Abstract
Accurate estimates of precipitation are fundamental for hydrometeorological and ecohydrological studies, but are more difficult in high mountainous areas because of the high elevation and complex terrain. This study compares and evaluates two kinds of precipitation datasets, the reanalysis product downscaled by the Weather Research and Forecasting (WRF) output, and the satellite product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product, as well as their bias-corrected datasets in the Middle Qilian Mountain in Northwest China. Results show that the WRF output with finer resolution performs well in both estimating precipitation and hydrological simulation, while the TMPA product is unreliable in high mountainous areas. Moreover, bias-corrected WRF output also performs better than bias-corrected TMPA product. Combined with the previous studies, atmospheric reanalysis datasets are more suitable than the satellite products in high mountainous areas. Climate is more important than altitude for the ‘falseAlarms’ events of the TRMM product. Designed to focus on the tropical areas, the TMPA product mistakes certain meteorological situations for precipitation in subhumid and semiarid areas, thus causing significant ‘falseAlarms’ events and leading to significant overestimations and unreliable performance. Simple linear bias correction method, only removing systematical errors, can significantly improves the accuracy of both the WRF output and the TMPA product in arid high mountainous areas with data scarcity. Evaluated by hydrological simulations, the bias-corrected WRF output is more reliable than the gauge dataset. Thus, data merging of the WRF output and gauge observations would provide more reliable precipitation estimations in arid high mountainous areas.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbaspour K C, Rouholahnejad E, Vaghefi S et al., 2015. A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524: 733–752. doi: https://doi.org/10.1016/j.jhydrol.2015.03.027
AghaKouchak A, Mehran A, Norouzi H et al., 2012. Systematic and random error components in satellite precipitation data sets. Geophysical Research Letters, 39(9): L09406. doi: https://doi.org/10.1029/2012GL051592
Beck H E, Vergopolan N, Pan M et al., 2017. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21(12): 6201–6217. doi: https://doi.org/10.5194/hess-21-6201-2017
Behrangi A, Khakbaz B, Jaw T C et al., 2011. Hydrologic evaluation of satellite precipitation products over a mid-size basin. Journal of Hydrology, 397(3–4): 225–237. doi: https://doi.org/10.1016/j.jhydrol.2010.11.043
Beven K, Binley A, 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes, 6(3): 279–298. doi: https://doi.org/10.1002/hyp.3360060305
Bharti V, Singh C, 2015. Evaluation of error in TRMM 3B42V7 precipitation estimates over the Himalayan region. Journal of Geophysical Research, 120(24): 12458–12473. doi: https://doi.org/10.1002/2015JD023779
Bitew M M, Gebremichael M, 2011. Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model. Water Resources Research, 47(6): W06526. doi: https://doi.org/10.1029/2010WR009917
Bourque C P A, Mir M A, 2012. Seasonal snow cover in the Qilian Mountains of Northwest China: its dependence on oasis seasonal evolution and lowland production of water vapour. Journal of Hydrology, 454–455: 141–151. doi: https://doi.org/10.1016/j.jhydrol.2012.06.008
Chen C, Yu Z, Li L et al., 2011. Adaptability ealuation of TRMM satellite rainfall and its application in the Dongjiang River Basin. Procedia Environmental Sciences, 10: 396–402. doi: https://doi.org/10.1016/j.proenv.2011.09.065
Chen J, Brissette F P, Chaumont D et al., 2013. Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resources Research, 49(7): 4187–4205. doi: https://doi.org/10.1002/wrcr.2033
Chen J, Li C, Brissette F P et al., 2018. Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. Journal of Hydrology, 560: 326–341. doi: https://doi.org/10.1016/j.jhydrol.2018.03.040
