Modeling in aquatic environment

Transcription

1 Modeling in aquatic environment Lecture 0 Data assimilation and data fusion in models Timo Huttula, Akiko Mano and Takauki Shuku Timo.huttula@environment.fi Timo Huttula, Finnish Environment Institute

2 Hdrological expertise in Finland Hdrological Modeling and Forecasting Sstem (4/4) User and operator interface (mobile) HYDROLOGICAL MODELING AND FORECASTING SYSTEM Public www Water situation description River discharges are low throughout the countr River discharges have decreased during sunn and dr period. Onl light rains are forecasted so discharges will sta low 2

3 Hdrological expertise in Finland Weather radar observations and nowcasts (/) Weather radar and LAPS in WSFS Weather radar Hourl radar data in 2x2km grid Used for 2 das in model Underestimates large rainfalls Corrected manuall against rain gauges Radar nowcasting 3hrs in Southern Finland Ensamble of 50 members LAPS Combines information from weather radar, automatic real-time rain gauges, road weather measurements, Will probabl be used in WSFS as a weather radar replacement in near future 33

4 Hdrological expertise in Finland Realtime and historical hdrometeorological and nutrients observations (/4) Observation networks Automatic realtime precipitation -hour measurements from 00 stations 380 discharge stations 220 with dail measurements 60 external stations with usuall dail measurements 660 water level stations 400 with dail measurements 260 external stations with usuall dail measurements Snow courses 40 montl measurements Water qualit measurements Snoptic weather stations, 50 dail measurements Other weather stations, 200 dail measurements

5 Hdrological expertise in Finland Realtime and historical hdrometeorological and nutrients observations (2/4) Measuring the snow water equivalent Snow water equivalent is measured b snow course measurements About 40 snow courses in Finland (less than what is shown at the map) Areal snow water equivalents are calculated for approximatel 0 areas Snow courses are 2-4 km long routes through various terrains 80 depth measurements 8 manual weightings Measurements are made once or twice a month

6 Hdrological expertise in Finland Realtime and historical hdrometeorological and nutrients observations (3/4) Measuring the snow water equivalent

7 Hdrological expertise in Finland Realtime and historical hdrometeorological and nutrients observations (4/4) Meteorological institute Local environmental agencies SYKE s automated measurement devices (water level, ) Hdropower companies Water suppl companies Sweden s SMHI FTP SMS GPRS http / https GSM Report form Realtime hdrometeorological data lines during normal da, man more during flood situations 22 lines / second are saved to database, on average Datacontrol SYKE Hdrological modelling and forecasting sstem Volunteer observers Cellular modem Other outside observers Traditional / snail mail 7

8 Hdrological expertise in Finland Data assimilation and control (/6) Manual measurements - Manual river discharge measurement - Manual precipitation observations - Snow line measurements Automatic observations - Automatic river water level observation - Automatic precipitation observations - Snow depth from automatic stations Derived results / algorithmic data - Weather radar precipitation - Wind corrected precipitation observations - Near-bcomplemented precipitation observations - Satellite value for snow water equivalent - Interpolated snow water equivalent Simulations - Areal precipitation - Runoff, discharge, water level, soil moisture, - Areal precipitation corrected to match water balance - Snow water equivalent, snow depth, ground frost depth - Ice thickness Observed data Calculated data

9 Hdrological expertise in Finland Data assimilation and control (2/6) Data assimilation algorithm To estimate the state of the hdrological sstem toda Assimilation observations of: discharge and water levels (over 400 stations) snow water equivalent (over 50 stations) SnowCoverArea satellite data flood cover area (experimental) Corrects inputs of the model (dail precipitation and temperature) Simulation is corrected to agree with observations on a -2 ear long period backward Expected result of the data assimilation: hdrological storages (snow, soil moisture, etc.) are more correct

10 Hdrological expertise in Finland Data assimilation and control (3/6) Example: forecast gone wrong when data is not filtered

11 Hdrological expertise in Finland Data assimilation and control (4/6) Example: forecast gone wrong when data is not filtered

12 Hdrological expertise in Finland Data assimilation and control (5/6) Data transfers in Watershed Simulation and Forecasting Sstem WWW server Mother Computer http User interface Weather and sea forecasts Weather radar Other data ftp ftp rsnc ftp HYDRO Computer A Computer Computer Computer A Computer A Computer A A L rsnc Data fetching Computer database connections HYDROTEMPO METEO HYDROFLOOD

