1. Introduction
Water assets in heterogeneous terrains, especially for the mountainous river basins, provides decision-makers and hydrologists with a critical and demanding situation due to the shortage of intensive in-situ rainfall tracking stations [
1]. Traditional rain gauge stations provide the most accurate rainfall data on a point basis. In addition, rainfall is one of the most important hydrologic parameters for many studies related to water resources and climate analysis. However, due to the high spatial variability of rainfall, it is difficult to capture the spatial and temporal variation of rainfall systems based on randomly distributed rain gauges, which do not meet the requirements of aquifer models and other related research [
2]. Therefore, now the focus is directed mainly toward Satellite Rainfall Products (SRPs) as an alternative to address all the shortcomings of ground rain gauge stations. Several SRPs are available, but not all the available types of SRPs can be used for one area of interest. This is mainly because the methods by which they are processed and extracted are different from each other [
3].
Several SRPs, such as the Integrated Multi-satellite Retrievals for GPM (IMERG), the Tropical Rainfall Measuring Mission (TRMM), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) group, are widely used he past studies as an alternative to the ground measured rainfall [
4,
5,
6]. The GPM_IMERG product by the National Aeronautics and Space Association (NASA) was produced by using algorithms that intercalibrate, merge and interpolate the data from satellite microwave estimates, microwave-calibrated infrared satellite estimates, precipitation gauge analysis, and several other precipitation estimators. Thus, the product is considered a strong and better product then extracting rainfall data by some researchers [
7,
8]. However, the TRMM 3B42 version 7 product was generated by a combined mission between NASA and Japan Aerospace Exploration Agency (JAXA), but the production was based on estimates obtained from TRMM Microwave Imager (TMI), and Precipitation Radar (PR) sources through the TRMM Satellite [
9,
10]. In addition, the PERSIANN group of SRPs was produced using the Artificial Neural Network (ANN) [
11]. Likewise, different SRPs were produced in different methods, and thereby it is very important to assess their applicability to different catchment areas before using them in practical situations.
As stated in the preceding paragraphs, research is being carried out for different areas of interest around the world to check the applicability of the selected SRPs. It has been proved by many researchers that the performance of SRPs varies with factors such as elevation, rainfall intensity, topography, etc. [
12,
13,
14,
15]. However, most of the river basins in Sri Lanka are not tested for these SRPs (except for a couple of river basins, such as the Mahaweli and Kelani River basins [
16,
17]).
Evaluation of SRPs for the Mahaweli River basin, which is the longest river in Sri Lanka, had already been carried out by Perera et al. [
16]. A similar study was carried out due to the importance of the Kelani River basin, as a frequently flooding river basin [
17]. Nevertheless, Sri Lanka geographically has a radial pattern of river basins covering most of its land and therefore, the river basins are the heart of the country [
18]. Sri Lanka has a dense rain gauge network (more than 500 rain gauges for a total of 65,610 km
2 area); however, some areas are not comprehensively covered and represent the spatial distribution of rainfall. In addition, some of the rain gauges were not functioned well in the past due to the war environment in northern and eastern Sri Lanka. In addition, some of the rain gauges were not maintained well due to various other reasons, such as financial restrictions and a lack of human resources.
Therefore, this study investigates the applicability of SRPs to three of the important river basins in Sri Lanka named Malwathu, Deduru, and Kalu river basins. Due to their significant contributions to society, these rivers are of utmost importance to the country. Thus, it is crucial to assess the applicability of such SRPs to be used in places of scarce rain gauge coverage in the Malwathu, Deduru, and Kalu catchments.
This research study focuses on evaluating IMERG V6, TRMM 3B42, TRMM 3B42RT, PERSIANN, PERSIANN-CCS, and PERSIANN-CDR precipitation products for the earlier stated river basins. Malwathu river basin is the second largest river basin in Sri Lanka. After originating in the Ritigala Hills and Inamaluwa Hills in the north-central province of Sri Lanka, the Malwathu River drifts through the Mannar region and exits into the Indian Ocean [
18]. The Kalu river basin is the third longest river in Sri Lanka. Starting from the Central Hills, it falls into the Indian ocean at Kalutara District, with a major part of the catchment in the highest rainfall area of the country [
19]. Deduru river basin is the sixth longest river in Sri Lanka, with major parts of the river basin in Kurunegala and Puttalam districts in the Northwestern Province through which it enters the sea at Chilaw while the other parts lie in the Kandy and Matale districts in the Central Province [
20]. This clearly shows the importance these river basins carry for the country in terms of agricultural use, drinking water supply, and many other economic benefits as well. The scarcity of rain gauge stations makes it of utmost importance to find an alternative method to obtain rainfall data for these river basins since rainfall data plays an important role in all of the hydrological applications associated with these basins. This is the first study to evaluate the selected SRPs for three major river basins, which fall into three different zones in the country. The previous studies carried out by Perera et al. [
16,
17] is not focusing on the dry and inter-mediate zones separately. Therefore, this particular study focuses on all three climatic zones of the country, thus marking the novelty and the great importance this study has to society.
5. Summary and Conclusions
Rainfall data of the three important river basins namely, Malwathu, Deduru, and Kalu of Sri Lanka were used to evaluate six selected SRPs namely, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, IMERG, TRMM-3B42, and TRMM-3B42RT. Four continuous evaluation indices, r, RMSE, PBIAS, and NSE, and four categorical indices, POD, FAR, CSI, and PC were used to evaluate the accuracy and prediction/detection accuracy of SRPs, respectively. Additionally, trend analysis was done in annual, seasonal, and monthly time scales to verify whether the trends predicted by SRPs match trends in observed data using the MK test and Sen’s Slope Estimation.
Mixed results were seen in the evaluation of categorical indices TRMM-3B42 performing well in the dry zone while IMERG performed well in the wet zone and intermediate zone. Overall, IMERG was the most consistent in terms of the CIs in the three basins. A clear worst performer cannot be identified as any of the SRPs consistently underperformed in all the CIs. The POD and PC results were fairly consistent among the three basins. However, the SRPs performed better in the wet zone in terms of CSI and FAR. A similar situation can be seen in the results of the CEIs with IMERG coming through as the overall best performer in terms of all four CEIs in the three basins. PERSIANN-CCS was the overall worst performer across the three basins. Once again, all the SRPs seemed to produce better results in the wet compared to the intermediate zone and dry zone. Despite the IMERG SRP performing better overall compared to the other SRPs, it was also not consistent enough.
The trend analysis showed that significant trends in the observed data from rain gauges were few and far between. Even when significant trends were present, they were very rarely reflected in the SRP predictions. TRMM-3B42_RT was the best performer in terms of trend prediction with two annual trend predictions, three monthly trend predictions, and four seasonal trend predictions. However, Sen’s slope analysis showed that even though the direction of the trend was predicted accurately, the magnitude of the trend prediction varied heavily from those of the observed data. Therefore, in conclusion, the SRPs tested in this study exhibit significant inaccuracies and cannot be recommended as a complete substitute for rain gauge measurements. Nevertheless, for regions with insufficient rain gauge data, SRPs such as IMERG could be used to obtain an overall idea about the rainfall in the region. However, basin-wise studies are recommended instead of zonal studies (wet, intermediate, and dry) for such studies. This concludes that the basin-wise recognition may not be valid overall for the zone to which that basin belongs. Therefore, more river basins in each climatic zone are recommended for future studies.