Drought Types Research Articles

Identical hierarchy of physical drought types for climate change signals and uncertainty

Climate change may have different impacts on different types of drought through its influence on the mechanisms of the propagation of a precipitation lack into a hydrological or agricultural drought. The involvement of additional processes in runoff and soil moisture modeling potentially leads to discrepancies in the projection uncertainties and signal-to-noise ratios between different drought types. This global study compares climate change signals, uncertainty, and signal-to-noise ratios between meteorological, hydrological, and agricultural droughts characterized by standardized precipitation index (SPI), standardized runoff index (SRI), and standardized soil moisture index (SSI), respectively. The comparison is made for five drought characteristics including median and peak intensity, median and longest duration, and frequency using 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) models for four Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. We find that the spatial extent and magnitude in all five drought characteristics increase from meteorological to hydrological to agricultural drought. This increase manifests itself, however, at the expense of augmented uncertainty, to the extent that uncertainty for agricultural drought is up to sevenfold larger compared to meteorological drought. Despite the augmentation of uncertainty from meteorological to agricultural drought, the hierarchy of drought types for climate change signals still holds for the spatial extent of significant signal-to-noise ratios.

Propagation and Characteristics of Hydrometeorological Drought Under Changing Climate in Irish Catchments

AbstractHydrometeorological droughts are complex hazards expressed through the relative deviation in water availability relative to long‐term average conditions. The development and propagation of drought is governed by hydrological processes at different spatial scales including precipitation, evapotranspiration, overland flow, soil moisture, groundwater storage and discharge. Detailed investigation is thus required to evaluate associated linkages among various types of hydrometeorological drought to understand the likely impacts of climate change on drought characteristics (magnitude, frequency, duration and propagation). This study explores the impact of climate change on hydrometeorological drought for 10 Irish catchments, using standardized drought indices representing different components of the hydrological system. We employ 12 Global Climate Models from the Coupled Model Inter‐comparison Project phase 6 (CMIP6), forced with SSP370 and bias corrected to catchment conditions to force a conceptual hydrological model to generate hydrological projections for each catchment. The results indicate substantial drying during summer with associated increases in summer drought magnitude and frequency. However, simulations show a wide range of change, especially for hydrological drought (discharge and baseflow). Only modest changes in the magnitude and frequency of hydrological drought events were found, with increases in winter and spring precipitation offsetting summer dryness. Similarly, the probability of meteorological drought propagating to hydrological events (Standardized Streamflow Index/Standardized Baseflow Index), shows modest increases under the climate change projections considered. Findings highlight that drought, especially during summer are a critical climate change risk for adaptation.

Construction and Assessment of a Drought-Monitoring Index Based on Multi-Source Data Using a Bias-Corrected Random Forest (BCRF) Model

Agricultural drought significantly impacts agricultural production, highlighting the need for accurate monitoring. Accurate agricultural-drought-monitoring models are critical for timely warning and prevention. The random forest (RF) is a popular artificial intelligence method but has not been extensively studied for agricultural drought monitoring. Here, multi-source remote sensing data, including surface temperature, vegetation index, and soil moisture data, were used as independent variables; the 3-month standardized precipitation evapotranspiration index (SPEI_3) was used as the dependent variable. Soil texture and terrain data were used as auxiliary variables. The bias-corrected RF model was used to construct a random forest synthesized drought index (RFSDI). The drought-degree determination coefficients (R2) of the training and test sets reached 0.86 and 0.89, respectively. The RFSDI and SPEI_3 fit closely, with a correlation coefficient (R) above 0.92. The RFSDI accurately reflected typical drought years and effectively monitored agricultural drought in Northeast China (NEC). In the past 18 years, agricultural drought in NEC has generally decelerated. The degree and scope of drought impacts from 2003 to 2010 were greater than those from 2010 to 2020. Agricultural drought occurrence in NEC was associated with dominant climatic variables such as precipitation (PRE), surface temperature (Ts), relative humidity (RHU), and sunshine duration (SSD), alongside elevation and soil texture differences. The agricultural drought occurrence percentage at 50–500 m elevations reached 94.91%, and the percentage of occurrence in loam and sandy soils reached 90.31%. Water and temperature changes were significantly correlated with the occurrence of agricultural drought. Additionally, NEC showed an alternating cycle of drought and waterlogging of about 10 years. These results have significant application potential for agricultural drought monitoring and drought prevention in NEC and demonstrate a new approach to comprehensively evaluating agricultural drought.

