Improvement of Multi-Satellite Real-Time Precipitation Products for Ensemble Streamflow Simulation in a Middle Latitude Basin in South China
- 487 Downloads
The real-time availability of several satellite-based precipitation products has recently provided hydrologists with an unprecedented opportunity to improve current hydrologic prediction capability for vast river basins, particularly for ungauged regions. However, the accuracy of real-time satellite precipitation data remains uncertain. This study aims to use three widely used real-time satellite precipitation products, namely, TRMM Multi satellite Precipitation Analysis real-time precipitation product 3B42 (TMPA 3B42RT), Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIAN), and NOAA/Climate Precipitation Center Morphing Technique (CMORPH), for ensemble stream flow simulation with the gridded xinanjiang (XAJ) model and shuffled complex evolution metropolis (SCEM-UA) algorithm in the middle-latitude Mishui basin in South China. To account the bias of the satellite precipitation data and consider the input uncertainty, two different methods, i.e. a precipitation error multiplier and a precipitation error model were introduced. For each precipitation input model, the posterior probability distribution of the parameters and their associated uncertainty were calibrated using the SCEM-UA algorithm, and 15,000 ensemble stream flow simulations were conducted. The simulations of the satellite precipitation data were then optimally merged using the Bayseian model averaging (BMA) method. The result shows that in Mishui basin, the three sets of real-time satellite precipitation data largely underestimated rainfall. Streamflow simulation performed poorly when the raw satellite precipitation data were taken as input and the model parameters were calibrated with gauged data. By implementing the precipitation error multiplier and the precipitation error model and then recalibrating the model, the behavior of the simulated stream flow and calculated uncertainty boundary were significantly improved. Furthermore, the BMA combination of the simulations from the three datasets resulted in a significantly better prediction with a remarkably reliable uncertainty boundary and was comparable with the simulation using the post-real-time bias-corrected research quality TMPA 3B42V7. The proposed methodology of bias adjustment, uncertainty analysis, and BMA combination collectively facilitates the application of the current three real-time satellite data to hydrological prediction and water resource management over many under-gauged basins. This research is also an investigation on the hydrological utility of multi-satellite precipitation data ensembles, which can potentially integrate additional more satellite products when the Global Precipitation Measuring mission with 9-satellite constellation is anticipated in 2014.
KeywordsSatellite precipitation Error adjustment Ensemble streamflow simulation Uncertainty analysis Hydrological model Bayesian model averaging
The current study was jointly supported by the Programme of Introducing Talents of Discipline to Universities by the Ministry of Education and the State Administration of Foreign Experts Affairs, China (the 111 Project, No. B08048), the Special Basic Research Fund by the Ministry of Science and Technology, China (No. 2011IM011000), the National Science Foundation for Young Scientists of China (No. 41,201,031), and the Fundamental Research Funds for the Central Universities. We also acknowledge the HyDROS Lab (HyDRometeorology and RemOte Sensing Laboratory: http://hydro.ou.edu) at the National Weather Center, Norman, OK for their guidance and support.
- Ajami NK, Duan QY, Sorooshian S (2007) An Integrated Hydrological Bayesian Multimodel Combination Framework: Confronting Input, Parameter, and Model Structural Uncertainty in Hydrologic Prediction. Water Resour Res 43, W01403Google Scholar
- Bitew MM, Gebremichael M (2011) Evaluation of Satellite Rainfall Products Through Hydrologic Simulation in a Full Distributed Hydrologic Model. Water Resour Res 47, W06526Google Scholar
- Hong Y, Hsu KL, Moradkhani H, Soroosh S (2006) Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response. Water Resour Res 42, W08421Google Scholar
- Jiang SH, Ren LL, Yong B, Yang XL, Shi L (2010) Evaluation of High-Resolution Satellite Precipitation Products With Surface Rain Gauge Observations from Laohahe Basin in Northern China. Water Sci Eng 3(4):405–417Google Scholar
- Jiang SH, Ren LL, Hong Y, Yong B, Yang XL, Yuan F, Ma MW (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. J Hydrol 452–453:213–225CrossRefGoogle Scholar
- Kubota T, Shige S, Hashizume H, Aonashi K, Takahashi N, Seto S, Takayabu YN, Ushio T, Nakagawa K, Iwanami K, Kachi M, Okamoto K (2007) Global Precipitation map Using Satellite Borne Microwave Radiometers by the GSMaP Project: Production and Validation. IEEE Trans Geosci Remote Sens 45:2259–2275CrossRefGoogle Scholar
- Kidd C, Huffman G (2011) Global Precipitation Measurement. Meteorol Appl 18(3):334–353Google Scholar
- Vrugt JA, Gupta HV, Bouten W, Sorooshian S (2003) A Shuffled Complex Evolution Metropolis Algorithm for Optimization and Uncertainty Assessment of Hydrologic Model Parameters. Water Resour Res 39:1201Google Scholar
- Xue XW, Hong Y, Limaye AS, Gourley JJ, Huffman GJ, Shan SI, Dorji C, Chen S (2013) 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? J Hydrol 499:91–99CrossRefGoogle Scholar
- Yong B, Hong Y, Ren LL, Gourley JJ, Huffman GJ, Chen X, Wang W, Khan SI (2012) Assessment of Evolving TRMM-Based Multi-Satellite Real-Time Precipitation Estimation Methods and Their Impacts on Hydrologic Prediction in a High Latitude Basin. J Geophys Res 117, D09108Google Scholar