ORCID Profile
0000-0002-7240-9265
Current Organisation
Boise State University
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Publisher: Elsevier BV
Date: 02-2021
Publisher: Copernicus GmbH
Date: 05-11-2019
Abstract: Abstract. Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe ecosystem in western North America, for ex le, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modeling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different plant functional types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrush-steppe ecosystem. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP) using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach. (1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. (2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. (3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two eddy covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. Our finding on preliminary parameterization of the sagebrush shrub PFT is an important step towards subsequent studies on shrubland ecosystems using EDv2.2 or any other process-based ecosystem model.
Publisher: Wiley
Date: 28-03-2019
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10919
Abstract: Soil water retention is important for the establishment and productivity of ecosystems through its role in governing the flux, depth distribution, and availability of soil moisture. With increasing application of global and regional hydrologic and climate models, there is a concomitant need to accurately predict and map soil hydraulic properties to parameterize these models and simulate soil water dynamics across spatiotemporal scales. Soil water retention functions created to fulfill this need typically assume a unimodal pore-size distribution, despite the common observation that soil pore-size distributions are multimodal due to soil structure and interpedal macropores. Existing dual porosity functions ide pores into two categories: larger pores, controlled by structure, and smaller pores, controlled by texture. Obtaining the parameters for the structural domain is difficult due to the poor characterization of large pores. Large pores cannot be characterized from water retention curves because measurement of water retention near saturation, and CT scans of soils rely on small soil s le volumes which limits the pore characterization to tens of millimeters in range, while pores may be much larger. In this study, we developed multiple PTFs to predict the van Genuchten parameters (& #593 and n) of the structural domain in dual porosity models, as well as the w coefficient, which reflects the relative abundance of these two types of pores in the dual porosity model. Our PTFs were developed from characterized pores 180 & #181 m from nine pedons across Kansas, USA, using recent advances of multistripe laser triangulation (MLT) scanning applications. MLT scanning pore characterization allowed us to characterize soil pores 10 cm and was conducted on 30-cm wide soil monoliths collected from excavation walls of each pedon that were either 20 or 40 cm tall depending on the thickness of the horizon. We used ImageJ to quantify pore-size distributions that were then used to estimate the water retention curve (WRC) and hydraulic conductivity of the structural domain. We fitted van Genuchten functions to characterize the WRCs in the structural domain, and ROSETTA 3.0 was used to characterize the WRCs in the matrix domain. These WRC fits were used to develop new PTFs that predict the parameters of the dual porosity model using mixed linear models with inputs including NRCS soil structure field descriptions along with standard physical and chemical properties (clay, sand, SOM, bulk density, coefficient of linear extensibility, cation exchange capacity, horizon midpoint depth, and quantified morphological descriptions of structural type, grade, solidity, roundness, and circularity). Using the predicted parameters, we estimated water retention for each horizon and achieved high levels of correlation and accuracy when compared with the water retention derived from the MLT scans. The approach for creating PTFs can be used to improve soil hydraulic property parameterization of soils with structure in regional hydrologic and climate models by providing a framework for integrating multiple recent advances such as the characterization of large pores using MLT and use of quantified soil structure from profile descriptions. Future studies will examine performance of these PTFs in numerical hydrologic models.
Publisher: American Geophysical Union (AGU)
Date: 02-2019
DOI: 10.1029/2018WR023903
Publisher: MDPI AG
Date: 14-09-2019
DOI: 10.3390/RS11182141
Abstract: The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems.
