Hydrosystems Modelling

School of Civil Engineering and GeoSciences

Catchment and River Modelling CEG 8506 (Hydrosystems Modelling) Coursework

Chinedum C. Eluwa (140538430) 7th January, 2015

This report compares the functionality and comparative performance of event-based and

continuous models in the prediction of floods, especially as regards model sources of uncertainty

Merits and demerits are investigated and expounded and model structure is found to be the

highest source of uncertainty in 1-dimensional river models. Continuous based models on the

other hand, are found to be data intensive and are susceptible to parameter uncertainties, and in

cases of sparse data sets, may not function efficiently.

These various merits and demerits, widen risks during non-expert model application and this

report demonstrates the importance of in-depth understanding of model limitations, and the

competent application of such models within these limits.

EXECUTIVE SUMMARY

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Contents 1. Introduction .................................................................................................................................... 1

1.1. Aims and Objectives ................................................................................................................ 1

1.2. Study Area ............................................................................................................................... 1

1.2.1. Location ........................................................................................................................... 1

1.2.2. Weather .......................................................................................................................... 2

1.3. Data ......................................................................................................................................... 2

2. Transfer Function Modelling: Generating a 100 - Year Flood Event ............................................... 2

3. NOAH 1-D: Routing a Flood ............................................................................................................. 4

3.1. Model Calibration ................................................................................................................... 5

3.2. Model Validation ..................................................................................................................... 5

3.3. Model Results ......................................................................................................................... 6

4. SHETRAN: Simulating Catchment Response to a Flood Event ........................................................ 7

4.1. Model Calibration ................................................................................................................... 7

4.2. Calibration Results .................................................................................................................. 7

5. Results Comparison and Critical Assessment of Methods .............................................................. 9

6. References .................................................................................................................................... 10

List of Tables Table 1: Important Physical Catchment Descriptors of the Wansbeck Catchment ................................ 2

Table 2: Table of Peak Discharge and Loss Factor .................................................................................. 4

Table 3: Calibration Process (Manning's Coefficient trial values) ........................................................... 5

Table 4: Calibration Results (Wansbeck stage at Oldgate Bridge) and Errors ........................................ 5

Table 5: Validation Results from 2nd Alteration of Mannings Coefficient on Validation period .......... 6

Table 6: Nash Sutcliffe and Logarithmic Nash Sutcliffe Efficiency Values .............................................. 8

List of Figures Figure 1: Map of Morpeth, (Source: Google Maps) ................................................................................ 1

Figure 2: Storm Profile for 1% AEP for Wansbeck Catchment ................................................................ 3

Figure 3: Transfer Function Model for Wansbeck Catchment ................................................................ 3

Figure 4: Various Flood Hydrographs on the 1% AEP rainfall event ....................................................... 4

Figure 5: Sensitivity plot of Discharge to Loss Factor ............................................................................. 4

Figure 6: Sensitivity plot of low and high river level simulations ........................................................... 6

Figure 7: Cross Section at selected portion of Wansbeck River. ..... 6

Figure 8: Response Surface of Nash-Sutcliffe Efficiency value ............................................................... 8

Figure 9: Time series of modelled and observed flow at Mitford .......................................................... 8

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1. Introduction Relevant information is necessary to support decision making at all levels. Therefore, hydrologic

models, through predictions and estimations, are designed to provide valuable spatial and temporal

information on catchment or regional scale responses to specific hydrologic events. Hydrologic models

are built to extrapolate measurements from available data (Pechlivanidis, et al., 2011) and also

incorporate the impact of future alterations within the hydrologic cycle at various levels. Due to their

important role in disaster mitigation, flood models must represent (as accurately as possible) expected

discharges and consequent water levels.

1.1. Aims and Objectives This paper is aimed at understanding and critically examining the various means to successfully

calibrate hydrologic models and realize best estimates of desired simulations. To achieve this, data

from a recent hydrologic and hydraulic event which occurred in Morpeth (a city in the Wansbeck

catchment) would be estimated by calibrating two different hydrologic models A 1-dimensional river

flow model (Noah-1D) and, a physically based distributed catchment model (SHETRAN).

