DSD-INT 2023 Hydrology User Days - Presentations - Day 2
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The Day 2 program of the Hydrology Suite International User Days 2023 includes several presentations on hydrological modeling techniques such as Wflow and Hydromt, with discussions on their applications and advancements. Key topics covered include machine learning, flood hazard modeling, and the democratization of global water resource information through platforms like Global Water Watch. The event features multiple expert speakers, including hydrologists and researchers, who emphasize the importance of these models in understanding and managing water resources amid challenges like climate change.
![Building a model
Steps to build a model
1. Select the data you want to use by
preparing a data catalog or using 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 using a pre-
defined one
4. Build your model using the Command
Line Interface (CLI) or Python
> hydromt build model “my_model” “{‘basin’: [x,y]}” -i model_build.ini -d data_catalog.yml -vv
43](https://image.slidesharecdn.com/dsd-int2023-hydrologyuserdays-presentations-day2-240120111121-dc71c2d8/85/DSD-INT-2023-Hydrology-User-Days-Presentations-Day-2-42-320.jpg)
![2. Select your region of interest
• Select the region of interest for which you
would like to build a wflow model
• The type of region:
− basin: full basin until sea outlet (eg. Rhine)
− subbasin: sub-basin until a specific point
outlet (e.g Moselle, tributary of the Rhine)
• Defining the location of your region: options:
− Point location [x, y]
− Bounding box [xmin, ymin, xmax, ymax]
− Geometry file ‘my_region.shp’
− Basin ID [ID1]
HydroMT documentation:
https://deltares.github.io/hydromt/latest/user_guide/model_region.html
45](https://image.slidesharecdn.com/dsd-int2023-hydrologyuserdays-presentations-day2-240120111121-dc71c2d8/85/DSD-INT-2023-Hydrology-User-Days-Presentations-Day-2-44-320.jpg)
![4. Run HydroMT
• Build your model using the HydroMT CLI (see example below)
− Call hydromt
− Select build to prepare a model from scratch to update an existing model
− Select which type or model, here wflow
− Path where the generated model will be stored: e.g. folder called “my_model”
− Select the region of interest, e.g. for a basin containing point x,y: “{‘basin’: [x,y]}”
− Path to the model configuration file listing the components to build with the –i option: e.g. -i wflow_build.ini
− Path to the data catalog with the input data sources with the –d option (several catalogs possible),
e.g. -d data_catalog.yml
− Change verbosity with -v option, e.g. -vv for extra verbosity
> hydromt build wflow “my_model” “{‘basin’: [x,y]}” -i wflow_build.ini -d data_catalog.yml -vv
HydroMT documentation:
https://deltares.github.io/hydromt_wflow/latest/user_guide/wflow_build.html
47](https://image.slidesharecdn.com/dsd-int2023-hydrologyuserdays-presentations-day2-240120111121-dc71c2d8/85/DSD-INT-2023-Hydrology-User-Days-Presentations-Day-2-46-320.jpg)
![Problem statement (cont.)
Page 3
van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden,
C., and Russell, B.: wflow_sbm v0.6.1, a spatially distributed hydrologic model: from global data to local applications, Geosci. Model Dev.
Discuss. [preprint], https://doi.org/10.5194/gmd-2022-182, in review, 2022.
Challenges:
• Calibrate fKh0?
• Uniform fKh0?
• Large-scale model?
Available PTFs are still insufficient
(Araya et al., 2019)
fKh0
Soil
Properties PTF
wflow_sbm
(Deltares)
175](https://image.slidesharecdn.com/dsd-int2023-hydrologyuserdays-presentations-day2-240120111121-dc71c2d8/85/DSD-INT-2023-Hydrology-User-Days-Presentations-Day-2-172-320.jpg)
DSD-INT 2023 Hydrology User Days - Presentations - Day 2
- 1. Day 2 Hydrology Suite International User Days 2023
- 6. Day 2 – MORNING PROGRAMME Arrival (8:30-9:00) wflow/HydroMT User Day 9:00 – 9:10 Opening and Welcome Hessel Winsemius, Hydrologist at Deltares 9:10 – 9:35 Introduction to wflow and HydroMT-wflow: Overview and New Developments Ali Meshgi, Product Owner at Deltares 9:35 – 10:00 Demo HydroMT-wflow, building a model from scratch Joost Buitink, Hydrologist at Deltares 10:00 – 10:20 Global Water Watch, a platform supporting the democratization of information about global water resources Björn Backeberg, Senior researcher at Deltares 10:20 – 10:40 Coffee break 10:40 – 11:00 Multi-threading in wflow_sbm: rapid derivation and model runtimes for Europe on high-performance clusters Ruben Imhoff, hydrometeorologist at Deltares 11:00 – 11:20 Application of wflow for water balance modelling of surface reservoirs in Burkina Faso Daan te Witt, Water management consultant at Witteveen & Bos 11:20 – 11:40 wflow_sbm application for one-way model coupling with groundwater flow model in Bandung, Indonesia. Steven Rusli, PhD Student at Wageningen University 11:40 – 12:00 Flood hazard modelling with coupled wflow and SFINCS models, supported by HydroMT Dirk Eilander, Hydrologist at Deltares/assistant professor at VU Amsterdam 12:00 – 12:20 Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability Awad Mohammed Ali - PhD student at Wageningen University 12:20 – 12:30 Closing remarks Hessel Winsemius, Hydrologist at Deltares Group photo and Lunch break (12:30 – 13:30) 7
- 7. Ali Meshgi Deltares Joost Buitink Deltares Björn Backeberg Deltares Ruben Imhoff Deltares Daan te Witt Witteveen+Bos Steven Rusli Wageningen University Dirk Eilander Deltares/VU Awad M. Ali Wageningen University Speakers of Hydrology Suite User Days (Day 2)
- 8. Ali Meshgi Ali Meshgi is a senior data scientist and urban groundwater hydrologist at Deltares, Department of Catchment and Urban Hydrology since 2018. He holds a Ph.D. in Civil and Environmental Engineering from the National University of Singapore and has 15 years of experience in both the private and government sectors, as well as in universities. His expertise spans various areas, including groundwater and hydrological studies, computer modelling and programming, data analytics, machine learning, project management/lead, and product owner. Currently, he holds the role of product owner for HydroMT and wflow. Introduction to wflow and HydroMT-wflow: Overview and New Developments Product Owner at Deltares
- 9. Overview and New Developments 29-11-2023 wflow and HydroMT-wflow
- 10. Why do we need models? 11 See level rise Subsidence Population growth Urbanization Climate change This requires all the smart minds and all the smart technology
