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Soil water content (SWC) and soil temperature (ST) are important properties in water-energy balance processes. Our objective was to analyze the SWC and ST variations at different soil depths (0–10, 10–40, 40–100, and 100–200 cm) and different timescales (daily, monthly, and annual) affected by precipitation and air temperature at seven sub-regions and entire mainland China. The sine function was used to fit the variations of daily and monthly ST. The results showed that the monthly and annual SWC and ST values were relatively low in northwest China and the alpine region of Qinghai-Tibetan plateau; however, higher values were shown in east China. SWC and ST fluctuated both randomly and periodically, especially at the 0–10 cm depth. The daily and monthly ST had regular time-lags and typical periodical changes, which could be fitted by a sine function for both the grids and sub-regions in China with a coefficient of determination (R2) of 0.855. Further, the correlations between SWC and precipitation were good in southern and northeastern China, but poor in northwestern China and Qinghai-Tibet Plateau at the depths < 40 cm. The correlations between ST and AT within the depths < 100 cm were generally good (R2 > 0.76). In conclusion, the spatiotemporal distribution of SWC and ST was greatly affected by precipitation and air temperature. The fitted sine functions for daily and monthly ST are very useful for elementary determination of the long-term mean ST values for a location. [ABSTRACT FROM AUTHOR]

Drought is one of the main factors that impacts crop yields. However, determining how different drought types affect winter wheat yields is difficult due to the lack of observation data. This work aimed to investigate the impacts of multi-timescale droughts on winter wheat yields. The winter wheat yields during 1981–2015 were simulated by the DSSAT-CERES-Wheat model. We analyzed the drought characteristics based on the standardized precipitation evapotranspiration index (SPEI) and soil moisture deficit index (SMDI) at timescales of 1 to 9 months at 108 sites in China. The modified Mann-Kendall (MMK) method was used to test the tendency of 1-, 3- and 6-month SPEI, SMDI and winter wheat yields. Pearson correlation analysis was used to explore the relationship between winter wheat yields and SPEI/SMDI at different timescales. The results showed that, DSSAT-CERES-Wheat generally performed well in simulating winter wheat anthesis date, maturity date and yields (0.64 < R 2 < 0.97, where R 2 is determination of coefficient). The dry or wet status for the 1- to 9-month timescales of SPEI and SMDI were generally consistent in the three subregions. Seasonal drought occurred more frequently in the Huang-Huai-Hai Plain than in the other two subregions. The 4-month SPEI and 1-month SMDI at the 0–10 cm depth affected the winter wheat yield more during the jointing to milk stages. For yields vs. 4-month SPEI, the number of stations with Pearson correlation coefficient r > 0.4 in March, April and May was 29, 35 and 23, respectively. For yields vs. 1-month SMDI, the number of stations with r > 0.4 in March, April and May was 16, 23 and 33, respectively. SMDI and SPEI explained more than 14% and less than 2% of the yield variability, respectively. Closer relations between SMDI and winter wheat yields were observed. This study provides useful references for preventing agricultural drought. • DSSAT-CERES-Wheat performed generally well for simulating winter wheat anthesis date, maturation date and yields. • The 4-month SPEI and 1-month SMDI at 0–10 cm depth affected winter wheat yields greater during jointing to milk stages. • SMDI was better than SPEI in denoting effects of drought on winter wheat yields. • This study obtained key timescales of SPEI and SMDI and key growth periods of winter wheat. [ABSTRACT FROM AUTHOR]

Drought poses a significant threat to global crop production. As the global community grapples with the escalating challenges of climate change, understanding the multifaceted impacts of concurrent drought on food security becomes imperative. This study delved into the response of wheat and maize, key staples in the global food system, to different types of drought, with a particular focus on the yield gaps resulting from concurrent meteorological and agricultural drought. The DSSAT-CERES model was adopted to simulate phenophase, rain-fed, and potential yields of maize and wheat in China from 1962 to 2100. Meteorological (Non-stationary Standard Precipitation Evapotranspiration Index, NSPEI) and