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

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.

A near‐term drought assessment using hydrological and climate forecasting in the Mekong River Basin

“…Drought indices are commonly used for historical drought assessments (Sheffield et al ., 2012), drought forecasting (Cancelliere et al ., 2007; Sehgal et al ., 2018; Sehgal and Sridhar, 2018; Kang and Sridhar, 2018b) and long‐term drought projections (Kang and Sridhar, 2017; 2018a; Thilakarathne and Sridhar, 2017). Various drought indices have been developed to assess different categories of drought: meteorological, hydrological, socio‐economic and agricultural droughts.…”

“…Macro‐scale hydrologic models are useful for estimating essential hydrometeorological variables (e.g., soil moisture, evapotranspiration and runoff) and in turn generating drought maps (Sridhar et al ., 2006; 2008). For instance, the Soil and Water Assessment Tool (SWAT; Arnold et al ., 1998) is a useful model for estimating hydrologic variables as well as assessing drought scenarios, which include historical drought assessments (Sehgal and Sridhar, 2018), seasonal drought forecasting (Sehgal and Sridhar, 2018; Kang and Sridhar, 2018b) and the effects of climate change on droughts (Kang and Sridhar, 2017; 2018a; Thilakarathne and Sridhar, 2017). SWAT has been applied to selected basins in the MRB.…”

Trends in Drought over the Northeast United States

“…Patterns in the covariability of precipitation and temperature anomalies have also been studied using model outputs and historic analysis of observations [62]. Modeling such patterns across timescales can aid in seasonal drought prediction [63,64].…”

Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin

“…The physical diagrams of these models are available in published literature and websites [23,24]. The range of applications to evaluate water resource problems includes drought [25][26][27][28], water management [29,30], and climate impacts [31][32][33][34]. The VIC model is grid-based, whereas SWAT is a hydrologic response unit (HRU)-based model that is defined using soil, slope, and land-use data.…”