b. tamrakar and knut alfredsen applicability of trmm...

B. Tamrakar and Knut Alfredsen: Applicability of TRMM satellite data in hydropower planning

Rentech Symposium Compendium, Volume 2, December 2012 60

Applicability of TRMM satellite data in hydropower planning Bijay Tamrakar*

1 and Knut Alfredsen

2

1Upper Solu Hydro-electric Project Nepal and

2Norwegian University of Science and Technology

Abstract- The computation of discharge through a watershed is

the first step for determination of capacity of a hydropower project.

The discharge can be simulated using a hydrological model where

precipitation is used as a basic input. The elaborated study of

precipitation demands the need of dense setup of rain gauge

network for a mountainous topography of Nepal which is

expensive and difficult for periodic maintenance. A smart

alternative could be the use of Satellite precipitation data which

also represents global precipitation. Wide ranges of satellite

products with varying spatial and temporal resolutions are available

but, they should be duly evaluated before using them in analysis. In

this study, Tropical Rainfall Measuring Mission (TRMM)

precipitation data are evaluated using ground based precipitation

stations over Nepal and fed in a rainfall-runoff model to estimate

monthly discharge through four of the major basins of Nepal. A

simple water balance model has been used initially developed by

Thornthwaite. Statistical parameters showed an under-estimation of

precipitation over major areas of Nepal. The results from water

balance model however presented quite a good estimation of

discharge through basins with an average correlation coefficient

(RR) of 0.8. This implies that TRMM data can be used for runoff

simulations during the planning stage of hydropower projects as

well as on ungauged catchments. 1

Index Terms- TRMM, satellite precipitation, Nepal,

Thornthwaite monthly water balance model

I. INTRODUCTION

The energy output through a hydropower project is

mainly governed by available discharge from the river and

net head available. Head remains constant for a project and

discharge varies based on precipitation falling over the

catchment. Flow through a catchment can be simulated

using a hydrological model where precipitation is used as a

major input. This needs the study of spatial and temporal

distribution of rainfall. Precipitation measurement is done

through a ground based rain gauge that represents point

measurement. An adequate number of these stations should

be spread over the catchment to understand its spatial and

temporal variability. Due to the mountainous topography of

Nepal, precipitation varies widely even in a small region.

This demands a dense setup of rain-gauge network which is

rather expensive and difficult for periodical maintenance [2].

An alternative to conventional measurement of

precipitation through rain gauge could be the use of satellite

precipitation data. Satellite precipitation map is an advanced

remote sensing technique that uses satellite observations of

infrared, passive microwave and space borne precipitation

radar for estimation of rainfall over globe. The estimation is

based on cloud characteristics and its temperature. Recent

development in remote sensing has introduced data with

high spatial (0.25°x0.25°) and temporal (3 hours)

resolutions.

A wide range of precipitation products have been

developed in recent years. The present study undertakes

* Corresponding author, [email protected]

evaluation of TRMM satellite product with relative to

ground based precipitation over whole country. The detailed

study of TRMM data over Nepal has been limited only to

Trishuli catchment [3], Narayani basin [4] and Bagmati

basin [5].

II. DATA PREPARATION

TRMM Satellite data

TRMM is jointly developed by NASA, USA and

NASDA, Japan to study rainfall and energy exchange on

tropical and subtropical regions of the world. TRMM

satellite data are available with different levels of processing

and resolution. Among them, TRMM 3B42 V6 has been

used in the study. The principal characteristics of this data

are presented in Table 1.

TABLE I

CHARACTERISTICS OF TRMM 3B42V6 DATA[1]

Description

Data Extent 1998 to present

Geographical coverage Latitude 50°S to 50°N

Longitude 180°W to 180°E

Temporal resolution 3 hours

Spatial resolution 0.25° x 0.25°

Grid Size 400 x 1440 pixels

Projection Geographic WGS 184

Data format HDF

Precipitation measurement mm/hr

Missing value -999.9

Rain gauge data

Point measurement data from 264 rain gauge stations are

employed for statistical evaluation of TRMM Satellite data.

The data were collected from 2001 to 2008 and obtained

from Department of Hydrology and Meteorology, GoN.

Methodology

Since two datasets are in different format; one as a point

measurement data and other as an aerial precipitation map,

either of them should be converted to other form. Point to

pixel method has been adopted where an aerial precipitation

map is converted to point measurement based on latitude

and longitudes of a rain gauge station. Daily, monthly and

annual accumulated datasets of both type of precipitation are

prepared. The conversion process is done are as shown in

Fig.1.

Since large amount of data are involved, a series of

scripts were prepared in ‘Python programming language’ to

automate data preparation. The data processing includes

format conversion, rotation, clipping and temporal

aggregation. The scripts were originally developed at NTNU

[6]and modified during the study.

