Upper mantle anisotropy beneath northeast India–Asia collision zone from shear-wave splitting analysis

Teleseismic earthquake data recorded by 11 broadband digital seismic stations deployed in the India–Asia collision zone in the eastern extremity of the Himalayan orogen (Tidding Suture) are analyzed to investigate the seismic anisotropy in the upper mantle. Shear-wave splitting parameters (Φ and δt) derived from the analysis of core-refracted SKS phases provide first hand information about seismic anisotropy and deformation in the upper mantle beneath the region. The analysis shows considerable strength of anisotropy (delay time ~0.85–1.9 s) with average ENE–WSW-oriented fast polarization direction (FPD) at most of the stations. The FPD observed at stations close to the Tidding Suture aligns parallel to the strike of local geological faults and orthogonal to absolute plate motion direction of the Indian plate. The average trend of FPD at each station indicates that the anisotropy is primarily originated by lithospheric deformation due to India–Asia collision. The splitting data analyzed at closely spaced stations suggest a shallow source of anisotropy originated in the crust and upper mantle. The observed delay times indicate that the primary source of anisotropy is located in the upper mantle. The shear-wave splitting analysis in the Eastern Himalayan syntaxis (EHS) and surrounding regions suggests complex strain partitioning in the mantle which is accountable for evolution of the EHS and complicated syntaxial tectonics.     

Potential CO<Subscript>2</Subscript> Emission Due to Loss of Above Ground Biomass from the Indian Sundarban Mangroves During the Last Four Decades

Sundarban, the largest single patch of mangrove forest of the world is shared by Bangladesh (~ 60 %) and India (~ 40 %). Loss of mangrove biomass and subsequent potential emission of carbon dioxide is reported from different parts of the world. We estimated the loss of above ground mangrove biomass and subsequent potential emission of carbon dioxide in the Indian part of the Sundarban during the last four decades. The loss of mangrove area has been estimated with the help of remotely sensed data and potential emission of carbon dioxide has been evaluated with the help of published above ground biomass data of Indian Sundarban. Total loss of mangrove area was found to be 107 km² between the year 1975 and 2013. Amongst the total loss ~60 % was washed away in the water by erosion, ~ 23 % was converted into barren lands and the rest were anthropogenically transformed into other landforms. The potential carbon dioxide emission due to the degradation of above ground biomass was estimated to be 1567.98 ± 551.69 Gg during this period, which may account to 64.29 million $ in terms of the social cost of carbon. About three-forth of the total mangrove loss was found in the peripheral islands which are much more prone to erosion. Climate induced changes and anthropogenic land use change could be the major driving force behind this loss of ‘blue carbon’.     

Hydrogeological component assessment for water resources management of semi-arid region: a case study of Gwalior,M.P., India

Groundwater management is of fundamental importance to meet the rapidly expanding urban, industrial and agricultural water requirements in semi-arid areas. To assess the current rate of groundwater withdrawal and possibility of recharge of potential aquifer in the semi-arid regions is essential for water management. The present study aimed to identify potential area for groundwater recharge structure in the Gwalior area based on land use, rainfall variation, hydrological component and statistical analysis. In this work, a stream survival approach was used for the assessment of water channel by using triangulated network and regression analysis to find out the correlation of individual component with reference to water management. Land use/land cover (LULC) map prepared from multispectral satellite images of the study area and used to validate the hydrological component and the results observed through the regression model shows good correlation. Therefore, immediate and effective water management schemes are required for sustainable water resource development and management in the area.     

Correction to: Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal,India

Flow pulsations in two-phase and single-phase near-critical fluids are considered as a possible source of ultra-low-frequency seismo-electromagnetic variations. The conditions for generation and suppression of density wave instability in the crust are analyzed and the surface electromagnetic effect due to streaming potential generation is estimated. The upper limit of amplitude of magnetic field variations due to density wave instability is about 0.1 nT for single-phase supercritical and 1 nT for two-phase flow oscillations in the frequency range \(10^{-4}{-}10^{-2}~\) Hz for the temperature gradients and spatial scales possible during strike slip events. The signal is characterized by a decaying amplitude with typical relaxation time of about several quasi-periods. The possibility of generation of very low-frequency flow pulsations in two-phase fluids via individual bubble evolution and interaction with external acoustic waves is discussed.     

General Regression Neural Network and Radial Basis Neural Network for the Estimation of Crop Variables of Lady Finger

Estimation of crop variables is necessary for crop type monitoring as well as crop yield forecast. At the present era artificial neural network methodology are widely used to the remote sensing domain for numerous applications like crop yield forecasting and crop type classification. In the present work, two neural network models namely general regression neural network (GRNN) and radial basis function neural network (RBFNN) are used to estimate crop variables: leaf area index (LAI), biomass (BM), plant height (PH) and soil moisture (SM) by using ground based X-band scatterometer data. The both networks are trained and tested with X-band scatterometer data. The performance of the GRNN and RBFNN networks are found that the radial basis approach is more suitable for crop variable estimation in comparison to the GRNN approach. This work presents the applicability of neural network as an estimator and method employed could be useful to estimate the crop variables of other crops.     