Publisher: Copernicus GmbH
Date: 04-02-2020
DOI: 10.5194/BG-2019-510
Abstract: Abstract. Wildfire incidents in sagebrush (Artemisia spp.) dominated semi-arid ecosystems in the western United States have risen dramatically in the last few decades. Severe wildfires often lead to the loss of native sagebrush communities and change the biogeochemical conditions which make it difficult for sagebrush to regenerate. Invasion of cheatgrass (Bromus tectorum) accentuates the problem by making the ecosystem more susceptible to frequent burns. Managers have implemented several techniques to cope with the cheatgrass-fire cycle, ranging from controlling undesirable fire effects by removing fuel loads either mechanically or via prescribed burns, to seeding the fire-affected areas with shrubs and native perennial forbs. There have been a number of studies at local scales to understand the direct impacts of wildfire on vegetation, however there is a larger gap in understanding these impacts at broad spatial and temporal scales. This need highlights the importance of global dynamic vegetation models (DGVMs) and remote sensing. In this study, we explored the influence of fire on vegetation composition and gross primary production (GPP) in the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model, a dynamic vegetation model. We selected Reynold Creek Experimental Watershed (RCEW) to run our simulation study, which represents sagebrush dominated ecosystems in the northern Great Basin. We ran point-based simulations at four existing flux-tower sites in the study area for a total 150 years after turning on the fire module in the 25th year. Results suggest dominance of shrub in a non-fire scenario, however under the fire scenario we observed contrasting phases of high and low shrub and C3 grass growth. Regional model simulations showed a gradual decline in gross primary production (GPP) for fire-introduced areas through the initial couple of years instead of killing all the vegetation in the affected area in the first year itself. We also compared the results from EDv2.2 with satellite data for the areas in RCEW affected by the 2015 Soda Fire. We observed a good spatial agreement between modeled GPP and a Landsat image-derived index for the study area with moderate to marginally strong correlations at the pixel level between maps of post-fire recovery GPP and the vegetation response observed in a post-fire Landsat image. This study contributes in understanding the application of ecosystem models to investigate temporal dynamics of vegetation under alternative fire regimes and the spatial behavior of post-fire ecosystem restoration.
Publisher: Copernicus GmbH
Date: 04-02-2020
Publisher: American Geophysical Union (AGU)
Date: 07-2022
DOI: 10.1029/2022WR032314
Abstract: Soil biota generates carbon that exports vertically to the atmosphere (CO 2 ) and transports laterally to streams and rivers (dissolved organic and inorganic carbon, DOC and DIC). These processes, together with chemical weathering, vary with flow paths across hydrological regimes yet an integrated understanding of these interactive processes is still lacking. Here we ask: How and to what extent do subsurface carbon transformation, chemical weathering, and solute export differ across hydrological and subsurface structure regimes? We address this question using a hillslope reactive transport model calibrated using soil CO 2 and water chemistry data from Fitch, a temperate forest at the ecotone boundary of the Eastern temperate forest and mid‐continent grasslands in Kansas, USA. Model results show that droughts (discharge at 0.08 mm/day) promoted deeper flow paths, longer water transit time, carbonate precipitation, and mineralization of organic carbon (OC) into inorganic carbon (IC) (∼98% of OC). Of the IC produced, ∼86% was emitted upward as CO 2 gas and ∼14% was exported laterally as DIC into the stream. Storms (8.0 mm/day) led to carbonate dissolution but reduced OC mineralization (∼88% of OC) and promoted DOC production (∼12% of OC) and lateral fluxes of IC (∼53% of produced IC). Differences in shallow‐versus‐deep permeability contrasts led to smaller difference ( %) than discharge‐induced differences and were most pronounced under wet conditions. High permeability contrasts (low vertical connectivity) enhanced lateral fluxes. Model results generally delineate hillslopes as active CO 2 producers and vertical carbon transporters under dry conditions, and as active DOC producers and lateral carbon transporter under wet conditions.
