Spatio-temporal trends of rainfall across Indian river basins

Theoretical and Applied Climatology - Daily gridded high-resolution rainfall data of India Meteorological Department at 0.25° spatial resolution (1901–2015) was analyzed to detect the...     

Detection and delineation of glacial lakes and identification of potentially dangerous lakes of Dhauliganga basin in the Himalaya by remote sensing techniques

Glaciers are retreating and thinning in the high altitude of the Himalayas due to global warming, causing into formation of numerous glacial lakes. It is necessary to monitor these glacial lakes consistently to save properties and lives downstream from probable disastrous glacial lake outburst flood. In this study, image processing software ArcGIS and ERDAS Imagine have been used to analyse multispectral image obtained by Earth resource satellite Landsat for delineating the glacial lakes with the help of image enhancement technique like NDWI. Landsat data since 1972 through 2013 have been used and maximum seven glacial lakes (L1–L7) have been detected and delineated in Dhauliganga catchment, they are situated above 4000 masl. The Glacial Lake L2 (Lat 30°26′45″E and Long 80°23′16″N) is the largest whose surface area was 132,300 m² in Sept 2009, and L6 (Lat 30°23′27″E and Long 80°31′52″N) is highly unstable with variation rate ?55 to +145 % with increasing trend. Additionally, glacial lakes L2 (Lat 30°26′45″E and Long 80°23′16″N) and L6 (Lat 30°23′27″E and Long 80°31′52″N) have been identified as potentially hazardous. These lakes may probably burst; as a result, huge reserve of water and debris may be released all on a sudden. This may transform into hazardous flash flood in downstream causing loss of lives, as well as the destruction of houses, bridges, fields, forests, hydropower stations, roads, etc. It is to note that Dhauliganga river considered in this study is a tributary of Kaliganga river, and should not be confused with its namesake the Dhauliganga river, which is a tributary of Alaknanda river.     

Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India

This study is an attempt to determine the trends in monthly, annual and monsoon total precipitation series over India by applying linear regression, the Mann-Kendall (MK) test and discrete wavelet transform (DWT). The linear regression test was applied on five consecutive classical 30-year climate periods and a long-term precipitation series (1851–2006) to detect changes. The sequential Mann-Kendall (SQMK) test was applied to identify the temporal variation in trend. Wavelet transform is a relatively new tool for trend analysis in hydrology. Comparison studies were carried out between decomposed series by DWT and original series. Furthermore, visualization of extreme and contributing events was carried out using the wavelet spectrum at different threshold values. The results showed that there are significant positive trends for annual and monsoon precipitation series in North Mountainous India (zone NMI) and North East India (NEI), whereas negative trends were detected when considering India as whole.

EDITOR A. Castellarin ASSOCIATE EDITOR S. Kanae     

Flow relevant covariance localization during dynamic data assimilation using EnKF

Multiphase dynamic data integration into high resolution subsurface models is an integral aspect of reservoir and groundwater management strategies and uncertainty assessment. Over the past two decades, advances in computing and the development and implementation of robust algorithms for automatic history matching have considerably reduced the time and effort associated with subsurface characterization and reduced the subjectivity associated with manual model calibration. However, reliable and accurate subsurface characterization continues to be challenging due to the large number of model unknowns to be estimated using a relatively smaller set of measurements. For ensemble-based methods in particular, the difficulties are compounded by the need for a large number of model replicates to estimate sample-based statistical measures, specifically the covariances and cross-covariances that directly impact the spread of information from the measurement locations to the model parameters. Statistical noise resulting from modest ensemble sizes can overwhelm and degrade the model updates leading to geologically inconsistent subsurface models. In this work we propose to address the difficulties in the implementation of the ensemble Kalman filter (EnKF) for operational data integration problems. The methods described here use streamline-derived information to identify regions within the reservoir that will have a maximum impact on the dynamic response. This is achieved through spatial localization of the sample-based cross-covariance estimates between the measurements and the model unknowns using streamline trajectories. We illustrate the approach with a synthetic example and a large field-study that demonstrate the difficulties with the traditional EnKF implementation. In both the numerical experiments, it is shown that these challenges are addressed using flow relevant conditioning of the cross-covariance matrix. By mitigating sampling error in the cross-covariance estimates, the proposed approach provides significant computational savings through the use of modest ensemble sizes, and consequently offers the opportunity for use with large field-scale groundwater and reservoir characterization studies.     

