INTRODUCTION TO YANQI BASIN CASE STUDY (CHINA) Wolfgang Kinzelbach, Yu Li ETH Zurich, Switzerland

Transcription

1 INTRODUCTION TO YANQI BASIN CASE STUDY (CHINA) Wolfgang Kinzelbach, Yu Li ETH Zurich, Switzerland

2 Outline Study area - Hydrological regime - Problems - Sustainability in Yanqi Distributed numerical model Box model Multi-objective evolutionary algorithm Homework 2

3 Study Area 3

4 The Yanqi Basin (China) Kaidu River Irrigation area Kongque River dam Lake Bosten 4

5 The Yanqi Basin (China) Hydrological Regime Precipitation 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 m 3 /s Irrigation abstraction 37 m 3 /s Evaporation by lake 57 m 3 /s Flow to downstream 39 m 3 /s 5

6 Land use Irrigation oasis Lake Salt marsh 6

7 The problems. Groundwater tablerise due to irrigation Soil salinization Decline of water level in lake Degradation of reed belt Increase of salinity in lake Die-off of fish Drying up of green corridor 7

8 Some more views Before 1983: One natural lake After 1983: Building of dam and operation as reservoir 8

9 Water balance of lake: discharge (10 8 m 3 /a) Safe lake level 9

10 Water balance of basin (average ) in m 3 /s Unproductive evaporation Resources to be harnessed Savings in irrigation water Bosten Lake Reduction of evaporation of lake 10

11 What does sustainability mean in Yanqi Basin? Lake Bosten Soil: - Water quality: TDS lower than 1000 mg/l - Lake level in the secure range between and 1048 m.a.s.l - Increased flow to the downstream to sustain its agricultural development and prevent die-off of the green corridor - 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 11

12 Mechanism of soil salinization Salts from irrigation water Irrigation water (salt) water vapour water, salts without drainage à accumulation of salts 12

13 Mechanism of soil salinization Salts from irrigation water Groundwater table rise Irrigation water (salt) water vapour Natural recharge Salts (not mobilized) Irrigation water (salts) water, salts without drainage à accumulation of salts irrigated Salts (deposited at surface) Salt (mobilized) Groundwater table rise, capillary rise, mobilization of salts, high evaporation and salt deposition 13

14 Possible measures contributing to the goal of sustainability 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 14

15 Challenges 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 15

16 Groundwater Pumping (initiated by World Bank project) Pumping keeps groundwater table below the extinction depth: no capillary rise maximum pumping rate = Initial phreatic evaporation rate Risks: - Over-explotation - Recirculation in a deep cone of depression 16

17 Groundwater Pumping (initiated by World Bank project) Pumping keeps groundwater below the extinction depth: no capillary rise maximum pumping rate = Initial phreatic evaporation rate Risks: - Over-explotation - Recirculation in a deep cone of depression 17

18 Distributed Numerical Model 18

19 Data sources available 58 years of observations! 19

20 Numerical model of Yanqi Basin: Ø Software: MIKESHE/MIKE11 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 Parameters: Collected +Calibrated 20

21 Numerical model of Yanqi Basin: ü Period: 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

22 Selected results from distributed model (500 m grid) 22

23 Reduced numerical model: Consecutive coarsening of grid 500 m by 500 m 1 km by 1 km 2 km by 2 km ü ü Parameters of coarsened grid: average values from sub grids. Coupling parameters (drain and river): effective parameters (modified based on mass conservation) 23

24 Results reduced numerical model: 24

25 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% and applying drip irrigation in one third of farm land from groundwater. 25

26 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

27 Prediction: Ensemble average results Lake level Discharge to downstream Kept constant by pumping excess water to downstream Salt accumulated in soil zone Lake salinity 27

28 Example for ensemble outputs of Scenario S1: Salt mass accumulation in soil Lake TDS IfU, BAUG, ETHZ 28 28

29 Predictive uncertainty (time horizon 50 years) Lake discharge Salt mass stored in soil Limit (10 8 t) Soil salinity: Salt disposal more efficient Lake salinity: Drip irrigation more efficient Fresh water TDS of lake Sustainability Go for a combination of salt disposal and water saving 29 Thesis Li Ning

30 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. 30

31 Box Model 31

32 A Schematic View of the System (water balance) Kaidu River crop field inflow into lake Aquifer Bosten Lake 32

33 A Schematic View of the System (water balance) Kaidu River crop consumption diversion crop field river infiltration pumping inflow into lake Aquifer Bosten Lake 33

34 A Schematic View of the System (water balance) Kaidu River crop consumption diversion crop field river infiltration seepage pumping Aquifer Phreatic evaporation inflow into lake surface drainage exfiltration Bosten Lake evaporation outflow 34

35 A Schematic View of the System (water balance) diversion surface drainage Kaidu River crop consumption crop field river infiltration seepage pumping Aquifer inflow into lake Phreatic evaporation exfiltration Bosten Lake evaporation outflow 35

36 A Schematic View of the System (water balance) Kaidu River crop consumption diversion crop field river infiltration seepage pumping Aquifer Phreatic evaporation inflow into lake surface drainage exfiltration Bosten Lake evaporation outflow 36

