{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T13:11:58Z","timestamp":1775740318123,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFF0711602"],"award-info":[{"award-number":["2022YFF0711602"]}]},{"name":"National Key R&amp;D Program of China","award":["42271479"],"award-info":[{"award-number":["42271479"]}]},{"name":"National Key R&amp;D Program of China","award":["2023A04J1993"],"award-info":[{"award-number":["2023A04J1993"]}]},{"name":"National Key R&amp;D Program of China","award":["2024GDASZH-2024010101"],"award-info":[{"award-number":["2024GDASZH-2024010101"]}]},{"name":"National Key R&amp;D Program of China","award":["2022GDASZH-2022010202"],"award-info":[{"award-number":["2022GDASZH-2022010202"]}]},{"name":"National Key R&amp;D Program of China","award":["2022GDASZH-2022020402-01"],"award-info":[{"award-number":["2022GDASZH-2022020402-01"]}]},{"name":"National Key R&amp;D Program of China","award":["2021B1212100006"],"award-info":[{"award-number":["2021B1212100006"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFF0711602"],"award-info":[{"award-number":["2022YFF0711602"]}]},{"name":"National Natural Science Foundation of China","award":["42271479"],"award-info":[{"award-number":["42271479"]}]},{"name":"National Natural Science Foundation of China","award":["2023A04J1993"],"award-info":[{"award-number":["2023A04J1993"]}]},{"name":"National Natural Science Foundation of China","award":["2024GDASZH-2024010101"],"award-info":[{"award-number":["2024GDASZH-2024010101"]}]},{"name":"National Natural Science Foundation of China","award":["2022GDASZH-2022010202"],"award-info":[{"award-number":["2022GDASZH-2022010202"]}]},{"name":"National Natural Science Foundation of China","award":["2022GDASZH-2022020402-01"],"award-info":[{"award-number":["2022GDASZH-2022020402-01"]}]},{"name":"National Natural Science Foundation of China","award":["2021B1212100006"],"award-info":[{"award-number":["2021B1212100006"]}]},{"name":"Science and Technology Program of Guangzhou","award":["2022YFF0711602"],"award-info":[{"award-number":["2022YFF0711602"]}]},{"name":"Science and Technology Program of Guangzhou","award":["42271479"],"award-info":[{"award-number":["42271479"]}]},{"name":"Science and Technology Program of Guangzhou","award":["2023A04J1993"],"award-info":[{"award-number":["2023A04J1993"]}]},{"name":"Science and Technology Program of Guangzhou","award":["2024GDASZH-2024010101"],"award-info":[{"award-number":["2024GDASZH-2024010101"]}]},{"name":"Science and Technology Program of Guangzhou","award":["2022GDASZH-2022010202"],"award-info":[{"award-number":["2022GDASZH-2022010202"]}]},{"name":"Science and Technology Program of Guangzhou","award":["2022GDASZH-2022020402-01"],"award-info":[{"award-number":["2022GDASZH-2022020402-01"]}]},{"name":"Science and Technology Program of Guangzhou","award":["2021B1212100006"],"award-info":[{"award-number":["2021B1212100006"]}]},{"name":"GDAS\u2019 Project of Science and Technology Development","award":["2022YFF0711602"],"award-info":[{"award-number":["2022YFF0711602"]}]},{"name":"GDAS\u2019 Project of Science and Technology Development","award":["42271479"],"award-info":[{"award-number":["42271479"]}]},{"name":"GDAS\u2019 Project of Science and Technology Development","award":["2023A04J1993"],"award-info":[{"award-number":["2023A04J1993"]}]},{"name":"GDAS\u2019 Project of Science and Technology Development","award":["2024GDASZH-2024010101"],"award-info":[{"award-number":["2024GDASZH-2024010101"]}]},{"name":"GDAS\u2019 Project of Science and Technology Development","award":["2022GDASZH-2022010202"],"award-info":[{"award-number":["2022GDASZH-2022010202"]}]},{"name":"GDAS\u2019 Project of Science and Technology Development","award":["2022GDASZH-2022020402-01"],"award-info":[{"award-number":["2022GDASZH-2022020402-01"]}]},{"name":"GDAS\u2019 Project of Science and Technology Development","award":["2021B1212100006"],"award-info":[{"award-number":["2021B1212100006"]}]},{"name":"Science and Technology Program of Guangdong","award":["2022YFF0711602"],"award-info":[{"award-number":["2022YFF0711602"]}]},{"name":"Science and Technology Program of Guangdong","award":["42271479"],"award-info":[{"award-number":["42271479"]}]},{"name":"Science and Technology Program of Guangdong","award":["2023A04J1993"],"award-info":[{"award-number":["2023A04J1993"]}]},{"name":"Science and Technology Program of Guangdong","award":["2024GDASZH-2024010101"],"award-info":[{"award-number":["2024GDASZH-2024010101"]}]},{"name":"Science and Technology Program of Guangdong","award":["2022GDASZH-2022010202"],"award-info":[{"award-number":["2022GDASZH-2022010202"]}]},{"name":"Science and Technology Program of Guangdong","award":["2022GDASZH-2022020402-01"],"award-info":[{"award-number":["2022GDASZH-2022020402-01"]}]},{"name":"Science and Technology Program of Guangdong","award":["2021B1212100006"],"award-info":[{"award-number":["2021B1212100006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite and reanalysis precipitation estimates of high quality are widely used for hydrological modeling, especially