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Title: Characterization of within-day beginning times of storms for stochastic simulation

item Bonta, James - Jim
item Hardegree, Stuart
item Cho, Jaepil

Submitted to: American Society of Agricultural and Biological Engineers
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/7/2012
Publication Date: 8/1/2012
Citation: Bonta, J.V., Hardegree, S.P., Cho, J. 2012. Characterization of within-day beginning times of storms for stochastic simulation. American Society of Agricultural and Biological Engineers. 55(4):1179-1192.

Interpretive Summary: Precipitation is a key input into a wide range of hydrologic, plant growth and natural resource models. The environmental processes driven by these inputs can be very responsive to diurnal patterns of precipitation, but most models do not account for sub-daily weather variability. We used precipitation data from the rain-gauge network at the USDA-Agricultural Research Service, Reynolds Creek Experimental Watershed in SW Idaho to quantify monthly and topographic variability in the diurnal pattern of the start of storm events. We found that elevation had no effect on the diurnal distribution of storm events, but that there was a significant change in distribution as a function of season. The strongest seasonal effect was the development of a significant bimodal distribution during the summer months. Two methods to quantify the variation of starting times of storms were investigated, and both worked well. These results will yield an improved methodology for parameterizing hydrologic and ecological models to better simulate realistic weather patterns during a 24-hr day. Potential users of these results are scientists and management agencies that use these models to understand environmental processes, and to inform management decisions regarding crop production, soil and erosion management, rangeland restoration, and other natural resource applications.

Technical Abstract: The beginning times of storms within a day are often required for stochastic modeling purposes and for studies on plant growth. This study investigated the variation in frequency distributions of storm-initiation time (SI time) within a day due to elevation changes and month. Actual storms without 24-hr constraints were used, as opposed to simply bursts of precipitation within a 24-hr period. Two methods of characterizing and quantifying these distributions were investigated – kernel-density estimation (KDE) and a mixed-doubly-truncated normal distribution (MDTN) method using nonlinear curve fitting subject to bounds on the parameters. Parameter-estimation methods were also investigated. Data came from the rain-gauge network maintained by USDA-Agricultural Research Service at the Reynolds Creek Experimental Watershed in SW Idaho over a 982-m elevation gradient. There was no difference between frequency distributions of SI time with elevation or precipitation type over the 147-km2 study area. There was a significant shift in SI-time distribution from earlier in the morning in late fall and winter, to early afternoon during the spring and summer. Both the KDE and MDTN methods accurately characterized the observed histograms, which included near-uniform, single-mode, and bimodal distributions. The MDTN method worked well most of the time (~97%) but can have mathematical convergence problems. An SI-time analysis based on a 24-h cycle starting at 2100 hours yielded a better fit to the data than a “standard day” defined to start at midnight using the MDTN method. Exploratory regressions between the four MDTN parameters and several readily available independent variables did not yield consistent or significant predictive relations. Cumulative distributions for either the KDE or MDTN methods are suggested for stochastic modeling purposes on a monthly basis as they represent well observed histograms of SI times. The KDE method is suggested for use because of its simplicity in ungauged areas as long as neighboring data are available. The methods have utility for characterizing time variation of other weather elements.