1. KAUTIR (Kerala Agricultural University Theses Information and Retrieval)
Permanent URI for this communityhttp://localhost:4000/handle/123456789/1
Browse
61 results
Search Results
Item Modelling the impact of conservation structures and climate change on water yield in a watershed(Department of Irrigationa and Drainage Engineering, Kelappaji College of Agricultural Engineering and Technology, Tavanur, 2020-12-07) Fousiya; Anu VarugheseHydrological models have been increasingly used for the impact assessment of climate change and management practices on hydrological processes. In the Thuthapuzha watershed, where extreme events due to climate change and resulting changes in patterns of river flow predominate, proper management of water resources through soil and water conservation needs to be adapted in the future. In this research, SWAT model was used to simulate hydrological processes on a daily time-step in Thuthapuzha watershed, subbasin of Bharathapuzha located in Kerala, India. SWAT performs satisfactorily with Nash-Sutcliffe efficiency value (NSE) of 0.88, coefficient of determination (R²) of 0.88 and Percent bias (PBIAS) of -1.4 for the calibration period (1989-2009) and R², NSE and PBIAS values of 0.8, 0.8 and 5.4 respectively for the validation period (2010-2017). The study concluded that the developed SWAT model can be used to predict streamflow from the watershed. So the developed model was then used for studying the impact of climate change and conservation structures on the hydrology of the watershed. Quantification of changes in the water balance and soil erosion over a long period of time is necessary for watershed management. The developed SWAT model was used to understand the impact of conservation practices on hydrological processes. Major conservation practices in the study area were modelled as ponds and Kanjirapuzha reservoir within the study area was modelled as dam. The results obtained were analysed to study the impact of conservation structures on streamflow and found that monthly streamflow increased during summer season (9-17%) when the river has a very lean flow with the effect of conservation practices which helps in maintaining a better environmental flow regime. Conservation structures impact on sediment yield was also analysed by comparing the outputs with and without the addition of structures. In addition to the structural details, sediment yield analysis requires equilibrium sediment concentration value which is very difficult to estimate. Thus, a calibration process was again done for calibrating equilibrium sediment concentration using sediment yield output at the Pulamanthole gauging station (Jalowska and Yuan, 2018). For the study, it was assumed that the sediment yield output obtained from the calibrated model as the sediment yield with the addition of structures. Monthly sediment yield showed a slight increase (0.001-0.04%) during the summer months whereas sediment yield decreased (0.2-1.3%) during peak flows with the addition of conservation structures. Climate data are collected from CMIP5 and CORDEX-SA datasets of GFDL-CM3 climate model for RCP4.5, RCP6 and RCP8.5 scenarios and the bias corrected weather data were used as input in SWAT model. Comparison of streamflow and drought intensity based on predicted climate change scenarios is evaluated. The results of the future simulations of streamflow in SWAT reveal that, river flow increased under all RCP scenarios with predominant increase in RCP6 scenario (37-60%) followed by RCP4.5 (13-16%) and RCP8.5 (9-16%) from 2021-2070. Significant increase in streamflow was found during the end periods of simulation for all the scenarios taken for the study purpose. Results show the importance of climate change effect on water resources, where it does not have only an effect on precipitation and temperature, but the streamflow is also directly influenced by climate change. Thus, necessary steps should be taken to mitigate the extreme events due to streamflow increase during future periods. In order to study the climatic condition in the Thuthapuzha watershed, drought intensity was calculated. Drought intensity was predicted using the SPI and RDI index for the period 1989-2017 and found that severely dry events have occurred once during 2015-16 when using SPI index. Comparison and regression analysis between both the indices showed that both were well correlated and similar trend with little variation in the drought period was observed. Thus, SPI index was selected for studying the impact of climate change on drought intensity and found that the wet years are more than drought years for all the RCP scenarios with RCP 8.5 shows more drought period followed by RCP4.5 and RCP6. For the projected period from 2021-70, extreme drought