Assessment of Satellite Precipitation Products in an Andean Catchment: Ambato River Basin, Ecuador

Abstract

Accurate precipitation data are essential for hydrological planning in mountainous regions with sparse opportunities for observation, such as the Ambato River basin in Ecuador. In this study, CHIRPS and IMERG satellite precipitation products were compared against six automatic rain gauges from 2014 to 2023, using both categorical metrics (to assess daily rainfall detection skill) and continuous validation (to evaluate rainfall amount), complemented by bias decomposition and spatiotemporal analysis. Our results show that IMERG demonstrated higher skill in detecting daily rainfall, while CHIRPS delivered a more stable performance during dry conditions, with fewer false alarms. Both products capture the main seasonal precipitation patterns but differ in bias behavior: CHIRPS tends to under-estimate daily rainfall less, whereas IMERG provides more reliable volumetric estimates overall. These findings suggest that IMERG may be best suited for flood risk and hydrological modelling, while CHIRPS could be preferred for drought monitoring and climatological studies in Andean catchments.

1. Introduction

Climate change has significantly altered rainfall regimes at local, regional, and global scales [1]. These alterations manifest in the distribution and intensity of precipitation, directly affecting ecosystem processes, increasing environmental vulnerability, and impacting human activities [2,3]. Accurate and reliable precipitation estimation, as a key meteorological variable, is therefore essential for understanding and predicting these climate variations [4].

Historically, ground-based meteorological stations have been the sole source of precipitation data for studies and forecasting. However, the scarcity of monitoring stations, limited accessibility, inconsistent data quality, lack of extreme event records, and low spatial and temporal coverage have constrained the availability of reliable data for integrated water resources management [5,6,7,8].

Currently, the availability and continuity of satellite-based precipitation records are transforming the way scientific and societal issues related to this variable are addressed, due to their rapid and easy accessibility through various data portals [9]. Several climate datasets have been developed at different spatiotemporal scales based on in situ observations. For instance, the Global Historical Climatology Network (GHCN) is an integrated database with approximately 31,000 stations and observations spanning the entire 20th century [10]. Satellite-based algorithms have enabled significant advances in estimating climate variables such as precipitation, temperature, and albedo, especially in data-scarce regions. However, these are indirect estimates that rely on cloud-top properties, utilizing infrared (IR) algorithms, and cloud liquid and ice content, utilizing polar microwave (PM) algorithms [11]

Quantifying the uncertainty and accuracy associated with each estimate permits users to infer the reliability of satellite precipitation products (SPPs) and improve their applicability across disciplines. Errors are assessed through validation studies that compare satellite-based precipitation estimates with ground truth, using rain gauges or radar observations [12].

Integrated Multi-satellite Retrievals for GPM (IMERG) [13] and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) are two widely used SPPs [14]. These tools have proven valuable for supplementing sparse ground station networks, especially in remote or poorly monitored areas, even though each product has advantages and limitations depending on its spatial and temporal resolution. Thus, combining the strengths of each product can enhance their overall performances [15,16]. CHIRPS is a high-resolution, quasi-global precipitation dataset spanning over 35 years, developed by the Climate Hazards Group at the University of California, Santa Barbara. It merges infrared satellite data with ground-based observations to generate accurate precipitation estimates in regions with limited station coverage [14]. IMERG, on the other hand, is a global precipitation dataset produced through the integration of multiple satellite sensors, developed as part of the Global Precipitation Measurement (GPM) mission, which is a collaboration between NASA and the Japan Aerospace Exploration Agency (JAXA) [13].

These products have been evaluated in various studies across different regions and scales. In China, daily and annual precipitation from nine satellite and reanalysis datasets were assessed between 2000 and 2020. The results indicated that CHIRPS performed best in the arid and semi-arid areas of the study region [17].

In South America, Benitez et al. [18] assessed the ability of satellite data to estimate rainfall across the Southeastern South America (SESA) region, covering Argentina, Uruguay, Brazil, and Paraguay. They compared four satellite products—IMERG V.06 Final Run, PERSIANN, PERSIANN CCS-CDR, and PDIR-NOW—against observations from 118 meteorological stations from 2001 to 2020. The results showed that IMERG and CCS-CDR better captured precipitation patterns at annual and seasonal scales. All products had difficulties estimating winter and summer rainfall, and accuracy was generally higher in humid regions and lower in dry zones. Although IMERG showed the highest accuracy (with correlation coefficients up to 0.95), all products struggled to capture extreme events and data-sparse areas.

In Bolivia, Mattos et al. [19] compared CHIRPS and IMERG satellite estimates with rain gauge measurements from 2002 to 2020 across three distinct topographic zones—highlands, valleys, and lowlands. IMERG performed better in detecting precipitation, particularly in the highlands, whereas CHIRPS provided more accurate precipitation measurements across all regions, with lower random errors and relative biases below 10%. IMERG, however, tended to overestimate rainfall, reaching up to 75% overestimation in the Bolivian Altiplano [19].

In Perú, Salas et al. [20] evaluated the performance of GPM IMERG_F v6 by comparing monthly satellite rainfall estimates with data from eight SENAMHI stations in the Madre de Dios basin. Using statistical techniques such as Nash–Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE), the authors found that, while satellite data are generally useful, they can exhibit over- or underestimation depending on the season, with an average RMSE of 146.48 mm. In some cases, such as the Iñapari station, NSE values fell below 0.65, indicating poor model performance in specific contexts. The study also highlights the benefits of combining satellite data with data from other sources to optimize water management and evaluate climate phenomena in areas with limited infrastructure.

