Ashok K. Keshari

An operational model for estimating Regional Evapotranspiration through Surface Energy Partitioning (RESEP)

Considerable work has been done on estimating surface energy fluxes and thus evapotranspiration at regional scale using remote sensing technique. Approaches currently used for estimating regional evapotranspiration are either based on empirical models or utilize a surface energy balance approach. The operationalization of these models has not been successful, because of the complexities involved in the procedure, strong dependence of heat transfer coefficient on a number of local meteorological parameters and accuracy at regional scale. This paper presents a remote sensing based simplified operational procedure to estimate sensible heat flux incorporating the local meteorological conditions. The model utilizes the surface reflectance in visible, infrared and thermal bands to generate surface albedo, surface temperature and leaf area index and thus surface energy fluxes to determine regional evapotranspiration. The developed model (Regional Evapotranspiration through Surface Energy Partitioning--RESEP) is applied to a part of the Western Yamuna Canal command in the State of Haryana, India to illustrate the methodology. The proposed procedure is computationally simple with reasonable accuracy of results. For a well-watered crop, average evapotranspiration by the proposed model is estimated as 2.1 mm day -1, whereas using Penman-Monteith equation it is calculated as 1.9 mm day -1 . The error involved in estimating evapotranspiration by the proposed model is calculated to be about 10%, which is acceptable for most cases.

Multiobjective management of a contaminated aquifer for agricultural use

Planned utilization of groundwater from a contaminated aquifer requires development of management strategies that determine the spatial distribution of withdrawal for allocation, as well as for control of water quality. Minimization of groundwater allocation for different purposes, and the control of contamination in the aquifer by a specified pumping strategy constitute a management problem with two conflicting objectives. In order to demonstrate possible tradeoffs between water quality control objective and minimum groundwater withdrawal objective, a multiobjective optimization model is formulated. The solution of the model specifies a strategy to control pollution distribution in the aquifer as per agricultural needs, and also evolve an optimal allocation policy to statisfy agricultural demands. Pareto-optimal solutions representing the tradeoff between the two noncommensurate objectives are established. The formulated model uses the embedding technique for simulating the flow and the transport processes in the aquifer. The constraint method is used to transform the multiobjective optimization model into a single objective optimization model. The resulting model is solved using the exterior penalty function method in conjunction with the Hooke-Jeeves method. The proposed model is easily adoptable for various agroclimatic regions and cropping patterns. For illustrative purposes, the model is applied to a specified study area. Although solutions of the model are presented and discussed as per agricultural requirements in terms of both quality and quantity, solutions for other kinds of water demands can be obtained using the same model with minor modifications. Results show that an optimal pumping strategy can be effectively utilized for controlling contamination in the aquifer.

Sensitivity analysis of water quality for Delhi stretch of the River Yamuna, India

Simulation models are used to aid the decision makers about water pollution control and management in river systems. However, uncertainty of model parameters affects the model predictions and hence the pollution control decision. Therefore, it often is necessary to identify the model parameters that significantly affect the model output uncertainty prior to or as a supplement to model application to water pollution control and planning problems. In this study, sensitivity analysis, as a tool for uncertainty analysis was carried out to assess the sensitivity of water quality to (a) model parameters (b) pollution abatement measures such as wastewater treatment, waste discharge and flow augmentation from upstream reservoir. In addition, sensitivity analysis for the “best practical solution” was carried out to help the decision makers in choosing an appropriate option. The Delhi stretch of the river Yamuna was considered as a case study. The QUAL2E model is used for water quality simulation. The results obtained indicate that parameters K1 (deoxygenation constant) and K3 (settling oxygen demand), which is the rate of biochemical decomposition of organic matter and rate of BOD removal by settling, respectively, are the most sensitive parameters for the considered river stretch. Different combinations of variations in K1 and K2 also revealed similar results for better understanding of inter-dependability of K1 and K2. Also, among the pollution abatement methods, the change (perturbation) in wastewater treatment level at primary, secondary, tertiary, and advanced has the greatest effect on the uncertainty of the simulated dissolved oxygen and biochemical oxygen demand concentrations.

A multi-model ensemble approach for quantifying sub-pixel land cover fractions in the urban environments

ABSTRACT Land cover information is essential for sustainable management of the environment in urban areas. Satellite images have increasingly been used to extract such information, yet the accuracy has been challenged by the spectral and spatial heterogeneity of urban land covers. This paper presents a framework to develop a more skilful and reliable model for estimating land cover fractions using a multi-model ensemble technique, named Bayesian Model Averaging (BMA). The BMA is a statistical technique that combines the estimates of different models using Bayesian probability theory. In the BMA, each individual estimate is assigned a weight that is optimised in such a way that the likelihood of an individual estimate given the observation is maximised. In this study, three methods, viz. Multi-layer Perceptron (MLP), Pre-screened and Normalised Multiple Endmember Spectral Mixture Analysis (PNMESMA) and Support Vector Regression (SVR) have been used to develop an Ensemble Model (EM). We used a cluster-based approach for applying the BMA to utilise the diverse advantages in individual models. First, the image pixels were separated into three clusters by applying Normalised Difference Vegetation Index (NDVI) thresholds. Second, an ensemble of models for each cluster was derived using the BMA, and these ensembles were finally combined to derive the final output. The EM was tested in a heterogeneous urban area, viz. South Delhi, India, using two multi-spectral images, including Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER). The modelled land cover fractions were compared with the reference land cover fractions derived from a high-resolution (approximately 1 m) panchromatic image of the OrbitView-3 satellite. The accuracy assessment revealed that the EM estimates more accurate and reliable land cover fractions than the individual models on both the images. The performance of the EM in terms of Root Mean Square Error (RMSE), bias and kappa coefficient (k) is generally superior to that of the best of the individual models. These findings can help improve the accuracy of land cover fractions in heterogeneous landscapes by combining the outputs of various diverse models.

A simple procedure for estimating distributed daily evapotranspiration using landsat-TM data

A procedure to estimate distributed daily evapotranspiration (ET) using remotely sensed data is presented. Landsat-TM data for a part of the Western Yamuna Canal command (Haryana) has been used for model application. The model utilizes the surface reflectance in visible, infrared and thermal bands to generate surface albedo, surface temperature and leaf area index and thus surface energy fluxes to determine distributed daily ET. Result reveals a reasonable estimate of distributed daily ET. For well-watered crop, average ET by the proposed model is estimated as 1.8 mm/d, whereas using Penmen-Monteith equation it is calculated as 1.9 mm/d. The error involved in estimating ET by the proposed model is calculated about 5%, which is quite acceptable for most applications. The proposed procedure is also found computationally simple and can also be applied on current Landsat ETM+ data.

Assessment of global environment using microwave radiometry

Abstract Developing countries are presently undergoing rapid industrialization and urbanization as a result numerous gases are being released into the atmosphere which affect the global environment and climate. In the present paper, we have studied the potentiality of microwave radiometers in mapping atmospheric anomalies such as air pollutants, aerosols, hydrometeors and sandstorms. The microwave attenuation and time series analysis of the dielectric constant of atmosphere are discussed for the Indian industrialized and urbanized cities to assess the climatic perturbations qualitatively and quantitatively.