1. A Distributed Biosphere-Hydrological Model System for Continental Scale River Basins 大陸河川のための分布型生物圈水文 モデルに関する研究 by Qiuhong Tang 7 Nov 2006 Hydro Seminar @ Land surface hydrology group of UW

2. Introduction ❶ Outline Evolution of Hydrological Modeling ❷ Analyses on Observed Data ❸ Development of a Distributed Biosphere-Hydrological Model ❹ Evaluation of the DBH Model System ❺ Long Term Change of Hydrological Cycles in the Yellow River Basin ❻ Conclusions and Recommendations ❼ ➢

3. The picture is adopted from Oki and Kanae Science (2006). Introduction ❶ Tang, Qiuhong 7 Nov 2006 Slide 3

4. ❷ ❸❹ ❺ ❻ Evolution of Hydrological Modeling ❼ Conclusions and Recommendations Introduction ❶ Tang, Qiuhong 7 Nov 2006 Slide 4

5. Introduction ❶ Outline Evolution of Hydrological Modeling ❷ Analyses on Observed Data ❸ Development of a Distributed Biosphere-Hydrological Model ❹ Evaluation of the DBH Model System ❺ Long Term Change of Hydrological Cycles in the Yellow River Basin ❻ Conclusions and Recommendations ❼ ➢

6. Conceptual Model: The first generation hydrological model (1960s – 1970s) Use statistical relationship between rainfall and discharge Integrate different components of hydrological processes in a lumped or fake-distributed way Representative models and methodology: Stanford model, Xin’an jiang model, Tank model, Unit Hydrograph etc. Distributed Model: The second generation hydrological model (1980s – 1990s) Recognize the effects of spatial heterogeneity with spatially varying data Solve the differential equations with powerful computer Representative models and methodology: SHE model, TOPMODEL, GBHM etc. Tang, Qiuhong 7 Nov 2006 Slide 6

7. Distributed Biosphere-Hydrological (DBH) Model: The third generation hydrological model (2006) Connect hydrological cycle with biosphere, climate system and human society. Physically represent hydrological cycle with nontraditional data Development of DBH model shows the new direction of hydrology. Few models can represent both biosphere and land surface hydrological cycle. (e.g. DHSVM, VIC, FOREST-BGC etc.) This study will develop a model system to bridge atmosphere-biosphere-land surface hydrology and human society. The scope of hydrology will broaden from rainfall- runoff relationship to climatology, biosphere, ecosystem, geosphere, remote sensing, and human society. Evolution of Hydrological Modeling ❷ Tang, Qiuhong 7 Nov 2006 Slide 7

8. Introduction ❶ Outline Evolution of Hydrological Modeling ❷ Analyses on Observed Data ❸ Development of a Distributed Biosphere-Hydrological Model ❹ Evaluation of the DBH Model System ❺ Long Term Change of Hydrological Cycles in the Yellow River Basin ❻ Conclusions and Recommendations ❼ ➢

9. IDW TS TPS Interpolation methods: Inverse Distance Weighted (IDW) Thin Plate Splines (TPS) Thiessen Polygons (TS) Analyses on Observed Data ❸ Tang, Qiuhong 7 Nov 2006 Slide 9 Get time series coverage from in situ observation.

10. Harmonize variant data sources. Information extracted from nontraditional data is compared with traditional data. G: Ground observation Rd: Data derived by DBH Ro: Data from CLAVR G1 G2 Rd Ro Data from: AVHRR NDVI dataset Spatial resolution: 16 km Temporal resolution: daily Study area: the Yellow River Basin Study period: 1995-2000 Satellite data Analyses on Observed Data ❸ Tang, Qiuhong 7 Nov 2006 Slide 10 Tang, Q., Oki, T., 2006. J. Appl. Meteorol., accepted.

