Standardized Soil Moisture Index Research Articles

Characterization of the Propagation of Meteorological Drought Using the Copula Model

Drought is a phenomenon that is caused by several factors and can be divided into meteorological drought, agricultural drought, hydrological drought, and socioeconomic drought. In this study, the characteristics of propagating from meteorological drought to agricultural (or hydrological) drought in the Andong Dam basin and Hapcheon Dam basin located in the Nakdong River basin in Korea were investigated. Standardized precipitation index (SPI), standardized soil moisture index (SMI), and standardized runoff index (SRI) were used to characterize meteorological, agricultural, and hydrological droughts, respectively. SPI-m (1–12) on various timescales and SMI-1 (or SRI-1) were selected as drought propagation timeseries, such that a correlation analysis was performed to evaluate the correlation and propagation time between meteorological and agricultural (or hydrological) drought. Propagation probability was quantified using a copula-based model. The correlation between meteorological and agricultural (or hydrological) droughts was not significantly affected by seasons. A relatively strong correlation was found in summer. A relatively weak correlation was shown in autumn. In addition, it was found that there was a difference in correlation between the Andong Dam basin and the Hapcheon Dam basin. On the other hand, in both watersheds, the propagation time was as long as 2 to 4 months in spring and decreased to 1 month in summer.

Trivariate Analysis of Changes in Drought Characteristics in the CMIP6 Multimodel Ensemble at Global Warming Levels of 1.5°, 2°, and 3°C

Abstract Drought is a major natural hazard with far-reaching social, economic, and environmental impacts whose characteristics are highly interdependent across different spatial and temporal scales. Traditional global warming impact assessments on drought at the global scale have, however, taken into account only one drought characteristic at a time, likely leading to an underestimation of the overall impact. Here, we perform a trivariate analysis of changes in drought conditions at 1.5°, 2°, and 3°C global warming levels using 25 CMIP6 GCMs. Drought properties are characterized by the Standardized Soil Moisture Index (SSI). The future joint return periods of droughts historically associated with 10-, 20-, and 30-yr return periods are computed under the warming levels using copula functions considering drought duration, peak, and severity. Our comparative assessments of global warming impact on drought properties between univariate and trivariate analyses corroborate the substantial underestimation of the impact by the univariate analysis. The trivariate analysis shows that around 63%–91% of the global land will be subject to more recurrent droughts, while the percentage of the land reduces to 41%–56% for the univariate analysis. The difference between the univariate and trivariate analyses enlarges with global warming levels and the extremity of drought events. Based on the trivariate analysis, a 30-yr drought would become at least threefold more recurrent in 11%, 15%, and 20% of the global land at 1.5°, 2°, and 3°C warming levels, respectively, but the univariate analysis could not reach such large increases in drought conditions.

Integrated Drought Index based on Vine Copula Modelling

AbstractAs a natural hazard, drought is a complex multivariate phenomenon that does not depend on only one hydrometeorological variable, as well as, considering a particular kind of drought may not be effective for drought management. Considering this, many multivariate drought indices have been developed based on linearity assumptions or conventional copulas assuming symmetric relationships among univariate drought indices. In this study, D‐vine copula was applied to construct a four‐dimensional index, named as Integrated Drought Index (IDI), by combining four univariate drought indices (Standardized Precipitation Index [SPI], Reconnaissance Drought Index [RDI], Standardized Soil moisture Index [SSI] and Standardized stream flow Drought Index [SDI]) to better reflect many hydrometeorological variables (precipitation, evapotranspiration, soil moisture and stream flow) and different kinds of drought (meteorological, agricultural and hydrological) simultaneously. Vine copula was used to solve nonlinear and asymmetric relationships among drought indices due to its flexibility over the free selection of copula(s) in each step of hierarchical structure in high dimensional modelling. The IDI was constructed for 1‐ and 4‐month timescales for the upper Tapti basin of the central region in India. The performance of IDI was tested with dependence measures (Pearson's correlation coefficient, Mutual Information) and evaluated against the Terrestrial Water Storage Anomaly data derived from the Gravity Recovery and Climate Experiment (GRACE) mission. Spatial analysis of drought was carried out by fuzzy c‐means (FCM) clustering algorithm with IDI. IDI based on vine copula solved the nonlinear and asymmetric relationships among different variables associated with the occurrence of droughts effectively with a reduction of uncertainty as compared to the single drought indices for different kinds of droughts. Analysis revealed spatially different drought risks in the upper and lower river basins. In general, the vine copula addresses nonlinear and asymmetric relationships that exist between the variables associated with natural hazards like drought.

