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Soil moisture data from the Oklahoma Mesonet have been used in numerous fields within the Earth sciences, including agriculture, hydrology, and meteorology. Soil matric potentials measured by heat dissipation sensors at Oklahoma Mesonet stations have been converted to soil volumetric water content estimates using soil water retention curve parameters estimated by the Rosetta pedotransfer function. Recently, an improved version of Rosetta, Rosetta3, was released. Informed by this new pedotransfer function and soil sampling at additional locations, an improved version of the Oklahoma Mesonet soil physical property database, MesoSoil v2.0, has been created. This article presents this new soil database, compares soil water retention parameters estimated using Rosetta3 with those derived from the original Rosetta model, and describes the effects of changes in those parameters on estimated volumetric water content. Using the Rosetta3 model led to changes in the estimated water retention parameters, most notably decreases in the parameter α, which is related to the inverse of the air-entry potential. These changes resulted in volumetric water content estimates that differed from those based on Rosetta1, with mean absolute differences averaging 0.02 cm3 cm–3 across all site-years and the greatest differences occurring during wet periods. The mean volumetric water content estimated using the Rosetta3 parameters across more than 100 sites was not significantly different from that determined by soil sampling. The updated database is publicly available and may be found here: http://soilphysics.okstate.edu/data.
Briana M. Wyatt; Tyson E. Ochsner; William G. Brown; D. Cole Diggins; Bradley G. Illston; Christopher A. Fiebrich. MesoSoil v2.0: An updated soil physical property database for the Oklahoma Mesonet. Vadose Zone Journal 2021, e20134 .
AMA StyleBriana M. Wyatt, Tyson E. Ochsner, William G. Brown, D. Cole Diggins, Bradley G. Illston, Christopher A. Fiebrich. MesoSoil v2.0: An updated soil physical property database for the Oklahoma Mesonet. Vadose Zone Journal. 2021; ():e20134.
Chicago/Turabian StyleBriana M. Wyatt; Tyson E. Ochsner; William G. Brown; D. Cole Diggins; Bradley G. Illston; Christopher A. Fiebrich. 2021. "MesoSoil v2.0: An updated soil physical property database for the Oklahoma Mesonet." Vadose Zone Journal , no. : e20134.
Many soil moisture networks monitor only one land cover type, typically grassland, and the availability of in-situ soil moisture data in other land cover types is severely limited. Satellite-based radiometers lack adequate resolution to match the spatial variability in land cover, which often occurs at the sub-kilometer scale. Thus, spatial and temporal dynamics of root zone soil moisture in regions with heterogeneous land cover types remain poorly understood. Our objective was to determine how effectively root-zone soil moisture for diverse land cover types can be estimated using a water balance model driven by normalized high-resolution, remotely sensed vegetation indices (VI) data and in-situ meteorological data. Root zone soil moisture dynamics under four different land cover types were estimated using normalized VI data as a proxy for the basal crop coefficient. Correlation coefficients (r) between measured and modeled soil moisture ranged from 0.50–0.92, mean absolute error (MAE) ranged from 0.03–0.06 m3 m−3, and mean bias error (MBE) ranged from -0.05–0.02 m3 m−3 across tallgrass prairie, cropland, mixed hardwood forest, and loblolly pine plantation sites. Model-estimated soil moisture under each land cover type was more accurate than both measured data from the nearest long-term grassland monitoring site and data from the NASA-USDA Enhanced Soil Moisture Active-Passive (SMAP) soil moisture product, providing evidence that in-situ meteorological data and remotely sensed VI data may be integrated into a simple water balance model to better estimate root zone soil moisture across diverse land cover types.
Briana M. Wyatt; Tyson E. Ochsner; Chris B. Zou. Estimating root zone soil moisture across diverse land cover types by integrating in-situ and remotely sensed data. Agricultural and Forest Meteorology 2021, 307, 108471 .