Content analysis of documents using neural networks： A study of Antarctic science research articles published in international journals

Content analysis of scientific papers emanating from Antarctic science research during the 25 years period （1980-- 2004） has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research articles published in Science Citation Indexed （SCI） journals were used for the study. Normalized co-word matrix from 35 most-used significant words was used to study the semantic association between the words. Structural Equivalence blocks were constructed from these 35 most-used words. Four-block model solution was found to be optimum. The density table was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent the thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with the blocks, characterizing thematic composition of Antarctic science research. The analysis has provided an analytical framework for carrying out studies on the con- tent of scientific articles. The paper has shown the utility of co-word analysis in highlighting the important areas of research in Antarctic science.     

A Novel Intelligent ELM-BBO Technique for Predicting Distance of Mine Blasting-Induced Flyrock

Natural Resources Research - Blasting is an economical technique for rock breaking in hard rock excavation. One of its complex undesired environmental effects is flyrock, which may result in human...     

Mooring forces in horizontal interlaced moored floating pipe breakwater with three layers

The paper presents the results from model scale experiments on the study of forces in the moorings of horizontally interlaced, multi-layered, moored floating pipe breakwaters. The studies are conducted with breakwater models having three layers subjected to waves of steepness H_i/L (H_i is the incident wave height and L the wavelength) varying from 0.0066 to 0.0464, relative width W/L (W is the width of breakwater) varying from 0.4 to 2.65, and relative spacing S/D (S is the spacing of pipes and D the diameter of pipe) of 2 and 4. The variation of measured normalized mooring forces on the seaward side and leeward side are analyzed by plotting non-dimensional graphs depicting f/γW² (f is the force in the mooring per unit length of the breakwater, γ the weight density of sea water) as a function W/L for various values of H_i/d (d is the depth of water). It is found that the force in the seaward side mooring increases with an increase in H_i/L for d/W values ranging between 0.081 and 0.276. The experimental results also reveal that the forces in the seaward side mooring decrease as W/L increases, up to a value of W/L=1.3, and then increases with an increase in W/L. It is also observed that the wave attenuation characteristics of breakwater model with relative spacing of 4 is better than that of the model with relative spacing of 2. The maximum force in the seaward side mooring for model with S/D=4 is lower compared to that for the breakwater model with S/D=2. A multivariate non-linear regression analysis has been carried out for the data on mooring forces for the seaside and leeside.     

Analysing transition of land surface temperature and derived indices with respect to elevation values in Kohima Saddar

Human activities have affected the urban environment resulting in a drastic change in the surface temperature. The impact of urban heat islands is noticeable in urban areas than in rural areas. The thermal band of Landsat 8 data is used to retrieve the spatial distribution of land surface temperature (LST) over Kohima Sadar for the years 2009, 2015 and 2020 with the Mono-window algorithm. Urban Thermal Field Variance Index (UTFVI) is used to assess the ecological condition in the area impacted by LST. Cartosat-1 Digital Elevation Model (Carto DEM) is used to understand the variations of LST and indices values with reference to the elevation profile located at different random points. The variations in the land cover are categorized as per the values of normalized difference vegetation index (NDVI) and built-up density index (BUI). This work estimates the influence of elevation over LST, vegetation, and the built-up area. Results implies a negative correlation between LST and NDVI whereas a positive correlation between LST and BUI. Likewise, NDVI and BUI show a strong negative correlation. It is observed that LST is independent of elevation profile but the variation of LST depends on the impact of change in topography urbanization, deforestation, and afforestation. There is no significant relationship of elevation with the variations in NDVI and BUI values. It is observed that the impact of emissivity influences the estimation of LST values. For the locations having the highest and lowest LST, NDVI, and BUI values, 50 random points are generated for the entire region, and validation is executed with the google earth historical image.