Application of Resistivity Method Wenner-Schlumberger Array to Evaluate Brantas River Embankment as Avoid Landslide

Anggraeni, Firdha Kusuma Ayu; Minarto, Eko; Realita, Arie

doi:10.12962/j23546026.y2017i2.2269

Cited by 2 publications

(1 citation statement)

References 1 publication

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“…The upstream is primarily composed of Andosols soil types. In 2018, at least seven landslides occurred that killed 28 lives [12]. Figure 1 illustrates the upper Brantas River Basin.…”

Section: Study Areamentioning

confidence: 99%

Comparisons of distributed and lumped rainfall-runoff model for soil moisture estimation

Hapsari

Syarifuddin

Putri

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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Soil moisture is an important parameter in landslides because of increased pore pressure and decreased shear strength. This research aims to derive soil moisture indicators from two hydrological models: the physically-based distributed hydrological model and the lumped model. Rainfall-Runoff-Inundation (RRI) Model is used to simulate the hydrological response of catchments to the rainfall-induced landslide in a distributed manner. Tank Model as a lumped hydrological model is also used in this study to simulate the dynamic of soil moisture. The study area is the upper Brantas River Basin, prone to landslides due to heavy rainfall and steep slope. Calibration of the model is conducted by tuning the model according to the river discharge data. The simulation indicates that acceptable performance is confirmed. Tank Model can provide the dynamic of the soil moisture. However, by using this approach, the spatial variation of the soil moisture cannot be presented. Regarding the quantitative amount of soil water content, RRI Model could make a reasonable simulation though the temporal variation is not adequately reproduced. Validation of this method with satellite soil moisture as well as ground measurement is also presented. The challenges of using these approaches to develop landslide hazard assessment are discussed.

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“…The upstream is primarily composed of Andosols soil types. In 2018, at least seven landslides occurred that killed 28 lives [12]. Figure 1 illustrates the upper Brantas River Basin.…”

Section: Study Areamentioning

confidence: 99%

Comparisons of distributed and lumped rainfall-runoff model for soil moisture estimation

Hapsari

Syarifuddin

Putri

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

Performance evaluation of soil water content observation from satellite images in sediment disaster-prone area

Hapsari

Putri

Aponno

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Soil water content observations from remote sensing are growing for landslide disaster management due to the potential of wide coverage and fine resolution. Characteristic evaluation of various soil water content products is necessary for improving the accuracy. This study assesses soil water content products from Soil Moisture Active Passive from Global Modelling and Assimilation NASA, Soil Moisture and Ocean Salinity from ESA, and Soil Water Index (SWI) from Copernicus Global Land Service. Results are compared with soil test in terms of spatial and temporal distribution, time series, and quantitative performance indices. The study is conducted in Brantas River Basin Indonesia, where the upstream is vulnerable for landslide disaster. The investigation has shown that with correlation coefficient 0.81, SWI is suitable for further application in the study area. This finding has important implications for modelling the landslide hazard using quantitative soil water content product from satellite imagery.

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Application of Resistivity Method Wenner-Schlumberger Array to Evaluate Brantas River Embankment as Avoid Landslide

Cited by 2 publications

References 1 publication

Comparisons of distributed and lumped rainfall-runoff model for soil moisture estimation

Comparisons of distributed and lumped rainfall-runoff model for soil moisture estimation

Performance evaluation of soil water content observation from satellite images in sediment disaster-prone area

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