Construction of an Agricultural Drought Monitoring Model for Karst with Coupled Climate and Substratum Factors—A Case Study of Guizhou Province, China

Droughts are becoming more frequent in the karst region of southwest China due to climate change, and accurate monitoring of karst agricultural droughts is crucial. To this end, in this study, based on random forest (RF) and support vector regression (SVR) algorithms, the monthly precipitation, monthly potential evapotranspiration, monthly normalised difference vegetation Index (NDVI), elevation, and karst development intensity from January to December 2001–2020 were used as independent variables, and the standardised soil moisture index (SSI) calculated by GLDAS soil moisture was used as the dependent variable to construct karst agricultural drought monitoring models at different timescales, using Guizhou Province as an example. The performance of the models constructed by the two algorithms was also evaluated using root mean square error (RMSE), coefficient of determination (R2), and correlation analysis, and the spatial and temporal evolution trends of karst agricultural drought at different timescales were analysed based on the model with better performance. The prediction of karst agricultural drought from January to December 2021–2025 was based on the seasonal difference autoregressive moving average (SARIMA) model and the analysis of change trends was performed using the Bayesian estimator of abrupt change, seasonal change, and trend (RBEAST). The results showed that (1) the drought model constructed by the RF regression algorithm performed better than the SVR algorithm at 1-, 3-, 6-, 9-, and 12-month timescales and was superior for monitoring karst agricultural drought. (2) The model showed that the overall trend of agricultural drought at different timescales was alleviated; 2010, 2011, and 2012 were typical drought years. At the same time, most regions showed a trend of drought mitigation, whereas a few regions (Bijie City, Liupanshui City, and Qianxinan Prefecture) showed a trend of aggravation. (3) The study predicted an overall high west–east distribution of drought intensity by 2021–2025. The 1- and 3-month timescales showed a trend of agricultural drought mitigation, and the 6-, 9-, and 12-month timescales showed a trend of aggravation; in 2021, 2022, and 2024, the abrupt change rates of autumn and winter droughts were higher. The results can provide a reference basis for the monitoring of agricultural drought in karst agriculture and the formulation of drought prevention and anti-drought measures.

Drought in Shanxi Province Based on Remote Sensing Drought Index Analysis of Spatial and Temporal Variation Characteristics

Drought is a natural disaster with long duration and which causes great harm. Studying the characteristics of drought evolution in Shanxi Province can grasp the regularity of drought occurrence and provide a basis for drought prevention and resistance. This study utilizes MODIS products to analyze and quantify the extent of drought in a specific area. The study calculates several indices, including the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI), using variables such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Evapotranspiration (ET), and Potential Evapotranspiration (PET). Additionally, three drought indices are analyzed for correlation with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and the most suitable drought index is selected through validation with typical drought events. Finally, the selected indices are used to investigate the spatiotemporal characteristics of drought in the study area from 2001 to 2020. The results show: (1) CWSI and sc-PDSI have a strong correlation both in terms of time and spatial analysis. Furthermore, CWSI has been shown to be more effective in monitoring significant drought events. (2) The multi-year mean values of CWSI range from 0.71 to 0.85, with a significant degree of spatial heterogeneity. In the study area, the percentage of the area affected by different levels of drought is in the following order: moderate drought > severe drought > mild drought > no drought. (3) The trend of CWSI changes shows that the drought situation in Shanxi Province has been alleviated from 2001 to 2020, and the overall spatial distribution indicates that the degree of drought alleviation in the southern region is greater than that in the northern region. The turning point from drought to wetness in the study area was in 2011, showing the overall characteristic of “dry in the north and wet in the south”.

