A Framework for Assessing Soil Moisture Deficit and Crop Water Stress at Multiple Space and Time Scales Under Climate Change Scenarios Using Model Platform, Satellite Remote Sensing, and Decision Support System

  • Binayak P. MohantyEmail author
  • Amor V. M. Ines
  • Yongchul Shin
  • Nandita Gaur
  • Narendra Das
  • Raghavendra Jana
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)


Better understanding of water cycle at different space–time scales would be a key for sustainable water resources, agricultural production, and ecosystems health in the twenty-first century. Efficient agricultural water management is necessary for sustainability of the growing global population. This warrants better predictive tools for aridity (based on precipitation, temperature, land use, and land cover), root zone (~top 1 m) soil moisture deficit, and crop water stress at farm, county, state, region, and national level, where decisions are made to allocate and manage the water resources. It will provide useful strategies for not only efficient water use but also for reducing potential risk of crop failure due to agricultural drought. Leveraging heavily on ongoing multiscale hydrologic modeling, data assimilation, soil moisture dynamics, and inverse model development research activities, and ongoing Land Data Assimilation (LDAS) and National Climate Assessment (NCA) indexing efforts we are developing a drought assessment framework. The drought assessment platform includes: (1) developing disaggregation methods for extracting various field-scale (1-km or less) climate indicators from the (SMOS, VIIRS, SMAP, AMSR-2) satellite / LDAS-based soil moisture in conjunction with a multimodel simulation–optimization approach using ensemble of Soil Vegetation Atmosphere Transfer, SVAT (Noah, CLM, VIC, Mosaic in LIS) models; (2) predicting farm/field-scale long-term root zone soil moisture status under various land management and climate scenarios for the past decades in hindcast mode and for the next decades in forecast mode across the USA using effective land surface parameters and meteorological input from Global Circulation Model (GCM) outputs; (3) assessing the potential risk of agricultural drought at different space–time scales across the USA based on predicted root zone soil moisture; and (4) evaluating various water management and cropping practices (e.g., crop rotation, soil modification, irrigation scheduling, better irrigation method/efficiency, water allocation, etc.) for risk reduction at field, county, state, region, and national scale using a web-based Decision Support System. This ongoing research provides a unifying global platform for forecasting several lagging indices for root zone soil moisture status as aridity index (AI), soil moisture deficit index (SMDI), and crop water stress index (CWSI) at the field, county, state, and regional scale on weekly, biweekly, monthly, and seasonal time scales by using various satellite and LDAS simulated data. Using available historical data, our approach is tested in various hydroclimatic regions (Great Plains, Midwest, West, Northeast, Southeast, and Southwest) across the USA. These indices form the basis for developing efficient management Decision Support Systems (DSS) for agricultural drought risk reduction and mitigation/adaption under the evolving climatic scenarios.


Root zone soil moisture Soil moisture deficit index Crop water stress index Aridity index Remote sensing Downscaling National Climate Assessment (NCA) Land Information System (LIS) Risk assessment Decision support system 


  1. Ajami NK, Duan Q, Sorooshian S (2007) An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resour Res 43(W01403):1–19Google Scholar
