Trichodesmium erythraeum (Ehrenberg) bloom along the southwest coast of India (Arabian Sea) and its impact on trace metal concentrations in seawater

The incidence of a large scale Trichodesmium erythraeum bloom along the southwest coast of India (Arabian Sea) observed in May 2005 is reported. Around 4802 filaments of T. erythraeum ml⁻¹ seawater was observed and a colony consisted of 3.6 × 10⁵ cells. The bloom was predominant off Suratkal (12° 59′N and 74° 31′E) with a depth of about 47 m, covering an area of 7 km in length and 2 km width. The concentrations of Zinc, Cadmium, Lead, Copper, Nickel and Cobalt were determined in samples collected from the bloom and non-bloom sites using stripping voltammetry. The observed hydrographical and meteorological parameters were found to be favorable for the bloom. The concentrations of Zinc, Cadmium and Nickel were found to be higher at bloom stations, while the concentrations of Lead, Copper and Cobalt were found to be very low at bloom stations. Elevated concentrations of Cadmium and Cobalt were observed at Valappad mainly due to the decomposition of detrital material produced in the bloom. Statistically significant differences (P > 0.01) in metal concentrations between the bloom and non-bloom stations were not observed except for Copper. Metals such as Lead, Copper and Cobalt were removed from the seawater at all places where bloom was observed. Cadmium was found to be slowly released during the decaying process of the bloom.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

Holocene climate forcings and lacustrine regime shifts in the Indian summer monsoon realm

Extreme climate events have been identified both in meteorological and long-term proxy records from the Indian summer monsoon (ISM) realm. However, the potential of palaeoclimate data for understanding mechanisms triggering climate extremes over long time scales has not been fully exploited. A distinction between proxies indicating climate change, environment, and ecosystem shift is crucial for enabling a comparison with forcing mechanisms (e.g. El-Niño Southern Oscillation). In this study we decouple these factors using data analysis techniques [multiplex recurrence network (MRN) and principal component analyses (PCA)] on multiproxy data from two lakes located in different climate regions – Lonar Lake (ISM dominated) and the high-altitude Tso Moriri Lake (ISM and westerlies influenced). Our results indicate that (i) MRN analysis, an indicator of changing environmental conditions, is associated with droughts in regions with a single climate driver but provides ambiguous results in regions with multiple climate/environmental drivers; (ii) the lacustrine ecosystem was ‘less sensitive’ to forcings during the early Holocene wetter periods; (iii) archives in climate zones with a single climate driver were most sensitive to regime shifts; (iv) data analyses are successful in identifying the timing of onset of climate change, and distinguishing between extrinsic and intrinsic (lacustrine) regime shifts by comparison with forcing mechanisms. Our results enable development of conceptual models to explain links between forcings and regional climate change that can be tested in climate models to provide an improved understanding of the ISM dynamics and their impact on ecosystems. © 2020 John Wiley & Sons, Ltd.     

Modelling Chorabari Lake outburst flood,Kedarnath, India

In this study, the Glacier Lake Outburst Flood(GLOF) that occurred over Kedarnath in June 2013 was modeled using integrated observations from the field and Remote Sensing(RS). The lake breach parameters such as area, depth, breach, and height have been estimated from the field observations and Remote Sensing(RS) data. A number of modelling approaches, including Snow Melt Runoff Model(SRM), Modified Single Flow model(MSF), Watershed Management System(WMS), Simplified Dam Breach Model(SMPDBK) and BREACH were used to model the GLOF. Estimations from SRM produced a runoff of about 22.7 m3 during 16–17, June 2013 over Chorabari Lake. Bathymetry data reported that the lake got filled to its maximum capacity(3822.7 m3) due to excess discharge. Hydrograph obtained from the BREACH model revealed a peak discharge of about 1699 m3/s during an intense water flow episode that lasted for 10–15 minutes on 17 th June 2013. Excess discharge from heavy rainfall and snowmelt into the lake increased its hydrostatic pressure and the lake breached cataclysmically.     

