Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4847-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4847-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Sep 2025
Research article |
| 30 Sep 2025
| 30 Sep 2025
Evaluation of remote-sensing- and reanalysis-based precipitation products for agro-hydrological studies in the semi-arid tropics of Tamil Nadu
Aatralarasi Saravanan
Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES), United Nations University, Dresden, 01067, Germany
Faculty of Environmental Sciences, Technische Universität Dresden, Dresden, 01069, Germany
Daniel Karthe
Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES), United Nations University, Dresden, 01067, Germany
Faculty of Environmental Sciences, Technische Universität Dresden, Dresden, 01069, Germany
Faculty of Engineering, German-Mongolian Institute for Resources and Technology (GMIT), Nalaikh, Mongolia
Selvaprakash Ramalingam
Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi, 110012, India
Niels Schütze
CORRESPONDING AUTHOR
Faculty of Environmental Sciences, Technische Universität Dresden, Dresden, 01069, Germany
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P. P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003.
Arjune, S. and Kumar, V. S.: Precision Agriculture: Influencing factors and challenges faced by farmers in delta districts of Tamil Nadu, in: 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON), 1–6, IEEE, Raigarh, Chhattisgarh, India, 8–10 February 2023, https://doi.org/10.1109/OTCON56053.2023.10113906, 2023.
Arumugam, S., Ashok, K. R., Kulshreshtha, S. N., Vellangany, I., and Govindasamy, R.: Yield variability in rainfed crops as influenced by climate variables: A micro level investigation into agro-climatic zones of Tamil Nadu, India, Int. J. Clim. Chang. Str., 7, 442–459, 2013.
Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., Nelson, B. R., and Prat, O. P.: PERSIANN-CDR Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies, B. Am. Meteorol. Soc., 96 , 69–71, 2015.
Balaganesh, G., Malhotra, R., Sendhil, R., Sirohi, S., Maiti, S., Ponnusamy, K., and Sharma, A. K.: Development of composite vulnerability index and district level mapping of climate change induced drought in Tamil Nadu, India, Ecol. Indic., 113, 106197, https://doi.org/10.1016/j.ecolind.2020.106197, 2020.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3 hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, 2017.
Beria, H., Nanda, T., Singh Bisht, D., and Chatterjee, C.: Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale, Hydrol. Earth Syst. Sci., 21, 6117–6134, https://doi.org/10.5194/hess-21-6117-2017, 2017.
Bosilovich, M. G., Lucchesi, R., and Suarez, M.: MERRA-2: File specification, GMAO Office Note No. 9 (Version 1.0), Microsoft Word – GMAO-OfficeNote-09-V1-M2FileSpec-Final.docx, 2015.
Bosilovich, M. G., Lucchesi, R., and Suarez, M.: MERRA-2: File specification, NASA GMAO Office Note 9 (version 1.1), 75 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf (last access: 10 January 2024), 2016.
Chandrasekar, K., Sesha Sai, M. V., Roy, P. S., Jayaraman, V., and Krishnamoorthy, R.: Identification of Agricultural Drought Vulnerable Areas of Tamil Nadu, India using GIS-based Multi-Criteria Analysis, Asian Journal of Environment and Disaster Management, 1, 40–61, 2009.
CHRS Data Portal: https://www.ncei.noaa.gov/data/precipitation-persiann/access/, last access: 10 December 2024.
Climate Data Store: https://cds.climate.copernicus.eu/datasets/derived-era5-land-daily-statistics, last access: 18 December 2023.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Bronnimann, S., Brunet, M., Crouthamel. R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok. H. Y., Nordli, O., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy. Meteor. Soc., 137, 1–28, 2011.
Dhar, O. N., Rakhecha, P. R., and Mandal, B. N.: Some facts about Indian rainfall – A brief appraisal from hydrological considerations, Indian. J. Power River Val. Dev., 31, 117–125, 1981.
Duan, Z., Liu, J., Tuo, Y., Chiogna, G., and Disse, M.: Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales, Sci. Total Environ., 573, 1536–1553, https://doi.org/10.1016/j.scitotenv.2016.08.213, 2016.
Dubey, S., Gupta, H., Goyal, M. K., and Joshi, N.: Evaluation of precipitation datasets available on Google earth engine over India, Int. J. Climatol., 41, 4844–4863, 2021.
