| Title: | Assessment of the future impact of climate change on the hydrology of the Mangoky River, Madagascar using ANN and SWAT |
| Authors: | Tanteliniaina, M.F.R., H. Rahaman and J. Zhai |
| Year: | 2021 |
| Journal: | Water |
| Volume (Issue): | 13(9) |
| Pages: | |
| Article ID: | 1239 |
| DOI: | 10.3390/w13091239 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic only |
| Primary Application Category: | climate change |
| Secondary Application Category: | hydrologic assessment |
| Watershed Description: | 55,750 km^2 Mangoky River, which flows from the Central Highland Region of the Mozambique Channel in southwest Madagascar. |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | |
| Abstract: | The assessment of the impacts of climate change on hydrology is important for better water resources management. However, few studies have been conducted in semi-arid Africa, even less in Madagascar. Here we report, climate-induced future hydrological prediction in Mangoky river, Madagascar using an artificial neural network (ANN) and the soil and water assessment tool (SWAT). The current study downscaled two global climate models on the mid-term, noted the 2040s (2041–2050) and long-term, noted 2090s (2091–2099) under two shared socioeconomic pathways (SSP) scenarios, SSP 3–7.0 and SSP 5–8.5. Statistical indices of both ANN and SWAT showed good performance (R2 > 0.65) of the models. Our results revealed a rise in maximum temperature (4.26– 4.69 °C) and minimum temperature (2.74–3.01 °C) in the 2040s and 2090s. Under SSP 3–7.0 and SSP 5–8.5, a decline in the annual precipitation is projected in the 2040s and increased the 2090s. This study found that future precipitation and temperature could significantly decrease annual runoff by 60.59% and 73.77% in the 2040s; and 25.18% and 23.45% in the 2090s under SSP 3–7.0 and SSP 5– 8.5, respectively. Our findings could be useful for the adaptation to climate change, managing water resources, and water engineering. |
| Language: | English |
| Keywords: | climate change; downscaling; ANN; SWAT model; Africa |