Seasonal Variation of Total Terrestrial Water Storage in Major River Basins

Transcription

1 1 Seasonal Variation of Total Terrestrial Water Storage in Major River Basins T Oki, P. Yeh, K Yoshimura, H Kim, Y Shen, N D Thanh, S Seto, and S Kanae Institute of Industrial Science, The University of Tokyo Third WCRP International Conference on Reanalysis, University of Tokyo, Japan, 29 January, 2008

2 2 Storage and Flux in Water Cycles (Oki, Global Energy and Water Cycles, CAP, 1999)

3 3 Land surface, Atmospheric, and Combined Water Balance Relative change of total terrestrial water storage can be estimated from water vapor convergence and river discharge information based on atmospheric water balance.

4 4 Peixoto [1969] Starr and Peixoto [1956]

5 5 Rasmusson [1968]

6 6 (Oki et al., Hydrol. Proc., 1995)

7 7 By AGCM Based on Observation (Oki, Global Energy and Water Cycles, 1999)

8 8 Based on Observation (Oki, Global Energy and Water Cycles, 1999)

9 9 Based on Observation By AGCM (Oki, Global Energy and Water Cycles, 1999)

10 10 Questions Is river water storage really significant for the seasonal cycle of total terrestrial water storage (TWS) in the Amazon river basin? Do we have any other independent estimation/validation data of TWS on continental basin scale?

11 11 Updating the Comparison Model estimation: offline simulation results by Global Soil Wetness Project 2 B0: Multi (8) LSM Ensemble B1: revised precipitation for an LSM Atmospheric water balance: reanalysis ERA40: 2.5 o x2.5 o, 6 hourly JRA25: 1.1 o x1.1 o, 6 hourly Satellite: GRACE by GFZ, JPL, and CSR until degree N=30 without C20

12 12 Target Period and Areas GSWP2-B0, -B1: (Jan 1986 Dec 1995) ERA40, JRA25: (Jan 1986 Dec 1995) GRACE: mean seasonal variations (Jan 2003 Dec 2006) Seven Basins in this study (Amazon, Mississippi, Missouri, Mackenzie, Ob, Yenisey, & Chao Phraya)

13 Data set production, comparison, and validation Global Soil Wetness Project GSWP is an ongoing GEWEX project, led by COLA (P. Dirmeyer) with IIS/UT (T. Oki), involving over a dozen modeling groups on four continents. GSWP1 used the ISLSCP I-1 data to examine Unlike the ocean, the land surface state variables (soil moisture, snow depth and coverage, soil temperature) are not routinely measured and reported. Land-surface models driven by observed meteorology give us a means to generate a surrogate observed data set. GSWP2, a 10-year ( ) global land-surface analysis, began in Regional studies are investigating issues of aggregation, sub-grid variability, and assimilation of remote sensing data.

14 14 Major Goal of the GSWP2 Produce the best global data sets of soil moisture, surface fluxes, and related hydrologic quantities (including runoff) for with 1x1 degree grid spacing considering the uncertainties associated with Land surface models: more than 20 LSMs, Model parameters: 2 kinds of vegetation etc., Forcing data: 3 kinds of reanalysis data, corrected (hybrid) precipitation, etc., and Temporal and spatial scales to run LSMs. (Dirmeyer et al., BAMS, 2006)

15 15 Offline Simulation of LSMs Radiation Evap. T c M c g c Precipitation Surface Met. Land Surface Model Runoff Soil Soil Moisture Vegetation Canopy Physical Param. CO 22 Flux Surface Layer T g M g W 1 P T e u c o p I S I L Root Zone W 2 z Recharge Zone T d W 3

16 16 Validation of GSWP-2 1m Column 1.0 Soil Moisture (IL, GSMDB) Total Field (1m, ) Correlation RMSE SSiB SiBUC VISA ISBA SWAPNOAH CLMMOSES ave 0 SSiB SiBUC VISA ISBA SWAPNOAH CLMMOSES ave Anomaly (1m, ) Correlation 0.4 RMSE (Dirmeyer, et. al, 2005) 0.0 SSiB ISBA SiBUC SWAP VISA NOAHMOSES CLM ave 0 SSiB ISBA SiBUC SWAP VISA NOAHMOSES CLM ave

17 17 Converting runoff into river discharge by TRIP (Oki and Sud, 1998)