Both models would be calibrated using data from the 2012 flood event which occurred in Morpeth.

However, only the 1-D model would be used to forecast responses to an artificial flood event. This

artificial flood event would be generated using a transfer function model.

1.2. Study Area

1.2.1. Location Morpeth (55.1630 N, 1.6780 W) is a market town which lies 13 miles north of Newcastle upon Tyne,

a similar distance to the south of Alnwick and 12 miles from the North Sea, extending over both north

and south banks of the River Wansbeck where it takes a broad southward loop (Northumberland

County Council; English Heritage, 2009). The river Wansbeck is a major river which flows through

Morpeth. It begins at the Sweethope Loughs lakes in the southern part of Northumberland, about

18 miles (29 km) west of Morpeth (Wikipedia, 2013). It is joined by Swilder Burn, Hart Burn and the

Font before reaching Morpeth and flowing to the coast south of Newbiggin-by-the-sea (Environment

Agency, 2009).

Oldgate Bridge (River level gauge)

River flow gauging station

Morpeth Mitford Wansbeck River North

0m 200m Map Scale

Figure 1: Map of Morpeth, showing the River Wansbeck and important data source points (Source: Google Maps)

140538430 Hydrosystems Modelling CEG 8506

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1.2.2. Weather The general climate in Morpeth is typical of usual maritime climate in the United Kingdom with no

defined dry season and precipitation quite evenly dispersed throughout the year. However, its

location in the North Eastern part of England leaves it prone to east or NE winds on the northern flank

of depressions passing to the south of the area (Met Office, 2013). Such frontal interactions (like the

occluded fronts) in the September months of 2008 and 2012 caused periods of intense and extended

rainfall duration. Catchment features responded to these events and riparian areas of the Wansbeck

in Morpeth were inundated.

1.3. Data In this study, observed hydrological, geological, geographical, hydraulic and meteorological data from

various sources (including Centre for Ecology and Hydrology; Environment Agency; Met Office;

Newcastle University Water Resources Group) was used. Simulated data from deterministic and

stochastic models was also used. The observed data was primarily from records taken during years

2008 and 2012 for the Morpeth town and Wansbeck River. Observed hydrological and hydraulic data

came from flow station at Mitford, and stage gauge at Oldgate Bridge, both logged in 15-minute time

series. Observed meteorological data included rainfall data (from 3 stations: Wallington, Harwood and

Font Res at 15-minute resolutions), temperature data (aggregated into two hourly resolved sets of

maximum and minimum), and potential evaporation data (aggregated into one dataset of hourly

resolutions). Other observed data such as cross sections of the Wansbeck River, vegetation, aquifer

characteristics, surface elevation, and soil cover were from GIS and geological surveys of the

catchment. The data used to generate a transfer function for the Wansbeck catchment came from the

Centre for Ecology and Hydrologys Flood Estimation Handbook. The transfer function model then

simulated hourly flood time series flow data which was routed through the 1-D model.

2. Transfer Function Modelling: Generating a 100 - Year Flood Event The objectives of this task are to design a rainfall event of 1% exceedance probability and generate

the runoff hydrograph resulting from this storm. The transfer function model (unit hydrograph) to be

used is based on the simple triangular version in the Centre for Ecology and Hydrologys Flood

Estimation Handbook. This simple triangular method used to generate the Unit Hydrograph is defined

by three parameters Time to Peak (Tp), Base time (TB) and Peak Discharge (Up), to be estimated for

the particular catchment under consideration. These parameters were estimated using standard

formulae and physical catchment descriptors from the Flood Estimation Handbook. Of these three

parameters, the most important parameter is the catchment time to peak. This is because, the other

parameters are based on various manipulations of the time to peak.

