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Prediction of low flow in mid-sized
natural basin using GRACE derived
daily Total Water Storage Anomaly
Durga Sharma1, and Basudev Biswal1,2
1Department of Civil Engineering, Indian Institute of Technology Hyderabad, 2Department of Civil Engineering, Indian Institute of Technology Bombay
SWAT 2018 conference IIT Chennai 10/1/2018 1
INTRODUCTION
BACKGROUND STUDY
DATA AND DATA ANALYSIS
METHODLOGY
ANALYSIS
CONCLUSION
10/1/2018 2
Outline
10/1/2018 3
Introduction
10/1/2018 4
Satellite storage measurement
J. S. Famiglietti et. al 2015
Paper Resolution Characteristic mega-basin water storage behavior using GRACE (Reager, J. T., & Famiglietti, J. S. (2013)
Spatial Resolution large Temporal Resolution – 1 month
Analysis of terrestrial water storage changes from GRACE and GLDAS Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J., & Wilson, C. R. (2008).
Spatial Resolution large Temporal Resolution – 1 month
GRACE storage-runoff hystereses reveal the dynamics of regional watersheds. Sproles, E. A., Leibowitz, S. G., Reager, J. T., Wigington, P. J., Famiglietti, J. S., & Patil, S. D. (2015).
Spatial Resolution large Temporal Resolution – 1 month
Daily GRACE gravity field solutions track major flood events in the Ganges-Brahmaputra DeltaGouweleeuw, B. T., Kvas, A., Grüber, C., Gain, A. K., Mayer-Gürr, T., Flechtner, F., & Güntner, A. (2017).
Spatial Resolution large Temporal Resolution – 1 day
Prediction of low flow using GRACE derived
daily Total Water Storage Anomaly (Our
Analysis)
Spatial Resolution - mid size river basin Temporal Resolution – 1 day
Work with GRACE
10/1/2018 5
10/1/2018 6
Storage-Discharge Relationship from GRACE
Terrestrial water storage is
the key entity that determines
flows in river channels TWSA
To use GRACE derived daily Total Water
Storage Anomaly for predicting low flow in
mid-sized natural basin
10/1/2018 7
Objective of our study
Data and Study Area
8
Daily Discharge data from USGS waterwatch ITSG-GRACE 2016 DATA
10/1/2018
Methodology
9 10/1/2018
S = (GW +SM+SWE+T+E+RS)
𝑄 = f (𝑆)
twsa (total water storage) = S
Methodology contd…
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TWSA_m = TWSA - min(TWSA)+1
Biswal and Marani 2010
Daily discharge data
Daily twsa data
Find recession events and corresponding twsa
Determine k at α = 2
Using this k, explore relationship between k and twsa
1
2
5
4
3
Brutsaert and Nieber
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Methodology contd…
We have
Our Approach
When only discharge is decreasing
When both discharge and TWSA are decreasing
12 10/1/2018
Results:
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Generating scatter plots for past twsa and k.
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D1 D2 D3 D4 D5 D6 D7 D8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Time Interval
R s
qua
re
D1 D2 D3 D4 D5 D6 D7 D8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Time Interval
R s
qua
re
Only discharge is decreasing
Strong relationship between power-law recession coefficient and initial storage (TWSA at the beginning of recession event).
Results contd. . .
Results contd. . .
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D1 D2 D3 D4 D5 D6 D7 D8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time Interval
R s
qua
re
D1 D2 D3 D4 D5 D6 D7 D8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Time Interval
R s
qua
re
Relationship increases significantly, when we consider decrease in both discharge and twsa.
When both discharge and twsa are decreasing
Results contd . . .
16
0 100 200 300 4000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
RsqQ
Time in Days
R s
qu
are
0 100 200 300 4000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
RsqT
Time in Days
R s
qu
are
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Appreciable relationships are observed between k and past TWSA values implying that storage takes time to deplete completely.
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Results contd . . .
Scatter plots of individual recession events
Exponential relationship
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Results contd . . .
2 Days 4 Days 6 Days 10 Days
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
Lead days
NS
E
Prediction Results
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Results contd . . .
Conclusions Daily storage-discharge relationship is highly dynamic, which generates large amount of scatter in storage-discharge plots.
There is a strong relationship between power-law recession coefficient and initial storage (TWSA).
Furthermore, appreciable relationships are observed between recession coefficient and past TWSA values implying that storage takes time to deplete completely.
With such a coarse data we got median Nash–Sutcliffe efficiency of 0.45.
Result will increase significantly by using finer resolution data
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21 10/1/2018
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