Effects of Biases in NEXRAD Precipitation estimates and Sub-Basin Resolution in the Hydrologic Modeling of Blue River Basin Using a Semi-distributed Hydrologic Model

1
Effects of Biases in NEXRAD Precipitation
estimates and Sub-Basin Resolution in the
Hydrologic Modeling of Blue River Basin Using a
Semi-distributed Hydrologic Model

• Zahidul Islam and Thian Y. Gan
• zahidul.islam_at_ualberta.ca
• tgan_at_ualberta.ca
• Department of Civil and Environmental Engineering
• University of Alberta, Edmonton, Canada

2
Structure 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

3
Introduction
Fully Distributed
Lumped
Semi- Distributed
DPHM-RS
4
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

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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.

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DPHM-RS
Semi-Distributed Physically based Hydrologic
Model using Remote Sensing

• 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.

DPHM-RS Applications

• 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

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Model Components of DPHM-RS

• Six Components
• Interception
• Evapotranspiration(ET)
• Soil Moisture
• Saturated Subsurface Flow
• Surface Flow
• Channel Routing

Fig.1 Model Component of DPHM-RS(Biftu and
Gan,2004)
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Model Components of DPHM-RS

• Interception

• The Rutter interception model ( Rutter et
  al.,1971) is used to estimate the rainfall
  interception

Fig.2 Rutter interception model (source Biftu
and Gan,2004)
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Model Components of DPHM-RS

• 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.

Fig.3 Two source model of Shuttleworth and
Gurney (1990) (source Biftu and Gan,2004)

• 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

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Model Components of DPHM-RS

• Evapotranspiration(ET) (..continued)

• Energy balance

Fig.3 Two source model of Shuttleworth and
Gurney (1990) (source Biftu and Gan,2004)
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Model Components of DPHM-RS

• 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)
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Model Components of DPHM-RS

• Soil Moisture (..continued)

• Apply soil water balance in two layers
• Case I Z2 gt0

• Case II Z2 0

Fig.4 Conceptual representation of soil
infiltration (source Biftu and Gan,2004)
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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.

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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.

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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.

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Model Components of DPHM-RS

• Channel Routing

• Muskingum-Cunge Flow routing method is used to
  route the flow through the drainage network.

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Blue River BasinSouth Central Oklahoma, USA
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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 )

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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)
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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)
Meteorological Hourly Precipitation Multisensor (NEXRAD and gauge) Precipitation Data
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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

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Methodology
Basin Sub-Division

• The entire catchment is divided into a number of
  sub-basins drained by a definite drainage
  network.

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Methodology
Generating Response Function
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Methodology
Distribution of Input Variables
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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.

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Calibration

• Calibrating parameters
• The exponential decay parameter of saturated
  hydraulic conductivity (f)
• Mannings 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.

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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
  Mannings n values for forest and bare soil, with
  the objective of matching the simulated with the
  observed hydrographs, especially the peak flows.
• The Mannings 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

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Results
Runoff at Calibration Period (1996-2002)
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Results
Runoff at Validation Period (2002-2006)
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Results
Monthly Mean Flow
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Results
Soil Moisture at Calibration Period
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Results
Soil Moisture at Validation Period
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Discussion on Results
Comparison with Other studies
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Discussion on Results
Biases of NEXRAD Precipitation Data
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Discussion on Results
Biases of NEXRAD Precipitation Data
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Discussion on Results
Biases of NEXRAD Precipitation Data
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Discussion on Results
Biases of NEXRAD Precipitation Data
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Discussion on Results
Biases of NEXRAD Precipitation Data
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Discussion on Results
Effects of Grid Resolution
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Discussion on Results
Effects of Grid Resolution
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Discussion 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.

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Discussion 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

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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

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Summary and Conclusions (Contd..)

• By adjusting NEXRAD precipitation data with
  rainfall measurements from 3 selected Mesonet
  stations, DPHM-RSs 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.

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Summary and Conclusions( Contd..)

• With respect to the Mesonets 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.

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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.

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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_l
  ink.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.

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Thank You
Comments Questions
?