28-6 Markov Chain order selection for prediction of daily precipitation in the High Plains

Session: Geoscience and Hydrogeology in the AI Era: From Predictive Models to Real-Time Applications (Posters)

Presenting Author:

Author:

Abstract:

Prediction of precipitation is extremely important in the field of hydrology, numerical modeling, and water resource management. Precipitation is also often considered as the primary variable when stochastically modelling other weather variables. Some numerical models require precipitation time series data extended over thousands of years. For those cases, development of stochastic precipitation models is required that can reliably generate synthetic precipitation series that have similar properties to those of the observed data.

Markov chain models are a commonly used statistical technique to generate realistic sequences of daily precipitation. Although a first-order model has been used for most studies, the use of higher order models has been recommended in many cases for robust estimates of dry spells and variabilities for some seasons and locations. The objective of this study is to choose the best order for Markov Chain to produce synthetic time series of daily precipitation in the High Plains area. In this study, Markov Chain-based precipitation generators using multiple orders were developed to simulate daily precipitation at sites across the High Plains. The time series produced by the models were compared for choosing the right model (order) for the prediction of daily precipitation across the High Plains.

Geological Society of America Abstracts with Program. Vol. 57, No. 6, 2025

doi: 10.1130/abs/2025AM-10427

© Copyright 2025 The Geological Society of America (GSA), all rights reserved.