Analysis of Rainfall Effect to Slope Stability in Ulu Klang, Malaysia

2015 ◽  
Vol 72 (3) ◽  
Author(s):  
Muhammad Mukhlisin ◽  
Siti Jahara Matlan ◽  
Mohamad Jaykhan Ahlan ◽  
Mohd Raihan Taha
Keyword(s):  
Slope Stability ◽  
Soil Water ◽  
Rainfall Threshold ◽  
Critical Threshold ◽  
Water Index ◽  
Triggering Factors ◽  
Landslide Event ◽  
Tropical Climates ◽  
Fatal Landslides

Malaysia is a country that is located near the equator line with tropical climates which receives high abundant rainfall, averaging 2,400mm annually. This makes Malaysia prone to the landslide events as rainfall is one of the main triggering factors that can cause landslide. Landslides in Malaysia are mainly attributed to frequent and prolonged rainfalls, in many cases associated with monsoon rainfalls. Of these, Ulu Klang area has received the most exposure. The area has constantly hit by fatal landslides since December 1993. This paper is aimed to investigate the correlation between the effective working rainfall and soil water index (SWI) methods with the landslide events in Ulu Klang, Malaysia. In this study 15 landslide events that occurred in Ulu Klang areas between years 1993 to 2012 were investigated and analyzed using rainfall threshold based on effective working rainfall and soil water index (SWI) methods. The analysis results showed that these methods are significant tools that can be used to identify the rainfall critical threshold of landslide event.  

Satellite Data Based Soil Water Index as a Tool to Monitor Droughts and Agricultural Crisis -An Example of Yavatmal District, Maharashtra, India

2020 ◽  
Vol 4 (1-2) ◽  
pp. 12-18
Author(s):  
Vijendra Boken
Keyword(s):  
Soil Water ◽  
Satellite Data ◽  
Suicide Rate ◽  
Crop Yields ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Coefficient Of Correlation ◽  
Water Index ◽  
Farmer Suicides ◽  
June July

Yavatmal is one of the drought prone districts in Maharashtra state of India and has witnessed an agricultural crisis to the extent that hundreds of its farmers have committed suicides in recent years. Satellite data based products have previously been used globally for monitoring and predicting of drought, but not for monitoring their extreme impacts that may include farmer-suicides. In this study, the performance of the Soil Water Index (SWI) derived from the surface soil moisture estimated by the European Space Agency’s Advanced Scatterometer (ASCAT) is assessed. Using the 2007-2015 data, it was found that the relationship of the SWI anomaly was bit stronger (coefficient. of correlation = 0.59) with the meteorological drought or precipitation than with the agricultural drought or crop yields of major crops (coefficient. of correlation = 0.50).  The farmer-suicide rate was better correlated with the SWI anomaly averaged annually than with the SWI anomaly averaged only for the monsoon months (June, July, August, and September). The correlation between the SWI averaged annually increased to 0.89 when the averages were taken for three years, with the highest correlation occurring between the suicide rate and the SWI anomaly averaged for three years. However, a positive relationship between SWI and the suicide rate indicated that drought was not a major factor responsible for suicide occurrence and other possible factors responsible for suicide occurrence need to examine in detail.

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

scholarly journals Analisis Numerik Pengaruh Infiltrasi Hujan Terhadap Stabilitas Lereng Di Pangkalan, Sumatera Barat

2020 ◽  
Vol 11 (1) ◽  
pp. 25-36
Author(s):  
Rokhmat Hidayat
Keyword(s):  
Slope Stability ◽  
Hydrological Modeling ◽  
Water Pressure ◽  
Laboratory Data ◽  
Soil Layer ◽  
Infiltration Process ◽  
The Road ◽  
Horizontal Drain ◽  
Landslide Event ◽  
Rain Infiltration

The landslides event was triggered by rain infiltration is an annual occurrence in Indonesia, majority of landslide occur in rainy season. In this research, the case of landslide taken in Pangkalan Area, District of Limapuluh Kota, West Sumatera. The location of the case study is the main access of West Sumatra-Riau, so the landslide in the location is certainly causing close the road. Research phase is geology mapping, geotechnical analysis, and hydrological modeling. Hydrological modeling is done by numerical simulation using laboratory data. The modeling results show that the rain infiltration process resulted in the formation of positive water pressure zone at the foot of the slope, then spread towards the top of the slope.  One day after the rainfall, the soil layer had been saturated. The soil layer will saturate the water, so that the slope stability will decrease and the landslide event will occur. To improve the slope stability, it can be done by preventing water from entering the permeable layer with the installation of the shotcrete layer, and draining the water from the slopes by the installation of horizontal drain.

scholarly journals Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event

2013 ◽  
Vol 1 (3) ◽  
pp. 2547-2587 ◽  
Author(s):  
D. W. Park ◽  
N. V. Nikhil ◽  
S. R. Lee
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Debris Flow ◽  
Slope Stability Analysis ◽  
Flow Paths ◽  
Infinite Slope ◽  
Erosion Rates ◽  
Landslide Event ◽  
Physically Based ◽  
Pore Pressure Response

Abstract. This paper presents the results from application of a regional, physically-based stability model: Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) for a catchment on Woomyeon Mountain, Seoul, Korea. This model couples an infinite-slope stability analysis with a one-dimensional analytical solution to predict the transient pore pressure response to the infiltration of rainfall. TRIGRS also adopts the Geographic Information Systems (GIS) framework for determining the whole behaviour of a slope. In this paper, we suggest an index for evaluating the results produced by the model. Particular attention is devoted to the prediction of routes of debris flow, using a runoff module. In this context, the paper compares observed landslide and debris flow events with those predicted by the TRIGRS model. The TRIGRS model, originally developed to predict shallow landslides, has been extended in this study for application to debris flows. The results predicted by the TRIGRS model are presented as safety factor (FS) maps corresponding to transient rainfall events, and in terms of debris flow paths using methods proposed by several researchers in hydrology. In order to quantify the accuracy of the model, we proposed an index called LRclass (landslide ratio for each predicted FS class). The LRclass index is mainly applied in regions where the landslide scar area is not well defined (or is unknown), in order to avoid over-estimation of the model results. The use of the TRIGRS routing module was proposed to predict the paths of debris flow, especially in areas where the rheological properties and erosion rates of the materials are difficult to obtain. Although an improvement in accuracy is needed, this module is very useful for preliminary spatiotemporal assessment over wide areas. In summary, the TRIGRS model is a powerful tool of use to decision makers for susceptibility mapping, particularly when linked with various advanced applications using GIS spatial functions.

A regionally explicit, global SWI calibration based on ISMN observations

2021 ◽  
Author(s):  
Manolis G. Grillakis
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Large Scale ◽  
Root Zone ◽  
Soil Depth ◽  
European Space Agency ◽  
Soil Layer ◽  
Added Value ◽  
Water Index ◽  
Soil Moisture Data

<p>Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.</p><p>Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 – 2018.</p><p>The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.</p>

Sign in / Sign up

Export Citation Format

Share Document