Satellite-Based Run-Off Model for Monitoring Drought in Peninsular Malaysia

: Traditional in situ observation interpolation techniques that provide rainfall data from rain gauges have limitations because they are discrete point-based data records, which may not be sufﬁcient to assess droughts from a spatiotemporal perspective. Considering this limitation, this study has developed a run-off model—a fully satellite-based method for monitoring drought in Peninsular Malaysia. The formulation of the run-off deﬁcit uses a water balance equation based on satellite-based rainfall and evapotranspiration data extracted respectively from calibrated TRMM multi-satellites precipitation analysis data (TMPA) and moderate resolution imaging spectroradiometer data (MODIS). The run-off deﬁcit was calculated based on per pixel spatial scale and allowed to produce the continuous and regular run-off maps. The run-off model was tested and evaluated in a one drought year (2005) within a span of three years (2003–2005) over the Kelantan (3448 km 2 ) and Hulu Perak (3672 km 2 ) catchments of Peninsular Malaysia. The validation results show that (1) monthly TMPA rainfall and MODIS evapotranspiration data signiﬁcantly improved after calibration; (2) satellite-based run-off data is not only strongly correlated with actual steam ﬂow, but also with spatiotemporal variation of run-off in drought-affected forest catchments. The most severely drought-affected forest catchments that experienced the run-off deﬁcits were Hulu Perak, Ulu Gading, Gunung Stong and Relai over Kelantan. The real time run-off change analysis shows that drought started in January and reached its peak in July of 2005. It was therefore demonstrated that this fully satellite-based run-off deﬁcit model is as good as a conventional drought-monitoring indicator, and can provide not only drought distribution information, but it also can reﬂect the drought-induced impacts on stream ﬂow, forest catchment and land-use.


Introduction
Drought is a natural disaster. There are four types of droughts: (1) meteorological (deficit in rainfall from normal); (2) agricultural (crop-response to deficit in soil moisture); (3) hydrological (deficit in runoff, or shortage of streamflow in catchments; and (4) socioeconomic (social response to the above  Table A1).

Datasets and Methods
The concept of drought assessment is to extract run-off information from multi-temporal satellite images, acquired during normal and drought conditions. The spatial-based drought assessment was carried out using the following analyses: (1) measurement of run-off deficit (Equation (1)); (2) analysis of run-off deficit versus land-use; and (3) comparison of satellite-based run-off values during the drought and the actual stream flow in a severely affected forest catchment for validation. The overall study methodology is illustrated in Figure 3. In addition, a qualitative assessment was performed using the visual analysis of multi-temporal run-off maps. This study considered that run-off variability of drought and non-drought years could potentially be used as an indicator of drought and hence as a proxy for hydrological drought characterization and assessment.
The run-off deficit (RD in %) was defined by the following formula: where Nr is the normal run-off value based on the monthly run-off without severe drought and Dr is the run-off during a drought season. The conventional water balance equation was used in developing the satellite-based run-off models described in Thornthwaite and Mather [40]. Procedures introduced by Mahmud et al. [41] were adapted. This equation is expressed below:

