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It is well established that sea surface temperature (SST) plays a significant role in the hydrologic cycle in which precipitation is the most important part. In this study, the influence of SST on Indian subdivisional monthly rainfall is investigated. Both spatial and temporal influences are investigated. The most influencing regions of sea surface are identified for different subdivisions and for different overlapping seasons in the year. The relative importance of SST, land surface temperature (LST) and ocean–land temperature contrast (OLTC) and their variation from subdivision to subdivision and from season to season are also studied. It is observed that LST does not show much similarity with rainfall series, but, in general, OLTC shows relatively higher influence in the pre‐monsoon and early monsoon periods, whereas SST plays a more important role in late‐ and post‐monsoon periods. The influence of OLTC is seen to be mostly confined to the Indian Ocean region, whereas the effect of SST indicates the climatic teleconnection between Indian regional rainfall and climate indices in Pacific and Atlantic Oceans. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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We explore the challenges of avalanche and debris flow hazard assessment for urban areas exposed in the Sakhalin region. Avalanches are a threat to more than 60 settlements in the region and debris flows to more than 30. Data are provided for avalanche and debris flow events that occurred in the Sakhalin region between 1928 and 2015. In this paper, the method for the design of hazard maps for snow avalanches and debris flows is described, providing the starting point for any planning constraints in general settlement planning schemes. These maps further allow conducting an assessment of avalanche and debris flow risk within a short time period for a larger territory and at minimum cost. 相似文献
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Forecasting of hydrologic time series, with the quantification of uncertainty, is an important tool for adaptive water resources management. Nonstationarity, caused by climate forcing and other factors, such as change in physical properties of catchment (urbanization, vegetation change, etc.), makes the forecasting task too difficult to model by traditional Box–Jenkins approaches. In this paper, the potential of the Bayesian dynamic modelling approach is investigated through an application to forecast a nonstationary hydroclimatic time series using relevant climate index information. The target is the time series of the volume of Devil's Lake, located in North Dakota, USA, for which it was proved difficult to forecast and quantify the associated uncertainty by traditional methods. Two different Bayesian dynamic modelling approaches are discussed, namely, a constant model and a dynamic regression model (DRM). The constant model uses the information of past observed values of the same time series, whereas the DRM utilizes the information from a causal time series as an exogenous input. Noting that the North Atlantic Oscillation (NAO) index appears to co‐vary with the time series of Devil's Lake annual volume, its use as an exogenous predictor is explored in the case study. The results of both the Bayesian dynamic models are compared with those from the traditional Box–Jenkins time series modelling approach. Although, in this particular case study, it is observed that the DRM performs marginally better than traditional models, the major strength of Bayesian dynamic models lies in the quantification of prediction uncertainty, which is of great value in hydrology, particularly under the recent climate change scenario. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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Sandeep Kar Jyoti Prakash Maity Jiin-Shuh Jean Chia-Chuan Liu Bibhash Nath Huai-Jen Yang Jochen Bundschuh 《Applied Geochemistry》2010,25(12):1805-1814
Hydrogeochemical characteristics and elemental features of groundwater and core sediments have been studied to better understand the sources and mobilization process responsible for As-enrichment in part of the Gangetic plain (Barasat, West Bengal, India). Analysis of water samples from shallow tubewells (depth 24.3–48.5 m) and piezometer wells (depth 12.2–79.2 m) demonstrate that the groundwater is mostly the Ca-HCO3 type and anoxic in nature (mean EhSHE = 34 mV). Arsenic concentrations ranged from <10–538 μg/L, with high concentrations only present in the shallow to medium depth (30–50 m) of the aquifer along with high Fe (0.07–9.8 mg/L) and relatively low Mn (0.15–3.38 mg/L) as also evidenced in core sediments. Most groundwater samples contained both As(III) and As(V) species in which the concentration of As(III) was generally higher than that of As(V), exhibiting the reducing condition. Results show lower concentrations of NO3, SO4 and NO2 along with higher values of DOC and HCO3, indicating the reducing nature of the aquifer with abundant organic matter that can promote the release of As from sediments into groundwater. Positive correlations of As with Fe and DOC were also observed. The presence of DOC may actively drive the redox processes. This study revealed that reduction processes of FeOOH was the dominant mechanism for the release of As into the groundwater in this part of the Ganges Delta plain. 相似文献
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Mampi Karmakar Saumen Maiti Amrita Singh Maheswar Ojha Bhabani Sankar Maity 《Marine Geophysical Researches》2018,39(3):407-419
Modeling and classification of the subsurface lithology is very important to understand the evolution of the earth system. However, precise classification and mapping of lithology using a single framework are difficult due to the complexity and the nonlinearity of the problem driven by limited core sample information. Here, we implement a joint approach by combining the unsupervised and the supervised methods in a single framework for better classification and mapping of rock types. In the unsupervised method, we use the principal component analysis (PCA), K-means cluster analysis (K-means), dendrogram analysis, Fuzzy C-means (FCM) cluster analysis and self-organizing map (SOM). In the supervised method, we use the Bayesian neural networks (BNN) optimized by the Hybrid Monte Carlo (HMC) (BNN-HMC) and the scaled conjugate gradient (SCG) (BNN-SCG) techniques. We use P-wave velocity, density, neutron porosity, resistivity and gamma ray logs of the well U1343E of the Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. While the SOM algorithm allows us to visualize the clustering results in spatial domain, the combined classification schemes (supervised and unsupervised) uncover the different patterns of lithology such of as clayey-silt, diatom-silt and silty-clay from an un-cored section of the drilled hole. In addition, the BNN approach is capable of estimating uncertainty in the predictive modeling of three types of rocks over the entire lithology section at site U1343. Alternate succession of clayey-silt, diatom-silt and silty-clay may be representative of crustal inhomogeneity in general and thus could be a basis for detail study related to the productivity of methane gas in the oceans worldwide. Moreover, at the 530 m depth down below seafloor (DSF), the transition from Pliocene to Pleistocene could be linked to lithological alternation between the clayey-silt and the diatom-silt. The present results could provide the basis for the detailed study to get deeper insight into the Bering Sea’ sediment deposition and sequence. 相似文献