- 11. 12 Why do we need models? We (approximately) know what happened… • In the past • At a certain location
- 12. 13 ? ➢ What will happen in the future? ➢ And what happens at other locations? ➢ What are the causes of current issues? ➢ Are the land-use management plans and mitigation measures efficient and sustainable? Why do we need models? Climate change Population growth
- 13. 14 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 ➢ …
- 14. • Open Source & free • For catchment-scale rainfall-runoff modelling • Fully distributed (grid-based) • Online documentation and examples • Different hydrological modelling concepts Hydrological modeling with wflow: General info 15
- 15. HBV SBM 16 Hydrological modeling with wflow: Concept Currently, most developments are in SBM concept • Conceptual • Based on HBV’96 concept • Implemented in the wflow framework by Deltares HBV concept • Flexible process-based model FLEX-Topo concept • Physically based • Based on Topog-SBM concept • Derived from the CQflow • Developed and maintained by Deltares SBM concept
- 16. 17 Hydrological modeling with wflow: Inputs & Outputs
- 17. Hydrological modeling with wflow: Use case examples 18 Sediment inflow to reservoirs/lakes Groundwater recharge calculation Pollutant fate and transport input Reservoir inflow calculation Data-scarce region use Large-scale application Inputs for flood modelling
- 18. Hydrological modeling with wflow 19 o Improve computational efficiency o Add Floodplain routing o Improve clearness of documentations o Enhance software stability Recent Developments Moving towards v1.0 by: o Providing comprehensive documentation o Ensuring the software is stable and free of critical bugs o Defining a support and maintenance plan Upcoming features and planning Other plannings: o Including water demand analysis o Better quantifications of NBS o Online coupling with MODFLOW & RIBASIM
- 19. Key Characteristics & Vision Integration Capability • Integrates with other models • Open source & free • Easy to set up using HydroMT-wflow • Clear documentation and support • Windows and Linux compatibility Usability • Simulates a wide range of hydrological processes and phenomena Versatility • Provides reliable results under a variety of conditions Robustness • Ongoing research on creating an emulator for wflow using a graph neural network Computational Efficiency • Ongoing pilot study on cloud storage Data Storage Solution • Works at different spatial and temporal scales Scalability • Accurately predicts hydrological variables Accuracy Hydrological modeling with wflow: Key Characteristics & Vision
- 20. 21 Hydrological modeling with wflow: Modelling Process • Physical catchment characteristics • Hydro- meteorological data • Satellite data Data collection • Setting up the model from data and the understanding of the relevant hydrological processes Model set-up • Getting values for the parameters Calibration • Confronting the model output with observations Evaluation
- 21. 22 Models need data… • Base model data: ➢ E.g.: elevation, land-use, river cross-sections… Construction data • Data to execute the model ➢ E.g.: meteorological data (precipitation, wind…), Execution data • Data to estimate the efficiency of the model ➢ E.g.: discharge, water level, flood maps… Validation data
- 22. 23 Time 1) Month 1-3 2) Month 3-6 3) Month 6 4) Month 7 5) Month 8-12 6) Month 12 The classical approach for Water Resources Management projects: 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 Models need data…
- 23. 24 GIS processing HydroMT was born! Models need data… ➢ Operate on these datasets. ➢ They are easy to use. ➢ Support the modelers by handling the "hard" and "boring" work. ➢ Support the development of enhancements for these datasets. ➢ Support the reuse of methods and workflows for different models. ➢ Good data becomes more widely available. ➢ Data becomes available at higher temporal and spatial resolutions. ➢ As the data improves, we aim to enhance our models. Need for tools
- 24. 25 ✓ Reproducible ✓ Automated ✓ Fast ✓ Flexible ✓ Scalable Open-source Python package that facilitates automated and reproducible pre- and postprocessing of spatial geoscientific models Build Update Plot Stat HydroMT, Model Building and Analysis: General Info
- 25. 26 HydroMT, Model Building and Analysis: General Info
- 26. 27 HydroMT, Model Building and Analysis: General Info Reproducible Automated Fast Flexible Scalable
- 27. 28 • 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 HydroMT, Model Building and Analysis: General Info
- 28. 29 HydroMT Core wflow Plugin SFINCS Plugin FIAT Plugin DELWAQ Plugin** iMOD Plugin HABITAT Plugin D-Flow FM Plugin* rainfall-runoff & sediment transport modelling water quality & emissions modelling hydrodynamic flood modelling flood impact modelling In development Developed groundwater modelling spatial environmental impact modelling coastal hydrodynamic modelling HydroMT, Model Building and Analysis: Plugins *D-Flow FM is the module of Delft3D FM Suite for hydrodynamic modelling **DELWAQ is the kernel of the D-Water Quality and D-Emissions modules of the Delft3D FM Suite
- 29. 30 HydroMT, Model Building and Analysis: Use case example ❑ Rapid model building /updating globally ❑ Small to large applications ❑ Operate on global and local datasets (data harmonization) ❑ Support developments in enhancing local datasets
- 30. 31 HydroMT, Model Building and Analysis: Use case example ❑ Rapid model building /updating globally ❑ Small to large applications ❑ Operate on global and local datasets (data harmonization) ❑ Support developments in enhancing local datasets
- 31. 32 Mines locations Regional statistics HydroMT, Model Building and Analysis: Use case example ❑ Rapid model building /updating globally ❑ Small to large applications ❑ Operate on global and local datasets (data harmonization) ❑ Support developments in enhancing local datasets Global population grid
- 32. 33 HydroMT, Model Building and Analysis: Use case example ❑ Rapid model building /updating globally ❑ Small to large applications ❑ Operate on global and local datasets (data harmonization) ❑ Support developments in enhancing local datasets
