A Methodology for Using Surface Wetness to Measure Flow in International River Basins: Application to the Zambezi, Mekong and Red River Basins

Transcription

1 A Methodology for Using Surface Wetness to Measure Flow in International River Basins: Application to the Zambezi, Mekong and Red River Basins Brian Blankespoor, Alan Basist, Neil Thomas World Bank, Eyes On Earth, Resource Data Incorporated 12 September 2016 Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

2 Overview 1 Motivation 2 Methodology 3 Results 4 Conclusion Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

3 Motivation Many river basins will likely face higher hydrologic variability, [Jury and Vaux, 2005, Milly et al. 2008] Extreme floods and droughts Economic and political consequences Increased variability promotes non-compliance among riparians [Drury and Olson, 1998, Nel and Righarts, 2008, Hendrix and Salehyan, 2012] Inter-state tensions may ensue resulting in economic shocks Intra-state violent conflict Due to fluctuations in flow, accurate monitoring provides benefits [Blankespoor et al. 2012, Dinar, 2015] Independent and objective observations of water management Identification of appropriate treaty stipulations and institutional mechanisms. Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

4 Previous models and challenges Satellite methods enhance models [Xu et al., 2014] Challenges to flow models Complexity limits external validity across basins Input data not readily available in data limited basins Requirements of input data restricts these models used in monitoring and mitigation capacity Review of 12 global hydrological models require 2-36 parameters [Sood and Smakhtin, 2015] see Table 2 Timing of water release from snow pack is challenging when rainfall is an input Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

5 Aim In order to offset these challenges, the Basist Wetness Index (BWI) has a simple yet robust measurement of surface wetness as the only input to model river flow BWI offers: Globally consistent data in near real time under most sky conditions. 25 year period of record allows calibration and validation of the model [Basist et al. 2001, Blankespoor et al. 2012] Integration of a multitude of factors in one variable Wetness Index: Rainfall Snowmelt Evapotranspiration rates Soil infiltration rates Irrigation Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

6 Model requirements and basin selection The one input to the simple model with 1-2 monthly lags summarized upstream of the gauge is either monthly BWI or precipitation data as a quadratic function of flow measured from the gauging station Model requirements and basin selection: Gauging station data from GRDC and Environment Canada Greater than 30 Km from major water bodies No impediments to natural flow upstream Sufficient amount of rain for detection SSM/I International river basin Sample areas are as large as possible to provide sufficient observations. Basin selection includes: the Mekong, Zambezi, and Red River Compare BWI-flow model to rainfall-flow model Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

7 Data Sources Basin database with treaties from the Transboundary Freshwater Dispute Database [TFDD, Yoffe et al. 2003] Runoff data from the Global Runoff Data Center and Environment Canada Hydrologically condition DEM to create watershed boundaries from HydroSHEDS [Lehner, 2008] Monthly precipitation data are derived from PRECipitation REConstruction over Land (PREC/L) [Chen et al. 2002] Surface Wetness index (BWI) empirically derived from the Special Sensor Microwave Imager (SSM/I)[Basist et al. 2001] Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

8 Basist Wetness Index (BWI) construction BWI as a surface wetness index that ranges from no water detected near the surface, to a percentage of the radiating surface that is liquid water. BWI = ɛ T s = β 0 [T b (υ 2 ) T b (υ 1 )] + 1 [T b (υ 3 ) T b (υ 2 )] where the change of emissivity (Basist et al. 2001), ɛ, is empirically determined from global SSM/I measurements, T s is surface temperature over wet or dry land, T b is the satellite brightness temperature at a particular frequency (GHz), υ n (n=1, 2, 3) is a frequency observed by the SSM/I instrument, β 0 and β 1 are estimated coefficients that correlate the relationship of the various channel measurements with the observed surface temperature at the time of the satellite overpass. Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

9 Table: Results of BWI and precipitation models for the Zambezi, Mekong and Red River basins ZAMB ZAMB MEKO MEKO REDN REDN BWI PREC BWI PREC BWI PREC bwi α α bwi α α β precl α α α precl α β Lag N Adj R Dependent Variable: flow Significance levels: γ p<0.1, β p<0.05, α p<0.01. Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

10 Red River model Flow estimates from BWI for a section of the Red River captures seasonal variation Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

11 Calibration and validation: Red River Calibration ( ) - more extreme events - signal/noise Validation ( ) Table: Validation of BWI and precipitation models for the Red River REDN REDN REDN REDN REDN REDN BWI BWI BWI PREC PREC PREC bwi α α α bwi β precl α α α precl β β Lag N Adj R Dependent Variable: flow Significance levels: γ p<0.1, β p<0.05, α p<0.01. Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

