Water Resources Management Plan 2014 Appendix 5 : Headroom

Transcription

Water Resources Management Plan
2014
Appendix 5 : Headroom
Appendix 5: Headroom
Executive Summary
1. We require an assessment of target headroom for the dWRMP14.
2. As part of the headroom assessment process, a review of the headroom models created for
WRMP09 has been undertaken, and the models updated with current data.
3. This appendix describes the various uncertainties in headroom, the key assumptions made in
the models for these uncertainties by taking into account the current supply demand
conditions and the outputs from the models.
4. The summary also lists the changes in assumptions made in the current headroom models in
comparison to those made for WRMP09. It should be noted that the headroom assessment
relies on data from several other tasks which are being undertaken concurrently and the
updated information from these tasks have been incorporated in this dWRMP14.
Contents Page
EXECUTIVE SUMMARY .....................................................................................................................................1
CONTENTS PAGE ..............................................................................................................................................2
INTRODUCTION ...............................................................................................................................................3
TARGET HEADROOM ..........................................................................................................................................3
HEADROOM UNCERTAINTY ..................................................................................................................................3
OBJECTIVES .....................................................................................................................................................4
THE MODEL ....................................................................................................................................................4
KEY ASSUMPTIONS ..........................................................................................................................................5
SUPPLY SIDE DATA ............................................................................................................................................5
S1, S2 and S3 Vulnerable Water Licences ..................................................................................................5
S4
Bulk Imports ................................................................................................................................5
S5 Gradual Pollution .............................................................................................................................6
S6 Accuracy of Supply Side Data ...........................................................................................................8
S7 Single Source Dominance .................................................................................................................8
S8 Uncertainty of Impact of Climate Change on Deployable Output.......................................................9
S9 New source uncertainty ...................................................................................................................9
DEMAND SIDE DATA..........................................................................................................................................9
D1
Accuracy of Sub–component Demand Data ..................................................................................9
D2
Demand Forecast Uncertainty.................................................................................................... 10
D3
Uncertainty of Climate Change on Demand ................................................................................ 10
D4
Uncertainty of Demand Management Measures ........................................................................ 11
OVERLAPPING AND CORRELATED .................................................................................................................. 12
OVERLAPPING ................................................................................................................................................ 12
CORRELATED ................................................................................................................................................. 12
RESULTS.........................................................................................................................................................13
MODELLED SCENARIOS..................................................................................................................................... 13
FINAL RESULTS ............................................................................................................................................... 13
Introduction
Target Headroom
5. When a supply and demand balance is calculated and forecast over a planning period there
are many sources of uncertainties that arise due to assumptions being made, incomplete or
unreliable data sets or a lack of understanding of factors that may influence the supply
demand balance such as climate change. Incorporating target headroom into the balance
helps to ensure that levels of service can be maintained despite uncertainties surrounding
supply demand forecasting.
6. Target headroom may be defined as “the minimum buffer that a prudent water company
should allow between supply and demand to cater for specified uncertainties (except for
those due to outages) in the overall supply and demand balance”.
7. The Guideline do not expect water companies to eliminate all potential uncertainties, but
they should certainly plan to eliminate the majority of the uncertainties in the immediate
future and less over the longer term as it is understood that there will be changes to the
supply demand balance later on in the planning period that could not be anticipated at the
beginning. Therefore, the current methodology allows headroom to be displayed as a
probability distribution known as headroom uncertainty.
Headroom Uncertainty
8. Headroom uncertainty addresses key components of the supply demand balance for the
entire planning period and, through the quantification of all foreseeable sources of
uncertainty provides an additional planning allowance added to the demand forecast.
Further to that the assessment of headroom uncertainty allows water companies to see
which components of their supply demand balance are the major sources of uncertainty.
When a company understands the uncertainties in its supply demand projections it can
make appropriate investment decisions to ensure levels of service are maintained.
9. The 2002 UKWIR methodology 1 suggests that there are eight sources of uncertainty in the
supply side data and four in the demand side. The methodology allows for water companies
to include as many or few as are relevant bearing in mind that within a 30 year planning
period there is likely to be many changes to the operation of a company and therefore many
additional sources of uncertainty. Some components may be specific to a particular source
such as a pollution issue, whilst others may affect a whole RZ such as licence reductions and
some will affect the company as a whole such as the impacts of climate change on supply
and demand.
10. After an assessment has been made on which components apply in each zone, and for what
period, the uncertainties must then be quantified. This process requires close liaison within
our Company with the personnel who have the most experience / knowledge in each
individual area. Many assumptions and simplifications are made, which must be
documented to ensure the approach is fully auditable and above all, robust enough to be
able to provide realistic targets throughout the planning horizon.
1
An improved methodology for assessing Headroom – final report (UKWIR, 2002).
11. The headroom assessment can then be integrated with other components of the supply –
demand balance in order to provide a tool that can aid planning and decision making
regarding the timing and nature of schemes designed to increase supply and manage
demand.
Objectives
12. The objective of this report was to summarise and complete a model for the assessment of
headroom uncertainty using all data currently available. The output is displayed as a
probability distribution that allows water companies to plan to manage uncertainties
throughout the planning period to 2040.
13. The assessment of headroom has been done so in accordance with the latest UKWIR
methodology (UKWIR, 2002). The approach is transparent and fully auditable and has been
done at a company, regional and Resource Zone (RZ) level. Two assessments have been
undertaken, one for Dry Year Annual Average (DYAA) and another for Average Day Peak
Week (ADPW).
14. The base year of the planning horizon is 2015; thereafter the results are displayed in five
year intervals up to 2040, consistent with each AMP period.
15. This report also details comprehensive methods of quantifying uncertainties for each of the
components that comprise the headroom assessment to enable future users of the model to
manipulate raw data in a way that ensures consistency across all components and that all
assumptions and simplifications are mathematically correct.
16. A further output of the model will be a clearly defined assessment of the degree to which
each component influences the headroom uncertainty throughout the planning period,
allowing the user to identify the key drivers of uncertainty in the supply demand balance.
17. To assess the headroom for DYCP the same approach has been adopted for DYAA calculation
but all deployable outputs used have been the peak deployable outputs and all calculations
involving demand figures have used the peak demand.
The Model
18. The model requires all uncertainties to be quantified in Ml/d and represented by probability
distributions which are then used to populate a Monte Carlo model. For this project @Risk
version 4.5 was used as the simulation tool.
19. The model is based on a template developed by UKWIR (2002), however a considerable
number of improvements have made to make the analysis more robust and to improve the
functionality, so the model can be used to test assumptions.
Key Assumptions
Supply Side Data
S1, S2 and S3 Vulnerable Water Licences
20. S1 and S2 uncertainties relate to vulnerable surface water and groundwater licences. As per
the WRPG (S 3.2), the Environment Agency has provided companies with “confirmed” and
“likely” sustainability changes required, and these have been included explicitly in baseline
deployable output according to the guidance. Companies are instructed not to make any
additional allowance for sustainability reduction in headroom and consequently these
components are set to zero in the model.
21. S3 allows for reductions in deployable output through time limited licences being reduced or
revoked upon expiration. The WRPG (S 5.3) states that we should not make allowances in
their headroom calculations for the risk of time-limited licences not being renewed or a
licence revoked because of a sustainability reduction. Consequently, the results presented in
this report do not take into account any uncertainty from time limited licences with the S3
component set to zero.
S4 Bulk Imports
22. S4 Allows for uncertainties in bulk imports that are out of the receiving water companies
control. Four imports have been identified and through discussions with our Operations
Control Centre we have reviewed historical operations and their reliability has been
approximately quantified. The four imports are summarised in table 1.
Table 1: Summary of Bulk Supplies
Donor Source
Resource
Current Agreed Volume
Zone
(Ml/d)
Average
Peak
Weir Wood
2
5.4
5.4
Affinity Water
4
36
36
Darwell
Reservoir
Belmont
Scheme
3
8
8
6
6.3
7.8
Weir Wood
23. Weir Wood Reservoir is owned and operated by Southern Water Services (SWS). The
treatment works at the reservoir transfers water into our system at five locations which are
metered and monitored. In recent years the full supply has not been guaranteed under the
severe drought conditions experienced so the uncertainties surrounding the Weir Wood
abstraction have been modelled using a triangular distribution based on the possibility that
the deployable output could reduce to 3.4 Ml/d. This has been derived from the operational
experience during the drought of 2011/12 when SWS reduced the supply to this level for
much of the period.
24. The distribution therefore has the following parameters for average and peak condition:
• Max loss 2.0 Ml/d
•
•
Min loss 0 Ml/d
Average loss 1.0 Ml/d
Affinity Water
25. Affinity (Central) Water supplies water from their existing WTW supply to our Surrey Hill
Reservoir in RZ4. The abstraction by Affinity Water is up to 101 Ml/d, of which we have an
assured supply of 36 Ml/d, however, should the abstraction decrease then our entitlement
will decrease accordingly. Based upon the experience in the recent drought and assurances
received from Affinity, the import is considered reliable, however the reduction in supply
that is out of our control has been assumed at 10% reduction of the agreement.
26. The distribution therefore has the following parameters for average and peak condition:
• Max loss 3.6 Ml/d
• Min loss 0 Ml/d
• Average loss 1.8 Ml/d
Darwell
27. We receive bulk supply from SWS’s Darwell Reservoir which provides raw water to our
Hazards Green Water Treatment Works in RZ3. The current agreement with SWS is for 8
Ml/d to be available. Historically Darwell Reservoir has been considered to be unreliable
with severe restrictions during droughts as the reservoir has been down to 10 – 20% full for
weeks at a time. Additional transfer capacity has been recently implemented to transfer
water from Bewl Reservoir to Darwell Reservoir, which is likely to increase the security of
the import in the future.
28. The scheme has not been operational long enough to confirm this but based on operational
data supplied by Southern Water the Darwell bulk import could reduce to 5.98Ml/d. This is
represented in the model by a triangular distribution with the following parameters for
both average and peak periods:
• Max loss 2.0Ml/d
• Min loss 0 Ml/d
• Average loss 1.0 Ml/d
Matts Hill
29. The import from SWS at Matts Hill into RZ6 (representing the Belmont Agreement) has
been identified as uncertain as there have been prolonged periods, particularly during
droughts, where the agreed quantity was not available. The current agreement with SWS
provides for 6.3 Ml/d average and 7.8 Ml/d peak to be available. During recent dry periods
SWS have reduced the supply directly due to source capability issues. It is on this basis that
a triangular distribution has been used to represent the uncertainty with the following
parameters:
• Max loss 1.1 Ml/d average and 2.6 Ml/d peak
• Min loss 0 Ml/d
• Average loss 0.55 Ml/d and 1.3 Ml/d peak (50% of the maximum)
S5 Gradual Pollution
30. S5 allows for uncertainties surrounding permanent loss of output through gradual pollution
of a source resulting in either abandonment or the installation of treatment. By contrast,
temporary losses are accounted for in the outage assessment which is reported elsewhere.
31. The risk of contamination has been quantified by using a standard approach of borehole
vulnerability which assesses the risk of contamination at each source using the following
parameters:
• Groundwater flow mechanism - Fissured aquifers are allocated a larger risk score;
• Distance from outcrop - For confined aquifers, risk is inversely proportional to distance
from outcrop;
• Superficial cover - Unconfined aquifers may have a decreased risk if they are overlain
by superficial deposits. The nature of the deposits is also considered;
• Unsaturated depth - Risk is inversely proportional to unsaturated depth;
• Primary land use - For unconfined aquifers the land use around the source has been
considered and for confined aquifers the land use around the outcrop has been
considered;
• Age of casing - If the last geophysical inspection of a borehole found the casing to be in
good order then degradation of the casing has been taken to increase the risk of
contamination. If a boreholes casing is known (from geophysical inspection) to be in
disrepair then it is discounted as the casing is assumed to have been replaced. And;
• Site specific risks - Subjective assessment based on presence of adits, proximity to
motorways, rivers etc.
32. To calculate figures for the total risk, the scores for all sources within the same aquifer has
been averaged and categorised into low, medium and high risk. This has then been applied
to all our sources constructed in the same type of aquifer. If the aquifer is confined the risk
has been halved. Through discussions with our Water Quality Manager, losses of output
due to gradual pollution have been estimated to be 10% of the risk value and this is
factored into the model.
33. For these models, it has been assumed that no source will be abandoned due to gradual
pollution uncertainty. The headroom risk has been related to the process losses associated
with additional treatment required due to pollution. The risk of contamination has been
quantified based on the type of aquifer as assumed for WRMP09 and shown in the
following table. The risk values, and therefore the probability values, used in the analysis
are shown in table 2.
Table 2: Risk of Contamination based on Aquifer types
Aquifer
Risk of Contamination
Chalk
High – 5%
Upper / Lower Greensand
Medium – 2.5%
Tunbridge Wells Sand
Medium – 2.5%
Ashdown Formation
Medium – 2.5%
Hythe Formation
Medium – 2.5%
Gravels
High – 5%
34. It should be noted that where a loss in S5 is identified in the headroom simulation the loss is
permanently excluded in the headroom assessment for the rest of the period. This matches
the model closer to what occurs in reality, where a source that is lost to gradual pollution
will be lost for the rest of the assessment period.
S6 Accuracy of Supply Side Data
35. S6 allows for uncertainties surrounding the measurement of outputs and the quantity of
water being abstracted at a source. For S6, all sources firstly had to be categorised
according to their constraints so the origin of the uncertainty can then be identified and
estimated.
36. Surface water sources have uncertainties surrounding the deployable output assessment.
These models are based on numerical models, which include assumptions and estimates.
On that basis, surface water deployable outputs have been assigned an uncertainty of +/10%.
37. The deployable output of groundwater sources that are constrained by the aquifer or the
Deepest Advisable Pumped Water Level (DAPWL) have uncertainties arising from the
method of the deployable output assessment. The deployable output assessment uses
historical water level and pumping rate data, which can often be incomplete or erroneous,
or may not fully represent the conditions that would exist during times of drought or peak
demand for example.
38. Based on the confidence level grading carried out in the output assessment, the sources
have been graded as 1 (Good), 2 (Fair), 3 (Poor). This has also been linked to the constraints
affecting an output value. This has been converted into a percentage uncertainty of the
deployable output to obtain the triangular distribution (Minimum, Average and Maximum
Values) for @Risk analysis as shown below.
Table 3: Percentage Loss for Triangular Distribution for S6 component
Confidence Grading
DO constraint
% Loss
No uncertainty
Licence constraint
1%
Good
Treatment/process
5%
Good
Hydrology/hydrogeology
5%
Fair
Treatment/process
10%
Fair
10%
Poor
Treatment/process
20%
Poor
20%
39. The Baseline deployable outputs used in this headroom assessment are the most robust
and up-to-date information available using the updated figures reported in the Supply
Section.
S7 Single Source Dominance
40. S7 has been omitted from the headroom assessment in accordance with UKWIR (2002) as it
is considered as an outage issue and will be included in that component of the dWRMP14.
S8 Uncertainty of Impact of Climate Change on Deployable Output
41. HR Wallingford has carried out an assessment of the impact of climate change on deployable
outputs which concluded that the range of uncertainty made for WRMP09 remains
reasonable using the latest information, although the projected impacts themselves are
marginally lower than those reported for WRMP09. This is in part due to this assessment
using UKCP09 climate change projections, resulting in different deployable output changes.
42. The impacted deployable outputs for mid, wet and dry scenarios have been supplied, which
may then be used to form triangular distributions at a RZ level the following equations:
• Maximum deployable output loss = (Mid deployable output) – (Dry deployable output)
• Minimum deployable output loss = (Mid deployable output) – (Wet deployable output)
• Base case = 0 (Mid deployable output – Mid deployable output)
43. Water Resources planning is based on dry years in accordance with the Level of Service.
Most research suggests that climate change will result in drier conditions in the south east of
England. Therefore, the triangular distributions have been skewed to 60% dry, 40% wet.
This ensures that the model will favour the dry climate change scenario.
44. Overall, therefore, no major changes have been applied to the WRMP09 models with regard
to the distribution of uncertainty in the models.
S9 New source uncertainty
45. S9 allows for uncertainties in estimations of the deployable output of new sources. At time
of calculation the results from the optimisation modelling for the dWRMP14 were not
available and consequently the baseline outputs for the S9 component has not been
included. However, the model still includes functionality to incorporate new sources at a
later stage.
46. However, it is considered reasonable that for the purposes of the baseline target headroom,
the options being considered deployable output include the yield uncertainty within their
own constraints at this stage, and consequently this current calculation is a reasonable
estimation of the S9 parameter.
Demand Side Data
D1
Accuracy of Sub–component Demand Data
47. The D1 parameter allows for uncertainties in the demand forecast that arise from the input
of the base year data of the forecast. Data for the base year, 2011/12 has been derived
from the June Returns/Annual Return.
48. Two methods are employed to quantify the demand over the year in the AR. A ‘bottom up’
approach sums the demand from each individual sub-component, while the source meters
sum the total distribution input (referred to as the ‘top-down’ approach). There is often a
small discrepancy between the two totals, which is addressed in a calculation which spreads
the imbalance across all components in proportion to their uncertainty.
49. A review of the return data and the imbalances reported suggests an appropriate
distribution is a uniform distribution ranging from -1% to +2.5%. This is included in the
baseline model. The high and low scenario difference therefore has been derived the current
best estimate DYAA forecast, with the lower margin set at -1% and the upper margin at
+2.5% of the demand. This range is unchanged to the distribution reported in the previous
WRMP09.
