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Effects of Biases in NEXRAD Precipitation estimates and Sub-Basin Effects of Biases in NEXRAD Precipitation estimates and Sub-Basin
Resolution in the Hydrologic Modeling of Blue River Basin Using a Resolution in the Hydrologic Modeling of Blue River Basin Using a
Semi-distributed Hydrologic ModelSemi-distributed Hydrologic Model
Zahidul Islam and Thian Y. Ganzahidul.islam@ualberta.ca
tgan@ualberta.ca
Department of Civil and Environmental EngineeringDepartment of Civil and Environmental EngineeringUniversity of Alberta, Edmonton, CanadaUniversity of Alberta, Edmonton, Canada
Structure of presentationStructure of presentation
• Introduction , Platform and Objectives
• Semi-distributed Hydrologic Model DPHM-RS
• Blue River Basin
• Data
• Research Methodology
• Calibration of DPHM-RS
• Discussions of Results
• Summary and Conclusions
• Recommendations of Future Works
Introduction Introduction
Fully Distributed Lumped Semi- Distributed
DPHM-RSDPHM-RS
Platform of the Study Platform of the Study
DMIP: Distributed Model Inter-comparison Project
• Sponsored by The Hydrology Laboratory (HL) of NOAA's National Weather Service
(NWS)
• Provided a forum to explore the applicability of distributed models using operational
quality data ( Smith et al.,2004 )
• Outcomes of the First phase are documented through Journal of Hydrology: DMIP
Special Edition, 2004.
DMIP 2: Distributed Model Inter-comparison Project Phase II
• Launched on February 2006
• Focussed outcomes : Journal of Hydrology: DMIP 2 Special Edition
Objective of the Study Objective of the Study
Our objectives are to apply DPHM-RS to model the hydrology of
BRB using the NEXRAD precipitation and North American
Regional Reanalysis (NARR) forcing data to address the following
issues:
– The effect of sub-basin resolution on hydrologic modeling for
long term simulation
– Effects of biases of NEXRAD precipitation data on basin-scale
hydrologic modeling.
DPHM-RS
• DPHM-RS is applied for Paddle River Basin of Central Alberta( Biftu and Gan, 2001 & 2004)
• DPHM-RS is also applied for Blue River Basin, Oklahoma, USA for event based simulation( Kalinga and Gan ,2006)
• Currently DPHM-RS is applying for Blue River Basin, Oklahoma, USA for continuous simulation
•Developed by Getu Fana Biftu and Thian Yew Gan (Biftu and Gan, 2001 & 2004)
•In DPHM-RS a basin is subdivided into an adequate number of sub-basins
•The model is designed to assimilate remotely sensed data.
Semi-Distributed Physically based Hydrologic Model using Remote Sensing
DPHM-RS Applications
Model Components of DPHM-RS
Fig.1 : Model Component of DPHM-RS(Biftu and Gan,2004)
Six Components:
•Interception•Evapotranspiration(ET)•Soil Moisture•Saturated Subsurface Flow•Surface Flow•Channel Routing
Model Components of DPHM-RS
Fig.2 : Rutter interception model(source: Biftu and Gan,2004)
Interception
•The Rutter interception model ( Rutter et al.,1971) is used to estimate the rainfall interception
Model Components of DPHM-RS
Fig.3 :Two source model of Shuttleworth and Gurney (1990)
(source: Biftu and Gan,2004)
Evapotranspiration(ET)
•Two source model of Shuttleworth and Gurney (1990)is used to compute ET
•Actual Evaporation from land surface and transpiration from vegetation canopy are computed separately.