- 33. 34 HydroMT, Model Building and Analysis 1. Improve the versatility and reproducibility of our data catalogue 2. Improve usability by introducing model checking 3. Improve clearness of documentations 4. Improve developer velocity by focusing on CI automation Recent Developments 1. Moving towards v1.0 by: o Stabilising our external API o Defining a support and maintenance plan o Improving the robustness of the code 2. Enabling Cloud support by: o Making data better available for collaboration o Optimising the data for cloud use o Optimising the code to take advantage of cloud architecture Upcoming features and planning
- 34. wflow & HydroMT Development Team Joost Buitink Developer Willem van Verseveld Developer Laurene Bouaziz Developer Brendan Dalmijn Developer Ali Meshgi Product Owner Dirk Eilander Developer Hélène Boisgontier Developer Sam Vente Developer Tjalling de Jong Developer Jaap Langemeijer Developer Anaïs Couasnon Developer
- 35. How do we work? 36 Start Market & research Agile product development Design Capacity planning Development Test Deploy Deliver
- 36. General links for wflow and HydroMT 37 Documentation for HydroMT How to access and install HydroMT Feature application example for HydroMT hydromt@deltares.nl Documentation for wflow How to access and install wflow Feature application example for wflow wflow@deltares.nl HydroMT webpage wflow webpage
- 37. Feedback 38
- 38. 39 Q & A
- 40. Joost Buitink Demo HydroMT-wflow, building a model from scratch Hydrologist at Deltares Joost Buitink works as a hydrologist at Deltares. He applies wflow in different catchments around the world, with a focus on understanding the effects of climate change on the hydrological response. Besides the work in projects, he is also one of the developers of wflow and hydromt_wflow
- 41. Translate data into a wflow model in only a couple of steps! 29-11-2023 Demo HydroMT-wflow
- 42. Building a model Steps to build a model 1. Select the data you want to use by preparing a data catalog or using 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 using a pre- defined one 4. Build your model using the Command Line Interface (CLI) or Python > hydromt build model “my_model” “{‘basin’: [x,y]}” -i model_build.ini -d data_catalog.yml -vv 43
- 43. 1. Select the data you want to use • Select the data you want to use and have it ready in one of the supported raster (tif, ascii, netcdf, zarr…), vector (shp, geojson, gpkg…) or geospatial time-series formats (netcdf, csv…) • If needed, create your own data catalog with a reference to your selected data, including: − Path to the data location − Data type and hydromt driver − Rename and unit conversion − Others… • Or use a pre-defined data catalog: − deltares_data → Deltares P drive − artifact_data − demo_data/data_catalog.yml HydroMT documentation: https://deltares.github.io/hydromt/latest/user_guide/data_prepare_cat.html 44
- 44. 2. Select your region of interest • Select the region of interest for which you would like to build a wflow model • The type of region: − basin: full basin until sea outlet (eg. Rhine) − subbasin: sub-basin until a specific point outlet (e.g Moselle, tributary of the Rhine) • Defining the location of your region: options: − Point location [x, y] − Bounding box [xmin, ymin, xmax, ymax] − Geometry file ‘my_region.shp’ − Basin ID [ID1] HydroMT documentation: https://deltares.github.io/hydromt/latest/user_guide/model_region.html 45
- 45. 3. Select the model components to build • Start from a template configuration file: https://deltares.github.io/hydromt_wflow/latest/user_guide/wflow_build.html#configuration-file • Edit / add / remove sections to select which components to prepare and change the building options such as data source, resampling method, default values, etc. HydroMT documentation: https://deltares.github.io/hydromt/latest/user_guide/model_config.html 46
- 46. 4. Run HydroMT • Build your model using the HydroMT CLI (see example below) − Call hydromt − Select build to prepare a model from scratch to update an existing model − Select which type or model, here wflow − Path where the generated model will be stored: e.g. folder called “my_model” − Select the region of interest, e.g. for a basin containing point x,y: “{‘basin’: [x,y]}” − Path to the model configuration file listing the components to build with the –i option: e.g. -i wflow_build.ini − Path to the data catalog with the input data sources with the –d option (several catalogs possible), e.g. -d data_catalog.yml − Change verbosity with -v option, e.g. -vv for extra verbosity > hydromt build wflow “my_model” “{‘basin’: [x,y]}” -i wflow_build.ini -d data_catalog.yml -vv HydroMT documentation: https://deltares.github.io/hydromt_wflow/latest/user_guide/wflow_build.html 47
- 47. So how does it look like in real life?
- 48. Björn Backeberg Global Water Watch, a platform supporting the democratization of information about global water resources Project Leader at Deltares Björn Backeberg is an experienced ocean expert with over 15 years of experience in academia, applied sciences, and industry. He specializes in understanding how the ocean affects our climate, using computer models, data analysis, and satellite information. He obtained his PhD at the University of Cape Town and played a key role in creating a system to predict ocean conditions in South Africa. Throughout his career, he has held various roles, including being a lead researcher and working on ocean and coastal data management. Currently, he works as a Senior Researcher and Project Leader at Deltares, where he's involved in European Commission projects and manages the BlueEarthData Platform. He's also a member of the Deltares Young Science Council.
- 49. A platform supporting the democratisation of information on water resources 29 November 2023 Global Water Watch Björn Backeberg Marine and Coastal Systems Environmental Hydrodynamics and Forecasting
- 50. Table of contents 51 • Sponsor and partners • The Team • The Challenge • Our Solution • Demo • Roadmap • Conclusion
- 51. Sponsors and partners 52 IMPACT CHALLENGE ON CLIMATE 2020 WATER PEACE AND SECURITY PARTNERSHIP EUROPEAN SPACE AGENCY
- 52. The Team 53
- 53. The challenge 54 HIGH-RESOLUTION DATA ON WATER RESOURCES ARE FREQUENTLY NOT AVAILABLE Example: Reservoirs / Dams Critical infastructure Anticipate water resource trends Data-driven management Peace and secutiry (deliver facts)
- 54. Our solution 55 Open Source Water Datasets Water Analytics Water Stories Support the democratisation of information on water resources • Near real-time methods to estimate reservoir area and volume dynamics from satellite data • Access to global datasets through a hosted web application and API.
