Measuring and modeling deep drainage, streamflow, and soil moisture in Oklahoma

This dissertation examines multiple components of the Oklahoma water balance in order to answer three independent research questions:i) Can long-term soil moisture monitoring data be used to estimate potential groundwater recharge rates? Daily drainage rates from the root zone were estimated for 78 sites using up to 17 years of soil moisture data from the Oklahoma Mesonet. Mean annual drainage rates ranged from 6 to 266 mm yr-1, with a statewide median of 67 mm yr-1. Drainage estimates were also modeled for four focus sites using HYDRUS1-D. Soil moisture-based drainage rates and HYDRUS1-D drainage rates agreed within 10 mm yr-1 at two drier sites but had discrepancies of >150 mm yr-1 at two sites with >1000 mm yr-1 precipitation.ii) Does incorporating soil moisture information improve seasonal streamflow forecast accuracy? A modified version of the standard Natural Resources Conservation Service (NRCS) principal component analysis and regression (PCR) model was developed to forecast streamflow in four rainfall-dominated watersheds. This model incorporated antecedent precipitation and soil moisture data from long-term monitoring networks into PCR analysis to predict seasonal streamflow volumes at 0-, 1-, 2-, and 3-month lead times. Including soil moisture data improved forecast accuracy by more than 50% over precipitation-based forecasts.iii) Can root zone soil moisture under diverse land cover types be effectively estimated by integrating ground-based meteorological data and remotely-sensed vegetation index data? Estimates of root zone soil moisture were made for four focus locations - a mixed hardwood forest, a loblolly pine plantation, cropland, and tallgrass prairie - by integrating ground-based meteorological data and basal crop coefficient curves derived from remotely-sensed vegetation index data within a soil water balance model. Results show that the model is able to estimate plant available water dynamics moderately well at the four focus locations, but needs further improvements before it can be used operationally

Nutrient loss and water quality

The Oklahoma Cooperative Extension Service periodically issues revisions to its publications. The most current edition is made available. For access to an earlier edition, if available for this title, please contact the Oklahoma State University Library Archives by email at [email protected] or by phone at 405-744-6311

Increasing Diversity, Equity, and Inclusion in the Soil and Agronomic Sciences through K-12 Outreach and Education

The SSSA K-12 Education and Outreach committee continually organizes and carries out activities, webinars, and workshops geared toward increasing K-12 student interest and participation in soil and agronomic sciences. This presentation highlights the efforts and impact of several committee members in conducting outreach work in their local communities targeted at improving recruitment, retention, and representation of historically marginalized groups- including young women, people of color, and underprivileged youth- in the sciences

Impacts of vegetation and topsoil removal on soil erosion, soil moisture, and infiltration

Abstract Soil erosion by water is a critical environmental problem that affects a large land area globally and is expected to increase substantially as a result of climate change‐induced rainfall intensification. This study sought to determine the impacts of soil mechanical disturbance and topsoil removal on soil erosion and deposition, soil moisture, soil water infiltration, and vegetation type in a central Texas subtropical grassland setting. The removal of topsoil increased soil erosion and caused changes in soil texture, soil moisture, infiltration rate, and the vegetation community within the disturbed area. Soil texture shifted from clay to sand dominated as sand particles were eroded from an upland area. This shift in soil texture led to decreased volumetric soil moisture (−14.6%) as compared to an adjacent undisturbed location. The removal of topsoil also led to an invasion of non‐native grasses that now dominate the vegetated portions of the disturbed area. Erosion and deposition measurements indicated an uneven gain and loss of soil across the disturbed area, but on average 0.85 cm of sand was deposited during the 1‐month study period. Our results show that high levels of erosion are occurring and degrading the immediate landscape, which is likely contributing a large amount of sediment to a nearby waterway, posing a potential water quality threat to an important surface water body

Estimating Deep Drainage Using Deep Soil Moisture Data under Young Irrigated Cropland in a Desert-Oasis Ecotone, Northwest China

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 ( L.) and wheat ( 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

Using a network of networks for high-frequency multi-depth soil moisture observations to infer spatial and temporal drivers of subsurface preferential flow

Preferential flow (PF) is defined as rapid subsurface bypass flow in the unsaturated soil and bedrock, and is a critical process that influences soil water availability and quality. Unfortunately, the lack of a mechanistic understanding of the controls on PF limits the ability to ensure ecological health and proper management of water supply and quality. However, recent developments in the availability of high-frequency and multi-depth soil moisture data from global monitoring networks across diverse landscapes (e.g., meteorology, ecology, and geology), as well as advances in data analysis methods (e.g., artificial intelligence and machine learning) make it possible to find answers to the fundamental questions of where and when PF occurs and what factors control PF occurrence. Outcomes of this synthesis work may be utilized to develop models capable of detecting and predicting PF events based on non-sequential wetting patterns, water flow velocity estimates, and other methods. In this presentation, we will describe the general approach and present initial results of a synthesis project and will interactively explore possibilities of additional data sets from conference participants