K P Sudheer

Physically-based distributed parameter models, such as the Soil and Water Assessment Tool (SWAT), have been widely used to predict watershed response. Calibrating and validating the hydrologic simulations of such models before they are applied to make watershed decisions is always a challenge, particularly if data from multiple sites are used for calibration. In this paper, we discuss three issues related to multi-site calibration of hydrologic models: use of a multi-objective function when conducting multi-site calibrations; multi-site calibration when sites are hydrologically connected by surface water; and simultaneous calibration of sites when conducting multi-site calibration. Another aspect of watershed model calibration is the selection of an objective (or multi-objective) function and the implied statistical assumptions associated with the objective function. Generally, watershed model objective functions are some form of minimizing errors. Hence, there exist statistical assumptions related to their residuals, which are rarely evaluated. However, our results indicate that assumptions should be evaluated for all calibrations. Results also indicated that evaluation of statistical assumptions varied depending on the calibration time scale. Hydrologic calibration of watersheds is complex and should include a well organized set of methods to ensure appropriate interpretation and application of the calibrated model.

Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.

Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and statistical analysis of the available data series. Several ANN models are developed that forecasts the water level of two observation wells. The results suggest that the model predictions are reasonably accurate as evaluated by various statistical indices. An input sensitivity analysis suggested that exclusion of antecedent values of the water level time series may not help the model to capture the recharge time for the aquifer and may result in poorer performance of the models. In general, the results suggest that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well. Such forecasts may be useful in conjunctive use planning of groundwater and surface water in the coastal areas that help maintain the natural water table gradient to protect seawater intrusion or water logging condition. © Springer Science + Business Media, Inc. 2006.

Artificial neural networks (ANNs), due to their excellent capabilities for modeling complex processes, have been successfully applied to a variety of problems in hydrology. However, one of the major criticisms of ANNs is that they are just black-box models, since a satisfactory explanation of their behavior has not been offered. They, in particular, do not explain easily how the inputs are related to the output, and also whether the selected inputs have any significant relationship with an output. In this paper, a perturbation analysis for determining the order of influence of the elements in the input vector on the output vector is discussed. The approach is illustrated though a case study of a river flow model developed for the Narmada Basin, India. The analyses of the results suggest that each variable in the input vector (flow values at different antecedent time steps) influences the shape of the hydrograph in different ways. However, the magnitude of the influence cannot be clearly quantified by this approach. Further it adds that the selection of input vector based on linear measures between the variables of interest, which is commonly employed, may still include certain spurious elements that only increase the model complexity. Journal of Hydrologic Engineering © ASCE.

Macrofungi have long been investigated for various scientific purposes including their food and medicinal characteristics. Their role in aerobiology as a fraction of the primary biological aerosol particles (PBAPs), however, has been poorly studied. In this study, we present a source of macrofungi with two different but interdependent objectives: (i) to characterize the macrofungi from a tropical dry evergreen biome in southern India using advanced molecular techniques to enrich the database from this region, and (ii) to assess whether identified species of macrofungi are a potential source of atmospheric PBAPs. From the DNA analysis, we report the diversity of the terrestrial macrofungi from a tropical dry evergreen biome robustly supported by the statistical analyses for diversity conclusions. A total of 113 macrofungal species belonging to 54 genera and 23 families were recorded, with Basidiomycota and Ascomycota constituting 96% and 4% of the species, respectively. The highest species richness was found in the family Agaricaceae (25.3%) followed by Polyporaceae (15.3%) and Marasmiaceae (10.8%). The difference in the distribution of commonly observed macrofungal families over this location was compared with other locations in India (Karnataka, Kerala, Maharashtra, and West Bengal) using two statistical tests. The distributions of the terrestrial macrofungi were distinctly different in each ecosystem. We further attempted to demonstrate the potential role of terrestrial macrofungi as a source of PBAPs in ambient air. In our opinion, the findings from this ecosystem of India will enhance our understanding of the distribution, diversity, ecology, and biological prospects of terrestrial macrofungi as well as their potential to contribute to airborne fungal aerosols.

Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remote sensing data. However, these models require information about the complex nature of underlying watershed characteristics and processes. Unlike regression-based models, Artificial Neural Network (ANN) provides an efficient alternative to map complex nonlinear relationships between an input and output datasets without requiring a detailed knowledge of underlying physical relationships. Limited or no information currently exist quantifying ability of ANN models to identify contrasting tillage practices from remote sensing data. In this study, a set of Landsat TM-based ANN models was developed to identify contrasting tillage practices in the Texas High Plains. Observed tillage data from Moore and Ochiltree Counties were used to develop and evaluate the models, respectively. The overall classification accuracy for the 15 models developed with the Moore County dataset varied from 74% to 91%. Statistical evaluation of these models against the Ochiltree County dataset produced results with an overall classification accuracy varied from 66% to 80%. The ANN models based on TM band 5 or indices of TM Band 5 may provide consistent and accurate tillage information when applied to the Texas High Plains. © 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.

