bangladesh metamodel network moduleqo1)m1 when qn75 =< 21100 q n80 = b2* (q n75 - q qo2)m2 when...

Bangladesh Metamodel

Network Module

v1.0

- Draft -

1. Summary

Water resources in Bangladesh are very dependent on the transport through the large number of rivers in

Bangladesh and the larger Ganges-Brahmaputra-Meghna (GBM) basin. At the core of the Bangladesh

Metamodel lies the network module, which describes transport of water through the major rivers in

Bangladesh. It keeps track of quantitative parameters as discharge, water level and tidal range, as well as

qualitative parameters as salinity for a large number of network nodes at a 10-day timestep.

The quantity of water in the network is for a large part dependent on transboundary flow from international

river basins and for a lesser part dependent on rainfall-runoff and groundwater flow within Bangladesh.

The Bay of Bengal provides downstream boundary conditions, including a substantial tidal range and

surface water salinity. This module is mainly based on existing detailed regional MIKE model information

and results, acknowledged by BWDB and maintained by IWM.

The network nodes are connected with the water balance calculation units, in order to exchange water

between land and river (rainfall-runoff) and vice verse between river and land (flooding).

This note describes the extent of the network module, its working, parameter values and verification results.

2. Purpose/Objective of the Module

The main objective of the Network Module is:

“To describe transport of water through the major rivers of Bangladesh and generate derived

parameters”

Specific objectives are:

Describing transport of water through the major rivers.

Discharge, water level, tidal range and salinity for all nodes.

Generating outputs for other modules as per their requirement.

3. Extent of Network Module

The major rivers, covering the hydrological regions (i.e. South West, North West, North East, North

Central, and South Central) have been considered for the current network module. Below map show the

extent of network module:

Figure 3.1: Extent of the Network Module

4. Approach and Methodology

4.1 Approach

The following Approach has been followed for developing the Network module:

Figure 4.1: Approach of Development of Network Module

4.2 Methodology

In the conceptualization phase the following activities have been performed:

• Selection of Main River Stretches for the module.

• Where to locate the nodes on branches and how to define them.

• Which nodes will be defined as boundary condition?

4.3 Setup of input files

The following input parameters have been primarily considered for the network module:

The current network of the module consists of

Major river network branches with their names.

Shape file of nodes with unique number (N10…N8001).

Figure 4.2: Extent of Network with Nodes

A network distribution file describes how water should be distributed through the network:

1. which nodes are connected and how much water (discharge Q and water level h) is diverted to the next

node;

2. in which order water is distributed (upstream to downstream);

3. parameter values for the rating curve (Q-h relationship).

Below, examples of these distribution files (one for waterlevels and one for discharges) are given. These

rating curves are derived from detailed regional models, acknowledged by BWDB and maintained by IWM.

Table 1: Water Level Distribution Format among Nodes in terms of Q-H relationship

Node TidalCalc OutletNode a1 n1 ho1 d1 Q_T a2 n2 ho2 d2 sq1 sq2 swl1 swl2 WaterLevelFactor

N10 N350*0 N350 17.52 0.48 4.80 0.00 200000.00 3.50 2.70 4.00 0.00 0.00 0.00 0.00 0 1

N20 N350*0 N350 3.39 0.39 5.96 0.00 200000.00 3.00 1.11 0.00 0.00 0.00 0.00 0.00 0 1

N30 N350*0 N350 2.43 0.40 2.20 0.00 1462.77 4.55 0.36 6.95 0.00 0.00 0.00 0.00 0 1

N1000 N350*0 N350 0.10 0.26 3.61 0.05 200000.00 2.08 0.40 2.38 0.14 0.00 0.00 0.00 0 1

N1001 N350*0 N350 50.28 2.45 0.67 1.49 60.00 49.61 0.67 0.78 1.41 0.00 0.00 0.00 0 1

N2000 N350*0 N350 2.08 0.40 2.38 0.14 200000.00 2.08 0.40 2.38 0.14 0.00 0.00 0.00 0 1

N2001 N350*0 N350 2.08 0.34 0.00 0.78 200000.00 2.08 0.40 2.38 0.14 0.00 0.00 0.00 0 1

N2003 N350*0 N350 50.92 12.45 0.16 1.10 45.00 138.61 1.15 0.00 1.39 0.00 0.00 0.00 0 1

N3000 N350*0 N350 36.43 3.19 5.28 0.00 35.00 3.80 0.40 3.69 0.00 0.00 0.00 0.00 0 1

N3001 N350*0 N350 355.00 186.53 5.55 0.00 45.00 345.44 1.42 2.90 0.00 0.00 0.00 0.00 0 1

Table Continues…

Table 2: Discharge Distribution Format among Nodes

No

de

Dis

char

g

eCal

c

QT_

0

B1

m1

Qo

1

QT_

1

B2

m2

Qo

2

Jan

Feb

Mar

Ap

r

May

Jun

Jul

Au

g

Sep

Oct

No

v

Dec

N10 N10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N20 N10=Diversion 110 0.1 1.15 100 860 0.6 0.96 450 0 0 0 0 0 0 0 0 0 0 0 0

N30 N10-N20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N1000 N1000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N1001 N1000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N2000 N2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N2001 N2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N2003 N2001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N3000 N3000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N3001 N3000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N3002 N3001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N4000 N4000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table Continues…

The current network module has eighteen (18) upstream boundaries. Ten days average discharge data of

time span 1985-2017 (measured) have been set as boundary condition for the following nodes:

N10 (Amalshid)

N50 (Noonkhawa)

N51 (Pateswari)

N52 (Taluk Simulbari)

N60 (Dalia)

N170 (Panchagarh)

N180 (Gaibandha)

N240 (Mokkarmpur)

N250 (Pankha)

N278 (Kobatak)

N1000 (Bibirbazar)

N2000 (Pipulia)

N3000 (Kaliachari)

N4000 (Kaptai)

N5000 (Bandarban)

N6000 (Kalaroa)

N7000 (Durgapur)

N8000 (Manu Rail Bridge)

Figure 4.3: Upstream Boundary Nodes (left) and Downstream Boundary Nodes (right)

The current network module also contains Eight (8) downstream boundaries. Ten days average tidal water

data of time span 1985-2017 (measured) have been set as boundary condition for the following nodes:

N330 (Chitalkhali)

N340 (Tetulia)

N350 (Daulatkhan)

N450 (Hiron Point)

N3002 (Feni Regulator)

N4002 (Sadarghat)

N5002 (Bannigram)

N6002 (Malancha)

4.4 Derivation of rating curves

Discharge

Water is distributed based on upstream flow (using flow rating curve) at major offtake point and fixed

distribution factors at other bifurcation and confluence points.