In Ecuador, Ballari et al. [21] validated monthly precipitation estimates from the TRMM 3B43 product using 14 rain gauges across Ecuador’s coastal plains, the Andes, and the Amazon. The study underlined the importance of rainfall monitoring for water availability, especially in mountainous areas with sparse measurements. Results showed that TRMM 3B43 captured precipitation seasonality well, with better performance in coastal and Amazon regions than in the Andes. The authors recommended further calibration and bias correction for satellite data in ungauged catchments.

Recently, Huber et al. [22] compared gridded global datasets of temperature and precipitation (IMERG, CHIRPS, ERA5, GLDAS) with data from a local automatic weather station network initiated in 2014. The findings emphasize that, while satellite products can help fill data gaps, their performance varies depending on location and precipitation type. IMERG and CHIRPS were found to be more effective under certain conditions, yet the lack of in situ data in regions such as the Amazon limits the accuracy of satellite estimates. Expanding the ground station network is therefore essential in improving validation and supporting water planning and climate monitoring in Ecuador.

The above studies demonstrate that, while satellite precipitation products are valuable tools for rainfall estimation, their performances depend on factors such as topography, station density and distribution, climate conditions, cloud characteristics, and temporal resolution. Therefore, evaluating their performances in different geographical and hydrological contexts remains essential.

The Ambato River Basin, located in Ecuador’s Sierra region, is part of the upper Pastaza River system. It plays a critical role in agricultural production in Tungurahua Province and is home to approximately 75% of the province’s population. The basin faces high water demand and contributes significantly to downstream pollution, affecting neighboring catchments such as the Patate River and tributaries of the Pastaza [23]. In 2013, the provincial government of Tungurahua deployed a network of automatic weather stations to monitor key meteorological variables and provide reliable data for risk prevention and water planning. However, complex topography leads to high climatic variability, and the existing stations are not sufficient to fully capture local precipitation dynamics [24,25].

In this context, the present study aims to evaluate the accuracy of satellite precipitation products CHIRPS and IMERG in the Ambato River Basin. The goal is to assess detection capacity using categorical and continuous statistics, as well as to calculate accuracy indicators through bias decomposition metrics to characterize the spatiotemporal rainfall patterns in the basin. This will contribute to improving the quality of available data for integrated water resources management and enhance our understanding of SPP applicability in high Andean catchments.

2. Materials and Methods

2.1. Study Area

The Ambato River Basin is located in the Western Cordillera of the central Ecuadorian Andes, within the administrative territory of Ambato, Tungurahua Province. It forms part of the upper Pastaza River basin system, which ultimately drains into the Amazon River. Covering an approximate area of ~130,000 ha, this hydrological unit accounts for 38.4% of the total surface area of Tungurahua Province. It is bounded to the north by the Cutuchi River Basin; to the south by the Pachanlica River Basin; to the east by the Cutuchi, Patate Rivers; and to the west by the Babahoyo and Yaguachi sub-basins [26] (Figure 1).

Climatically, the Ambato River Basin exhibits a predominantly temperate and dry mountain climate, with the presence of multiple microclimates being due to abrupt altitudinal gradients and varied topographic exposure [27]. According to WorldClim v2.1 [28], the basin receives relatively low annual precipitation, ranging from 600 to 1200 mm/year, and has mean annual temperatures between 8 °C and 14 °C, depending on elevation. At higher altitudes, conditions become markedly colder, with permanent snow cover on nearby peaks such as Carihuairazo and Chimborazo, especially over 4800 m a.s.l. Compared to adjacent regions, this basin is drier than the Amazonian foothills (>3000 mm/year) and the coastal plains (~1500 mm/year), highlighting the rain-shadow effect of the Andean cordillera. The temperature regime is relatively stable throughout the year, but the basin shows high interannual variability in precipitation.

Land cover in the basin is distributed as follows: 49% natural ecosystems, 44% agricultural land, 4% areas undergoing erosion processes, 1% water bodies, and 2% human settlements [26].

The hydrological cycle in the basin begins at elevations above 3600 m above sea level, in the páramo ecosystems of the Casahuala, Pilishurco, and Chimborazo Wildlife Production Reserve. These areas function as water regulators and give rise to 127 rivers, 237 streams, 80 lakes, and 244 creeks. The basin supplies water to five of the nine cantons (a second-level political division) of Tungurahua Province, with an estimated population of 320,315 inhabitants and an average population density of 130.9 inhabitants/km². The predominant economic activities are agriculture and livestock farming, particularly in rural zones and transitional areas between the páramos and urban regions [29].

2.2. Data Sources

2.2.1. Rain Gauges Data

Daily precipitation records from eight automatic meteorological stations within the Ambato River basin were obtained from the Decentralized Autonomous Government of Tungurahua Province (Figure 1) for 2014–2023.

For the core validation over the full 2014–2023 period, six stations with multi-year records of sufficient length and continuity were used. Tamboloma and Pilahuin stations were not retained because their records are limited to 2019–2023 and contain substantial gaps, which prevents the consistent calculation of validation metrics across the entire study period. Both excluded stations are located near the basin center, close to Calamaca station (Figure 1), so their omission does not create a data gap in the south part of the catchment. However, the southern sector (coincident with the Chimborazo and Carihuairazo volcanic slopes) lacks rain gauges; this coverage limitation is acknowledged.

Following Veiga et al. [30], the six gauge series underwent quality control (QC), including gross-error checks, fixed-range checks, and temporal continuity tests to ensure internal consistency. The characteristics of the six retained stations are reported in

Table 1. Characteristics of the meteorological stations in the Ambato River basin. Coordinates are referred to UTM zone 17 S. Completeness values are computed over 2014–2023. ‘Years used’ counts the years with ≥80% daily data completeness, which are the years included in the annual statistics of mean annual precipitation (MAP) and standard deviation (SD).

