The Global Paleomonsoon as seen through speleothem records from Asia and the Americas

The regional monsoons of the world have long been viewed as seasonal atmospheric circulation reversal—analogous to a thermally-driven land-sea breeze on a continental scale. This conventional view of monsoons is now being integrated at a global scale and accordingly, a new paradigm has emerged which considers regional monsoons to be manifestations of global-scale seasonal changes in response to overturning of atmospheric circulation in the tropics and subtropics, and henceforth, interactive components of a singular Global Monsoon (GM) system. The paleoclimate community, however, tends to view ‘paleomonsoon’ (PM), largely in terms of regional circulation phenomena. In the past decade, many high-quality speleothem oxygen isotope (δ¹⁸O) records have been established from the Asian Monsoon and the South American Monsoon regions that primarily reflect changes in the integrated intensities of monsoons on orbital-to-decadal timescales. With the emergence of these high-resolution and absolute-dated records from both sides of the Equator, it is now possible to test a concept of the ‘Global-Paleo-Monsoon’ (GPM) on a wide-range of timescales. Here we present a comprehensive synthesis of globally-distributed speleothem δ¹⁸O records and highlight three aspects of the GPM that are comparable to the modern GM: (1) the GPM intensity swings on different timescales; (2) their global extent; and (3) an anti-phased inter-hemispheric relationship between the Asian and South American monsoon systems on a wide range of timescales.     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Prediction of SST anomalies of east Pacific Ocean (Nino 3 region) using a statistical model

Summary  Average SST anomalies of OCT-DEC months for Nino-3 region are predicted using the following parameters – (i) April rain over Himachal Pradesh, (ii) Darwin pressure change (January–April), (iii) Southern Oscillation Index (Tahiti–Darwin) and (iv) SST anomalies of Nino-3 region in the month of May. Principal component analysis is used to orthogonalise the predictors before using them in the regression equation. The first two principal components, which explain nearly 73% of the variance, are used to fit a regression line. The period 1951–1985 is used as the calibration period for the model and the period 1986–1997 as the verification period for the forecast. The Brier score with respect to a reference forecast (persistence) for the independent period is found to be 0.82 which is indicative of good forecast skill. Received April 1, 1999 Revised January 17, 2000     

Integrated approach for demarcating the fracture zone for well site location : A case study near Gumla and Lohardaga,Bihar

In recent years satellite remote sensing techniques have greatly aided identification of fractures/joints/faults in connection with groundwater exploration. However, due to some constraints in filtering out the type of fracture i.e. shear fracture, tensional fracture, mineralised fracture etc. as to their “open or closed” nature from groundwater aspects, there is need of adding geophysical survey, particularly resistivity survey to further enhance the accuracy, and hence minimise drilling failures. The area under study is a part of Ranchi plateau and lithologically comprising of granite-gneiss. The present paper lays stress on an integrated approach for localising well sites through satellite data analysis and resistivity profiling along with vertical electrical sounding which is based on fracture patterns. With this combined effort i.e. firstly considering the surfacial features like drainage, geomorpho-logy, lithology and lineaments, and secondly taking into account geophysical survey aspects, i.e. depth and thickness of fracture zone, lateral extent, different layer parameter, etc., fairly accurate results are achieved. In the presnt study this type of an integrated survey has been carried out in Lohardaga and Gumla districts in Bihar, the results of which are highlighted in the paper.     

Validating the use of object-based image analysis to map commonly recognized landform features in the United States

ABSTRACT

The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is currently limited to 2D point locations. Geographic object-based image analysis (GEOBIA) was determined to be a promising method to approach both goals. An existing GEOBIA workflow was modified and the resulting segmented objects and terrain categories tested for a strategically chosen physiographic province in the mid-western US, the Ozark Plateaus. The chi-squared test of independence confirmed that there is significant overall spatial association between terrain categories of the GEOBIA and GNIS feature classes. Contingency table analysis also suggests strong category-specific associations between select GNIS and GEOBIA classes. However, 3D visual analysis revealed that GEOBIA objects resembled segmented regions more than they did individual landform objects, with their boundaries often failing to correspond to match what people would likely perceive as landforms. Still, objects derived through GEOBIA can provide initial baseline landscape divisions that can improve the efficiency of more specialized feature extraction methods.     