•This model calculate the sensible heat flux and latent heat flux and then apply the energy balance for three layer :
•Above canopy
•Within canopy
•Soil
Model Components of DPHM-RS
Fig.3 :Two source model of Shuttleworth and Gurney (1990)
(source: Biftu and Gan,2004)
Evapotranspiration(ET) (..continued)
•Energy balance
secee
sc
ncnsn
ssens
ccenc
en
ELELEL
HHH
RRR
GHELR
HLR
HLR
Where,
0
0
0
Model Components of DPHM-RS
Fig.4 Conceptual representation of soil infiltration (source: Biftu and Gan,2004)
Soil Moisture
•Soil Profile of three homogeneous layer is used to model the soil moisture:
•Active layer:
• unsaturated, 15-30 cm
• Simulates rapid changes of soil moisture content.
• Transmission layer:
• unsaturated
•layer between base of the active layer and top of capillary fringe
•Simulates seasonal changes of soil moisture
•Groundwater Zone:
•Saturated
Model Components of DPHM-RS
Fig.4 Conceptual representation of soil infiltration (source: Biftu and Gan,2004)
Soil Moisture (..continued)
•Apply soil water balance in two layers:
•Case I: Z2 >0
•Case II: Z2 =0
Model Components of DPHM-RS
Saturated Subsurface Flow
•The water table equation from Sivapalan et al. (1987) is modified to simulate the average water table for each sub-basin.
Local Topographic Soil Index: From DEM of DTED
Catchments Average value of
Exponential Decay of Saturated Hydraulic Conductivity Ks
Most Important Calibration parameter for Soil Moisture
Model Components of DPHM-RS
Surface Runoff
•The surface runoff from bare soil:
•The surface runoff from vegetated soil:
•In DPHM-RS the resulting runoff becomes a lateral inflow to the stream channel within the sub-basin
•The surface runoff transferred into stream flow using and average response function for each sub basin.
Kinemsatic Response Function for Sub-basin 1
0
2
4
6
8
10
12
0 50 100 150 200 250
Time (hr)
Dis
ch
arg
e (
m3
/s)
Model Components of DPHM-RS
Surface Runoff
•Finding response function:
• A reference runoff (e.g. 1 cm ) is made available for one time step for all grid cells within the sub-basin.
•Kinematic wave equation is applied for each grid cell and flow is routed from cell to cell based on 8 possible flow direction until the total volume of water corresponding to reference runoff for a sub-basin is completely evacuated.
•Finding resultant runoff:
•The actual surface runoff for each sub-basin is then computed based on that average response function.
Kinemsatic Response Function for Sub-basin 1
0
2
4
6
8
10
12
0 50 100 150 200 250
Time (hr)
Dis
ch
arg
e (
m3
/s)
Model Components of DPHM-RS
Channel Routing
•Muskingum-Cunge Flow routing method is used to route the flow through the drainage network.
x
t
i Δx i+1
j
Δ
t
j+
1
+ C4
(Included Lateral Inflow )
Blue River BasinSouth Central Oklahoma, USA
Blue River BasinSouth Central Oklahoma, USA
•Catchment Type : Non regulated
•Terrain:
• Flat• elevation ranging from 150 m to 350 m (msl)
•Catchment area: 1233 km2
•Major Soil Group:
•Silty Clay loam (Sub-basin 1,2,3) •Sandy Clay ( Sub-basin 4)•Clay (Sub-basin 5,6,7)
•Dominant Vegetation:
•Woody Savanah ( Occupying 80% area )
Input Data to DPHM-RS model(modified from Kaninga and Gan ,2006)
Data Type Parameters Source
Topographic •Mean Altitude•Aspects •Flow direction•Surface slope•Drainage network•Topographic soil index
DEM of USGS National Elevation Dataset
Land use •Spatial distribution of land use classes •Surface Albedo•Surface emissivity•Leaf Area Index
•NASA LDAS•NOAA-AVHRR Satellite data
Soil Properties •Spatial distribution of soil types •Antecedent moisture content•Soil hydraulic properties
•US. State Soil Geographic (STATSGO)•Soil Propeties of Rawls and Brakensiek (1985)
Input Data to DPHM-RS model(modified from Kaninga and Gan ,2006)
Data Type Parameters Source
Stream Flow •Hourly streamflow data at the catchment outlet •Channel cross section
USGS
Meteorological •Shortwave radiation•Wind speed•Air temperature •Ground temperature •Relative humidity •Net radiation •Ground heat flux
North American Regional Reanalysis (NARR)
•Hourly Precipitation Multisensor (NEXRAD and gauge) Precipitation Data
Input Data to DPHM-RS model
•Data Resolution
•DEM : 100 m•Soil Texture : 1 km•Vegetation : 1 km•Precipitation : 4 km•Energy Forcing : 32 km
Methodology
Basin Sub-Division
•The entire catchment is divided into a number of sub-basins drained by a definite drainage network.