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. doi: https://doi.org/10.1016/j.atmosres.2017.02.011
Ding Yongjian, Ye Baisheng, Zhou Wenjuan, 1999. Temporal and spatial precipitation distribution in the Heihe catchment, Northwest China, during the past 40 a. Journal of Glaciolgy and Geocryology, 21(1): 42–48. (in Chinese)
Ebert E E, Janowiak J E, Kidd C, 2007. Comparison of near-realtime precipitation estimates from satellite observations and numerical models. Bulletin of the American Meteorological Society, 88(1): 47–64. doi: https://doi.org/10.1175/BAMS88147
Essou G R C, Sabarly F, Lucas-Picher P et al., 2016. Can precipitation and temperature from meteorological reanalyses be used for hydrological modeling? Journal of Hydrometeorology, 17(7): 1929–1950. doi: https://doi.org/10.1175/JHM-D-15-0138.1
Ghajarnia N, Liaghat A, Arasteh P D, 2015. Comparison and evaluation of high resolution precipitation estimation products in Urmia Basin-Iran. Atmospheric Research, 158–159: 50–65. doi: https://doi.org/10.1016/j.atmosres.2015.02.010
Gudmundsson L, Tallaksen L M, Stahl K et al., 2012. Comparing large-scale hydrological model simulations to observed runoff percentiles in Europe. Journal of Hydrometeorology, 13(2): 604–620. doi: https://doi.org/10.1175/jhm-d-11-083.1
Henn B, Newman A J, Livneh B et al., 2018. An assessment of differences in gridded precipitation datasets in complex terrain. Journal of Hydrology, 556: 1205–1219. doi: https://doi.org/10.1016/j.jhydrol.2017.03.008
Huai B J, Li Z Q, Wang S J et al., 2014. RS analysis of glaciers change in the Heihe River Basin, Northwest China, during the recent decades. Journal of Geographical Sciences, 24(6): 993–1008. doi: https://doi.org/10.1007/s11442-014-1133-z
Huffman G J, Bolvin D T, Nelkin E J et al., 2007. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1): 38–55. doi: https://doi.org/10.1175/JHM560.1
Huffman G J, Bolvin D T, 2018. Real-Time TRMMMulti-Satellite Precipitation Analysis Data Set Documentation. Available at: https://pmm.nasa.gov/sites/default/files/document_files/3B4XRT_doc_V7_180426.pdf
Jiang S H, Ren L L, Hong Y et al., 2012. Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method. Journal of Hydrology, 452–453: 213–225. doi: https://doi.org/10.1016/j.jhydrol.2012.05.055
Kalin L, Isik S, Schoonover J E et al., 2010. Predicting water quality in unmonitored watersheds using artificial neural networks. Journal of Environmental Quality, 39(4): 1429–1440. doi: https://doi.org/10.2134/jeq2009.0441
Kneis D, Chatterjee C, Singh R, 2014. Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi). Hydrology and Earth System Sciences, 18: 2493–2502. doi: https://doi.org/10.5194/hess-18-2493-2014
Kumar D, Gautam A K, Palmate S S et al., 2017. Evaluation of TRMM multi-satellite precipitation analysis (TMPA) against terrestrial measurement over a humid sub-tropical basin, India. Theoretical and Applied Climatology, 129(3–4): 783–799. doi: https://doi.org/10.1007/s00704-016-1807-9
Lafon T, Dadson S, Buys G et al., 2013. Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. International Journal of Climatology, 33(6): 1367–1381. doi: https://doi.org/10.1002/joc.3518
Li C M, Tang G Q, Hong Y, 2018. Cross-evaluation of ground-based, multi-satellite and reanalysis precipitation products: applicability of the triple collocation method across mainland China. Journal of Hydrology, 562: 71–83. doi: https://doi.org/10.1016/j.jhydrol.2018.04.039
Li L, Hong H, Wang J H et al., 2009b. Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa. Natural Hazards, 50: 109–123. doi: https://doi.org/10.1007/s11069-008-9324-5
Li Z L, Xu Z X, Shao Q X et al., 2009a. Parameter estimation and uncertainty analysis of SWAT model in upper reaches of the Heihe river basin. Hydrological Processes, 23(19): 2744–2753. doi: https://doi.org/10.1002/hyp.7371
