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Open Access
Hyetograph-hydrograph transformation model for small ungauged watersheds
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2004-06) Sachan, Shatendra; Akhilesh Kumar
The well established techniques used for determination of runoff hydrograph require historical runoff data and other complex information for evaluating various parameters, which are generally not available in case of ungauged watersheds. Therefore, an attempt has been made to develop a model which is capable of transforming available rainfall hyetograph in to direct runoff hydrograph by using information about land use pattern and topographical features of the area. In the development of models, the storm runoff has been estimated by using SCS curve number method. The model formulation was attempted considering uniform and nonuniform rainfall distribution patterns. In case of uniform rainfall distribution, the model was formulated on the basis of one step rainfall input and accordingly named as One Step Rainfall Input Model (OSRIM). While in case of nonuniform rainfall distribution, the entire storm duration was divided into smaller time increments in a way that the rainfall intensity within an increment is almost constant and the model was formulated considering multiple step rainfall input and called as Multiple Step Rainfall Input Model (MSRIM). The developed models were applied for their verification using the observed data of a small hilly watershed known as “Jandoo-Nala watershed” comprising an area of 17.71 ha in Dehradun district of Uttaranchal State. In case of small ungauged watersheds, the developed methodology will be very useful in designing, planning and operation of various soil and water conservation structures, flood control works, water storage & conveyance structures and also in watershed management & planning. It was found that the value of initial abstraction ratio λ = 0.15 provided a better prediction of direct runoff volume using SCS curve number method for the study area. In case of One Step Rainfall Input Model (OSRIM), the coefficient of correlation between model predicted and observed values of the peak rate of runoff was found to be 73.0%. The values of peak rate of runoff predicted by using Multiple Step Rainfall Input Model (MSRIM) were found to be yielding a good correlation with the corresponding observed values as the coefficient of correlation was found to be 83.0%. A non-linear model of exponential form between peak runoff rate (qp, m3/s), rainfall depth (P, mm), maximum potential retention (S, mm) and curve number (CN) was developed having coefficient of determination (R2) as 96.61%. In case of OSRI model, the time to peak coincided with the time of concentration while in the NLR model because of its inherent weakness did not predict time to peak value. It was observed that there was a very good correlation (97.0%) and coefficient of efficiency (97.19%) between the observed and MSRI model predicted time to peak values. The qualitative analysis revealed that, in general, the predicted ordinates of hydrographs of the selected storm events were in reasonably close agreement with the corresponding ordinates of observed hydrographs.
Open Access
Forecasting of daily reference evapotranspiration using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-07) Sah, Namrata; Kashyap, P.S.
Open Access
Analysis of spatio-temporal trends of rainfall in Jodhpur and Kota zones of Rajasthan
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-06) Yogesh Kumar; Anil Kumar
The trend is one of the deterministic components of a time series. The trend component is described as long term or regular fluctuation in a time series, on an average basis, which could either be increasing or decreasing in nature. The anthropogenic and climatic activities influence many hydrometeorological processes in a persistent manner, whose effect emerges in form of trends in rainfall, temperature, evapo-transpiration, stream flows, etc. These trends may be with respect to time (temporal trend) and space (spatial trend). These trends in rainfall are of paramount importance for carrying out studies related to climate change aspects. The knowledge of trends in rainfall is extremely important for agricultural engineers, hydrologists, water scientists etc. Without studying trends it may sometimes lead to overestimation or underestimation of the parameters for the design and operation of water infrastructures, water stresses, water shortages and agricultural failures. Rajasthan is a state which covers north-western and western parts of Indian sub-continent. The state has always been under the threat of droughts and planning and management of water resources and agriculture becomes a crucial issue in the state. There should be proper management of water resources and agricultural operations to prevent such extreme events. Hence it is very important to study the nature of rainfall in form of trends both temporally and spatially. This study was conducted to analyze the temporal and spatial trends of annual and monsoonal rainfall, and annual rainy days in Jodhpur and Kota zones of Rajasthan. Non- Parametric statistical methods namely Mann-Kendall (MK) and Modified Mann-Kendall (MMK) test were employed for detection of temporal trends. The magnitude of the identified trends was assessed by using Theil-Sen slope estimator test. A recently proposed Innovative trend analysis (ITA) method was also used for detection of temporal trends. The turning point of potential trends was identified using Sequential Mann-Kendall (SQMK) test. The spatial analysis of trends was done using Inverse Distance Weighing (IDW) technique in GIS environment. On the basis of application of above methods, fluctuating trends of different magnitudes in both Jodhpur and Kota zones were identified, which could be possible due to climatic and anthropogenic activities taking place in the study area over the years. This could be a sign of climate change in the study area.
