Assessing the Performance of the Satellite-Based Precipitation Products (SPP) in the Data-Sparse Himalayan Terrain
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Observed Hydro-Meteorological Data
2.3. Satellite-Based Precipitation Products (SPPs)
2.4. Statistical Evaluation Method
2.5. Ranking of SPPs Using MCDM Method
2.6. Assessment of the Hydrological Utility of SPPs
3. Results
3.1. Performance Comparison among SPPs against Rain Gauge Observation
3.1.1. Performance Comparison Regarding the Spatial Distribution
3.1.2. The Lumped Performance Comparison
3.2. Rank of SPPs
3.3. Suitability of SPPs for Streamflow Estimation
3.3.1. Calibration Results of GR4J Model
3.3.2. Discharge Simulation Capability of SPPs
4. Discussion
5. Conclusions
- Our analysis is restricted to a small part of the Himalayan region, and it might not be robust for other morphologically complicated mountains. As a result, further research should be conducted on a wide range of small to large watersheds using our novel comprehensive framework, which can systematically rank different datasets and identify the most suitable for hydro-meteorological application in the ungauged river basin.
- Since no SPPs were found to be outstanding (like previous studies) in capturing rainfall events and magnitude and simulating the discharge, future research can therefore be focused on bias correction of SPPs to improve the performance of SPPs.
- A snowmelt model has not been taken into account in this work while simulating discharge using a hydrological model. In addition, our hydrological model has not been calibrated and validated using each SPPs incorporating snowmelt model that might be included in future research to investigate the precipitations’ product-specific simulation capability.
- This study has only evaluated the SSPs for daily and monthly streamflow simulation, but in terms of hydrological studies, the time of occurrence of any extrema event is critical; therefore, further investigation should apply at sub-daily time scales.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Input Data | Method and Technique | Lowest Frequency | Spatial Resolution | Temporal Coverage | Latency | Reference |
---|---|---|---|---|---|---|---|
Terrain (Digital Elevation Model) | Radar interferometry | NASA Shuttle Radar Topographic Mission (SRTM) | - | 30 m × 30 m | - | - | https://lpdaac.usgs.gov/about/citing_lp_daac_and_data (accessed on 1 January 2021). |
Gauge-based Precipitation | - | Gauge | h | 18 stations | 2003–2017 | 1y | http://www.dhm.gov.np/ (accessed on 1 January 2020). |
Discharge | - | - | h | 1 station | 2003–2015 | NA | http://www.dhm.gov.np/ (accessed on 1 January 2020). |
Reanalysis-based Potential Evapotranspiration (RPET) | Model and gauge-based observations | 4D-Var data assimilation and model forecasts | h | 0.25° × 0.25° | 1979–Present | 5 d | [45] |
CHIRPS | Infrared Cold Cloud Duration (CCD) observations, satellite imagery, and ground-based observed rainfall | Interpolation techniques | d | 0.05° × 0.05° | 1981-Present | 1 m | [35] |
CMORPH V1.0 | Low orbiter satellite microwave observations and geostationary satellite IR data | CPC MORPHing technique | 30 min | 0.25° × 0.25° | 2002-Present | NA | [40] |
IMERG_Early (06) | Temperature and humidity ancillary data from a constellation of passive microwave satellites | Forward propagation GPROF algorithm | 30 min | 0.1° × 0.1° | 2000-Present | 4 h | [46] |
IMERG_Late (06) | Same as IMERG_Early | Both forward and backward propagation GPROF algorithm | 30 min | 0.1° × 0.1° | 2000-Present | 12 h | [46] |
IMERG_Final (06) | Same as IMERG_Late | Same with IMERG_Late GPROF algorithm and Gauge correction technique | 30 min | 0.1° × 0.1° | 2000-Present | 3.5 m | [46] |
PERSIANN | Geostationary longwave infrared imagery | Neural network function classification procedures | h | 0.25° × 0.25° | 2000-Present | 2 d | [41] |
PERSIANN_CCS | Satellite imagery | Variable threshold cloud segmentation | h | 0.04° × 0.04° | 2003-Present | 1 d | [41] |
PERSIANN_CDR | GridSat-B1 infrared data and GPCP data | Artificial Neural Networks—Climate Data Record | d | 0.25° × 0.25° | 1983-Present | NA | [41] |
Dataset | Average LP | Rank |
---|---|---|
CMORPH | 0.045665 | 1 |
IMERG_Final | 0.091280 | 2 |
PERSIANN_CDR | 0.117600 | 3 |
PERSIANN_CCS | 0.202612 | 4 |
PERSIANN | 0.215901 | 5 |
IMERG_Early | 0.218660 | 6 |
CHIRPS | 0.231086 | 7 |
IMERG_Late | 0.242115 | 8 |
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Kumar, S.; Amarnath, G.; Ghosh, S.; Park, E.; Baghel, T.; Wang, J.; Pramanik, M.; Belbase, D. Assessing the Performance of the Satellite-Based Precipitation Products (SPP) in the Data-Sparse Himalayan Terrain. Remote Sens. 2022, 14, 4810. https://doi.org/10.3390/rs14194810
Kumar S, Amarnath G, Ghosh S, Park E, Baghel T, Wang J, Pramanik M, Belbase D. Assessing the Performance of the Satellite-Based Precipitation Products (SPP) in the Data-Sparse Himalayan Terrain. Remote Sensing. 2022; 14(19):4810. https://doi.org/10.3390/rs14194810
Chicago/Turabian StyleKumar, Sonu, Giriraj Amarnath, Surajit Ghosh, Edward Park, Triambak Baghel, Jingyu Wang, Malay Pramanik, and Devesh Belbase. 2022. "Assessing the Performance of the Satellite-Based Precipitation Products (SPP) in the Data-Sparse Himalayan Terrain" Remote Sensing 14, no. 19: 4810. https://doi.org/10.3390/rs14194810
APA StyleKumar, S., Amarnath, G., Ghosh, S., Park, E., Baghel, T., Wang, J., Pramanik, M., & Belbase, D. (2022). Assessing the Performance of the Satellite-Based Precipitation Products (SPP) in the Data-Sparse Himalayan Terrain. Remote Sensing, 14(19), 4810. https://doi.org/10.3390/rs14194810