Bipal K. Jana

Accumulation of Carbon Stock Through Plantation in Urban Area

Emission of carbon dioxide (CO2) in urban area is higher compared to the rural area due to the presence of different emission sources within a small area. Carbon is sequestered by the plant photosynthesis and stored as biomass in different parts of the tree. Carbon sequestration rate (CSR) has been measured for young species (6 years age) of Albizzia lebbek in Indian Botanic Garden in Howrah district and Artocarpus integrifolia at Banobitan within Kolkata in the lower Gangetic plain of West Bengal in India by Automated Vaisala Made Instrument, GMP343 and aboveground biomass carbon has been analyzed by CHN analyzer. The specific objective of this article is to measure carbon sequestration rate and accumulation of biomass carbon stock of two young species of A. lebbek and A. integrifolia. The carbon sequestration rates (mean) as CO2 from the ambient air as obtained by A. lebbek and A. integrifolia were 14.86 and 4.22 g/h, respectively. The annual carbon sequestration rates from ambient air were estimated at 11.97 t C/ ha by A. lebbek and 3.33 t C/ha by A. integrifolia. The percentages of carbon content (except root) in the aboveground biomass of A. lebbek and A. integrifolia were 47.12 and 43.33, respectively. The total accumulated aboveground biomass carbon stocks in 6 years as estimated for A. lebbek and A. integrifolia were 6.26 and 7.28 t C/ha, respectively, in these forest stands. Therefore, urban plantation based on better carbon sequestrated species will help to accumulate more biomass carbon stock as well as to offset the increasing CO2 level in ambient air.

Estimation of Soil Carbon Stock and Soil Respiration Rate of Recreational and Natural Forests in India

Soil contains good amount of carbon stock. The amount of carbon stock depends on soil texture, climatic parameters, vegetation, land-use pattern, and soil moisture. The study has been conducted at four sites in the recreational and natural forests in India. The main objective of this study is to estimate the soil carbon stock and soil respiration rate of recreational and natural forests in plain land in eastern India. At Banobitan – a recreational forest, soil was slightly alkaline; moisture content ranged between 7.26% and 9.74%, and soil texture was sandy loam. Total carbon and soil organic carbon (SOC) ranged from 24.2 to 36.5 and 2.8–8.3 g/kg, respectively. At Indian Botanic Garden – a recreational forest, soil was slightly acidic in nature; moisture content varied between 16.2% and 21.7%, and soil texture was clayey loam. Total carbon and soil organic carbon in the soil varied between 58 and 80.1 and 8.3 and 12.6 g/kg, respectively. At Chandra – a natural forest, soil was slightly acidic in nature; moisture content ranged between 3.2% and 11.4%, and soil texture was sandy loam. Total carbon and soil organic carbon ranged from 15 to 23.2 and 1.4–1.5 g/kg, respectively. At Chilapata forest – a natural forest, soil was slightly acidic in nature; moisture content varied between 22.1% and 26.0% and soil texture was loamy. Total carbon and soil organic carbon in the soil varied between 45.7 and 62.5 and 7.4 and 12.8 g/kg, respectively. Estimated mean soil total carbon and mean soil organic carbon stock at Banobitan, Indian Botanic Garden, Chandra, and Chilapata forests were 43.70 and 7.99, 96.32 and 14.57, 27.31 and 2.07, and 75.52 and 13.73 Mg C/ha, respectively. Estimated annual soil respiration rates of Banobitan, Indian Botanic Garden, Chandra, and Chilapata were 2.07, 3.34, 0.61, and 4.18 t C/ha/year, respectively.

Application of Parity Classified Neurogenetic Models to Analyze the Impact of Climatic Uncertainty on Water Footprint

Water footprint of an individual, community, or business is defined as the total volume of freshwater that is used to produce the goods and services consumed by the individual or community, or produced by the business. Neurogenetic models were widely used in the prediction of hydrologic variables, and outcome of such applications were found to be satisfactory. The irregular rainfall and temperature pattern, and degradation of watersheds were causing worldwide reduction of water availability (UNFCC). As water footprint is directly related to water availability and also shows the demand from industrial consumers, the present study tried to estimate the impact of climate change on water footprint between two river basins of East India with the help of neurogenetic models. The climate change scenarios were generated with the help of PRECIS climate models, and future runoff was estimated by a neurogenetic model trained with orthopareto dataset. The output from the neurogenetic model, named as PARITYCGD, was compared with a neurogenetic model trained with normal dataset (NGHYD) and conceptual hydrologic models. According to the results, the neurogenetic model trained with orthopareto dataset was selected as the better model among the five models, which shows that neural models trained with orthopareto dataset learn a problem better than a neurogenetic model trained with normal dataset. From the prediction of stream flow, water footprint of the sampling regions were calculated and according to the estimations, water footprint would be reduced in both A2 and B2 climate change scenarios where reductions would be more pronounced in A2 than in B2. Although, due to data dependency of neurogenetic models, the PARITYCGD model may not work for other basins but for the present study, it was found to have better accuracy than the conceptual hydrologic model.

