Thorough understanding of the rainfall-runoff processes that influence watershed hydrological response is important and can be incorporated into the planning and management of watershed resources. Soft computing techniques and inferential statistics were used to assess 2 rainfall-runoff models and their runoff predictive accuracy. The 1954 simplified SCS runoff model was statistical in-significant under two Null hypotheses rejection and paved way for the model calibration study to produce regional specific model through calibration according to regional hydrological conditions. New model out-performed non-calibrated SCS model and reduced RSS by 27%. A 3D runoff difference model was created as a collective visual representation between non-calibrated and calibrated new model, it also showed that both under and over design risks were less significant at high CN area and more profound under high rainfall depths. On average, rural catchments of Peninsula Malaysia faced 7% (lower CN area as much as 22%) CN down scaling adjustment due to regional hydrological calibration in order to achieve better runoff predictions.
[1]
Rochowicz,et al.
Bootstrapping Analysis, Inferential Statistics and EXCEL
,
2010
.
[2]
Lloyd Ling,et al.
Inferential statistics of claim assessment
,
2014
.
[3]
Daniel B. Wright,et al.
Understanding Statistics: An Introduction for the Social Sciences
,
1997
.
[4]
L Ling,et al.
A micro focus with macro impact: Exploration of initial abstraction coefficient ratio (λ) in Soil Conservation Curve Number (CN) methodology
,
2014
.
[5]
D. C. Howell.
Statistical methods for psychology, 3rd ed.
,
1992
.