气候变化领域集成服务门户(CCPortal): Monitoring hydropower reliability in Malawi with satellite data and machine learning

CCPortal

DOI	10.1088/1748-9326/ab6562
	Monitoring hydropower reliability in Malawi with satellite data and machine learning
	Falchetta G.; Kasamba C.; Parkinson S.C.
发表日期	2020
ISSN	17489318
卷号	15 期号:1
英文摘要	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. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	energy-climate-water nexus; extreme hydroclimatic events; hydroelectricity; random forests; remote sensing; vulnerability
语种	英语
scopus关键词	Cooling water; Decision making; Decision trees; Hydroelectric power; Power quality; Random forests; Regression analysis; Reliability; Remote sensing; Satellites; Climatic variability; energy-climate-water nexus; Hydroclimatic; Infrastructure development; Modelling framework; Regression algorithms; Remotely sensed data; vulnerability; Machine learning; algorithm; AMSR-E; cooling water; electricity; environmental assessment; extreme event; hydroelasticity; hydroelectric power; hydrometeorology; Jason; machine learning; model validation; MODIS; monitoring; prediction; reliability analysis; remote sensing; satellite data; satellite mission; TOPEX-Poseidon; urban area; VIIRS; vulnerability; Malawi
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154126
作者单位	Future Energy Program, FEEM - Fondazione Eni Enrico Mattei, Italy; Department of International Economics, Institutions and Development, Catholic University, Italy; Department of Energy, Ministry of Natural Resources, Energy and Mines of Malawi, Malawi; Energy Program, International Institute for Applied Systems Analysis (IIASA), Austria; Institute for Integrated Energy Systems, University of Victoria, Canada
推荐引用方式 GB/T 7714	Falchetta G.,Kasamba C.,Parkinson S.C.. Monitoring hydropower reliability in Malawi with satellite data and machine learning[J],2020,15(1).
APA	Falchetta G.,Kasamba C.,&Parkinson S.C..(2020).Monitoring hydropower reliability in Malawi with satellite data and machine learning.Environmental Research Letters,15(1).
MLA	Falchetta G.,et al."Monitoring hydropower reliability in Malawi with satellite data and machine learning".Environmental Research Letters 15.1(2020).