Abstract
The present study evaluates and compares the performance of different rainfall products, namely, Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), India Meteorological Department (IMD) gridded, Prediction of Worldwide Energy Resource (POWER), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR) with gauge-based measurements over Narmada River basin, India. The ground-based daily rainfall data (1981–2020) of 11 gauging stations have been collected from the Water Resources Department, Madhya Pradesh and the evaluation of rainfall product has been accomplished on a point-to-grid basis (nearest neighbor method) at annual and seasonal scales with the help of continuous and categorical statistical metrics. The results reveal a strong positive correlation (> 0.75) between rainfall estimates of different products and gauge-based measurements at annual scale demonstrating higher similarity in rainfall estimates and observed data, whereas seasonal estimates have exhibited comparatively weaker relationship. Likewise, percent bias (PBIAS) demonstrates least bias in annual and monsoon rainfall estimates and high in other seasons. These findings reveal that rainfall estimates tend to improve with increasing time scale (season to annual). However, majority of the rainfall products have overestimated the low rainfall (western region) and underestimated the high rainfall (eastern and southeastern regions). Further, the values of critical success index (CSI) indicate IMD gridded product outperforms in detecting rainfall events accurately followed by POWER, PERSIANN-CDR, and CHIRPS. These results suggest that IMD gridded estimates provide the best alternate to ground-based rain measurements. However, rainfall estimates from POWER, PERSIANN-CDR and CHIRPS can also be used in various hydrometeorological investigations over Narmada River basin.
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All data included in this study are available upon request.
References
Akinyemi, D. F., Ayanlade, O. S., Nwaezeigwe, J. O., & Ayanlade, A. (2020). A comparison of the accuracy of multi-satellite precipitation estimation and ground meteorological records over southwestern Nigeria. Remote Sensing in Earth System Sciences, 3, 1–12. https://doi.org/10.1007/s41976-019-00029-3
Awasthi, N., Tripathi, J. N., Petropoulos, G. P., Gupta, D. K., Singh, A. K., & Kathwas, A. K. (2023). Performance assessment of Global-EO-based precipitation products against gridded rainfall from the Indian Meteorological Department. Remote Sensing, 15, 3443. https://doi.org/10.3390/rs15133443
Bandyopadhyay, A., Nengzouzam, G., Singh, W. R., Hangsing, N., & Bhadra, A. (2018). Comparison of various re-analyses gridded data with observed data from meteorological stations over India. Epic Series in Engineering, 3, 190–198. https://doi.org/10.29007/clsf
Banerjee, A., Chen, R., Meadows, M. E., Singh, R. B., Mal, S., & Sengupta, D. (2020). An analysis of long-term rainfall trends and variability in the Uttarakhand Himalaya using Google Earth Engine. Remote Sensing, 12(40), 709. https://doi.org/10.3390/rs12040709
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., Dijk, A. I. J. M., Brocca, L., Pappenberger, F., Huffman, J., & Wood, E. F. (2017). Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modelling. Hydrology and Earth System Sciences, 21, 6201–6217. https://doi.org/10.5194/hess-21-6201-2017
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., Dijk, A. I. J. M., Van Huffman, G. J., Adler, R. F., & Wood, E. F. (2019). Daily evaluation of 26 precipitation datasets using stage-IV gauge-radar data for the CONUS. Hydrology and Earth System Sciences, 23, 207–224. https://doi.org/10.5194/hess-23-207-2019
Bharti, V., Singh, C., Ettema, J., & Turkington, T. (2016). Spatiotemporal characteristics of extreme rainfall events over the Northwest Himalaya using satellite data. International Journal of Climatology, 36(12), 3949–3962. https://doi.org/10.1002/joc.4605
Darand, M., & Khandu, K. (2020). Statistical evaluation of gridded precipitation datasets using rain gauge observations over Iran. Journal of Arid Environments, 178, 104172. https://doi.org/10.1016/j.jaridenv.2020.104172
Darand, M., Amanollahi, J., & Zandkarimi, S. (2017). Evaluation of the performance of TRMM multi-satellite precipitation analysis (TMPA) estimation over Iran. Atmospheric Research, 190, 121–127. https://doi.org/10.1016/j.atmosres.2017.02.011
Dharmayasa, G. N. P., Simatupang, C. A., & Singa, M. S. (2022). NASA Power’s: An alternative rainfall data resource for hydrology research and planning activities in Bali Island, Indonesia. Journal of Infrastructure Planning and Engineering, 1(1), 1–7. https://doi.org/10.22225/jipe.1.1.2022.1-7
