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Ahmed, Akram
- Influence of Canopy Architecture on Stemflow in Agroforestry Trees in Western Himalayas
Abstract Views :235 |
PDF Views:80
Authors
Affiliations
1 Indian Grassland and Fodder Research Institute, Jhansi 284 003, IN
2 Central Soil and Water Conservation Research and Training Institute, 218 Kaulagarh Road, Dehradun 248 195, IN
1 Indian Grassland and Fodder Research Institute, Jhansi 284 003, IN
2 Central Soil and Water Conservation Research and Training Institute, 218 Kaulagarh Road, Dehradun 248 195, IN
Source
Current Science, Vol 109, No 4 (2015), Pagination: 759-764Abstract
Rainfall event on a tree can be partitioned into throughfall, interception loss and stemflow. In this study, stemflow was measured for 39 rainfall events in 5-year-old plantations of 3 trees each, belonging to Morus alba and Grewia optiva in Dehradun, India. Diameter of selected Morus and Grewia trees varies from 7 to 9.3 and 8.12 to 10 cm respectively, whereas height varies from 4 to 4.5 and 5.5 to 6.5 m respectively. The minimum and maximum rainfall events recorded during the study period were 1.01 and 121.70 mm per day respectively. When the rainfall magnitude was less than or equal to 50 mm and more than 50 mm, stemflow volume from Morus was approximately 2.72 and 1.85 fold higher respectively, compared to Grewia. Maximum stemflow volume recorded for Morus and Grewia was 48,065 and 30,633 ml with respect to rainfall magnitude of 109.58 and 121.70 mm respectively. The generation of higher stemflow volume in case of Morus is due to concave orientation of branches and leaves. Results showed that a significant amount of nutrients leached from Grewia and Morus through stemflow process.Keywords
Canopy Architecture, Interception Loss, Rainfall, Stemflow, Throughfall.Full Text
- Scaling of Hydraulic Functions in Heterogeneous Soil Using Nonlinear Least Squares Minimization Method
Abstract Views :272 |
PDF Views:77
Authors
Affiliations
1 Division of Farm Machinery and Post Harvest Technology, Indian Grassland and Fodder Research Institute, Jhansi 284 003, IN
1 Division of Farm Machinery and Post Harvest Technology, Indian Grassland and Fodder Research Institute, Jhansi 284 003, IN
Source
Current Science, Vol 114, No 05 (2018), Pagination: 1046-1054Abstract
Presenting soil heterogeneity precisely in various spatial scales is the main key to simulate water and solute transport through it. The method described by Richards is mostly used to study water flow through vadose zone. It requires spatial representation of hydraulic functions and water retention relationship in the soil. To represent the spatial relationship of soil hydraulic functions, scaling approach is being used since the last few decades. In this study, a simple scaling method using nonlinear least squares minimization technique has been used to scale soil matric potential, hydraulic conductivity as well as simultaneous scaling of soil matric potential and hydraulic conductivity data. Simultaneous scaling is necessary as it reduces the volume of data by producing a single set of scale factors for hydraulic functions in a heterogeneous soil. Van Genuchten’s semi-empirical expressions were used in this study to parameterize soil hydraulic functions. Results showed that correlation coefficient from raw and descaled data was superior when soil matric potential and hydraulic conductivity data were scaled separately than simultaneously. Improvement of correlation coefficient in simultaneous scaling can be obtained by adding more weight to the soil matric potential data than unsaturated hydraulic conductivity data, which enhances the overall correlation coefficient in simultaneously scaling. Statistical analysis of the scale factors showed that they are lognormally distributed. Scale factors calculated by solving simple equations obtained using the method described in this study can be used to simulate water movement through heterogeneous soil conditions using HYDRUS model.Keywords
Effective Saturation, Lognormal Distribution, Scaling, Soil Matric Potential, Unsaturated Hydraulic Conductivity.Full Text
References
- Richards, L. A., Capillary conduction of liquids through porous mediums. Physics, 1931, 1, 318–333.
- Van Genuchten, M. T., A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J., 1980, 44, 892–898.
- Mualem, Y., A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res., 1976, 13(3), 513–522.
- Miller, E. E. and Miller, R. D., Physical theory for capillary flow phenomena. Appl. Phys. J., 1956, 27, 324–332.
