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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-181
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 12 Oct 2018

Model description paper | 12 Oct 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Discrete k-nearest neighbor resampling for simulating multisite precipitation occurrence and adaption to climate change

Taesam Lee1 and Vijay P. Singh2 Taesam Lee and Vijay P. Singh
  • 1Department of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju-daero, Jinju, Gyeongnam, South Korea, 660-701
  • 2Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University, 321 Scoates Hall, College Station, Texas, United States, 77843

Abstract. Stochastic weather simulation models are commonly employed in water resources management and agricultural applications. The data simulated by these models, such as precipitation, temperature, and wind, are used as input for hydrological and agricultural models. Stochastic simulation of multisite precipitation occurrence is a challenge because of its intermittent characteristics as well as spatial and temporal cross-correlation. Employing a nonparametric technique, k-nearest neighbor resampling (KNNR), and coupling it with Genetic Algorithm (GA), this study proposes a novel simulation method for multisite precipitation occurrence. The proposed discrete version of KNNR (DKNNR) model is compared with an existing parametric model, called multisite occurrence model with standard normal variate (MONR). The datasets simulated from both the DKNNR model and the MONR model are tested using a number of statistics, such as occurrence and transition probabilities as well as temporal and spatial cross-correlations. Results show that the proposed DKNNR model can be a good alternative for simulating multisite precipitation occurrence. We also tested the model capability to adapt climate change. It is shown that the model is capable but further improvement is required to have specific variations of the occurrence probability due to climate change. Combining with the generated occurrence, the multisite precipitation amount can then be simulated by any multisite amount model.

Taesam Lee and Vijay P. Singh
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Status: open (until 14 Dec 2018)
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Taesam Lee and Vijay P. Singh
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Latest update: 13 Nov 2018
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Short summary
A novel simple technique for simulating multisite occurrence of precipitation is proposed. The proposed technique employs the nonparametric approaches k-nearest neighbor and genetic algorithm. We tested this technique in various ways and proved that this simple technique can be useful and comparable to existing one.
A novel simple technique for simulating multisite occurrence of precipitation is proposed. The...
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