- 55. Global Water Watch platform demo Follow along at https://www.globalwaterwatch.earth/map/
- 56. The Global Water Watch Platform 57 Our current reservoirs time series database (1984-2023) includes 71 208 reservoirs
- 59. Global Water Watch 60 The global water watch platform https://www.globalwaterwatch.earth/
- 60. Global Water Watch 61 The global water watch platform https://www.globalwaterwatch.earth/
- 63. Global Water Watch 64 The global water watch platform https://www.globalwaterwatch.earth/
- 70. Global Water Watch 71 The global water watch platform https://www.globalwaterwatch.earth/
- 71. Global Water Watch 72 The global water watch platform https://www.globalwaterwatch.earth/
- 72. Global Water Watch 73 The global water watch platform https://www.globalwaterwatch.earth/
- 73. Global Water Watch 74 The global water watch platform https://www.globalwaterwatch.earth/
- 75. Global Water Watch 76 The global water watch platform https://www.globalwaterwatch.earth/
- 76. Global Water Watch 77 The global water watch platform https://www.globalwaterwatch.earth/
- 77. Global Water Watch 78 The global water watch platform https://www.globalwaterwatch.earth/
- 78. Global Water Watch 79 The global water watch platform https://www.globalwaterwatch.earth/
- 80. Global Water Watch 81 The global water watch platform https://www.globalwaterwatch.earth/
- 81. Global Water Watch 82 The global water watch platform https://www.globalwaterwatch.earth/
- 82. Global Water Watch 83 The global water watch platform https://www.globalwaterwatch.earth/
- 83. Global Water Watch 84 The global water watch platform https://www.globalwaterwatch.earth/
- 85. Global Water Watch 86 The global water watch platform https://www.globalwaterwatch.earth/
- 86. Global Water Watch 87 The global water watch platform https://www.globalwaterwatch.earth/
- 87. Global Water Watch 88 The global water watch platform https://www.globalwaterwatch.earth/
- 88. Global Water Watch 89 The global water watch platform https://www.globalwaterwatch.earth/
- 89. Global Water Watch platform demo For the more technical user https://api.globalwaterwatch.earth/docs
- 90. Leverage the power of Global Water Watch data through our API 91
- 91. Roadmap 92 Platform enhancement and community building • Improve and extend reservoir database • Add AI-based dam detection and mapping • Add uncertainty estimates • Monitor very large reservoirs • Outreach and uptake • Impact stories 2024 Extend platform scope • Add surface water changes • Citizen science capabilities 2025 Extend platform scope • Add data-driven and process modelling based forecasting capabilities • Extend to monitoring and forecasting rivers and lakes 2026
- 92. 93 EARTH OBSERVATIONS MACHINE LEARNING WATER EXPERTISE CLOUD COMPUTING FARMERS POLICY MAKERS GOVERNMENTS INSURANCES HYDROPOWER DRINKING WATER CONSERVATION Summary • Example of co-creation and research to operations • Platform monitors water dynamics on a planetary scale • Intuitive web app with data analytics supporting decision-making and storytelling • API for downstream application development and integration
- 93. Thank you on behalf of the team 94 Join the community: https://www.globalwaterwatch.earth/ Help us improve the platform!
- 95. Delft Software Days Hydrology Suite We continue at:
- 96. Ruben Imhoff Multi-threading in wflow_sbm: rapid derivation and model runtimes for Europe on high-performance clusters Hydrometeorologist at Deltares Ruben Imhoff is a hydrometeorologist working in the Operational Water Management and Early Warning Department at the research institute Deltares. He primarily focuses on hydrological modeling and short-term hydrometeorological forecasting systems for rapidly responsive catchments and urban areas, often utilizing radar-based methods like nowcasting. This aligns with Ruben's academic background, as he recently obtained his Ph.D. in rainfall nowcasting for flood early warning from Wageningen University in the Netherlands.
- 97. Ruben Imhoff Willem van Verseveld Joost Buitink Albrecht Weerts Rapid deployment and model runtimes for Europe on high-performance clusters 29 November 2023 Multi-threading in wflow_sbm
- 98. Table of contents 99 • The ACROSS project: low-latency weather forecasts, climate projections and subsequent hydrological model simulations • Speeding up the wflow_sbm model using Julia’s multi-threading − From Python to Julia − Multi-threading improvements − Tests on Karolina • Rapid deployment of high-resolution hydrological model whenever new forcing is available − Direct interaction with new meteorological forecasts and climate model simulations − Model setup for Europe − Integration of all steps using Streamflow workflow manager − Model validation
- 99. The ACROSS project: low-latency hydrological model simulations with wflow_sbm
- 100. The ACROSS project: low-latency hydrological model simulations with wflow_sbm 101 Forecasts Climate projections Storage Storage Obtain, regrid and pre-process forcing data for wflow_sbm models Data / observations Download data through API Hydrological simulation Post processing
- 101. The ACROSS project: low-latency hydrological model simulations with wflow_sbm 102 System by ECMWF and MPI: “BORGES” (Better ORGanized Earth System model data) data system Needs: • Direct interaction and pre-processing of forcing • Fast model runtimes
- 102. Speeding up the wflow_sbm model using Julia’s multi- threading
- 103. From Python to Julia 104
- 104. Testing Julia’s multi-threading with wflow (for the Rhine) 105 Kinematic wave land and river Kinematic wave land – local inertial river 1D Floodplain routing
- 105. Multi-threading improvements 106 Tested solutions to improve multi-threading: • Minimize overhead by using a minimum ‘basesize’, which sets the number of elements of the iteration space processed by each thread. • Replacing Julia’s @threads macro with the @spawn macro in combination with a minimum ‘basesize’. • Using low overhead threading of Polyester.jl, as a replacement of the native Julia threading functionality. • Setting Julia environment variable JULIA_EXCLUSIVE=1 to pin threads to CPU-cores. For the local-inertial routing: • This routing scheme does not rely on a specific execution order → suited for loop vectorization • Further speedup of computations by using SIMD (Single Instruction, Multiple Data) vectorization and loop reordering, provided by the Julia package LoopVectorization.jl.
