Arid
DOI10.1007/s12665-017-6814-3
Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India
Gupta, Ankit1,2; Kamble, Trupti2; Machiwal, Deepesh3
通讯作者Gupta, Ankit
来源期刊ENVIRONMENTAL EARTH SCIENCES
ISSN1866-6280
EISSN1866-6299
出版年2017
卷号76期号:15
英文摘要

Rainfall, a vital component of hydrological cycle, plays a key role in appropriate planning and sustainable management of water resources in water-short regions. However, its uneven distribution over the space and time necessitates getting knowledge about its variability. This study aimed at evaluating efficacy of ordinary and Bayesian kriging techniques in depicting spatial and temporal variability of annual rainfall in arid and semi-arid regions of north-west India. Using 35-year gridded rainfall datasets of India Meteorological Department (IMD at 0.5 degrees resolution, 1971-2005) and Climate Forecast System Reanalysis (CFSR at 0.3 degrees resolution, 1979-2013), geostatistical modelling was performed by employing five kriging techniques with novelty of exploring potential of empirical Bayesian kriging (EBK) technique, for the first time, for rainfall datasets in this study. Performance of the kriging techniques was evaluated by cross-validation based on five criteria. Both exponential ordinary kriging (EOK) and EBK revealed better performance over other kriging techniques. Thus, EOK and EBK were further examined by adopting goodness-of-fit criteria of correlation coefficient (r) and root-mean-square error (RMSE). Very high r values indicated equally excellent performance of both the techniques; however, less RMSE values in case of EBK suggested it as the best-fit technique, which was then used for developing spatially distributed rainfall raster. Spatially distributed 35-year mean annual rainfall (MAR) and coefficient of variation (CV) highlighted very high temporal variability of the annual rainfall (CV > 120%) in the western arid lands of the country with <150 mm MAR values. Thus, under the scenario of scarce availability and high rainfall variability, urgent actions are needed to be taken such as implementation of rainwater-harvesting and groundwater-recharging structures, adoption of less water-requiring crops and micro-irrigation methods in agriculture. Moreover, these findings are useful for the decision makers, planners and resource managers to formulate appropriate strategies for conserving and sustainably managing precious rainwater quantities in arid regions worldwide.


英文关键词Arid and semi-arid regions Coefficient of variation Gridded rainfall dataset Empirical Bayesian kriging Goodness-of-fit criteria Rainfall variability Geostatistical modelling
类型Article
语种英语
国家India
收录类别SCI-E
WOS记录号WOS:000407545200005
WOS关键词GROUNDWATER CONTAMINATION ; INTERPOLATION METHODS ; EXTREME RAINFALL ; PRECIPITATION ; RIVER ; SCALE ; GEOSTATISTICS ; TEMPERATURE ; VALIDATION ; REANALYSIS
WOS类目Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向Environmental Sciences & Ecology ; Geology ; Water Resources
来源机构ICAR Central Arid Zone Research Institute
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/198675
作者单位1.ISRO, NRSC, DOS, Reg Remote Sensing Ctr West, CAZRI Campus, Jodhpur 342003, Rajasthan, India;
2.ICAR Indian Inst Soil & Water Conservat, Res Ctr, Anand 388306, Gujarat, India;
3.ICAR Cent Arid Zone Res Inst, Reg Res Stn, Bhuj 370105, Gujarat, India
推荐引用方式
GB/T 7714
Gupta, Ankit,Kamble, Trupti,Machiwal, Deepesh. Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India[J]. ICAR Central Arid Zone Research Institute,2017,76(15).
APA Gupta, Ankit,Kamble, Trupti,&Machiwal, Deepesh.(2017).Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India.ENVIRONMENTAL EARTH SCIENCES,76(15).
MLA Gupta, Ankit,et al."Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India".ENVIRONMENTAL EARTH SCIENCES 76.15(2017).
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