in ungauged or data-scarce regions. To improve flood simulations by merging different precipitation inputs or directly merging streamflow outputs, this study comprehensively evaluates the accuracy and hydrological utility of nine corrected and uncorrected precipitation products (TMPA-3B42V7, TMPA-3B42RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, and PERSIANN-CCS) from 2006 to 2018 on a daily timescale using the Coupled Routing and Excess Storage (CREST) hydrological model in two flood-prone tributaries, the Beijiang and Dongjiang Rivers, of the Pearl River Basin, China. The results indicate that (1) all the corrected precipitation products had better performance (higher CC, CSI, KGE\u2019, and NSCE values) than the uncorrected ones, particularly in the Beijiang River, which has a larger drainage area; (2) after re-calibration under Scenario II, the two daily merged precipitation products (NSCE values: 0.73\u20130.87 and 0.69\u20130.82 over the Beijiang and Dongjiang Rivers, respectively) outperformed their original members for hydrological modeling in terms of BIAS and RMSE values; (3) in Scenario III, four evaluation metrics illustrated that merging multi-source streamflow simulations achieved better performance in streamflow simulation than merging multi-source precipitation products; and (4) under increasing flood levels, almost all the performances of streamflow simulations were reduced, and the two merging schemes had a similar performance. These findings will provide valuable information for improving flood simulations and will also be useful for further hydrometeorological applications of remote sensing data.<\/jats:p>","DOI":"10.3390\/rs15225349","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T02:20:37Z","timestamp":1699928437000},"page":"5349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhen","family":"Gao","sequence":"first","affiliation":[{"name":"Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0923-583X","authenticated-orcid":false,"given":"Guoqiang","family":"Tang","sequence":"additional","affiliation":[{"name":"Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO 80305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8021-3943","authenticated-orcid":false,"given":"Wenlong","family":"Jing","sequence":"additional","affiliation":[{"name":"Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2170-3250","authenticated-orcid":false,"given":"Zhiwei","family":"Hou","sequence":"additional","affiliation":[{"name":"Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4092-2264","authenticated-orcid":false,"given":"Ji","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"}]},{"given":"Jia","family":"Sun","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 511485, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3527","DOI":"10.1038\/s41467-022-30727-4","article-title":"Flood exposure and poverty in 188 countries","volume":"13","author":"Rentschler","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2020EF001778","DOI":"10.1029\/2020EF001778","article-title":"Increased Flood Exposure Due to Climate Change and Population Growth in the United States","volume":"8","author":"Swain","year":"2020","journal-title":"Earth\u2019s Future"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4403","DOI":"10.5194\/hess-25-4403-2021","article-title":"Compound flood potential from storm surge and heavy precipitation in coastal China: Dependence, drivers, and impacts","volume":"25","author":"Fang","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"E1133","DOI":"10.1175\/BAMS-D-20-0001.1","article-title":"Multisourced Flood Inventories over the Contiguous United States for Actual and Natural Conditions","volume":"102","author":"Huang","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gao, Z., Huang, B., Ma, Z., Chen, X., Qiu, J., and Liu, D. (2020). Comprehensive Comparisons of State-Of-The-Art Gridded Precipitation Estimates for Hydrological Applications over Southern China. Remote Sens., 12.","DOI":"10.3390\/rs12233997"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S103","DOI":"10.1175\/BAMS-D-20-0135.1","article-title":"Anthropogenic Influences on Heavy Precipitation during the 2019 Extremely Wet Rainy Season in Southern China","volume":"102","author":"Li","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"E1844","DOI":"10.1175\/BAMS-D-20-0299.1","article-title":"The Global Satellite Precipitation Constellation: Current Status and Future Requirements","volume":"102","author":"Kidd","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TGRS.2010.2057513","article-title":"Satellite remote sensing and hydrologic modeling for flood inundation mapping in Lake Victoria basin: Implications for hydrologic prediction in ungauged basins","volume":"49","author":"Khan","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jhydrol.2013.06.042","article-title":"Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins?","volume":"499","author":"Xue","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"126390","DOI":"10.1016\/j.jhydrol.2021.126390","article-title":"Evaluation