uncertainties and the effect of how the uncertainties propagate from precipitation data
through models need to be evaluated as a whole so that the users have a certain degree
of confidence while making decisions (Figure 13.1). Additionally, evaluation of
uncertainties associated with precipitation products and its propagation into model
behavior is an indispensable element of evaluating data quality and improving
hydrological simulation techniques. As an evaluation result, the uncertainty of
satellite-based precipitation estimation error is a function of several factors, including
spatial and temporal resolution of estimates, the estimated rain rates, and sampling
frequency. Furthermore, when compared with the conventional error propagation
procedure, that is, fixed ratio error estimate, this strategy not only provides more
realistic quantification of precipitation estimation error but also offers improved
uncertainty assessment of the error propagation from the precipitation input into the
hydrological models (Figure 13.8).
The proposed uncertainty analysis framework in this case study not only
provides a general framework of scale-based satellite precipitation estimation error
quantification procedure but also can assess its influence on the uncertainty of
hydrological prediction through Monte Carlo simulation of the error propagation.
As a first attempt to quantify uncertainty of one of the major satellite-based
precipitation data sets (PERSIANN-CCS) at the fine scale (0.04° ´ 0.04° and
hourly), the satellite rainfall error model [Equation (13.1)] does not consider
rainfall detection and false-alarm probabilities. In our continuing effort to quantify
the uncertainty of high-resolution satellite-based rainfall estimates, the error
property of spatial–time integrated precipitation related to the undetected rainfall
and false-alarm scenarios will be investigated and embedded in the end-to-end
uncertainty analysis framework report here.
REFERENCES
Anagnostou, E. N., and Krajewski, W. F. 1998. Calibration of the WSR-88D precipitation
processing subsystem. Weather Forecast 13:396–406.
Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z. Y., and Smith, M. 2001.
Toward improved streamflow forecasts: Value of semidistributed modeling. Water
Resources Research 37:2749–2759.
Burges, S. J. 2003. Process representation, measurements, data quality, and criteria for
parameter estimation of watershed models, in Calibration of Watershed Models. Water
Science and Application 6:283–299.
Gourley, J. J., Hong, Y., Flamig, Z. L., Li, L., and Wang, J. H. 2010. Intercomparison of rainfall
estimates from radar, satellite, gauge, and combinations for a season of record rainfall.
Journal of Applied Meteorology and Climatology 49:437–452.
Habib, E., Krajewski, W. F., and Kruger, A. 2001. Sampling errors of tipping-bucket rain gauge
measurements. Journal of Hydrologic Engineering 6:159–166.
Hong, Y., Hsu, K.-L., Moradkhani, H., and Sorooshian, S. 2006. Uncertainty quantification
of satellite precipitation estimation and Monte Carlo assessment of the error propagation
into hydrologic response. Water Resources Research 42:W08421.
REFERENCES
263
through models need to be evaluated as a whole so that the users have a certain degree
of confidence while making decisions (Figure 13.1). Additionally, evaluation of
uncertainties associated with precipitation products and its propagation into model
behavior is an indispensable element of evaluating data quality and improving
hydrological simulation techniques. As an evaluation result, the uncertainty of
satellite-based precipitation estimation error is a function of several factors, including
spatial and temporal resolution of estimates, the estimated rain rates, and sampling
frequency. Furthermore, when compared with the conventional error propagation
procedure, that is, fixed ratio error estimate, this strategy not only provides more
realistic quantification of precipitation estimation error but also offers improved
uncertainty assessment of the error propagation from the precipitation input into the
hydrological models (Figure 13.8).
The proposed uncertainty analysis framework in this case study not only
provides a general framework of scale-based satellite precipitation estimation error
quantification procedure but also can assess its influence on the uncertainty of
hydrological prediction through Monte Carlo simulation of the error propagation.
As a first attempt to quantify uncertainty of one of the major satellite-based
precipitation data sets (PERSIANN-CCS) at the fine scale (0.04° ´ 0.04° and
hourly), the satellite rainfall error model [Equation (13.1)] does not consider
rainfall detection and false-alarm probabilities. In our continuing effort to quantify
the uncertainty of high-resolution satellite-based rainfall estimates, the error
property of spatial–time integrated precipitation related to the undetected rainfall
and false-alarm scenarios will be investigated and embedded in the end-to-end
uncertainty analysis framework report here.
REFERENCES
Anagnostou, E. N., and Krajewski, W. F. 1998. Calibration of the WSR-88D precipitation
processing subsystem. Weather Forecast 13:396–406.
Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z. Y., and Smith, M. 2001.
Toward improved streamflow forecasts: Value of semidistributed modeling. Water
Resources Research 37:2749–2759.
Burges, S. J. 2003. Process representation, measurements, data quality, and criteria for
parameter estimation of watershed models, in Calibration of Watershed Models. Water
Science and Application 6:283–299.
Gourley, J. J., Hong, Y., Flamig, Z. L., Li, L., and Wang, J. H. 2010. Intercomparison of rainfall
estimates from radar, satellite, gauge, and combinations for a season of record rainfall.
Journal of Applied Meteorology and Climatology 49:437–452.
Habib, E., Krajewski, W. F., and Kruger, A. 2001. Sampling errors of tipping-bucket rain gauge
measurements. Journal of Hydrologic Engineering 6:159–166.
Hong, Y., Hsu, K.-L., Moradkhani, H., and Sorooshian, S. 2006. Uncertainty quantification
of satellite precipitation estimation and Monte Carlo assessment of the error propagation
into hydrologic response. Water Resources Research 42:W08421.
REFERENCES
263