Better Drought Index between SPEI and SMDI and the Key Parameters in Denoting Drought Impacts on Spring Wheat Yields in Qinghai, China

Abstract

Drought has great negative impacts on crop growth and production. In order to select appropriate drought indices to quantify drought influences on crops to minimize the risk of drought-related crops as much as possible, climate and spring wheat yield-related data from eight sites in the Qinghai Province of China were collected for selecting better drought index between standardized precipitation evapotranspiration index (SPEI, denoting meteorological drought) and soil moisture deficit index (SMDI, denoting agricultural drought) as well as the key parameters (timescale and month) in denoting drought impacts on spring wheat yields. The spring wheat yields during 1961–2018 were simulated by the DSSAT–CERES–Wheat model. Pearson correlations were used to investigate the relationship between SPEI and SMDI and between spring wheat yields and drought indices at different timescales. The results showed that: (1) SMDI reflected more consistent dry/wet conditions than SPEI when the timescales changed and (2) There were one- and two-month lags in SMDI compared to SPEI (with the higher correlation coefficients values of 0.35–0.68) during May to August and (3) May (the jointing period of spring wheat) and the two-month timescale of SMDI0–10 (with the higher correlation coefficients values of 0.21–0.37) were key parameters denoting drought influences on spring wheat yield and (4) The correlations between the linear slopes of spring wheat yield reduction rate and linear slopes of SMDI0–10 in May at the studied eight sites were considerable between 1961–2018 (r = 0.85). This study provides helpful references for mitigating the drought risk of spring wheat.

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

“…Because of the ignorance of the drainage and leakage, the calculation was not quite in line with the actual situation. Thus, the model was improved by considering the paddy field leakage and the thickness difference in the water layer between the beginning and the end of the growth stage: (7) where P i is the rainfall depth during the rice growth period (mm); W gi is the irrigation water during the time interval (mm); L i is the seepage during the time period, 2 mm/d (Hu et al, 2019); ET i is the field water requirement during each growth stage by using the α-value method (mm) [34]; and λ i are the sensitivity coefficients in various growth stages [35].…”

“…λ i n i=1 (7) where P i is the rainfall depth during the rice growth period (mm); W gi is the irrigation water during the time interval (mm); L i is the seepage during the time period, 2 mm/d (Hu et al, 2019); ET i is the field water requirement during each growth stage by using the α-value method (mm) [34]; and λ i are the sensitivity coefficients in various growth stages [35].…”

“…Investigating agricultural drought risk has become the focus of scientific research. Many scholars used meteorological drought indicators to characterize agricultural drought [7,8]. The precipitation anomaly percentage (Pa) index could easily directly reflect the drought caused by abnormally reduced precipitation in Guizhou [9].…”

Drought Characterization and Potential of Nature-Based Solutions for Drought Risk Mitigation in Eastern Ethiopia

“…The cumulative deficit with the SPEI or SMDI values consistently lower than 0 can be used to calculate the drought intensity, which measures the severity of the drought during the period of the drought. This study used the aforementioned methods to estimate the drought characteristics of the Eerer sub-basin utilizing SPEI and SMDI as the dry/wet state categorization shown in Table 2 [54].…”

A regional model for soil moisture concentration from remote sensing data and its application for agricultural drought hazard mapping

“…numerous indices have been used to quantitatively examine the characteristics of drought events, such as the standardized precipitation index (SPI) (McKee et al 1993;Tsakiris & Vangelis 2004), soil moisture drought index (SMDI) (Narasimhan and Srinivasan 2005;Wambua 2019;Hou et al 2022), the soil wetness deficit index (SWDI) (Martínez-Fernández et al 2015), the Palmer's drought severity index (PDSI) (Yan et al 2016), and the standardized precipitation evapotranspiration index (SPEI) (Beguería et al 2014;Li et al 2020). However, such indices are computed based on time series from local meteorological stations, being, thus, affected by the uncertainties associated with temporal and spatial data interpolation (Phillips & Marks 1996;Fernández et al 2013, Morid et al 2007, Svoboda et al 2015.…”