D2
Demand Forecast Uncertainty
50. Sources of uncertainty in the demand forecast include assumptions in population growth,
per capita consumption and changes and growth in non-household demand.
51. In order to quantify the uncertainties around the central demand forecast, an upper and a
lower demand forecast has been estimated with the changes in demand as shown in the
table below. The forecast is weighted with a higher level of uncertainty towards the upper,
or higher forecast, which the significant reductions in household consumption adopted in
the base forecast and our assessment the likelihood of these reductions in demand
management being achieved. This upper end forecast is some 3 times higher than the lower
end forecast which we consider is a reasonable reflection of the conservative nature of the
base forecast.
Table 4: D2: High and Low Forecast Range
Change to Forecast
Low
High
2014/15
-0.5%
0.5%
2019/20
-0.8%
1.5%
2024/25
-1.0%
3.0%
2029/30
-1.5%
5.0%
2034/35
-2.0%
7.0%
2039/40
-2.5%
9.0%
52. Triangular distributions have been modelled for each year using the following equations:
• Maximum increase in demand = High – Mid
• Minimum increase in demand = Low – Mid
• Base case = 0 (Mid – Mid)
D3
Uncertainty of Climate Change on Demand
53. Modelling the impact of climate change on demand has been undertaken by HR Wallingford
for the company. Demand figures for a mid, wet and dry scenarios have been supplied, the
climate change impacted deployable outputs may then be used to form a triangular
distribution at a RZ level using the following equations:
• Maximum increase in demand = High – Mid
• Minimum increase in demand = Low – Mid
• Base case = 0 (Mid – Mid)
54. In order to quantify the uncertainties around the central demand forecast, an upper and a
lower demand forecast has been estimated with the changes in demand as shown in the
table below.
Table 5: D3: High and Low Forecast Range
Change to Forecast
Low
High
2014/15
0.00%
0.10%
2019/20
0.00%
0.21%
2024/25
-0.05%
0.32%
2029/30
-0.11%
0.44%
2034/35
-0.17%
0.58%
2039/40
-0.24%
0.71%
D4
Uncertainty of Demand Management Measures
55. D4 is the demand equivalent of S9 and although not included in the baseline analysis, the
functionality is included in the model. The study has identified a number of demand
management measures and has estimated the uncertainty surrounding water savings on an
scale of 0 – 100. As with S9 the score has been converted into a percentage by assuming
that a score of 0 equates to no saving uncertainty, whilst a score of 100 equates to a saving
uncertainty of 50%.
56. Each scheme within the shortlist has been included in the model with its own triangular
distribution. The scheme can be activated by assigning a ‘1’ to the scheme and deactivated
by assigning a ‘0’ to the scheme. This may be done at any modelled year within the planning
period to allow the user to control which schemes will affect the headroom uncertainty.
57. However, this parameter has currently been set to zero and has not been used in our
assessment of target headroom.
Overlapping and Correlated
58. Headroom components may be related or dependent, which will often need to be
incorporated into the statistical analysis. Relationships between headroom components will
be in one of the following forms:
• Overlapping
• Correlated
Overlapping
59. Headroom components are said to overlap when only the larger of the two uncertainties
needs to be included. S1/S2 and S5 are the only overlapping headroom components in the
model and as the former are not included in this model, being set to zero, there are no
further parameters which overlap.
Correlated
60. Headroom components are correlated when they are influenced by the same factors. The
following correlations have been used:
• S8, D3 Correlation coefficient 0.75
61. If climate change has a large impact on supply then it will also have a large impact on
demand. In the future climate change cannot be wet and dry at the same time, so if the dry
scenario is selected for one component it should also be selected for the other. However, as
demand is generally driven by summer discretionary use, resulting from hot dry summers
and supply is mostly dependant on water recharge it is not appropriate to use a correlation
coefficient of 1 so 0.75 has been adopted.
• S8, S4 Correlation coefficient 0.5
62. If climate change has a large impact on supply then the uncertainty around bulk imports is
likely to increase. The company does not have detailed modelling on the yields of bulk
supplies, so it may not be appropriate use a correlation coefficient of 1. As an estimate a
figure of 0.5 has been used.
Results
Modelled Scenarios
63. The model runs were completed for the baseline forecast and deployable output and a full
suite of percentile plots were produced for each of the average and peak scenarios for each
of the 8 WRZs. An example plot is shown below, for WRZ3 Average condition. The full set of
tables, for each WRZ are included in Appendix A and B for the dry year annual average and
the dry year summer peak periods respectively, together with the relevant tornado plots
Appendices C and D.
Figure 1: Average Headroom Percentile plot, WRZ3
64. We have considered a range of issues in assessing the appropriate level of headroom for the
dWRMP14 and consider that the 65 percentile reflects the most appropriate level of risk
across the planning period. Previous percentiles have ranged from 90 %ile down to 50%ile,
but, in comparison with target headroom figures produced for WRMP09 and the current
level of risk experienced in the industry, the 65%ile represents:
• Reasonable consistency with historic levels of target headroom;
• A figure which is proportionate with the demands and resources of the company; and
• A level of risk that is within the range of other companies estimates.
Final Results
65. The final figures for this dWRMP14 are presented in Table 6 below.
Table 6: Summary of Target Headroom for dWRMP14
2015
2020
2025
Ml/d
Ave
Peak
Ave
Peak
Ave
Peak
RZ1
0.9
1.5
2.4
3.4
3.0
4.3
RZ2
2.4
2.7
6.1
8.0
7.4
10.0
RZ3
2.0
1.6
4.1
4.7
5.6
6.6
RZ4
4.7
5.5
7.1
8.2
8.6
10.6
RZ5
0.7
1.2
1.7
2.6
2.1
3.2
RZ6
1.5
2.1
1.9
2.4
2.5
3.4
RZ7
1.4
1.2
2.6
2.5
2.8
2.7
RZ8
0.9
1.0
2.5
3.0
3.6
4.2
TOTAL 14.6
16.7
28.5
35.0
35.6
45.0
2030
Ave
3.8
8.4
7.6
10.3
2.8
3.1
3.0
4.8
43.8
Peak
5.1
11.2
9.3
13.7
4.1
4.1
2.9
5.6
56.1
2035
Ave
4.7
9.2
9.3
12.1
3.2
3.9
3.2
6.2
51.7
Peak
6.4
12.2
11.7
16.5
5.1
4.9
3.2
7.2
67.2
2040
Ave
5.5
10.6
11.3
13.5
4.0
4.7
3.4
7.6
60.7
Peak
7.0
13.6
13.6
19.5
5.5
5.8
3.6
8.6
77.2
66. We consider that the final headroom figures presented in this report are consistent,
transparent, robust and are comparable with the rest of the region. These results are in line
with other neighbouring water companies’ headroom as reported previously, as a
percentage of distribution input and in terms of levels of confidence.
Headroom Model Results for
dWRMP14 Final Report
Feb 2013
APPENDIX A – WATER RESOURCE ZONE HEADROOM MODEL
SPREADSHEET DATA
DRY YEAR ANNUAL AVERAGE
Feb 2013
Headroom Spreadsheet
Scenario Ref
Resource Zone Ref
South East Water
DYAA
RZ1
Version Phase 4.5
Date 13/02/2013
Component
S1 & S2
Correlated
With By
Conti
nuou
Depe
nden
t
Overl
appi
ng
Company Name
Parameters
Headroom Component (Ml/d)
2019/20
2024/25
2011/12
2014/15
2029/30
2034/35
2039/40
0.0000
0.0000
0.0000
0.000
0.000
0.0000
0.0000
0.0000
0.0000
0.000
0.000
0.000
0.40
0.000
0.81
1.21
1.62
2.02
2.43
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.00
0.00
0.00
0.00
0.00
0.000
0.000
0.000
0.000
0.000
0.000
Vulnerable surface water and groundwater licences
Now included in S5/S1 & S2 below
S4
Bulk transfers
FALSE
S5/S1 & S2 Max of Gradual pollution/Vulnerable surface water and groundwater licences
)
Type
C
S6
Accuracy of supply side data
S8
Uncertainty of impact of climate change on source yields
0.00
FALSE
Type
FALSE
0.00
FALSE
Type
0.00
FALSE
S9
Type
FALSE
D1
0.00
FALSE
0.30
FALSE
Type
0.66
0.72
0.77
0.000
0.000
0.000
0.00
0.00
0.00
0.00
0.00
0.00
0.000
0.000
0.000
0.000
0.000
0.000
0.30
0.30
0.30
0.30
0.30
0.30
0.000
0.000
0.000
0.000
0.000
0.000
0.27
FALSE
0.00
0.10
0.27
0.48
0.69
0.91
0.000
0.000
0.000
0.000
0.000
0.000
Uncertainty of impact of climate change on demand
Type
FALSE
D4
0.60
0.000
Demand forecast variation
FALSE
D3
0.54
0.000
Accuracy of sub-component data
Type
D2
0.00
0.000
Uncertainty over New Sources
0.00
FALSE
0.01
0.03
0.04
0.04
0.05
0.06
0.000
0.000
0.000
0.000
0.000
0.000
Uncertainty of impact of demand management
Type
FALSE
0.00
FALSE
Type
Param. A
Param. B
Param. C
0.00
Triangular
0.00
Min
Best
Max
0.00
0.00
0.00
0.00
0.03
0.00
14
Overlapping components
0.00
Triangular
Min
Best
Max
0.00
0.00
0.06
0.00
16
0.00
Triangular
Min
Best
Max
0.00
0.00
0.09
0.00
18
0.00
Triangular
Min
Best
Max
0.00
0.00
0.13
0.00
20
0.00
Triangular
Min
Best
Max
0.00
0.00
0.15
0.00
22
0.00
Triangular
Min
Best
Max
Triangular
0.00
Min
Best
Max
0.00
0.18
0.00
20
0.00
22
Group 1
Group 2
Group 3
Sum of Headroom components
Distribution Profile Parameters
Profile
A
B
C
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max Max
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Beta
A
Alpha
B
Beta
Scl
Scale
Exponential
Rate Rate
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Uniform
Min
Min Estimate
Max Max Estimate
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Gama
Loc
Location
Scl
Scale
Shp
Shape
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Extreme value Mod Mode
Other
Scl
Scale
2011/12
0.57
D
2014/15
0.72
2019/20
1.78
2024/25
2.42
2029/30
3.10
2034/35
3.79
2039/40
4.48
Analysis Statistics
p2
Probability 2
Mean
Minimum
25th percentile
50th percentile
75th percentile
Maximum
Std Deviation
Variance
Skewness
0.581
-0.738
0.187
0.610
0.951
1.937
0.509
0.259
0.018
0.712
-0.383
0.350
0.705
1.074
1.766
0.458
0.210
-0.042
1.769
-2.964
0.473
1.679
2.958
6.444
1.739
3.024
0.141
2.316
-2.381
0.953
2.212
3.655
7.520
1.860
3.460
0.178
3.076
-2.625
1.543
2.918
4.588
9.205
2.146
4.607
0.167
3.859
-2.189
2.149
3.699
5.538
10.779
2.398
5.749
0.155
4.496
-2.035
2.621
4.343
6.343
12.407
2.675
7.158
0.161
Feb 2013
South East Water
DYAA
RZ2
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2011/12
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
FALSE
0.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
FALSE
0.00
0.41
0.83
1.24
1.66
2.07
2.49
FALSE FALSE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
FALSE
0.00
0.00
1.54
1.72
1.89
2.06
2.23
FALSE FALSE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
FALSE
0.56
0.56
0.56
0.56
0.56
0.56
0.56
FALSE FALSE
0.31
0.00
0.18
0.49
0.87
1.25
1.65
FALSE FALSE
0.00
0.02
0.05
0.07
0.08
0.10
0.12
S4
Bulk transfers
)
C
S6
S8
S9
D1
D2
D3
D4
FALSE FALSE
0.00
14
0.00
16.00
0.00
18
0.00
20
0.00
22
0.00
20
0.00
22
Group 1
2011/12
0.86
Group 2
2014/15
2.00
2019/20
4.16
2024/25
5.07
2029/30
6.05
2034/35
7.04
2039/40
8.04
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
0.9
1.97
4.2
5.2
6.1
7.0
8.0
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-1.4
-0.68
-8.2
-7.8
-8.8
-8.7
-8.7
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
0.2
1.22
0.4
1.0
1.6
2.3
2.4
Beta
A
Alpha
B
Scl
Scale
50th percentile
0.9
1.96
3.8
4.8
5.7
6.6
7.6
Exponential
Rate Rate
75th percentile
1.5
2.69
8.0
9.1
10.7
11.6
13.2
Custom
x1
Value 1
x2
Maximum
3.2
4.88
17.8
19.0
22.7
27.0
27.5
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
0.9
1.01
5.1
5.6
6.4
6.8
7.3
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
0.8
1.02
26.1
31.8
40.8
46.8
53.8
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.09
0.2
0.2
0.1
0.2
0.2
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Beta
Value 2
Scale
p1
Probability 1
p2
Probability 2
Feb 2013
South East Water
DYAA
RZ3
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2011/12
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
1.0000
1.0000
1.0000
2.0000
3.0000
4.0000
5.0000
0.00
0.000
0.25
0.000
0.51
0.000
0.76
0.000
1.02
0.000
1.27
0.000
1.52
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.79
0.000
0.88
0.000
0.96
0.000
1.05
