Standardized Soil Moisture Index Research Articles

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.

Assessment of global meteorological, hydrological and agricultural drought under future warming based on CMIP6

Quantifying the changes and propagation of drought is of great importance for regional eco-environmental safety and water-related disaster management under global warming. In this study, phase 6 of the Coupled Model Intercomparison Project was employed to examine future meteorological (Standardized Precipitation Index, SPI, and Standardized Precipitation–Evapotranspiration Index, SPEI), hydrological (Standardized Runoff Index, SRI), and agricultural (Standardized Soil moisture Index, SSI) drought under two warming scenarios (SSP2-4.5 and SSP5-8.5). The results show that, across the globe, different types of drought events generally exhibit a larger spatial extent, longer duration, and greater severity from 1901 to 2100, with SPEI drought experiencing the greatest increases. Although SRI and SSI drought are expected to be more intensifying than SPI drought, the models show higher consistency in projections of SPI changes. Regions with robust drying trends include the southwestern United States, Amazon Basin, Mediterranean, southern Africa, southern Asia, and Australia. It is also found that meteorological drought shows a higher correlation with hydrological drought than with agricultural drought, especially in warm and humid regions. Additionally, the maximum correlation between meteorological and hydrological drought tends to be achieved at a short time scale. These findings have important implications for drought monitoring and policy interventions for water resource management under a changing climate.摘要在全球变暖背景下, 分析和预测干旱的变化趋势和传播规律对于区域生态环境安全和灾害管理具有重要意义. 本文基于第六次国际耦合模式比较计划 (CMIP6), 分析了SSP2-4.5和SSP5-8.5两种变暖情景下的气象 (标准化降水指数SPI和标准化降水蒸发指数SPEI) , 水文 (标准化径流指数SRI) 和农业 (标准化土壤水分指数SSI) 干旱情况. 从1901年到2100年, 全球范围内不同类型的干旱事件普遍都表现出空间范围, 持续时间和严重程度上的增长, 其中SPEI干旱的增长最为显著. 干旱加剧的地区主要包括美国西南部, 亚马逊地区, 地中海地区, 非洲南部, 亚洲南部和澳大利亚. 尽管结果显示SRI和SSI干旱程度强于SPI干旱, 但不同模式对SPI干旱的预测具有更高的一致性. 此外, 研究发现气象干旱与水文干旱的相关性高于气象干旱与农业干旱的相关性, 在温暖湿润地区尤为明显, 同时水文干旱与短时间尺度的气象干旱具有更好的相关性. 本文的研究结果将有助于气候变化下的干旱监测和水资源管理.

Investigation about the correlation and propagation among meteorological, agricultural and groundwater droughts over humid and arid/semi-arid basins in China

The propagation of meteorological drought in the hydrological cycle not only leads to the deficits of surface soil moisture, which results in agricultural drought, but also probably affects groundwater to trigger groundwater drought. Improving the understanding about the correlation and propagation among meteorological, agricultural and groundwater droughts is necessary to lessen the risks resulted from them. Moreover, the distinction of the correlation and propagation over the basins with different climate conditions is not yet sufficiently understood. As a case study of the humid and arid/semi-arid basins in China, the standardized precipitation index, the standardized soil moisture index and groundwater drought index based on GRACE (Gravity Recovery and Climate Experiment) are used to characterize meteorological, agricultural and groundwater drought respectively over the Yangtze River Basin with a humid climate condition and Yellow River Basin with an arid/semi-arid climate condition. The Spearman rank correlation coefficient is applied to investigate the correlation and propagation among the above three types of drought in the two basins. The results indicated that: (1) The meteorological, agricultural and groundwater droughts in the Yangtze River Basin and the meteorological and agricultural droughts in the Yellow River Basin are decreasing from April 2002 to March 2020, while the groundwater drought in the Yellow River Basin is aggravating in recent years. (2) There is a strong link between meteorological and agricultural droughts, and the propagation time from meteorological to agricultural drought in summer and autumn is shorter than that in winter and spring in the two basins. When comparing the propagation time in the two selected basins, it is longer in the Yangtze River Basin. (3) Groundwater extraction may be the main factor in the aggravation of groundwater drought in the two basins, which is different from the contributors to agricultural drought, which is mainly the propagation of meteorological drought.

