Q
Quanxi Shao
Researcher at Commonwealth Scientific and Industrial Research Organisation
Publications - 149
Citations - 6284
Quanxi Shao is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Climate change & Evapotranspiration. The author has an hindex of 40, co-authored 134 publications receiving 5164 citations. Previous affiliations of Quanxi Shao include University of Melbourne.
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Water balance modeling over variable time scales based on the Budyko framework – Model development and testing
TL;DR: In this article, the Budyko framework was used to model water balance at four temporal scales (mean annual, annual, monthly and daily) to predict streamflow for ungauged catchments in Australia.
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Quantitative assessment of the impact of climate variability and human activities on runoff changes: a case study in four catchments of the Haihe River basin, China
TL;DR: In this paper, the effect of climate variability and human activities on runoff in the Haihe River basin from 1957 to 2000 were quantitatively evaluated with the help of three methods and based on the assumption that climate and human activity are the only drivers for streamflow and are independent of each other.
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Regional frequency analysis and spatio-temporal pattern characterization of rainfall extremes in the Pearl River Basin, China
TL;DR: In this paper, a method for regional frequency analysis and spatio-temporal pattern characterization of rainfall-extreme regimes (i.e. extremes, durations and timings) in the Pearl River Basin (PRB) using the well-known L-moments approach together with advanced statistical tests including stationarity test and serial correlation check, which are crucial to the valid use of Lmoments for frequency analysis.
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Predicting and understanding home garden water use
TL;DR: In this paper, the authors made estimates of external water use, the total water use outside home (e.g. on lawns, gardens, or swimming pools) for 397 households in detached housing in Perth, WA.
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An improved statistical approach to merge satellite rainfall estimates and raingauge data.
Ming Li,Quanxi Shao +1 more
TL;DR: In this article, a nonparametric kernel smoothing method is employed in this merging strategy with emphasis on discontinuity correction and spatial interpolation adapting for sparse design, and a cross-validation study was undertaken to blend observations from the Australian raingauge network and satellite derived TRMM Multisatellite Precipitation Analysis (TMPA) 3B42.