Browsing by Author "Asha Joseph"

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Assessment of evapotranspiration models for the humid tropical region of Tavanur
(Department of Irrigation and Drainage Engineering, Kelappaji College of Agricultural Engineering and Technology, Tavanur, 2017) Pravalika, Y R; Asha Joseph
World is facing an acute water crisis due to the increase of world population, droughts, land degradation, and food demand. This increases the concern over conservation of water. One of the most important factors related to water management is crop evapotranspiration. In the present research work, the reference crop evapotranspiration (ETo) is estimated by using ten empirical models which are widely used in Indian conditions namely, Thornthwaite (1948), Hargreaves et al., (1985), Turc (1961), Christiansen (1968) Pan Evaporation, FAO-24 Blaney-Criddle (1977), FAO-24 Modified Penman (1977), FAO-24 Open Pan (1977), Preistly-Taylor, Makkinik and FAO-56 Penman-Monteith (1991). The accuracy of these reference evapotranspiration models were evaluated by comparing it with FAO-56 Penman-Monteith using six years monthly average meteorological data for the period January, 2011-December, 2016. Then the models were validated with lysimetric data. The weekly water balance studies were conducted in lysimeter to find the actual reference evapotranspiration. The model values were estimated using weekly meteorological data for the period January-May 2017 during which the lysimeter study was conducted. Then best fit relations were developed between the estimated values (EToEST) and observed values (EToLYM) for the humid tropical region. Among the different empirical models, Turc model showed the highest ETo value (14.92 mm/day) while the Priestly-Taylor showed the lowest (0.62 mm/day). Thornthwaite, Blaney-Criddle and Modified Penman model gave closer values to each other 7.32, 8.9 and 7.09 mm/day respectively. While Christiansen, Penman-Monteith, Open Pan and Makkinik models gave values like 3.08, 3.23, 3.24 and 2.22 mm/day respectively which were slightly lower compared to the values obtained from the Hargreaves model (4.7 mm/day). The statistical comparison was made by considering FAO-56 PMM as the standard model using six year average monthly meteorological data. The Modified Penman model gave the best performance with R2 of 0.96 with RMSE 3.95 and RelRMSE 1.22 followed by Hargreaves model. The Open Pan method ranked the third one. The models, Christiansen, Priestly-Taylor and Makkinik were underestimated while Thornthwaite, Turc and Blaney-Criddle models overestimated. For validation of the models, weekly ETo estimated from models were compared with ETo observed from lysimeter for the period January-May, 2017. The Hargreaves model showed the best performance with R2 0.83 and RMSE 0.82. The Turc model was highly over estimated while Blaney-Criddle and Modified Penman models were only slightly overestimated. The Penman- Monteith and Makkinik models were slightly underestimated while Priestly- Taylor highly underestimated with R2 0.56 and the RMSE 4.29. Hence it is concluded that Hargreaves (HAM), Open Pan (OPM) and Christiansen (CHM) models were found to be in close agreement with lysimetric data and hence these models were suggested for use in this humid tropical region. Therefore relationships were developed between these empirical model output and the lysimetric data (LYM). The relationships developed were as follows: ETo LYM = 0.79HAM + 0.45, EToLYM = 0.79CHM + 1.60 and EToLYM = 0.63OPM + 2.04. Finally the results of this research can be recommended for humid tropical region for irrigation scheduling, selection of cropping pattern, optimum allocation of water resources and efficient use of water.
