a methodology for using surface wetness to measure flow in...

A Methodology for Using Surface Wetness to MeasureFlow 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

[email protected], [email protected]

12 September 2016

Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP)BWI Applied to International Basins 12 September 2016 1 / 21

Overview

1 Motivation

2 Methodology

3 Results

4 Conclusion


Motivation

Many river basins will likely face higher hydrologic variability,[Jury and Vaux, 2005, Milly et al. 2008]

Extreme floods and droughtsEconomic 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 shocksIntra-state violent conflict

Due to fluctuations in flow, accurate monitoring provides benefits[Blankespoor et al. 2012, Dinar, 2015]

Independent and objective observations of water managementIdentification of appropriate treaty stipulations and institutionalmechanisms.


Previous models and challenges

Satellite methods enhance models [Xu et al., 2014]

Challenges to flow models

Complexity limits external validity across basinsInput data not readily available in data limited basinsRequirements of input data restricts these models used in monitoringand 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 isan input


Aim

In order to offset these challenges, the Basist Wetness Index (BWI)has a simple yet robust measurement of surface wetness as the onlyinput 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:

RainfallSnowmeltEvapotranspiration ratesSoil infiltration ratesIrrigation


Model requirements and basin selection

The one input to the simple model with 1-2 monthly lags summarizedupstream of the gauge is either monthly BWI or precipitation data asa quadratic function of flow measured from the gauging station

Model requirements and basin selection:

Gauging station data from GRDC and Environment CanadaGreater than 30 Km from major water bodiesNo impediments to natural flow upstreamSufficient amount of rain for detection SSM/IInternational river basinSample 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


Data Sources

Basin database with treaties from the Transboundary FreshwaterDispute Database [TFDD, Yoffe et al. 2003]

Runoff data from the Global Runoff Data Center and EnvironmentCanada

Hydrologically condition DEM to create watershed boundaries fromHydroSHEDS [Lehner, 2008]

Monthly precipitation data are derived from PRECipitationREConstruction over Land (PREC/L) [Chen et al. 2002]

Surface Wetness index (BWI) empirically derived from the SpecialSensor Microwave Imager (SSM/I)[Basist et al. 2001]


Basist Wetness Index (BWI) construction

BWI as a surface wetness index that ranges from no water detected nearthe surface, to a percentage of the radiating surface that is liquid water.

BWI = ∆ε ∗ Ts = β0 ∗ [Tb(υ2) − Tb(υ1)] + 1[Tb(υ3) − Tb(υ2)]

where the change of emissivity (Basist et al. 2001), ∆ε, is empiricallydetermined from global SSM/I measurements, Ts is surface temperatureover wet or dry land, Tb is the satellite brightness temperature at aparticular frequency (GHz), υn (n=1, 2, 3) is a frequency observed by theSSM/I instrument, β0 and β1 are estimated coefficients that correlate therelationship of the various channel measurements with the observedsurface temperature at the time of the satellite overpass.


Table: Results of BWI and precipitation models for the Zambezi, Mekong andRed River basins

ZAMB ZAMB MEKO MEKO REDN REDNBWI PREC BWI PREC BWI PREC

bwi -460.179α 303.707 90.743α

bwi2 748.559α 886.618α 2.037β

precl 71.897α 75.915α 35.825α

precl2 -0.780α 0.297 -0.469β

Lag 2 2 2 2 1 1N 198 198 44 44 298 298Adj R2 0.892 0.524 0.948 0.966 0.786 0.489

Dependent Variable: flowSignificance levels: γ p<0.1, β p<0.05, α p<0.01.


Red River model

Flow estimates from BWI for a section of the Red River captures seasonalvariation


Calibration and validation: Red River

Calibration (2002.09 - 2014.12) - more extreme events - signal/noiseValidation (1988.01 - 2002.08)

Table: Validation of BWI and precipitation models for the Red River

REDN REDN REDN REDN REDN REDNBWI BWI BWI PREC PREC PREC

’88-’14 ’02-’14 ’88-’02 ’88-’14 ’02-’14 ’88-’02

bwi 90.743α 102.641α 74.669α

bwi2 2.037β 1.564 2.902precl 35.825α 39.312α 37.426α

precl2 -0.469β -0.583 -0.671β

Lag 2 2 2 2 1 1N 298 149 149 298 149 149Adj R2 0.786 0.832 0.715 0.489 0.512 0.432

Dependent Variable: flowSignificance levels: γ p<0.1, β p<0.05, α p<0.01.


