1. Introduction
Rainfall signifies an important physical variable in modulating varying ecosystem functions, including water availability [
1], soil productivity [
2], biodiversity, and agricultural production [
3], and is of importance in supporting land and water management, such as soil erosion and flood risk assessment. Rainfall has been traditionally measured using a gauge in real-time, on a daily basis. Given the importance of rainfall data, therefore, accurate and complete rainfall data availability is essential for multi-users’ decision-making processes [
4]. However, despite being considered as the most accurate rainfall data, the rainfall data are only available to the corresponding point or location of the gauges [
5]. In tropical developing countries, rainfall gauges’ measurement records are often limited by lower spatial and temporal coverage, due to costly maintenance, infrastructure and difficult terrain conditions, especially in mountainous regions where an orographic influence is evident.
To provide rainfall information for ungauged places, estimation has commonly been implemented through varying techniques of interpolation, including gauge points such as arithmetic mean, Thiessen polygon, isohyet, and geo-statistics [
6,
7,
8]. However, these methods require extensive gauge coverage and representative gauge distribution. When these factors are compromised, high bias or errors are often the consequence. The increasingly operational satellite data have offered gridded rainfall estimates in varying scales. The products offer a promising approach to providing rainfall data that support varying applications, such as drought monitoring, flood risk assessments and water balance studies [
9]. However, despite their potential to provide complete rainfall data coverage, satellite-based rainfall estimates introduce a degree of error. Information about the errors of rainfall estimates is of importance in that it enables the users to identify the most suitable rainfall estimates and incorporate the errors in sequential analysis [
10]. This is even more critical for hydrological applications, since the rainfall variability has often been quantified as the primary source of errors [
11,
12,
13].
Despite improved development of satellite-based rainfall data, which has made data more reliable and accurate [
14,
15], a number of studies on error assessments of satellite-based rainfall data reveal that errors may vary from one region to another [
10,
16]. Several studies also demonstrated the inconsistency of the performance of satellite-derived rainfall data. For example, the Climate Prediction Center Morphing Method (CMORPH) data are better than Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN) in tropical landscapes in Bali and the Ethiopian highlands [
17,
18], but it showed larger bias in Sumatra [
19]. As modeling products, rainfall estimates from satellite data introduce inherent errors and uncertainties due to several aspects, such as methods in spatial and temporal samplings, estimation methods of data, and climatic and terrain conditions [
20,
21]. Besides, satellite rainfall errors have also been found to have an association with externalities, such as geographic, topographic, and climatic regimes [
22,
23,
24]. This condition drives the need for a comprehensive validation assessment in varying geographic areas to improve the usability of remote sensing-based rainfall products.
Indonesia is a typical tropical country with distinct monsoonal characteristics in most parts. The presence of extensive mountainous regions signifies the role of orographic influences. As in other humid tropic regions, primary traits include intensified rainfall energy, leading to a great amount of runoff, and subjectivity to massive land-surface disturbance [
25]. When national and local rain gauges’ data coverage is inaccessible, incomplete, and insufficient, a reliance on satellite rainfall data is an increasingly used alternative for varying assessment. However, peer-reviewed studies about satellite rainfall products in the region are rare. To our knowledge, there have been several studies focusing on satellite rainfall assessment. This includes assessment on CMORPH, PERSIANN, Tropical Rainfall Measuring Mission (TRMM), and university-based gridded from Delaware University, in scattered locations, namely in Bali, Jakarta, Lampung, and Central Java [
19,
26,
27,
28]. In addition, impacts of the quality of satellite rainfall estimates on hydrological responses has never been explored. All these studies employed short to medium timespans (3–13 years) data, and there have been no studies exploring the satellite rainfall products in East Java, or in a longer timespan. Sampling size can affect the result, especially with small numbers of samples from a short period, and therefore assessment of a longer period (>20 years) would be essential in obtaining a more complete understanding. Several studies documented the errors of satellite products which are associated with biophysical conditions, such as wind, landforms, topography, evapotranspiration, solar radiation, and vegetation [
10,
24]. Such quantification of this phenomenon has never been examined for a complex humid tropic region, such as the Brantas watershed. This information is beneficial not only in providing an insight for parameterizations to improve the satellite rainfall estimates, but also in recognizing the potential limitations of satellite data applications in certain conditions.
