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by Tang, Qiuhong 26 June 2006 Lab. meeting presentation

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 Tang, Qiuhong 26 June 2006 Lab. meeting presentation. Outline. ❶. ➢. Introduction. A Historical Perspective of Land Surface Hydrology. ❷.

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by Tang, Qiuhong 26 June 2006 Lab. meeting presentation

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  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 Tang, Qiuhong 26 June 2006 Lab. meeting presentation

  2. Outline ❶ ➢ Introduction A Historical Perspective of Land Surface Hydrology ❷ Development of a Distributed Biosphere-Hydrological Model ❸ Forcing Data and Parameters Analysis ❹ Application of the DBH Model System ❺ A Comprehensive Application in a Continental Scale River Basin ❻ Conclusions and Recommendations ❼

  3. The picture is adopted from Oki and Kanae (2006). ❶ Introduction Tang, Qiuhong 26 June 2006 Slide 3

  4. ❷ A Historical Perspective of Land Surface Hydrology ❸ ❹ ❻ ❺ ❼ Conclusions and Recommendations ❶ Introduction Tang, Qiuhong 26 June 2006 Slide 4

  5. Outline ❶ Introduction ➢ A Historical Perspective of Land Surface Hydrology ❷ Development of a Distributed Biosphere-Hydrological Model ❸ Forcing Data and Parameters Analysis ❹ Application of the DBH Model System ❺ A Comprehensive Application in a Continental Scale 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-distributedway 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.

  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 science. Few models can represent both biosphere and land surface hydrological cycle. (e.g. DHSVM, VIC, FOREST-BGC etc.) This study will develop a DBH 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. ❷ Historical Perspective of Land Surface Hydrology Tang, Qiuhong 26 June 2006 Slide 7

  8. Outline ❶ Introduction A Historical Perspective of Land Surface Hydrology ❷ Development of a Distributed Biosphere-Hydrological Model ➢ ❸ Forcing Data and Parameters Analysis ❹ Application of the DBH Model System ❺ A Comprehensive Application in a Continental Scale River Basin ❻ Conclusions and Recommendations ❼

  9. DBH model strategy One dimensional model (Hydrotopes) River Routing Scheme ❸ Development of a DBH Model Tang, Qiuhong 26 June 2006 Slide 9

  10. New features of DBH model: Biosphere, Nontraditional data sources. ❸ Development of a DBH Model Tang, Qiuhong 26 June 2006 Slide 10

  11. New features of DBH model: Biosphere, Nontraditional data sources. Global Climate Stations SiB2 Land Use 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 AVHRR / LAI ❸ Development of a DBH Model Tang, Qiuhong 26 June 2006 Slide 11

  12. Performance of the DBH model system in the Yellow River Basin. Averaged Monthly discharge comparison Monthly discharge comparison Bias = -1.1% RMSE = 136 m3/s RRMSE = 0.2 MSSS =0.923 Bias = -1.1% RMSE = 233 m3/s RRMSE = 0.3 MSSS =0.828 Annual Largest Flood Peak comparison (m3/s, day) MSSS (mean square skill score, Murphy, 1988, recommended by WMO) Daily discharge comparison Bias = -1.1% RMSE = 297 m3/s RRMSE = 0.4 MSSS =0.759 Bias > 50% Tdelay > 5 days Bias < 10% ❸ Development of a DBH Model Tang, Qiuhong 26 June 2006 Slide 12

  13. Outline ❶ Introduction A Historical Perspective of Land Surface Hydrology ❷ Development of a Distributed Biosphere-Hydrological Model ❸ ➢ Forcing Data and Parameters Analysis ❹ Application of the DBH Model System ❺ A Comprehensive Application in a Continental Scale River Basin ❻ Conclusions and Recommendations ❼

  14. Get time series coverage from in situ observation. IDW TPS Current available interpolation methods in the DBH model system: Inverse Distance Weighted (IDW) Thin Plate Splines (TPS) Thiessen Polygons (TS) TS ❹ Forcing Data and Parameters Analysis Tang, Qiuhong 26 June 2006 Slide 14