Evaluation of drought propagations with multiple indices in the Yangtze River basin.

This paper explored the drought propagation phenomenon based on meteorological, hydrological, and agricultural aspects in the Yangtze River basin (YRB), China. To evaluate meteorological, hydrological, and agricultural droughts, this paper used three drought indices, standardized precipitation evapotranspiration index (SPEI), standardized runoff index (SRI), and standardized soil moisture index (SSMI), respectively. The community land model (CLM) in the YRB to generate the monthly evapotranspiration, soil moisture, runoff data, which are required for the estimation of drought index, were applied. Different mean durations (6-and 12-month) were used for drought estimation, and propagations of meteorological to hydrological and meteorological and agricultural droughts were investigated for different durations as SPEI6-SRI6, SPEI6-SSMI6, SPEI12-SRI12, SPEI12-SSMI12. The average drought propagation between 1950 and 2010 presented the highest autocorrelation and correlation with one-month lags in four combinations of drought indices in SPEI6-SRI6, SPEI6-SSMI6, SPEI12-SRI12, and SPEI12-SSMI12. Additionally, this paper estimated the optimal lags of SPEI-SRI and SPEI-SSMI drought propagations using mean 6-and 12-month lag times for six representative drought periods. Therefore, the propagation phenomenon of meteorological to hydrological and to agricultural droughts were confirmed in the YRB.

Analysis of Vegetation Vulnerability Dynamics and Driving Forces to Multiple Drought Stresses in a Changing Environment

Quantifying changes in the vulnerability of vegetation to various drought stresses in different seasons is important for rational and effective ecological conservation and restoration. However, the vulnerability of vegetation and its dynamics in a changing environment are still unknown, and quantitative attribution analysis of vulnerability changes has been rarely studied. To this end, this study explored the changes of vegetation vulnerability characteristics under various drought stresses in Xinjiang and conducted quantitative attribution analysis using the random forest method. In addition, the effects of ecological water transport and increased irrigation areas on vegetation vulnerability dynamics were examined. The standardized precipitation index (SPI), standardized precipitation-evapotranspiration index (SPEI), and standardized soil moisture index (SSMI) represent atmospheric water supply stress, water and heat supply stress, and soil water supply stress, respectively. The results showed that: (1) different vegetation types responded differently to water stress, with grasslands being more sensitive than forests and croplands in summer; (2) increased vegetation vulnerability under drought stresses dominated in Xinjiang after 2003, with vegetation growth and near-surface temperature being the main drivers, while increased soil moisture in the root zone was the main driver of decreased vegetation vulnerability; (3) vulnerability of cropland to SPI/SPEI/SSMI-related water stress increased due to the rapid expansion of irrigation areas, which led to increasing water demand in autumn that was difficult to meet; and (4) after ecological water transport of the Tarim River Basin, the vulnerability of its downstream vegetation to drought was reduced.

Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

The characteristics of the drought propagation from meteorological drought (MD) to agricultural drought (AD) differ in various climatic and underlying surface conditions. However, how these factors affect the process of drought propagation is still unclear. In this study, drought propagation and influencing factors were investigated in an arid region of Northeast Asia (ARNA) during 1982–2014. Based on run theory, the drought characteristics were detected using the standardized precipitation index (SPI) and standardized soil moisture index (SMI), respectively. The propagation time from MD to AD was investigated, and the influence factors were identified. Results demonstrated that five clusters (C1–C5) based on land cover distribution were further classified by the K-means cluster algorithm to discuss the spatial and seasonal propagation variation. MD and AD in ARNA became more severe during the study period in all five clusters. The propagation times from MD to AD in all five clusters were shorter (1–3 months) in summer and autumn and longer (5–12 months) in spring and winter. This result suggested that the impact of vegetation on the seasonal drought propagation time was more obvious than that of the spatial drought propagation time. Precipitation and vegetation were the major impactors of AD in spring, summer and autumn (p < 0.05). The impact of precipitation on AD was more noticeable in summer, while vegetation mainly influenced AD in spring and autumn. The research also found that drought propagation time had a negative relationship (p < 0.05) with precipitation, evapotranspiration, soil moisture and NDVI in this region, which indicated that a rapid hydrological cycle and vegetation can shorten the propagation time from MD to AD. This study can help researchers to understand the drought propagation process and the driving factors to enhance the efficiency of drought forecasting.

Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings

Assessing the risk on agricultural system is important for agricultural sustainability. The present study analyses the agricultural drought risk with respect to different drought severities. Different drought indices namely SPEI, SSI, VCI and TCI are used to evaluate the conditional probability. The non-stationary analysis is carried out for SPEI and SSI drought indices to incorporate the impact of large-scale oscillations and regional hydrological variability. The copula analysis is performed between drought conditions and different crop yield anomalies over the Maharashtra, India during 1998-2015. The outcomes suggest that SPEI is found as significant drought indicator over the maximum number of districts in all the crops. The SST and ISMI are selected as suitable covariates to model the non-stationarity in SPEI time series. The drought risk is estimated to increase with the drought severity for all the selected crops. It is observed that the exclusion of non-stationarity will underestimate the agricultural risk.

Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change

This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980–2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020–2056 period. Based on the normalized root mean square error (NRMSE), Cramer-von mises statistic (Sn), and Nash Sutcliffe (NS) evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe and longer droughts will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, the joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it will decrease for extreme droughts in the future.

Propagation characteristics and mechanism from meteorological to agricultural drought in various seasons

Agricultural drought poses a serious threat to national food security, and the investigation on the propagation from meteorological to agricultural drought plays an important role in drought mitigation and social stability. Nevertheless, the seasonal dynamic propagation from meteorological to agricultural drought and its dominant influence factors in a changing environment remain unresolved. To this end, the meteorological drought and agricultural drought are characterized by the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSMI), respectively. The propagation time from meteorological to agricultural drought is determined by selecting the maximum conditional probability value between SPI with various timescales and SSMI with time scale of 1 month (SSMI-1). Furthermore, the seasonal dynamic propagation patterns are obtained based on the moving window and Mann-Kendall trend test. Subsequently, the influence factors are explored based on the diverse hydro-meteorological factors and irrigation water consumption. The Wei River Basin (WRB), a typical arid and semi-arid as well as an important agricultural production area in China, is taken as the research object. Results manifest that: (1) it is more suitable to determine the drought propagation time by conditional probability method considering linear and nonlinear relations simultaneously; (2) the seasonal propagation time of the whole basin is getting shorter as a whole, especially in the main stream basin; (3) the increasing temperature and decreasing soil moisture and precipitation have strong effects on the propagation dynamics while irrigation water consumption has a little influence, and teleconnection factors may indirectly affect drought propagation by influencing hydro-meteorological factors. Overall, it is of great significance to study the propagation of meteorological drought to agricultural drought for drought prevention, drought resistance and food security.

Regional frequency analysis of drought severity and duration in Karkheh River Basin, Iran using univariate L-moments method.

Drought is one of the natural disasters that causes a great damage to human life and natural ecosystems. The main differences are in the gradual effect of drought over a relatively long period, impossibility of accurately determining time of the beginning and end of drought, and geographical extent of the associated effects. On the other hand, lack of a universally accepted definition of drought has added to the complexity of this phenomenon. In the last decade, due to increasing frequency of drought in Iran and reduction of water resources, its consequences have become apparent and have caused problems for planners and managers. So in this research, regional frequency analysis using L-moments methods was performed to investigate severity and duration of Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSI) and to study of meteorological, agricultural, and hydrological droughts in Karkheh River Basin in Iran. Using K-means clustering method, basin was divided into four homogeneous areas. Uncoordinated stations in each cluster were removed. The best regional distribution function was selected for each homogeneous region, and it was found that Pearson type (3) has the highest fit on the data set in the basin. Based on Hosking and Wallis heterogeneity test, Karkheh Basin with H1 < 1 was identified as acceptable homogeneous in all clusters. The results showed that hydrological drought occurs with a very short time delay in Karkheh River Basin after the meteorological drought, and two indicators show meteorological and hydrological drought conditions well. Agricultural drought occurs after meteorological and hydrological drought, respectively, and its severity and duration are less than the other indicators. Meteorological, hydrological, and agricultural droughts do not occur at the same time in all of the years. In general, the SPI drought index shows the most severe droughts compared with the other three indices. By this way, in 5- to 20-year return period with severity of 3SPI and in 20- to 100-year return period with severity of 7SPI, region IV or the western and northwestern areas of the basin has been affected by severe meteorological drought. By using the regional standardized quantities, it is possible to estimate the probability of drought in any part of the catchment that does not have sufficient data for hydrological studies.