Table 1: Important Physical Catchment Descriptors of the Wansbeck Catchment River Wansbeck AREA 286.88 Area of catchments (square kilometres)

PROPWET 0.45 Proportion of Time with Soil Moisture above field capacity

DPLBAR 21.85 Average drainage path length (kilometres)

DPSBAR 50.8 Average drainage path slope (metre per kilometre)

SAAR 793 Standard Annual Average Rainfall (1961-1990) (millimetres)

BFIHOST 0.347 Base Flow Index derived from Hydrology of Soil Types

URBEXT1990 0.002 Extent of urban and suburban land cover (year 1990)

Important catchment descriptors which help determine the catchment time to peak, using the

formulae below, are given in Table 1.


3

() = 1.56 1.09 0.60 (1 + 1990)

3.34 0.28;

=0.65 ()

3.6 () ; = 2.52

The unit hydrograph is based on the rainfall-runoff relationship and therefore its convolution requires

a design storm as input. In order to ensure that the entire catchment is contributing to runoff, the

storm duration is calculated using the formula below.

= (1 +

1000)

Using this storm duration, a combination

of Areal Reduction Factors, Seasonal

Correction Factors and Depth-Duration-

Frequency curves (Kjeldsen, 2007), the

total rainfall with a 100-year return period

was estimated. This rainfall depth was

then transformed to a dimensionless curve

(storm profile) also known as mass curve,

with cumulative fraction of time (storm

duration) and total precipitation on the

horizontal and vertical axes, respectively

(Ellouze, et al., 2009). For the Wansbeck

catchment, the storm duration was 15

hours. This yielded a storm profile shown

in Figure 2.

Usually, the peak discharge from the 100

year hyetograph may be assumed to be

the 100 year flood (Viglione & Bloschl,

2009). However, there is great uncertainty

associated with this assumption because

of the difficulty in correctly assessing the

current soil moisture conditions at the

onset and during the design storm. To

incorporate soil moisture conditions, a loss

factor is applied to the storm profile

before it is combined with the unit

hydrograph. Kjeldsen (2007) describes a

method of properly estimating and

applying this loss factor to derive effective

rainfall based partly on the likelihood that the catchment soil moisture would be above or below field

capacity (PROPWET) at the onset of a rainfall event and infiltration capacity during the event;

however, this is beyond the scope of this section.

For the given catchment characteristics, the time to peak for the Wansbeck catchment was calculated

to be 8 hours. The peak discharge was calculated to be 6.52 m3/s/mm-of-effective-rain; base time was

calculated to be 19 hours. These parameters defined the transfer function model and produced the

profile in Figure 3.

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Figure 2: Storm Profile for 1% AEP for Wansbeck Catchment

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Figure 3: Transfer Function Model for Wansbeck Catchment


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The combination of the design 100-year

storm, unit hydrograph convolutions,

baseflow and other catchment

characteristics, such as the antecedent soil

moisture conditions, produced the

required flood hydrograph. The variability

of effective rainfall due to soil moisture

conditions represented by various loss

factors gave a range of peak discharges,

shown in Figure 4 which all correspond to

the 1% AEP rainfall event. This range

introduces high uncertainty and makes it

difficult to determine the 100-year flood

based on the 100-year storm alone.

Table 2: Table of Peak Discharge and Loss Factor

Loss Factor

Change in Loss Factor

Peak Discharge

(m3/s)

Change in Peak

Discharge

0.3 0 107.3 0

0.4 0.1 141 33.7

0.5 0.2 176 68.7

0.6 0.3 210 102.7

0.7 0.4 244 136.7

0.8 0.5 278 170.7

0.9 0.6 312 204.7

1 0.7 346 238.7

The range of possible peak discharges

from application of loss factors from 0.3 to

1 is 238.7 (m3/s), as can be seen from

Figure 4. This is a highly significant value

that reflects the level of uncertainty

involved in selecting a 100 year design flood based on rainfall.