Prediction of flow

SSM/I instrument is currently operational, we use the fitted model topredict recent runoff from monthly wetness values beyond thecalibration period especially with regards to the prediction ofseasonality: low flow (e.g. droughts), and high flow events (e.g.floods).


Calibration and Prediction period of the Zambezi

Flow estimates from BWI for a section of the Zambezi River capturesseasonal variation and the 2010 flood

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 values are due to the lack of reliable SSM/I data.

0

1000

2000

3000

4000

5000

6000

7000

J-88 O-90 J-93 M-96 D-98 S-01 J-04 M-07 D-09 A-12

Flow

PredictedFlow-Calibrated PredictedFlow-Model


Zambezi flood: April 2009

NASA Image of a severe flood of the Zambezi river, its water backing upinto the Chobe basin

January 2009 shows the base flow,when the Chobe basin to the southand Zambezi basin to the north arecompletely separate

April 14 2009 shows that largestZambezi flood in over 40 yearsinundating a vast area, andimpacting over 300,00 people in theregion


Zambezi flood: May 2010

acquired May 8, 2010


Zambezi Surface Wetness: prediction April 2010

BWI estimated flow for a section of the Zambezi River: April 2010, where.00–.05 (red) means that less than 5% of the time is it this dry, 45-.55(white) is the expected normal soil moisture, and .95-1.0 (purple) meansless than 5% of the time is it this wet


Calibration and Prediction period of the Mekong

Flow estimates from BWI for a section of the Mekong River capturesseasonal variation and the 1995 flood


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 andeffective allocation of limited water resources.


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), 297-308.

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., 1-56.

Chen, M., Xie, P., Janowiak, J.E. and Arkin, P.A., 2002.

Global land precipitation: A 50-yr monthly analysis based on gauge observations.

Journal of Hydrometeorology 3(3), pp. 249-266.

Dinar, S., Katz, D., De Stefano, L. and Blankespoor, B., 2015.

Climate change, conflict, and cooperation: Global analysis of the effectiveness ofinternational river treaties in addressing water variability.

Political Geography 45, pp. 55-66.


References

Drury, A.C. and Olson, R.S., 1998.

Disasters and Political Unrest: An Empirical Investigation.

Journal of Contingencies & Crisis Management 6(3), pp. 153.

Hendrix, C.S. and Salehyan, I., 2012.

Climate change, rainfall, and social conflict in Africa.

Journal of Peace Research, 49(1) pp. 35-50.

Jury, W.A. and Vaux, H., 2005.

The role of science in solving the world’s emerging water problems.

Proceedings of the National Academy of Sciences of the United States of America102(44), pp. 15715-15720.

Lehner, B., Verdin, K. and Jarvis, A., 2008.

New global hydrography derived from spaceborne elevation data.

EOS, TRANSACTION, American Geophysical Union 89(10),93-104.

Milly, P., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, Z.W.,Lettenmaier, D.P. and Stouffer, R.J., 2008.

Stationarity is dead: whither water management?

Science 319(1 February), pp. 573-574.Brian Blankespoor, Alan Basist, Neil Thomas (WB,EoE,WP)BWI Applied to International Basins 12 September 2016 19 / 21

References

Nel, P. and Righarts, M., 2008. Natural

Disasters and the Risk of Violent Civil Conflict.

International Studies Quarterly 52(1), pp. 159-185.

Sood, A. and Smakhtin, V., 2015.

Global hydrological models: a review.

Hydrological sciences journal 60(4), pp. 549-565.

(TFDD) Transboundary Freshwater Dispute Database. Product of theTransboundary Freshwater Dispute Database, Department of Geosciences, OregonState University. http://www.transboundarywaters.orst.edu

Xu, X., Li, J. and Tolson, B.A., 2014.

Progress in integrating remote sensing data and hydrologic modeling.

Progress in Physical Geography 38(4), pp. 464-498.

Yoffe, S., Wolf, A.T. and Giordano, M., 2003.

Conflict and cooperation over international freshwater resources indicators ofbasins at RISR1.

JAWRA Journal of the American Water Resources Association 39(5), pp.1109-1126.


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