Full-Cycle Evaluation of Multi-Source Precipitation Products for Hydrological Applications in the Magat River Basin, Philippines
Abstract
Highlights
- Developed an integrated framework with bias correction and multi-criteria ranking.
- Nine SPPs showed distinct seasonal strengths in the Magat River Basin, Philippines.
- Corrected SPPs improve flood forecasting, drought monitoring, and water balance.
- Provides a replicable method for data-scarce, localized, and ungauged basins.
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Evaluation Metrics
2.4. Multi-Criteria Decision Analysis (EWM-TOPSIS)
2.5. Bias Correction Using Quantile Mapping
2.6. Post-Correction Re-Ranking and Decision Support
3. Results
3.1. Comparison of SPPs and Rain Gauge Observations
3.2. Performance of SPPs Based on Metrics
3.2.1. Statistical Accuracy and Detection Skills
3.2.2. Bias and Volume Accuracy
3.2.3. Erosive Precipitation Characteristics
3.3. Pre-Ranking Using EWM-TOPSIS Analysis
3.4. Bias Correction Impact Assessment
3.5. Post-Correction Suitability Analysis for the Best SPP
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
SPP | Satellite Precipitation Product |
PP | Precipitation Products |
IMERG | Integrated Multi-satellitE Retrievals for GPM |
CHIRPS | Climate Hazards Group InfraRed Precipitation with Station Data |
ClimGridPh-RR | Climate Grid Philippines Reanalysis, daily gridded rainfall dataset for the Philippines |
APHRODITE | Asian Precipitation–Highly Resolved Observational Data Integration Towards Evaluation |
ERA5 | ECMWF Reanalysis v5 |
MSWEP | Multi-Source Weighted Ensemble Precipitation |
SM2RAIN-ASCAT | Soil Moisture to Rain–Advanced Scatterometer |
GSMaP | Global Satellite Mapping of Precipitation |
PERSIANN-CDR | Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record |
MRB | Magat River Basin |
CRB | Cagayan River Basin |
NIA-MARIIS | National Irrigation Administration–Magat River Integrated Irrigation System |
PAGASA | Philippine Atmospheric, Geophysical and Astronomical Services Administration |
QM | Quantile Mapping |
MCDA | Multi-Criteria Decision Analysis |
NSE | Nash-Sutcliffe Efficiency |
RMSE | Root Mean Square Error |
MAE | Mean Absolute Error |
R2 | Correlation Coefficient |
RB | Relative Bias |
VR | Volume Ratio |
SORS | Sum of Residuals |
POD | Probability of Detection |
FAR | False Alarm Ratio |
CSI | Critical Success Index |
ARI | Average Rainfall Intensity |
NRDY | Number of Rainy Days in a Year |
NHRD | Number of Heavy Rain Days |
MDR | Maximum Daily Rainfall |
EWM | Entropy Weight Method |
TOPSIS | Technique for Order of Preference by Similarity to Ideal Solution |
LGU | Local Government Unit |
LOCI | Local Intensity Scaling |
masl | Meters Above Sea Level |
ECMWF | European Centre for Medium-Range Weather Forecasts |