It is also evident from Figure 5 that the value of peak discharge is highly sensitive to changes in the

loss factor with a regression slope of 341. This means that a change in loss factor of 0.1 accounts for

a change in discharge by about 34 (m3/s). This sensitivity represents the magnitude of the likely error

made in discharge estimation if the wrong loss factor is used.

3. NOAH 1-D: Routing a Flood In the event of a storm, generated runoff from all areas of the catchment would travel towards the

main drainage channel. If the generated flood supersedes the volumetric capacity (design discharge)

of the channel, one of two things occur depending on whether the channel is open or closed. In an

open channel, water levels rise above channel edges (banks) and surrounding areas are inundated.

Otherwise, excess storm water backs up through closed channels (pipes) and out into drains. This risk

of fluvial or pluvial flooding depends on the intensity of the storm generating runoff. Fluvial flooding

is the focus of this paper.

The previous chapter generated a storm over the Wansbeck catchment and consequent hydrograph.

This chapter will present the modelled response of the Wansbeck River to the design flood. The

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300

0 4 8 12 16 20 24 28 32 36

Dis

char

ge (

m3/s

)

Time (Hours)

Loss Factor = 0.7

Loss Factor = 0.3

Loss Factor = 0.5

Loss Factor = 0.58

Figure 4: Various Flood Hydrographs corresponding various loss factors on the 1% AEP rainfall event

y = 341.18x + 0.075

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0 0.2 0.4 0.6 0.8

Ch

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acto

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Change of Loss Factor relative to 0.3

Peak DischargeVariation

Linear (PeakDischargeVariation)

Figure 5: Sensitivity plot of Discharge to Loss Factor


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accuracy of the modelled response is dependent on the ability of the model to reflect actual behaviour

of the river and channel. Model parameters were altered until a suitable fit was found.

3.1. Model Calibration The model used in this section was the NOAH 1-D river routing model. Being a 1-D hydraulic model, it

assumes flow as predominantly one dimensional (in the stream-wise direction) and follows the St.

Venant equations. In the model, estimation of Mannings roughness coefficient is very important to

the simulation of open channel flows because the coefficient includes the components of surface

friction resistance, form resistance, wave resistance and resistance due to flow unsteadiness (Ding &

Wang, 2004). Because the 1-D model aggregates flow velocity over the cross-sectional area, there is

the underlying assumption that stream flow is normal to the cross section. This means that the

geometry of individual cross sectional areas are quite important to the model.

After model configuration using data from GIS surveys of cross sectional area, initial conditions were

included. Initial conditions require constant inflow over the inflow cross section, this is due to the

model representation of the St Venant momentum equation (University of Technology Hamburg,

2010). This means that the model starts up with an empty channel and constantly fills until it matches

the set initial conditions at the particular time step. An eleven-day period in the 2012 flow time series

(20/09/2012 30/09/2012) was selected, to calibrate the model. The flow hydrograph of this period

was used as initial conditions of the model. The model calibration took place through a repeated

procedure of trial and error involving visual comparisons between field measurements and

simulations of water level (at Oldgate Bridge) whilst changing the roughness coefficient (Table 3). Few

number of trials are due to familiarity with the study area through online maps and tutorials on the

model.

Table 3: Calibration Process (Manning's Coefficient trial values)

Mannings Coefficient

Calibration Steps River Bed Riparian Areas Special Features

(Weirs, stepping stones etc.)

Model (as configured) 0.03 0.03 0.03

1st Alteration 0.03 0.05 0.05

2nd Alteration 0.03 0.05 0.015

Table 4: Calibration Results (Wansbeck stage at Oldgate Bridge) and Errors

The adopted calibration (2nd Alteration) was the trial which resulted in the least combined error (Table

4) averaged over high and low flows.

3.2. Model Validation When the proper Manning coefficients resulting in satisfactory error margins (considered acceptable

due to other model constraints) were established, the model was validated using a different period

(also of high and low flows) of the same 2012 flow dataset (28/06/2012 07/07/2012). Similar results

were observed for the validation period (Table 5). This verified the acceptability of the model to be

used for design purposes.

