Your search found 2 records
1 Obahoundje, Salomon; Diedhiou, A.; Akpoti, Komlavi; Kouassi, K. L.; Ofosu, E. A.; Kouame, D. G. M. 2024. Predicting climate-driven changes in reservoir inflows and hydropower in Côte d'Ivoire using machine learning modeling. Energy, 302:131849. (Online first) [doi: https://doi.org/10.1016/j.energy.2024.131849]
Climate change ; Climate prediction ; Reservoirs ; Dams ; Machine learning ; Modelling ; Time series analysis ; Water power ; Hydroelectric power generation ; River basins ; Climate variability / Côte d'Ivoire / Buyo Dam / Kossou Dam / Taboo Dam
(Location: IWMI HQ Call no: e-copy only Record No: H052857)
(18.20 MB)
This study investigates the impact of climate change and variability on reservoir inflow and hydropower generation at three key hydropower plants in Côte d'Ivoire including Buyo, Kossou, and Taboo. To simulate inflow to reservoir and energy generation, the Random Forest (RF), a machine-learning algorithm allowing fewer input variables was applied. In three-step, RF k-fold cross validation (with k = 5) was used; (i) 12 and 6 multiple lags of precipitation and temperature at monthly increments were used as predictors, respectively; (ii) the five most important variables were used in addition to the current month's precipitation and temperature; and (iii) a residual RF was built. The bias-adjusted ensemble mean of eleven climate models output of the COordinated Regional Downscaling Experiment was used for the representative concentration pathways (RCP4.5 and RCP8.5). The model output was highly correlated with the observations, with Pearson correlations >0.90 for inflow and >0.85 for energy for the three hydropower plants. The temperature in the selected sub-catchments may increase significantly from 0.9 to 3 °C in the near (2040–2069) and from 1.7 to 4.2 °C in far (2070–2099) future periods relative to the reference period (1981–2010). A time series of precipitation showed a change in range -7 and 15 % in the near and -8 to 20 % in the far future and more years are with increasing change. Depending on the sub-catchment, the magnitude of temperature and precipitation changes will increase as greenhouse gas emissions (GHG)(greater in RCP8.5 than RCP4.5) rise. At all time scales (monthly, seasonal, and annual), the simulated inflow and energy changes were related to climate variables such as temperature and precipitation. At the annual time scale, the inflow is projected to change between -10 and 37 % and variability may depend on the reservoir. However, the energy change is promised to change between -10 and 25 %, -30 to 15 %, and 5–40 % relative to the historical (1981–2010) period for Taabo, Kossou, and Buyo dams, respectively at an annual scale. The changes may vary according to the year, the RCPs, and the dam. Consequently, decision-makers are recommended to take into consideration an energy mix plan to meet the energy demand in these seasons.
2 Akaffou, F. H.; Obahoundje, Salomon; Didi, S. R. M.; Koffi, B.; Coulibaly, W. B.; Habel, M.; Kadjo, M. M. F.; Kouassi, K. L.; Diedhiou, A. 2024. Analyzing inflow to Faye Reservoir sensitivity to climate change using CMIP6 and random forest algorithm. International Journal of River Basin Management, 21p. (Online first) [doi: https://doi.org/10.1080/15715124.2024.2354707]
Reservoirs ; Climate change ; Climate models ; Climate variability ; Forecasting ; Hydroelectric power generation ; Dams ; Precipitation ; Temperature / Côte d'Ivoire / Faye Reservoir / Faye Dam
(Location: IWMI HQ Call no: e-copy only Record No: H052858)
(4.95 MB)
In the era of Climate Change and Climate Variability (CC and CV), renewable energy sources such as Hydropower (HP) have a significant role to play in mitigation. However, inflow to reservoir which is the key fuel for HP generation is vulnerable to CC and CV. Thus, there is a need to investigate the potential impacts of CC and CV on HP systems in the future. This study attempts to assess the potential impacts of CC and CV on the Faye reservoir inflow using the Random Forest (RF) algorithm. For this purpose, bias-adjusted precipitation and temperature data of thirteen climate model outputs and their ensemble mean from Coupled Model Inter-comparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways scenarios (SSP1-2.6; SSP2-4.5 and SSP5-8.5) were used as predictors. The potential changes in reservoir inflows were evaluated in the near (2025–2049), mid (2050–2074) and far (2075–2099) futures relative to the reference period (1990–2014). The results show the good performance of the RF algorithm in simulating reservoir inflows with Cor > 0.6 for all models. The annual inflows to the Faye reservoir are noted to increase in the future compared to the reference period despite the potential decrease in future precipitation probably due to land use/cover change. For the ensemble mean of models, this projected increase is estimated to around 16%, 23% and 10%, respectively under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios for all projection periods. The largest annual increase is noted under the SSP2-4.5 scenario while the lowest increase is noted under the SSP5-8.5 scenario for all projection periods. This study could help the small dam managers better consider the implications of CC and CV on inflow management.
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