- 107. Rapid deployment of high-resolution hydrological model whenever new forcing is available
- 108. Direct interaction with new meteorological forecasts and climate model simulations 109 Forecasts Climate projections On-the-fly regridding and downscaling of forcing variables for wflow FDB BORGES FDB request with pyfdb and pre-processing Karolina HPC wflow_sbm
- 109. Model setup for Europe • High-resolution (1 km2) basin delineation based on MERIT DEM (90x90 m) − Automatic basin delineation using HydroMT (Eilander et al., 2023, Journal of Open Source Software) − Automatic parameterization of land, soil and river parameters (Imhoff et al., 2020, Water Resources Research; Eilander et al., 2021, Hydrology and Earth System Sciences) • Merging of small catchments with larger ones → reduction to 108 basins that can be run in parallel 110 European catchments at 1- km resolution Merged catchments after 12,000 km2 catchment area constraint
- 110. Integration of all steps using StreamFlow workflow manager 111 wflow_sbm models for 108 catchments Run in parallel with Julia multi-threading on Karolina Post processing Forecasts Climate projections FDB BORGES On-the-fly regridding and downscaling of forcing variables for wflow FDB request with pyfdb and pre- processing
- 111. Results 112 • ECMWF forecast speed up in absolute amounts not enormous for Rhine and Meuse, but: − Based on 1 member: with 100 ensemble members, this becomes 100x the shown speed up − Forecast up to 240h in advance, true usage up to 15d and 46d in advance by Rijkswaterstaat − True power in running this for all of Europe at once. With multi-threading and parallel distribution using StreamFlow, this can be done in approx. the same time Meuse Rhine 1 thread 32 threads Speed up 1 thread 32 threads Speed up ERA5 daily run from 1988 – 2022 8.6 h 4.6 h 1.9x 33.2 h 9.3 h 3.6x ECMWF forecast, hourly up to 240 hours 178 s 118 s 1.5x 321 s 156 s 2.1x
- 112. Summary 113 ACROSS project: low-latency weather forecasts, climate projections and subsequent hydrological model simulations − Collaboration with ECMWF and MPI Sped up wflow_sbm by optimally using Julia’s multi-threading − Reduction in overhead with multi-threading (run times do not go up anymore with many threads) − 2 – 11 times faster on top of speed up due to transition to Julia Rapid deployment of high-resolution hydrological model whenever new forcing is available − Direct interaction with new meteorological forecasts and climate model simulations − Model setup for Europe − Integration of all steps using StreamFlow workflow manager
- 114. Extra: Multi-threading tests Moselle River 115
- 115. Daan te Witt Application of wflow for water balance modelling of surface reservoirs in Burkina Faso Hydrologist at Witteveen+Bos Daan te Witt has over 3 years of experience using wflow. Daan graduated in 2021 from Delft University of Technology in Water Management. His master thesis was about the calibration of a wflow model using the spatio-temporal patterns of satellite datasets to simulate the discharge in the Volta river basin in West-Africa. Daan works as a hydrologist at the Water Management department of Witteveen+Bos for 2 years. He set up and applied wflow models for different water resources projects all over the world (Indonesia, Brazil, Guatemala, Kazakhstan, Burkina Faso)
- 116. Rehabilitation of Large Water Reservoirs in Burkina Faso Phase 1: Preliminary design for reservoirs Boulsa, Boussouma, Loumbilla and Toécé wflow user day, 29 Nov 2023
- 117. Goal of the project 118 ˗ Problems in the reservoirs: · Siltation · Large (evaporation) losses · Water quality ˗ Goal: To develop an integrated set of measures to improve: · reservoir storage capacity · limit siltation
- 118. Consortium 119 ˗ Witteveen+Bos ˗ Deltares ˗ Bera ˗ Netics ˗ Bureau M2
- 119. Project area 120 ˗ Burkina Faso ˗ 4 Reservoirs ˗ Volta & Niger catchments ˗ Toécé, Loumbila, Boussouma and Boulsa
- 120. Project area 121 ˗ Burkina Faso ˗ 4 Reservoirs ˗ Volta & Niger catchments ˗ Toécé, Loumbila, Boussouma and Boulsa
- 121. Application of wflow 122 ˗ Simulation of discharge dynamics · Water balance · Extreme value analysis · Effects of climate change ˗ Simulation of erosion and sedimentation processes
- 122. Application of wflow 123 ˗ Simulation of discharge dynamics · Water balance · Extreme value analysis · Effects of climate change ˗ Simulation of erosion and sedimentation processes
- 123. Model setup 124 ˗ Modules · wflow_sbm · Reservoir module · Sediment module ˗ Data · Model basis was set up using HydroMT · We added precipitation data (ERA5, CHIPRS, local data) · We added evaporation data (ERA5, GLEAM, local data, and more) · We added reservoir data (GRanD/HydroLakes)
- 124. Reservoirs & local forcing data 125
- 125. Precipitation data: point-to-pixel analysis 126
- 126. Evaporation data 127 ˗ Important factor in this region ˗ Huge differences between available datasets ˗ Little local data available ˗ Ended up using the local data but corrected by ERA5
- 127. Calibration of the hydrological model 128 ˗ Little (reliable) discharge data was available ˗ Why are we not able to simulate the first discharge peak of the year? · Crust formation? · Low infiltration capacity? ˗ Peak flows are often overestimated
- 128. Calibration of the reservoir module 130 ˗ Calibration of reservoirs using reservoir parameters: · max volume · target fraction full · target minimum full fraction · demand · max release · area
- 129. Calibration of the reservoir module 131 ˗ Only WL-timeseries for 2 reservoirs ˗ Used satellite data to detect the surface area of other 2 reservoirs
- 130. Calibration of the reservoir module 132 ˗ Would be nice to add: · Infiltration · Abstractions · HAV-relations
- 131. Result 134 ˗ Water balance per reservoir · Inflows · Outflows · Reservoir volume
- 132. What we also did 135 ˗ Set up an erosion/sediment model · Identify erosion hotspots · Estimate the sedimentation ˗ Estimated effects of climate change ˗ Calculated the effect of different measures on the water balance · Solar panels, dredging, catchment reforestation, raising the dam lvl., etc.