Brantas is a major watershed in East Java, Indonesia, and is home to more than 21 million inhabitants. Topographically, Brantas watershed is marked with a complex terrain, due to the presence of seven mountains within the watersheds. With increasing reports on water resource-related issues in Brantas, the demand for accurate rainfall data with sufficient spatio-temporal coverage for varying purposes, such as flood modelling, erosion management, and water supply assessment, is increasing. This study is therefore aiming to (1) validate the daily and monthly satellite-based rainfall data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Global Precipitation Measurement (GPM), and PERSIANN using the publicly available rainfall field data from 1990 to 2020, (2) characterize the errors of the associated products and examine the contribution of physical factors, namely temperature, elevation, land-use, wind, and soil moisture, to the errors of the rainfall estimates, and (3) examine the impact of satellite rainfall quality on hydrological responses, as observed through a hydrological modelling approach.
4. Discussion
In this study, we assessed the performance of the three globally used daily satellite rainfall products, which had varied spatial resolutions, using long-term ground rainfall data from 1990 to 2020. The ground rainfall data came from six varying locations and had similar traits in wind and evapotranspiration statistics, but relatively different traits in terrain and temperature characteristics. Overall, the performance of the three daily satellite rainfall products were not satisfactory, given only CC of 0.22–0.36 at combined daily data. The RMSE and NRMSE values were also not very good, with ranges between 11–21 mm/day and 7–30%, respectively. As expected, the errors in the wet season are larger than in the dry season due to larger rain magnitudes. These results are comparable to those from other tropical studies, such as in Bali [
17], but are somewhat better than in tropical Malaysia [
57].
In all gauges, both CHIRPS and PERSIANN, despite their relatively better RMSE than that of GPM (
Table 4), show a tendency to underestimation (
Figure 3). This underestimation was also found in tropical parts of Brazil [
58]. These two products are less capable in capturing high rainfall rates.
Figure 8 confirms this finding, where the errors of CHIRPS and PERSIANN are exponentially larger with the increase in rainfall rates, mainly higher rates (>20 mm/day). The inability of satellite products to sense high rain rates was probably due to the presence of rainfall/hydrology extremes [
40,
59].
As opposed to other studies, which show substantial spatial differences in the performance of satellite rainfall data, this study shows that among six locations, there have been no distinct differences in error patterns. This could partially be attributed to the scale of assessment, which is only within 11,000 km
2, so that the extreme spatial climatic variations within the area were not found. The only location that almost consistently has the largest error is the PS location (>1470 m above sea level). Mountainous terrain can introduce a significant effect to variations of rainfall due to the generation of local wind currents, producing orographic impacts in tropics, especially those close to the maritime region (e.g., [
60,
61]). This was probably due to the potentially strongest orographic influence in this location. While a study in Bali [
17] shows that the land-water interface might contribute to the errors, this study shows that two locations, JU and TP, that have considerably close proximity to coastal areas (~2 km), do not exhibit major difference in performance. This was due in part to the fact that the number of gauges used in this study is limited, and therefore cannot sufficiently represent rainfall variability within the pixel boundary. Discrepancies between satellite products and gauge measurements, therefore, do not necessarily indicate satellite retrieval errors, but instead can originate from low density or nonuniform gauge coverage.
The categorical accuracy assessments reveal that GPM has the highest ability to detect (highest POD), however CHIRPS is more consistent, given its higher FOH, lower FAR, and higher agreement (DA). GPM shows its superiority in sensing wetter climates where rain events are evident, rain-rate magnitudes are high, and rain events are very extreme [
31]. This information would offer an opportunity for the GPM to evaluate the spatial rainfall variability in tropical humid regions, especially in Indonesia, which might experience unexpected climate change, and are predicted to be much wetter and exposed to higher incidents of weather extremes [
62].