Ebita, A., Kobayashi, S., Ota, Y., Moriya, M., Kumabe, R., Onogi, K., Harada, Y., Yasui, S., Miyaoka, K., Takahashi, K., Kamahori, H., Kobayashi, C., Endo, H., Soma, M., Oikawa, Y., and Ishimizu, T.: The Japanese 55 year reanalysis “JRA-55”: an interim report, Sola, 7, 149–152, 2011.
Gardas, B. B., Raut, R. D., and Narkhede, B.: Evaluating critical causal factors for post harvest losses (PHL) in the fruit and vegetables supply chain in India using the DEMATEL approach, J. Clean. Prod., 199, 47–61, 2018.
Gebrechorkos, S. H., Hülsmann, S., and Bernhofer, C.: Evaluation of multiple climate data sources for managing environmental resources in East Africa, Hydrol. Earth Syst. Sci., 22, 4547–4564, https://doi.org/10.5194/hess-22-4547-2018, 2018.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 14, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Government of Tamil Nadu: Statistical Handbook 2020–2021: Sub chapter: Rainfall, Department of Economics and Statistics of the Tamil Nadu Planning, Development and Special Initiatives Department, https://www.tn.gov.in/deptst/climateandrainfall.pdf (last access: 23 March 2024), 2022a.
Government of Tamil Nadu: Statistical Handbook 2020–2021: Sub chapter: Irrigation, Department of Economics and Statistics of the Tamil Nadu Planning, Development and Special Initiatives Department, https://www.tn.gov.in/deptst/irrigation.pdf (last access: 23 March 2024), 2022b.
Global Modeling and Assimilation Office library: https://disc.gsfc.nasa.gov/datasets?page=1&subject=Precipitation&source=Models%20MERRA-2&project=MERRA-2&temporalResolution=1%20day&spatialResolution=0.5%20%C2%B0%20x%200.625%20%C2%B0, last access: 22 December 2023.
GloH2O: https://www.gloh2o.org/mswep/, last access: 19 December 2023.
Gupta, A., Jain, M. K., Pandey, R. P., Gupta, V., and Saha, A.: Evaluation of global precipitation products for meteorological drought assessment with respect to IMD station datasets over India, Atmos. Res., 297, 107104, https://doi.org/10.1016/j.atmosres.2023.107104, 2024.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.,: The Global Precipitation Measurement Mission, B. Am. Meteorol. Soc., 95, 701–722, 2014.
Huffman, G. J. and Pendergrass, A.: The Climate Data Guide: TRMM: Tropical Rainfall Measuring Mission, edited by: National Center for Atmospheric Research Staff, https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission (last access: 1 January 2024), 2023.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, 2007.
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Gu, G.: Improving the global precipitation record: GPCP Version 2.1, Geophys. Res. Lett., 36, L17808, https://doi.org/10.1029/2009GL040000, 2009.
IPCC: Summary for Policymakers, in: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Lee, H., and Romero, J., IPCC, Geneva, Switzerland, 1–34, https://doi.org/10.59327/IPCC/AR6-9789291691647, 2023.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5, 487–503, 2004.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., and Woollen, J.: The NCEP/NCAR 40 year reanalysis project, in: Renewable energy, edited by: Sorenson, B., Routledge, Vol1_146-Vol1_194, https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2, 2018.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J., Fiorino, M., and Potter, G. L.: NCEP–DOE AMIP–II Reanalysis (R-2), B. Am. Meteorol. Soc., 83, 1631–1644, 2002.
Kolluru, V., Kolluru, S., and Konkathi, P: Evaluation and integration of reanalysis rainfall products under contrasting climatic conditions in India, Atmos. Res., 246, 105121, https://doi.org/10.1016/j.atmosres.2020.105121, 2020.
Kucera, P. A., Ebert, E. E., Turk, F. J., Levizzani, V., Kirschbaum. D., Tapiador. F. J., Loew. A., and Borsche, M.: Precipitation from space: advancing Earth system science, B. Am. Meteorol. Soc., 94, 365–375, https://doi.org/10.1175/BAMS-D-11-00171.1, 2013.
