Precise information on spatiotemporal rainfall is critical for accurate hydrology predictions and simulations in humid tropical regions. Satellite precipitation data are useful for supporting in-situ measurements, because they provide wide coverage, are publicly available, and are grid-based. However, their suitability for small basins is hindered by their coarse grid size [1
]. This is conspicuous for most humid tropical catchments in Southeast Asia, where the region comprises of small land–sea ration area—especially islands and peninsula. Hence, the spatial variability of tropical rainfall variation is rather high [3
], and is expected to increase [4
]. Although the new satellite precipitation data product from Global Precipitation Mission and GsMAP has higher resolution (0.1°) than its predecessor, the Tropical Rainfall Measuring Mission (TRMM), it is only available from 2015 onwards. Effective climate-hydrologic analysis requires continuous data, especially historical, and therefore it is important to improve those datasets. Due to that conflict, numerous efforts have been made to improve the coarse grids by spatial downscaling.
However, spatial-downscaling algorithms for satellite precipitation data for humid, tropical environments have rarely been reported. Currently, advances in spatial downscaling of satellite precipitation data are centered on using rainfall-related environmental parameters at higher spatial resolutions as predictors. Based on the strong relationship between the rainfall and its site-specific explanatory proxy variables, the rainfall values for a smaller grid were estimated through the regression coefficient. Often, multiple regression analyses are used to assess the relationships between rainfall, vegetation, and elevation [5
]. Those variables were selected due to their significant relationship at a specific temporal period. In the temperate region, the relationship between seasonal rainfall and vegetation was strong, particularly during late spring and summer, where the photosynthetic rate increased. Meanwhile, topographic variations have significantly influenced regional or local rainfall patterns and distribution, especially in the hilly areas. These orographic effects can be relatively stronger if that region received air masses from the significant seasonal wind flows (e.g., monsoon).
Employing these variables for robust downscaling in humid tropical regions might be less suitable because of the weaker relationships between rainfall, vegetation, and elevation compared to temperate regions. Although applying multivariate regression could be effective in statistically increasing the predictive power of the model, the approach is constrained by several doubts: first is the possibility of a declining relationship between predictors and rainfall from low to finer resolution scales, and second is whether the high predictive power agreed with the physics of the rainfall-environmental perspectives [11
]. Merging the rain gauge data to downscale the satellite precipitation in the tropics is useful, such as the process done by [14
]. However, their method did not improved the spatial resolution of the precipitation. Efforts by [15
] in applying the fractal downscaling is effective, but limited by the real-time support of wind and other meteorological data through complex processes. Therefore, an alternative initiative for an effective, operational, and less complex transformation of the satellite spatial downscaling in humid tropics is required.
The proxy variable in humid tropics should be one that influences rainfall patterns and, most importantly, one for which the surrogate data is available at a higher resolution than the satellite precipitation (<0.1°). Anders and Nesbitt [16
] highlighted significant variables that influenced the satellite precipitation gradient in the tropics. On a local scale, precipitation was influenced by hydro-meteorological variables, namely prevailing winds, atmospheric moisture, and convective mode. Another important criteria for the spatial downscaling method is the operational aspect. Most of the satellite precipitation spatial downscaling models were developed based on the single or multivariate relationship over specific times and conditions; therefore, downscaling of the future satellite data requires the recalibration or redevelopment of the model, because either the predictor or the rainfall itself might change and influence the predictive power of the regression model (e.g., [8
Rainfall distribution in the tropics is closely associated with water vapor [17
] and monsoons [18
]. However, the high resolution data for water vapor is not available regularly, and therefore not suitable to be used as proxy downscaling variable. The Asian monsoon season contributed significantly to the variation in local rainfall in many tropical regions of Southeast Asia [19
]. The seasonal rainfall pattern is found to be less variable on a local scale, and exhibits specific local zoning [21
]. Using the ratio product between the satellite precipitation and the corresponding rain gauge to calibrate the satellite precipitation is a well-developed approach in quantitative downscaling, and widely used in merging algorithms [24
]. Theoretically, if the rainfall pattern was historically consistent over space and time, the ratio between the satellite and the rain gauge should follow a similar trend. We could expand this concept to developing a spatial downscaling method that is suitable for the humid tropics.
It is our aim to produce high-resolution satellite precipitation data by two process: first, by re-gridding the raw satellite precipitation data; and second, by recalculating the values of each pixel using the historical satellite–rain gauge ratio value. The appropriately high resolution would depend on two main factors: the density of rain gauges and the desired scale. For humid tropics, the challenge is to model atmospheric and hydro-meteorological variables at a mesoscale resolution (2–20 km) or lower [25
]. We hypothesized that in humid tropical regions of low seasonal rainfall variability, the bias ratio between the previous satellite and ground measurements is consistent, and therefore can be used as a coefficient to estimate the accurate rainfall values of the future satellite precipitation datasets. If the site-specific coefficient were available at a smaller grid, a fine-scale estimation of the satellite precipitation would be achievable.
