Source: TEXAS A&M UNIVERSITY submitted to
SOIL, WATER, AND ENVIRONMENTAL PHYSICS TO SUSTAIN AGRICULTURE AND NATURAL RESOURCES
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
REVISED
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
1021289
Grant No.
(N/A)
Project No.
TEX0-2-3188
Proposal No.
(N/A)
Multistate No.
W-4188
Program Code
(N/A)
Project Start Date
Nov 25, 2019
Project End Date
Sep 30, 2024
Grant Year
(N/A)
Project Director
Mohanty, BI.
Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
Biological & Agricultural Engineering
Non Technical Summary
Our multi-scale soil hydrology and biogeochemistry effort will fill in a much-needed gap in linked water, energy, nutrient, and carbon cycle science including better understanding of mechanistic processes and develop new predictive modeling tools and decision support system for various applications including agriculture, water management, soil health, and adaption to climate change.
Animal Health Component
0%
Research Effort Categories
Basic
(N/A)
Applied
70%
Developmental
30%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
1010110205020%
1020120205020%
1110210205020%
1120310205020%
1320399205020%
Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships; 111 - Conservation and Efficient Use of Water; 112 - Watershed Protection and Management; 132 - Weather and Climate; 101 - Appraisal of Soil Resources;

Subject Of Investigation
0120 - Land; 0310 - River basins; 0110 - Soil; 0210 - Water resources; 0399 - Watersheds and river basins, general;

Field Of Science
2050 - Hydrology;
Goals / Objectives
1. Connect new understandings of storage and transport of mass and energy to assess environmental change. See attachment. 2. Develop and test new instrumentation, methods and models to improve the mechanistic understanding of soil processes and the quality of soil information and knowledge. See attachment. 3. Integrate scale-appropriate methods to improve decisions related to the management of soil and water resources.
Project Methods
We will use in situ, air-borne, and space-borne sensors for understanding soil hydrologic processes and related parameterization across various space and time scales under different hydro-climatic conditions. Our recently developed insitu sensor network (Texas Water Observatory) will be used for various scientific investigations and validation of new hypotheses, models, data analysis and scaling tools. Specifically we will explore - Soil moisture and ET causative relationships at different scales- Soil moisture drydown patterns and hydraulic parameters at footprint scale- Develop numerical modeling linking soil, ground water, and streamflow for water cycle at multiple scales- Spatial hierarchical data fusion to estimate multi-scale hydrologic fluxes including recharge and ET- Web-based decision support system for agriculture water management using scale-appropriate models and parameters

Progress 11/25/19 to 09/30/20

Outputs
Target Audience:The scientific community. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?Research findings were disseminated via refereed journal publications, conference proceedings, and a number of presentations at national and international meetings (see the publication section below). Developed and managed the operation of Texas Water Observatory on Brazos River Basin in Texas, a testbed for better understanding of coupled water, carbon, and energy cycle at different scales. What do you plan to do during the next reporting period to accomplish the goals?Continue as outlined in the objectives.

Impacts
What was accomplished under these goals? In 2020, field monitoring and laboratory experiments were conducted at Texas Water Observatory sites under different land use land covers for improved understanding of soil moisture, temperature, and carbon dynamics and soil hydraulic and retention properties variation from local, regional, to global scale. Using soil monitoring stations, Eddy Covariance towers, satellite, and International Space Station (ECOSTRESS) observations and improved process modeling concepts, we developed new soil moisture and ET retrieval, dry-down patterns, scaling, fusion, gap-filling, and forecasting techniques at multiple space-time scales. In addition, we measured soil hydraulics in different land covers and landscape positions and studied the linkages between surface water, ground water and vadose zone with bidirectional exchange schemes.