Duhan, D., & Pandey, A. (2013). Statistical analysis of long term spatial, temporal trends of precipitation during 1901–2002 at Madhya Pradesh India. Atmospheric Research, 122, 136–149. https://doi.org/10.1016/j.atmosres.2012.10.010
Gottardi, F., Obled, C., Gailhard, J., & Paquet, E. (2012). Statistical reanalysis of precipitation fields based on ground network data and weather patterns: Application over French mountains. Journal of Hydrology, 432, 154–167. https://doi.org/10.1016/j.jhydrol.2012.02.014
Gupta, H., & Chakrapani, G. J. (2005). Temporal and spatial variations in water flow and sediment load in Narmada River basin, India: Natural and man-made factors. Environmental Geology, 48, 579–589. https://doi.org/10.1007/s00254-005-1314-2
Islam, M. A. (2018). Statistical comparison of satellite-retrieved precipitation products with rain gauge observations over Bangladesh. International Journal of Remote Sensing, 39(9), 2906–2936. https://doi.org/10.1080/01431161.2018.1433890
Khan, M. K. U., Iqbal, M. F., Mahmood, I., Shahzad, M. I., Zafar, Q., & Khalid, B. (2023). Evaluation of precipitation products over different climatic zones of Pakistan. Theoretical and Applied Climatology, 151, 1301–1321. https://doi.org/10.1007/s00704-022-04355-3
Kishore, P., Jyothi, S., Basha, G., Rao, S. V. B., Rajeevan, M., Velicogna, I., & Sutterley, T. C. (2016). Precipitation climatology over India: Validation with observations and reanalysis datasets and spatial trends. Climate Dynamics, 46, 541–556. https://doi.org/10.1007/s00382-015-2597-y
Kumar, S. K., Rao, N. T., Saikranthi, K., & Rao, P. M. (2015). Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data. Journal of Geophysical Research: Atmospheres, 120, 8987–9005. https://doi.org/10.1002/2015JD023437
Kumar, B., Patra, K. C., & Lakshmi, V. (2016). Daily rainfall statistics of TRMM and CMORPH: A case for trans-boundary Gandak River basin. Journal of Earth System Sciences, 125(5), 919–934. https://doi.org/10.1007/s12040-016-0710-1
Kumar, P., & Singh, A. K. (2022). A comparison between MLR, MARS, SVR and RF techniques: Hydrological time-series modelling. Journal of Human, Earth, and Future, 3(1), 90-98. https://www.hefjournal.org/index.php/HEF/article/view/137
Liu, C., Aryastana, P., Liu, G., & Huang, W. (2020). Assessment of satellite precipitation product estimates over Bali Island. Atmospheric Research, 244, 105032. https://doi.org/10.1016/j.atmosres.2020.105032
Mirabbasi, R., Kisi, O., Sanikhani, H., & GajbhiyeMeshram, S. (2019). Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models. Neural Computing and Applications, 31, 6843–6862. https://doi.org/10.1007/s0052-018-3519-9
Moazami, S., Golian, S., Kavianpour, M. R., & Hong, Y. (2013). Comparison of PERSIANN and V7TRMM Multi-Satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran. International Journal of Remote Sensing, 34(22), 8156–8171. https://doi.org/10.1080/01431161.2013.833360
Mondal, A., Lakshmi, V., & Hashemi, H. (2018). Intercomparison of trend analysis of multisatellite monthly precipitation products and gauge measurements for river basins of India. Journal of Hydrology, 565, 779–790. https://doi.org/10.1016/j.jhydrol.2018.08.083
Mulungu, D. M. M., & Mukama, E. (2023). Evaluation and modelling of accuracy of satellite-based CHIRPS rainfall data in Ruvu sub basin, Tanzania. Modelling Earth System and Sciences, 9, 1287–1300. https://doi.org/10.1007/s40808-022-01555-8
Nair, A. S., & Indu, J. (2017). Performance assessment of multi-source weighted-ensemble precipitation (MSWEP) product over India. Climate, 5(1), 1–50. https://doi.org/10.3390/cli5010002
Ng, C.K.-C., Kong, R.W.-H., Foo, G.-H., & Khoo, G. (2023). Rapid comparison of precipitation data between satellite and in situ observations. Environmental Monitoring and Assessment, 195, 228–245. https://doi.org/10.1007/s10661-022-10789-z
Omar, G. M., Paturel, J.-E., Salles, C., Mahe, G., Jalludin, M., Satge, F., & Nour, M. I. (2023). Evaluation of rainfall products in semi-arid areas: Application to the southeast of the Republic of Djibouti and a focus on the Ambouli catchment. Water, 15, 2168. https://doi.org/10.3390/w15122168
Pandey, V., Srivastava, P. K., Mall, R. K., Arriola, F. M., & Han, D. (2022). Multi-satellite precipitation products for meteorological drought assessment and forecasting in Central India. Geocarto International, 37(7), 1899–1918. https://doi.org/10.1080/10106049.2020.1801862
Peng, Z., Wang, Q. J., Bennett, J. C., Pokhrel, P., & Wang, Z. (2014). Seasonal precipitation forecasts over China using monthly large scale oceanic-atmospheric indices. Journal of Hydrology, 519, 792–802. https://doi.org/10.1016/j.jhydrol.2014.08.012