Evaluation results of the relationship between errors and physical variables or land characteristics of the locations show that errors are larger in areas that have higher average elevation and steeper slopes. This suggests that accuracy decreases in the regions that have higher altitudes and topographic gradients. This could potentially be linked to the fact that higher elevation areas are associated with mountainous regions, where orographic influence is stronger [
63]. In addition, larger errors can also be linked to the wind and evapotranspiration, where faster winds and higher evapotranspiration are associated with higher precipitation [
64], thus introducing larger errors at higher rain rates. The larger errors in lower PET regions in tropics might be attributed to the ability of the algorithms of the satellite rainfall products. The ability of a satellite to detect and estimate the rate of rain is affected by the complex relationship between rain rates and brightness temperatures, which needs adjustment based on the evaporated water and relative humidity [
65]. Larger errors in lower PET regions might be attributed to these variables, since these two variables are associated with the evapotranspiration and wind in the atmosphere. With regards to the land physical cover, interestingly, all three rainfall datasets did not show any observable association to the CV of NDVI values within the corresponding pixel boundaries. While the CV of NDVI values can provide an inference about variations of land-uses, these findings suggest that within the resolution of interest (5 km~25 km), the variations of land-uses/land-cover should not be a concern. Among the three rainfall products, GPM is the only satellite product whose errors show correlation with NDVI (r = 0.16). NDVI values represent the density of vegetative cover. Denser coverage induces infrared scattering from leaf structures. In addition, higher NDVI values are usually forested areas where precipitation is high. GPM’s algorithm is designed to intercalibrate, merge, and interpolate “all” satellite microwave rainfall estimates, in conjunction with microwave-calibrated infrared (IR) satellite estimates and precipitation gauge analysis [
66]. Forest areas are mostly located in higher elevations, sparsely gauged or even ungauged, and often marked with high precipitation. Apart from the ground condition, the weakness of IR-based algorithms in the satellite rainfall estimate, where cloud thickness and cloud top temperature as estimated by the IR, do not always convey with the amount of rainfall. On the other hand, the microwave-based algorithms are also affected by water vapor, cloud liquid, oxygen, surface temperature, and the surface emissivity, which makes it very difficult to differentiate rain from the background, especially in the low rain regime [
65]. This combined effect could potentially lead to an association between vegetation cover and errors in rainfall estimates.
This study expanded the assessment of rainfall potentials to hydrological applications. Findings reveal that CHIRPS performed better than GPM and PERSIANN products, delivering generated daily flow with high R
2 and highest NSE value (R
2 = 0.59–0.62, NS = 0.54 and 055). This indicates that CHIRPS data has a slightly better ability for hydrological modeling.
Table 8 shows that ratios of CHIRPS and PERSIANN for three water balance components are generally better (close to one). The performance from CHIRPS for hydrological modeling is apparently influenced by its finer spatial resolution and station-based interpolator integrated in the algorithms. The results support the potential of CHIRPS for being complementary of rainfall gauge data. While for PERSIANN, it should be noted that PERSIANN’s resolution is 25 km
2, which probably is not suitable to support the hydrologic applications at the sub-catchments level, which are essential to sustainability resources, disaster management and environmental safety. While GPM, despite its better ability for rain event detection, is too poor to apply the datasets as a surrogate to ground rainfall data due to its extreme overestimation (3–4 times larger than gauge data). Some studies show the potential ways of improving the usability of satellite-based rainfall data. These include a blended method of interpolation and correlation [
67], machine learning [
68], and correction of orographic influence when applying hydrological modeling [
69]. While these approaches were beyond this study, the findings from the study provide insight on the potential of satellite-based rainfall data for hydrological application in a humid tropic country, especially in Indonesia.
5. Conclusions
In this study, we evaluated three satellite rainfall products using long-term daily rainfall data (1990–2020) from six locations in a humid tropic watershed. Based on numerical (quantitative) accuracy measures, GPM is consistently the product with the largest errors in all selected timespans (daily, monthly, and seasonal), while CHIRPS and PERSIANN show similar degrees of performance. However, based on categorical measures, CHIRPS shows relatively consistent performance, as shown by its high POD, FOH, DA and low FAR. On the other hand, GPM shows better sensitivity in capturing rainfall variability, especially during high rainfall events (>40 mm/day). This means that s GPM has more potential for applications related to extreme rain events and hazard assessment. Terrain physical conditions, such as wind, slope, and evaporations, exhibit a degree of association with the magnitude of errors. This information provides insight about future potential satellite data calibration and downscaling efforts. Examination into hydrological modelling shows that CHIRPS slightly shows the best ability to model monthly flow and water balance at a finer resolution, followed by PERSIANN, and GPM being the worst. However, its coarse resolution impedes PERSIANN applications for local and regional analysis supporting watershed management.