Kumar, P., Srivastava, S. S., Jivani, N., Varma, A. K., Yokoyama, C., and Kubota, T.: Long-term assessment of ERA5 reanalysis rainfall for lightning events over India observed by Tropical Rainfall Measurement Mission Lightning Imaging Sensor, Q. J. Roy. Meteor. Soc., 150, 2472–2488, 2024.
Lakshmi, S., Nivethaa, E. A. K., Ahamed Ibrahim, S. N., Ramachandran, A., and Palanivelu, K.: Prediction of Future Extremes during Northeast monsoon in the Coastal District of Tamil Nadu state in India based on ENSO, Pure Appl. Geophys., 178, 3207–3228, 2021.
Lal, M.: Global Climate Change – India's Monsoon and its variability, Journal of Environmental Studies and Policy, 6, 1–34, 2003.
Lalmuanzuala, B., Sathyamoorthy, N., Kokilavani, S., Jagadeeswaran, R., and Kannan, B.: Drought analysis in southern region of Tamil Nadu using meteorological and remote sensing indices, MAUSAM, 74, 973–988, 2023.
Legates, D. R. and McCabe, G. J.: Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233–241, https://doi.org/10.1029/1998WR900018, 1999.
Liu, J., Duan, Z., Jiang, J., and Zhu, A. X.: Evaluation of three satellite precipitation products TRMM 3B42, CMORPH, and PERSIANN over a subtropical watershed in China, Adv. Meteorol., 151239, https://doi.org/10.1155/2015/151239, 2015.
Malaiarasan, U., Paramasivam, R., and Felix, K. T.: Crop diversification: determinants and effects under paddy-dominated cropping system, Paddy Water Environ., 19, 417–432, 2021.
McDonald, M. G. and Harbaugh, A. W.: A modular three-dimensional finite-difference ground-water flow model, U. S. Geological Survey Open-File Report, 83–875, 528 p., https://doi.org/10.3133/twri06A1, 1984.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Nair, A. S. and Indu, J.: Performance assessment of multi-source weighted-ensemble precipitation (MSWEP) product over India, Climate, 5, 2, https://doi.org/10.3390/cli5010002, 2017.
National Aeronautics and Space Administration: https://disc.gsfc.nasa.gov/datasets?keywords=TRMM_3B42_Daily_7&page=1, last access: 15 December 2023.
National Aeronautics and Space Administration: https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary?keywords=%22IMERG%20final%22, last access: 5 January 2024.
National Centre for Environmental Information: https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/access/daily/0.25deg/, last access: 20 December 2023.
Neitsch, S. L., Arnold, J. G., and Srinivasan, R.: Pesticides fate and transport predicted by the soil and water assessment tool (SWAT). Atrazine, Metolachlor and Trifluralin in the Sugar Creek Watershed: BRC Report, 3, https://swat.tamu.edu/media/1335/sugarcreekin.pdf (last access: 10 February 2023), 2002.
Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Buechner, M., Cerezo-Mota, R., Christensen, O. B., Deque, M., Fernandez, J., Haensler, A., van Meijgaard, E., Samuelsson, P., Sylla, M. B., and Sushama, L.: Precipitation Climatology in an Ensemble of CORDEX-Africa Regional Climate Simulations, J. Climate, 25, 6057–6078, https://doi.org/10.1175/JCLI-D-11-00375.1, 2012.
NOAA Physical Science Laboratory: https://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html, NCEP/DOE Reanalysis II: NOAA Physical Sciences Laboratory NCEP/DOE Reanalysis II, last access: 1 December 2024.
Paramasivam, P.: Hundreds stranded as parts of India's Tamil Nadu flooded after heavy rain, Reuters, 2023.
Prigent, C.: Precipitation retrieval from space: An overview, CR Geoscience, 342, 380–389, 2010.
Radhakrishnan, S., Duraisamy Rajasekaran, S. K., Sujatha, E. R., and Neelakantan, T. R.: A Comparative Study on 2015 and 2023 Chennai Flooding: A Multifactorial Perspective, Water, 16, 2477, https://doi.org/10.3390/w16172477, 2024.