Name	Location			Completeness		Years Used	MAP	SD
	Coord X	Coord Y	Altitude	Daily (%)	Monthly (%)	(y)	(mm/y)	(mm/y)
Chiquiurco	743,682	9,866,064	3763	98.6	80.8	10	1108	88.1
Aeropuerto Ambato	769,929	9,865,679	2590	74.5	56.7	7	587	298
P. Fermín Cevallos	765,641	9,849,972	2910	95.0	80.0	9	478	83.7
Calamaca	742,705	9,858,860	3483	90.3	75.8	8	757	122
Mula Corral	741,602	9,867,738	3890	85.6	65.8	9	958	130
Quisapincha	753,559	9,865,921	3670	96.6	85.8	10	988	185

. After QC, these series are internally consistent and suitable for the analyses presented here.

2.2.2. Satellite Data

This study uses two satellite datasets that have been proved to have a better performance for the conditions in Ecuador: CHIRPS and IMERG. The Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) is a quasi-global rainfall dataset developed by the Climate Hazards Group at the University of California, Santa Barbara. It offers high-resolution precipitation estimates (0.05° spatial resolution, ~5 km) from 1981 to the present by combining satellite infrared data with in situ observations from weather stations [14].

The CHIRPS data generation process involves two main steps. First, satellite-derived infrared precipitation (IRP) pentads are calculated by assessing the proportion of time that cold cloud tops (<235 K) are detected in infrared imagery during each five-day period. These frequencies are then converted into rainfall amounts (in mm) through a calibrated regression using TRMM 3B42 pentad precipitation data. To account for climatic variability, the IRP pentads are normalized against their long-term means (1981–2012), resulting in unitless anomaly values indicating deviations from average precipitation (i.e., below-normal, near-normal, or above-normal conditions). These normalized values are multiplied by corresponding pentads from the CHPclim monthly climatology to produce gridded, bias-corrected rainfall estimates known as CHIRP. In the second step, CHIRP estimates are merged with ground-based station data to generate the final CHIRPS product, which improves accuracy across complex terrains and regions with sparse monitoring networks [14].

The Integrated Multi-satellite Retrievals for GPM (IMERG) dataset, developed by the NASA Goddard Space Flight Center’s Precipitation Processing System, provides high-resolution, satellite-based precipitation estimates through the integration of passive microwave and infrared sensor data with ground-based rain gauge observations [31]. The IMERG algorithm performs inter-calibration, data merging, and temporal interpolation to generate seamless precipitation records.

IMERG offers near-global coverage, spanning latitudes between 60° N and 60° S, with a spatial resolution of approximately 10 km. Its temporal resolution ranges from 30 min intervals to monthly accumulations, making it suitable for a wide range of hydrological and climatological applications, particularly in data-scarce regions or areas with low-quality observational networks [31].

In this study, daily and monthly precipitation data from both the CHIRPS and IMERG datasets were retrieved using the Google Earth Engine platform [32]. For CHIRPS, “UCSB-CHG/CHIRPS/DAILY” image collection was filtered to extract daily precipitation values corresponding to the geographic coordinates of the meteorological stations located in the Ambato River basin. Similarly, for IMERG, the GPM L3/IMERG V07 collection (IMERG-Final run) was used to obtain daily and monthly precipitation estimates, which were spatially matched to the same set of stations across the basin.

2.3. Evaluation Methods

CHIRPS and IMERG were evaluated at two temporal scales: daily, to assess rainfall daily detection, and monthly, to assess accumulated amounts. Satellite values were extracted from the native grid cell containing each gauge (point-to-pixel pairing). Daily accumulations for gauges and SPEs were aligned to local time (UTC-05:00). Days with missing data in either source were excluded pairwise; no gap infilling was applied. A rainy day was defined as gauge precipitation ≥1.0 mm day⁻¹ [33], used for all categorical metrics. This denotes daily rain occurrence and does not correspond to a hydrologically coherent storm; daily aggregation can merge or split sub-daily storms. Monthly totals are calendar aggregates suitable for climatological characterization and volumetric accuracy, but catchment response periods need not align with calendar months

To ensure hydrological relevance, the evaluation targets both daily rainy-day detection and rainfall amounts. The daily rainy-day detection skill was assessed with the following measures: Probability of Detection (POD), False Alarm Ratio (FAR), Frequency Bias Index (FBI), and Heidke Skill Score (HSS); these capture misses, false alarms, systematic over/under-detection, and chance-corrected categorical skill. These are key attributes for flood early warnings and daily modelling. Quantitative agreement was evaluated with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Index of Agreement (d), and bias; these relate to error magnitude, pattern fidelity, and systematic deviation that influence water balance closure and drought assessments. To diagnose error sources, bias was decomposed into hit, miss, and false components (HB, MB, FB), which are informative for the mountainous Andean terrain where orographic and seasonal effects can differentially affect peak flow simulations, baseflow conditions, and soil moisture recharges.

2.3.1. Categorical Validation Statistics

To evaluate the ability of satellite-based precipitation estimates (SPEs) to detect rainfall, categorical validation statistics were applied based on daily comparisons with ground-based rain gauge measurements [19,33]. Categorical metrics were computed on the intersection of days with valid gauge, IMERG, and CHIRPS records at each station; daily rainy-day detections were classified as occurring when daily precipitation exceeded 1 mm per day [33], for both gauge and SPPs. Missing values are not imputed. Four categorical metrics were employed to assess performance: Probability of Detection (POD), False Alarm Ratio (FAR), Frequency Bias Index (FBI), and Heidke Skill Score (HSS); each of these provides a distinct measure of agreement between IMERG and CHIRPS estimates and ground observations.

