Spatio-temporal analysis of groundwater levels and projection of future trend of Agra city,Uttar Pradesh,India

Access to water resources is one of the major challenges being faced worldwide. Water scarcity, particularly groundwater resource, is the major ubiquitous concern for the country. Almost half of the country is reeling under severe ground water crisis due to anthropogenic and natural reasons (basalt rock surface). Agra region situated in the western part of Uttar Pradesh state of India has a semi-arid climate. The study area, which has a history of water scarcity since medieval ages, has seen a spurt of acute water shortage in recent times owing to the expansion of a very dense built-up area and excessive haulage accompanied by decline in rainfall. A study was under taken for identifying the trends in pre- and post-monsoon groundwater levels for Agra city, Uttar Pradesh. Pre-monsoon and post-monsoon groundwater depth data of 16 observation wells for the 2007–2016 period were collected and analyzed using ARC GIS 10.2 software. The rainfall trend during the study period was also studied to understand its role in groundwater fluctuation level. Statistical tests like Mann-Kendall, Sen’s slope estimator, and linear regression model were applied to understand the trend and rate of change in groundwater level. The land use/land cover map of the study area was integrated with groundwater map to have a primary understanding of the spatial trend of groundwater scenario of the study area. The result obtained is quite alarming for the city’s groundwater scenario. Results showed that the groundwater levels had significantly declined during 2007–2016. Average rates of water level decline were 0.228 and 0.267 m/year during pre- and post-monsoon seasons, respectively. There was a rapid decline in water level between 2008 and 2009 and between 2013 and 2014. The average rate of decline of pre- and post-monsoon groundwater level in the city during this period is 0.32 and 0.30 m/year, respectively. Significant decrease in groundwater level is found in 84.21% of wells for pre- and post-monsoon as obtained through Mann-Kendall analysis at 95% confidence level. During pre-monsoon season, the rate of decline according to Sen’s slope estimator varied between 0.74 and 2.05 m/year. Almost similar picture of decline is portrayed through linear regression slope wherein the computed rate of decline varied between 0.75 and 2.05 m/year. During post-monsoon, the rate of decline according to Sen’s slope varied between 0.13 and 1.94 m/year. Similar trend statistic is obtained through linear regression method where the declining rate is between 0.14 and 1.91 m/year. Comparison of the three statistical tests indicates similar nature of declining trend. The result of this research raises concern about the future of groundwater resources in Agra city. The findings of this study will assist planners and decision-makers in developing better land use and water resource management.     

Simplified SMA-inspired 1-parameter SCS-CN model for runoff estimation

This study proposes a simplified 1-parameter SCS-CN model (M5) based on Mishra-Singh (2002) model and soil moisture accounting (SMA) procedure for surface runoff estimation and compares its performance with the existing SCS-CN method (SCS, 1956) (M1), Michel 1-P model (Water Resour Res 41:1-6, 2005) (M2), Sahu 1-P model (Hydrol Process 21:2872-2881, 2007) (M3), and Ajmal et al. model (J Hydrol 530:623-633, 2015) (M4) using large rainfall–runoff dataset of 48,763 events from123 USDA-ARS watersheds. The performance of models was evaluated using three statistical error indices such as Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), percentage bias (PBIAS), and rank and grading system (RGS). Based on the results obtained, the models can be ranked as follows: M5?>?M4?>?M3?>?M1?>?M2, i.e., model M5 outperformed all the remaining four models M1–M4 and hence is recommended for field applications.     

Detection of Changes in Glacier Mass Balance Using Satellite and Meteorological Data in Tirungkhad Basin Located in Western Himalaya

Himalayan glaciers and their mass balance are poorly sampled through direct mass balance measurements. Thus, based on Landsat datasets of ETM⁺ (2000), ETM⁺ (2006) and TM (2011), mass balance studies of 32 glaciers was carried out using accumulation area ratio (AAR) method in the Tirungkhad river basin, a tributary of Satluj River, located in western Himalayan region. The overall specific mass balance was negative varying from ?27 cm (2000) to ?41 cm (2011). Out of 32 glaciers, 27 glaciers (81.2 %) showed negative mean mass balance and 5 glaciers (18.7 %) showed positive mean mass balance. Mean of specific mass balance for the year 2000, 2006 and 2011 was found to be ?48 cm, ?55 cm and ?0.61 cm respectively, in case of glaciers with negative mass balance while in case of glaciers with positive mass balance, it was 0.67 cm (2000), 0.56 cm (2006) and 0.47 cm (2011). The investigations suggested a loss of ?0.034 km³ of glacial ice for 2000, 0.036 km³ for 2006 and 0.038 km³ for 2011 respectively. The negative mass balance of the glaciers since 2000 correlates well with the increasing trend of annual mean temperature and decreasing trend of precipitation (snow water equivalent (SWE) and rainfall). Based on Mann Kendall test the temperature and SWE trends were significant at 95 % confidence level, however, the rainfall trend was insignificant.     