- Clausnitzer, V., Hopmans, J. W. and Nielsen, D. R., Simultaneous scaling of soil water retention and hydraulic conductivity curves. Water Resour. Res., 1992, 28(1), 19–31.
- Warrick, A. W., Mullen, G. J. and Nielsen, D. R., Scaling field-measured soil hydraulic properties using similar media concept. Water Resour. Res., 1977, 13(2), 355–362.
- Russo, D. and Bresler, E., Scaling soil hydraulic properties of a heterogeneous field. Soil Sci. Soc. Am. J., 1980, 44, 681–684.
- Simmons, C. S., Nielsen, D. R. and Biggar, J. W., Scaling of field-measured soil-water properties. I. Methodology, II. Hydraulic conductivity and flux, Agricultural Experiment Station. Hilgardia, 1979, 47(4), 349–358.
- Peck, A. J., Luxmoore, R. J. and Stolzy, J. L., Effects of spatial variability of soil hydraulic properties in water budget modeling. Water Resour. Res., 1977, 13(2), 248–354.
- Ahuja, L. R., Naney, J. W. and Nielsen, D. R., Scaling soil water properties and infiltration modeling. Soil Sci. Soc. Am. J., 1984, 48, 970–973.
- Hopmans, J. W. and Stricker, J. N. M., Soil hydraulic properties in the study area Hupselse Beek as obtained from three different scales of observation: an overview. Research Report, Department of Hydraulics & Catchment Hydrology, Wageningen, 1987.
- Lascano, R. J. and van Bavel, C. H. M., Spatial variability of soil hydraulic and remotely sensed soil parameters. Soil Sci. Soc. Am. J., 1982, 46, 223–228.
- Unlu, K., Nielsen, D. R. and Biggar, J. W., Stochastic analysis of unsaturated flow: one-dimensional Monte Carlo simulations and comparisons with spectral perturbation analysis and field observations. Water Resour. Res., 1990, 26(9), 2207–2218.
- Brooks, R. H. and Corey, A. T., Properties of porous media affecting fluid flow. J. Irrig. Drain E, 1966, 72, 61–88.
- Vachaud, G., Vauclin, M. and Balabanis, P., Stochastic approach of soil water flow through the use of scaling factors: measurement and simulation. Agric. Water Manage., 1988, 13, 249–261.
- Tuli, A., Kosugi, K. and Hopmans, J. W., Simultaneous scaling of soil water retention and unsaturated hydraulic conductivity functions assuming lognormal pore-size distribution. Adv. Water Resour., 2001, 24, 677–688.
- Nasta, P., Kamai, T., Chirico, G., Hopmans, J. W. and Romano, N., Scaling soil water retention functions using particle-size distribution. J. Hydrol., 2009, 374(3), 223–234.
- Nasta, P., Romano, N., Assouline, S., Vrugt, J. A. and Hopmans, J. W., Prediction of spatially variable unsaturated hydraulic conductivity using scaled particle-size distribution functions. Water Resour. Res., 2013, 49, 4219–4229.
- Stoffregen, H. and Wessolek, G., Scaling the hydraulic functions of a water repellent sandy soil. Int. Agrophys., 2014, 28, 349–358.
- Shukla, M. K., Soil Physics: An Introduction, CRC Press, Florida, 2013.
- Simúnek, J., Sejna, M. and van Genuchten, M. T., Hydrus-2d. User manual. International Ground Water Modeling Center, Colorado School of Mines, 1996.
- Marquardt, D. W., An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math., 1963, 11(2), 431–441.
- Schmitz, G. H., Schutze, N. and Wohling, T., Irrigation Control: Towards a New Solution of an Old Problem, IHP-HWRP, Germany, 2005, vol. 5.