Datasets and Methods
The concept of drought assessment is to extract run-off information from multi-temporal satellite images, acquired during normal and drought conditions. The spatial-based drought assessment was carried out using the following analyses: (1) measurement of run-off deficit (Equation (1)); (2) analysis of run-off deficit versus land-use; and (3) comparison of satellite-based run-off values during the drought and the actual stream flow in a severely affected forest catchment for validation. The overall study methodology is illustrated in Figure 3. In addition, a qualitative assessment was performed using the visual analysis of multi-temporal run-off maps. This study considered that run-off variability of drought and non-drought years could potentially be used as an indicator of drought and hence as a proxy for hydrological drought characterization and assessment.
The run-off deficit (R D in %) was defined by the following formula: where Nr is the normal run-off value based on the monthly run-off without severe drought and Dr is the run-off during a drought season. The conventional water balance equation was used in developing the satellite-based run-off models described in Thornthwaite and Mather [40]. Procedures introduced by Mahmud et al. [41] were adapted. This equation is expressed below: where R O is run-off, P and E are satellite rainfall and evapotranspiration, respectively, and S m is soil moisture. The available water capacity was fixed at 250 mm based on two main assumptions: (i) the average rooting depth of the vegetation is about 1.5 m [42]; and (ii) the soils in Peninsular Malaysia are predominantly silt loams (58%), clay loams (30%) and clays (12%) [43]. The soil moisture retention information was taken from Thornthwaite and Mather [41]. The soil moisture (S m ) and the accumulated potential water loss (A PWL ) have the following exponential expression: where both a and b are constants. Given a fixed water holding capacity of 250 mm, the values for a and b are 249.5 and´0.0040, respectively.
where RO is run-off, P and are satellite rainfall and evapotranspiration, respectively, and Sm is soil moisture. The available water capacity was fixed at 250 mm based on two main assumptions: (i) the average rooting depth of the vegetation is about 1.5 m [42]; and (ii) the soils in Peninsular Malaysia are predominantly silt loams (58%), clay loams (30%) and clays (12%) [43]. The soil moisture retention information was taken from Thornthwaite and Mather [41]. The soil moisture (Sm) and the accumulated potential water loss (APWL) have the following exponential expression: (3) where both a and b are constants. Given a fixed water holding capacity of 250 mm, the values for a and b are 249.5 and -0.0040, respectively.

Rainfall Retrieval from TRMM Multi Satellites Precipitation Analysis Data (TMPA)
Rainfall is one of the primary variables of the satellite-based run-off model. The TRMM Multisatellite Precipitation Analysis (TMPA) version 7 data, released by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) consists of rainfall observations at three temporal resolutions: 3-hourly (3B42), daily (3B42 derived), and monthly (3B43). The TMPA 3B42 version 7 data product provides a standard 3 hourly rain rate at global scale with 0.25 degrees resolution covering 50 degrees latitude in the northern and southern hemisphere. The data can be directly downloaded from the Internet link: [44]. The daily rainfall amount is obtained by multiplying the provided three-hour average rain rate of eight datasets per day by three. The daily rainfall amount is then accumulated to estimate monthly rainfall. The equations used for the calculation of daily (Dr) and monthly (Mr) rainfall are as follows: where Rr is the three hour rain rate, Mr is the monthly rainfall, and Nd is the number of days per month. To maximize the ability of 0.25 degrees (27 km) TMPA rainfall data to depict local rainfall patterns, a spatial downscaling reduced the grid to 1 km. The study area, categorized as small-size region, experienced large measurement errors. These errors could possibly be caused by the conflict between the insensitive coarse grid data and dynamic local hydrometeorology properties [45]. The spatial downscaled algorithm suggested by Mahmud et al. [41] was employed for determination of

