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Spatial statistics analysis of precipitation in the Urmia Lake Basin
ID Aghamohammadi, Hossein (Author), ID Behzadi, Saeed (Author), ID Moshtaghinejad, Fatemeh (Author)

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Abstract
Most of the world's population lives in areas facing a severe water crisis. Climatology researchers need precipitation information, pattern analysis, modeling of spatial relationships, and more to cope with these conditions. Therefore, in this paper, a comprehensive approach is developed for describing geographic phenomenon using various geostatistical techniques. Two main methods of interpolation (Inverse Distance Weighting and Kriging) are used and their results are compared. The Urmia Lake Basin in Iran was selected as a case-study area that has faced critical conditions in recent years. Precipitation was initially modeled using both conventional, non-statistical approaches and advanced geo-statistical methods. The result of the comparison shows that ordinary Kriging is the best interpolation method for precipitation, with an RMS of 4.15, and Local Polynomial Interpolation with the exponential kernel function is the worst method, with an RMS of 5.02. Finally, a general regression analysis was conducted on precipitation data to examine its relationship with other variables. The results show that the latitude variable was identified as the dependent variable with the most influence on precipitation, with an impact factor of 81%, and that the slope has the lowest impact on precipitation, at nearly zero percent. The influence of latitude on precipitation appears to be localized, suggesting that it may not be a significant variable for predicting global environmental threats.

Language:English
Keywords:precipitation estimation, geostatistics, spatial relationship modeling, hydrology, kriging interpolation
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FGG - Faculty of Civil and Geodetic Engineering
Publication status:Published
Publication version:Version of Record
Year:2023
Number of pages:Str. 139-154
Numbering:Vol. 36, no. 65
PID:20.500.12556/RUL-163186 This link opens in a new window
UDC:502/504:551.577.5(55)(078.7)
ISSN on article:0352-3551
DOI:10.15292/acta.hydro.2023.09 This link opens in a new window
COBISS.SI-ID:210122499 This link opens in a new window
Publication date in RUL:03.10.2024
Views:112
Downloads:24
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Record is a part of a journal

Title:Acta hydrotechnica
Publisher:Fakulteta za gradbeništvo in geodezijo
ISSN:0352-3551
COBISS.SI-ID:3664386 This link opens in a new window

Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.

Secondary language

Language:Slovenian
Title:Prostorska statistična analiza padavin v porečju jezera Urmia
Abstract:
Večina svetovnega prebivalstva živi na območjih, ki se soočajo s hudo krizo zaradi pomanjkanja vode. Klimatologi za spopadanje s temi izzivi potrebujejo informacije o padavinah, analize prostorskih vzorcev in modele prostorskih odnosov. V prispevku opisujemo celoviti pristop k opisovanju geografskega pojava z uporabo različnih geostatističnih tehnik. Uporabljeni sta dve glavni metodi interpolacije (metoda inverzne utežene razdalje in Kriging) ter primerjani njuni rezultati. Kot območje študije primera je bilo izbrano porečje jezera Urmia, ki se je v zadnjih letih soočalo s kritičnimi razmerami. Padavine smo najprej modelirali s klasičnimi in geostatističnimi metodami. Rezultati kažejo, da je navadni Kriging najboljša interpolacijska metoda za padavine – z vrednostjo RMS 4,15, metoda z eksponentno jedrno funkcijo pa je najslabša – z vrednostjo RMS 5,02. Na koncu je bila izvedena splošna regresijska analiza padavin. Rezultati kažejo, da je bila spremenljivka širine najvplivnejša odvisna spremenljivka s faktorjem vpliva 81 %, naklon pa ima najmanjši vpliv na padavine s skoraj nič odstotki. Zdi se, da je vpliv zemljepisne širine lokalne narave in morda ne predstavlja pomembne globalne okoljske grožnje.

Keywords:ocena padavin, geostatistika, modeliranje prostorskih odnosov, hidrologija, krigiranje

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