Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region

This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets include two raw satellite datasets, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center MORPHing raw satellite dataset (CMORPH RAW); four satellite-gauge datasets, Tropical Rainfall Measuring Mission 3B42 (TRMM), PERSIANN Climate Data Record (PERSIANN CDR), CMORPH bias-corrected (CMORPH CRT), and gauge blended datasets (CMORPH BLD); and two satellite-reanalysis-gauge datasets, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS). The uncertainty related to hydrologic model physics is investigated using two different hydrological models. A set of statistical indices is utilized to comprehensively evaluate the precipitation datasets from different perspectives, including detection, systematic, random errors, and precision for simulating extreme precipitation. Results show that CMORPH BLD and MSWEP generally perform better than other datasets. In terms of hydrological simulations, all satellite-based datasets show significant dampening effects for the random error during the transformation process from precipitation to runoff; however, these effects cannot hold for the systematic error. Even though different hydrological models indeed introduce uncertainties to the simulated hydrological processes, the relative hydrological performance of the satellite-based datasets is consistent in both models. Namely, CMORPH BLD performs the best, which is followed by MSWEP, CMORPH CRT, and TRMM. PERSIANN CDR and CHIRPS perform moderately well, and two raw satellite datasets are not recommended as proxies of gauged observations for their worse performances.

[1]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[2]  E. Epstein,et al.  A Spectral Climatology , 1988 .

[3]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[4]  Zhao Ren-jun,et al.  The Xinanjiang model applied in China , 1992 .

[5]  U. Schneider,et al.  Terrestrial Precipitation Analysis: Operational Method and Required Density of Point Measurements , 1994 .

[6]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[7]  S. Sorooshian,et al.  Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks , 1997 .

[8]  J. Janowiak,et al.  The Global Precipitation Climatology Project (GPCP) combined precipitation dataset , 1997 .

[9]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[10]  R. E. Livezey,et al.  A Comparison of the NCEP-NCAR Reanalysis Precipitation and the GPCP Rain Gauge-Satellite Combined Dataset with Observational Error Considerations , 1998 .

[11]  Christian D. Kummerow,et al.  Beamfilling Errors in Passive Microwave Rainfall Retrievals. , 1998 .

[12]  P. Jones,et al.  Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology , 1999 .

[13]  A. Chang,et al.  Nonsystematic Errors of Monthly Oceanic Rainfall Derived from SSM/I , 1999 .

[14]  Jeffrey W. White,et al.  Interpolation techniques for climate variables , 1999 .

[15]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[16]  Thomas L. Bell,et al.  Dependence of Satellite Sampling Error on Monthly Averaged Rain Rates:Comparison of Simple Models and Recent Studies , 2000 .

[17]  Katsuhiko Nishikawa,et al.  Development of precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite , 2001, IEEE Trans. Geosci. Remote. Sens..

[18]  Grant W. Petty,et al.  The Sensitivity of Microwave Remote Sensing Observations of Precipitation to Ice Particle Size Distributions , 2001 .

[19]  M. Jha,et al.  Impacts of Climate Change on Stream Flow in the Upper Mississippi River Basin: A Regional Climate Model Perspective, The , 2003 .

[20]  Chris Kidd,et al.  Satellite Rainfall Estimation Using a Combined Pasive Microwave and Infrared Algorithm. , 2003 .

[21]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[22]  Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure , 2004 .

[23]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[24]  J. Janowiak,et al.  COMPARISON OF NEAR-REAL-TIME PRECIPITATION ESTIMATES FROM SATELLITE OBSERVATIONS AND NUMERICAL MODELS , 2007 .

[25]  Dennis P. Lettenmaier,et al.  Evaluation of Precipitation Products for Global Hydrological Prediction , 2008 .

[26]  Denis Ruelland,et al.  Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa , 2008 .

[27]  Phillip A. Arkin,et al.  An Intercomparison and Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly Gauge Data , 2009 .

[28]  M. Tippett,et al.  Optimal Estimation of the Climatological Mean , 2009 .

[29]  Misako Kachi,et al.  Verification of High-Resolution Satellite-Based Rainfall Estimates around Japan Using a Gauge-Calibrated Ground-Radar Dataset , 2009 .

[30]  G. Huffman,et al.  The TRMM Multi-Satellite Precipitation Analysis (TMPA) , 2010 .

[31]  Uang,et al.  The NCEP Climate Forecast System Reanalysis , 2010 .

[32]  T. Skaugen,et al.  Simulated precipitation fields with variance-consistent interpolation , 2010 .