B. Tamrakar and

Rentech Symposium Compendium, Volume

The statistical parameters used for the comparison of

TRMM Satellite data and rain gauge data are

Nash-sutcliffe coefficient of efficiency (R²)

correlation (RR), Normalized Accumulated Difference

(NAD), Root mean square Difference (RMSD), Mean

Absolute Difference (MAD), Mean Relative Absolute

Difference (MRAD), Estimation Bias (EB), Satellit

Conditional Probability of Detection (CPOD_S), Gauge

Conditional Probability of Detection (CPOD_D)

III. RESULTS OF COMPARISON

The daily, monthly and annual datasets of gauge station

and TRMM satellite was compared. The statistical

parameters revealed following results.

TABLE II

COMPARISON RESULTS[

Statistical

Parameters

Daily Monthly

Max Min Max Min

R² 0.18 -5.42 0.88 -19.47

RR 0.60 -0.06 0.95

RMSD 56.54 8.05 519.41 42.90

MAD 32.83 4.85 346.67 29.94

NAD 53.23 -75.95 164.08 -78.68

MRAD 11.61 0.74 7.98

CPOD_S 0.89 0.47 1.00

CPOD_G 0.89 0.14 0.99

EB 332.03 -78.44 233.44 -78.65

The parameters showed a slight deviation of TRMM data

with respect to gauge data as R² value is low and negative in

most cases. Monthly accumulated data showed better results

than daily and annual datasets. They appear with high RR

value and R² lying between 0.5 and 1.Th

probably resulted due to accumulation of deviations in

precipitation estimation by TRMM. The spatial variability of

R² over gauging locations for monthly dataset is shown in

Fig. 2.

B. Tamrakar and Knut Alfredsen: Applicability of TRMM satellite data in

Rentech Symposium Compendium, Volume 2, December 2012

Fig. 1: Sequence of data processing

The statistical parameters used for the comparison of

TRMM Satellite data and rain gauge data are scatter plot,

sutcliffe coefficient of efficiency (R²)[7], coefficient of

correlation (RR), Normalized Accumulated Difference

(NAD), Root mean square Difference (RMSD), Mean

Absolute Difference (MAD), Mean Relative Absolute

Difference (MRAD), Estimation Bias (EB), Satellite

Conditional Probability of Detection (CPOD_S), Gauge

Conditional Probability of Detection (CPOD_D).

OMPARISON

The daily, monthly and annual datasets of gauge station

and TRMM satellite was compared. The statistical

[1]

Monthly Annual

Min Max Min

19.47 0.48 -51.82

0.81 0.80 -0.86

42.90 3914.97 167.99

29.94 3851.18 145.29

78.68 359.67 -78.90

0.38 6.15 0.15

0.91 1.00 1.00

0.55 1.00 0.83

78.65 359.67 -78.90

showed a slight deviation of TRMM data

with respect to gauge data as R² value is low and negative in

most cases. Monthly accumulated data showed better results

than daily and annual datasets. They appear with high RR

value and R² lying between 0.5 and 1.This could have

probably resulted due to accumulation of deviations in

precipitation estimation by TRMM. The spatial variability of

R² over gauging locations for monthly dataset is shown in

Fig. 2: Spatial variation of R² over gauging stations

It is also seen that Estimation bias is negative in most of the

cases that shows underestimation. But, an average of

CPOD_S of 0.7 reflects good detection efficiency of

TRMM. A significant decreasing trend of R² is seen with

increasing elevation. The R² vs. e

precipitation datasets is presented in Fig. 3.

Fig. 3: R² vs elevation for daily dataset comparison

Spatial variabilitywithin a single pixel is also studied. A

comparison is made between 11 gauging stations lying

within same pixel and TRMM pixel value. Fig. 4 shows the

pixel and underlying gauging stations within the pixel and

the annual and mean monthly precipitation records of the

pixel.

atellite data in hydropower planning

61

Spatial variation of R² over gauging stations

is also seen that Estimation bias is negative in most of the

cases that shows underestimation. But, an average of

CPOD_S of 0.7 reflects good detection efficiency of

TRMM. A significant decreasing trend of R² is seen with

increasing elevation. The R² vs. elevation for daily

precipitation datasets is presented in Fig. 3.

