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.
Highlights
Droughts have negatively affected water resource management, agriculture, the environment and the economy around the globe [1,2]
A reliable drought monitoring and forecasting framework is essential for water resource management and drought mitigation strategies
This study investigated a retrospective drought simulation and near-real-time drought forecasting method using simulated hydrometeorological variables using the Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models with Coupled Forecast System Model version 2 (CFSv2) for the contiguous U.S (CONUS)
Summary
Droughts have negatively affected water resource management, agriculture, the environment and the economy around the globe [1,2]. Several drought forecasting systems have evolved to perform the evaluation of hydrologic/drought conditions across the CONUS, such as the Climate Prediction Center’s (CPC) Seasonal Drought Outlook [9], the National Integrated Drought Information System’s (NIDIS’s) Drought Early Warning Systems (DEWS), Princeton University’s drought forecast system [10], and a probabilistic drought forecasting framework [11] These studies have proposed some advanced drought forecasting methods, such as probabilistic drought forecasting, the model-based Drought Monitoring and Prediction System (DMAPS) [10], and the dynamical or statistical-dynamical seasonal forecasting [12], the effects of climate change on droughts across various regions of the globe suggests the need for more reliable methods in predicting extreme events because climate change may lead to more extreme hydrometeorological conditions, such as floods or droughts [13,14]
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