A mechanistic modeling and data assimilation framework for Mojave Desert ecohydrology

Abstract

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 nonGaussianity 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.

Identifying multiple time scale rainfall controls on Mojave Desert ecohydrology using an integrated data and modeling approach for Larrea tridentata

“…To integrate Kelso Valley observations with the model, this study uses the ecohydrological model and data assimilation framework developed by Ng et al [2014], briefly summarized here. The coupled ecohydrological model CLM4.0-CN [Oleson et al, 2010] simulates soil moisture (at 10 computational layers) and vegetation states (e.g., carbon pools in various plant compartments) as prognostic variables.…”

“…For distinct surficial geological units, Nimmo et al [2009] and Mirus et al [2009] carried out field and numerical experiments to determine hydraulic states and properties in the shallow unsaturated zone, to which they ascribed distinct influences on root uptake. Our study focuses on four instrumented sites (''TB5,'' ''TB4,'' ''TA3,'' and ''TA1'') in a cluster 1 km across on the lower reach (average elevation 770 m) of an alluvial fan in Kelso Valley (see Figure 1 of Ng et al [2014], reproduced in supporting information Figure S1). All sites are located on relatively homogeneous and coarse early Holocene soils that comprise a thick unsaturated zone overlying an extremely deep water table (100 m depth).…”

“…Due to their proximity, they have similar meteorology, with a 50 year mean annual rainfall of 103 mm. The redundancy among sites is important for filling data gaps and reducing uncertainty in model simulations [Ng et al, 2014].…”

“…Previous field studies have shown different soil and terrain properties to produce very distinct ecohydrological conditions [McAuliffe, 1994;Smith et al, 1995;Hamerlynck et al, 2000aHamerlynck et al, , 2002Nimmo et al, 2009;Bedford et al, 2009]. Ng et al [2014] demonstrated significant sensitivity to parameter uncertainty when modeling the slow growth of Larrea tridentata. To address this, we calibrate model parameters to observations from the Mojave National Preserve in southeastern California (USA) using the approach by Ng et al [2014].…”

“…Ng et al [2014] demonstrated significant sensitivity to parameter uncertainty when modeling the slow growth of Larrea tridentata. To address this, we calibrate model parameters to observations from the Mojave National Preserve in southeastern California (USA) using the approach by Ng et al [2014]. Details of our simulation results are specific to our study site, but they provide data-conditioned insights into how soil moisture dynamics can control the effect of short to long-term rainfall on Larrea tridentata growth.…”