0.000
1.14
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.47
0.000
0.47
0.000
0.47
0.000
0.47
0.000
0.47
0.000
0.47
0.000
0.47
0.00
0.000
0.00
0.000
0.15
0.000
0.41
0.000
0.73
0.000
1.05
0.000
1.38
0.00
0.000
0.02
0.000
0.04
0.000
0.05
0.000
0.07
0.000
0.08
0.000
0.10
S4
Bulk transfers
FALSE
)
C
S6
S8
S9
D1
D2
D3
D4
Type
FALSE
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2011/12
1.47
Group 2
2014/15
1.74
2019/20
2.96
2024/25
4.57
2029/30
6.24
2034/35
7.92
2039/40
9.61
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
1.5
1.7
3.1
4.6
6.4
8.1
9.8
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-0.5
-0.1
-3.7
-2.9
-2.5
-2.2
-0.7
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
0.9
1.1
1.4
2.4
4.0
5.6
6.8
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
1.5
1.7
3.0
4.3
6.3
7.9
9.6
Exponential
Rate Rate
75th percentile
2.0
2.3
5.0
6.6
8.6
10.6
12.7
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
3.4
3.7
10.2
12.8
15.6
18.6
22.1
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
0.8
0.8
2.6
2.9
3.3
3.6
4.1
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
0.6
0.6
6.6
8.4
10.9
13.3
16.7
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.1
0.1
0.2
0.1
0.1
0.0
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
South East Water
DYAA
RZ4
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
1.8000
1.8000
1.8000
1.8000
1.8000
1.8000
1.8000
0.00
0.000
0.36
0.000
0.72
0.000
1.08
0.000
1.44
0.000
1.80
0.000
2.16
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.97
0.000
1.08
0.000
1.18
0.000
1.29
0.000
1.40
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
1.45
0.000
1.45
0.000
1.45
0.000
1.45
0.000
1.45
0.000
1.45
0.000
1.45
-1.90
0.000
0.00
0.000
0.47
0.000
1.26
0.000
2.21
0.000
3.18
0.000
4.16
0.00
0.000
0.06
0.000
0.13
0.000
0.17
0.000
0.21
0.000
0.25
0.000
0.29
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2009/10
1.34
Group 2
2014/15
3.67
2019/20
5.53
2024/25
6.83
2029/30
8.29
2034/35
9.77
2039/40
11.26
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
1.4
3.6
5.4
6.8
8.2
9.7
11.0
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-7.7
-3.1
-5.5
-5.3
-7.1
-8.7
-6.6
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
-0.8
1.8
2.5
3.5
4.5
5.0
5.9
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
1.3
3.6
5.4
6.8
8.0
9.4
10.9
Exponential
Rate Rate
75th percentile
3.6
5.4
8.4
10.1
12.0
13.9
15.6
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
9.9
10.3
18.4
21.8
25.3
30.3
33.5
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
3.1
2.3
4.1
4.6
5.4
6.4
7.0
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
9.5
5.3
17.0
21.1
29.6
41.1
49.2
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.0
0.1
0.1
0.0
0.2
0.2
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
South East Water
DYAA
RZ5
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.00
0.000
0.22
0.000
0.43
0.000
0.65
0.000
0.86
0.000
1.08
0.000
1.29
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.35
0.000
0.39
0.000
0.42
0.000
0.46
0.000
0.50
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.30
0.000
0.30
0.000
0.30
0.000
0.30
0.000
0.30
0.000
0.30
0.000
0.30
0.04
0.000
0.00
0.000
0.10
0.000
0.26
0.000
0.46
0.000
0.66
0.000
1.21
0.00
0.000
0.01
0.000
0.03
0.000
0.03
0.000
0.04
0.000
0.05
0.000
0.06
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2009/10
0.34
Group 2
2014/15
0.53
2019/20
1.20
2024/25
1.63
2029/30
2.09
2034/35
2.56
2039/40
3.37
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
0.3
0.5
1.2
1.7
2.2
2.5
3.4
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-0.6
-0.5
-1.7
-1.7
-2.1
-1.7
-1.4
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
0.0
0.2
0.4
0.8
1.1
1.2
2.0
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
0.3
0.5
1.2
1.6
2.1
2.5
3.3
Exponential
Rate Rate
75th percentile
0.7
0.9
2.1
2.5
3.3
3.7
4.6
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
1.3
1.6
4.3
5.4
6.7
7.6
8.8
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
0.4
0.4
1.2
1.3
1.5
1.8
1.9
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
0.2
0.2
1.4
1.6
2.3
3.1
3.5
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.0
0.1
0.2
0.2
0.2
0.2
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
South East Water (ex MKW)
DYAA
RZ6
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.6500
0.6500
0.6500
0.5500
0.5500
0.5500
0.5500
0.00
0.000
0.00
0.000
0.23
0.000
0.46
0.000
0.69
0.000
0.92
0.000
1.15
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.04
0.000
0.06
0.000
0.09
0.000
0.11
0.000
0.13
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.49
0.000
0.49
0.000
0.49
0.000
0.49
0.000
0.49
0.000
0.49
0.000
0.49
0.00
0.000
0.00
0.000
0.16
0.000
0.45
0.000
0.80
0.000
1.18
0.000
1.57
0.00
0.000
0.02
0.000
0.04
0.000
0.06
0.000
0.07
0.000
0.08
0.000
0.10
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2009/10
1.14
Group 2
2014/15
1.16
2019/20
1.62
2024/25
2.07
2029/30
2.69
2034/35
3.33
2039/40
3.99
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
1.1
1.2
1.6
2.1
2.6
3.3
4.0
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-1.3
-1.4
-1.1
-0.9
-0.5
-1.0
-1.3
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
0.5
0.5
0.9
1.4
1.7
2.0
2.5
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
1.1
1.1
1.5
2.1
2.6
3.2
3.8
Exponential
Rate Rate
75th percentile
1.8
1.8
2.2
2.8
3.5
4.5
5.5
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
3.4
3.4
4.1
5.6
6.7
9.5
9.9
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
0.8
0.8
0.9
1.1
1.3
1.7
2.0
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
0.7
0.7
0.8
1.1
1.7
2.9
4.2
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.0
0.1
0.1
0.2
0.3
0.3
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
DYAA
RZ7
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.0000
1.0000
2.0000
2.0000
2.0000
2.0000
2.0000
0.00
0.000
0.03
0.000
0.06
0.000
0.09
0.000
0.11
0.000
0.14
0.000
0.17
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.06
0.000
0.09
0.000
0.12
0.000
0.15
0.000
0.18
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.16
0.000
0.16
0.000
0.16
0.000
0.16
0.000
0.16
0.000
0.16
0.000
0.16
0.02
0.000
0.00
0.000
0.05
0.000
0.14
0.000
0.25
0.000
0.36
0.000
0.48
0.00
0.000
0.01
0.000
0.01
0.000
0.02
0.000
0.02
0.000
0.03
0.000
0.03
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2009/10
0.18
Group 2
2014/15
1.20
2019/20
2.35
2024/25
2.50
2029/30
2.67
2034/35
2.84
2039/40
3.02
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
0.2
1.2
2.4
2.5
2.7
2.8
3.0
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-1.3
-0.6
0.0
-0.2
-0.1
-0.3
-0.2
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
-0.1
0.8
1.9
1.9
2.1
2.0
2.2
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
0.2
1.2
2.4
2.5
2.6
2.7
3.0
Exponential
Rate Rate
75th percentile
0.5
1.5
2.9
3.0
3.3
3.4
3.7
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
1.5
2.8
5.3
4.7
5.4
6.5
7.1
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
0.4
0.5
0.8
0.8
0.9
1.0
1.1
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
0.2
0.3
0.6
0.6
0.8
1.1
1.3
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.0
0.0
-0.1
0.1
0.1
0.2
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
DYAA
RZ8
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.00
0.000
0.60
0.000
1.21
0.000
1.81
0.000
2.42
0.000
3.02
0.000
3.63
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.02
0.000
0.04
0.000
0.05
0.000
0.06
0.000
0.07
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.64
0.000
0.64
0.000
0.64
0.000
0.64
0.000
0.64
0.13
0.000
0.00
0.000
0.22
0.000
0.60
0.000
1.08
0.000
1.58
0.000
2.12
0.00
0.000
0.03
0.000
0.06
0.000
0.07
0.000
0.09
0.000
0.11
0.000
0.13
S4
Bulk transfers
FALSE
Type
S5/1
S6
Summary of S5 components (sheet S5)
C
FALSE FALSE
Type
S6/1
S8
FALSE FALSE
Type
S8/1
S9
Summary of S8 components (sheet S8)
FALSE
Type
S9/1
D1
FALSE FALSE
Type
D1/1
D2
Summary of D1 components (sheet D1)
FALSE
Type
D2/1
D3
FALSE FALSE
Type
D3/1
D4
FALSE FALSE
Type
D4/1
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
Group 2
Group 3
Profile
A
B
C
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Beta
A
Alpha
B
Beta
Scl
Scale
Exponential
Rate Rate
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Uniform
Min
Min Estimate
Max Max Estimate
Weibull
Loc
Location
Scl
Scale
Shp
Gama
Loc
Location
Scl
Scale
Shp
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
Other
Scl
Scale
M/M
2009/10
0.13
D
2014/15
0.63
2019/20
2.15
2024/25
3.16
2029/30
4.27
2034/35
5.41
2039/40
6.59
Analysis Statistics
Mean
0.1
0.6
2.1
3.2
4.2
5.5
6.6
Minimum
-1.0
-0.8
0.0
0.1
0.5
0.6
1.0
Shape
25th percentile
-0.3
0.2
1.4
2.3
3.1
3.9
4.6
Shape
50th percentile
0.1
0.7
2.1
3.2
4.1
5.2
6.4
75th percentile
0.6
1.1
2.9
4.0
5.3
7.0
8.4
Maximum
1.3
2.0
5.0
6.3
8.8
11.7
13.6
Std Deviation
0.5
0.5
1.0
1.2
1.6
2.1
2.6
Variance
0.3
0.3
1.0
1.4
2.5
4.4
6.7
Skewness
0.0
0.0
0.2
0.1
0.2
0.4
0.3
Min/Max
p2
Probability 2
Feb 2013
APPENDIX B – WATER RESOURCE ZONE HEADROOM MODEL
SPREADSHEET DATA
DRY YEAR CRITICAL PERIOD
Feb 2013
Scenario Ref
Resource Zone Ref
South East Water
ADPW
RZ1
Version Phase 4.5
Date 13/02/2013
Component
S1 & S2
Correlated
With By
Conti
nuou
Depe
nden
t
Overl
appi
ng
Company Name
2011/12
2014/15
FALSE
0.0000
0.0000
0.0000
0.000
0.000
0.83
0.000
Parameters
2019/20
2024/25
2029/30
2034/35
2039/40
0.0000
0.0000
0.0000
0.0000
0.000
0.000
0.000
0.000
1.25
1.66
2.08
2.49
2.91
0.000
0.000
0.000
0.000
0.000
S4
Bulk transfers
)
Type
C
S6
0.42
FALSE
Type
FALSE
S8
0.00
FALSE
Type
0.00
FALSE
S9
D1
Type
FALSE
0.00
FALSE
Type
0.37
FALSE
D2
FALSE
0.00
0.00
0.00
0.00
0.000
0.000
0.000
0.000
0.00
0.82
0.92
1.01
1.10
1.19
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.00
0.00
0.00
0.00
0.00
0.000
0.000
0.000
0.000
0.000
0.000
0.37
0.37
0.37
0.37
0.37
0.37
0.000
0.000
0.000
0.000
0.000
0.000
0.27
FALSE
0.00
0.12
0.34
0.60
0.88
1.18
0.000
0.000
0.000
0.000
0.000
0.000
Type
FALSE
D4
0.00
0.000
Type
D3
0.00
0.000
0.00
FALSE
0.02
0.03
0.04
0.05
0.06
0.06
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.00
0.00
0.00
0.00
0.00
Type
FALSE
0.00
FALSE
14
16
18
20
22
20
22
Group 1
Group 2
Group 3
Profile
A
B
C
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Beta
A
Alpha
B
Beta
Scl
Scale
Exponential
Rate Rate
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Uniform
Min
Min Estimate
Max Max Estimate
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Gama
Loc
Location
Scl
Scale
Shp
Shape
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
2011/12
1.05
D
2014/15
1.21
2019/20
2.59
2024/25
3.32
2029/30
4.11
2034/35
4.91
2039/40
5.71
Analysis Statistics
p2
Probability 2
Mean
Minimum
25th percentile
50th percentile
75th percentile
Maximum
Std Deviation
Variance
Skewness
1.1
-0.5
0.6
1.1
1.5
2.6
0.6
0.3
0.0
1.2
-0.2
0.8
1.3
1.7
2.7
0.6
0.3
0.0
2.6
-3.5
0.7
2.4
4.3
9.8
2.6
6.8
0.2
3.3
-3.4
1.0
3.1
5.5
11.3
3.0
8.7
0.2
4.0
-3.8
1.7
3.8
6.2
12.7
3.3
10.8
0.2
5.0
-4.5
2.3
4.8
7.4
15.4
3.7
13.6
0.2
5.6
-3.8
2.7
5.4
8.3
17.1
4.0
16.0
0.2
Feb 2013
South East Water
ADPW
RZ2
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.43
0.000
0.87
0.000
1.30
0.000
1.73
0.000
2.17
0.000
2.60
0.000
3.03
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
2.57
0.000
2.85
0.000
3.03
0.000
3.21
0.000
3.39
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.22
0.000
0.22
0.000
0.22
0.000
0.22
0.000
0.22
0.000
0.22
0.000
0.22
0.38
0.000
0.00
0.000
0.22
0.000
0.61
0.000
1.09
0.000
1.59
0.000
2.11
0.00
0.000
0.03
0.000
0.06
0.000
0.08
0.000
0.10
0.000
0.12
0.000
0.14
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2009/10
1.04
Group 2
2014/15
2.12
2019/20
5.37
2024/25
6.49
2029/30
7.60
2034/35
8.74
2039/40
9.89
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
0.9
2.1
5.2
6.6
7.3
8.5
9.8
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-2.3
-1.5
-11.9
-14.4
-16.1
-14.8
-16.6
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
-0.1
1.1
-0.5
-0.2
0.4
1.3
2.6
Beta
A
Alpha
B
Scl
Scale
50th percentile
0.9
2.0
4.7
5.8
6.6
7.9
9.5
Exponential
Rate Rate
75th percentile
2.0
3.1
10.8
13.4
14.0
14.9
16.8
Custom
x1