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SPP | Temporal Coverage | Resolution (Spatial and Temporal) | Spatial Coverage | Latency | Category | References |
---|---|---|---|---|---|---|
APHRODITE V1901 | 1951–2025 (2000–2015 used) | 0.25°/Daily | 60°N–60°S | N/A (Historical) | Gauge-based | [50] |
ERA5 | 2000–2024 | 0.1°/Hourly | Global | ~5 days | Reanalysis | [51] |
MSWEP V2.2 | 2000–2013 | 0.1°/3-hourly | 60°N–60°S | Several days | Merged | [52] |
SM2RAI-ASCAT V2.1.2n | 2007–2022 | 0.125°/Daily | 60°N–60°S (land) | Historical (offline) | Satellite-only | [53] |
GSMaP V8 | 2000–2024 | 0.1°/Hourly | 60°N–60°S | 12–24 h | Satellite-only | [54] |
PERSIANN°CDR V1.0 | 1983–present (2000–2024 used) | 0.25°/Daily | 60°N–60°S | ~6–12 months | Merged | [55] |
CHIRPS V2.0 | 2000–2024 | 0.05°/Daily | 50°N–50°S | ~24 h | Merged | [56] |
IMERG V07B (Final Run) | 2000–2024 | 0.1°/30 min | 65°N–65°S | ~3 months | Merged | [57] |
ClimGridPh-RR | 2001–2020 | 0.01°Daily | Philippines | Few months | Merged | [15] |
Group | Metric | Formula and Criteria | Ideal Value | Purpose/ Interpretation |
---|---|---|---|---|
Statistical Accuracy | RMSE | 0 | Measures the average magnitude of the error. | |
MAE | 0 | Measures the average absolute difference. | ||
R2 | 1 | Indicates the strength and direction of a linear relationship. | ||
NSE | Criterion: ≥0.8 (VG), ≥0.7 (G), ≥0.5 (S) | 1 | Indicates predictive power of SPP vs. observations. | |
Detection Skill | POD | D11—Number of times both satellite and rain gauge detected precipitation (i.e., hits) D01—Number of times the rain gauge detected precipitation, but the satellite did not (i.e., misses) | 1 | Measures how well precipitation events are detected. |
FAR | D10—Number of times the satellite detected precipitation, but the rain gauge did not (false alarms) | 1 | Measures frequency of false detections. | |
CSI | 1 | Balances hits, misses, and false alarms. | ||
Bias and Volume Accuracy | RB | 0 | Measures under- or overestimation in total precipitation. | |
SORS | Total precipitation observed (used for bias and volume comparisons). | |||
VR | 1 | The ratio of estimated to observed total precipitation. | ||
Erosive Precipitation Characteristics | MDR | — | Highest daily precipitation—relevant for flood and erosion. | |
ARI | Pi—Daily precipitation on a day, where Pi > 0 Pi—Number of rainy days (i.e., days with precipitation > 0 mm) | — | Average intensity of precipitation during events. | |
NRDY | — | Days with any measurable precipitation. | ||
NHRD | — | Days with heavy precipitation linked to flood/sediment risk. |
Application | Performance Parameter | Justification/Basis |
---|---|---|
Drought | RB | Detects systematic over- or underestimation in dry conditions |
NRDY | Captures precipitation frequency critical for drought detection | |
