Exploring the performance of spatio-temporal assimilation in an urban cellular automata model. Issue 11 (2nd November 2017)

• Record Type: Journal Article
• Authors: Li, Xuecao; Lu, Hui; Zhou, Yuyu; Hu, Tengyun; Liang, Lu; Liu, Xiaoping; Hu, Guohua; Yu, Le
• Is Part Of: International journal of geographical information science. Volume 31:Issue 11(2017)
• Page Start: 2195
• Page End: 2215
• Publication Date: 2017-11-02
• Subjects: EnKF -- Logistic-CA -- block size -- assimilation window length -- sensitivity analysis; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2017.1357821
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Urban cellular automata (CA) models propagate and accumulate errors during the modeling process due to the model structure or stochastic processes involved. It is feasible to assimilate real-time observations into an urban CA model to reduce model uncertainties. However, the assimilation performance is sensitive to the spatio-temporal units in the assimilation algorithm, that is, spatial block size and window length (temporal interval). In this study, we coupled an assimilation model, an ensemble Kalman filter (EnKF) and a Logistic-CA model to simulate the urban dynamic in Beijing over a period of two decades. Our results indicate that the coupled EnKF-CA model outperforms the CA-alone counterpart by about 10% in terms of the figure of merit, which reflects the agreement of modeled pixels. We also find that the assimilation performance using a finer block (1 km) is better than that using a coarser block (5 km and 10 km) because of the better depiction of spatial heterogeneity using a finer block. Moreover, the improvement of intermediate outputs using the coupled EnKF-CA model is effective for a certain period (e.g. 5 years). This implies that a high-frequency assimilation may not significantly improve the model performance. The sensitivity analyses of spatio-temporal assimilation in the EnKF-CA model provide a better understanding of the assimilation mechanism that couples with land-use change models.