Observed

River Stage Model (as

configured) Error (%)

1st Alteration

Error (%)

2nd Alteration

Error (%)

Average of High Flows

25.72 25.35 -1.45% 25.27 -1.74% 25.34 -1.49%

Peak Flow 27.12 26.65 -1.75% 26.54 -2.15% 26.64 -1.76%

Average of Low Flows

24.47 24.08 -1.60% 24.10 -1.50% 24.11 -1.47%

Lowest Recorded Flow

24.33 24.04 -1.21% 24.07 -1.08% 24.08 -1.04%


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Table 5: Validation Results from 2nd Alteration of Mannings Coefficient on Validation period

Observed River Stage 2nd Alteration Error (%)

Average of High Flows 25.34 24.98 -1.44%

Peak Flow 25.80 25.48 -1.24%

Average of Low Flows 24.46 24.11 -1.43%

Lowest Flow 24.37 24.08 -1.17%

3.3. Model Results The hydrograph generated from the 100-year

storm event (with antecedent conditions of

0.7) was then set up as model initial conditions,

and routed through the calibrated river

channel. Model watches were set up at the

Oldgate Bridge to log the time at exceedance

of average low flow levels and maximum levels.

Map views and photographs of sections of the

river (from Google Maps) corresponding to

survey chainages (as shown in Figure 7) were

used to determine river banks for the model

watch.

The design flood from the 100-year storm caused an overflow in the river banks after about 5 hours,

53 minutes. The maximum flood level recorded at Oldgate Bridge (26.67m about 2.29m above

average low flows) occurred 15 hours 23 minutes into the event. This peak flood with river levels

increased 2.29 metres above average low flow was less than the recorded levels from the peak flood

in 2012 as observed from dataset (2.65m above low flows). This may be used to roughly validate the

assumed approximation for catchment antecedent conditions, because the inundation from the 100

Figure 7: Cross Section at selected portion of Wansbeck River. Reach 5 WANS05_1147 Top: Google Map image of the section immediately downstream of Road bridge A192; red line showing approximated riparian inundation line during design flood event (loss factor 0.7). Bottom: NOAH 1-D Model section diagram of same section with water levels (blue line) at low flow; purple line showing maximum water levels during design flood event.

0.25

0.30

0.35

0.40

0.45

0.50

0 0.0025 0.005 0.0075 0.01 0.0125

Dif

fere

nce

be

twe

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ob

serv

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an

d s

imu

late

d le

vels

(m

)

Change in Manning's Coefficient (relative to default value 0.3)

High Flows

Low Flows

Figure 6: Sensitivity plot of low and high river level simulations against changes in Manning's coefficient during calibration


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year event supersedes that caused by 2012 flood event (estimated as a 1 in 16 year event) (Carlyon,

et al., 2013).

Sensitivity tests of the model show (Figure 6) steeper curve gradients on visual inspection for the high

flows. This means that the model sensitivity to the Mannings roughness coefficient varies for low and

high flows. This discrepancy shows that the prediction of the low flows may depend on some other

parameter which either is not included in the model, or is not adequately represented or

compensated. This highlights one area of uncertainty in the model as well as a general limitation of

the application of 1-D models on complex flows. Both of these would will be covered more in chapter

5.

4. SHETRAN: Simulating Catchment Response to a Flood Event In the previous chapter, a specific event was generated and used to predict river levels during the

event. Most of the uncertainty of that method arose from the initial soil moisture due to antecedent

conditions at the start time of the model. Continuous catchment simulation seeks to minimize this

uncertainty by initializing an entire time series and constantly updating antecedent conditions during

simulation.

