Determination of Forest Water Yield in Malaysian Tropical Watershed Using Calibrated Satellite-based Rainfall Data

Mohd. Rizaludin Mahmud¹, Mazlan Hashim²

¹Department of Surveying Science and Geomatics Faculty of Architecture, Planning, and Surveying

Universiti Teknologi MARA Perlis 02600, Arau,Perlis, MALAYSIA

²Institute of Geospatial Technology

Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia

81310,Skudai, Johor, MALAYSIA

ABSTRACT

This study investigates the potential of the calibrated satellite-based rainfall estimates data product from Tropical Rainfall Measuring Mission (TRMM) satellites, known as TRMM 3B43 Version 6 or TRMM Multi-Satellites Precipitation Analysis (TMPA) water run-off modeling at tropical forested watershed scale in Peninsular Malaysia. The calibrated satellite-based rainfall is used as the main input in the Thornthwaite water balance equation to estimate the water run-off (yield) at monthly basis from 1998 to 2007. Four forested watershed which located in each main region in the Peninsular Malaysia are used as sample to evaluate the model performance. Preliminary assessment of the calibrated satellite-based rainfall at the selected forested watershed indicates good agreement with the ground rainfall measurement with average bias of ±43.35mm per month. The monthly estimated water run-off for all forested watershed in all region also shows good agreement and comparable with the simulated run-off using the rain gauged data. The average bias for the satellite-based derived water run-off for all forested watershed in all Peninsular Malaysia region is 22.7mm per month except for east region where higher values are recorded which reach 55.5mm per month.

1. INTRODUCTION Water is a limited and valuable resource that is

essential for sustaining life. In most developing countries such as Malaysia, the search for an effective water management approach remain a national interest in order to support huge populations growth, rapid economic development, large irrigation areas, and increasing domestic water consumption. It is identified that the watersheds are the main water resources with rainfalls as their main water inputs. Therefore, accurate rainfall measurement is crucial for run-off estimations from watershed.

However, water run-off assessment from rainfall-

run-off based models is commonly inadequate due to the existing weakness of conventional rainfall measurements

[1]. Even though that the ground rain gauged provides high accuracy measurements, it is only reliable for the surrounding areas. In developing region, where the rain gauge is sparse, especially in remote areas, the uncertainties of rainfall measurements are increased. High cost maintenance and deployment are the main constraints of establishing the effective rain gauge distributions, especially for large areas. Therefore, the usage of satellite-based technology as an alternative data source is considered to be a practical solution [2].

Advancement of satellite-based technology enables

us to estimate the rainfall before it reaches the ground surface, through radar reflectivity [3,4]. The nature of the orbiting satellite at high altitude in space provides consistent and regular rainfall measurements over large coverage areas. Recent studies show that the satellite-based precipitation data is able to provide good measurement with reliable accuracy for many regions in the world [5,6]. The latest version of the satellite-based precipitation data from Tropical Rainfall Measuring Mission (TRMM) is capable to provide daily rainfall estimation with three-hour interval measurement at high spatial resolution of 0.25 degrees. In addition, the data can be further localized to reach higher spatial resolution [7].

Peninsular Malaysia, the main region of Malaysia,

which is located nearly at the centre of the Southeast Asia, is in need of more comprehensive water monitoring supports tools in order to cope with the increasing water demand as well as water induced disaster. Although the distributions of rain gauge stations are quite abundant throughout the entire regions, the rain gauged measurements, however, is inconsistent and poorly conducted during heavy rainfall. Furthermore, the high rainfall dynamics over different monsoon season bring difficulties for the existing discrete-based conventional

2010 International Conference on Science and Social Research (CSSR 2010), December 5 - 7, 2010, Kuala Lumpur, Malaysia

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rain measurement approach in proviaccurate rainfall measurement especial[8]. Therefore, the utilization oprecipitation data for hydrologic assesrun-off, has been widely conductenvironments [9,10,11].

