Zahidul Islam and Thian Y. Gan zahidul.islam@ualberta tgan@ualberta

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. [email protected]

[email protected]

Department of Civil and Environmental EngineeringDepartment of Civil and Environmental EngineeringUniversity of Alberta, Edmonton, CanadaUniversity of Alberta, Edmonton, Canada

mailto:[email protected]

mailto:[email protected]

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


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


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


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


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


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


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


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)


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)


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


Date

Dis

char

ge (

m3/

s)

Measured

Simulated

R =0.79

0

20

40

60

80

100

120

140

160

180

200


Date

Dis

char

ge (

m3/

s)

Measured

Simulated

R =0.42

0

20

40

60

80

100

120


Date

Dis

char

ge (

m3/

s)

Measured

Simulated

R =0.64

0

50

100

150

200

250

300

350


Date

Dis

char

ge (

m3/

s)

Measured

Simulated

R =0.61

0

100

200

300

400

500

600

700

800


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


Date

Dis

char

ge (m

3/s

)

Measured

Simulated

0

50

100

150

200

250

300


Date

Dis

char

ge (m

3/s

)

Measured

Simulated

0

50

100

150

200

250

300


Date

Dis

char

ge (m

3 /s)

Measured

Simulated

0

50

100

150

200

250

300


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


Biases of NEXRAD Precipitation Data


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.



• 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.

http://www.weather.gov/oh/hrl/dmip/2/data_link.html

Thank You

Comments & Questions

Zahidul Islam and Thian Y. Gan zahidul.islam@ualberta tgan@ualberta

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