11. Data analysis. Detect climate change magnitude (1960- 2000) : Precipitation on the Loess Plateau decreases Cloudy decreases, humidity decreases, temperature and ET increase, in irrigation districts (Drier). LAI increase in irrigation districts. Precipitation (%)Reference ET (%) Relative humidity (%)Sunshine time (%) Cloud amount (%)LAI (%) Mean Temperature (K)Min. Temp. (K) Max. Temp. (K)DTR (diurnal temp. range, K) I II Temperature increases, LAI decreases on the Tibet Plateau The Loess Plateau, the IDs, and the Tibet Plateau can be precipitation, human activity, and temperature hot spots of Yellow River drying up, respectively. III Analyses on Observed Data ❸ Tang, Qiuhong 7 Nov 2006 Slide 11 Tang, Q., Oki, T., Kanae, S., Hu, H., 2006. Hydrol. Process., accepted.

12. Introduction ❶ Outline Evolution of Hydrological Modeling ❷ Analyses on Observed Data ❸ Development of a Distributed Biosphere-Hydrological Model ❹ Evaluation of the DBH Model System ❺ Long Term Change of Hydrological Cycles in the Yellow River Basin ❻ Conclusions and Recommendations ❼ ➢

13. One dimensional model River Routing Scheme (Hydrotopes) Development of a DBH Model ❹ Tang, Qiuhong 7 Nov 2006 Slide 13 DBH model strategy Tang, Q., Oki, T., Hu, H., 2006. Ann. J. Hydraul. Eng. JSCE 50, 37-42. http://hydro.iis.u-tokyo.ac.jp/DBH/

14. New features of DBH model: Biosphere, Nontraditional data sources. Development of a DBH Model ❹ Tang, Qiuhong 7 Nov 2006 Slide 14

15. New features of DBH model: Biosphere, Nontraditional data sources. AVHRR / LAI SiB2 Land Use Global Climate Stations Data sources used in the DBH model system: Remote sensing (RS) : AVHRR/NDVI, LAI, FPAR, ISCCP-FD RadFlux, HYDRO1K, etc. Ground observations: Global Surface Summary of Day Data, Global Soil Bank, etc. Statistical survey data: Global Soil Map, Global Irrigation Area Development of a DBH Model ❹ Tang, Qiuhong 7 Nov 2006 Slide 15

16. Introduction ❶ Outline Evolution of Hydrological Modeling ❷ Analyses on Observed Data ❸ Development of a Distributed Biosphere-Hydrological Model ❹ Evaluation of the DBH Model System ❺ Long Term Change of Hydrological Cycles in the Yellow River Basin ❻ Conclusions and Recommendations ❼ ➢

17. Evaluation of the DBH Model System ❺ Tang, Qiuhong 7 Nov 2006 Slide 17 DBH model application in the Yellow River Basin The Yellow River Basin Area: 794,712 km 2 River length: 5,464 km Topographic condition : Tibetan Plateau – Loess Plateau – North China Plain Climatic Condition: Annual precipitation < 200 – 800 mm Simulation: Spatial: 10*10 km; Time step: hourly;

18. Model Calibration and Validation Monthly discharge comparison Bias = -1.1% RMSE = 233 m 3 /s RRMSE = 0.3 MSSS =0.828 MSSS (mean square skill score, Murphy, 1988, recommended by WMO) MSSS: -∞ To 1.0 Bias = -6% RMSE = 333 m 3 /s RRMSE = 0.48 MSSS =0.646 Calibration (1983-1993) Validation (1962-1982) Monthly discharge comparison Slope: FAO soil map, slope f=2.0 Evaluation of the DBH Model System ❺ Tang, Qiuhong 7 Nov 2006 Slide 18