Rainfall Retrieval from TRMM Multi Satellites Precipitation Analysis Data (TMPA)
Rainfall is one of the primary variables of the satellite-based run-off model. The TRMM Multi-satellite Precipitation Analysis (TMPA) version 7 data, released by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) consists of rainfall observations at three temporal resolutions: 3-hourly (3B42), daily (3B42 derived), and monthly (3B43). The TMPA 3B42 version 7 data product provides a standard 3 hourly rain rate at global scale with 0.25 degrees resolution covering 50 degrees latitude in the northern and southern hemisphere. The data can be directly downloaded from the Internet link: [44]. The daily rainfall amount is obtained by multiplying the provided three-hour average rain rate of eight datasets per day by three. The daily rainfall amount is then accumulated to estimate monthly rainfall. The equations used for the calculation of daily (D r ) and monthly (M r ) rainfall are as follows: where R r is the three hour rain rate, M r is the monthly rainfall, and N d is the number of days per month.
To maximize the ability of 0.25 degrees (27 km) TMPA rainfall data to depict local rainfall patterns, a spatial downscaling reduced the grid to 1 km. The study area, categorized as small-size region, experienced large measurement errors. These errors could possibly be caused by the conflict between the insensitive coarse grid data and dynamic local hydrometeorology properties [45]. The spatial downscaled algorithm suggested by Mahmud et al. [41] was employed for determination of the monthly site specific co-efficients. The co-efficients were obtained from historical bias records based on specific monsoon seasons. Thus, the site specific mean of each pixel was calculated. The equations used for the downscaling process are as follows: DSat pi,jq " R satpi,jq where HRC is the high resolution co-efficient, Rsat is the satellite-based rainfall data that was re-gridded at 1 km resolution, Rg is the interpolated ground rainfall at 1 km resolution, n is total number of pixels, and DSat is the downscaled satellite rainfall (at 1 km). A comparison of ground-based measurements with the satellite-based TMPA data is given in Table 2. The evapotranspiration (E T ) values were obtained by calibrating the MODIS 16 data product. MODIS provides the evapotranspiration estimates at varied spatial (from 500 m to 5 km) and temporal resolutions (from daily to monthly scales). It uses vegetation cover reflectance and temperature information coupled with ancillary atmospheric parameters in an energy balance model to estimate the potential evapotranspiration [45].
A run-off map was produced based on estimated values derived from spatial inputs in the water balance equation. Applying the same procedure to multitemporal run-off images enabled us to visualize the effect and impacts of differences between pre-and post-drought conditions. Satellite-based rainfall data used in this study are TRMPA and MODIS daily data from January 2000 to December 2010 ( Figure 2). Because the variability of seasons affects the amount of rainfall in the Peninsular Malaysia, the calibration of multitemporal TRMPA and MODIS data sets at micro-climatic scale is required. For the calibration of TMPA rainfall, gain and offset errors were computed by correlating the TMPA rainfall with rain gauge data at a monthly time scale. Regression analysis between MODIS data and in situ observations collected from meteorological stations in the Peninsular Malaysia was used for the calibration of the MODIS evapotranspiration data sets (MODIS 16A). Ground data pertaining to this study was supplied by the Malaysia Meteorological Department (MMD), collected from 26 stations (Table A1).
In this study, the monthly run-off maps were produced based on datasets of three consecutive Observations on the run-off pattern for continuous months enabled us to study the spatiotemporal aspects of the drought. The run-off deficit was calculated based on per pixel spatial scale and enabled us to produce the continuous and regular run-off maps. The standard procedures for estimating the run-off deficit from the satellite-based data is shown in Figure 4.  The geographical based calibration requires location-specific corrections. The equations used for the calibration are as follows: where ( , ) is the satellite-based rainfall and ( , ) is the rain gauge data at a location (latitude (i), longitude (j)), ( , ) is the downscaled satellite-based rainfall, ∆ ( , ) is the correction values, d is the distance between the centroid and neighbouring points, r is the order of the interpolation, and ( , ) is the calibrated rainfall, and ( , ) is the uncorrected satellite-based rainfall.

Calibration for Evapotranspiration (ET) retrieval from MODIS16A Data Set
Evapotranspiration (ET) is the second parameter used in developing the satellite-based run-off model. The algorithm used to estimate the ET from the satellite data was developed by Mu et al. [45]. However, in this research, the ET was retrieved from the digital number (DN) of MODIS 16A data product. The ET values were multiplied by a constant to convert DN into millimeter per month (Equation (11)).
Where is the total evapotranspiration estimates at monthly intervals (mm/month), 16 is unitless in HDF format, is a constant which is set to 0.1, = ( )/ = denotes slope of the curve relating saturated water vapor pressure ( ) to temperature, is available energy partitioned between sensible heat, latent heat and soil flux on land surface, is air density, is the specific heat capacity of air, is the aerodynamic resistance and is the surface resistance. Surface resistance was parameterized using satellite leaf area index and vegetation fraction cover. is the psychrometric constant.
The detailed procedure for estimating MODIS ET is presented in Figure 5. The rainfall data was calibrated based on the Geographical Difference Analysis approach, introduced by Bastiaanssen et al. [46] which was tested for the Indus Catchment. Because of the differences between the satellite-and ground-based rainfall data, calibration is essential to minimize existing errors over the study area [47]. Details of the calibration scheme were provided in Nadzri and Hashim [48] and therefore only a brief description is given here.
The geographical based calibration requires location-specific corrections. The equations used for the calibration are as follows: ∆R ippi,jq " where R satpi,jq is the satellite-based rainfall and R gpi,jq is the rain gauge data at a location (latitude (i), longitude (j)), D sat pi,jq is the downscaled satellite-based rainfall, ∆R ippi,jq is the correction values, d is the distance between the centroid and neighbouring points, r is the order of the interpolation, and R csatpi,iq is the calibrated rainfall, and R rawpi,jq is the uncorrected satellite-based rainfall.

