Controls on intense silicate weathering in a tropical river, southwestern India

The Silicate Weathering Rate (SWR) and associated Carbon dioxide Consumption Rate (CCR) in tropical silicate terrain is assessed through a study of the major ion chemistry in a small west flowing river of Peninsular India, the Nethravati River. The specific features of the river basin are high mean annual rainfall and temperature, high runoff and a Precambrian basement composed of granitic-gneiss, charnockite and minor metasediments. The water samples (n = 56) were collected from three locations along the Nethravati River and from two of its tributaries over a period of twelve months. Chemical Weathering Rate (CWR) for the entire watershed is calculated by applying rainwater correction using river chloride as a tracer. Chemical Weathering Rate in the Nethravati watershed is estimated to 44 t.km(-2).y(-1) encompassing a SWR of 42 t.km(-2).y(-1) and a maximum carbonate contribution of 2 t.km(-2).y(-1). This SWR is among the highest reported for granito-gneissic terrains. The assessed CCR is 2.9 . 10(5) mol.km(-2).y(-1). The weathering index (Re). calculated from molecular ratios of dissolved cations and silica in the river, suggests an intense silicate weathering leading to kaolinite-gibbsite precipitation in the weathering covers. The intense SWR and CCR could be due to the combination of high runoff and temperature along with the thickness and nature of the weathering cover. The comparison of silicate weathering fluxes with other watersheds reveals that under similar morpho-climatic settings basalt weathering would be 2.5 times higher than the granite-gneissic rocks. (C) 2012 Elsevier B.V. All rights reserved.

Physically Based, Hydrologic Model Results Based on Three Precipitation Products

The main objective of the study is to examine the accuracy of and differences among simulated streamflows driven by rainfall estimates from a network of 22 rain gauges spread over a 2,170 km2 watershed, NEXRAD Stage III radar data, and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite data. The Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a physically based, distributed parameter, grid-structured, hydrologic model, was used to simulate the June-2002 flooding event in the Upper Guadalupe River watershed in south central Texas. There were significant differences between the rainfall fields estimated by the three types of measurement technologies. These differences resulted in even larger differences in the simulated hydrologic response of the watershed. In general, simulations driven by radar rainfall yielded better results than those driven by satellite or rain-gauge estimates. This study also presents an overview of effects of land cover changes on runoff and stream discharge. The results demonstrate that, for major rainfall events similar to the 2002 event, the effect of urbanization on the watershed in the past two decades would not have made any significant effect on the hydrologic response. The effect of urbanization on the hydrologic response increases as the size of the rainfall event decreases.

Characterization of groundwater chemistry under the influence of lithologic and anthropogenic factors along a climatic gradient in Upper Cauvery basin, South India

Hydrogeological and climatic effect on chemical behavior of groundwater along a climatic gradient is studied along a river basin. `Semi-arid' (500-800 mm of mean annual rainfall), `sub-humid' (800-1,200 mm/year) and `humid' (1,200-1,500 mm/year) are the climatic zones chosen along the granito-gneissic plains of Kabini basin in South India for the present analysis. Data on groundwater chemistry is initially checked for its quality using NICB ratio (<+/- 5 %), EC versus TZ+ (similar to 0.85 correlation), EC versus TDS and EC versus TH analysis. Groundwater in the three climatic zones is `hard' to `very hard' in terms of Ca-Mg hardness. Polluted wells are identified (> 40 % of pollution) and eliminated for the characterization. Piper's diagram with mean concentrations indicates the evolution of CaNaHCO3 (semi-arid) from CaHCO3 (humid zone) along the climatic gradient. Carbonates dominate other anions and strong acids exceeded weak acids in the region. Mule Hole SEW, an experimental watershed in sub-humid zone, is characterized initially using hydrogeochemistry and is observed to be a replica of entire sub-humid zone (with 25 wells). Extension of the studies for the entire basin (120 wells) showed a chemical gradient along the climatic gradient with sub-humid zone bridging semi-arid and humid zones. Ca/Na molar ratio varies by more than 100 times from semi-arid to humid zones. Semi-arid zone is more silicaceous than sub-humid while humid zone is more carbonaceous (Ca/Cl similar to 14). Along the climatic gradient, groundwater is undersaturated (humid), saturated (sub-humid) and slightly supersaturated (semi-arid) with calcite and dolomite. Concentration-depth profiles are in support of the geological stratification i.e., not approximate to 18 m of saprolite and similar to 25 m of fracture rock with parent gneiss beneath. All the wells are classified into four groups based on groundwater fluctuations and further into `deep' and `shallow' based on the depth to groundwater. Higher the fluctuations, larger is its impact on groundwater chemistry. Actual seasonal patterns are identified using `recharge-discharge' concept based on rainfall intensity instead of traditional monsoon-non-monsoon concept. Non-pumped wells have low Na/Cl and Ca/Cl ratios in recharge period than in discharge period (Dilution). Few other wells, which are subjected to pumping, still exhibit dilution chemistry though water level fluctuations are high due to annual recharge. Other wells which do not receive sufficient rainfall and are constantly pumped showed high concentrations in recharge period rather than in discharge period (Anti-dilution). In summary, recharge-discharge concept demarcates the pumped wells from natural deep wells thus, characterizing the basin.

