Mrinmoy Majumder

An Introduction and Current Trends of Damodar and Rupnarayan River Network

Rivers are the important primary resource of landed community for their primary sustenance. Losses of navigability, gradient fall within a short distance, deltaic formations in lower reaches, anthropogenic actions or manipulations such as construction of embankments and guard walls, silt depositions or encroachment of river beds, monsoon induced changes, etc., can cause a river to die. As per the current status of West Bengal (Eastern part of India) and Jharkhand rivers, the effect of the creation of reservoirs, industrial extractions, and climate change can be observed easily. Damodar and Rupnarayan river systems are two major river networks of eastern India which are one of the major sources of water for irrigation, agriculture, and industrial purposes of the people living in the river banks. The present note tries to give an overview of the current trends, geomorphological characteristics, and economical resources of the two rivers which can give an idea of the impact of vulnerabilities on the natural water resources of the two catchments.

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.

Conservation of Natural Resource with the Application of Carbon Sequestration and Carbon Economy

Trees and plants absorb carbon dioxide (CO2) from the atmosphere and store as biomass carbon in different parts of it. Afforestation is a cost-effective approach to assimilate increased ambient CO2, which mitigate the predicted effects of global climate change. It is necessary to create public awareness of multiple benefits and environmental services provided by the forests and thereby encourage people’s participation in the conservation, protection, and management of forests. Joint Forest Management (JFM) is an approach for sharing of products, responsibilities, control, and decision-making authority over forest land between Forest Departments and local user groups based on a formal agreement. Carbon credit can be generated through carbon sequestration by plantation like reforestation and afforestation project, and this credit can be distributed to the poor people in and around the forest area through different schemes such as employment xadgeneration, education, child welfare, small-scale cottage industries, biogas generation, etc. A major part of the earned amount by the carbon credit can be distributed to the forest neighbors who are directly dependent on forest and forest produce for their livelihood. This will give them a fresh look to the forest protection from the forestdegradation or illegal felling. Green belt or afforestation is a statutory condition for any development activities where carbon sequestration has not been considered in the concept of green belt or afforestation. This could be recommended to include and select more effective carbon sequestrated plant species in the design of green belt or afforestation for effective mitigation of environmental pollution along with reducing of regional climatic temperature for the sustainable development of any project.

Identification of Water-Stressed Regions of Two Tropical and Subtropical River Basins with the Help of Representative Elementary Area Concept and Neurogenetic Models

The present study tried to identify water-stressed regions of two river basins of East India. Availability of water was calculated with the help of UNFCC-recommended equation, which use rainfall, evapotranspiration, and basin loss. The result from the calculation was fed to Falkenmar- and Pastor -prescribed conditions for water stress, which propose that a region with less than 1,000 m3/capita/year water availability would be under stress for water. One of the major reasons for faulty hydrologic models could be attributed to the area dependency of hydrologic parameter. For example, when river runoff is estimated from a lumped and distributed model the predicted result would be different. When precipitation is converted to runoff, a portion of the rainfall volume is lost due to evaporation, transmission, and infiltration. A lumped model does not consider loss incurred by sub-basins of a river basin, whereas the distributed model predicts runoff from a river basin considering the influence of each of the sub-basin. But even distributed models yield erroneous results when they consider the influence of gauged catchment and ignore the un-gauged one. A continuously distributed hydrologic model considers the influence of both gauged and un-gauged sub-basins of a river and for that model also there would be noticeable amount of error though it would be less than that of the distributed hydrologic models. A distributed or continuously distributed hydrologic model requires a lot of parameters and constants to imitate the hydrologic processes involved in generation of river runoff. Such models are often tedious and time-consuming to develop and also need a lot of computational energy to perform each estimation. Representative elementary area (REA) is an area where change in basin area has no impact on change in basin runoff. If a relationship can be estimated with runoff and area of sub-basins, runoff of entire river basin and REA, a more accurate prediction of river runoff can be made. The present study tried to estimate river runoff with the help of REA where the interrelationship between runoff and area of sub-basin was estimated by neurogenetic models because of their ability to identify patterns more efficiently than the conceptual hydrologic models. The models developed in such a way were compared with a distributed and continuously distributed hydrologic model along with NGHYD (Chapter 4) neurogenetic model. The result of comparison can reveal the validity of the REA concept. The selected model was also used to estimate river runoff from which water-stressed regions of the two river basins considered for the present study was identified. According to the results, REA model was found to be a better model among the five models used in the present study according to the performance validation criteria, like root mean square error, correlation coefficient, coefficient of efficiency, and first-order uncertainty analysis. According to the model estimations, the northeast region of the two river basins would be under water stress in face of the future climatic uncertainty.

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.