This chapter will focus on calibrating a physically based distributed model for continuous simulation

of the catchment in focus. The model used here is the SHETRAN model, set up on a 1km square grid

for use with the yearlong 2012 flow data series from the Mitford gauge. For this calibration only one

aspect of the catchment response (discharge within the major drainage channel at the Mitford flow

gauge) would be optimized. Usually, in physically distributed models, the number of parameters

contained in the model and potentially subjected to calibration is huge and increases with model

complexity. According to the principle of parsimony (Hill, 1998), the calibration problem is better

posed if its dimensionality is reduced whilst retaining satisfactory results (Blasone, et al., 2007).

4.1. Model Calibration Prior to calibration the current model was set up with initial conditions to simplify computations. Two

parameters (hydraulic conductivity and Stricklers surface roughness coefficient) were identified as

particularly influential on model simulations. These were singled out for the calibration process. Other

parameters in the model are soil type, soil depth, and residual and saturated water content values.

Data required by the model was mainly from GIS survey of elevations, land cover, rainfall over the

catchment, potential evaporation and temperature. A systematic method of alteration of parameter

values was adopted. The realistic ranges of parameter values were first established and combined in

the model to determine the practical extremities of responses. After these extreme value sets, other

combinations were taken. For saturated hydraulic conductivity (K), the values for sandstone, which

make up majority of the subsurface formation in the catchment were used. In the case of surface

roughness, the entire range of Stricklers roughness coefficient (C) was used.

Model calibration was assessed using the Nash-Sutcliffe efficiency value because it is sensitive to

timing errors, is suitable for continuous simulation modelling and it can be easily transformed (by the

logarithmic functions) to give more emphasis to low flows. Also other objective functions a multi-

objective calibration was used to assess the optimum parameter sets for better understanding.

4.2. Calibration Results In this exercise, the highest Nash-Sutcliffe value attained was 0.75. This showed generally satisfactory calibration, at least at an operational level (Zhang & Savenije, 2005). This highest efficiency occurred for only one of the parameter sets (K = 0.000026 (wet soil); C = 20 (slow flow)) from the selected range


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(Table 6). This case of single-valued finality rarely occurs in reality and may have happened due to the coarse resolution of manually selected and modelled conductivity values. Certain parameter sets produced poor Nash-Sutcliffe values but better values for logarithmic Nash Sutcliffe values, showing overland flow sensitivity to aquifer conditions. Table 6: Nash Sutcliffe and Logarithmic Nash Sutcliffe Efficiency Values

Mass balance (Inflow-Outflow-Storage) for the highest model efficiency was calculated to be 349mm. This difference from zero may be responsible for antecedent moisture conditions in the catchment quite sensible for a wet soil of depth 20.4 metres (from model input file) and model accuracy with low flows. These little verification details available in the model demonstrate one of the merits of continuous catchment models. It would be worthwhile to investigate the reaction of the model to deeper soils and to verify storage changes.

Multi-criteria calibration was used to generate a response surface (Figure 8) for the Nash-Sutcliffe efficiency value. This surface (within the uncertainty due to resolution of selected values) shows that the model simulates peak catchment contribution to river flows better at low values of hydraulic conductivity. The model predictive efficiency is relatively less sensitive to Stricklers surface roughness coefficient. This can be seen in the steeper

Saturated Hydraulic Conductivity (K) m/day

Strickler's Roughness Coefficient (C)

20 40 50 60 80

NSE Ln (NSE) NSE Ln (NSE) NSE Ln (NSE) NSE Ln (NSE) NSE Ln (NSE)

0.52 0.07 0.473 0.08 0.48 0.08 0.48 0.08 0.48 0.08 0.473

0.26 0.02 0.35 0.02 0.354 0.03 0.353 0.03 0.353 0.03 0.35

0.026 -0.03 -0.055 -0.03 -0.066 -0.03 -0.066 -0.02 -0.067 -0.03 -0.059

0.0026 0.01 -0.05 0.01 -0.049 0.01 -0.050 0.01 -0.051 0.01 -0.05

0.00026 0.60 0.299 0.63 0.339 0.62 0.335 0.62 0.332 0.60 0.299

0.000026 0.75 0.598 0.73 0.588 0.68 0.573 0.69 0.57 0.68 0.559

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Figure 9: Time series of modelled and observed flow at Mitford