  15. Harmonize variant data sources of the DBH model system. Compare Cloud amount from variant data sources with the DBH model system G: Ground observation Rd: Data derived by DBH Ro: Data from CLAVR Ro Rd Satellite data G1 G2 Satellite data Rd G2 Data from: AVHRR NDVI dataset Spatial resolution: 16 km Temporal resolution: daily Study area: the Yellow River Basin Study period: 1995-2000 G1 G1 ❹ Forcing Data and Parameters Analysis Tang, Qiuhong 26 June 2006 Slide 15

  16. Data analysis with the DBH model system. I III Precipitation (%) Reference ET (%) Mean Temperature (K) Min. Temp. (K) Relative humidity (%) Sunshine time (%) Max. Temp. (K) DTR (diurnal temp. range, K) II Detect climate change magnitude (1960-2000) with the DBH model system: Precipitation on the Loess Plateau decreases Cloudy decreases, humidity decreases, Temperature and ET increase, in irrigation districts (Drier). LAI increase in irrigation districts. III Cloud amount (%) LAI (%) 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. ❹ Forcing Data and Parameters Analysis Tang, Qiuhong 26 June 2006 Slide 16

  17. Outline ❶ Introduction A Historical Perspective of Land Surface Hydrology ❷ Development of a Distributed Biosphere-Hydrological Model ❸ Forcing Data and Parameters Analysis ❹ Application of the DBH Model System ➢ ❺ A Comprehensive Application in a Continental Scale River Basin ❻ Conclusions and Recommendations ❼

  18. DBH model application in the Yellow River Basin The Yellow River Basin Area: 794,712 km2 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; Period: 1983-2000 ❺ Application of the DBH Model System Tang, Qiuhong 26 June 2006 Slide 18

  19. Target: Effects of natural and anthropogenic heterogeneity Methodology: Anthropogenic heterogeneity Precipitation heterogeneity Calibrate with Tangnaihai station a=b=4 withdraw from specific river section withdraw from nearest river section Irrigated area data is from AQUASTAT dataset. Experiments: Case 1 : no irrigation, no precipitation heterogeneity Case 2 : no irrigation, with precipitation heterogeneity Case 3 : irrigation, with precipitation heterogeneity ❺ Application of the DBH Model System Tang, Qiuhong 26 June 2006 Slide 19

  20. Results: Case 1 : no precipitation heterogeneity Case 2 : with precipitation heterogeneity With consideration of natural heterogeneity, total runoff increase because surface runoff increase. Case 2 : no irrigation Case 3 : with irrigation Case 3 With consideration of anthropogenic heterogeneity, Runoff Absorbing Zone (RAZ) can be simulated. Case 2 41% discharge increases 59% decreasing discharge (RAZ) ❺ Application of the DBH Model System Tang, Qiuhong 26 June 2006 Slide 20

  21. Effects of human activities on water components: Annual mean water components (1983-2000) in the Yellow River Basin AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN Irrigation Water shortage 65% 42% 44% 100% 0% 1.9 7.7 11.7 37.1 0 AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN -0.25 0.8 1.2 26.4 -8.6 2.1 6.9 10.5 22 0 Evaporation increase Runoff increase Averaged (AVG) In Irrigated Districts (ID) Irrigated Fraction>0.3(IF3) MAX MIN ❺ Application of the DBH Model System Tang, Qiuhong 26 June 2006 Slide 21

  22. Effects of human activities on energy components: Mean energy components in peak irrigation month (JJA, 1983-2000) AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN -0.1 -0.32 -0.4 0 -1.6 -0.06 -0.23 -0.31 0 -1.2 Canopy temperature change Ground temperature change AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN -2.5 -.7.7 -10.2 0 -37.8 3.3 11.2 15.5 43.3 0 Latent heat fluxes change Sensible heat fluxes change Averaged (AVG) In Irrigated Districts (ID) Irrigated Fraction>0.3(IF3) MAX MIN ❺ Application of the DBH Model System Tang, Qiuhong 26 June 2006 Slide 22