12 Prediction of flow SSM/I instrument is currently operational, we use the fitted model to predict recent runoff from monthly wetness values beyond the calibration period especially with regards to the prediction of seasonality: low flow (e.g. droughts), and high flow events (e.g. floods). Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

13 Calibration and Prediction period of the Zambezi Flow estimates from BWI for a section of the Zambezi River captures seasonal variation and the 2010 flood Flow J-88 O-90 J-93 M-96 D-98 S-01 J-04 M-07 D-09 A-12 Predicted Flow - Calibrated Predicted Flow - Model Figure 5 : The Zambezi values of runoff (m³/s per month, y-axis) and time (month / years--, January 1988 through July 2013) display seasonality with both the predicted flow from the period of record (with observed gauging station data) (blue) and the predicted values from the Zambezi runoff BWI model after the period of record (red) (see Table 1 for equation). Missing Brian Blankespoor, values Alanare Basist, due Neil to the Thomas lack of (WB,EoE,WP) reliable BWI Applied SSM/I to data. International Basins 12 September / 21

14 Zambezi flood: April 2009 NASA Image of a severe flood of the Zambezi river, its water backing up into the Chobe basin January 2009 shows the base flow, when the Chobe basin to the south and Zambezi basin to the north are completely separate April shows that largest Zambezi flood in over 40 years inundating a vast area, and impacting over 300,00 people in the region Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

15 Zambezi flood: May 2010 acquired May 8, 2010 Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

16 Zambezi Surface Wetness: prediction April 2010 BWI estimated flow for a section of the Zambezi River: April 2010, where (red) means that less than 5% of the time is it this dry, (white) is the expected normal soil moisture, and (purple) means less than 5% of the time is it this wet Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

17 Calibration and Prediction period of the Mekong Flow estimates from BWI for a section of the Mekong River captures seasonal variation and the 1995 flood Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

18 Conclusion: Advantages of BWI-flow model Simple robust model to estimate flow Independent and objective remotely sensed measurement Best fit models provides 1-2 months lag time Early warning capacity is essential to building climate resilience and effective allocation of limited water resources. Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

19 References Basist, A., C. Williams Jr, T. F. Ross, M. J. Menne, N. Grody, R. Ferraro, S. Shen, and A. T. C. Chang (2001) Using the Special Sensor Microwave Imager to monitor surface wetness Journal of Hydrometeorology 2(3), Blankespoor, B., A. Basist, A. Dinar and S. Dinar (2012) Assessing Economic and Political Impacts of Hydrological Variability on Treaties: Case Studies of the Zambezi and Mekong Basins Policy Research Working Paper No. 5996, World Bank, Washington, DC., Chen, M., Xie, P., Janowiak, J.E. and Arkin, P.A., Global land precipitation: A 50-yr monthly analysis based on gauge observations. Journal of Hydrometeorology 3(3), pp Dinar, S., Katz, D., De Stefano, L. and Blankespoor, B., Climate change, conflict, and cooperation: Global analysis of the effectiveness of international river treaties in addressing water variability. Political Geography 45, pp Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

20 References Drury, A.C. and Olson, R.S., Disasters and Political Unrest: An Empirical Investigation. Journal of Contingencies & Crisis Management 6(3), pp Hendrix, C.S. and Salehyan, I., Climate change, rainfall, and social conflict in Africa. Journal of Peace Research, 49(1) pp Jury, W.A. and Vaux, H., The role of science in solving the world s emerging water problems. Proceedings of the National Academy of Sciences of the United States of America 102(44), pp Lehner, B., Verdin, K. and Jarvis, A., New global hydrography derived from spaceborne elevation data. EOS, TRANSACTION, American Geophysical Union 89(10), Milly, P., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, Z.W., Lettenmaier, D.P. and Stouffer, R.J., Stationarity is dead: whither water management? Science 319(1 February), pp Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

21 References Nel, P. and Righarts, M., Natural Disasters and the Risk of Violent Civil Conflict. International Studies Quarterly 52(1), pp Sood, A. and Smakhtin, V., Global hydrological models: a review. Hydrological sciences journal 60(4), pp (TFDD) Transboundary Freshwater Dispute Database. Product of the Transboundary Freshwater Dispute Database, Department of Geosciences, Oregon State University. Xu, X., Li, J. and Tolson, B.A., Progress in integrating remote sensing data and hydrologic modeling. Progress in Physical Geography 38(4), pp Yoffe, S., Wolf, A.T. and Giordano, M., Conflict and cooperation over international freshwater resources indicators of basins at RISR1. JAWRA Journal of the American Water Resources Association 39(5), pp Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21

22 The End Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP) BWI Applied to International Basins 12 September / 21