Fuzzy Hydrologic Model in Tropical Watershed - Advances in Data, Methods, Models and Their Applications in Geoscience - page 294

Fig. 8. CN maps obtained by the Fuzzy SCSCN model (A) and by the standard SCSCN
model (B)

Rule no. (R i ) If HSG and LPI Then CN

R 1 If D ( v. low inf. ) and pasture then 80 ( v. high)

R 2 If D ( v. low inf. ) and forest then 69 (medium-high)

R 3 If C ( low inf.) and pasture then 74 (High)

R 4 If C ( low inf. ) and forest then 62 (Medium)

R 5 If A ( high inf. ) and pasture then 39 (medium-low)

R 6 If A ( high inf. ) and forest then 26 (Low)

R 7 If B ( moderate inf. ) and pasture then 61 (Medium)

R 8 If B ( moderate inf. ) and forest then 52 (medium-low)

Table 3. Fuzzy rule-based model for providing the CN parameters for the study area.

6. Result discussions

The CNs used here were selected on the basis of calibrations between modeled and
observed runoffs. Key characteristics of the watershed, chiefly the hydrologic soil group,
land cover and antecedent moisture conditions, plus CN tables available in the literature
(e.g., SCS 1972; Thompson 1999), guided the CN selection. Once the CNs were selected, the
runoff modelling was tested through a comparison between the modeled runoff depth and
the recorded runoff depth observed in field. The validation of the CNs for the Quilombo
River watershed was carried out for 16 rain events (Table 4). The results indicate that the
modeled and the observed runoffs are akin and, therefore, the employed CNs proved
suitable.