Your search found 4 records
1 Obahoundje, S.; Diedhiou, A.; Dubus, L.; Alamou, E. A.; Amoussou, E.; Akpoti, Komlavi; Ofosu, Eric Antwi. 2022. Modeling climate change impact on inflow and hydropower generation of Nangbeto Dam in West Africa using multi-model CORDEX ensemble and ensemble machine learning. Applied Energy, 325:119795. [doi: https://doi.org/10.1016/j.apenergy.2022.119795]
Climate change ; Modelling ; Dams ; River basins ; Hydropower ; Hydroelectric power generation ; Reservoirs ; Climate variability ; Temperature ; Precipitation ; Machine learning ; Datasets ; Forecasting ; Energy generation / West Africa / Togo / Nangbeto Dam / Mono River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051375)
(4.52 MB)
Climate change (CC) poses a threat to renewable hydropower, which continues to play a significant role in energy generation in West Africa (WA). Thus, the assessment of the impacts of climate change and climate variability on hydropower generation is critical for dam management. This study develops a framework based on ensemble climate models and ensemble machine learning methods to assess the projected impacts of CC on inflow to the reservoir and hydropower generation at the Nangbeto Hydropower plant in WA. Inflow to reservoir and energy generation for the future (2020–2099) is modeled using climate models output data from Coordinated Regional Downscaling Experiment to produce a publicly accessible hydropower dataset from 1980 to 2099. The bias-adjusted ensemble mean of eleven climate models for representative concentration pathways (RC4.5 and RCP8.5) are used. The added value of this approach is to use fewer input data (temperature and precipitation) while focusing on their lagged effect on inflow and energy. Generally, the model output strongly correlates with the observation (1986–2005) with a Pearson correlation of 0.86 for energy and 0.82 for inflow while the mean absolute error is 2.97% for energy and 9.73% for inflow. The results reveals that both inflow and energy simulated over the future periods (2020–2039, 2040–2059, 2060–2079, and 2080–2099) will decrease relative to the historical period (1986–2005) for both RCPs in the range of (2.5–20.5% and 1–8.5% for inflow and energy, respectively), at annual, monthly and seasonal time scales. Therefore, these results should be considered by decision-makers when assessing the best option for the energy mix development plan.
2 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.
3 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.
4 Fourat, E.; Blanchart, E.; CueRio, M.; Darias, M. J.; Diedhiou, A.; Droy, I.; Jacob, F.; Janin, P.; Bars, M. L.; Lourme-Ruiz, A.; Mekki, I.; Meral, P.; Moiti-Maizi, P.; Seghieri, J.; Verger, E. O. 2024. Holistic approaches to assess the sustainability of food systems in low- and middle-income countries: a scoping review. PLOS Sustainability and Transformation, 3(7): e0000117. [doi: https://doi.org/10.1371/journal.pstr.0000117]
(Location: IWMI HQ Call no: e-copy only Record No: H052966)
https://journals.plos.org/sustainabilitytransformation/article/file?id=10.1371/journal.pstr.0000117&type=printable
https://vlibrary.iwmi.org/pdf/H052966.pdf
https://vlibrary.iwmi.org/pdf/H052966.pdf
(0.95 MB) (976 KB)
Food systems and their sustainability have been extensively studied in high-income countries (HICs), yet less so in low- and middle-income countries (LMICs), despite their importance for global food security. In this study, we conducted a systematic scoping review to describe the extent, range, and nature of peer-reviewed literature assessing the sustainability performance of food systems in LMICs. The review revealed a recent and heterogeneous literature. From this diversity, 3 archetypes of epistemological approaches emerged, classified by their purpose: observational, modeling, and transformative. All 3 approaches apply existing or tailored methods to specifically study food systems, and their objectives are to observe, model, or transform different parts of the food systems towards sustainability. Gaps in the literature include inconsistent definitions of food systems and frameworks and understudied drivers of food systems sustainability. Therefore, the development of a comprehensive and systematic inventory of frameworks and their sustainability is crucial to determine the most suitable interdisciplinary methodologies for specific contexts and generate actionable knowledge for food systems transformation.
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