- 133. Conclusions & Recommendations 136 ˗ Easy to set up a hydrological model in a data scarce regions using wflow ˗ However, obtaining good forcing data remains an issue ˗ And model calibration with limited calibration data is a challenge ˗ Nevertheless, we were able to set up a good reservoir water balance and sediment model, which formed the basis for developing measures for the reservoirs ˗ It would be nice to add some functionalities to the reservoir module
- 134. Other project in which we applied wflow Country Vrbas river Croatia Masterplan Welang river Indonesia Rehabilitation of 4 reservoirs in Burkina Faso Burkina Faso Backwater curve impact fence Rio Las Vacas Guatemala Masterplan remining Gelado dam Brasil Water balance of a Lagoon Ivory Coast Disaster Risk Reduction Kazachstan Kazachstan Water as leverage Cartagena Colombia Questions? 137
- 135. www.witteveenbos.com
- 136. Steven Rusli wflow_sbm application for one-way model coupling with groundwater flow model in Bandung, Indonesia. PhD Student at Wageningen University Steven Reinaldo Rusli obtained his bachelor's degree in the Civil Engineering Department at Universitas Katolik Parahyangan, Indonesia. His undergraduate thesis delved into flood control in a tidal-influenced river on Borneo Island. Following stints in engineering consultation and part-time teaching, he pursued a double degree in Water Resources Engineering from Universitas Katolik Parahyangan, Indonesia, and Hohai University, China, graduating in 2015. His master's thesis focused on assessing parameter uncertainties in hydrological models across different temporal variabilities. In 2019, he embarked on his PhD at Wageningen University and Research, researching groundwater flow modeling in the data-scarce Bandung groundwater basin, a region heavily reliant on groundwater.
- 137. Hydrology Suite User Days – wflow and HydroMT wflow_sbm application for one-way model coupling with groundwater flow model in Bandung, Indonesia 140
- 138. • To understand the current situation in the designated study area Motivation Rusli, et al. (2021) 141
- 139. Motivation • Bandung groundwater basin, Indonesia ▪ ±1700 km2 (two cities and three regencies) ▪ Steep slope (±2500 m ASL to ±650 m ASL) ▪ ±2000 mm of annual rainfall ▪ Discharge measurement at Nanjung ±75 m3/s ▪ Volcanic deposits (breccia, tuffs, and clays) ▪ Multi-layer aquifers (lake Bandung) ▪ ±11 million population by the end of 2023 ▪ ±65% of domestic water supply from groundwater ▪ Rapid industrial development ▪ Estimated groundwater abstraction of 400Mm3/year 142
- 140. Motivation • Estimating water balance components to determine water storage status Data Average value (mm/day) Precipitation Rainfall station 5.40 SACA&D 6.47 TRMM 7.50 CHIRPS 7.80 Actual evaporation GLEAM 3.13 ERA5 2.82 Discharge Observation 3.65 GloFAS-ERA5 6.12 Groundwater abstraction estimates 0.57 Basin storage changes ‘lowest’ combination -4.42 ‘highest’ combination +0.76 𝐼 − 𝑂 = ∆𝑆 Precipitation Evaporation, river discharge, groundwater abstraction Storage (groundwater, surface water, soil moisture) 143
- 141. Overview of the wflow_sbm model setup 144
- 142. wflow_sbm model simulation results • Average discharge: • Observed: 83.16 m3/s • Simulated: 96.48 m3/s • Evaluation metrics: • KGE: 0.46 • NSE: -0.58 • RMSE: 90.78 m3/s • Context • MaxLeakage • Fluctuation pattern • Observation data quality Rusli, et al. (2023a) 145
- 143. One-way coupling of wflow_sbm and MODFLOW Hydrological simulation using wflow_sbm Groundwater flow simulation using MODFLOW Model parameterization Model forcing Model results CHIRPS rainfall ERA5 PET Groundwater recharge Groundwater recharge (Pedo)transfer functions with global dataset Field-based parameter estimates Model transient spin-up Soil moisture change Basin-integrated groundwater budget Groundwater storage change Water storage change (simulation) GRACE dataset on water storage change Basin-scale groundwater status assessment Evaluation comparison Calibration data River discharge Groundwater table elevation data One-way model coupling Rusli, et al. (2023a) 146
- 144. Results: groundwater recharge Result: (newly found) correlation between recharge and slope Rusli, et al. (2023b) Rusli, et al. (2023b) 147
- 145. Results: soil moisture Rusli, et al. (2023a) 148
- 146. Results: total water storage changes Result: comparable response and behavior of water storage changes Limitation: spatial scale & domain differences (±1700 km2 vs ±12000 km2) Rusli, et al. (2023a) 149
- 147. On-going research: EWT data involvement Rusli, et al. (2023b) Rusli, et al. (2023b) Rusli, et al. (2023b) 150
- 148. On-going research: aquifer interaction Rusli, et al. (2023b) Rusli, et al. (2023b) 151
- 149. On-going research: future projection Rusli, et al. (2023c) 152
- 150. Take-home messages ▪ wflow_sbm flexibility to be applied in many areas ▪ The possibility of one-way coupling the wflow_sbm model with groundwater flow model (MODFLOW) ▪ Opportunity to calibrate the one-way coupled model with satellite data (GRACE) ▪ Even further usage is feasible (aquifer interaction, future projection, etc) 153
- 151. References ▪ Rusli, S., A. Weerts, A. Taufiq, and V. Bense (2021). “Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin”. Journal of Hydrology: Regional Studies 37, 100911. DOI: https://doi.org/10.1016/j.ejrh.2021.100911 ▪ Rusli, S., V. Bense, A. Taufiq, and A. Weerts (2023a). “Quantifying basin-scale changes in groundwater storage using GRACE and one-way coupled hydrological and groundwater flow model in the data-scarce Bandung groundwater basin, Indonesia”. Groundwater for Sustainable Development 22, 100953. DOI: https://doi.org/10.1016/j.gsd.2023.100953 ▪ Rusli, S., A. Weerts, S. Mustafa, D. Irawan, A. Taufiq, and V. Bense (2023b). “Quantifying aquifer interaction using numerical groundwater flow model evaluated by environmental water tracer data: Application to the data-scarce area of Bandung groundwater basin, West Java, Indonesia”. Journal of Hydrology: Regional Studies. accepted ▪ Rusli, S., V. Bense, S. Mustafa, and A. Weerts (2024). “Assessing the significance of global climate model projections and groundwater abstraction scenarios on future basin-scale groundwater availability”. Hydrology and Earth System Sciences. In preparation. 154
- 152. Dirk Eilander Flood hazard modelling with coupled wflow and SFINCS models, supported by HydroMT Hydrologist at Deltares, Assistant Professor at VU Dirk Eilander is an expert in flood risk modeling. His work focuses on improving flood risk management anywhere globally in co-creation with stakeholders, through development of models, methods, and datasets. He obtained his PhD degree at the Vrije Universiteit on large- scale compound flood risk modeling in coastal deltas. Currently, he combines his work as hydrologist and lead- developer of HydroMT at Deltares with an Assistant professorship position at the Vrije Universiteit in Amsterdam.