Climate change impact on irrigation water requirement and crop water productivity of rice
(Department of Irrigation and Drainage Engineering, Kelappaji College of Agricultural Engineering and Food Technology, Tavanur, 2025-02-19) Midhula, B N.; Asha Joseph
The study evaluated the impact of climate change on the irrigation water requirement (IWR) and crop water productivity (CWP) of rice in Pattambi, Kerala, using observed climate data from 1991-2022 and projected data for 2025-2095. Climate projections were based on four Global Climate Models (GCMs), MPI-ESM 1-2-HR, ACCESS-ESM 1-5, MPI-ESM 1-2-LR, and INM-CM-5-0 under SSP2 4.5 and SSP5 8.5 scenarios. GCM data was bias-corrected using linear scaling for temperature and power transformation for precipitation. The AquaCrop model, calibrated and validated with RMSE (0.3527-0.3728) and NSE (0.97-0.99), simulated rice yields and CWP, while CROPWAT 8.0 estimated ETo, ETc, and IWR for the baseline and future periods (2025-2049 (2035s), 2050- 2074 (2055s) and 2075- 2095 (2085s)). The climate model INM-CM 5-0 exhibited strong agreement between observed and model-derived data with RMSE (1.5-4.80) and R² (0.5-0.85) in acceptable range. Future projections for the period 2025-2095 indicated that maximum temperatures could rise by +0.6, +0.84, and +0.89°C, minimum temperatures by +0.57, +0.85, and +1.2°C, and precipitation by +96.19, +122, and +214.23 cm during 2035s, 2055s and 2085s respectively under the SSP2 4.5. Under SSP5 8.5, the maximum temperature could rise by +0.66, +1.33, and +1.97°C, minimum temperatures by +0.67, +1.48, and +2.46°C, and precipitation by +159.3, +699.9, and +415.57 cm for the same time horizons. The AquaCrop model was calibrated and validated with RMSE (0.3527-0.3728) and NSE (0.99- 0.97) in the acceptable range for simulating rice yield. Future projections of IWR indicated a remarkable rise in water demand both in Virippu (1st crop) and Mundakan (2nd crop) seasons. During Virippu, IWR is expected to increase by up to +42.63% and +37.97% under SSP2 4.5 and SSP5 8.5, respectively, while the same for Mundakan was found to be +4.20% and +11.65% respectively. This reflected higher water requirements for rice production under future climate change scenarios. Future yield projections showed a reduction in yield both in Mundakan (-51.72% and -42.12%) and Virippu season (-77.38% and -81.97%) under SSP2 4.5 and SSP5 8.5, respectively. However, the Virippu season showed a more prominent reduction in yield than Mundakan. This significantly impacted CWP during Virippu, which showed a sharp reduction of -87.92% and - 90.82%, and Mundakan showed a reduction of -66.36% and -46.36% under SSP2 4.5 and SSP5 8.5, respectively. Adopting early transplanting dates, particularly on April 21st, will help to increase yields (+26.2%) and reduce irrigation water requirements (-1.97%), while late transplanting should be avoided due to significant yield reduction in Virippu. But during the Mundakan season, transplanting dates on Oct 12th (in 2035s), Nov 11th (in 2055s), and Nov 21st (in 2085s) were found optimal due to increased yield (+2 - 9.8%). Adopting drip irrigation reduced water use by 20% and improved rice yields by +2.5%. Hence, it is concluded that, rising temperatures and rainfall under future climate scenarios are projected to increase IWR, reduce rice yields, and significantly lower CWP. Hence, adaptation measures are recommended to combat the effect of climate change and enhances CWP.
Flood frequency analysis and modelling of flood using HEC-HMS for a river basin: a case study
(Tavanur Department of Irrigation and Drainage Engineering, Kelappaji Collge of Agricultural Engineering and Technology, 2020) Riyola George; Asha Joseph
IOT based real-time microclimate monitoring and controlling system for green house
(Department of irrigation and drainage engineering, Kelappaji college of agricultural engineering and technology,Tavanur, 2023-10-07) Angitha, K; Asha Joseph
IoT has revolutionized the agriculture with real-time monitoring and controlling systems. Though, greenhouse provides a controlled environment for cultivating crops, maintaining optimal microclimatic conditions inside the greenhouse in real-time is crucial for maximizing yield and quality of produce. Hence, a study was conducted to develop a web enabled microcontroller embedded system with sensors and IoT technology for greenhouse, to monitor and control the various microclimate parameters in real-time. The study was conducted in a naturally ventilated polyhouse. The web enabled system consists of microcontroller, temperature & humidity sensor, light sensor and actuators (exhaust fans and foggers). The developed system was evaluated with and without crop inside polyhouse.