Value 1
x2
Maximum
4.3
5.9
26.3
30.3
32.3
36.5
40.4
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
1.4
1.4
8.0
9.2
9.4
9.9
10.6
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
1.9
1.9
63.3
85.3
88.0
98.1
111.6
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.1
0.2
0.2
0.2
0.2
0.1
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Beta
Value 2
Scale
p1
Probability 1
p2
Probability 2
Feb 2013
South East Water
ADPW
RZ3
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
1.0000
1.0000
1.0000
2.0000
3.0000
4.0000
5.0000
0.21
0.000
0.41
0.000
0.62
0.000
0.83
0.000
1.03
0.000
1.24
0.000
1.44
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
1.54
0.000
1.71
0.000
1.83
0.000
1.95
0.000
2.06
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.19
0.000
0.52
0.000
0.94
0.000
1.37
0.000
1.82
0.00
0.000
0.03
0.000
0.05
0.000
0.07
0.000
0.08
0.000
0.10
0.000
0.10
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2009/10
1.21
Group 2
2014/15
1.44
2019/20
3.40
2024/25
5.12
2029/30
6.88
2034/35
8.65
2039/40
10.43
Group 3
Profile
A
B
C
D
Analysis Statistics
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
1.2
1.4
3.2
4.9
6.7
9.2
10.6
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-0.1
0.1
-7.6
-8.0
-6.8
-5.0
-8.2
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
0.9
1.1
-0.4
1.1
2.2
4.3
5.2
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
1.2
1.4
2.9
4.4
6.0
8.8
10.4
Exponential
Rate Rate
75th percentile
1.5
1.8
6.4
8.3
11.0
14.2
15.7
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
2.3
2.8
15.7
18.9
23.4
26.7
28.5
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
0.5
0.5
4.9
5.2
6.0
6.4
7.2
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
0.2
0.2
23.9
27.2
36.3
41.1
51.4
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
-0.1
0.0
0.3
0.2
0.2
0.1
0.1
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
South East Water
ADPW
RZ4
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.7500
0.7500
0.7500
0.7500
0.7500
0.7500
0.7500
0.80
0.000
1.59
0.000
2.39
0.000
3.18
0.000
3.98
0.000
4.77
0.000
5.57
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
1.00
0.000
1.11
0.000
1.23
0.000
1.34
0.000
1.45
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
1.69
0.000
1.69
0.000
1.69
0.000
1.69
0.000
1.69
0.000
1.69
0.000
1.69
0.31
0.000
0.04
0.000
0.76
0.000
1.89
0.000
3.42
0.000
5.02
0.000
6.67
0.00
0.000
0.08
0.000
0.16
0.000
0.20
0.000
0.25
0.000
0.31
0.000
0.31
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2009/10
3.54
Group 2
2014/15
4.14
2019/20
6.74
2024/25
8.82
2029/30
11.32
2034/35
13.87
2039/40
16.44
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
3.6
4.2
6.7
8.6
11.6
14.1
16.9
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-3.5
-2.4
-6.4
-3.1
-2.8
-3.6
-2.2
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
1.6
2.1
3.7
5.1
7.6
9.4
11.7
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
3.6
4.1
6.6
8.6
11.5
13.9
16.6
Exponential
Rate Rate
75th percentile
5.6
6.4
9.6
12.0
15.5
18.7
22.0
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
10.7
10.4
19.7
23.1
30.0
35.6
39.0
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
2.6
2.7
4.1
4.8
5.5
6.4
7.5
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
6.5
7.1
17.2
23.1
30.6
41.2
56.6
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.0
0.1
0.1
0.2
0.1
0.2
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
South East Water
ADPW
RZ5
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.24
0.000
0.48
0.000
0.72
0.000
0.96
0.000
1.20
0.000
1.44
0.000
1.68
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.51
0.000
0.57
0.000
0.63
0.000
0.68
0.000
0.74
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.41
0.000
0.41
0.000
0.41
0.000
0.41
0.000
0.41
0.000
0.41
0.000
0.41
0.04
0.000
0.00
0.000
0.19
0.000
0.52
0.000
0.94
0.000
1.37
0.000
1.73
0.00
0.000
0.02
0.000
0.03
0.000
0.04
0.000
0.05
0.000
0.06
0.000
0.06
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Triangular
0.00
14
Triangular
0.00
16
Triangular
0.00
18
Triangular
0.00
20
Triangular
0.00
22
Triangular
0.00
20
Triangular
0.00
22
Group 1
2009/10
0.69
Group 2
2014/15
0.90
2019/20
1.87
2024/25
2.50
2029/30
3.22
2034/35
3.96
2039/40
4.62
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
0.7
0.9
2.0
2.5
3.2
4.0
4.5
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-0.7
-0.6
-2.5
-3.4
-2.8
-3.1
-1.8
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
0.2
0.5
0.7
1.1
1.5
2.0
2.5
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
0.7
0.9
2.0
2.4
3.1
3.8
4.4
Exponential
Rate Rate
75th percentile
1.2
1.4
3.2
3.9
4.9
5.9
6.3
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
2.1
2.4
6.5
8.5
10.5
12.0
12.7
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
0.6
0.6
1.7
2.0
2.4
2.7
2.7
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
0.3
0.4
3.0
3.9
5.9
7.3
7.4
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.1
-0.1
0.1
0.2
0.2
0.2
0.2
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
ADPW
RZ6
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.9800
0.9800
0.9800
1.3000
1.3000
1.3000
1.3000
0.00
0.000
0.00
0.000
0.15
0.000
0.31
0.000
0.46
0.000
0.61
0.000
0.76
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.05
0.000
0.07
0.000
0.09
0.000
0.12
0.000
0.14
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.59
0.000
0.59
0.000
0.59
0.000
0.59
0.000
0.59
0.000
0.59
0.000
0.59
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
FALSE FALSE
0.00
Type
FALSE FALSE
D4
0.00
0.19
0.54
0.98
1.45
1.96
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.03
0.05
0.07
0.08
0.10
0.12
0.000
0.000
0.000
0.000
0.000
0.000
Type
FALSE FALSE
0.00
14
0.00
16
0.00
18
0.00
20
0.00
22
0.00
20
0.00
22
Group 1
2009/10
1.57
Group 2
2014/15
1.59
2019/20
2.01
2024/25
2.87
2029/30
3.50
2034/35
4.17
2039/40
4.87
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
1.5
1.6
2.0
2.9
3.5
4.2
4.9
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-2.0
-1.7
-1.3
-1.1
-1.0
-0.7
-2.4
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
0.7
0.7
1.1
1.9
2.3
2.8
2.9
Beta
A
Alpha
B
Scl
Scale
50th percentile
1.5
1.6
1.9
2.8
3.5
4.1
4.7
Exponential
Rate Rate
75th percentile
2.3
2.5
2.8
3.8
4.6
5.6
6.5
Custom
x1
Value 1
x2
Maximum
4.8
5.5
5.4
7.6
8.5
10.8
13.7
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
1.2
1.2
1.3
1.4
1.6
2.0
2.6
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
1.3
1.4
1.6
1.9
2.6
4.1
6.9
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.1
0.1
0.2
0.1
0.2
0.3
0.4
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Beta
Value 2
Scale
p1
Probability 1
p2
Probability 2
Feb 2013
ADPW
RZ7
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.0000
1.0000
2.0000
2.0000
2.0000
2.0000
2.0000
0.00
0.000
0.03
0.000
0.07
0.000
0.10
0.000
0.13
0.000
0.17
0.000
0.20
-0.24
0.000
-0.24
0.000
-0.24
0.000
-0.24
0.000
-0.24
0.000
-0.24
0.000
-0.24
0.00
0.000
0.00
0.000
0.09
0.000
0.13
0.000
0.17
0.000
0.22
0.000
0.26
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.20
0.000
0.20
0.000
0.20
0.000
0.20
0.000
0.20
0.000
0.20
0.000
0.20
0.02
0.000
0.00
0.000
0.07
0.000
0.18
0.000
0.32
0.000
0.47
0.000
0.62
0.00
0.000
0.01
0.000
0.02
0.000
0.02
0.000
0.03
0.000
0.03
0.000
0.03
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
2009/10
-0.01
Group 2
2014/15
1.01
2019/20
2.20
2024/25
2.40
2029/30
2.62
2034/35
2.85
2039/40
3.08
Group 3
Profile
A
B
C
Analysis Statistics
D
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Mean
0.0
1.0
2.2
2.4
2.6
2.8
3.1
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Minimum
-0.9
-0.4
-0.1
0.1
-0.5
-0.8
-0.3
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
25th percentile
-0.3
0.7
1.7
1.9
2.0
2.1
2.2
Beta
A
Alpha
B
Beta
Scl
Scale
50th percentile
0.0
1.0
2.2
2.4
2.6
2.8
3.1
Exponential
Rate Rate
75th percentile
0.2
1.3
2.7
3.0
3.3
3.5
3.9
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Maximum
0.9
2.3
4.4
4.7
5.6
6.8
7.1
Uniform
Min
Min Estimate
Max Max Estimate
Std Deviation
0.3
0.5
0.7
0.8
0.9
1.1
1.2
Weibull
Loc
Location
Scl
Scale
Shp
Shape
Variance
0.1
0.2
0.5
0.7
0.9
1.2
1.5
Gama
Loc
Location
Scl
Scale
Shp
Shape
Skewness
0.0
0.0
-0.1
0.1
0.1
0.2
0.2
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
M/M
Min/Max
Other
Scl
Scale
p2
Probability 2
Feb 2013
ADPW
RZ8
Correlated
With
S1 & S2
By
Overlapping
Component
Component
Dependent
Component
Version Phase 4.5
Date 13/02/2013
Continuous /
intermittent
Company Name
Scenario Ref
Resource Zone Ref
Parameters
2009/10
2014/15
2019/20
2024/25
2029/30
2034/35
2039/40
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.00
0.000
0.65
0.000
1.29
0.000
1.94
0.000
2.58
0.000
3.23
0.000
3.87
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.02
0.000
0.03
0.000
0.04
0.000
0.05
0.000
0.06
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.000
0.00
0.00
0.000
0.00
0.000
0.81
0.000
0.81
0.000
0.81
0.000
0.81
0.000
0.81
0.13
0.000
0.00
0.000
0.28
0.000
0.77
0.000
1.41
0.000
2.10
0.000
2.84
0.00
0.000
0.03
0.000
0.07
0.000
0.09
0.000
0.11
0.000
0.14
0.000
0.14
S4
Bulk transfers
FALSE
)
Type
C
S6
S8
S9
D1
D2
D3
D4
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE
Type
FALSE FALSE
Type
FALSE FALSE
Type
FALSE FALSE
0.000
0.00
0.00
14
16
0.000
0.00
18
0.000
0.00
20
0.000
0.00
22
0.000
0.00
20
0.000
0.00
22
Group 1
Group 2
Group 3
Profile
A
B
C
Fixed
Val
Value
Triangular
Min
Min
Best Best Estimate
Max
Max
Normal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Lognormal
Avg
Mean
Max Max Estimate
SD
St'd Deviation
Beta
A
Alpha
B
Beta
Scl
Scale
Exponential
Rate Rate
Custom
x1
Value 1
x2
Value 2
p1
Probability 1
Uniform
Min
Min Estimate
Max Max Estimate
Weibull
Loc
Location
Scl
Scale
Shp
Gama
Loc
Location
Scl
Scale
Shp
Logistic
Avg
Mean
Scl
Scale
Preto
Loc
Location
Shp Shape
Other
Scl
Scale
M/M
2009/10
0.13
D
2014/15
0.68
2019/20
2.47
2024/25
3.64
2029/30
4.96
2034/35
6.33
2039/40
7.73
Analysis Statistics
Mean
0.1
0.7
2.5
3.6
4.9
6.2
7.6
Minimum
-1.5
-1.0
-0.1
-0.3
0.0
0.3
-0.2
Shape
25th percentile
-0.4
0.1
1.4
2.4
3.4
4.2
4.9
Shape
50th percentile
0.1
0.7
2.5
3.6
4.8
6.0
7.2
75th percentile
0.7
1.2
3.4
4.7
6.2
8.1
9.9
Maximum
1.6
2.4
5.9
7.8
11.4
14.6
17.5
Std Deviation
0.7
0.7
1.3
1.5
2.0
2.8
3.4
Variance
0.4
0.5
1.6
2.3
4.0
7.7
11.7
Skewness
0.0
0.0
0.1
0.1
0.2
0.4
0.4
Min/Max
p2
Probability 2
APPENDIX C – RESOURCE ZONE 2019 MODEL
SIMULATION HEADROOM DATA
DRY YEAR ANNUAL AVERAGE
RZ1 Simulation Results for
DYAA 2019/U50
Simulation Summary Information
Workbook Name
RZ1_v4.5.xlsm
Number of Simulations
1
Number of Iterations
1000
Number of Inputs
482
Number of Outputs
168
Sampling Type
Monte Carlo
Simulation Start Time
3/18/13 16:09:14
Simulation Duration
00:00:05
Random # Generator
RAN3I
Random Seed
50
Summary Statistics for 2019
Statistics
Minimum
-2.96
Percentile
5% -0.88
Maximum
6.44
10% -0.49
Mean
1.77
15% -0.17
Std Dev
1.74
20% 0.19
Variance
3.023653637
25% 0.47
Skewness
0.141126304
30% 0.76
Kurtosis
2.472983958
35% 1.00
Median
1.68
40% 1.23
Mode
2.42
45% 1.44
Left X
-0.88
50% 1.68
Left P
5%
55% 1.95
Right X
4.71
60% 2.20
Right P
95%
65% 2.43
Diff X
5.59
70% 2.68
Diff P
90%
75% 2.96
#Errors
0
80% 3.36
Filter Min
Off
85% 3.72
Filter Max
Off
90% 4.18
#Filtered
0
95% 4.71
Regression and Rank Information for 2019
Rank
1
Name
Regr
S8, WRZ 1 / 2019/20
0.941
Corr
0.960
2
D1, RZ1 / 2019/200.226
0.254
3
D2, RZ1 / 2019/200.112
0.097
4
S6, Kemsing / 2019/20
0.086
0.103
5
S6, Hartlake / 2019/20
0.074
0.119
6
S6, Tonbridge (Gravels)
0.043/ 2019/20
0.001
7
S6, Cramptons Rd 0.020
/ 2019/20
-0.020
8
2019/20
0.014
0.034
9
2014/15
0.014
0.032
10
D3, RZ1 / 2020/210.011
0.702
11
2019/20
0.010
0.018
12
2014/15
0.010
-0.034
13
S6, Saints Hill / 2019/20
0.008
0.026
14
S6, Oak Lane / 2019/20
0.006
0.028
DYAA 2019 / U50
Summary Information
Workbook Name
Simulation
RZ2 - phase 3.3 v4.xls
1
Summary Information
1000
Workbook Name
RZ2_v4.5.xlsm
Number of Outputs
1
Sampling Type
1000
Simulation Start
Time of Inputs
Number
607
04/11/2008 11:23
Simulation Stop
Time of Outputs
Number
140
04/11/2008 11:26
Simulation Duration
Sampling Type
Monte Carlo
3/18/13 15:26:56