A global perspective on the probability of propagation of drought: From meteorological to soil moisture

With a copula-based probabilistic model, this study presents a global view on the propagation of meteorological to soil moisture drought. Meta-Gaussian and three-dimensional vine copulas were employed to construct the bivariate and trivariate conditional joint drought distributions, respectively, using the time series of standardized precipitation evapotranspiration index (SPEI) and standardized soil moisture index (SMI) derived from the Global Land Data Assimilation System (GLDAS) simulations. Three different cases, including the probability and magnitude of soil moisture drought (SMI) conditioned on 1-month meteorological drought (SPEI1), seasonal meteorological drought (SPEI3), and the joint effects of SPEI1 and antecedent soil moisture conditions (SMIlag-1), were analyzed. Then, the duration of soil moisture drought in response to meteorological drought, and potentially affected cropland area were analyzed to assess potential agricultural impacts. Results showed that the likelihood of soil moisture drought conditioned on SPEI1 in the Amazon rainforest, southern and eastern China, Southeast Asia, southeastern United States, and west Africa was generally higher than in other regions. Under the impact of seasonal meteorological drought, except for the Qinghai-Tibet Plateau in China, northern Russia, and North Africa, the probability of soil moisture drought occurrence overall increased with significant increments in Europe and North America. The antecedent soil moisture condition also influenced the probability of drought propagation, and this effect was particularly significant for Northern China, Russia, the U.S. Midwest, Canada, and Australia. Spatially, the western U.S., northern Canada, South Africa, eastern Europe, North China, northern Russia, and most parts of Australia present longer duration of soil moisture drought in response to meteorological drought. Comprehensive considerations of the conditional probability, duration, and potentially affected area of soil moisture drought for croplands revealed that among five continents, South America was more vulnerable to agricultural drought under the condition of meteorological drought.

Use of a multiscalar GRACE-based standardized terrestrial water storage index for assessing global hydrological droughts

A multiscalar standardized terrestrial water storage index (STI) is proposed to assess global hydrological droughts in this study. STI is defined as the standardized statistical deviation of terrestrial water storage (TWS) measured by GRACE to the climatology at multiple time scales. Because of the statistical nature of its definition, the proposed index is comparable to other standardized drought indices in different time scales, e.g., standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI) across different regions at multiple time scales. Our comparisons of STI with multiscalar drought indices (i.e. SPI and SPEI) and uni-scalar indices (i.e. self-calibrating palmer drought severity index [scPDSI], standardized soil moisture index [SM-Z], standardized runoff index [R-Z], and GRACE-based drought severity index [DSI]) show that STI can capture drought events with less noise than other indicators in persistent drought events. As a hydrological drought index based on TWS (which includes groundwater, soil moisture, etc.), STI at short-term time scales (e.g. 3 and 6 months) have higher correlations (mostly > 0.7) with SPI/SPEI at long-term scales (e.g. 18 and 24 months). Since the TWS measured by GRACE incorporates the impacts of the accumulated changes in meteorological variables, hydrological processes, and human activities as well as the advantage of multiscale, STI better represents vegetation growth reflected by Normalized Difference Vegetation Index (NDVI) than uni-scalar drought indices including the GRACE-based DSI, suggesting STI is a robust indicator for hydrological droughts and vegetation changes.

Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

AbstractMonitoring and prediction of agricultural drought are paramount to food security at the global and regional scales, particularly under the influence of climate change and anthropogenic activities. Soil moisture is an effective indicator for monitoring and characterizing agricultural drought. Soil moisture (agricultural drought) is mainly affected by precipitation (meteorological drought) and temperature (hot conditions). Owing to the flexibility of vine copulas in handling multidimensional variables by decomposing them into pair copula constructions (PCCs), we propose a novel drought prediction method using three predictors, namely antecedent meteorological drought, previous hot conditions, and persistent agricultural drought, based on the conditional distributions of C‐vine copulas in a four‐dimensional scenario. The proposed model was applied to agricultural drought (characterized by the standardized soil moisture index (SSI)) prediction with 1–2‐months lead time for the summer season (i.e., August at a 6‐months timescale) in China. Taking two severe agricultural drought events that occurred in many regions across China in August of 2006 and 2014 as validation cases, the SSI predictions with 1–2‐months lead time using the conditional C‐vine copulas model were found to be generally consistent with the corresponding historical SSI observations in most parts of China. Performance evaluation using the Nash‐Sutcliffe efficiency (NSE), coefficient of determination (R2), and F1 score (F1S) for different climate regions also indicated that this model provided a reliable prediction of agricultural drought for most areas of China. The outcome of this study can serve as a guidance for drought prediction, early warning, and drought mitigation.