- Assessment of Rainfall Variability and its Impact on Groundnut Yield in Bundelkhand Region of India
Abstract Views :240 |
PDF Views:78
Authors
Affiliations
1 ICAR Research Complex for Eastern Region, ICAR Parisar, P. O. Bihar Veterinary College, Patna 800 014, IN
2 Indian Grassland and Fodder Research Institute, Near Pahuj Dam, Gwalior Road, Jhansi 284 003, IN
1 ICAR Research Complex for Eastern Region, ICAR Parisar, P. O. Bihar Veterinary College, Patna 800 014, IN
2 Indian Grassland and Fodder Research Institute, Near Pahuj Dam, Gwalior Road, Jhansi 284 003, IN
Source
Current Science, Vol 117, No 5 (2019), Pagination: 794-803Abstract
Bundelkhand region, one of the vulnerable areas in central India, is prone to frequent drought and crop failure due to annual rainfall variability. In this study, long-term (113 years) fine resolution (0.25° × 0.25°) daily gridded rainfall data has been analysed to depict a spatial variation of annual rainfall over Bundelkhand. An increase in annual rainfall has been observed from north to south of the study area. A declining trend varying from 0.49 to 2.16 mm per year is observed in annual rainfall time series in most parts of the study area. Trend analysis of monsoon rainfall shows overall declining trend over the study area. Rainfall events are categorized in various classes and their spatial trends over Bundelkhand are depicted. Kharif crop calendar (July–September) as well as its yield in India, including Bundelkhand, is primarily based on monsoonal rainfall parameters. A study on the relationship between groundnut yield and monsoonal rainfall parameters for Jhansi district in Bundelkhand shows highest correlation (0.46) between groundnut yield and rainfall class 3 events (16 ≤ rainfall intensity, mm day–1 < 32) occurred in a year followed by cumulative rainfall amount precipitated during June–July (JJ). The frequency of rainfall class 5 type (64 ≤ rainfall intensity, mm day–1 < 128) as well as a delay in onset of monsoonal rainfall have shown a negative correlation with groundnut yield. This study depicts rainfall pattern over the study area and identifies the vulnerable areas that are likely to experience more water stress due to rainfall variability.-Keywords
Bundelkhand Region, Groundnut Yield, Indian Monsoon Rainfall, Rainfall Intensity Class.Full Text
References
- Granados, R., Soria, J. and Cortina, M., Rainfall variability, rainfed agriculture and degree of human marginality in North Guanajuato, Mexico. Singapore J. Trop. Geo., 2017, 38, 153–166.
- Ayoade, J. O., Climate Change, Vantage Publishers, Ibadan, 2004.
- Bannayan, M., Lotfabadi, S. S., Sanjani, S., Mohamadian, A. and Aghaalikhani, M., Effects of precipitation and temperature on crop production variability in northeast Iran. Int. J. Biometeorol., 2011, 55, 387–401.
- Gautam, H. R., Bhardwaj, M. L. and Kumar, R., Climate change and its impact on plant diseases. Curr. Sci., 2013, 105, 1685– 1691.
- López, R. E., Thomas, V. and Troncoso, P., Climate Change and Natural Disasters, STD 414, Santiago, 2015.
- Core Writing Team, Pachauri, R. K. and Meyer, L. A. (eds), IPCC, Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva, Switzerland, 2014.
- Hundecha, Y. and Bárdossy, A., Trends in daily precipitation and temperature extremes across western Germany in the second half of the 20th century. Int. J. Climatol., 2005, 25, 1189–1202.
- Alexander, L. V. et al., Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 2006, 111, 1–22.
- Volosciuk, C., Maraun, D., Semenov, V. A., Tilinina. N., Gulev, S. K. and Latif, M., Rising Mediterranean sea surface temperatures amplify extreme summer precipitation in central Europe. Sci. Rep., 2006, 6, 32450.
- Zhao, Y., Xu, X., Huang, W., Wang, Y., Xu, Y., Chen, H. and Kang, Z., Trends in observed mean and extreme precipitation within the Yellow River Basin, China. Theor. Appl. Climatol., 2008; https://doi.org/10.1007/s00704-018-2568-4.
- Kumar, M. R., Krishnan, R., Sankar, S., Unnikrishnan, A. S. and Pai, D. S., Increasing trend of ‘break-monsoon’ conditions over India – role of ocean–atmosphere processes in the Indian Ocean. IEEE Geosci. Remote Sensing, 2009, 6, 332–336.
- Vishnu, S., Francis, P. A., Shenoi, S. S. C. and Ramakrishna, S. S. V. S., On the decreasing trend of the number of monsoon depressions in the Bay of Bengal. Environ. Res. Lett., 2016, 11, 014011; https://doi.org/10.1088/1748-9326/11/1/014011.
