The fluvial geochemistry, contributions of silicate, carbonate and saline-alkaline components to chemical weathering flux and controlling parameters: Narmada River (Deccan Traps), India

The Narmada River in India is the largest west-flowing river into the Arabian Sea, draining through the Deccan Traps, one of the largest flood basalt provinces in the world. The fluvial geochemical characteristics and chemical weathering rates (CWR) for the mainstream and its major tributaries were determined using a composite dataset, which includes four phases of seasonal field (spot) samples (during 2003 and 2004) and a decade-long (1990-2000) fortnight time series (multiannual) data. Here, we demonstrate the influence of minor lithologies (carbonates and saline-alkaline soils) on basaltic signature, as reflected in sudden increases of Ca²⁺-Mg²⁺ and Na⁺ contents at many locations along the mainstream and in tributaries. Both spot and multiannual data corrected for non-geological contributions were used to calculate the CWR. The CWR for spot samples (CWR_spot) vary between 25 and 63 ton km⁻² year⁻¹, showing a reasonable correspondence with the CWR estimated for multiannual data (CWR_multi) at most study locations. The weathering rates of silicate (SilWR), carbonate (CarbWR) and evaporite (Sal-AlkWR) have contributed ∼38-58, 28-45 and 8-23%, respectively to the CWR_spot at different locations. The estimated SilWR (11-36 ton km⁻² year⁻¹) for the Narmada basin indicates that the previous studies on the North Deccan Rivers (Narmada-Tapti-Godavari) overestimated the silicate weathering rates and associated CO₂ consumption rates. The average annual CO₂ drawdown via silicate weathering calculated for the Narmada basin is ∼0.032 × 10¹² moles year⁻¹, suggesting that chemical weathering of the entire Deccan Trap basalts consumes approximately 2% (∼0.24 × 10¹² moles) of the annual global CO₂ drawdown. The present study also evaluates the influence of meteorological parameters (runoff and temperature) and physical weathering rates (PWR) in controlling the CWR at annual scale across the basin. The CWR and the SilWR show significant correlation with runoff and PWR. On the basis of observed wide temporal variations in the CWR and their close association with runoff, temperature and physical erosion, we propose that the CWR in the Narmada basin strongly depend on meteorological variability. At most locations, the total denudation rates (TDR) are dominated by physical erosion, whereas chemical weathering constitutes only a small part (<10%). Thus, the CWR to PWR ratio for the Narmada basin can be compared with high relief small river watersheds of Taiwan and New Zealand (1-5%) and large Himalayan Rivers such as the Brahmaputra and the Ganges (8-9%).     

Petrophysical properties of the Himalayan granitoids: Implication on composition and source

The paper reports the characterization of density, magnetic susceptibility, magnetic anisotropy, seismic wave velocities, attenuation as well as mineralogy and major element chemistry of the four generation of granitoids from the Indian Himalaya. Based on these petrophysical properties, only the Cretaceous granitoids of the Trans-Himalayan region by virtue of their mantle affinity and domination of magnetite and/or magnetite–ilmenite series qualify to be the I-type granitoid. On the other hand, rest of the 3 suites of granitoids have a crustal affinity and can be categorized as S-type granitoids enriched with ilmenite and/or hemo-ilmenite series. Beside this general classification, some anomalous petrophysical properties can be related to distinctive mineralogy, stages of magmatic crystallization, and intensity of deformation in different class of granitoids. For example; (i) presence of heavy minerals like hornblende and magnetite accounts for the significantly high density and seismic wave velocity of the Cretaceous granitoids; (ii) fractional crystallization of mantle melts leads to hornblende-rich granitoids (rich in magnetite) in the earlier stage where biotite-rich granitoids (low magnetite) crystallize in the later stage, thus explaining bimodal distribution of magnetic susceptibility in Cretaceous granitoids; (iii) in S-type granitoids, high quartz content (45%) account for the lowest density recorded in Saruna Proterozoic granitoids whereas high content of micaceous minerals reduce the seismic wave and are responsible for the lowest S-wave velocity in the Early Palaeozoic Mandi granitoids; (iv) further, the effect of texture is seen as varying attenuation character of P- and S-waves on grain size. In general, the higher the grain size, the greater the attenuation. Once again Cretaceous granitoids negate this well established relation. Incorporation of this anomalous dependence of physical properties on mineralogical, tectonic fracturing, texture will help the translation of geophysical maps to more a realistic region specific crustal tectonic evolution models.     