Linking drought propagation with episodes of climate-Induced water insecurity in Pernambuco state - Northeast Brazil

The semiarid region of Northeast Brazil is characterized by recurrent drought episodes. The magnitude of drought events may be assessed using standardized climate indices (SCIs) based on hydrometeorological variables, including precipitation, evapotranspiration, soil moisture, and streamflow. Drought monitoring may be improved using the concept of drought propagation, which considers the existing link between different types of droughts. Thus, correlations between the SCIs can be used to identify propagation patterns and, subsequently, predict the magnitude of a drought occurring after the first identification of a meteorological drought. In this study, five river basins located in the Pernambuco state in Northeast Brazil were selected for SCI application. The results showed that drought duration plays an important role in the final value of the SCI. Further, events between 2012 and 2017 had more severe SSMI (Standardized Soil Moisture Index) and SSI (Standardized Streamflow Index) values than SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index). Thus, the SPI alone may not be able to properly detect the magnitude of some droughts. The results of this study show the potential of the SCIs for monitoring systems and how information of soil moisture and streamflow, besides precipitation, can be useful for drought preparedness.

Agricultural drought prediction in China based on drought propagation and large-scale drivers

Agricultural drought is generally defined as a deficit in soil moisture, and can affect plant growth and crop yields. Accurate prediction of agricultural drought with sufficient lead time can aid agricultural planning and reduce losses in agricultural production. In this study, the meta-Gaussian model was employed to predict agricultural drought (standardized soil moisture index, or SSI) during spring and summer in China based on the initial soil moisture conditions, antecedent meteorological drought (standardized precipitation index, or SPI) and large-scale drivers (El Niño-Southern Oscillation, or ENSO). Monthly precipitation and soil moisture data from Global Land Data Assimilation System, version 2 (GLDAS-2.0) were used to compute the meteorological and agricultural drought indicators. The conditional distribution of agricultural drought given multiple predictors was used for the statistical prediction. The autocorrelation of agricultural drought, the correlation between meteorological drought and agricultural drought, and the correlation between ENSO and agricultural drought were first evaluated to understand the predictors from soil moisture persistence, drought propagation, and large-scale drivers. We then employed the conditional distribution to predict agricultural drought over the period from 1948 to 2014, in which contributions of antecedent meteorological drought and large-scale drivers to the prediction performance were evaluated. Results showed that the prediction method performed well in semi-arid and sub-humid regions during spring, but did not perform well in humid regions during summer. In addition, the incorporation of ENSO provided useful predictability for long lead time prediction in certain regions (with significant influence of ENSO). The results obtained from this study can provide useful information for early agricultural drought warning across China.

Can multi-scalar meteorological drought indices detect soil moisture droughts? A study of Indian regions

The present study aims at exploring the potential of multi-scalar meteorological drought indices in detecting soil moisture drought events. The standardized soil moisture index(SSMI), standardized precipitation index(SPI), standardized evapo-transpiration index(SEI), standardized precipitation evapo-transpiration index(SPEI) and multi-variate moisture anomaly index(MMAI) were computed using long-term (1980-2015) MERRA-2 soil moisture, precipitation, evapo-transpiration data products, respectively. The performances of the meteorological indices were evaluated based on zone-wise and spatial correlation approach along with failure rate (FR) and false alarm rate (FAR) values. The spatial correlation was highest in SEI, followed by MMAI, in comparison to both SPI and SPEI. FR and FAR values denoted that SEI is the best index for detecting soil moisture drought events. Whereas, MMAI outperformed other indices in representing combined drought events, i.e. meteorological or/and soil moisture droughts. The outcome of the study may be useful in retrieving information about soil moisture drought over a region exclusively using meteorological parameters.

