Operational Satellite-based Watershed Monitoring Systems (SAWMOS) for Large Humid Tropical Catchment Environment

M. 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 & Real Estate Universiti Teknologi Malaysia

81310, Skudai, Johor, Malaysia.

ABSTRACT

Operational monitoring the productivity of forested catchment areas is a pivotal task as it provides the water supply for the reservoir and the streamflow. The limitation of conventional method to perform those tasks especially in spatial basis environment and large, remote forested areas made the use of satellite-based techniques could be a useful solution. This study introduces the satellite-based watershed monitoring systems (SAWMOS). SAWMOS is specifically developed to assess the water yield productivity of a watershed using two key hydrological variables; (i) rainfall and (ii) evapotranspiration. The Hulu Perak catchment, at the humid tropical climate region of Peninsular Malaysia which situated at the Southeast Asia is selected as the experimental site. Tropical Rainfall Measuring Misssion (TRMM) and MODIS are used as primary satellite data to estimate both rainfall and ET respectively. The satellite-based water yield estimation is comparable with the actual river flow. The Nash-Sutcliffe Model Efficiency Index indicates acceptable performance of the systems. The forest water yield maps derived from 2003 to 2007 is proved to be reliable to be used for operational spatial-based water yield monitoring at both monthly and annual basis.

Keywords—Satellite, Watershed, Water Yield

I. INTRODUCTION

The challenges of monitoring the impacts of climate change on water resources and assessment of

physical water scarcity are among the global agenda. Ensure an adequate water supply is necessary to drive the sustainable economic growth. In consequences, an effective water management system is needed and intensive monitoring of the water catchment areas productivity is one of the pivotal parts. Lately, frequent occurrences of regional weather phenomena can influence the water yield from the catchment areas especially in Asia, where their spatial temporal and variability of freshwater is large [1]. However, assessing water yield through conventional techniques may insufficient especially in describing the spatial changes and distribution of them especially in operational fashion. Conventional water yield assessment which conducted through the measurement of water surplus from precipitation and the corresponding evapotranspiration of an area by Thornthwaite & Mather [2] is only provide accurate measurement on their surroundings. Meanwhile, the isohyets surface which produced by interpolating the point basis measurement suffer from several factors; (i) insufficient data coverage [3], (ii) limitation of the interpolation model [4,5], and (iii) deal with the local variations over large areas [6].

Respond to those constraints, the use of satellite-

based data which is enabled to provide large coverage areas on temporal basis made it a useful solution. In addition, their cost effective and technologically sound has lead to their increasing used in hydrology and water resources application. However, most of the studies emphasized on parameterize only single hydrological variables especially either precipitation or

2011 IEEE Colloquium on Humanities, Science and Engineering Research (CHUSER 2011), Dec 5-6 2011, Penang

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evapotranspiration (ET) [7,8,9]. This means that the so called hybrid hydrology model is still largely depended on ground measurement data, which is could be the main limitation factor towards operational water resources monitoring. Furthermore, many of the studies which utilized the satellite-based precipitation data in their hydrologic model [10,11], only simulate the river flow of the catchment without identifying which part of the catchment is productive or not. This missing gap of the spatial knowledge of the water yield changes is one of the key issues which will be cover in this study.

Another inherent limitation of the current

satellite-based hybrid model to operational monitoring task is the selection of data input. Many of the study regarding ET are using low temporal satellite data such as [12, 13]and [14]. The implication of using such model for operational monitoring by substituting by other satellite data is the affluences to the model performance. This is simply because every sensor system is difference than each other. Furthermore, the use of series of satellite data such as Landsat TM and ASTER made the operational monitoring work is highly expensive due to large of high data costing and time ineffective caused by the burden of longer processing time. Even though that there are satellite-based ET model which utilized the use of more operational satellite, such as NOAA-AVHRR [15] and MODIS [16], but their suitability of the model to the humid tropical rainforest is unknown. There is also an effort to use multi sensor of satellite data to the hydrological model by Pan et al. [7], but they are focus on soil moisture changes instead of water yield. In addition, the model is involved with complex computation besides they are not being tested to other study site with differences of climatic characteristics.

Therefore it is suggested that for more

operational monitoring purposes of water yield especially for forested catchment areas where located at remote and dense thick rainforest, there should be a good balance between reliability of estimation, temporal aspect of the data, simplicity of the computation and cost effective. Thus, this paper has several objectives; (i) to estimate the rainfall and ET using satellite-based data, (ii) to utilize both derived satellite-based information to estimate forest water yield, and (iii) analyze the satellite-based forest water yield spatial distribution and pattern. The output of this study is to propose a simpler yet reliable approach on the fully utilization of satellite-based data to assess forest water yield in dynamic climatic condition of Asian region. The information derived from the water yield maps could later be used for other respective hydrologic and water resources assessment such as monitoring the impact of water borne disaster and identifying the productivity of the catchment area.

