Geochemical behaviour of dissolved trace elements in a monsoon-dominated tropical river basin, Southwestern India

The study presents a 3-year time series data on dissolved trace elements and rare earth elements (REEs) in a monsoon-dominated river basin, the Nethravati River in tropical Southwestern India. The river basin lies on the metamorphic transition boundary which separates the Peninsular Gneiss and Southern Granulitic province belonging to Archean and Tertiary-Quaternary period (Western Dharwar Craton). The basin lithology is mainly composed of granite gneiss, charnockite and metasediment. This study highlights the importance of time series data for better estimation of metal fluxes and to understand the geochemical behaviour of metals in a river basin. The dissolved trace elements show seasonality in the river water metal concentrations forming two distinct groups of metals. First group is composed of heavy metals and minor elements that show higher concentrations during dry season and lesser concentrations during the monsoon season. Second group is composed of metals belonging to lanthanides and actinides with higher concentration in the monsoon and lower concentrations during the dry season. Although the metal concentration of both the groups appears to be controlled by the discharge, there are important biogeochemical processes affecting their concentration. This includes redox reactions (for Fe, Mn, As, Mo, Ba and Ce) and pH-mediated adsorption/desorption reactions (for Ni, Co, Cr, Cu and REEs). The abundance of Fe and Mn oxyhydroxides as a result of redox processes could be driving the geochemical redistribution of metals in the river water. There is a Ce anomaly (Ce/Ce*) at different time periods, both negative and positive, in case of dissolved phase, whereas there is positive anomaly in the particulate and bed sediments. The Ce anomaly correlates with the variations in the dissolved oxygen indicating the redistribution of Ce between particulate and dissolved phase under acidic to neutral pH and lower concentrations of dissolved organic carbon. Unlike other tropical and major world rivers, the effect of organic complexation on metal variability is negligible in the Nethravati River water.

Climate change scenarios of surface solar radiation in data sparse regions: a case study in Malaprabha River Basin, India

A variety of methods are available to estimate future solar radiation (SR) scenarios at spatial scales that are appropriate for local climate change impact assessment. However, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. Three methodologies to guide the estimation of SR are discussed in this study, namely: Case 1: SR is measured, Case 2: SR is measured but sparse and Case 3: SR is not measured. In Case 1, future SR scenarios are derived using several downscaling methodologies that transfer the simulated large-scale information of global climate models to a local scale ( measurements). In Case 2, the SR was first estimated at the local scale for a longer time period using sparse measured records, and then future scenarios were derived using several downscaling methodologies. In Case 3: the SR was first estimated at a regional scale for a longer time period using complete or sparse measured records of SR from which SR at the local scale was estimated. Finally, the future scenarios were derived using several downscaling methodologies. The lack of observed SR data, especially in developing countries, has hindered various climate change impact studies. Hence, this was further elaborated by applying the Case 3 methodology to a semi-arid Malaprabha reservoir catchment in southern India. A support vector machine was used in downscaling SR. Future monthly scenarios of SR were estimated from simulations of third-generation Canadian General Circulation Model (CGCM3) for various SRES emission scenarios (A1B, A2, B1, and COMMIT). Results indicated a projected decrease of 0.4 to 12.2 W m(-2) yr(-1) in SR during the period 2001-2100 across the 4 scenarios. SR was calculated using the modified Hargreaves method. The decreasing trends for the future were in agreement with the simulations of SR from the CGCM3 model directly obtained for the 4 scenarios.

Forest area estimation and reporting: implications for conservation, management and REDD

Periodic estimation, monitoring and reporting on area under forest and plantation types and afforestation rates are critical to forest and biodiversity conservation, sustainable forest management and for meeting international commitments. This article is aimed at assessing the adequacy of the current monitoring and reporting approach adopted in India in the context of new challenges of conservation and reporting to international conventions and agencies. The analysis shows that the current mode of monitoring and reporting of forest area is inadequate to meet the national and international requirements. India could be potentially over-reporting the area under forests by including many non-forest tree categories such as commercial plantations of coconut, cashew, coffee and rubber, and fruit orchards. India may also be under-reporting deforestation by reporting only gross forest area at the state and national levels. There is a need for monitoring and reporting of forest cover, deforestation and afforestation rates according to categories such as (i) natural/primary forest, (ii) secondary/degraded forests, (iii) forest plantations, (iv) commercial plantations, (v) fruit orchards and (vi) scattered trees.

Characterising weather patterns associated with fire in a seasonally dry tropical forest in southern India

Anthropogenic fires in seasonally dry tropical forests are a regular occurrence during the dry season. Forest managers in India, who presently follow a fire suppression policy in such forests, would benefit from a system of assessing the potential risk to fire on a particular day. We examined the relationship between weather variables (seasonal rainfall, relative humidity, temperature) and days of fire during the dry seasons of 2004-2010, based on MODIS fire incident data in the seasonally dry tropical forests of Mudumalai in the Western Ghats, southern India. Logistic regression analysis showed that high probabilities of a fire day, indicating successful ignition of litter and grass fuel on the forest floor, were associated with low levels of early dry season rainfall, low daily average relative humidity and high daily average temperatures. These weather conditions are representative of low moisture levels of fine fuels, suggesting that the occurrence of fire is moderated by environmental conditions that reduce the flammability of fine fuels in the dry tropics. We propose a quantitative framework for assessing risk of a fire day to assist forest managers in anticipating fire occurrences in this seasonally dry tropical forest, and possibly for those across South Asia.