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A series of experiments have been carried out in a developed liquid sloshing setup to estimate the pressure developed on the tank walls and the free surface displacement of water from the mean static level. The square tank attached to a shaking table can be moved to and fro by a cam arrangement driven by a DC motor. Pressure and displacement studies are done on the basis of changing excitation frequency of the shaking table and fill level in the tank. Experiments were carried out without and with baffles, and the consequent changes in the parameters are observed. 相似文献
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It is well recognized that the time series of hydrologic variables, such as rainfall and streamflow are significantly influenced by various large‐scale atmospheric circulation patterns. The influence of El Niño‐southern oscillation (ENSO) on hydrologic variables, through hydroclimatic teleconnection, is recognized throughout the world. Indian summer monsoon rainfall (ISMR) has been proved to be significantly influenced by ENSO. Recently, it was established that the relationship between ISMR and ENSO is modulated by the influence of atmospheric circulation patterns over the Indian Ocean region. The influences of Indian Ocean dipole (IOD) mode and equatorial Indian Ocean oscillation (EQUINOO) on ISMR have been established in recent research. Thus, for the Indian subcontinent, hydrologic time series are significantly influenced by ENSO along with EQUINOO. Though the influence of these large‐scale atmospheric circulations on large‐scale rainfall patterns was investigated, their influence on basin‐scale stream‐flow is yet to be investigated. In this paper, information of ENSO from the tropical Pacific Ocean and EQUINOO from the tropical Indian Ocean is used in terms of their corresponding indices for stream‐flow forecasting of the Mahanadi River in the state of Orissa, India. To model the complex non‐linear relationship between basin‐scale stream‐flow and such large‐scale atmospheric circulation information, artificial neural network (ANN) methodology has been opted for the present study. Efficient optimization of ANN architecture is obtained by using an evolutionary optimizer based on a genetic algorithm. This study proves that use of such large‐scale atmospheric circulation information potentially improves the performance of monthly basin‐scale stream‐flow prediction which, in turn, helps in better management of water resources. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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The regional climate model (RegCM4) is customized for 10-year climate simulation over Indian region through sensitivity studies on cumulus convection and land surface parameterization schemes. The model is configured over 30° E–120° E and 15° S–45° N at 30-km horizontal resolution with 23 vertical levels. Six 10-year (1991–2000) simulations are conducted with the combinations of two land surface schemes (BATS, CLM3.5) and three cumulus convection schemes (Kuo, Grell, MIT). The simulated annual and seasonal climatology of surface temperature and precipitation are compared with CRU observations. The interannual variability of these two parameters is also analyzed. The results indicate that the model simulated climatology is sensitive to the convection as well as land surface parameterization. The analysis of surface temperature (precipitation) climatology indicates that the model with CLM produces warmer (dryer) climatology, particularly over India. The warmer (dryer) climatology is due to the higher sensible heat flux (lower evapotranspiration) in CLM. The model with MIT convection scheme simulated wetter and warmer climatology (higher precipitation and temperature) with smaller Bowen ratio over southern India compared to that with the Grell and Kuo schemes. This indicates that a land surface scheme produces warmer but drier climatology with sensible heating contributing to warming where as a convection scheme warmer but wetter climatology with latent heat contributing to warming. The climatology of surface temperature over India is better simulated by the model with BATS land surface model in combination with MIT convection scheme while the precipitation climatology is better simulated with BATS land surface model in combination with Grell convection scheme. Overall, the modeling system with the combination of Grell convection and BATS land surface scheme provides better climate simulation over the Indian region. 相似文献
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In this study, the nature of basin‐scale hydroclimatic association for Indian subcontinent is investigated. It is found that, the large‐scale circulation information from Indian Ocean is also equally important in addition to the El Niño‐Southern Oscillation (ENSO), owing to the geographical location of Indian subcontinent. The hydroclimatic association of the variation of monsoon inflow into the Hirakud reservoir in India is investigated using ENSO and EQUatorial INdian Ocean Oscillation (EQUINOO, the atmospheric part of Indian Ocean Dipole mode) as the large‐scale circulation information from tropical Pacific Ocean and Indian Ocean regions respectively. Individual associations of ENSO & EQUINOO indices with inflow into Hirakud reservoir are also assessed and found to be weak. However, the association of inflows into Hirakud reservoir with the composite index (CI) of ENSO and EQUINOO is quite strong. Thus, the large‐scale circulation information from Indian Ocean is also important apart form the ENSO. The potential of the combined information of ENSO and EQUINOO for predicting the inflows during monsoon is also investigated with promising results. The results of this study will be helpful to water resources managers due to fact that the nature of monsoon inflow is becoming available as an early prediction. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献