19. Averaged Monthly discharge comparison Bias = -1.1% RMSE = 136 m 3 /s RRMSE = 0.2 MSSS =0.923 Daily discharge comparison Bias = -1.1% RMSE = 297 m 3 /s RRMSE = 0.4 MSSS =0.759 YearQobvTpeakQsimTpeakQsim-obvTsim-obv 1983356014-Jul325314-Jul-3070 1984366017-Jul309915-Jul-561-2 1985335021-Sep338918-Sep39-3 198626204-Jul27665-Jul1461 1987215025-Jun325227-Jun11022 1988148010-Oct13407-Oct-140-3 1989414023-Jun267026-Jun-14703 1990143017-Sep130913-Sep-121-4 1991159018-Aug175117-Aug161 199227107-Jul232222-Jun-388-15 1993204021-Jul226423-Jul2242 Annual Largest Flood Peak comparison (m 3 /s, day) Bias < 10% Bias > 50% Tdelay > 5 days Evaluation of the DBH Model System ❺ Tang, Qiuhong 7 Nov 2006 Slide 19 Bias = -6% RMSE = 459 m 3 /s RRMSE = 0.6 MSSS =0.419 Daily discharge comparison Calibration (1983-1993) Validation (1962-1982) Model Calibration and Validation

20. Evaluation of the DBH Model System ❺ Tang, Qiuhong 7 Nov 2006 Slide 20 Model Calibration and Validation Validation (1960s-1970s) Calibration (1980s-1990s) ReportedSimulated 1960s1777024980 1970s1990023181 Unit: 10 6 m 3 / year ReportedSimulated 1980s2961026886 1990-952996029879 Unit: 10 6 m 3 / year Canal coefficient: 0.3 The canal coefficient in Yellow River basin is about: 0.3 – 0.5. (Wang H., Cai P., Zhou H. Yellow River News, YRCC, 2005)

21. Evaluation of the DBH Model System ❺ Tang, Qiuhong 7 Nov 2006 Slide 21 Target: Effects of natural and anthropogenic heterogeneity Methodology: withdraw from nearest river section withdraw from specific river section Irrigated Fraction data is from AQUASTAT dataset. Precipitation heterogeneity Calibrate with Tangnaihai station a=b=4 Anthropogenic heterogeneity Experiments: Case 1 : no irrigation, no precipitation heterogeneity Case 2 : no irrigation, with precipitation heterogeneity Case 3 : irrigation, with precipitation heterogeneity Area Precipitation Tang, Q., Oki, T., Kanae, S., Hu, H., 2006. J. Hydromet., accepted. Review of studies on this topic: Effect of natural, not anthropogenic, heterogeneity is presented. The new generation hydrological model makes it possible to represent both natural and anthropogenic heterogeneity.

22. Evaluation of the DBH Model System ❺ Tang, Qiuhong 7 Nov 2006 Slide 22 Results: Case 1 : no precipitation heterogeneity Case 2 : with precipitation heterogeneity Case 1 : no precipitation heterogeneity Case 2 : with precipitation heterogeneity With consideration of natural heterogeneity, total runoff increase because surface runoff increase. decreasing discharge discharge increases 59% 41% (RAZ) Case 2 : no irrigation Case 3 : with irrigation Case 2 : no irrigation Case 3 : with irrigation With consideration of anthropogenic heterogeneity, Runoff Absorbing Zone (RAZ) can be simulated.

23. Effects of human activities on water components: Water shortage Evaporation increaseRunoff increase Irrigation Averaged (AVG) In Irrigation Districts (ID) Irrigated Fraction>0.3(IF3) MAX MIN Annual mean water components (1983-2000) in the Yellow River Basin 65% 42% 44% 100% 0% 1.9 7.7 11.7 37.1 0 2.1 6.9 10.5 22 0 -0.25 0.8 1.2 26.4 -8.6 AVG ID IF3 MAX MIN Evaluation of the DBH Model System ❺ Tang, Qiuhong 7 Nov 2006 Slide 23

24. Ground temperature change Latent heat fluxes changeSensible heat fluxes change Canopy temperature change -0.1 -0.32 -0.4 0 -1.6 -0.06 -0.23 -0.31 0 -1.2 3.3 11.2 15.5 43.3 0 -2.5 -.7.7 -10.2 0 -37.8 AVG ID IF3 MAX MIN Effects of human activities on energy components: Averaged (AVG) In Irrigation Districts (ID) Irrigated Fraction>0.3(IF3) MAX MIN Mean energy components in peak irrigation month (JJA, 1983-2000) Evaluation of the DBH Model System ❺ Tang, Qiuhong 7 Nov 2006 Slide 24