Calibration for Evapotranspiration (E T ) retrieval from MODIS16A Data Set
Evapotranspiration (E T ) is the second parameter used in developing the satellite-based run-off model. The algorithm used to estimate the E T from the satellite data was developed by Mu et al. [45]. However, in this research, the E T was retrieved from the digital number (DN) of MODIS 16A data product. The E T values were multiplied by a constant to convert DN into millimeter per month (Equation (11)).
where E T is the total evapotranspiration estimates at monthly intervals (mm/month), MODIS16A HDF is unitless E T in HDF format, a is a constant which is set to 0.1, s " d pe sat q {dts = denotes slope of the curve relating saturated water vapor pressure pe sat q to temperature, A is available energy partitioned between sensible heat, latent heat and soil flux on land surface, ρ is air density, C ρ is the specific heat capacity of air, r a is the aerodynamic resistance and r s is the surface resistance. Surface resistance was parameterized using satellite leaf area index and vegetation fraction cover. γ is the psychrometric constant. The detailed procedure for estimating MODIS E T is presented in Figure 5. The satellite-based ET provides data at global scales. Although several validations of those datasets have been done for other climates, such as America and Asia [49], specific validation, particularly for tropical regions was conducted by Nadzri and Hashim [48]. Those studies suggested that it is necessary to calibrate data to obtain better results. Moreover, those studies have shown that errors are associated with seasonal variations that need to be calibrated by using linear regression based on monsoon characteristics following the expression given below: is the calibrated satellite evapotranspiration, and a and c are the coefficients of calibration. Table 3 shows the variations between satellite-and ground-based s values. Table 3. Computed mean and satellite based gauge (S/G)-ratios of the potential evapotranspiration (ET) measurements using ground temperature and MODIS data for the meteorological stations (referred to Figure 1 and Table A1).  The satellite-based E T provides data at global scales. Although several validations of those datasets have been done for other climates, such as America and Asia [49], specific validation, particularly for tropical regions was conducted by Nadzri and Hashim [48]. Those studies suggested that it is necessary to calibrate data to obtain better results. Moreover, those studies have shown that errors are associated with seasonal variations that need to be calibrated by using linear regression based on monsoon characteristics following the expression given below:

Mean MODIS ET (mm/month) Monthly S/G Ratio
where E Tcali is the calibrated satellite evapotranspiration, and a and c are the coefficients of calibration. Table 3 shows the variations between satellite-and ground-based E T s values. Table 3. Computed mean and satellite based gauge (S/G)-ratios of the potential evapotranspiration (E T ) measurements using ground temperature and MODIS data for the meteorological stations (referred to Figure 1 and Table A1).

Accuracy Assessment
To assess the accuracy of the calibrated TMPA and MODIS 16, validation tests were conducted. Eighty percent of the rain gauge data randomly selected from 18 stations was used for the TMPA calibration and the remaining 20% were used for validation. The MODIS data was calibrated using rain gauge data collected in the periods between where R g is the observed ground rainfall, R sat is the satellite-based rainfall, and n is the number of pixel.