Publisher: Copernicus GmbH
Date: 13-12-2018
Publisher: Copernicus GmbH
Date: 13-12-2018
DOI: 10.5194/GMD-2018-264
Abstract: Abstract. Gross primary production (GPP) is one of the most critical processes in the global carbon cycle, but is difficult to quantify in part because of its high spatiotemporal variability. Direct techniques to quantify GPP are lacking, thus, researchers rely on data inferred from eddy covariance (EC) towers and/or ecosystem dynamic models. The latter are useful to quantify GPP over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. However, such models have also been associated with internal uncertainties and complexities arising from distinct qualities of the ecosystem being analyzed. Widely distributed sagebrush-steppe ecosystems in western North America are threatened by anthropogenic disturbance, invasive species, climate change, and altered fire regimes. Although land managers have focused on different restoration techniques, the effects of these activities and their interactions with fire, climate change, and invasive species on ecosystem dynamics are poorly understood. In this study, we applied an ecosystem dynamic model, Ecosystem Demography (EDv2.2), to parameterize and predict GPP for sagebrush-steppe ecosystems in the Reynolds Creek Experimental Watershed (RCEW), located in the northern Great Basin. Our primary objective was to develop and parameterize a sagebrush (Artemisia spp.) shrubland Plant Functional Type (PFT) for use in the EDv2.2 model, which will support future studies to model estimates of GPP under different climate and management scenarios. To accomplish this, we employed a three-tiered approach. First, to parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, gathered information from existing sagebrush literature, and borrowed values from other PFTs in EDv2.2. Second, we identified the five most sensitive parameters out of thirteen that were found to be influential in GPP prediction based on previous studies. Third, we optimized the five parameters using an exhaustive search method to predict GPP, and performed validation using observations from two EC sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. We expect that, with further refinement, the resulting sagebrush PFT will permit explicit scenario testing of potential anthropogenic modifications of GPP in sagebrush ecosystems, and will contribute to a better understanding of the role of sagebrush ecosystems in shaping global carbon cycles.
Publisher: Copernicus GmbH
Date: 22-03-2021
Abstract: Abstract. Wildfires in sagebrush (Artemisia spp.)-dominated semi-arid ecosystems in the western United States have increased dramatically in frequency and severity in the last few decades. Severe wildfires often lead to the loss of native sagebrush communities and change the biogeochemical conditions which make it difficult for sagebrush to regenerate. Invasion of cheatgrass (Bromus tectorum) accentuates the problem by making the ecosystem more susceptible to frequent burns. Managers have implemented several techniques to cope with the cheatgrass–fire cycle, ranging from controlling undesirable fire effects by removing fuel loads either mechanically or via prescribed burns to seeding the fire-affected areas with shrubs and native perennial forbs. There have been a number of studies at local scales to understand the direct impacts of wildfire on vegetation however there is a larger gap in understanding these impacts at broad spatial and temporal scales. This need highlights the importance of dynamic global vegetation models (DGVMs) and remote sensing. In this study, we explored the influence of fire on vegetation composition and gross primary production (GPP) in the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model, a dynamic global vegetation model. We selected the Reynolds Creek Experimental Watershed (RCEW) to run our simulation study, an intensively monitored sagebrush-dominated ecosystem in the northern Great Basin. We ran point-based simulations at four existing flux tower sites in the study area for a total of 150 years after turning on the fire module in the 25th year. Results suggest dominance of shrubs in a non-fire scenario however under the fire scenario we observed contrasting phases of high and low shrub density and C3 grass growth. Regional model simulations showed a gradual decline in GPP for fire-introduced areas through the initial couple of years instead of killing all the vegetation in the affected area in the first year itself. We also compared the results from EDv2.2 with satellite-derived GPP estimates for the areas in the RCEW burned by a wildfire in 2015 (Soda Fire). We observed moderate pixel-level correlations between maps of post-fire recovery EDv2.2 GPP and MODIS-derived GPP. This study contributes to understanding the application of ecosystem models to investigate temporal dynamics of vegetation under alternative fire regimes and post-fire ecosystem restoration.
Publisher: MDPI AG
Date: 22-09-2017
DOI: 10.3390/RS9100981
Publisher: Elsevier BV
Date: 09-2023
Location: United States of America
Start Date: 2013
End Date: 2018
Funder: National Science Foundation - Directorate for Geosciences
View Funded ActivityStart Date: 2015
End Date: 2017
Funder: National Science Foundation - Directorate for Biological Sciences
View Funded ActivityStart Date: 2013
End Date: 2017
Funder: National Science Foundation - Office of the Director
View Funded Activity