排序方式： 共有37条查询结果，搜索用时 421 毫秒
11.
12.
Ocean Dynamics - The performance of three different mixing schemes implemented in the HYbrid Coordinate Ocean Model (HYCOM), namely, K-Profile Parameterization (KPP), Goddard Institute of Space... 相似文献
13.
Afroosa M. Rohith B. Paul Arya Durand Fabien Bourdallé-Badie Romain Joseph Sudheer Prerna S. Shenoi S. S. C. 《Ocean Dynamics》2022,72(7):523-538
Ocean Dynamics - An intraseasonal see-saw has been observed in the Indo-Pacific barotropic sea level anomaly during boreal winters. This see-saw carries a significant amount of energy and is... 相似文献
14.
P. Athira K. P. Sudheer R. Cibin I. Chaubey 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(4):1131-1149
Regionalization of model parameters by developing appropriate functional relationship between the parameters and basin characteristics is one of the potential approaches to employ hydrological models in ungauged basins. While this is a widely accepted procedure, the uniqueness of the watersheds and the equifinality of parameters bring lot of uncertainty in the simulations in ungauged basins. This study proposes a method of regionalization based on the probability distribution function of model parameters, which accounts the variability in the catchment characteristics. It is envisaged that the probability distribution function represents the characteristics of the model parameter, and when regionalized the earlier concerns can be addressed appropriately. The method employs probability distribution of parameters, derived from gauged basins, to regionalize by regressing them against the catchment attributes. These regional functions are used to develop the parameter characteristics in ungauged basins based on the catchment attributes. The proposed method is illustrated using soil water assessment tool model for an ungauged basin prediction. For this numerical exercise, eight different watersheds spanning across different climatic settings in the USA are considered. While all the basins considered in this study were gauged, one of them was assumed to be ungauged (pseudo-ungauged) in order to evaluate the effectiveness of the proposed methodology in ungauged basin simulation. The process was repeated by considering representative basins from different climatic and landuse scenarios as pseudo-ungauged. The results of the study indicated that the ensemble simulations in the ungauged basins were closely matching with the observed streamflow. The simulation efficiency varied between 57 and 61 % in ungauged basins. The regional function was able to generate the parameter characteristics that were closely matching with the original probability distribution derived from observed streamflow data. 相似文献
15.
M. Sudheer Kumar Ratnakar Dhakate G. Yadagiri K. Srinivasa Reddy 《Arabian Journal of Geosciences》2017,10(4):83
In order to assess the impact of fluoride-rich groundwater of Shasilair Vagu watershed on groundwater regime, more than hundred groundwater samples for pre- and post-monsoon seasons were collected from bore wells/dug wells and analyzed for major ions. Water quality analysis of major ion chemistry shows elevated concentration of fluoride in groundwater samples. The fluoride concentration ranges from 1.4 to 5.9 mg/l and 1.5 to 5.8 mg/l in pre- and post-monsoons, respectively. The result clearly shows that the seasonal variation of fluoride in groundwater is due to recharge of rain water during monsoon. The water quality data was analyzed by hydrochemical facies (Piper diagram), Gibbs plot, and various plots. Plots of Na versus Cl, Ca versus SO4, and (Na+Cl)-(SO4+HCO3) versus (Na+K-Cl) shows positive and negative values, indicating that their source of high concentration are aquifer, evapotranpiration, and other anthropogenic sources. Saturation index of halite and gypsum shows that all groundwater samples were undersaturated and suggests that carbonate minerals influence the concentration. Using multivariate statistical techniques, viz., principal component (factor analysis and cluster analysis), the analysis brought out impact of intensity of excess use of fertilizers and excess withdrawal of groundwater regime. Multivariate statistical techniques are potential tools and provide greater precision for identifying contaminant parameter linkages. 相似文献
16.
K.?S.?KasiviswanathanEmail author K.?P.?Sudheer 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(7):1659-1670
Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement. 相似文献
17.