Broadband wavelength-selective reflectance and selective polarization by a tip-bent vertically aligned multi-walled carbon nanotube forest

The tunable optical properties of the bulk structure of carbon nanotubes (CNT) were recently revealed as a perfect black body material, optically reflective mirror and solar absorber. The present study demonstrates an enhanced optical reflectance of up to similar to 15% over a broad wavelength range in the near infrared region followed by a mechanical modification of the surface of a bulk CNT structure, which can be accounted for due to the grating-like surface abnormalities. In response to the specific arrangement of the so-formed bent tips of the CNT, a selective reflectance is achieved and results in reflecting only a dominant component of the polarized ight, which has not been realized so far. Modulation of this selective-optical reflectance can be achieved by ontrolling the degree of tip bending of the nanotubes, thus opening up avenues for the construction of novel dynamic light polarizers and absorbers.

Temporal variability of forest communities: empirical estimates of population change in 4000 tree species

Long-term surveys of entire communities of species are needed to measure fluctuations in natural populations and elucidate the mechanisms driving population dynamics and community assembly. We analysed changes in abundance of over 4000 tree species in 12 forests across the world over periods of 6-28years. Abundance fluctuations in all forests are large and consistent with population dynamics models in which temporal environmental variance plays a central role. At some sites we identify clear environmental drivers, such as fire and drought, that could underlie these patterns, but at other sites there is a need for further research to identify drivers. In addition, cross-site comparisons showed that abundance fluctuations were smaller at species-rich sites, consistent with the idea that stable environmental conditions promote higher diversity. Much community ecology theory emphasises demographic variance and niche stabilisation; we encourage the development of theory in which temporal environmental variance plays a central role.

An assessment of terrestrial water storage, rainfall and river discharge over Northern India from satellite data

Terrestrial water storage (TWS) plays a key role in the global water cycle and is highly influenced by climate variability and human activities. In this study, monthly TWS, rainfall and Ganga-Brahmaputra river discharge (GBRD) are analysed over India for the period of 2003-12 using remote sensing satellite data. The spatial pattern of mean TWS shows a decrease over a large and populous region of Northern India comprising the foothills of the Himalayas, the Indo-Gangetic Plains and North East India. Over this region, the mean monthly TWS exhibits a pronounced seasonal cycle and a large interannual variability, highly correlated with rainfall and GBRD variations (r > 0.8) with a lag time of 2 months and 1 month respectively. The time series of monthly TWS shows a consistent and statistically significant decrease of about 1 cm year(-1) over Northern India, which is not associated with changes in rainfall and GBRD. This recent change in TWS suggests a possible impact of rapid industrialization, urbanization and increase in population on land water resources. Our analysis highlights the potential of the Earth-observation satellite data for hydrological applications.

Species and acoustic diversity of bats in a palaeotropical wet evergreen forest in southern India

The Western Ghats of India is among the top 25 biodiversity hotspots in the world. About 43% of the reported 117 bat species in India are found in this region, but few quantitative studies of bat echolocation calls and diversity have been carried out here thus far. A quantitative study of bat diversity was therefore conducted using standard techniques, including mist-netting, acoustical and roost surveys in the wet evergreen forests of Kudremukh National Park in the Western Ghats of Karnataka. A total of 106 bats were caught over 108 sampling nights, representing 17 species, 3 belonging to Megachiroptera and 14 to Microchiroptera. Acoustical and roost surveys added three more species, two from Microchiroptera and one from Megachiroptera. Of these 20 species, 4 belonged to the family Pteropodidae, 10 to Vespertilionidae, 3 to Rhinolophidae, 2 to Megadermatidae and 1 to Hipposideridae. We recorded the echolocation calls of 13 of the 16 microchiropteran species, of which the calls of 4 species (Pipistrellus coromandra, Pipistrellus affinis, Pipistrellus ceylonicus and Harpiocephalus harpia) have been recorded for the first time. Discriminant function analyses of the calls of 11 species provided 91.7% correct classification of individuals to their respective species, indicating that the echolocation calls could be used successfully for non-invasive acoustic surveys and monitoring of bat species in the future.

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial ``dilution'' bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Sources of major ions and processes affecting the geochemical and isotopic signatures of subsurface waters along a tropical river, Southwestern India

Systematic monitoring of subsurface hydrogeochemistry has been carried out for a period of one year in a humid tropical region along the Nethravati-Gurupur River. The major ion and stable isotope (delta O-18 and delta H-2) compositions are used to understand the hydrogeochemistry of groundwater and its interaction with surface water. In the study, it is observed that intense weathering of source rocks is the major source of chemical elements to the surface and subsurface waters. In addition, agricultural activities and atmospheric contributions also control the major ion chemistry of water in the study area. There is a clear seasonality in the groundwater chemistry, which is related to the recharge and discharge of the hydrological system. On a temporal scale, there is a decrease in major cation concentrations during the monsoon which is a result of dilution of sources from the weathering of rock minerals, and an increase in anion concentrations which is contributed by the atmosphere, accompanied by an increase in water level during the monsoon. The stable isotope composition indicates that groundwater in the basin is of meteoric origin and recharged directly from the local precipitation during the monsoonal season. Soon after the monsoon, groundwater and surface water mix in the subsurface region. The groundwater feeds the surface water during the lean river flow season.

