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United States Department of Agriculture

Agricultural Research Service

Title: Stochastic Simulation of Storm Occurrence, Depth, Duration, and Within-Storm Intensities

Author
item BONTA, JAMES

Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 28, 2004
Publication Date: September 30, 2004
Citation: Bonta, J.V. 2004. Stochastic simulation of storm occurrence, depth, duration, and within-storm intensities. Transactions of the ASAE. 47(5):1573-1584.

Interpretive Summary: Newer watershed models require detailed continuous precipitation having short time increments to make watershed models work. The variability of precipitation inputs to models is a major source of variability in watershed flows that is often evaluated in the context of risks. However, available short-term-increment rainfall data are not adequate. A storm-generator model ("StormGen") was developed and tested using precipitation data from Coshocton, OH that synthesizes 'storms' directly (several storms per day to several days per storm). The concepts for modeling the four components of actual storms (storm occurrence, storm duration and depth, and within-storm intensities), and methods of quantifying the model (parameterization) using statistics is presented. Supporting studies have been conducted to help with practical parameterization in areas where there is no rainfall information. Times between storms (TBS) are represented and simulated well by using exponential frequency distributions. Storm durations are characterized by observed distributions of durations in a month. Storm depths require parameterization and simulation that depends on storm durations. Within-storm intensities utilize probabilistic information contained in a special type of graph that summarizes rainfall intensities within storms ('Huff curves'). Initial performance evaluation of three of the components of the model for a 200-year simulation shows that the model works well. TBS was modeled best (monthly average deviation of -1.3%, with values ranging from -3.2 to 3.0%). Monthly average storm duration deviations ranged from -6.2% to +1.6% with an average of -1.3%. Monthly average storm depth deviations ranged from -17.1% to +0.1% with an average of -9.2%, although actual magnitudes ranged only from -2.2 mm to 0 mm. Average deviations between simulated and measured average monthly precipitation was +1.7%, ranging from -8.6% to +12.6%. Corresponding depth differences ranged from -10.0 mm to 10.8 mm, with an average of +0.5 mm. Total simulated precipitation for the entire period deviated from measured precipitation by +0.6%, corresponding to +0.5 mm. The model has utility for a variety of natural resources and engineering uses. A major advantage of the storm generator is its ability to generate long records of storms in areas where there are few or no data. The results of this study are encouraging, and further research should help achieve the potential uses of the model. The model is useful to university and government scientists working on modeling objectives.

Technical Abstract: Newer watershed models require detailed continuous temporal precipitation to drive modeled hydrologic processes. The variability of precipitation inputs to models is a major source of variability in watershed flows that is often evaluated in the context of risks. However, available short-term-increment rainfall data are not adequate. A storm-generator model ("StormGen") was developed and tested using precipitation data from Coshocton, OH that synthesizes 'storms' directly (several storms per day to several days per storm). The concepts for modeling the four elements of actual storms (storm occurrence, storm duration and depth, and within-storm intensities), and storm-model characterization and parameterization using an empirical and statistical approach is presented. Supporting studies have been conducted to help with practical parameterization in ungauged areas. Times between storms (TBS) are represented and simulated well by exponential distributions. Storm durations are characterized by empirical distributions of durations in a month. Storm depths require conditional simulation given storm durations. Within-storm intensities utilize probabilistic information contained in Huff curves. Initial performance evaluation of three of the elements of the model for a 200-year simulation shows that the model works well. TBS was modeled best (monthly average deviation of -1.3%, with values ranging from -3.2 to 3.0%). Monthly average storm duration deviations ranged from -6.2% to +1.6% with an average of -1.3%. Monthly average storm depth deviations ranged from -17.1% to +0.1% with an average of -9.2%, although actual magnitudes ranged only from -2.2 mm to 0 mm. Average deviations between simulated and measured average monthly precipitation was +1.7%, ranging from -8.6% to +12.6%. Corresponding depth differences ranged from -10.0 mm to 10.8 mm, with an average of +0.5 mm. Total simulated precipitation for the entire period deviated from measured precipitation by +0.6%, corresponding to +0.5 mm. The Poisson assumption for the model is validated using Coshocton data, with the ratio of average storm duration to average TBS of only 0.107. The results of this study suggest that StormGen model is promising and that modeling concepts and characterization deserve further investigation.

Last Modified: 8/19/2014
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