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.

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 AUTOMATED GIS-BASED TECHNIQUE FOR EVALUATION OF INDIRECT GROWING SEASON ESTIMATIONS

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 209-211
Author(s):  
I. Rykin ◽  
E. Panidi ◽  
V. Tsepelev
Keyword(s):  
Meteorological Data ◽  
Growing Season ◽  
Google Earth ◽  
Water Quantity ◽  
Special Significance ◽  
Water Index ◽  
Atmospheric Scattering ◽  
The Past ◽  
Normalized Difference Water Index ◽  
Automated Technique

<p><strong>Abstract.</strong> This article is based on NDWI (Normalized Difference Water Index) which is automatically computed from the daily MODIS data. The main purpose of the article is to tell how the evaluation of NDWI-based growing season estimations can be automated. The NDWI is used as an indicator of liquid water quantity in vegetation, which is less sensitive to atmospheric scattering effect then the famous growing index (NDVI). The NDWI is computed using cloud-based platform (Google Earth Engine was applied) and compared with the daily meteorological data. The available meteorological data is collected for the past 130 years and NDWI data is collecting for the past 20 years. An automated technique has been probated on the example of republic of Komi, as it has a different climate forming factors. This approach can be used to evaluate growing season estimations for other territories that contain vegetation. Due to the accumulated amount of data, the study is relevant and has a special significance for areas with sparse hydrometeorological network.</p>

scholarly journals LONG-TERM REMOTE MONITORING OF THREE TYPICAL LAKE AREA VARIATIONS IN THE NORTHWEST CHINA OVER THE PAST 40 YEARS

2018 ◽  
Vol XLII-3 ◽  
pp. 1165-1168
Author(s):  
Y. Liu ◽  
Y. Lu ◽  
Y. Li ◽  
H. Yue
Keyword(s):  
Development Strategy ◽  
Meteorological Data ◽  
Northwest China ◽  
Qinghai Lake ◽  
Lake Area ◽  
Bosten Lake ◽  
Water Index ◽  
The Past ◽  
Normalized Difference Water Index ◽  
The Impact

water resources management and sustainable development strategy, but also provide reference for assessing the impact of climate change and human activities. This paper selects three inland lakes in Northwest China, using Landsat MSS/TM/ETM+/OLI data from 1970 to 2015, Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) were used to extract lake area and analysed the dynamic trends. Meteorological station rainfall, evaporation and other meteorological data of the lakes were used to analyse reasons for the area change. The results showed that area of Hongjiannao Lake in the past 40&amp;thinsp;a was reduced, the groundwater impoundment and underground coal mining are the main cause of area reduction; the area of Bosten Lake in recent 40&amp;thinsp;a showed a decreasing trend after the first increase, the area was mainly affected by the surface runoff and snowmelt; the area of Qinghai Lake in the past 40&amp;thinsp;a shows a trend of decreasing first and then increasing, the change of its area is mainly affected by regional precipitation and the inflow.

FEATURES OF MONITORING OF DEGRADED LANDS TO ASSESS POSSIBILITIES FOR THEIR REHABILITATION

2020 ◽  
pp. 37-43
Author(s):  
B.I. KORZHENEVSKIY ◽  
◽  
N.V. KOLOMIYTSEV ◽  
G.YU. TOLKACHEV
Keyword(s):  
Agricultural Land ◽  
Meteorological Data ◽  
Morphological Characteristics ◽  
Energy Reserves ◽  
Land Restoration ◽  
Degraded Land ◽  
Degraded Lands ◽  
The Past ◽  
Unused Land ◽  
Restoration Work

Putting out of using large areas of agricultural lands in the central region over the past years has led to worsening the prospects of their purposed use, although the problem of the relevance of their restoration still remains. For many years the unused land was exposed to both natural exogenous processes such as erosion, suffusion, etc. and biological and chemical changes, usually for the worse for agriculture. There are considered elements of monitoring aimed at assessing the prospects or lack of perspectives of rehabilitation of degraded lands. An energy approach to assessing the state of slopes and soils located within these slopes is presented. The main factors of natural and anthropogenic character in assessing the prospects for land restoration are their steepness, excess relative to local bases of erosion other morphological characteristics of slopes which in general is reduced to an assessment of the energy provision of slopes and soils. So the higher the energy capacity of slopes – they are less promising for development, for soils – there is a reverse picture – the higher their energy reserves, the more promising is their use. Approaches to zoning the territory for monitoring from larger taxons of natural and anthropogenic genesis to the sites of special surveillance within which the prospects for rehabilitation of the agricultural land are evaluated. The most important factor is the material expediency of such actions, i.e. before starting the restoration work it is necessary to assess the profitability or loss of the proposed event. In cases of the material expediency it is feasible as further actions to include energy assessments of slopes and soils; zoning of the object according to the steepness and oriented characteristics of soil washout; and the possibility of obtaining agronomic and meteorological data on a timely basis. The result of the work is a forecast assessment of the prospects for restoring degraded land for the intended purpose using modern databases and WEB-systems.

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 Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

Optimal tuned direct adaptive controller for seismic protecting of structures

2021 ◽  
pp. 1045389X2098878
Author(s):  
Amin Hosseini ◽  
Touraj Taghikhany ◽  
Milad Jahangiri
Keyword(s):  
Sensitivity Analysis ◽  
Adaptive Control ◽  
Optimization Problem ◽  
Adaptive Controller ◽  
Sensitivity Analyses ◽  
Building Structures ◽  
Building Structure ◽  
The Past ◽  
Innovative Method ◽  
Story Building

In the past few years, many studies have proved the efficiency of Simple Adaptive Control (SAC) in mitigating earthquakes’ damages to building structures. Nevertheless, the weighting matrices of this controller should be selected after a large number of sensitivity analyses. This step is time-consuming and it will not necessarily yield a controller with optimum performance. In the current study, an innovative method is introduced to tuning the SAC’s weighting matrices, which dispenses with excessive sensitivity analysis. In this regard, we try to define an optimization problem using intelligent evolutionary algorithm and utilized control indices in an objective function. The efficiency of the introduced method is investigated in 6-story building structure equipped with magnetorheological dampers under different seismic actions with and without uncertainty in the model of the proposed structure. The results indicate that the controller designed by the introduced method has a desirable performance under different conditions of uncertainty in the model. Furthermore, it improves the seismic performance of structure as compared to controllers designed through sensitivity analysis.

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