Based on the above-mentioned concept and hypothesis, we attempted to conduct an experiment. To test this hypothesis, Peninsular Malaysia was selected as an experimental site, because the coefficient of variance (COV) for seasonal precipitation is low [27
]. The experiment has two main objectives: (1) Derive the site-specific coefficient (SSC) for each individual pixel, using the average bias ratio between the high-resolution ground rainfall data and re-gridded satellite rainfall data; and (2) validate the SSC to produce high-resolution precipitation maps. The proposed downscaling algorithm can be used to create high-resolution precipitation maps in the highly dynamic hydro-meteorological status quo of humid tropics, with less complex computation and more reliable results.
The use of the SSC-downscaling method was able to produce a high-resolution precipitation map (0.05°) with improved quantitative accuracy. In addition, it was effective in spatially downscaling the future dataset without the input from rain gauges. Most of the present or previous merging, or other spatial resolution improvement methods, require multi-dataset or the ground in-situ preferences’ surrogate information [5
]. In the context of a tropical region, our results had a better performance compared to the downscaling based on multivariate regression done by [12
] in mountainous, coastal, and forested environments. On the other hand, although our results’ performance slightly underachieved in using the super ensemble method developed by Yatagai et al. (2014), we successfully produced higher-resolution precipitation data. Furthermore, our computation was less complex, and fewer input variables were required. Therefore, we can conclude that incorporating a dense rain gauge network [39
], as well as monsoon rainfall seasonality and variability proved to be the effective in robustly downscaling satellite precipitation for various environmental contexts in the humid tropics.
Prior to positive results, this technique can be useful to the humid tropical regions, which have small land–sea ratio, many islands, and highly-variable seasonal rainfall patterns. Those characteristics are common and significant, especially for many areas in Southeast Asia [2
]. It is also one of the regions in the world that receives large rainfall excess with high intensity [42
], and is prone to extreme rainfall events [43
]. The availability of high-resolution precipitation information would be significant for understanding the dynamics of tropical rainfall at a microscale.
In addition, smaller tropical catchment or sub-basin hydrology modelling from space will be possible. Current satellite precipitation data had limitations to representing the catchment scale rainfall, due to coarse resolution [2
]. From a water resources perspective, with the availability of the global data, many humid tropical catchments for important reservoirs were categorized as smaller catchments (<10 km²) [45
]. They were located in thick, remote, and mountainous tropical forests, which are difficult to access. Utilizing an operational infrastructure could be expensive and laborious. Literature had showed that a substantial number of them were inadequately monitored and require support mechanisms [46
Another positive implication of the downscaling method is the opportunity to develop higher-resolution historical tropical precipitation data from satellite datasets. This was a critical parameter that was missing from precise regional climate modelling, which is the primary domain of future climate and environmental sustainability efforts [25
]. There were substantial amounts of coarse-resolution satellite precipitation data before TRMM, especially from the early METEOSAT missions [48
]. Performing our SSC-downscaling technique to those datasets is plausible, under the condition that the site-specific coefficient should be derived first.
Despite the promising outcomes of this study, there were a few limitations. First was the requirement of a large rain gauge dataset. Because the downscaling coefficient was eventually derived by correcting the bias factors at a smaller grid, it is necessary to have as large a rain gauge network as possible. This could be a limitation for hydrological data conflict areas (HDCS). An HDCS is an area which has experienced one or more of these conflicts: sparse rain gauges, missing rain gauge data, inefficient data sharing policies, or ineffective data management. The second limitation was whether the downscaling coefficient could be used for other satellite precipitation data besides TRMM. Hypothetically, it can be used, but a further investigation is needed.
The third limitation is that there was emerging evidence on the change in seasonal monsoon rainfall patterns, due namely to an external factor: El Nino Southern Oscillation (ENSO) [22
]. This effect, however, was neglected in our study, due to lack of ENSO data at local scale. The final limitation was the effect of decreasing temporal correlation after the downscaling process. It was believed to be caused by the high-resolution output grid. Because the original TRMM gridded data was coarse, it tended to homogenize the local rainfall pattern. Therefore, as the grid was transformed to be smaller, the high heterogeneity of local rainfall patterns appeared. This effect, however, was minimal, and did not affect the output performance.
Anticipating the second and third limitations by testing the usability of the coefficient on other satellite precipitation data, and excluding samples that affected by ENSO, could be future work in to improve this study. In an effort to further localize the satellite precipitation data, utilizing the role of topographic control as a proxy variable is promising. This is especially true for high-altitude regions in the tropics. In addition, experimenting with the similar downscaling procedures at a higher temporal scale (i.e., weekly) could be worthwhile, because the rainfall in humid tropics is highly dynamic.