- Guhathakurta, P. and Rajeevan, M., Trends in the rainfall pattern over India. Int. J. Climatol., 2008, 28, 1453–1469.
- Thomas, T., Nayak, P. C. and Ghosh, N. C., Irrigation planning for sustainable rain-fed agriculture in the drought-prone Bundelkhand region of Madhya Pradesh, India. J. Water Climate Change, 2014, 5, 408–426.
- Thomas, T., Nayak, P. C. and Ghosh, N. C., Spatiotemporal analysis of drought characteristics in the Bundelkhand region of Central India using the standardized precipitation index. J. Hydrol. Eng., 2015, 20, 05015004; https://doi.org/10.1061/(ASCE)HE.19435584.0001189.
- Mallya, G., Mishra, V., Niyogi, D., Tripathi, S. and Govindaraju, R. S., Trends and variability of droughts over the Indian monsoon region. Weather Climate Extremes, 2016, 12, 43–68.
- Gosain, A. K., Rao, S. and Basuray, D., Climate change impact assessment on hydrology of Indian river basins. Curr. Sci., 2016, 90, 346–353.
- Kumar, V., Jain, S. K. and Singh, Y., Analysis of long-term rainfall trends in India. Hydrol. Sci. J., 2010, 55, 484–496.
- Rajeevan, M., Bhate, J. and Jaswal, A. K., Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophys. Res. Lett., 2008, 35, L18707; https://doi.org/10.1029/2008GL035143.
- Roxy, M. K. et al., A threefold rise in widespread extreme rain events over central India. Nat. Commun., 2017, 8, 708; http://dx.doi.org/10.1038/s41467-017-00744-9.
- Phalguna, M. and Mirajkar, A. B., The trend analysis of rainfall in the Wainganga River Basin, India. Int. J. Eng. Res. Appl., 2018, 8, 72–85.
- Kumar, K. K., Kumar, K. R., Ashrit, R. G., Deshpande, N. R. and Hansen, J. W., Climate impacts on Indian agriculture. Int. J. Climatol., 2004, 24, 1375–1393.
- Prasanna, V., Impact of monsoon rainfall on the total foodgrain yield over India. J. Earth Syst. Sci., 2014, 123, 1129–1145.
- Mukherjee, J., Das, D. K., Sehgal, V. K., Vashisth, A., Singh, R. and Barari, S. K., South west monsoon and food grain production of India. J. Agric. Phys., 2014, 14, 73–79
- Selvaraju, R., Impact of El Niño–southern oscillation on Indian foodgrain production. Int. J. Climatol., 2003, 23, 187–206.
- Subash, N. and Gangwar, B., Statistical analysis of Indian rainfall and rice productivity anomalies over the last decades. Int. J. Climatol., 2014, 34, 2378–2392.
- Rai, S. K., Behari, P., Satyapriya, A., Rai, K. and Agrawal, R. K., Long term trends in rainfall and its probability for crop planning in two districts of Bundelkhand region. J. Agrometeorol., 2012, 14, 74–78.
- Rai, S. K., Kumar, S., Rai, A. K. and Palsaniya, D. R., Climate change, variability and rainfall probability for crop planning in few districts of Central India. Atmos. Climate Sci., 2014, 4, 394– 403.
- Deo, K., Tripathi, P., Kumar, A., Singh, K., Mishra, S., Mishra, A. and Singh, A., Trend of rainfall in different sectors of Uttar Pradesh under present scenario of climate change. Int. J. Environ. Sci., 2015, 6, 303–310.
- Kar, S. K., Thomas, T. and Singh, R. M., Identification of drought prone areas and trend analysis of rainfall phenomenon in Dhasan Basin. Indian J. Dryland Agric. Res. Dev., 2016, 31, 9–14.
- Jain, V. K., Pandey, R. P., Jain, M. K. and Byun, H. R., Spatiotemporal rainfall trends in the twentieth century for Bundelkhand region, India. J. Water Climate Change, 2017, 8, 441–455.
- Kumari, M., Singh, O. P. and Meena, D. C., Crop water requirement, water productivity and comparative advantage of crop production in different regions of Uttar Pradesh, India. Int. J. Curr. Microbiol. App. Sci., 2017, 6, 2043–2052.