VR | Measures agreement in total precipitation volume | |
MAE | Reflects the general accuracy of estimation | |
R2 | Temporal consistency and seasonality agreement | |
NSE | Predictive skill in modeling low flow thresholds (drought onset) | |
NHRD | Detects missed high-intensity events that affect agricultural drought resilience | |
Flood | MDR | Key driver for peak flows and flood simulation |
POD | Ability to capture actual heavy precipitation events | |
FAR | Avoidance of false flood warnings | |
CSI | Combined skill measure of detection and false alarm trade-off | |
NSE | Overall predictive power for extreme flows | |
RMSE | Penalizes larger errors (important for extremes) | |
R2 | Captures seasonal flood timing alignment | |
NHRD | High rainfall counts contribute to threshold exceedance | |
Water Balance | VR | Reflects catchment water input estimation accuracy |
SORS | Total baseline volume for comparison | |
MAE | Affects soil moisture and baseflow estimation | |
RMSE | Penalizes deviations in total water input | |
R2 | Interannual and seasonal flow representation | |
NSE | Hydrologic performance proxy in water yield modeling | |
POD | Needed to reflect daily water input detection | |
Sedimentation | NHRD | Indicator of erosive precipitation (R-factor component) |
ARI | Drives detachment and sediment transport | |
MDR | Captures high-intensity events linked to mass erosion | |
RB | Affects long-term erosion and deposition estimates | |
VR | Overestimation of volume can inflate erosion risk predictions | |
RSME | Large over/underestimates distort suspended sediment transport | |
CSI | Captures correct event-type distributions linked to sediment bursts |
Metrics | Season | APHRO -DITE V1901 | SM2RAIN-ASCAT V2.1.2n | CHIRPS V2.0 | ERA5 | GSMaP V8 | IMERG V07B | MSWEP V2.2 | PERSI -ANN -CDR V1.0 | Clim -GridPh-RR |
---|---|---|---|---|---|---|---|---|---|---|
Dry | 9.03 | 7.93 | 10.99 | 10.28 | 9.80 | 15.16 | 16.32 | 11.30 | 13.56 | |
RSME | Wet | 9.57 | 9.69 | 11.27 | 11.40 | 11.07 | 16.53 | 14.89 | 12.58 | 11.19 |
Overall | 9.30 | 8.86 | 11.13 | 10.86 | 10.45 | 15.86 | 15.62 | 11.96 | 12.43 | |
Dry | 4.66 | 5.51 | 5.63 | 5.70 | 7.29 | 5.59 | 8.69 | 5.93 | 5.37 | |
MAE | Wet | 5.06 | 5.84 | 5.86 | 5.82 | 8.27 | 5.58 | 8.16 | 6.53 | 5.20 |
Overall | 4.86 | 5.68 | 5.74 | 5.76 | 7.78 | 5.58 | 8.43 | 6.23 | 5.29 | |
Dry | 0.23 | 0.22 | 0.27 | 0.13 | 0.06 | 0.29 | 0.00 | 0.18 | 0.32 | |
R2 | Wet | 0.26 | 0.21 | 0.22 | 0.15 | 0.07 | 0.22 | 0.00 | 0.11 | 0.42 |
Overall | 0.24 | 0.21 | 0.24 | 0.14 | 0.06 | 0.25 | 0.00 | 0.14 | 0.36 | |
Dry | −0.04 | 0.15 | −0.58 | −0.74 | −3.89 | −2.00 | −2.24 | −0.71 | −1.39 | |
NSE | Wet | 0.15 | 0.17 | −0.26 | −0.31 | −3.80 | −1.72 | −0.84 | −0.56 | −0.11 |
