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Subsurface drainage modules are important components of the Bio-ecological Drainage System (BIOECODS) which is a system designed to manage stormwater quantity and quality using constructed grass swales, subsurface modules, dry and wet ponds. BIOECODS is gradually gaining attention as one of the most ecologically sustainable solutions to the frequent flash floods in Malaysia and the rest of the world with a focus on the impact of the subsurface modules to the effectiveness of the system. Nearly two decades of post-construction research in the BIOECODS technology, there is need to review findings and areas of improvement in the system. Thus, this study highlighted the key advances and challenges in these subsurface drainage modules through an extensive review of related literature. From the study, more work is required on the hydraulic characteristics, flow attenuation and direct validation methods between field, laboratory, and numerical data. Also, there is concern over the loss of efficiency during the design life especially the infiltration capacity of the module, the state of the geotextile and hydronet over time. It is recommended for the sake of higher performance, that there should be an onsite methodology to assess the permeability, rate of clogging and condition of the geotextile as well as the hydronet over time.

Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of some crucial conditioning factors (CFs) termed dynamic CFs whose impacts on the accuracy have not been investigated. Thus, this study evaluates erosion susceptibility under the influence of both non-redundant static and dynamic CFs using support vector machine (SVM), remote sensing and GIS. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as non-redundant static factors, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity as the dynamic factors. The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. Using area under the curve (AUC) and Cohen’s kappa index (k) as the validation criteria, the results showed that polynomial function had the highest performance followed by linear and radial basis function. However, sigmoid SVM underperformed having the lowest AUC and k values coupled with higher classification errors. The CFs’ weights were implemented for the development of soil erosion susceptibility map. The map would assist planners and decision makers in optimal land-use planning, prevention of soil erosion and its related hazards leading to sustainable watershed management.

Global Land Cover (GLC) maps have been extensively used as background data for numerous applications such as land-use planning, climate change monitoring, and urban planning. While countries in Europe and North America have greatly developed land cover maps, several developing countries in Asia and Africa lack such vital data sources. GlobeLand30's 30 m resolution dataset has the potential to meet the mapping needs of these developing regions. However, differences in accuracy of the dataset between countries can be significant making it unsuitable to adopt regional accuracy directly for assessing individual country's accuracy level. Therefore, it is critical to develop reliable localized assessments. This research assesses the accuracy of GlobeLand30 for Malaysia using multiple evaluation approaches. Seven classes of land cover data were evaluated using 197 samples in the country. An overall accuracy of 63.45% and 65.70% was obtained from the Error Matrix using sample counts and the new unbiased area estimator respectively. We conclude that the overall accuracy of GlobeLand30 in tropical developing countries like Malaysia is generally lower than the accuracy in developed countries and arid developing countries thereby requiring significant improvement for optimal use. However, despite the errors due largely to misclassification, the GlobeLand 30 data has the potential to complement other LULC data to estimate and monitor vital land-use changes in Malaysia and the ASEAN region in general for sustainable development. [ABSTRACT FROM AUTHOR]

Soil erosion is a devastating land degradation process that needs to be spatially analyzed for identification of critical zones for sustainable management. Geospatial prediction through susceptibility analysis assesses the occurrence of soil erosion under a set of causative factors (CFs). Previous studies have considered majorly static CFs for susceptibility analysis, but neglect dynamic CFs. Thus, this study presents an evaluation of erosion susceptibility under the influence of both non-redundant static and dynamic CFs using multivariate logistic regression (MLR), remote sensing and geographic information system. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as static CFs, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity representing the dynamic CFs. These were parameterized to establish geospatial relationships with the occurrence of erosion. The results showed that length-slope had the highest positive impact on the occurrence of erosion, followed by lineament density. During the MLR classification process, predicted accuracies for the eroded and non-eroded locations were 89.1% and 83.6% respectively, with an overall prediction accuracy of 86.6%. The model's performance was satisfactory, with 81.9% accuracy when validated using the area-under-curve method. The output map of this study will assist decision makers in sustainable watershed management to alleviate soil erosion. [ABSTRACT FROM AUTHOR]