At bifurcation points and confluence points,

• Distribution factors are between zero (0) and one (1);

• For each node the sum of all downstream factors should be 1.

Calculate from upstream to downstream,

• Find upstream nodes and discharge and factors;

• Calculate downstream discharge.

At major offtake point,

For calculating discharge at each node, going downstream from upstream, a rating curve formula is applied:

Q_downstream-1 = B1 * (Q_upstream - QQo)m1

Q_downstream-2 = Q_upstream - Q_downstream-1

Example case at Old Brahmaputra offtake:

QN80 = 0 when QN75 < 4900

QN80 = B1*(QN75 - QQo1)m1 when QN75 =< 21100

QN80 = B2* (QN75 - QQo2)m2 when QN75 > 21100

QN90 = QN75 – QN80

B1 m1 Qo1

B2 m2 Qo2

0.00003 1.52 4800

0.00006 1.583 11990

Figure 4.4: Example rating curve at Old Brahmaputra offtake

Water Level

For calculating a water level at each node, going downstream to upstream, a rating curve formula is applied:

Where

• Qnode is calculated in the discharge calculation

• hdownstream is water level boundary condition

• h0,node comes from input network distribution table

• anode comes from input network distribution table

• nnode comes from input network distribution table

Salinity

For calculating salinity (in ppt) at each node, going downstream to upstream, following preliminary formula

is developed:

Where

• sq1node comes from input distribution table

• Qnode calculated in the discharge calculations

• sq2node comes from input distribution table

• Salinitydownstreamboundary comes from boundary conditions input file

• sw1node comes from input distribution table

• WLnode comes from water level calculation

• sw2node comes from input distribution table

𝑺𝒂𝒍𝒊𝒏𝒊𝒕𝒚𝒏𝒐𝒅𝒆 = 𝑺𝒒𝟏𝒏𝒐𝒅𝒆 × 𝑸

𝒏𝒐𝒅𝒆

𝑺𝒒𝟐 × 𝑺𝒘𝟏𝒏𝒐𝒅𝒆 × 𝑾𝒍 𝒏𝒐𝒅𝒆

𝑺𝒘𝟐 × 𝑺𝒂𝒍𝒊𝒏𝒊𝒕𝒚𝒅𝒐𝒘𝒏𝒔𝒕𝒓𝒆𝒂𝒎𝒃𝒐𝒖𝒏𝒅𝒂𝒓𝒚

Tidal Range

For each node:

Take the tidal range of the first downstream point,

multiply with factor as defined in TidalCalc (column C)

from network distribution table, for example:

TidalRangeN320 = TidalRangeN350 * 0.5

TidalRangeN160 = TidalRangeN320 * 0.15

Generic formula for calculating tidal range:

TidalRangenode = TidalRangeonedownstreamnode * factornode

Figure 4.5: Screenshot Network distribution file

Navigation: Flow depth and top width of water surface are important parameters for navigation. Network

module can calculate flow depth and top width of water surface using elevation of lowest point of riverbed

and S-function of depth vs top width of water surface accordingly.

Figure 4.6: Typical Cross-Section of river.

Flow Depth

Riverbed level data at every node of network module are collected from detail model. Flow depth is the

difference between riverbed level and water level. Network module is capable flow depth at every node per

time step after calculating water level.

Figure 4.7: S-function (Elevation vs Top Width of Water Surface) at Node N75

Top Width of Water Surface

S-Function curve (Elevation vs Top Width of Water Surface) is made based detail model result at every node

of Network module. After calculating water level at every time step, network module calculates top width

of water surface based one S-function.

4.5 Water exchange with waterbalance module

The network module calculates flow and water level at each node in every time step. The waterbalance

module calculates each timestep, based on the existing (from previous timestep) water level in the river, a

certain water demand or supply for each upazila nearest to each river network node. Based on this demand

or supply, the network module updates the discharges and water level in the network, based on the rating

curves and distribution file above. If possible (enough water in network and positive head) and required,

the network module provides water to the upazilas or the network module ingest water from the upazilas

(rainfall-runoff). More details on exchange of water with waterbalance, is in waterbalance module.

4.6 Scripting in Python

The next phase in obviously scripting in python which gives the final shape of the module. Generic scripting

steps are shown below:

"""

A class used to represent a the network for the Bangladesh Metamodel

Inputs from framework

------

[None]

Results to framework

----------

discharge : pandas series with discharge of current timestep for each node

waterlevel : pandas series with waterlevel of current timestep for each node

tidalrange : pandas series with tidalrang of current timestep for each node

Methods

-------

initialzeModuleforRun()

reads the networkdefinition from file

reads the boundary conditions

upstream discharge

downstream waterlevels

downstream tidal range

sets the initial values for the network values

dotimestep(currentTimeIndex)

updates discharge, waterlevel and tidalrange to contain the results for the currenttimestep

postprocessing()

currently does nothing

Scenarios

---------------------

Scenarios- 1: Base condition

Scenarios- 2: Ganges Barrage is active

Scenarios- 3: 1m sea level Rises

Scenarios- 4: 1m + Ganges Barrage

"""

4.7 Testing, calibration and validation

Test simulation of the network module was done for the time span 1985-2017. And the output was 10 days

average for each of the parameters.