- 153. Dirk Eilander Email: dirk.eilander@deltares.nl Flood hazard modelling with coupled wflow and SFINCS models, supported by HydroMT SOURCE: Janicki, J. (2019) Flood extent delineation in Mozambique after Cyclone Idai using Sentinel-1 data.
- 154. Flood hazard modelling - intro 157 • Over the past 50 years (1970-2019) floods have caused an average of 6,600 deaths and 22.32 billion USD in losses per year (WMO, 2021) • Understanding the flood hazard is key to prevention, mitigation, preparedness and response (Sendai Framework)
- 155. SFINCS 158 • Fast hydrodynamic computations: − Bathtub models too simple − Advanced models like D-Flow FM module of the Delft3D FM Suite: high detail, but too slow for quick scan analysis for 1000km coast − Alternative: reduced-complexity solvers • Local Inertial equations based on Bates et al. 2010 • Added processes for coastal compound flooding (e.g., wind drag, waves) • Subgrid schematisation • Spatially varying infiltration & roughness Leijnse et al. (2021) – Coast Eng
- 156. 159 HydroMT Setup reproducible models Quick setup based on global data Easy to setup model experiments Easy to improve with local data 159
- 157. Case study 1: Sofala Mozambique Reuters Reuters BBC
- 158. wflow – SFINCS model coupling with HydroMT 161 • Reproducible sensitivity experiment with wflow-SFINCS model chain − Rainfall data (ERA5, Chrips) − Land cover data (Globcover, VITO) • Workflow: − Build SFINCS model (incl river inflow points) − Build wflow model based on the SFINCS domain
- 159. Sensitivity analyses with HydroMT 162 • Reproducible sensitivity experiment with wflow-SFINCS model chain − Rainfall data (ERA5, Chrips) − Land cover data (Globcover, VITO) • Workflow: − Build SFINCS model (incl river inflow points) − Build wflow model based on the SFINCS domain − 4x Update wflow and run simulation − 4x Update SFINCS and run simulation (2 shown) • Results: − (obvious) large sensitivity to rainfall product
- 160. FAIR modelling with HydroMT 163 https://github.com/DirkEilander/hydromt-wflow-sfincs/
- 161. Case study 2: Toamasina, Madagascar 164 Reuters
- 162. A globally-applicable framework for compound flood hazard modelling 165 Eilander et al. (2023) – NHESS • GTSM forced with ERA5 • wflow forced with ERA5 HydroMT SFINCS wflow • DTM • Bathymetry • Hydrography • Landuse / landcover
- 163. A globally-applicable framework for compound flood hazard modelling 166 Eilander et al. (2023) – NHESS • GTSM forced with ERA5 • wflow forced with ERA5 HydroMT SFINCS wflow • DTM • Bathymetry • Hydrography • Landuse / landcover • Highres DTM • Canal / stream bathymetry
- 164. Flood model building – local data 167
- 165. Flood hazard modelling - workflow 168 (~100)
- 166. Flood adaptation – rehabilitation of drainage system 169 BASE Canal rehabilitation Flood reduction
- 167. Case study 3 - Operational forecasting in Australia 170 170 Leijnse et al. (2022) – ICCE22 conference
- 168. Take home message 171 • Coupled wflow + SFINCS models, supported by HydroMT, provide a strong toolset for (compound) flood hazard modeling • With HydroMT, the model setup up: − is reproducible − is fast − can be updated to facilitate model experiments (e.g., different forcing, land use) − can easily be improved with local data − is scalable Dirk Eilander Email: dirk.eilander@deltares.nl
- 169. Awad Mohammed Ali Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability PhD Student at Wageningen University Awad M. Ali, a Sudanese Civil Engineering professional, obtained his Bachelor of Science from the University of Khartoum in 2018 and completed a Master's program in Earth and Environment at Wageningen in August 2023. In the three years between his undergraduate and postgraduate studies, Awad worked at the Water Research Centre in Sudan, contributing to research in hydrological drought and dams’ management in the Nile basin. His expertise includes remote sensing in ungauged basins, hydrological modelling, and machine learning integration. Currently, he's pursuing a Ph.D., focusing on developing a global dataset for transparent water management in transboundary basins, building on his four-month internship at Deltares this year.