R² vs elevation for daily dataset comparison

within a single pixel is also studied. A

comparison is made between 11 gauging stations lying

TRMM pixel value. Fig. 4 shows the

pixel and underlying gauging stations within the pixel and

the annual and mean monthly precipitation records of the

B. Tamrakar and

Rentech Symposium Compendium, Volume 2

Fig. 4: 0.25°x0.25° pixel with annual and mean monthly precipitation comparison

From the figure, it is seen that precipitation varies widely

even within a small area of 27.5 km x 27.5 km. Also, it is

seen that TRMM represents an average precipitation for the

pixel. This demonstrates that the pixel resolution also

governs efficiency and accuracy of estimation.

IV. MONTHLY WATER BALANCE M

The precipitation data are used as an input in a rainfall

runoff model for the computation of discharge through the

catchment. A monthly water balance model initially

developed by Thornthwaite[8]and revised by Alleys (1984)

has been employed in this study. The study of monthly water

balance model is done for long term forecasting of water

resources distribution [9]. The model is programmed in R

software by Emmanuel Jjunju during his PhD research at

NTNU. The framework of Thornthwaite model structure is

presented in Fig. 5.

Fig. 5: Thornthwaite Monthly WB model


2, December 2012

ixel with annual and mean monthly precipitation comparison[

From the figure, it is seen that precipitation varies widely

even within a small area of 27.5 km x 27.5 km. Also, it is

seen that TRMM represents an average precipitation for the

es that the pixel resolution also

governs efficiency and accuracy of estimation.

MODEL

The precipitation data are used as an input in a rainfall-

runoff model for the computation of discharge through the

balance model initially

and revised by Alleys (1984)

has been employed in this study. The study of monthly water

balance model is done for long term forecasting of water

. The model is programmed in R

software by Emmanuel Jjunju during his PhD research at

NTNU. The framework of Thornthwaite model structure is

Thornthwaite Monthly WB model

The aerial precipitation and temperature map of 0.25° x

0.25° resolution is used as major input to the model.

Potential evapotranspiration is computed through Hamon’s

equation[10]and is based on lattitude and longitude of the

location. Snow contribution in runoff is computed relating

average temperature in each month with threshold for snow

or rain[11]. The model uses 3 parameters; direct runoff

factor, surplus runoff factor and soil moisture capacity.

Direct runoff factor governs immediate runoff from the

catchment, soil moisture capacity marginalize limit for

moisture storage in soil and surplus runoff factor determines

excess runoff after saturating soil moisture storage.

parameters have been adjusted for best fit of simulated

discharge curve and it is seen that param

values for all of the basins.

The model simulation has been done for 6 years from

2001 to 2006. The major basins of Nepal with their

measured outlet points are shown in Fig. 6.

Fig. 6: Major basins of Nepal

V. SIMULATION

The runoff is simulated using a monthly water balance

model for 4 major basins of Nepal. They are plotted with

measured discharge at the basin outlet as shown in Fig. 7.


62

[1]

al precipitation and temperature map of 0.25° x

0.25° resolution is used as major input to the model.

Potential evapotranspiration is computed through Hamon’s

and is based on lattitude and longitude of the

location. Snow contribution in runoff is computed relating

average temperature in each month with threshold for snow

. The model uses 3 parameters; direct runoff

factor, surplus runoff factor and soil moisture capacity.

Direct runoff factor governs immediate runoff from the

moisture capacity marginalize limit for

moisture storage in soil and surplus runoff factor determines

excess runoff after saturating soil moisture storage.The

parameters have been adjusted for best fit of simulated

seen that parameters possess similar

The model simulation has been done for 6 years from

2001 to 2006. The major basins of Nepal with their

measured outlet points are shown in Fig. 6.

Major basins of Nepal

IMULATION RESULTS

runoff is simulated using a monthly water balance

model for 4 major basins of Nepal. They are plotted with

measured discharge at the basin outlet as shown in Fig. 7.

B. Tamrakar and

Rentech Symposium Compendium, Volume

Fig.7:

The parameter adjustment for best fit of simulated

discharge with observed showed hig direct runoff factor. It

means rainfall is drained immediately after their occurrence.

The results showed quite a good prediction of flow except in

some years. The mean monthly observed and simulated

discharges are also compared; one for Koshi basin is shown

in Fig. 8. It can be seen that the model has better estimati

of rising limb; but recession limb is not yet perfectly

predicted.

The correlation coefficient for Karnali, Narayani,

Bagmati and Koshi basins are obtained as 0.74, 0.83, 0.80

and 0.82 respectively. This shows it is not worth using a

TRMM data for computation of monthly flow through a

basin.

Fig.8: Mean monthly discharge for K

An average annual runoff map is prepared from the

simulated monthly discharge, as shown in Fig. 9. The map

holds the pixel size as that of TRMM satellite data. The map

can be used during planning phase of water resource

developments like hydropower projects to estimate

discharge through a point.