condition will occur only once and severe drought condition will occur six times for RCP8.5 whereas no extreme and severe drought conditions were observed for RCP4.5 and RCP6. SWAT successfully achieved the aim of this research; to assess the impact of climate change and conservation practices in the Thuthapuzha watershed. Nevertheless, uncertainty cannot be avoided in this study since climate model datasets were used for making the future prediction. The results of the entire research work will give an insight to hydrologists in solving climate change related issues as well as provides water resources managers with an effective tool for the integrated catchment management.Item Global change and subterranean ecosystems of Kerala(College of Climate Change and Environmental Science , Vellanikkara, 2023-01-31) Arya Shaji; Rajeev RaghavanItem Assessment of marine plastics n selected blue carbon ecosystems along the Indian coast(College of Climate Change and Environmental Science, Vellanikkara, 2023-01-30) Subadra, K M; Ratheesh Kumar, RItem Future projections of Indian summer monsoon rainfall in CMIP6(College of Climate Change and Environmental Science, Vellanikkara, 2022-11-30) Lekshmi, M S; Roxy Mathew KollThe state-of-the-art climate model simulations of the Indian monsoon must be examined since future climate policy choices rely on accurate monsoon projections. This study examines the fidelity of 22 CMIP6 models in simulating the Indian summer monsoon from 1950 to 2014. The models are rated based on how well they reproduce seasonal mean precipitation and monsoon circulation characteristics. The multimodel mean of the better models reported in this study is expected to give more reliable Indian monsoon estimates. The Indian summer monsoon's near-term, mid-term, and long-term variations have been studied under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. According to the findings, future changes in Indian monsoon rainfall are closely tied to changes in low level monsoon winds. Under a global warming scenario, the low level monsoon wind over the North Arabian Sea (north of 15°N) is expected to strengthen, whereas the low level monsoon wind over the South Arabian Sea (south of 15°N) is expected to weaken. This pattern is caused by a poleward shift in the low-level monsoon. This poleward shift causes changes in precipitation over land and ocean. Long-term strengthening of the North Arabian Sea wind speed would result in enhanced rainfall over central India as well as the sinking of air (reduced rainfall) south of the equator, completing the cycle. The quantity of rainfall that may fall on the southern peninsula as a result of global warming, however, will be limited by a decrease in the South Arabian Sea wind speed. Weak winds (south of 15°N) will hinder the moisture buildup caused by the warming of the West Indian Ocean from reaching the subcontinent; rather, it will rise above the ocean, causing an increase in rainfall over the Arabian Sea. Keywords: CMIP6, Indian Summer Monsoon Rainfall, Monsoon Circulation, Climate ProjectionsItem Carbon sink potential of selected forest plantations in Kerala(College of Climate Change and Environmental Science, Vellanikkara, 2023-04-27) Karthik, M S; Sandeep, SNowadays the global climate is changing due to the accumulation of carbon dioxide due to fossil fuel emissions and anthropogenic activities. Forest ecosystems can act as both source and sink of carbon and thus play a crucial role in global carbon cycles. Also, it is one of the most carbon rich habitats on the planet, and their protection is vital in reducing the greenhouse gases in the atmosphere. The objective of the study is to assess baseline scenarios and determine the carbon storage potentials of the selected forest plantations under the Kerala Forest development corporation (KFDC). The study was conducted in the selected forest plantations Marottichal and Kulathupuzha under the Kerala Forest development Corporation. Teak and acacia are considered as the major plantation at Marottichal and sandal mixed plantation was selected in the Kulathupuzha range. The estimation of carbon storage in these plantations was conducted by non-destructive methods. The methods employed included measuring the GBH of the trees, collecting soil from different depths across the profile, and collecting litter and undergrowth from the plots. The GBH data obtained from the field measurements were used as inputs for the allometric equations of the trees thus giving its biomass. In teak plantations, 2008, 2009 and 2010 planted trees showed a carbon storage of 230.85, 216.31 and 239.67 (t/ha) respectively. In all the soil profiles, there was a significant decrease in organic matter content with depth. On the other hand, eight-year-old (2014) acacia plantation accounts for 234.07 t/ha carbon. Soil organic carbon accounts for the major carbon pool (134.64 t/ha). Twelve-and eleven – year – old sandal mixed plantations showed relatively lesser carbon storage than teak and acacia, accounting for 133.51 and 122.19 (t/ha) carbon. This is because the above ground pools accounted for very limited contributions to the carbon storage caused by slow growth of sandal trees. The present study data of carbon storage potential of selected plantations could be added to the data repository and would be useful in carbon accounting during implementation of projects such as Carbon Neutral Kerala.Item Energy analysis of rice cultivation in kole lands of Thrissur district(College of Climate Change and Environmental Science, Vellanikkara, 2022-12-30) Resmi Dhanapal; Mary Regina, FA study was conducted to analyse the energy input and output of rice cultivation in the Kole lands of Thrissur district of Kerala. A total of 358 farmers belonging to two different Padashekharam were surveyed for the study. Two Padashekharams namely Sangam Padashekharam and Porathur Padav Padashekharam were selected for the survey. The persolal details, operational details and machinery details were collected through face-to-face interview. Energy input by human labour, fuel, machinery, electricity, pesticide, fertilizers and energy output as grain and straw were estimated. All the quantified inputs were transformed into energy values using their respective equivalent energy coefficients. Energy efficiency, energy productivity, specific energy and net energy were calculated using standard procedures. The energy inputs were divided into direct energy, indirect energy, renewable energy, non-renewable energy, commercial energy and non-commercial energy. The inputs were also used to find the carbon emission from the field. The results show that the Sangam Padashekharam has a greater energy consumption than Porathur Padav Padashekharam. However, the grain yield is found to be greater in Sangam Padashekharam. The total input and output of Sangam Padashekharam are 33959.53 MJha-1 and 198912.5 MJha-1 respectively, whereas the total input and output of Porathur Padav Padashekharam are 25782.75 MJha-1 and 92225 MJha-1. The energy pattern consists of 34% fertilizers, 26% electricity, 16% diesel, 8% chemical, 8% human labour, 5% seed and 3% machinery. The specific energy, net energy, energy efficiency and energy productivity in this region was 0.51 MJkg-1, 115697.62 MJha-1, 4.71 and 2.31KgMJ-1 respectively. It was also found that the methane emission from paddy field is the largest contributor to carbon emission than any other inputs used for paddy cultivation and it is about 1078.514 kgCO2 eq. The second major contributor is electricity which is about 551.915 kgCO2 eq. The findings revealed that the energy input for fertilizer is higher in the Kole region. This higher input was due to farmers’ practice of applying fertilizer at rates higher than the PoP recommendation. Energy consumption and production costs can be reduced by using the recommended amount of fertilizer. The second highest energy input is electricity, which can be reduced by using a more efficient pump.Item Influence of climatic variables on selected marine fish populations in south estern Arabian Sea(College of Climate Change and Environmental Science, Vellanikkara, 2023-01-27) Reshma, R; Najmudeen, T MThe potential impacts of climate variables on commercially important marine fish population in the southeastern Arabian Sea are mostly unexplored. There are large knowledge gaps that prevent a comprehensive understanding the impacts of climatic variables from the other factors which influence the fish biomass and catches is vital for studying the stock dynamics of fishery resources. The study used Biomass Dynamic Models to investigate relationships between environmental variables and the biomass of commercially important fish species along southwest coast of India and to predict their future changes under various RCP scenarios. For modeling, the time-series data on species/resource wise, gear-wise annual catch and fishing effort for the harvest of selected fishery resources for the period 1985-2019 were used, along with the environmental variables such as annual average data on Sea Surface Temperature (SST), Coastal Upwelling Index (CUI), Marine Heat Waves (MHW) and precipitation (PPT). The results indicate that the models attempted for all the fish groups gave very good fit as observed by the regression plots of observed catch against expected catch with high regression coefficients. The biomass estimated using the model parameters indicate that, the climate variables considered in the present study showed significant lagged effects on biomass