Validation of TMPA Rainfall Calibrated with Un-Calibrated Data
For the comparison of un-calibrated and calibrated TMPA data, we analyze the statistical indices mentioned above and present the results in Figure 6. Statistical bias is an important and useful statistical metric that reflects the precision of rainfall after calibration. Figure 6a shows the improvement in the calibrated rainfall values, with reduction of 35 mm in the Alor Setar station after calibration compared to the other four stations. Despite that the Bayan Lepas and Alor Setar are in the similar climatic region, i.e., in the north of Peninsular Malaysia, bias for the former station was higher (around 40%) than the latter, which may be due to a maritime effect around Bayan Lepas, which is located on the Penang island. The differences in bias for Alor Setar, Kluang, and Kuala Terengganu stations were less than 20%, indicating a similar pattern of improvement after calibration across those stations. Although the sample used to validate the calibration showed a reduction in quality, Figure 7 indicates an overall improvement due to calibration, which is evident from wide spread regression values (from light to dark blue legend in Figure 7a,b). In this study, the hydrologic model that used TMPA data for the calibration of rainfall to generate spatial maps was subject to sources of negligible errors for Hulu Perak compared to that of Kelantan catchments, as evident from a visual assessment (Figure 7). The Correlation co-efficient also supports the improvement after calibration (r = 0.7´0.9). The mean absolute error (M AE ) and Nash-Sutcliffe (NS), shown respectively in Figure 6b,d, show an almost similar trend for all stations, while the later matrices are +0.2,´0.8 and´0.4, respectively, for Alor Setar, Prai and Kuala Terengganu stations. However, the differences for Kuala Terengganu are small and at an acceptable range. However, the trends of NS (+0.55,´0.04) and M AE (+76 mm, +1 mm) for Petaling Jaya and Kluang stations are opposite (Figure 6d). The E RMS (Figure 6c) rainfall values, estimated from TMPA calibrated data, are similar in trend with the M AE for all the stations. Most of the high-ranged rainfall values were located around the eastern region, while the low-ranged were located around the southeast region (Figure 7). However, smaller extent of the red area (~200 mm) shown in Figure 7c compared to that of Figure 7d indicate the rainfall data sets improved, after calibrations in terms of the E RMS values. This data calibration improvement pattern agrees well with the temporal variations presented in Figure 2, where the differences between calibrated TMPA and ground measurements are evident. of the red area (~200 mm) shown in Figure 7c compared to that of Figure 7d indicate the rainfall data sets improved, after calibrations in terms of the ERMS values. This data calibration improvement pattern agrees well with the temporal variations presented in Figure 2, where the differences between calibrated TMPA and ground measurements are evident.  Figure 1 and Table A1), illustrating the differences in bias (a); mean absolute error (b); root mean square error (c); and Nash-Sutcliffe value (d) before and after calibration.  Figure 1 and Table A1), illustrating the differences in bias (a); mean absolute error (b); root mean square error (c); and Nash-Sutcliffe value (d) before and after calibration.

Assessment of MODIS 16 Calibrated Data
The performance of the calibrated MODIS 16, estimated from the statistical measures, is presented in Figure 8. Figure 8a shows that all months were calibrated with maximum value differences during the IM season (25 mm during March-April). As for the ( Figure 8b) and the ERMS (Figure 8c), the trends are similar to each other and the error difference ranges from −10 mm to −20 mm. Calibration of MODIS evapotranspiration led to improvements in drought monitoring (rainfall measurements) which is indicated by the NSE values ( Figure 8d) with a range between −0.36 (IM) and 0.5 (SWM). In the aspects of seasonal rainfall prediction, larger improvements were indicated by NSE ranges from −0.36 to −0.25 for IM and −2.0 to 0.3 for SWM, while there was no improvement for NEM season after calibration.

Assessment of MODIS 16 Calibrated Data
The performance of the calibrated MODIS 16, estimated from the statistical measures, is presented in Figure 8. Figure 8a shows that all months were calibrated with maximum value differences during the IM season (25 mm during March-April). As for the M AE (Figure 8b) and the E RMS (Figure 8c), the trends are similar to each other and the error difference ranges from´10 mm to´20 mm. Calibration of MODIS evapotranspiration led to improvements in drought monitoring (rainfall measurements) which is indicated by the NSE values ( Figure 8d) with a range between´0.36 (IM) and 0.5 (SWM). In the aspects of seasonal rainfall prediction, larger improvements were indicated by NSE ranges froḿ 0.36 to´0.25 for IM and´2.0 to 0.3 for SWM, while there was no improvement for NEM season after calibration.

Assessment of Spatial-Based Run-off Deficit
The satellite-based run-off map of Kelantan state during normal conditions (in July), the drought season (in July) of 2005, and run-off relative deficiency are shown, respectively, in Figures 9a-c. In order to assist with a comprehensive visual aided analysis of spatial variations in run-off and landuse, the corresponding land-use map is also added to this illustration (Figure 9d).

