Improving land surface temperature prediction using spatiotemporal factors through a genetic-based selection procedure (Case Study: Tehran, Iran)

Karimi, Amer; Ghajari, Yasser Ebrahimian

doi:10.1016/j.asr.2022.02.004

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…The GTWR model (Wang et al, 2018) is an extension of the geographically weighted regression model, with the addition of temporal latitude, to form a three-dimensional model. GTWR models have been widely applied in climate studies (Guo et al, 2020;Shi et al, 2021;Karimi and Ghajari, 2022). Moreover, it has also been applied for the analysis of water bodies (Chu et al, 2018).…”

Section: Center Of Gravity Gtwr Modelmentioning

confidence: 99%

Meteorological driving factors effecting the surface area of Ebinur Lake and determining associated trends and shifts

Yao

Wang²,

Chen³

et al. 2022

Front. Environ. Sci.

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Changes in lake water resources and regional hydrological processes in response to climate change and human activity necessitates timely and accurate access to lake change information to monitor water cycles and water security. The Ebinur Lake Basin has experienced a serious ecological crisis in recent years, which is majorly due to excess use of its water. Therefore, in this study, we used Ebinur Lake as a research object, and used Geographic Information System and remote sensing technology, Landsat, Sentinel, and MODIS images, the Google Earth Engine platform, and the water body index method to determine the changes in lake area from April to October of 2011–2020. Daily data from the Alashankou and Jinghe meteorological stations from 2011 to 2020 were collected. The center of gravity-geographically and temporally weighted regression model was used to analyze the factors changes in surface area. The results showed obvious spatial and temporal heterogeneities for the surface area. Except for 2016, which had unusual/extreme weather, in the last decade, the lake surface area generally showed a monthly decreasing trend from April to October. When Ebinur Lake is one water body, the surface area ranges between 530 and 560 km2. We concluded that the spatio-temporal characteristics of Ebinur Lake can be divided into two levels. When the southeast and northwest regions of the lake merge, it represents the largest possible surface area of Ebinur Lake; this was called the “water storage level”. Historically, Ebinur Lake has lost significant amounts of water, and there is no water body connecting the two main parts of the lake (thereby dividing the lake into two areas); this was called “water demand level”. The trajectory of the center of gravity of the lake is linear, with a slope of 45° and a direction of northwest to southeast. The lake gravity center has different aggregation states. According to the season, it can be divided into spring gathering and autumn gathering. The variation in the surface area of Ebinur Lake is highly correlated with the meteorological and hydrological variation during the year. The highest correlation was observed between lake surface area and wind speed. This study aimed to supplement other studies that explore the lake annually and provide a reference for future water resources management and planning.

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