Precipitation & Runoff modeling of Savinja catchment

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1 Precipitation & Runoff modeling of Savinja catchment Hidrološko modeliranje porečja Savinje 36. Goljevščkov spominski dan Ljubljana,

2 The drainage basin hydrological cycle The drainage basin hydrological system Source: 2

3 Drainage Basin Flow Chart Lakes/ Reservoars Source: 3

4 The basic principle 4

5 Which hydrological model? various ongoing researches are there on topics like which model will give more compatible results compared to P-R relations HSPF, HSPEXP+, TOPMODEL, HBV-96, WFLOW Py(distributed HBV), MIKE- SHE, SWAT, SWMM, RS Minerve HBV-light & PEST HBV-light: PEST: 5

6 HBV - Hydrologiska Byråns Vattenbalansavdelning (Hydrological Agency Water Balance Department) The HBV model (Bergström, 1976, 1992) is a rainfall-runoff model, which includes conceptual numerical descriptions of hydrological processes at the catchment scale. The general water balance can be described as d P E Q = + dt [ SP + SM + TZ + UZ + LZ lakes] Where P = precipitation E = evapotranspiration Q = runoff SP = snow pack SM = soil moisture TZ = storage in soil top zone (introduced in HBV-light) UZ = upper groundwater zone storage LZ = lower groundwater zone storage lakes = lake volume Source: 6

7 Semi-distribution Subdivides a large problem into smaller, simpler parts with unique characteristic Elevation zones Vegetation zones 7

8 HBV overview The HBV model is a simple multi-tanktype model for simulating runoff. Rainfall and air temperature data as well as estimated potential evaporation data based on the American Society of Civil Engineers Penman Monteith method are inputs to the model, which consists of four commonly used routines: snow; soil moisture; response; and routing. Picture: Help HBV-light An Overview of the HBV Model 8

9 Model of Computed Runoff Source: Help HBV-light Three GW Box (Distributed STZ and SUZ) Model 9

10 Equations Overview Source: Help HBV-light An Overview of the HBV Model

11 Catchment Parameters Three GW Box (Distributed STZ and SUZ) Model

12 Vegetation Zone Parameters Source: Help HBV-light Parameter Overview 12

13 Effect of T T Picture: Help HBV-light 13

14 Savinja Catchment Enclosed Area of sq km min_elev_m=190.1 m max_elev_m= m avg_elev_m=604.5 m avg_aspect=se (127 ) Older_Celje_elev=~238.0 m 14

15 Flood 1954 Savinja-Celje 15

16 Josephinische Landesaufnahme ( ) 16

17 Flood 1990 Savinja-Laško 17

18 Relational Hydro and Meteo Data Data source: MOP -ARSO,

19 HIGRIS Hydrologic Graphical IS - basically designed with Global Mapper and has more than 160 layers and a lot of external links GIS (Global Mapper [LiDAR], Map Window, SAGA, ILWIS, Google Earth Pro) CAD (AutoCAD MAP 3D, QuickSurf, Surfer) Graphic design (PhotoLine, PaintShop) DB (ASCII, MS Access, PostGIS) Statistic (MS Excell, Origin, Scilab) Programming (SQL, PowerBasic_CC, Python with NumPy) P-R model (HBV-light_CLI, HBV-light-GUI) Calibration Tools (PEST, GAP and Monte Carlo are included in HBV-light) File navigation and data preview (Total Commander, IrfanView, Acrobat) 19

20 Savinja 21 sub-catchments; I. model 20

21 Savinja 77 sub-catchments; II. model 21

22 Geology [Alluvi-Karst] 22

23 Vegetation zones 3 vegetation zones 23

24 Elevation zones 16 elevation zones 24

25 Precipitation RR hour 33 precipitation stations 25

26 What is PEST? Parameter Estimation and Uncertainty Analysis * parameter are estimated on the basis of past events * some of them cannot be estimated uniquely 26

27 BeoPEST, PEST++ are tools for model calibration and uncertainty analysis 27

28 Model Calibration Source: Help HBV-light Model Calibration 28

29 [PTQ screen] 29

30 Flood Wave Celje

31 Goodnes of Fit for calibration period - year 2007 Average model efficiency of Savinja River to Gračnica inflow for whole calibration period Average model efficiency for flood wave

