K. S. Hari Prasad

Estimation of water cloud model vegetation parameters using a genetic algorithm

Abstract The water cloud model is used to account for the effect of vegetation water content on radar backscatter data. The model generally comprises two parameters that characterize the vegetated terrain, A and B, and two bare soil parameters, C and D. In the present study, parameters A and B were estimated using a genetic algorithm (GA) optimization technique and compared with estimates obtained by the sequential unconstrained minimization technique (SUMT) from measured backscatter data. The parameter estimation was formulated as a least squares optimization problem by minimizing the deviations between the backscatter coefficients retrieved from the ENVISAT ASAR image and those predicted by the water cloud model. The bias induced by three different objective functions was statistically analysed by generating synthetic backscatter data. It was observed that, when the backscatter coefficient data contain no errors, the objective functions do not induce any bias in the parameter estimation and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the cases considered, the objective function based on the sum of squares of normalized deviations with respect to the computed backscatter coefficient resulted in the best possible estimates. A comparison of the GA technique with the SUMT was undertaken in estimating the water cloud model parameters. For the case considered, the GA technique performed better than the SUMT in parameter estimation, where the root mean squared error obtained from the GA was about half of that obtained by the SUMT. Editor D. Koutsoyiannis; Associate editor L. See Citation Kumar, K., Hari Prasad, K.S. and Arora, M.K., 2012. Estimation of water cloud model vegetation parameters using a genetic algorithm. Hydrological Sciences Journal, 57 (4), 776–789.

Model for Nonlinear Root Water Uptake Parameter

AbstractAn empirical relationship is developed for the nonlinear root water uptake parameter in the O-R moisture uptake model from easily measurable plant physiological parameters, such as maximum daily transpiration, maximum root depth, and time to attain the maximum transpiration. A nondimensional parameter, termed specific transpiration, that involves the plant physiological parameters is used in this empirical relationship. Data for determining this relationship are obtained by minimizing the deviations between the field observed moisture depletions of 28 crops reported in the literature, and the Richards equation–based numerically simulated soil moisture depletions combined with the moisture uptake model accounting for root water uptake. In addition to cross-validation, field experiments on three Indian crops (maize, Indian mustard, and wheat) are conducted to further validate the proposed empirical relationship. Comparisons of model predictions with field observations of soil moisture profiles and m...

Evaluation of a Nonlinear Root-Water Uptake Model

Soil-water movement due to root-water uptake, is a key process for plant growth and transport of water in the soil plant system. There are different root-water uptake models to determine the extraction rate of soil moisture by roots. The present study examines the performance of different root-water extraction models using available data as well as data generated under controlled conditions. Data pertaining to moisture uptake in respect to two crops: wheat (Triticum aestivum L.) and maize (Zea mays L.) along with soil-water characteristics have been monitored at the Indian Institute of Technology Roorkee, agricultural farm. For this purpose, a numerical model is also formulated by incorporating different moisture extraction terms as sink terms in the Richards equation. A nonlinear root-water uptake model selected as the base model was evaluated for its moisture uptake efficiency. The work establishes the merits of the base model over other extraction terms considered, particularly constant and linear extr...

Virus Transport through Unsaturated Zone: Analysis and Parameter Identification

AbstractThis paper presents numerical and parameter estimation models for the analysis of virus transport and the identification of transport parameters in the unsaturated zone. The numerical model couples a mass conservative fully implicit finite difference model simulating moisture flow in the unsaturated zone with the hybrid finite volume model for virus transport. The accuracy of the numerical scheme is tested for both advection- and dispersion-dominated transport. The comparison of the numerical model with the analytical solution indicates that the numerical model’s predictions are in excellent agreement with the analytical predictions. The parameter estimation is formulated as a nonlinear least-squares minimization problem in which the parameters are estimated by minimizing the deviations between the model-predicted and experimentally observed virus concentrations. A parameter estimation procedure is developed by coupling the numerical model simulating one-dimensional virus transport in the unsatura...

Estimation of Unsaturated Hydraulic Parameters from Infiltration and Internal Drainage Experiments

Inverse problem of determining unsaturated soil hydraulic properties from transient infiltration and internal drainage events are analyzed. Hydraulic properties are assumed to be described by van Genuchtens relationships. The inverse problem is solved using Levenberg-Marquardt method while the forward problem is solved using a mass conservative finite difference numerical scheme. The bias induced by different objective functions on the parameter estimates with error free and noisy data are analyzed. Field experiments are conducted at two sites to compare the parameter estimates obtained from the infiltration and internal drainage tests. The results indicate that some objective functions induce undue bias in the estimated parameters in the presence of noise in the data and as such selecting a suitable objective function should be given due importance in the parameter estimation. The comparison of the parameter estimates from infiltration and internal drainage experiments at two sites indicates that the parameter estimates are close to each other. It is concluded that infiltration experiment, which is simpler and of short duration can be an alternative to internal drainage experiment for estimating the unsaturated soil parameters.