Rentech Symposium Compendium, Volume 2, December 2012

Simulated and Gauge discharge for major basins of Nepal [1]

The parameter adjustment for best fit of simulated

discharge with observed showed hig direct runoff factor. It

after their occurrence.

The results showed quite a good prediction of flow except in

some years. The mean monthly observed and simulated

discharges are also compared; one for Koshi basin is shown

in Fig. 8. It can be seen that the model has better estimation

of rising limb; but recession limb is not yet perfectly

The correlation coefficient for Karnali, Narayani,

Bagmati and Koshi basins are obtained as 0.74, 0.83, 0.80

and 0.82 respectively. This shows it is not worth using a

tation of monthly flow through a

Mean monthly discharge for Koshi basin

An average annual runoff map is prepared from the

simulated monthly discharge, as shown in Fig. 9. The map

holds the pixel size as that of TRMM satellite data. The map

can be used during planning phase of water resource

developments like hydropower projects to estimate

Fig.9: Average Annual Runoff map of Nepal

VI. CONCLUSION

The study showed an under

satellite data for most of the region of the country. Similar

conclusions were drawn from Barros

Ghaju[4]. Yet, the results need to be verified with further

investigations and a biasness correction factor could be

developed to scale up the TRMM rainfall data.

The study used simple monthly water balance model for

runoff simulation and still obtained better results. That

means, TRMM satellite data can be employed for runoff

simulation of the catchments for an early investigations. A

better model can be used for the study of detailed

characteristics for TRMM satelli

concrete conclusion.


63

[1]

Average Annual Runoff map of Nepal

ONCLUSION

The study showed an under-estimation of TRMM

of the region of the country. Similar

conclusions were drawn from Barros[3], Shrestha [5]and

. Yet, the results need to be verified with further

investigations and a biasness correction factor could be

developed to scale up the TRMM rainfall data.

The study used simple monthly water balance model for

runoff simulation and still obtained better results. That

means, TRMM satellite data can be employed for runoff

simulation of the catchments for an early investigations. A

better model can be used for the study of detailed

characteristics for TRMM satellite data before deriving a

B. Tamrakar and Knut Alfredsen: Applicability of TRMM satellite data in hydropower planning

Rentech Symposium Compendium, Volume 2, December 2012 64

REFERENCES

[1] Tamrakar, B., Evaluation of precipitation distribution over Nepal

using satellite data and its applications in Hydrological Modelling,

2011, Norwegian University of Science and Technology.

[2] Islam, M.N., S. Das, and H. Uyeda, Calibration of TRMM derived

rainfall over Nepal during 1998–2007. The Open Atmospheric

Science Journal, 2010. 4: p. 12-23.

[3] Barros, A.P., et al., A study of the 1999 monsoon rainfall in a

mountainous region in central Nepal using TRMM products and rain

gauge observations. Geophys. Res. Lett., 2000. 27(22): p. 3683-3686.

[4] Ghaju, S., Evaluation of Satellite based precipitation for Hydrological

modelling in Narayani basin in Nepal, 2010, Norwegian University of

Science and Technology.

[5] Shrestha, M., et al., Using satellite‐ based rainfall estimates for

streamflowmodelling: Bagmati Basin. Journal of Flood Risk

Management, 2008. 1(2): p. 89-99.

[6] Abdella, Y. and K. Alfredsen, A GIS toolset for automated processing

and analysis of radar precipitation data. Comput. Geosci., 2010. 36(4):

p. 422-429.

[7] Nash, J.E. and J.V. Sutcliffe, River flow forecasting through

conceptual models part I — A discussion of principles. Journal of

Hydrology, 1970. 10(3): p. 282-290.

[8] Thornthwaite, C.W., An approach toward a rational classification of

climate. Geographical review, 1948. 38(1): p. 55-94.

[9] Xiong, L. and S. Guo, A two-parameter monthly water balance model

and its application. Journal of Hydrology, 1999. 216(1): p. 111-123.

[10] Hamon, W.R. Estimating potential evapotranspiration. in Proc. Amer.

Soc. civ. Engrs. 1961.

[11] McCabe, G.J. and D.M. Wolock, Recent declines in western US

snowpack in the context of twentieth-century climate variability.

Earth Interactions, 2009. 13(12): p. 1-15.

BIOGRAPHIES

Mr. Bijay Tamrakar has completed MSc in Hydropower development

from NTNU, Norway. He is working as a Hydropower Engineer in Upper

Solu Hydro-electric Project.

Prof. Dr. Knut Alfredsen is a Professor at Department of Hydraulic and

Environmental Engineering, Norwegian University of Science and

Technology. His areas of research are hydrological modeling, cold climate

hydrology and environmental impacts of hydropower.

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