Assessment of Spatial-Based Run-off Deficit
The satellite-based run-off map of Kelantan state during normal conditions (in July), the drought season (in July) of 2005, and run-off relative deficiency are shown, respectively, in Figure 9a-c. In order to assist with a comprehensive visual aided analysis of spatial variations in run-off and land-use, the corresponding land-use map is also added to this illustration (Figure 9d).
From a visual inspection, it can clearly be identified that the run-off decreased in many areas when the drought reached its peak during July 2005. The average run-off computed for normal seasons (91.9 mm/month), i.e., in the years 2003 and 2004, was significantly greater than the drought season (84.0 mm/month), i.e., in the year 2005. The relative run-off deficiency maps (Figure 9c), estimated for Kelantan state and Hulu Perak show that approximately 34.7% and 42.9% of the total area experienced a deficit greater than 40%. A large portion of Kelantan (41%) and Hulu Perak (43%) experienced moderate run-off deficits which ranges from 20% to 39%. In contrast, a small portion of land over the Kelantan (24.1%) and Hulu Perak (13.8%) experienced a small run-off at low spatial extent (<20%). From a visual inspection, it can clearly be identified that the run-off decreased in many areas when the drought reached its peak during July 2005. The average run-off computed for normal seasons (91.9 mm/month), i.e., in the years 2003 and 2004, was significantly greater than the drought season (84.0 mm/month), i.e., in the year 2005. The relative run-off deficiency maps (Figure 9c), estimated for Kelantan state and Hulu Perak show that approximately 34.7% and 42.9% of the total area experienced a deficit greater than 40%. A large portion of Kelantan (41%) and Hulu Perak (43%) experienced moderate run-off deficits which ranges from 20% to 39%. In contrast, a small portion of land over the Kelantan (24.1%) and Hulu Perak (13.8%) experienced a small run-off at low spatial extent (<20%).

Land-Use Versus Run-off Deficiency Analysis
The classification of the run-off deficit to their respective land-use is shown in Table 4 and their distribution patterns are shown in Figures 9 (c) and (d). A combined analysis of land-use and run-off deficits has revealed that forested areas experienced the highest run-off deficits. About 87.5% of Kelantan experienced more than 60% run-off deficit. Other major land-uses, rubber (6.7%), oil palm (1.1%) and paddy (2.1%), experienced less than 10% run-off deficit. This indicates that approximately 29.1% of the total forest areas experienced more than 40% run-off deficit, which is comparable to the homogenous areas of Hulu Perak (22.2%). Those affected areas are within Kelantan forest reserve (FR) catchment areas, including Ulu Gading FR, Sg. (Sungai = river) Durian FR, Sg. Rek FR, Sg. Relai FR, Sg. Lebir, and Gunung Stong Utara FR. Statistics also shows that about 20% of the forest areas, representing approximately 4062 km 2 , experienced more than 40% run-off deficit. There was

Land-Use Versus Run-off Deficiency Analysis
The classification of the run-off deficit to their respective land-use is shown in Table 4 and their distribution patterns are shown in Figure 9c,d. A combined analysis of land-use and run-off deficits has revealed that forested areas experienced the highest run-off deficits. About 87.5% of Kelantan experienced more than 60% run-off deficit. Other major land-uses, rubber (6.7%), oil palm (1.1%) and paddy (2.1%), experienced less than 10% run-off deficit. This indicates that approximately 29.1% of the total forest areas experienced more than 40% run-off deficit, which is comparable to the homogenous areas of Hulu Perak (22.2%). Those affected areas are within Kelantan forest reserve (FR) catchment areas, including Ulu Gading FR, Sg. (Sungai = river) Durian FR, Sg. Rek FR, Sg. Relai FR, Sg. Lebir, and Gunung Stong Utara FR. Statistics also shows that about 20% of the forest areas, representing approximately 4062 km 2 , experienced more than 40% run-off deficit. There was catchment-wide (Kelantan and Hulu Perak) reduction in evapotranspiration, possibly caused by the rainfall deficits during April-June in 2005 ( Figure 10). catchment-wide (Kelantan and Hulu Perak) reduction in evapotranspiration, possibly caused by the rainfall deficits during April-June in 2005 ( Figure 10).