The POD metric quantifies the proportion of actual rainfall events that were correctly identified by the satellite product, with values approaching 1 indicating better detection performance. Its calculation involves two key variables: A (hits, where both the SPE and gauge detect precipitation) and C (misses, where precipitation is recorded by the gauge but not detected by the SPE), as shown in Equation (1). POD values range from 0 to 1, with 1 representing perfect detection.

$$POD={\displaystyle \frac{A}{A+C}}$$

(1)

In contrast to POD, the False Alarm Ratio (FAR) quantifies the proportion of precipitation events falsely detected by the satellite-based estimates, that is, cases where the SPE reported rainfall, but no precipitation was recorded by the ground-based rain gauges. This metric incorporates variable B, representing false alarms, defined as instances where precipitation was estimated by the SPE but not observed in situ (Equation (2)). FAR values range from 0 to 1, with lower values indicating better performance and 0 being the ideal outcome.

$$FAR={\displaystyle \frac{B}{A+B}}$$

(2)

The Frequency Bias Index (FBI) assesses the tendency of satellite-based precipitation estimates to either overestimate or underestimate the occurrence of daily precipitation when compared to ground-based observations. It is calculated using both hits (A) and false alarms (B), as shown in Equation (3). FBI values range from 0 to ∞, with an ideal value of 1. A value below 1 suggests that the SPE underestimates precipitation frequency, while a value above 1 indicates an overestimation relative to rain gauge records.

$$FBI={\displaystyle \frac{A+B}{A+\mathrm{C}}}$$

(3)

The Heidke Skill Score (HSS) is a categorical metric that evaluates the overall accuracy of precipitation estimates, correcting for the influence of random chance (Equation (4)). It incorporates all four elements of the contingency table [34]: hits (A), false alarms (B), misses (C), and correct negatives (D). Here, D corresponds to days with no precipitation detected by either the SPE or the ground-based rain gauge.

HSS values range from −∞ to 1. A score below 0 indicates that random chance outperforms the SPE, a value of 0 implies no predictive skill, and a value of 1 represents perfect agreement between the satellite-based estimates and ground observations in identifying precipitation days.

$$HSS={\displaystyle \frac{2\left(AD-BC\right)}{\left[\left(A+C\right)\left(C+D\right)+\left(A+B\right)\left(B+D\right)\right]}}$$

(4)

2.3.2. Continuous Validation Statistics

To assess the quantitative agreement between satellite-based precipitation estimates (SPEs) and ground-based observations, a set of continuous validation statistics was computed. These included the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Index of Agreement (d), and bias, as defined in Equations (5)–(8). All metrics were calculated separately for daily and monthly datasets.

MAE and RMSE quantify the magnitude of error, with ideal values tending toward 0. In contrast, d and Bias evaluate relative agreement and systematic deviation, respectively, with optimal values equal to 1.

$$MAE=\left({\displaystyle \frac{1}{N}}\right)\sum _{i=1}^N\left|S_i-E_i\right|$$

(5)

$$RMSE=\sqrt{\left({\displaystyle \frac{1}{N}}\right)\sum _{i=1}^N{\left(S_i-E_i\right)}^2}$$

(6)

$$d=1-{\displaystyle \frac{\sum {(S-E)}^2}{\sum {(\left|S-\overline{E}\right|+\left|E-\overline{E}\right|)}^2}}$$

(7)

$$Bias={\displaystyle \frac{ \sum S_i}{\sum E_i}}$$

(8)

where $S_i$ is the satellite-derived precipitation at time i, $E_i$ is the rain gauge observation at time i, $\overline{E}$ means the observed precipitation over the comparison period, and $N$ is the total number of paired observations.

2.3.3. Bias Decomposition Analysis

To better characterize the sources of error in satellite-based precipitation estimates, a bias decomposition analysis was conducted using three complementary metrics: Hit Bias (HB), Miss Bias (MB), and False Bias (FB). These indicators quantify the relative contributions of different types of disagreement between SPE products and ground-based rainfall observations.

HB represents the systematic difference in precipitation volumes during coincident detection events, that is, when both SPE and rain gauges register rainfall. This metric captures whether the SPE tends to overestimate or underestimate rainfall intensity and is calculated using Equation (9). Values close to zero indicate strong agreement, whereas positive values denote overestimation and negative values indicate underestimation.

$$HB=(\sum (S-E)/\sum E)\times 100for:S>0andE>0$$

(9)

The Miss Bias (MB) quantifies undetected precipitation events—those observed by ground stations but not captured by the SPE. It is computed as shown in Equation (10) and reflects the proportion of missed rainfall relative to the total observed precipitation. MB values are always negative, and greater absolute values indicate larger under-detection.

$$MB=(-\sum E_1/\sum E)\times 100for:S=0andE>0$$

(10)

The False Bias (FB) measures the magnitude of precipitation estimated by the SPE in the absence of corresponding observations from rain gauges. It highlights false positive detections and is calculated according to Equation (11). As with the other metrics, values closer to zero indicate better performance.

$$FB=(\sum S/\sum E)\times 100for:S>0andE=0$$

(11)

2.4. Analyzing Interannual Spatiotemporal Precipitation Patterns

To analyze spatiotemporal rainfall patterns at the grid-cell level, annual precipitation averages over the study period were mapped using IMERG and CHIRPS datasets. The spatial variability of precipitation was assessed by calculating the coefficient of variation (CV) using Equation (12):

$$CV={\sigma }_P/{\mu }_P$$

(12)

where ${\sigma }_P$ is the standard deviation of precipitation and ${\mu }_P$ is the mean precipitation. The resulting CV maps provide insights into spatial differences in precipitation variability within the study area. Higher CV values indicate greater relative variability across the grid cells, while lower values suggest more uniform precipitation patterns.

