MODFLOW-6, Flopy and SALib

Sensitivity analysis refers to the uncertainty analysis in model results from the uncertainties on the model inputs (or something close to this definition). We usually perform this analysis on observation points (points that we know) with certain predefined ranges over the hydraulic parameters. Python is an object oriented programming language and everything can be treated as an object like a MODFLOW-6 model. Having a pumping test model as an object allows us to use powerful Python libraries as SALib to perform sensibility analysis. This webinar covers the whole procedure to perform a sensitivity analysis over a 72 hour pumping test plus recovery on the hydraulic response in an observation piezometer located at 11 meters from the well. Since the study case it’s a transient model the sensitivities will vary over time and stage of pumping/recovery.

Instructor / Instructor:

Saul Montoya M.Sc

Hydrogeologist - Numerical Modeler

Mr. Montoya is a Civil Engineer graduated from the Catholic University in Lima with postgraduate studies in Management and Engineering of Water Resources (WAREM Program) from Stuttgart University – Germany with mention in Groundwater Engineering and Hydroinformatics. Mr Montoya has a strong analytical capacity for the interpretation, conceptualization and modeling of the surface and underground water cycle and their interaction. He is in charge of numerical modeling for contaminant transport and remediation systems of contaminated sites. Inside his hydrological and hydrogeological investigations Mr. Montoya has developed an holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system.

Language / Lenguaje:

English

Event date / Fecha del evento:

Wednesday, Jan 17 2024 6:00 p.m. Amsterdam Time

Hosted by / Organizado por:

Hatarilabs

Stream link / Enlace de transmisión:

https://meet.google.com/ihp-fajd-opm

Input data / Datos de entrada:

https://owncloud.hatarilabs.com/s/inyE6XC9heQtgIr

Additional instructions / Instrucciones adicionales:

Password to download data: Hatarilabs. You need to have Anaconda installed on your computer.