TWI computation: a comparison of different open source GISs

Mattivi, Pietro; Franci, Francesca; Lambertini, Alessandro; Bitelli, Gabriele

doi:10.1186/s40965-019-0066-y

Cited by 113 publications

(69 citation statements)

References 36 publications

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“…TWI is a measure of available soil moisture of a given site in the landscape and is calculated as ln( α /tan β ), where α is the specific upslope catchment area and β is the local surface slope, assuming steeper slopes offer less opportunity for water retention (Le Roux et al., 2013; Mattivi et al., 2019). It predicts groundwater occurrence (as channels, seeps, or riparian water tables) in convergent topographies with large upslope catchment areas and at the foot of hillslopes where slope gradients drop dramatically (McLaughlin et al., 2017).…”

Section: Methodsmentioning

confidence: 99%

From glacial refugia to hydrological microrefugia: Factors and processes driving the persistence of the climate relict tree Zelkova sicula

Garfì

Carimi

Fazan

et al. 2021

Ecology and Evolution

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Section: Methodsmentioning

confidence: 99%

From glacial refugia to hydrological microrefugia: Factors and processes driving the persistence of the climate relict tree Zelkova sicula

Garfì

Carimi

Fazan

et al. 2021

Ecology and Evolution

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“…SAGA-GIS software was applied to map TWI. The TWI was estimated with the help of the following method:

where,

refers to the catchment area, and

represent slope angle [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

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“…The following categories have been shown in previous studies to influence gully erosion: elevation, slope aspect, plan curvature, slope angle, Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), Land Use and Land Cover (LULC), lithology, soil type and distance from stream [23,26,37,38,90]. Except for lithology, data for all categories were obtained via open access resources.…”

Section: Variables Affecting Gully Erosionmentioning

confidence: 99%

Human movement and gully erosion: Investigating feedback mechanisms using Frequency Ratio and Least Cost Path analysis in Tigray, Ethiopia

et al. 2021

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The cost of human movement, whether expressed in time, effort, or distance, is a function of natural and human related variables. At the same time, human movement itself, whether on land, air or sea, causes environmental cost. We are looking into the long-term environmental relationship of this interplay. Gullies—linear landforms, which dissect the landscape—are considered to be a cost for human movement, as they can form unpassable barriers destroying present path networks. On the other hand, human movement creates pathways, which flatten the surface and decrease the water permeability potential. This process results in runoff generation and possibly gully erosion. Accordingly, the spatial relationship between pathways and gullies is investigated. In the Tigray region of the Northern Ethiopian Highlands, gullies and pathways were mapped using remote sensing data. Frequency Ratio was used for assessing pathways as a variable affecting the location of gullies while Least Cost Paths were tested to evaluate the possible constraining impact gullies have on mobility. Based on these results, it is concluded that a positive feedback exists between the cost of human movement and gully erosion. We further discuss possible effects gullies may have had on trade, territory, and political affairs in Tigray. Consequently, we suggest that movement cost and gullying may not only hold strictly environmental or movement-related implications, but also socio-cultural ones.

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TWI computation: a comparison of different open source GISs

Cited by 113 publications

References 36 publications

From glacial refugia to hydrological microrefugia: Factors and processes driving the persistence of the climate relict tree Zelkova sicula

From glacial refugia to hydrological microrefugia: Factors and processes driving the persistence of the climate relict tree Zelkova sicula

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Human movement and gully erosion: Investigating feedback mechanisms using Frequency Ratio and Least Cost Path analysis in Tigray, Ethiopia

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