  2. Arsenault KR, Houser PR, De Lannoy GJM (2014) Evaluation of the MODIS snow cover fraction product. Hydrol Process 28(3):980–998. doi: 10.1002/hyp.9636 CrossRefGoogle Scholar
  3. Bindlish R, Barros AP (2002) Subpixel variability of remotely sensed soil moisture: an inter-comparison study of SAR and ESTAR. IEEE Trans Geosci Remote Sens 40:326–337CrossRefGoogle Scholar
  4. Chervin RM (1981) On the comparison of observed GCM simulated climate ensembles. J Atmos Sci 38:885–901CrossRefGoogle Scholar
  5. Cosh MH, Jackson TJ, Bindlish R, Prueger JH (2004) Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates. Remote Sens Environ 92:427–435CrossRefGoogle Scholar
  6. Crow WT, Berg A, Cosh MH, Loew A, Mohanty BP, Panciera R, De Rosnay P, Ryu D, Walker J (2012) Upscaling sparse ground-based soil moisture observations for the validation of satellite surface soil moisture products. Rev Geophys 50:RG2002. doi: 10.1029/2011RG000372 CrossRefGoogle Scholar
  7. Dai Y, Zeng X, Dickinson R, Baker I, Bonan G, Bosilovich M, Denning S, Dirmeyer P, Houser P, Niu G, Oleson K, Schlosser A, Yang Z-L (2003) The common land model (CLM). Bull Am Meteorol Soc 84(4):1013e1023. doi: 10.1175/BAMS-84-8-1013 Google Scholar
  8. Das NN, Mohanty BP (2006) Root zone soil moisture assessment using remote sensing and vadose zone modeling. Vadose Zone J 5:296–307CrossRefGoogle Scholar
  9. Das NN, Mohanty BP (2008) Temporal dynamics of PSR-based soil moisture across spatial scales in an agricultural landscape during SMEX02: a wavelet approach. Remote Sens Environ 112(2):522–534CrossRefGoogle Scholar
  10. Das NN, Mohanty BP, Cosh MH, Jackson TJ (2008a) Modeling and assimilation of root zone soil moisture using remote sensing observations in walnut gulch watershed during SMEX04. Remote Sens Environ 112(2):415–429CrossRefGoogle Scholar
  11. Das NN, Mohanty BP, Njoku EG (2008b) A Markov chain Monte Carlo algorithm for upscaled soil-vegetation-atmosphere-transfer modeling to evaluate satellite-based soil moisture measurements. Water Resour Res 44. doi: 10.1029/2008WR006472
  12. Das NN, Mohanty BP, Njoku EG (2010a) Profile soil moisture across spatial scales under different hydroclimatic conditions. Soil Sci 175(7):315–319CrossRefGoogle Scholar
  13. Das NN, Mohanty BP, Efendiev Y (2010a) Characterization of saturated hydraulic conductivity in agricultural field using Karhunen-Loève expansion with the Markov chain Monte Carlo technique. Water Resour Res 46. doi: 10.1029/2007WR007100
  14. Das NN, Mohanty BP, Seo D, Efendiev Y (2016) Data-driven downscaling of satellite-based surface soil moisture using high resolution physical controls information. Remote Sensing of Environment, in Review.Google Scholar
  15. Duan Q, Ajami NK, Gao X, Sorooshian S (2007) Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv Water Resour 30:1371–1386CrossRefGoogle Scholar
  16. Ek M, Mitchell K, Yin L, Rogers P, Grunmann P, Koren V, Gayno G, Tarpley J (2003) Implementation of Noah land surface model advances in the NCEP operational mesoscale Eta model. J Geophys Res 108(D22):8851. doi: 10.1029/2002JD003296 CrossRefGoogle Scholar
  17. Engman T (1991) Application of remote sensing of soil moisture for water resources and agriculture. Remote Sens Environ 35:213–226CrossRefGoogle Scholar
  18. Entekhabi D, Asrar GR, Betts AK, Beven KJ, Bras RL, Duffy CJ (1999) An agenda for land surface hydrology research and a call for the second international hydrological decade. Bull Am Meteorol Soc 80(10):2043–2058CrossRefGoogle Scholar
  19. Entekhabi D, Njoku EG, O’Neill PE, Kellogg KH, Crow WT, Edelstein WN, Entin JK, Goodman SD, Jackson TJ, Johnson J, Kimball J, Piepmeier JR, Koster RD, Martin N, McDonald KC, Moghaddam M, Moran S, Reichle R, Shi JC, Spencer MW, Thurman SW, Tsang L, Zyl JV (2010) The soil moisture active passive (SMAP) mission. Proc IEEE 98(5):704–716CrossRefGoogle Scholar
