Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.     

Impact of Climate Change on Indian Agriculture: A Review

During the recent decade, with the growing recognition of the possibility of climate change and clear evidence of observed changes in climate during 20^th century, an increasing emphasis on food security and its regional impacts has come to forefront of the scientific community. In recent times, the crop simulation models have been used extensively to study the impact of climate change on agricultural production and food security. The output provided by the simulation models can be used to make appropriate crop management decisions and to provide farmers and others with alternative options for their farming system. It is expected that in the coming decades with the increased use of computers, the use of simulation models by farmers and professionals as well as policy and decision makers will increase. In India, substantial work has been done in last decade aimed at understanding the nature and magnitude of change in yield of different crops due to projected climate change. This paper presents an overview of the state of the knowledge of possible effect of the climate variability and change on food grain production in India. An erratum to this article can be found at     

Current status of multimodel superensemble and operational NWP forecast of the Indian summer monsoon

In the last thirty years great strides have been made by large-scale operational numerical weather prediction models towards improving skills for the medium range time-scale of 7 days. This paper illustrates the use of these current forecasts towards the construction of a consensus multimodel forecast product called the superensemble. This procedure utilizes 120 of the recent-past forecasts from these models to arrive at the training phase statistics. These statistics are described by roughly 10⁷ weights. Use of these weights provides the possibility for real-time medium range forecasts with the superensemble. We show the recent status of this procedure towards real-time forecasts for the Asian summer monsoon. The member models of our suite include ECMWF, NCEP/EMC, JMA, NOGAPS (US Navy), BMRC, RPN (Canada) and an FSU global spectral forecast model. We show in this paper the skill scores for day 1 through day 6 of forecasts from standard variables such as winds, temperature, 500 hPa geopotential height, sea level pressure and precipitation. In all cases we noted that the superensemble carries a higher skill compared to each of the member models and their ensemble mean. The skill matrices we use include the RMS errors, the anomaly correlations and equitable threat scores. For many of these forecasts the improvements of skill for the superensemble over the best model was found to be quite substantial. This real-time product is being provided to many interested research groups. The FSU multimodel superensemble, in real-time, stands out for providing the least errors among all of the operational large scale models.     

Probabilistic seismic hazard analysis for Kakrapar atomic power station, Gujarat, India

Seismic ground motion caused by earthquakes mainly affects the constructions and structures around its area of influence. In this context, the probabilistic seismic hazard analysis (PSHA) is a scientific step towards the safety analysis of any major construction such as nuclear power plant. Thus, the present study focused to estimate seismic hazard level at different probabilities for Kakrapar nuclear power plant located in the Western India. The hazard curves for the study area are developed following the procedure of PSHA suggested by Cornell–McGuire. Three source zones, Narmada-Tapti zone (NTZ), Rann of Kuchchh (ROK), and west passive margin (WPM), are classified on the basis of seismicity and tectonic setting of the study area. The estimated maximum magnitude (m _max) for NTZ, ROK, and WPM are 6.9 ± 0.57, 6.5 ± 0.64, and 6.1 ± 0.64, respectively. Logic tree approach has been used for the development of hazard curves to account the epistemic uncertainties associated with the analysis. For maximum credible earthquake [MCE, i.e., the probability of exceedance of 2 % in 50 years (return period of ~2,500 years)], the peak spectral acceleration (i.e., PSA at 0.2 s) expected around 5 km of the Kakrapar nuclear power plant (site) is 0.23 g from all source zones; however, at exact site location, it is 0.18 g. The PSA values due to NTZ, ROK, and WPM based on MCE are 0.22, 0.065, and 0.052 g, respectively. In case of design-based earthquake (DBE, i.e., 50 % probability in 50 years (return period of ~110 years)), the calculated maximum spectral acceleration (SA) from all source zones is about 0.045 g. The PSA distribution for the DBE from the NTZ has reached a maximum value of 0.042 g; however, PSA for ROK and WPM is considerably low with a maximum value of 0.022 and 0.021 g, respectively. Considering the MCE and DBE, the estimated PSA at 0.2 s has a highest value of ~0.23 g from all source zones. Spectral accelerations (SAs) correspond to different periods are presented, and SA plots for NTZ zone can be considered as response spectra for the KAPS site. Deaggregation of PSHA in the present study is also discussed. PGA values reported in seismic zonation map and global seismic hazard analysis program around the present study area range from 0.05 to 0.2 g which is slightly lower than the peak acceleration obtained in this study. The results of this study would facilitate in the performance of the site-specific seismic probabilistic safety analysis.     

Effects of Non-Plastic Silts on Liquefaction Potential of Solani Sand

Loose saturated cohesionless soils are most susceptible to liquefaction, however there are strong historical evidence suggesting that soils containing fines such as silty sands are also prone to liquefaction during earthquakes. The liquefaction of silty sands has been observed in a number of recent case studies. This paper presents the effects of fine silts on liquefaction potential of sandy soil. Tests have been conducted on the vibration table at different accelerations and pore water pressure is measured. During the lab investigation, locally (Roorkee, India) available Solani Sand and Dhanauri Silt have been used. The soil samples have been prepared by varying silt content and the initial relative density. The results of the study performed are used to clarify the effects of non-plastic fines content on the Solani sand. As the silt content increases, the number of cycles required to produce maximum pore water pressure increases. For a particular level of excitation, rate of pore water pressure generation is maximum at critical silt content. It is observed that critical silt content to generate maximum pore water pressure is different for different accelerations. Further, effect of silt content is very much dependent on relative density.     

Prioritization of cyclone preparedness activities in humanitarian supply chains using fuzzy analytical network process

Natural Hazards - During recent years, the importance of preparedness has increased with increasing frequency of disasters and the cost associated with relief and response activities. Therefore,...     

Analysis of solute transport in rivers with transient storage and lateral inflow: An analytical study

Solute transport in rivers with transient storage and lateral inflow has been studied by Transient Storage Model (TSM). Analytical solution of the TSM is obtained by means of Laplace transform. In order to illustrate the use of present analytical solution, physical transport parameters are estimated for the observational data of Uvas Creek tracer experiment. Analytical concentration-time breakthrough curves are found to be in good agreement with the observed concentration-time breakthrough curves. A sensitivity analysis has been performed in order to identify the most critical parameter for predicting concentration. It has been found that lateral inflow rate is the most sensitive and the ratio of cross-sectional areas is the least sensitive parameter. A hypothetical situation has been considered to study the effects of background concentrations, lateral inflow concentration and its rate. The analytical solutions show that the solute concentration gets diluted or concentrated due to lateral inflow. Physical mechanisms of the problems are well reproduced by the present analytical solutions and these results can be used for analysis of tracer experiments.