Evolution of narrow-band spectrum of two random Stokes wavetrains in deep water

Ocean Dynamics - Fourth-order nonlinear evolution equation (NLEE), which is an excellent starting point for the analysis of nonlinear deep water surface waves, are used here to investigate the...     

Aquifer modelling of the Ganga–Mahawa sub‐basin,a part of the Central Ganga Plain,Uttar Pradesh,India

The Ganga–Mahawa sub‐basin, which has an area of 1280 km² forms the western part of the Central Ganga Plain in the Moradabad and Badaun districts of western Uttar Pradesh, India. The Bundelkhand granite forms the basement complex, overlain unconformably by the upper Vindhyan sequence, which is further overlain by the Neogene (Middle and Upper) Siwaliks and finally by Quaternary alluvium. Four geomorphological units, the Varanasi older alluvial plain, Aligarh older alluvial plain, terrace zones and the Ganga recent floodplain, abandoned channels, channel scars and meander scars represent various landforms. The hydrogeological cross‐sections indicate the occurrence of a single aquifer down to 120 m. Some influent seepage from the River Ganga could be seen around Gangeswari, but the rest of the River Ganga is effluent. Groundwater‐flow modelling was carried out to assess the degree of Ganga river and aquifer interaction. The River Ganga marks the western boundary; boundaries to the northeast and southeast are set as fixed heads to simulate lateral inflow into and outflow from the sub‐basin respectively. The eastern boundary is simulated as a no‐flow condition. The Mahawa and Badmar rivers are considered to be effluent. The area modelled is covered by a grid of 34 rows×46 columns with three layers, viz., an unconfined aquifer, an aquitard which is underlain by a semi‐confined to confined aquifer. The permeability distribution was inferred from morphometric analysis and pumping tests. Natural recharge due to monsoon rainfall forms the main input. The River Ganga stage data at Ahar, Naora and Ramghat has been used for assigning surface water levels and river bed elevations in the model. Abstraction from all existing deep and shallow tube wells has been assigned as output at various cells. A steady state flow simulation was carried out and calibrated against the June 1986 water level; subsequent transient conditions were calibrated up to May 1995. The computed groundwater balance was comparable to that estimated from field investigations. The aquifer modelling study has attempted to integrate all available information and provided a tool that could be used for predictive simulation. Copyright © 2000 John Wiley & Sons, Ltd.     

Geoenvironmental effects of groundwater regime in Andhra Pradesh, India

 The Indian subcontinent has the largest semi-arid tropical (SAT) area among developing nations. The State of Andhra Pradesh falls under the SAT region in India and is mostly covered by compact and hard rocks, characterized by seasonal rainfall of a highly fluctuating nature, in both space and time. As a consequence of the green revolution and an increase in industrial activity, there has been an increase in the utilization of groundwater resources during the last two decades in Andhra Pradesh. The development has also caused a number of problems, such as water table decline, decrease in well yields and seawater intrusion. Although major irrigation projects have contributed to improved agricultural production, the associated problems of waterlogging, salinization and loss of valuable bioresources have led to the gradual degradation of the land, affecting agricultural productivity. Surface water and groundwater have also been polluted in several parts of the State because of untreated discharge of effluents from the industries into nearby streams or open lands. A brief account of the overall scenario of the hydrogeological framework and geo-environmental effects on the groundwater regime in Andhra Pradesh is presented. Possible management practices and conservation methods are suggested. Received: 9 August 1999 · Accepted: 10 July 2000     

Use of regionalisation approach to develop fire frequency curves for Victoria,Australia

It is important to perform fire frequency analysis to obtain fire frequency curves (FFC) based on fire intensity at different parts of Victoria. In this paper fire frequency curves (FFCs) were derived based on forest fire danger index (FFDI). FFDI is a measure related to fire initiation, spreading speed and containment difficulty. The mean temperature (T), relative humidity (RH) and areal extent of open water (LC2) during summer months (Dec–Feb) were identified as the most important parameters for assessing the risk of occurrence of bushfire. Based on these parameters, Andrews’ curve equation was applied to 40 selected meteorological stations to identify homogenous stations to form unique clusters. A methodology using peak FFDI from cluster averaged FFDIs was developed by applying Log Pearson Type III (LPIII) distribution to generate FFCs. A total of nine homogeneous clusters across Victoria were identified, and subsequently their FFC’s were developed in order to estimate the regionalised fire occurrence characteristics.     