Calibration:

The primary calibration has been done separately for dry and monsoon season by performing visual

comparison and goodness-of-fit statistics between “Observed Hydrodynamic Parameter” and “Simulated

Hydrodynamic Parameters” for 2007 to 2017 hydrological year. Parameters as in Water Level, Discharge

etc. Calibration results at different renowned places are graphically represented below and visually the

calibration looks satisfactory for that particular node:

Figure 4.8 : Water level and Discharge Comparison at Godagari on Ganges River

Figure 4.9 : Water level and Discharge Comparison at Baruria on Padma River

Figure 4.10 : Water level and Discharge Comparison at Bahadurabad on Jamuna River

Again, the network module has been calibrated in combination with the water balance. Calibration has been

done for dry and monsoon season separately.

Figure 4.11 : Water level and Discharge Comparison at Godagari on Ganges River (With WB module)

Figure 4.12 : Water level and Discharge Comparison at Baruria on Padma River (With WB module)

Figure 4.11 : Water level and Discharge Comparison at Bahadurabad on Jamuna River (With WB module)

Table 3(a). Model Performance Presented as Goodness-of-Fit Statistics for Discharge Calibration with

Water Balance Module (2007–2017, May to October)

Node MAE NSE PBAIS RMSE RSR R_square Log_NSE Remarks

N10 0 1 0 0 0 1 1

N50 6.85 1 -0.02 7.98 0 1 1

N51 4.91 1 -0.66 5.74 0.01 1 1

N52 0 1 0 0 0 1 1

N60 5.18 1 -0.45 6.1 0.01 1 1

N170 17.01 0.82 -28.18 19.66 0.42 0.98 0.77


N180 38.04 0.73 -37.56 44.95 0.52 0.96 0.75

N240 20.3 1 -4.6 24.12 0.04 1 0.98

N250 0 1 0 0 0 1 1

N1000 6.84 0.97 -7.7 10.65 0.16 0.99 0.93

N20 75.29 0.94 -10.86 96.65 0.24 0.99 0.96

N30 88.47 0.98 -3.41 109.91 0.16 0.99 0.94

N8001 247.75 0.81 5.58 308.79 0.44 0.85 0.85

N53 781.93 1 2.22 1108.03 0.07 1 1

N54 1181.85 0.99 3.24 1538.3 0.09 1 0.99

N70 1795.37 0.98 5.15 2245.42 0.13 1 0.99

N75 3898.82 0.9 1.33 5237.7 0.32 0.9 0.78

N90 3969.42 0.89 2.2 5201 0.32 0.9 0.78

N95 4083.91 0.88 1.32 5413.81 0.34 0.89 0.77

N140 2299.72 0.97 5.63 3030.38 0.18 0.98 0.98

N173 81.43 0.52 21.47 117.73 0.69 0.66 0.68

N175 83.73 0.57 18.27 120.77 0.65 0.67 0.72

N185 57.92 0.66 23.14 89.63 0.58 0.78 0.7

N190 53.68 0.73 -7.93 75.9 0.52 0.74 0.67

N230 3189.11 0.94 7.64 4197.76 0.24 0.97 0.96

N260 808.54 0.99 2.34 1456.09 0.1 0.99 0.98

N261 896.5 0.99 1.89 1504.22 0.1 0.99 0.98

N265 1080.46 0.99 1.87 1659.36 0.11 0.99 0.98

N270 288.61 0.91 15.72 411.12 0.31 0.95 0.93

N271 271.78 0.91 13.51 394.43 0.29 0.95 0.94

N275 294.65 0.9 15.04 427.59 0.31 0.95 0.9

N280 1072.46 0.99 1.9 1563.22 0.12 0.99 0.98

N290 3819.13 0.97 5.76 5068.22 0.18 0.98 0.97

N295 4646.43 0.94 0.4 6485.51 0.25 0.94 0.89

N300 4752.84 0.93 1.2 6667.77 0.27 0.93 0.88

N310 701.83 0.59 -25.71 808.53 0.64 0.69 0.56

N315 4755.03 0.93 1.24 6669.51 0.27 0.93 0.87

N316 39.91 0.55 -36.59 45.43 0.67 0.72 0.39

N320 5023.49 0.93 0.96 6727.26 0.26 0.94 0.92

N321 5212.04 0.92 -1.88 6873.22 0.28 0.92 0.92

N322 6277.45 0.89 -5.79 8168.95 0.33 0.9 0.88

N304.5 595.65 0.56 16.17 695.41 0.67 0.74 0.22

N306 864.05 0.62 8.77 1139.49 0.62 0.69 0.47

N350 6250.55 0.89 -5.92 8213.1 0.33 0.9 0.88

Table 3(b). Model Performance Presented as Goodness-of-Fit Statistics for Water Level Calibration with