Constraining complex aquifer geometry with geophysics (2-D ERT and MRS measurements) for stochastic modelling of groundwater flow

Stochastic modelling is a useful way of simulating complex hard-rock aquifers as hydrological properties (permeability, porosity etc.) can be described using random variables with known statistics. However, very few studies have assessed the influence of topological uncertainty (i.e. the variability of thickness of conductive zones in the aquifer), probably because it is not easy to retrieve accurate statistics of the aquifer geometry, especially in hard rock context. In this paper, we assessed the potential of using geophysical surveys to describe the geometry of a hard rock-aquifer in a stochastic modelling framework. The study site was a small experimental watershed in South India, where the aquifer consisted of a clayey to loamy-sandy zone (regolith) underlain by a conductive fissured rock layer (protolith) and the unweathered gneiss (bedrock) at the bottom. The spatial variability of the thickness of the regolith and fissured layers was estimated by electrical resistivity tomography (ERT) profiles, which were performed along a few cross sections in the watershed. For stochastic analysis using Monte Carlo simulation, the generated random layer thickness was made conditional to the available data from the geophysics. In order to simulate steady state flow in the irregular domain with variable geometry, we used an isoparametric finite element method to discretize the flow equation over an unstructured grid with irregular hexahedral elements. The results indicated that the spatial variability of the layer thickness had a significant effect on reducing the simulated effective steady seepage flux and that using the conditional simulations reduced the uncertainty of the simulated seepage flux. As a conclusion, combining information on the aquifer geometry obtained from geophysical surveys with stochastic modelling is a promising methodology to improve the simulation of groundwater flow in complex hard-rock aquifers. (C) 2013 Elsevier B.V. All rights reserved.

Biodiversity and ecological assessments of Indian sacred groves

Sacred groves are patches of forests preserved for their spiritual and religious significance. The practice gained relevance with the spread of agriculture that caused large-scale deforestation affecting biodiversity and watersheds. Sacred groves may lose their prominence nowadays, but are still relevant in Indian rural landscapes inhabited by traditional communities. The recent rise of interest in this tradition encouraged scientific study that despite its pan-Indian distribution, focused on India's northeast, Western Ghats and east coast either for their global/regional importance or unique ecosystems. Most studies focused on flora, mainly angiosperms, and the faunal studies concentrated on vertebrates while lower life forms were grossly neglected. Studies on ecosystem functioning are few although observations are available. Most studies attributed watershed protection values to sacred groves but hardly highlighted hydrological process or water yield in comparison with other land use types. The grove studies require diversification from a stereotyped path and must move towards creating credible scientific foundations for conservation. Documentation should continue in unexplored areas but more work is needed on basic ecological functions and ecosystem dynamics to strengthen planning for scientifically sound sacred grove management.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Probable maximum precipitation estimation for catchments in Mahanadi river basin

Probable maximum precipitation (PMP) is a theoretical concept that is widely used by hydrologists to arrive at estimates for probable maximum flood (PMF) that find use in planning, design and risk assessment of high-hazard hydrological structures such as flood control dams upstream of populated areas. The PMP represents the greatest depth of precipitation for a given duration that is meteorologically possible for a watershed or an area at a particular time of year, with no allowance made for long-term climatic trends. Various methods are in use for estimation of PMP over a target location corresponding to different durations. Moisture maximization method and Hershfield method are two widely used methods. The former method maximizes the observed storms assuming that the atmospheric moisture would rise up to a very high value estimated based on the maximum daily dew point temperature. On the other hand, the latter method is a statistical method based on a general frequency equation given by Chow. The present study provides one-day PMP estimates and PMP maps for Mahanadi river basin based on the aforementioned methods. There is a need for such estimates and maps, as the river basin is prone to frequent floods. Utility of the constructed PMP maps in computing PMP for various catchments in the river basin is demonstrated. The PMP estimates can eventually be used to arrive at PMF estimates for those catchments. (C) 2015 The Authors. Published by Elsevier B.V.

Analytical approach to quantile estimation in regional frequency analysis based on fuzzy framework

Regional frequency analysis is widely used for estimating quantiles of hydrological extreme events at sparsely gauged/ungauged target sites in river basins. It involves identification of a region (group of watersheds) resembling watershed of the target site, and use of information pooled from the region to estimate quantile for the target site. In the analysis, watershed of the target site is assumed to completely resemble watersheds in the identified region in terms of mechanism underlying generation of extreme event. In reality, it is rare to find watersheds that completely resemble each other. Fuzzy clustering approach can account for partial resemblance of watersheds and yield region(s) for the target site. Formation of regions and quantile estimation requires discerning information from fuzzy-membership matrix obtained based on the approach. Practitioners often defuzzify the matrix to form disjoint clusters (regions) and use them as the basis for quantile estimation. The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds. The lost information cannot be utilized in quantile estimation, owing to which the estimates could have significant error. To avert the loss of information, a threshold strategy (TS) was considered in some prior studies. In this study, it is analytically shown that the strategy results in under-prediction of quantiles. To address this, a mathematical approach is proposed in this study and its effectiveness in estimating flood quantiles relative to DFA and TS is demonstrated through Monte-Carlo simulation experiments and case study on Mid-Atlantic water resources region, USA. (C) 2015 Elsevier B.V. All rights reserved.