An interactive multi-objective optimization framework for groundwater inverse modeling

The groundwater inverse problem of estimating heterogeneous groundwater model parameters (hydraulic conductivity in this case) given measurements of aquifer response (such as hydraulic heads) is known to be an ill-posed problem, with multiple parameter values giving similar fits to the aquifer response measurements. This problem is further exacerbated due to the lack of extensive data, typical of most real-world problems. In such cases, it is desirable to incorporate expert knowledge in the estimation process to generate more reasonable estimates. This work presents a novel interactive framework, called the ‘Interactive Multi-Objective Genetic Algorithm’ (IMOGA), to solve the groundwater inverse problem considering different sources of quantitative data as well as qualitative expert knowledge about the site. The IMOGA is unique in that it looks at groundwater model calibration as a multi-objective problem consisting of quantitative objectives – calibration error and regularization – and a ‘qualitative’ objective based on the preference of the geological expert for different spatial characteristics of the conductivity field. All these objectives are then included within a multi-objective genetic algorithm to find multiple solutions that represent the best combination of all quantitative and qualitative objectives. A hypothetical aquifer case-study (based on the test case presented by Freyberg [Freyberg DL. An exercise in ground-water model calibration and prediction. Ground Water 1988;26(3)], for which the ‘true’ parameter values are known, is used as a test case to demonstrate the applicability of this method. It is shown that using automated calibration techniques without using expert interaction leads to parameter values that are not consistent with site-knowledge. Adding expert interaction is shown to not only improve the plausibility of the estimated conductivity fields but also the predictive accuracy of the calibrated model.     

Model Averaging Techniques for Quantifying Conceptual Model Uncertainty

In recent years a growing understanding has emerged regarding the need to expand the modeling paradigm to include conceptual model uncertainty for groundwater models. Conceptual model uncertainty is typically addressed by formulating alternative model conceptualizations and assessing their relative likelihoods using statistical model averaging approaches. Several model averaging techniques and likelihood measures have been proposed in the recent literature for this purpose with two broad categories—Monte Carlo-based techniques such as Generalized Likelihood Uncertainty Estimation or GLUE (Beven and Binley 1992) and criterion-based techniques that use metrics such as the Bayesian and Kashyap Information Criteria (e.g., the Maximum Likelihood Bayesian Model Averaging or MLBMA approach proposed by Neuman 2003) and Akaike Information Criterion-based model averaging (AICMA) (Poeter and Anderson 2005). These different techniques can often lead to significantly different relative model weights and ranks because of differences in the underlying statistical assumptions about the nature of model uncertainty. This paper provides a comparative assessment of the four model averaging techniques (GLUE, MLBMA with KIC, MLBMA with BIC, and AIC-based model averaging) mentioned above for the purpose of quantifying the impacts of model uncertainty on groundwater model predictions. Pros and cons of each model averaging technique are examined from a practitioner's perspective using two groundwater modeling case studies. Recommendations are provided regarding the use of these techniques in groundwater modeling practice.     

Incorporating subjective and stochastic uncertainty in an interactive multi-objective groundwater calibration framework

The interactive multi-objective genetic algorithm (IMOGA) combines traditional optimization with an interactive framework that considers the subjective knowledge of hydro-geological experts in addition to quantitative calibration measures such as calibration errors and regularization to solve the groundwater inverse problem. The IMOGA is inherently a deterministic framework and identifies multiple large-scale parameter fields (typically head and transmissivity data are used to identify transmissivity fields). These large-scale parameter fields represent the optimal trade-offs between the different criteria (quantitative and qualitative) used in the IMOGA. This paper further extends the IMOGA to incorporate uncertainty both in the large-scale trends as well as the small-scale variability (which can not be resolved using the field data) in the parameter fields. The different parameter fields identified by the IMOGA represent the uncertainty in large-scale trends, and this uncertainty is modeled using a Bayesian approach where calibration error, regularization, and the expert’s subjective preference are combined to compute a likelihood metric for each parameter field. Small-scale (stochastic) variability is modeled using a geostatistical approach and added onto the large-scale trends identified by the IMOGA. This approach is applied to the Waste Isolation Pilot Plant (WIPP) case-study. Results, with and without expert interaction, are analyzed and the impact that expert judgment has on predictive uncertainty at the WIPP site is discussed. It is shown that for this case, expert interaction leads to more conservative solutions as the expert compensates for some of the lack of data and modeling approximations introduced in the formulation of the problem.