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EN
The problem of flood vulnerability has been reviewed in several studies, however, the reviews focused exclusively either on the social or on the physical component of the problem. The components of flood vulnerability are interdependent and each of them makes an equally important contribution to the flood vulnerability index. This study identifies and evaluates the integrated flood vulnerability index (FVI) of an area by considering its multiple components (social, economic, and environmental). The Analytic Hierarchy Process (AHP) method was applied to evaluate the weight of each component. The evaluation was based on the judgements of experts working at local government policy-making agencies. The input data for the AHP were acquired through a questionnaire survey. Eleven indicators that delivered significant results were then selected. The FVI results show high flood vulnerability at the local scale. The FVI provides the basis for the identification of villages with high vulnerability indices. The results provide essential information about pluvial flood vulnerability at the local scale, about the area with the highest vulnerability index, and the most vulnerable villages. The results also show that the components that have a significant impact on the flood vulnerability index include environmental components (43.4%), social components (28.5%), and physical components (28.1%).
EN
In Indonesia, ground-based rainfall monitoring is uneven and sometimes lacks continuity especially in small watersheds, which makes hydrological modeling difficult. This paper aims to the performance evaluation of the HBV Light model from the manual measurement of rainfall (MMR), Global Precipitation Measurement (GPM3IMERGDF), and Tropical Rainfall Measuring Mission (TRMM-3B42) as input for the hydrological model. The Hydrologiska Byrans Vattenbalansavdelning (HBV) Light hydrological model is applied to three small watersheds, namely Sampean Baru, Bedadung, and Mayang. The model’s performance evaluation is assessed based on the correlation between the average rainfall data for the satellite product area and the MMR product, the stationarity of the rainfall and discharge data, and the model accuracy. The model simulation results show that the MMR rainfall in all watersheds provides a better discharge response than the other two products. Meanwhile, the simulation model of the GPM-3IMERGDF satellite product is slightly better than TRMM-3B42. The stationarity test of rainfall and discharge data needs to be enforced before modeling.
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