Estimation of radon as an earthquake precursor: A neural network approach

An artificial neural networks (ANN) approach combined with Fourier Transform based selection of time period in the time series Radon Emission Data has been presented and shown to improve event prediction rates and reduce false alarms in Earthquake Event Identification over the traditional multiple linear regression techniques. The paper presents a neural networks system using radial basis function (RBF) network as an alternative to traditional statistical regression technique in isolating Radon Emission Anomaly caused by seismic activities. The RBF model has been developed to accept and predict earthquakes events based on a known data set of Radon Emanation, Metrological parameters and actual earthquake events. Subsequently, the model was tested and evaluated on a future data set and a prediction rate of 87.8%, if a reduced false alarm was achieved, the results obtained are better than the traditional techniques.     

Hydrograph separation and precipitation source identification using stable water isotopes and conductivity: River Ganga at Himalayan foothills

The observed retreat of several Himalayan glaciers and snow packs is a cause of concern for the huge population in southern Asia that is dependent on the glacial‐fed rivers emanating from Himalayas. There is considerable uncertainty about how cryospheric recession in the Himalayan region will respond to climate change, and how the water resource availability will be affected. As a first step towards quantifying the contribution of glacier‐melt water, hydrograph separation of River Ganga at Rishikesh into its constituent components, namely (i) surface runoff, (ii) glacial ice‐melt and (iii) groundwater discharge has been done in this paper. A three‐component mixing model has been employed using the values of δ¹⁸O and electrical conductivity (EC) of the river water, and its constituents, to estimate the time‐varying relative fraction of each component. The relative fraction of the surface runoff peaks (70–90%) during winter, due to the near‐zero contribution of glacial ice‐melt, essentially represents the melting of surface snow from the catchment. The contribution of glacial ice‐melt to the stream discharge peaks during summer and monsoon reaches a maximum value of ～40% with an average of 32%. The fraction of groundwater discharge varies within a narrow range (15 ± 5%) throughout the year. On the basis of the variation in the d‐excess values of river water, it is also suggested that the snow‐melt and ice‐melt component has a significant fraction derived from winter precipitation with moisture source from mid‐latitude westerlies (also known as western disturbances). Copyright © 2010 John Wiley & Sons, Ltd.     

Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.     

Hierarchical Approach for Developing Baseline Soil Organic Carbon Database and Its Distribution in India

This study involves generation and logical integration of non-spatial and spatial data in a geographical information system framework to address the gap in national level soil organic carbon estimates. Remote sensing derived inputs and other spatial layers are corrected and integrated using same geographical standards. A relational data base of soil organic carbon density of Indian forest was prepared with attribute information. Hierarchical approach was followed to stratify and verify each sample from the data base using the corrected input layers in GIS and the resulting spatially distributed data is called Indian forest soil organic carbon database. The estimated mean soil organic carbon density for Indian forest is 70 t ha^?1 and varied from 35.4 t ha^?1 in Tropical thorn forest to 104.2 t ha^?1 in Himalayan moist temperate forest in the upper 30 cm of soil depth. Due to large variations in the surface layers the estimated standard error ranged from ±1.5 to 15 % for the upper 30 cm layer which is generally higher than the bottom soil layers. The level of detail in the data base helps to establish base line information for global, national and regional level studies.     

Delineation of shallow resistivity structure around Malvan, Konkan region, Maharashtra by neural network inversion using vertical electrical sounding measurements

Modeling resistivity profiles, especially from hard rock areas, is of specific relevance for groundwater exploration. A method based on Bayesian neural network (BNN) theory using a Hybrid Monte Carlo (HMC) simulation scheme is applied to model and interpret direct current vertical electrical sounding measurements from 28 locations around the Malvan region, in the Sindhudurg district, southwest India. The modeling procedure revolves around optimizing the objective function using the HMC based sampling technique which is followed by updating each trajectory by integrating the Hamiltonian differential equations via a second order leapfrog discretization scheme. The inversion results suggest a high resistivity structure in the north-western part of the area, which correlates well with the presence of laterites. In the south-western part, a very high conductive zone is observed near the coast indicating an extensive influence of saltwater intrusion. Our results also show that the effect of intrusion of saline water diminishes from the south-western part to the north-eastern part of the region. Two dimensional modeling of four resistivity profiles shows that the groundwater flow is partly controlled by existing lineaments, fractures, and major joints. Groundwater occurs at a weathered/semi-weathered layer of laterite/clayey sand and the interface of overburden and crystalline basement. The presence of conduits is identified at a depth between 10 and 15 m along the Dhamapur–Kudal and Parule–Oros profiles, which seems to be potential zone for groundwater exploration. The NW–SE trending major lineaments and its criss-cross sections are indentified from the apparent and true resistivity surface map. The pseudo-section at different depths in the western part of the area, near Parule, shows extensive influence of saltwater intrusion and its impact reaching up to a depth of 50 m from the surface along the coastal area. Further, the deduced true electrical resistivity section against depth correlates well with available borehole lithology in the area. Present analyses suggest that HMC-based BNN method is robust for modeling resistivity data especially in hard rock terrains. These results are useful for interpreting fractures, major joints, and lineaments and crystalline basement rock and also for constraining the higher dimensional models.     

