INTRODUCTION TO YANQI BASIN CASE STUDY (CHINA)

Wolfgang Kinzelbach, Yu LiETH Zurich, Switzerland

Outline

• Study area- Hydrological regime- Problems - Sustainability in Yanqi

• Distributed numerical model

• Box model

• Multi-objective evolutionary algorithm

• Homework

2

Study Area

3

The Yanqi Basin (China)

Lake Bosten

Irrigation area

Kaidu River

damKongqueRiver

4

The Yanqi Basin (China)

Hydrological RegimePrecipitation 74 mm/year (Switzerland 1500 mm/year)

PET 1400 mm/year

Elevation 4000 to 1046 m.a.s.l from north to south

Inflow by rivers 133 m3/s

Irrigation abstraction 37 m3/s

Evaporation by lake 57 m3/s

Flow to downstream 39 m3/s

5

Land use

6

Irrigation oasis

Salt marsh

Lake

Die-off of fish

Soil salinization

Groundwater table rise due to irrigation

Drying up of „green corridor“

Degradation of reed belt

Decline of water level in lake

Increase of salinity in lake

The problems….

7

Before 1983: One natural lake After 1983: Building of dam and operation as reservoir

Some more views

8

Water balance of lake: 1958-2008

disc

harg

e (1

08m

3 /a)

Safe lake level

9

Water balance of basin (average 1950-2000) in m3/s

Unproductive evaporation

Savings in irrigation water

Reduction ofevaporation of lake

Bosten Lake

Resources tobe harnessed

10

• Lake Bosten- Water quality: TDS lower than 1000 mg/l

- Lake level in the secure range between 1045.5 and 1048 m.a.s.l

- Increased flow to the downstream to sustain its agricultural development and prevent die-off of the green corridor

• Soil:- Salt concentration of irrigated soils should be stabilized at acceptable level

(assume 6000 mg/l)

• Groundwater- No deterioration of quality (salinity, agrochemicals)

- Groundwater level around the lake should be higher than lake level in order to prevent a reversal of groundwater flow

• Measures must be economically acceptable for farmers

What does sustainability mean in Yanqi Basin?

11

Mechanism of soil salinization

Irrigation water (salt)

Salts from irrigation water

water vapour

water, salts

without drainage à accumulation of salts

12

Mechanism of soil salinization

Irrigation water (salt)

without drainage à accumulation of saltsGroundwater table rise, capillary rise, mobilization of salts, high evaporation and salt deposition

water, salts

Salts from irrigation water Groundwater table rise

Natural recharge

Salts (not mobilized)

irrigated

Irrigation water (salts)

Salt (mobilized)

Salts (deposited at surface)

water vapour

13

• Water saving irrigation- drip irrigation, plastic mulching

• Use of groundwater to lower groundwater table- reduce phreatic evaporation and thus salinization

• Diversion of saline water to evaporation ponds in the desert

• Reduction of agricultural area- Return to a more natural system

• Renovation of irrigation channels to reduce water transport losses- Increase efficiency

• Maintenance of drainage channels to flush salts out

Possible measures contributing to the goal of sustainability

14

• Cost increase – pumping energy, drip equipment

• Groundwater pumping– Risk of over-exploitation

• Drip irrigation: accumulation of salts– requires flooding every 3 to 4 years to remove residual salts

• Drainage efficiency: difficult to raise

• Opposition to reduction of agricultural area

• Unknown: climate risk

Challenges

15

Groundwater Pumping (initiated by World Bank project)

Pumping keepsgroundwater table belowthe extinction depth:no capillary rise

maximum pumping rate =Initial phreatic evaporation rate

Risks:

- Over-explotation- Recirculation in a deep

cone of depression

16

Groundwater Pumping (initiated by World Bank project)

Pumping keepsgroundwater belowthe extinction depth:no capillary rise

maximum pumping rate =Initial phreatic evaporation rate

Risks:

- Over-explotation- Recirculation in a deep

cone of depression

17

Distributed Numerical Model

18

Data sources available

58 years of observations!