Node MAE NSE PBAIS RMSE RSR R_square LOG_NSE Remarks

N10 0.53 0.93 -3.8 0.67 0.27 0.98 0.94

N50 0.26 0.9 -0.69 0.5 0.31 0.91 0.91

N51 0.5 0.59 -0.52 0.63 0.64 0.62 0.58

N52 0.57 0.18 -1.66 0.7 0.9 0.69 0.2

N60 0.19 0.76 -0.23 0.26 0.49 0.81 0.76

N170 0.32 0.67 0.29 0.41 0.58 0.74 0.67

N180 0.67 0.33 -2.2 0.82 0.82 0.83 0.35

N240 0.76 0.91 -0.58 0.96 0.3 0.91 0.92

N250 0.32 0.98 -0.27 0.42 0.15 0.99 0.97

N5000 4.1 -7.99 -53.21 4.36 3 0.66 -4.47

N4000 1.39 -4.3 49.65 1.44 2.3 0.72 -8.73

N2000 0.77 -0.82 -13.17 0.84 1.35 0.89 -0.31

N1000 0.47 0.5 -2.32 0.64 0.71 0.64 0.52

N8000 0.92 -1.01 -5.19 1.03 1.42 0.61 -0.96

N20 0.55 0.93 -4.26 0.67 0.27 0.97 0.92

N30 0.78 0.85 -6.21 0.94 0.39 0.97 0.82

N3002 0 1 0 0 0 1 1

N6001 0.02 0.97 1.68 0.02 0.17 0.99 0.97

N6002 0 1 0 0 0 1 1

N8001 0.38 0.8 1.55 0.48 0.45 0.82 0.8

N53 0.26 0.89 -1 0.53 0.33 0.91 0.9

N54 0.42 0.82 -1.51 0.82 0.43 0.85 0.83

N70 0.38 0.9 1.59 0.56 0.32 0.93 0.9

N75 0.52 0.77 0.86 0.75 0.48 0.85 0.71

N80 0.8 0.59 -4.02 0.92 0.64 0.88 0.61

N81 1.81 -0.05 -25.3 2.18 1.03 0.78 0.38

N90 0.84 0.54 4.24 1.01 0.68 0.83 0.48

N95 0.47 0.76 1.3 0.71 0.49 0.82 0.61

N100 2.3 -2.52 -53.85 2.38 1.88 0.75 -1.31

N110 0.63 0.58 -2.67 0.81 0.65 0.78 0.66

N140 0.61 0.84 2.27 0.75 0.39 0.87 0.84

N160 1.78 -3.56 -48.7 1.87 2.13 0.67 -2.22

N171 0.48 0.25 0.75 0.56 0.86 0.74 0.26

N173 0.5 0.56 0.5 0.65 0.66 0.64 0.57

N174 0.58 0.47 2.84 0.75 0.72 0.7 0.5

N175 0.46 0.7 1.32 0.59 0.55 0.73 0.71

N176 0.66 0.63 3.38 0.84 0.61 0.71 0.67

N185 0.4 0.76 0.11 0.52 0.49 0.76 0.76

N190 1.65 -1.25 -9.82 1.76 1.5 0.73 -1.09


N200 0.28 0.32 1.22 0.34 0.83 0.69 0.33

N210 0.81 0.67 -0.03 1 0.57 0.67 0.68

N220 0.83 0.67 8.32 0.99 0.58 0.87 0.62

N230 0.78 0.72 3.3 0.94 0.53 0.75 0.68

N260 0.16 0.99 0.44 0.35 0.11 0.99 0.98

N261 0.29 0.98 -0.15 0.41 0.13 0.99 0.98

N265 0.48 0.94 3.79 0.69 0.25 0.97 0.89

N270 0.35 0.95 1.93 0.46 0.22 0.96 0.96

N271 0.36 0.96 4.99 0.46 0.2 0.97 0.97

N276 0.21 0.34 -8.26 0.26 0.81 0.86 0.57

N280 0.61 0.89 5.54 0.88 0.34 0.97 0.75

N290 0.4 0.9 1.14 0.51 0.32 0.9 0.85

N295 0.42 0.88 5.65 0.56 0.35 0.91 0.83

N300 0.36 0.9 1.95 0.47 0.32 0.91 0.83

N310 0.42 0.89 -0.35 0.53 0.34 0.89 0.84

N315 0.22 0.92 2.3 0.29 0.29 0.93 0.89

N316 0.23 0.91 0.84 0.29 0.29 0.92 0.89

N301 0.24 0.84 -5.09 0.31 0.4 0.87 0.85

N320 0.16 0.9 2.34 0.2 0.31 0.93 0.88

N303 0.14 0.25 9.89 0.16 0.86 0.79 0.2

N277 0.14 0.61 9.2 0.17 0.63 0.93 0.67

N279 0 1 0.06 0 0.03 1 1

N301.5 0.24 0.82 -2.88 0.31 0.42 0.86 0.84

N321.5 0.19 0.79 -4.81 0.25 0.46 0.93 0.83

N302 0.17 0.77 -1.7 0.22 0.48 0.87 0.76

N321 0.14 0.88 3.02 0.18 0.34 0.92 0.86

N322 0.12 0.81 4.56 0.15 0.44 0.92 0.77

N322.5 0.12 0.81 4.25 0.15 0.44 0.93 0.77

N304 0.15 0.71 5.68 0.18 0.54 0.85 0.66

N305.5 0.08 0.67 -3.64 0.1 0.57 0.9 0.72

N304.5 0.07 0.83 0.89 0.08 0.41 0.84 0.83

N330 0 1 0 0 0 1 1

N340 0 1 0 0 0 1 1

N350 0 1 0 0 0 1 1

N450 0 1 0 0 0 1 1

Table 4(a). Model Performance Presented as Goodness-of-Fit Statistics for Discharge Calibration with

Water Balance Module (2007–2017, November to April)


N10 0 1 0 0 0 1 1

N50 0.5 1 -0.01 0.92 0 1 1

N51 0.56 1 -0.47 1.07 0.02 1 1

N52 0 1 0 0 0 1 1

N60 0.71 1 -0.51 1.35 0.01 1 1

N170 1.41 0.98 -5.37 2.69 0.13 0.99 0.76

N180 1.8 0.87 -17.9 3.1 0.36 0.92 0.78

N240 3.04 0.99 -10.44 4.12 0.11 1 0.95

N250 0 1 0 0 0 1 1

N1000 3.78 0.8 -12.48 6.88 0.45 0.88 0.75

N20 13.8 0.92 -43.36 30.86 0.28 0.98 0.09

N30 40.37 0.96 -18.49 56.24 0.21 0.98 0.76

N8001 77.56 0.88 -15.59 120.61 0.34 0.91 0.8

N53 119.03 1 1.82 143.58 0.05 1 1

N54 152.84 0.99 2.16 222.95 0.07 1 1

N70 321.38 0.98 4.79 393.04 0.13 0.99 0.98

N140 490.21 0.96 6.13 619.98 0.2 0.98 0.96

N171 15.33 0.57 8.12 20.27 0.65 0.59 -1.27

N172 15.7 0.61 0.85 20.04 0.63 0.62 -0.61

N173 14.93 0.63 -8.25 19.07 0.61 0.63 -0.49

N175 17.8 0.53 13.58 22.4 0.68 0.57 -0.44

N230 760.22 0.91 9.87 940.58 0.29 0.96 0.88

N260 150.8 0.98 3.22 270.63 0.15 0.98 0.95

N261 160.26 0.98 3.17 279.95 0.15 0.98 0.95

N265 195.41 0.97 4.71 317.28 0.17 0.98 0.95

N270 56.09 0.76 -16.17 80.28 0.49 0.79 0.43

N271 58.18 0.77 -24.29 80.07 0.47 0.81 0.39

N275 61.5 0.8 -16.23 84.4 0.45 0.84 0.33

N276 28.48 0.69 18.49 39.93 0.56 0.74 0.15

N280 310.38 0.94 10.71 458.17 0.25 0.98 0.93

N290 1059.16 0.91 10.22 1354.15 0.29 0.96 0.91

N320 2229.65 0.69 1.65 2889.41 0.56 0.69 0.62

N321 2373.24 0.58 -14.52 2982.13 0.65 0.67 0.49

Table 4(b). Model Performance Presented as Goodness-of-Fit Statistics for Water Level Calibration with