- 170. Awad M. Ali Supervisors (Deltares): Ruben Imhoff and Albrecht H. Weerts Supervisor (WUR): Lieke A. Melsen 29 November 2023 Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability Hydrology Suite User Days Day 2 - wflow and HydroMT 173
- 171. (Pedo)transfer Functions (PTFs) Imhoff, R. O., van Verseveld, W. J., van Osnabrugge, B., & Weerts, A. H. (2020). Scaling point-scale (pedo) transfer functions to seamless large- domain parameter estimates for high-resolution distributed hydrologic modeling: An example for the Rhine River. Water Resources Research, 56, e2019WR026807. https://doi.org/10. 1029/2019WR026807 • PTFs are predictive functions that estimate soil hydraulic properties from structural soil data • To lower the number of calibrated global parameters • A sensitive parameter without a PTF is KsatHorFrac (fKh0) (Wannasin et al., 2021) (Imhoff et al., 2020) KsatVer KsatHor 𝐾𝑠𝑎𝑡𝐻𝑜𝑟 = 𝑓𝐾ℎ0 × 𝐾𝑠𝑎𝑡𝑉𝑒𝑟 174
- 172. Problem statement (cont.) Page 3 van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B.: wflow_sbm v0.6.1, a spatially distributed hydrologic model: from global data to local applications, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-182, in review, 2022. Challenges: • Calibrate fKh0? • Uniform fKh0? • Large-scale model? Available PTFs are still insufficient (Araya et al., 2019) fKh0 Soil Properties PTF wflow_sbm (Deltares) 175
- 173. Objective & Research Questions (RQs) Page 4 How accurately can ML algorithms predict fKh0 compared to a calibrated benchmark? RQ1: Can ML-based fKh0 predictions improve wflow_sbm performance? How certain we are? RQ2: How reliable and robust are ML-based fKh0 predictions if tested in a different region? RQ3: To explore the possibilities of using Machine Learning (ML) algorithms in estimating PTFs for fKh0 prediction Main Objective 176
- 174. Study area and data Page 5 Data Study Area Great Britain (1970 – 2015) Loire Basin, France (1978 – 2020) Discharge CAMELS-GB Hydro platform Precipitation CEH-GEAR E-OBS Temperature & Evapotranspiration ERA5 Soil maps SoilGrids v1.0 • Great Britain: 551 subbasins • Loire basin (117,000 km2): 559 subbasins 177
- 175. SoilGrids data in subbasin scale Page 6 Step 1: Rasterize wflow subbains Step 3: Average over layers Trapezoidal rule Organic carbon content (‰ or g Kg-1) Sand (%) Clay (%) Silt (%) Absolute depth to bedrock (cm) Bulk density (kg/m3) pH index measured in water solution (pH) Organic carbon content (‰ or g Kg-1) Sand (%) Clay (%) Silt (%) Absolute depth to bedrock (cm) Bulk density (kg/m3) pH index measured in water solution (pH) Step 2: Average each layer over subbasins SoilGrids 250m Layer 1 Layer 1 Layer 1 Layer 1 Layer 1 Layer 1 178
- 176. Optimized 𝑓Kh0 Page 7 Duc, L. and Sawada, Y.: A signal-processing-based interpretation of the Nash–Sutcliffe efficiency, Hydrol. Earth Syst. Sci., 27, 1827–1839, https://doi.org/10.5194/hess-27-1827-2023, 2023. 179
- 177. SoilGrids 250m Soil attributes Sand % Silt % Clay % Org. carbon ‰ Bulk density pH idx (water) Bedrock depth Optimized (𝑓𝐾ℎ0) ML-based PTFs Training (75%) Test (25%) Random Sampling Standardization Standardization Mean & St.D. Random Forest (RF) Boosted Regression Trees (BRT) Machine Learning Algorithm Parameter Tuning Prediction Model Evaluation + Feature Importance 5-Fold Cross Validation Page 8 Araya, S. N., & Ghezzehei, T. A. (2019). Using machine learning for prediction of saturated hydraulic conductivity and its sensitivity to soil structural perturbations. Water Resources Research, 55(7), 5715-5737. 180
- 178. Tree-based models Page 9 (a) Random Forest (RF) (b) Boosted Regression Trees (BRT) Error Iterations + + + RF: Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. BRT: Friedman, J. H. (2002). Stochastic gradient boosting. Computational statistics & data analysis, 38(4), 367-378. 181
- 180. Objective & Research Questions (RQs) How accurately can ML algorithms predict fKh0 compared to a calibrated benchmark? RQ1: Can ML-based fKh0 predictions improve wflow_sbm performance? How certain we are? RQ2: How reliable and robust are ML-based fKh0 predictions if tested in a different region? RQ3: To explore the possibilities of using Machine Learning (ML) algorithms in estimating PTFs for fKh0 prediction Main Objective 183
- 181. PTF evaluation Optimized Uncalibrated Limited by model accuracy Accepted Rejected Better performance 184
- 182. Uncertainty and implications Page 13 185
- 183. Objective & Research Questions (RQs) Page 14 How accurately can ML algorithms predict fKh0 compared to a calibrated benchmark? RQ1: Can ML-based fKh0 predictions improve wflow_sbm performance? How certain we are? RQ2: How reliable and robust are ML-based fKh0 predictions if tested in a different region? RQ3: To explore the possibilities of using Machine Learning (ML) algorithms in estimating PTFs for fKh0 prediction Main Objective 186
- 184. Study area and data Data Study Area Great Britain (1970 – 2015) Loire Basin, France (1978 – 2020) Discharge CAMELS-GB Hydro platform Precipitation CEH-GEAR E-OBS Temperature & Evapotranspiration ERA5 Soil maps SoilGrids v1.0 • Great Britain: 551 subbasins • Loire basin (117,000 km2): 559 subbasins 187
- 185. Globally predicted 𝑓Kh0 Page 16 188
- 186. PTF testing 189
- 188. Conclusion • ML-based PTFs obtained from RF and BRT effectively predicted fKh0 and RF slightly outperformed BRT • Two globally seamless fKh0 maps were generated and are readily available for use • PTFs notably enhanced wflow_sbm performance compared to the uncalibrated scenario (fKh0 = 100), increased the median KGE from 0.55 to 0.75 • The utilization of RF and BRT improved performance in around 75% of subbasins within the Loire basin in France, resulting in a KGE that on average 0.06 higher 191
- 189. Hydrology Suite User Days Day 2 - wflow and HydroMT Thank you for your attention
- 190. Closing remarks By Hessel Winsemius
- 191. Group Photo 194
- 192. Lunch break 195
- 193. Hands-on training in Ganges Delta 196 We are here
- 194. Delft Software Days Hydrology Suite We continue at: in meeting room Ganges Delta