Methodology Generating Response Function
For 1 Sub-Basin
0
5
10
15
20
25
30
35
40
0 50 100 150 200 250 300
Time (hr)
Dis
char
ge(m
3/s)
Sub-Baisn 1
For 5 Sub-Basin
0
5
10
15
20
25
0 50 100 150 200 250 300
Time (hr)
Dia
char
ge (m
3/s) Sub-Basin 1
Sub-Basin 2
Sub-Basin 3
Sub-Basin 4
Sub-Basin 5
For 7 Sub-Basin
0
5
10
15
20
25
30
0 20 40 60 80
Time (hr)
Dia
char
ge (m
3/s)
Sub-Basin 1
Sub-Basin 2
Sub-Basin 3
Sub-Basin 4
Sub-Basin 5
Sub-Basin 6
Sub-Basin 7
For 13 Sub-Basin
0
5
10
15
20
25
30
0 10 20 30 40 50 60
Time (hr)
Dia
char
ge (m
3/s)
Sub-Basin 1
Sub-Basin 2
Sub-Basin 3
Sub-Basin 4
Sub-Basin 5
Sub-Basin 6
Sub-Basin 7
Sub-Basin 8
Sub-Basin 9
Sub-Basin 10
Sub-Basin 11
Sub-Basin 12
Sub-Basin 13
For 20 Sub-Basin
0
5
10
15
20
25
30
0 10 20 30 40
Time (hr)
Dia
char
ge (
m3/
s)
Sub-Basin 1Sub-Basin 2Sub-Basin 3Sub-Basin 4Sub-Basin 5Sub-Basin 6Sub-Basin 7Sub-Basin 8Sub-Basin 9Sub-Basin 10Sub-Basin 11Sub-Basin 12Sub-Basin 13Sub-Basin 14Sub-Basin 15Sub-Basin 16Sub-Basin 17Sub-Basin 18Sub-Basin 19Sub-Basin 20
Methodology Distribution of Input Variables
Methodology Model Parameterization
•Model parameters of DPHM-RS
Vegetation
Soil
Channel
•The vegetation parameters are taken from Kalinga and Gan (2006)
•The depth of the active soil layer: 20 cm
•Initial moisture content of the active soil layer : 60%
•The mean water table depth: 8.0 m.
Calibration
Calibrating parameters
•The exponential decay parameter of saturated hydraulic conductivity (f)
• Manning’s roughness coefficient (n) for soil and vegetation
• Mean cross sectional top width
• n for the channel
Sensitivity
• f directly affects the depth of the local GWT and the amount of base flow
• n for soil and vegetation significantly changes the response function
• n for channel and top width affect the shape of the simulated hydrograph.
Calibration
Calibrations Steps
• f was manually adjusted by a trial and error approach so as to simulate adequate base
flows with respect to the observed
• Calibrated f values: 1.0 m-1 for silty clay loam, 0.7 m-1 for sandy clay and 0.4 m-1 for clay.
• The response functions for the seven sub-basins were further calibrated by manually
adjusting Manning’s n values for forest and bare soil, with the objective of matching the
simulated with the observed hydrographs, especially the peak flows.