- Shepard, D., A two-dimensional interpolation function for irregularlyspaced data. In Proceedings of the 23rd ACM National Conference, New York, USA, 1968, pp 517–524.
- Machiwal, D. and Jha, M. K., Comparative evaluation of statistical tests for time series analysis: application to hydrological time series. Hydrol. Sci. J., 2008, 53, 353–366.
- Yeh, C. F., Wang, J., Yeh, H. F. and Lee, C. H., Spatial and temporal streamflow trends in northern Taiwan. Water, 2015, 7, 634–651.
- Jayawardena, A., Environmental and Hydrological Systems Modeling, CRC Press, Boca Raton, 2014.
- Buishand, T. A., Some methods for testing the homogeneity of rainfall records. J. Hydrol., 1982, 58, 11–27.
- Hamed, K. H. and Rao, A. R., A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol., 1998, 204, 182–196.
- Theil, H., A rank-invariant method of linear and polynomial regression analysis. In Henri Theil’s Contributions to Economics and Econometrics. Advanced Studies in Theoretical and Applied Econometrics (eds Raj, B. and Koerts, J.), Springer, Dordrecht, Netherlands, 1992, vol. 23.
- Sen, P. K., Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc., 1968, 63, 1379–1389.
- Alpert, P. et al., The paradoxical increase of Mediterranean extreme daily rainfall in spite of decrease in total values. Geophys. Res. Lett., 2002, 29, 311–314.
- Curtarelli, M., Leão, J., Ogashawara, I., Lorenzzetti, J. and Stech, J., Assessment of spatial interpolation methods to map the bathymetry of an Amazonian hydroelectric reservoir to aid in decision making for water management. ISPRS Int. J. Geo. Inf., 2015, 4, 220–235.
- Boote, K. J., Growth stages of peanut (Arachis hypogaea L.). Pean. Sci., 1982, 9, 35–40.
- Reddy, T. Y., Reddy, V. R. and Anbumozhi, V., Physiological responses of groundnut (Arachis hypogea L.) to drought stress and its amelioration: a critical review. Plant. Growth Regul., 2003, 41, 75–88.
- Oppong-Sekyere, D., Akromah, R., Nyamah, E. Y., Ninfaa, A. D., Braimah, M. M., Akpalu, M. M. and Salifu, A. R. S., Assessment of postharvest practices of groundnuts in northern Ghana based on the participatory rural appraisal technique. J. Sci. Res. Rep., 2016, 10, 1–17.
- Niazi, S., In India’s parched Bundelkhand, drought brings a tide of migration [WWW Document]; https://www.preventionweb.net/news/view/59147 (accessed on 5 October 2018).
- Saxena, M., Bundelkhand water crisis: Fourth successive drought and vanishing water bodies compound farmer misery, trigger migration [WWW Document]. https://www.firstpost.com/india/bundelkhand-water-crisis-fourth-successive-drought-and-vanishing-water-bodies-compound-farmer-misery-trigger-migration-4468313.html (accessed on 5 October 2018).
- Bisht, H., Climate Resilient Development in Bundelkhand Region of Madhya Pradesh – Synthesis Report. Development Alternatives, New Delhi, 2013.
- Mohan, C., Developing Climate Resilient Farming in Bundelkhand [WWW Document]. https://krishijagran.com/news/developing-climate-resilient-farming-in-bundelkhand/ (accessed on 9 October 2018).
- Rakhecha, P. and Singh, V. P., Applied Hydrometeorology, Capital Publishing Company, New Delhi, 2009.
- Adamgbe, E. M. and Ujoh, F., Effect of variability in rainfall characteristics on maize yield in Gboko, Nigeria. J. Environ. Prot., 2013, 4, 881–887.
- Naveen, P., Daniel, K. V., Subramaniyam, P. and Kumar, P. S., Response of irrigated groundnut to agro management. Indian J. Agron., 1992, 37, 82–85.
- IOPEPC, Kharif – 2017 Survey of Groundnut Crop. Indian Oilseeds and Produce Export Promotion Council. Govt of India, Mumbai, 2017.
- Chandrika, V., Parameswari, P. and Sreenivas, G., Effect of sowing time and rainfall distribution on yield of rainfed groundnut (Arachis hypogaea L.) in southern agro-climatic zone of Andhra Pradesh. Legume Res., 2008, 31, 54–56.