Response of vegetation phenology to soil moisture dynamics in the Mongolian Plateau

Soil moisture (SM) plays a paramount role in maintaining plant growth. However, the understanding of the influences of SM dynamics on vegetation phenology is still insufficient. This is especially true for the arid and semi-arid regions, where moisture is the main limiting factor for vegetation growth. Therefore, by selecting the typical arid and semi-arid Mongolian Plateau as the study region, we evaluated the dynamics of SM and drought characterized by the standardized soil moisture index (SSMI) from 1982 to 2019 based on SM product derived from the fifth generation of the European Center for Medium-Range Weather Forecasts Interim Re-Analysis (ERA5). Their impacts on vegetation phenological indices, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS), were further investigated. Our results showed that SM in the growing season had experienced a significant negative trend accompanying with the increment of SM droughts. A significantly abrupt change of SM occurred around 2000. The influences of SM on vegetation phenology displayed strong seasonal variations and weakened with soil depths. Except for some alpine areas in northwestern and southeastern parts and areas with artificial plantations, negative correlations between spring SM and SOS were generally found in the Mongolian Plateau, indicating that a decreasing SM was not conductive to an earlier SOS. Most plain areas showed a positive correlation between summer SM and EOS, while the relationship became stronger in autumn. Summer SM also had a strong control on prolonging the growing season. Furthermore, vegetation phenology was sensitive to drought, with a higher sensitivity in the EOS. Our findings have derived a better understanding on the effects of climate change, but also provided vital references to maintain sustainable development of ecosystems for the Mongolian Plateau and other arid and semi-arid regions worldwide.

A novel index for ecological drought monitoring based on ecological water deficit

Although the concept of ecological drought was first defined by the Science for Nature and People Partnership (SNAPP) in 2016, there remains no widely accepted ecological drought monitoring index. Therefore, this study constructed a new ecological drought index, the standardized ecological water deficit index (SEWDI). The SEWDI is based on the difference between ecological water requirement and consumption, referred to as the standardized precipitation index (SPI) method, which was used to monitor ecological drought in Northwestern China (NWRC). The performance of the SEWDI was compared with that of other widely–used drought indices, including standardized root soil moisture index (SSI), self–calibrated Palmer drought index (scPDSI), standardized precipitation–evaporation drought index (SPEI), and SPI, using the Pearson correlations between these indices and standardized normalized difference vegetation index (SNDVI) under different time scales, wetness and water use efficiencies (WUE) of vegetation. The SEWDI at a 12–month scale was decomposed in NWRC during 1982–2015 using the rotational empirical orthogonal function (REOF) in order to demarcate five ecological drought regions, including southeastern sub–region (SE), southwestern sub–region (SW), northwestern sub–region (NW), northeastern sub–region (NE), and central sub–region (CT). The characteristics of ecological drought in NWRC, such as intensity, duration, and frequency, were extracted using the run theory. The return periods of five types of drought were calculated using wavelet analysis. Results showed that the performance of SEWDI in monitoring ecological drought was the best among the drought indices under different time scales, and the 12–month–scale was largely unaffected by wetness and WUE. Results of monitoring indicated that serious ecological droughts in the NWRC mainly occurred in 1982–1986, 1990–1996, and 2005–2010, primarily in SE, SW, and CT, SW and NE, and NW, NE, and CT, respectively. Furthermore, SEWDI in the arid SW, NW and CT showed a longer return period compared to that in the humid NE and SE, and the evolution of ecological drought in arid regions was mainly influenced by meteorological drought and the scarcity of root soil moisture. This study provides ant approach for quantifying ecological drought severity across natural vegetation areas which can be employed by decision makers

Global Dust Variability Explained by Drought Sensitivity in CMIP6 Models

AbstractBoth the atmospheric and land surface conditions affect the dust cycle in the climate system. In particular, the occurrence of drought can modulate the emission of dust on interannual time scales. Studies have shown, however, that models generally do not represent dust variability, and that there is a large intermodel spread in the simulation of dust. In this study, we compare the relationship between drought and dust in 19 Global Circulation Models participating in Phase Six of the Coupled Model Intercomparison Project for historical (1950–2014) and future (2050–2100: SSP585) scenarios and MERRA‐2 reanalysis. The relationships between drought and dust (dust sensitivity to drought) are based on linear regression analysis. Our results show that MERRA‐2 reanalysis highly underestimates models' average dust emission. The Standardized Soil Moisture Index better explains the dust variability over most regions than the Standardized Precipitation Index, highlighting the importance of the condition of the land surface. Across models, the strength of the dust‐drought relationship explains much of the spread in interannual variability of dust emission over Southern Africa, Sahel, India, Australia, and North America, indicating models that capture this relationship generate greater variability. We also find that the correlation between models' dust‐drought relationship and mean emission is generally weaker compared to that with dust variability. In future scenarios, the intermodel spread in the projected changes in the dust variability is correlated to the intermodel spread in the projected changes in the models' dust sensitivity to drought in Australia, India, Middle East, South America, and Southern Africa.