N10 0.69 0.82 -9.91 0.74 0.42 0.98 0.71

N50 0.17 0.94 -0.31 0.31 0.24 0.94 0.94

N60 0.38 0.12 0.07 0.51 0.94 0.74 0.11

N240 0.38 0.76 -2.01 0.5 0.49 0.83 0.74

N250 0.43 0.77 -3.08 0.5 0.47 0.97 0.75

N5000 2.7 -99.55 -47.83 3.14 10.03 0.58 -58.26

N2000 0.17 0.68 -3.07 0.25 0.57 0.9 0.73

N8000 0.44 -1.19 -2.63 0.58 1.48 0.61 -1.1

N20 1.21 0.3 -20.98 1.45 0.83 0.74 -0.03

N30 1.03 0.6 -17.1 1.1 0.63 0.95 0.5

N3002 0 1 0 0 0 1 1

N6001 0.01 0.99 -0.41 0.02 0.11 0.99 0.99

N6002 0 1 0 0 0 1 1

N8001 0.38 0.81 -7.53 0.52 0.44 0.87 0.75

N53 0.16 0.93 -0.61 0.33 0.26 0.94 0.94

N54 0.22 0.92 -0.47 0.36 0.28 0.94 0.92

N70 0.16 0.94 0.64 0.22 0.25 0.95 0.94

N75 0.63 0.43 1.25 0.88 0.75 0.6 0.42

N90 0.54 0.5 2.55 0.83 0.71 0.6 0.51

N95 0.65 0.42 3.38 0.89 0.76 0.59 0.36

N110 0.29 0.45 6.36 0.36 0.74 0.59 0.31

N140 0.61 0.61 1.39 0.73 0.62 0.62 0.63

N171 0.22 0.52 -0.04 0.26 0.69 0.53 0.52

N175 0.26 0.47 0.63 0.34 0.73 0.53 0.45

N230 0.52 0.58 3.2 0.67 0.65 0.6 0.56

N260 0.11 0.97 0.14 0.21 0.19 0.97 0.96

N261 0.34 0.84 -3.7 0.46 0.41 0.9 0.8

N265 0.43 0.82 -5.08 0.5 0.42 0.87 0.78

N270 0.25 0.7 -3.01 0.31 0.54 0.79 0.68

N271 0.27 0.74 -11.73 0.33 0.51 0.85 0.64

N275 0.19 0.1 -12.82 0.22 0.95 0.61 0.07

N276 0.06 0.86 -2.97 0.08 0.38 0.88 0.87

N280 0.29 0.88 2.04 0.4 0.35 0.9 0.84

N290 0.53 0.55 -7.84 0.62 0.67 0.64 0.49

N315 0.14 0.71 3.75 0.19 0.54 0.74 0.72

N316 0.14 0.72 1.32 0.19 0.53 0.73 0.73

N301 0.15 0.44 -9.69 0.2 0.75 0.68 0.35

N320 0.1 0.82 3.33 0.13 0.43 0.85 0.82

N301.75 0.82 -11.56 -62.48 0.9 3.54 0.61 -5.69


N303 0.04 0.9 -1.3 0.05 0.32 0.91 0.9

N277 0.04 0.9 1.31 0.05 0.31 0.93 0.91

N279 0 1 -0.09 0 0.02 1 1

N301.5 0.16 0.38 -11.28 0.21 0.79 0.69 0.3

N321.5 0.08 0.87 -1.12 0.1 0.36 0.88 0.87

N302 0.15 0.45 10.62 0.19 0.74 0.74 0.32

N321 0.09 0.86 3.82 0.11 0.38 0.89 0.87

N322 0.12 0.8 8.63 0.13 0.45 0.94 0.81

N322.5 0.12 0.8 8.63 0.13 0.45 0.94 0.81

N304 0.14 0.45 12.64 0.17 0.74 0.85 0.28

N305.5 0.06 0.81 -3.24 0.07 0.44 0.86 0.8

N304.5 0.09 0.48 -6.98 0.11 0.72 0.71 0.51

N306 0.02 0.96 1.45 0.04 0.21 0.96 0.97

Validation:

The primary validation has been done separately for dry and monsoon season by performing visual

comparison and goodness-of-fit statistics based on same parameters those are used in calibration time for

2000 to 2006 hydrological year. Validation result at different renowned places are graphically represented

below and visually the calibration looks satisfactory for that particular node:

Figure 4.12 : Water level and Discharge Comparison at Hardinge-Bridge on Ganges River

Figure 4.13: Water level and Discharge Comparison at Meghna on Meghna River

Figure 4.14 : Water level and Discharge Comparison at Sirajganj on Jamuna River

The network module is further validated in combination with the water balance. Validation has been be

done for dry and monsoon season, separately.

