Presentation by Hélène Boisgontier (Deltares) at the Symposium Models and decision-making in the wake of climate uncertainties, during the Deltares Software Days - Kampala 2023 (DSD-Kampala 2023). Wednesday, 4 October 2023, Kampala, Uganda.

1. Hélène Boisgontier
Deltares Software Days – Kampala 2023
Modelling in a data scarce
environment: the story of
HydroMT

2. Contents
• Why HydroMT ?
• About HydroMT
• Demo: Building a model with HydroMT
2

3. Why HydroMT ?

4. Why do we need models?
We (approximately) know what happened…
• In the past
• At a certain location
4

5. Why do we need models?
But what will happen in the future?
• Tomorrow
• Next week
• Next year(s): E.g. climate change
And what happens at other locations?
What are the causes of current issues
(water scarcity, poor quality…)?
Or are the land-use management
plans or mitigation measures efficient
and sustainable?
• Reforestation
• Increase of agricultural land…
5
?
Climate Change Population growth

6. Why do we need models?
Model can help with:
• Better understanding of a complex system
• Assess effects of scenarios or measures and strategies
• Predict into the future
Model can have different forms:
• Simple / lumped
• Complex / gridded
6

7. Models need data…
• Model construction data
− Base model data (often static or do not vary much in time)
− E.g.: elevation, land-use, river cross-sections…
• Model execution data
− Data to execute the model, like boundary or initial conditions
(often dynamic)
− E.g.: meteorological data (precipitation, wind…), hydrological
data (discharge, groundwater recharge…)
• Model validation data:
− Data to estimate the efficiency of the model, to calibrate and
validate it.
− E.g.: discharge, water level, flood maps…
7

8. Classical approach
1) Collect local data
2) Collect more local data
3) Collect even more local data
4) Analyse data
5) Build a model
6) Throw away 80% of the collected data
1) Month 1-3
2) Month 3-6
3) Month 6
4) Month 7
5) Month 8-12
6) Month 12
8
Models need data…
Time

9. Rationale
Examples of (new) datasets
2015 2017
• (Good) data becomes more widely available
• At higher temporal and spatial resolution
• As the data improves, we want to improve our models

10. Rationale
• (Good) data becomes more widely available
• At higher temporal and spatial resolution
• As the data improves, we want to improve our models
• Need for tools that:
− Operate on these datasets
− Are easy to use (for modelling experts (!))
− Support the modellers by doing the “hard” & “boring” work
− Support developments in enhancing these datasets
− Support re-using methods and workflows for different models
• Hence, HydroMT was born!
10
GIS
processing

11. Classical approach
1) Collect local data
2) Collect more local data
3) Collect even more local data
4) Analyse data
5) Build a model
6) Throw away 80% of the collected data
New approach
1) Build an initial model base on available
(global) data
2) Discuss where improvements are required and
which data is missing
3) Collect local data
4) Analyse data
5) Improve the model with the new data
11
Model approach with hydroMT

12. About HydroMT
12 October 2022

13. About HydroMT
HydroMT (Hydro Model Tools) is an open-source Python package at the interface between
data, users, and water system models.
It includes GIS, hydrological, statistical and plotting methods needed for rapidly building inter-connected
environmental models for hydrology, water quality, groundwater, water resources and environmental
impacts applications.
13
Build Update Plot Stat
Cloud-ready
Scalable
Reproducible
Flexible
Fast

14. About HydroMT
• Open Source
• Available on Github
• MIT license
• Releases on pypi and conda for easier installation
• Online documentation and interactive examples
with Binder
• Works via command line
14
> hydromt build wflow “my_model” “{‘basin’: [x,y]}”
-i wflow_build.ini -d data_catalog.yml -vv

15. About HydroMT
• Data-centred model building with HydroMT
− Rapid model building/updating globally
15
Useful in quick scan analysis
Ventiane Laos example: flood hazard and impact mapping
wflow + SFINCS + Delft-FIAT
Useful in data-scarce regions
Coumpound flood modelling Mozambique