Irrigation planning and management of a canal irrigated command using geospatial tools
(Department of irrigation and drainage engineering, Kelappaji college of agricultural engineering and technology,Tavanur, 2023-07-14) G Nair., Priya; Asha Joseph
Most of the irrigation command in India is facing low water use efficiency and in unequitable distribution of water. The current canal water release policies in India are supply based and not meeting the actual water requirement of the existing cropping pattern of the command area. Hence most of the major irrigation command areas in India suffer from inadequate and unreliable supply of water. The water is to be delivered over a large area in a canal command with spatially variable soils, crops and weather conditions. This spatial and temporal variability in soil, crop and climate are to be addressed to estimate the actual irrigation demand in a canal command. Distributary is the basic unit in a canal irrigation system. Hence the estimation of distributary wise irrigation demand and water delivery schedule is very important for proper planning and management of irrigation water. Spatial data management tool like Geographic Information System (GIS) can effectively handle the spatial variability of soil, crop, and weather conditions. GIS with water balance model can deal this complex problem of irrigation water demand estimation in a quick and easy way. Hence a study was conducted in Gayathri Irrigation Project command area to assess the crop-based water demand using GIS and water balance model and to develop a demand-based water delivery schedule. The Gayathri irrigation project (GIP), is one of the medium irrigation projects in Palakkad district. The canal network consists of 19 distributaries with 11 distributaries in left bank canal (LBC) and 8 distributaries in right bank canal (RBC). The study area comes under rain shadow area of Palakkad gap and comes under the Agro ecological unit (AEU) 23. Geo spatial data base for canal network, land use, and soil was created in ArcGIS. ArcMap for distributary command, its land use, soil and land use-soil intersect maps were generated to identify the crop in a specific soil series. CROPWAT 8.0 model was used as the field water balance model for finding the net water requirement (NIR) of different crops in the command area of the distributary. The gross irrigation water requirement (GIWR) of the command area was calculated by adding the seepage loss with NIR of various crops in the command area. A demand-based water delivery schedule for the operation of distributaries were developed based on the existing canal water releasing schedule. This new water delivery schedule was compared with the prevailing canal roster for demand supply analysis and to find the surplus/deficit in various distributaries of the canal command for efficient planning and management. The paddy was the major crop which occupies about 48 per cent of total gross command area. The other crops were coconut, mango, vegetables, banana and arecanut. The various soil series identified in the command area were Bhavani Nagar, Karinganthodu, Mungilmada, Tolanur, Kozhinjampara and Vadavannur, which occupies 30.99, 28.56, 14.77, 11.62, 10.83 and 3.40 per cent of command area respectively. The Bhavani Nagar series was with sandy clay texture, while all other series were with sandy clay loam texture. The NIR of different crops estimated by CROPWAT 8.0 model showed that NIR varied with respect to for season, soil, crop and climatic condition. NIR of rice (mundakan) varied from 1004.5 to 1576.3 mm between head and tail reaches. The high value in tail reach was due to high infiltration rate of the soil series ‘Karinganthode’. The variation in NIR of rice (mundakan) was also found in the same soil series among the different distributaries (Bhavani Nagar series with 1056.5mm and 1004.5 mm in head and middle reaches respectively). This was mainly due to the variations of rainfall in the area. The average NIR values for other crops viz. rice (virippu), coconut, mango, vegetable, banana and arecanut were found 1081mm, 483.3, 372 and 417.4, 752.3 and 166.9 mm respectively. The total annual NIR of different distributaries showed that it was more than 10000 m3 /ha for all the distributaries. The highest NIR of about 25000 m3 /ha was obtained for DB14 (Peringhotukavu) followed by DB7 (Ootara). The lowest NIR of below 5000 m3 /ha was obtained for DB1 (Parakkalchalla) The estimated seepage loss for computation of GIWR showed that the seepage loss values ranged from 0.003 to 0.029 m3 /m2 /day. The gross irrigation