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Singh, Vijay Kumar
- Assessment of Meteorological Drought for Parbhani District of Maharashtra, India
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1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
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International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 260-267Abstract
A study was carried out to estimate the drought occurrences for rainfed area of Parbhani district of Maharashtra, India. Rainfall plays an important role during crop growth in rainfed agriculture system. Rainfall data of 32 years (1983 - 2014) have been analyzed on annual, seasonal, monthly and weekly basis to find out drought occurrences at Parbhani. The drought analysis indicated that during the study duration the drought, normal and wet years were found to be 9.37, 68.75 and 21.87 per cent, respectively. The occurrences of drought, normal and wet seasons were 7.29, 73.95 and 18.75 per cent, respectively. The percentage of drought, normal and wet months were observed to be 48.43, 38.80 and 12.76 per cent, respectively while drought, normal and wet weeks were observed with a frequency of 70.07, 16.28 and 13.64 per cent, respectively. The research revealed that 9 years showed moderate drought intensity, 9 years showed mild drought intensity while the remaining 14 years observed with no drought condition. No severe or extreme drought was observed during this study duration. The mean value, standard deviation and coefficient of variation of annual rainfall were found to be 947.5 mm, 312.3 mm and 32.96 %, respectively. The analysis also indicated the need of assured irrigation during late winter and summer season.Keywords
Rainfall Analysis, Meteorological Drought, Drought Year.
References
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- Land Surface Temperature Estimation Using Split Window Approach over US Nagar District of Uttarakhand State, India
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Authors
Affiliations
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Soil and Water Conservation Engineering, Gobind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Soil and Water Conservation Engineering, Gobind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
Source
International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 354-359Abstract
To estimate land surface temperature (LST) has an important role for agriculture as well as global change of climate, growth of vegetation and glacier melting. It combines the results of all surface atmosphere interactions and energy fluxes between the surface and the atmosphere. Now-a-days, estimation of temperature of land surface is being calculated with the help of satellite images containing thermal infrared band. Though land surface temperature derived from satellite, could be a beneficial complement to conventional land surface temperature data sources. This research, proposed a methodology for determining land surface temperature through using a structured mathematical algorithm viz., split window (SW) algorithm. Split window algorithm has been used on LANDSAT 8 with operational land imager i.e. OLI sensor and thermal infrared sensor i.e. TIRS dataset of Udham Singh Nagar district. TIRS shows two thermal bands i.e. band 10 and band 11. SW approach requires brightness temperature value of both band 10 and band 11 as well as land surface emissivity which is calculated from OLI bands i.e. NIR and Red, for the estimation of land surface temperature. The spectral radiance was determined using thermal infrared bands i.e. band 10 and band 11. Emissivity was calculated by using normalized difference vegetation index i.e. NDVI threshold technique for which OLI bands 2, 3, 4 and 5 were utilized. SW approach uses brightness temperature of two bands of thermal infrared, mean and difference in land surface emissivity for estimating land surface temperature. In this paper, 6 Dec. 2015 date was selected as an example to show the approach of using SW technique to estimate the LST of Udham Singh Nagar district of Uttarakhand state in India.Keywords
Split Window Approach, Fractional Vegetation Cover, Land Surface Emissivity, Land Surface Temperature.References
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- Weather Parameter Based Crop Planning in Tarai Region of Uttarakhand
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Authors
Affiliations
1 Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
3 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu Univesity, Varanasi (U.P.), IN
1 Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
3 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu Univesity, Varanasi (U.P.), IN
Source
International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 360-366Abstract
The major weather parameters like temperature, relative humidity, rainfall, wind speed and sunshine hour for a period of 43 years were collected and analyzed. This was done for crop planning and to develop an appropriate irrigation scheduling for different crops. The annual rainfall record indicated that in 40.47 per cent cases the normal rainfall (average ± 19%) was received in the study area, whereas, the per cent of below normal and above normal rainfall was found as 33.33 and 26.20 per cent, respectively. The highest PET was obtained in April and the lowest in December. The maximum net irrigation requirements for Rabi and Kharif season crops were found in February, March, April, June, September, October and November months. June to September months received the highest rainfall when the rainfall was received about 86 per cent of the total amount of annual rainfall. It appears that surplus rainfall (Rainfall>PET) during mid-June to August received and it can be harvested and use in high irrigation demand months.Keywords
Rainfall, Probability Analysis, Irrigation Water Requirement, Crop Planning.References
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- A Comparative Study of Artificial Intelligence and Conventional Techniques for Rainfall-Runoff Modeling
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Authors
Affiliations
1 Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu Univesity, Varanasi (U.P.), IN
3 Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
1 Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu Univesity, Varanasi (U.P.), IN
3 Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
Source
International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 441-449Abstract
The essential for accurate modeling of the rainfall–runoff process has grown rapidly in the past decades. However, considering the high stochastic property of the process, many models are still being developed in order to define such a complex phenomenon. In this study, two AI-based models which are reliable in capturing the periodicity features of the process are introduced for river rainfall–runoff modeling. In the first model, the ANN model, an ANN is used to five different type training algorithms namely momentum, Quickprop, Delta-Bar-Delta, Conjugate Gradient and Levenberg Marquardt. In the second model, ANFIS model trained used to two different type membership function (MFs) viz., Gaussian and generalized bell and conventional techniques was used multiple linear regression (MLR). The artificial intelligence performed better than the conventional techniques for rainfall-runoff modelling of study area. The ANFIS models performing the best results, ANN models gives the satisfactory results and MLR model having poor result in runoff prediction for Arpa River basin. Also gamma test (GT) was used for identifying the best input combination of input variables.Keywords
Artificial Neural Network, Adaptive Neural-Fuzzy Inference System, Multiple Linear Regression, Gamma Test.References
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