A new framework for the identification of flash drought: Multivariable and probabilistic statistic perspectives

AbstractFlash drought is distinguished with seasonal drought by its characteristics of sudden onset and rapid intensification, and has attracted increasing concern due to the severe negative impacts on socioeconomic development and ecosystems. To accurate identify the onset time and propagation of flash drought is still a big challenge, as it is influenced by many factors (e.g., deficit in precipitation, increase in evapotranspiration and temperature, shortage in soil moisture, and etc.). This study aims to develop a probabilistic and multivariable flash drought identification method (PMFDI) from the perspective of considering the interaction effects from different driving factors on flash drought occurrence and propagation with a multivariable statistical tool. The PMFDI uses the weekly standardized precipitation evapotranspiration index and the standardized soil moisture index to describe the abnormally changes in multiple hydrometeorological variables over a short duration. The linearly multivariable index (LMI) is employed to describe the correlation relationship between multiple hydrometeorological variables and to identify flash droughts from the aspects of sudden onset and rapid intensification. We select the Loess Plateau (LP) in China to verify the efficiency of the developed PMFDI with the comparison to the other two recently proposed flash drought identification methods. The results indicate that PMFDI can identify high‐ and low‐ frequency regions similar to those identified by the other two methods and can generally recognize actual flash droughts. In addition, the PMFDI can capture the comprehensive impact of multiple hydrometeorological variables as well as their interaction on flash drought occurrence, which provides a useful perspective to understand the propagation processes of the actual flash droughts in the LP.

Drought propagation and construction of a comprehensive drought index based on the Soil and Water Assessment Tool (SWAT) and empirical Kendall distribution function (<i>K</i><sub>C′</sub>): a case study for the Jinta River basin in northwestern China

Abstract. Monitoring drought and mastering the laws of drought propagation are the basis for regional drought prevention and resistance. Multivariate drought indicators considering meteorological, agricultural and hydrological information may fully describe drought conditions. However, series of hydrological variables in cold and arid regions that are too short or missing make it difficult to monitor drought. This paper proposed a method combining Soil and Water Assessment Tool (SWAT) and empirical Kendall distribution function (KC′) for drought monitoring. The SWAT model, based on the principle of runoff formation, was used to simulate the hydrological variables of the drought evolution process. Three univariate drought indexes, namely meteorological drought (standardized precipitation evapotranspiration index; SPEI), agricultural drought (standardized soil moisture index; SSI) and hydrological drought (standardized streamflow drought index; SDI), were constructed using a parametric or non-parametric method to analyze the propagation time from meteorological drought to agricultural drought and hydrological drought. The KC′ was used to build a multivariable comprehensive meteorology–agriculture–hydrology drought index (MAHDI) that integrated meteorological, agricultural and hydrological drought to analyze the characteristics of a comprehensive drought evolution. The Jinta River in the inland basin of northwestern China was used as the study area. The results showed that agricultural and hydrological drought had a seasonal lag time from meteorological drought. The degree of drought in this basin was high in the northern and low in the southern regions. MAHDI proved to be acceptable in that it was consistent with historical drought records, could catch drought conditions characterized by univariate drought indexes, and capture the occurrence and end of droughts. Nevertheless, its ability to characterize mild and moderate droughts was stronger than severe droughts. In addition, the comprehensive drought conditions showed insignificant aggravating trends in spring and summer and showed insignificant alleviating trends in autumn and winter and at annual scales. The results provided theoretical support for the drought monitoring in the Jinta River basin. This method provided the possibility for drought monitoring in other watersheds lacking measured data.