requirement was found more for distributaries in middle and tail reaches than the head reach. The highest GIWR of 25369.71 m3 /ha was obtained for DB14 (Peringhotukavu) followed by DB7 (Ootara). The lowest GIWR of 4390.965 m3 /ha was obtained for DB 1 (Parakkalchalla). The newly developed crop water demandbased water delivery schedule consists of 12 irrigation cycles which starts from 20th of October and ends on 14th May. The water demand was found high in 1st and 10th irrigation cycles in almost all distributaries. The maximum water demand of 3.017 Mm3 was seen in DB19 (Kollengode distributary) which is having the highest total irrigable area of 554.837 ha. The lowest water demand of 0.002 Mm3 was seen in DB1 (Parakkalchalla) followed by DB2 (Pappanchalla). The water demand of other distributaries ranged from 0.003 to 1.497 Mm3 . The newly developed water delivery schedule was compared with the existing water delivery schedule for demand supply analysis and it showed that actual supply was very low in majority of the distributaries, which was below 1.6 Mm3 . Demand supply gap was found maximum in DB19 (Kollengode distributary) with +7.447 Mm3 . The demand was also compared with design volume of distributaries. Both the actual water supply and design volume was found more for DB19, (Kollengode distributary) than other distributaries. The distributaries in the head reach of canal irrigation system showed negative value (supply more than the demand) while all other distributaries showed positive values (supply less than demand). The demand and design volume gap showed negative values for distributaries DB1, DB3, DB4, DB 5, DB 6, DB11, DB12 and DB13 (supply more than design volume) while all other distributaries showed positive values (supply less than design demand). The highest negative value was found for in DB12 (Peruvemb -2.486 Mm3 ). The highest positive value was found for DB19, Kollengode distributary (+ 5.438 Mm3 ). Canal performance by adequacy indicator showed that the distributaries in head reach of canal irrigation system, are getting sufficient water and showed good performance with performance adequacy (PA) value near to one. All other distributaries were having PA value less than 0.7 indicated poor performance. The performance indices equity (PIE) and efficiency (PIEF) of different distributaries were also determined. The distributaries DB1 and DB6 showed poor equity (PIE) values of 0.704 and 0.375 indicated the spatial distribution of irrigation water is not equal with respect to demand. All other distributaries were showed fair or good performance. In case of efficiency (PIEF), distributaries DB1 and DB6 showed values of 0.37 and 0.57 showed poor efficiency and the system was not efficient to meet the requirements of the region. The PIEF values were observed greater than 0.7 for all other distributaries indicated that system was efficient to meet the requirement of the region. Thus, it could be concluded that spatial variations in climate, soil and crop are to be considered for the estimation of irrigation water demand in the canal command. GIS with water balance model was found an effective tool for addressing this spatial variability in water demand. The effective planning and management of water resources of the canal command should be based on the optimum use of irrigation water. A crop water demand-based delivery schedule is essential for achieving this goal
Mapping, inventory and change detection of wetlands of Tavanur grama panchayath
(Department of Irrigation and Drainage Engineering, Kelappaji College of Agricultural Engineering and Technology, Tavanur, 2022) Chithra, M R; Asha Joseph
A wetland is a distinct ecosystem that is flooded by water (Keddy, 2010). It is distinguished by the characteristic aquatic vegetation (Ramsar convention, 2010). Wetlands Play a critical role in climate change, biodiversity, hydrology and human well-being (Ramsar, 2001). Worldwide, wetlands are in peril now. Wetlands are either being polluted, drained or filled for development also wetlands are being destroyed and LULC pattern altered. Wetland classification maps, inventory and spatio-temporal change information is very important for ecological protection and local government decisions. Use of high resolution remote sensing data along with GIS and GPS is very effective for mapping and change detection of wetlands. No proper documentation on wetland mapping, its inventory, change detection and water dynamics of Tavanur Panchayat. Hence the study was undertaken. LULC mapping and change dynamics between 2008 and 2018 were done by