Evaluation of indicators for desertification risk assessment in part of Thar Desert Region of Rajasthan using geospatial techniques

Desertification has emerged as a major economic, social and environmental problem in the western part of India. The best way of dealing with desertification is to take appropriate measures to arrest land degradation, especially in areas prone to desertification. This requires an early warning system for desertification based on scientific inputs. Hence, in the present study, an attempt has been made to develop a comprehensive model for the assessment of desertification risk in the Jodhpur district of Rajasthan, India, using 23 desertification indicators. Indicators including soil, climate, vegetation and socio-economic parameters were integrated into a GIS environment to get environmental sensitive areas (ESAs) to desertification. Desertification risk index (DRI) was calculated based on ESAs to desertification, the degree of land degradation and significant desertification indicators obtained from the stepwise multiple regression model. DRI was validated by using independent indicators such as soil organic matter content and cation exchange capacity. Multiple regression analysis shows that 16 indicators out of 23 were found to be significant for assessing desertification risk at a 99% confidence interval with \(R^{2}=0.83\). The proposed methodology provides a series of effective indicators that would help to identify where desertification is a current or potential problem, and what could be the actions to alleviate the problem over time.     

Parameter estimation of SWAT and quantification of consequent confidence bands of model simulations

Soil and Water Assessment Tool (SWAT) is a river basin scale model widely used to study the impact of land management practices in large, complex watersheds. Even though model output uncertainties are generally recognized to affect watershed management decisions, those uncertainties are largely ignored in model applications. The uncertainties of SWAT simulations are quantified using various methods, but simultaneous attempt to calibrate a model so as to reduce the uncertainty are seldom done. This study aims to use an uncertainty reduction procedure that helps calibrate the SWAT model. The shuffled complex evolutionary metropolis algorithm for uncertainty analysis is employed for this purpose, and is demonstrated using the data from the St. Joseph River basin, USA. The values of the performance indices, the r² and the Nash–Sutcliffe efficiency (NSE) for the simulations during calibration period was found to be 0.81 (same for r² and NSE) and 0.79 for validation period indicating a good simulation by the model. The results also indicate that the algorithm helps reduce the uncertainty (percentage of coverage?=?62% and average width?=?19.2 m³/s), and also identifies the plausible range of parameters that simulate the processes with less uncertainty. The confidence bands of simulations are obtained that can be employed in making uncertainty-based decisions on watershed management practices.     

Hydrochemical classification and multivariate statistical analysis of groundwater from Wadi Sahba area in central Saudi Arabia

The central region of Saudi Arabia is underlain by thick sedimentary formations belonging to the Mesozoic and Cenozoic era. These sedimentary formations form a prolific aquifer supplying groundwater for agricultural and domestic usage in and around Riyadh. The region south of Riyadh City is well known for agricultural activities. Wadi Sahba, which is an eastward extension of Wadi Nisah, has readily available groundwater resources in the Cretaceous Biyadh sandstone aquifer to sustain agricultural activities. The objective of the present study was the hydrochemical assessment of groundwater in the area to understand the main hydrological processes which influence groundwater chemistry. To achieve this objective, 20 groundwater samples were collected from agricultural farms in the Wadi Sahba in central Saudi Arabia, and the major physiochemical constituents were analyzed and interpreted. The average TDS value of the analyzed samples is 1578.05 mg/l, whereas the average EC concentration is 3220.05 μS/cm. Groundwater facies classification inferred from the Piper plot shows that groundwater in the study area belongs to the Ca-SO₄-Cl type and Ca-Na-SO₄-Cl type. The Ca-SO₄-Cl type of groundwater facies is influenced mainly by gypsum dissolution and base ion exchange, whereas the Ca-Na-SO₄-Cl type is influenced by gypsum and halite dissolution. All the groundwater samples are undersaturated with respect to these two principal mineral phases. The Q-mode cluster analysis results in two main groups of groundwater samples, mainly based on the TDS content. Cluster 1 has an average TDS value of 1980 mg/l, whereas cluster 2 has an average TDS of 1176 mg/l. The groundwater facies identified through the Piper plot reflects the major hydrological processes controlling groundwater chemistry in the area and was found to be more useful in this study as compared to cluster analysis.     