Overall | 0.07 | 0.16 | −0.40 | −0.49 | −3.84 | −1.84 | −1.40 | −0.62 | −0.63 | |
Dry | 0.89 | 1.00 | 0.63 | 1.00 | 0.76 | 0.82 | 0.95 | 0.76 | 0.82 | |
POD | Wet | 0.88 | 1.00 | 0.63 | 0.99 | 0.77 | 0.81 | 0.96 | 0.77 | 0.82 |
Overall | 0.89 | 1.00 | 0.63 | 1.00 | 0.77 | 0.82 | 0.95 | 0.76 | 0.82 | |
Dry | 0.21 | 0.25 | 0.15 | 0.27 | 0.20 | 0.17 | 0.33 | 0.21 | 0.20 | |
FAR | Wet | 0.23 | 0.28 | 0.16 | 0.29 | 0.22 | 0.19 | 0.32 | 0.21 | 0.23 |
Overall | 0.22 | 0.27 | 0.15 | 0.28 | 0.21 | 0.18 | 0.32 | 0.21 | 0.21 | |
Dry | 0.72 | 0.75 | 0.57 | 0.73 | 0.64 | 0.70 | 0.65 | 0.63 | 0.68 | |
CSI | Wet | 0.70 | 0.72 | 0.56 | 0.71 | 0.64 | 0.68 | 0.66 | 0.64 | 0.66 |
Overall | 0.71 | 0.73 | 0.57 | 0.72 | 0.64 | 0.69 | 0.65 | 0.63 | 0.67 | |
Dry | −6.85 | 31.22 | 15.16 | 28.57 | 33.84 | 31.10 | 46.69 | 13.49 | 27.00 | |
RB | Wet | −13.89 | 34.34 | 13.37 | 27.07 | 50.81 | 28.49 | 15.59 | 17.45 | 17.98 |
Overall | −10.49 | 32.77 | 14.26 | 27.81 | 42.42 | 29.79 | 30.11 | 15.50 | 22.33 |
Metric | Entropy (Ej) | Diversification (Dj) | Weight (Wj) |
---|---|---|---|
RSME | 0.023 | 0.977 | 0.085 |
MAE | 0.003 | 0.997 | 0.087 |
R2 | 0.001 | 0.999 | 0.087 |
NSE | 0.011 | 0.989 | 0.086 |
POD | 0.296 | 0.704 | 0.061 |
FAR | 0.438 | 0.562 | 0.049 |
CSI | 1.000 | 0.010 | 0.001 |
RB | 0.010 | 0.990 | 0.086 |
SORS | 0.003 | 0.997 | 0.087 |
VR | 0.001 | 0.999 | 0.087 |
MDR | 0.062 | 0.938 | 0.081 |
ARI | 0.018 | 0.982 | 0.085 |
NRDY | 0.625 | 0.375 | 0.033 |
NHRD | 0.004 | 0.996 | 0.087 |
Metrics | Season | APHRODITE V1901 | SM2RAIN-ASCAT V2.1.2n | CHIRPS V2.0 | ClimGridPh -RR | Ideal/ Observed Value |
---|---|---|---|---|---|---|
Dry | 6.36 | 9.17 | 7.92 | 6.50 | 0 | |
RSME | Wet | 12.15 | 11.84 | 10.99 | 9.99 | 0 |
Overall | 9.70 | 10.59 | 9.57 | 8.43 | 0 | |
Dry | 2.67 | 3.29 | 3.51 | 2.91 | 0 | |
MAE | Wet | 7.63 | 7.48 | 6.83 | 5.86 | 0 |
Overall | 5.15 | 5.38 | 5.16 | 4.39 | 0 | |
Dry | 0.41 | 0.15 | 0.23 | 0.41 | 1 | |
R2 | Wet | 0.17 | 0.13 | 0.19 | 0.34 | 1 |
Overall | 0.25 | 0.17 | 0.24 | 0.38 | 1 | |
Dry | −0.01 | −0.25 | −0.02 | 0.39 | 1 | |
NSE | Wet | −0.35 | −0.28 | −0.14 | 0.10 | 1 |
Overall | −0.01 | −0.20 | −0.03 | 0.25 | 1 | |
Dry | 0.67 | 0.68 | 0.39 | 0.60 | 1 | |
POD | Wet | 0.93 | 0.93 | 0.80 | 0.95 | 1 |
Overall | 0.82 | 0.82 | 0.63 | 0.80 | 1 | |
Dry | 0.23 | 0.19 | 0.25 | 0.29 | 0 | |
FAR | Wet | 0.17 | 0.12 | 0.11 | 0.15 | 0 |
Overall | 0.19 | 0.15 | 0.15 | 0.20 | 0 | |
Dry | 0.56 | 0.59 | 0.35 | 0.48 | 1 | |
CSI | Wet | 0.78 | 0.82 | 0.73 | 0.81 | 1 |
Overall | 0.69 | 0.72 | 0.56 | 0.67 | 1 | |
Dry | −30.62 | −19.86 | −16.39 | −26.75 | 0 | |
RB | Wet | 17.58 | 4.21 | 2.77 | 9.34 | 0 |
Overall | 3.17 | −2.83 | −2.87 | −1.59 | 0 | |
Dry | 2528.42 | 3037.60 | 5004.51 | 3532.55 | 5696.774 | |
SORS | Wet | 10,046.95 | 9551.98 | 14,332.95 | 12,134.29 | 13,647.21 |