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-0.100.000.100.200.300.400.500.600.700.80

NA

SH-S

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-0.10-0.00 0.00-0.10 0.10-0.20 0.20-0.30 0.30-0.400.40-0.50 0.50-0.60 0.60-0.70 0.70-0.80

Figure 8: Response Surface of Nash-Sutcliffe Efficiency value to calibration parameters (Hydraulic Conductivity and Surface roughness)

K (m/day)

(C) Stricklers Roughness


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gradient along the hydraulic conductivity axis on the response surface plot.

5. Results Analysis and Critical Assessment of Methods In the generation of storm profiles, errors accumulated from frequency statistics of extreme rainfall

contained in rainfall depth-duration-frequency curves (Overeem, et al., 2008). The effects of these

errors on model are however reduced on appropriate application to the specific catchment. Because

this sort of modelling is based on a particular event, not an entire time series, its greatest uncertainty

is due to initial conditions. The loss factor applied in this method was a simplistic aggregation which

tried to account for antecedent soil moisture conditions. It would be worth investigating the

differences when hydrograph results are compared to the more robust initial conditions method as

stated in the revitalized flood handbook (Kjeldsen, 2007). Another drawback of this method is that it

transfers its uncertainty to any other model which depends on its output.

Predicting flood levels from NOAH 1-D was associated with uncertainty from input hydrograph, model

structure and parameters. The model was configured using physical survey data which may also

contain errors. More importantly, the model was limited in the simulation of certain natural hydraulic

scenarios as are typical of the constantly meandering reaches used in this study. Curved reaches were

assumed straight, therefore water levels over riparian regions enclosed within meanders are not as

realistic as expected. The model was also unable to accurately simulate hydraulic behaviour around

and above obstacles (such as weirs) during low flows (which do not drown such obstacles), especially

when these obstacles are not exactly perpendicular to the flow, thus reducing the reliability of

predictions at low flows. Also, without physical site inspection, or additional data, modellers cannot

define where river banks begin from surveyed data alone. This creates uncertainties in the

reconstruction of flood level progress in time and in the application of results. Also, it is quite difficult

(without adequate knowledge of the varied features and surfaces within and around the river banks)

to apply correct parameter values for Mannings coefficient. These limitations impose uncertainties

(especially underestimations) in the model prediction of flood water levels. Nevertheless, the model

performance, with a few corrections for the systematic errors and proper understanding of its

limitations, was simple enough and sufficient to understand catchment behaviour during high flood

conditions. Results were sufficient for the level of detail and uncertainty involved. For low flows, other

higher level models are more appropriate.

As a distributed model, the large data requirements of SHETRAN expose it to wide uncertainty margins

through error propagation. A major advantage of the physically distributed model is the continuous

simulation which constantly updates initial conditions to current model status. The most uncertainty

was associated with the choice of parameters to collectively and concurrently enhance optimization.

The problem of equifinalty did not occur in this study probably because the model was significantly

simplified and only a few combinations were tried. The model assumed parameter calibration values

for the entire catchment. This is not exactly so in reality, as such values vary spatially. Another

drawback was that the continuous model required about six months of data (half the dataset) to

stabilize during the spin up period after initialization. For this sort of data intensity, a split sample

calibration would yield abysmal results. It would be worth investigating methods and algorithms to

incorporate spatial variation in parameterization (to more realistically represent catchment variables)

and also to reduce spin up periods in models for application to sites with short data series. This lack

of data also affected the Nash-Sutchliffe Efficiency as it was calculated using the entire hydrograph

series including spin up times. The model timings were appropriate, but observed discharges were

underestimated, by about 22% (linear regression () = . () + . ; R2 = 0.83)

even at a Nash-Sutcliffe Value of 0.75 and index of agreement (as defined by Krause, et al. (2005)) of

0.95.


