Spatial characteristics of thunderstorm rainfall fields and their relation to runoff

Abstract

The main aim of this study was to assess the ability of simple geometric measures of thunderstorm rainfall in explaining the runoff response from the watershed. For calculation of storm geometric properties (e.g. areal coverage of storm, areal coverage of the high-intensity portion of the storm, position of storm centroid and the movement of storm centroid in time), spatial information of rainfall is needed. However, generally the rainfall data consists of rainfall depth values over an unevenly spaced network of raingauges. For this study, rainfall depth values were available for 91 raingauges in a watershed of about 148 km 2. There was a question about which interpolation method should be used for obtaining uniformly gridded data. Therefore, a small study was undertaken to compare cross-validation statistics and computed geometric parameters using two interpolation methods (kriging and multiquadric). These interpolation methods were used to estimate precipitation over a uniform 100 m £ 100 m grid. The cross-validation results from the two methods were generally similar and neither method consistently performed better than the other did. In view of these results we decided to use multiquadric interpolation method for the rest of the study. Several geometric measures were then computed from interpolated surfaces for about 300 storm events occurring in a 17-year period. The correlation of these computed measures with basin runoff were then observed in an attempt to assess their relative importance in basin runoff response. It was observed that the majority of the storms (observed in the study) covered the entire watershed. Therefore, it was concluded that the areal coverage of storm was not a good indicator of the amount of runoff produced. The areal coverage of the storm core (10-min intensity greater than 25 mm/h), however, was found to be a much better predictor of runoff volume and peak rate. The most important variable in runoff production was found to be the volume of the storm core. It was also observed that the position of the storm core relative to the watershed outlet becomes more important as the catchment size increases, with storms positioned in the central portion of the watershed producing more runoff than those positioned near the outlet or near the head of the watershed. This observation indicates the importance of interaction of catchment size and shape with the spatial storm structure in runoff generation. Antecedent channel wetness was found to be of some importance in explaining runoff for the largest of the three

Evolution of aquatic insect behaviours across a gradient of disturbance predictability

“…In the midsized catchments (approx. 10 km 2 ), rainfall cues and floods are well correlated, in part because monsoon storms occur at this spatial scale (9 km 2 ; Syed et al 2003). Thus, flood events can be predicted from rainfall events somewhat reliably in the mid-sized catchments.…”

Product‐error‐driven generator of probable rainfall conditioned on WSR‐88D precipitation estimates

“…[2] It is commonly agreed that the quality of real-time hydrologic forecasts depends on the adequacy of the rainfall runoff model structure and parameters as well as the accuracy of space-time representation of rainfall input [e.g., Kitanidis and Bras, 1980;Georgakakos and Hudlow, 1984;Georgakakos, 1986;Georgakakos and Smith, 1990;Krajewski et al, 1991;Pessoa et al, 1993;Michaud and Sorooshian, 1994;O'Connell and Todini, 1996;Winchell et al, 1998;Bell and Moore, 2000;Ogden et al, 2000;Borga, 2002;National Research Council, 2002;Syed et al, 2003;Smith et al, 2004;Ajami et al, 2007;Gabellani et al, 2007]. While errors due to both factors have received much recent attention in the literature [e.g., Finnerty et al, 1997;Butts et al, 2004;Carpenter and Georgakakos, 2004;Wagener and Gupta, 2005;Borga et al, 2006;Huard and Mailhot, 2006;Kavetski et al, 2006aKavetski et al, , 2006bKuczera et al, 2006;Oudin et al, 2006], data assimilation and ensemble forecasting frameworks are considered effective means of improving forecasts in the face of uncertainty [e.g., Kavetski et al, 2002;Vrugt et al, 2005;Carpenter and Georgakakos, 2006a;Kavetski et al, 2006a;Moradkhani et al, 2005aMoradkhani et al, , 2005bMoradkhani et al, , 2006Oudin et al, 2006;Russo et al, 2006;…”

Influence of rainfall observation network on model calibration and application

“…In a rainfall-runoff model, accurate knowledge of precipitation is very essential for accurately estimating discharge. This is due to that fact that representation of precipitation is important in determining surface hydrological processes (Syed et al, 2003;Zehe et al, 2005). Beven (2001) noted that no model, however well founded in physical theory or empirically justified by past performance, will be able to produce accurate hydrograph predictions if the inputs to the model do not characterize the precipitation inputs.…”