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Water Resource Planning and Management - MACE40402
创建日期：2024-04-07 14:28:21
所属分类：管理Management
标签： MACE40402
Water Resource Planning and Management

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Coursework Brief - Surface water
Water Resource Planning and Management - MACE40402

Assignment description
To support water resource management, a water company has hired you to develop a rainfall-runoff model to assess
available water resources in one of their catchments. The company is especially interested in the ability to predict
reliably the occurrence of future low flow periods in order to understand potential risks posed by climate and land
use change to water users and freshwater ecosystems in the catchment.
Based on this brief, your task is to implement, calibrate and validate the GR4J rainfall-runoff model using observed
climatological and streamflow data for the catchment. Before starting your analysis, you will need to access and
download: (i) observation data (climate, streamflow, and information about relevant catchment characteristics) for
your assigned catchment, (ii) copy of the Matlab toolbox and associated scripts needed to perform calibration and
validation of the GR4J rainfall-runoff model. More details on how to access these are provided on page 3 of this brief
under section ‘Observation data and code’.
Once you have downloaded observation data for your catchment and model code files, your task is then to use
the information covered in lectures and supplementary reading for Module 1 of this unit to complete the following
analyses and then write a short report about your results and findings. Details about the report format and guidelines
are provided on page 2 of this brief under the section heading ‘Report structure’.
1. Conduct a preliminary analysis of the streamflow observation data for your assigned catchment. Explore
how streamflow varies over the observed historical record (both between and within years), and assess how
streamflow variability relates to climatic, topographic, land cover, soil and/or hydrogeological characteristics
of the catchment provided within the supplementary dataset files.
2. Modify and complete the Matlab script ‘MACE40402 CW1 AutoCalibration.m’. Once all missing parts of this
script are completed, run the script to perform an automated calibration of the GR4J model for your catch-
ment using the inbuilt local gradient-based search algorithm function (fminsearchbnd). Record the optimised
parameter values you obtain, along with relevant performance statistics illustrating the quality of the model
fit to observed data. Repeat this analysis for different initial parameter guesses, and explore if and how these
choices affect the resulting optimised values of model parameters and resulting fit to observed streamflow.
3. Modify and complete the Matlab script ‘MACE40402 CW1 URS.m’. Once all missing parts of this script are
completed, run the script to perform a batch run of the GR4J model for different potential parameter sets
using a uniform random sampling approach. Explore how model performance differs from results obtained
previously using the local gradient-based optimisation algorithm in part 2, and evaluate how any differences in
model performance using the uniform random sampling approach are affected by the choice of sample size.
1
Assessment and submission
You should produce a short report (pdf format) summarising the findings of your analysis. The length of the report
must not exceed 2000 words, not counting any title, contents, or bibliography pages. A suggested structure for the
report is provided in the following section of this document, along with a breakdown of marks allocated to each
component of the analysis and report. You must include appropriate figures, tables, and supporting references when
discussing your analysis and the findings you obtain. Please provide a statement of your word count on the title page
- any reports exceeding the allowable word limit will be marked down.
The deadline for submitting this piece of coursework is 6pm on Friday 26 March 2021. You should submit your
report using the Turnitin link in the folder ‘.../Coursework/CW1 - Surface water/...’ on Blackboard. Please also
submit the completed versions of the two Matlab scripts used in parts 2 and 3 of your coursework using the respective
assignment submission links in the same folder. Coursework will be checked for plagiarism and collusion using the
Turnitin software, and any evidence of academic malpractice will be taken very seriously by the department and the
University.
This assignment is worth 20% of the total mark for this unit. Late submissions will be deducted 10% per day, and
any submissions received more than 4 days late (i.e. after 6pm on Tuesday 30 March) will be graded zero.
Report structure
It is suggested that you begin your report with a short introductory paragraph describing the objectives of the pro-
posed work. This should be in your own words - do not simply copy from the brief! The main body of your report
should then contain the following information relating to the three core tasks outlined above, with marks weighted
as outlined in brackets below:
Part 1 (20%): Describe the location, physical characteristics/attributes, and streamflow regime for your catchment.
You description should clearly evidence how streamflow varies over the observed historical record (both between and
within years), and explain this in terms of catchment characteristics and dominant runoff generation processes. You
should use relevant information provided in the supplementary files provided in the CAMELS-GB dataset to support
this discussion and evaluation of your catchment’s streamflow responses and regime.
Part 2 (40%): Report the optimised parameter values you obtain using gradient-based optimisation, along with
relevant performance statistics illustrating the quality of the model fit to observed data. Include a description and
justification of the objective function used to evaluate model performance, and provide suggestions for any discrep-
ancies between simulated and observed streamflow values. Discuss to what extent your results are affected by initial
parameter guesses, and explain why these choices do or do not affect the resulting optimised values of model param-
eter and fit to observed streamflow data.
Part 3 (40%): Report how optimised parameter values obtained using uniform random sampling differ from those
obtained using the gradient-based optimisation algorithm in part 2, and to what extent these changes are affected
by the choice of sample size. Based on these results discuss the relative strengths and weaknesses of uniform random
sampling as a calibration approach relative to local gradient-based optimisation. You may wish to comment on the
computational efficiency of this approach (and suggestions for how this could be enhanced) along with the usefulness
of this approach for understanding parameter sensitivity and uncertainty.
While no specific marks are allocated for the quality of the report presentation, please pay careful attention to the
way you structure, report and present your work. Unclear or incomplete wording, a lack of relevant supporting
figures and/or tables, and failure to include relevant supplementary references will lead to a loss of marks. A title
page and bibliography should also be included in your report.

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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