10

In conclusion, both models, performed satisfactorily within their limitations. However, the continuous

catchment simulation model performed better during low flows. Large data requirements are the

major drawback of physically distributed models, however, their ability to simulate entire catchment

responses over long periods complements the simplicity of river routing models. After ensuring data

quality, reducing uncertainty in model predictions depends finally on the competence of the modeller

and familiarity with the limitations of the modelling tools.

6. References Blasone, R. S., Madsen, H. & Rosbjerg, D., 2007. Parameter estimation in distributed hydrological modelling:

comparison of global and local optimisation techniques. Nordic Hydrology, 38(4), pp. 451-476.

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Ding, Y. & Wang, S. S., 2004. Identification of Mannings Roughness Coefficients in Channel Network Using Adjoint Analysis. International Journal of Computational Fluid Dynamics, 00(0), pp. 1-11.

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Kjeldsen, T. R., 2007. Flood Estimation Handbook: Supplementary Report No. 1 (The revitalised FSR/FEH rainfall-runoff method), Wallingford: Centre for Ecology & Hydrology.

Krause, P., Boyle, D. P. & Base, F., 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, pp. 89-97.

Met Office, 2013. North East England: climate. [Online] Available at: http://www.metoffice.gov.uk/climate/uk/regional-climates/ne [Accessed 29 December 2014].

Northumberland County Council; English Heritage, 2009. Morpeth: Northumberland Extensive Urban Survey, Morpeth: Northumberland City Council.

Overeem, A., Buishand, A. & Holleman, I., 2008. Rainfall depth-duration-frequency curves and their uncertainties. Journal of Hydrology, Volume 348, pp. 124-134.

Pechlivanidis, I., Jackson, B., McIntyre, N. & Wheather, H., 2011. CATCHMENT SCALE HYDROLOGICAL MODELLING: A REVIEW OF MODEL TYPES, CALIBRATION APPROACHES AND UNCERTAINTY ANALYSIS METHODS IN THE CONTEXT OF RECENT DEVELOPMENTS IN TECHNOLOGY AND APPLICATIONS. Global NEST Journal, 13(3), pp. 193-214.

University of Technology Hamburg, 2010. 1D Hydrodynamic Models. [Online] Available at: http://daad.wb.tu-harburg.de/tutorial/flood-probability-assessment/hydrodynamics-of-floods/1d-hydrodynamic-models/theory/fundamentals-of-mathematical-river-flow-modelling-1d-water-level-calculation/derivation-of-the-basic-equation/ [Accessed 31 December 2014].

Viglione, A. & Bloschl, G., 2009. On the role of storm duration in the mapping of rainfall to flood return periods. Hydrology and Earth System Science, Issue 13, pp. 205-216.

Warmink, J. J., Klis, H. V. d., Booij, M. J. & Hulscher, S. J. M. H., 2011. Identification and Quantification of Uncertainties in a Hydrodynamic River Model Using Expert Opinions. Journal of Water Resource Management, Volume 25, pp. 601-622.

Wikipedia, 2013. Sweethope Loughs. [Online] Available at: http://en.wikipedia.org/wiki/Sweethope_Loughs [Accessed 29 December 2014].

Zhang, G. P. & Savenije, H. H. G., 2005. Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach. Hydrology and Earth System Sciences Discussions, pp. 345-359.

1. Introduction1.1. Aims and Objectives1.2. Study Area1.2.1. Location1.2.2. Weather

1.3. Data

2. Transfer Function Modelling: Generating a 100 - Year Flood Event3. NOAH 1-D: Routing a Flood3.1. Model Calibration3.2. Model Validation3.3. Model Results

4. SHETRAN: Simulating Catchment Response to a Flood Event4.1. Model Calibration4.2. Calibration Results

5. Results Analysis and Critical Assessment of Methods6. References

Documents

Hydrosystems Modelling