Research on Provincial-Level Soil Moisture Prediction Based on Extreme Gradient Boosting Model

As one of the physical quantities concerned in agricultural production, soil moisture can effectively guide field irrigation and evaluate the distribution of water resources for crop growth in various regions. However, the spatial variability of soil moisture is dramatic, and its time series data are highly noisy, nonlinear, and nonstationary, and thus hard to predict accurately. In this study, taking Jiangsu Province in China as an example, the data of 70 meteorological and soil moisture automatic observation stations from 2014 to 2022 were used to establish prediction models of 0–10 cm soil relative humidity (RHs10cm) via the extreme gradient boosting (XGBoost) algorithm. Before constructing the model, according to the measured soil physical characteristics, the soil moisture observation data were divided into three categories: sandy soil, loam soil, and clay soil. Based on the impacts of various factors on the soil water budget balance, 14 predictors were chosen for constructing the model, among which atmospheric and soil factors accounted for 10 and 4, respectively. Considering the differences in soil physical characteristics and the lagged effects of environmental impacts, the best influence times of the predictors for different soil types were determined through correlation analysis to improve the rationality of the model construction. To better evaluate the importance of soil factors, two sets of models (Model_soil&atmo and Model_atmo) were designed by taking soil factors as optional predictors put into the XGBoost model. Meanwhile, the contributions of predictors to the prediction results were analyzed with Shapley additive explanation (SHAP). Six prediction effect indicators, as well as a typical drought process that happened in 2022, were analyzed to evaluate the prediction accuracy. The results show that the time with the highest correlations between environmental predictors and RHs10cm varied but was similar between soil types. Among these predictors, the contribution rates of maximum air temperature (Tamax), cumulative precipitation (Psum), and air relative humidity (RHa) in atmospheric factors, which functioned as a critical factor affecting the variation in soil moisture, are relatively high in both models. In addition, adding soil factors could improve the accuracy of soil moisture prediction. To a certain extent, the XGBoost model performed better when compared with artificial neural networks (ANNs), random forests (RFs), and support vector machines (SVMs). The values of the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), Nash–Sutcliffe efficiency coefficient (NSE), and accuracy (ACC) of Model_soil&atmo were 0.69, 11.11, 4.87, 0.12, 0.50, and 88%, respectively. This study verified that the XGBoost model is applicable to the prediction of soil moisture at the provincial level, as it could reasonably predict the development processes of the typical drought event.

Construction of a Time-Variant Integrated Drought Index Based on the GAMLSS Approach and Copula Function

Construction of an integrated drought index is a fundamental task to conducting drought disaster risk management and developing drought resistance planning strategies. Given the evident non-consistent features during the drought evolution process, firstly, the GAMLSS approach was utilized to construct multiple combination scenarios of time-variant parameters and corresponding probability distribution functions. Then, the time-variant comprehensive drought index integrating the variable characteristics of precipitation and soil moisture was established by means of the copula function. Finally, the reliability of the time-variant comprehensive drought index was verified through its application in frequency analysis and return period determination of drought hazard system in Huaibei Plain, China. The application results demonstrated that: (1) The variation of the time-variant integrated drought indicator presented strong consistency with both soil moisture and precipitation during historical years in Huaibei Plain. (2) The overall variation of the drought hazard system characterized by drought duration and severity presented a gradual mitigation trend from west to east and north to south in Huaibei Plain, which agrees with the geographic differences and water resources availability distribution features. (3) Drought recognition results, including the frequency of drought events and typical drought processes with extreme grades, are in agreement with the practical statistics and observed data series. On the whole, the proposed time-variant integrated drought indicator is capable of extracting complex variation characteristics within the drought hazard evolution process, and can be further applied in drought monitoring, recognition and assessment research fields.

Improved Understanding of Flash Drought from a Comparative Analysis of Drought with Different Intensification Rates