And yet it shrinks: A novel method for correcting bias in forest tree growth estimates caused by water-induced fluctuations

Accuracy in tree woody growth estimates is important to global carbon budget estimation and climate-change science. Tree growth in permanent sampling plots (PSPs) is commonly estimated by measuring stem diameter changes, but this method is susceptible to bias resulting from water-induced reversible stem shrinkage. In the absence of bias correction, temporal variability in growth is likely to be overestimated and incorrectly attributed to fluctuations in resource availability, especially in forests with high seasonal and inter-annual variability in water. We propose and test a novel approach for estimating and correcting this bias at the community level. In a 50-ha PSP from a seasonally dry tropical forest in southern India, where tape measurements have been taken every four years from 1988 to 2012, for nine trees we estimated bias due to reversible stem shrinkage as the difference between woody growth measured using tree rings and that estimated from tape. We tested if the bias estimated from these trees could be used as a proxy to correct bias in tape-based growth estimates at the PSP scale. We observed significant shrinkage-related bias in the growth estimates of the nine trees in some censuses. This bias was strongly linearly related to tape-based growth estimates at the level of the PSP, and could be used as a proxy. After bias was corrected, the temporal variance in growth rates of the PSP decreased, while the effect of exceptionally dry or wet periods was retained, indicating that at least a part of the temporal variability arose from reversible shrinkage-related bias. We also suggest that the efficacy of the bias correction could be improved by measuring the proxy on trees that belong to different size classes and census timing, but not necessarily to different species. Our approach allows for reanalysis - and possible reinterpretation of temporal trends in tree growth, above ground biomass change, or carbon fluxes in forests, and their relationships with resource availability in the context of climate change. (C) 2014 Elsevier B.V. All rights reserved.

Reduced Hornbill Abundance Associated with Low Seed Arrival and Altered Recruitment in a Hunted and Logged Tropical Forest

Logging and hunting are two key direct threats to the survival of wildlife in the tropics, and also disrupt important ecosystem processes. We investigated the impacts of these two factors on the different stages of the seed dispersal cycle, including abundance of plants and their dispersers and dispersal of seeds and recruitment, in a tropical forest in north-east India. We focused on hornbills, which are important seed dispersers in these forests, and their food tree species. We compared abundances of hornbill food tree species in a site with high logging and hunting pressures (heavily disturbed) with a site that had no logging and relatively low levels of hunting (less disturbed) to understand logging impacts on hornbill food tree abundance. We compared hornbill abundances across these two sites. We, then, compared the scatter-dispersed seed arrival of five large-seeded tree species and the recruitment of four of those species. Abundances of hornbill food trees that are preferentially targeted by logging were two times higher in the less disturbed site as compared to the heavily disturbed site while that of hornbills was 22 times higher. The arrival of scatter-dispersed seeds was seven times higher in the less disturbed site. Abundances of recruits of two tree species were significantly higher in the less disturbed site. For another species, abundances of younger recruits were significantly lower while that of older recruits were higher in the heavily disturbed site. Our findings suggest that logging reduces food plant abundance for an important frugivore-seed disperser group, while hunting diminishes disperser abundances, with an associated reduction in seed arrival and altered recruitment of animal-dispersed tree species in the disturbed site. Based on our results, we present a conceptual model depicting the relationships and pathways between vertebrate-dispersed trees, their dispersers, and the impacts of hunting and logging on these pathways.

Regeneration of Juvenile Woody Plants after Fire in a Seasonally Dry Tropical Forest of Southern India

Woody tree species in seasonally dry tropical forests are known to have traits that help them to recover from recurring disturbances such as fire. Two such traits are resprouting and rapid post-fire growth. We compared survival and growth rates of regenerating small-sized individuals (juveniles) of woody tree species after dry season fire (February-March) at eight adjacent pairs of burnt and unburnt transects in a seasonally dry tropical forest in southern India. Juveniles were monitored at 3-mo intervals between August 2009 and August 2010. High juvenile survivorship (>95%) was observed in both burnt and unburnt areas. Growth rates of juveniles, analyzed at the community level as well as for a few species individually (especially fast-growing ones), were distinctly higher in burnt areas compared to unburnt areas after a fire event, particularly during the pre-monsoon season immediately after a fire. Rapid growth by juveniles soon after a fire may be due to lowered competition from other vegetative forms such as grasses, possibly aided by the availability of resources stored belowground. Such an adaptation would allow a juvenile bank to be retained in the understory of a dry forest, from where individuals can grow to a possible fire-tolerant size during favorable conditions.

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.