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Entropy-Based Measurement of Long-Term Precipitation Variability across India
Published in Surendra Kumar Chandniha, Anil Kumar Lohani, Gopal Krishan, Ajay Krishna Prabhakar, Advances in Hydrology and Climate Change, 2023
Prabhash Kumar Mishra, Hemant Singh, Swagatam Das, Surendra Kumar Chandniha
Literally, variability refers to the quality of being subject to variation. In other words, it is the degree of being variable or changeable. Statistically, it is defined as the degree of extent a data point that differs from the mean value. Median and mean represent a distinct value that differs from the actual values. The extent to which these median and mean vary depends upon the variability or dispersion in the original data. Datasets become highly dispersed when they contain values considerably higher and lower than the median or the mean value. There are four commonly used measures of variability: range, mean, variance, and standard deviation. The commonly used statistical measure of variability is the variance, which measures the spread of the data points. Hence, variance is a method to capture the degree of spread of the dataset. Variability may also be measured spatially as well as temporally for a dataset. Spatial variability is represented by different values that are measured at different geographical locations. For example, spatial variability between the network of rain gauges can be investigated for the contribution of precipitation from individual station to the total amount of rainfall received in the region. Temporal variability measures randomness of a data series over different time scales.
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Published in Megh R. Goyal, Susmitha S. Nambuthiri, Richard Koech, Technological Interventions in Management of Irrigated Agriculture, 2018
Farmers experience spatial and temporal variability in grain yield even though they manage field crops spatially homogeneously. Spatial and temporal variability in crop production systems is due to the interaction among various factors such as soil, crop, weather, topography, and other factors in the field. A better understanding of the factors and processes causing spatial variability is important in developing site-specific management strategies.
Evaluation of ground water quality and health risk assessment due to nitrate and fluoride in the Middle Indo-Gangetic plains of India
Published in Human and Ecological Risk Assessment: An International Journal, 2020
Jitendra Maurya, Satya Narayana Pradhan, A. K. Ghosh
The suitability of ground water for drinking purposes was evaluated by a comprehensive PCA based water quality index (WQI) by evaluating weight of each variable as a ratio of variance of that water quality parameter to total variance obtained from PCA, instead of using arbitrary values used in literature and then multiplying the weight by score and finally adding all the products (WQI = ∑ Wi × Si) (Mahapatra et al. 2012). Finally the WQI was standardized andthe PCA based WQI was classified into four categories and was excellent if WQI was > 0.95; good in between 0.75 and 0.95; poor in between 0.5 and 0.75; and very poor when WQI was < 0.50. Spatial variability map of WQI (Figure 2) suggests that only a few groundwater samples in the western region of the study are suitable for drinking purposes and only 2.7% and 13.5% water samples were classified as “excellent” and “good” respectively; whereas 59.5% and 24.3% (totalling 83.8%) of the ground sample were categorized as poor and very poor water quality respectively and were not recommended for drinking and other domestic purposes.
Analysis of spatiotemporal variability of water productivity in Ethiopian sugar estates: using open access remote sensing source
Published in Annals of GIS, 2020
Moti Girma Gemechu, Taye Alemayehu Hulluka, Yoseph Cherinet Wakjira
To meet the future demands, water productivity in this cropping system needs considerable improvement as water is becoming scarcer and competition among agriculture and other sectors is increasing (Ahmad, Masih, and Turral 2004). Appropriate benchmarks for water productivity (WP) are needed to help identify and diagnose inefficiencies in crop production and water management in irrigated systems (Grassini et al. 2010). Water productivity in agriculture needs to be improved significantly in the coming decades to secure food supply to a growing world population. To assess on a global scale where water productivity can be improved and what the causes are for not reaching its potential, the current levels must be understood(Zwart, Bastiaanssena, and Charlotte de Fraiturec 2010). The value of water productivity will be varying both spatially and temporally. The spatial variability is mainly due to difference in water use, sowing dates, fertilizer use, soil quality and socio-economic conditions. Rainfall, amount and timing, emerged as the most important factor inducing temporal changes (Ahmad, Masih, and Turral 2004).
Calibration of foundation movements for AASHTO LRFD bridge design specifications
Published in Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2019
Naresh C. Samtani, John M. Kulicki
The temporal uncertainties are from a time-related variability that may occur at a given support location and the possibility that this variability is not the same at all support locations. In contrast, variability that can occur over different support locations at a given time is referenced as spatial variability. Analytical models, e.g. Schmertmann, Hough, etc., use simplified assumptions to account for these variabilities, but their success in doing so is a function of the level of subsurface investigations performed (field and laboratory) and interpretations of the subsurface data. These uncertainties can be reduced by increased and better subsurface investigations using appropriate investigative and interpretive techniques but can never be completely addressed. This is further complicated by factors such as uncertainties due to variabilities in regional design and construction practices, maintenance protocols, and local environment leading to deterioration.