3. Results

3.1. Daily Rainfall Detection

Across the basin, IMERG detects daily rainfall more consistently than CHIRPS at all gauges. POD values for IMERG were around 0.4 at every station (often >0.4), whereas CHIRPS remains <0.3 in most cases; FAR is comparable between products (≈0.28;

Table 2. Detection skill scores for daily rainfall estimates from CHIRPS and IMERG across the six selected stations. The variable n common number of valid gauge–IMERG–CHIRPS days (2014–2023).

		Daily Categorical Validation—IMERG
Station	n	POD	FAR	FBI	HSS
Chiquiurco	3599	0.47	0.37	0.74	0.17
Aeropuerto Ambato	2721	0.39	0.52	0.81	0.2
P.F. Cevallos	3468	0.47	0.55	1.02	0.23
Calamaca	3297	0.46	0.49	0.89	0.13
Mula Corral	3126	0.45	0.39	0.74	0.15
Quisapincha	3526	0.38	0.33	0.57	0.18
Mean		0.44	0.44	0.8	0.18
		Daily Categorical Validation—CHIRPS
Station	n	POD	FAR	FBI	HSS
Chiquiurco	3599	0.23	0.35	0.35	0.1
Aeropuerto Ambato	2721	0.26	0.53	0.56	0.14
P.F. Cevallos	3468	0.3	0.55	0.68	0.16
Calamaca	3297	0.23	0.44	0.42	0.1
Mula Corral	3126	0.21	0.36	0.33	0.09
Quisapincha	3526	0.24	0.35	0.36	0.1
Mean		0.24	0.43	0.45	0.12

).

Spatially, the strongest agreement for IMERG occurs at the eastern station (P. Fermín Cevallos) and the northwestern station (Chiquiurco). In the northwestern sector (Mula Corral, Chiquiurco), CHIRPS shows the weakest daily rainfall detection, while IMERG still outperforms CHIRPS though with more moderate skill than at the eastern/northeastern sites. The northern station (Quisapincha) exhibits intermediate performance, with IMERG consistently ahead of CHIRPS.

The frequency bias further indicates systematic under-detection by CHIRPS (FBI < 1 across most stations), whereas IMERG is closer to the ideal (FBI → 1); for example, CHIRPS attains its highest FBI at P.F. Cevallos (~0.68) yet remains below unity. HSS follows the same pattern, with IMERG higher at nearly all gauges. Overall, these daily occurrence results point to basin-wide superiority of IMERG and limited categorical skill for CHIRPS, particularly in the northwest (Figure 1; Table 2).

3.2. Daily Precipitation Totals (Continuous Validation)

At the daily scale, IMERG and CHIRPS show broadly comparable performance, with IMERG exhibiting a slight advantage in bias and index of agreement (d), and marginally higher MAE than CHIRPS (

Table 3. Continuous validation statistics at the daily time scale. The variable n indicates the number of paired data points considered in the comparison.

		Daily Continuous Validation—IMERG
Station	n	MAE	RMSE	d	Bias
Chiquiurco	3599	3.33	5.99	0.62	0.86
Aeropuerto Ambato	2721	2.55	6.09	0.48	1.26
P.F. Cevallos	3468	2.41	5.74	0.54	1.71
Calamaca	3297	2.94	5.73	0.55	1.25
Mula Corral	3126	3.16	5.81	0.62	0.95
Quisapincha	3526	2.94	5.33	0.65	0.76
Mean		2.89	5.78	0.58	1.13
		Daily Continuous Validation—CHIRPS
Station	n	MAE	RMSE	d	Bias
Chiquiurco	3599	3.5	6.24	0.47	0.52
Aeropuerto Ambato	2721	2.35	4.88	0.43	0.88
P.F. Cevallos	3468	2.97	6.88	0.38	1.83
Calamaca	3297	2.89	5.6	0.43	0.83
Mula Corral	3126	3.02	5.38	0.46	0.43
Quisapincha	3526	3.1	5.41	0.55	0.58
Mean		2.97	5.73	0.45	0.84

). IMERG tends to overestimate daily totals on average, whereas CHIRPS generally underestimates them.

Spatially, the largest daily discrepancies for both products occur at the northwestern station Chiquiurco, reflected in elevated MAE and RMSE. In the north sector (Quisapincha), performance is intermediate; IMERG achieves the highest d observed across the network at Quisapincha (≈0.65). The central western station Calamaca and the eastern station P.F. Cevallos show comparatively better agreement patterns, consistent with the station-wise means.

Bias results indicate contrasting tendencies: IMERG averages bias ≈ 1.13, i.e., an overestimation of ~13% in daily totals, while CHIRPS averages bias ≈ 0.84, i.e., an underestimation of ~16%. Station-wise, IMERG is close to unity at Mula Corral and Chiquiurco (≈0.95 and ≈0.86, northwest), whereas CHIRPS shows pronounced underestimation across sites; at Mula Corral (northwest) its bias is ~0.43

IMERG provides slightly higher daily agreement (higher d) but with a tendency to overestimate amounts, while CHIRPS yields slightly lower error magnitudes on average (RMSE) yet systematically underestimates daily totals; this is particularly evident at the northwestern station Chiquiurco and Mula Corral.

3.3. Daily-Scale Bias Decomposition

The bias decomposition analysis at daily time scale for the IMERG and CHIRPS products was conducted to evaluate their detection performance, as well as the magnitude of undetected precipitation and false detections. The overall bias was disaggregated into three components: Hit Bias (HB), Miss Bias (MB), and False Bias (FB) (

Table 4. Bias decomposition statistics at the daily time scale. The variable n common number of valid gauge–IMERG–CHIRPS days (2014–2023).