Cavitation by Nonlinear Interaction Between Inertial Alfvén Waves and Magnetosonic Waves in Low Beta Plasmas

This paper presents the model equations governing the nonlinear interaction between dispersive Alfvén wave (DAW) and magnetosonic wave in the low-β plasmas (β≪m _e/m _i; known as inertial Alfvén waves (IAWs); here \upbeta = 8pn₀T /B₀²\upbeta = 8\pi n_{0}T /B_{0}^{2} is thermal to magnetic pressure, n ₀ is unperturbed plasma number density, T(=T _e≈T _i) represents the plasma temperature, and m _e(m _i) is the mass of electron (ion)). This nonlinear dynamical system may be considered as the modified Zakharov system of equations (MZSE). These model equations are solved numerically by using a pseudo-spectral method to study the nonlinear evolution of density cavities driven by IAW. We observed the nonlinear evolution of IAW magnetic field structures having chaotic behavior accompanied by density cavities associated with the magnetosonic wave. The relevance of these investigations to low-β plasmas in solar corona and auroral ionospheric plasmas has been pointed out. For the auroral ionosphere, we observed the density fluctuations of ∼ 0.07n ₀, consistent with the FAST observation reported by Chaston et al. (Phys. Scr. T84, 64, 2000). The heating of the solar corona observed by Yohkoh and SOHO may be produced by the coupling of IAW and magnetosonic wave via filamentation process as discussed here.     

GIS based spatial distribution mapping and suitability evaluation of groundwater quality for domestic and agricultural purpose in Kaithal district, Haryana state, India

Groundwater is a major resource for meeting huge domestic and agricultural requirements of Kaithal district in Haryana. Therefore, evaluation of its quality in terms of suitability for domestic and agricultural sectors is necessary for sustainable management of the resource. The present study has analyzed pre- and post-monsoon physico-chemical data of groundwater samples from bore wells spread over the entire district. Spatial distribution maps were generated for hydrogen ion concentration, total dissolved solids, total hardness, electrical conductivity, sodium adsorption ratio, residual sodium carbonate and percent sodium using the geographic information system. Furthermore, the study area was demarcated into different groundwater quality zones for domestic and agricultural use by applying various national and international standards. It was observed from the study that the groundwater was predominantly hard, alkaline and saline in nature. However, it was within safe limits for domestic use. Further, it was also experienced from the analysis that in about two-third parts of the district, groundwater was in desirable-to-permissible quality class for agricultural use but hazardous for soil as well as for crops in the remaining part. Also, a moderation in water quality was observed after the monsoon season, which can be attributed to a possible dilution due to groundwater recharge.     

Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India

Drought is a serious climatic condition that affects nearly all climatic zones worldwide, with semi-arid regions being especially susceptible to drought conditions because of their low annual precipitation and sensitivity to climate changes. Drought indices such as the standardized precipitation index (SPI) using meteorological data and vegetation indices from satellite data were developed for quantifying drought conditions. Remote sensing of semi-arid vegetation can provide vegetation indices which can be used to link drought conditions when correlated with various meteorological data based drought indices. The present study was carried out for drought monitoring for three districts namely Bhilwara, Kota and Udaipur of Rajasthan state in India using SPI, normalized difference vegetation index (NDVI), water supply vegetation index (WSVI) and vegetation condition index (VCI) derived from the Advanced Very High resolution Radiometer (AVHRR). The SPI was computed at different time scales of 1, 2, 3, 6, 9 and 12 months using monthly rainfall data. The NDVI and WSVI were correlated to the SPI and it was observed that for the three stations, the correlation coefficient was high for different time scales. Bhilwara district having the best correlation for the 9-month time scale shows late response while Kota district having the best correlation for 1-month shows fast response. On the basis of the SPI analysis, it was found that the area was worst affected by drought in the year 2002. This was validated on the basis of NDVI, WSVI and VCI. The study clearly shows that integrated analysis of ground measured data and satellite data has a great potential in drought monitoring.