•The Manning’s n derived were 0.08 for forest, 0.07 for bare soil and 0.015 for the channel
•Based on the Muskingum-Cunge method for channel routing we did not find the need to
adjust the mean top width of the channel reaches (Biftu and Gan, 2001) and we ended up
using the cross-sectional measurements provided by DMIP 2
ResultsResults Runoff at Calibration Period (1996-2002)
0
100
200
300
400
500
600
1-Oct-96 30-Nov-96 29-Jan-97 30-Mar-97 29-May-97 28-Jul-97 26-Sep-97
Date
Dis
char
ge (
m3/
s)
Measured
Simulated
R =0.90
0
50
100
150
200
250
1-Oct-97 30-Nov-97 29-Jan-98 30-Mar-98 29-May-98 28-Jul-98 26-Sep-98
Date
Dis
char
ge (
m3/
s)
Measured
Simulated
R =0.79
0
20
40
60
80
100
120
140
160
180
200
1-Oct-98 30-Nov-98 29-Jan-99 30-Mar-99 29-May-99 28-Jul-99 26-Sep-99
Date
Dis
char
ge (
m3/
s)
Measured
Simulated
R =0.42
0
20
40
60
80
100
120
1-Oct-99 30-Nov-99 29-Jan-00 29-Mar-00 28-May-00 27-Jul-00 25-Sep-00
Date
Dis
char
ge (
m3/
s)
Measured
Simulated
R =0.64
0
50
100
150
200
250
300
350
1-Oct-00 30-Nov-00 29-Jan-01 30-Mar-01 29-May-01 28-Jul-01 26-Sep-01
Date
Dis
char
ge (
m3/
s)
Measured
Simulated
R =0.61
0
100
200
300
400
500
600
700
800
1-Oct-01 30-Nov-01 29-Jan-02 30-Mar-02 29-May-02 28-Jul-02 26-Sep-02
Date
Dis
char
ge (
m3/
s)
Measured
Simulated
R =0.77
e) f)
d)c)
a) b)
ResultsResults Runoff at Validation Period (2002-2006)
0
50
100
150
200
250
300
1-Oct-02 30-Nov-02 29-Jan-03 30-Mar-03 29-May-03 28-Jul-03 26-Sep-03
Date
Dis
char
ge (m
3/s
)
Measured
Simulated
0
50
100
150
200
250
300
1-Oct-03 30-Nov-03 29-Jan-04 29-Mar-04 28-May-04 27-Jul-04 25-Sep-04
Date
Dis
char
ge (m
3/s
)
Measured
Simulated
0
50
100
150
200
250
300
1-Oct-04 30-Nov-04 29-Jan-05 30-Mar-05 29-May-05 28-Jul-05 26-Sep-05
Date
Dis
char
ge (m
3 /s)
Measured
Simulated
0
50
100
150
200
250
300
1-Oct-05 30-Nov-05 29-Jan-06 30-Mar-06 29-May-06 28-Jul-06 26-Sep-06
Date
Dis
char
ge (m
3 /s)
Measured
Simulated
a) b)
c) d)
ResultsResults Monthly Mean Flow
ResultsResults
Soil Moisture at Calibration Period
ResultsResults
Soil Moisture at Validation Period
Discussion on ResultsDiscussion on Results
Comparison with Other studies
Discussion on ResultsDiscussion on Results
Biases of NEXRAD Precipitation Data
Discussion on ResultsDiscussion on Results
Biases of NEXRAD Precipitation Data
Discussion on ResultsDiscussion on Results
Biases of NEXRAD Precipitation Data
Discussion on ResultsDiscussion on Results
Biases of NEXRAD Precipitation Data
Discussion on ResultsDiscussion on Results
Biases of NEXRAD Precipitation Data
Discussion on ResultsDiscussion on Results
Effects of Grid Resolution
Discussion on ResultsDiscussion on Results
Effects of Grid Resolution
Discussion on ResultsDiscussion on Results
Effects of Grid Resolution
•Increasing the number of sub-basin
causes higher simulated runoff in both
high and low flow seasons for the same
total precipitation input which causes
generally leads to an increase in the
correlation during high flow and a
decrease in the correlation during low
flow.