		Bias Decomposition IMERG
Station	n	HB	MB	FB
Chiquiurco	3599	7.82	−43.5	20.99
Aeropuerto Ambato	2721	20.35	−49.36	57.79
P.F. Cevallos	3468	39.63	−35.14	69.73
Calamaca	3297	21.61	−41.31	45.64
Mula Corral	3126	13.83	−43.5	24.54
Quisapincha	3526	7.45	−46.99	16.33
Mean		18.45	−43.3	39.17
		Bias Decomposition CHIRPS
Station	n	HB	MB	FB
Chiquiurco	3599	5.62	−66.95	16.2
Aeropuerto Ambato	2721	7.66	−62.5	49.42
P.F. Cevallos	3468	51.42	−53.01	92.99
Calamaca	3297	15.27	−64.33	37.42
Mula Corral	3126	−1.07	−67.97	15.47
Quisapincha	3526	6.24	−62.91	17.99
Mean		14.19	−62.94	38.25

).

Network-wide, CHIRPS exhibits a lower HB on average (+14.2%) than IMERG (+18.5%), indicating less overestimation on coincident rainy days. In contrast, MB is substantially less negative for IMERG (≈−43.3%) than for CHIRPS (≈−62.9%), implying that IMERG misses a smaller fraction of gauge-observed daily rainfall. For FB, CHIRPS is slightly lower on average (38.25%) than IMERG (39.17%), indicating fewer false-positive rainfall detections.

At the station scale, patterns align with basin-relative location. On the eastern side (P.F. Cevallos), both products exhibit their largest Hit Bias (HB; CHIRPS 51.42%, IMERG 39.63%) together with elevated False Bias (FB; CHIRPS 92.99%, IMERG 69.73%), indicating strong overestimation on coincident wet days and a high frequency of false-positive rainfall detections. In the northwestern sector (Mula Corral, Chiquiurco), CHIRPS shows the most negative Miss Bias (MB; down to −67.97% at Mula Corral and −66.95% at Chiquiurco), evidencing substantial missed rainfall, while FB remains low (≈15%). IMERG in this sector reduces the magnitude of MB (≈−43% at both stations), with modest positive HB (7–13%) and low-to-moderate FB (≈20%). In the northeast (Quisapincha, Ambato Airport), IMERG attains its lowest FB at Quisapincha (16.33%) but records its most negative MB within its range at Ambato Airport (−49.36%); CHIRPS also underestimates at both sites (MB −62.91% and −62.5%, respectively), with FB of 17.99% and 49.42%. In the central west (Calamaca), IMERG presents one of its least negative MB values (−41.3%), consistent with comparatively better daily detection there; CHIRPS shows moderate HB (15.27%), moderate FB (37.42%), and strongly negative MB (−64.33%).

Overall, the decomposition reveals complementary behaviors. IMERG reduces the fraction of missed rainfall (less-negative MB) across the network but tends to overestimate on coincident wet days (higher HB); and, on average, it incurs slightly higher FB than CHIRPS. CHIRPS, in turn, produces fewer product-only wet days (lower FB) and lower HB, yet misses a larger share of gauge-observed rainfall (more-negative MB), particularly in the northwest. These station-wise contrasts are consistent with the categorical findings and help explain the product-specific tendencies reported elsewhere in Section 3 (Table 4).

3.4. Monthly Precipitation Totals (Continuous Validation)

At the monthly scale, interpreted here as calendar aggregates for climatological/volumetric evaluation, IMERG generally shows tighter agreement with gauges than CHIRPS, with higher index of agreement (d) and, on average, lower or comparable MAE/RMSE (

Table 5. Continuous validation statistics at the monthly time scale. The variable n indicates the number of paired data points considered in the comparison.

		Monthly Continuous Validation—IMERG
Station	n	MAE	RMSE	d	Bias
Chiquiurco	97	40.20	54.99	0.58	0.87
Aeropuerto Ambato	69	35.29	50.94	0.38	1.32
P.F. Cevallos	96	38.96	50.31	0.55	1.72
Calamaca	91	39.41	52.45	0.55	1.28
Mula Corral	79	38.42	52.24	0.57	0.95
Quisapincha	103	36.46	44.03	0.65	0.75
Mean		38.12	50.83	0.55	1.15
		Monthly Continuous Validation—CHIRPS
Station	n	MAE	RMSE	d	Bias
Chiquiurco	97	52.45	65.60	0.42	0.53
Aeropuerto Ambato	69	24.23	33.16	0.55	0.93
P.F. Cevallos	96	40.64	51.33	0.51	1.79
Calamaca	91	34.85	44.82	0.47	0.87
Mula Corral	79	55.87	66.52	0.39	0.41
Quisapincha	103	44.13	54.26	0.50	0.55
Mean		42.03	52.62	0.47	0.85

). Bias indicates opposing tendencies: IMERG overestimates monthly totals by ~15% on average (bias ≈ 1.15), whereas CHIRPS underestimates by ~15% (bias ≈ 0.85).

Spatially, the largest monthly discrepancies for both products occur at the northwestern station, Chiquiurco (elevated MAE/RMSE). In the north sector, IMERG attains the highest d across the network at Quisapincha (≈0.65), while CHIRPS reaches its best d at Ambato Airport (≈0.55). An outlier appears at the eastern station, P.F. Cevallos, where both products show positive bias (IMERG ≈ 1.72, CHIRPS ≈ 1.79).

The gauge–satellite scatter plots (Figure 2) corroborate these patterns: IMERG (blue) points cluster more tightly around the 1:1 line (red dashed line), whereas CHIRPS (black) points more often fall below 1:1, reflecting underestimation; Ambato Airport is a notable exception where CHIRPS aligns relatively well with observations.

