Precise spatio-temporal measurements of rainfall during seasonal monsoons are critical for accurate hydrologic analyses in the tropical regions of Southeast Asia. The use of satellite precipitation data is technologically sound but requires downscaling to minimize inherent uncertainties. The uncertainties at a local climate regime that are essential to be resolved are rarely reported; consequently, such work needs attention. To address this problem, we validated the Tropical Rainfall Measuring Mission (TRMM) precipitation data using high-resolution areal precipitation (0.125 deg.) at a seasonal scale. This study examined the performance of the monthly rainfall data product (TRMM 3B43) at the seasonal monsoon scale in the local climate region of Peninsular Malaysia. The high-resolution areal precipitation data (0.125 deg.) were derived from a dense rain gauge network ever collected in Peninsular Malaysia (n = 984). Three relevant performance elements were evaluated: (i) the ability to depict temporal rainfall variation (ii) the quantitative error between TRMM and ground rainfall; and (iii) the ability to estimate the actual rainfall amount. We found that the ability of monthly TRMM data to depict rainfall variation and its tendency to propagate large errors varied seasonally. The correlation between TRMM and ground rainfall was good during the wettest period in all local climate regions. The error was related to the northeast monsoon and inter-monsoon 2 (September–October). Meanwhile, the TRMM ratio varied regionally, rather than seasonally. Determining the local-scale uncertainties will facilitate future downscale activities using TRMM satellite data in this region.

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 km²) and Hulu Perak (3672 km²) 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.

This paper described the development of a spatial downscaling algorithm to produce finer grid resolution for satellite precipitation data (0.05°) in humid tropics. The grid resolution provided by satellite precipitation data (>0.25°) was unsuitable for practical hydrology and meteorology applications in the high hydrometeorological dynamics of Southeast Asia. Many downscaling algorithms have been developed based on significant seasonal relationships, without vegetation and climate conditions, which were inapplicable in humid, equatorial, and tropical regions. Therefore, we exploited the potential of the low variability of rainfall and monsoon characteristics (period, location, and intensity) on a local scale, as a proxy to downscale the satellite precipitation grid and its corresponding rainfall estimates. This study hypothesized that the ratio between the satellite precipitation and ground rainfall in the low-variance spatial rainfall pattern and seasonality region of humid tropics can be used as a coefficient (constant value) to spatially downscale future satellite precipitation datasets. The spatial downscaling process has two major phases: the first is the derivation of the high-resolution coefficient (0.05°), and the second is applying the coefficient to produce the high-resolution precipitation map. The first phase utilized the long-term bias records (1998–2008) between the high-resolution areal precipitation (0.05°) that was derived from dense network of ground precipitation data and re-gridded satellite precipitation data (0.05°) from the Tropical Rainfall Measuring Mission (TRMM) to produce the site-specific coefficient (SSC) for each individual pixel. The outcome of the spatial downscaling process managed to produce a higher resolution of the TRMM data from 0.25° to 0.05° with a lower bias (average: 18%). The trade-off for the process was a small decline in the correlation between TRMM and ground rainfall. Our results indicate that the SSC downscaled method can be used to spatially downscale satellite precipitation data in humid, tropical regions, where the seasonal rainfall is consistent.

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds.

Changes in land-use–land-cover (LULC) affect the water balance of a region by influencing the water yield (WY) along with variations in rainfall and evapotranspiration (ET). Remote sensing satellite imagery offers a comprehensive spatiotemporal distribution of LULC to analyse changes in WY over a large area. Hence, this study mapped and analyse successive changes in LULC and WY between 2000 and 2015 in the Johor River Basin (JRB) by specifically comparing satellite-based and in-situ-derived WY and characterising changes in WY in relation to LULC change magnitudes within watersheds. The WY was calculated using the water balance equation, which determines the WY from the equilibrium of precipitation minus ET. The precipitation and ET information were derived from the Tropical Rainfall Measuring Mission (TRMM) and moderate-resolution imaging spectroradiometer (MODIS) satellite data, respectively. The LULC maps were extracted from Landsat-Enhanced Thematic Mapper Plus (ETM+) and Landsat Operational Land Imager (OLI). The results demonstrate a good agreement between satellite-based derived quantities and in situ measurements, with an average bias of ±20.04 mm and ±43 mm for precipitation and ET, respectively. LULC changes between 2000 and 2015 indicated an increase in agriculture land other than oil palm to 11.07%, reduction in forest to 32.15%, increase in oil palm to 11.88%, and increase in urban land to 9.82%, resulting in an increase of 15.76% WY. The finding can serve as a critical initiative for satellite-based WY and LULC changes to achieve targets 6.1 and 6.2 of the United Nations Sustainable Development Goal (UNSDG) 6.