Articles written in Journal of Earth System Science
Volume 126 Issue 2 March 2017 Article ID 0021
An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi- Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysisrevealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.
Volume 126 Issue 7 October 2017 Article ID 0106
Cratonic areas experience complex process-response changes due to their operative endogenic and exogenic forces varying in intensity and spatiality over long timescales. Unlike zones of active deformation, the surface expression of the transient signals in relatively tectonically stable areas are usually scant. The Subarnarekha River Basin, in eastern India, is a prime example of a Precambrian cratonic landscape, overlain in places by Tertiary and Quaternary deposits. A coupled quantitative-qualitative approach is employed towards deciphering tectonic and geological influences across linear and areal aspects, at the basin and sub-basin scale. Within this landscape, the transient erosional signatures are explored, as recorded in the disequilibrium conditions of the longitudinal profiles of the major streams, which are marked by a number of waterfalls at structural and lithological boundaries. Mathematical expressions derived from the normalized longitudinal profiles of these streams are used to ascertain their stage of development. Cluster analysis and chi plots provide significant interpretations of the role of vertical displacements or litho-structural variations within the basin. These analyses suggest that a heterogeneous, piece-meal response to the ongoing deformation exists in the area, albeit, determining the actual rate of this deformation or its temporal variation is difficult without correlated chronological datasets.
Volume 130 All articles Published: 4 February 2021 Article ID 0015 Research article
Vegetation community plays a pivotal role in various geomorphic processes. The growth of vegetation intrinsically depends on the effective shear stresses exerted by the flow of material (e.g., water or soil) along the slope. We comparatively assess the growth and decay of vegetation using linear and logistic growth model coupled with a runoff erosion model. The linear growth model predicts a sharp decrease in the non-dimensional vegetation profile from the upper reach to the lower part along the slope. However, the logistic growth model delivers a smooth gradual decrease in the vegetation extent. Additionally, we propose a stochastic model to capture the role of internal and external factors in the dynamics of vegetation growth using two Gaussian noises. The steady probability distribution functions from the stochastic model provide insights about the role of different noises on the reaction of the system and suggest that bio-environmental factors are difficult to segregate from one another.
$\bullet$ Comparative study between logistic and linear vegetation growth model coupled with a surface erosion model.
$\bullet$ Steady-state equilibrium vegetation profile along any slope suggests that the logistic growth model is more realistic.
$\bullet$ Effect of Gaussian noises in vegetation growth has been demonstrated with the aid of stationary probability distribution.
Volume 130, 2021
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