[33]  Faisal Hossain,et al.  Understanding the Scale Relationships of Uncertainty Propagation of Satellite Rainfall through a Distributed Hydrologic Model , 2010 .

[34]  A. Getirana,et al.  Assessment of different precipitation datasets and their impacts on the water balance of the Negro River basin , 2011 .

[35]  Chris Kidd,et al.  Global Precipitation Measurement , 2008 .

[36]  Pingping Xie,et al.  A conceptual model for constructing high‐resolution gauge‐satellite merged precipitation analyses , 2011 .

[37]  Jinzhong Yang,et al.  The thin plate spline robust point matching (TPS-RPM) algorithm: A revisit , 2011, Pattern Recognit. Lett..

[38]  Kuolin Hsu,et al.  Hydrologic evaluation of satellite precipitation products over a mid-size basin , 2011 .

[39]  M. Gebremichael,et al.  Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands , 2011 .

[40]  Y.‐C. Gao,et al.  Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau , 2012 .

[41]  Harald Kunstmann,et al.  The Hydrological Cycle in Three State-of-the-Art Reanalyses: Intercomparison and Performance Analysis , 2012 .

[42]  Chuntian Cheng,et al.  Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model , 2012 .

[43]  Michael Smith,et al.  Analysis of inconsistencies in multi-year gridded quantitative precipitation estimate over complex terrain and its impact on hydrologic modeling , 2012 .

[44]  Yang Hong,et al.  Assessment of evolving TRMM-based multisatellite real-time precipitation estimation methods and their impacts on hydrologic prediction in a high latitude basin , 2012 .

[45]  Guoxiong Wu,et al.  Diurnal variations of summer precipitation over the Asian monsoon region as revealed by TRMM satellite data , 2012, Science China Earth Sciences.

[46]  Ali Behrangi,et al.  On the quantification of oceanic rainfall using spaceborne sensors , 2012 .

[47]  E. Biggs,et al.  Assessing the accuracy and applied use of satellite-derived precipitation estimates over Nepal , 2012 .

[48]  A. Kitoh,et al.  APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges , 2012 .

[49]  Chong-yu Xu,et al.  Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China , 2013 .

[50]  Kelin Wang,et al.  Application of the SWAT model to the Xiangjiang river watershed in subtropical central China. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[51]  G. Xue A gridded daily observation dataset over China region and comparison with the other datasets , 2013 .

[52]  S. Jain,et al.  Validation of a new meteorological forcing data in analysis of spatial and temporal variability of precipitation in India , 2014, Stochastic Environmental Research and Risk Assessment.

[53]  Yang Hong,et al.  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? , 2013 .

[54]  A. Ye,et al.  Would the ‘real’ observed dataset stand up? A critical examination of eight observed gridded climate datasets for China , 2014 .

[55]  Zhenchun Hao,et al.  Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau , 2014 .

[56]  E. Anagnostou,et al.  Error Analysis of Satellite Precipitation Products in Mountainous Basins , 2014 .

[57]  Y. Fujihara,et al.  Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin , 2014 .

[58]  Yang Hong,et al.  Improvement of Multi-Satellite Real-Time Precipitation Products for Ensemble Streamflow Simulation in a Middle Latitude Basin in South China , 2014, Water Resources Management.

[59]  Matti Kummu,et al.  Using Reanalysis and Remotely Sensed Temperature and Precipitation Data for Hydrological Modeling in Monsoon Climate: Mekong River Case Study , 2014 .

[60]  A. Hou,et al.  The Global Precipitation Measurement Mission , 2014 .

[61]  Sadiq I. Khan,et al.  Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan , 2014 .

[62]  G. Balsamo,et al.  The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data , 2014 .

[63]  Satya Prakash,et al.  An evaluation of high-resolution multisatellite rainfall products over the Indian monsoon region , 2014 .

[64]  Yihun T. Dile,et al.  Evaluation of CFSR climate data for hydrologic prediction in data‐scarce watersheds: an application in the Blue Nile River Basin , 2014 .

[65]  Xiaojing Wang,et al.  Corrigendum: Proteomic analysis of colon and rectal carcinoma using standard and customized databases , 2015, Scientific Data.

[66]  Arthur P. Cracknell,et al.  Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia , 2015, Remote. Sens..

[67]  Peter Bauer-Gottwein,et al.  Evaluation of Remotely Sensed Precipitation and Its Performance for Streamflow Simulations in Basins of the Southeast Tibetan Plateau , 2015 .

[68]  Emmanouil N. Anagnostou,et al.  Hydrologic evaluation of satellite and reanalysis precipitation datasets over a mid-latitude basin , 2015 .