of the fish species in varying intensities. The SST component in the biomass was highest for the pelagic fish species ribbonfishes and the contribution to biomass due to SST ranged from 25.7% to 26.4% with an average of 26.0%, and lowest for squids. In general, the influence of SSST on the biomass of two pelagic species are greater that of the demersal species. The influence of precipitation on the biomass of the fish species selected for study were always lower than that of SST both in pelagic as well as demersal resources. The influence of CUI and MHW were much lower in all the species studied compared to that of SST and precipitation, with MHW became the least influencing variable. The predicted biomass tends to decrease over the future periods in the SST model, and increase for the precipitation along the region. In both the projections, lowest values were observed for RCP 6 and highest values for RCP 8.5 scenario for all the species. Coupled with overexploitation, and habitat degradation, climate change impacts are reported to cause challenges in the sustainability of marine ecosystems and fisheries along the coastal waters of India. Present study throws light on the use of biomass dynamics models to unravel the effects of climate variables on the fish biomass and the results indicate that the dynamics of climate variables should also be taken into consideration while determining the harvestable potential of each fishery resource, especially, for the pelagic fish stocks.Item Soil erosion risk assessment in a sub-watershed of karuvannur river basin, Thrissur Kerala using rusle model(College of Climate Change and Environmental Science, Vellanikkara, 2023-05-09) Akash, P J; Mary Regina, FA study was conducted to assess the soil erosion risk regions of a sub-watershed and to classify the whole area based on the severity of the erosion rate classes. Kurumali watershed in the Karuvannur river basin in Thrissur district of Kerala, India was selected for the study. The RUSLE soil erosion model is used to calculate the annual value o soil loss and the intensity of soil erosion. The ArcGIS software was used to implement the RUSLE factors. Parameters like Rainfall erosivity factor (R ), Soil erodibility factor (K), Slope length steepness factor (LS), Cover management factor ( C), and Practice factor (P ) were used for the estimation of soil erosion or the working of the RULE model. The Digital Elevation Model (DEM) of the sub-watershed was used to estimate the LS factor. The land use data of the sub-watershed, collected from Kerala State Land Use Board, were used for estimating the C factor and P factor. 20-year (2000-2020) daily rainfall data of two stations (Pudukkad and Varandarapilly) were collected from Irrigation Design and Research Board (IDRB) for calculating R factor. Soil samples were collected from various physiographic units in the Kurumali watershed for evaluating soil organic matter, texture, structure, and permeability and these were used to calculate the K factor. The soil erosion risk map was produced in the ArcGIS environment after analysing the RUSLE parameters, R, K, LS, C and P. The maps were converted to raster format using a raster calculator and then combined to produce the soil erosion model. The soil loss values for the Kurmali watershed range from 0 to 70.26 tonnes ha-1y-1 with a standard deviation of 1.24. The average soil erosion rate estimated for the watershed ranges between 0 to 1 tonnes ha-1year-1. It was found that about 82.5% of the area (34969.86 ha) comes under the ‘Very slight’(<1 tonnes ha-1 year-1 ) soil erosion class. 11.1% of areas are classified under ‘Slight’ erosion. The ‘Moderate’ erosion class has an area coverage of 1.97%. About 4.42% of the area is under the ‘Moderately high’ to ‘Very high’ (>10 tonnes ha-1 year-1). Among different land use types barren lands has the highest erosion rate followed by plantations. And dense forest has the least erosion rate, due to its dense canopy cover. Measures must be taken to reduce the soil erosion of the study area. Some practices that can be performed to prevent soil erosion in the study area have been suggested.Item Impacts of ENSO events on Gujarat coast and its implications on selected marine fish resources(College of Climate Change and Environmental Science, Vellanikkara, 2023-01-04) Arya, S Jagadan; Shelton PaduaENSO events exert an enormous influence on the global weather pattern. El Nino has become very visible in recent years as a dominant source of interannual climate variability around the world. ENSO episodes are known to change the environmental characteristics of coastal waters which are the major habitats for the fish resources that are harvested all along the Indian coasts. The effects of various ENSO episodes from 