Publications

  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Fisher, J.B., B. Lee, A. Purdy, G.H. Halverson, M.B. Dohlen, K. Cawse?Nicholson, A. Wang, R.G. Anderson, B. Aragon, M.A. Arain, D.D. Baldocchi, J.M. Baker, H. Barral, C.J. Bernacchi, C. Bernhofer, S.C. Biraud, G. Bohrer, N. Brunsell, B. Cappelaere, S. Castro?Contreras, J. Chun, B.J. Conrad, E. Cremonese, J. Demarty, A.R. Desai, A.De Ligne, L. Folt�nov�, M.L. Goulden, T.J. Griffis, T. Gr�nwald, M.S. Johnson, M. Kang, D. Kelbe, N. Kowalska, J.?H. Lim, I. Ma�nassara, M.F. McCabe, J.E.C. Missik, B.P. Mohanty, C.E. Moore, L. Morillas, R. Morrison, J.W. Munger, G. Posse, A.D. Richardson, E.S. Russell, Y. Ryu, A. Sanchez?Azofeifa, M. Schmidt, E. Schwartz, I. Sharp, L. `igut, Y. Tang, G. Hulley, M. Anderson, C. Hain, A. French, E. Wood, and S. Hook, ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration from the International Space Station. Water Resources Research. 56, doi.org/10.1029/2019WR026058, 2020.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Neelam, M., A. Colliander, B.P. Mohanty, M.H. Cosh, S. Misra, and T.J. Jackson, Multi-Scale Surface Roughness for Improved Soil Moisture Estimation. IEEE Trans. Geoscience and Remote Sensing. doi:10.1109/TGRS.2019.2961008, 2020.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Jena, S., R.K. Panda, M. Ramadas, B.P. Mohanty, S.K. Pattanaik, Delineation of Groundwater Storage and Recharge Potential Zones using RS-GIS-AHP: Application in Arable Land Expansion. Remote Sensing Applications: Society and Environment. doi.org/10.1016/j.rsase.2020.100354, 2020.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: *Neelam, M., and B.P. Mohanty, On the Radiative Transfer Model for Soil Moisture Across Space, Time and Hydro-climates. Remote Sensing. 12, 2645; doi:10.3390/rs12162645, 2020.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Bayat, H., G. Ebrahimzadeh, and B.P. Mohanty, Investigating the Capability of Estimating Soil Thermal Conductivity using Topographical Attributes for the Southern Great Plains. Soil & Tillage Research. 206, https://doi.org/10.1016/j.still.2020.104811, 2020.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: *Hong, M., B.P. Mohanty, and Z. Sheng, An Explicit Scheme to Represent the Bidirectional Hydrologic Exchanges Between the Vadose Zone, Phreatic Aquifer, and River. Water Resources Research. 56, 10.1029/2020WR027571, 2020.
  • Type: Other Status: Published Year Published: 2020 Citation: Montzka, C., M. Cosh, B. Bayat, A. Al Bitar, A. Berg, R. Bindlish, H. R. Bogena, J. D. Bolten, F. Cabot, T. Caldwell, S. Chan, A. Colliander, W. Crow, N. Das, G. De Lannoy, W. Dorigo, S. R. Evett, A. Gruber, S. Hahn, T. Jagdhuber, S. Jones, Y. Kerr, S. Kim, C. Koyama, M. Kurum, E. Lopez-Baeza, F. Mattia, K. McColl, S. Mecklenburg, B. Mohanty, P. ONeill, D. Or, T. Pellarin, G. P. Petropoulos, M. Piles, R. H. Reichle, N. Rodriguez-Fernandez, C. R�diger, T. Scanlon, R. C. Schwartz, D. Spengler, P. Srivastava, S. Suman, R. van der Schalie, W. Wagner, U. Wegm�ller, J.