Estimation of unconfined aquifer parameters by genetic algorithms

Abstract Unconfined aquifer parameters, viz. transmissivity, storage coefficient, specific yield and delay index from a pumping test are estimated using the genetic algorithm optimization (GA) technique. The parameter estimation problem is formulated as a least-squares optimization, in which the parameters are optimized by minimizing the deviations between the field-observed and the model-predicted time–drawdown data. Boultons convolution integral for the determination of drawdown is coupled with the GA optimization technique. The bias induced by three different objective functions: (a) the sum of squares of absolute deviations between the observed and computed drawdown; (b) the sum of squares of normalized deviations with respect to the observed drawdown; and (c) the sum of squares of normalized deviations with respect to the computed drawdown, is statistically analysed. It is observed that, when the time–drawdown data contain no errors, the objective functions do not induce any bias in the parameter estimates and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the case considered, defining the objective function as the sum of the squares of absolute deviations between the observed and simulated drawdowns resulted in the best possible estimates. A comparison of the GA technique with the curve-matching procedure and a conventional optimization technique, such as the sequential unconstrained minimization technique (SUMT), is made in estimating the aquifer parameters from a reported field pumping test in an unconfined aquifer. For the case considered, the GA technique performed better than the other two techniques in parameter estimation, with the sum-of-squares errors obtained from the GA about one fourth of those obtained by the curve matching procedure, and about half of those obtained by SUMT. Citation Rajesh, M., Kashyap, D. & Hari Prasad, K. S. (2010) Estimation of unconfined aquifer parameters by genetic algorithms. Hydrol. Sci. J. 55(3), 403–413.

Estimation of Border-Strip Soil Hydraulic Parameters

The inverse problem of determining soil hydraulic parameters (saturated hydraulic conductivity and water retention parameters) of border-strip irrigation from irrigation event data is analyzed. The inverse problem is solved using sequential unconstrained minimization technique. The forward problem involves the solution of coupled Saint-Venants equation governing overland flow and Richards equation governing subsurface flow. Saint-Venants equations are solved using the MacCormack scheme-based finite-difference method while Richards equation is solved using a mass conservative fully implicit finite-difference method. Field experiments are conducted on two border strips to obtain surface and subsurface irrigation data such as irrigation advance, recession, flow depth, and soil moisture content. The soil hydraulic parameters, i.e., saturated hydraulic conductivity and soil retention parameters, are estimated by minimizing the deviations between the model-predicted and field-observed irrigation data. The results indicate that defining the objective function in terms of flow depths results in the optimization algorithm converging to the true values as compared to the use of irrigation advance data. Further, it is observed that underestimating the initial guess results in the least number of iterations for the optimization algorithm to converge to the true values. It is also observed that simultaneous estimation of all three soil hydraulic parameters is not possible even with the inclusion of subsurface moisture content data in the objective function. DOI: 10.1061/(ASCE)IR.1943-4774.0000398.

Estimation and Characterization of Deep Percolation from Rice and Berseem Fields Using Lysimeter Experiments on Sandy Loam Soil

AbstractDeep percolation from the root zone of water intensive crops reduces irrigation efficiency, minimizes water productivity and becomes an environmental threat by carrying chemical residues to groundwater systems. Quantification of the percolation process is often made indirectly without actual field observations. In the present case study, simple, locally constructed drainage type lysimeters were utilized to monitor daily deep percolation from the root zone of unpuddled sandy loam soil throughout the growth periods of rice and berseem fodder crops. Similarly, other water balance components were monitored on daily time steps during the crop growth periods (2013 and 2014). It was observed that a large volume of water is returned as deep percolation loss as physically demonstrated from lysimeter measurements. Overall, approximately 82% of the input water volume in rice season and 61.8% in berseem season accounted for deep percolation in unpuddled sandy loam soil of the experimental field. A simple wate...

A FINITE VOLUME MODEL FOR THE SOLUTION OF THE ADVECTION-DISPERSION EQUATION

ABSTRACT The present study is concerned with the modeling of conservative as well as non-conservative solute transport in ground water. The model is based on an operator split approach which uses an Eulerian frame work with finite volume method for the advective transport and fully implicit central difference method for the dispersive transport. This formulation helps in accurately simulating both highly advective and dispersive transport cases with less restriction on the grid size and time step. The numerical solution is compared with exact as well as the approximate analytical solution and the maximum error as percentage of peak concentration is presented for different values of decay constant at varying distances. It is found that finite volume method provides accurate solutions of both conservative and non-conservative solutes for both advection and dispersion dominated situations.

Effect of fine sediments on river hydraulics – a research review

Fine sediments that join rivers from various channel and non channel sources considerably affect on the hydraulics of the river. The hydraulics of river carrying water with fine sediment has a different fluid and flow characteristics as compared to that carrying clear water. Basic fluid properties like viscosity and density and flow properties like resistance to flow, bed load carrying capacity, etc. are affected by the presence of fines in suspension. Several studies have been carried out to understand the changes associated with the presence of fine sediment in flow. The present paper presents a comprehensive review of these studies.