II. MATERIALS AND METHODS

A. Operational Satellite-based Watershed Systems Framework

The operational satellite-based watershed systems (SAWMOS) operates based on the traditional water balance concept by Thornthwaite and Mather (1955) with newly parameter input from satellite-based measurements. Two primary hydrological variables from the water balance equation, namely rainfall and evapotranspiration undergo parameterization using satellite-based inputs. The SAWMOS provides four main information which critical in determination of watershed condition; (i) rainfall, (ii) evapotranspiration, (iii) water yield, and (iv) water balance information. The information produces by SAWMOS is strategically on monthly basis measurement in spatially gridded format of 500m pixel. The flow of the SAWMOS computation is illustrated in figure 1.

B. Satellite-based Water Yield Estimation Procedures

The water yield is measured based on the net value of the rainfall minus evapotranspiration, applying the same principle of the conventional water balance equation. Uniquely in this study, both variables are estimated using the satellite-based data, eliminating dependencies on any ground measurement data. Equation 1 below expressed the satellite-based water yield calculation: Wi= Ri -Ei (1) Where W is the water yield at month i in mm, R and E is the total amount of rainfall at month i.

Since both of the data input in the model are typically

on continuous pixel basis mode, therefore, the water yield is actually estimated on pixel basis which later then produced a forest water yield map over the study area. In this study, the sized of the pixel grid is set to be 1km to 1km.

C. Estimating Rainfall from TMPA Satellite Data

The rainfall which is one of the primary variables in the model is retrieved from the rain rate information given in the Version 6 of TMPA 3B43 data product. The monthly average rain rate provided by the data product is then rescaled to the total amount of monthly rainfall as shown in equation 1. The pixel size of the TMPA data is downscaled into 1km x 1km grid size to produce a continuous rainfall surface over the study area. Ri= Mi * Ni * 24 (2)

2011 IEEE Colloquium on Humanities, Science and Engineering Research (CHUSER 2011), Dec 5-6 2011, Penang

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where M is the monthly average rain rmm/hour, N is the number of days of the total number of hours per day.

Figure 1. Flow of the SAWMOS Proc

D. Estimating Evapotranspiration fromData

The other crucial parameter, evaestimated using the simplified surfacbased ET algorithm known as SateEvapotranspiration which also known developed by Jiang et al. (2009). In thithe MODIS land surface temperature idata instead of AVHRR data product version. The advantages of this algorisimplicity of operation where makepractical and operational purposes, developed on tropical condition envirquite identical with the climatic condiarea. Since the objective of this studyalgorithm based on monthly basis ETmodification is done where all the inchanged to monthly average basis inacquire monthly average evapotranspira

The monthly estimation of ET in Saout by multiplication of the evaporativenet solar radiation (see Equation 3).fraction (EF) is obtained by two maEquation 4); one is the φ, and the otheris the slope of saturated vapor pretemperature (see Equation 6). Befcalculated, the bound of the temperatuindices space (T-VI) need to be determithe temperature is obtained from the Mproduct, while MODIS NDVI data prodVI inputs. Therefore, for that particutime of satellite scanning, specific vaestimated for the whole study area whdifferent land cover area which is expr5. The advantage of using the T-VI eliminate the use of surface air tedifficult to be computed within space acan increase the efficiency of this explanation of the SatDAET algorithmJiang et al. (2009).

MET = EF . Rn EF = φ . ∆

∆ + γ

φ ≈ φmax . Tmax-T

Tmin-Tmax

rate of month i in month i and 24 is

cedures

m MODIS Satellite

apotranspiration is ce energy balance ellite-based Daily as SatDAET was

is study, however, is used as primary as in the original

ithm lies on their e it suitable for

and also it is ronment where is ition of our study y is to assess the

T, some additional nput variables are nstead of daily to ation.

atDAET is carried e fraction with the The evaporative

ain functions (see r one then ∆ which essure at the air fore φ is being ure and vegetation ined. In this study, MODIS LST data duct is used as the ular instantaneous alue of φ can be hich is unique for ressed in equation space figure is to emperature which and time therefore algorithm. Detail

m can be found in

(3) (4)

(5)

∆ = 26297.77( Ta- 29.65 )²

exp 17

where EF is the monthly averagRn is the monthly average solthe ground measurement. φmax and Taylor, 1972), T is MODIdata. Tmin and Tmax is the temperature respectively in psychrometric constant (hPa/KTa is the mean inland water sur

E. Experimental Site Descripti

Figure 2. Study Site – Hu

Hulu Perak watershed locat

Peninsular Malaysia with the square. The topographic conditfrom lowland tropical forest (0forest (>500m). The watershedmonsoon season, northeast (May-Sept). The watershed freshwater supplies either to doand also to generate electricitlocation is shown in figure 2.