Crustal evolution and tectonics of the Archean Bundelkhand craton, Central India

Magnetotelluric studies over the Bundelkhand craton indicates a high resistivity sub-structure, typically observed in the Archean-Proterozoic regions. The geoelectric section shows a single high resistivity layer in the northern part of the craton, extending from surface to a depth of about 60 km and a three layered resistivity structure overlying a conductive bottom in its southern part. The geological studies reported earlier have delineated an EW trending zone of ultramafic rocks, called the Bundelkhand tectonic zone (BTZ), which marks the divide between the two electrical resistivity patterns. The geoelectric structure is broadly indicative of a northward dipping tectonic fabric in this region which conforms to the Himalayan subduction, to the immediate north of this craton. However this observation cannot explain the findings from geochemical, isotope analysis and geological studies, suggesting possible vertical block movements in the region, which are also indicated in the Bouguer gravity studies. The geoelectric structure beneath the Vindhyan group to the south shows low resistivities even up to 60 km, suggesting that the Bundelkhand craton which is characterized by high resistivity rocks, does not extend to the south beneath the Vindhyans, as was believed by the earlier researchers. A low resistivity body with an extremely high conductance of about 100,000 Siemens is delineated at the mid crustal depths beneath the exposed Bijawars south of Bundelkhand craton. The causative factors behind this low resistivity are not immediately apparent, but some possibilities are discussed here.     

Stochastic Simulation of Nonstationary Rainfall Fields, Accounting for Seasonality and Atmospheric Circulation Pattern Evolution

A model for generating daily spatial correlated rainfall fields suitable for evaluating the impacts of climate change on water resources is presented. The model, termed Stochastic Rainfall Generating Process, is designed to incorporate two major nonstationarities: changes in the frequencies of different precipitation generating mechanisms (frontal and convective), and spatial nonstationarities caused by interactions of mesoscale atmospheric patterns with topography (orographic effects). These nonstationarities are approximated as discrete sets of the time-stationary Stochastic Rainfall Generating Process, each of which represents the different spatial patterns of rainfall (including its variation with topography) associated with different atmospheric circulation patterns and times of the year (seasons). Each discrete Stochastic Rainfall Generating Process generates daily correlated rainfall fields as the product of two random fields. First, the amount of rainfall is generated by a transformed Gaussian process applying sequential Gaussian simulation. Second, the delimitation of rain and no-rain areas (intermittence process) is defined by a binary random function simulated by sequential indicator simulations. To explore its applicability, the model is tested in the Upper Guadiana Basin in Spain. The result suggests that the model provides accurate reproduction of the major spatiotemporal features of rainfall needed for hydrological modeling and water resource evaluations. The results were significantly improved by incorporating spatial drift related to orographic precipitation into the model.     

Coupled hydromechanical‐fracture simulations of nonplanar three‐dimensional hydraulic fracture propagation

This paper presents an algorithm and a fully coupled hydromechanical‐fracture formulation for the simulation of three‐dimensional nonplanar hydraulic fracture propagation. The propagation algorithm automatically estimates the magnitude of time steps such that a regularized form of Irwin's criterion is satisfied along the predicted 3‐D fracture front at every fracture propagation step. A generalized finite element method is used for the discretization of elasticity equations governing the deformation of the rock, and a finite element method is adopted for the solution of the fluid flow equation on the basis of Poiseuille's cubic law. Adaptive mesh refinement is used for discretization error control, leading to significantly fewer degrees of freedom than available nonadaptive methods. An efficient computational scheme to handle nonlinear time‐dependent problems with adaptive mesh refinement is presented. Explicit fracture surface representations are used to avoid mapping of 3‐D solutions between generalized finite element method meshes. Examples demonstrating the accuracy, robustness, and computational efficiency of the proposed formulation, regularized Irwin's criterion, and propagation algorithm are presented.