Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response

The aim of this paper is to foster the development of an end‐to‐end uncertainty analysis framework that can quantify satellite‐based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite‐based precipitation estimates is assumed as a nonlinear function of rainfall space‐time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high‐resolution satellite‐based precipitation estimates and gauge‐corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5° × 5° latitude‐longitude grids shows about 12–16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall‐runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall‐generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval.

[1]  R. Moore The probability-distributed principle and runoff production at point and basin scales , 1985 .

[2]  D. Rosenfeld,et al.  Climatologically tuned reflectivity-rain rate relations and links to area-time integrals , 1990 .

[3]  Witold F. Krajewski,et al.  A Monte Carlo Study of rainfall sampling effect on a distributed catchment model , 1991 .

[4]  R. Bras,et al.  Analysis of Darwin Rainfall Data: Implications on Sampling Strategy. , 1996 .

[5]  Matthias Steiner,et al.  Uncertainty of Estimates of Monthly Areal Rainfall for Temporally Sparse Remote Observations , 1996 .

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

[7]  George J. Huffman,et al.  Estimates of Root-Mean-Square Random Error for Finite Samples of Estimated Precipitation , 1997 .

[8]  Witold F. Krajewski,et al.  Investigations of error sources of the Global Precipitation Climatology Project emission algorithm , 1998 .

[9]  Norman C. Grody,et al.  Detailed analysis of the error associated with the rainfall retrieved by the NOAA/NESDIS SSM/I algorithm: 1. Tropical oceanic rainfall , 1998 .

[10]  Matthias Steiner,et al.  Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation , 1999 .

[11]  Emmanouil N. Anagnostou,et al.  Uncertainty Quantification of Mean-Areal Radar-Rainfall Estimates , 1999 .

[12]  W. Krajewski,et al.  On the estimation of radar rainfall error variance , 1999 .

[13]  Roman Krzysztofowicz,et al.  Bayesian theory of probabilistic forecasting via deterministic hydrologic model , 1999 .

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

[15]  Chris Kidd,et al.  Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network , 2000 .

[16]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods , 2000 .

[17]  Witold F. Krajewski,et al.  Initial Validation of the Global Precipitation Climatology Project Monthly Rainfall over the United States , 2000 .

[18]  P. A. Arkin,et al.  A combined microwave/infrared rain rate algorithm , 2001 .

[19]  Soroosh Sorooshian,et al.  Toward improved streamflow forecasts: value of semidistributed modeling , 2001 .

[20]  J. Susskind,et al.  Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations , 2001 .

[21]  A. Gruber,et al.  GOES Multispectral Rainfall Algorithm (GMSRA) , 2001 .

[22]  Robert J. Kuligowski,et al.  A Self-Calibrating Real-Time GOES Rainfall Algorithm for Short-Term Rainfall Estimates , 2002 .

[23]  Witold F. Krajewski,et al.  Error Uncertainty Analysis of GPCP Monthly Rainfall Products: A Data-Based Simulation Study , 2003 .

[24]  Matthias Steiner,et al.  Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Dataset , 2003 .

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

[26]  J. Janowiak,et al.  GPCP Pentad Precipitation analyses: An experimental dataset based on gauge observations and satellite estimates , 2003 .

[27]  Matthias Steiner,et al.  Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Dataset , 2003 .

[28]  Witold F. Krajewski,et al.  Zero-covariance hypothesis in the error variance separation method of radar rainfall verification , 2003 .

[29]  J. Janowiak,et al.  The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present) , 2003 .

[30]  Paul D. Bates,et al.  Attenuating reaches and the regional flood response of an urbanizing drainage basin , 2003 .

[31]  Faisal Hossain,et al.  Sensitivity analyses of satellite rainfall retrieval and sampling error on flood prediction uncertainty , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[32]  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 .

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

[34]  Konstantine P. Georgakakos,et al.  Continuous streamflow simulation with the HRCDHM distributed hydrologic model , 2004 .

[35]  Faisal Hossain,et al.  Assessment of current passive-microwave- and infrared-based satellite rainfall remote sensing for flood prediction , 2004 .

[36]  Soroosh Sorooshian,et al.  Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .

[37]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[38]  Y. Hong,et al.  Self‐organizing nonlinear output (SONO): A neural network suitable for cloud patch–based rainfall estimation at small scales , 2005 .

[39]  Faisal Hossain,et al.  A two-dimensional satellite rainfall error model , 2006, IEEE Transactions on Geoscience and Remote Sensing.