Hanoi, January 28 th 2015 Quang Dinh DEIB – Politecnico di Milano IMRR Project Emulators of the Delta model 7 – Emulators of the Delta model INTEGRATED AND SUSTAINABLE WATER MANAGEMENT OF RED-THAI BINH RIVER SYSTEM IN A CHANGING CLIMATE

IMRR phases econnaissance odeling the system ndicators identification cenarios definition lternative design valuation R M I S A E omparison C 2

Delta model  320 Rivers & canals with 4200 km  ~ 8000 Cross sections  29 Bridges  148 Drainage culverts  89 Sluice gates  160 Pumping stations from main river  303 Pumping stations from 11 irrigation districts  complete description of the system at each time step  ~ 2 days for 16 years simulation MIKE11 More than state variables! 3

point to point information is required ~ 350 million years simulation for 1 policy  The model simulation must be extremely fast (1 yr in few milliseconds) In the IMRR Project Lumped model, computationally efficientEmulators MIKE11 4

Emulators Reference: S. Galelli and A.Castelletti (2013), Tree-based iterative input variable selection for hydrological modeling, Water Resources Research, 49(7), Select among the PB model (Delta model) output components, one component y, the dynamic of which we like to emulate 5

6 JSJS JHJH JFJF Emulators Step 0: Output selection

7 JSJS JHJH JFJF h t HN – mực nước ngày tại Hà Nội Emulators Step 0: Output selection

8 JSJS JHJH JFJF d t : tổng lượng nước thiếu vùng đồng bằng trong ngày t Emulators Step 0: Output selection

Evaluation of other indicators q ST : daily flow at Son Tay control station (for the environmental indicators) h PL & h TQ : daily water level in Pha Lai & Tuyen Quang (for the flood indicators) 9  Choose: h HN, d, q ST, h PL & h TQ

Emulators Prepare a sample data set 10

h t HN D t q t ST h t TQ h t PL  r HB, r TB, r TQ  q HY, q YB  minor & lat. flows   t   t   V t  d t Emulators Step 1: Sample dataset t, t-1,t-2,… Dataset plays a critical role in building the emulator constituted by N tuples {inputs, output} 11

10. Các sự kiện thủ văn cực đoan Tomorrow 9:00-10:00 Emulators Step 1: Sample dataset List of experiments: Exp1: 17 yrs (Oct,1994-Oct, 2010), using historical flows Exp2: 17 yrs (Oct,1994-Oct, 2010), using natural flows (the case in which reservoirs were not presented) Exp3: 2 yrs in which big flood occurred (1969 & 1971) Exp4: 1969, 1971, 1996 with 300 & 500 yrs return periods Exp5: 10 yrs, corresponding to 10 extreme yrs (5 floods + 5 droughts) Exp6: 10 yrs with 100, 200, 300 & 500 yrs return period More than 22,630 tuples! 12

Emulators Step 1: Sample dataset List of experiments: Exp1: 17 yrs (Oct,1994-Oct, 2010), using historical flows Exp2: 17 yrs (Oct,1994-Oct, 2010), using natural flows (the case in which reservoirs were not presented) Exp3: 2 yrs in which big flood occurred (1969 & 1971) Exp4: 1969, 1971, 1996 with 300 & 500 yrs return periods Exp5: 10 yrs, corresponding to 10 extreme yrs (5 floods + 5 droughts) Exp6: 10 yrs with 100, 200, 300 & 500 yrs return period Data set was splitted in two sub-sets: trainings & validation (cross-validation) 13

Emulators model is identified (but extra-tree)  adopt ANN to emulate this model to, later on, embed it into MO optimization framework The input that are most relevant in explaining I-O behavior of the PB model, with respect to y, are recursively selected, until all the selected state variables are given a dynamic description 14

ANN: Artificial Neural NetworkEmulators inputs neurons output 15

Emulators How to choose the number n of its neurons? n too low reduces the accuracy, but the ANN computation is faster n too high: opposite  the identification was repeated for different values of n 16