However, due to lack of study on

precipitation data of TRMM in the tSoutheast Asia, there are huge uncertaperformances. In consequences, succethese satellite-based precipitation data as Malaysia which situated in Southhowever, remains a major challedetermining the reliability of thprecipitation data for hydrologic assegiven high priority towards supporting tmanagement. Significant to that, thisdetermine the potential of the satellite-bto estimate the water yield on humidwatershed.

2. MATERIALS AND MET

2.1 Data Description The main satellite-based data used

obtained from the Tropical Rainfall M(TRMM) Version 6 3B43 which also Multi-Satellites Precipitation Data Andata product which provides monthestimates from multiple satellites and otThe data was downloaded via internetwebsites of National Space Agency collaboration of Japanese Aerospace Ex(JAXA).The 3B43 data product also prerror estimates data for each pixel whremove the relative error effects.

This TMPA data provides rainfa

high spatial accuracy, where each gr2.4km. In this study, the satellite-basefive years period starting from year 2obtained, which consists of 60 conobservations. In order to evaluate thTMPA data and also the derived water rthe rain gauged information and grounalso being obtained for the same perioinformation is provides by the MalaysiDepartment and Department of Irrigatiwhere measurement is conducted on then summed to the monthly scale average for the ground temperature.

2.2 Study Site Description This study focused on five

watershed in the Peninsular Malaysia rof them representing different region onamely i) Pedu watershed (F1-norther

iding precise and lly for large areas of satellite-based sment particularly ted in different

the satellite-based tropical region of ainties about their essful adaption of on local area such

heast Asia region, enge. Efforts in he satellite-based ssment should be the effective water s paper aimed to based rainfall data

d tropical forested

THODS

d in this study was Measuring Mission

known as TRMM nalysis (TMPA), a hly basis rainfall ther data products. t from the official (NASA) with the xploration Agency ovides the relative hich then used to

all information in rid is sampled at d rainfall data for

2003 until 2007 is ntinuous monthly e accuracy of the run-off from them, nd temperature are od of time. Those an Meteorological ion and Drainage, daily basis which

for rainfall and

tropical forested region, where each on the peninsular, rn region), Kenyir

watershed (F2-region), Sg. Selregion), Sg.Muar watershed (Sg. Kermoi watershed (F5-inwatersheds are among the cridams are located for water locations of the study sites are details are summarized in table

Table 1. Summary of the selected fore

Forested Watershed ID Region L

F1 (Pedu) Northwest 2F2 (Kenyir) East -

F3 (Sg. Selangor) West 6F4 (Sg. Muar) Southwest 6F5 (Sg. Kermoi) Inland 6

Fig. 1. Stud

Fig. 2. Forested watershed loMalaysia r

2.3 Methodology The study is divided into

phase 1 – data preparation, determination and iii) phase 3phase 1, the calibrated satellit

langor watershed (F3-west (F4-southwest region) and land). All of the forested itical in the region where

reservoir purposes. The shown in figure 1 and their

e 1.

ested watershed in the study area.

River Length (km)

Basin Acreage

(km²)

Annual rainfall (mm)

22.69 407.2 2466 - 2709.6 4315

68.27 1985.3 2907 63.15 1853.6 2121 63.56 1126 2395

dy site

ocation on the Peninsular region.

o several phase, include i) ii) phase 2- water yield

3-accuracy assessment. In te-based rainfall data from

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year 2003 to 2007 using the calibration procedures by Rizaludin et al [13] are used to estimate the monthly rainfall along the study period. These monthly basis rainfalls are the product from the multiplication of rain rate given by the satellite-based measurement with the total days and hours per day for the whole month, which expressed on the equation 1. P R D H (Eqn. 1) where Pi is the monthly rainfall for month, Ri is the rain rate for month, Di is the number of days in

month, and Ha is the total hour per day which assumed to be constant.

