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

Summary This study first focuses on comprehensive evaluating three widely used satellite precipitation products (TMPA 3B42V6, TMPA 3B42RT, and CMORPH) with a dense rain gauge network in the Mishui basin (9972 km 2 ) in South China and then optimally merge their simulated hydrologic flows with the semi-distributed Xinanjiang model using the Bayesian model averaging method. The initial satellite precipitation data comparisons show that the reanalyzed 3B42V6, with a bias of −4.54%, matched best with the rain gauge observations, while the two near real-time satellite datasets (3B42RT and CMORPH) largely underestimated precipitation by 42.72% and 40.81% respectively. With the model parameters first benchmarked by the rain gauge data, the behavior of the streamflow simulation from the 3B42V6 was also the most optimal amongst the three products, while the two near real-time satellite datasets produced deteriorated biases and Nash–Sutcliffe coefficients (NSCEs). Still, when the model parameters were recalibrated by each individual satellite data, the performance of the streamflow simulations from the two near real-time satellite products were significantly improved, thus demonstrating the need for specific calibrations of the hydrological models for the near real-time satellite inputs. Moreover, when optimally merged with respect to the streamflows forced by the two near real-time satellite precipitation products and all the three satellite precipitation products using the Bayesian model averaging method, the resulted streamflow series further improved and became more robust. In summary, the three current state-of-the-art satellite precipitation products have demonstrated potential in hydrological research and applications. The benchmarking, recalibration, and optimal merging schemes for streamflow simulation at a basin scale described in the present work will hopefully be a reference for future utilizations of satellite precipitation products in global and regional hydrological applications.

[1]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[2]  Inge Sandholt,et al.  Evaluation of remote‐sensing‐based rainfall products through predictive capability in hydrological runoff modelling , 2010 .

[3]  S. Sorooshian,et al.  Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .

[4]  Misako Kachi,et al.  Global Precipitation Map Using Satellite-Borne Microwave Radiometers by the GSMaP Project: Production and Validation , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Quanxi Shao,et al.  An improved statistical approach to merge satellite rainfall estimates and raingauge data. , 2010 .

[6]  Yudong Tian,et al.  Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications , 2007 .

[7]  Roongroj Chokngamwong,et al.  Thailand Daily Rainfall and Comparison with TRMM Products , 2008 .

[8]  Yang Hong,et al.  Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and Its Utility in Hydrologic Prediction in the La Plata Basin , 2008 .

[9]  D. Lettenmaier,et al.  Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification , 1996 .

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

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

[12]  J. Janowiak,et al.  A Real–Time Global Half–Hourly Pixel–Resolution Infrared Dataset and Its Applications , 2001 .

[13]  Soroosh Sorooshian,et al.  Optimal use of the SCE-UA global optimization method for calibrating watershed models , 1994 .

[14]  Liliang Ren,et al.  Evaluation of high-resolution satellite precipitation products with surface rain gauge observations from Laohahe Basin in northern China , 2010 .

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

[16]  S. Sorooshian,et al.  A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters , 2002 .

[17]  Peiyan Chen,et al.  Verification of Tropical Cyclone–Related Satellite Precipitation Estimates in Mainland China , 2009 .

[18]  Yang Hong,et al.  Hydrologic evaluation of Multisatellite Precipitation Analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe basin, China , 2010 .

[19]  Faisal Hossain,et al.  How Much Can A Priori Hydrologic Model Predictability Help in Optimal Merging of Satellite Precipitation Products , 2011 .

[20]  S. J. Connor,et al.  Validation of high‐resolution satellite rainfall products over complex terrain , 2008 .

[21]  F. J. Turk,et al.  Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[22]  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 .

[23]  F. Joseph Turk,et al.  Evaluating High-Resolution Precipitation Products , 2008 .

[24]  Qingyun Duan,et al.  An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction , 2006 .

[25]  Mekonnen Gebremichael,et al.  Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model , 2011 .

[26]  Y. Hong,et al.  Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System , 2004 .

[27]  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 .

[28]  Faisal Hossain,et al.  A first approach to global runoff simulation using satellite rainfall estimation , 2007 .

[29]  Y. Hong,et al.  Merging multiple precipitation sources for flash flood forecasting , 2007 .

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

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

[32]  C. Keller,et al.  Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW) , 1996 .

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

[34]  Valentine G. Anantharaj,et al.  Optimally Merging Precipitation to Minimize Land Surface Modeling Errors , 2010 .