supervised classification and visual interpretation technique. The accuracy of mapping checked by Confusion matrix (error matrix) and Change detection analysis was carried out by PCC method Wetlands of Tavanur Panchayath in 2008 and 2018 were classified and mapped by visual interpretation technique also the water dynamics of Tavanur Panchayath were studied by analysing Depth to water table, Water table contour map, Water table fluctuation map and areal extend of surface water body. The percentage change in land use was found highest for aquaculture (100%) followed by fallow land (56.06%) and paddy converted to coconut (-36.15%). Paddy land experienced the highest transition among the different LULC classes. No appreciable transition was found in the case of river, pond, road and well. Thavanur kayal, Ayankalam kayal, Maravancheri kayal, Varo kayal, Ayankalam aqua culture, Mathur aquaculture and some farm ponds are the main wetlands of Tavanur Panchayath. Marshy type is the common type of wetland in Thavanur other than Aquaculture pond and river. Total area of wetland was estimated 414.17 ha (16.37 %) in 2008 and 409.52 ha (16.87 %) in 2018, it decreased by 4.65 ha only. By studying the water table fluctuation of this Panchayath Kadakassery north , Vallancheri kadavu and Kanjirakutty areas were the most vulnerable areas with a water table fluctuation of 4.92 - 5.44m and Athallur and Kadakassery (centre)regions were less xii vulnerable with fluctuation of 0.08-1.31m only. The area of surface water body increased from 112.97 ha to 192.83 ha during 2008-2013 and from 192.83 ha to 212.92 ha during 2013- 2018. For mapping wetlands of Tavanur Panchayat, visual interpretation technique was found more accurate than supervised classification. The study found that there were not much conspicuous changes in the land use pattern of Tavanur Panchayat during the last decade. Wetlands were found slowly disappearing in Tavanur. Hence an urgent need to make aware of the importance and preservation of these wetlands to prevent further degradation. The analysis of water dynamics showed that water is available for cultivation in the area except in the month of March and April
Spatio-temporal groundwater drought assessment based on ANN model and GIS for a sub-basin of Bharathapuzha
(Department of Irrigation and Drainage Engineering, Kelappaji College of Agricultural Engineering and Technology, ,Tavanur, 2023-07-13) Rabeea Assainar, K K; Asha Joseph
Groundwater is one of the most precious and significant sources of water in the world. Understanding the effects of both natural and human-made factors on groundwater reserves and exploitation is crucial for developing appropriate management strategies to deal with unsustainable use. The understanding of groundwater level variability and trend is crucial for water resource planning in a region. The groundwater level fluctuation is a non-linear phenomenon. Hence, Artificial Neural Networks (ANN) proves to be one of the best tools for modelling non-linear relationship between input and output datasets. Once the groundwater level is modelled, it is easy to assess the groundwater drought conditions of a region using Standardized Groundwater level Index (SGI). Therefore, systematic information about likely occurrence and distribution of drought may assist in preparedness and mitigation of drought disasters. The present study was conducted in Kalpathypuzha sub-basin of Bharathapuzha to analyze the variability and trend of groundwater level, to develop ANN model for groundwater level prediction and to assess the groundwater drought using Standardised Groundwater level Index (SGI). Twelve observation wells evenly distributed in the blocks of Kuzhalmannam, Palakkad, Malampuzha and Chittur were selected. The groundwater level variability was analyzed by various descriptive statistics such as mean, standard deviation, coefficient of variation, skewness and kurtosis. The groundwater level trend was estimated using Mann- Kendall test and Sens slope estimator. ANN models were developed separately for each well to predict the groundwater level using MATLAB R2016a software. The input parameters used were precipitation, maximum and minimum temperature and output data used was groundwater level collected for a period of 15 years from 2007 to 2021. SGI values were estimated for both observed and predicted groundwater level data to assess the groundwater drought scenario of the study area and to develop spatio-temporal groundwater drought map. Monthly groundwater level and drought conditions were predicted for the year 2023 using the developed