In phase 1 also, the amount of potential evapotranspiration for each month is computed using the ground measured temperature at selected locations through out the Peninsular Malaysia. The detail of these procedures is described in the section 2.5.

Phase 2 is focused on the forest water yield estimation. Both the satellite-based rainfall and rain gauge measurement are used to simulate the water yield variations for the whole area of the forested watershed. Detail descriptions are given in section 2.4.

The final phase of this study is to assess the accuracy of the satellite-based derived run-off. The rain gauge based run-off is used as reference values to determine the reliability and performance of the satellite-based derived run-off for the period from 2003 to 2007 due to the data constraint condition.

2.4 Modified Thornthwaite’s Water Balance Model for Satellite-based Derived Water Yield In this study, the original water balance model

introduced by Thornthwaite et. al [16] is modified to estimate the forest water yield. The calibrated satellite-based rainfall data is reinforced to the hydrological model, instead of using the rain gauge information in the conventional approach. Other critical parameter of the water balance, evapotranspiration, on the other hand is being acquired using the ground measured temperature data. Section 2.5 explained the procedures in detail.

The soil moisture components in this study are set to be constant at 250mm followed the recommendation on the historical studies by the Department of Irrigation and Drainage [17]. This value are computed based on the soil moisture depletion equation on static condition of vegetation root depth at 1.5m and soil properties was predominantly was silt loams, clay loams, and clays. The modified equation of the water balance model after those conditions and assumption is described in the equation 2. Wi= Pi- Eti (Eqn. 2)

where Wi are the forest water yield in month i, Pi are the satellite-based rainfall data in month i, and Eti are the potential evapotranspiration at month i.

2.5 Thornthwaite’s Evapotranspiration Estimation

The equation utilized the mean monthly air temperature together with latitude data as the main input to estimate the potential evapotranspiration (PEt). The implementation of Thornthwaite methods of describing the potential evapotranspiration is made based on several assumptions, which are i) the net radiation is set as constant based on sensible and latent heat function, ii) no evapotranspiration is generated below zero degree Celcius, iii) vegetation effects is negligible [18]. For this study, the average temperature from the meteorological observation stations was used as sole data inputs.

The corresponding equation used to estimate the potential evapotranspiration is expressed in equation 3.

Etp=16 10Ta

Ia (Eqn. 3)

where Etp is the monthly potential evapotranspiration in (mm), Ta is the monthly mean temperature (°C), a is the location dependent co-efficient described in equation 2.12 and I is the annual heat index described in the equation 2.11.

In order to determine Etp and a, the annual heat index should be computed first: =

Tai

5

1.514n

i=1

(Eqn. 4)

Tai is the mean monthly temperature for the month i (°C) for the location of interest. The co-efficient a meanwhile is computed as follows:

a = 0.49 + 0.0179I – 0.000077I² + 0.000000675I³ (Eqn. 5)

The computed PEt is the unadjusted

evapotranspiration which represents the measurement for a standard month with 360 hours of daylight; therefore it must be corrected for the actual varying length of day with latitude using the correction factor which can be acquired in Dunne et al [15]. The monthly distribution of PEt for the entire watershed is then estimated by performing simple kriging interpolation of the computed PEt from the ground temperature measurements.

3. RESULTS AND DISCUSSION

3.1 Accuracy Assessment of the Calibrated Satellite-Based Rainfall and Water Yield for Each Forested Watershed Areas

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The accuracy of the calibrated TMPA rainfall data over selected forested watershed is showed in table 2. On general, the TMPA rainfall data have good agreement with the corresponding in-situ rainfall measurement at all forested watershed (R² > 0.8; P < 0.003). However, the actual TMPA performance is slightly varies among those watersheds. The best performances are identified in the west (F3-Sg.Selangor) and inland forested watershed (F5-Sg.Kermoi) where relatively have the lowest error compared to the other regions (monthly biases < 37mm). Meanwhile, the east region watershed (F2-Kenyir) experienced the largest bias measurement (monthly biases > 90mm), almost twice than the values scored by other region. These phenomena could possibly driven by the heavy rainfalls and strong wind flows during the northeast monsoon season [14, 19], which made the rainfall uncertainties during these period are relatively large. Since the satellite measured the reflected radar backscatter from the potential cloud, while rain gauge measured the actual rainfall which occurred on the ground, therefore due to the strong wind flows, there are possibilities where both measurements are not inaccurate.

