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Article

Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records

Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 5, 15780 Zographou, Greece
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Academic Editor: Ataur Rahman
Water 2021, 13(23), 3429; https://doi.org/10.3390/w13233429
Received: 3 November 2021 / Revised: 29 November 2021 / Accepted: 1 December 2021 / Published: 3 December 2021
(This article belongs to the Special Issue Hydroinformatics and Integrated Urban Water Management)
The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities based solely on available information at coarser time scales. The methodology is based on three key elements: (a) the analysis of statistics’ behaviour across multiple temporal scales; (b) the use of parametric functions to model this behaviour; and (c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to a 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements. View Full-Text
Keywords: downscaling of statistical quantities; fine temporal scales; multi-scale analysis; scaling laws; residential water demand; smart meters; cost-effective enrichment of records downscaling of statistical quantities; fine temporal scales; multi-scale analysis; scaling laws; residential water demand; smart meters; cost-effective enrichment of records
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MDPI and ACS Style

Kossieris, P.; Tsoukalas, I.; Efstratiadis, A.; Makropoulos, C. Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records. Water 2021, 13, 3429. https://doi.org/10.3390/w13233429

AMA Style

Kossieris P, Tsoukalas I, Efstratiadis A, Makropoulos C. Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records. Water. 2021; 13(23):3429. https://doi.org/10.3390/w13233429

Chicago/Turabian Style

Kossieris, Panagiotis, Ioannis Tsoukalas, Andreas Efstratiadis, and Christos Makropoulos. 2021. "Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records" Water 13, no. 23: 3429. https://doi.org/10.3390/w13233429

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