Lloyd Ling

Impacts of land-use and climate variability on hydrological components in the Johor River basin, Malaysia

Abstract This study aims to investigate separate and combined impacts of land-use and climate variability on hydrological components in the Johor River Basin (JRB), Malaysia. The Mann-Kendall and Sen’s slope tests were applied to detect the trends in precipitation, temperature and streamflow of the JRB. The Soil and Water Assessment Tool (SWAT) was calibrated and validated using measured monthly streamflow data. The validation results showed that SWAT was reliable in the tropical JRB. The trend analysis showed that there was an insignificant increasing trend for streamflow, whereas significant increasing trends for precipitation and temperature were found. The combined (climate + land-use change) impact caused the annual streamflow and evaporation to increase by 4.4% and 1.2%, respectively. Climate (land-use) raised annual streamflow by 4.4% (0.06%) and evaporation by 2.2% (−0.2%). Climate change imposed a stronger impact than land-use change on the streamflow and evaporation. These findings are useful for decision makers to develop better water and land-use policies. Editor Z. W. Kundzewicz; Guest editor V. Krysanova

Inferential statistics of claim assessment

Initial abstraction coefficient ratio (λ) within the runoff prediction model proposed by the United States Department of Agriculture (USDA), Soil Conservation Services (SCS) in 1954 produced inconsistent runoff results according to worldwide research findings. SCS proposed a linear correlation between initial abstraction (Ia) and total abstraction (S) where Ia = λS. The proposed correlation by then was re-assessed using non-parametric inferential statistics to deduce a different conclusion in this study. Practitioners are encouraged to validate and employ the runoff prediction model with caution.

The Collective Visual Representation of Rainfall-Runoff Difference Model

Inconsistent model prediction results were reported worldwide against SCS (now USDA) runoff model since its inception in 1954. Non parametric inferential statistics was used to reject two Null hypotheses and guided the numerical analysis optimization study to formulate a statistical significant new runoff prediction model. The technique performed regional hydrological conditions calibration to SCS base runoff model and improved runoff prediction by 27 % compared to the non-calibrated empirical model. A rainfall runoff difference model was created as a collective visual representation of runoff prediction error from the non-calibrated SCS empirical model under multiple rainfall depths and CN scenarios in Peninsula Malaysia. Statistical significant correction equations were formulated through swift data mining from the model to study the under and over-design worse case scenarios which are nearly impossible to quantify by solving the complex mathematical equation. Critical curve number concept was introduced in this study.