-P. Wigneron, F. Camacho, and J. Nickeson, Soil Moisture Product Validation Good Practices Protocol Version 1.0. In: C. Montzka, M. Cosh, J. Nickeson, F. Camacho (Eds.): Good Practices for Satellite Derived Land Product Validation (p. 123), Land Product Validation Subgroup (WGCV/CEOS), Goddard Space Flight Center-NASA, doi:10.5067/doc/ceoswgcv/lpv/sm.001, 2020.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Mao, H., N. Duffield, X. Liu, H. Yuan, S. Ji and B.P. Mohanty, Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis, Procs. of 19th IEEE International Conference on Data Mining (ICDM 2020), Sorrento, Italy, November 17-20, 2020.
  • Type: Other Status: Published Year Published: 2019 Citation: Seyednasrollah, B., A.M. Young, K. Hufkens, T. Milliman, M.A. Friedl, S. Frolking, A.D. Richardson, M. Abraha, D.W. Allen, M. Apple, M.A. Arain, J. Baker, J.M. Baker, C.J. Bernacchi, J. Bhattacharjee, P. Blanken, D.D. Bosch, R. Boughton, E.H. Boughton, R.F. Brown, D.M. Browning, N. Brunsell, S.P. Burns, M. Cavagna, H. Chu, P.E. Clark, B.J. Conrad, E. Cremonese, D. Debinski, A.R. Desai, R. Diaz-Delgado, L. Duchesne, A.L. Dunn, D.M. Eissenstat, T. El-Madany, D.S.S. Ellum, S.M. Ernest, A. Esposito, L. Fenstermaker, L.B. Flanagan, B. Forsythe, J. Gallagher, D. Gianelle, T. Griffis, P. Groffman, L. Gu, J. Guillemot, M. Halpin, P.J. Hanson, D. Hemming, A.A. Hove, E.R. Humphreys, A. Jaimes-Hernandez, A.A. Jaradat, J. Johnson, E. Keel, V.R. Kelly, J.W. Kirchner, P.B. Kirchner, M. Knapp, M. Krassovski, O. Langvall, G. Lanthier, G.l. Maire, E. Magliulo, T.A. Martin, B. McNeil, G.A. Meyer, M. Migliavacca, B.P. Mohanty, C.E. Moore, R. Mudd, J.W. Munger, Z.E. Murrell, Z. Nesic, H.S. Neufeld, T.L. O'Halloran, W. Oechel, A.C. Oishi, W.W. Oswald, T.D. Perkins, M.L. Reba, B. Rundquist, B.R. Runkle, E.S. Russell, E.J. Sadler, A. Saha, N.Z. Saliendra, L. Schmalbeck, M.D. Schwartz, R.L. Scott, E.M. Smith, O. Sonnentag, P. Stoy, S. Strachan, K. Suvocarev, J.E. Thom, R.Q. Thomas, A.K. Van den berg, R. Vargas, C.S. Vogel, J.J. Walker, N. Webb, P. Wetzel, S. Weyers, A.V. Whipple, T.G. Whitham, G. Wohlfahrt, J.D. Wood, S. Wolf, J. Yang, X. Yang, G. Yenni, Y. Zhang, Q. Zhang, and D. Zona. 2019. PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1674
  • Type: Other Status: Published Year Published: 2019 Citation: Milliman, T., B. Seyednasrollah, A.M. Young, K. Hufkens, M.A. Friedl, S. Frolking, A.D. Richardson, M. Abraha, D.W. Allen, M. Apple, M.A. Arain, J. Baker, J.M. Baker, C.J. Bernacchi, J. Bhattacharjee, P. Blanken, D.D. Bosch, R. Boughton, E.H. Boughton, R.F. Brown, D.M. Browning, N. Brunsell, S.P. Burns, M. Cavagna, H. Chu, P.E. Clark, B.J. Conrad, E. Cremonese, D. Debinski, A.R. Desai, R. Diaz-Delgado, L. Duchesne, A.L. Dunn, D.M. Eissenstat, T. El-Madany, D.S.S. Ellum, S.M. Ernest, A. Esposito, L. Fenstermaker, L.B. Flanagan, B. Forsythe, J. Gallagher, D. Gianelle, T. Griffis, P. Groffman, L. Gu, J. Guillemot, M. Halpin, P.J. Hanson, D. Hemming, A.A. Hove, E.R. Humphreys, A. Jaimes-Hernandez, A.A. Jaradat, J. Johnson, E. Keel, V.R. Kelly, J.W. Kirchner, P.B. Kirchner, M. Knapp, M. Krassovski, O. Langvall, G. Lanthier, G.l. Maire, E. Magliulo, T.A. Martin, B. McNeil, G.A. Meyer, M. Migliavacca, B.P. Mohanty, C.E. Moore, R. Mudd, J.W. Munger, Z.E. Murrell, Z. Nesic, H.S. Neufeld, W. O