AI Article Synopsis
- Lake water surface temperature (LWST) is vital for understanding how freshwater ecosystems react to climate change, and traditional methods view water bodies as unchanging.
- The newly developed open-source web application, IMPART, uses Landsat and MODIS data to estimate dynamic LWST from 2004 to 2022, showing a minor deviation of 0.86°C compared to traditional methods.
- The analysis indicates that 57% of studied lakes show significant differences in static vs. dynamic LWST, highlighting the need to recognize water bodies as dynamic for better monitoring and response to climate change impacts.
Article Abstract
Lake water surface temperature (LWST) is a critical component in understanding the response of freshwater ecosystems to climate change. Traditional estimation of LWST estimation considers water surface bodies to be static. Our work proposes a novel open-source web application, IMPART, designed for estimating dynamic LWST using Landsat reflectance and MODIS temperature datasets from 2004 to 2022. Results presented globally for over 342 lakes reveal a root mean square deviation of 0.86 °C between static and dynamic LWST. Additionally, our results demonstrate that 57% of the lakes exhibit a statistically significant difference between the static and dynamic LWST values. Improved LWST will ultimately enhance our ability to comprehensively monitor and respond to the impacts of climate change on freshwater ecosystems worldwide. Furthermore, based on the Koppen-Geiger climate classification, our zonal analysis demonstrates the deviation between static and dynamic LWST. It identifies specific zones where considering waterbodies as dynamic entities is essential.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jenvman.2024.122075 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!