Embedded Files
Week 1: Data exploration and analysis
Week 1: Data exploration and analysis
Explore the rainfall and flow data available to you.
Analyse the data, use basic statistics (mean, media, variance...), plots and other tools
Make interpretations
Take decision to fill in any missing values
The slides can be found here: Kick-Off presentation
The Google Collab can be found here: Python Code
Available data (you can add other relevant data):
Temporal Data
Rainfall data from 3 stations (coordinates here) inside/near the Ouseburn catchment:
->jesmond
Spatial Data a
Week 2: Frequency (extreme value) Analysis
Week 2: Frequency (extreme value) Analysis
Understand extreme value data (rainfall) using the Annual Maxima method.
Assess and fit extreme value distributions using L-moments method.
Select the suitable EV distribution based on return period and fitting.
Perform uncertainty analysis using the Jackkniffe method.
The slides can be found here: EV analysis
The Google Colab to extract the AM daily data can be found here: AM daily rainfall
The Google Colab to perform the EV analysis using L-Moments can be found here: EV analysis
Week 3: Design storm profiles
Week 3: Design storm profiles
Understand design storm profiles introduced by the UK Flood Estimation Handbook (FEH), focusing on summer and winter storms.
Learn about the effect of shifted profiles, including front-loaded, center-loaded, and back-loaded storm profiles, where the peak intensity occurs at different times.
Using the Scaling Relationship Method, generate Intensity-Duration-Frequency (IDF) curves based on return periods.
Using multiple linear regression, determine the IDT equation to compute rainfall intensity as a function of duration and return period.
Develop design storm hyetographs using the Alternating Block Method to arrange rainfall intensities over time.
The slides can be found here: Design_storms
The Google Colab for the Design Storm using the Alternating Block Method: IDF-DS
The Excel file for the Design Storm using the Alternating Block Method: Excel_IDF-DS
The Google Colab for 2 methods to get different profiles: Storm Profiles
Week 4: Hydrological Modelling
Week 4: Hydrological Modelling
Understand the different types of hydrological models: black box, conceptual, and physically-based models, focusing on their characteristics and applications.
Learn about the model development workflow, including defining the purpose of the model, data requirements, and conceptualization.
Explore the calibration and validation process for hydrological models to improve model accuracy.
Discuss the limitations of hydrological models and the importance of understanding that all models are approximations of real-world behaviors.
The slides can be found here: Hydrological_Modelling
The Google Colab for the HBV conceptual model: HBV_model
Data for the HBV and SIMHYD models: Data_HBV
The Google Colab for the SIMHYD conceptual model: SIMHYD_model
Video explaining the SIMHYD conceptual model: Conceptual_model_video
Week 5: Hydrological Modelling 2 -SHETRAN-
Week 5: Hydrological Modelling 2 -SHETRAN-
Present Shetran hydrological model.
Check the quality of the simulation.
Explore the calibration and validation process for Shetran to improve model accuracy.
Run Shetran with different storm designs (front, centre, back and DDF)
The slides can be found here: Shetran Hydrological Model
Data (make sure to unzip the folders):
Ouse__input (input data to use for Shetran simulation)
Ouse_output (example of Shetran results after simulation using the previous input data)
Shetran (This is the hydrologocal model, run 'start-snow.exe')
Design_Storms (This folder contains 5 different design storms to experiment)
The Google Colab for an Introduction to Shetran: Introduction_Shetran_model
The Google Colab to Calibrate Shetran: Shetran_calibration
The Google Colab to experiment with different design storms: Shetran_experimenting_design_storms
Useful documentation about Shetran (do not forget to unzip): Shetran_docs
Further reading: Great British Rainstorms – An events-based characterization of the properties of sub-hourly to daily annual maximum producing rainfall events
Week 6: Hydrological Modelling 3- Flood Estimation-
Week 6: Hydrological Modelling 3- Flood Estimation-
Understand Rainfall-Runoff modelling for design flood estimation (ReFH model from FEH).
Understand the difference in modelling gauged and ungauged sites.
Explore with different methods to estimate design floods -Unit Hydrograph UH & Curve Number SCS method
The slides can be found here: Flood Estimation
The FEH (Flood Estimation Handbook) can be found here: FEH
The Google Colab of Unit Hydrograph : UH-RDDF
The Google Colab of the CN and UH using the SCS method: CN-UH-SCS
The Excel file of the CN and UH using the SCS method: Excel CN-UH-SCS
Week 6: Hydrological Modelling 3- Flood Estimation-
Week 6: Hydrological Modelling 3- Flood Estimation-
Understand Rainfall-Runoff modelling for design flood estimation (ReFH model from FEH).
Understand the difference in modelling gauged and ungauged sites.