Table 2. Accuracy assessment of the calibrated satellite-based rainfall on the selected forested watershed area.

Forested Watershed R² Biases (mm) P

Significance Values

F1 – Pedu (Northern) 0.824 49.1 0.003 F2 – Kenyir (East) 0.880 93.3 0.002 F3 – Sg. Selangor (West) 0.877 37.4 0.002 F4 –.Sg. Muar (Southwest) 0.806 44.4 0.003 F5 –.Sg. Kermoi (Inland) 0.816 36.6 0.002

Table 3. Accuracy assessment of the estimated satellite-based water yield on the selected forested watershed area.

Forested Watershed R² Biases (mm) P significance Values

F1 – Pedu (Northern) 0.824 57.9 0.002

F2 – Kenyir (East) 0.866 85.8 0.002 F3 – Sg. Selangor (West) 0.816 44.9 0.002 F4 –.Sg. Muar (Southwest) 0.835 51.0 0.002 F5 –.Sg. Kermoi (Inland) 0.819 33.5 0.001

In addition, similar trend of accuracy are showed of

the estimated satellite-based water yield (see Table 3). Despite the strong agreement with the in-situ based simulated water yield, the accuracy are varies between region. The east region watershed produces largest monthly bias with 85.8mm where in most cases the estimated satellite-based water yield (SBWY) is underestimate simulated in-situ based water yield (ISWY) especially in year 2005 and 2007 (see fig. 3b). Time-series analysis between the SBWY and ISWY also reveal that most of the time both of them is demonstrates similar pattern and most errors are propagate during the large water run-off production in monsoon season. 3.3 Forest Water Yield Analysis

Fig. 4 illustrates the monthly average of water yield in forested watershed in the Peninsular Malaysia region for five years period from 2003 to 2007. Each watershed produces different variation of water yield patterns, both on amount and run-off fluctuations. Results obtained has almost a similar results with the previous studies by M. Rizaludin [14] which utilized the same datasets and study region with complete water balance scheme. The east region watershed (F2-Kenyir), have their maximum water yield on the northeast monsoon period where December is the peak water yield is generated (see figure 3b). The west and southwest region watershed are also has their highest water yield production during this period, but with lower magnitudes (see figure 3c and 3d). Meanwhile, the watershed on the inland and northwest region produces high water yield during the inter-monsoon periods which occur on the month March to April and September to October. However, unlike the inland region watershed (F5-Sg. Kermoi) the F1-Pedu watershed which located on the northern part is only sensitive during the inter-monsoon period on the September and October (see figure 3a).

Fig. 3. Time-series analyses between the satellite-based estimated water

yield (SBWY) and in-situ based simulated water yield (ISWY).

On annual basis time frame, the summary of the annual average forest water yield for the 5 years periods are tabulated in table 4. The F2-Kenyir watershed on the east region produces the highest amount of water yield relatively (30%), followed by the west (22%), inland (17%), northern (16%) and southwest region (15%), and their spatial distribution is showed in Fig. 5. The

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northeast monsoon period which strongly influences the Peninsular Malaysia rainfall [20], and forest water yield production in the east, west and southwest region become the most critical period for the peninsular regional water resources where it contributes 47% of the total annual average water yield of the entire peninsular. The inter-monsoon period on the other hand, supplied about 36% from the total peninsular average water yield. The southwest monsoon meanwhile give less significant impact of the water yield generation where on all watershed, it contributes only about 19% on average of the total forest water yield amount. On overall perspectives, the forest water yield which evaluated on spatial basis using satellite measurement agrees with most previous rainfall and climatology studies such as Desa et al [21], Juneng et al [22] and Juneng et al [23].