  20. Entekhabi et al. 2014, SMAP Handbook, Jet Propulsion Lab, NASA, p. 180Google Scholar
  21. Famiglietti JS, Devereaux JA, Laymon CA, Tsegaye T, Houser PR, Jackson TJ, Graham ST, Rodell M, van Oevelen P (1999) Ground-based investigation of soil moisture variability within remote sensing footprints during the Southern Great Plains (1997) hydrology experiment. Water Resour Res 35:1839–1851CrossRefGoogle Scholar
  22. Gaur N, Mohanty BP (2013) Evolution of physical controls for soil moisture in humid and sub-humid watersheds. Water Resour Res 49:1–15. doi: 10.1002/wrcr.20069 CrossRefGoogle Scholar
  23. Gaur N, Mohanty BP (2015) Land-surface controls on near-surface soil moisture dynamics: 1 traversing remote sensing footprints. Water Resour Res. RevisedGoogle Scholar
  24. Gaur N, Mohanty BP (2016) Land-surface controls on near-surface soil moisture dynamics: Traversing remote sensing footprints. Water Resour Res, 52, doi:  10.1002/2015WR018085
  25. Gong X, Barnston AG, Ward MN (2003) The effect of spatial aggregation on the skill of seasonal rainfall forecasts. J Clim 16:3059–3071CrossRefGoogle Scholar
  26. Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95CrossRefGoogle Scholar
  27. Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour Res 30:1535–1546CrossRefGoogle Scholar
  28. Hughes JP, Guttorp P, Charles SP (1999) A non-homogenous hidden Markov model for precipitation occurrence. Appl Stat 48:15–30Google Scholar
  29. Ines AVM (2004) GCM bias correction tool. Version 0.3a. IRI-Columbia University, New YorkGoogle Scholar
  30. Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138:44–53CrossRefGoogle Scholar
  31. Ines AVM, Honda K (2005) On quantifying agricultural and water management practices from Low spatial resolution RS data using genetic algorithms: a numerical study for mixed pixel environment. Adv Water Resour 28:856–870CrossRefGoogle Scholar
  32. Ines AVM, Mohanty BP (2008a) Near-surface soil moisture assimilation to quantify effective soil hydraulic properties using genetic algorithm. I. Conceptual modeling. Water Resour Res 44. doi 10.1029/2007WR005990
  33. Ines AVM, Mohanty BP (2008b) Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: II. Using airborne remote sensing drying SGP97 and SMEX02. Water Resour Res 45. doi 10.1029/2008WR007022
  34. Ines AVM, Mohanty BP (2009) Parameter conditioning with a noisy Monte Carlo genetic algorithm for estimating effective soil hydraulic properties from space. Water Resour Res 44:W08441. doi: 10.1029/2007WR006125 Google Scholar
  35. Ines AVM, Mohanty BP, Shin Y (2013) An unmixing algorithm for remotely sensed soil moisture. Water Resour Res 49:408–425. doi: 10.1029/2012WR012379 CrossRefGoogle Scholar
  36. Jackson TJ, Le Vine DM, Hsu AY, Oldak A, Starks PJ (1999) Soil moisture mapping at regional scales using microwave radiometry: the Southern Great Plains hydrology experiment. IEEE Trans Geosci Remote Sens 37:2136–2151CrossRefGoogle Scholar
  37. Jackson TJ, Bindlish R, Gasiewski AJ, Stankov B, Klein M, Njoku EG, Bosch D, Coleman TL, Laymon C, Starks PJ (2005) Polarimetric scanning radiometer C and X band microwave observations during SMEX03. IEEE Trans Geosci Remote Sens 43(11):2418–2429CrossRefGoogle Scholar
  38. Jacobs J, Mohanty BP, Hsu E-C, Miller D (2004) Field scale variability and similarity of soil moisture during SMEX02. Remote Sens Environ 92:436–446CrossRefGoogle Scholar
  39. Jana R (2010) Scaling characteristics of soil hydraulic parameters at varying spatial resolutions. Ph.D. Dissertation, Texas A&M University, p 264Google Scholar
  40. Jana R, Mohanty BP (2012a) On topographic controls of soil hydraulic parameter scaling at hillslope scales. Water Resour Res 48:W02518. doi: 10.1029/2011WR011204 Google Scholar