AMA StyleBriana M. Wyatt, Tyson E. Ochsner, Chris B. Zou. Estimating root zone soil moisture across diverse land cover types by integrating in-situ and remotely sensed data. Agricultural and Forest Meteorology. 2021; 307 ():108471.
Chicago/Turabian StyleBriana M. Wyatt; Tyson E. Ochsner; Chris B. Zou. 2021. "Estimating root zone soil moisture across diverse land cover types by integrating in-situ and remotely sensed data." Agricultural and Forest Meteorology 307, no. : 108471.
Evapotranspiration (ET) is the dominant water loss flux in mesic tallgrass prairie. Partitioning of ET into its two components—soil evaporation (E) and plant transpiration (T)—is challenging but critical for unraveling biophysical processes underlying ecosystem functioning and sustainability in a changing environment. Because of the pulsed nature of ecophysiological processes in this water-limited ecosystem, we carried out two field campaigns during wetting–drying episodes following precipitation pulses. We applied a two-source isotopic mixing model for ET partitioning. The isotopic compositions of ET, E, and T (δET, δE, and δT) were determined by the Keeling-plot method, the Craig–Gordon model, and midday plant xylem water, respectively. We found that the ET partitioning results (T/ET) could be more accurately quantified with 2H than with 18O, because of (1) the better performance of 2H in Keeling-plot regressions of high-temporal-frequency isotopic measurements of water vapor, and (2) the stronger sensitivity of 2H to the equilibrium fractionation. Using 2H values, we found that the mean ± standard deviation of T/ET was 0.84 ± 0.05 and 0.92 ± 0.06 during two field campaigns. Soil water near the surface (especially the top 10 cm) responded actively during these two wetting–drying episodes and was the major source for the total ET flux during the initial drying periods. Only after shallow soil moisture had become substantially exhausted did deeper soil layers (up to 1 m) increasingly become the major source for the T flux, while the E flux declined progressively to a negligible level.
Xiangmin Sun; Bradford Wilcox; Chris B. Zou; Elaine Stebler; Jason B. West; Briana Wyatt. Isotopic partitioning of evapotranspiration in a mesic grassland during two wetting–drying episodes. Agricultural and Forest Meteorology 2021, 301-302, 108321 .
AMA StyleXiangmin Sun, Bradford Wilcox, Chris B. Zou, Elaine Stebler, Jason B. West, Briana Wyatt. Isotopic partitioning of evapotranspiration in a mesic grassland during two wetting–drying episodes. Agricultural and Forest Meteorology. 2021; 301-302 ():108321.
Chicago/Turabian StyleXiangmin Sun; Bradford Wilcox; Chris B. Zou; Elaine Stebler; Jason B. West; Briana Wyatt. 2021. "Isotopic partitioning of evapotranspiration in a mesic grassland during two wetting–drying episodes." Agricultural and Forest Meteorology 301-302, no. : 108321.
The recent novel coronavirus pandemic led to global changes in higher education as universities transitioned to online learning in order to slow the spread of the virus. In the U.S., this transition occurred during the spring of 2020, and the compulsory shift to online learning led to frustrations from students and instructors alike. I studied student participation during the online portion of a university‐level soil physics course taught in Spring 2020. Participation was quantified using the number of student posts in weekly discussion boards, the number of student views of asynchronous videos, and the number of video views during each week of online instruction. Relationships between video length and number of student views and between student participation and final exam grades were also examined. My findings show that student views of mini‐lecture videos were low and decreased throughout the online learning period. Conversely, views of example problem videos and the number of posts on graded discussion boards were high and remained high throughout the online learning period, suggesting that students were more engaged with online material that affected their grades. I also found that the level of student engagement in online material was positively correlated with higher final exam scores. The findings presented here may be used to improve the development and delivery of online coursework in natural science disciplines, both during current and future emergencies. This article is protected by copyright. All rights reserved
Briana Wyatt. Insights into student participation in a soil physics course during COVID‐19 emergency online learning. Natural Sciences Education 2020, 50, 1 .