37 A Schematic View of the System (salt balance) Kaidu River crop consumption diversion crop field river infiltration surface drainage seepage pumping inflow into lake Soil capillary rise washout Aquifer exfiltration Bosten Lake outflow 37

38 A Schematic View of the System (salt balance) Kaidu River crop consumption diversion crop field river infiltration seepage pumping Soil capillary rise washout Aquifer inflow into lake surface drainage exfiltration Bosten Lake outflow 38

39 How to Run the Program 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 39

40 Interface put your own numbers in the left panel to define your decisions 40

41 Interface see final performance on the right panel 41

42 Check state-variables in Results folder 42

43 Homework 43

44 Tasks 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 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 44

45 Goals 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 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 45

46 Optimizing our decisions Criteria Salt concentration in Bosten Lake no higher than 600 mg/l Bosten Lake level stays in between 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; 46

47 Optimizing our decisions Criteria Objective Salt concentration in Bosten Lake no higher than 600 mg/l min{ salt concentration in lake } Bosten Lake level stays in between and 1048 m.a.s.l Salt concentration in soil layer is not higher than 4000 mg/l min{ salt concentration in soil} max{ aquifer head lake level } max{ net profit } On average, the aquifer water table is higher than the lake level to prevent reversal of groundwater flow; constraint{ 1045 < lake level [m] <1048 } 47

48 Results Monte- Carlo simulation 48

49 Performance: 1 objective 49

50 Performance: 2 objectives 50

51 Performance: 2 objectives Question: can you see conflict of objectives? 51

52 Performance: 2 objectives a 1-D Pareto front 52

53 Performance: 3 objectives 53

54 Performance: 4 objectives Difference between aquifer head and lake level 54

55 Parallel coordinate plots minimization 55

56 Parallel coordinate plots minimization each Y-axis is an objective 56

57 Parallel coordinate plots each line represents a solution minimization 57

58 Parallel coordinate plots two lines intersecting each other reveals a conflict minimization 58

59 Parallel coordinate plots screening out unacceptable performances minimization 59

60 Multi-objective optimization with MOEA 60

61 Multi-objective Evolutionary Algorithm (MOEA) Initialize Population A typical flowchart of genetic algorithm Evaluate smart guess Selection Crossover Mutation Create New Population Termination Criterion Solution Set Q1: how to perform the smart guess Q2: how to ensure the gradual improvement of the solution 61

62 Multi-objective Evolutionary Algorithm (MOEA) Initialize Population A population is a group of possible solutions a1 a2 a3 a4 Evaluate b1 c1 b2 c2 b3 c3 b4 c4 smart guess Selection Crossover Mutation Create New Population Termination Criterion Solution Set 62

63 Multi-objective Evolutionary Algorithm (MOEA) Initialize Population A population is a group of possible solutions a1 a2 a3 a4 Evaluate b1 c1 b2 c2 b3 c3 b4 c4 smart guess Selection Crossover Mutation Create New Population y1 y2 y3 y4 Termination Criterion y2 > y4 > y1 > y2 Solution Set 63

64 Multi-objective Evolutionary Algorithm (MOEA) Initialize Population A population is a group of possible solutions a1 a2 a3 a4 Evaluate b1 c1 b2 c2 b3 c3 b4 c4 smart guess Selection Crossover Mutation Create New Population y1 y2 y3 y4 Termination Criterion y2 > y4 > y1 > y2 Solution Set 64

65 Multi-objective Evolutionary Algorithm (MOEA) cross-over operation Initialize Population a2 a4 Evaluate b2 c2 b4 c4 smart guess Selection Crossover Mutation Create New Population a2 a4 Termination Criterion b4 c2 b2 c4 Solution Set 65

66 Multi-objective Evolutionary Algorithm (MOEA) mutation operation Initialize Population a2 a4 Evaluate b2 c2 b4 c4 smart guess Selection Crossover Mutation Create New Population + - Termination Criterion A2 A4 Solution Set B2 C2 B4 C4 66

67 Multi-objective Evolutionary Algorithm (MOEA) Three-objective Test Problem Heuristic method: flexibility for stochastic problems with unknown gradients Search balances convergence and diversity Reed, Patrick M., et al. "Evolutionary multiobjective optimization in water resources: The past, present, and future." Advances in water resources 51 (2013):

68 Approximated Pareto Front with NSGAII 68

69 Acceptable solution with parallel plot Solution B Solution A 69

70 Acceptable solution with parallel plot Solution B Solution A Question: if the inflow from Kaidu River is changing in future, are the solutions still acceptable? 70

71 Robustness Based Assessment objective scenario 71

72 Robustness Based Assessment objective Success acceptable threshold Failure scenarios 72

73 Robustness Based Assessment objective Success acceptable threshold Failure scenarios 73

74 The robustness under changing inflow rates With inflow changing by 20% Solution A: Acceptable performance = 31% Solution B: Acceptable performance = 0 % 74

75 The robustness under changing inflow rates With inflow changing by 10% Solution A: Acceptable performance = 51% Solution B: Acceptable performance = 0 % Conclusion: Solution A is more robust! 75

76 THANK YOU for your attention Follow our project on: 76