2007-2020 on Gujarat’s marine fish resources were studied. The monthly catch of major pelagic fish resources like Gold spotted grenadier anchovy (Coilia dussumieri), Bombay duck (Harapadon nehereus), Ribbon fish, Torpedo scad (Megalaspis cordyla), Long tail tuna (Thunnus tonggol), Kawakawa (Euthynnus offinis), Yellowfin tuna (Thunnus albacares); demersal fishes like Croakers, Threadfin bream, and Greater lizard fish (Saurida tumbil); crustacean fishes like Crabs, Stomatopods, Lobsters and Penaeus spp.; and molluscans like Sepia pharonis, Octopus spp., Sepia spp and squids by major gears for the period 2007-2020 were collected and the catch per unit effort (CPUE) was estimated. The ENSO indices like Nino 1+2, Nino 4, Nino 3.4, ONI, MEI, SOI, EMI, DMI, TNI, and ocean-atmospheric parameters such as Chlorophyll a (CHL_A), Salinity (SALT), Sea Surface Height (SSH), Sea Surface Temperature (SST), Local Temperature Anomaly (LTA), Rainfall (RF), Ocean Current Direction (OCD) and Ocean Current Velocity (OCV) were analyzed. GAM (Generalized Additive Model) was used to study the influence of the ENSO phenomenon on different ocean-atmospheric parameters by considering ocean-atmospheric parameters as the response variables and the different ENSO indices as predictors. GAM model results indicated that the ENSO could explain 47.5% of the deviance in local temperature anomaly (R2.adj=0.41), 45.6% of the deviance in SALT (R2.adj=0.39), 45.5% of the deviance in SST(R2.adj=0.38), 41.8%of the deviance in monthly Rainfall (R2.adj=0.33), 35.5% of the deviance in Sea Surface Height (R2. Aj = 0.3) and 31.2% of the deviance in Chla. The ENSSO episodes could explain 39.6% deviance in the abundance of Ribbon fish, 33.3%, 22.9% and 21.8% deviance in the abundance of tuna species such as Thunnus tonggol, Euthynnus affinis, and Thunnus albacares respectively, 34.6% deviance in the abundance of Croakers. 26.6% deviance in the abundance of Threadfin breams, 53.8% deviance in the abundance of Stomatopods and 29.9% deviance in the abundance of Sepia pharonis. The combined model explained 66.6% deviance of Ribbon fish (R2.adj=0.58), 74.1% deviance of Saurida tumbil (R2.adj=0.75), 79% deviance of Stomatopods (R2.adj=0.71) and 75.1% deviance of Sepia spp (R2.adj=0.64). The ENSO episodes alone could explain 32.8% of deviance (R2.adj=0.27) and combination of ENSO and ocean-atmospheric parameters could explain 76.3% of the deviance (R2.adj=0.64) in the abundance of total fish resources the over the Gujarat coast.Item Emergence of Kerala coast as disaster hotspot-implications for management(College of Climate Change and Environmental Science, Vellanikkara, 2022-11-30) Sreelakshmi, M; Shijo JosephCoastal areas hold ecological, economic as well as strategic functions that directly or indirectly benefit a nation. Their geographic location along with high population pressure and industrial developments make them prone to disasters. With the current scale of climate change, weather related extreme events are going to be more frequent and intense. Evidence based research is carried out to analyze the frequency of hazardous events in the Kerala coast for last decade to identify the hotspot zones. The result found that a remarkable increase in the hazards was found in the central zone and further disaster risk was carried out here. A multi risk approach is adopted to identify hotspot areas using a combination of indicators and indices. The study provides a preliminary insight into the risk experienced by the coastline along central Kerala-in Alappuzha, Ernakulam, and Thrissur. Risk assessment is adopted following the typology of risk give by IPCC AR5 where a function of hazard, vulnerability, and exposure contribute to risk. Indicators under hazard (SLR, precipitation intensity, shoreline change rate, proximity to cyclone track, storm surge height, and number of sea surge events), vulnerability (population density and LU/LC) and exposure (slope, elevation, Mean SWH, and drainage density) are integrated into GIS environment to develop a risk index at the regional level. Risk ranking is assigned to individual variables followed by mapping the composite risk that yield a visual representation of the realities. The study found that 2% of the study area is under very high risk, 6% of it under high risk area, 21% falls into medium risk, 43% of it under low risk, and 28% under very low risk. Coastal areas require a different yet comprehensive approach for their sustenance and utilization. Thus, for the sustainable use or resources, management implications include retreat, accommodate, and protection measures. The outcome is expected to be a valuable tool aiming at disaster risk reduction and community development within the context of integrated coastal zone management.