Holocene climate variability from lake sediment core in Larsemann Hills,Antarctica

A sediment core (L2) from Larsemann Hills, Antarctica was analyzed for Biogenic Silica (BSi), Sand (%) and Total Organic Carbon (TOC). The 78 cm core length represents the time span of ∼8.3 cal ka BP. The values of BSi from the core show prominent high productivity from ∼8.3 to ∼6 cal ka BP in comparison to less productivity in mid-late Holocene (∼6 cal ka BP to recent). Moreover, high sand (%) infers the glacio-fluvial deposition from ∼8.3 to ∼5 cal ka BP TOC shows little variation through out the core, except in the upper ∼10 cm (∼4 cal ka BP) part wherein it is comparatively high. The increased TOC in the upper part of the core possibly indicates presence of algal mat due to exposure of the lake to the ice free (glacier free) conditions.     

Measurements of radon flux and soil-gas radon concentration along the Main Central Thrust, Garhwal Himalaya, using SRM and RAD7 detectors

Radon in the Earth’s crust or soil matrix is free to move only if its atoms find their way into pores or capillaries of the matrix. ²²²Rn atoms from solid mineral grains get into air, filling pores through emanation process. Then ²²²Rn enters into the atmosphere from air-filled pores by exhalation process. The estimation of radon flux from soil surface is an important parameter for determining the source term for radon concentration modeling. In the present investigation, radon fluxes and soil-gas radon concentration have been measured along and around the Main Central Thrust (MCT) in Uttarkashi district of Garhwal Himalaya, India, by using Scintillation Radon Monitor (SRM) and RAD7 devices, respectively. The soil radon gas concentration measured by RAD7 with soil probe at the constant depth was found to vary from 12 ± 3 to 2330 ± 48 Bq·m^?3 with geometrical mean value of 302 ± 84 Bq·m^?3. Th significance of this work is its usefulness from radiation protection point of view.     

Estimation of heavy rainfall during cyclonic storms from microwave observations using nonlinear approach over Indian Ocean

In this study, an effort has been made to study heavy rainfall events during cyclonic storms over Indian Ocean. This estimate is based on microwave observations from tropical rainfall measuring mission (TRMM) Microwave Imager (TMI). Regional scattering index (SI) developed for Indian region based on measurements at 19-, 21- and 85-GHz brightness temperature and polarization corrected temperature (PCT) at 85?GHz have been utilized in this study. These PCT and SI are collocated against Precipitation Radar (PR) onboard TRMM to establish a relationship between rainfall rate, PCT and SI. The retrieval technique using both linear and nonlinear regressions has been developed utilizing SI, PCT and the combination of SI and PCT. The results have been compared with the observations from PR. It was found that a nonlinear algorithm using combination of SI and PCT is more accurate than linear algorithm or nonlinear algorithm using either SI or PCT. Statistical comparison with PR exhibits the correlation coefficients (CC) of 0.68, 0.66 and 0.70, and root mean square error (RMSE) of 1.78, 1.96 and 1.68?mm/h from the observations of SI, PCT and combination of SI and PCT respectively using linear regressions. When nonlinear regression is used, the CC of 0.73, 0.71, 0.79 and RMSE of 1.64, 1.95, 1.54?mm/h are observed from the observations of SI, PCT and combination of SI and PCT, respectively. The error statistics for high rain events (above 10?mm/h) shows the CC of 0.58, 0.59, 0.60 and RMSE of 5.07, 5.47, 5.03?mm/h from the observations of SI, PCT and combination of SI and PCT, respectively, using linear regression, and on the other hand, use of nonlinear regression yields the CC of 0.66, 0.64, 0.71 and RMSE of 4.68, 5.78 and 4.02?mm/h from the observations of SI, PCT and combined SI and PCT, respectively.