Prakash, S. (2019). Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India. Journal of Hydrology, 571, 50–59. https://doi.org/10.1016/j.jhydrol.2019.01.036
Prakash, S., Mitra, A. K., Rajagopal, E. N., & Pai, D. S. (2016). Assessment of TRMM-based TMPA-3B42 and GSMaP precipitation products over India for the peak southwest monsoon season. International Journal of Climatology, 36, 1614–1631. https://doi.org/10.1002/joc.4446
Reddy, M. V., Mitra, A. K., Momin, I. M., Mitra, A. K., & Pai, D. S. (2019). Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period. International Journal of Remote Sensing, 12, 4577–4603. https://doi.org/10.1080/01431161.2019.1569786
Ren, P., Li, J., Feng, P., Guo, Y., & Ma, Q. (2018). Evaluation of multiple satellite precipitation products and their use in hydrological modelling over the Luanhe River basin, China. Water, 10(6), 677–691. https://doi.org/10.3390/w10060677
Rickards, N., Thomas, T., Kaelin, A., Carr, H. H., Jain, S. K., Mishra, P. K., Manish, K., Nema, M. K., Dixon, H., Rahman, M. M., Horan, R., Jenkins, A., & Rees, G. (2020). Understanding future water challenges in a highly regulated Indian river basin-Modelling the impact of climate change on the hydrology of the upper Narmada. Water, 12(1762), 1–19. https://doi.org/10.3390/w12061762
Salami, F., & Fenta, A. A. (2022). Spatio-temporal evaluation of open access precipitation products with rain gauge observations in Nigeria. Arabian Journal of Geosciences, 15(1785), 1–20. https://doi.org/10.1007/s12517-022-11071-9
Sanikhani, H., Kisi, O., Mirabbasi, R., & Meshram, S. G. (2018). Trend analysis of rainfall pattern over the central India during 1901–2010. Arabian Journal of Geosciences, 11(15), 437. https://doi.org/10.1007/s12517-018-3800-3
Satge, F., Defrance, D., Sultan, B., Bonnet, M. P., Seyler, F., Rouché, N., Pierron, F., & Paturel, J. E. (2020). Evaluation of 23 gridded precipitation datasets across West Africa. Journal of Hydrology, 581, 124412. https://doi.org/10.1016/j.jhydrol.2019.124412
Setti, S., Maheswaran, R., Sridhar, V., Barik, K. K., Merz, B., & Agarwal, A. (2020). Inter-comparison of gauge-based gridded data, reanalysis and satellite precipitation product with an emphasis on hydrological modelling. Atmosphere, 11, 1252. https://doi.org/10.3390/atmos11111252
Setti, S., Yumnam, K., Rathinasamyd, M., & Agarwal, A. (2022). Assessment of satellite precipitation products at different time scales over a cyclone prone coastal river basin in India. Journal of Water and Climate Change, 14, 1–28. https://doi.org/10.2166/wcc.2022.166
Singh, A. K., Tripathi, J. N., Singh, K. K., Singh, V., & Sateesh, M. (2019). Comparison of different satellite-derived rainfall products with IMD gridded data over Indian meteorological subdivisions during Indian Summer Monsoon (ISM) 2016 at weekly temporal resolution. Journal of Hydrology, 575, 1371–1379. https://doi.org/10.1016/j.jhydrol.2019.02.016
Suchithra, A. S., & Agarwal, S. (2021). Intercomparison of trend analysis using multi satellite precipitation products and gauge measurements. Indian Journal of Ecology, 48(4), 955–963.
Sun, Q., Miao, C., & h., Duan, Q., Ashouri, H., Sorooshian, S., & Hsu, K. L. (2018). A review of global precipitation datasets: Data sources, estimation, and inter-comparisons. Reviews of Geophysics, 56(1), 79–107. https://doi.org/10.1002/2017RG000574
Thomas, T., Gunthe, S. S., Ghosh, N. C., & Sudheer, K. P. (2015). Analysis of monsoon rainfall variability over Narmada basin in central India: Implication of climate change. Journal of Water and Climate Change, 6(3), 615–627. https://doi.org/10.2166/wcc.2014.041
Tiwari, D. K., Tiwari, H. L., & Nateriya, R. (2022). Runoff modeling in Kolar River basin using hybrid approach of wavelet with artificial neural network. Journal of Water and Climate Change, 13(2), 963–974. https://doi.org/10.2166/wcc.2021.246
Yang, X., Yong, B., Hong, Y., Chen, S., & Zhang, X. (2015). Error analysis of multi- satellite precipitation estimates with an independent rain gauge observation network over a medium-sized humid basin. Hydrological Sciences Journal, 61(10), 1813–1830. https://doi.org/10.1080/02626667.2015.1040020
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Jyoti Sharma and Omvir Singh designed the research. J. Sharma wrote the original draft; both authors conducted the manuscript review and editing, and wrote the final paper.
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Sharma, J., Singh, O. Comparison of different rainfall products with gauge-based measurements over Narmada River Basin, India. Environ Monit Assess 196, 114 (2024). https://doi.org/10.1007/s10661-023-12206-5
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DOI: https://doi.org/10.1007/s10661-023-12206-5