Open Access
Monthly rainfall modelling using ANN for central Telangana region
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-06) Kota Spandana; Devendra Kumar
In the present study, artificial neural network technique has been employed to predict monthly rainfall for Medak, Khammam and Warangal stations of Central Telangana, India. Eighty five years of rainfall data (January, 1901 to December, 1985) were used for training of models and twenty eight years of rainfall data (January, 1986 to December, 2014) were used for testing of models. Gamma test, autocorrelation function and cross correlation function were used for selection of appropriate input variables. The ANN models were trained using multilayer perceptron with two learning rules i.e. Levenberg-Marquardt and Delta-bar-delta and two transfer functions viz. sigmoid axon and Tanh axon. The performance of the models was evaluated qualitatively by visual observation and quantitatively by using different performance indices viz. Root Mean Square Error, Correlation Coefficient, Coefficient of Efficiency, Percent Bias and Integral Square Error. It was observed that the better results of monthly rainfall prediction of developed models were observed when the rainfall data of adjoining stations were used as the input variable as compared the lagged rainfall of the same station. The higher value of Correlation Coefficient and Coefficient of Efficiency and lower value of Integral Square Error, Percent Bias and Root Mean Square Error suggest that the M-8 model, K-7 model and W-5 may be used to predict monthly rainfall of Medak, Khammam and Warangal stations respectively for Central Telangana region.
Open Access
Drought assessment based on standardized precipitation index and its forecasting using neural networks
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-06) Rai, Priya; Singh, Praveen Vikram
Drought is one of the most important environmental problems affecting the planet. Drought means scarcity of water, which disturbs the entire ecosystem and adversely affects various sectors of society, e.g. agriculture, hydropower generation, water supply and industry. Occurrence of drought and its forecasting are critical components of hydrology which play a major role in risk management, drought preparedness and mitigation. The identification, characterization and monitoring of droughts are of great importance in water resources planning and management. Based on its nature, drought may be divided into four different categories, viz., metrological, agricultural, hydrological and socioeconomical. Of these, the meteorological drought signifies the paucity of rainfall over a region for a considerable period of time. Drought occurs in nearly all climatic zones of the world at one time or other, but this creeping phenomenon mostly affects tropics and adjoining regions. This study was conducted using the rainfall data for 44 years (1971-2014) of Parbhani district of Marathwada region with specific objectives to find the best fit probability distribution, determine occurrence of meteorological drought based on Standardized Precipitation Index (SPI) for different time scales, develop Multilayer Perceptron Neural Network (MLPNN) models and assess the performance of these models for meteorological drought forecasting. The data were used to determine best fit probability distribution among Normal, log Normal, Gamma distribution and Weibull distribution based on KS goodness of fit statistic at 1% and 5% level of significance. The rainfall data were used to calculate drought and wet conditions in the region based on SPI values considering time scales of 1-, 3-, 6-, 9-, 12- and 24-month for the best fit distribution. The forecasting of the occurrence of the drought based on SPI was done at considered time scales using Multilayer Perceptron Neural Network (MLPNN) models. The lag found for different time scales i.e., SPI-1, SPI-3, SPI-6, SPI-12 and SPI-24 were 1, 2, 4, 7, 8 and 12 respectively. The NeuroSolutions Software was used with Levenberg-Marquardt learning rule and activation function was hyperbolic tangent with range -1 to 1. The performance of developed models was assessed using statistical indices such as root mean squared error (RMSE), Coefficient of determination (R2) and Coefficient of efficiency (CE). The Normal and Gamma distribution were found to be the best fitted on the rainfall data, however, Gamma distribution had an edge over the Normal distribution. During the total period of 528 months (1971-2014), 32 extreme drought, 69 severe drought, 82 extreme wet and 160 severe wet events occurred. The multilayer perceptron neural network models can be used only for forecasting of long duration SPI. The network architecture 7-8-1, 8-11-1 and 12-9-1 can be used for SPI-9, -12 and -24 months respectively for the study area.