Discussion on ResultsDiscussion on Results
Effects of Grid Resolution
• With smaller sub-basin areas water has
to travel a shorter distance via interflow to
the saturated areas compared to larger
sub-basin areas.
• So increasing the number of sub-basins
causes a quicker drainage of water
because of the shorter travel distance
than for larger sub-basin areas
• Higher moisture content at larger sub-basin areas give rise to higher actual evaporation, thus
lowering the effective precipitation (the difference between actual precipitation and
evaporation) and so the net outflow from the entire basin decreased as number of sub-basins
decrease
Summary and Conclusions Summary and Conclusions
• Even as a semi-distributed, physically based hydrologic model and using 7 sub-basins,
DPHM-RS performed comparably at the calibration stage with three other hydrologic
models that are either TIN-based (Ivanov et al. 2004; Bandaragoda et al., 2004), or with
21 sub-basins (Carpenter and Georgakakos, 2004), and marginally better in the
validation stage;
• Considering there could be other sources of errors, the degradation of model
performance at the validation stage for DPHM-RS can partly be attributed to biases
associated with NEXRAD precipitation even though it is already merged with rain gauge
data, as evident in some cases where high precipitation based on NEXRAD data under
reasonable antecedent moisture content resulted in minimal observed runoff;
Summary and Conclusions (Contd..)Summary and Conclusions (Contd..)
• By adjusting NEXRAD precipitation data with rainfall measurements from 3 selected
Mesonet stations, DPHM-RS’s performance improve marginally in the calibration stage
and significantly in the validation stage, which supports our suspicion on the biases
associated with NEXRAD data. Therefore we suggest that whenever possible, NEXRAD
precipitation data should first be compared and adjusted to local conditions (e.g., rain
gauge data) before applying the data to simulate basin hydrology.
• For a given climatic regime and river basin characteristics (topography, vegetation and
geology), there might be an optimum level of discretization in modeling basin hydrology
and for BRB it turned out to be 7 sub-basins (170 km2 per sub-basin), which is still the
same as that of Kalinga and Gan (2006) even though we used long-term instead of event
based simulations.
Summary and Conclusions( Contd..) Summary and Conclusions( Contd..)
• With respect to the Mesonet’s soil moisture estimates, it seems that DPHM-RS simulated
realistic soil moisture, which together with realistic simulated runoff hydrograph,
demonstrate the physical basis of the semi-distributed model, which should be subjected
to more extensive testing to confirm this observation.
Recommendations for Future Studies Recommendations for Future Studies
1. The uncertainties of NEXRAD precipitation should be further examined
2. The current development of satellite based precipitation estimates e.g., CMOPRPH
(Climate Prediction Center morphing method), TMPA (TRMM Multi-satellite Precipitation
Analysis), SCaMPR (Self-Calibrating Multivariate Precipitation Retrieval) can be a future
alternative of radar precipitation data.
Acknowledgement Acknowledgement
The first author is supported by FS Chia PhD Scholarship of the University of Alberta and
Alberta Ingenuity PhD Graduate Student Scholarship.
The data used in this study were downloaded through the links provided in the website of
DMIP2 (http://www.weather.gov/oh/hrl/dmip/2/data_link.html ), of the US National
Weather Services (NWS) and Office of Hydrologic Development (OHD).
In addition, Oklahoma Mesonet data were provided by the Oklahoma Mesonet, a
cooperative venture between Oklahoma State University and The University of Oklahoma
and supported by the taxpayers of Oklahoma
The research support group of Academic Information and Communication Technologies
(AICT), University of Alberta for significant amount of technical support in data decoding.
Thank You
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