Notably, IMERG showed stronger concordance with observations at the Quisapincha, Calamaca, and Mula Corral stations, which aligns with the results from the continuous validation metrics (Table 4). Conversely, CHIRPS tends to underestimate monthly precipitation values when compared to gauge data, as evidenced by most scatter points lying below the 1:1 line. An exception to this pattern is observed at the Ambato Airport station, where CHIRPS demonstrates relatively better agreement, consistent with the continuous validation results: bias = 0.93 and d = 0.55.

Monthly climatology data (2014–2023; Figure 3) indicate that both products reproduce the seasonal cycle only at two stations—Calamaca (central west) and Quisapincha (north)—and show limited skill at the remaining four gauges. Consistent with the metrics, IMERG tends to lie above the observed monthly totals, while CHIRPS tends to lie below them.

3.5. Interannual Spatiotemporal Precipitation Patterns

To investigate the spatiotemporal behavior of rainfall across the study area, annual mean precipitation was calculated for both satellite-based products: IMERG and CHIRPS (Figure 4). IMERG estimates ranged from 788 mm to 1577 mm per year (Figure 4a), while CHIRPS estimates varied between 333 mm and 1187 mm annually (Figure 4c).

The coefficient of variation (CV), used to assess interannual variability, further revealed spatial differences between the two products. For IMERG, the highest CV reached 25% (Figure 4b), mainly concentrated in the northeastern sector of the Ambato River basin. The rest of the basin exhibited relatively low CV values, suggesting stable annual precipitation over the study period. In the case of CHIRPS, the maximum CV was slightly lower at 21% (Figure 4d), located to the northwest of the catchment, outside the basin boundaries. Despite these differences, both products revealed similar spatial patterns of interannual variability within the core area of the basin.

Within the study area, three distinct rainfall regimes can be observed. The first corresponds to the inter-Andean corridor spanning the northeastern to southeastern sectors of the catchment. In this corridor, IMERG estimates show a gradual increase in annual precipitation from around 800 mm in the northeast to 1000 mm in the southeast of the catchment. CHIRPS, in contrast, reports lower values ranging from approximately 500 mm to 900 mm.

The second region encompasses the eastern slopes of the Western Andes in the northwest part of the basin, an area of higher elevations with páramo ecosystems. IMERG indicates mean annual precipitation of around 1000 mm, whereas the CHIRPS estimates are significantly lower, near 600 mm.

The third region lies in the southwest sector along the eastern flank of the Western Andes. This area overlaps substantially with the Chimborazo and Carihuairazo volcanoes. This is the largest inter-product discrepancy in the basin (Figure 4e), with IMERG exceeding CHIRPS by ~900 mm.

These spatial contrasts further support the patterns revealed in the validation analyses, highlighting the consistent overestimation by IMERG and the underestimation by CHIRPS across various sectors of the basin.

4. Discussion

Satellite precipitation products such as IMERG and CHIRPS have become indispensable tools for hydrological analysis in regions with sparse ground-based data, particularly in mountainous and forested catchments. However, their performance varies depending on topographic complexity, data processing algorithms, and the temporal resolution of evaluation. For instance, in a study conducted across Bolivia’s diverse terrain, IMERG showed superior skill in detecting daily rainfall (POD > 0.5) compared to CHIRPS, especially in high-altitude areas, but suffered from a high false alarm ratio and rainfall overestimation of up to 75% in the highlands [19]. Similarly, in the Madre de Dios basin in Peru, Salas-Choquehuanca et al. [20] reported that IMERG_F v6 data presented notable deviations from observed values, with RMSEs averaging 146 mm and Nash–Sutcliffe Efficiency (NSE) values often below acceptable thresholds (< 0.65). Although the Pearson correlation coefficients (r > 0.8) suggested a generally consistent trend, systematic over- and underestimations persisted across the stations. These results collectively emphasize that, while IMERG can capture general rainfall patterns, it tends to overestimate intensities and may underperform at finer scales or in specific microclimates.

In the Ambato River catchment, characterized by steep topography, the categorical validation metrics (POD, FAR, FBI, HSS) confirmed the superior performance of IMERG over CHIRPS in detecting daily rainfall. IMERG achieved higher POD and HSS values, reflecting its greater ability to correctly identify precipitation occurrences, while both products showed similar FAR values. This aligns with findings from Benítez et al. [18], who highlighted IMERG’s capability to detect rainy days (POD > 0.7) and its high agreement with observed precipitation in the wetter regions of southeastern South America. However, as also observed in Bolivia [19], our analysis revealed that IMERG exhibited a higher False Bias Index (FBI), suggesting the presence of false rainfall detections not supported by gauge observations.

In terms of continuous validation at a daily scale, both products produced comparable results for MAE and RMSE. Nevertheless, IMERG showed a slight advantage in MAE and presented a lower bias index, suggesting less systematic deviation from observed rainfall. Interestingly, IMERG tended to overestimate precipitation, whereas CHIRPS consistently underestimated it—a pattern also reported by [19,20]. At the monthly scale, this behavior became more pronounced, with IMERG consistently providing higher rainfall totals. The scatterplot analysis of the monthly estimates confirmed this trend, as IMERG’s predictions clustered more closely around the 1:1 line, whereas CHIRPS values tended to fall below it, indicative of underestimation. One factor that may have influenced the monthly validation results is the selection of only those months with complete datasets, whereas daily validation included time series that may have come from months with missing data.