agricultural (Standard Soil Moisture Index, SSMI) drought indices were calculated from crop seeding to maturity stages. We employed bivariate and multiple cross-wavelet as well as vine Copula to qualitatively and quantitatively analyze the response of yield gaps to different drought types. Finally, we determined the relative dependence weights of maize and wheat on NSPEI and SSMI by least squares regression. Spanning from 2022 to 2100, a trend of shortened growth periods for these crops were detected, accompanied by increasingly drier conditions. These situations exacerbated the crops' vulnerability to concurrent drought, leading to considerable yield reductions. Our projections indicated that future yield gaps due to concurrent drought could be, on average, 2–30% higher than those caused by single-type drought. Concurrent drought affected wheat (5–50%) more severely than maize (0–35%). Western regions would be more affected than the Eastern regions. Under the SSP (Shared socioeconomic pathway) 5-8.5 scenario in 2022–2100, all four crops would have higher dependence weights on SSMI (51–99%) than NSPEI (26–59%), emphasizing the critical role of soil moisture in agricultural drought monitoring and yield loss alleviation. Our findings highlight the urgent need for integrated drought management strategies that address the compounded risks of concurrent drought, thereby contributing to the resilience of agricultural systems and global food security in a changing climate. Our research proposes to consider the relative weights of meteorological and agricultural drought in the future development of composite drought monitoring indicators for addressing food drought risk under climate change. [Display omitted] • The response of yield gaps to drought indices differed by regional distribution, crop species, and drought types. • Concurrent droughts were identified as a major risk to global wheat and maize yields. • Projected yield gaps to widen by 2–30% due to concurrent droughts by 2100. • Soil moisture retention is key to mitigating future yield gaps. [ABSTRACT FROM AUTHOR]

• HYDRUS-1D coupled APSIM could be used for projection water dynamics in water repellent soils. • Climate change shortened the summer maize growth period in the Northwestern China. • Soil water repellency increased soil water storage and evaporation. • Soil water repellency decreased evapotranspiration and root water uptake. Soil water repellency greatly affects crop growth and soil water movement. The aim of this study was to estimate dynamics of soil water storage (SWS), actual evapotranspiration (ET a), root water uptake (RWU) and actual evaporation (E a) under an annual crop grown in water repellent (WR) soils at future climate scenarios. The soil hydraulic parameters were calibrated and validated for HYDRUS-1D based on the experimental data in 2016 and 2017. The summer maize growth periods and irrigation schedules were generated with Agricultural Production Systems Simulator (APSIM). The daily SWS, ET a , RWU and E a values from five water repellent treatments were simulated for summer maize growth periods during 1981–2000, 2030–2059 and 2060–2089 using eight selected global climate models under two representative concentration pathways (RCP 4.5 and RCP 8.5). Due to the increased temperature, the growth period reduced by 12–27 days, the total SWS, ET a , RWU and E a decreased by 8.1%-21.1%, 2.2%-11.1%, 0.5%-9.7% and 0.8%-9.6% compared to the baseline period, respectively. Changes of total SWS, ET a , RWU and E a during the whole summer maize growth periods under RCP 4.5 were greater than RCP 8.5 during the same period. Values of total SWS, ET a , RWU and E a in 2030–2059 were higher than 2060–2089 for the same RCP scenario. With increasing initial water droplet penetration time, total SWS and E a increased, while ET a and RWU decreased. The global circulation model (GCM) and Period contributed greatly to uncertainty. The results implied that it is necessary to adjust the planting date of summer maize. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard that may decrease agricultural production. To investigate crop growth and yield responses to drought conditions are vital for drought prevention during crop growth periods. This study aims to analyze the impacts of meteorological and agricultural droughts on wheat/maize yields from multiple perspectives and to select the key parameters which describe the best relationship between crop yield and drought indices. Using standardized precipitation evapotranspiration index (SPEI) and soil moisture deficit index (SMDI) at 1- to 9-month timescales, the drought characteristics of different crop growth periods at the selected 98 sites in different subregions were analyzed. DSSAT-CERES-Wheat/Maize models were used to simulate the leaf area index (LAI), biomass and yield of spring wheat, spring and summer maize over 1961 − 2018. The relationships between yield related factors and SPEI/SMDI 0–10 at different timescales were investigated using Pearson correlation. The key timescale and growth period which showed the best correlations between crop yield/growth and SPEI/SMDI were determined and used to obtain the yield/growth equations using multivariable linear regression. The results showed that: (1) The temporal variations of SPEI and SMDI 0–10 differed with different timescales, months and subregions. DSSAT-CERES generally performed well in simulating growth and yields of wheat and maize over 1961–2018. (2) For spring wheat, the correlations of yield and SMDI were highest at 3-month timescale in July, at 5-month timescale in July and at 3-month timescale in June in subregions I, II and IV, respectively. For spring maize, in subregion I, yield correlated with 1-month SPEI in June best), while yield was correlated largest with 4-month SPEI in August in subregion III and IV. For summer maize, the best correlations occurred in August between yield and 4-month SPEI. Therefore, different crop had varying key parameters for drought prevention measures. (3) The multivariable linear equations described yield/growth vs. drought indices relationship well for different crops. The results are referable for providing measures for agricultural production practice under drought. • Agricultural drought index SMDI (soil moisture deficit index) can better identify drought in spring wheat growth period, and meteorological drought index SPEI (standardized precipitation evapotranspiration index) has a greater correlation with maize yield. • The continuous drought during jointing-tasseling period has the greatest influence on the spring maize yield. • For summer maize, drought had the greatest effect on yield during the anthesis period, the significant positive correlation between yield SPEI and at the 4-month timescale in August was the best. • The linear slopes of SMDI has greater influence on the linear slopes of yield related factors of spring wheat than that of SPEI，but the opposite was true for maize. [ABSTRACT FROM AUTHOR]

Drought frequency analysis provides valuable information for drought risk assessment. Nonparametric kernel density estimation (KDE) is applied for agricultural drought frequency analysis at the global scale. Agricultural drought over 1950–2020 is described by the standardized soil moisture index (SSMI), and drought variables (i.e., duration, severity, and peak) are extracted using run theory. The univariate and multivariate joint distributions of drought variables are established by KDE. Given that the averages for drought duration, severity, and peak are 3.10 (months), 1.59, and 0.60, respectively, the spatial distributions of multivariate return periods are mapped to determine regions with higher drought risk. The results showed that: (1) The mean values of drought duration, severity, and peak over different regions were in the ranges of 1.94–5.18 (months), 0.92–2.81, and 0.49–0.72, respectively. (2) Drought severity had higher correlations with drought duration (0.83) and peak (0.91), while the correlation coefficient between drought duration and peak was lower (0.73). (3) KDE can establish reliable joint distributions of drought variables after passing Kolmogorov-Smirnov (K S) and Anderson-Darling (A-D) tests at the 5% significance level with an average root-mean-square error of 0.04. (4) When the univariate return period was equal to 100 years, the multivariate joint return period of the "or" case was generally less than 70 years but that of the "and" case was mainly greater than 200 years. (5) Compared with other regions, West North America, North-East Brazil, Southeastern South America, Central Asia, and the Tibetan Plateau experienced higher drought risks. Accordingly, countermeasures should be established in these regions to alleviate drought impacts. [Display omitted] • Multivariate drought frequency analysis was carried out by applying nonparametric kernel density estimation (KDE) approach. • Drought return period in the "or" case was always shorter than that in the "and" case. • Drought risk was high in West North America, North-East Brazil, Southeastern South America, Central Asia and Tibetan Plateau. [ABSTRACT FROM AUTHOR]