Browsing by Author "Singh, Shailesh Kumar"

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Open Access
Enhancing hydrological model calibration through hybrid strategies in data‐scarce regions
(2024) Anand, Vicky; Oinam, Bakimchandra; Wieprecht, Silke; Singh, Shailesh Kumar; Srinivasan, Raghavan
Calibration of a hydrological model is a challenging task, especially in basins that are data scarce. With the incorporation of regional information and integration with satellite data, the parameters of hydrological models can be estimated for a basin with scant or no discharge records. The main objective of this study is to calibrate and validate a hydrological model based on a limited amount of in‐situ measured and remote sensing satellite datasets in a data‐sparse region. Multiple techniques were applied for the model calibration: (1) stage‐discharge curves using a spatial proximity approach, (2) Simplified Surface Energy Balance actual evapotranspiration, (3) river discharge using a physical similarity regionalization approach, and (4) a new hybrid approach by integrating remote sensing datasets along with field measured river bathymetry data to estimate the river discharge. To demonstrate the methodology, we employed the widely used Soil and Water Assessment Tool (SWAT) hydrological model in Manipur River Basin, India. The sensitivity, calibration, and validation of the SWAT model were carried out by using the Sequential Uncertainty Fitting Technique. During calibration, the coefficient of determination (R2) and the Kling Gupta Efficiency (KGE) were found to be in the range of 0.46-0.81 and 0.41-0.83, whereas during validation R2 and KGE were found to be in the range of 0.40-0.79 and 0.53-0.77 for the four different techniques. Among all the four techniques applied in this study, calibration based on (i) stage‐discharge curve using spatial proximity approach and (ii) new hybrid approach by integrating remote sensing datasets and river bathymetry were found as the better approaches as indicated by the statistical indices. The performance evaluation of the model through a new hybrid approach by integrating remote sensing and in‐situ measured datasets for rivers with narrow width represents a promising technique for use in a data sparse region.
Open Access
Robust parameter estimation in gauged and ungauged basins
(2010) Singh, Shailesh Kumar; Bárdossy, András ( Prof. Dr. rer. nat. Dr.-Ing. habil. )
Hydrological modeling has become a widely accepted theoretical tool for water resources engineering and management. Rainfall-runoff models are used both for short and medium time management (for example flood forecasting) and long-time design purposes. However, the application of hydrological models is limited due to several reasons. One important limitation is imposed by the availability of data and parameter estimation. Discharges are only measured at a few selected river cross sections, leading to a small number of catchments for which the runoff calculated from the models might be verified. Further, the high spatial and temporal variability of the meteorological input (such as precipitation, temperature or wind) cannot fully be captured by the usually small number of meteorological stations. Radar measurement of precipitation can provide more detailed space time information on precipitation but unfortunately the reliability of the data is at present still low. Other influencing factors such as soil properties also vary considerably in space and even to some extent in time (for example macropores in soils). These problems among others make models which are based on physical principles only infeasible for many practical applications. Models which to some extent use analogous concepts can partly smoothen out the effects of variability and thus can often be successfully used for practical purposes. The limitation of these models lies in the fact that some of their parameters are not directly related to physically measurable quantities. Therefore those have to be estimated from observations using calibration techniques. This research work was aimed at developing an efficient, practical and robust methodology for parameter estimation (calibration) for a reliable hydrological modeling at gauged and ungauged basin. The main focus of this research was to bring more insight into the process of parameter estimation techniques in hydrological modeling. The other objective of this research work was to develop a methodology that enables regional estimation of parameters of a conceptual continuous water balance model based on physical catchment descriptor, which includes the land use, soil type, stream network, elongation and topographic attributes of the catchment. It aims at improving the weakness inherent in the traditional two-step regionalization approach in estimating the relationship between the model parameters and the physical catchment descriptor. The specific objectives of the research were to answer some basic question as listed below: - How can we estimate hydrologically reliable parameters for modeling? - How do different objective functions map parameter space during calibration? - Can we calibrate a hydrological model using carefully selected critical events? - Can we improve prediction and model diagnosis by including dynamic variability in parameters? - How can we extend hydrologically reliable parameters from gauged to ungauged basins? In this research, several algorithms, for example, ROPE, SRWP, HOP, ICE, RDPE and SAV algorithm were developed to answer the basic questions mentioned above. These algorithms were very useful for the robust and reliable hydrological modeling in gauged and ungauged basins.