AMA StyleBriana Wyatt. Insights into student participation in a soil physics course during COVID‐19 emergency online learning. Natural Sciences Education. 2020; 50 (1):1.
Chicago/Turabian StyleBriana Wyatt. 2020. "Insights into student participation in a soil physics course during COVID‐19 emergency online learning." Natural Sciences Education 50, no. 1: 1.
Seasonal streamflow forecasting methods are less skillful in rainfall-dominated catchments than snow-dominated catchments, where measurements of water storage in the snowpack enhance predictability. Recent research in snow-dominated catchments showed that forecasts can be further enhanced by also including soil moisture measurements, but the impact of soil moisture data on forecast performance in rainfall-dominated watersheds remains unknown. Our objective was to evaluate the potential improvements gained by including in-situ soil moisture data in seasonal streamflow forecasting models in rainfall-dominated watersheds. Precipitation and soil moisture data from four watersheds in the U.S. were incorporated into a modified principal components analysis and regression method to predict seasonal (4-month) streamflow totals at 0-, 1-, 2-, and 3-month lead times. Forecasts derived from antecedent precipitation alone were often statistically insignificant and explained less than 30% of the variance in seasonal streamflow, as indicated by the Nash-Sutcliffe efficiency coefficient. Conversely, forecast models that included soil moisture information explained up to 87% of seasonal streamflow variance at the 0-month lead time, up to 81% at the 1-month lead time, up to 71% at the 2-month lead time, and up to 52% at the 3-month lead time. The root mean square errors for forecasts which included soil moisture data were on average 55% lower than for those based on antecedent precipitation alone. The soil moisture-based forecasts for rainfall-dominated watersheds exhibited accuracies comparable to those previously reported in snow-dominated watersheds. This new forecast method shows strong potential for use in surface water management in rainfall-dominated regions.
Briana M. Wyatt; Tyson E. Ochsner; Erik S. Krueger; Eric T. Jones. In-situ soil moisture data improve seasonal streamflow forecast accuracy in rainfall-dominated watersheds. Journal of Hydrology 2020, 590, 125404 .
AMA StyleBriana M. Wyatt, Tyson E. Ochsner, Erik S. Krueger, Eric T. Jones. In-situ soil moisture data improve seasonal streamflow forecast accuracy in rainfall-dominated watersheds. Journal of Hydrology. 2020; 590 ():125404.
Chicago/Turabian StyleBriana M. Wyatt; Tyson E. Ochsner; Erik S. Krueger; Eric T. Jones. 2020. "In-situ soil moisture data improve seasonal streamflow forecast accuracy in rainfall-dominated watersheds." Journal of Hydrology 590, no. : 125404.
Deep drainage reduces agricultural water productivity under cropland recently converted from native desert soils (i.e., young cropland) and increases the risks of nutrient and pesticide leaching into groundwater in the desert-oasis ecotone. However, the deep drainage rates under young cropland in these oasis environments remain unclear, especially for winter irrigation, a common practice in Northwest China. The objective of this study was to estimate the deep drainage rate using the HYDRUS-1D model based on soil moisture data in the deep vadose zone. Soil moisture at depths ranging from 0 to 200 cm was measured using HydraProbe II soil sensors in maize (Zea mays L.) and wheat (Triticum aestivum L.) fields in 2015 and 2017, respectively. Using a novel simulation approach based on soil moisture data in the deep vadose zone, the HYDRUS-1D model provided reliable estimates of deep drainage as confirmed by comparison with estimates from the soil water balance method and prior studies in the region. The annual deep drainage averaged 468 mm, and the annual deep drainage coefficient averaged 43% in the young croplands. The winter irrigation amount averaged 265 mm, and the deep drainage coefficient during winter averaged 21% in the young croplands. The sandy soil of the young cropland and inefficient irrigation scheduling are detrimental to water conservation, causing relatively large deep drainage losses and enhancing the risks of groundwater pollution. Copyright © 2019. . © 2019 The Author(s).