Implementing a Spatial Model to Derive Potential Fishing Zones in the Northern Bay of Bengal Lying Adjacent to West Bengal Coast,India

Potential fishing zones (PFZ’s) are those regions where the fishes aggregate due to an abundance of food and they are demarcated by tracing those regions in the ocean, where a sharp sea surface temperature (SST) gradient along with optimal chlorophyll (Chl) concentration co-exists at a given time. In this regard, Indian National Centre for Ocean Information Services (INCOIS) disseminates the daily PFZ forecasts in Bay of Bengal and Arabian Sea to aid the fishermen community. The present study is an endeavor to develop a local spatial model derived Potential Fishing Zone (PFZ) in the northern Bay of Bengal (nBoB) lying adjacent to the West Bengal coast. Satellite derived SST and chlorophyll data obtained for two consecutive winter seasons of 2010–11 and 2011–12 were used to generate line density (LD) raster. Shapefiles of INCOIS predicted PFZs were overlaid on these LD raster to extract the corresponding pixel values. Histogram ranges of the extracted pixels were fixed and same values lying in the LD raster of both SST and chlorophyll other than INCOIS PFZs were detected by a spatial model in ERDAS. The PFZs thus derived were validated against the ground fish catch data and it was observed that good fish catch was seen in the model derived additional PFZs also. The catch per unit effort (CPUE) values was found to be very close to that of the CPUE value of PFZ advisories of INCOIS. However, the CPUE in the non PFZ areas were significantly lower than the former two categories.     

Optimal operation of reservoirs for downstream water quality control using linked simulation optimization

A methodology is developed for optimal operation of reservoirs to control water quality requirements at downstream locations. The physicochemical processes involved are incorporated using a numerical simulation model. This simulation model is then linked externally with an optimization algorithm. This linked simulation–optimization‐based methodology is used to obtain optimal reservoir operation policy. An elitist genetic algorithm is used as the optimization algorithm. This elitist‐genetic‐algorithm‐based linked simulation–optimization model is capable of evolving short‐term optimal operation strategies for controlling water quality downstream of a reservoir. The performance of the methodology developed is evaluated for an illustrative example problem. Different plausible scenarios of management are considered. The operation policies obtained are tested by simulating the resulting pollutant concentrations downstream of the reservoir. These performance evaluations consider various scenarios of inflow, permissible concentration limits, and a number of management periods. These evaluations establish the potential applicability of the developed methodology for optimal control of water quality downstream of a reservoir. Copyright © 2007 John Wiley & Sons, Ltd.     

Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok,India

In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020.     

Medium-range forecast of cyclogenesis over North Indian Ocean with multilayer perceptron model using satellite data

An attempt is made in this study to develop a model to forecast the cyclonic depressions leading to cyclonic storms over North Indian Ocean (NIO) with 3 days lead time. A multilayer perceptron (MLP) model is developed for the purpose and the forecast quality of the model is compared with other neural network and multiple linear regression models to assess the forecast skill and performances of the MLP model. The input matrix of the model is prepared with the data of cloud coverage, cloud top temperature, cloud top pressure, cloud optical depth, cloud water path collected from remotely sensed moderate resolution imaging spectro-radiometer (MODIS), and sea surface temperature. The input data are collected 3 days before the cyclogenesis over NIO. The target output is the central pressure, pressure drop, wind speed, and sea surface temperature associated with cyclogenesis over NIO. The models are trained with the data and records from 1998 to 2008. The result of the study reveals that the forecast error with MLP model varies between 0 and 7.2 % for target outputs. The errors with MLP are less than radial basis function network, generalized regression neural network, linear neural network where the errors vary between 0 and 8.4 %, 0.3 and 24.8 %, and 0.3 and 32.4 %, respectively. The forecast with conventional statistical multiple linear regression model, on the other hand, generates error values between 15.9 and 32.4 %. The performances of the models are validated for the cyclonic storms of 2009, 2010, and 2011. The forecast errors with MLP model during validation are also observed to be minimum.     

Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.     

Extreme rainfall events over the Himalayas between 1871 and 2007

Abstract

Currently there is much discussion regarding the impact of climate change and the vagaries of the weather, in particular extreme weather events. The Himalayas form the main natural water resource of the major river systems of the Indian region. We present a brief review of the available information and data for extreme rainfall events that were experienced in different sectors of the Himalayas during the last 137 years (1871–2007). Across the entire Himalayas, from east to west, there are now 822 rainfall stations. There was an increase in the rainfall station network from 1947 onwards, especially in the Nepal and Bhutan Himalayas. Extreme one-day rainfall has been picked out for each station irrespective of the period for which data are available. The decadal distribution of these extreme one-day rainfalls shows that there is a considerable increase in the frequencies during the decades 1951–1960 to 1991–2000, whereas there is a sudden decrease in the frequencies in the present decade during 2001–2007, indicating the need to understand the response of the systems to global change and the associated physical and climatological changes. This is essential in terms of preserving this natural resource and to encourage environmental management and sustainable development of mountain regions.

Citation Nandargi, S. & Dhar, O. N. (2011) Extreme rainfall events over the Himalayas between 1871 and 2007. Hydrol. Sci. J. 56(6), 930–945.