Impact of climatic parameters on statistical stream flow sensitivity analysis for hydro power

Large area of Himalayas covered with seasonal snow during winter are rapidly changing during summer, significantly affects the stream flow of many rivers originating from Himalayas. This necessitates the efficient time series monitoring of seasonal snow cover in the rugged mountainous region throughout the winter and summer periods for weekly/monthly as well as seasonal forecast of stream run-off for water management and other developmental activities.     

A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation

ABSTRACT

The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.     

Seismic quality factor observations for gas-hydrate-bearing sediments on the western margin of India

Any propagating wave undergoes attenuation, which is primarily governed by the physical properties of the medium, determined in terms of quality factor (Q). Research into the characteristics of both P- and S-wave Q with reference to gas-hydrates exploration remains in its infancy. Presence of gas-hydrates increases the Q, and this again depends on the nature of distribution and amount of hydrates within the sediments. Thus, estimation of Q provides useful input for both the detection and quantitative assessment of gas-hydrates. Here we propose a simple technique of deriving Q from prestack surface seismic reflection data based on the logarithm of spectral ratio (LSR), and apply the method to marine multi-channel seismic (MCS) data collected on the western margin of India where a bottom simulating reflector (BSR), which is a prime marker for gas-hydrates, has already been identified. The Q (256 ± 11) estimated over the region with a strong BSR is found to be more than double the Q (101 ± 9) derived for the region without any BSR or a weak BSR. The anomalously high Q with respect to the background can be used to detect gas-hydrates in areas where the BSR is not very clearly observed on seismic sections.     

Analysis of groundwater levels for detection of trend in Sagar district,Madhya Pradesh

Groundwater being an important component of the hydrological cycle as it sustains the streamflow during precipitation free periods and is a major source of water supply. The dependence on the groundwater has increased drastically over the years leading to over exploitation of the aquifers. Therefore, it is imperative to assess the extent of exploitation and analyse the groundwater level scenarios in the area of interest. The existence of a trend in a hydrological time series can be detected by statistical tests. The present study investigates the application of various methods for identification of trends in groundwater levels in few blocks of Sagar district, which faces severe water scarcity owing to the declining groundwater levels. The non-parametric Kendal rank correlation test as well as the parametric linear regression test has been used for trend detection based on the analysis of the seasonal groundwater levels. Kendal’s rank correlation test, has been applied to identify the trend persisting in the data and the linear regression test is used to identify the significance of the slope. The analysis indicates that the time series of groundwater levels are cyclical with characteristics of seasonal variation in all the blocks coupled with a declining trend at Sagar, Khurai and Bina.     

Morphometric analysis of Morar River Basin, Madhya Pradesh, India, using remote sensing and GIS techniques

Hydrogeological mapping and drainage analysis can form an important tool for groundwater development. Assessment of drainage and their relative parameters have been quantitatively carried out for the Morar River Basin, which has made positive scientific contribution for the local people of area for the sustainable water resource development and management. Geographical Information System has been used for the calculation and delineation of the morphometric characteristics of the basin. The dendritic type drainage network of the basin exhibits the homogeneity in texture and lack of structural control. The stream order ranges from first to sixth order. The drainage density in the area has been found to be low which indicates that the area possesses highly permeable soils and low relief. The bifurcation ratio varies from 2.00 to 5.50 and the elongation ratio (0.327) reveals that the basin belongs to the elongated shaped basin category. The results of this analysis would be useful in determining the effect of catchment characteristics such as size, shape, slope of the catchment and distribution of stream net work within the catchment.     

Petrographic and depositional characteristics of Tadkeshwar lignite deposits (Cambay Basin), Gujarat

Tadkeshwar lignite deposits are one of the important Lower Tertiary lignite deposits of Cambay Basin, Gujarat. These lignites are rich in huminite macerals followed by liptinite and inertinite with moderate to high proportions of associated mineral matter. Under fluorescence mode these are found to be rich in liptinite macerals, in particular the liptodetrinite and resinite. The type of huminite suggests that the lignite is formed from mixed vegetal source, i.e. woody forest vegetation and herbs, shrubs. The composition of macerals indicates the deposition of lignite in sub-aqueous condition in wet-reducing environment with intermittent exposure and subsidence of the peat surface. The rank of the Main and the Leader seams, determined through reflectance measurement, indicate that the lignite is less mature. The considerable amount of perhydrous huminite may have contributed to the lower reflectance values in some of the samples. Low rank and appreciable amount of mineral matter make the lignite suitable for its utilization in thermal power plants, however when upgraded or mixed with better quality coals it can be used for other industries.     

Snow Cover Area Mapping Using Synthetic Aperture Radar in Manali Watershed of Beas River in the Northwest Himalayas

The current study has used Synthetic Aperture Radar (SAR) satellite data to estimate the Snow Cover Area (SCA) in Manali watershed of Beas River in Northwest Himalayas of Himachal Pradesh, India. SAR data used in this study is of Radarsat-2 (RS2) and Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR). The SAR preprocessing was done with SAR image processing tools for converting raw SAR images into calibrated geo-coded backscatter images. Maps for forest, built area, layover and shadow were created and used for masking snow cover in these areas. The backscattering ratio of wet snow to reference image threshold method with value range from ?2 to ?3 db was used to estimate wet SCA for study area. In this technique, if the threshold is too high (≥-2 db) wet SCA is overestimated and if it is too low (≤-3db), this method underestimates the SCA. The wet SCA is under/over estimated (+6 % to?8 % on average) in late spring season due to the inherent terrain and SAR imaging effects of layover/foreshortening and shadow and also due to the masking of forest areas. Overall, the SCA derived from SAR data matches well when compared with total SCA derived from cloud free optical remote sensing data products, especially during wet season.     

A Review of “Politics and Practice in Economic Geography”

Abstract

This study examines the spatial distribution and enforcement of air polluting facilities in the state of New Jersey, as listed in the U.S. Environmental Protection Agency's Aerometric Information Retrieval System. Results show that air-polluting facilities tend to concentrate near minority neighborhoods, although this relationship is partially explained by factors of population density, manufacturing employment, and land use. Other results suggest that facilities in areas with a relatively high percentage of minority population tend to have a weaker record of environmental enforcement as compared to other facilities. Of the socioeconomic variables considered, employment in manufacturing appears to be the most strongly related to environmental enforcement.