Evaluating Satellite Rainfall Products
for Hydrological Applications
Mekonnen Gebremichael, and Dawit Zeweldi
Civil & Environmental Engineering Department
University of Connecticut
Outline
Introduction
+ The Approach
+ The Study Region
Evaluating Satellite Rainfall Products (PERSIANN)
+ Performance Statistics
Evaluating Utility in Hydrological Applications
+ A Blueprint
Conclusions
Mekonnen Gebremichael
University of Connecticut
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Introduction
Two validation approaches:
1. Evaluating against independent rainfall observations
2. Evaluating error propagation in hydrological applications
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University of Connecticut
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Introduction
High-resolution satellite rainfall products analyzed: PERSIANN,
CMORPH (to follow)
PERSIANN
+ IR-PMW merged algorithm: Neural Network
+ 4-km hourly over the United States
Validation Approach
+ Evaluation against NEXRAD radar rainfall observations
+ Evaluation in hydrological applications (to follow)
Study region
+ Little Washita watershed in Oklahoma, USA
+ Good quality NEXRAD data; subject of several major experiments (NASA,
USDA, etc.)
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University of Connecticut
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Introduction: The Little Washita Watershed
Area ~ 600 km2
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University of Connecticut
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Introduction: Inter-annual variability
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University of Connecticut
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Performance Statistics: 4-km, hourly time scale
Bias
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University of Connecticut
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Performance Statistics: watershed-averaged, hourly time scale
DJF
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University of Connecticut
JJA
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Performance Statistics: e-folding distance
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University of Connecticut
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Performance Statistics of the Largest Storm
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University of Connecticut
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Performance Statistics of Storms
Storm Duration
Storm rainfall , mm
E-folding
distance:
Beginning
Ending
NEXRAD
RMSE
mm
Corr
elati
on
eP / eN
06/04/02:22
06/05/02:16
37.31
47.62
8.60
0.92
6.11
0.57
08/11/04:07
08/11/04:14
30.07
26.46
8.17
0.62
2.76
0.63
03/03/04:18
03/04/04:23
53.82
58.12
0.89
0.94
8.59
0.63
10/07/04:09
10/07/04:18
36.21
28.16
0.94
0.70
2.82
0.40
09/19/02:04
09/19/02:13
24.90
14.95
4.26
1.34
8.45
0.23
05/17/02:07
05//17/02:12
36.59
18.78
0.41
0.42
3.20
0.40
05/14/03:03
05/14/03:13
21.56
9.30
1.07
0.46
5.74
0.46
10/08/02:06
10/09/02:22
72.46
15.84
0.55
0.26
7.16
0.48
10/28/02:19
10/29/02:06
30.73
13.40
0.71
0.79
5.07
0.25
09/08/02:16
09/09/02:19
44.95
5.49
0.63
0.09
8.00
0.76
Mekonnen Gebremichael
University of Connecticut
PERSIANN
Peak
Storm
Rate:
qP / qN
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Performance Statistics: Scaling with Temporal Scale
DJF
RMSE
 T 0.38
R
JJA
RMSE
 T 0.55
R
RMSE
R
What is the appropriate space-time scale?
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University of Connecticut
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Hydrological Application
Which is the hydrologic model apt for the satellite rainfall observations?
Numerical simulations of catchment hydrologic processes require a method
for representing a basin. Methods can be categorized as lumped versus
distributed modeling (contours, grids, polygons, TINs).
versus
Basin-Averaged Models
(e.g. HEC-HMS)
Raster-Grid Models
(e.g. MIKE SHE)
Predictive performance of hydrologic models as a function of model complexity
and data availability (Grayson and Bloschl 2001).
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University of Connecticut
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Performance Statistics: A Blueprint for Hydrological Applications
Observed
Watershed
Response
Reference
Rainfall Products
Satellite
Rainfall Products
Hydrologic Model
Hydrologic Model
Mekonnen Gebremichael
University of Connecticut
Hydrologic
Model
Error
Simulated
Watershed
Response
Satellite
Rainfall Error
Propagation
Simulated
Watershed
Response
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Conclusions
Need for rigorous validation of high-resolution satellite rainfall products, at
various space-time scales, for different regimes
Need for identifying hydrologic model complexity level apt for satelliterainfall inputs, and sensitivity to space-time resolutions
Mekonnen Gebremichael
University of Connecticut
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Thank you
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University of Connecticut
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