Lab

Vadose Zone Research Group at Texas A&M University

About the lab

The Vadose Zone Research Group at Texas A&M carries out research on a wide spectrum of topics related to the unsaturated zone like (but not restricted to) numerical modeling of hydrological processes, understanding soil bio-geo-chemistry, understanding scaling issues in hydrology, and, field- scale observations and measurements.

Featured projects (1)

Project
Soil moisture and hydrologic fluxes in the root zone (land surface to shallow groundwater table) are known to significantly influence atmospheric boundary layer, groundwater recharge, and surface and subsurface runoff production. Results of most previous studies indicate soil moisture spatio-temporal pattern reflects a conjoint variability of soil, topography, vegetation, and precipitation, and is “dominated” by soil properties at the field scale, topographic features at the catchment/watershed scale, vegetation characteristics and precipitation patterns at the regional scale and beyond. Ensemble hydrologic fluxes (including evapotranspiration, infiltration, shallow groundwater recharge) within and across the vadose zone reflect the evolution of soil moisture at a particular spatial scale (field, watershed, or region) and can be “effectively” represented by one or more linear/nonlinear hydrologic scale parameters. Overarching the above concepts, we hypothesized that effective soil hydraulic property in the root zone at the footprint-scale is an effective indicator for combined soil, topography, and vegetation heterogeneities in land-atmosphere interaction models at different spatial scales. To test this overarching scientific hypothesis we will utilize state-of-the-art remotely sensed (RS) near-surface soil moisture data at multiple resolutions with a newly developed inverse model including a soil-water-atmosphere-plant model and advanced parameter estimation techniques. Effective root zone soil hydraulic parameters estimated using deterministic or stochastic inverse modeling approach (top-down) will be tested and compared for relative performance against numerical-cum-measurement upscaling schemes (bottom-up) for steady and transient conditions inclusive of various soil textures and structures, small and large scale topographic features, and a range of land covers and root distributions. We will evaluate our proposed inverse modeling (top-down) approach at three hydro-climatic regions of humid Iowa, semi-humid Oklahoma, and semiarid Arizona by using thematically-measured root zone soil hydraulics followed by appropriate upscaling (bottom-up) treatment. Predictive watershed scale modeling will test our hypothesis that “effective” hydraulic properties with larger RS footprint-scale measurement support are better predictors than “upscaled” hydraulic properties using local-scale measurements and scaling rules, based on estimated hydrologic fluxes (runoff/streamflow, ET, and soil moisture) and their uncertainties. Once validated, effective soil hydraulic property database for the continental USA will be generated using SMAP soil moisture products at 36 km X 36 km resolution. Determining “effective root zone soil hydraulic properties” in complex landscapes from remote sensing data will open up a new paradigm and will have tremendous impacts on our ability to predict terrestrial hydrology, weather, climate, and global circulation of water, energy, and chemicals in the environment.

Featured research (7)