Emulators Step 2-4: Emulators Step 2-4: Iterative Input variable Selection & Emulator Building Water level at Ha Noi Total supply deficit Flow at Son Tay Water level at Tuyen Quang & Pha Lai 17

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi OrderVariable ΔR2 ΔR2 R2R2 1QtDQtD h t HN tt h t HN : the daily mean water level at Ha Noi section between [t- 1,t)  t : daily maximum tide at river mouth 18

5 neurons 1 output wl t+1 HN 3 inputs  Q t D  h t HN   t Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi 19

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi StatisticMonolithic emulator R2R mean err [%] st. dev. err [%] max err [m] min err [m] max(err99) [m] μ(|err99|) [m] min(err47) [m] μ(|err47|) [m]

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi dry season: interested in the effects of low water levels flood season: interested in the effects of the high water levels  would it not be better to consider specialized emulators in the different seasons? Build a cluster of 3 different emulators: - dry season (15/ /5) - flood season (1/7 – 15/9) - two intermediate seasons 21

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi dry season: 7 neurons flood & intermediate seasons: 5 neurons 22

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi 23 Dynamic emulator: Quy hoạch động ngẫu nhiên Stochastic Dynamic Programming (SDP) Giải thuật di truyền Genetic Algorithm (GA)

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi Non-dynamic emulator: by excluding water level at Hanoi we identified an ANN emulator with 5 neurons 23 Dynamic emulator:

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi Dynamic Non-dynamic 24

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Water level at Ha Noi Dynamic Non-dynamic 25

Emulators Step 2-4: Emulators Step 2-4: Iterative Input variable Selection & Emulator Building Water level at Ha Noi Total supply deficit Flow at Son Tay Water level at Tuyen Quang & Pha Lai 26

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Total supply deficit OrderVariable ΔR2 ΔR2 R2R2 1 tt VtVt QtDQtD tt ! Canals system behaves like a reservoir: store water when it can be withdrawn from the river supply it to the fields when the water demand requires it. We identified an ANN emulator with 5 neurons 27

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Total supply deficit OrderVariable ΔR2 ΔR2 R2R2 1 VtVt tt tt QtDQtD we identified an ANN emulator with 5 neurons V t would not be known without the Delta model  identify one more emulator to evaluate it 28

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Total supply deficit Delta model Emulator 29

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Total supply deficit Delta model Emulator 30

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Total supply deficit 31

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Total supply deficit OrderVariable ΔR2 ΔR2 R2R2 1 tt QtDQtD tt we identified an ANN emulator with 5 neurons Non-dynamic V emulator: by removing the water volume V stored in the canals from the possible inputs 32

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Total supply deficit Non-dynamic V emulator 33

Emulators Step 2-4: Emulators Step 2-4: Iterative Input variable Selection & Emulator Building Water level at Ha Noi Total supply deficit Flow at Son Tay Water level at Tuyen Quang & Pha Lai 34

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Flow at Son Tay network has to be dynamic & we consider q ST among inputs tide & water demand are not relevant in Son Tay sum Q D of the upstream flows (releases + unregulated flows) can be used instead of using each single output Delay time is generally lower than 1 day  considered 2 cases 1 day delay no delay 35

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Flow at Son Tay we identified an ANN emulator with 5 neurons 36

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Flow at Son Tay

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Flow at Son Tay

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Flow at Son Tay q ST is used to evaluate the environmental indicators  the emulator has to fit well especially for low flows training & validation dataset only flows below 5400 m 3 /s (75 th quantile of the historical time series of flows at ST) 39

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Flow at Son Tay All have high performances in term of R 2 the last one gives better fitting (also considering errors on the extremes) 40

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Flow at Son Tay

Emulators Step 2-4: Emulators Step 2-4: IIS & EB – Flow at Son Tay

Emulators Step 2-4: Emulators Step 2-4: Iterative Input variable Selection & Emulator Building Water level at Ha Noi Total supply deficit Flow at Son Tay Water level at Tuyen Quang & Pha Lai 43

Thanks for your attention XIN CẢM ƠN 41