Monitoring hydropower reliability in Malawi with satellite data and machine learning

16244 14 89712 2 257934 document 89712 Falchetta_2020_Environ._Res._Lett._15_014011.pdf application/pdf 1cc6e5f525357944b5f513c1a9d1f237 MD5 1586526 2020-03-09 06:37:31 https://pure.iiasa.ac.at/id/eprint/16244/7/Falchetta_2020_Environ._Res._Lett._15_014011.pdf 16244 7 7 application/pdf text en public cc_by_4

Falchetta_2020_Environ._Res._Lett._15_014011.pdf

published 89713 2 257938 document 89713 lightbox.jpg image/png b2b89c3145527e40c33c46ef458da02e MD5 8968 2020-03-09 06:38:03 https://pure.iiasa.ac.at/id/eprint/16244/8/lightbox.jpg 16244 8 8 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/89712 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/89712 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/89712 archive 353 disk0/00/01/62/44 2020-01-07 08:29:47 2021-10-21 05:00:34 2020-01-07 08:29:47 article show Falchetta G. 9187 0000-0003-2607-2195 Kasamba C. Parkinson S. 2028 0000-0002-4753-5198 Monitoring hydropower reliability in Malawi with satellite data and machine learning pub prog_ene Hydro-climatic extremes can affect the reliability of electricity supply, in par-ticular in countries that depend greatly on hydropower or cooling water andhave a limited adaptive capacity. Assessments of the vulnerability of the powersector 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 intro-duce and validate an energy-climate-water framework linking remotely-senseddata from multiple satellite missions and instruments (TOPEX/POSEIDON.OSTM/Jason, VIIRS, MODIS, TMPA, AMSR-E) and field observations. Theplatform exploits random forests regression algorithms to mitigate data scarcityand predict river discharge variability when ungauged. The validated predic-tions are used to assess the impact of hydroclimatic extremes on hydropowerreliability and on the final use of electricity in urban areas proxied by night-time light radiance variation. We apply the framework to the case of Malawifor the periods 2000-2018 and 2012-2018 for hydrology and power, respectively.Our results highlight the significant impact of hydro-climatic variability anddry extremes on both the supply of electricity and its final use. We thus showthat a modelling framework based on open-access data from satellites, machinelearning algorithms, and regression analysis can mitigate data scarcity and im-prove the understanding of vulnerabilities. The proposed approach can supportlong-term infrastructure development monitoring and identify vulnerable pop-ulations, in particular under a changing climate. 2020-01-17 published Institute of Physics Publishing (IOP) 10.1088/1748-9326/ab6562 3119 225 public Environmental Research Letters 15 1 e014011 TRUE 1748-9326 FALSE no info:eu-repo/semantics/article