Explore with different methods to estimate design floods -Unit Hydrograph UH & Curve Number SCS method
The slides can be found here: Flood Estimation
The FEH (Flood Estimation Handbook) can be found here: FEH
The Google Colab of Unit Hydrograph : UH-RDDF
The Google Colab of the CN and UH using the SCS method: CN-UH-SCS
The Excel file of the CN and UH using the SCS method: Excel CN-UH-SCS
Week 7: Climate Change & Hydrological modelling
Week 7: Climate Change & Hydrological modelling
Understand different climate change scenarios and how they affect precipitation.
Understand downscaling methods.
Develop and analyse design storm hyetographs from climate change projections under different scenarios.
The slides can be found here: Climate Change
The Google Colab of Unit Hydrograph under climate change scenarios : CC_EV_Analysis
Data for Valencia Spain: Data
The Climate change projected (documentation of the data) can be found here:
This data can be directly downloaded from the Wekeo services as demonstrated in the slides.
Further reading:
- Climate Models | MIT Climate Portal
- What are emission scenarios?
- Chapter 4 | Climate Change 2021: The Physical Science Basis
Link to the UKCP18 website: https://ukclimateprojections-ui.metoffice.gov.uk/ui/home
The document to use the UKCP18 website and get data (optional tasks included): https://icedrive.net/s/5DhBRNiuhD3vCAx2Zwhv4tZGX2Ak
Week 8: Stochastic Rainfall using the Markov chain model
Week 8: Stochastic Rainfall using the Markov chain model
Develop a first‐order Markov chain for wet/dry day occurrence using historical daily rainfall data.
Fit an exponential distribution to wet‐day rainfall amounts and generate a synthetic series.
Apply delta change factors (from UKCP18) to adjust mean rainfall intensity for future scenarios.
Compare baseline and future synthetic precipitation to identify changes in average and extreme events.
The slides can be found here: Stochastic daily rainfall
The Google Colab to generate stochastic daily rainfall and apply the delta change factor for CC analysis: Markov_model
Daily rainfall data (this is the output from Week 1 without the dates and headers): Daily_rainfall
Markov chain model main paper: Markov_chain_model_(Haan)
Applied Hydrology book (read chapter 11 to understand Statistics in Hydrology): Read_exponential_distribution
Link to the UKCP18 website: https://ukclimateprojections-ui.metoffice.gov.uk/ui/home
Week 9: Hydrodynamic flood risk modelling using CityCAT
Week 9: Hydrodynamic flood risk modelling using CityCAT
Understand the Flood risk modelling in urban areas.
Understand the potential of hydrodynamic flood risk modelling in cloud computing.
Run the flood risk simulation using the provided data, ensuring the model accurately represents the urban environment and potential flood scenarios.
Visualize and analyse different rainfall scenarios (return periods) and their impact on urban areas.
The slides can be found here: Flood risk modelling
CityCAT user manual, to setup the model follow the guidelines here: User Manual v4
The data needed to run the simulation can be found here: Data (this data and only this needs to be with the CityCat.exe in the same folder)
To extract building and green spaces geometries: Colab extract data , the data to be extracted: buildings and green spaces shapefiles
To plot maps on Qgis follow the guidelines here: Plot maps Qgis , If you wanna plot it using Python: Colab plot maps , you can use these tiff files in your map plots depending on the rainfall even you choose: Tiff
If you want to convert ascii (.rsl) to tiff: Convert to Tiff
(You first need to install these packages from Anaconda prompt:
conda create -n spatial-dev.guru python=3.10
conda activate spatial-dev.guru
conda install -c conda-forge geocube
conda install -c conda-forge pygeos
And when you open spyder:
pip install geocube
(then restart the klernel)
)
Further reading:
Bertsch, R., Glenis, V., & Kilsby, C. (2022). Building level flood exposure analysis using a hydrodynamic model. Environmental Modelling & Software, 156, 105490. https://doi.org/10.1016/j.envsoft.2022.105490
Glenis, V., Kutija, V., & Kilsby, C. G. (2018). A fully hydrodynamic urban flood modelling system representing buildings, green space and interventions. Environmental Modelling and Software, 109, 272-292. https://doi.org/10.1016/j.envsoft.2018.07.018
Kilsby, C., Glenis, V., & Bertsch, R. (2020). Coupled surface/sub-surface modelling to investigate the potential for blue-green infrastructure to deliver urban flood risk reduction benefits. In C. Thorne (Ed.), Blue-Green Cities: Integrating urban flood risk management with green infrastructure (ICE Bookshop - civil engineering publications ed., pp. 37-50). ICE Publishing.
McClean, F., Dawson, R., & Kilsby, C. (2023). Intercomparison of global reanalysis precipitation for flood risk modelling. Hydrol. Earth Syst. Sci., 27(2), 331-347. https://doi.org/10.5194/hess-27-331-2023
Page updated
Google Sites
Report abuse