scholarly journals review of "Evaluation of Soil Water Index of distributed Tank Model in a small basin with field data"

2020 ◽  
Author(s):  
Anonymous
Keyword(s):  
Soil Water ◽  
Field Data ◽  
Tank Model ◽  
Water Index ◽  
Small Basin

scholarly journals Evaluation of Soil Water Index of distributed Tank Model in a small basin with field data

2020 ◽  
Author(s):  
Sofia Melo Vasconcellos ◽  
Masato Kobiyama ◽  
Aline de Almeida Mota
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Retention Curve ◽  
Topographic Wetness Index ◽  
Tank Model ◽  
Water Index ◽  
Soil Water Tension ◽  
Spatially Distributed ◽  
Reliable Performance ◽  
Water Tension

Abstract. The objective of the present study was to determine the spatial behaviour of the Soil Water Index (SWI) by applying a distributed version of the Tank Model (D-Tank Model) to the Araponga river basin (5.26 ha) in southern Brazil and to verify its reliability through the comparison to soil moisture estimated with the measured water-tension values and the water retention curve. The study area has a monitoring system for rainfall, discharge (5-min interval), and soil-water tension (10-min interval). The simulation results showed that the D-Tank Model has a reliable performance. The correlation between SWI and HAND was reasonable (r = 0.6) meanwhile that between SWI and the Topographic Wetness Index was high (r = 0.88). The comparison between the spatially distributed values of the SWI and soil moisture confirmed the high potential of the SWI derived from the D-Tank Model to be applied for predictions related to hydrological and environmental sciences.

scholarly journals Application of Soil Water Index to landslide prediction in snowy regions: sensitivity analysis in Japan and preliminary results from Tomsk, Russia

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hiroshi Matsuyama ◽  
Hitoshi Saito ◽  
Valerii Zemtsov
Keyword(s):  
Sensitivity Analysis ◽  
Soil Water ◽  
Meteorological Data ◽  
Tank Model ◽  
Water Index ◽  
Hourly Precipitation ◽  
The Past ◽  
Maximum Value ◽  
Hourly Temperature ◽  
Severe Flood

AbstractSoil Water Index (SWI) represents the conceptual water stored in the soil and is calculated using a three-layer tank model with hourly precipitation. In Japan, landslide disasters are likely to occur when SWI in an event exceeds the maximum value of the past 10 years; however, snowmelt-driven landslide disasters have not been considered yet. Using the tank model that simultaneously calculates SWI and runoff, we implemented the snowfall-accumulation-snowmelt processes into the original SWI and applied the modified SWI to meteorological data in Tomsk, Russia, in spring 2010 when severe flood and landslide disasters had occurred. We conducted a sensitivity analysis of hourly precipitation in snowy region in Japan considering that meteorological data in Russia are available every 3 h. When we input the average of the three-hourly accumulated precipitation to calculate SWI, the result was almost identical to that of the observed hourly precipitation being given. We then estimated the hourly temperature by linearly interpolating the data every 3 h, and set the threshold of liquid/solid precipitation. The degree-hour method was employed to calculate the snowmelt. The modified SWI predicted the occurrence of snowmelt-driven landslide disasters in Japan when the calculated SWI exceeded the maximum value in the snowmelt season (March–May) for the past 10 years. When applied to meteorological data in Tomsk, the modified SWI and calculated runoff captured the timing of snowmelt-driven flood and landslide disasters in spring 2010. We demonstrated that by focusing on the maximum value of SWI in the snowmelt season for the past 10 years, we can predict snowmelt-driven landslide disasters.

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.

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.  

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.

Preliminary field data of selected deep-rooted vegetation effects on the slope-vegetation-atmosphere interaction: results from an in-situ test 

2021 ◽  
Author(s):  
Vito Tagarelli ◽  
Federica Cotecchia ◽  
Osvaldo Bottiglieri
Keyword(s):  
Soil Water ◽  
Pore Water ◽  
Field Data ◽  
Test Site ◽  
Grass Species ◽  
In Situ Test ◽  
South Eastern ◽  
Atmosphere Interaction ◽  
The Stability

<p>The soil-vegetation-atmosphere interaction is becoming more and more the subject of intense scientific research, motivated by the wish of using smart vegetation implants as sustainable mitigation measure for erosive phenomena and slope instability processes. <br>The use of novel naturalistic interventions making use of vegetation has been already proven to be successful in the reduction of erosion along sloping grounds, or in increasing the stability of the shallow covers of slopes, whereas the success of vegetation as slope stabilization measure still needs to be scientifically proven for slopes location of deep landslides, whose current activity is climate-induced, as frequent in the south-eastern Apennines. Recently, though, peculiar natural perennial grass species, which develop deep root systems, have been found to grow in the semi-arid climate characterizing the south-eastern Apennines and to determine a strong transpirative flow. Therefore, their peculiar leaf architecture, their crop density, combined with their perennial status and transpiration capacity, make such grass species suitable for the reduction of the net infiltration rates, equal to the difference between the rainfall rate and the sum of the runoff plus the evapotranspiration rate. As such, the grass species here of reference have been selected as vegetation measure intended to determine a reduction of the piezometric levels in the slope down to large depths, in order to increase the stability of deep landslide bodies. <br>At this stage, only preliminary field data representing the interaction of clayey soils with the above cited vegetation species are available. These have been logged within a full scale in-situ test site, where the deep-rooted crop spices have been seeded and farmed. The test site (approximatively 2000 m<sup>2</sup>) has been set up in the toe area of the climate-induced Pisciolo landslide, in the eastern sector of the Southern Apennines.<br>The impact of the vegetation on the hydro-mechanical state of the soil is examined in terms of the spatial and temporal variation of the soil water content, suction an pore water pressure from ground level down to depth, both within the vegetated test site and outside it, where only spare wild vegetation occur, in order to assess the effects of the implant of the selected vegetation. The soil water contents, suctions and pore water pressures have been also analyzed taking into account of the climatic actions, monitored by means of a meteorological station. </p>

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>

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