The rapid intensification of drought, commonly known as flash drought, has recently drawn widespread attention from researchers. However, how the characteristics and drivers, as well as the ecological impacts of rapidly intensified droughts, differ from those of slowly intensified ones still remains unclear over the globe. To this end, we defined three types of droughts based on the root zone soil moisture (RZSM) decline rates, flash droughts, general droughts, and creep droughts, and then implemented a comparative analysis between them across the globe and the 26 Intergovernmental Panel on Climate Change Special Report on Extremes (IPCC-SREX) regions. The ensemble of RZSM from multiple reanalysis datasets was used to reduce the uncertainties. According to the frequency analysis, our findings suggest that flash droughts contributed to the majority of drought events during 1980–2019, indicating the prevalence of rapid transition from an energy-limited to a water-limited condition in most of the regions. The comparative results of vegetation responses show that flash droughts are more likely to happen in the growing season, leading to faster but relatively minor vegetation deterioration compared to the slowly intensified ones. By analyzing the precipitation and temperature anomalies in the month of drought onset, we found the role of temperature (precipitation) on drought intensification can be generalized as the warmer (drier) the climate is or the faster the drought intensifies, but the main driving forces vary by region. Unlike temperature dominating in midwestern Eurasia and northern high latitudes, precipitation plays a prominent role in the monsoon regions. However, the temperature is expected to be the decisive driver in the warming future, given its monotonically increased contribution over the past four decades.

Spatial Heterogeneity of Vegetation Resilience Changes to Different Drought Types

AbstractResilience is a fundamental concept for vegetation health. The increasing drought frequency and severity may pose severe threat to vegetation resilience. However, it is still not clear how vegetation resilience is evolving in response to climate change in pivotal biographical zones. Here, we examined the resilience changes in terms of leaf area index (LAI, an indicator of canopy structure) and gross primary productivity (GPP, an indicator of carbon uptake) in responding to the Standardized Precipitation‐Evapotranspiration Index (SPEI) and vapor pressure deficit (VPD) over China's Loess Plateau and Qinling Mountains. Linking remote sensing variables and tree ring width allows the upscaling of plot‐based vegetation growth information. We further explored potential explanatory factors associated with the heterogeneous spatial distributions of resilience changes. Results revealed that the resilience of GPP weakened more than LAI in response to drought, suggesting that compared to LAI, productivity requires more time to recover to the pre‐drought levels. Regionally, the change of vegetation resilience on the Loess Plateau and in high‐altitude areas was highly susceptible to SPEI and VPD, respectively. The observed spatial heterogeneity in resilience changes was mainly attributed by climate zone, water deficit, and their interactions. Our findings provide direct and empirical evidence that the vegetation in the Loess Plateau and Qinling Mountains is gradually losing resilience. The results indicate that sustained ecosystem water deficit and atmospheric dryness will continue to threaten vegetation survival and terrestrial ecosystem service.

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent studies (from 2010 to 2022) that have explored drought features in Turkey. To this end, a total of 71 articles were selected from the Web of Science (WoS) and Scopus databases. The selected papers were clustered into two categories: (i) drought monitoring studies and (ii) drought forecasting articles. Then, the representative papers were reviewed in detail regarding the implemented indices, models (techniques), case study area, and source of the indicators used to derive drought indices. The review results showed that most of the studies aimed at meteorological drought monitoring and forecasting. An increasing trend was also observed in the use of machine learning for short-term meteorological and hydrological drought prediction. On the other hand, the emerging RS technology and satellite-driven indicators were rarely used in the country. The review showed that there is room for more research on agricultural and hydrological drought monitoring, forecasting, and pattern detection in Turkey.

Spatiotemporal Drought Monitoring Using Remote Sensing Technique in Babel-Iraq

Drought is one of the most natural hazards that may harm human life and property under different weather and environmental conditions. This study used remote sensing data to monitor agricultural and meteorological drought in Babel Governorate. Drought maps were drawn using Landsat 8 images based on Normalized Difference Vegetation Index (NDVI) for 2015, 2018, and 2021. The meteorological drought was assessed using a standardized precipitation index (SPI 12) for the same years. The results showed that the SPI-12 indicated near-normal drought types in 2015 and 2018, whereas SPI values showed a lower value in 2021. Two drought categories were identified which were moderate drought and severe drought. The NDVI results showed that the vegetation area decreased in 2021 by 9 % in comparison to 2015. This result could help the government planners to develop and manage the drought impacts.

