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Open AccessArticle

Development of a Parametric Regional Multivariate Statistical Weather Generator for Risk Assessment Studies in Areas with Limited Data Availability

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Department of Civil and Environmental Engineering, Colorado State University, 1372 Campus Delivery, Fort Collins, CO 80523-1372, USA
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Iraqi Ministry of Water Resources, Planning and Follow up Directorate, Palestine Street, Baghdad, Iraq
*
Author to whom correspondence should be addressed.
Deceased on March 28 2020; formerly, Professor, Dept. of Civil and Environmental Engineering.
Climate 2020, 8(8), 93; https://doi.org/10.3390/cli8080093
Received: 10 July 2020 / Revised: 6 August 2020 / Accepted: 7 August 2020 / Published: 11 August 2020
(This article belongs to the Special Issue Application of Climatic Data in Hydrologic Models)
Risk analysis of water resources systems can use statistical weather generators coupled with hydrologic models to examine scenarios of extreme events caused by climate change. These require multivariate, multi-site models that mimic the spatial, temporal, and cross correlations of observed data. This study developed a statistical weather generator to facilitate bottom-up approaches to assess the impact of climate change on water resources systems for cases of limited data. While existing weather generator models have impressive features, this study suggested a simple weather generator which is straightforward to implement and can employ any distribution function for variables such as precipitation or temperature. It is based on (1) a first-order, two-state Markov chain to simulate precipitation occurrences; (2) the use of Wilks’ technique to produce correlated weather variables at multiple sites with the conservation of spatial, temporal, and cross correlations; (3) the capability to vary the statistical parameters of the weather variables. The model was applied to studies of the Diyala River basin in Iraq, which is a case with limited observed records. Results show that it exhibits high values (e.g., over 0.95) for the Nash–Sutcliffe and Kling–Gupta metric tests, preserves the statistical properties of the observed variables, and conserves the spatial, temporal, and cross correlations among the weather variables in the meteorological stations. View Full-Text
Keywords: statistical weather generator; stochastic process; Diyala River basin; Wilks’ technique statistical weather generator; stochastic process; Diyala River basin; Wilks’ technique
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MDPI and ACS Style

Waheed, S.Q.; Grigg, N.S.; Ramirez, J.A. Development of a Parametric Regional Multivariate Statistical Weather Generator for Risk Assessment Studies in Areas with Limited Data Availability. Climate 2020, 8, 93.

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