[69]  Guoqing Wang,et al.  Changes in precipitation and temperature in Xiangjiang River Basin, China , 2016, Theoretical and Applied Climatology.

[70]  K. Moffett,et al.  Remote Sens , 2015 .

[71]  Amir AghaKouchak,et al.  Error characterization of TRMM Multisatellite Precipitation Analysis (TMPA-3B42) products over India for different seasons , 2015 .

[72]  K. Sunilkumar,et al.  Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data , 2015 .

[73]  S. Sorooshian,et al.  PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies , 2015 .

[74]  P. Peterson,et al.  The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0 Dataset: 35 year Quasi-Global Precipitation Estimates for Drought Monitoring , 2015 .

[75]  J. Michaelsen,et al.  The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes , 2015, Scientific Data.

[76]  Gilles R. C. Essou,et al.  Comparison of climate datasets for lumped hydrological modeling over the continental United States , 2016 .

[77]  Nemati Amirreza,et al.  DAILY PRECIPITATION CLIMATE DATA RECORD FROM MULTISATELLITE OBSERVATIONS FOR HYDROLOGICAL AND CLIMATE STUDIES , 2016 .

[78]  Viviana Maggioni,et al.  A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era , 2016 .

[79]  Chong-yu Xu,et al.  Similarity and difference of global reanalysis datasets (WFD and APHRODITE) in driving lumped and distributed hydrological models in a humid region of China , 2016 .

[80]  Bernd Diekkrüger,et al.  Evaluating the performance of remotely sensed and reanalysed precipitation data over West Africa using HBV light , 2016 .

[81]  S. Benabdallah,et al.  Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—Application in data-scarce rural Tunisia , 2016 .

[82]  Jaap Schellekens,et al.  MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data , 2016 .

[83]  Chong-yu Xu,et al.  Feasibility and uncertainty of using conceptual rainfall-runoff models in design flood estimation , 2016 .

[84]  A. Dezetter,et al.  Hydrological Evaluation of TRMM Rainfall over the Upper Senegal River Basin , 2016 .

[85]  Danqing Huang,et al.  Assessment of summer monsoon precipitation derived from five reanalysis datasets over East Asia , 2016 .

[86]  Markus Disse,et al.  Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. , 2016, The Science of the total environment.

[87]  Minha Choi,et al.  A SWAT modeling approach to assess the impact of climate change on consumptive water use in Lower Chenab Canal area of Indus basin , 2016 .

[88]  Junfeng Gao,et al.  Modeling the combined impact of future climate and land use changes on streamflow of Xinjiang Basin, China , 2016 .

[89]  François Brissette,et al.  Can Precipitation and Temperature from Meteorological Reanalyses Be Used for Hydrological Modeling , 2016 .

[90]  Donghee Lee,et al.  Hydrological Utility and Uncertainty of Multi-Satellite Precipitation Products in the Mountainous Region of South Korea , 2016, Remote. Sens..

[91]  Botao Zhou,et al.  Changes in temperature and precipitation extreme indices over China: analysis of a high‐resolution grid dataset , 2016 .

[92]  Z. Duan,et al.  Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy). , 2016, The Science of the total environment.

[93]  Fan Yang,et al.  Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China , 2017 .

[94]  Yonghua Zhu,et al.  Evaluating the Applicability of Four Latest Satellite-Gauge Combined Precipitation Estimates for Extreme Precipitation and Streamflow Predictions over the Upper Yellow River Basins in China , 2017, Remote. Sens..

[95]  Kuolin Hsu,et al.  Evaluation of a new satellite‐based precipitation data set for climate studies in the Xiang River basin, southern China , 2017 .

[96]  Chris Kidd,et al.  Global precipitation measurements for validating climate models , 2017 .

[97]  M. Shrestha,et al.  Assessment of high‐resolution satellite rainfall estimation products in a streamflow model for flood prediction in the Bagmati basin, Nepal , 2017 .

[98]  Gholam Reza Rakhshandehroo,et al.  Evaluation of satellite rainfall climatology using CMORPH, PERSIANN‐CDR, PERSIANN, TRMM, MSWEP over Iran , 2017 .

[99]  F. Pappenberger,et al.  Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling , 2017 .

[100]  Chong-yu Xu,et al.  Tracking the error sources of spatiotemporal differences in TRMM accuracy using error decomposition method. , 2018 .

[101]  S. Sorooshian,et al.  A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons , 2018 .

[102]  Christian Massari,et al.  On the performance of satellite precipitation products in riverine flood modeling: a review. , 2018 .