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Improved Drought Prediction Using Near Real-Time Climate Forecasts and Simulated Hydrologic Conditions

Author

Listed:
  • Hyunwoo Kang

    (Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA)

  • Venkataramana Sridhar

    (Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA)

Abstract

Short-term drought forecasting is helpful for establishing drought mitigation plans and for managing risks that often ensue in water resource systems. Additionally, hydrologic modeling using high-resolution spatial and temporal data is used to simulate the land surface water and energy fluxes, including runoff, baseflow, and soil moisture, which are useful for drought forecasting. In this study, the Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models are used for short-term drought forecasting in the contiguous United States (CONUS), as many areas in this region are frequently affected by varying drought intensities. Weekly-to-seasonal meteorological inputs are provided by the Climate Prediction Center (CPC) for the retrospective period (January 2012 to July 2017) and Climate Forecasting System version 2 (CFS v2) for the forecasting period (August 2017 to April 2018), and these inputs are used to estimate agricultural and groundwater drought conditions. For drought assessment, three drought indices, namely, the Standardized Soil Moisture index (SSI), the Multivariate Standardized Drought Index (MSDI), and the Standardized Baseflow index (SBI), were analyzed. The accuracy of the forecasting results was verified using several a performance measure (Drought area agreement (%); DA). Generally, eight weeks of lead time forecasting showed good drought predictability from both the SWAT and VIC models for the MSDI simulations (62% for SWAT and 64% for VIC for all drought categories). However, the DA values for eight weeks lead time forecasting for SSI were 23% (SWAT) and 10% (VIC) and 7% (SWAT) and 7% (VIC) for the SBI, respectively. In addition, the accuracies of drought predictions remarkably decreased after eight weeks, and the average DA values were 36% for SWAT and 38% for VIC due to an increase in the uncertainties associated with meteorological variables in CFS v2 products. For example, there are increases in the total number of grids where the absolute values of monthly differences between CFSv2 and CPC observations exceed 20 mm and 1 °C during the forecasting period. Additionally, drought forecasting using only one variable (i.e., SSI and SBI) showed low prediction performances even for the first eight weeks. The results of this study provide insights into drought forecasting methods and provide a better understanding to plan for timely water resource management decisions.

Suggested Citation

  • Hyunwoo Kang & Venkataramana Sridhar, 2018. "Improved Drought Prediction Using Near Real-Time Climate Forecasts and Simulated Hydrologic Conditions," Sustainability, MDPI, vol. 10(6), pages 1-29, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1799-:d:149711
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    References listed on IDEAS

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    4. Troy Sternberg, 2011. "Regional drought has a global impact," Nature, Nature, vol. 472(7342), pages 169-169, April.
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    Cited by:

    1. Neeta Nandgude & T. P. Singh & Sachin Nandgude & Mukesh Tiwari, 2023. "Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies," Sustainability, MDPI, vol. 15(15), pages 1-19, July.
    2. Vinit Sehgal & Venkataramana Sridhar & Luke Juran & Jactone Arogo Ogejo, 2018. "Integrating Climate Forecasts with the Soil and Water Assessment Tool (SWAT) for High-Resolution Hydrologic Simulations and Forecasts in the Southeastern U.S," Sustainability, MDPI, vol. 10(9), pages 1-27, August.
    3. Koppuravuri Ramabrahmam & Venkata Reddy Keesara & Raghavan Srinivasan & Deva Pratap & Venkataramana Sridhar, 2021. "Flow Simulation and Storage Assessment in an Ungauged Irrigation Tank Cascade System Using the SWAT Model," Sustainability, MDPI, vol. 13(23), pages 1-18, November.

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