Rainfall-runoff information is critical for water resource and river basin management. Runoff can be estimated by using two methods; gauged method (direct measurement) and ungauged method (indirect formula and equation). The in-situ measurement provides real-time and accurate yet required time-consuming operation and inaccessibility topography. Therefore, the runoff estimation modelling and equation was developed to overcome the limitation of in-situ measurement. SCS-CN is a simple model of ungauged method, where runoff volume (Q) resulting from rainfall (P) is formulated using equation of (Q= (P-Ia) 2 / (P-Ia + S). It was known as the best technique to be adopted for large basin study where time and manpower also accessibility are limited. SCS-CN method also is widely use in prediction software as it taken into consideration of the effects of soil, properties, land cover and antecedent moisture. Curve Number is well developed in USA for the agriculture purpose with many investigations to validate and calibrate the values of curve number. It was applied in numerous river basins in temperate and other regions e.g. US, Argentina, India, China, South Korea, Palestine and Malaysia. However, the reliability of the CN in the tropics is doubtable due to different land use characteristics, soil type, climate, geological features and rainfall pattern and variability. Based on the reviewed conceptual and applications of SCS-CN in temperate and tropics, numerous studies found the SCS-CN method is reliable and practical for runoff estimation in tropics region.
The aim of this study was to propose a groundwater quality index (GWQI) that presents water quality data as a single number and represents the water quality level. The development of the GWQI in agricultural areas is vital as the groundwater considered as an alternative water source for domestic purposes. The insufficiency of the groundwater quality standard in Malaysia revealed the importance of the GWQI development in determining the quality of groundwater. Groundwater samples were collected from thirteen groundwater wells in the Northern Kuala Langat and the Southern Kuala Langat regions from February 2018 to January 2019. Thirty-four parameters that embodied physicochemical characteristics, aggregate indicator, major ions, and trace elements were considered in the development of the GWQI. Multivariate analysis has been used to finalize the important parameters by using principal component analysis (PCA). Notably, seven parameters-electrical conductivity, chemical oxygen demand (COD), magnesium, calcium, potassium, sodium, and chloride were chosen to evaluate the quality of groundwater. The GWQI was then verified by comparing the groundwater quality in Kota Bharu, Kelantan. A sensitivity analysis was performed on this index to verify its reliability. The sensitivity GWQI has been analyzed and showed high sensitivity to any changes of the pollutant parameters. The development of GWQI should be beneficial to the public, practitioners, and industries. From another angle, this index can help to detect any form of pollution which ultimately could be minimized by controlling the sources of pollutants.
Currently, the available indices to measure mangrove health are not comprehensive. An integrative ecological-socio economic index could give a better picture of the mangrove ecosystem health. This method explored all key biological, hydrological, ecological and socio-economic variables to form a comprehensive mangrove quality index. A total of 10 out of 43 variables were selected based on principal component analysis (PCA). They are aboveground biomass, crab abundance, soil carbon, soil nitrogen, number of phytoplankton species, number of diatom species, dissolved oxygen, turbidity, education level and fishing time spent by fishers. Two types of indices were successfully developed to indicate the health status viz., (1) Mangrove quality index for a specific category (MQISi ) and, (2) Overall mangrove quality index (MQI) to reflect the overall health status of the ecosystem. The indices for the five different categories were mangrove biotic integrity index ( M Q I S 1 ), mangrove soil index ( M Q I S 2 ), marine-mangrove index ( M Q I S 3 ), mangrove-hydrology index ( M Q I S 4 ) and mangrove socio-economic index ( M Q I S 5 ). The quality of the mangroves was classified from 1 to 5 viz. 1 (worst), 2 (bad), 3 (moderate), 4 (good), 5 (excellent). These MQI class could reflect the quality of mangrove forest which could be managed with the objective of improving its quality. Advantages of this method include: •PCA to select metrics from ecological-socioeconomic variables•Formulation of MQI based on selected metrics•Comprehensive index to classify mangrove ecosystem health.