  23. Outline ❶ Introduction A Historical Perspective of Land Surface Hydrology ❷ Development of a Distributed Biosphere-Hydrological Model ❸ Forcing Data and Parameters Analysis ❹ Application of the DBH Model System ❺ ➢ A Comprehensive Application in a Continental Scale River Basin ❻ Conclusions and Recommendations ❼

  24. A comprehensive application (Both data analysis and model simulation) Study area: the Yellow River Basin (1960-2000) Target: what contributes to the Yellow River drying up? Methodology: Irrigated area change/ no change The distribution of irrigated area data is from AQUASTAT dataset. The amount of irrigated area is obtained from reports or literatures. ❻ A Comprehensive Application in YRB Tang, Qiuhong 26 June 2006 Slide 24

  25. Climate conditions linear change/ no linear change (mean value is the mean value of the 1960s) / no pattern change Precipitation Mean Temp. Min. Temp. Max. Temp. Climate conditions without pattern change (repeat the climate condition in the 1960s) Sunshine time Relative Humidity ❻ A Comprehensive Application in YRB Tang, Qiuhong 26 June 2006 Slide 25

  26. Vegetation conditions change / no change LAI FPAR Experiments: Scenario1 : control simulation with most realistic condition (all conditions are changing) Scenario2 : non-climate linear change Scenario3 : non-vegetation change Scenario4 : non-irrigated area change Scenario5 : stable without linear tendency (non-climate linear, no vegetation, no irrigated area change) Scenario6 : stable without climate pattern change (non-climate pattern, no vegetation, no irrigated area change) 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 ❻ A Comprehensive Application in YRB Tang, Qiuhong 26 June 2006 Slide 26

  27. Results: Model performance of annual discharge at main stem stations of the Yellow River MSSS >= 0.5 MSSS (mean square skill score, Murphy, 1988, recommended by WMO) Simulated and reported water withdrawals at the Yellow River basin ❻ A Comprehensive Application in YRB Tang, Qiuhong 26 June 2006 Slide 27

  28. Results: Hydrological components change contributed by climate, vegetation, irrigated area change. (S1-S5) Main results: 1) Climate change is dominated in upper/middle reaches, human activity is dominated in lower reaches. 2) Climate pattern change rather than linear change 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. ❻ A Comprehensive Application in YRB Tang, Qiuhong 26 June 2006 Slide 28

  29. Outline ❶ Introduction A Historical Perspective of Land Surface Hydrology ❷ Development of a Distributed Biosphere-Hydrological Model ❸ Forcing Data and Parameters Analysis ❹ Application of the DBH Model System ❺ A Comprehensive Application in a Continental Scale River Basin ❻ Conclusions and Recommendations ➢ ❼

  30. Conclusions 1) A new generation hydrological model, DBH model, is developed and validated. The model is intended to be as physically, biologically, and hydrologically realistic as possible. It can be used for hydrological simulation in continental scale river basin. 2) The agreement between nontraditional data and traditional ground observation suggests that spatial distribution of land characteristics and climate features can be captured by the DBH model. The data analysis in the Yellow River Basin indicates that the Loess Plateau, the Tibetan Plateau, and the irrigation districts are precipitation, temperature, and human activity hot spots of the Yellow River drying up, respectively. 3) The new generation model can demonstrate the effects of natural and anthropogenic heterogeneity. Accounting for precipitation heterogeneity improved the runoff simulation. 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 reasons for the Yellow River drying up. The results indicate 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 for Yellow River drying up. ❻ Conclusions and Recommendations Tang, Qiuhong 26 June 2006 Slide 30

  31. Recommendations 1) Further data collection efforts would continuously benefit research on land surface hydrology. Hydrologists should improve communications with data maker community. 2) Data on the chemical composition of water can be used for modeling water flow paths. The transport processes of chemical traces could be incorporated into the third generation model and improve flow path simulation 3) Further, the model can extend to simulation 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. ❻ Conclusions and Recommendations Tang, Qiuhong 26 June 2006 Slide 31

  32. Life was like football match. You never know what you're gonna get. The picture is adopted from www.lqqm.org.

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