Number of Inputs
Random Seed
624
140
Monte Carlo
00:03:17
Simulation Duration
00:00:05
Summary Statistics
RAN3I
Statistic Random # GeneratorValue
%tile
Minimum
-9.83
5%
Random Seed
50
Maximum
Mean
Std Dev
-0.25
0
0.25
Std b Coefficients
0.5
0.75
20%
0.44
25%
1.41
30%
2.03
2.750210723
10% -2.42
35%
2.69
Median
Mean
4.18
4.41
15% -1.37
40%
3.25
Std Dev
5.11
20% -0.52
3.71
45%
3.73
-3.23
50%
4.41
Left P
Variance
26.12993984
25% 0.39
5%
55%
5.14
Right X
Skewness
0.179910168
30% 12.96
1.39
60%
5.83
Right P
Kurtosis
2.458051907
95%
35% 1.98
65%
6.63
Diff X
Median
3.79
40% 16.19
2.55
70%
7.13
Mode
2.98
90%
45% 3.18
75%
7.88
0
80%
9.01
Filter Min
Left X
-3.81
50% 3.79
85%
10.08
Filter Max
Left P
5%
55% 4.52
90%
11.54
#Filtered
Right X
13.13
60% 5.160
95%
12.96
Right P
95%
65% 6.05
#1
Sensitivity
16.94
70% 6.94
Name
Regr
Diff
P
90%
75% 7.96 0.916
S8, WRZ 2 / 2019/20 / $G$18
#2
#Errors
S6, Barcombe /02019/20 / $J$58
80% 8.82
0.264
0.264
#3
D2, RZ2
Filter
Min/ 2019/20
Off / $G$77
85% 9.69
0.197
0.161
#4
D1, RZ2
Filter
Max/ 2019/20
Off / $L$32
90% 11.25 0.149
0.107
0.059
0.051
Rank
1
-0.61
4.95
17.79
Regression Sensitivity for 2019/U50
-0.5
15%
Maximum
#Errors
-0.75
-1.64
4.72
Kurtosis
Diff P
-1
10%
-8.23
Left X
.266
.2
.149
.059
.043
.037
.016
.013
.005
.005
.004
.004
S6, Coggins Mill/Sharnden .../R42
D3, RZ2 / 2030/31/M73
S8, WRZ 4 / 2019/20/G20
-3.23
19.69
Percentile
24.49722684
5% -3.81
0.168281482
Mode
.91
Value
Statistics
Skewness Minimum
Variance
S8, WRZ 2 / 2019/20/G18
S6, Barcombe / 2019/20/J58
D2, RZ2 / 2019/20/G77
D1, RZ2 / 2019/20/L32
S6, Cowish / 2019/20/J46
S4, Weir Wood / 2019/20/H14
S6, Shell Brook/J55
S6, Coggins Mill/Sharnden .../J45
D3, RZ2 / 2020/21/I62
S5, Saddlescombe / 2009/10.../M36
S5, Saddlescombe / 2019/20.../Q36
S5, Offham Springs / 2014/.../O33
S5, Coombe Down / 2034/35/W26
-.004
-.003
-.003
50
#5
#6
Diff X
S6, Cowish / 2019/20 / $J$46
#Filtered
0
0.921
0.041
0.497
0.037
-0.036
#8
Regression
and/ Rank
Information
for 20190.028
S6, Groombridge
2019/20
/ $J$43
0.021
#9
S6, Poverty Bottom
Rank
Name/ 2019/20 / $J$49
Regr
Corr 0.015
2019/20
/ $J$45
1S6, Coggins Mill/Sharnden
S8, WRZ 2 / /2019/20
0.944
0.980 0.015
0.024
#7
#10
#11
#12
S4, Weir Wood / 2019/20 / $H$14
95% 13.13
Corr
S6, Shell Brook / $J$55
S6, Hempstead / 2019/20 / $J$44
2
D1, RZ2 / 2019/200.149
S6, Cockhaise Well / 2019/20 / $J$51
0.076
0.053
0.010
0.018
0.009
-0.015
#14
3S6, Forest RowS4,
Weir Wood
/ 2019/20
0.079 0.524 0.007
/ 2019/20
/ $J$42
4D3, RZ2 / 2020/21
S6, Barcombe
/ 2019/20
0.077 0.091 0.007
/ $I$62
#15
2019/20
$J$52
5S6, Rathfinny / D2,
RZ2 // 2019/200.067
0.038 0.005
0.011
#16
/ 2009/10 // $M$36
6S5, Saddlescombe
S6, Rathfinny
2019/20
0.022
0.049 0.004
-0.053
#13
7
S6, Poverty Bottom
0.018
/ 2019/20
0.018
8
D3, RZ2 / 2020/210.007
9
S5, Saddlescombe0.004
/ 2014/15
-0.021
10
S6, Cockhaise Well0.003
/ 2019/20
-0.031
11
S6, Shell Brook / 2019/20
0.002 -0.029
12
S6, Hempstead / 2019/20
0.002 -0.051
13
S5, Whitelands / 2014/15
0.001 0.055
14
S5, Eridge / 2014/15
0.001
0.702
0.034
-0.008
0.671
DYAA 2019 / U50
Summary Information
Workbook Name
1
Workbook Name
RZ3_v4.5.xlsm
1000
Number of Inputs
1
629
Number of Outputs
1000
Sampling Type
140
Monte Carlo
Number of Inputs
653
Number of Outputs
140
Simulation Duration
Sampling Type
Monte Carlo
Random Seed
3/18/13 16:17:00
Simulation Stop Time
Simulation Duration
04/11/2008 12:02
04/11/2008 12:06
00:03:16
50
00:00:06
Random # Generator
RAN3I
Summary Statistics
StatisticRandom Seed
Value
%tile
50
Minimum
Std b Coefficients
35%
2.43
2.57
20% 0.85
3.67
40%
2.86
Mode
Variance
6.595277205
25% 1.36
3.99
45%
3.29
Left X
Skewness
0.11431686
50%
3.67
Left P
Kurtosis
2.474100524
30% -1.03
1.67
35% 2.06
5%
55%
4.01
Right X
Median
3.01
8.82
40% 2.45
60%
4.39
Right P
Mode
2.95
95%
45% 2.69
65%
4.93
9.84
50% 3.01
90%
70%
5.35
55% 3.29
75%
5.80
0
80%
6.32
85%
7.14
90%
7.85
95%
8.82
Diff X
Left X
-0.96
Diff P
Left P
5%
Percentile
2.98
5% -0.96
10% -0.20
15% 0.30
Right X
7.48
60% 3.62
Right P
95%
65% 4.13
Diff X
8.44
70% 4.47
Diff P
90%
75% 4.96
#Errors
0
Rank Filter Min
Off
0
Sensitivity80% 5.48
85% 6.11 Regr
Name
Corr
#1
S8, WRZ
/ $G$19
Filter
Max 3 / 2019/20
Off
90% 6.63
0.821
0.906
#2
D2, RZ3 / 2019/20
/ $G$78
#Filtered
0
95% 7.48
0.253
0.235
#3
D1, RZ3 / 2019/20 / $L$33
0.208
0.234
20190.176
0.215
0.135
0.550
#6
#7
#8
#9
#10
S6, Arlingtonand
/ 2019/20
$J$56
Regression
Rank/ Information
for
S4, Darwell Reservoir / 2019/20 / $G$10
Rank
Name
Regr
S6, Wallers Haven / $J$55
1
S8, WRZ 3 / 2019/20
0.947
Corr
0.091
0.946
2
0.205
S6, Stonegate / 2019/20 / $J$46
0.144
0.041
0.008
0.033
0.036
3 S6, River Rother
S4, at
Darwell
Reservoir
/ 2019/20
0.122 0.027
Crowhurst
Br0.161
/ $J$53
4 S6, CrowhurstD2,
RZ3
/
2019/200.114
Bridge / 2019/20 / $J$42 0.101 0.027
0.044
D1, RZ3 / 2019/200.245
S6, Birling Farm / 2019/20 / $J$47
0.046
-0.013
#12
5 S6, Friston & Deep
S6, Wallers
0.055
/ 2019/20
Dean / Haven
2019/20
/ $J$350.067 0.022
6 2014/15 / $J$28
2014/15
0.014 0.118 0.014
#13
7 2019/20 / $L$28
S6, Friston & Deep0.013
Dean / 2019/20
0.050 0.014
-0.063
#14
8 2009/10 / $H$28
2019/20
0.013
0.025 0.014
-0.008
#15
2019/20
/ $J$45
9 S6, Turzes Farm
D3,/RZ3
/ 2020/210.011
0.711 0.012
0.037
#16
S6, Water Works Rd / 2019/20 / $J$50
0.010
0.024
#11
1
2.03
2.606538542
Std Dev
#5
0.75
30%
Median
#4
0.5
1.69
0.176731223
3.15
0.25
25%
Mean
#Filtered
0
1.18
8.884103909
10.20
Filter Max
-0.25
0.61
20%
Maximum
Filter Min
-0.5
15%
-3.73
#Errors
-0.75
-0.02
3.79
Statistics
Minimum
Kurtosis
-1
10%
Mean
Skewness
.255
.211
.176
.134
.092
.034
.029
.026
.019
.018
.015
.013
.006
.006
.006
-1.03
12.92
Variance
.816
5%
Maximum
Std Dev
S8, WRZ 3 / 2019/20/G19
D2, RZ3 / 2019/20/G78
D1, RZ3 / 2019/20/L33
S6, Arlington / 2019/20/J56
S4, Darwell Reservoir / 20.../G10
S6, Wallers Haven/J55
S6, Birling Farm / 2019/20.../J47
S6, Crowhurst Bridge / 201.../J42
S6, River Rother at Crowhu.../J53
2019/20/L28
2014/15/J28
D3, RZ3 / 2020/21/I63
S6, Turzes Farm / 2019/20/J45
S6, Sweet Willow Rd / 2019.../J36
S6, Powder Mill (BH1) / 20.../N33
2014/15/J25
Value
-4.15
10
S6, Birling Farm / 2019/20
0.010 0.046
11
S6, Crowhurst Bridge
0.009
/ 2019/20
0.020
12
2014/15
13
S6, Wallers Haven0.005
Augmentation
-0.017 BHs / 2019/20
14
2019/20
0.005
0.005
-0.043
0.029
0.057
DYAA 2019 / U50
Summary Information
Workbook Name
Workbook Name
RZ4_v4.5.xlsm
1
1000
Number of Inputs
1000
600
Number of Outputs
590
Number of Inputs
Sampling Type
1
133
Monte Carlo
Number of Outputs
126
Sampling Type
Monte Carlo
3/18/13 16:25:41
Simulation Duration
00:00:06
Random # Generator
RAN3I
Simulation Duration
Random Seed
05/11/2008 11:48
05/11/2008 11:51
50
Random Seed Summary Statistics
50
Statistic
Value
%tile
Minimum Summary Statistics for 2019
-6.39
5%
Maximum Statistics
Minimum
-5.52
Std Dev
Maximum
18.36
10% 5.22
0.05
20%
2.71
Variance
Mean
5.44
27.2450667
15% 0.97
25%
3.54
Skewness Std Dev
Kurtosis Variance
4.13
0.061399978
20% 1.75
30%
4.37
17.04516276
2.593131526
25% 2.47
35%
5.16
Median
Skewness
0.102877761
30% 7.58
3.16
40%
5.90
Kurtosis
2.644119562
35% 3.75
11.09
45%
6.85
Median
5.41
-1.08
40% 4.27
50%
7.58
Mode
5.87
5%
45% 4.74
55%
8.33
Left X
-1.24
16.36
50% 5.41
60%
8.97
5%
55% 5.89
65%
9.57
Diff X
Left P
95%
17.44
70%
10.33
Diff P
Right X
12.49
60% 90%
6.40
75%
11.04
#Errors
Right P
95%
65% 7.07
0
80%
11.76
Filter Min
Diff X
13.73
70% 7.70
85%
12.91
90%
75% 8.40
90%
14.24
#Errors
0
80% 8.94
0
95%
16.36
Filter Min
Off
85% 9.77
Filter Max
Off
Left P
Right X
Right P
Filter Max Diff P
#Filtered
-1
-0.75
-0.5
-0.25
Rank #Filtered
#1
#2
#3
#4
.214
.14
.117
.067
.054
.044
.043
.027
.023
.016
.014
.009
0
0.25
Std b Coefficients
#5
1.86
Sensitivity 90% 10.97
Name
95% 12.49Regr
S8, WRZ 4 / 2019/20 / $G$20
Corr
0.573
0.670
D2, RZ4 / 2019/20
/ $G$79
0.462
Regression
and Rank
Information for 2019
0.462
S6, Bray South / 2019/20 / $J$49
0.352
Rank
Name
Regr
D1, RZ4 / 2019/20 / $L$34
1
S8, WRZ 4 / 2019/20
0.727
Corr
0.347
0.800
2
D1, RZ4 / 2019/200.469
0.457
D2, RZ4 / 2019/200.211
0.179
0.341
0.407
0.118
0.100
0.068
0.081
0.043
#10
5 S6, West HamS4,
Valleys
/0.172
2019/200.481 0.055
PSThree
/ 2019/20
/ $J$42
6 S6, Boxhalls Lane
S6, College
Avenue0.094
2019/20
0.102 0.044
LGS / 2019/20
//$J$39
#11
7 S6, West HamS6,
Hurley
/ 2019/20
0.066
Park
/ 2019/20
/ $J$43
0.046 0.042
8 S6, Itchel / 2019/20
S6, West
Ham Park
0.063
/ 2019/20
0.075 0.026
/ $J$40
0.047
0.031
#14
9 S6, Woodgarston
S6, West
Ham/ PS
/0.033
2019/200.065 0.023
/ 2019/20
$J$46
/ $I$64
10D3, RZ4 / 2020/21
D3, RZ4
/ 2020/210.020 0.640 0.017
#15
/ 2019/20
$J$31 0.016
11S6, Cookham S6,
Itchel / 2019/20
#16
Heath
/ 2019/20
/0.015
$J$47
0.033
12S6, BeenhamsS6,
Beenhams
Heath
(including
0.050W0.014
Waltham) / 2019/20
#9
#12
#13
3
4
S6, Hurley / 2019/20 / $J$38
0.389
0.181
#8
1
0.73
15%
0.139
#7
0.75
0
10%
0.214
#6
0.5
-1.08
Mean
Left X
.572
.461
.353
.347
Value
Percentile
22.42
5% 7.45
-1.24
Mode
S8, WRZ 4 / 2019/20/G20
D2, RZ4 / 2019/20/G79
S6, Bray South / 2019/20/J49
D1, RZ4 / 2019/20/L34
S6, Hurley / 2019/20/J38
S4, Three Valleys / 2019/2.../G14
S6, College Avenue / 2019/.../J45
S6, Bray Gravels / 2019/20.../J44
S6, West Ham PS / 2019/20/J42
S6, Boxhalls Lane LGS / 20.../J39
S6, West Ham Park / 2019/2.../J43
S6, Itchel / 2019/20/J40
S6, Woodgarston / 2019/20/J46
D3, RZ4 / 2020/21/I64
S6, Beenhams Heath / 2019/.../J47
S6, Boxhalls Lane Chalk / .../J34
00:03:30
S4, Three Valleys / 2019/20 / $G$14
S6, College Avenue / 2019/20 / $J$45
S6, Bray Gravels / 0.177
2019/200.167
S6, Bray Gravels / 2019/20 / $J$44
0.052 0.015
13
S6, Woodgarston /0.015
2019/200.033
14
S6, Lasham / 2019/20
0.009
0.030
0.036
0.123
0.505
-0.015
DYAA 2019 / U50
Summary Information
Workbook Name
Summary Information
Number Simulation
of Simulations
RZ5_v4.5.xlsm
1000
Number Number
of Inputsof Simulations
1
523
Number Number
of Outputs
of Iterations
1000
140
Sampling
Type of Inputs
Number
510
Monte Carlo
Simulation
Start Time
Number
of Outputs
140
05/11/2008 12:15
Sampling Type
Monte Carlo
05/11/2008 12:18
3/18/13 16:32:40
Simulation Duration
00:00:05
Random # Generator
RAN3I
Simulation Duration
Random Seed
Summary Statistics
Value 50
%tile
Random Seed
Statistic
Minimum
Summary Statistics for
Maximum
2019
-0.25
0
0.25
0.64
0.179238319
15% -0.07 30%
0.82
KurtosisStd Dev
1.18
2.631792274
20% 0.18
35%
0.99
Median Variance
Mode Skewness
1.402462576
25%1.57
0.36
40%
1.21
0.093043381
30%1.36
0.52
45%
1.37
Left X
Kurtosis
2.507451832
35% 0.68
-0.49
50%
1.57
Median
1.17
5%
40% 0.82
55%
1.71
Mode
1.55
3.92
45% 1.02
60%
1.91
Left X
-0.73
95%
50% 1.17
65%
2.03
4.41
70%
2.23
Left P
5%
55%90%
1.34
75%
2.46
#Errors Right X
3.17
60% 1.50
0
80%
2.72
Filter MinRight P
95%
65% 1.67
85%
3.00
Diff X
Filter Max
3.91
70% 1.82
90%
3.36
#FilteredDiff P
90%
75% 2.05
0
95%
3.92
#Errors
0
80% 2.27
Filter Min
Off
Rank
Filter Max
Off
Std b Coefficients
#1
S8, WRZ 05 / 2019/20 / $G$21 95% 3.17 0.845
#Filtered
Corr
0.842
D2, RZ5 / 2019/20 / $G$80
0.350
0.332
#3
D1, RZ5 / 2019/20 / $L$35
0.322
0.310
0.161
0.163
#6
#7
1
Sensitivity
85% 2.52
Name
90% 2.82 Regr
0.42
#2
#5
0.75
0.21
1.20
#4
0.5
-0.09
Skewness
Mean
-0.5
10%
1.713826706
10% -0.32 25%
Diff P
-0.75
-0.49
5.53
4.26
Diff X
-1
5%
VarianceMaximum
Right P
.16
.102
.099
.032
.022
.014
.012
.009
.009
.007
.007
.006
.006
Value
-2.16
-1.69
Right X
.35
.318
50
Percentile
1.59
15%
5%1.31
-0.73 20%
Left P
.846
00:02:33
Statistics
Std Dev Minimum
Mean
S8, WRZ 5 / 2019/20/G21
D2, RZ5 / 2019/20/G80
D1, RZ5 / 2019/20/L35
S6, Greatham / 2019/20/J35
S6, The Bourne / 2019/20/J40
S6, Britty Hill / 2019/20/J39
S6, Oakhanger / 2019/20/J31
S6, Tilford WR / 2019/20/J28
S6, Rushmoor / 2019/20/J33
D3, RZ5 / 2020/21/I65
2019/20/L30
2019/20/L34
2009/10/H30
2009/10/H33
2009/10/H34
2014/15/J34
1
Number Workbook
of Iterations
Name
Regression and Rank Information for 2019
S6, Greatham / 2019/20 / $J$35
Rank
Name
Regr
S6, The Bourne / 2019/20 / $J$40
1
S8, WRZ 5 / 2019/20
0.911
Corr
0.103
0.926
2
0.310
S6, Britty Hill / 2019/20 / $J$39
D1, RZ5 / 2019/200.335
S6, Tilford Meads / 2019/20 / $J$27
0.069
0.098
0.084
0.033
0.022
0.123 0.032
0.067
#8
3
D2, RZ5
/ 2019/200.156
S6, Oakhanger
/ 2019/20
/ $J$31
#9
4