Open Access
Daily and weekly rainfall modelling using Artificial Neural Network
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Joshi, Neeraj; Devendra Kumar
Artificial Neural Network technique has been employed to predict daily and weekly rainfall for Nainital station of Uttarakhand, India. The meteorological data from year (2003 to 2013) were used for training of daily and weekly rainfall prediction models and meteorological data from (2014 to 2018) were used for testing of daily and weekly rainfall prediction models. Gamma test was used for the selection of appropriate input variables of daily and weekly rainfall prediction models. The ANN models were trained using multilayer perceptron with one learning rule i.e. Levenberg - Marquardt and two transfer functions viz. Tanh Axon and Sigmoid Axon. The performance of the models was evaluated qualitatively by visual observation and quantitatively using different performance indices viz. Root Mean Square Error, Correlation Coefficient, Coefficient of Efficiency, Percent Bias and Integral Square Error. It was observed that both Tahn Axon and Sigmoid Axon activation functions are capable of predicting the daily and weekly rainfall with almost equal prediction efficiency, for daily rainfall model structure of 5 – 8 – 8 - 1 with L evenberg – M arquardt and Tahn Axon, and model structure 5 -10 - 10-1 with Levenberg –M arquardt and Sigmoid Axon performed well. And, in case of weekly rainfall the model structure of 5 -10-10-1 was found to be working well in both the cases for Nainital station of Uttarakhand, India.
Open Access
Daily monsoon rainfall prediction using Artificial Neural Network (ANN) and Wavelet Based Artificial Neural Network (WANN) models
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-07) Rawat, Amit; Pravendra Kumar
Rainfall is the most complex and difficult elements of hydrologic cycle to understand and to model due to the complexity of atmospheric processes. Long term Rainfall prediction is very important for countries whose economy depends mainly on agriculture. It is widely used in the energy industry for efficient resource planning and management including famine and disease control, rainwater catchment and ground water management. Considering these facts, a study has been carried out to assess the monsoon rainfall prediction in Parbhani Maharastara, India. The daily monsoon meteorological data of 30 years (1st June, 1985 – 30th Sept, 2014) were collected from meteorological observatory located at Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani of Maharastra State, India. In the present study, Artificial Neural Network (ANN) and wavelet based Artificial Neural Network (WANN) techniques were used to predict the daily monsoon rainfall. The daily data for monsoon period (1st June to 30th September) of years 1985-2008 and 2008-2014 were used to train and test models, respectively. The best combination of input variables was selected on the basis of Gamma statistic. In case of ANN and WANN, back-propagation algorithm and tan sigmoid activation function were used to train and test the models. The performance of the models were evaluated qualitatively by visual observations and quantitatively using various performances indices Viz. RMSE, correlation coefficient, MSE, coefficient of efficiency and pooled average relative error. Finally, it can be concluded from the results that the performance of the WANN model is better than the ANN model.
Open Access
Sediment particle movement for different packing patterns under varying channel slope and discharge conditions
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-05) Kethavath, Ajaykumar; Akhilesh Kumar
Sediment particle movement depends on not only the discharge and surface topography but also on the inherent properties of sediment particle including its shape, size, specific weight apart from their orientation. The shear force exerted on a sediment particle by flowing water plays a dominant role in deciding the rate of movement. The movement of sediment particles starts only when the shear stress acting on the particle is more than the critical shear stress. A number of studies have been conducted to study initiation of particle known as the condition of incipient motion and critical shear stress. Mostly, these studies have been conducted considering sediment particle as uniform and spherical which are never met in practice. In case of non-spherical and irregular shaped particles their orientation becomes a deciding factor for the shear force acting on the particle as it varies with changing orientation under similar conditions. Similarly, in case of non-uniform sediment material, the packing pattern of the sediment particles also plays a dominant role in deciding the magnitude of acting shear force and their movement behaviour. This study was conducted to study the effect of sediment particle packing pattern on their movement speed under varying discharge and channel slope conditions. Laboratory experiments were conducted using 6 m long slope adjusting hydraulic tilting flume on artificially constructed regular shaped sediment particles. For this purpose, sediment particles of three different sizes were constructed in the laboratory and designated as P, Q and R. These particles were arranged in different packing patterns on the bed of the flume and particle movement speed were observed for selected packing patterns with five different discharges i.e.11.1 l/s/m, 15.3 l/s/m, 19 l/s/m, 23 l/s/m, and 26.7 l/s/m, and three different channel bottom slopes of 1%, 2% and 3%. In this way the experiment was conducted in total for 405 runs for different packing patterns, slope and discharge conditions. On the basis of the analysis of observed data, it was observed that in case of patterns 1R3, 2R3 and 3R3, the maximum movement of 78.61 cm/s was observed in 1R3 pattern at 3% land slope and the minimum movement was found to be 30.14 cm/s for 3R3 pattern at 1% channel slope. The maximum movement of Q particle was found to be 68.32 cm/s for 2Q3-2P3 pattern and 62.27 cm/s for 2P3-2Q3 pattern with a discharge of 26.7 l/s/m at 3% land slope. Similarly, the maximum movement speed of R particle was observed to be as 81.56 cm/s for 2 (1R3-1P3-1Q3) pattern which came down to 79.09 cm/s for 2P3-2Q3-2R3 and 78.14 cm/s for 2(1Q3-1R3-1P3) patterns at 3% channel bottom slope for a discharge of 26.7 l/s/m. In general, however, the magnitude of shear force, increased with increase in discharge for a particular packing pattern at a slope. Similarly, for a given discharge, the shear force increased with increase in channel slope. However, it was observed that for a given discharge by changing packing pattern of particles, the exposed area changed and the shear force and particle movement changed accordingly.
