Performance of multisite stochastic precipitation models for a tropical monsoon region
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ORIGINAL PAPER
Performance of multisite stochastic precipitation models for a tropical monsoon region Tue M. Vu1
•
Ashok K. Mishra1
Accepted: 31 August 2020 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Stochastic weather generator (SWG) produces synthetic time series of weather data based on the statistical characteristics of observed weather for a given location. Although SWG models are extensively evaluated and applied in different hydroclimate related studies, they often ignore the spatial correlation between weather patterns observed at multiple locations. This can limit the value of some spatial impact assessments such as flood modeling, agricultural crop modeling, water resources management and urban infrastructure design. To address such limitations, multisite SWG models are implemented to preserve the spatial characteristics of weather variables. In this study, we compared the performance of three multisite stochastic precipitation models, which includes modified Wilks model (modWilks), RainSim V3 (RSIM) and perturbed K-Nearest Neighbor (pKNN) models. The performances of these models are investigated for a study area located in the tropical monsoon climate region over Central Highland, Vietnam. The models are evaluated based on their performance for simulating precipitation occurrence and amount statistics on a wet day, extreme cumulative wet/dry days, transition and joint probability of wet/dry state, cross-correlation across all sites as well as the behavior of precipitation amount in relation to neighboring station state. The performance of model depends on the type of the precipitation characteristics, for example, the RSIM model performed well in term of the mean precipitation intensity. Overall, the pKNN model outperformed other models in term of temporal statistics, spatial characteristics, as well as extreme events measured based on Intensity–Duration–Frequency (IDF) curves. Keywords Multisite stochastic precipitation model Perturbed KNN Modified Wilks RainSim Tropical monsoon region Vietnam
1 Introduction Climate change likely to alter the seasonal precipitation and evaporation pattern resulting in more flood and drought events in different parts of the world (Konapala et al. 2020), for example the extreme rainfall pattern has recently increased over the USA (Vu and Mishra 2019). In order to develop appropriate strategies to mitigate such climate extreme events, long term data sets are needed to provide risk-based information for infrastructure development. & Tue M. Vu [email protected] Ashok K. Mishra [email protected] 1
Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29631, USA
However, the long-term data sets are often very limited in most part of the world, especially in developing countries (Mishra and Coulibaly 2009). Stochastic weather generators (SWG) are able to generate long-term synthetic weather data for applications in water resources planning, urban infrastructure design and environmental models. Different types of
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