Comparison between Satellite-Based Run-Off and Actual River Flow
Comparison between the measured river flow in the Kelantan River and the corresponding satellite-based run-off values on three forest catchments indicates that low run-off leads to river flow reduction ( Figure 11). The three major catchments, namely Ulu Gading, Gunung Stong, and Relai contribute water flow to the Kelantan River, while only one catchment contributes water flow to the Hulu Perak River. In addition, the average monthly run-off of those three catchments consistently declined from January and reached a minimum in July, 2005 (Figure 12). The run-off of those forest catchments declined by 57% during January-March, 80% during April-June, and 90% during July-September 2005. The lowest average run-off was less than 3 mm/month in July, while normal run-off was 18 mm/month.

Comparison between Satellite-Based Run-Off and Actual River Flow
Comparison between the measured river flow in the Kelantan River and the corresponding satellite-based run-off values on three forest catchments indicates that low run-off leads to river flow reduction ( Figure 11). The three major catchments, namely Ulu Gading, Gunung Stong, and Relai contribute water flow to the Kelantan River, while only one catchment contributes water flow to the Hulu Perak River. In addition, the average monthly run-off of those three catchments consistently declined from January and reached a minimum in July, 2005 (Figure 12). The run-off of those forest catchments declined by 57% during January-March, 80% during April-June, and 90% during July-September 2005. The lowest average run-off was less than 3 mm/month in July, while normal run-off was 18 mm/month. The three statistical error matrices including bias, Nash-Sutcliffe, and root mean square were computed to capture spatiotemporal variability of drought-related variables such as water yield for the Hulu Perak and Kelantan rivers ( Figure 13). Overall, the results show that there is a nearly perfect water yield between satellite-based predicted water yield and ground observations. The NSE values ranged between 0.0 and 0.7 for Hulu Perak, and 0.4 and 0.8 for Kelantan catchments, indicating algorithms are hydrologically consistent for drought monitoring, except unsatisfactory statistical measures computed for Kelantan in April and for Hulu Perak in February. Those sources of error might be associated with reduced sensor sensitivity due to reduced atmospheric water during the dry season. The overall performance between the actual and satellite based river flow is presented in Figure 14. The double mass curve (DBM) shows that there is an underestimation of water yield for Kelantan, and an overestimation for Hulu Perak. However, NS used to measure the capability and reliability of the hydrologic model shows that, overall, 91.6% of data is in high agreement with ground measurement for both areas.  Figure 11. Comparison between the actual river flow and satellite run-off estimates from severely affected sub-catchments in the Kelantan basin.
The three statistical error matrices including bias, Nash-Sutcliffe, and root mean square were computed to capture spatiotemporal variability of drought-related variables such as water yield for the Hulu Perak and Kelantan rivers ( Figure 13). Overall, the results show that there is a nearly perfect water yield between satellite-based predicted water yield and ground observations. The NSE values ranged between 0.0 and 0.7 for Hulu Perak, and 0.4 and 0.8 for Kelantan catchments, indicating algorithms are hydrologically consistent for drought monitoring, except unsatisfactory statistical measures computed for Kelantan in April and for Hulu Perak in February. Those sources of error might be associated with reduced sensor sensitivity due to reduced atmospheric water during the dry season. The overall performance between the actual and satellite based river flow is presented in Figure 14. The double mass curve (DBM) shows that there is an underestimation of water yield for Kelantan, and an overestimation for Hulu Perak. However, NS used to measure the capability and reliability of the hydrologic model shows that, overall, 91.6% of data is in high agreement with ground measurement for both areas.
The model is based on a measure of water stress, calculated by run-off deficit (Equations (1) and (2)) before (2002)(2003) and during (2005; El Niño) drought events, where we employed the concept of water balance equation (Figure 2). Besides run-off deficits, satellite-based estimates of vegetation, temperature, rainfall conditions [2], and ground-based information about land-use, cover, and soils [53], were also used for drought monitoring.
The concept of integrating rainfall and evapotranspiration variability into run-off deficit mapping on a continuous and regular per pixel basis is a potentially important new approach to monitor spatial drought. Unlike other ground-or satellite-based drought indices [25], the advantageous aspect of the proposed approach is the detectability of changes for assessing hydrologic conditions, especially across tropical humid regions where local wind and climate patterns dramatically change [31]. Furthermore, the simplicity of the calculation process should make the technique practical for operational drought monitoring systems (Figures 8 and 10). The TMPA rainfall and MODIS satellite data, which are the primary data inputs to the proposed model, can be retrieved through an open source internet data sharing policy.
The proposed technique was proved to be useful in identifying the impact of the drought on land-use (Figures 9 and 10). In the forest mensuration aspect, the logged timber would be difficult to be transported through shallow-river trawling. Furthermore, a forest catchment severely affected by long-term drought may lead to a reduction in river flow and, consequently, may cause scarcity of freshwater supplies [54,55]. Such drought conditions may affect the whole community due to water scarcity for irrigated crops and, thereby, may reduce crop production [56]. Water rationing would need to be implemented in those critical areas identified by an efficient drought monitoring system. The operational procedure of this draught monitoring system can be used to identify the spatial extent of drought-vulnerabilities for a given land-use, and to map drought risks if socioeconomic factors are taken into account [56], although approaches need to be thoroughly investigated across a wide range of socioeconomic dimensions and climate regions.
The multitemporal and spatial observation of the run-off changes can be a useful indicator for the onset of drought warning and monitoring systems. The acquired information would enable scientists and decision-makers to study the spatiotemporal patterns and behavior of the run-off. Temporal rainfall deficiency detection within forested catchment areas which can be the major factor in the drought occurrence in tropical areas can be made in a more comprehensive manner.
The operational hydrological drought monitoring based on satellite observations could effectively identify the complex spatio-temporal drought distribution [57]. Understanding the global perspective of drought distribution is fundamental to monitoring the cause and effects of specific regional droughts [58]. The identification of drought-vulnerable areas and suitable locations for climate monitoring stations can be designed based on satellite-based drought products. The establishment of more systematic and effective drought management systems, such as the Famine Early Warning System (FEWS-NET) and the Monitoring Agricultural Resources (MARS) in Africa can be done, but they should be more suited to the local climatic systems. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecasted, with more frequent extreme events. Initiatives for managing climate change and increased climate variability therefore requires baseline information on satellite-based drought indicators for future monitoring and anomaly detection [59].
Findings have indicated that the fully satellite-based run-off models can assess and quantify drought impacts from a local catchment perspective of Peninsular Malaysia. Studies [60,61] show that in the east and northeast regions of the Peninsular Malaysia where critical drought occur, the models provide a powerful tool for monitoring drought conditions. Given the ground-based observations are very limited and unevenly distributed across Peninsular Malaysia, satellite-based run-off models could lead to improvement in drought monitoring, and seasonal forecasts in this region. The developed drought monitoring methods provide complementary information, and it could lead to a better understanding of (i) the operational application of drought monitoring at spatiotemporal scale ( Figure 10); (ii) the ecosystem response to hydro-climatic variability (Figures 11 and 12); and (iii) the ability to assessing a precise and comprehensive grid-based drought conditions and distributions (Figures 13 and 14).