Yongyong Zhang; Wenzhi Zhao; Tyson E. Ochsner; Briana M. Wyatt; Hu Liu; Qiyue Yang. Estimating Deep Drainage Using Deep Soil Moisture Data under Young Irrigated Cropland in a Desert‐Oasis Ecotone, Northwest China. Vadose Zone Journal 2019, 18, 1 -10.
AMA StyleYongyong Zhang, Wenzhi Zhao, Tyson E. Ochsner, Briana M. Wyatt, Hu Liu, Qiyue Yang. Estimating Deep Drainage Using Deep Soil Moisture Data under Young Irrigated Cropland in a Desert‐Oasis Ecotone, Northwest China. Vadose Zone Journal. 2019; 18 (1):1-10.
Chicago/Turabian StyleYongyong Zhang; Wenzhi Zhao; Tyson E. Ochsner; Briana M. Wyatt; Hu Liu; Qiyue Yang. 2019. "Estimating Deep Drainage Using Deep Soil Moisture Data under Young Irrigated Cropland in a Desert‐Oasis Ecotone, Northwest China." Vadose Zone Journal 18, no. 1: 1-10.
Groundwater supplies ~20% of global freshwater withdrawals, and accurate information regarding groundwater recharge rates is needed for sustainable groundwater management. Recharge rates are often limited by the rates of drainage from the soil profile, which are influenced by soil moisture conditions. Soil moisture monitoring has expanded dramatically in recent decades with the advent of large-scale networks like the Oklahoma Mesonet, which has monitored soil moisture statewide since 1996. Using those data with site-specific soil hydraulic properties and a unit-gradient assumption, we estimated daily drainage rates at 60 cm for 78 sites for up to 17 yr. Our working hypothesis was that these drainage rates are indicative of potential groundwater recharge rates. Mean annual drainage rates ranged from 6 to 266 mm yr−1, with a statewide median of 67 mm yr−1. These rates agreed well with prior recharge estimates for major Oklahoma aquifers. To provide a further independent check on our results, drainage was modeled using HYDRUS-1D for four focus sites across 17 yr. Soil-moisture-based drainage rates and HYDRUS-1D drainage rates agreed to within 10 mm yr−1 at the drier two sites but had discrepancies of > 150 mm yr−1 at two sites with > 1000 mm yr−1 precipitation. Simulations also showed that for a semiarid site the unit-gradient assumption was likely violated at the 60-cm depth, highlighting the need for deeper soil moisture monitoring. Despite these limitations, this simple method for estimating drainage through long-term soil moisture monitoring shows unique potential to provide valuable information for hydrology and groundwater management. Copyright © 2017. . Copyright © by the Soil Science Society of America, Inc.
Briana M. Wyatt; Tyson E. Ochsner; Christopher A. Fiebrich; Christopher R. Neel; David S. Wallace. Useful Drainage Estimates Obtained from a Large-Scale Soil Moisture Monitoring Network by Applying the Unit-Gradient Assumption. Vadose Zone Journal 2017, 16, 1 .
AMA StyleBriana M. Wyatt, Tyson E. Ochsner, Christopher A. Fiebrich, Christopher R. Neel, David S. Wallace. Useful Drainage Estimates Obtained from a Large-Scale Soil Moisture Monitoring Network by Applying the Unit-Gradient Assumption. Vadose Zone Journal. 2017; 16 (6):1.
Chicago/Turabian StyleBriana M. Wyatt; Tyson E. Ochsner; Christopher A. Fiebrich; Christopher R. Neel; David S. Wallace. 2017. "Useful Drainage Estimates Obtained from a Large-Scale Soil Moisture Monitoring Network by Applying the Unit-Gradient Assumption." Vadose Zone Journal 16, no. 6: 1.