We have developed an applied case of groundwater model discretized from ESRI Shapefiles with refinement areas. Boundary conditions are also set up from spatial data with the intersect functionality of Flopy. Model surface and layer bottom are imported / processed from xyz point data. The simulation is run for one steady  and ten transient stress periods and results are plotted for head on aerial view and cross section.

In order to improve the accuracy of a groundwater flow simulation we need a strong conceptual model, a high quality observation dataset and a numerical code that can handle specific characteristics of the groundwater flow we want to evaluate. MODFLOW 6 can deal with variable density flow with the BUY package and also with variable viscosity with the VSC package that is implemented on the latest versions of Model Muse. This webinar covers an applied case of variable viscosity modeling due to the injection of salinity and heat to a model with regional groundwater flow. The main features for the implementation of the VSC package are explained and the resulting concentrations on two observation points are evaluated with Flopy codes.

This webinar covers an applied case of regional groundwater modeling with a group of regional fractures that define areas of small cell size. The applied case covers the construction of a groundwater model with a refinement that ranges from 100m on the outer parts of the model to 15 meters in the area close to the faults.

The application of PEST to real conditions is a challenge since it involves the setup of the automated parameter estimation and the complexities of the groundwater response. We have developed an applied case of automatic parameter estimation on a groundwater flow model related to a site drainage with 6 pumps and steel sheet piles. The calibration is based on observations from a pumping well and two outer monitoring points over a period of 120 days. The case involves as well the sensitivity analysis of hydraulic conductivities from heterogeneous soil and sheet piles together with the conductance of neighboring water channels.

There are many topics to take into account when we create a groundwater flow model. One of the most important is to represent the available geological information into the distribution the hydraulic parameters. We have researched a simple and reproducible workflow to create a geological model from point shapefile and insert the modeled geological units into Model Muse with corresponding K values. This webinar cover the whole procedure to create a geological model with Gempy and Aquifer App and the necessary codes to create a xyz shapefile that is later imported in Model Muse with the Kx value extracted from the attribute table.

Aquifer App offers a friendly, clean and powerful way to create Gempy scripts for geological modeling. We have developed an applied case of a 3D petroleum system model for a part of the Williston Basin, USA that contains the Nesson Anticline. Top layer information was provided as raster format where elevation and orientation for random points were extracted with Python codes using the Gemgis package and exported in Gempy input file format. On Aquifer App the processed CSV files were inserted and the geological model was set up with the corresponding geological sequence. 

Lithium is present in some salt lakes along with other brines in different concentrations. In order to simulate the right groundwater flow regime of Lithium brine along other brines we need to model variable density flow from multiple species. This type of modeling is possible on the latest versions of Modflow 6 with the BUY package and Model Muse. 

We have developed an applied case of variable density flow modeling on a andean salt lake that is pumped from 3 wells in 10 years. The model has a grid refinement close to the lake and works with 6 brines: B(OH)3, NaHCO3, NaCl, KHSO4, LiHSO4 and NaHSO4. The example implements the BUY package with all the required parameters together with the configuration of the boundary conditions and initial conditions. Concentration and water balances are processed with Python to evaluate the brine migration over time and the impact of pumping on the water balance.

We have developed an applied case of automatic calibration with PEST over a MODFLOW 6 model for a pumping test. The model represents a 72 hour pumping test over a confined aquifer with an observation point located 11 meters from the well.  Model Muse was used for the model construction, Pest activation and parameter setup.

We have developed an applied case of automatic calibration with PEST over a MODFLOW 6 model for a pumping test. The model represents a 72 hour pumping test over a confined aquifer with an observation point located 11 meters from the well.  Model Muse was used for the model construction, Pest activation and parameter setup.

This webinar covers the procedure to implement a neural network based on a set of parameters set with corresponding head values; the study case is on a transient pumping test model with an observation point located around 10 meters away from the pump well. The analysis predicts the calibration parameters from the spatially interpolated observed head values. 

Sensitivity analysis is referred 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 (point 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. the Since the study case it’s a transient model the sensitivities will vary over time and stage of pumping/recovery.

We have done an applied groundwater model for a pumping test on a confined aquifer. The wellsite is located on the Clairborne aquifer (Georgia, USA) and has two observations on different layers besides the well itself. The aquifer test has two periods of pumping and recovery that last 3 days each one and the numerical model was constructed with MODFLOW-6 based on a Python script with the FloPy package. Comparison among observed and simulated wells were done with plots in Matplotlib.

An applied case for the recognition of missing crop vegetation areas based on a drone orthophoto. Contours have been identified from an enhanced combination of raster bands with a marching squares method to find constant valued contours and then exported as geospatial polygons.

NetCDF has become a popular choice for storing and delivering precipitation and water resources related data. Its capacities to store multiple geospatial raster layers over time allow another level of abstraction on data analysis, however the format is of limited use on normal desktop applications and most times we are required to use a programming language such as Python or R.

We have done an applied example of exploration analysis from a NetCDF file that has the CHIRPS dataset for year 2022. The dataset is opened with Python and Rasterio and clipped to country extent where an interactive visualization of precipitation is performed. Finally, a daily precipitation hydrograph is generated over the coordinates of a given city.

If you want to speed some steps in groundwater modeling you might be interested in the tools presented on this webinar. We have developed some online tools that replace intense and time consuming tasks on desktop software and web servers and provide vector and raster data on the formats required by Model Muse. The webinar covers the steps of DEM generation as Surfer Grid File (*.grd) with the definition of watersheds and river network based on two online services provided by Hatarilabs.

Modeling tidal waves on groundwater flow models is a challenge due to the several stress periods required to represent the wave and the response on the aquifer system. Standard tools on GUI software fall short to implement multiple stress periods, multiple boundary condition parameters and represent selected output with the tidal elevations.

We have done an “hybrid” approach where a coastal aquifer is built on Model Muse and later imported by Flopy where the boundary conditions that represent the sea elevation is replaced by a time series, new stress periods are inserted, workspace has changed and the model is simulated. The applied case ends up with the head representation and the comparison among the tidal elevation and the piezometers located at different distances and depths.

Large amount of spatial data is indexed and delivered through files in Hierarchical Data Format (HDF). These files are compatible with desktop GIS software as QGIS but they are not so easy to open/read/process with standard Python libraries as Rasterio, or with dedicated libraries. On our research we found the spatial functionality on the powerful GDAL binaries and library for Python. 

We have done an applied case for the download, reproject (to WGS84) and clip to the area of interest of several MYD16A2 actual evapotranspiration images. We have also create an interactive visualization of the clipped images that allow us to examine the precipitation trends and the challenges in the use of these data products. 

Having more tools and managing more data enhances us to reach a superior level of data analysis. This time we have developed an applied case of automatic calibration with PEST of a previously manual calibrated MF6 model on a hillslope area. Our main purpose was to analyze the increase / decrease in the calibration statistics for the automatic versus the manual calibration model and discuss what the new parameters tell us from the original hydrogeological conceptual model.