ANN model. Results of trend analysis showed a decreasing pre-monsoon groundwater level trend in three wells, well 129 of Palakkad block and wells 133 and 142 of Malampuzha block while decreasing post-monsoon groundwater level trend in well 139 of Chittur block. But there was no trend in all other wells for both pre-monsoon and post-monsoon. Feed forward ANN models were developed for all the twelve wells in the study area and the performance indicators correlation coefficient r (0.93 to 0.74), Root Mean Square Error RMSE (0.11 to 0.45 m), and coefficient of determination R2 (0.87 to 0.69) were found in the acceptable range. The best model performance for training was for the well PKD S-4 with model configuration 3-10- 1 and r = 0.92 whereas, during testing it was found for the well 129 with model configuration 3-14-1 and r = 0.93. ANN predicted groundwater level was found in close agreement with that of the observed groundwater level in this study. Hence the model developed could be safely and effectively applied in the study area. The SGI was estimated for pre-monsoon months Jan, Feb, Mar, Apr and May of the study period from 2007 to 2021 for all the twelve wells as drought was more severe during these months. SGI values ranged from -3.7 to 1.1 indicated exceptional to no drought condition in the study area. The computed SGI values indicated that the years 2013, 2016, 2017 were the severe drought years of the study area. According to Spatial distribution of SGI values for the years 2013, 2016 and 2017 Chittur and Malampuzha block were the most drought affected areas followed by Kuzhalmannam and Palakkad block. Hence the study revealed that the majority of Kapathypuzha sub-basin is drought prone and immediate measures are to be adopted to prevent the extend of severity in the area.
Subsurface drip irrigation of ladies finger in sandy loam soil
(Department of Irrigation and Drainage Engineering, Kelappaji College of Agricultural Engineering and Technology, Tavanur, 2007) Nisha, T V; Asha Joseph
Water availability and climatic water balance for a selected cropped area
(Department of Irrigation and Drainage Engineering, Kelappaji College of Agricultural Engineering and Technology, Tavanur, 2018) Venkata Sai, K; Asha Joseph
Water conservation measures and cropping pattern for a watershed using geospatial techniques and swat modelling
(Department of Irrigation and Drainage Engineering, Kelappaji College of Agricultural Engineering and Technology, 2020) Panchamy Balan; Asha Joseph
The Manali watershed located in Thrissur district of Kerala with a drainage area of 140.94 km2 receives an average annual rainfall of 2501.08 mm. But the watershed experiences increased water level rise during monsoon and scarcity of water during non-monsoon season. In order to address the problem of water scarcity in the watershed, an attempt was made to plan conservation measures and cropping pattern using geospatial techniques and SWAT modelling. SWAT model was used effectively for the hydrologic water balance assessment and water availability in the watershed. Water demand was estimated as the sum of agricultural and non-agricultural water demand. Agricultural water demand was estimated using CROPWAT 8 model. An analysis of monthly water availability and water demand was carried out to know the status of water in the watershed. Site suitability modelling was done using GIS to locate water conservation measures and IMSD guidelines were applied to select the type of water conservation measures. Cropping pattern was proposed based on existing crops, soil type, physiography and aridity index. The model was calibrated and validated satisfactorily for the watershed with NSE values 0.71 and 0.61 and R2 values 0.81 and 0.61 during calibration and validation respectively. The highest water availability (71.57 Mm³) was found in the month of June and lowest (1.28 Mm³) in the month of January. Water demand was highest in the month of January (8.91 Mm³) and lowest in the month of June (1.23 Mm³). Water surplus was observed in almost all the months of the year except January, February, March and December. The annual total water surplus in the watershed was obtained as 227.43 Mm3. Hence conservation measures were proposed for the watershed. Thus 32 farm ponds, 7 percolation ponds and 4 check dams were suggested to construct in the watershed area. Farm ponds were found to be the most suitable conservation measure in the area. Suitable cropping pattern like sequential cropping and intercropping were also suggested to improve the productivity and economic status of the watershed.