25. Introduction ❶ Outline Evolution of Hydrological Modeling ❷ Analyses on Observed Data ❸ Development of a Distributed Biosphere-Hydrological Model ❹ Evaluation of the DBH Model System ❺ Long Term Change of Hydrological Cycles in the Yellow River Basin ❻ Conclusions and Recommendations ❼ ➢

26. A comprehensive application ( Both data analysis and model simulation ) Study area: the Yellow River Basin (1960-2000) Target: potential reasons for the Yellow River drying up Long Term Change of Hydrological Cycles in YRB ❻ Tang, Qiuhong 7 Nov 2006 Slide 26 Review of studies on this topic: Analyze hydro-climate data (Fu et al 2004; Yang et al 2004, Xu 2005) Analyze water use/irrigation data (Liu and Zhang 2002) Statistical relationship between climate data, water use, and discharge data Climate condition Human activity Hydrology cycle DBH Distributed Numerical The new generation hydrological model makes it possible to numerically simulate connections (internal relation) between climate condition, human activity and hydrology cycle.

27. Methodology: The distribution of irrigated area data is from AQUASTAT dataset. The amount of irrigated area is obtained from reports or literatures. Irrigated area change/ no change Long Term Change of Hydrological Cycles in YRB ❻ Tang, Qiuhong 7 Nov 2006 Slide 27 To watch the hydrological response to hydrological forcing data. The simulation difference between ‘no change’ and ‘change’ forcing data shows the contribution of the hydrological components.

28. Long Term Change of Hydrological Cycles in YRB ❻ Tang, Qiuhong 7 Nov 2006 Slide 28 Climate conditions linear change/ no linear change (mean value is the mean value of the 1960s) / no pattern change PrecipitationMean Temp. Min. Temp.Max. Temp. Relative Humidity Sunshine time Climate conditions without pattern change (repeat the climate condition in the 1960s)

29. Long Term Change of Hydrological Cycles in YRB ❻ Tang, Qiuhong 7 Nov 2006 Slide 29 Vegetation conditions change / no change LAIFPAR Experiments: S1-S2: linear climate change contribution S1-S3: vegetation change contribution S1-S4: irrigated area change contributions S1-S5: all linear changes contribution (S1-S5) – (S1-S6): climate pattern change contribution ScenariosClimateVegetationIrrigated Area Scenario 1/// Scenario 2--// Scenario 3/--/ Scenario 4//-- Scenario 5-- Scenario 6O-- / With change-- No linear changeO No pattern and no linear change

30. Long Term Change of Hydrological Cycles in YRB ❻ Tang, Qiuhong 7 Nov 2006 Slide 30 Results: Model performance of annual discharge at main stem stations of the Yellow River Simulated and reported water withdrawals at the Yellow River basin MSSS = 0.5 MSSS = 0.7 Scenario 1

31. Hydrological components change contributed by climate, vegetation, irrigated area change. (S1-S5) Results: Long Term Change of Hydrological Cycles in YRB ❻ Tang, Qiuhong 7 Nov 2006 Slide 31 Runoff_ChangeET_Change Withdrawal_ChangeTg_Change

32. Conclusion Remarks: 1) Climate change (75%) is dominated in upper/middle reaches, human activity is dominated in lower reaches. 2) Climate pattern change (30%) rather than linear change (10%) is more important for Yellow River drying up. 3) The reservoirs make more stream flow consumption for irrigation on one hand, and help to keep environment flow and counter zero-flow in the river channel on the other hand. Long Term Change of Hydrological Cycles in YRB ❻ Tang, Qiuhong 7 Nov 2006 Slide 32 Tang, Q. et al, 2006. xxx, xxx (manuscript ready for submission).