The spatial variations derived from the satellite-

based measurement (see fig. 5) indicates that the rainfall within the tropical forested watersheds area are not homogenous. In F2 station (see fig. 5b, for example, only the southern part of the watersheds which produces massive amount of run-off, and the run-off is gradually decrease towards the major river stream. Almost the same patterns are identified in the west and southern watersheds (see fig.5c and 5d). This indicates that the tropical watersheds in Malaysia has dynamic run-off patterns, which strongly driven by rainfall even though in small sized watersheds. The capabilities of the satellite-based measurement to map out the rainfall spatial variability at local scale indicate that its potential for practical hydrologic prediction and assessment.

Table 4. Annual satellite-based estimated water yield composition

based on monsoon season for the selected forested watersheds.

Forested Watershed

NEM SWM IM Annualmm % mm % mm % mm

F1 - Pedu (Northern) 193 22 223 25 446 52 861 F2 - Kenyir (East) 1042 67 180 26 340 22 1562 F3 - Sg. Selangor (West) 435 38 211 23 489 43 1135 F4 -.Sg. Muar (Southwest) 447 59 114 27 194 26 755 F5 -.Sg. Kermoi (Inland) 319 35 205 26 384 42 908

Fig. 4. Monthly average of the satellite-based estimated water yield for

the year 2003 to 2007.

Fig. 5. Annual average water yield for selected forested watershed for

the five years period from 2003 to 2007.

4. CONCLUSION The results of the study indicate that the calibrated

monthly basis satellite-based rainfall estimates of the TRMM Multi-Satellites Precipitation Analysis (TMPA)

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3B43 data can be used for water yield determination for the local forested watershed areas. The satellite-based simulated run-off illustrates good agreement with the in-situ data simulated run-off values for the whole selected forested watershed areas in Peninsular Malaysia. The tolerable accuracy produces by the satellite-based simulated run-off indicates its reliability to describe the water yield variability within space and time. However, the average accuracy between the watersheds in different region is varies and strongly influenced by the local monsoon season. In future, the study of the satellite-based run-off could be evaluated in more comprehensive framework.

REFERENCES [1] B. Collischonn, W. Collischonn, and C.E.M.Tucci C. E. M.,

“Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates,” Journal of Hydrology , 360, 207– 216, 2008.

[2] J. P. Aguttes, J. Schrive, C.Goldstein, M. Rouze, G.Raju, “MEGHA-TROPIQUES, a satellite for studying the water cycle and energy exchanges in the tropiques”, IEEE/IGARSS Conference 2000, Honolulu, HI, July 24-28,2000.

[3] T. Kozu, T., Kawanishi, H. Kuroiwa, M. Kojima, K. Oikawa, H. Kumagai, K. Okamoto, M.Okumura, H. Nakatsuka, and K. Nishikawa, “Development of precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM)”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 39, No. 1, January 2001.

[4] H. Kumagai, T. Kozu, M. Satake, H. Hanado, and K. Okamoto, “Development of an active radar calibrator for the TRMM precipitation radar,” IEEE Trans. Geosci. Remote Sensing, vol. 33, pp. 1316–1318, Nov. 1995.

[5] M.N. Islam, and H. Uyeda, “Use of TRMM in determining the climatic characteristics of rainfall over Bangladesh,” Remote Sensing of Environment, 108, 264-276, 2007.

[6] B. Bookhagen and C.W. Burbank, “Topography, relief, and TRMM-derived rainfall variations along the Himalaya,” Geophysical Research Letters, Vol. 33, L08405, 2006.

[7] W.W. Immerzeel, M.M. Rutten, and P. Droogers, “Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula,” Remote Sensing of Environment, Vol. 113, Issue 2, pp 362-370, 16 Feb 2009.