19

Numerical model of Yanqi Basin:

• River flow : Saint Venant equation, 1D, simulating the lake as a set of river channels with wide cross-section

• Transpiration : Kristensen and Jensen method

• Unsaturated flow : Richards’ equation, 1D,van Genuchten’s formula

• Saturated flow : flow equation, 3D

Ø Software: MIKESHE/MIKE11

Parameters:

Collected+Calibrated

20

ü Period: 1958---2008

ü Model discretization: - horizontal: 500m x 500m, 169 x 370 cells- vertical: 4 aquifer layers and- unsaturated zone: 0.05m to 2m from top to bottom

ü Manual calibration: results acceptable on the basis of different kinds of observations

Considerable computation time

21

Numerical model of Yanqi Basin:

Selected results from distributed model(500 m grid)

22

Reduced numerical model:

Consecutive coarsening of grid

ü Parameters of coarsened grid: average values from sub grids.

ü Coupling parameters (drain and river): effective parameters (modified based on mass conservation)

500 m by 500 m 1 km by 1 km 2 km by 2 km

23

24

Results reduced numerical model:

Now use model in predictive mode to evaluate management scenarios

üS0_basin: Business-as-usual scenario: randomly generates one 50 years’ time series with hydrological characteristics of historical data.

üS1_basin: Salt deposit scenario: Transfer all drainage discharge from the irrigated area to the desert.

üS2_basin: Drip irrigation scenario: One third of agricultural land is assumed to adopt the new technology within the prediction time horizon. 10% of irrigation water is saved and drip irrigation water is supplied from groundwater.

üS3_basin: Reduced agricultural area scenario plus introduction of drip irrigation: reducing cultivated land by 20% andapplying drip irrigation in one third of farm land from groundwater.

25

Coping with uncertainty

• Parameter uncertainty (including correlation of parameters)

• Uncertainty of hydrology• Both can be taken into account by ensemble method

(Monte Carlo method)• Many model runs with an ensemble of realizations of

the model with different parameter combinations and hydrologic sequences instead of one single model run

26

Prediction: Ensemble average results

Lake level Discharge to downstream

Salt accumulated in soil zone Lake salinity

Kept constant by pumpingexcess water to downstream

27

28IfU, BAUG, ETHZ

Example for ensemble outputs of Scenario S1:

Salt mass accumulation in soil

Lake TDS

28

29

Fresh water

Predictive uncertainty (time horizon 50 years)

Lake dischargeSalt mass stored in soil

TDS of lake

SustainabilityGo for a combinationof salt disposaland watersaving

Soil salinity: Salt disposal moreefficientLake salinity:Drip irrigation moreefficient

Limit

Thesis Li Ning

(108t)

Conclusions

Ø A distributed 3D flow and transport model is constructed using MIKESHE/MIKE11 with the grid size of 500 m by 500 m. Running this numerical model is too time consuming, so a reduced but still adequate numerical model with the grid size of 2 km by 2 km is obtained by consecutive coarsening of the grid of the finer model.

Ø The coarser model is used in an ensemble approach to cover prediction uncertainty.

Ø All 3 strategies lead with some probability to a sustainable situation. The strategy of delivering the salt flux from the drain system to the desert instead of returning it to the lake is the most robust.

Ø The uncertainty of the scenarios does not yet include the uncertainty of climate change. The latter may be small because all rivers are dammed in the upstream.

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Box Model

31

BostenLake

Kaid

uRi

ver

A Schematic View of the System (water balance)

Aqu

ifer

crop fieldin

flow

into

lake

32

BostenLake

diversion

pumpingKa

idu

Rive

r

A Schematic View of the System (water balance)

Aqu

iferriver infiltration

crop field

crop consumption

inflo

w in

to la

ke

33

BostenLake

river infiltration

diversionseepage

pumping

surfa

ce d

rain

age

Kaid

uRi

ver

A Schematic View of the System (water balance)

Aqu

ifer

inflo

w in

to la

ke

crop field

crop consumption

Phreatic evaporation

evaporation

outflow

exfilt

ratio

n

34

BostenLake

diversion

surfa

ce d

rain

age

Kaid

uRi

ver

A Schematic View of the System (water balance)

inflo

w in

to la

ke

crop field

crop consumption

evaporation

outflow

river infiltration

seepage

pumping

Aqu

ifer Phreatic evaporation

exfilt

ratio

n

35

river infiltration

seepage

pumping

Aqu

ifer Phreatic evaporation

exfilt

ratio

n

river infiltration

diversionseepage

pumpingKa

idu

Rive

r

A Schematic View of the System (water balance)