Conclusions
The use of satellite measurements to assess drought is a significant and relevant step to understanding global climatic changes. This study provides a basic guideline on how droughts can be monitored and assessed operationally using satellite observations. Many demonstrations on the operational use of satellites have been developed for detailed monitoring and mapping of floods and droughts with the combination of in situ data. Gathering sufficient information on the spatial and temporal nature of droughts will require the installation of additional ground-stations at high spatial coverage, which would be logistically expensive. Fully satellite-based drought monitoring can supplement in situ data by providing consistent observations with global coverage and would be cost-effective. The spatiotemporal maps have the ability to identify drought vulnerable areas, indicating that the satellite-based run-off model of rainfall and evapotranspiration can be used for climate change impact assessment from local to global scales. Knowledge gained from this study could be useful for developing spatiotemporal drought identification methods and assessing and quantifying the impact of drought on water-related ecosystems (e.g., freshwater productivity) across tropical regions-similar to hydro-climatic characteristics of Peninsular Malaysia. Rainfall is a vital component of the hydrologic cycle and plays a key role in monitoring drought, especially in water-limited ecosystems, and in conserving watershed areas to support economic activities. The rapid changes of rainfall and evapotranspiration in the Southeast Asian humid tropical areas, particularly under climatic or hydrologic extremes such as drought, can be effectively monitored at a regional scale using satellite multi-sensor products. The impact of drought on the carbon cycle, water storage, and broadly drought-induced climate changes can therefore be studied in a comprehensive manner.