Drought monitoring and evaluation using ESA CCI and GLDAS-Noah soil moisture datasets across China

Soil moisture (SM) plays a fundamental role in governing the water and energy balance at land-atmosphere interfaces and in controlling plant growth and biological interactions, which makes it a key indicator in drought identification. We compared and evaluated two types of surface SM datasets (Global Land Data Assimilation System-Noah-simulated (GLDAS-Noah); Europe Space Agency’s Climate Change Initiative (ESA CCI)) for drought analysis in China over 1979–2014. The cumulative density function (CDF) matching method was employed to fill the data gap of ESA CCI data using the GLDAS-Noah SM products. Drought characteristics of duration, severity, and frequency were appraised on a grid basis using the Standardized Soil Moisture Index (SSI). The results show that the SSI values calculated based on these two SM products are significantly correlated (p < 0.05) over most parts (70%) of China, with similar patterns of average drought duration, severity, and frequency. The duration and severity at the arid and semiarid regions (with duration over 3 months; with an average severity of −3.1) are generally higher than those over humid regions (with the duration of 2 months; with an average severity of −2.7), but both SM datasets show higher drying trends in humid regions. However, the two SM datasets exhibit large discrepancies in the spatial patterns of drought duration, severity, and frequency trends, especially in arid and cold regions. Both SM products are capable of monitoring extreme drought events reported in southwestern, southern, and northern China compared with the Standardized Precipitation Index (SPI). Overall, both data sources have the potential to be used for drought monitoring; however, caution should be paid in high altitude and latitude regions where a large discrepancy exists.

Improving regional wheat drought risk assessment for insurance application by integrating scenario-driven crop model, machine learning, and satellite data

CONTEXTAccurate estimation of yield losses from natural disasters on a regional scale can guide agronomic management and agricultural insurance, transfer disaster risk, and ensure food security. Conventional yield losses, however, mainly depend on historical events, for which detailed records of locations and losses are unavailable. OBJECTIVEThe development of a disaster vulnerability model has consequently been hindered by the lack of sufficient samples. To improve regional yield estimates, a novel method for estimating yield losses and the pure insurance rate is thus strongly needed. METHODSUsing 129 major wheat-producing counties on the North China Plain as an example, we developed a drought assessment system based on crop growth modeling, machine learning, and satellite data. The initial model used was the model to simulate the crop–weather relationship over a large area (MCWLA) applied to wheat (MCWLA-Wheat), which was then calibrated by multi-step-assimilation with multi-source data. We first established various drought scenarios to simulate the impacts of drought at different growth stages on wheat yield by driving the calibrated MCWLA-Wheat. RESULTS AND CONCLUSIONSAccording to our results, drought-sensitive stages of wheat varied by drought severity, with insufficient water supply during the Greenup–Heading stage having the most significant impact on grain yields. Based on the outputs of the simulation and three drought indicators—standardized precipitation index (SPI), standardized soil moisture index (SSMI), and relative leaf area index (RLAI), we then established vulnerability models coupling the MCWLA-Wheat with statistical models (random forest [RF] and multiple linear regression [MLR]). The vulnerability model MCWLA+RF showed higher accuracy, with a root mean square error (RMSE) of 6% (i.e., 365 kg ha−1), relative to that of the MCWLA+MLR (RMSE = 1175 kg ha−1). Ranked in descending order, the relative importance of drought indicators at Greenup–Heading was SPI, SMMI-20 cm, and RLAI. SIGNIFICANCEOur study has highlighted a potential method for quantifying crop disaster risk—integration of a scenario-driven crop growth model, machine learning, and satellite data—that can be applied to other crops and regions.

Assessment of basin-wise future agricultural drought status across India under changing climate

AbstractMost of the existing studies on meteorological drought suggest more intense and frequent drought events due to changing climate. However, basin-scale assessment of future agricultural drought is lacking due to many reasons. In this study, the intensity and frequency of future agricultural drought (characterized by the Standardized Soil Moisture Index, SSMI) for 226 sub-basins across India are analyzed, and vulnerable basins are identified. The prediction of the future agricultural drought status is achieved using the wavelet-based drought temporal consequence modeling of meteorological drought with the best performing bias-corrected Coordinated Regional Downscaling Experiment (CORDEX) simulations, selected by Multi-Criteria Decision-Making frameworks. This study reveals a geographically contrasting change in future agricultural drought that indicates more intense agricultural drought in north, north-east, and central India as compared with south India. The area under drought is also expected to increase, and about 20 and 50% of the Indian mainland is expected to suffer from extreme (SSMI ≤ −2) and moderate (SSMI ≤ −1) agricultural drought conditions by the end of this century. Sub-basins lying in north and central India are expected to have a longer time under drought conditions. Thus, the findings of this study will be useful for future planning and preparedness against agricultural productivity.