Rajkumar, R., Kumaran, M. M., Prasath, M. R. K., and Senthilshanmugavelayuthm, S. P.: Agro-Climatic Zonation for Tamil Nadu Using GIS and AHP Techniques, IOP Conf. Ser.-Mat. Sci., 1006, 012015, https://doi.org/10.1088/1757-899X/1006/1/012015, 2020.
Reddy, N. M. and Saravanan, S.: Evaluation of the accuracy of seven gridded satellite precipitation products over the Godavari River basin, India, Int. J. Environ. Sci. Te., 20, 10179–10204, https://doi.org/10.1007/s13762-022-04524-x, 2023.
Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P., Koster, R. D., and De Lannoy, G. J.: Assessment of MERRA-2 land surface hydrology estimates, J. Climate, 30, 2937–2960, 2017.
Renard, K. G., Foster, G. R., and Weesies, G. A.: Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equatio (RUSLE), USDA agricultural handbook No. 703, US Department of Agriculture, Agriculture Handbook No.703USDA, USDA, Washington DC, 404 pp., ISBN 0-16-048938-5 , 1997.
Saha, S., Moorthi, S., Pan, H. L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., and Liu, H.: The NCEP climate forecast system reanalysis, B. Am. Meteorol. Soc., 91, 1015–1057, https://doi.org/10.1175/2010BAMS3001.1, 2010.
Sahoo, A. K., Sheffield, J., Pan, M., and Wood, E. F.: Evaluation of the tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA) for assessment of large-scale meteorological drought, Remote Sens. Environ., 159, 181–193, 2015.
Sapiano, M. R. P. and Arkin, P. A.: An Intercomparison and Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly Gauge Data, J. Hydrometeorol., 10, 149–166, https://doi.org/10.1175/2008JHM1052.1, 2009.
Shaowei, N., Jie, W., Juliang, J., Xiaoyan, X., Yuliang, Z., Fan, S., and Linlin, Z.: Comprehensive evaluation of satellite-derived precipitation products considering spatial distribution differences of daily precipitation over eastern China, J. Hydrol.: Regional Studies, 44, 101242, https://doi.org/10.1016/j.ejrh.2022.101242, 2022.
Shen, Z., Yong, B., Yi, L., Wu, H., and Xu, H.: From TRMM to GPM, how do improvements of post/near-real-time satellite precipitation estimates manifest?, Atmos. Res., 268, 106029, https://doi.org/10.1016/j.atmosres.2022.106029, 2022.
Shukla, A. K., Ojha, C. S. P., Singh, R. P., Pal, L., and Fu, D.: Evaluation of TRMM precipitation dataset over Himalayan Catchment: the upper Ganga Basin, India, Water (Switzerland), 11, 613, 1–25, https://doi.org/10.3390/w11030613, 2019.
Singh, A. K., Singh, V., Singh, K. K., Tripathi, J. N., Kumar, A., Soni, A. K., Sateesh, M., and Khadke, C.: A case study: Heavy rainfall event comparison between daily satellite rainfall estimation products with IMD gridded rainfall over peninsular India during 2015 winter monsoon, J. Indian Soc. Remote, 46, 927–935, 2018.
Singh, A. K., Tripathi, J. N., Singh, K. K., Singh, V., and Sateesh, M.: 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, J. Hydrol., 575, 1371–1379, 2019.
Smith, E. A., Asrar, G., Furuhama, Y., Ginati, A., Mugnai, A., Nakamura, K., Adler, R. F., Chou, M.-D., Desbois, M., and Durning, J. F.: International global precipitation measurement (GPM) program and mission: An overview, Measuring 920 precipitation from space: EURAINSAT and the future, 611–653, 2007.
Sorooshian, S., Hsu, K., Gao, X., Gupta, H. V., Imam, B., and Braithwaite, D.: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall, B. Am. Meteorol. Soc., 81, 2035–2046, https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2, 2000.
Sorooshian, S., Gao, X., Hsu, K., Maddox, R. A., Hong, Y., Gupta, H. V., and Imam, B.: Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information, J. Climate, 15, 983–1001, 2002.
Surendran, U., Raja, P., Jayakumar, M., and Subramoniam, S. R.: Use of efficient water saving techniques for production of rice in India under climate change scenario: A critical review, J. Clean. Prod., 309, 127272, https://doi.org/10.1016/j.jclepro.2021.127272, 2021.
