PMID- 34178466
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210628
IS  - 2167-8359 (Print)
IS  - 2167-8359 (Electronic)
IS  - 2167-8359 (Linking)
VI  - 9
DP  - 2021
TI  - Design and analysis of statistical probability distribution and non-parametric 
      trend analysis for reference evapotranspiration.
PG  - e11597
LID - 10.7717/peerj.11597 [doi]
LID - e11597
AB  - Accurate estimates of reference evapotranspiration are critical for 
      water-resource management strategies such as irrigation scheduling and operation. 
      Therefore, knowledge of events such as spatial and temporal reference 
      evapotranspiration (ET(o)) and their related principle of statistical probability 
      theory plays a vital role in amplifying sustainable irrigation planning. 
      Spatiotemporal statistical probability distribution and its associated trends 
      have not yet has explored in Pakistan. In this study, we have two objectives: (1) 
      to determine the most appropriate statistical probability distribution that 
      better describes ET(o) on mean monthly and seasons wise estimates for the design 
      of irrigation system in Khyber Pakhtunkhwa, and (2) to check the trends in ET(o) 
      on a monthly, seasonal, and annual basis. To check the ET(o) trends, we used the 
      modified version of the Mann-Kendall and Sen Slope. We used Bayesian Kriging for 
      spatial interpolation and propose a practical approach to the design and study of 
      statistical probability distributions for the irrigation system and water 
      supplies management. Also, the scheme preeminent explains ET(o), on a monthly and 
      seasonal basis. The statistical distribution that showed the best fit ET(o) 
      result occupying 58.33% and 25% performance for the design of irrigation scheme 
      in the entire study region on the monthly level was Johnson SB and Generalized 
      Pareto, respectively. However, according to the Anderson-Darling (AD) and 
      Kolmogorov-Smirnov (KS) goodness of fit measure, seasonal ET(o) estimates were 
      preferably suited to the Burr, Johnson SB & Generalized Extreme Value. More 
      research work must be conduct to assess the significance of this study to other 
      fields. In conclusion, these findings might be helpful for water resource 
      management and policymaker in future operations.
CI  - (c)2021 Gul et al.
FAU - Gul, Sajid
AU  - Gul S
AD  - Henan Academy of Big Data, Zhengzhou University, Zhengzhou, Henan, China.
AD  - School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, Henan, 
      China.
FAU - Ren, Jingli
AU  - Ren J
AD  - Henan Academy of Big Data, Zhengzhou University, Zhengzhou, Henan, China.
AD  - School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, Henan, 
      China.
FAU - Xiong, Neal
AU  - Xiong N
AD  - Departments of Mathematics and Computer Sciences, Northeastern State University, 
      OK, United States of America.
FAU - Khan, Muhammad Asif
AU  - Khan MA
AD  - School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 
      Jiangxi, China.
AD  - Department of Statistics, Islamia College University, Peshawar, Khyber 
      Pakhtunkhwa, Pakistan.
LA  - eng
PT  - Journal Article
DEP - 20210618
PL  - United States
TA  - PeerJ
JT  - PeerJ
JID - 101603425
PMC - PMC8216168
OTO - NOTNLM
OT  - Anderson darling
OT  - Irrigation
OT  - Khyber Pakhtunkhwa
OT  - Kolmogorove-Smirnov
OT  - Seasonal
COIS- The authors declare there are no competing interests.
EDAT- 2021/06/29 06:00
MHDA- 2021/06/29 06:01
PMCR- 2021/06/18
CRDT- 2021/06/28 06:02
PHST- 2021/02/22 00:00 [received]
PHST- 2021/05/21 00:00 [accepted]
PHST- 2021/06/28 06:02 [entrez]
PHST- 2021/06/29 06:00 [pubmed]
PHST- 2021/06/29 06:01 [medline]
PHST- 2021/06/18 00:00 [pmc-release]
AID - 11597 [pii]
AID - 10.7717/peerj.11597 [doi]
PST - epublish
SO  - PeerJ. 2021 Jun 18;9:e11597. doi: 10.7717/peerj.11597. eCollection 2021.