Figure 4.9 : Water level and Discharge Comparison at Hardinge-Bridge on Ganges River (With WB module)

Figure 4.10: Water level and Discharge Comparison at Meghna on Meghna River (With WB module)

Figure 4.11: Water level and Discharge Comparison at Sirajganj on Jamuna River (With WB module)

Table 5(a). Model Performance Presented as Goodness-of-Fit Statistics for Discharge Validation with



N10 11.38 1 -0.01 17.45 0.02 1 1

N50 5101.36 0.87 1.36 6060.38 0.36 0.89 0.66

N52 209.99 0.69 9.74 369.9 0.56 0.72 0.79

N60 273.58 0.84 7.41 409.06 0.41 0.86 0.8

N170 30.45 0.65 -24.75 45.96 0.59 0.75 0.68


N250 2098.74 0.96 5.46 3083.27 0.21 0.96 0.95

N1000 19.97 0.69 -2.67 38.56 0.56 0.7 0.77

N20 69.19 0.95 -9.17 89.84 0.22 0.98 0.92

N30 85.93 0.98 -3.39 105.05 0.15 0.99 0.95

N8001 280.54 0.78 9.42 349.99 0.47 0.86 0.82

N53 5566.61 0.86 3.61 6502.91 0.37 0.9 0.69

N54 5617.43 0.87 5.88 6652.07 0.36 0.91 0.72

N61 288.23 0.83 8.98 418.15 0.41 0.86 0.79

N62 327.37 0.81 12.66 455.51 0.44 0.85 0.75

N70 6075.76 0.85 6.56 7092.8 0.39 0.9 0.73

N75 2666.24 0.94 -1.29 3402.6 0.24 0.94 0.94

N90 2640.46 0.94 -0.46 3289.75 0.24 0.94 0.94

N95 2851.36 0.93 -1.33 3634.36 0.27 0.93 0.93

N140 6357 0.83 7.57 7515.76 0.41 0.89 0.72

N172 80.83 0.54 14.13 117.95 0.68 0.59 0.69

N173 94.82 0.51 7.13 131.1 0.7 0.52 0.66

N175 100.89 0.55 4.18 137.45 0.67 0.57 0.7

N185 82 0.65 26.75 113.51 0.59 0.85 0.69

N190 66.64 0.78 -1.53 85.92 0.47 0.78 0.72

N230 6846.43 0.81 9.95 8272.32 0.44 0.88 0.73

N260 2120.07 0.96 0.92 3012.79 0.2 0.96 0.95

N261 2165.31 0.96 0.44 3081.63 0.2 0.96 0.95

N265 2268.57 0.95 0.47 3230.34 0.21 0.96 0.95

N270 370.64 0.86 17.2 494.35 0.37 0.92 0.93

N271 343.6 0.88 15.14 469.76 0.35 0.92 0.92

N275 374.42 0.86 16.12 506.14 0.37 0.92 0.85

N280 2253.66 0.95 0.22 3097.35 0.23 0.95 0.95

N290 7685.3 0.9 6.49 9403.49 0.32 0.93 0.83

N295 5420.35 0.9 6.87 8968 0.32 0.91 0.95

N300 5667.82 0.89 8.38 9052.19 0.34 0.91 0.94

N320 5967.83 0.9 6.79 8438.75 0.31 0.93 0.94

N321 5515.45 0.9 5.48 8217.27 0.32 0.92 0.94

N322 5534.32 0.91 2.89 8109.11 0.31 0.92 0.93

N306 842.04 0.65 1.88 1105.47 0.59 0.67 0.56

N350 5559.87 0.91 2.52 8116.75 0.31 0.91 0.93

Table 5(b). Model Performance Presented as Goodness-of-Fit Statistics for Water Level Validation with