  41. Jana R, Mohanty BP (2012b) A topography-based scaling algorithm for soil hydraulic parameters at hillslope scales: field testing. Water Resour Res 48:W02519. doi: 10.1029/2011WR011205 Google Scholar
  42. Jana R, Mohanty BP (2012c) A comparative study of multiple approaches to soil hydraulic parameter scaling applied at the hillslope scale. Water Resour Res 48:W02520. doi: 10.1029/2010WR010185 Google Scholar
  43. Jana R, Mohanty BP, Springer EP (2007) Multi-scale pedo-transfer functions for soil water retention. Vadose Zone J 6(4):868–878CrossRefGoogle Scholar
  44. Jana R, Mohanty BP, Springer E (2008) Bayesian implementation of multi-scale pedo-transfer functions with Non-linear bias corrections. Water Resour Res 44:W08408. doi: 10.1029/2008WR006879 CrossRefGoogle Scholar
  45. Jasinski M, Arsenault K, Beaudoing H, Bolten J, Borak J, Kempler S, Kumar S, Peters-Lidard C, Li B, Liu Y, Mocko D, Rodell M, Vollmer B (2014) NCA-LDAS: an integrated terrestrial water analysis system for development, evaluation, and dissemination of climate indicators. In: American geophysical union fall meeting, San Francisco, p GC51B–0405Google Scholar
  46. Joshi C, Mohanty BP (2010) Physical controls of near‐surface soil moisture across varying spatial scales in an agricultural landscape during SMEX02. Water Resour Res 46:12503. doi: 10.1029/2010WR009152 CrossRefGoogle Scholar
  47. Joshi C, Mohanty BP, Jacobs J, Ines AVM (2011) Spatiotemporal analyses of soil moisture from point to footprint scale in two different hydroclimatic regions. Water Resour Res 47. doi: 10.1029/2009WR009002
  48. Kim G, Barros AP (2002) Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data. Remote Sens Environ 83:400–413CrossRefGoogle Scholar
  49. Kim J, Mohanty BP, Shin Y (2015) Effective soil moisture estimates and its uncertainty using multi-model simulation based on Bayesian model averaging. J Geophys Res Atmos 120:8023–8042. doi: 10.1002/2014JD022905 CrossRefGoogle Scholar
  50. Kirshner S, Smyth P, Robertson AW (2004) Conditional Chow-Liu tree structures for modeling discrete-valued vector time series. Technical Report UCI-ICS 04-04. Information and Computer Science. University of California, IrvineGoogle Scholar
  51. Koster R, Suarez M (1996) Energy and water balance calculations in the Mosaic LSM. Technical memorandum 104606, NASA Goddard Space Flight CenterGoogle Scholar
  52. Kumar SV, Peters-Lidard CD, Tian Y, Houser PR, Geiger J, Olden S, Lighty L, Eastman JL, Doty B, Dirmeyer P, Adams J, Mitchell K, Wood EF, Sheffield J (2006) Land information system: an interoperable framework for high resolution land surface modeling. Environmental Modeling & Software 21:1402–1415CrossRefGoogle Scholar
  53. Kumar SV, Peters-Lidard CD, Mocko D, Tian Y (2013) Multiscale evaluation of the improvements in surface snow simulation through terrain adjustments to radiation. J Hydrometeorol 14:220–232.
  54. Kumar SV, Peters-Lidard CD, Mocko D, Reichle R, Liu Y, Arsenault K, Xia Y, Ek M, Riggs G, Livneh B, Cosh M (2014) Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation. J Hydrometeorol 15(6):2446–2469. doi: 10.1175/JHM-D-13-0132.1 CrossRefGoogle Scholar
  55. Leung LR, Qian Y, Bian X, Washington WM, Han J, Roads JO (2004) Mid-century ensemble regional climate change scenarios for the Western United States. Climate Change 62:75–113CrossRefGoogle Scholar
  56. Liang X, Lettenmaier D, Wood E (1996) One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model. J Geophys Res 101(D16):21403e21422CrossRefGoogle Scholar
  57. Liu Y, Peters-Lidard CD, Kumar SV, Foster JL, Shaw M, Tian Y, Fall GM (2013) Assimilating satellite-based snow depth and snow cover products for improving snow predictions in Alaska. Adv Water Resour 54:208–227.