Sylla, M. B., Giorgi, F., Coppola, E., and Mariotti, L.: Uncertainties in daily rainfall over Africa: assessment of gridded observation products and evaluation of a regional climate model simulation, Int. J. Climatol., 33, 1805–1817, https://doi.org/10.1002/joc.3551, 2013.
Tapiador, F. J., Kidd, C., Levizzani, V., and Marzano, F. S.: A neural networks-based fusion technique to estimate half-hourly rainfall estimates at 0.18 resolution from satellite passive microwave and infrared data, J. Appl. Meteorol., 43, 576–594, 2004.
Tapiador, F. J., Turk, F. J., Petersen, W., Hou, A. Y., Garcia-Ortega, E., and Machado, L. A. T.: Global precipitation measurement: methods, datasets and applications, Atmos. Res., 104, 70–97, 2012.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
TN-ENVIS: Tamil Nadu – Status of Environment and Related Issues, http://tnenvis.nic.in/Database/TN-ENVIS_791.aspx (last access: 5 May 2024), 2023.
Ushio, T., Sasashige, K., Kubota, T., Shige, S., Okamoto, K., Aonashi, K., Inoue, T., Takahashi, N., Iguchi, T., and Kachi, M.: A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data, J. Meteor. Soc. Japan, 87, 137–151, 2009.
Varadan, R. J., Kumar, P., Jha, G. K., Pal, S., and Singh, R.: An exploratory study on occurrence and impact of climate change on agriculture in Tamil Nadu, India., Theor. Appl. Climatol., 127, 993–1010, 2017.
Venkadesh, S., Pazhanivelan, S., and Mrunalini, K.: Performance of satellite-based precipitation products using dense network of rain-gauge observations in Tamil Nadu, SKUAST Journal of Research, 23, 53–59, 2021.
Wilby, R. L. and Yu, D.: Rainfall and temperature estimation for a data sparse region, Hydrol. Earth Syst. Sci., 17, 3937–3955, https://doi.org/10.5194/hess-17-3937-2013, 2013.
Willmott, C. J.: On the Validation of Models, Phys. Geogr., 2, 184–194, 1981.
Wu, W.-S., Purser, R. J., and Parrish, D. F.: Three-dimensional variational analysis with spatially inhomogeneous co-variances, Mon. Weather Rev., 130, 2905–2916, https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2, 2002.
Xie, P., Janowiak, J. E., Arkin, P. A., Adler, R., Gruber, A., Ferraro, R., Huffman, G. J., and Curtis, S.: GPCP pentad precipitation analyses: An experimental dataset based on gauge observations and satellite estimates, J. Climate, 16, 2197–2214, 2003.
Xie, P., Arkin, P. A., and Janowiak, J. E.: CMAP: The CPC Merged Analysis of Precipitation. In: Levizzani, V., Bauer, P., and Turk, F. J. (eds.): Measuring Precipitation From Space, in: Advances in Global Change Research, vol. 28. Springer, Dordrecht, https://doi.org/10.1007/978-1-4020-5835-6_25, 2007.
Yaswanth, P., Kannan, B. A. M., Bindhu, V. M., Balaji, C., and Narasimhan, B.: Evaluation of Remote Sensing Rainfall Products, Bias Correction and Temporal Disaggregation Approaches, for Improved Accuracy in Hydrologic Simulations, Water Resour. Manag., 37, 3069–3092, https://doi.org/10.1007/s11269-023-03486-0, 2023.
Zambrano-Bigiarini, M., Nauditt, A., Birkel, C., Verbist, K., and Ribbe, L.: Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile, Hydrol. Earth Syst. Sci., 21, 1295–1320, https://doi.org/10.5194/hess-21-1295-2017, 2017.
Short summary
In water-scarce regions, precipitation is a highly variable and essential resource for crop production. Developing countries like India have an uneven distribution of rain gauges, so reliance on satellite- and reanalysis-based precipitation products is critical for their prudent management. Hence, this study statistically evaluated different precipitation products against station data for water-scarce regions in Tamil Nadu and found that the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 Land (ERA5-Land) performed the best, followed by the Multi-Source Weighted-Ensemble Precipitation (MSWEP).
In water-scarce regions, precipitation is a highly variable and essential resource for crop...
