The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.
Landslide is one of the most dangerous hazards worldwide. It could be caused by several factors and could have a massive destructive impact on the environment. A landslide event occurred in one of the urban cities in Indonesia. In the middle of 2002, a landslide disaster due to rainfall occurred in the Ciliwung River's floodplain, precisely in the South Jakarta area. The landslide profoundly affected large areas of the region and seriously injured many people. Several circumstances that could trigger landslide occurrences are the building load in the settlement area around the river, increase in the rainfall intensity, slope, and soil characteristics in the Ciliwung River area. This research proposes a combination of nonstructural and structural disaster mitigation methods for water-related landslide by investigating the safety factor (SF) of the river bank's slope in one of the impacted sites, i.e., the area under the main bridge of Grand Depok City regency. This site is located in the boundary area between Depok City and South Jakarta. The authors simulate analytical and numerical modeling to estimate the SF of the slopes. This research concludes that the minimum SF in the analyzed location is recognized as a safety criterion for society. The condition becomes less secure when an earthquake occurs. Furthermore, high rainfall intensity could become the worst scenario that generates considerable damage. The proposed structural mitigation for river bank with anchor or snail increases the SF. However, this reinforcement program is not recommended because of its high cost and ineffectiveness in solving problems. Hence, green infrastructure (GI) is highly suggested for nature-based mitigation to prevent rainfall-triggered landslides in the Ciliwung River area. The authors conduct the preliminary design of the study and recommend further analysis of GI or soil bioengineering to ensure its effectiveness and applicability in the research area.
This research uses Resource Modelling Associate (RMA) program to conduct a two-dimension model of Agathis Lake to analyse velocity and sediment transport distribution. The main programs are RMA-10 for velocity distribution and RMA-11 for the pollutant distribution. The sediment transport focuses on total suspended solids (TSS) pollutant. The research aims to construct total suspended solid model. Firstly, RMA Generation produces mesh of Agathis Lake. Next, RMA-10 and RMA-11 would perform both velocity and TSS simulation model to represent the actual condition. This research still gives temporary result due to limited sampling data. Calibration analysis of the model is needed to make the result of the program more accurate and more representative to the real condition.
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