  58. Madigan D, Raftery AE, Volinsky C, Hoeting J (1996) Bayesian model averaging. In: Proceedings of the AAAI workshop on integrating multiple learned models, AAAI Press, Portland, p 77–83Google Scholar
  59. Mohanty BP, Skaggs TH (2001) Spatio-temporal evolution and time stable characteristics of soil moisture within remote sensing footprints with varying soils, slopes, and vegetation. Adv Water Resour 24:1051–1067CrossRefGoogle Scholar
  60. Mohanty BP, Zhu J (2007) Effective hydraulic parameters in horizontally and vertically heterogeneous soils for steady-state land–atmosphere interaction. J Hydrometeorol 8(4):715–729CrossRefGoogle Scholar
  61. Mohanty BP, Skaggs TH, Famiglietti JS (2000a) Analysis and mapping of field-scale soil moisture variability using high-resolution ground based data during the Southern Great Plains 1997 (SGP97) hydrology experiment. Water Resour Res 36:1023–1032CrossRefGoogle Scholar
  62. Mohanty BP, Famiglietti JS, Skaggs TH (2000b) Evolution of soil moisture spatial structure in a mixed-vegetation pixel during the SGP97 hydrology experiment. Water Resour Res 36(12):3675–3686CrossRefGoogle Scholar
  63. Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) Emissions scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge/New YorkGoogle Scholar
  64. Narasimhan B, Srinivasan R (2005) Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric For Meteorol 133:69–88CrossRefGoogle Scholar
  65. Peters-Lidard CD, Houser PR, Tian Y, Kumar SV, Geiger J, Olden S, Lighty L, Doty B, Dirmeyer P, Adams J, Mitchell K, Wood EF, Sheffield J (2007) High-performance earth system modeling with NASA/GSFC’s land information system. Innov Syst Softw Eng 3(3):157–165CrossRefGoogle Scholar
  66. Robertson AW, Kirshner S, Smyth P (2004) Downscaling of daily rainfall occurrence over Northeast Brazil using a hidden Markov model. J Clim 17:4407–4424CrossRefGoogle Scholar
  67. Roeckner E, Arpe K, Bengtsson L, Claussen CM, Dümenil L, Esch M, Giorgetta M, Schiese U, Schulzweida U (1996) The atmospheric general circulation model ECHAM-4: model description and simulation of present-day climate. Max-Planck-Institute for Meteorology. Report No. 218, HamburgGoogle Scholar
  68. Rui H, Teng W, Vollmer B, Jasinski M, Mocko D, Kempler S (2014) National Climate Assessment-Land Data Assimilation System (NCA-LDAS) Data at NASA GES DISC. In: American geophysical union fall meeting, San Francisco, p GC51B–0405Google Scholar
  69. Santanello JA, Kumar SV, Peters-Lidard CD, Harrison KW, Zhou S (2013) Impact of land model calibration on coupled land-atmosphere prediction. J Hydrometeorol 14:1373–1400.
  70. Shin Y, Mohanty BP (2013) Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications. Water Resour Res 49:10. doi: 10.1002/wrcr.20495 CrossRefGoogle Scholar
  71. Shin Y, Mohanty BP, Ines AVM (2012) Soil hydraulic properties in one-dimensional layered soil profile using layer-specific soil moisture assimilation scheme. Water Resour Res 48:W06529. doi: 10.1029/2010WR009581 CrossRefGoogle Scholar
  72. Sivapalan M, Beven K, Wood EF (1987) On hydrologic similarity 2. A scaled model of storm runoff production. Water Resour Res 23:2266–2278CrossRefGoogle Scholar
  73. Sud Y, Mocko D (1999) New snow-physics to complement Ssib Part I: design and evaluation with ISLSCP initiative I datasets. J Meteorol Soc Jpn 77(1B):335–348Google Scholar
  74. Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21:530–548CrossRefGoogle Scholar
  75. Wilby RL, Wigley TML, Conway D, Jones PD, Hewistson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34:2995–3008CrossRefGoogle Scholar
  76. Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the Eastern United States. J Geophys Res 107(D20):4429. doi: 10.1029/2001JD000659 CrossRefGoogle Scholar
  77. Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62:189–216CrossRefGoogle Scholar
  78. Zaitchik BF, Santanello JA, Kumar SV, Peters-Lidard CD (2013) Representation of soil moisture feedbacks during drought in NASA Unified WRF (NU-WRF). J Hydrometeorol 14:360–367.

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Binayak P. Mohanty
    • 1
    Email author
  • Amor V. M. Ines
    • 2
  • Yongchul Shin
    • 3
  • Nandita Gaur
    • 1
  • Narendra Das
    • 4
  • Raghavendra Jana
    • 5
  1. 1.Department of Biological and Agricultural EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Michigan State UniversityEast LansingUSA
  3. 3.School of Agricultural Civil & Bio-Industry Engineering, College of Agriculture and Life Science, Kyungpook National UniversityDaeguSouth Korea
  4. 4.Jet Propulsion Laboratory, NASAPasadenaUSA
  5. 5.King Abdullah University of Science and TechnologyThuwalSaudi Arabia

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