An Overview: Flood Catastrophe of Kelantan Watershed in 2014

Jaafar, Aminah Shakirah; Sidek, Lariyah Mohd; Basri, Hidayah; Zahari, Nazirul Mubin; Jajarmizadeh, Milad; Noor, Hanapi Mohamad; Osman, Sazali; Mohammad, Abdul Hafiz; Azad, W. H.

doi:10.1007/978-981-10-0500-8_2

Cited by 7 publications

(8 citation statements)

References 3 publications

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“…The delineated area is calculated using the Digital Terrain Analysis (DTA) in the DECIPHeR as 12,142 Km 2 . The population of Kelantan based on 2015 data is 1.718 million [47] with approximately one third of the population living in the downstream area of the catchment [34], which is the most flood-prone.…”

Section: Study Areamentioning

confidence: 99%

“…The six segmented behavioral model simulation plots are presented in Figure 4. Of special note for Peak 1, Figure 4(a), it includes the year 2014 characterised by a big flood event reported [34], [68] as the highest peak since year 1967 [69]. The recorded data, however, contains missing data at the peak event, which captured the highest amount of only 7613.5 m 3 /s.…”

Section: Streamflow Simulation and Model Performancementioning

confidence: 99%

“…The study area, Kelantan catchment (12,142 Km 2 ) Malaysian Peninsular, is a large catchment with a tendency to experience extreme flooding [34] and significant land use change. Recent flood events were in 2014 and 2017 [35,36].…”

Section: Introductionmentioning

confidence: 99%

“…Recent flood events were in 2014 and 2017 [35,36]. The highest water level recorded in the Kelantan catchment was in 2014, which highlights the important work of hydrological research [34]. A land use classification study for 20 years, from 1994 to 2014, revealed a 13.7 % decrease in forest land, and a 6.2 % increase in oil palm plantations [37] in the catchment.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Streamflow Simulation for a Tropical Forested Catchment using Dynamic TOPMODEL – Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Umar¹,

Zulkafli²,

Yusuf³

et al. 2020

Preprint

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Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1 - considering the flexibility, modularity and portability - and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e. the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. 32 years' continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatterplot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modelling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.

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Section: Study Areamentioning

confidence: 99%

Section: Streamflow Simulation and Model Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Streamflow Simulation for a Tropical Forested Catchment using Dynamic TOPMODEL – Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Umar¹,

Zulkafli²,

Yusuf³

et al. 2020

Preprint

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“…Rapid development and deployment of electrochemical biosensors for pathogen quantification is needed for use in flood-stricken areas where drinking-water supplies and sanitation systems are disrupted and making possible serious waterborne diseases. These outbreaks of waterborne diseases become more serious when flooding occurs in rural areas with limited resources, such the case of Kuala Krai, Kelantan, East Coast of Malaysia [1]. In the resource-constrained setting of Kuala Krai, water samples are filtered through microfiltration techniques that remove only large debris and are ineffective for removing bacteria and viruses [2,3].…”

Section: Introductionmentioning

confidence: 99%

Reduced Graphene Oxide on Screen-Printed Carbon Electrodes as Biosensor for Escherichia coli O157:H7 Detection

Barthasarathy

Salim

2020

The 1st International Electronic Conference on Biosensors

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Mixture of drinking-water supplies with sewage discharges poses disease threats in flood-stricken areas. In such exigent conditions, on-site testing of water samples is the only option, as water samples cannot be transported to laboratories owing to severely impacted transportation services. Hence, we developed a low-cost electrochemical biosensor fabricated from a screen-printed carbon electrode (SPCE) to detect E. coli O157:H7, a virulent pathogen often found in sewage discharges. We focused on understanding antigen-antibody interaction when the antibody used is not specific for E. coli O157:H7. We found that antibody immobilized on a reduced graphene oxide (rGO)–modified SPCEs distinguished between E. coli O157:H7 concentrations of 4 × 108 and 4 CFU/ml, with lowest current reported for 4 × 108 CFU/ml. In contrast, a reduced graphene oxide–modified SPCEs without antibody immobilization does not produce a prominent peak that distinguishes the highest and lowest E. coli concentrations. However, a few E. coli cells were still attached to the rGO/SPCEs in the absence of antibody, as shown in FESEM images. A processing step of differential readings from reference and active electrodes needs to be programmed into an Arduino® microprocessor to realize a prototype of a bacteria sensor for field use.

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