16. About HydroMT
• Data-centred model building with HydroMT
− Rapid model building /updating globally
− Small to large applications
16
AXA Western Europe
World Water Quality Assessment

17. About HydroMT
• Data-centred model building with HydroMT
− Rapid model building /updating globally
− Small to large applications
− Operate on global and local datasets (data harmonization)
17
Global population grid
Mines locations
Regional statistics
Water Quality Diagnostic in Peru
Mix of 40+ emission data from global and local sources

18. About HydroMT
• Data-centred model building with HydroMT
− Rapid model building /updating globally
− Small to large applications
− Operate on global and local datasets (data harmonization)
− Support developments in enhancing these local datasets
18
BMA Bangkok
Improving local data to build Delft3DFM models

19. About HydroMT
• Data-centred model building with HydroMT
− Rapid model building /updating globally
− Small to large applications
− Operate on global and local datasets (data harmonization)
− Support developments in enhancing these local datasets
− Modular tool: methods and workflows can be reused for different
models (plugins)
19

20. Steps to run HydroMT

21. Building a model
• Steps to build a model
1. Select the data you want to use, download if needed,
and prepare a data catalog or use a pre-defined one.
2. Select your region of interest.
3. Select the model components you want to build
and the options (data source, resampling method
etc.) by preparing a model configuration file or
use a pre-defined one.
4. Build your model using the Command Line Interface (CLI) or Python.
21
HydroMT documentation:
https://deltares.github.io/hydromt/latest/user_guide/model_build.html
> hydromt build wflow “my_model” “{‘basin’: [x,y]}” -i wflow_build.ini -d data_catalog.yml -vv

22. Demo video

23. Building a first wflow model for the Mara river

24. Building another one for Mbalangeti river

25. Updating the climate data

26. Building a Delft3D FM model

27. Take away

28. Using HydroMT
• Approach data at the center of model building
− First get a model from (global) available data
− Improve with local data
• Usage
− Many types of projects and a lot of applications use HydroMT directly
− Tools powered by HydroMT: Climate Stress Test Toolbox, FloodAdapt
• Available plugins
− wflow
− D-Flow FM (Delft3D FM Suite)
− SFINCS
− Delft-FIAT
− DELWAQ (kernel of the D-Water Quality and D-Emissions modules of the Delft3D FM Suite)
28

29. Upcoming features
• Data: reduce effort to download data and write data catalogs
− Use data from public clouds like Google GCS or Amazon AWS
− Use data available via API like OpenStreetMap or Copernicus
− Use directly download-able data through https like wind atlas or many more
• Models: make it easier to prepare data without a plugin
− ‘Generic’ models like GridModel (regular grid), MeshModel (unstructured grid), VectorModel (lumped and
semi-distributed), NetworkModel (networks)
− Generic models helps prepare all data required but in common GIS formats rather than model formats
• Others:
− Commands and tools to help analyze or export and share data
− Commands and tools to help quickly visualize and analyze model results
− Conversion commands to convert model data to common GIS formats (new data or connect models)
29

30. Links HydroMT and HydroMT-wflow
• HydroMT docs: https://deltares.github.io/hydromt/latest/
• Github HydroMT: https://github.com/Deltares/hydromt
• Plugins:
− HydroMT wflow: https://deltares.github.io/hydromt_wflow/
− HydroMT SFINCS: https://deltares.github.io/hydromt_sfincs/
− HydroMT FIAT: https://deltares.github.io/hydromt_fiat/
− HydroMT DELWAQ: https://deltares.github.io/hydromt_delwaq/
− HydroMT D-Flow FM: https://deltares.github.io/hydromt_delft3dfm/

31. Thank you for your attention!
www.deltares.nl
info@deltares.nl
@deltares
@deltares
linkedin.com/company/deltares
facebook.com/deltaresNL