Accuracy assessment of remotely sensed data to analyze lake water balance in semi-arid region

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Joy Bhattacharjee, Mehedi Rabbil, Nasim Fazel, Hamid Darabi, Bahram Choubin, Md. Motiur Rahman Khan, Hannu Marttila, Ali Torabi Haghighi. Accuracy assessment of remotely sensed data to analyze lake water balance in semi-arid region. Science of The Total Environment 797 (2021), 149034, ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2021.149034

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Title: Accuracy assessment of remotely sensed data to analyze lake water balance in semi-arid region
Author: Bhattacharjee, Joy; Rabbil, Mehedi; Fazel, Nasim; Darabi, Hamid; Choubin, Bahram; Khan, Md. Motiur Rahman; Marttila, Hannu; Haghighi, Ali Torabi
Publisher: Elsevier
Date: 2021
Language: en
Belongs to series: Science of the Total Environment 797 (2021), 149034
ISSN: 0048-9697
URI: http://hdl.handle.net/10138/334385
Abstract: Lake water level fluctuation is a function of hydro-meteorological components, namely input, and output to the system. The combination of these components from in-situ and remote sensing sources has been used in this study to define multiple scenarios, which are the major explanatory pathways to assess lake water levels. The goal is to analyze each scenario through the application of the water balance equation to simulate lake water levels. The largest lake in Iran, Lake Urmia, has been selected in this study as it needs a great deal of attention in terms of water management issues. We ran a monthly water balance simulation of nineteen scenarios for Lake Urmia from 2003 to 2007 by applying different combinations of data, including observed and remotely sensed water level, flow, evaporation, and rainfall. We used readily available water level data from Hydrosat, Hydroweb, and DAHITI platforms; evapotranspiration from MODIS and rainfall from TRMM. The analysis suggests that the consideration of field data in the algorithm as the initial water level can reproduce the fluctuation of Lake Urmia water level in the best way. The scenario that combines in-situ meteorological components is the closest match to the observed water level of Lake Urmia. Almost all scenarios showed good dynamics with the field water level, but we found that nine out of nineteen scenarios did not vary significantly in terms of dynamics. The results also reveal that, even without any field data, the proposed scenario, which consists entirely of remote sensing components, is capable of estimating water level fluctuation in a lake. The analysis also explains the necessity of using proper data sources to act on water regulations and managerial decisions to understand the temporal phenomenon not only for Lake Urmia but also for other lakes in semi-arid regions.
Description: Highlights • Water balance algorithms were used to simulate semi-arid lake water levels. • Scenarios were formed by combining in-situ and remote sensing data sources. • The proposed combinations can reproduce lake water level even without in-situ data. • Using in-situ data as initial water level matched best to simulate lake water level. • 9 out of 19 scenarios did not vary significantly with in-situ water level.
Subject: inland water
scenario
water management
meteorological data
lake water level fluctuation
lakes
water
water analysis
water level height
hydrology
water resources
climate changes
arid regions
remote sensing
algorithms
simulation
modelling
in-situ data
Lake Urmia
Iran
Subject (ysa): meteorologia
simulointi
järvet
sisävedet
kaukokartoitus
vedenkorkeus
data
hydrologia
skenaariot
Urmia (järvi)
mallintaminen
vesi
algoritmit
vesitase
vesianalyysi
aridiset alueet
ilmastonmuutokset
vesivarat
vesienhoito
meteorologinen data
Rights: https://creativecommons.org/licenses/by-nc-nd/4.0/


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