Tectonic geomorphology of Lolab Watershed, Northwestern Himalayas, India

Abaas Ahmad Mir, Ahsan Afzal Wani, Zahoor Ul Islam and Pervez Ahmed

Page No. 1-9

The present study examines tectonic geomorphology of Lolab Watershed situated in Jhelum basin in Northwestern Himalayas under complex tectonic conditions due to presence of the faults such as Main Boundary Thrust, Main Central Thrust, Zanskar Thrust and the recently identified Balapur Fault from different sides. Geomorphic indices such as relevant to the morphology of the drainage in the watershed, were analyzed to assess tectonic activity.

The results infer that tectonically the study area is slightly to moderately active. The Smf, Eb and KA values indicate dominance of tectonic forces on erosional activity in the watershed whereas the Vf, Hi and Af values indicate that the watershed is uplifting and with valleys deeply incised. Moreover, the topography is high relative to the mean.

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Variation in ratio of maximum 1-hourly to 24-hourly rainfall at western coastal site in India

Saha Dauji, Srivastava Pankaj Kumar and Bhargava Kapilesh

Page No. 10-17

The number and the intensity of the extreme events in India are on an alarming upward trend over the last century. The monthly rainfall for different rainfall subdivisions in the country show varying trends over the time. Contribution of each individual month to the total monsoon rainfall for a particular subdivision also shows increasing and decreasing trends for different subdivisions. Analysis of hourly rainfall records for a typical location in India would throw more light on the actual characteristics of the monsoon rainfall. In order to understand typical characteristics of monsoon rainfall, analyses of continuous hourly rainfall dataset from year 1997 to 2013 of a western coastal site of India have been performed. The site predominantly receives the southwest monsoon on average 90 rainfall days for four months (June to September) which accounts for nearly 90% of its annual rainfall.

The analysis shows that for the maximum number of rainfall events, the hours of rainfall is limited to up to 8 hours, thereby, confirming the deluge/torrential pattern of a tropical rainfall. Also, maximum 1-hourly to 24-hourly rainfall ratio has been studied to derive a realistic design basis for typical monsoon rainfall. It was found that the curves developed for foreign countries, if used for India, would yield uneconomic hydraulic design. In India, rainfall data is available primarily on daily (24-hourly) basis and hence, the present study would serve as a general design basis of other Indian locations where continuous rainfall record is not available.

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Comparing carbon in sediment of primary and artificially generated mangrove forests

Muhammad Arif Asadi, Defri Yona and Muhammad Zuhal Fikri

Page No. 18-26

Soil from primary/natural and artificially generated mangrove forests from Lamongan, Indonesia, was analyzed for bulk density, carbon content and nitrogen content. Samplings were carried out at a total of 12 stations and up to 1 m depth (0-10, 10-25, 25-40, 40-70 and 70-100 cm). Organic matter contents were analyzed using loss-on-ignition and values were converted to organic carbon content using an accepted conversion factor of 0.58. The results showed that the primary forest had lower bulk density and higher carbon and nitrogen contents than those of the artificial forest. On average, the bulk densities of natural and artificial forests were 0.73 g cm-3 and 0.96 g cm-3 respectively, while carbon contents were 6.39% and 3.67% respectively.

Carbon and nitrogen stocks of the primary forest were 477.82±70.75 MgC ha-1 and 38.91±22.84 MgN ha-1 respectively while they were lower at the artificial forest (363.54±52.66 MgC ha-1 and 11.75±2.54 MgN ha-1 respectively). Higher carbon content on the natural forest significantly contributes to higher amount of carbon stocks in the forest. Carbon/nitrogen ratios were 21 and 31 for primary and artificial mangrove forests respectively. Marine based carbon pools might also play a role in the primary mangrove forest as it had lower carbon/nitrogen ratios.

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Time-series Analysis and Forecasting of Rainfall at Idukki district, Kerala: Machine Learning Approach

Kamath R.S. and Kamat R.K.

Page No. 27-33

We report machine learning model for the time-series analysis and forecasting of rainfall at Iddukki district, Kerala. We have used the rainfall dataset from Knoema, a free to use web based open data platform. This work exhibits performance evaluation of various time-series analysis models and compares the forecast accuracy. The models include the Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Exponential Smoothing State Space (ETS).

The comparative study is conducted on modelling fits and outputs. Each of these techniques is applied to model the monthly rainfall at Idukki district for the duration from January 2006 to December 2016. The reported investigation depicts ARIMA modelling outperformed the rest of the models. The performance of the model is evaluated with reference to Root Mean Squared Error (RMSE) and model fit.

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Landslide susceptibility zonation of Gangtok city, Sikkim using Knowledge Driven Method (KDM)

Gupta Srimanta, Kaur Harjeet, Parkash Surya and Thapa Raju

Page No. 34-43

In this study, remote sensing and GIS technique have been employed for the mapping of landslide susceptibility zones within Gangtok Municipal Corporation (GMC) area. In this research work, landslide susceptibility map (LSM) of Gangtok is derived by weighted overlay method (WOM) where weights of various triggering factors are evaluated through expert opinion.

Twelve influencing factors like slope morphometry, elevation, geology/lithology, lineament, land use/land covers (LULC), building density, rainfall, water regime, soil type, soil thickness, soil liquefaction and relative relief are extracted from the database of Sikkim State Disaster Management Authority, Geoogical Survey of India and other Government agencies with limited availability on past landslide information.

Susceptibility map categorizes 19.14% and 31.78 % of the GMC area under very high and high landslide hazard zone respectively whereas, rest of the areas i.e. 30.95% and 18.11 % come under medium and low susceptible zone respectively. LSM is validated by superimposing the reported landslide data as well as success rate curve.

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