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Climate change or regional human impacts? Remote sensing tools, artificial neural networks, and wavelet approaches aim to solve the problem
Author(s) -
Ehsan Foroumandi,
Vahid Nourani,
Elnaz Sharghi
Publication year - 2020
Publication title -
hydrology research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 48
eISSN - 1996-9694
pISSN - 0029-1277
DOI - 10.2166/nh.2020.112
Subject(s) - evapotranspiration , climate change , environmental science , precipitation , structural basin , surface runoff , agriculture , vegetation (pathology) , hydrology (agriculture) , inflow , water resources , ecosystem , climatology , physical geography , geography , meteorology , ecology , geology , medicine , paleontology , geotechnical engineering , archaeology , pathology , biology
Lake Urmia, as the largest lake in Iran, has suffered from water-level decline and this problem needs to be investigated accurately. The major reason for the decline is controversial. The current paper aimed to study the hydro-environmental variables over the Lake Urmia basin using remote sensing tools, artificial neural networks, wavelet transforms, and Mann–Kendall trend tests from 1995 to 2019 in order to determine the primary reason of the decline and to find the most important hydrologic periodicities over the basin. The results indicated that for the monthly-, seasonally-, and annually-based time series, the components with 4-month and 16-month, 24and 48-month, and 2and 4-year, respectively, are the most dominant periodicities over the basin. The agricultural increase according to the vegetation index and evapotranspiration and their close relationship with the waterlevel change indicated that human land-use is the main reason for the decline. The increasing agriculture, in the situations that the precipitation has not increased, caused the inflow runoff to the lake to decline and the remaining smaller discharge is not sufficient to stabilize the water level. Temperature time series, also, has experienced a significant positive trend which intensified the water-level change.

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