Browsing by Author "Li, Bonan"

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    Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community
    (Wiley, 2021-12) Nagy, R. Chelsea; Balch, Jennifer K.; Bissell, Erin K.; Cattau, Megan E.; Glenn, Nancy F.; Halpern, Benjamin S.; Ilangakoon, Nayani; Johnson, Brian; Joseph, Maxwell B.; Marconi, Sergio; O’Riordan, Catherine; Sanovia, James; Swetnam, Tyson L.; Travis, William R.; Wasser, Leah A.; Woolner, Elizabeth; Zarnetske, Phoebe; Abdulrahim, Mujahid; Adler, John; Barnes, Grenville; Bartowitz, Kristina J.; Blake, Rachael E.; Bombaci, Sara P.; Brun, Julien; Buchanan, Jacob D.; Chadwick, K. Dana; Chapman, Melissa S.; Chong, Steven S.; Chung, Y. Anny; Corman, Jessica R.; Couret, Jannelle; Crispo, Erika; Doak, Thomas G.; Donnelly, Alison; Duffy, Katharyn A.; Dunning, Kelly H.; Duran, Sandra M.; Edmonds, Jennifer W.; Fairbanks, Dawson E.; Felton, Andrew J.; Florian, Christopher R.; Gann, Daniel; Gebhardt, Martha; Gill, Nathan S.; Gram, Wendy K.; Guo, Jessica S.; Harvey, Brian J.; Hayes, Katherine R.; Helmus, Matthew R.; Hensley, Robert T.; Hondula, Kelly L.; Huang, Tao; Hundertmark, Wiley J.; Iglesias, Virginia; Jacinthe, Pierre‐Andre; Jansen, Lara S.; Jarzyna, Marta A.; Johnson, Tiona M.; Jones, Katherine D.; Jones, Megan A.; Just, Michael G.; Kaddoura, Youssef O.; Kagawa‐Vivani, Aurora K.; Kaushik, Aleya; Keller, Adrienne B.; King, Katelyn B. S.; Kitzes, Justin; Koontz, Michael J.; Kouba, Paige V.; Kwan, Wai‐Yin; LaMontagne, Jalene M.; LaRue, Elizabeth A.; Li, Daijiang; Li, Bonan; Lin, Yang; Liptzin, Daniel; Long, William Alex; Mahood, Adam L.; Malloy, Samuel S.; Malone, Sparkle L.; McGlinchy, Joseph M.; Meier, Courtney L.; Melbourne, Brett A.; Mietkiewicz, Nathan; Morisette, Jeffery T.; Moustapha, Moussa; Muscarella, Chance; Musinsky, John; Muthukrishnan, Ranjan; Naithani, Kusum; Neely, Merrie; Norman, Kari; Parker, Stephanie M.; Perez Rocha, Mariana; Petri, Laís; Ramey, Colette A.; Record, Sydne; Rossi, Matthew W.; SanClements, Michael; Scholl, Victoria M.; Schweiger, Anna K.; Seyednasrollah, Bijan; Sihi, Debjani; Smith, Kathleen R.; Sokol, Eric R.; Spaulding, Sarah A.; Spiers, Anna I.; St. Denis, Lise A.; Staccone, Anika P.; Stack Whitney, Kaitlin; Stanitski, Diane M.; Stricker, Eva; Surasinghe, Thilina D.; Thomsen, Sarah K.; Vasek, Patrisse M.; Xiaolu, Li; Yang, Di; Yu, Rong; Yule, Kelsey M.; Zhu, Kai; Earth Sciences, School of Science
    It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.
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    The impact of fog on soil moisture dynamics in the Namib Desert
    (Elsevier, 2018-03) Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Vogt, Roland; Li, Lin; Seely, Mary; Earth Science, School of Science
    Soil moisture is a crucial component supporting vegetation dynamics in drylands. Despite increasing attention on fog in dryland ecosystems, the statistical characterization of fog distribution and how fog affects soil moisture dynamics have not been seen in literature. To this end, daily fog records over two years (Dec 1, 2014–Nov 1, 2016) from three sites within the Namib Desert were used to characterize fog distribution. Two sites were located within the Gobabeb Research and Training Center vicinity, the gravel plains and the sand dunes. The third site was located at the gravel plains, Kleinberg. A subset of the fog data during rainless period was used to investigate the effect of fog on soil moisture. A stochastic modeling framework was used to simulate the effect of fog on soil moisture dynamics. Our results showed that fog distribution can be characterized by a Poisson process with two parameters (arrival rate λ and average depth α (mm)). Fog and soil moisture observations from eighty (Aug 19, 2015–Nov 6, 2015) rainless days indicated a moderate positive relationship between soil moisture and fog in the Gobabeb gravel plains, a weaker relationship in the Gobabeb sand dunes while no relationship was observed at the Kleinberg site. The modeling results suggested that mean and major peaks of soil moisture dynamics can be captured by the fog modeling. Our field observations demonstrated the effects of fog on soil moisture dynamics during rainless periods at some locations, which has important implications on soil biogeochemical processes. The statistical characterization and modeling of fog distribution are of great value to predict fog distribution and investigate the effects of potential changes in fog distribution on soil moisture dynamics.
