Lagtimes and times of concentration are frequently determined parameters in hydrological design and greatly aid in understanding natural watershed dynamics. In unmonitored catchments, they are usually calculated using empirical or semiempirical equations developed in other studies, without critically considering where those equations were obtained and what basic assumptions they entailed. In this study, we determined the lagtimes (LT) between the middle point of rainfall events and the discharge peaks in a watershed characterized by volcanic soils and swamp forests in
southern Chile. Our results were compared with calculations from 24 equations found in the literature. The mean LT for 100 episodes was 20 hours (ranging between 0.6–58.5 hours). Most formulae that only included physiographic predictors severely underestimated the mean LT, while those including the rainfall intensity or stream velocity showed better agreement with the average value. The duration of the rainfall events related significantly and positively with LTs. Thus, we accounted for varying LTs within the same watershed by including the rainfall duration in the equations that showed the best results, consequently improving our predictions. Izzard and velocity methods are recommended, and we suggest that lagtimes and times of concentration must be locally determined with hyetograph-hydrograph analyses, in addition to explicitly considering precipitation patterns.
There is an emerging challenge within water resources on how, and to what extent, borrowing concepts from landscape ecology might help re-define traditional concepts in hydrology in a more tangible manner.
A stepwise regression model was adopted in this study to assess whether the time of concentration of catchments could be explained by five landscape structure-representing metrics for land use/land cover, soil and geological patches, using spatial data from 39 catchments.
The models suggested that the times of concentration of the catchments could be predicted using the measures of four landscape structure-representing metrics, which include contiguity index (r2 = 0.46, p ≤0.05), fractal dimension index (r2 = 0.51, p ≤0.05), related circumscribing circle (r2 = 0.52, p ≤0.05), and shape index (r2 = 0.47, p ≤0.05).
The models indicated that the regularity or irregularity in land use/land cover patch shape played a key role in affecting catchment hydrological response. Our findings revealed that regularity and irregularity in the shape of a given patch (e.g., urban and semi-urban, rangeland and agricultural patches) can affect patch functions in retarding and/or increasing flow accumulation at the catchment scale, which can, in turn, decrease or increase the times of concentration in the catchments.
Time of concentration (TC) of surface flow in watersheds depends on the coupled response of hillslopes and stream networks. The important point in this background is to study the effects of the geometry and the shape of complex hillslopes on the time of concentration considering the degree of flow convergence (convergent, parallel or divergent) as well as the profile curvature (concave, straight or convex). In this research, the shape factor of complex hillslopes as introduced by Agnese et al. (2007) is generalized and linked to the TC. A new model for calculating TC of complex hillslopes is presented, which depends on the plan shape, the type and degree of profile curvature, the Manning roughness coefficient, the flow regime, the length, the average slope, and the excess rainfall intensity. The presented model was compared to that proposed by Singh and Agiralioglu (1981a,b) and Agiralioglu (1985). Moreover, the results of laboratory experiments on the travel time of surface flow of complex hillslopes were used to calibrate the model. The results showed that TC for convergent hillslopes is nearly double those of parallel and divergent ones. TC in convex hillslopes was very close to that in straight and concave hillslopes. While the effect of convergence on TC is considerable, the curvature effect confirmed insignificant. Finally, in convergent hillslopes, TC increases with the degree of convergence, but in divergent hillslopes, it decreases as degree of divergence increases.