代做ENEN90031 Quantitative Environmental Modelling Modelling Assignment 2帮做Python编程

ENEN90031 Quantitative Environmental Modelling

Modelling Assignment 2

Due Midnight on Friday 24th May 2024

Introduction

This assignment concentrates on the use of global model analysis techniques that can be applied to optimisation, sensitivity analysis and model uncertainty analysis. The final step involves applying your model results to determine the reliability of a hydroelectric scheme for reservoirs of different volumes and to estimate the impact of climate change on that relationship. The key parts of the assignment are:

1 Calibration and validation of your model using the global optimization technique Differential Evolution;

2 Undertaking a regional sensitivity analysis to understand the effects of different parameters on the model performance;

3 Undertaking an uncertainty analysis using GLUE; and

4 Assessing the reliability of a hydroelectric scheme for reservoirs of different volumes and the impact of climate change on that relationship.

The assessment for this assignment is as follows.

• Model calibration and validation (part 1): (Technical 5%, Discussion 15%)

• Regional sensitivity analysis (part 2) (Technical 7%, Discussion 13%)

• Parameter uncertainty analysis (part 3) (Technical 7%, Discussion 13%)

• Reservoir assessment (part 4) (Technical 5%, Discussion 10%)

• Discussion and conclusions (part 5) (Discussion 10%)

• Overall quality of report presentation 10%

• Quality of participation in the discussion forum 5%. Note that this participation could involve asking either technical questions about coding or about interpretation of the analyses. It could also involve contribution to answering questions or discussing points about the assignment.

Submission

Late Penalty: assignments submitted late will be penalised 10% per day or part thereof; assignments submitted more than 5 days late will not be marked.

Over-length reports: we will stop marking at the end of page 12 of the main body of your report.

Your report must be submitted using the submission point on Canvas. There is also a trial submission link on Canvas (TurnItIn test submissions “assignment”) that you can use to generate and view Turn-It-In reports before your official submission.

Your report should contain the following: aims, introduction, results, discussion, conclusions, and references. The report must be no more than 12 pages (including graphs but excluding the appendix) and any additional pages will not be assessed. In writing your report, try to demonstrate to the reader your knowledge of the methodologies and their limitations using your understanding of the techniques used and modelling results. Please submit your report using the LMS assignment link. Please also submit your Jupyter Notebook (or assignment script. if you use a different platform).

Note that you can discuss issues you encountered without including figures for everything. Prioritize the figures to best support the most important model results and points that you want to make in your report. Seventy percent of the assignment mark is for the report. Of that, 60% relates to the report content and 10% to the report quality – that is, report structure, grammar, plots and tables appropriately formatted and labelled, reference list formatting. Twenty-five percent is for technical aspects related to achieving and displaying (e.g. the quality of graphs) sound results through your Python code and the readability of your Python code, including comments and formatting. The final 5% relates to the discussion forum.

Engineering Practice Hurdle

This assignment can be used as the final piece of your Engineering Practice Hurdle Written Communication submission. STEP workshops and online lessons are available to help you further develop your writing skills.

See the Skills Towards Employment Program community for more details on the Engineering Practice Hurdle.

Code notes

We have provided partial code in a Jupyter notebook. You may need to install the pyDOE library using pip install pyDOE from Anaconda command – see the Jupyter notebook for guidance. You also need hbvModel.py, and read_camels_oz.py, along with all the csv data files supplied. These files need to be in the same directory as your Jupyter Notebook.

You have, or soon will have, undertaken a range of similar analyses in tutorial classes and should refer back to that code and adapt it for various parts of this assignment.

Part 1: Global Calibration and Validation using Differential Evolution

The aim of this part is to calibrate and validate the HBV model for the study catchment. Note, in using Differential Evolution you can assume it is reliably converging to the global optimum (we have check this for you). Using the Differential Evolution algorithm and approaches learnt in class, you should:

a) Use a split-sample calibration and validation analysis to assess how well the model predicts runoff behaviour for periods that are independent of the calibration period. In making this assessment consider both mean squared error and coefficient of efficiency. Also consider the model bias in different periods of calibration and validation.

b) Assess whether the parameters depend on the calibration period and how different the model predictions are for different parameter sets.

Note: Differential Evolution will take a while to run. While you get your code working you might want to set maxiter to 2 and then increase it for final runs.

