Stereo Cartosat-1 satellite remote sensing data in assessing topographic potential of soil erosion

Stereo Cartosat-1 satellite data was processed to generate high spatial resolution digital elevation model (DEM) using ground control points (GCPs) collected through geodetic single frequency GPS in differential GPS mode. DEM was processed to generate bare earth DEM by removing heights of natural and man made features from DEM. The bare earth DEM was further analysed in GIS environment to generate terrain-topographic indices viz. wetness index (WI), stream power index (SPI) and sediment transport index (STI) to characterize topographic potential of soil erosion. Hillslopes in the studied watershed (part of Shiwalik hills of Dehradun district, Uttarakhand state) were characterized as low wetness index values indicating dry areas whereas high wetness index values at lower reaches of the watershed indicating as possible source areas for generation of saturated overland flow. Higher STI values were observed in hilly as well as upper part of the piedmont plain and at along sides of the streams in upper piedmont indicating areas susceptible to severe soil erosion. GIS based these topographic indices provided an easy and quick appraisal and scientific basis to identify spatial variability of soil erosion risk in a hilly watershed.     

Isotope–geochemical characterization and geothermometrical modeling of Uttarakhand geothermal field,India

Uttarakhand geothermal area, located in the central belt of the Himalayan geothermal province, is one of the important high temperature geothermal fields in India. In this study, the chemical characteristics of the thermal waters are investigated to identify the main geochemical processes affecting the composition of thermal waters during its ascent toward the surface as well as to determine the subsurface temperature of the feeding reservoir. The thermal waters are mainly Ca–Mg–HCO₃ type with moderate silica and TDS concentrations. Mineral saturation states calculated from PHREEQC geochemical code indicate that thermal waters are supersaturated with respect to calcite, dolomite, aragonite, chalcedony, quartz (SI > 0), and undersaturated with respect to gypsum, anhydrite, and amorphous silica (SI < 0). XRD study of the spring deposit samples fairly corroborates the predicted mineral saturation state of the thermal waters. Stable isotopes (δ¹⁸O, δ²H) data confirm the meteoric origin of the thermal waters with no oxygen-18 shift. The mixing phenomenon between thermal water with shallow ground water is substantiated using tritium (³H) and chemical data. The extent of dilution is quantified using tritium content of thermal springs and non-thermal waters. Classical geothermometers, mixing model, and multicomponent fluid geothermometry modeling (GeoT) have been applied to estimate the subsurface reservoir temperature. Among different classical geothermometers, only quartz geothermometer provide somewhat reliable estimation (96–140 °C) of the reservoir temperature. GeoT modeling results suggest that thermal waters have attained simultaneous equilibrium with respect to minerals like calcite, quartz, chalcedony, brucite, tridymite, cristobalite, talc, at the temperature 130 ± 5 °C which is in good agreement with the result obtained from the mixing model.     

Multivariate statistical approach for assessment of subsidence in Jharia coalfields,India

Indian coalfields, one of the major coal producers, are facing serious problem of subsidence now-a-days. This paper attempts to investigate various factors and their influence on magnitude and extent of subsidence. The study was conducted in the Jharia coalfields, India where extraction of thick seams at shallow depths has damaged the ground surface in the form of subsidence. For precise pre-estimation of subsidence, it is therefore necessary to know the contribution of each factor to the occurrence of subsidence. In order to achieve the objectives of this study, several multivariate statistical techniques such as factor analysis (FA), principal component analysis (PCA) and cluster analysis (CA) have been used. Two factors were extracted using FA. Factor 1 and factor 2 account for 42.327% and 24.661% of the variability respectively. Factor 1 represents “natural factor” whereas factor 2 represents “subsidence coefficient”. Spatial variations in regarding susceptibility to the subsidence were determined from hierarchical CA using the linkage distance. Further, the findings of this study would be helpful for prediction of magnitude of subsidence empirically.     

The major lightning regions and associated casualties over India

Natural Hazards - Lightning, a climate-related highly localized natural phenomenon, claims lives and damage properties. These losses could only be reduced by the identification of active seasons...     