The probability distributions and fractal dimension of sunspot cycles associated with ENSO phenomena

Various methods have been used to secure the certainty of significant relations among the sunspot cycles and some of the terrestrial climate parameters such as temperature, rainfall, and ENSO. This study investigates the behavior of ENSO cycles and mean monthly sunspot cycles. Sunspot cycles range from 1755 to 2016 whereas, ENSO cycles range from 1866 to 2012. In this regard, the appropriateness of distributions is investigated with the help of Kolmogorov-Smirnov D, Anderson-Darling, and chi-square tests. It is found that most of the sunspot cycle follows generalized Pareto distribution whereas, generalized extreme value distribution was found appropriate for ENSO cycles. Probability distribution is used to analyze the behavior of each sunspot cycle and ENSO cycle separately. Probability distribution indicates the tail behavior of each cycle; tail explored correlation cycles. Furthermore, self-similar and self-affine fractal dimension methods are used to compute Hurst exponents to determine the persistency of the available data. Fractal dimension has an ability to study the complexity involved in sunspot and ENSO cycles. The fractal dimension and Hurst exponent describe persistency (smoothness) and complexity of data. Hurst exponent measures long-term behavior of time series, making it more helpful for forecasting. This is the measure of regularity or irregularity (chaos) of the time function in the form of their persistency or anti-persistency, respectively. Hurst exponents are computed using rescaled range analysis method and box counting methods. Both these methods are suitable for long-term forecasting. The results of this study confirm that during the period 1980–2000, ENSO cycles were very active. Simultaneously, ENSO was active for the periods 1982–1983, 1986–1987, 1991–1993, 1994–1995, and 1997–1998; these periods include two strongest periods of the century viz., 1982–1983 and 1997–1998. Sunspot cycles and ENSO cycles both were found to be persistent. Self-similar fractal dimensions exhibited a better persistency and a better correlation as compared to self-affine fractal dimension. This research is a part of a larger research project investigating the correlation of sunspot cycles and ENSO cycles, and the influence of ENSO cycles on variations of the local climatic parameters which in turn depends on solar activity changes.     

Effect of Forest Fire on Snow Reflectance in Uttarakhand Region: An Analysis Using AWiFS and INSAT-3D Data

In the summer of 2016, fire broke out in the forests of Uttarakhand state. The effect of forest fire was thought to have severe impact on nearby glaciated region in terms of faster melting. It is understood that contamination and heat enhance the snowmelt, which reduces the reflectance in different part of EM spectrum. In order to assess the effect, AWiFS and IMAGER data from Resourcesat-1 and INSAT-3D, respectively, for the months of April and May of the years 2012, 2013, 2014 and 2016 were used to compare the reflectance of snow. It was observed that pre- and post-fire data of 2016 show drop in reflectance in comparison with earlier years. The change in reflectance for locations near to forest fire was significantly high in comparison with previous year images, whereas far-away locations did not show much change. This drastic drop may be attributed to deposition of black carbon on nearby locations in snow-covered area. AWS data of nearby Joshimath observatory were also analysed to avoid any anomalous change in temperature for the same duration.     

Site response analysis for deep and soft sedimentary deposits of Dhaka City,Bangladesh

Natural Hazards - The ground motion intensity of an earthquake is significantly changed when seismic waves propagate from the bedrock to the near-surface soft geological materials. The ground where...     

Bianchi Type I string dust cosmological model with magnetic field in general relativity

Bianchi Type I string dust cosmological models in presence and absence of magnetic field following the techniques used by Letelier and Stachel, are investigated. To get the deterministic solution, we have assumed that σ ₁¹ is proportional to the expansion (θ) where σ ₁¹ is the eigen value of shear tensor (σ _i ^j ) and which leads to A=N(BC)⁻ⁿ , n>0 where A,B,C are metric potentials and , N and ℓ are constants. The behaviour of the models in presence and absence of magnetic field are discussed. The other physical and geometrical aspects of the model are also discussed.