Abstract
Drought is one of the most environmentally impactful hydrologic processes with devastating economic consequences for many rural communities in arid and semi-arid countries all over the world. In this research, we have employed satellite data and a stochastic approach for forecasting the changes in lake surface areas and demonstrated for the application of the new technique for the case study of the Lake Gregory in Australia. High-resolution Landsat satellite images are used on a monthly time scale from Landsat 5, 7, and 8, on days that are not cloudy. The software ENVI 5.3, using normalized difference vegetation index (NDVI), and modify normalized difference water index (MNDWI) indices were employed to obtain the lake surface maps, and satellite images have been split into water and non-water using a decision tree. The ArcGIS 10.3 software was used to calculate the area of the Lake monthly. The overall trend data shows that from 2004 to 2019, the LS is steadily declining, reaching its lowest area in 2019.The TRMM satellite monthly precipitation (P) and temperature (T) measurement were obtained to investigate the correlation between these changes and regional precipitation. We developed a novel generalized group method of data handling (GGMDH) to forecast lake surface (LS) fluctuations, in which the LS time-series database is extracted from the satellite imagery. For downscaling, precipitation and three different scenarios are defined based on climate change projections to forecast the LS in the 2020–2060 period. The comparison of the GGMDH with stochastic models integrated with preprocessing scenarios indicates the GGMDH in long-term LS forecasting outperforms the stochastic model. The result showed GGMDH is the best model among other ones to modeling lake surface by R2 (%) = 94.16, RMSE = 8.77 for the forecasting stage. The forecasted surface of the Lake Gregory fluctuated from 226 to 0.008 km2 in the future.
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Soltani, K., Amiri, A., Zeynoddin, M. et al. Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods. Theor Appl Climatol 143, 713–735 (2021). https://doi.org/10.1007/s00704-020-03419-6
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DOI: https://doi.org/10.1007/s00704-020-03419-6