Overall | 12,575.37 | 12,589.58 | 19,337.46 | 15,666.84 | 19,343.98 | |
Dry | 0.44 | 0.53 | 0.88 | 0.62 | 1 | |
VR | Wet | 0.74 | 0.70 | 1.05 | 0.89 | 1 |
Overall | 0.65 | 0.65 | 1.00 | 0.81 | 1 | |
Dry | 68.08 | 172.45 | 150.05 | 74.09 | 172.45 | |
MDR | Wet | 172.45 | 78.40 | 172.45 | 172.45 | 81.22 |
Overall | 172.45 | 172.45 | 172.45 | 172.45 | 172.45 | |
Dry | 3.84 | 4.57 | 8.75 | 4.32 | 4.92 | |
ARI | Wet | 9.49 | 9.07 | 10.80 | 9.12 | 9.1 |
Overall | 7.32 | 7.33 | 10.18 | 7.29 | 7.28 | |
Dry | 600.00 | 608.00 | 572.00 | 740.00 | 1068 | |
NRDY | Wet | 1030.00 | 1022.00 | 1327.00 | 1298.00 | 1456 |
Overall | 1630.00 | 1630.00 | 1899.00 | 2038.00 | 2524 | |
Dry | 63.00 | 74.00 | 135.00 | 99.00 | 152 | |
NHRD | Wet | 346.00 | 334.00 | 507.00 | 412.00 | 481 |
Overall | 409.00 | 408.00 | 642.00 | 511.00 | 633 |
Metrics | Seasonal (wj) in % | Application (wj) in % | ||||||
---|---|---|---|---|---|---|---|---|
Dry | Wet | Overall | Overall (Raw) | Drought | Flood | Water Balance | Sedimentation | |
RSME | 14.81 | 20.9 | 16.67 | 8.5 | 15.29 | 14.28 | 19.04 | |
MAE | 17.62 | 24.13 | 24.65 | 8.7 | 18.26 | 14.29 | ||
R2 | 3.51 | 2.78 | 1.93 | 8.7 | 18.27 | 15.56 | 14.29 | |
NSE | 17.85 | 20.9 | 17.95 | 8.6 | 18.29 | 15.6 | 14.29 | |
POD | 0.92 | 0.09 | 0.26 | 6.1 | 15.32 | 14.28 | ||
FAR | 15.42 | 14.14 | 22.12 | 4.9 | 15.68 | |||
CSI | 0.82 | 0.05 | 0.18 | 0.1 | 15.3 | 19.14 | ||
RB | 18.02 | 13.12 | 13.2 | 8.6 | 17.75 | 17.81 | ||
SORS | 1.43 | 0.55 | 0.75 | 8.7 | 14.29 | |||
VR | 1.43 | 0.55 | 0.75 | 8.7 | 18.18 | 14.28 | 19.09 | |
MDR | 3.44 | 1.78 | 0 | 8.1 | 0.65 | 0.92 | ||
ARI | 2.64 | 1.11 | 0.51 | 8.5 | 16.47 | |||
NRDY | 0.22 | 0.31 | 0.22 | 3.3 | 8.28 | |||
NHRD | 1.88 | 0.6 | 0.83 | 8.7 | 0.97 | 6.53 | 7.53 |
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Gacu, J.G.; Kantoush, S.A.; Nguyen, B.Q. Full-Cycle Evaluation of Multi-Source Precipitation Products for Hydrological Applications in the Magat River Basin, Philippines. Remote Sens. 2025, 17, 3375. https://doi.org/10.3390/rs17193375
Gacu JG, Kantoush SA, Nguyen BQ. Full-Cycle Evaluation of Multi-Source Precipitation Products for Hydrological Applications in the Magat River Basin, Philippines. Remote Sensing. 2025; 17(19):3375. https://doi.org/10.3390/rs17193375
Chicago/Turabian StyleGacu, Jerome G., Sameh Ahmed Kantoush, and Binh Quang Nguyen. 2025. "Full-Cycle Evaluation of Multi-Source Precipitation Products for Hydrological Applications in the Magat River Basin, Philippines" Remote Sensing 17, no. 19: 3375. https://doi.org/10.3390/rs17193375
APA StyleGacu, J. G., Kantoush, S. A., & Nguyen, B. Q. (2025). Full-Cycle Evaluation of Multi-Source Precipitation Products for Hydrological Applications in the Magat River Basin, Philippines. Remote Sensing, 17(19), 3375. https://doi.org/10.3390/rs17193375