N10 0.51 0.93 -3.8 0.64 0.27 0.98 0.95

N50 0.72 0.71 -0.97 1.12 0.54 0.8 0.66

N51 0.69 0.3 -1.94 0.77 0.84 0.65 0.27

N52 0.58 0.08 -1.8 0.69 0.96 0.73 0.09

N60 0.19 0.77 0.01 0.25 0.48 0.83 0.77

N170 0.6 -0.26 0.86 0.66 1.12 0.79 -0.28

N180 0.83 0.19 -2.74 0.93 0.9 0.91 0.23

N240 2.63 -0.17 -15.46 3.09 1.08 0.69 0.15

N250 0.48 0.95 -0.14 0.63 0.22 0.97 0.95

N2000 0.44 0.41 -7.2 0.51 0.77 0.88 0.46

N1000 0.41 0.37 -3.07 0.5 0.79 0.75 0.4

N8000 0.84 -0.05 -4.27 0.93 1.02 0.68 -0.03

N20 0.61 0.91 -4.92 0.74 0.3 0.97 0.88

N30 0.74 0.84 -6.04 0.9 0.4 0.97 0.82

N1001 0.43 0.66 -1.85 0.53 0.59 0.67 0.68

N6001 0.03 0.97 2.51 0.03 0.17 1 0.97

N6002 0 1 0 0 0 1 1

N8001 0.38 0.78 2.51 0.47 0.47 0.83 0.79

N53 0.7 0.71 -1.27 1.13 0.54 0.81 0.66

N54 0.81 0.72 -1.55 1.27 0.53 0.81 0.67

N61 0.25 0.59 -0.53 0.33 0.64 0.85 0.6

N62 0.27 0.79 -0.09 0.35 0.46 0.83 0.79

N70 0.67 0.8 -0.48 0.93 0.44 0.85 0.77

N75 0.3 0.92 0.45 0.4 0.29 0.93 0.92

N80 0.8 0.57 -4.26 0.86 0.66 0.94 0.59

N81 1.48 0.12 -20.3 1.85 0.94 0.75 0.44

N90 0.65 0.69 3.51 0.74 0.55 0.93 0.69

N95 0.31 0.91 0.54 0.39 0.3 0.91 0.9

N100 1.87 -1.27 -40.61 1.99 1.51 0.75 -0.74

N110 0.66 0.6 8.26 0.81 0.63 0.75 0.66

N140 0.66 0.8 -4.68 0.98 0.45 0.89 0.73

N160 1.4 -1.49 -34.62 1.57 1.58 0.61 -1.08

N171 0.26 0.66 0.3 0.35 0.58 0.79 0.67

N172 0.54 0.05 -1.11 0.63 0.97 0.66 0.05

N174 0.54 0.53 1.84 0.7 0.68 0.62 0.55

N175 0.46 0.66 -0.87 0.57 0.58 0.68 0.67

N176 0.57 0.53 -0.73 0.74 0.69 0.61 0.54

N185 0.42 0.8 -0.34 0.53 0.44 0.81 0.8

N190 1.78 -0.94 -10.41 1.92 1.39 0.74 -0.79

N200 0.37 0.2 1.82 0.46 0.89 0.78 0.22

N210 0.76 0.72 -4.22 0.94 0.53 0.79 0.71

N220 0.71 0.81 4.61 0.82 0.44 0.89 0.83


N230 0.67 0.83 3.96 0.77 0.42 0.9 0.84

N260 0.4 0.97 -0.69 0.57 0.18 0.97 0.96

N261 0.55 0.95 -2.65 0.75 0.22 0.97 0.93

N265 0.5 0.95 0.58 0.64 0.23 0.95 0.92

N270 0.42 0.94 2.82 0.53 0.24 0.96 0.95

N271 0.46 0.94 3.41 0.57 0.25 0.94 0.94

N276 0.26 0.09 -13.05 0.33 0.95 0.84 0.48

N280 0.59 0.92 1.96 0.73 0.27 0.94 0.86

N290 0.45 0.9 3.63 0.52 0.32 0.96 0.92

N295 0.43 0.88 7.5 0.6 0.35 0.95 0.92

N300 0.28 0.92 3.46 0.43 0.27 0.94 0.95

N310 0.35 0.91 1.29 0.5 0.3 0.93 0.94

N315 0.19 0.93 2.86 0.27 0.26 0.94 0.95

N316 0.17 0.94 0.93 0.25 0.24 0.94 0.96

N301 0.18 0.87 2.7 0.3 0.36 0.91 0.91

N320 0.16 0.9 4.04 0.22 0.32 0.93 0.92

N303 0.11 0.65 6.57 0.13 0.59 0.84 0.69

N277 0.15 0.63 8.93 0.17 0.61 0.91 0.69

N279 0 1 -0.01 0 0.03 1 1

N301.5 0.23 0.79 5.3 0.36 0.45 0.91 0.86

N321.5 0.14 0.88 -3.16 0.2 0.34 0.92 0.92

N302 0.15 0.84 3.2 0.19 0.39 0.89 0.83

N321 0.14 0.88 4.46 0.19 0.35 0.92 0.9

N322 0.1 0.84 4 0.12 0.4 0.93 0.82

N322.5 0.1 0.84 3.61 0.12 0.4 0.94 0.82

N304 0.17 0.65 7.82 0.19 0.59 0.91 0.61

N305.5 0.1 0.64 -6.96 0.12 0.6 0.93 0.73

N304.5 0.06 0.85 -1.01 0.08 0.39 0.86 0.86

N330 0 1 0 0 0 1 1

N340 0 1 0 0 0 1 1

N350 0 1 0 0 0 1 1

N450 0 1 0 0 0 1 1

Table 6(a). Model Performance Presented as Goodness-of-Fit Statistics for Discharge Validation with



N10 15.52 0.98 -4.52 65.51 0.15 0.98 0.97

N52 30.84 0.6 10.74 43.21 0.63 0.67 0.56

N60 48.01 0.58 18.4 78.29 0.64 0.62 0.59

N1000 6.37 0.65 -7.06 12.71 0.59 0.71 0.68


N20 19.95 0.92 -60.82 40.69 0.28 0.98 -0.04

N30 40.3 0.95 -14.16 67.16 0.23 0.96 0.85

N8001 75.81 0.84 -6.41 142.59 0.4 0.86 0.86

N61 57.01 0.51 23.42 85.34 0.7 0.59 -0.27

N62 58.11 0.53 23.91 86 0.69 0.61 -0.24

N75 973.38 0.84 -1.67 1130.67 0.4 0.86 0.78

N90 965.2 0.84 -1.25 1123.58 0.4 0.86 0.78

N95 959.32 0.84 -0.84 1130.84 0.4 0.86 0.79

N171 4.52 0.67 -5.71 5.95 0.57 0.78 0.62

N190 14.75 0.62 -4.15 22.22 0.62 0.62 0.15

N270 106.47 0.67 -21.25 144.44 0.58 0.74 0.37

N271 108.23 0.69 -25.76 141.93 0.56 0.77 0.35

N275 117.32 0.69 -19.43 155.79 0.56 0.78 0.24

N276 40.81 0.61 -14.38 51.62 0.62 0.64 0.24

N295 1535.95 0.87 -9.37 1851.44 0.37 0.93 0.79

N300 1399.58 0.88 -6.35 1757.59 0.35 0.93 0.82

N320 2004.59 0.81 -9.18 2502.7 0.43 0.87 0.72

N321 3047.08 0.57 -27.54 3443.88 0.65 0.86 0.3

Table 6(b). Model Performance Presented as Goodness-of-Fit Statistics for Water Level Validation with