Ma Y Z, Yang Y, Han Z Y et al., 2018. Comprehensive evaluation of Ensemble Multi-Satellite Precipitation Dataset using the Dynamic Bayesian Model Averaging scheme over the Tibetan plateau. Journal of Hydrology, 556: 634–644. doi: https://doi.org/10.1016/j.jhydrol.2017.11.050
Marques J E, Samper J, Pisani B et al., 2011. Evaluation of water resources in a high-mountain basin in Serra da Estrela, Central Portugal, using a semi-distributed hydrological model. Environmental Earth Sciences, 62(6): 1219–1234. doi: https://doi.org/10.1007/s12665-010-0610-7
Maurer E P, Das T, Cayan D R, 2013. Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction. Hydrology and Earth System Sciences Discussions, 10: 1657–1691. doi: https://doi.org/10.5194/hessd-10-1657-2013
Meng J, Li L, Hao Z C et al., 2014. Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River. Journal of Hydrology, 509: 320–332. doi: https://doi.org/10.1016/j.jhydrol.2013.11.049
Moriasi D N, Arnold J G, Van Liew M W et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers, 50(3): 885–900. doi: https://doi.org/10.13031/2013.23153
Mourre L, Condom T, Junquas C et al., 2016. Spatio-temporal assessment of WRF, TRMM and in situ precipitation data in a tropical mountain environment (Cordillera Blanca, Peru). Hydrology and Earth System Sciences, 20(1): 125–141. doi: https://doi.org/10.5194/hess-20-125-2016
Nasrollahi N, Hsu K, Sorooshian S, 2013. An artificial neural network model to reduce false alarms in satellite precipitation products using MODIS and CloudSat observations. Journal of Hydrometeorology, 14(6): 1872–1883. doi: https://doi.org/10.1175/JHM-D-12-0172.1
Nastos P T, Kapsomenakis J, Philandras K M, 2016. Evaluation of the TRMM 3B43 gridded precipitation estimates over Greece. Atmospheric Research, 169: 497–514. doi: https://doi.org/10.1016/j.atmosres.2015.08.008
Nguyen H, Mehrotra R, Sharma A, 2017. Can the variability in precipitation simulations across GCMs be reduced through sensible bias correction? Climate Dynamics, 49(9): 3257–3275. doi: https://doi.org/10.1007/s00382-016-3510-z
Nkiaka E, Nawaz N R, Lovett J C, 2017. Evaluating global reanalysis precipitation datasets with rain gauge measurements in the Sudano-Sahel region: case study of the Logone catchment, Lake Chad Basin. Meteorological Applications, 24(1): 9–18. doi: https://doi.org/10.1002/met.1600
Pan X D, Li X, Shi X K et al., 2012. Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China. Frontiers of Earth Science, 6(3): 314–323. doi: https://doi.org/10.1007/s11707-012-0306-2
Peng B, Shi J C, Ni-Meister W et al., 2014. Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) Products and Their Potential Hydrological Application at an Arid and Semiarid Basin in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3915–3930. doi: https://doi.org/10.1109/JSTARS.2014.2320756
Qin Y X, Chen Z Q, Shen Y et al., 2014. Evaluation of satellite rainfall estimates over the Chinese mainland. Remote Sensing, 6(11): 11649–11672. doi: https://doi.org/10.3390/rs61111649
Rienecker M M, Suarez M J, Todling R et al., 2008. The GEOS-5 Data Assimilation System-Documentation of versions 5.0.1, 5.1.0, and 5.2.0.https://ntrs.nasa.gov/api/citations/20120011955/downloads/20120011955.pdf
Seyyedi H, Anagnostou E N, Beighley E et al., 2014. Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications. Hydrology and Earth System Sciences, 18(12): 5077–5091. doi: https://doi.org/10.5194/hess-18-5077-2014
Simmons A, Uppala S, Dee D et al., 2007. ERA-interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter, 110: 25–35. doi: https://doi.org/10.21957/pocnex23c6
Su F G, Hong Y, Lettenmaier P P, 2008. Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin. Journal of hydrometeorology, 9(4): 622–640. doi: https://doi.org/10.1175/2007JHM944.1
Sun Q H, Miao C Y, Duan Q Y et al., 2018. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Reviews of Geophysics, 56(1): 79–107. doi: https://doi.org/10.1002/2017RG000574