Open Access
Effect of additives on geotechnical properties of soil in relation to erosion under simulated rainfall conditions
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-01) Joshi, Pranjay; Akhilesh Kumar
The vulnerability of soil erosion towards erosion may be reduced by having a good vegetative cover over the soil surface, slope improvement and improving soil properties so that it is not easily detached and transported. However, establishment of proper vegetative cover is time taking process as it takes time for a seed to germinate and attain maturity. As an alternative approach, if soil resistance is increased by increasing the shear strength of soil against erosive forces offered by eroding agents, the soil system will become capable of withstanding the detachment of its particles on the application of shear stress. Under this practice, an additive is mixed to the soil which increases the shear strength of soil. The additives range from biodegradable to non-biodegradable or partially biodegradable inert materials. Recently, the use of guargum polymer in agriculture has become quite popular. Some recent applications of Guar gum are in the field of drag reduction in pipes, evaporation control, soil stabilization and erosion control. Accordingly, in this study, additives namely jute fibre and guargum and their combinations were mixed with soil in different proportions to study various geo-technical properties such as dry density, optimum water content, California bearing ratio (CBR) and tri-axial shear parameters. Index properties like specific gravity, grain size distribution and consistency limits and geotechnical properties such as dry density and optimum moisture content of treated and untreated soils were determined. California bearing ratio (CBR) value was used to evaluate mechanical strength of soil. There were nine treatments for which CBR values were determined. for two stages i.e., for the1st day of treatment and on the 4th day after the treatment. The tri-axial test was performed to obtain values of shear parameters at three confining pressures of 1kg/cm2, 1.5kg/cm2 and 2 kg/cm2 for a particular percentage of additives. Deviator stress was subjected from the top and the loading rate of 1.25 mm per minute was kept throughout the experiment. The unconsolidated undrained (UU) conditions were maintained throughout the experiment. For untreated soil, the value of cohesion was found to be 0.49 kg/cm2and angle of internal friction was found to be 28.66 degree on the1st day. The cohesion and angle of internal friction increased to 1.593kg/cm2and 55.35 degrees respectively on 7thday. Similarly, these values were obtained for all treatments applied to soil to determine shear parameters. To produce rainfall a small size portable rainfall simulation system was developed in the laboratory which was1m x1m in size and operated from a height of 5 meter. The simulator was calibrated for its rainfall generation capabilities which commensurate with natural rainfall conditions. The simulation system was calibrated for producing rainfall intensities of 12.8 cm/h t and 17.5 cm/h for water pressures of 0.4 kg/cm2 and 0.5 kg/cm2 which occurred almost uniformly over the entire test plot. The soil was treated with guargum having 0.3%, 0.5%, and 0.7% concentration by weight and with jute fiber having 0.3%, 0.5%, and 0.7% by weight and also with guargum and jute fiber combinations as 0.3%guargum and 0.3% Jute; 0.5% guargum and 0.5% jute; and 0.7% guargum and 0.7% jute mixture by weight. The recorded observations clearly revealed that as the value of shear strength of soil increased as the result of the applied treatments, the soil loss rate/ sediment outflow rate decreased for every combination of land slope and rainfall intensity. It was also found that for a particular value of cohesion and angle of internal friction, the runoff rate increased with rainfall intensity for every land slope while the sediment concentration and sediment outflow rate increased with rainfall intensity as well as land slope.

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