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Knowledge of drought onset and its relationship with drought severity (deficit volume) is crucial for providing timely information for reservoir operations, irrigation scheduling, devising cropping choices and patterns and managing surface and groundwater water resources. An analysis of the relationship between drought onset timing and deficit volume can help in drought hazard assessments and associated risks. Despite its importance, little attention has been paid to understand the drought onset timing and its potential linkage with deficit volume for effective drought monitoring and its impact assessment. Further, only a few studies have explored the role of environmental controls, encompassing the interaction between climate, catchment and land‐surface processes in influencing streamflow droughts and associated characteristics such as onset time and severity. This study leverages quality‐controlled streamflow observations from 1965 to 2018 to unveil regional patterns of streamflow drought onset, at‐site trends in deficit volume and detect non‐linear relationships between onset timing and deficit volume across 82 rain‐fed catchments in peninsular India (8°–24° N, 72°–87° E). We show that around 12% of catchments show an earlier onset of streamflow droughts in conjunction with a decreasing trend in deficit volume. Further, approximately one‐third of the catchments show a significant non‐linear dependency between drought deficit volume and onset time. Among catchment controls, such as soil and topographic properties, we found soil organic carbon stock and stock as dominant drivers controlling the streamflow drought onset time. Likewise, sand content and vertical distance to channel network control the streamflow deficit volume. Finally, the linkages between inferred dominant low‐flow generation mechanisms and the specific combinations of environmental controls are synthesized in a conceptual diagram that might assist in developing appropriate models for low‐flow simulations and predictions, especially across ungauged sites. The new insights add value to understanding the chain of physical processes linking climatic and physiographic controls on streamflow droughts, which can support drought forecasting and climate impact assessment efforts. [ABSTRACT FROM AUTHOR]

In this study, the gross calorific value (GCV) of coal has been estimated using two types of coal analysis data. These are proximate data and spectral reflectance data in visible to near infrared range (wavelength: 400–2500 nm). The present study aims in developing two GCV prediction models and compare the accuracy of the predicted values using those models with the experimental value. Multiple linear regression (MLR) method is used to develop these models. In the first model, different properties of coal such as ash, moisture, and volatile matter contents are correlated with the GCV. Whereas in the second model, the GCV is predicted from the maximum absorption band depth at five different wavelength ranges. The performance accuracy has been evaluated using R2, RMSE, and MAPE values of the two regression models. These two models yield R2, RMSE, and MAPE values of 0.84, 2.26, 5.24 and 0.92, 1.6, 4.84, respectively. It has been concluded that the GCV predicted from the spectral reflectance data provides better accuracy than those predicted from the proximate data. Therefore, hyperspectral sensor based technology could be used for rapid determination of the GCV of coal with greater accuracy. [ABSTRACT FROM AUTHOR]

Evaluation of flow and transport processes in a watershed-scale requires that the watershed be divided into homogenous spatial units referred to as hydrologically similar units (HSUs). Although a few discretization schemes are already in use, a universally acceptable method of obtaining HSUs is yet to emerge. In this study, we developed a fuzzy inference system (FIS) to classify the saturated hydraulic conductivity ( K) and two water-retention parameters α and n into fuzzy logic-based soil hydrologic classes (FSHCs). Analysis of these classes showed that soil properties within an FSHC have less variability and those between two FSHCs have large variability. This result suggested that soils belonging to a specific FSHC may be more similar than those across different FSHCs and may be grouped together to represent an HSU. Soils within a specific hydrologic class were aggregated to delineate HSUs within the watershed. For the Dengei Pahad micro-watershed (DPW), this approach showed five distinct regions representing a discretized zone having similar soil hydraulic properties. Application of this approach on a larger international database of soil hydraulic properties revealed that the developed hydrologic classes are quite comparable across different databases. The delineated HSUs based on these FSHCs were also better than the soil series map of the watershed in maintaining the soil heterogeneity of the watershed. Moreover, this new discretization scheme using the SWAT modelling environment showed better performance than the soil series-based discretization approach. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]