18 18 (Oki and Sud, 1998)

19 19 Simulated River Discharge by TRIP (Yoshimura, et. al, HRL, under revision)

20 20 Synthesized Global Water Cycle Artificial reservoir 7 (Oki and Kanae, Science, 2006) 3

21 21 (P-obs: CRU) Mean Annual Cycle of Precipitation

22 Mean Annual Cycle of Runoff (1)

23 Mean Annual Cycle of Runoff (2)

24 24 Characteristics of Estimates Estimating Storage (Change) Mean relative error of annual runoff in 6 basins (%) Offline Simulation by LSM GSWP2-B0 no explicit 43 GSWP2-B1 ground water -6 Reanalysis ERA40 Imbalance -22 Atmospheric ERA40 28 Water Theoretically Balance JRA25 everything 39 included Satellite GRACE -

25 25 How to adjust divq? To prevent the imbalance in the estimates, mean annual WV convergence are adjusted by: Bias reduction: constant subtractions or additions of monthly divq. Ratio method: multiply constant coefficient to monthly divq.

26 26 Runoff/WV Convergence (mm/month) Two ways to adjust Water Vapor Convergence data Observation WV Convergence Bias Ratio Mean bias = 8.2mm/month WVC/Obs. = 1.62 ec Month

27 27 GRACE: Monthly Series

28 28 Results Comparisons of TWS in Continental Scale River Basins

29 29 Relative Storage Amazon Components

30 30 Components of GSWP2-B1 Relative S to ra g e (m m ) A M A Z O N (a t O b idos) Month B1_River B1_Snow B1_SoilM Grace ERA40(Bias) JRA25(Bias) Old Version of GRACE estimates (at AGU Fall Meeting, 2006)

31 31 Relative Storage Chao Phraya Components

32 32 Relative Storage Mississippi Components

33 33 Relative Storage Missouri Components

34 34 Relative Storage Components Mackenzie

35 35 Relative Storage Components Ob

36 36 Relative Storage Components Yenisey

37 37 Uncertainties in Estimates Offline simulation of LSM (e.g., GSWP2) Inherits uncertainties in forcing data No explicit ground water representation Atmospheric Water Balance Imbalance due to discrete updates inconsistent with terrestrial water balance bias reduction ambiguities GRACE Higher order (N>30) variation in space? C20?

38 38 Remarks It is nice to have three independent estimates of TWS. Seasonal changes of total TWS estimates. GRACE and GSWP2-B1 are similar except for Mississippi and Missouri. GSWP2-B0 overestimates the amplitudes in high latitudes AWB estimates by JRA25 and ERA40 are similar and corresponding to GRACE estimates for continental scale river basins Major Components of the Seasonal Cycle of TWS River channel water storage in tropical river basins. Soil moisture in mid latitude river basins Snow water equivalent in high latitude river basins New estimates are consistent with old results based on very primitive data Thank you for your attention!

39 39 Taikan Oki Institute of Industrial Science, The University of Tokyo

40 Snow 209 Subsurface runoff Global Terrestrial Water Budget Rainfall (Dirmeyer et. al, BAMS, 2006) Unit: mm/year ET 242 Surface runoff Soil water storage Legend Inter-model range Total runoff Interannual range Average of 12 models ( ) 633

41 41 Obs. GSWP2-B0 GSWP2-B1 ERA40 ERA40 (mm/year) Runoff Runoff Runoff Runoff Convg. JRA25 Convg. Amazon (Obidos) Mississippi (Vicksburg) Missouri (Hermann) McKenzie (Arctic Red River) Ob (Salekhard) Yenisey (Igarka) Chao Phraya (Khai Chira Prawat)

42 42 Seneviratne et al. [2004]

43 43 Star & Peixoto [1956] Rasmusson [1968] Previous Works: Benton et al [1950]; Benton and Estoque [1954]; Star and Peixoto[1956]; Rasmusson[1967, 1968, 1971]; Peixoto [1969]; Seneviratne et al. [2004]; Hirschi et al [ 2006]; Peixoto [1969]

44 44 GRACE: Ave. Annual Cycle of

45 Runoff: Amazon and Chao Phraya

46 Runoff: Mississippi and Missouri

47 Runoff: Mackenzie, Ob, & Yenisey