echel, A.C. Oishi, W.W. Oswald, T.D. Perkins, M.L. Reba, B. Rundquist, B.R. Runkle, E.S. Russell, E.J. Sadler, A. Saha, N.Z. Saliendra, L. Schmalbeck, M.D. Schwartz, R.L. Scott, E.M. Smith, O. Sonnentag, P. Stoy, S. Strachan, K. Suvocarev, J.E. Thom, R.Q. Thomas, A.K. Van den berg, R. Vargas, C.S. Vogel, J.J. Walker, N. Webb, P. Wetzel, S. Weyers, A.V. Whipple, T.G. Whitham, G. Wohlfahrt, J.D. Wood, J. Yang, X. Yang, G. Yenni, Y. Zhang, Q. Zhang, and D. Zona. 2019. PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1689.
  • Type: Other Status: Published Year Published: 2020 Citation: Mooney, M., P.V. Caldwell, S. Zhang, A. Deshpande, C. Adkison, C. E. Cooper, B.P. Mohanty, G. Miller, M. Everett, J. Brumbelow, A. Cross, and G.W. Moore, Using Wet-Dry Forest Extremes to Construct Water Budgets and Improve Watershed Modeling in Hardwood Forests of East Texas USA, AGU Fall Meeting Abstract, 2020.
  • Type: Other Status: Published Year Published: 2020 Citation: Mbabazi, D, B.P. Mohanty, W. Kustas, and N. Gaur, Disaggregation of Eddy Covariance Evapotranspiration Fluxes from Heterogeneous Landscapes, AGU Fall Meeting Abstract, 2020.
  • Type: Other Status: Published Year Published: 2020 Citation: Rokad, B., N. Gaur, V. Sehgal, and B.P. Mohanty, Heterogeneity Index: Quantifying the Variability and Co-Variability in Land-Surface Heterogeneity at the Global Scale, AGU Fall Meeting Abstract, 2020.
  • Type: Other Status: Published Year Published: 2020 Citation: Sehgal, V. and B.P. Mohanty, Multiscale Soil Hydraulic Parameterization for Improved Hydrologic Simulations Using SMAP, AGU Fall Meeting Abstract, 2020.
  • Type: Other Status: Published Year Published: 2020 Citation: Sedaghatdoost, A., B.P. Mohanty, and Y. Huang, Effect of Soil Physical and Chemical Heterogeneity on the Distribution of Carbon, Nitrogen, and Phosphorous in Subsurface Soils, AGU Fall Meeting Abstract, 2020.
  • Type: Other Status: Published Year Published: 2020 Citation: Kathuria, D., B.P. Mohanty, and K. Cawse-Nicholson, Linking ECOSTRESS, MODIS and Eddy-Covariance for High-Resolution Daily Evapotranspiration, AGU Fall Meeting Abstract, 2020.
  • Type: Other Status: Published Year Published: 2020 Citation: Mohanty, B.P., and D. Kathuria, Looking Beyond Physical Models and Machine Learning: Novel Insights into Soil Moisture Dynamics Using Multi-Scale Big Data Geostatistics, AGU Fall Meeting Abstract, 2020.
  • Type: Other Status: Published Year Published: 2020 Citation: Gaur, N., B. Rokad, V. Sehgal, and B.P. Mohanty, Land-Surface Heterogeneity Index to Classify Continental Scale Near-Surface Soil Moisture Dynamics, AGU Fall Meeting Abstract, 2020.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Artificial Intelligence Enhanced Decision Making in Agriculture: Challenges and Opportunities, Online Workshop on Artificial Intelligence in Agriculture, via Zoom, Texas A&M Institute for Data Science, July 17, 2020.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Invited Speaker On Soil Moisture Remote Sensing, Modeling, and Fusion across Scales, Consortium of International Agricultural Research Centers (CGIAR) Meeting, University of California, Santa Barbara, October 30, 2020.