Aqu

ifer

crop field

crop consumption

Phreatic evaporation

BostenLake

surfa

ce d

rain

age

inflo

w in

to la

ke

evaporation

outflow

exfilt

ratio

n

36

BostenLake

surfa

ce d

rain

age

inflo

w in

to la

ke

evaporation

outflow

exfilt

ratio

n

BostenLake

diversion

surfa

ce d

rain

age

Kaid

uRi

ver

A Schematic View of the System (salt balance)

inflo

w in

to la

ke

crop field

crop consumption

outflow

river infiltration

seepage

pumping

Aqu

ifer

Soil capillary

rise

washout

exfilt

ratio

n

37

river infiltration

seepage

pumping

Aqu

ifer

Soil capillary

rise

washout

exfilt

ratio

n

river infiltration

diversionseepage

pumpingKa

idu

Rive

r

A Schematic View of the System (salt balance)

Aqu

ifer

crop field

crop consumption

Soil capillary

rise

washout

BostenLake

surfa

ce d

rain

age

inflo

w in

to la

ke

outflow

exfilt

ratio

n

38

BostenLake

surfa

ce d

rain

age

inflo

w in

to la

ke

outflow

exfilt

ratio

n

• Install the Matlab Runtime Environment corresponding to your operating system

• Double click the executable file (.exe) to open the program

• Type values in the box to define inputs

• Click “calculate” to run the model. Results and figures are stored in the “Results” folder

How to Run the Program

39

Interfaceput your own numbers in the left panel to define your decisions

40

Interfacesee final performance on the right

panel

41

Check state-variables in “Results” folder

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Homework

43

■ Try with box model program by:– Changing water allocation scheme– Changing irrigation scheme (i.e., methods and area)– Changing cropping scheme– Changing simulation horizon

■ Play 20 times with 20 years horizon, check the performance of the your decisions based on following criteria:

– Salt concentration in Bosten Lake no higher than 1000 mg/l– Bosten Lake level stays in between 1045.5 and 1048 m.a.s.l– Salt concentration in soil layer is not higher than 6000 mg/l– On average, the aquifer water table is higher than the lake level to

prevent reversal of groundwater flow;

■ Collect all resulted “.csv” files into a folder named as “”, and send it to instructor

Tasks

44

■ Get a feel for box model by:– Changing water allocation scheme– Changing irrigation scheme (i.e., methods and area)– Changing cropping scheme– Changing simulation horizon

■ Play 20 times with 20 years’ time horizon, check the performance of your decisions based on the following criteria:

– Salt concentration in Bosten Lake no higher than 1000 mg/l– Bosten Lake level stays in between 1045.5 and 1048 m.a.s.l– Salt concentration in soil layer is not higher than 6000 mg/l– On average, the aquifer water table is higher than the lake level to

prevent reversal of groundwater flow;

■ Collect all resulted “.csv” files into a folder named as “”, analyze, describe your findings in words. Send calculation results to instructor

Goals

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Salt concentration in Bosten Lake no higher than 600 mg/l

Bosten Lake level stays in between 1045.5 and 1048 m.a.s.l

Salt concentration in soil layer is not higher than 4000 mg/l

On average, the aquifer water table is higher than the lake level to prevent reversal of groundwater flow;

Optimizing our decisions

Criteria

46

Optimizing our decisions

min{ salt concentration in lake }

min{ salt concentration in soil}

max{ aquifer head – lake level }

max{ net profit }

constraint{ 1045 < lake level [m] <1048 }

Criteria Objective

Salt concentration in Bosten Lake no higher than 600 mg/l

Bosten Lake level stays in between 1045.5 and 1048 m.a.s.l

Salt concentration in soil layer is not higher than 4000 mg/l

On average, the aquifer water table is higher than the lake level to prevent reversal of groundwater flow;

47

Results Monte-Carlo simulation

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Performance: 1 objective

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Performance: 2 objectives

50

Performance: 2 objectives

51

Question: can you see conflict of objectives ?