The efficiency of the Standardized Evapotranspiration Deficit Index (SEDI) in assessing the impact of drought on vegetation cover.

It is important to choose an indicator that can optimally demonstrate the effects of drought intensity on soil moisture access, evapotranspiration and the changes in vegetation cover at a regional scale. Therefore, herein, SEDI was developed by the fit of the experimental distribution of Gringorten on evapotranspiration deficit based on TerraClimate data at the time scales of 1, 3, 6 and 12months, and its relationship with Standardized Precipitation-Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSMI), Normalized Ecosystem Drought Index (NEDI) and Normalized Difference Vegetation Index (NDVI) were investigated. The results indicated that SEDI has the highest significant correlation (above 95%) with NEDI and SPEI, especially for the 1-month time scale. This index also revealed the lowest correlation (less than 25%) with SPI on short-term time scales. The relationship between SEDI and SSMI indicated the high sensitivity of SEDI to the cumulative reduction of low amounts of soil moisture. According to the findings, the 6-month SEDI with NDVI showed the highest correlation with a 1-month delay (r = 0.64) and the best fit between them occurred in wet months. However, in the dry months, the relationship between SEDI with NDVI was affected via water availability stresses, grazing intensity and pest infestation. Finally, the use of SEDI at a regional scale, especially in arid and semi-arid regions like Lorestan, could be recognized as an important index in depicting the effects of drought on vegetation cover, due to the use of the actual evapotranspiration factor.

Development of a New Multivariate Composite Drought Index for the Blue Nile River Basin

Drought is a slow-onset phenomenon that evolves over a season or even years. Drought affects people more than any other natural disaster due to its widespread and significant negative impacts. Population growth and associated water demand add further stress on water resources, especially in periods of drought. Drought indices represent a single value resulting from processing a considerable amount of data. These indices provide a short message to stakeholders to adapt water resource management strategies. Since drought results from interconnected phenomena, designing a composite drought index that includes several drought indices can accurately capture drought events. Drought assessment over a large-scale basin (e.g., the Blue Nile) is a challenging objective that has not been deeply tackled before except for small portions of the basin. This paper assessed droughts over the whole basin by evaluating meteorological, agricultural, and hydrological drought indices. The calculated drought indices (Standardized Runoff Index (SRI), Standardized Precipitation Index (SPI), and standardized soil moisture index (SSI)) in addition to the development of a new standardized evapotranspiration index (sETI) are jointly integrated into a novel composite drought index for the Blue Nile (BNI). The optimal weights for SPI, SRI, sETI, and SSI were 0.33, 0.26, 0.2, and 0.19, respectively, in the designed BNI.

Evaluation of agricultural drought in Luanhe River Basin based on the standardized soil moisture index.

Drought is one of the main disasters causing agricultural production losses. Accurate and effective agricultural drought monitoring is of great significance to reduce agricultural production losses. In this study, the standardized soil moisture index for agricultural drought evaluation was established by using the historical time series of soil moisture. Based on the index, the temporal and spatial distribution of drought events in the Luanhe River Basin from 2002 to 2019 were analyzed, and the agricultural drought was quantitatively evaluated. The results showed that soil moisture of most grid units in the Luanhe River Basin obeyed normal distribution during the study period, and that the soil moisture of a few grid units obeyed generalized extreme distribution. The changes of drought and flood in each grid unit had good temporal consistency, that is, the changes of drought and flood was consistent. The changes of drought and flood in the middle of the basin was relatively significant, while the agricultural drought generally showed a trend of reduction. The spatial distribution of agricultural drought severity was not obvious. Relatively speaking, the drought characteristics (the frequency, duration and intensity of drought) were higher in the middle and northwest of the basin and lower in the southeast. The results could provide a reference for agricultural drought prediction and the formulation of drought prevention and mitigation measures in the Luanhe River Basin.