S6, park
Greatham
/ 2019/20
0.090
S6, Headley
/ 2019/20
/ $J$32 0.101 0.028
#10
5
Britty
Hill / 2019/20
0.054
S6, TilfordS6,
WR
/ 2019/20
/ $J$28
0.074 0.020
-0.037
#11
6
D3, RZ5
/ 2020/210.016
S6, East Meon
/ 2019/20
/ $J$41
0.676 0.019
0.006
#12
7
Tilford/ Meads
2019/20-0.0390.015
D3, RZ5 /S6,
2020/21
$I$65 /0.016
0.649
#13
8
S6, Rushmoor
/ 2019/20 (including
/ $J$33
-0.029
S6, Oakhanger
0.015 oaklands
-0.0170.014
and southlands)
/ 2019/20
#14
9
2019/20 / 2019/20
$L$30
0.013
0.045 0.009
0.050
#15
10
2014/15 / 2019/20
$J$30
0.013
0.029 0.008
0.012
#16
11
2019/20 / 2014/15
$L$34
0.013
0.015 0.008
0.050
12
2019/20
0.012
-0.018
13
2014/15
0.012
0.014
14
S6, Hawkley / 2019/20
0.011
-0.023
0.024
DYAA 2019 / U50
Summary Information
Workbook Name
Simulation
Summary Information
Workbook Name
1
RZ6_v4.5.xlsm
Number of Inputs
Number of Outputs
693
1
140
Sampling Type
1000
Number
Simulation Start
Timeof Inputs
629
05/11/2008 12:29
Simulation Stop
Timeof Outputs
Number
140
05/11/2008 12:31
Simulation Duration
Sampling Type
Monte Carlo
Random Seed
3/18/13 16:36:47
Monte Carlo
Simulation Duration
00:00:06
Summary Statistics
RAN3I
StatisticRandom # GeneratorValue
Minimum
Random Seed
50
Maximum
Mean
Std Dev
Statistics
Variance
Skewness Minimum
Maximum
Kurtosis
-0.25
0
0.25
Std b Coefficients
1.17
1.19
20%
1.38
25%
1.59
30%
1.77
-1.06
Percentile
1.412203795
5% 0.08
0.091444931
4.06
10% 0.35
2.74827377
35%
1.93
2.12
20% 4.15
0.72
45%
2.27
Left X
Variance
0.847923168
25% 0.88
0.50
50%
2.42
Skewness
0.059484608
5%
30% 1.03
55%
2.57
4.47
60%
2.74
Right P
Kurtosis
2.602221986
35% 95%
1.17
65%
2.90
Diff X
Median
1.53
70%
3.07
Diff P
Mode
1.33
40% 3.97
1.31
45% 90%
1.42
75%
3.23
#Errors
Left X
0.08
0
50% 1.53
80%
3.48
Left P
5%
55% 1.67
85%
3.73
Right X
3.13
60% 1.81
0
90%
3.96
95%
4.47
Right P
95%
65% 1.94
Diff X
3.04
Rank Diff P
#1
#2
90% Name
D2, RZ6 / 2019/20 / $G$81
75% 2.21 Regr
#Errors
0
80% 2.34
Filter Min
Off
85% 2.53
D1, RZ6 / 2019/20 / $L$36
S4, Matts Hill / 2019/20 / $G$13
Corr
0.706
0.652
0.579
0.554
0.220
0.247
#4
Filter
Max / 2019/20
Off
S6, Trosley
/ $J$54
90% 2.76
0.209
0.216
#5
#Filtered
0 / 2019/20 / $J$52 95% 3.13
S6, Hartley Chalk
0.148
0.053
0.122
0.093
S6, Thurnham
Bh Rank
11 / 2019/20
/ $J$48 for 2019
0.113
Regression
and
Information
0.112
#7
#8
S6, Thurnham Bh 13 / 2019/20 / $J$49
S8, RZ6 / 2019/20 / $G$15
0.113
#10
Rank
Name
Regr
Corr
S6, Hockers lane / 2019/20 / $J$50
0.080
1 S6, Hartley Greensand
D1, RZ6 //2019/200.731
0.753 0.077
2019/20 / $J$43
#11
2 S6, Forstal BhD2,
RZ6 / 2019/200.334
4 / 2019/20
/ $J$41
#9
1
15%
0.92
#6
0.75
0.92
2.44
Std Dev
#3
0.5
10%
Mode
-0.5
0.50
6.39
40%
#Filtered
-0.75
Value
5%
15% 2.42
0.57
Filter Max
-1
%tile
-0.88
1.56
Filter Min
.22
.205
.148
.122
.114
.113
.081
.08
.077
.067
.048
.044
.04
.033
50
Mean
Right X
.703
.576
00:02:34
Median
Left P
D2, RZ6 / 2019/20/G81
D1, RZ6 / 2019/20/L36
S4, Matts Hill / 2019/20/G13
S6, Trosley / 2019/20/J54
S6, Hartley Chalk / 2019/2.../J52
S6, Thurnham Bh 13 / 2019/.../J49
S6, Thurnham Bh 11 / 2019/.../J48
S8, RZ6 / 2019/20/G15
S6, Hockers lane / 2019/20.../J50
S6, Hartley Greensand / 20.../J43
S6, Forstal Bh 4 / 2019/20.../J41
S6, Boxley Well Source / 2.../J51
S6, Borough Green / 2019/2.../J46
S6, Thurnham Bh 12 / 2019/.../J42
S6, Ridley / 2019/20/J53
S6, Boxley Greensand / 201.../J45
1000
0.217
0.094
0.129
0.085
#12
0.283 0.076
2019/20
/ $J$51 0.354 0.071
3 S6, Boxley Well
S4,Source
Matts /Hill
/ 2019/20
0.279
#13
/ 2019/20
/ $J$46
4 S6, Borough Green
S6, Forstal
Sourceworks
0.246 / 2019/20
0.204 0.045
0.040
#14
5
#15
#16
6
0.043
S6, SWS Burham / $J$57
0.044
0.025
S6, Ridley / 2019/20 / $J$53
0.042
0.104
S6, Thurnham Bh 12 / 2019/20 / $J$42
0.041
0.042
S6, Thurnham Bhs0.237
/ 2019/20
0.225
S6, Hartley Chalk /0.169
2019/200.178
7
S8, RZ6 / 2019/20 0.145
0.280
8
S6, Matts Hill (Belmont)
0.143 / 2019/20
0.139
9
S6, Trosley / 2019/20
0.135
10
S6, Cossington Greensand
0.055 0.049
/ 2019/20
11
S6, Boxley Greensand
0.053
/ 2019/20
0.038
12
S6, Hockers lane /0.050
2019/200.082
13
S6, Hartley Greensand
0.050/ 2019/20
0.083
14
S6, Boxley Well Source
0.043/ 2019/20
0.034
0.150
DYAA 2019 / U50
Summary Information
Workbook Name
Simulation
RZ7_v4.5.xlsm
Number of Outputs
1
Sampling Type Number of Iterations
1000
Number of Inputs
140
Monte Carlo
Random Seed
5%
-0.40
2.42
10%
-0.19
0.55
15%
-0.05
0.58
20%
0.07
0.340382063
25%
0.16
30%
0.25
50
Variance
Statistics
Minimum
0.02
Percentile
0.027691689
5% 1.05
3.084046579
35%
0.33
Median
Maximum
5.32
10% 1.37
0.56
40%
0.40
Mode
Mean
2.37
15% 1.57
0.19
45%
0.49
Left X
Std Dev
0.78
-0.40
20% 1.73
50%
0.56
Left P
Variance
0.601882773
5%
25% 1.86
55%
0.63
Skewness
0.048107958
1.48
30% 1.97
60%
0.69
95%
65%
0.78
Diff X
Kurtosis
3.18314579
35% 2.08
1.89
70%
0.86
Diff P
Median
2.38
40% 2.19
90%
75%
0.95
#Errors
Mode
2.65
45% 2.280
80%
1.04
85%
1.14
90%
1.31
95%
1.48
Right X
Right P
Filter Min
Filter Max
#Filtered
-0.25
0
0.25
Std b Coefficients
0.5
Rank
1
Left X
1.05
50% 2.38
Left P
5%
55% 2.47
Right X
3.63
60% 2.55
Right P
95% Sensitivity 65% 2.64
2.58Name
70% 2.74 Regr
Diff X
#1
S6, Bewl Res / 2019/20 / $J$25
#2
D2, RZ7 / 2019/20 / $G$82
#3
D1, RZ7 / 2019/20 / $L$37
#4
Min
Off/ $G$16
S8,Filter
WRZ7
/ 2019/20
0
Corr
0.669
0.655
0.431
0.412
0.347
0.346
85% 3.18
0.323
0.347
#5
S6,Filter
Goudhurst
8, 9 and 14 / 2019/20 / 90%
$J$223.31
Max BhsOff
0.252
0.218
#6
S6,#Filtered
Bewl Bridge Bhs
0 / 2019/20 / $J$21
0.217
0.230
#7
S6, Goudhurst Bh 11 & 13 / 2019/20 / $J$23
0.132
0.117
#8
S6, Lamberhurst Sourceworks / 2019/20 / $J$19
0.035
-0.038
#9
D3, RZ7 / 2020/21 / $I$67
#10
Name/ $L$5
S5,Rank
Goudhurst / 2009/10
#11
0.75
Value
-1.19
Kurtosis
-0.5
50
Std Dev
Skewness
-0.75
00:02:49
3/18/13 16:47:31
Summary Statistics
Simulation Duration
00:00:04
Value
%tile
Random # Generator
RAN3I
Mean
-1
05/11/2008 12:46
Random Seed Sampling Type
Maximum
.431
.347
.323
.252
.217
.132
.035
.017
.016
.016
.015
.009
.008
.008
.003
05/11/2008 12:44
308
140
Statistic
292
Monte Carlo
Number of Outputs
Simulation Duration
Minimum
.669
1000
Workbook Name
Number of Inputs
S6, Bewl Res / 2019/20/J25
D2, RZ7 / 2019/20/G82
D1, RZ7 / 2019/20/L37
S8, WRZ7 / 2019/20/G16
S6, Goudhurst Bhs 8, 9 and.../J22
S6, Bewl Bridge Bhs / 2019.../J21
S6, Goudhurst Bh 11 & 13 /.../J23
S6, Lamberhurst Sourcework.../J19
D3, RZ7 / 2020/21/I67
S5, Goudhurst / 2009/10/L5
S5, Goudhurst / 2014/15/N5
S5, Goudhurst / 2019/20/P5
S6, Maytham Farm / 2019/20.../J17
1
Summary Information
#12
Diff P
90%
75% 2.87
#Errors
0
80% 3.03
95% 3.63
Regression and Rank Information for 20190.017
Regr
S5,1 Goudhurst / 2014/15
/ $N$5
Burham
/ 2019/200.530
Corr 0.016
0.563 0.016
#13
S5,2 Goudhurst / 2019/20
/ $P$5 SW / 2019/20
Bewl Bridge
0.517 0.482 0.015
S5, Goudhurst / 2009/10 / $L$6
0.009
#14
S5, Goudhurst / 2019/20 / $P$6
#15
S5, Goudhurst / 2014/15 / $N$6
#16
3
S6, Bewl Bridge Bhs
0.424
/ 2019/20
0.398
4
D1, RZ7 / 2019/200.278
0.008
0.324 0.008
Goudhurst
Sourceworks
0.268 0.243
/ 2019/20
S6,5 Maytham FarmS6,
/ 2019/20
/ $J$17
0.003
6
S8, WRZ7 / 2019/20
0.239
0.236
7
D2, RZ7 / 2019/200.128
0.183
8
S6, Goudhurst Bh 11
0.075
& 13 /0.097
2019/20
9
S6, Bewl Res / 2019/20
0.044
0.016
10
D3, RZ7 / 2020/210.013
0.170
11
S6, Lamberhurst Sourceworks
0.013 0.020
/ 2019/20
12
S5, Goudhurst / 2019/20
0.012 -0.039
13
0.012 0.068
14
0.003 -0.006
0.278
0.050
-0.024
-0.023
0.007
0.039
0.025
-0.027
DYAA 2019 / U50
Summary Information
Workbook Name
Simulation
Summary Information
Workbook Name
Number of Inputs
602
1
1000
Number of Outputs
Sampling Type
1
140
Monte Carlo
Number
Simulation Start
Time of Inputs
572
05/11/2008 14:29
Number
Simulation Stop
Time of Outputs
140
05/11/2008 14:30
Simulation Duration
Sampling Type
Monte Carlo
Random SeedSimulation Start Time
3/18/13 16:53:03
Simulation Duration
00:00:05
Random Seed
50
Summary Statistics
RAN3I
Minimum
0.10
5.82
10%
0.44
2.19
15%
0.71
1.34
Percentile
1.79123971
5% 0.63
20%
0.95
25%
1.18
0.124418484
30%
1.37
2.49074447
35%
1.56
Statistics
Minimum
-0.01
Kurtosis
Maximum
4.98
Median
Mean
2.13
15% 1.00
2.20
40%
1.79
Mode
Std Dev
0.98
20% 1.20
3.41
45%
2.00
Left X
Variance
0.961466464
0.10
25% 1.38
50%
2.20
Left P
Skewness
0.150164999
5%
30% 1.51
55%
2.37
Kurtosis
2.32580547
35% 1.65
4.48
60%
2.54
2.12
40% 1.81
4.38
65%
2.69
Diff X
Median
95%
70%
2.88
Diff P
Mode
1.64
45% 1.96
90%
75%
3.10
#Errors
Left X
0.63
0
50% 2.12
80%
3.36
Filter Min
Left P
5%
55% 2.26
85%
3.59
Filter Max
Right X
3.81
60% 2.37
90%
4.02
Right P
95%
65% 2.53
95%
4.48
Diff X
3.18
Right P
#Filtered
-0.75
-0.5
-0.25
0
0.25
Std b Coefficients
0.5
0.75
0
#1
#2
D1,
RZ8
/ 2019/20
Filter
Min
Off/ $L$38
85% 3.20
0.582
0.598
#3
S6, Wichling / 2019/20 / $J$47
0.229
0.200
#4
S6, Newnham / 2019/20 / $J$49
0.199
0.168
#5
S6, Wineycock Shaw / 2019/20 / $J$48
0.106
0.159
#6
S6, Charing Bhs 2, 3 and 4 / 2019/20 / $J$43
0.087
0.123
#7
Regression
Rank
Information
S6,
Westwell / and
2019/20
/ $J$45
for 20190.069
0.067
#8
Corr 0.057
0.888 0.053
0.060
#9
S6,
2019/20 / $J$30 Regr
RankThanington /Name
S8,
/ $G$17
1 WRZ8 / 2019/20
D1, RZ8
/ 2019/200.874
#10
S6, Charing Bh 5 / 2019/20 / $J$44
0.048
0.117
#11
S6, Howfield / 2019/20 / $J$31
#12
S6, Chilham / 2019/20 / $J$33
#13
4 Godmersham
S6,/ 2019/20
Newnham
/ 2019/20
0.071
S6,
/ $J$34
#14
Rank
1
10% 0.85
Diff P
90% Name
75% 2.88 Regr
D2,
RZ8 / 2019/20
#Errors
0 / $G$83
80% 3.02 0.710
-1
Value
5%
Std Dev
Right X
.577
%tile
Skewness
.223
.196
.106
.088
.07
.057
.051
.047
.04
.039
.038
.032
.026
.014
50
Mean
Variance
.708
00:00:57
-1.18
Maximum
D2, RZ8 / 2019/20/G83
D1, RZ8 / 2019/20/L38
S6, Wichling / 2019/20/J47
S6, Newnham / 2019/20/J49
S6, Wineycock Shaw / 2019/.../J48
S6, Charing Bhs 2, 3 and 4.../J43
S6, Westwell / 2019/20/J45
S6, Thanington / 2019/20/J30
S8, WRZ8 / 2019/20/G17
S6, Charing Bh 5 / 2019/20.../J44
S5, Chilham / 2019/20/P5
S6, Howfield / 2019/20/J31
S5, Thanington / 2019/20/P12
D3, RZ8 / 2020/21/I68
S5, Howfield / 2019/20/P11
S5, Newnham / 2019/20/P15
1000
RZ8_v4.5.xlsm
Filter Max
Off
90% 3.44
#Filtered
0
95% 3.81
2
D2, RZ8 / 2019/200.421
0.395
3
S6, Wichling / 2019/20
0.076
0.132
Corr
0.707
0.077
0.043
0.040
0.042
-0.026
0.068 0.042
0.073 0.040
0.012
5 Chilham / 2019/20
S8, WRZ8
/ 2019/20
0.064
S5,
/ $P$5
#15
S5,
6 Godmersham
S5,/ 2019/20
Chilham/ /$P$6
2014/15
0.056
0.066 0.039
0.030
#16
S5,
2019/20
/ $P$12 /0.051
7 Thanington /S5,
Godmersham
2014/150.017 0.037
0.056
8
S5, Chilham / 2019/20
0.050
0.063
9
D3, RZ8 / 2020/210.047
0.062
10
S5, Thanington / 2014/15
0.044 0.056
11
0.039 0.081
12
S5, Howfield / 2014/15
0.038
13
S6, Wineycock Shaw
0.033
/ 2019/20
0.045
14
S5, Howfield / 2019/20
0.032
0.021
-0.037
0.046
APPENDIX D – RESOURCE ZONE 2019 MODEL
SIMULATION HEADROOM DATA
DRY YEAR CRITICAL PERIOD
ADPW 2019 all - peak / U55
Summary Information
Workbook Name
RZ1- phase 3.3 v4.xls
1
Workbook Name
RZ1_v4.5.xlsm
1000
Number of Inputs
1
492
Number of Outputs
1000
Number of Inputs
482
Number of Outputs
168
Simulation Duration
Sampling Type
Monte Carlo
Random Seed
3/18/13 16:09:14
Sampling Type
Simulation Duration
168
Monte Carlo
04/11/2008 09:44
04/11/2008 09:47
00:03:14
50
00:00:05
Random # Generator
RAN3I
Summary Statistics
Statistic Random Seed
Value
%tile
50
Minimum
Maximum
Mean
0
0.25
Std b Coefficients
35%
1.14
40%
1.53
45%
1.84
Mode
Variance
6.765095541
20% 2.23
0.30
25% -0.49
0.68
Left X
Skewness
0.223969141
30% -1.73
1.13
50%
2.23
Left P
Kurtosis
2.535514477
5%
35% 1.45
55%
2.63
Right X
Median
2.40
40% 6.90
1.80
60%
3.03
Right P
Mode
2.39
45% 95%
2.04
65%
3.47
Diff X
Left X
-1.43
50% 8.63
2.40
70%
3.92
90%
75%
4.32
0
80%
4.95
85%
5.42
90%
6.09
95%
6.90
15% -0.37
Left P
5%
55% 2.76
Right X
7.20
60% 3.13
Right P
95%
65% 3.43
Diff X
8.63
70% 3.86
0
Diff P
90%
75% 4.27
#Errors
0
Rank Filter Min
Off Name
85% 5.49 Regr
Corr
#1
S8, WRZ
/ $G$37
Filter
Max 1 / 2019/20
Off
90% 6.14
0.945
0.943
#2
D2, RZ1 / 2019/20
#Filtered
0 / $G$86
95% 7.20
0.260
0.214
#3
D1, RZ1 / 2019/20 / $L$42
0.172
0.193
S6, Kemsing / 2019/20 / $J$29
Regression
and Rank Information for 20190.079
0.057
#6
#7
#8
1
0.79
2.436995697
2.60
#5
0.75
30%
Std Dev
#4
0.5
0.38
0.134665614
Median
#Filtered
-0.25
25%
2.57