For this section, your report should:

1.1. outline your methods for the split sample analysis;

1.2. provide evidence to support your assessment of the impact of calibration period;

1.3. provide any comments you may have on the adequacy and limitations of your analysis; and

1.4. summarise your findings.

Part 2: Regional sensitivity analysis (RSA)

The aim of Regional Sensitivity Analysis is to understand the relationships between the model predictions and the parameters. This is based on a Monte Carlo analysis of the model. Here you should:

a) Assess the sensitivity of HBV to each parameter;

b) Assess the parameter interactions; and

c) Assess to what degree your answer depends on the threshold you use to define behavioural runs

For this section, your report should provide an interpretation of results of the Regional Sensitivity Analysis in terms of:

2.1. how sensitive the objective function value is to each parameter;

2.2. whether there are significant parameter interactions;

2.3. which parameters are most likely to be reliably identified through calibration;

2.4. whether these results help you interpret your part 1 results, and if so, how; and

2.5. what limitations there might be for this analysis.

Part 3: Prediction uncertainty analysis

Having now undertaken both the global optimisation and the sensitivity analysis, it is now time to focus in on how uncertain the model predictions are. To estimate the prediction uncertainty, please use the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. To do this you should adapt the code from Tutorial 9.

For this section of the assignment you should:

a) Choose an appropriate threshold to define behavioural runs; and

b) Assess the resulting 90 percent prediction intervals (i.e. 5 to 95%) for runoff and the frequency with which observed flows fall within, above and below that prediction interval.

Your report should:

3.1. Justify your choice of behavioural threshold, including through a discussion of point b above;

3.2. Present the resulting flow prediction intervals; and

3.3. Critique your results and the overall analysis approach.

Note the similarities in RSA and GLUE and take this into account when writing your report. For example, there is no need to repeat figures that have very similar content and your discussion should not repeat the same/similar things.

Part 4: Hydroelectric reliability and climate change assessment

Now that the model has been calibrated and evaluated, the model can be used to make predictions. By also using the parameter uncertainties, the prediction error can be quantified. This part of the assignment looks at the volume and reliability of a hydroelectric system. This system consists of:

• The catchment

• A reservoir with a certain storage capacity (you will look at the effect of different capacities on reliability).

• A hydroelectric power plant that takes water from the reservoir and generates electricity.

Hydroelectric systems can be started and stopped rapidly and are often used when meeting peak electricity demands. We have supplied an electricity demand timeseries which contains non-zero demands at peak demand times and we also adjust this series within the supplied Jupyter notebook so that it is consistent with the amount of water available from you catchment.

We have provided a simple model of the hydroelectric system – the hydropower function within the Jupyter notebook. It assumes the only inflows are from the stream and the only outflows are to meet generation demands or spills to the downstream river when the reservoir is completely full – in reality there would be other things to account for such as environmental releases. For our purposes here, you should assess reliability as:

• Reliability in time = days electricity demand is met / total number of days

• Reliability of meeting demand = electrical energy generated / total energy demanded

The function simulating the hydroelectric system (supplied in the notebook) calculates both these metrics.

Hydroelectric systems have a long life so it is also important to estimate future reliability for each system. HBV can be used to estimate changes in flows with changed climate and these can then be used to see how reliable the hydroelectric system is.

There are two tasks here:

a) Using the model calculate both types of system reliability and plot a curve of these against reservoir storage capacity. To do this choose a number of reservoir storage capacities and use the behavioural models and period for which electricity demand data is available. You should use a warm-up period of at least 1 year before this period. Note that the hydroelectricity function automatically trims the data to a common period of flow and demand data when it simulates the hydroelectricity system. You should indicate the uncertainty in reliability.

b) Repeat a) for a climate change scenario where there is a 15% reduction in rainfall, a 5% increase in PET (i.e. run HBV with rainfall multiplied by 0.85 and PET by 1.05). Also indicate an estimate of the uncertainty resulting from HBV.

In writing up your assessment of the impact of climate change on the hydroelectricity reliability, you should evaluate how well you think these uncertainties have been captured and comment on potential improvements in the method that you can think of. Comment on other potential sources of uncertainty.

Part 5: Discussion and conclusions

Lastly, in your report you need to discuss the strengths and weakness of your modelling for this assignment and summarise the overall findings from each part of the model analysis.