Estimation of Reservoir Discharge with the Help of Clustered Neurogenetic Algorithm

This chapter presents a new approach of reservoir out flow prediction using a clustered neurogenetic algorithm. The algorithm combines the learning ability of artificial neural networks with searching capability of the genetic algorithm. The model is tested on the Panchet reservoir in river Damodar using the historical, hydrological, and water supply dataset. The values of the input parameters are classified into six groups based on the magnitude of the input parameters. The results showed a highly adaptive and flexible investigating ability of the model in prediction of nonlinear relationships among different variables.

Estimation of the Spatial Variation of Water Quality by Neural Models and Surface Algorithms

The present study was a continuation of the scientific investigation described in Chapter 9. The present research tried to estimate spatial variation of water quality, expressed by Weighted Average Water Quality (WAWQ), from the estimated spatial variation of stream flow as explained in Chapter 9. The relationship between WAWQ and stream flow was estimated with the help of neurogenetic models, and the spatial variation was predicted by radial basis surface algorithm. According to the results, upstream of Damodar River was found to have low quality of water than the upstream of river Barakar, downstream of river Damodar, and the entire river networks of Rupnarayan. But in the future, quality of river water will be estimated to degrade with time for both the scenarios of climate change, which was depicted by the surface diagrams of the future, where area of low WAWQ circles were seemed to be increased with time from 2010 to 2100. The change was more or less similar for both A2 and B2 scenario of climate change.

Estimation of Groundwater Quality from Surface Water Quality Variables of a Tropical River Basin by Neurogenetic Models

According to the hydrological cycle, after rainfall infiltration becomes high and once the soil pores become saturated, surface runoff begins. The infiltrated water is added to the groundwater and depressions and canals are utilized to store or drain out excess water. Because surface water and groundwater have the same source, their quality is related, but the physiochemical properties of the soil layers and geological characteristics of the catchments also influence the quality of water in the surface and ground. Many scientific studies have established that surface water is not as pure and fit for drinking as groundwater. Groundwater is free of turbidity, suspended impurities, and organic and inorganic micropollutants. This reactive nature of water is almost neutral. Although groundwater is affected by dissolved metals (like arsenic, iron, etc.), volatile organic compounds and toxic gases, but the intensity of groundwater pollutants varies with location and surrounding geophysical and ecological structures. In most of the places people use groundwater for drinking without adopting any means of purification. If the source is free of organic and inorganic pollutants and if the metal and gaseous concentrations are low, then the ground/surface water can easily be used for drinking or washing purposes without much threat to human health. But if the surface water contaminates the source through leakage or accidental removal of the impervious layers, then it may contaminate the source, and use of the contaminated groundwater could cause affect public health. The present study attempts to predict the quality of groundwater with the help of surface water quality parameters along with some climatic and geophysical parameters. The study utilized neurogenetic models for predicting the quality of groundwater. The results show that predictions of pH and chlorine levels based on the parameters was found to be more accurate and reliable than the prediction of any other quality variables. Thus it can be concluded that if surface water and groundwater are mixed, the pH and turbidity will undergo the most dramatic change among all other quality variables.

Use of Forest Index or PLANOBAY in Estimation of Water Availability Due to Climate Change

The present study tried to estimate future water availability with the help of Forest Index or Plantation-Prioritized Basin Yield Estimation (PLANOBAY) Hydrologic model, which is a multi-event, discharge prediction model based on variation of discharge with basin area and canopy cover. RCM-PRECIS model was applied to generate future weather scenarios. The observed rainfall along with Vegetated Area Index (VAIn) was used as input to estimate basin runoff. Presence of vegetated area (forest, plantations, cropped land) in any basin would impact the quantity of basin runoff as vegetated areas could hold water with greater capacity than any nonvegetated area. Hence the estimation of runoff from vegetated and nonvegetated catchment must differ and for former, models must include or consider the relationship between vegetated area and the amount of basin runoff. In PLANOBAY, VAIn represents the relationship between vegetated area and basin runoff. VAIn represented the variance of basin area and vegetated area with respect to basin runoff. A neurogenetic model was developed to identify the patterns associated with VAIn, rainfall, and basin runoff. Dataset of 3 decades (1970–2002) was employed to train the model. After the successful completion of training, models were compared with three conceptual models, namely, Hydrologic Engineering Centre – Hydrologic Modeling System (HECHMS), Trend Research Manual of 1955 (TR55), and MODified RATional (MODRAT) hydrologic model. The better model among the four was identified with the help of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (E), and first-order uncertainty analysis (U). Future water availability was estimated with the help of estimated stream flow from the selected model, estimated rainfall from PRECIS climatic model-generated weather scenarios, and Water Budget equation. According to the results, PLANOBAY model was selected as better model among the four, and according to the estimations from the same model, future water availability of the two river basins would reduce for both A2 and B2 scenario of climate change where the water scarcity would be more pronounced in A2 than in B2.