A simulation-optimization (S-O) framework is developed for the hybrid stochastic modeling of multi-site multi-season streamflows. The multi-objective optimization model formulated is the driver and the multi-site, multi-season hybrid matched block bootstrap model (MHMABB) is the simulation engine within this framework. The multi-site multi-season simulation model is the extension of the existing single-site multi-season simulation model. A robust and efficient evolutionary search based technique, namely, non-dominated sorting based genetic algorithm (NSGA - II) is employed as the solution technique for the multi-objective optimization within the S-O framework. The objective functions employed are related to the preservation of the multi-site critical deficit run sum and the constraints introduced are concerned with the hybrid model parameter space, and the preservation of certain statistics (such as inter-annual dependence and/or skewness of aggregated annual flows). The efficacy of the proposed S-O framework is brought out through a case example from the Colorado River basin. The proposed multi-site multi-season model AMHMABB (whose parameters are obtained from the proposed S-O framework) preserves the temporal as well as the spatial statistics of the historical flows. Also, the other multi-site deficit run characteristics namely, the number of runs, the maximum run length, the mean run sum and the mean run length are well preserved by the AMHMABB model. Overall, the proposed AMHMABB model is able to show better streamflow modeling performance when compared with the simulation based SMHMABB model, plausibly due to the significant role played by: (i) the objective functions related to the preservation of multi-site critical deficit run sum; (ii) the huge hybrid model parameter space available for the evolutionary search and (iii) the constraint on the preservation of the inter-annual dependence. Split-sample validation results indicate that the AMHMABB model is able to predict the characteristics of the multi-site multi-season streamflows under uncertain future. Also, the AMHMABB model is found to perform better than the linear multi-site disaggregation model (MDM) in preserving the statistical as well as the multi-site critical deficit run characteristics of the observed flows. However, a major drawback of the hybrid models persists in case of the AMHMABB model as well, of not being able to synthetically generate enough number of flows beyond the observed extreme flows, and not being able to generate values that are quite different from the observed flows.

The quantification of uncertainty in the simulations from complex physically based distributed hydrologic models is important for developing reliable applications. The generalized likelihood uncertainty estimation method (GLUE) is one of the most commonly used methods in the field of hydrology. The GLUE helps reduce the parametric uncertainty by deriving the probability distribution function of parameters, and help analyze the uncertainty in model output. In the GLUE, the uncertainty of model output is analyzed through Monte Carlo simulations, which require large number of model runs. This induces high computational demand for the GLUE to characterize multi-dimensional parameter space, especially in the case of complex hydrologic models with large number of parameters. While there are a lot of variants of GLUE that derive the probability distribution of parameters, none of them have addressed the computational requirement in the analysis. A method to reduce such computational requirement for GLUE is proposed in this study. It is envisaged that conditional sampling, while generating ensembles for the GLUE, can help reduce the number of model simulations. The mutual relationship between the parameters was used for conditional sampling in this study. The method is illustrated using a case study of Soil and Water Assessment Tool (SWAT) model on a watershed in the USA. The number of simulations required for the uncertainty analysis was reduced by 90 % in the proposed method compared to existing methods. The proposed method also resulted in an uncertainty reduction in terms of reduced average band width and high containing ratio.

The estimation of maximum storm surges along a coast is indispensable for emergency action planning, design, and adaptation of coastal infrastructure. The existing studies on the Bay of Bengal coast have used synthetic tracks with constant track parameters such as the maximum wind speed, radius of maximum wind, and central pressure to estimate the probable maximum storm surges. However, the analysis of the best tracks from the Joint Typhoon Warning Center during 1978–2019 shows that the track parameters vary from origin to landfall locations. The reported studies along the Bay of Bengal have failed to capture such variations and overestimated probable maximum storm surges due to constant track parameters. Therefore, this study proposes a methodology for estimating wind speed of various return periods and associated track parameters that vary along a synthetic track. The wind speed of different return periods is computed at each eye location using the probabilistic approach. Thus, the calculated wind speeds vary from origin to landfall locations, and such a pattern of wind speed variation is observed to be similar to the historical cyclones. The radius of maximum wind and central pressure is calculated using the regression equations derived from historical tracks. The accuracy of the proposed methodology is investigated by simulating the cyclones Thane and Vardah that occurred along the Tamil Nadu coast. The results suggest that the varying track parameters using the proposed methodology produce realistic surge values similar to parameters from best track data, and it overcomes the overestimation of surge heights. The proposed methodology is further utilized to analyse the maximum surge scenarios along the Tamil Nadu coast resulting from various track shifts and angles of attack. The proposed methodology is expected to improve the estimation of storm surges in other basins.

This paper presents a novel hybrid linear-neural (LN) model formulation to effectively model rainfall-runoff processes. The central idea of the proposed model framework is that the hidden layer of an artificial neural network (ANN) model be designed with a combination of linear and nonlinear neurons. A training algorithm for the proposed model is designed based on minimum description length criteria. The advantage of the algorithm is that the final architecture of the LN model is arrived at during the training process, thus avoiding selection from a class of models. The proposed model has been developed and evaluated for its performance for forecasting the river flow of Kolar basin, in India. The values of three performance evaluation criteria, namely, the coefficient of efficiency, the root-mean-square error, and the coefficient of correlation, were found to be very good and consistent for flows forecasted 1 hour in advance by the LN model. The value of the relative error in peak flow prediction was within reasonable limits for the model. The forecasts by the LN model at higher lead times (up to 6 hours) are found to be good. A relative evaluation of LN model performance with that of an ANN model and of a multiple linear regression model indicates that the LN model effectively combines the strength of the other two, implying that the LN model seems to be well suited to exploit the information to model the nonlinear dynamics of the rainfall-runoff process. Copyright 2006 by the American Geophysical Union.