Over the years, Cameron Highlands have witnessed extensive land-use and land-cover (LULC) changes due to the massive agricultural and urbanization activities. This significantly contributed to the erosion problems in the area. Rainfall erosivity that measures the aggressiveness of raindrop in triggering soil erosion is one of its major components that could be influenced by the LULC changes in watersheds. However, the research relating to the-LULC changes with the erosivity especially in the complex landscape is scarce. Hence, this study applies geographic information system (GIS) and remote sensing techniques to assess the LULC changes and their influence on the rainfall erosivity distribution in mountainous watershed of Cameron Highlands. Four Landsat images and the rainfall data from the period of thirty years were analysed for the development of LULC and erosivity maps respectively in ArcGIS environment. The study showed that the study area experienced immense land-use changes especially in agriculture and urbanization which affected the erosivity distribution. The LULC change for agriculture increased linearly in the last 30 years from 7.9% in 1986 to almost 16.4% in 2016. The results showed that urban development increased from 5.1% in 1986 to 11.4% in 2016. The increasing urbanization trend was targeted to meet up with tourism requirement in Cameron Highlands. However, forest class declined tremendously due to the exploration of land for agriculture practice and other various types of development. Watershed managers and other stakeholders should find this study beneficial in tackling erosion and its associated ecological challenges. [ABSTRACT FROM AUTHOR]

Rainfall-runoff is the active agent of soil erosion which often resulted in land degradation and water quality deterioration. Its aggressiveness to induce erosion is usually termed as rainfall erosivity index or factor (R). R-factor is one of the factors to be parameterized in the evaluation of soil loss using the Universal Soil Loss Equation and its reversed versions (USLE/RUSLE). The computation of accurate R-factor for a particular watershed requires high temporal resolution rainfall (pluviograph) data with less than 30-minutes intensities for at least 20 yrs, which is available only in a few regions of the world. As a result, various simplified models have been proposed by researchers to evaluate R-factor using readily available daily, monthly or annual precipitation data. This study is thus aimed at estimating R-factor and to establish an approximate relationship between R-factor and rainfall for subsequent usage in the estimation of soil loss in Cameron highlands watershed. The results of the analysis showed that the least and peak (critical) R-factors occurred in the months of January and April with 660.82 and 2399.18 MJ mm ha-1 h-1year-1 respectively. Also, it was observed that erosivity power starts to increase from the month of January through April before started falling in the month of July. The monthly and annual peaks (critical periods) may be attributed to increased rainfall amount due to climate change which in turn resulted to increased aggressiveness of rains to cause erosion in the study area. The correlation coefficient of 0.985 showed that there was a strong relationship rainfall and R-factor. [ABSTRACT FROM AUTHOR]

The flow resistance of the existing modules in the bio-ecological drainage system (BIOECODS) is high and may lead to flood instead of its mitigation. As part of efforts to enhance the performance of the system, the river engineering and urban drainage research center (REDAC) module was developed. This study modelled the hydrodynamics of flow through this module using FLOW-3D and laboratory experiments for two cases of free flow without module (FFWM) and flow with a module (FWM) to understand and visualize the effects of the module. With less than 5% error between the numerical and experimental results, REDAC module altered the flow pattern and created resistance by increasing the Manning's roughness coefficient at the upstream, depth-averaged flow velocity (43.50 cm/s to about 46.50 cm/s) at the downstream and decreasing water depth (7.75–6.50 cm). These variations can be attributed to the complex nature of the module pattern with further increase across the porous openings. Therefore, the technique used herein can be applied to characterize the behavior of fluids in larger arrangments of modules and under different flow conditions without the need for expensive laboratory experiments. [ABSTRACT FROM AUTHOR]