Open AccessMonitoring hydropower reliability in Malawi with satellite data and machine learning
Author(s) -
Giacomo Falchetta,
Chisomo Kasamba,
Simon Parkinson
Publication year - 2020
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/ab6562
Subject(s) - hydropower , environmental science , climate change , vulnerability (computing) , scarcity , reliability (semiconductor) , water scarcity , environmental resource management , satellite , water resources , computer science , meteorology , power (physics) , geography , engineering , ecology , physics , computer security , quantum mechanics , aerospace engineering , electrical engineering , economics , biology , microeconomics
Hydro-climatic extremes can affect the reliability of electricity supply, in particular in countries that depend greatly on hydropower or cooling water and have a limited adaptive capacity. Assessments of the vulnerability of the power sector and of the impact of extreme events are thus crucial for decision-makers, and yet often they are severely constrained by data scarcity. Here, we introduce and validate an energy-climate-water framework linking remotely-sensed data from multiple satellite missions and instruments (TOPEX/POSEIDON. OSTM/Jason, VIIRS, MODIS, TMPA, AMSR-E) and field observations. The platform exploits random forests regression algorithms to mitigate data scarcity and predict river discharge variability when ungauged. The validated predictions are used to assess the impact of hydroclimatic extremes on hydropower reliability and on the final use of electricity in urban areas proxied by nighttime light radiance variation. We apply the framework to the case of Malawi for the periods 2000–2018 and 2012–2018 for hydrology and power, respectively. Our results highlight the significant impact of hydro-climatic variability and dry extremes on both the supply of electricity and its final use. We thus show that a modelling framework based on open-access data from satellites, machine learning algorithms, and regression analysis can mitigate data scarcity and improve the understanding of vulnerabilities. The proposed approach can support long-term infrastructure development monitoring and identify vulnerable populations, in particular under a changing climate.