{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T22:42:12Z","timestamp":1772664132799,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52179006"],"award-info":[{"award-number":["52179006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51909010"],"award-info":[{"award-number":["51909010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUT20RC(3)019"],"award-info":[{"award-number":["DUT20RC(3)019"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study evaluated and intercompared seven near-real-time (NRT) versions of satellite-based precipitation products (SPPs) with latencies of less than one day, including GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-NOW, IMERG-Early, IMERG-Late, TMPA 3B42RT, and PERSIANN-CCS for wet seasons from 2008 to 2019 in a typical middle\u2013high latitude temperate monsoon climate basin, namely, the Nierji Basin in China, in four aspects: flood sub-seasons, rainfall intensities, precipitation events, and hydrological utility. Our evaluation shows that the cell-scale and area-scale intercomparison ranks of NRT SPPs are similar in these four aspects. The performances of SPPs at the areal scale, at the event scale, and with light magnitude are better than those at the cell scale, at the daily scale, and with heavy magnitude, respectively. Most SPPs are similar in terms of their Pearson Correlation Coefficient (CC). The main difference between SPPs is in terms of their root-mean-square error (RMSE). The worse performances of TMPA 3B42RT are mainly caused by the poor performances during main flood seasons. The worst performances of PERSIANN-CCS are primarily reflected by the lowest CC and the underestimation of precipitation. Though GSMaP-NOW has the highest RMSE and overestimates precipitation, it can reflect the precipitation variation, as indicated by the relatively high CC. The differences among SPPs are more significant in pre-flood seasons and less significant in post-flood seasons. These results can provide valuable guidelines for the selection, correction, and application of NRT SPPs and contribute to improved insight into NRT-SPP retrieval algorithms.<\/jats:p>","DOI":"10.3390\/rs13224552","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"4552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evaluation of Seven Near-Real-Time Satellite-Based Precipitation Products for Wet Seasons in the Nierji Basin, China"],"prefix":"10.3390","volume":"13","author":[{"given":"Yanhong","family":"Dou","sequence":"first","affiliation":[{"name":"School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China"}]},{"given":"Lei","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China"}]},{"given":"Jiayan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China"}]},{"given":"Huicheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8545","DOI":"10.1029\/2018WR023749","article-title":"If Precipitation Extremes Are Increasing, Why Aren\u2019t Floods?","volume":"54","author":"Sharma","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6519","DOI":"10.5194\/hess-22-6519-2018","article-title":"The probability distribution of daily precipitation at the point and catchment scales in the United States","volume":"22","author":"Ye","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.jclepro.2018.05.194","article-title":"Water-energy-food nexus: Concepts, questions and methodologies","volume":"195","author":"Zhang","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1016\/j.jhydrol.2018.06.045","article-title":"Evaluation of hydrological utility of IMERG Final run V05 and TMPA 3B42V7 satellite precipitation products in the Yellow River source region, China","volume":"567","author":"Yuan","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.atmosres.2010.09.013","article-title":"A systematic review of sensitivities in the Swedish flood-forecasting system","volume":"100","author":"Arheimer","year":"2011","journal-title":"Atmos. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"125908","DOI":"10.1016\/j.jhydrol.2020.125908","article-title":"Multi-source error correction for flood forecasting based on dynamic system response curve method","volume":"594","author":"Liang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s00376-014-4155-7","article-title":"Evaluation of Radar and Automatic Weather Station Data Assimilation for a Heavy Rainfall Event in Southern China","volume":"32","author":"Tuanjie","year":"2015","journal-title":"Adv. Atmos. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.atmosres.2017.06.016","article-title":"Cross-evaluation of re fl ectivity from the space-borne precipitation radar and multi-type ground-based weather radar network in China","volume":"196","author":"Zhong","year":"2017","journal-title":"Atmos. Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Y., Grimaldi, S., Walker, J., and Pauwels, V. (2016). Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review. Remote Sens., 8.","DOI":"10.3390\/rs8060456"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ebert, E.E., Janowiak, J.E., and Kidd, C. (2007). Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Am. Meteorol. Soc., 47\u201364.","DOI":"10.1175\/BAMS-88-1-47"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1175\/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2","article-title":"Evaluation of PERSIANN system satellite-based estimates of tropical rainfall","volume":"81","author":"Sorooshian","year":"2000","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1175\/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2","article-title":"CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution","volume":"5","author":"Joyce","year":"2004","journal-title":"J. Hydrometeorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1175\/JHM560.1","article-title":"The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales","volume":"8","author":"Huffman","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_14","unstructured":"Huffman, G.J., Gsfc, N., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E.J., Sorooshian, S., and Tan, J. (2018, February 07). Algorithm Theoretical Basis Document (ATBD) NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), Available online: https:\/\/gpm.nasa.gov\/resources\/documents\/gpm-integrated-multi-satellite-retrievals-gpm-imerg-algorithm-theoretical-basis-."