Determination of Urbanization Impact on Rain Water Quality with the Help of Water Quality Index and Urbanization Index

Rain water quality is a vital factor for deciding whether the water is drinkable or not. But increase in urbanization could degrade the quality of rain water. In the present study, rain water was collected from different sampling locations near and far from urban centers. The influences of urbanization were analyzed with the help of the relationship between water quality index (WQI) of the collected samples and urbanization index (UI) of the sampling location. The WQI was developed with the help of different water quality parameters and their standards. The index was developed as per the standards of drinking water prescribed by All India Public Health Engineering and with the help of “Water Classifier” software developed by Majumder (2008). The urbanization index for the present study was developed as a function of population density, change in population within sampling locations, and density of residential and commercial complexes areas within a radius of 5 km of the sampling locations. According to the results, the relationship between WQI and UI was inversely proportional in sampling locations of both South and North 24 Parganas, which are located in southern and northern outskirts of Kolkata but the slope of the relationship is more tilted in case of sampling points located in South 24 Parganas, than in case of the samples taken from North 24 Parganas. As most of the polluting commercial complexes (leather, textile) were situated in southern outskirts of Kolkata and huge number of residential complexes were present or in verge of completion in the region, there was a massive migration of population from different parts of Kolkata to South 24 Parganas, The service and IT sectors, the nonpolluting industries of Kolkata were concentrated in the northern outskirts. The justification of the relationship between WQI and UI in southern and northern outskirts of Kolkata can be attributed to the above fact. The present study, thus, concluded that there is an impact of urbanization on quality of rain water in Kolkata. The same study can be made in other metro cities of India to verify the veracity of the relationship. The present study was conducted with very few sampling locations but still the locations were situated in regions of different UI and WQI. The study can be repeated with more sampling locations within the city with samples, which have more diverse WQI and UI.

Impact of Climate Change on the Availability of Virtual Water Estimated with the Help of Distributed Neurogenetic Models

Impact of climate change on virtual water of a tropical multireservoir system was estimated with the help of models developed by neural network and genetic algorithm. Virtual water or embedded water or embodied water, or hidden water refers to the water used in the production of goods or services. For instance, it takes 1,300 m3 of water on an average to produce 1 t of wheat. The precise volume can be more or less depending on climatic conditions and agricultural practice. The virtual water has major impacts on productive use of water and global trade policy especially in water-scarce regions. The impact of climate change on virtual water could open a path for the efficient use of virtual water in the face of climatic uncertainties, which may directly impact availability of raw water. The present study tried to estimate the future virtual water with the help of neurogenetic models, which estimates stream flow as function of various hydrological, meterological variables, and basin characteristics. The models prepared were distributed in nature and also consider temporal variability. In total, two models were prepared with rainfall, time of concentration, and catchment loss as input and stream flow as output. One model was prepared by classifying the dataset, based on the magnitude of the variable, and the other model was prepared with normal dataset. First, the better performing model was identified and then output from RCM-PRECIS model was applied to the chosen model to estimate the impact of climate change on stream flow. The estimation results were used to calculate the amount of virtual water, and the result was compared with the present-day virtual water to analyze the change in virtual water availability due to climate change. According to the results, model prepared with normal dataset was identified as a better model, and from the estimations it could be concluded that virtual water availability would increase in case of both A2 and B2 scenario of climate change where the change would be more pronounced in case of the latter.

Estimating Spatial Variation of River Discharge in Face of Desertification Induced Uncertainty

Climate change and global warming along with wide scale forest degradation have induced desertification in different parts of the world including India. The problem of desertification includes excess runoff, soil erosion, etc., which ultimately leads to catchment degradation. A study was performed to analyze the impact of desertification on river discharge. River Ajay, a small tributary of river Bhagirathi in the west of West Bengal was chosen as the study area due to the semideserted condition of the catchment. DIStributed COupled RATional Model (DISCORAT) where Orange County rational method (Rational OC) and MODified RATional (MODRAT) were coupled to estimate river runoff due to desertification-induced uncertainty. The desertification-induced uncertainty was generated by three scenarios where two scenarios represent extreme desertification (Actual-50%) and semi-desertification (Actual-5%). The input variables were modified according to the generated scenarios and applied to DISCORAT model for estimation of stream flow. As the catchment was divided into 16 15/15 grids and contribution of each grid was included in the estimation, the predicted stream flow for the desertification scenarios would give a distributed variation of stream flow and impact of desertification for each grid could be observed from the estimated stream flow at the grids. Cumulatively, a continuous variation of stream flow due to desertification could be generated and analysis could be made about the impact of desertification on stream flow. According to the results, reduction of stream flow was observed due to desertification and the relationship between desertification and reduction of stream flow was found to be inversely proportional, that is, more intense desertification would imply more reduction of stream flow except in the outlet of the river basin where an opposite relationship was observed between desertification and stream flow. A reason for this estimation could be contributed to the reduction of rainfall as considered in the scenarios of desertification. The reversal of relationship at the outlet could be because of runoff-rainfall ration, which was considered to be well above 150% in Actual-50% scenario of desertification.

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.