Tang G Q, Long D, Hong Y, 2016. Systematic anomalies over inland water bodies of high mountain Asia in TRMM precipitation estimates: no longer a problem for the GPM era? IEEE Geoscience and Remote Sensing Letters, 13(12): 1762–1766. doi: https://doi.org/10.1109/LGRS.2016.2606769
Tang G Q, Behrangi A, Long D et al., 2018. Accounting for spatiotemporal errors of gauges: a critical step to evaluate gridded precipitation products. Journal of Hydrology, 559: 294–306. doi: https://doi.org/10.1016/j.jhydrol.2018.02.057
Tapiador F J, Turk F J, Petersen W et al., 2012. Global precipitation measurement: methods, datasets and applications. Atmospheric Research, 104–105: 70–97. doi: https://doi.org/10.1016/j.atmosres.2011.10.021
Thiemig V, Rojas R, Zambrano-Bigiarini M et al., 2013. Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology, 499: 324–338. doi: https://doi.org/10.1016/j.jhydrol.2013.07.012
Tong K, Su F G, Yang D Q et al., 2014. Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan plateau. Journal of Hydrology, 519: 423–437. doi: https://doi.org/10.1016/j.jhydrol.2014.07.044
Viviroli D, Weingartner R, 2004. The Hydrological significance of mountains: from regional to global scale. Hydrology and Earth System Sciences, 8(6): 1017–1030. doi: https://doi.org/10.5194/hess-8-1017-2004
Worqlul A W, Yen H, Collick A S et al., 2017. Evaluation of CF-SR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions: the upper Blue Nile Basin, Ethiopia. CATENA, 152: 242–251. doi: https://doi.org/10.1016/j.catena.2017.01.019
Yin Z Y, Liu X D, Zhang X Q et al., 2004. Using a geographic information system to improve Special Sensor Microwave Imager precipitation estimates over the Tibetan Plateau. Journal of Geophysical Research, 109(D3): D03110. doi: https://doi.org/10.1029/2003jd003749
Zhang L H, Jin X, He C S et al., 2016. Comparison of SWAT and DLBRM for hydrological modeling of a mountainous watershed in arid Northwest China. Journal of Hydrologic Engineering, 21(5): 04016007. doi: https://doi.org/10.1061/(ASCE)HE.1943-5584.0001313
Zhang L H, He C S, Zhang M M et al., 2019. Evaluation of the SMOS and SMAP soil moisture products under different vegetation types against two sparse in situ networks over arid mountainous watersheds, Northwest China. Science China Earth Sciences, 62(4): 703–718. doi: https://doi.org/10.1007/s11430-018-9308-9
Zhao C Y, Nan Z R, Cheng G D, 2005. Methods for modelling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian mountains, China. Ecological Modelling, 189(1–2): 209–220. doi: https://doi.org/10.1016/j.ecolmodel.2005.03.016
Zhu H L, Li Y, Huang Y W et al., 2018. Evaluation and hydrological application of satellite-based precipitation datasets in driving hydrological models over the Huifa river basin in Northeast China. Atmospheric Research, 207: 28–41. doi: https://doi.org/10.1016/j.atmosres.2018.02.022
Zhu Q, Xuan W D, Liu L et al., 2016. Evaluation and hydrological application of precipitation estimates derived from PER-SIANN-CDR, TRMM 3B42V7, and NCEP-CFSR over humid regions in China. Hydrological Processes, 30(17): 3061–3083. doi: https://doi.org/10.1002/hyp.10846
Author information
Authors and Affiliations
Corresponding authors
Additional information
Foundation item
Under the auspices of National Natural Science Foundation of China (No. 42030501, 41877148, 41501016, 41530752), Scherer Endowment Fund of Department of Geography, Western Michigan University and the Fundamental Research Funds for the Central Universities (No. lzujbky-2019-98)
Rights and permissions
About this article
Cite this article
Zhang, L., He, C., Tian, W. et al. Evaluation of Precipitation Datasets from TRMM Satellite and Down-scaled Reanalysis Products with Bias-correction in Middle Qilian Mountain, China. Chin. Geogr. Sci. 31, 474–490 (2021). https://doi.org/10.1007/s11769-021-1205-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11769-021-1205-9