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    The impact of rainfall and fog on soil moisture dynamics in the Namib Desert
    (2017-07) Li, Bonan; Wang, Lixin
    Soil moisture is a key variable in dryland ecosystems. Knowing how and to what extent soil moisture is influenced by rainfall and non-rainfall waters (e.g., dew, fog, and water vapor) is essential to understand dryland dynamics. The hyper-arid environment of the Namib Desert with its frequent occurrence of fog events provides an ideal place to conduct research on the rainfall and non-rainfall effects on soil moisture dynamics. Rainfall and soil moisture records was collected from three locations (gravel plain at Gobabeb (GPG), sand dune at Gobabeb (SDG), and gravel plain at Kleinberg (GPK)) within the Namib Desert using CS655 Water Content Reflectometer and tipping-buckets, respectively. The fog data was collected from the FogNet stations. Field observations of rainfall and soil moisture from three study sites suggested that soil moisture dynamics follow rainfall patterns at two gravel plain sites, whereas no significant relationships was observed at the sand dune site. The stochastic modeling results showed that most of soil moisture dynamics can be simulated except the rainless periods. Model sensitivity in response to different soil and vegetation parameters was investigated under diverse soil textures. Sensitivity analyses suggested that soil hygroscopic point (sh), field capacity (sfc) were two main parameters controlling the model output. Despite soil moisture dynamics can be partially explained by rainfall, soil moisture dynamics during rainless period still poorly understood. In addition, characterization of fog distribution in the Namib Desert is still lacking. To this end, nearly two years’ continuous daily records of fog were used to derive fog distribution. The results suggested that fog is able to be well - characterized by a Poisson process with two parameters (arrival rate and average depth). Field observations indicated that there is a moderate positive relationship between soil moisture and fog at GPG and the relationship tend to be less significant at the other two sites. A modified modeling results suggested that mean and general patterns of soil moisture can be captured by the modeling. This thesis is of practical importance for understanding soil moisture dynamics in response to the rainfall and fog changing conditions.
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    The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert.
    (PLOS, 2016) Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.; Department of Earth Sciences, School of Science
    Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert.
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    A multi-scale analysis of Namibian rainfall over the recentdecade – comparing TMPA satellite estimates and groundobservations
    (Elsevier, 2016-12) Lu, Xuefei; Wang, Lixin; Pan, Ming; Kaseke, Kudzai F.; Li, Bonan; Department of Earth Sciences, School of Science
    Study region Namibia. Study focus The lack of ground observations has long been a major obstacle in studying rainfall patterns in many dryland regions, particularly in the data scarce African continent. In this study, a continuous 6-year (2008–2013) daily record of ground observations collected from Weltevrede Farm at the edge of the Namib Desert was used to evaluate TRMM Multi-satellite Precipitation Analysis (TMPA, 0.25° resolution) daily rainfall estimates of this area. A Mann-Kendall trend analysis was conducted using all the available annual TMPA satellite data (1998–2015) to examine long-term trends in rainfall amount, intensity, frequency and seasonal variations over four locations across a rainfall gradient. New hydrological insights for the region The agreement between ground and satellite rainfall data was generally good at annual/monthly scales but large variations were observed at the daily scale. Results showed a spatial variability of rainfall trends across the rainfall gradient. We observed significant changes in frequency along with insignificant changes in intensity and no changes in total amount for the driest location, but no changes in any of the rainfall parameters were observed for the three wetter locations. The results also showed increased rainfall variability for the driest location. This study provided a useful approach of using TMPA data associated with trend analysis to extend the data record for ecohydrological studies for similar data scarce conditions. The results of this study will also help constrain IPCC predictions in this region.