of different precipitation inputs on streamflow simulation in Himalayan River basin","volume":"599","author":"Khatakho","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Usman, M., Ndehedehe, C.E., Farah, H., Ahmad, B., Wong, Y., and Adeyeri, O.E. (2022). Application of a Conceptual Hydrological Model for Streamflow Prediction Using Multi-Source Precipitation Products in a Semi-Arid River Basin. Water, 14.","DOI":"10.3390\/w14081260"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"125878","DOI":"10.1016\/j.jhydrol.2020.125878","article-title":"Blending multi-satellite, atmospheric reanalysis and gauge precipitation products to facilitate hydrological modelling","volume":"593","author":"Yin","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112754","DOI":"10.1016\/j.rse.2021.112754","article-title":"Review of GPM IMERG performance: A global perspective","volume":"268","author":"Pradhan","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1175\/JHM574.1","article-title":"Evaluation of PERSIANN-CCS Rainfall Measurement Using the NAME Event Rain Gauge Network","volume":"8","author":"Hong","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_17","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_18","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_19","first-page":"30","article-title":"NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG)","volume":"4","author":"Huffman","year":"2015","journal-title":"Algorithm Theor. Basis Doc. (ATBD) Version"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol.Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5801","DOI":"10.5194\/hess-22-5801-2018","article-title":"The PERSIANN family of global satellite precipitation data: A review and evaluation of products","volume":"22","author":"Nguyen","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1002\/2017RG000574","article-title":"A review of global precipitation data sets: Data sources, estimation, and intercomparisons","volume":"56","author":"Sun","year":"2018","journal-title":"Rev. Geophys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1175\/JHM-D-19-0073.1","article-title":"Machine Learning\u2014Based Blending of Satellite and Reanalysis Precipitation Datasets: A Multiregional Tropical Complex Terrain Evaluation","volume":"20","author":"Nikolopoulos","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124412","DOI":"10.1016\/j.jhydrol.2019.124412","article-title":"Evaluation of 23 gridded precipitation datasets across West Africa","volume":"581","author":"Defrance","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"127783","DOI":"10.1016\/j.jhydrol.2022.127783","article-title":"Effective multi-satellite precipitation fusion procedure conditioned by gauge background fields over the Chinese mainland","volume":"610","author":"Li","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127389","DOI":"10.1016\/j.jhydrol.2021.127389","article-title":"Performance of satellite-based and reanalysis precipitation products under multi-temporal scales and extreme weather in mainland China","volume":"605","author":"Zhang","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102531","DOI":"10.1007\/s11356-023-29572-9","article-title":"Evaluating the necessity of post-processing techniques on d4PDF data for extreme climate assessment","volume":"30","author":"Maneechot","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Maggioni, V., Massari, C., and Kidd, C. (2022). Errors and Uncertainties Associated with Quasiglobal Satellite Precipitation Products, Elsevier.","DOI":"10.1016\/B978-0-12-822973-6.00023-8"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"125660","DOI":"10.1016\/j.jhydrol.2020.125660","article-title":"Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland","volume":"595","author":"Jiang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1029\/2009WR008965","article-title":"Hydrologic evaluation of Multisatellite Precipitation Analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe basin, China","volume":"46","author":"Yong","year":"2010","journal-title":"Water Resour. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1175\/JHM-D-15-0059.1","article-title":"Statistical and Hydrological Comparisons between TRMM and GPM Level-3 Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA 3B42V7?","volume":"17","author":"Tang","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6201","DOI":"10.5194\/hess-21-6201-2017","article-title":"Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling","volume":"21","author":"Beck","year":"2017","journal-title":"Hydrol. Earth Syst.Sci."