13 Hdrological expertise in Finland Data assimilation and control (6/6) State: UNKNOWN (at the beginning) Data Control State Machine State: OK State: DISCARD State: SUSPICION U_AO (accept observ.) U_FO O_AO (accept observ.) O_FO D_AO (accept observ.) D_FO S_AO (accept observ.) S_FO U_FO (fetch observ.) U_RF O_FO (fetch observ.) O_RF D_FO (fetch observ.) D_RF S_FO (fetch observ.) S_RF U_RF (read flag) ACCEPTABL. U_AO REVISED O_AO SUSPICIOUS U_AO NULL O_AO O_RF (read flag) ACCEPTABLE O_AO REVISED O_AO SUSPICIOUS O_TR NULL O_TR D_RF (read flag) ACCEPTABLE D_AO REVISED O_AO SUSPICIOUS D_TR NULL D_TR S_RF (read flag) ACCEPTABLE S_AO REVISED O_AO SUSPICIOUS S_TR NULL S_TR O_TR (test rejection) PASS O_TS FAIL D_FO D_TR (test rejection) PASS D_TS FAIL D_FO S_TR (test rejection) PASS S_TS FAIL D_FO O_TS (test suspicion) PASS O_AO FAIL S_AO D_TS (test suspicion) PASS O_AO FAIL S_AO S_TS (test suspicion) PASS O_AO FAIL S_AO

14 Wind data assimilation to Coherens (Shuku&Suito) Local wind fields strongl impact on water current fields (Suito et al., 204) Coupled simulations between air flow and water flow are recommended Time consuming Complex (Confusing) ( How should we deal with the dilemma?

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16 Data and methods Models COHERENS V2(Luten, 20) Open boundar conditions at 3 river mouths: River discharge (observation extracted from Hertta data base of the Finnish Environmental Administration ) Temperature (model results provided b VEMALA*) Total suspended sediment (observation interpolated b linear function) Surface data Meteorological data such as wind speed and direction, air temperature, humidit, cloud coverage and air pressure. (observation provided b Finnish Meteorological Institute) *VEMALA: the water qualit component of the Watershed Simulation and Forecasting Sstem (Vehviläinen B et al., 2005) of the Finnish Environment Institute. This sstem simulates variables such as the transport of total phosphorus and nitrogen and suspended solids in land area, rivers and lakes. (Huttunen I. et al., 2008). Huttunen I, Huttunen M, Tattari S, Vehviläinen B Large scale phosphorus load modelling in Finland. In Northern Hdrolog and its Global Role, Volume 2, Sveinsson ÓGB, Garðarsson SM, Gunnlaugsdóttir S (eds). XXV Nordic Hdrological Conference NHP Report No. 50. Icelandic Hdrological Committee: Rekjavik; Vehviläinen B, Huttunen M, Huttunen I Hdrological forecasting and real time monitoring in Finland: The watershed simulation and forecasting sstem (WSFS). In Innovation, Advances and Implementation of Flood Forecasting Technolog, Conference Papers, Tromsø, Norwa, 7 9 October 2005.

17 Taneli Duunari-Töntekijäinen, SYKE Objective Aim:sensitivit test of assimilation Total Suspended Sediment. River discharge Temperature As much as possible 0 das interval 20 das interval 30 das interval No assimilation Meteorological data SPM(no biomass) 0 times 2 times 3 times 5 times 6 times Model Model Model Model Model 7

18 /30/206 Huttula Finnish Environment Institute, SYKE 8

19 Taneli Duunari-Töntekijäinen, SYKE Target area Stud area Eurajoki(out) Phäjoki(in) Ylaneenjoki(in) (Reports of Finnish Environment Institute, 2008) Reports of Finnish Environment Institute 5/2008, 73 p. URN:ISBN: ISBN: (PDF).Vehviläinen B, Huttunen M, Huttunen I Hdrological forecasting and real time monitoring in Finland: The watershed simulation and forecasting sstem (WSFS). In Innovation, Advances and Implementation of Flood Forecasting Technolog, Conference Papers, Tromsø, Norwa, 7 9 October

20 /30/206 Huttula Finnish Environment Institute, SYKE 20

21 Turbidir TSM Model 06 No biomass Amap Model08 0das Model09 20das Model0 30das Model None 6/ 6/8 6/8 6/2 6/26 7/6

22 Results

23 /30/206 Huttula Finnish Environment Institute, SYKE 23

24 /30/206 Huttula Finnish Environment Institute, SYKE 24

25 Observation Station Meteorological data wind direction, wind speed, temperature, humidit Palokkajärvi Tuomiojärvi

26 Observation Station Palokka Ranta-Niemela Kaijala Laajavuori JEnergia Lehtisaari

27 Temporal Modeling Auto-Regressive (AR) Model n m i i n i n v a n a i v n :n th time-series data : i th AR coefficient : white noise m : autoregressive order <Yule-Walker method> m m m m m m C C C a a a C C C C C C C C C ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ i Ĉ : auto-correlation function i a : AR coefficient

28 Spatial Modeling

29 Ordinar Kriging ) ( ) ( ) ( 0 ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( w w w n n n n n n n n Sstem equation 0 ) ~ i( n : Unknown value (wind speed and wind direction) : Known value (wind speed and wind direction) : Mean value : Variogram (auto correlation function) i w : Weight

30 Interpolated data Y (m) Y (m) Y (m) X (m) X (m) X (m) st August 20 th August 30 th August Y (m) Y (m) X (m) X (m) 0 th September 20 th September

31 Estimated local wind field Y (m) X (m)