Phenotyping drought tolerance and yield performance of barley using a combination of imaging methods

Drought resistance represents a complex of traits that are differently employed depending on drought type, severity and timing. Hence, a relatively comprehensive assessment of morphological and physiological phenotypic response to drought is required. We evaluated the dynamic responses of six barley genotypes, representing a wide range of drought tolerance, to continuous drying and re-watering using non-invasively measured parameters based on red-green-blue (RGB), thermal infrared and chlorophyll fluorescence imaging within an automated phenotyping platform. We identified three critical points in drought progress: i) 50% level of available soil water, ii) wilting point, iii) full plant recovery after re-watering. However, the individual monitored parameters showed the potential to evaluate drought sensitivity at different points since the onset of drying. The correlation with relative yield response gradually increased for the side projected leaf area (SPA) and reached the maximum at the point of full recovery. The actual quantum yield of photosystem II (ΦPSII) showed the highest correlation with a relative grain yield around wilting point. In contrast, the relative leaf temperature difference demonstrated a high correlation with yield response earlier, at 50% of available water. The highest correlations with the relative yield response were obtained for the colour RGB analysis at the wilting point and after recovery, particularly for khaki, beige, dark-green and olive-green colours. Multiple regression with parameters providing Pearson’s correlation coefficient R > 0.5 slightly improved the estimation of relative yield response to drought but ensured significant improvement of absolute grain yield estimation under drought stress. This study shows that combining the phenotyping methods representing different morphological and physiological traits allows not only the assessment of drought tolerance (based on relative yield response to drought) which is crucial for selecting the genetic resources for the subsequent breeding process but also allows to test the yield performance of new genotypes under drought stress.

Compound droughts slow down the greening of the Earth.

Vegetation response to soil and atmospheric drought has raised extensively controversy, however, the relative contributions of soil drought, atmospheric drought, and their compound droughts on global vegetation growth remain unclear. Combining the changes in soil moisture (SM), vapor pressure deficit (VPD), and vegetation growth (normalized difference vegetation index [NDVI]) during 1982-2015, here we evaluated the trends of these three drought types and quantified their impacts on global NDVI. We found that global VPD has increased 0.22 ± 0.05 kPa·decade-1 during 1982-2015, and this trend was doubled after 1996 (0.32 ± 0.16 kPa·decade-1 ) than before 1996 (0.16 ± 0.15 kPa·decade-1 ). Regions with large increase in VPD trend generally accompanied with decreasing trend in SM, leading to a widespread increasing trend in compound droughts across 37.62% land areas. We further found compound droughts dominated the vegetation browning since late 1990s, contributing to a declined NDVI of 64.56%. Earth system models agree with the dominant role of compound droughts on vegetation growth, but their negative magnitudes are considerably underestimated, with half of the observed results (34.48%). Our results provided the evidence of compound droughts-induced global vegetation browning, highlighting the importance of correctly simulating the ecosystem-scale response to the under-appreciated exposure to compound droughts as it will increase with climate change.

Drought vulnerability modeling over Mandawa watershed, northern Iraq, using GIS-AHP techniques

Drought is among the most severe natural calamities induced by lack of water, having a negative implication on water resources and agriculture in the affected area. Drought types and severity vary by location, so understanding the spatial distribution can aid in developing measures to overcome this natural hazard. In this study, the areas vulnerable to droughts in the Mandawa watershed in the Kurdistan region of Iraq were determined by employing seven associated factors: rainfall, temperature, LULC, surface slope, soil texture, elevation, and distance to rivers. Satellite imagery of Landsat 8 OLI for 2021 was employed to create the Land Use and Land Cover (LULC) and distance to rivers maps. The elevation and surface slope maps have been generated from the Digital Elevation Model at 30 m resolution, soil texture map was extracted from The FAO Digital Soil Map of the World and the inverse distance weighting method was utilized to interpolate the rainfall and temperature throughout the watershed. Analytical Hierarchy Process (AHP) was used to create a pairwise compression matrix to obtain the weight of each parameter. In the Geographic Information System (GIS) environment, the combined impact of affecting factors was utilized to create the area's drought zonation map. The results indicated that only 5.2% and 13.8% of the study area is vulnerable to extreme and severe droughts, respectively. While more than 35% of the watershed is hardly vulnerable to droughts.