Precipitation and Soil Moisture Spatio-Temporal Variability and Extremes over Vietnam (1981-2019): Understanding Their Links to Rice Yield.

Vietnam, one of the three leading rice producers globally, has recently seen an increased threat to its rice production emanating from climate extremes (floods and droughts). Understanding spatio-temporal variability in precipitation and soil moisture is essential for policy formulations to adapt and cope with the impacts of climate extremes on rice production in Vietnam. Adopting a higher-order statistical method of independent component analysis (ICA), this study explores the spatio-temporal variability in the Climate Hazards Group InfraRed Precipitation Station’s (CHIRPS) precipitation and the Global Land Data Assimilation System’s (GLDAS) soil moisture products. The results indicate an agreement between monthly CHIRPS precipitation and monthly GLDAS soil moisture with the wetter period over the southern and South Central Coast areas that is latter than that over the northern and North Central Coast areas. However, the spatial patterns of annual mean precipitation and soil moisture disagree, likely due to factors other than precipitation affecting the amount of moisture in the soil layers, e.g., temperature, irrigation, and drainage systems, which are inconsistent between areas. The CHIRPS Standardized Precipitation Index (SPI) is useful in capturing climate extremes, and the GLDAS Standardized Soil Moisture Index (SSI) is useful in identifying the influences of climate extremes on rice production in Vietnam. During the 2016–2018 period, there existed a reduction in the residual rice yield that was consistent with a decrease in soil moisture during the same time period.

Short-term agricultural drought prediction based on D-vine copula quantile regression in snow-free unfrozen surface area, China

This study aims to propose an agricultural drought prediction model based on D-vine copula quantile regression considering several factors related to agricultural drought occurrence mechanisms in China. The proposed model is applied for short-term agricultural drought prediction (represented by 1-month time scale of the standardized soil moisture index (SSI-1)) considered antecedent soil moisture (represented by SSI-3), real-time rainfall (represented by 1-month time scale of the Standardized Precipitation Index (SPI-1)) and vegetation cover (represented by the Normalized Difference Vegetation Index (NDVI)) based on monthly soil moisture data from the Climate Change Initiative (CCI) program of European Space Agency (ESA), precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and NDVI products from MODIS. The proposed model was employed and evaluated in China and results showed it performed well in snow-free unfrozen surface area such as south-east China. The outcome of this study can contribute to early warning for agricultural drought.

Multivariate global agricultural drought frequency analysis using kernel density estimation

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 (KS) 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.

Assessment of Regional Spatiotemporal Variations in Drought from the Perspective of Soil Moisture in Guangxi, China

Understanding the changes in regional droughts is important for promoting overall sustainable development. However, the spatiotemporal dynamics of soil droughts in Guangxi under the background of global warming and regional vegetation restoration have not been studied extensively, and the potential causes are scarcely understood. Here, using TerraClimate soil moisture data, we constructed a monthly standardized soil moisture index (SSMI), analyzed the seasonal and annual spatiotemporal distribution of droughts from the perspective of soil moisture, and studied past soil drought events in Guangxi. Migration methods of drought centroid, trend analysis, and principal component decomposition were used. In the interannual dynamics, the overall SSMI increased, indicating that the soil drought situation was gradually alleviated in Guangxi. Further, the frequency of extreme and severe droughts decreased with time, mainly in autumn and winter. During early drought stages, the migration path was short, which extended as the droughts progressed. Ocean temperature and soil moisture were strongly correlated, indicating that abnormal ocean surface temperature may drive soil moisture. This study provides scientific guidance for the early warning, prevention, and mitigation of losses associated with soil droughts in Guangxi and serves as valuable reference for understanding the impacts of large-scale climate anomalies on soil moisture.