N10 0.74 0.68 -10.21 0.88 0.57 0.91 0.53

N52 0.19 0.09 0.29 0.24 0.95 0.69 0.08

N60 0.91 -2.22 1.69 1.12 1.79 0.64 -2.33

N170 0.9 -56.43 1.32 0.91 7.58 0.78 -57.31

N180 0.18 0.34 -0.32 0.23 0.81 0.77 0.34

N1000 0.24 -0.17 1.4 0.3 1.08 0.73 -0.24

N20 1.78 -0.26 -34.37 2.02 1.12 0.71 -0.59

N30 1 0.49 -15.99 1.09 0.71 0.93 0.36

N6001 0.01 0.99 0.31 0.02 0.09 1 0.99

N6002 0 1 0 0 0 1 1

N8001 0.34 0.81 -4.14 0.46 0.43 0.84 0.79

N75 0.37 0.8 -0.42 0.44 0.44 0.83 0.79

N90 0.37 0.76 1.87 0.48 0.49 0.84 0.76

N95 0.37 0.81 -0.55 0.44 0.44 0.84 0.78

N140 0.66 0.24 -12.35 0.82 0.87 0.71 0.28

N171 0.22 -1.46 -0.38 0.25 1.57 0.68 -1.45

N172 0.99 -14.49 -2.34 1 3.94 0.61 -14.25


N210 0.46 0.47 -4.43 0.6 0.72 0.64 0.43

N220 0.27 0.84 -1.35 0.37 0.41 0.84 0.85

N230 0.25 0.84 -1.4 0.35 0.4 0.85 0.86

N270 0.41 0.5 -5.7 0.5 0.71 0.72 0.45

N271 0.47 0.54 -19.4 0.55 0.68 0.79 0.37

N275 0.25 0.27 -22.24 0.27 0.85 0.89 0.11

N276 0.07 0.85 -5.91 0.09 0.38 0.92 0.84

N290 0.64 0.45 -17.32 0.72 0.74 0.91 0.28

N295 0.13 0.86 -3.16 0.17 0.38 0.88 0.83

N300 0.12 0.85 -3.07 0.16 0.38 0.88 0.82

N310 0.19 0.72 -8.25 0.23 0.53 0.88 0.69

N315 0.09 0.87 -2.62 0.11 0.36 0.9 0.86

N316 0.11 0.79 -5.5 0.14 0.46 0.9 0.77

N301 0.08 0.84 -1.26 0.1 0.4 0.87 0.84

N320 0.07 0.88 0.75 0.08 0.35 0.89 0.87

N301.75 0.93 -14.46 -65.91 0.97 3.93 0.84 -8.67

N303 0.04 0.94 -2.24 0.04 0.25 0.96 0.93

N277 0.05 0.91 -0.14 0.06 0.29 0.95 0.91

N279 0 1 -0.36 0 0.03 1 1

N301.5 0.08 0.84 -4.09 0.1 0.39 0.9 0.83

N321.5 0.07 0.83 -3.64 0.09 0.41 0.89 0.81

N302 0.11 0.58 6.3 0.13 0.65 0.76 0.47

N321 0.06 0.88 1.57 0.07 0.35 0.9 0.87

N322 0.11 0.52 8.28 0.12 0.69 0.98 0.44

N322.5 0.11 0.52 8.28 0.12 0.69 0.98 0.44

N304 0.09 0.64 7.16 0.11 0.6 0.91 0.63

N305 0.1 0.55 -4.25 0.13 0.67 0.69 0.6

N305.5 0.06 0.85 -6.34 0.07 0.39 0.94 0.85

N304.5 0.08 0.7 -6.7 0.09 0.55 0.89 0.67

N306 0.02 0.91 1.62 0.05 0.31 0.92 0.94

Table 7: Definitions of Goodness-of-Fit Statistics and Their General Reported Ratings (Data from Rossi et

al. 2008)

5. Follow-up activities

1. BDP2100 scenarios have a direct influence on the boundary conditions of the network module.

Timeseries of upstream (discharge) and downstream (sea level rise) boundary conditions will be adapted

according to BDP2100 scenario information.

2. The impact of some BDP2100 measures, e.g. river barrages, dredging, new river stretches can be

reflected in the network distribution file and related rating curves. In order to update Q-H relations, some

measures will require new detailed model runs.

3. Salinity module will be tested and calibrated.

6. Simulation of Example Cases

Four example cases have been developed as below. Unique RUN ID’s have been provided for each of the

cases.

Cases RUN ID’s Remarks

Base Condition 1 Separate Exemplary Distribution Files have been

created named as

“NetwrokDistribution_WithGanges” and

“NetworkDistribution_WIthoutGanges”. Different

scenarios for example Productive 2030 and Active

2050 is run based Deltaplan 2100 scebarios.

Ganges Barrage 2

Productive 2030 3

Active 2050 4

Main objective of Ganges Barrage is to divert water in Gorai River to reduce salinity intrusion and store

water in upstream for irrigation. Based on IWM study (Mathematical modelling for the Ganges project) are

implemented in network module. This study has recommended month specific flow requirement round the

year. This month specific flow is diverted in Gorai River in the network module.

Table 12: Discharge Distribution Format among Nodes in Presence of Barrage

No

de

Dis

char

geC

alc

QT_

0

B1

m1

Qo

1

QT_

1

B2

m2

Qo

2

Jan

Feb

Mar

Ap

r

May

Jun

Jul

Au

g

Sep

Oct

No

v

Dec

N270 N265=Barrage 275 0.1 1.15 100 860 0.6 0.96 450 225 227 225 230 194 500 2500 7600 7600 250 2500 1000

N280 (Green Dot) represents Ganges Barrage and N320 have been used to show the effect of sea level rise.

Some of the simulation results considering the cases are graphically shown below:

Figure 6.1: Ganges Barrage Location Figure 6.2: Ganges Barrage Effect on Gorai at Gorai-RB

Figure 6.3: Meghna River at Chandpur Figure 6.4: Sea level rise at Chandpur based on different

scenarios

Figure 6.5: Jamuna River at Aricha Figure 6.6: flow and WL changes at Aricha based on different

scenarios

References

• IWM (2019), Updating and Validation of North Central Region Model for 2017 Hydrological

Year, Validation Reports.

• IWM (2019), Updating and Validation of North West Region Model for 2017 Hydrological Year,

Validation Reports.

• IWM (2019), Updating and Validation of North East Region Model for 2017 Hydrological Year,

Validation Reports.

• IWM (2019), Updating and Validation of South East Region Model for 2017 Hydrological Year,

Validation Reports.

• IWM (2019), Updating and Validation of South West Region Model for 2017 Hydrological Year,

Validation Reports.

• IWM (2019), Updating and Validation of Eastern Hilly Region Model for 2017 Hydrological

Year, Validation Reports.

• WL, Q, Tidal Range and Salinity data (years:1985-2017, IWM Region Model)

• Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L.

(2007). Model evaluation guidelines for systematic quantification of accuracy in watershed

simulations. Transactions of the ASABE, 50(3), 885-900.

bangladesh metamodel network moduleqo1)m1 when qn75 =< 21100 q n80 = b2* (q n75 - q qo2)m2 when...

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