D. Sudheer Reddy 《Journal of the Indian Society of Remote Sensing》2010,38(4):664-669
In the applications of remote-sensing it is a common task of finding out the overlap area of coverage between two images. There are several methods available to find overlap area of varying time complexities. In this paper a method based on Monte Carlo approach is presented along with an algorithm to find common area using only corner coordinate information of the images. This method take less time than compared to the image matching methods via correlations when complete images are given. Further, this algorithm facilitate finding optimal pairs(automatically) that can be mosaicked depending on the overlap area requirement. Another simplest and considerably fast algorithm is also elaborated for evaluation. A comparison of both methods is done with a sample of Cartosat-2 images and the results are presented. 相似文献
18.
Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty 
下载免费PDF全文
下载免费PDF全文 Stream flow predictions in ungauged basins are one of the most challenging tasks in surface water hydrology because of nonavailability of data and system heterogeneity. This study proposes a method to quantify stream flow predictive uncertainty of distributed hydrologic models for ungauged basins. The method is based on the concepts of deriving probability distribution of model's sensitive parameters by using measured data from a gauged basin and transferring the distribution to hydrologically similar ungauged basins for stream flow predictions. A Monte Carlo simulation of the hydrologic model using sampled parameter sets with assumed probability distribution is conducted. The posterior probability distributions of the sensitive parameters are then computed using a Bayesian approach. In addition, preselected threshold values of likelihood measure of simulations are employed for sizing the parameter range, which helps reduce the predictive uncertainty. The proposed method is illustrated through two case studies using two hydrologically independent sub‐basins in the Cedar Creek watershed located in Texas, USA, using the Soil and Water Assessment Tool (SWAT) model. The probability distribution of the SWAT parameters is derived from the data from one of the sub‐basins and is applied for simulation in the other sub‐basin considered as pseudo‐ungauged. In order to assess the robustness of the method, the numerical exercise is repeated by reversing the gauged and pseudo‐ungauged basins. The results are subsequently compared with the measured stream flow from the sub‐basins. It is observed that the measured stream flow in the pseudo‐ungauged basin lies well within the estimated confidence band of predicted stream flow. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
19.
A hybrid model that blends two non‐linear data‐driven models, i.e. an artificial neural network (ANN) and a moving block bootstrap (MBB), is proposed for modelling annual streamflows of rivers that exhibit complex dependence. In the proposed model, the annual streamflows are modelled initially using a radial basis function ANN model. The residuals extracted from the neural network model are resampled using the non‐parametric resampling technique MBB to obtain innovations, which are then added back to the ANN‐modelled flows to generate synthetic replicates. The model has been applied to three annual streamflow records with variable record length, selected from different geographic regions, namely Africa, USA and former USSR. The performance of the proposed ANN‐based non‐linear hybrid model has been compared with that of the linear parametric hybrid model. The results from the case studies indicate that the proposed ANN‐based hybrid model (ANNHM) is able to reproduce the skewness present in the streamflows better compared to the linear parametric‐based hybrid model (LPHM), owing to the effective capturing of the non‐linearities. Moreover, the ANNHM, being a completely data‐driven model, reproduces the features of the marginal distribution more closely than the LPHM, but offers less smoothing and no extrapolation value. It is observed that even though the preservation of the linear dependence structure by the ANNHM is inferior to the LPHM, the effective blending of the two non‐linear models helps the ANNHM to predict the drought and the storage characteristics efficiently. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
20.
Many of the continuous watershed models perform all their computations on a daily time step, yet they are often calibrated at an annual or monthly time-scale that may not guarantee good simulation performance on a daily time step. The major objective of this paper is to evaluate the impact of the calibration time-scale on model predictive ability. This study considered the Soil and Water Assessment Tool for the analyses, and it has been calibrated at two time-scales, viz. monthly and daily for the War Eagle Creek watershed in the USA. The results demonstrate that the model's performance at the smaller time-scale (such as daily) cannot be ensured by calibrating them at a larger time-scale (such as monthly). It is observed that, even though the calibrated model possesses satisfactory ‘goodness of fit’ statistics, the simulation residuals failed to confirm the assumption of their homoscedasticity and independence. The results imply that evaluation of models should be conducted considering their behavior in various aspects of simulation, such as predictive uncertainty, hydrograph characteristics, ability to preserve statistical properties of the historic flow series, etc. The study enlightens the scope for improving/developing effective autocalibration procedures at the daily time step for watershed models. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献