Characterization of agricultural drought propagation over China based on bivariate probabilistic quantification

Agricultural drought has become a serious threat to the world food security and sustainable development. Although various characteristics have been investigated for agricultural drought assessment and early warning, the propagation of meteorological drought to agricultural drought is less than clear. This study investigated the spatiotemporal variations of characteristics of the propagation from meteorological drought to agricultural drought over China during the period of 1953–2012. The Standardized Precipitation Index (SPI) and the Standardized Soil moisture Index (SSI) were used to characterize meteorological drought and agricultural drought, respectively. The SSI-1 and SPI-m (1–12) were chosen as drought propagation series by correlation analysis to evaluate the correlation and propagation time between these two droughts. The probability of propagation in different regions of China was quantified using copula-based models. Results indicated that the strongest and the weakest correlations between meteorological drought and agricultural drought were found in summer (0.8–0.9) and in winter (0.5–0.8), respectively, while the propagation time increased from 1 to 2 months in summer to 2–7 months in the next spring. Spatially, correlation and propagation sensitivity between meteorological drought and agricultural drought were greater in humid areas than in other climatic zones. Further, the probability of agricultural drought occurrence was found to synchronously increase with the severity of meteorological drought. This study will help improve drought warning and forecasting systems.

How reliable are the evapotranspiration estimates by Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models for catchment-scale drought assessment and irrigation planning?

Catchment-scale assessment of evapotranspiration (ET) becomes indispensable for evaluating agricultural drought hazard and irrigation water management. As direct estimation of ET is quite intricate under sparse data availability scenarios, the recent indirect ET estimation approaches heavily depend on the water balance-based ET estimates from the popular land surface models, such as, Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC-3L) model or the MOD16A2 satellite product. Along with the land uses, the irrigation application methods of alternate wetting and drying (AWD) could also influence the actual ET (ETact) estimates. This study evaluates the usability of these indirect ET estimation methods with reference to the moisture-stress specific FAO-56 Penman-Monteith (PM) equation for catchment-scale drought and irrigation planning in a real paddy-dominated integrated reservoir-catchment command. The results reveal that the SWAT-derived ET in the non-irrigated paddy fields was consistent with the FAO-56 PM based ET estimates during two crop growing seasons; however, significant overestimation was observed by the VIC-3L, and MOD16A2 products showed a relatively better estimate. The SWAT and VIC-3L derived Standardized Soil Moisture Index (SSI) and Standardized Evapotranspiration Drought Index (SEDI) for the study area revealed that the VIC-3L based drought severity was continuously overestimated or underestimated; whereas the SWAT-derived SEDI indices were almost consistent with the benchmark MOD16A2-ET and ESA-CCI soil moisture derived estimates. Finally, this study endorses to use these indirect catchment-scale drought assessment tools with caution with a need to revise the existing ET subroutines in these hydrological models to deal with the crop dynamics and water management options realistically.

Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data

Increasing frequency and intensity of extreme drought events have harmed the environment, ecosystem, and agricultural productivity. However, the characteristics of agricultural drought in China have not been well understood. The remote sensing (RS) based gridded monthly precipitation, soil moisture, land surface temperature (LST), and normalized difference vegetation index (NDVI) datasets over 1982–2018 were utilized to derive standardized precipitation index (SPI), standardized soil moisture index (SSI), multivariate standardized drought index (MSDI), and vegetation health index (VHI). The variation patterns and trends of SPI, SSI, and MSDI at the 1-, 3-, and 6-month timescales against monthly VHI anomaly were compared to identify the best agricultural drought index in China. The drought variations in the four sub-regions (northwest, north, Qinghai-Tibet area, and south area) were also investigated. The results showed that: (1) Temporal patterns of VHI anomaly were similar to relative soil moisture and slightly different from precipitation. The spatial patterns of MSDI matched with VHI the best than SPI and SSI. (2) All three indices showed positive correlations with VHI at the three timescales. The highest correlation coefficients (r) between MSDI and VHI ranged from 0.25 to 0.67, 0.22 to 0.78, 0.23 to 0.69, and 0.19 to 0.74 in northwest China, north China, Qinghai-Tibet Plateau, and south China, respectively. (3) The connections between monthly VHI and the three drought indices were weaker at the 1-month timescale (0.16 < r < 0.25) than the 3-month (0.39 < r < 0.78) and 6-month (0.26 < r < 0.68) timescales. (4) The VHI significantly increased in most of China except north China. Overall, MSDI showed better performance for monitoring agricultural drought in China's mainland.