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1109\/TGRS.2007.895337","article-title":"Global Precipitation Map Using Satellite-Borne Microwave Radiometers by the GSMaP Project: Production and Validation","volume":"45","author":"Kubota","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1002\/2017JD027606","article-title":"To What Extent is the Day 1 GPM IMERG Satellite Precipitation Estimate Improved as Compared to TRMM TMPA-RT?","volume":"123","author":"Gebregiorgis","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1175\/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2","article-title":"The Tropical Rainfall Measuring Mission (TRMM) Sensor Package","volume":"15","author":"Kummerow","year":"1998","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L. (2017). A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons. Rev. Geophys., 79\u2013107.","DOI":"10.1002\/2017RG000574"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1175\/BAMS-D-13-00164.1","article-title":"The Global Precipitation Measurement Mission","volume":"95","author":"Hou","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3931","DOI":"10.1109\/JSTARS.2014.2320960","article-title":"Evaluation of Precipitation Estimates by at-Launch Codes of GPM\/DPR Algorithms Using Synthetic Data from TRMM\/PR Observations","volume":"7","author":"Kubota","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","first-page":"2699","article-title":"Evaluation and Validation of GPM Dual-Frequency Classification Module after Launch","volume":"33","author":"Le","year":"2016","journal-title":"Am. Meteorol. Soc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111697","DOI":"10.1016\/j.rse.2020.111697","article-title":"Have satellite precipitation products improved over last two decades ? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets","volume":"240","author":"Tang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.jhydrol.2018.01.039","article-title":"On the performance of satellite precipitation products in riverine flood modeling: A review","volume":"558","author":"Maggioni","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lu, D., and Yong, B. (2020). A preliminary assessment of the gauge-adjusted near-real-time GSMaP precipitation estimate over Mainland China. Remote Sens., 12.","DOI":"10.3390\/rs12010141"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.atmosres.2017.11.005","article-title":"Systematical estimation of GPM-based global satellite mapping of precipitation products over China","volume":"201","author":"Zhao","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"124376","DOI":"10.1016\/j.jhydrol.2019.124376","article-title":"Comparison analysis of six purely satellite-derived global precipitation estimates","volume":"581","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1175\/JHM-D-11-086.1","article-title":"Evaluation of Global Satellite Rainfall Products over Continental Europe","volume":"13","author":"Stampoulis","year":"2011","journal-title":"J. Hydrometeorol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Navarro, A., Garc\u00eda-Ortega, E., Merino, A., S\u00e1nchez, J.L., Kummerow, C., and Tapiador, F.J. (2019). Assessment of IMERG precipitation estimates over Europe. Remote Sens., 11.","DOI":"10.3390\/rs11212470"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Palharini, R.S.A., Vila, D.A., Rodrigues, D.T., Quispe, D.P., Palharini, R.C., de Siqueira, R.A., and de Sousa Afonso, J.M. (2020). Assessment of the Extreme Precipitation by Satellite Estimates over South America. Remote Sens., 12.","DOI":"10.3390\/rs12132085"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105341","DOI":"10.1016\/j.atmosres.2020.105341","article-title":"Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan","volume":"249","author":"Arshad","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.atmosres.2018.12.029","article-title":"Spatiotemporal evaluation of the GPM satellite precipitation products over the United Arab Emirates","volume":"219","author":"Mahmoud","year":"2019","journal-title":"Atmos. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Llauca, H., Lavado-Casimiro, W., Le\u00f3n, K., Jimenez, J., Traverso, K., and Rau, P. (2021). Assessing Near Real-Time Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Peruvian Andes. Remote Sens., 13.","DOI":"10.3390\/rs13040826"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105203","DOI":"10.1016\/j.atmosres.2020.105203","article-title":"Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Peninsular Malaysia","volume":"248","author":"Noor","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shi, J., Yuan, F., Shi, C., Zhao, C., Zhang, L., Ren, L., Zhu, Y., Jiang, S., and Liu, Y. (2020). Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region. Water, 12.","DOI":"10.3390\/w12041006"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mastrantonas, N., Bhattacharya, B., Shibuo, Y., Rasmy, M., Espinoza-D\u00c1VALOS, G., and Solomatine, D. (2019). Evaluating the Benefits of Merging Near-Real-Time Satellite Precipitation Products: A Case Study in the Kinu Basin Region, Japan. J. Hydrometeorol., 1213\u20131233.","DOI":"10.1175\/JHM-D-18-0190.1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1111\/1752-1688.12610","article-title":"Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin, Texas","volume":"54","author":"Omranian","year":"2018","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Belabid, N., Zhao, F., Brocca, L., Huang, Y., and Tan, Y. (2019). Near-real-time flood forecasting based on satellite precipitation products. Remote Sens., 11.","DOI":"10.3390\/rs11030252"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yuan, F., Zhang, L., Soe, K., Ren, L., Zhao, C., Zhu, Y., Jiang, S., and Liu, Y. (2019). Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar. Remote Sens., 11.","DOI":"10.3390\/rs11020140"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"125975","DOI":"10.1016\/j.jhydrol.2021.125975","article-title":"Two-decades of GPM IMERG early and final run products intercomparison: Similarity and difference in climatology, rates, and extremes","volume":"594","author":"Li","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e2020EA001090","DOI":"10.1029\/2020EA001090","article-title":"Can the GPM IMERG Hourly Products Replicate the Variation in Precipitation During the Wet Season Over the Sichuan Basin, China?","volume":"7","author":"Wang","year":"2020","journal-title":"Earth Sp. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s00704-018-2391-y","article-title":"Performance of near real-time Global Satellite Mapping of Precipitation estimates during heavy precipitation events over northern China","volume":"135","author":"Chen","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s40645-021-00425-8","article-title":"Flood inundation simulations based on GSMaP satellite rainfall data in Jakarta, Indonesia","volume":"8","author":"Priyambodoho","year":"2021","journal-title":"Prog. Earth Planet. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Falanga Bolognesi, S., Pasolli, E., Belfiore, O., De Michele, C., and D\u2019Urso, G. (2020). Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy. Remote Sens., 12.","DOI":"10.3390\/rs12081275"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"de Carvalho, M.\u00c2.C.C., Uliana, E.M., da Silva, D.D., Aires, U.R.V., Martins, C.A.d.S., de Sousa Junior, M.F., da Cruz, I.F., and Mendes, M.A.d.S.A. (2020). Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado\u2013Amazon Transition, Brazil. Water, 12.","DOI":"10.3390\/w12123366"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105007","DOI":"10.1016\/j.atmosres.2020.105007","article-title":"Evaluation of remotely sensed precipitation sources for drought assessment in Semi-Arid Iraq","volume":"242","author":"Suliman","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Getirana, A., Kirschbaum, D., Mandarino, F., Ottoni, M., Khan, S., and Arsenault, K. (2020). Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil. Remote Sens., 12.","DOI":"10.3390\/rs12244095"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Huang, W., Liu, P., Hsu, J., Li, X., and Deng, L. (2021). Assessment of Near-Real-Time Satellite Precipitation Products from GSMaP in Monitoring Rainfall Variations over Taiwan. Remote Sens., 13.","DOI":"10.3390\/rs13020202"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1002\/2016WR019452","article-title":"Multiobjective hedging rules for flood water conservation","volume":"53","author":"Ding","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-019-8392-z","article-title":"Hydrological characteristic-based methodology for dividing flood seasons: An empirical analysis from China","volume":"78","author":"Jiang","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1080\/02626667.2010.481087","article-title":"Flood season segmentation based on the probability change-point analysis technique","volume":"55","author":"Liu","year":"2010","journal-title":"Hydrol. Sci. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.jhydrol.2018.10.026","article-title":"Nierji reservoir flood forecasting based on a Data-Based Mechanistic methodology","volume":"567","author":"Wei","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1175\/JAM2173.1","article-title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","volume":"43","author":"Hong","year":"2004","journal-title":"J. Appl. Meteorol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/978-3-030-24568-9_20","article-title":"Global Satellite Mapping of Precipitation (GSMaP) Products in the GPM Era","volume":"1","author":"Kubota","year":"2020","journal-title":"Satell. Precip. Meas."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Levizzani, V., Kidd, C., Kirschbaum, D.B., Kummerow, C.D., Nakamura, K., and Turk, F.J. (2020). Satellite Precipitation Measurement, Springer Nature Switzerland AG.","DOI":"10.1007\/978-3-030-35798-6"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1109\/JSTARS.2018.2825336","article-title":"Tracing the Error Sources of Global Satellite Mapping of Precipitation for GPM (GPM-GSMaP) Over the Tibetan Plateau, China","volume":"11","author":"Zhu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1016\/j.scitotenv.2016.08.213","article-title":"Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales","volume":"573","author":"Duan","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_57","first-page":"2019","article-title":"Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation","volume":"612","author":"Huffman","year":"2015","journal-title":"NASA\/GSFC Code"},{"key":"ref_58","unstructured":"Huffman, G.J., Bolvin, D.T., Nelkin, E.J., Stocker, E.F., and Tan, J. (2018). V05 IMERG Final Run Release Notes, NASA Goddard Earth Sciences Data and Information Services Center."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1080\/02626667.2011.546602","article-title":"Guide to Hydrological Practices","volume":"56","author":"Rodda","year":"2011","journal-title":"Hydrol. Sci. J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jhydrol.2010.01.023","article-title":"An improved statistical approach to merge satellite rainfall estimates and raingauge data","volume":"385","author":"Li","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wu, L., Xu, Y., and Wang, S. (2018). Comparison of TMPA-3B42RT Legacy Product and the Equivalent IMERG Products over Mainland China. Remote Sens., 10.","DOI":"10.3390\/rs10111778"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/0022-1694(92)90096-E","article-title":"The Xinanjiang model applied in China","volume":"135","author":"Zhao","year":"1992","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4552\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:29:18Z","timestamp":1760167758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":62,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224552"],"URL":"https:\/\/doi.org\/10.3390\/rs13224552","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,12]]}}}