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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

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Remote Sensing of Hydrological Extremes

Part of the book series: Springer Remote Sensing/Photogrammetry ((SPRINGERREMO))

Abstract

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.

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Authors and Affiliations

  1. Department of Biological and Agricultural Engineering, Texas A&M University, 2117 TAMU, College Station, TX, USA

    Binayak P. Mohanty Ph.D. & Nandita Gaur

  2. Michigan State University, East Lansing, MI, USA

    Amor V. M. Ines

  3. School of Agricultural Civil & Bio-Industry Engineering, College of Agriculture and Life Science, Kyungpook National University, Daegu, South Korea

    Yongchul Shin

  4. Jet Propulsion Laboratory, NASA, Pasadena, CA, USA

    Narendra Das

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Raghavendra Jana

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  1. Binayak P. Mohanty Ph.D.
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  2. Amor V. M. Ines
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  3. Yongchul Shin
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  4. Nandita Gaur
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Correspondence to Binayak P. Mohanty Ph.D. .

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  1. Department of Geophysics, Stanford University, Stanford, California, USA

    Venkat Lakshmi

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Mohanty, B.P., Ines, A.V.M., Shin, Y., Gaur, N., Das, N., Jana, R. (2017). 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. In: Lakshmi, V. (eds) Remote Sensing of Hydrological Extremes. Springer Remote Sensing/Photogrammetry. Springer, Cham. https://doi.org/10.1007/978-3-319-43744-6_9

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