Time Series Modelling of Pan Evaporation-A Case Study in the Northeast IndiaView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000699</doi_batch_id><timestamp>1714538758</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 2</title></titles><publication_date><month>05</month><year>2024</year></publication_date><journal_volume><volume>16</volume></journal_volume><issue>2</issue></journal_issue><journal_article><titles><title>Time Series Modelling of Pan Evaporation-A Case Study in the Northeast India</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><div> <p>In the present study, a monthly time series model was developed for pan evaporation (E<sub>pan</sub>) in Jorhat (Assam), northeast India. We assume that the E<sub>pan</sub> series can be decomposed into deterministic and stochastic components, and a turning point test was used to identify the presence of a trend component in the series. A trend-free E<sub>pan</sub> time series was further obtained by eliminating the trend component from the original E<sub>pan</sub> time series, and later used in identifying the periodic component using Fourier series analysis. Results show that the first three harmonics explained the total variance by 88,2 %. Before modelling the stochastic dependent component, the periodic component was removed from the time series. In the stochastic dependent component modelling, the moving average (MA) model of order 2 was found suitable on the basis of minimum value of the Bayesian information criterion (BIC). The independent stochastic component was modelled using the normal distribution function. Overall, a very good agreement is observed between generated and historical records, with the Nash-Sutcliffe coefficient being equal to 0,98.</p> </div> <p> </p> </p></abstract><publication_date><month>02</month><day>04</day><year>2014</year></publication_date><pages><first_page>280</first_page><last_page>292</last_page></pages><publisher_item><identifier id_type="pii">gnest_01317</identifier></publisher_item><doi_data><doi>10.30955/gnj.001317</doi><timestamp>1714538758</timestamp><resource>https://journal.gnest.org/publication/gnest_01317</resource></doi_data></journal_article></journal></body></doi_batch> </div>