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"128103","DOI":"10.1016\/j.jhydrol.2022.128103","article-title":"Hydrologic utility of satellite precipitation products in flood prediction: A meta-data analysis and lessons learnt","volume":"612","author":"Hinge","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"129384","DOI":"10.1016\/j.jhydrol.2023.129384","article-title":"Statistical comparison and hydrological utility evaluation of ERA5-Land and IMERG precipitation products on the Tibetan Plateau","volume":"620","author":"Wu","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.jhydrol.2015.05.042","article-title":"Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins\u2013Araguaia basin in Brazil","volume":"527","author":"Falck","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_37","first-page":"1987","article-title":"Assessment of Precipitation Error Propagation in Discharge Simulations over the Contiguous United States","volume":"22","author":"Nanding","year":"2021","journal-title":"J. Hydrometeorol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., and Hsu, K. (2021). Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling. Remote Sens., 13.","DOI":"10.3390\/rs13163061"},{"key":"ref_39","first-page":"100148","article-title":"Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model","volume":"18","author":"Hartke","year":"2023","journal-title":"J. Hydrol. X"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.jhydrol.2016.03.063","article-title":"Comparison of climate datasets for lumped hydrological modeling over the continental United States","volume":"537","author":"Essou","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101105","DOI":"10.1016\/j.ejrh.2022.101105","article-title":"Assessment of global reanalysis precipitation for hydrological modelling in data-scarce regions: A case study of Kenya","volume":"41","author":"Wanzala","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"128920","DOI":"10.1016\/j.jhydrol.2022.128920","article-title":"How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling","volume":"617","author":"Gu","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"124076","DOI":"10.1016\/j.jhydrol.2019.124076","article-title":"Evaluating precipitation datasets for large-scale distributed hydrological modelling","volume":"578","author":"Mazzoleni","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.jhydrol.2019.03.024","article-title":"Dependence of flood peaks and volumes in modeled discharge time series: Effect of different uncertainty sources","volume":"572","author":"Brunner","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105","DOI":"10.5194\/hess-25-105-2021","article-title":"Flood spatial coherence, triggers, and performance in hydrological simulations: Large-sample evaluation of four streamflow-calibrated models","volume":"25","author":"Brunner","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"126705","DOI":"10.1016\/j.jhydrol.2021.126705","article-title":"How reliable are the satellite-based precipitation estimations in guiding hydrological modelling in South China?","volume":"602","author":"Su","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"17956","DOI":"10.1038\/s41598-021-97432-y","article-title":"Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China","volume":"11","author":"Jiao","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1080\/02626667.2019.1612522","article-title":"Merging multi-source precipitation products or merging their simulated hydrological flows to improve streamflow simulation","volume":"64","author":"Zhu","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"125040","DOI":"10.1016\/j.jhydrol.2020.125040","article-title":"Hydrological evaluation of merged satellite precipitation datasets for streamflow simulation using SWAT: A case study of Potohar Plateau, Pakistan","volume":"587","author":"Rahman","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"124629","DOI":"10.1016\/j.jhydrol.2020.124629","article-title":"Interpolated or satellite-based precipitation? Implications for hydrological modeling in a meso-scale mountainous watershed on the Qinghai-Tibet Plateau","volume":"583","author":"Zhang","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101134","DOI":"10.1016\/j.ejrh.2022.101134","article-title":"Hydrological evaluation of radar and satellite gauge-merged precipitation datasets using the SWAT model: Case of the Terauchi catchment in Japan","volume":"42","author":"Mtibaa","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"055002","DOI":"10.1088\/1748-9326\/ab79e2","article-title":"Intercomparison of annual precipitation indices and extremes over global land areas from in situ, space-based and reanalysis products","volume":"15","author":"Alexander","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1002\/joc.4341","article-title":"Validation and comparison of a new gauge-based precipitation analysis over mainland China","volume":"36","author":"Shen","year":"2016","journal-title":"Int. J. Climatol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.jhydrol.2015.12.008","article-title":"Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales","volume":"533","author":"Tang","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mega, T., Ushio, T., Kubota, T., Kachi, M., Aonashi, K., and Shige, S. (2014, January 16\u201323). Gauge adjusted global satellite mapping of precipitation (GSMaP_Gauge). Proceedings of the 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS), Beijing, China.","DOI":"10.1109\/URSIGASS.2014.6929683"},{"key":"ref_56","unstructured":"Copernicus Climate Change Service (C3S) (2017). ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernic. Clim. Change Serv. Clim. Data Store (CDS), 15, 2020."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1002\/qj.828","article-title":"The ERA-Interim reanalysis: Configuration and performance of the data assimilation system","volume":"137","author":"Dee","year":"2011","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1080\/02626667.2010.543087","article-title":"The coupled routing and excess storage (CREST) distributed hydrological model","volume":"56","author":"Wang","year":"2011","journal-title":"Hydrol. Sci. J."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.jhydrol.2016.04.007","article-title":"A method for probabilistic flash flood forecasting","volume":"541","author":"Hardy","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1175\/BAMS-D-15-00130.1","article-title":"Hydrological Modeling and Capacity Building in the Republic of Namibia","volume":"98","author":"Clark","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.jhydrol.2017.05.025","article-title":"Assessing the potential of satellite-based precipitation estimates for flood frequency analysis in ungauged or poorly gauged tributaries of China\u2019s Yangtze River basin","volume":"550","author":"Gao","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"S514","DOI":"10.1111\/jfr3.12250","article-title":"Using high-resolution satellite precipitation for flood frequency analysis: Case study over the Connecticut River Basin","volume":"11","author":"Dis","year":"2018","journal-title":"J. Flood Risk Manag."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"126522","DOI":"10.1016\/j.jhydrol.2021.126522","article-title":"Mapping dynamic non-perennial stream networks using high-resolution distributed hydrologic simulation: A case study in the upper blue river basin","volume":"600","author":"Gao","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"04016061","DOI":"10.1061\/(ASCE)HE.1943-5584.0001442","article-title":"Refining a Distributed Linear Reservoir Routing Method to Improve Performance of the CREST Model","volume":"22","author":"Shen","year":"2017","journal-title":"J. Hydrol. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1029\/91WR02985","article-title":"Effective and efficient global optimization for conceptual rainfall-runoff models","volume":"28","author":"Duan","year":"1992","journal-title":"Water Resour. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1175\/BAMS-88-1-47","article-title":"Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations","volume":"88","author":"Ebert","year":"2007","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lawal, I.M., Bertram, D., White, C.J., Jagaba, A.H., Hassan, I., and Shuaibu, A. (2021). Multi-Criteria Performance Evaluation of Gridded Precipitation and Temperature Products in Data-Sparse Regions. Atmosphere, 12.","DOI":"10.3390\/atmos12121597"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e2023EA002980","DOI":"10.1029\/2023EA002980","article-title":"Advantages of GSMaP Data for Multi-Timescale Precipitation Estimation in Luzon","volume":"10","author":"Lee","year":"2023","journal-title":"Earth Space Sci."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Pan, X., Wu, H., Chen, S., Nanding, N., Huang, Z., Chen, W., Li, C., and Li, X. (2023). Evaluation and Applicability Analysis of GPM Satellite Precipitation over Mainland China. Remote Sens., 15.","DOI":"10.3390\/rs15112866"},{"key":"ref_70","unstructured":"Xu, L. (1994). Two-Layer Variable Infiltration Capacity Land Surface Representation for General Circulation Models, University of Washington."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TGRS.2013.2238636","article-title":"Estimation of Satellite Rainfall Error Variance Using Readily Available Geophysical Features","volume":"52","author":"Gebregiorgis","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.advwatres.2017.09.012","article-title":"Decomposing the satellite precipitation error propagation through the rainfall-runoff processes","volume":"109","author":"Mei","year":"2017","journal-title":"Adv. Water Resour."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1175\/JHM-D-12-0113.1","article-title":"Effects of Resolution of Satellite-Based Rainfall Estimates on Hydrologic Modeling Skill at Different Scales","volume":"15","author":"Vergara","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Nijssen, B., and Lettenmaier, D.P. (2004). Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites. J. Geophys. Res. Atmos., 109.","DOI":"10.1029\/2003JD003497"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5349\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:22:30Z","timestamp":1760131350000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5349"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"references-count":74,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15225349"],"URL":"https:\/\/doi.org\/10.3390\/rs15225349","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,13]]}}}