32 Savinja - vp Nazarje i vp Savinja - Nazarje : : : : : : :00 Series1 Series2 32

33 Savinja - vp Letuš 1 5 vp Savinja -Letuš I : : : : : : :00 Series1 Series2 33

34 Savinja vp Medlog vp Savinja - Medlog : : : : : : :00 Series1 Series2 34

35 Savinja vp Celje II _ brv vp Savinja - Celje II - brv : : : : : : :00 Series1 Series2 35

36 Savinja vp Laško 19 vp Savinja - Laško : : : : : : :00 Series1 Series2 36

37 Dreta vp Kraše 4 vp Dreta - Kraše : : : : : : :00 Series1 Series2 37

38 Bolska vp Dolenja vas vp Bolska - Dolenja vas : : : : : : :00 Series1 Series2 38

39 Gračnica vp Vodiško I vp Gračnica - Vodiško I : : : : : : :00 Series1 Series2 39

40 Goodnes of Fit for validation period - year 1990 WS2 WS2_Name NS ( ) 1 Savinja do VP Solčava I Dreta do VP Kraše Ložnica do VP Levec I Savinja do VP Celje II - brv Hudinja do VP Škofja Vas Voglajna do VP Celje II Savinja do VP Laško Savinja do VP Veliko Širje I

41 Flood Wave vp Laško

42 Savinja vp Laško Savinja-Laško I :00: :00: :00: :00: :00: :00: :00: :00: :00:00 Series1 Series2 42

43 Ložnica vp Levec I 90 Ložnica-Levec I : : : : : : : : :00 Series1 Series2 43

44 Dreta vp Kraše Dreta-Kraše :00: :00: :00: :00: :00: :00: :00: :00: :00:00 Series1 Series2 44

45 Voglajna vp Celje II 70 Voglajna-Celje II : : : : : : : : :00 Series1 Series2 45

46 1. Why use a P-R modeling? for education for decision support for data quality control for water balance studies for drought runoff forecasting (irrigation) for fire risk warning for runoff forecasting/prediction (flood warning and reservoir operation) for what happens if questions 46

47 2. Why use a P-R modeling? to compute design floods for flood risk detection to extend runoff data series (or filling gaps) to compute design floods for dam safety to compute energy production to investigate the effects of land-use changes within the catchment to simulate discharge from ungauged catchments to simulate climate change effects 47

48 Designed Flood Predictions based on flood event 2007 (50 year return period) 24 hour Precipitation Event for Q10, Q20, Q50, Q100, Q200 and Q500 based on flood event 1990 and 1998 (100 year return period) 48 hour Precipitation Event for Q10, Q20, Q50, Q100, Q200 and Q500 48

49 Sprememba rabe tal zmanjšanje površine gozda za 20% 800 VP Letuš Sim&Obs-20% gozd Qobs_12 12_20 10 % povečanje konice vala 13 % povečanje volumna 4.5 mio. m3 bi zadržal gozd 1h visokovodni val nastopi 1 uro prej 49

Razporeditev padavin 2000 1800 1600 1400 1200 1000 800 600 400 200 0 VP Veliko Širje 2007

50 Razporeditev padavin VP Veliko Širje 2007 Scn01 Scn02 Scn03 Scn04 Scn

51 Ground water 51

Vpliv zadrževalnikov na Dreti -VP Celje 1200 45_Savinja do VP Celje II brv_ 2007 1000 800 600 400 200 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37

52 Vpliv zadrževalnikov na Dreti -VP Celje _Savinja do VP Celje II brv_ Qsim_WS2 Q_zadrzano sprememba Qvk [%] sprememba Qvk [m3] 1.63 zadržani volumen [mio. m3] 52

[m] Povprečni padec odseka [%] Savinja 58,4 934 62,5 0,3 Dreta 15,7 407 38,8 0,4

53 River Analysis System (HEC-RAS)5.0.3 (1D,2D) Vodotok Dolžina [km] Število profilov Povprečna oddaljenost med profili [m] Povprečni padec odseka [%] Savinja 58, ,5 0,3 Dreta 15, ,8 0,4 Voglajna ,5 0,2 Hudinja 17, ,9 0,6 Ložnica 2, ,3 0,3 Koprivnica 2, ,3 0,2 Sušnica 1, ,6 0,4 53

54 Vpliv zajezitve Voglajne zaradi povečanih pretokov Hudinje 54

55 Vpliv zadrževalnikov na Savinji v spodnji Savinjski dolini - VP Celje 55

The CaMa-Flood model description

Japan Agency for Marine-Earth cience and Technology The CaMa-Flood model description Dai Yamazaki JAMTEC Japan Agency for Marine-Earth cience and Technology 4 th ep, 2015 Concepts of the CaMa-Flood development