33. Introduction ❶ Outline Evolution of Hydrological Modeling ❷ Analyses on Observed Data ❸ Development of a Distributed Biosphere-Hydrological Model ❹ Evaluation of the DBH Model System ❺ Long Term Change of Hydrological Cycles in the Yellow River Basin ❻ Conclusions and Recommendations ❼ ➢

34. ❼ Tang, Qiuhong 7 Nov 2006 Slide 34 Conclusions 1) A new generation hydrological model, DBH model, is developed and validated. 2) Spatial distribution of land characteristics and climate features can be captured by the DBH model with nontraditional datasets. 3) The new generation model can demonstrate the effects of natural and anthropogenic heterogeneity. Accounting for anthropogenic heterogeneity can simulate negative runoff contribution which cannot be represented by traditional models. 4) The DBH model was used to interpret the potential reasons for the Yellow River drying up. Climate change is dominated in upper/middle reaches, human activity is dominated in lower reaches. Climate pattern change rather than linear change is more important.

35. Recommendations Conclusions and Recommendations ❼ Tang, Qiuhong 7 Nov 2006 Slide 35 1) Data collection efforts would continuously benefit research on land surface hydrology. Hydrologists should improve communications with data maker community. 2) Model validation is needed for the new generation model. Data on the chemical composition of water can be used for modeling water flow paths. 3) Further, the model can extend to simulate hydrological cycle over the global land surface with global datasets. The ocean-land surface-atmosphere model system will explore and variability and predictability of climate and hydrological variations. 4) With the consideration of climate, biosphere, land surface hydrology and human activity, the new generation model has potential great societal benefits. The development and application of the new model will benefit both science and society.

36. Hydro Seminar @ Land surface hydrology group of UW http://hydro.iis.u-tokyo.ac.jp/DBH/

38. Publications (Accepted and Published): Tang, Q., Oki, T., Hu, H., 2006. A distributed biosphere hydrological model (DBHM) for large river basin. Ann. J. Hydraul. Eng. JSCE 50, 37-42. Tang, Q., Oki, T., 2006. Daily NDVI relationship to cloud cover. J. Appl. Meteorol., accepted. Tang, Q., Oki, T., Kanae, S., Hu, H., 2006. The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation. J. Hydromet., accepted. Tang, Q., Oki, T., Kanae, S., Hu, H., 2006. A spatial analysis of hydro-climatic and vegetation condition trends in the Yellow River Basin. Hydrol. Process., accepted. Tang, Q., Hu, H., Oki, T., Tian, F., 2006. Water balance within intensively cultivated alluvial plain in an arid environment. Water Resource Management., accepted. Tang,Q., Hu, H., Oki, T., 2006. Groundwater recharge and discharge in a hyperarid alluvial plain (Akesu, Taklimakan Desert, China), Hydrological Processes, accepted. Tang, Q., Hu, H., and Oki, T., Hydrological processes within an intensively cultivated alluvial plain in an arid environment, Sustainability of Groundwater Resources and its Indicators (Pro- ceedings of symposium S3 held during the Seventh IAHS Scientific Assembly at Foz do Iguacu, Brazil, April 2005). IAHS Publ. 302, 2006. Tang, Q., Tian, F., and Hu, H., Runoff-evaporation hydrological model for arid plain oasis II: the model application, Shuikexue Jinzhan/Advances in Water Science, 15 (2): 146-150, 2004. (in Chinese with English abstract) Hu, H., Tang, Q., Lei, Z., and Yang, S., Runoff-evaporation hydrological model for arid plain oasis I: the model structure, Shuikexue Jinzhan/Advances in Water Science, 15 (2): 140-145, 2004. (in Chinese with English abstract)

39. Land Surface Model Groundwater-soil water transfer: Relationship between soil moisture potential and soil moisture: (Clapp and Hornberger, 1978)

40. Groundwater-River Interaction Groundwater flow to a ditch over a sloping impermeable bed. Assuming that the flow lines are approximately parallel to the bed, according to the Dupuit-Forchheimer approximation, the flow of water per unit width of the river is estimated. (Childs, 1971; Towner, 1975)

41. Overland flow on the Hillslope Flow in river channel

42. Model Validation Criteria Mean Error: Relative Bias: Mean Absolute Error: Mean square error: Relative RMSE: Mean Square Skill Score: (Murphy, 1988) Recommended by WMO.