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Estimation of terrestrial water budget at global and regional scales are essential for efficient agricultural water management, flood predictions, and, hydrological modeling. In hydrological modeling, it is a challenging task to quantify the major hydrological components like runoff, evapotranspiration (ET), and total water storage (TWS) due to improper and limited availability of detailed meteorological datasets. Furthermore, there has been no consensus to answer a-decade-long critical question that a less data-intensive models can be an alternate to robust data-intensive models in data scarce conditions. This study aims at multi-model approach over the single models usage for representing the hydrological behaviour in the Kangsabati River Basin (KRB), India. It is done by applying the standard model selection criteria over various hydrological models. Two hydrological models are selected, a semi- distributed model, Variable Infiltration Capacity (VIC-3 L), and a conceptually lumped model, Identification of unit Hydrograph and Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES). Both models were calibrated against the observed daily discharge at the KRB outlet for the period of 2001–2006 and validated for 2008–2010. The results show that both VIC-3 L and IHACRES produce reasonable runoff estimates at daily and monthly time scale in the KRB. The ET estimates show that VIC-3 L and IHACRES captured the seasonal variations with the percent change of 0.4% and 6.6% respectively. As IHACRES is simpler, parsimonious, fewer parameters, and better performances, it can be useful for hydrological modeling in data-scarce regions.
相似文献Hydrological responses corresponding to the agricultural land use alterations are critical for planning crop management strategies, water resources management, and environmental evaluations. However, accurate estimation and evaluation of these hydrological responses are restricted by the limited availability of detailed crop classification in land use and land cover. An innovative approach using state-of-the-art Variable Infiltration Capacity (VIC) model is utilized by setting up the crop-specific vegetation parameterization and analyse the effect of uniform and heterogeneous agricultural land use over the hydrological responses of the basin, in the Kangsabati River Basin (KRB). Thirteen year simulations (1998–2010) based on two different scenarios i.e., single-crop in agricultural land use (SC-ALU) and multi-crop in agricultural land use (MC-ALU) patterns are incorporated in the model and calibrated (1998–2006) and validated (2007–2010) for the streamflow at Reservoir and Mohanpur in the KRB. The results demonstrated that the VIC model improved the estimates of hydrological components, especially surface runoff and evapotranspiration (ET) at daily and monthly timescales corresponding to MC-ALU than SC-ALU (NSC?>?0.7). Grid-scale ET estimates are improved after incorporating heterogeneous agricultural land use (NSC?>?0.55 and R2?>?0.55) throughout the period of 1998–2010. This study improves our understanding on how the change in agricultural land use in the model settings alters the basin hydrological characteristics, and to provide model-based approaches for best management practices in irrigation scheduling, crop water requirement, and management strategies in the absence of flux towers, eddy covariance, and lysimeters in the basin.
相似文献Medical images are more typical than any other ordinary images, since it stores patient’s information for diagnosis purpose. Such images need more security and confidentiality as total diagnosis depends on it. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data. The embedded data is scrambled using a unique key and then a transform domain based hybrid watermarking technique is used to embed the scrambled data into the transform coefficients of the host image. The data embedding in medical images involves more attention, so that the diagnosis part must not be affected by any modification. Therefore, Support Vector Machine (SVM) is used as a classifier, which classify a medical image into two regions i.e. Non Region of Interest (NROI) and Region of Interest (ROI) to embed watermark data into the NROI part of the medical image, using the proposed embedding algorithm. The objective of the proposed model is to avoid any quality degradation to the medical image. The simulation is performed to measure the Peak Signal to Noise Ratio (PSNR) for imperceptibility and Structural Similarity Index (SSIM) to test the robustness. The experimented result shows, robustness and imperceptibility with SSIM of more than 0.50 and PSNR of more than 35 dB for proposed watermarking model.
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