Application of semi‐distributed hydrological model for basin level water balance of the Ken basin of Central India

In the present study, a semi‐distributed hydrological model soil and water assessment tool (SWAT) has been employed for the Ken basin of Central India to predict the water balance. The entire basin was divided into ten sub basins comprising 107 hydrological response units on the basis of unique slope, soil and land cover classes using SWAT model. Sensitivity analysis of SWAT model was performed to examine the critical input variables of the study area. For Ken basin, curve number, available water capacity, soil depth, soil evaporation compensation factor and threshold depth of water in the shallow aquifer (GWQ_MN) were found to be the most sensitive parameters. Yearly and monthly calibration (1985–1996) and validation (1997–2009) were performed using the observed discharge data of the Banda site in the Ken basin. Performance evaluation of the model was carried out using coefficient of determination, Nash–Sutcliffe efficiency, root mean square error‐observations standard deviation ratio, percent bias and index of agreement criterion. It was found that SWAT model can be successfully applied for hydrological evaluation of the Ken basin, India. The water balance analysis was carried out to evaluate water balance of the Ken basin for 25 years (1985–2009). The water balance exhibited that the average annual rainfall in the Ken basin is about 1132 mm. In this, about 23% flows out as surface run‐off, 4% as groundwater flow and about 73% as evapotranspiration. Copyright © 2013 John Wiley & Sons, Ltd.     

A cascade approach to continuous rainfall data generation at point locations

High-resolution temporal rainfall data sequences serve as inputs for a range of applications in planning, design and management of small (especially urban) water resources systems, including continuous flow simulation and evaluation of alternate policies for environmental impact assessment. However, such data are often not available, since their measurements are costly and time-consuming. One alternative to obtain high-resolution data is to try to derive them from available low-resolution information through a disaggregation procedure. This study evaluates a random cascade approach for generation of high-resolution rainfall data at a point location. The approach is based on the concept of scaling in rainfall, or, relating the properties associated with the rainfall process at one temporal scale to a finer-resolution scale. The procedure involves two steps: (1) identification of the presence of scaling behavior in the rainfall process; and (2) generation of synthetic data possessing same/similar scaling properties of the observed rainfall data. The scaling identification is made using a statistical moment scaling function, and the log–Poisson distribution is assumed to generate the synthetic rainfall data. The effectiveness of the approach is tested on the rainfall data observed at the Sydney Observatory Hill, Sydney, Australia. Rainfall data corresponding to four different successively doubled resolutions (daily, 12, 6, and 3 h) are studied, and disaggregation of data is attempted only between these successively doubled resolutions. The results indicate the presence of multi-scaling behavior in the rainfall data. The synthetic data generated using the log–Poisson distribution are found to exhibit scaling behaviors that match very well with that for the observed data. However, the results also indicate that fitting the scaling function alone does not necessarily mean reproducing the broader attributes that characterize the data. This observation clearly points out the extreme caution needed in the application of the existing methods for identification of scaling in rainfall, especially since such methods are also prevalent in studies of the emerging satellite observations and thus in the broader spectrum of hydrologic modeling.     

On conformity of the EEJ based ionospheric model to the fountain effect and resulting improvements

The total electron content (TEC) of the equatorial ionosphere is controlled by photochemical processes as well as the transport of the ionospheric plasma near the magnetic equator. The transport phenomenon is initiated by the vertical drift driven by the eastward electric field, which also drives the Equatorial Electrojet. The empirical relation between the Equatorial Electrojet and the anomaly component of the equatorial TEC has already been established. Taking this relation as a reference, a simplified physical model of the anomaly component of equatorial TEC is obtained as a function of Equatorial Electrojet. Influence of other factors like the solar incidence angle and the solar flux are also considered here and the extent of their influence are also investigated. This has been done using TEC data obtained from dual frequency GPS receivers during the low solar activity period of 2005. The derived model is based on the physics of the underlying fountain effect and matches with the observed empirical relation to a fair extent. Obtained results are found to corroborate with previous findings and these physical model values are found to have improved correlation with the observed data than the reference empirical relation. This establishes the conformity between the EEJ based ionospheric model and the physical phenomenon of the fountain effect.     

Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass

Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R² = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R² = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R² = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.     

Observations of PC5 pulsations near field-aligned current regions

Examples of long period Pc5 magnetic field pulsations near field-aligned current (FAC) regions in the high-latitude magnetosphere, observed by INTERBALL-Auroral satellite during January 11, April 11 and June 28, 1997 are shown. Identification of corresponding magnetosphere regions and subregions is provided by electrons and protons in the energy-range of 0.01–100 keV measured simultaneously onboard the spacecraft. The examined Pc5 pulsations reveal a compressional character. A fairly good correlation is demonstrated between these ULF Pc5 waves and the consecutive injection of magnetosheath low energy protons. The ULF Pc5 wave occurrence is observed in both upward and downward FACs.