Modeling hydrological non-stationarity to analyze environmental impacts on drought propagation

Climate variation and anthropic activities are two key driving forces that impact the hydrologic cycle as well as the relationships between different drought types. Thus, it is essential to evaluate the impacts of environmental variations on the relationship between meteorological and hydrological droughts. In this study, abrupt changes in the yearly hydrological time series (streamflow) of the Han River Basin (HRB) were detected using a non-linearity-based empirical segmentation approach. The Standardized Precipitation Evapotranspiration Index (SPEI) was employed to model meteorological drought, while the Generalized Additive Model for Location, Scale and Shape (GAMLSS) algorithm was adopted to model the non-linear hydrological time series to obtain the non-stationarity based Standardized Runoff Index (SRINS). Correlation analyses were conducted on meteorological droughts (as presented by SPEI) and the hydrological drought data (as presented by the SRINS). A Bayesian network model (BNM) was employed to calculate the propagation likelihood of different categories of meteorological droughts resulting in hydrological droughts. Change points in the hydrological regime were identified based on the empirical segmentation analysis after the 1990s. Significant increasing trends in urbanization, gross domestic product, and population were observed after the change points. The correlation analysis showed that the seasonal (3-month) timescale of SPEI corresponded best to the three-month SRINS. The BNM revealed that the average propagation likelihoods of severe and extreme categories of meteorological drought resulting in severe and extreme categories of hydrological drought were 23.6% and 18.2%, respectively, due to the influence of climate change. These probabilities were increased by 53.9% and 70.8%, respectively, in the human impacted era due to high pressure on water resources caused by increased population, industrialization, water extraction, etc. In conclusion, the likelihood of extreme conditions of meteorological drought resulting in extreme hydrological drought was increased significantly after the change points.

Partitioning the Uncertainties in Compound Hot and Dry Precipitation, Soil Moisture, and Runoff Extremes Projections in CMIP6

AbstractA comprehensive assessment of compound hot and dry extremes based on different drought conditions (low precipitation, runoff, or soil moisture) and associated uncertainties is necessary to fully understand the possible risks. Here, we analyze changes in the likelihood of compound hot and dry conditions associated with low precipitation, runoff, and soil moisture using Coupled Model Intercomparison Project Phase6 (CMIP6) simulations for present‐day climate (+1°C) and additional global warming levels (+1.5°C, +2°C, +3°C). Further, we investigate the contributions of different components (e.g., global warming levels, climate models, copula types) to the total spread in their future projections. Results show the significance of global warming levels in governing risks of rising compound hot and dry extremes. The hotspot regions include the Mediterranean, South Central America, Amazonia, and Sahara. The rising risks are also accompanied by rising uncertainty as the spread in changing likelihood is significantly contributed by Earth System Models (ESMs), global warming levels, their interactions, and the statistical estimation error. The uncertainty due to ESMs spread was observed to be most significant in the case of compound hot and low soil moisture extremes, which also corresponds to some of the most impactful conditions. It was observed that the estimation error dominates the uncertainty in compound hot and low precipitation extremes as compared to the two other combinations. Our findings indicate that the regional likelihood and associated uncertainties of compound hot‐dry events in CMIP6 projections are functions of both the selection of drought types and the methodology of deriving the joint extremes.

A new integrated index for drought stress monitoring based on decomposed vegetation response factors

Drought is a complex process that usually stems from aberrant climatic conditions such as precipitation deficits and temperature anomalies. Drought can pose a serious threat to vegetation growth, which in turn regulates the process of drought evolution. An in-depth understanding of the interaction between drought and vegetation is crucial not only for drought monitoring but also for drought mitigation. To this end, this study divided the drought process into two sub-processes: (1) from physical dry conditions to drought stress and (2) from drought stress to vegetation destruction, and introduced vegetation response factors to quantitatively depict the vegetation contributions in each sub-process. Response factors of different vegetation types were decomposed respectively from Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Precipitation Condition Index (PCI) in the first sub-process, and from Vegetation Condition Index (VCI) in the second sub-process. Further, this study proposed a novel integrated drought index named Type Response-Aided Drought Index (TRADI) based on the response factors of different vegetation types. Vegetation response factors were applied to fine-tune several commonly used single drought indices to verify their effectiveness and the proposed TRADI was evaluated by comparing it with several combined indices, including Vegetation Health Index (VHI), Scaled Drought Condition Index (SDCI), Microwave Integrated Drought Index (MIDI), and Integrated Drought Monitoring Index (IDMI). Moreover, the TRADI was qualitatively evaluated with the maps from U.S. Drought Monitor (USDM) in the 2011 and 2012 drought events across the CONtinental United States (CONUS). Our results showed that adjusted by vegetation response factors, single drought indices as well as combined drought indices showed a closer correlation with the Palmer Drought Severity Index (PDSI) than their original forms, indicating the strength of response factors in modifying existing indices. Furthermore, the proposed TRADI performed better than VHI, SDCI, MIDI, and IDMI, and was consistent with USDM in typical drought events. This study provides a new perspective on understanding the process of drought affecting vegetation and highlights the importance of considering vegetation response in drought index establishment.