[8] R.C. Ward and M. Robinson, Principles of Hydrology. 4th Edition, Mc Graw Hill, United Kingdom, 2000.

[9] S. Wagner, H. Kunstmann, A. Bárdossy, C. Conrad, and R.R. Colditz, “Water balance estimation of a poorly gauged catchment in West Africa using dynamically downscaled meteorological fields and remote sensing information,” Physics and Chemistry of the Earth,34,225–235,2009.

[10] K.K. Yilmaz, T.S. Hogue, K.L. Hsu, S. Sorooshian, V.H. Gupta, and T. Wagener, “Intercomparison of rain gauge, radar, and

satellite-based precipitation estimates with emphasis on hydrologic forecasting,” J. Hydrometeor., 6, 497–517, 2005.

[11] G.A.Vicente and M.H. Costa, “Real time satellite rainfall estimation over the Amazon region for hydrological applications,” Geoscience and Remote Sensing Symposium, IGARSS 2001, pp 2121-2123,vol. 5, IEEE International, 2001.

[12] M. M. Rizaludin, and M. Hashim “ Validation of TRMM multi-satellite precipitation analysis for determination of rainfall in forested-watershed: case study in Kuantan watershed”, Proc. FKSG Postgraduate Seminar, Universiti Teknologi Malaysia, 14-15 July, 2009.

[13] M. M. Rizaludin, and M. Hashim, “Validation of satellite based rainfall measurement on malaysian tropical forest water catchment areas”, Proc. 30th Asian Conference on Remote Sensing, Beijing, China, 17-20 October, 2009.

[14] M.M. Rizaludin, G. Noorzalianee, M. Zuraihan, N. Masayu and H. Mazlan, “Utilization of satellite-based rainfall measurement for run-off simulation in tropical Malaysian basin”, Proc. 6th MRSS International Remote Sensing and GIS Conference & Exhibition, Putra World Trade Centre, Kuala Lumpur, 28-29 April, 2010.

[15] T. Dunne, and L.B. Leopold, “Water in Environmental Planning”, W.H. Freeman and Company, New York,1978.

[16] C.W. Thornthwaite and J.R. Mather. “The Water Balance”. Publication In Climatology. Volume VIII(1). Centerton, New Jersey: Drexel Institute of Technology, Laboratory of Climatology,1955.

[17] Department of Irrigation and Drainage Malaysia, “Average annual and surface water resources of Peninsular Malaysia”, Water Resources Publication No.12, 1982.

[18] H.L. Penman, “Natural evaporation from open water, bare soil, and grass”. Proceedings of the Royal Society of London, 139(A), 120-146,1948.

[19] S.M. Deni, A.A Jemain, and K. Ibrahim, 2008. “The spatial distribution of wet and dry spells over Peninsular Malaysia,” Theoretical of Applied Climatology, doi:10.1007/s00704-007-0355-8, 2008.

[20] C.L. Wong, R.Venneker, S. Uhlenbrook, A.B.M. Jamil, and Y Zhou, “Variability of rainfall in Peninsular Malaysia,” Hydrol. Earth Syst. Sci. Discuss., 6, 5471–5503,2009.

[21] M.M.N, Desa, and P.R. Rakhecha, “Probable maximum precipitation for 24-h duration over an equatorial region: part 2 – Johor, Malaysia”. Atmospheric Research, 84, 84–90, 2007.

[22] L. Juneng, and F.T., “Evolution of ENSO-related rainfall anomalies in Southeast Asia region and its relationship with atmosphere–ocean variations in Indo-Pacific sector,” Climate Dynamics, 25, 337–350, 2005.

[23] L., Juneng, L., and F.T. Tangang, “Level and source of predictability of seasonal rainfall anomalies in Malaysia using canonical correlation analysis”. International Journal of Climatology, 90, 9–99. doi:10.1002/joc.1617, 2007.

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