Filter Max
-0.5
0.05
7.177009959
Mean
Filter Min
-0.75
-0.47
20%
10% -0.84
#Errors
-1
15%
9.84
Diff P
D2, RZ7 / 2029/30/K82
2029/30/P22
S6, Pembury (TW) / 2029/30.../N32
.006
.005
-1.03
2.41
-3.49
Kurtosis
.259
.171
.077
.023
.014
.011
.011
.009
.008
.007
10%
Maximum
Skewness
.943
-1.73
9.49
Percentile
2.68
5% -1.43
Variance
S8, WRZ 1 / 2019/20/G37
D2, RZ1 / 2019/20/G86
D1, RZ1 / 2019/20/L42
S6, Kemsing / 2019/20/J29
S6, Hartlake / 2019/20/J31
S6, Tonbridge (Gravels) / .../J34
S6, Oak Lane / 2019/20/J28
2009/10/H28
D3, RZ1 / 2020/21/I72
D2, RZ2 / 2014/15/E87
D2, RZ6 / 2009/10/C81
-.006
-.006
-.006
D2, RZ3 / 2009/10/C88
2014/15/J27
5%
Statistics
Minimum
Std Dev
Regression Sensitivity for 2019 all peak/U55
Value
-4.40
S6, Saints Hill / 2019/20 / $J$30
0.052
0.021
0.033
-0.009
Rank
Name
Regr
S6, Pembury (Ash) / 2019/20 / $J$33
1
S8, WRZ 1 / 2019/20
0.968
Corr
0.044
0.973
2S6, Hartlake / 2019/20
D1, RZ1 // $J$31
2019/200.187
0.174 0.024
S6, Cramptons Rd / 2019/20 / $J$25
0.012
0.035
0.034
#10
3S6, Tonbridge D2,
RZ1 / 2019/200.090
(Gravels)
/ 2019/20 / $J$34 0.079 0.015
4S6, Oak Lane /S6,
Kemsing
/ 2019/20
0.065 0.080 0.011
2019/20
/ $J$28
#11
5D3, RZ1 / 2020/21
S6, Hartlake
0.049
/ $I$72 / 2019/20
0.707
#12
0.021 0.008
62019/20 / $L$28
S6, Tonbridge (Gravels)
0.028/ 2019/20
0.041 0.007
#13
72009/10 / $H$28
S6, Cramptons Rd 0.017
/ 2019/20
0.003 0.007
-0.047
#14
82014/15 / $J$28
2019/20
0.010
-0.014 0.006
-0.003
#15
92019/20 / $L$23
2014/15
0.010
-0.009 0.006
-0.004
0.006
0.022
#9
#16
2009/10 / $H$23
10
2009/10
0.009
-0.003
11
D3, RZ1 / 2020/210.009
0.711
12
2014/15
0.009
-0.008
13
2019/20
0.009
-0.039
14
2009/10
0.009
0.022
0.026
0.005
ADPW 2019 / U50
Summary Information
Workbook Name
Simulation
1
Summary Information
1000
Workbook Name
RZ2_v4.5.xlsm
Number of Outputs
1
Sampling Type
1000
Simulation Start
Time of Inputs
Number
607
04/11/2008 11:23
Simulation Stop
Time of Outputs
Number
140
04/11/2008 11:26
Number of Inputs
Simulation Duration
624
140
Monte Carlo
00:03:17
Sampling Type
Monte Carlo
3/18/13 16:00:13
Random Seed
Simulation Duration
00:00:06
Summary Statistics
%tile
RAN3I
Minimum
-12.28
5%
Random Seed
50
Maximum
Mean
Std Dev
-2.54
8.10
20%
-0.72
25%
0.71
30%
1.67
35%
2.92
5.95
40%
3.86
12.73
45%
4.94
50%
5.95
Maximum
26.32
2.591396398
10% -5.07
Mean
5.23
15% -3.20
Left X
Std Dev
7.95
20% -6.48
-1.76
Left P
Variance
63.26406322
25% -0.47
5%
55%
7.07
Right X
Skewness
0.23901322
30% 20.45
0.62
60%
8.17
Right P
Kurtosis
2.521596518
95%
35% 1.64
65%
9.00
Median
4.68
40% 2.73
26.93
70%
10.16
Mode
7.99
90%
45% 3.64
0
75%
11.68
#Errors
80%
13.05
Filter Min
Left X
-7.36
50% 4.68
85%
15.13
Filter Max
Left P
5%
55% 5.62
90%
17.83
#Filtered
Right X
19.45
0
60% 6.90
95%
20.45
Right P
95%
#1
65% 8.03
Sensitivity
26.81Name
70% 9.02 Regr
Diff
P
90% / $G$38
75% 10.77 0.949
S8, WRZ
2 / 2019/20
#2
S6, Barcombe / 02020/21 / $J$58
#Errors
80% 12.29 0.163
0.164
#3
D2, RZ2
/ 2019/20
Filter
Min
Off/ $G$87
85% 14.02 0.150
0.223
0.106
0.094
0.048
0.052
Rank
#4
#5
Diff X
D1, RZ2 / 2019/20 / $L$43
Filter Max
Off
S6, Cowish / 2020/21 / $J$44
#6
#Filtered
0
S4, Weir Wood / 2019/20 / $H$14
#7
S6, Shellbrook / $J$55
90% 16.35
95% 19.45 0.025
0.023
-.003
S6, Shellbrook/H55
#8
S6, Groombridge
/ $J$51
Regression
and/ 2020/21
Rank Information
for 20190.018
-.003
-.003
-.002
-.002
.003
S5, Cockhaise Well / 2014/.../O24
S6, Rathfinny / 2014/15/H52
S5, Coombe Down / 2024/25/S26
S5, Coggins Mill/Sharnden .../O25
#9
S6, Eridge / 2020/21
Rank
Name/ $J$43
Regr
S6, Poverty Bottom / 2020/21 / $J$46
1
S8, WRZ 2 / 2019/20
0.982
D1, RZ2 / 2034/35/R32
-0.5
15%
Kurtosis
Diff P
-0.75
-4.17
6.32
-11.92
Diff X
-1
10%
Skewness
Mode
.162
.15
.108
.048
.024
.023
.008
.007
.004
-0.25
0
0.25
Std b Coefficients
0.5
0.75
1
-6.48
28.20
Percentile
65.5561531
0.169987033
5% -7.36
Median
.946
Value
Statistics
Minimum
Variance
S8, WRZ 2 / 2019/20/G38
S6, Barcombe / 2020/21/J58
D2, RZ2 / 2019/20/G87
D1, RZ2 / 2019/20/L43
S6, Cowish / 2020/21/J44
S4, Weir Wood / 2019/20/H14
S6, Shellbrook/J55
D3, RZ2 / 2020/21/I73
S6, Forest Row / 2020/21/J50
S6, Rathfinny / 2020/21/J52
50
#10
#11
Corr 0.012
0.009
0.983
S6, Coggins Mill/Sharnden / 2020/21 / $J$42
0.965
0.039
0.050
0.007
-0.015
-0.004
0.026
#13
0.091 0.007
3S6, Hempstead S6,
Barcombe
/ 2020/21
0.077 0.110 0.006
/ 2020/21
/ $J$41
#14
/ 2020/21
/ $J$50
4S6, Forest Row D2,
RZ2 / 2019/200.053
0.053 0.006
0.041
#15
$I$73Wood / 2019/20
5D3, RZ2 / 2020/21
S4,/Weir
0.050 0.043 0.005
0.711
S6, Rathfinny / 2020/21 / $J$52
0.057
#12
#16
2S6, Cockhaise Well
D1, RZ2
/ 2019/200.145
/ 2020/21 / $J$49
0.009
Corr
6
S6, Rathfinny / 2020/21
0.020
0.052
7
S6, Poverty Bottom
0.016
/ 2020/21
0.030
8
S6, Cow wish / 2020/21
0.006
0.026
9
D3, RZ2 / 2020/210.006
0.748
10
S6, Eridge / 2020/21
0.004
-0.015
11
/ 2020/21
0.020
12
/ 2014/15
0.074
13
S6, Clayton / 2020/21
0.002
0.028
14
S5, Rathfinny / 2019/20
0.002
0.045
0.004
-0.008
0.008
ADPW 2019 / U50
Summary Information
Workbook Name
RZ3_v4.5.xlsm
1000
Number of Inputs
1
629
Number of Outputs
1000
140
Sampling Type
Number of Inputs
653
Monte Carlo
Simulation Start
Timeof Outputs
Number
140
04/11/2008 12:02
Simulation Stop
Time Type
Sampling
Monte Carlo
04/11/2008 12:06
Simulation Duration
3/18/13 16:17:00
Simulation Duration
00:00:06
Random # Generator
RAN3I
Random Seed
Statistic
Minimum
Maximum
0.74
3.17
15% -2.21
0.192252634
30%
1.34
2.47944665
20% -1.23
35%
2.11
Kurtosis
Std Dev
4.89
Median
Variance
23.88253037
4.32
25% -0.38
40%
2.79
Mode
Skewness
0.28139112
6.50
30% 0.26
45%
3.56
Left X
Kurtosis
2.447415005
35% -3.73
0.87
50%
4.32
Median
2.86
55%
4.97
Mode
1.25
5%
40% 1.50
13.29
60%
5.71
Left X
-4.15
95%
50% 2.86
65%
6.47
Left P
5%
17.03
55% 3.49
70%
7.12
Right X
11.87
90%
60% 4.04
75%
8.03
0
80%
8.94
Right P
95%
65% 4.71
85%
10.10
Filter Max Diff X
16.02
70% 5.57
90%
11.54
90%
75% 6.41
0
95%
13.29
#Errors
0
80% 7.71
Filter Min
Off
Rank Filter Max
Off
S8, WRZ 3 / 2019/20
/ $G$39
#Filtered
0
#2
D2, RZ3 / 2019/20 / $G$88
0.970
0.180
0.215
#3
S6, Arlingtonand
/ 2019/20
$J$56
Regression
Rank/ Information
for 20190.099
0.041
#4
S4, Darwell Reservoir / 2019/20 / $G$15
Rank
Name
Regr
Corr 0.079
S6, Darwell Reservoir / 2019/20 / $J$54
1
S8, WRZ 3 / 2019/20
0.988 0.992 0.063
0.554
#7
#8
2019/20/L32 #9
#10
Std b Coefficients
2
3
4
5
0.077
S6, Friston & Deep Dean / 2019/20 / $J$37
0.062
0.075
S6, Wallers Haven / 2019/20 / $J$55
0.054
-0.001
S4, Darwell Reservoir
0.084
/ 2019/20
0.061
D2, RZ3 / 2019/200.076
S6, Cornish / 2019/20 / $J$46
0.065
0.035
0.053
S6, Stonegate / 2019/20 / $J$41
0.024
-0.006
S6, Birling Farm / 2019/20 / $J$42
S6, Wallers Haven0.029
/ 2019/20
0.064
S6, Darwell Reservoir
0.017
/ 2019/20
0.046
0.019
0.015
6 S6, Sweet Willow
S6, Friston
& Deep0.009
Dean / 2019/20
-0.062 0.018
Rd / 2019/20 / $J$43
0.054
7 S6, CrowhurstS6,
Powder
Mill (Group)
0.027 0.018
Bridge
/ 2019/20
/ 0.008
$J$38 / 2019/20
0.049
0.009
#14
8 S6, River Rother
D3,atRZ3
/ 2020/210.007
Crowhurst
Br / $J$53 0.721 0.016
9 S6, Holywell (E.Bourne)
S6, Birling/Farm
/ 2019/20
0.005
2019/20
/ $J$45 0.083 0.015
#15
10S6, Turzes Farm
S6, /Sweet
Willow
Wood
0.005 / 2019/20
-0.027 0.013
2019/20
/ $J$40
#16
112014/15 / $J$28
2009/10
0.002
0.027 0.011
12
2009/10
0.002
-0.047
13
2019/20
0.002
0.049
14
2019/20
0.002
-0.062
2024/25/N26 #11
S6, Friston & Deep Dean / .../R35
#12
.005
1
Corr
95% 11.87 0.919
#6
0.75
Sensitivity85% 8.60
90% 10.15 Regr
Name
#1
#5
0.5
45% 2.11
Diff P
#Filtered
0.25
-0.31
25%
Filter Min
0
-1.23
20%
10% -3.03
26.57066368
#Errors
-0.25
-2.35
15%
15.74
Diff P
-0.5
10%
Maximum
Diff X
-0.75
-3.73
19.09
Variance
Right P
-1
Value
5%
-7.57
Right X
.006
%tile
-7.46
Std Dev
Left P
.177
.099
.082
.065
.061
.034
.019
.018
.009
.008
50
Percentile
4.44
5% -4.15
5.15
Skewness Mean
.911
Summary Statistics
50
Value
Random Seed
00:03:16
Statistics
Minimum
Mean
S8, WRZ 3 / 2019/20/G39
D2, RZ3 / 2019/20/G88
S6, Arlington / 2019/20/J56
S4, Darwell Reservoir / 20.../G15
S6, Friston & Deep Dean / .../J37
S6, Darwell Reservoir / 20.../J54
S6, Cornish / 2019/20/J46
S6, Crowhurst Bridge / 201.../J38
S6, Sweet Willow Rd / 2019.../J43
D3, RZ3 / 2020/21/I74
S6, Hazards Green / 2019/2.../J44
-.008
S6, Holywell (E.Bourne) / .../J44
-.006
-.006
S6, Powder Mill (Twr Fm) /.../L39
1
Workbook Name
#13
0.031
0.022
-0.011
ADPW 2019 / U50
Summary Information
Workbook Name
Simulation
Summary Information
1
Workbook Name
RZ4_v4.5.xlsm
1000
Number of Inputs
1
600
Number of Outputs
1000
Sampling Type
Number of Inputs
590
133
Monte Carlo
05/11/2008 11:48
Number of Outputs
126
Sampling Type
Monte Carlo
3/18/13 16:25:41
Simulation Duration
00:00:06
Simulation Duration
Random Seed
05/11/2008 11:51
00:03:30
50
Random # Generator
RAN3I
Summary Statistics
Value
%tile
50
Minimum
Maximum
Mean
-0.25
0
0.25
4.13
Median
Std Dev
4.14
20% 6.07
3.09
40%
4.89
Mode
Variance
17.1586282
25%10.97
3.74
45%
5.41
Left X
Skewness
0.078056402
30%-1.28
4.40
50%
6.07
Left P
Kurtosis
2.848366947
5%
35% 4.96
55%
6.75
Right X
Median
6.57
40%15.07
5.53
60%
7.53
Mode
8.90
45% 5.97
95%
65%
8.38
Left X
0.03
16.34
50% 6.57
70%
9.14
5%
55% 7.12
0
75%
9.76
#Errors
Left P
90%
80%
10.74
Filter Min
Right X
13.48
60% 7.59
85%
11.85
95%
65% 8.24
90%
13.11
Diff X
13.45
0
70% 8.88
95%
15.07
Diff P
90%
75% 9.59
#Errors
0
#1
Std b Coefficients
Rank Filter Min
Off
Name
Regr
85% 11.09
S8, WRZ 4 / 2019/20 / $G$40
Corr
0.629
0.617
0.618
0.584
0.417
0.392
0.127
0.135
#5
Regression
and Rank
Information
S6, College Avenue
/ 2019/20
/ $J$40 for 2019
0.122
0.127
#6
0.062
#7
S6, Boxhalls Lane
Chalk / 2019/20
/ $J$30Corr 0.110
Rank
Name
Regr
2019/20
/ $J$39
1 S6, Bray Gravels
S8, /WRZ
4 / 2019/20
0.722 0.739 0.073
#8
PS RZ4
/ 2019/20
/ $J$37
2 S6, West HamD1,
/ 2019/200.547
0.568 0.059
0.000
#9
Park / 2019/20 / $J$38
3 S6, West HamD2,
RZ4 / 2019/200.212
0.226 0.050
0.025
#2
#10
#11
#12
1
3.58
35%
#3
0.75
30%
15% 2.38
2.957150831
Filter Max
Off
90% 11.95
#Filtered
0
95% 13.48
D2, RZ4 / 2019/20 / $G$89
D1, RZ4 / 2019/20 / $L$45
#4
0.5
2.91
0.190892418
6.67
-0.5
25%
Mean
#Filtered
-0.75
2.27
25.31811084
Kurtosis
Filter Max Right P
-1
1.39
20%
10% 1.50
Diff P
.008
.007
.007
.007
15%
19.73
Diff X
S8, WRZ 4 / 2024/25/I20
0.36
6.46
-6.38
Right P
.125
.122
.111
.077
.052
.016
.014
.009
10%
Maximum
Skewness
.416
-1.28
21.95
Percentile
5.03
5% 0.03
Variance
.628
.614
5%
Statistics
Minimum
Std Dev
S8, WRZ 4 / 2019/20/G40
D2, RZ4 / 2019/20/G89
D1, RZ4 / 2019/20/L45
S6, Lasham / 2019/20/J34
S6, College Avenue / 2019/.../J40
S6, Boxhalls Lane Chalk / .../J30
S6, Bray Gravels / 2019/20.../J39
S6, Boxhalls Lane LGS / 20.../J31
S6, Tongham / 2019/20/J32
D3, RZ4 / 2020/21/I75
S6, Beenhams Heath / 2039/.../R47
-.009
2039/40/T29
2034/35/R37
S8, WRZ 1 / 2039/40/O37
2039/40/T39
Value
-8.57
#13
#14
#15
#16
S6, Lasham / 2019/20 / $J$34
0.044
S6, Boxhalls Lane LGS / 2019/20 / $J$31
0.049
0.057
S6, Itchel / 2019/20 / $J$33
0.028
-0.020
S6, Woodgarston / 2019/20 / $J$41
0.025
0.013
6 S6, BeenhamsS6,
College
Avenue
/ 2019/20
0.054 0.024
Heath
/ 2019/20
/ 0.094
$J$43
7 D3, RZ4 / 2020/21
S6, Lasham
0.078 0.090 0.020
/ $I$75 / 2019/20
0.015
4
5
S6, Bray Gravels / 0.174
2019/200.167
S6, Boxalls Lane Chalk
0.131/ 2019/20
0.116
0.460
8 S6, Cookham S6,
West Ham
Park
0.065
/ 2019/20
0.120 0.016
/ 2019/20
/ $J$27
0.031
2019/20
/ $J$32
0.050
9 S6, Tongham /S6,
Beenhams
Heath
0.050
(including
0.047W0.012
Waltham) / 2019/20
10
S6, Cookham / 2019/20
0.050
11
S6, West Ham PS /0.036
2019/200.077
12
D3, RZ4 / 2020/210.024
13
S6, Boxalls Lane GS
0.017
/ 2019/20
0.020
14
2019/20
0.015
0.021
0.544
0.057
ADPW 2019 / U50
Summary Information
Workbook Name
Simulation
Summary Information
RZ5_v4.5.xlsm
1000
Number of Inputs
1
523
Number of Outputs
1000
Number of Inputs
510
Sampling Type
Sampling Type
Simulation Duration
Monte Carlo
Random Seed
3/18/13 16:32:40
Minimum
Summary Statistics
Random # Generator
RAN3I
Value
Random Seed
50
-0.99
-0.34
2.14
15%
0.10
20%
0.37
-2.49
1.98
Percentile
3.909043564
5% -0.71
25%
0.72
6.47
0.070591554
10% -0.23
30%
1.04
Mean
2.01
2.89401061
35%
1.26
Std Dev
1.72
2.13
20% 0.43
40%
1.58
3.60
45%
1.88
Left X
Variance
2.960581559
25%-0.99
0.68
50%
2.13
Left P
Skewness
