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A mechanistic modeling and data assimilation framework for Mojave Desert ecohydrology
Author(s) -
Ng GeneHua Crystal,
Bedford David R.,
Miller David M.
Publication year - 2014
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2014wr015281
Subject(s) - ecohydrology , larrea , data assimilation , ensemble kalman filter , environmental science , desert (philosophy) , subarctic climate , deserts and xeric shrublands , climatology , shrub , hydrology (agriculture) , kalman filter , meteorology , ecosystem , ecology , statistics , mathematics , geography , geology , extended kalman filter , philosophy , geotechnical engineering , epistemology , biology , habitat
This study demonstrates and addresses challenges in coupled ecohydrological modeling in deserts, which arise due to unique plant adaptations, marginal growing conditions, slow net primary production rates, and highly variable rainfall. We consider model uncertainty from both structural and parameter errors and present a mechanistic model for the shrub Larrea tridentata (creosote bush) under conditions found in the Mojave National Preserve in southeastern California (USA). Desert‐specific plant and soil features are incorporated into the CLM‐CN model by Oleson et al. (2010). We then develop a data assimilation framework using the ensemble Kalman filter (EnKF) to estimate model parameters based on soil moisture and leaf‐area index observations. A new implementation procedure, the “multisite loop EnKF,” tackles parameter estimation difficulties found to affect desert ecohydrological applications. Specifically, the procedure iterates through data from various observation sites to alleviate adverse filter impacts from non‐Gaussianity in small desert vegetation state values. It also readjusts inconsistent parameters and states through a model spin‐up step that accounts for longer dynamical time scales due to infrequent rainfall in deserts. Observation error variance inflation may also be needed to help prevent divergence of estimates from true values. Synthetic test results highlight the importance of adequate observations for reducing model uncertainty, which can be achieved through data quality or quantity.