Assessing the characteristics of recent drought events in South Korea using WRF-Hydro

Drought is a complex and slow-moving disaster that is difficult to monitor and define. This study, therefore, aims to demonstrate the characteristics of recent droughts occurring from 2008 to 2015 over South Korea using a process-based land and hydrologic model, Weather Research and Forecasting-Hydro modeling system (WRF-Hydro). To drive the standalone WRF-Hydro, gridded meteorological data (5 km) were generated using station-based observations and the Parameter-elevation Regressions on Independent Slopes Model (PRISM). The model was calibrated and evaluated using inflow observations at four locations with dams; for 2008–2010 (calibration) and 2011–2015 (evaluation), it demonstrated average R2 values of 0.80 and 0.75, respectively. While Standardized Precipitation Index is used for calculating meteorological drought using precipitation from PRISM, Standardized Soil Moisture Index and Standardized Streamflow Index, at different timescales, are used to calculate agricultural and hydrological droughts, respectively, with WRF-Hydro simulations. The correlation coefficients between SPI and both SSFI and SSMI were calculated to detect their response times. The hydrological and agricultural droughts showed response times 0.5–1 month later than meteorological drought. In 2008–2015, agricultural and hydrological drought events occurred 1.6 times per year on average in South Korea, whereas meteorological droughts occurred 4.3 times per year on average. Agricultural and hydrological droughts lagged behind meteorological droughts by up to 53, 65, and 83 days, when using 1-, 3-, and 6-month SPI, respectively. Moreover, hydrological droughts were less severe than meteorological droughts due to the propagation of drought by attenuation. This study demonstrates that WRF-Hydro can be used to quantitatively determine the different types of drought events and their propagation, which could help policy makers manage drought risks.

Unraveling the Relationship Between Tropical Storms and Agricultural Drought

AbstractDrought and tropical storm (TS) are associated with water deficit and surplus, respectively. Soil moisture is a key component in the hydrological cycle that plays an important role in monitoring drought and reflects the infiltrated or stored water due to TS rainfall. Therefore, soil moisture information can be used for the assessment of whether TS can ameliorate severe drought conditions. Here, we use downscaled 1 km Soil Moisture Active Passive data set generated at the Center for Complex Hydrosystems Research at the University of Alabama, and Hurricane Database 2nd generation to examine the coincidence of extreme events between agricultural droughts and Atlantic TS in the contiguous United States from 2015 to 2019. The Cumulative Distribution Function (CDF) matching approach is employed to correct the bias using the root zone soil moisture data provided by the North American Land Data Assimilation System Phase 2 (NLDAS‐2), and then weekly Standardized Soil Moisture Index is calculated for characterization of agricultural drought. As a result, we estimate the frequency of TS impacted regions in the US, the ratio of droughts ameliorated and exacerbated by TS, and the regions where TS highly affect the offset of drought. Our findings indicate detailed spatial information of the offset of drought conditions based on a high‐resolution data set and provide potential information in terms of mitigating drought and TS for the future.

High-resolution propagation time from meteorological to agricultural drought at multiple levels and spatiotemporal scales

Meteorological drought (MD) is the source of agricultural drought (AD), and in-depth understanding of the propagation process from MD to AD can help AD early warning. However, previous studies primarily focused on average state of propagation time (PT) rather than from the perspective of various drought levels, and had coarse spatiotemporal resolutions (monthly and basin scales), and also ignored the PT during critical stages of crop water demand. To fill the knowledge gap, this study used the standardized precipitation index (SPI) and standardized soil moisture index (SSMI) to characterize MD and AD. Then, a framework for high-resolution PT identification under different drought levels based on Copula theory and conditional probability was proposed. Taking the rain-fed agricultural region of Loess Plateau (RFLP) as an example, the PT of the critical stages of water demand for local crops (spring maize, winter wheat) was calculated respectively. Result showed that: (1) PT in summer and autumn is shorter than those in winter and spring, and the higher drought level, the faster the spreading rate, in which the PT in spring, summer, autumn, and winter under extreme MD conditions were 3–15, 1–6, 1–8 and 4–16 ten days, respectively; (2) it should be noted that the central part of RFLP is a hotspot, with a shorter PT than any surrounding area; (3) PT is mainly related to local water and heat characteristics, and high potential evapotranspiration tends to accelerate the propagation process, while sufficient soil moisture has a good buffering effect on MD and can slow down the propagation; (4) it was observed that PT in the jointing-heading stage of wheat was significantly shortened, which should be of great concern to the local agricultural authorities. In general, this study sheds new insights into high spatiotemporal resolution drought propagation, which is helpful for AD risk management.