0.14143254
30% 0.89
5%
55%
2.39
Right X
Kurtosis
2.457953114
35% 5.50
1.25
60%
2.65
Right P
Median
1.96
40% 95%
1.50
65%
2.90
Diff X
Mode
1.27
45% 6.49
1.73
70%
3.21
Left X
-0.71
50% 1.96
90%
75%
3.49
Left P
5%
0
55% 2.18
80%
3.76
85%
4.17
Filter Max Right X
4.97
60% 2.42
90%
4.69
Right P
95%
65% 2.64
0
95%
5.50
Diff X
5.67
70% 2.95
Diff P
90%
Median
Mode
Diff P
#Errors
Filter Min
#Filtered
0
0.25
Std b Coefficients
0.5
Statistics
Minimum
#1
Rank #Errors
#2
Name
80% 3.55 Regr
Off
D2, RZ5 / 2019/20 / $G$90
85% 3.86
#3
Filter
Max
Off
S6, Oakhanger / 2019/20 / $J$34
#4
#Filtered
0 / $L$46
D1, RZ5 / 2019/20
0.815
0.311
0.307
90% 4.34 0.300
95% 4.97 0.274
0.259
0.205
0.157
#6
S6, Britty Hilland
/ 2019/20
$J$36
0.114
Regression
Rank/ Information
for 2019
0.106
#7
S6, Headley park / 2019/20 / $J$37
Rank
Name
Regr
S6, The Bourne / 2019/20 / $J$38
1
S8, WRZ 5 / 2019/20
0.920
Corr 0.111
0.081
0.925
0.138
2
0.293
#9
#10
S6, Greatham / 2019/20 / $J$29
Corr
0.827
0.147
#8
S6, Windmill Hill, Alton / 2019/20 / $J$33
D1, RZ5 / 2019/200.308
0.113
0.056
0.024
-0.026
#12
0.166 0.021
4 S6, Tilford WRS6,
Greatham
/ 2019/20
0.082 0.072 0.015
/ 2019/20
/ $J$26
#13
/ 2019/20
/ $J$39
5 S6, East MeonS6,
Britty Hill
/ 2019/20
0.066
0.046 0.014
-0.015
#14
/ 2019/20
/ $J$27
6 S6, RushmoorS6,
Headley
park /0.057
2019/200.075 0.014
-0.016
#15
/ $I$76
7 D3, RZ5 / 2020/21
S6, Sheet
/ 2019/20
0.031
#16
S6, Sheet / 2019/20 / $J$40
0.072
0.055
#11
1
0
15% 0.21
S8, WRZ 5 / 2019/20 / $G$41
Filter Min
#5
0.75
Value
10%
Skewness Maximum
Kurtosis
-0.25
%tile
8.22
Variance
-0.5
50
5%
Std Dev
-0.75
00:02:33
-4.31
Mean
-1
05/11/2008 12:18
00:00:05
Maximum
.311
.301
.27
.145
.115
.11
.08
.074
.016
.014
.012
.008
.007
.005
.005
05/11/2008 12:15
140
Statistic
.822
140
Monte Carlo
Number
Simulation Stop
Timeof Outputs
Simulation Duration
S8, WRZ 5 / 2019/20/G41
D2, RZ5 / 2019/20/G90
S6, Oakhanger / 2019/20/J34
D1, RZ5 / 2019/20/L46
S6, Greatham / 2019/20/J29
S6, Headley park / 2019/20.../J37
S6, Britty Hill / 2019/20/J36
S6, The Bourne / 2019/20/J38
S6, Windmill Hill, Alton /.../J33
S6, Tilford WR / 2019/20/J26
D3, RZ5 / 2020/21/I76
2014/15/J34
S6, Hawkley / 2019/20/J30
S6, Hindhead LDN Rd / 2019.../J31
2034/35/R24
S8, WRZ 4 / 2034/35/M40
1
Workbook Name
3 S6, Tilford Meads
D2, RZ5
/ 2019/200.215
/ 2019/20
/ $J$25
8
0.038
-0.050 0.013
0.604
0.010
0.015
S6, Hindhead Tower Rd / 2019/20 / $J$32
S6, Oakhanger (including
0.014 oaklands
0.020 and southlands) / 2019/20
9
D3, RZ5 / 2020/210.013
10
S6, Tilford Meads /0.011
2019/200.056
0.674
11
2014/15
0.010
-0.021
12
2019/20
0.010
0.016
13
2014/15
0.009
-0.054
14
2019/20
0.009
0.028
ADPW 2019 / U50
Summary Information
Workbook Name
1
Workbook Name
RZ6_v4.5.xlsm
1000
Number of Inputs
1
693
Number of Outputs
1000
140
Sampling Type
629
Monte Carlo
Number of Inputs
05/11/2008 12:29
Number of Outputs
140
Sampling Type
Monte Carlo
3/18/13 16:44:48
Simulation Duration
00:00:06
Simulation Duration
Random Seed
05/11/2008 12:31
00:02:34
50
Random # Generator
RAN3I
Summary Statistics
Value
50
%tile
Minimum
-0.25
0
0.25
Std b Coefficients
0.5
0.75
Variance
Maximum
5.40
3.088491598
10% 0.33
25%
1.50
Skewness
Mean
1.96
0.173085057
15% 0.62
30%
1.73
2.884470626
35%
2.04
2.63
40%
2.24
2.09
45%
2.46
Std Dev
1.27
Variance
1.600392782
25% 1.11
Skewness
0.168758612
30% 1.26
-0.04
50%
2.63
Left P
Kurtosis
2.687632975
35% 1.39
5%
55%
2.84
Right X
Median
1.87
40% 1.57
5.62
60%
3.15
Right P
Mode
1.35
45% 1.75
95%
65%
3.37
Diff X
Left X
-0.05
50% 1.87
5.66
70%
3.63
Diff P
Left P
5%
90%
55% 2.06
75%
3.88
#Errors
Right X
4.19
0
60% 2.23
80%
4.24
Filter Min
Right P
95%
65% 2.41
85%
4.55
Filter Max
Diff X
4.24
70% 2.58
90%
4.96
Diff P
90%
0
95%
5.62
#Errors
0
Filter Min
Off
Filter Max
Off
Rank
1
1.23
-1.28
-0.5
0.80
20%
Std Dev
2.72
Percentile
1.76
5% -0.05
#Filtered
-0.75
0.50
15%
Statistics
Minimum
Left X
-1
10%
Mean
Mode
.225
.181
.101
.093
.09
.088
.084
.077
.068
.066
.053
.05
.035
-0.04
8.49
Median
.616
.489
.361
5%
Maximum
Kurtosis
D2, RZ6 / 2019/20/G91
D1, RZ6 / 2019/20/L47
S4, Matts Hill / 2019/20/G18
S6, Trosley / 2019/20/J50
S6, Forstal Bhs 3 and Well.../J55
S6, Hartley Chalk / 2019/2.../J51
S6, Halling Greensand / 20.../J40
S6, Thurnham Bh 11 / 2019/.../J53
S8, RZ6 / 2019/20/G32
S6, Halling Chalk (Inc No..../J58
S6, Forstal Bh 4 / 2019/20.../J59
S6, Ridley / 2019/20/J52
S6, Nepicar Lane / 2019/20.../J46
S6, Boxley Well Source / 2.../J56
S6, Hartley Greensand / 20.../J48
S6, SWS Burham/J61
Value
-1.89
20% 0.93
75% 2.79
80% 3.01
Sensitivity
85% 3.36
Name
Regr
90% 3.73
#1
D2, RZ6 / 2019/20 / $G$91
#2
#Filtered
0 / $L$47
D1,
RZ6 / 2019/20
#3
S4, Matts Hill / 2019/20 / $G$18
#4
Regression
and Rank
Information for
S6,
Trosley / 2019/20
/ $J$50
#5
S6,
Forstal Bhs 3
and Well / 2019/20
/ $J$55Corr
Rank
Name
Regr
#6
Corr
0.620
0.628
0.487
0.481
0.363
0.441
20190.225
0.253
95% 4.19
0.181
0.218
S6,
/ 2019/20
/ $J$51
1 Hartley Chalk
D1,
RZ6 / 2019/200.638
0.662 0.103
0.108
#7
S6,
/ 2019/20
/ $J$40
2 Halling Greensand
S4, Matts
Hill / 2019/20
0.499
0.522 0.094
0.076
#8
S6,
11RZ6
/ 2019/20
/ $J$53
3 Thurnham Bh
D2,
/ 2019/200.302
0.298 0.088
0.073
#9
S8,
$G$32 Sourceworks
4 RZ6 / 2019/20
S6,/ Forstal
0.214 / 2019/20
0.186 0.084
0.308
#10
S6, Halling Chalk (Inc No.7) / $J$58
0.082
0.132
#11
S6, Forstal Bh 4 / 2019/20 / $J$59
0.076
0.052
#12
S6, Ridley / 2019/20 / $J$52
0.065
0.044
#13
S6, Nepicar Lane / 2019/20 / $J$46
0.065
0.092
#14
S6, Hockers lane / 2019/20 / $J$54
0.055
0.032
#15
9 Boxley Well S8,
RZ6 // 2019/20
2019/20/ 0.112
S6,
Source
$J$56
0.337 0.054
10
S6,
Paddlesworth
0.051
/
2019/20
-0.015 0.051
S6, Hartley Greensand / 2019/20 / $J$48
0.041
#16
5
S6, Thurnham Bhs0.201
/ 2019/20
0.214
6
S6, Trosley / 2019/20
0.161
7
S6, Hartley Chalk /0.126
2019/200.123
8
S6, Matts Hill (Belmont)
0.124 / 2019/20
0.113
0.178
11
S6, Ridley / 2019/20
0.046
0.076
12
S6, Boxley Greensand
0.041
/ 2019/20
0.075
13
S6, Hockers lane /0.038
2019/200.054
14
S6, Boxley Well Source
0.037/ 2019/20
0.050
0.065
ADPW 2019 / U50
Summary Information
Workbook Name
Workbook Name
Number
of Iterations
Number
of Inputs
Number
of Inputs
Number
of Outputs
Number
Sampling
Typeof Outputs
Sampling
Simulation
StartType
Time
Simulation
Start Time
Simulation
Stop Time
Simulation
Duration
Simulation
Duration
Random
Seed # Generator
Random
Random Seed
RZ7_v4.5.xlsm
1
1
1000
1000
292
308
140
140
Monte Carlo
Monte Carlo
05/11/2008 12:44
3/18/13 16:47:31
05/11/2008 12:46
00:00:04
00:02:49
50
RAN3I
50
Summary Statistics
Statistic
%tile
Summary StatisticsValue
for 2019
Minimum
Statistics
Maximum
Minimum
-0.12
Mean Maximum
4.41
Std Dev
Mean
Variance
Std Dev
Skewness
Variance
Kurtosis
Skewness
Median
Kurtosis
Mode
Median
Left X
Mode
Left P
Left X
Right X
Left P
Right P
Diff X Right X
-1
-0.75
-0.5
-0.25
0
0.25
Std b Coefficients
0.18
30%
0.29
35%
0.41
40%
0.48
45%
0.59
50%
0.66
55%
0.77
60%
0.91
65%
1.00
3.42
60%2.51
2.42
65%90%
2.53
70%
1.12
75%
1.24
80%
1.38
85%
1.52
0
80% 2.85
90%
1.70
#Filtered
Filter Min
Off
0
85% 2.98
95%
1.94
Filter Max
Off
90% 3.16
#Filtered
0
#5
#6
#7
#8
#9
#10
#11
1
0.03
25%
#Errors
Filter Max
#3
0.75
-0.13
20%
90%
#4
0.5
15%
Diff P
Filter Min
#2
.209
.192
.121
.103
.101
.018
.012
.012
.011
.008
.007
.007
-0.31
10%0.70
1.26
0.77
2.22
15% 1.44
0.587649405
0.74
20% 1.62
0.034183181
0.548779468
25% 1.72
2.644493992
-0.065609476
30% 1.82
0.66
2.843260377
35% 1.90
0.63
2.21
40% 2.02
-0.57
2.81
45% 2.12
5%
1.00
50%1.94
2.21
5%
55%95%
2.32
70% 2.64
0
75% 2.74
#1
.41
.353
.342
-0.57
10%
95%
Rank
.724
Value
5%
Diff P Right P
Diff X
#Errors
DO estimate / 2019/20/I23
D2, RZ7 / 2019/20/G92
D1, RZ7 / 2019/20/L48
S8, WRZ7 / 2019/20/G33
D3, RZ7 / 2020/21/I78
S5, Lamberhurst / 2014/15/N18
S5, Lamberhurst / 2009/10/L18
-1.58
Percentile
5%2.91
1.00
2.42
Sensitivity
95% 3.42
Name
Regr
DO estimate
/ 2019/20
/ $I$23 for 2019
0.724
Regression
and Rank
Information
Rank
1
2
3
4
5
6
7
8
#12 9
#13 10
#14 11
#15 12
#16 13
14
D2, RZ7 / 2019/20 / $G$92
Name
Regr
D1, RZ7
/ 2019/20
/ $L$48
Burham
/ 2019/20
0.566
0.410
Corr
0.570 0.353
S8, WRZ7 / 2019/20 / $G$33
0.342
Bewl Bridge SW / 2019/20
0.549 0.513
DO estimate / 2019/20 / $I$19
0.209
D1, RZ7 / 2019/200.373 0.346
DO estimate / 2019/20 / $I$16
0.192
S8, WRZ7 / 2019/20
0.364 0.368
DO estimate / 2019/20 / $I$17
0.121
D2, RZ7 / 2019/200.167 0.200
DO estimate / 2019/20 / $I$21
0.103
DO estimate / 2019/20
0.164 0.133
DO estimate / 2019/20 / $I$18
0.101
0.157 0.195
D3, RZ7 / 2020/21 / $I$78
0.017
0.098 0.060 0.012
S5, Goudhurst / 2019/20 / $P$16
DO estimate
/ 2019/20
0.085
S5, Goudhurst
/ 2009/10
/ $L$16 0.040 0.012
DO estimate
/ 2019/20
0.060
S5, Goudhurst
/ 2014/15
/ $N$16 0.038 0.012
DO estimate
/ 2019/20
0.039
S5, Goudhurst
/ 2014/15
/ $N$17 0.067 0.008
D3, RZ7 // 2020/210.016
S5, Goudhurst
2019/20 / $P$17 0.293 0.008
S5, Goudhurst
/ 2014/15
0.013
S5, Goudhurst
/ 2009/10
/ $L$17 0.015 0.007
0.013 -0.010
Corr
0.677
0.401
0.295
0.323
0.176
0.164
0.092
0.085
0.102
0.234
-0.022
0.070
0.051
-0.001
0.061
0.045
ADPW 2019 / U50
Summary Information
Simulation
Summary Information
Workbook
Name
Name
Number Workbook
of Simulations
RZ8_v4.5.xlsm
of Simulations
Number Number
of Iterations
1
1000
Number Number
of Inputsof Iterations
1000
602
Number Number
of Outputs
of Inputs
572
140
Sampling
Type of Outputs
Number
140
Monte Carlo
Sampling Type
Monte Carlo
3/18/13 16:53:03
Simulation Duration
00:00:05
Random # Generator
RAN3I
Simulation Duration
Random Seed
1
05/11/2008 14:29
05/11/2008 14:30
50
Random Seed
50
Summary Statistics
Statistic
Value
%tile
MinimumSummary Statistics for 2019
-2.43
5%
Maximum
Statistics
Minimum
-0.12
7.48
10%
Percentile
5%2.50
0.51 15%
Std Dev Maximum
5.94
10%1.60
0.77 20%
Mean
Variance
Mean
2.47
Std Dev
1.26
Variance
1.576753718
Mode
Skewness
0.082877882
Left X
Kurtosis
2.14403854
Left P
Median
2.48
Skewness
Kurtosis
Median
-.01
-.009
-1
-0.75
-0.5
-0.25
0
0.25
Std b Coefficients
0.5
0.75
1
1.09
0.78
2.567593414
25%
1.35
0.168178571
30%
1.54
2.747283839
35%
1.74
2.49
40%
2.06
15% 1.04
20% 1.19
25% 1.43
30%2.37
1.63 45%
35%
1.85 50%
-0.09
2.29
2.49
Right P Left X
Diff X Left P
0.51
50%95%
2.48 65%
3.07
5%
55%5.22
2.70 70%
3.33
Diff P
Right X
4.41
60%90%
2.84 75%
3.61
#Errors
Right P
95%
0
80%
65% 3.00
3.87
85%
4.17
90%
4.60
95%
5.13
S5, Newnham / 2019/20/P36
0.45
2.40
Diff X
3.90
70% 3.20
Diff P
90%
75% 3.41
#Errors
0
80% 3.72
Filter Min
Off
Filter Max
Rank
Off
#Filtered
.207
.181
.159
.111
.097
.072
.051
.033
.016
.015
.013
S5, Newnham / 2014/15/N15
.01
S8, WRZ6 / 2019/20/G15
-0.09
Right X Mode
Filter Max
.716
.603
Value
40% 5%
2.07 55%
45%5.13
2.27 60%
Filter Min
D2, RZ8 / 2019/20/G93
D1, RZ8 / 2019/20/L49
Flow meter / 2019/20/I45
None / 2019/20/I36
S8, WRZ8 / 2019/20/G17
00:00:57
0
Sensitivity85% 3.93
90% 4.19Regr
Name
2.69
2.86
Corr
#1
#Filtered
D2, RZ8 / 02019/20 / $G$93
95% 4.41 0.726
0.697
#2
D1, RZ8 / 2019/20 / $L$49
0.610
0.595
#3
DO estimate
2019/20
/ $I$46
0.206
Regression
and /Rank
Information
for 2019
0.166
#4
/ 2019/20 / $I$45Regr
Rank Flow meter
Name
DO estimate
2019/20
/ $I$39
1
D1,/RZ8
/ 2019/200.901
Corr 0.183
0.9010.155
0.121
0.116
0.072
0.101
0.035
0.074
0.073
0.053
0.106
#5
#6
#7
#8
#9
2
3
4
DO estimate / 2019/20 / $I$47
D2, RZ8 / 2019/200.408
0.349
None / 2019/20 0.081
0.117
D3, RZ8 / 2020/210.074
0.028
DO estimate / 2019/20 / $I$37
DO estimate / 2019/20 / $I$42
Flow meter / 2019/20 / $I$31
0.161
#10
5
Flow
meter / /2019/20
0.071
DO estimate
/ 2019/20
$I$43
0.0490.040
0.014
#11
6
estimate
/ 2019/20
0.061
S8, WRZ8DO
/ 2019/20
/ $G$34
0.0230.039
0.016
#12
7
DO
estimate/ $I$30
/ 2019/20
0.047
Flow meter
/ 2019/20
0.0320.036
0.067
#13
8
S5, Godmersham
/ 2019/20 /0.045
/ 2014/15
$P$27 0.0460.035
S5, Godmersham
0.001
#14
9
0.008
#15
10
Flow meter
/ $I$29
S5,/ 2019/20
Thanington
/ 2014/15
0.043 0.0820.035
Flow meter
S5,/ 2019/20
Chilham//$I$32
2014/15
0.042 -0.0160.033
11
S5, Thanington
/ 2019/20
/ $P$33
S5, Chilham
/ 2019/20
0.041
0.009
#16
0.0610.032
12
0.041 0.039
13
0.034
0.065
14
Flow meter / 2019/20
0.032
0.061
0.000

Water Resources Management Plan 2014 Appendix 5 : Headroom

Transcription

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