Global analysis of the correlation and propagation among meteorological, agricultural, surface water, and groundwater droughts

Groundwater drought monitoring relies on ground observation data, which cannot be used to reflect large-scale droughts in groundwater resources. The Gravity Recovery and Climate Experiment Satellite (GRACE) improved the situation and provided a new solution for groundwater drought research. However, the propagation relationship among global different drought types has not been fully explored. We employed CRU precipitation data, MERR2 reanalysis soil moisture data, GLDAS and GRACE data to calculate SPI (Standardized precipitation index), SSI (Standardized soil moisture index), SRI (Standardized runoff index), and GDI (Groundwater drought index), characterizing meteorological, agricultural, surface water and groundwater droughts, respectively. The Pearson correlation coefficient was adopted to study the propagation time of these four types of droughts. The results showed that the average propagation times for the different drought types are meteorological drought to surface water drought (3.5 months), meteorological drought to agricultural drought (5.7 months), agricultural drought to groundwater drought (12.97 months), surface water drought to groundwater drought (13.78 months), and meteorological drought to groundwater drought (14.47 months) from longest to shortest. (2) Climate conditions had a significant impact on the propagation time of different drought types. Low temperatures in cold climates resulted in the longest drought propagation time, while dry summer climates in temperate climates reduced drought propagation time. There were weaker propagation relationships in arid climates. In tropical climates, precipitation may not be the main driving factor for drought propagation. (3) Different land cover types show significant differences in the propagation of groundwater droughts, with forests having a longer propagation time from meteorological drought to agricultural drought or surface water drought than grassland and cropland, and forests having the shortest propagation time when meteorological drought, agricultural drought, and surface water drought is propagated to groundwater drought. Woody plants have deeper root systems than herbaceous plants and can draw up deeper groundwater. Forests have greater water storage capacity and weaker groundwater recharge than grasslands and croplands, resulting in forests being more resistant to agricultural and surface water droughts and less resistant to groundwater droughts during meteorological droughts. This study can help to clarify the propagation laws among different drought types and understand the internal mechanisms that affect the development of drought during the water cycle.

From meteorological to agricultural drought: Propagation time and probabilistic linkages

Study regionYangtze River Basin, China Study focusAs agricultural drought originates from meteorological drought, exploring the propagation from meteorological to agricultural drought is an important step in providing early warning of agricultural drought. In this study, Standardized Precipitation Index (SPI) and Improved Soil Moisture Anomaly Percentage Index (ISMAPI) were adopted to indicate meteorological and agricultural droughts, respectively. The propagation time from meteorological drought to agricultural drought was identified, and probabilistic linkages between the two types of drought were investigated using the copula function and Bayesian network. New hydrological insightsThe average propagation time from meteorological to agricultural drought in the Yangtze River Basin (YRB) is 48 d. The propagation times in the summer and autumn are shorter than those in the spring and winter. The occurrence probability of more serious agricultural drought gradually increases with meteorological drought intensity. Agricultural and meteorological drought events were matched using the proposed drought event-matching method. In the YRB, approximately 72.4% of meteorological drought events may translate into agricultural drought events. Generally, an agricultural drought event that has the same drought category as that of the given meteorological drought event is most likely to occur. Regional differences in drought propagation time and occurrence probability of agricultural drought may be related to variations in climatic conditions across regions.

Simultaneous Monitoring of Different Drought Types Using Linear and Nonlinear Combination Approaches

Simultaneous Monitoring of Different Drought Types Using Linear and Nonlinear Combination Approaches