Hydro-climatological changes in the Colorado River Basin over a century

This study evaluates changes in streamflow, temperature and precipitation over a time span of 105 years (1906–2010) in the Colorado River Basin (CRB). Monthly precipitation and temperature data for 29 climate divisions, and streamflow data for 29 naturalized gauges were analyzed. Two variations of the Mann-Kendall test, considering lag-1 auto correlation and long-term persistence, and the Pettitt test were employed to assess trends and shifts, respectively. Results indicated that streamflow increased during the winter–spring months and decreased during the summer– autumn period. Decreasing trends in winter precipitation were identified over snow-dominated regions in the upper basin. Significant increases in temperature were detected over several months. Major shifts were noticed in 1964, 1968 and in the late 1920s. Increasing temperature while decreasing streamflow and precipitation were noticed after major shifts in the 1930s, and these shifts coincided with coupled phases of El Niño Southern Oscillation and Pacific Decadal Oscillation.

EDITOR A. Castellarin; ASSOCIATE EDITOR R. Hirsch     

Optimal allocation of water and land resources for maximizing the farm income and minimizing the irrigation-induced environmental problems

The continuous decrease in good quality water and land resources and concurrent increase in global population accentuates the need of optimal allocation of these resources to fulfilling the rising food requirements. This study presents the formulation and application a management model for the optimal allocation of available good quality water and land resources to maximize the farm revenue of a canal command area. A groundwater balance constraint was imposed on the model, which moderates the irrigation-induced environmental problems of waterlogging and salinization, while making the optimal allocation of resources. The model results show a reduction in mustard, rice, and gram crop areas against an increase in sorghum, millets, and wheat areas. The net annual revenue from the command area increased by about 18 % under the optimal allocation plans. The farmers and stakeholders concerned in the actual agricultural production process are suggested to use groundwater and canal water conjunctively to maximizing the farm income. This strategy would also mitigate the hydrological imbalances to the groundwater system without installing costly drainage systems which is not viable as the quality of groundwater is poor and drainage water may cause a serious disposal problem. The developed model can be used as a reliable decision tool for taking the farm and regional level decisions of optimal land and water resources allocation and is able to solve the irrigation-induced environmental problems of agricultural systems.     

Effect of Monetary Incentives on Mail Survey Response Rates for Midwestern Farmers

Response rates to mail-based surveys have declined in recent decades, and survey response rates for farmers tend to be low overall. Maintaining high response rates is necessary to prevent non-response bias. Historically, incentives have been an effective tool to increase response rates with general populations. However, the effect of incentives on farmers has not been well tested. In this study, we experimentally manipulated the use of a $2 incentive in two surveys targeted at farmers. We tested both the use of the incentive and the timing of incentive distribution in the survey process. We found the incentive significantly increased response rates with farmers but there was no significant effect of when the incentive was distributed. Additionally, we evaluated the cost-effectiveness of using the incentive. While the incentive increased response rate, the cost per survey response also increased and the cost of the incentive was not offset by the increased response rate.     

Using large‐scale climatic patterns for improving long lead time streamflow forecasts for Gunnison and San Juan River Basins

In a water‐stressed region, such as the western United States, it is essential to have long lead times for streamflow forecasts used in reservoir operations and water resources management. Current water supply forecasts provide a 3‐month to 6‐month lead time, depending on the time of year. However, there is a growing demand from stakeholders to have forecasts that run lead times of 1 year or more. In this study, a data‐driven model, the support vector machine (SVM) based on the statistical learning theory, was used to predict annual streamflow volume with a 1‐year lead time. Annual average oceanic–atmospheric indices consisting of the Pacific decadal oscillation, North Atlantic oscillation (NAO), Atlantic multidecadal oscillation, El Niño southern oscillation (ENSO), and a new sea surface temperature (SST) data set for the ‘Hondo’ region for the period of 1906–2006 were used to generate annual streamflow volumes for multiple sites in the Gunnison River Basin and San Juan River Basin, both located in the Upper Colorado River Basin. Based on the performance measures, the model showed very good forecasts, and the forecasts were in good agreement with measured streamflow volumes. Inclusion of SST information from the Hondo region improved the model's forecasting ability; in addition, the combination of NAO and Hondo region SST data resulted in the best streamflow forecasts for a 1‐year lead time. The results of the SVM model were found to be better than the feed‐forward, back propagation artificial neural network and multiple linear regression. The results from this study have the potential of providing useful information for the planning and management of water resources within these basins. Copyright © 2012 John Wiley & Sons, Ltd.     

Rapid flash flood calamity in Chamoli,Uttarakhand region during Feb 2021: an analysis based on satellite data

Natural Hazards - The present study investigates the accelerating factors for extreme flash flood at Chamoli district of Uttarakhand on 7 February 2021. The Sentinel-2A and 2B satellite data have...