Abstract
Satellite-based precipitation retrieval techniques and algorithms have been developed to estimate precipitation from satellite observation. The realistic characterization of uncertainty in satellite precipitation estimate and the corresponding uncertain hydrologic response can better aid water resources managers in their decision making. In this study, the standard error of satellite-based PERSIANN-CCS rainfall estimates conditioning on the assumed true field (i.e. radar rainfall) is obtained according to a multivariate function considering the spatial and temporal scales. Accepting the multiplicative nature of this error, the Monte Carlo simulation is used to generate the ensemble of precipitation and propagate them into a conceptual hydrologic model to investigate the impact of input error on streamflow simulation. The statistical assessment of the results through probabilistic measures explores the more in-depth quality and reliability of the hydrologic response resulted from input error characterization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adler, F.R. et al. (2003), The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present), Journal of Hydrometeorology, 4, 1147–1167.
Anderson, J.L. (2001), An ensemble adjustment kalman filter for data assimilation, Monthly Weather Review, 129(12), 2884–2903.
Boyle, D.P., H.V. Gupta, and S. Sorooshian (2000), Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resources Research, 36(12), 3663–3674.
Bradley, A.A., S.S. Schwartz, and T. Hashino (2004), Distributions-oriented verification of ensemble streamflow predictions, Journal of Hydrometeorology, 5(3), 532–545.
Burnash, R.J.C. (1995), The NWS river forecast system- catchment model, in Computer Models of Watershed Hydrology, edited by V.P. Singh , pp. 311–365, Water Resources Publications, Littleton, CO.
Burnash, R.J., R.L. Ferral, and R.A. McGuire (1973), A generalized streamflow simulation system: Conceptual modeling for digital computers, Technical Report, Joint Federal-State River Forecast Center, US National Weather Service and California Department of Water Resources, Sacramento, CA, 2004 pp.
Duan, Q.S., S. Sorooshian, and V.K. Gupta (1992), Effective and efficient global optimization for conceptual rainfall- runoff models, Water Resources Research, 28(4), 1015–1031.
Forman, B.A., E.R. Vivoni, and S.A. Margulis (2008), Evaluation of ensemble-based distributed hydrologic model response with disaggregated precipitation products, Water Resources Research, 44(12), W12409.
Gebremichael, M., W.F. Krajewski, M.M. Morrissey, G.J. Huffman, and R.F. Adler (2005), A detailed evaluation of GPCP 1-degree daily rainfall estimates over the Mississippi River Basin, Journal of Applied Meteorology, 44, 665–681.
Hamill, T.M. (2001), Notes and correspondence on “Interpretation of rank histograms for verifying ensemble forecasts”, Monthly Weather Review, 129, 550–560.
Hong, Y., K. Hsu, X. Gao, and S. Sorooshian (2004), Precipitation estimation from remotely sensed imagery using artificial neural network-cloud classification system, Journal of Applied Meteorology, 43, 1834–1853.
Hong, Y., K. Hsu, H., Moradkhani, and S. Sorooshian. (2006), Uncertainty quantification of satellite precipitation estimation and monte carlo assessment of the error propagation into hydrologic response, Water Resources Research, 42(8), W08421.
Hossain, F. and E.N. Anagnostou (2006), A two-dimensional satellite rainfall error model, IEEE Transactions on Geoscience and Remote Sensing, 44, 1511–1522.
Hossain, F. and E.N. Anagnostou (2005), Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture, Advances in Water Resources, 28, 1336–1350.
Hsu, K., X. Gao, S. Sorooshian, and H.V. Gupta (1997), Precipitation estimation from remotely sensed information using artificial neural networks, Journal of Applied Meteorology, 36, 1176– 1190.
Huard, D. and A. Mailhot (2006), A Bayesian perspective on input uncertainty in model calibration: Application to hydrological model ‘‘abc’, Water Resources Research, 42(7), W07416.
Huffman, G.J., R.F. Adler, M. Morrissey, D. Bolvin, S. Curtis, S. Joyce, B. McGavock, and J. Susskind (2001), Global precipitation at one-degree daily resolution from multisatellite observations, Journal of Hydrometeorology, 2, 36–50.
Jolliffe, I.T. and D.B. Stephenson (Eds.) (2003), Introduction to Forecast Verification, A Practitioners Guide in Atmospheric Sciences, Wiley Inc. Press, New York.
Kavetski, D., G. Kuczera, and S.W. Franks (2006), Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resources Research, 42, W03407.
Margulis, S.A., D. Entekhabi, and D. McLaughlin (2006), Spatiotemporal disaggregation of remotely sensed precipitation for ensemble hydrologic modeling and data assimilation, Journal of Hydrometeorology, 7, 511–533.
Moradkhani, H., K.L. Hsu, H. Gupta, and S. Sorooshian (2005b), Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter, Water Resources Research, 41(5), W05012, doi: doi:10.1029/2004WR003604.
Moradkhani, H., K. Hsu, Y. Hong, and S. Sorooshian (2006), Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting, Geophysics Research Letters, 33, L12401.
Moradkhani, H. and S. Sorooshian (2008), General review of rainfall-runoff modeling: Model calibration, data assimilation, and uncertainty analysis, in Hydrological Modeling and Water Cycle, Coupling of the Atmospheric and Hydrological Models, vol. 63, Anonymous, pp. 1–23, Springer, Water Science and Technology Library, New York.
Moradkhani, H., S. Sorooshian, H.V. Gupta, and P.R. Houser (2005a), Dual state–parameter estimation of hydrological models using ensemble Kalman filter, Advances in Water Resources, 28(2), 135–147.
Olson, W.S., et al., (2006), Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: Improved method and uncertainties, Journal of Applied Meteorology and Climatology, 45, 702–720.
Steiner, M., T.L. Bell, Y. Zhang, and E.F. Wood (2003), Comparison of two methods for estimating the sampling-related uncertainty of satellite rainfall averages based on a large radar dataset, Journal of Climate, 16, 3759–3778.
Toth, Z., O. Talagrand, G. Candille, and Y. Zhu (2003), Probability and ensemble forecasts, in Environmental Forecast Verification: A Practitioner’s Guide in Atmospheric Science, edited by I.T. Jollife and D.B. Stephenson. Wiley, New York.
Vrugt, J.A., C.J.F. Braak, M.P. Clark, and J.M. Hyman (2008), Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resources Research, 44, W00B09.
Wilks, D.S. (2006), Statistical Methods in the Atmospheric Sciences, Second Edition, Elsevier Academic Press, Amsterdam.
Acknowledgements
The partial financial support for this work was provided by NOAA-CPPA grant NA070AR4310203.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Moradkhani, H., Meskele, T.T. (2010). Probabilistic Assessment of the Satellite Rainfall Retrieval Error Translation to Hydrologic Response. In: Gebremichael, M., Hossain, F. (eds) Satellite Rainfall Applications for Surface Hydrology. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2915-7_14
Download citation
DOI: https://doi.org/10.1007/978-90-481-2915-7_14
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-2914-0
Online ISBN: 978-90-481-2915-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)