David R. Bedford

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

The perennial shrub Larrea tridentata is widely successful in North American warm deserts but is also susceptible to climatic perturbations. Understanding its response to rainfall variability requires consideration of multiple time scales. We examine intra-annual to multiyear relationships using model simulations of soil moisture and vegetation growth over 50 years in the Mojave National Preserve in southeastern California (USA). Ecohydrological model parameters are conditioned on field and remote sensing data using an ensemble Kalman filter. Although no specific periodicities were detected in the rainfall record, simulated leaf-area-index exhibits multiyear dynamics that are driven by multiyear (∼3 years) rains, but with up to a 1 year delay in peak response. Within a multiyear period, Larrea tridentata is more sensitive to winter rains than summer. In the most active part of the root zone (above ∼80 cm), >1 year average soil moisture drives vegetation growth, but monthly average soil moisture is controlled by root uptake. Moisture inputs reach the lower part of the root zone (below ∼80 cm) infrequently, but once there they can persist over a year to help sustain plant growth. Parameter estimates highlight efficient plant physiological properties facilitating persistent growth and high soil hydraulic conductivity allowing deep soil moisture stores. We show that soil moisture as an ecological indicator is complicated by bidirectional interactions with vegetation that depend on time scale and depth. Under changing climate, Larrea tridentata will likely be relatively resilient to shorter-term moisture variability but will exhibit higher sensitivity to shifts in seasonal to multiyear moisture inputs.

A mechanistic modeling and data assimilation framework for Mojave Desert ecohydrology

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.

Dust on a Hawaiian volcano: A regional model using field measurements to estimate transport and deposition: Quantifying Rates of Dust Transport and Deposition on Mauna Kea, HI

The western slopes of Hawaii’s Mauna Kea volcano are mantled by fine-grained soils, the record of volcanic airfall and eolian deposition. Where exposed, strong winds transport this sediment across West Hawaii, affecting tourism and local communities with decreased air and water quality. Operations on US Army’s Ke’amuku Maneuver Area (KMA) have the potential to increase dust flux from these deposits. The USGS established 18 ground monitoring sites and sampling locations surrounding KMA. For over 3 years, each station measured vertical and horizontal dust flux, while co-located anemometers measured wind speed and direction. We used these datasets to develop a parsimonious regional model for dust supply and transport to assess whether KMA is a net dust sink or source. We found that dust transport is most highly correlatedwith thresholdwind speeds of 8m/s.We used this value as the regional average threshold wind speed for dust entrainment. Using a model that partitions measured horizontal dust flux into inwardand outward-directed components, we estimate that KMA is currently a net dust sink. Geochemical analysis of dust samples illustrates that local organics and carbonate make up 64% of dust mass, the remainder being volcanic silt and fine sand. Measured vertical dust deposition rates of 0.006mm/yr are similar to 0.004mm/yr of deposition predicted from taking the divergence of dust across KMA’s boundary. These rates are low compared with pre-historic rates of ~0.2–0.3mm/yr, from radiocarbon dating of buried soils. KMA’s soils record persistent deposition over millennia, at rates that imply episodic dust storms. Such events created a soil-mantled landscape in the middle of a largely Pleistocene rocky landscape. A substantial portion of fine-grained soils in other leeward Hawaiian Island landscapes may have formed from similar eolian deposition, and not direct weathering of parent rock. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.