Performance: 2 objectives

52

a 1-D Pareto front

Performance: 3 objectives

53

Performance: 4 objectivesDifference between aquifer head and lake level

54

Parallel coordinate plotsm

inim

izatio

n

55

Parallel coordinate plots

each Y-axis is an objective

min

imiza

tion

56

Parallel coordinate plotseach line represents a solution

min

imiza

tion

57

Parallel coordinate plots

two lines intersecting each other reveals a “conflict” m

inim

izatio

n

58

Parallel coordinate plots

screening out unacceptable performances

min

imiza

tion

59

Multi-objective optimization with MOEA

60

Initialize Population

Evaluate

Selection

Crossover

Mutation

Termination Criterion

Create New Population

Solution Set

smar

t gue

ss

A typical flowchart of genetic algorithm

Q1: how to perform the smart guess Q2: how to ensure the gradual

improvement of the solution

Multi-objective Evolutionary Algorithm (MOEA)

61

Initialize Population

Evaluate

Selection

Crossover

Mutation

Termination Criterion

Create New Population

Solution Set

smar

t gue

ssMulti-objective Evolutionary Algorithm (MOEA)

62

A “population” is a group of possible solutions

a1

b1

c1

a2

b2

c2

a3

b3

c3

a4

b4

c4

Initialize Population

Evaluate

Selection

Crossover

Mutation

Termination Criterion

Create New Population

Solution Set

smar

t gue

ssMulti-objective Evolutionary Algorithm (MOEA)

63

A “population” is a group of possible solutions

a1

b1

c1

a2

b2

c2

a3

b3

c3

a4

b4

c4

y1 y2 y3 y4

y2 > y4 > y1 > y2

Initialize Population

Evaluate

Selection

Crossover

Mutation

Termination Criterion

Create New Population

Solution Set

smar

t gue

ssMulti-objective Evolutionary Algorithm (MOEA)

64

A “population” is a group of possible solutions

a1

b1

c1

a2

b2

c2

a3

b3

c3

a4

b4

c4

y1 y2 y3 y4

y2 > y4 > y1 > y2

Initialize Population

Evaluate

Selection

Crossover

Mutation

Termination Criterion

Create New Population

Solution Set

smar

t gue

ssMulti-objective Evolutionary Algorithm (MOEA)

65

“cross-over” operation

a2

b2

c2

a4

b4

c4

a2

b4

c2

a4

b2

c4

Initialize Population

Evaluate

Selection

Crossover

Mutation

Termination Criterion

Create New Population

Solution Set

smar

t gue

ssMulti-objective Evolutionary Algorithm (MOEA)

66

a2

b2

c2

a4

b4

c4

A2

B2

C2

A4

B4

C4

“mutation” operation

+ ∆ - ∆’

Three-objective Test Problem■ Heuristic method: flexibility for stochastic problems with unknown gradients

■ Search balances convergence and diversity

Multi-objective Evolutionary Algorithm (MOEA)

67

Reed, Patrick M., et al. "Evolutionary multiobjective optimization in water resources: The past, present, and future." Advances in water resources 51 (2013): 438-456.

Approximated Pareto Front with NSGAII

68

Acceptable solution with parallel plot

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Solution A

Solution B

Acceptable solution with parallel plot

Question: if the inflow from Kaidu River is changing in future, are the solutions still acceptable ?

70

Solution A

Solution B

Robustness Based Assessment

objective

scenario

71

Robustness Based Assessment

72

scenarios

objective

acceptablethreshold

Success

Failure

Robustness Based Assessment

73

objective

acceptablethreshold

scenarios

Success

Failure

The robustness under changing inflow rates

74

Solution A:

Acceptable performance = 31%

Solution B:

Acceptable performance = 0 %

With inflow changing by 20%

The robustness under changing inflow rates

75

Solution A:

Acceptable performance = 51%

With inflow changing by 10%

Solution B:

Acceptable performance = 0 %

Conclusion: Solution A is more robust!

THANK YOUfor your attention

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