In this context, the findings of López-Bermeo et al. [35] offer relevant evidence, as they conducted a robust validation of CHIRPS using 75 rain gauge stations across the diverse topography of Antioquia, Colombia. Their results revealed that CHIRPS generally performs well at annual and interannual scales, particularly in Andean regions, but tends to overestimate precipitation in most stations and underestimates in warmer, lowland subregions. These biases become more pronounced at the daily scale, where CHIRPS exhibited limited accuracy. This supports our current analysis, which also shows that, while CHIRPS captures the general precipitation patterns, its reliability varies by subregion and time scale, and local overestimation or underestimation must be accounted for when interpreting results against station data.

Quispe et al. [36] assessed the performance of GPM IMERG products (early, late, and final versions) across the Lake Titicaca Basin and found that accuracy improved with coarser temporal scales, with the monthly IMERG-F (final) product consistently outperforming its early and late versions. This finding aligns with our results, in which IMERG showed stronger agreement with rain gauge data, particularly in monthly accumulations.

The bias decomposition analysis further clarified the strengths and limitations of each product. CHIRPS exhibited better performance in terms of Hit Bias (HB), meaning that, on average, its estimations more closely matched the number of actual rainy days. However, IMERG had a better Miss Bias (MB), with fewer undetected precipitation events compared to CHIRPS. Regarding False Bias (FB), CHIRPS again outperformed IMERG, supporting previous findings that IMERG’s overestimation is primarily driven by spurious rainfall detections, particularly in regions of complex terrain and variable surface emissivity [19].

Spatial analysis revealed further discrepancies. CHIRPS produced lower annual average precipitation estimates than IMERG, especially in the southwestern region of the basin, where CHIRPS suggested values below 400 mm, while IMERG exceeded 1300 mm. This contrast is critical, as prior studies by Hunink et al. [27] suggest that annual rainfall in this area should be closer to 1000 mm. Therefore, while IMERG may overestimate rainfall amounts, its estimates may still offer a closer approximation to reality in certain high-precipitation subzones.

The implications of these findings are particularly relevant for hydrological modeling, flood forecasting, and water resource planning in the Ambato River basin and similar Andean catchments. The tendency of CHIRPS to underestimate precipitation may lead to underestimation of runoff and water availability in rainfall-runoff models, potentially compromising water allocation and drought risk assessments. Conversely, the overestimation by IMERG—especially in the highland zones—could inflate simulated streamflow values, influencing flood hazard assessments and infrastructure design. Given these trade-offs, the selection of an appropriate product should be context-specific: CHIRPS may be more suitable for long-term climatological analyses and drought studies, where minimizing false rainfall is crucial, while IMERG might be better suited for applications requiring accurate detection of rainfall occurrence, such as early warning systems and daily hydrological modeling. Integrating bias-correction techniques or combining both products using ensemble or fusion methods may further enhance their applicability in operational settings.

Nonetheless, these results should be interpreted considering certain limitations that may affect their generalizability and accuracy. Although six automatic rain gauge stations were used, their spatial distribution may be insufficient for fully representing the heterogeneity of rainfall patterns in such a topographically complex Andean basin. This limited density may reduce the precision of ground-based validation, especially in areas with strong orographic influence. In addition, although a strict quality control process was applied to ensure the reliability of the data, missing records from some stations were not gap-filled. This decision was taken to preserve the integrity of the validation analysis, avoiding artificial bias. While this approach favors methodological rigor, it may also restrict the completeness of the comparison with satellite estimates. Despite these constraints, the study offers valuable insight for hydrometeorological applications in data-scarce regions, where ground-based networks are sparse and satellite precipitation products serve as essential tools. Future research could further enhance validation robustness by integrating complementary data sources and expanding the observational network.

The present comparative assessment of CHIRPS and IMERG reveals that, while both products exhibit valuable capabilities, they also present significant limitations that must be accounted for in hydrometeorological applications. IMERG outperforms CHIRPS in rainfall detection and correlation with observed data, but suffers from higher false detections and systematic overestimation in certain zones. CHIRPS provides more conservative and spatially coherent estimates, though it underrepresents precipitation in high-rainfall regions. These findings reinforce the importance of regional validation of SPEs and support the use of multi-source strategies to improve rainfall estimation accuracy in topographically complex and data-scarce Andean environments.

5. Conclusions

This study provides a comprehensive evaluation of the performance of CHIRPS and IMERG satellite precipitation products against rain gauge observations in the Ambato River catchment, a mountainous Andean basin characterized by steep topography and variable climate conditions. The findings demonstrate that both products capture general precipitation patterns but differ markedly in their accuracy, bias structure, and suitability depending on the temporal scale and intended hydrometeorological application.

IMERG outperformed CHIRPS in rainfall daily rainy-day detection (POD = 0.44 vs. 0.24), and monthly Mean Absolute Error (MAE = 38.12 vs. 42.03), making it more reliable for rainfall-driven hydrological applications. These results suggest that IMERG is more reliable for daily hydrological modeling, flood forecasting, and early warning systems, especially when accurate detection of rainfall occurrence and intensity is required. However, its tendency to overestimate rainfall and produce false alarms (FAR = 0.44) may compromise accuracy in high-resolution or risk-sensitive applications.

Conversely, at a daily time scale, CHIRPS showed better performance in Hit Bias (HB) and False Bias (FB), along with more conservative rainfall estimates, making it potentially more suitable for long-term climatological assessments or drought monitoring, where underestimations are more acceptable than false detections. Its stability under dry conditions further supports this use case.

These results highlight the importance of regional validation before operational use of satellite precipitation estimates. For hydrological modeling, drought assessment, or flood forecasting in the Andes, product selection should be context-specific. Future research should explore bias-correction techniques and multi-sensor integration to improve rainfall representation in complex terrains. Ultimately, while satellite products are indispensable in data-scarce regions, their limitations must be explicitly accounted for to ensure accurate and reliable hydrometeorological analyses.