Abrupt onset and swift intensification characterize flash droughts. Global surface soil moisture (θRS) from NASA's Soil Moisture Active Passive (SMAP) satellite can facilitate a near-real-time assessment of emerging flash droughts at a 36-km footprint. However, a robust flash drought monitoring using θRS must account for the i) short observation record of SMAP, ii) non-linear geophysical controls over θRS dynamics, and iii) emergent meteorological drivers of flash droughts. We propose a new method for near-real-time characterization of droughts using Soil Moisture Stress (SMS, drought stress) and Relative Rate of Drydown (RRD, drought stress intensification rate) ─ developed using SMAP θRS (March 2015-May 2021), footprint-scale seasonal soil water retention parameters and land-atmospheric coupling strength. SMS and RRD are nonlinearly combined to develop Flash Drought Stress Index (FDSI) to characterize emerging flash droughts (FDSI ≥ 0.71 for moderate to high RRD and SMS). Globally, FDSI shows a high correlation with concurrent meteorological anomalies. A mechanistic evaluation of flash droughts is presented for the Northern Great Plains, Central South Africa, and Eastern Australia using FDSI, SMS, and RRD. About 5.6% of the earth’s landmass experienced flash droughts of varying intensity and duration during 2015-2021 (FDSI ≥ 0.71 for >30 consecutive days), majorly in global drylands. FDSI shows high skill in forecasting vegetation health with a lead of 0-2 weeks, with exceptions in irrigated croplands and mixed forests. With readily available parameters, low data latency, and no dependence on model simulations, we provide a robust tool for global near-real-time flash drought monitoring using SMAP.
Flash droughts are characterized by an abrupt onset and swift intensification. Global surface soil moisture (θRS) from NASA's Soil Moisture Active Passive (SMAP) satellite can facilitate a near-real-time assessment of emerging flash droughts at 36-km footprint. However, a robust flash drought monitoring using θRS must account for the i) short observation record of SMAP, ii) non-linear geophysical controls over θRS dynamics, and, iii) emergent meteorological drivers of flash droughts. We propose a new method for near-real-time characterization of droughts using Soil Moisture Stress (SMS, drought stress) and Relative Rate of Drydown (RRD, drought stress intensification rate) ─ developed using SMAP θRS (March 2015-2019) and footprint-scale seasonal soil water retention parameters and land-atmospheric coupling strength. SMS and RRD are nonlinearly combined to develop Flash Drought Stress Index (FDSI) to characterize emerging flash droughts (FDSI ≥ 0.71 for moderate to high RRD and SMS). Globally, FDSI shows high correlation with concurrent meteorological anomalies. A retrospective evaluation of select droughts is demonstrated using FDSI, including a mechanistic evaluation of the 2017 flash drought in the Northern Great Plains. About 5.2% of earth’s landmass experienced flash droughts of varying intensity and duration during 2015-2019 (FDSI ≥ 0.71 for >30 consecutive days), majorly in global drylands. FDSI shows high skill in forecasting vegetation health with a lead of 0-2 weeks, with exceptions in irrigated croplands and mixed forests. With readily available parameters, low data latency, and no dependence on model simulations, we provide a robust tool for global near-real-time flash drought monitoring using SMAP.
Understanding the global soil moisture (SM) dynamics and its governing controls beyond the Darcy Scale is critical for various hydrologic, meteorological, agricultural, and environmental applications. In this study, we parameterize the pathways of the seasonal drydowns using global surface soil moisture (θRS) observation from SMAP satellite (between 2015 and 2019) at 36 km resolution. We develop a new data‐driven non‐parametric approach to identify the canonical forms of θRS drydown, followed by a nonlinear least‐squares parameterization of the seasonal drydown pathways at each SMAP footprint. The derived parameters provide the effective soil water retention parameters (SWRPeff), land‐atmospheric coupling strength and soil hydrologic regimes for SMAP footprint. Depending on footprint heterogeneity, climate and season, the characteristics curves comprising different drydown phases are discovered at SMAP footprints. Drydown curves respond to the within‐footprint changes in the meteorological drivers, land‐surface characteristics and the soil‐vegetative and atmospheric dynamics. Drydown parameters display high inter‐seasonal variability, especially in grasslands, croplands and savannah landscapes due to significant changes in the landscape characteristics and moisture patterns at the subgrid‐scale. Soil texture exerts influence on the soil water retention and drydown parameters only when the footprint mean θRS is low, specifically in arid and sparsely vegetated regions. The influence of soil texture on the inter‐seasonal variability of SWRPeff is low compared to landuse and climate at the RS‐footprint scale. The global understanding of SM drydown features at SMAP footprints provides a significant step towards a scale‐specific, effective soil hydrologic parameterization for various applications.
Soil water retention parameters (SWRPs) are important in determining the plant available water at field-scale and are crucial inputs to all land-surface models. However, at large special scales, climate and vegetation moderate the effective soil water retention, thus diminishing the significance of these parameters which are defined (rather obscurely) only for fine-scale processes. The physical manifestation of the influence of plants and climate on soil moisture dynamics is engrained in the soil moisture drydown curves (rate of loss of moisture v/s initial soil moisture in the profile). Recent advancements in the remote sensing of soil moisture has made it possible to observe and parameterize these signature patterns at large spatial scales. Using four years of remote sensing observations from Soil Moisture Active-Passive (SMAP) at 36-km resolution, we parameterize surface soil moisture drydown curves for the Contiguous US and evaluate the spatial patterns across the continent. We observe that the characteristic drydown signatures of surface soil moisture show a significant influence of climate, vegetation (and landuse). A comparison is made with the estimated SWRPs using a pedotransfer function to highlight the difference between the upscaled and the empirically observed values of the SWRPs. The proposed parameterization of the soil moisture drydown curves is a step forward in recognizing the dominance of the coupled influence of the climate and vegetation on soil moisture drydowns, and possible segregation of the governing controls on the soil moisture dynamic at large spatial scales.

Lab head

B. P. Mohanty
Department
  • Department of Biological & Agricultural Engineering (Dwight Look College of Engineering )

Members (10)

Bhavna Arora
  • Lawrence Berkeley National Laboratory
Jonggun Kim
  • Texas A&M University
Vinit Sehgal
  • Texas A&M University
Joseph A. P. Pollacco
  • Manaaki Whenua - Landcare Research
Raghavendra Belur Jana
  • Skolkovo Institute of Science and Technology
Nandita Gaur
  • University of Georgia
Dhruva Kathuria
  • Texas A&M University
Deanroy Mbabazi
  • Texas A&M University
Yongchul Shin
Yongchul Shin
  • Not confirmed yet
Maheshwari Neelam
Maheshwari Neelam
  • Not confirmed yet
Zijuan Chen
Zijuan Chen
  • Not confirmed yet