III. RES

A. Annual Hydrological Summ Table 1. Annual Summary of Hydrolog

Monsoon Season Precipita

(mm)

Northeast (Oct - Jan) 254.9

Southwest monsoon (May - Sept) 199.3

Inter-monsoon (Feb - Apr) 146.1

The annual summary of thethe Hulu Perak catchment is sh51% of the water yield prodproduced during the northeasheaviest rainfall occurred. Thewater yield has stronglyevapotranspiration (ET) rate

7.67. (Ta - 273.15)Ta-29.65

(6)

ge evaporative fraction and lar radiation acquired from

is equal to 1.26 (Priestley S land surface temperature minimum and maximum T-VI space, γ is the

K) which equal to 1.37, and rface temperature.

ion

ulu Perak Watershed

ted at the northern part of acreage nearly 6588 km tion of the watershed range -300m) up to mountaneous d experienced two distinct (Oct-Jan) and southwest is critical as it provides omestic water consumption ty turbine. The study site

SULTS

mary of Watershed

gical Variables from SAWMOS.

ation )

Evapotranspiration (mm)

Water Yield (mm)

9 127.5 133.3

3 142.9 68.7

1 119.7 59.9

e hydrological variables for howed in Table 1. Almost

duces by the watershed is st monsoon season where e spatial distribution of the y influenced by the

(Figure 3). In details,

2011 IEEE Colloquium on Humanities, Science and Engineering Research (CHUSER 2011), Dec 5-6 2011, Penang

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additional analysis with the forest elevahigher water yield volume is proddipterocarp forest (0-300m).

Figure 3. Annual Spatial based Information of WaEvapotranspiration in Hulu Perak produced by SAWMO

Figure 4. Annual Spatial Variations of WateEvapotranspiration in Hulu Perak in different seasonal co

The spatial variation of the water ydetermine by the rainfall seasonal patterfigure 4. The northeast monsoon seasonwhere large volume of water yield arelowland dipterocarp forest. The lowforests are consistently produced waterentire year range from 56 to 130mm per

B. Accuracy Assessment of SAWMOS

The performance of SAWMOS are three kind of approaches, (i) NashEfficiency (NSME) Index & Root M(RMSE), (ii) Comparison with groundyield, and (iii) Comparison with actuaNSME index determine the efficiency in this study the SAWMOS is evaluseasonal condition. The mean diffSAWMOS water yield estimation fromdata simulated water yield are also qushowed the NSME index and theSAWMOS. The SAWMOS tend to prodand this lead to the inconsistenperformances in heavy rainfall seasons On average, the SAWMOS performancwith the ground based measurement.

ation, indicates that duced in lowland

ater Yield, Precipitation, OS.

er Yield, Precipitation, ondition.

yield meanwhile is rns as described in n has been critical e produced in the

wland dipterocarp r yield through the r month.

carried out using h-Sutcliffe Model

Mean Square Error d data based water al river flow. The of the model, and uated at different ferences between

m the ground based uantified. Table 2 e RMSE of the duces large RMSE ncies of model (NSME ~ 0.286).

ce was comparable

Table 2. Annual Summary of Hydrolo

Monsoon Season

N

Ef

Northeast (Oct - Jan)

Southwest monsoon (May - Sept)

Inter-monsoon (Feb - Apr)

Figure 5. Double mass curves analysis befrom SAWMOS with ground data based sim

In order to measure the c

based water yield over long pfive years monthly based compbased water yield is conducteThe trend was quite obvious wyield tend to overestimate the approximately 50% after fianticipated however, the overeuncalibrated of hydrological inpcorrection should be carried ou

Figure 6. Double mass curves analysis yield from SAWMOS with ground data

Despite the long period co

its relative significant with the its reliability in describing thewatershed (Figure 6). The sevrevealed that both SAWMOS pattern with actual river flowwatershed.

ogical Variables from SAWMOS

Nash-Sutcliffe Model

fficiency Index (NSME)

RMSE (mm)

0.286 181.1

0.185 90.4

0.444 88.1

etween the satellite-based water yield

mulated water yield.

consistency of SAWMOS period of measurement, a parison against the ground

ed and shown in figure 5. where the SAWMOS water

ground based water yield five years period. It is estimation could due to the puts. Therefore, significant

ut to validate the outputs.

between the satellite-based water

a based simulated water yield.

onsistencies of SAWMOS, actual river flow indicates

e water productivity in the ven month comparison has

water yield has agreeable w at the river outlet of the

2011 IEEE Colloquium on Humanities, Science and Engineering Research (CHUSER 2011), Dec 5-6 2011, Penang

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C. Commercial Value of SAWMOS

Unlike any other hydrological and watershed model in the market, the advantages of SAWMOS lies in its capability to carry out both qualitative and quantitative assessment of watershed simultaneously. The needed of data requirement has been minimal, to reduce cost, and its operability is limitless to monthly of daily basis measurement, depend to the data input. The simplicity of the model and effectiveness of output made SAWMOS could be use for various kind of water-related assessment such as disasters, water resources, and drought monitoring applications. The cost of operation has been effectively less complex than other hydrological systems. The frequency of field visit can be reduced, thus make it cost effective. The temporal mode of satellite data has barely made the SAWMOS be very operational. As comprehensive large watershed assessment require both field and spatial-based information, the incorporation of SAWMOS as useful supporting system for decision making and further hydrological analysis.

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