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Persistent droughts pose a threat to agricultural production, and the changing environment worsens the risk of drought exposure. Understanding the propagation of drought in changing environments and assessing possible impact factors can help in the early detection of drought, guiding agricultural production practices. The current study cannot reflect the propagation status of drought to the total terrestrial hydrological drought, so this work creatively investigated the atmospheric to hydrological drought propagation time in the Yangtze River Basin under the dynamic and static perspectives based on the Standardized Precipitation Evapotranspiration Index and the Terrestrial Water Storage Anomalous Drought Index, fine-tuned the time scale to the seasonal scale, and explored the contributing capacity of the variable interactions. The results show that: (1) under the dynamic perspective, while the propagation time is decreasing in the annual scale, the spring season shows the opposite trend; and (2) large variability exists in the timing of drought propagation at spatial scales, with elevation playing the most important influential role, and bivariate interactions contributing stronger explanations compared to single variables. This study highlights the importance of considering the impact of variable interactions and contributes to our understanding of the response of secondary droughts to upper-level droughts, providing valuable insights into the propagation of droughts to total terrestrial hydrologic drought.

Understanding the propagation characteristics and driving factors from meteorological drought to hydrological drought is essential for alleviating drought and for early warning systems regarding drought. This study focused on the Weihe River basin (WRB) and its two subregions (the Jinghe River (JRB) and the middle reaches of the Weihe River (MWRB)), utilizing the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) to characterize meteorological and hydrological drought, respectively. Based on Copula theory and conditional probability, a quantification model for the propagation time (PT) of meteorological–hydrological drought was constructed. The dynamic characteristics of PT on annual and seasonal scales were explored. Additionally, the influences of different seasonal meteorological factors and underlying surface factors on the dynamic changes in PT were analyzed. The main conclusions were as follows: (1) The PT of meteorological–hydrological drought was characterized by faster propagation during the hot months (June–September) and slower propagation during the cold months (December to March of the following year); (2) Under the same level of hydrological drought, as the level of meteorological drought increases, the PT of the drought shortens. The propagation thresholds of meteorological to hydrological drought in the WRB, the JRB, and the MWRB are −0.69, −0.81, and −0.78, respectively. (3) In the dynamic changes in PT, the WRB showed a non-significant decrease; however, both the JRB and the MWRB exhibited a significant increase in PT across different drought levels. (4) The influence of the water and heat status during spring, summer, and winter on PT was more pronounced, while in autumn, the impact of the basin’s water storage and discharge status was more significant in the JRB and the MWRB.

Drought is one of the most frequent types of natural disasters in the world, and it has been classified into several different categories. Generally, meteorological drought is considered to be the beginning of a drought disaster, while socio-economic drought is the possible ultimate result. However, controversy remains around the main impact factors in the propagation from meteorological drought to socio-economic drought over the past decades. In this study, a comprehensive investigation of the 2022 drought event in the city of Lishui, China was conducted to build a model for analyzing the main impact factors in the propagation from meteorological drought to socio-economic drought. The results showed that the 2022 drought event had a great impact on the city’s socio-economic activities. According to governmental reports on socio-economic drought and basic information on water sources, a random forest attribution analysis model was built. The model demonstrated a great performance in distinguishing whether a socio-economic drought had occurred, with an accuracy of 0.9935, a true positive rate of 0.9489 and a false positive rate of 0.0021. Additionally, the variables related to water sources—including drainage area, covered population and daily water supply volume—were found to be more important than the other variables related to meteorological conditions in the model, meaning that the capacity of water sources is the main impact factor in the propagation between meteorological drought and socio-economic drought. In other words, it is feasible to prevent the propagation of meteorological drought to socio-economic drought through water conservancy engineering construction.

Gaining a comprehensive understanding of the characteristics and propagation of precipitation-based meteorological drought to terrestrial water storage (TWS)-derived hydrological drought is of the utmost importance. This study aims to disentangle the frequency–time relationship between precipitation-derived meteorological and TWS-based hydrological drought from June 2002 to June 2017 based on the Standardized Precipitation Index (SPI) and Standardized Terrestrial Water Storage Index (STI) by employing wavelet coherence rather than a traditional correlation coefficient. The possible influencing factors on drought propagation in 28 regions across the world are examined. The results show that the number of drought months detected by the STI is higher than that detected by the SPI worldwide, especially for slight and moderate drought. Generally, TWS-derived hydrological drought is triggered by and occurs later than precipitation-based meteorological drought. The propagation characteristics between meteorological and hydrological droughts vary by region across the globe. Apparent intra-annual and interannual scales are detected by wavelet analysis in most regions, but not in the polar climate region. Drought propagation differs in phase lags in different regions. The phase lag between hydrological and meteorological drought ranges from 0.5 to 4 months on the intra-annual scale and from 1 to 16 months on the interannual scale. Drought propagation is influenced by multiple factors, among which the El Niño–Southern Oscillation, North Atlantic Oscillation, and potential evapotranspiration are the most influential when considering one, two, or three factors, respectively. The findings of this study improve scientific understanding of drought propagation mechanisms over a global scale and provide support for water management in different subregions.

The Nile River Basin (NRB) has experienced a notable rise in drought episodes in recent decades. The propagation of meteorological, agricultural, and groundwater drought dynamics in the NRB was investigated in this study. The following drought indices examined the correlation and propagation among meteorological, agricultural, and groundwater droughts. These are the standardized precipitation evapotranspiration index (SPEI), soil moisture index, Gravity Recovery and Climate Experiment, and GRACE Follow-On (GRACE/GRACE-FO)-derived groundwater drought index (GGDI). These droughts were comprehensively evaluated in the NRB from 2003 to 2022. The cross-wavelet transform approach highlighted the links between droughts. The following are the key findings: (1) In the NRB, the cross-wavelet energy spectrum of wavelet coherence can indicate the internal connection between meteorological versus (vs.) agricultural and agricultural versus (vs.) groundwater drought. The time scale with the most significant correlation coefficient is the drought propagation time. (2) The El Niño–Southern Oscillation (ENSO) correlated with agricultural and groundwater drought much more than the Indian Ocean Dipole (IOD), demonstrating that ENSO has an important impact on drought advancement. (3) The R2 values were 0.68 for GGDI vs. standardized soil moisture index (SSI), 0.71 for Blue Nile Region (BNR) GGDI vs. SSI, and 0.55 for SSI vs. Standardized Precipitation Evapotranspiration Index (SPEI). Similarly, in the Lake Victoria Region (LVR), GGDI vs. SSI was 0.51 and SSI vs. SPEI was 0.55, but in the Bahr-el-Ghazal Region (BER), GGDI vs. SSI was 0.61 and SSI vs. SPEI was 0.27 during the whole research period with varied lag durations ranging from 1 to 6 months. Thus, the propagation of drought (i.e., meteorological, agricultural, and groundwater drought) dynamics has the potential to reshape our understanding of drought evolution, which could lead to early drought forecasting across the NRB and similar climatic regions.

As extreme climate events become more common with global warming, groundwater is increasingly vital for combating long-term drought and ensuring socio-economic and ecological stability. Currently, the mechanism of meteorological drought propagation to groundwater drought is still not fully understood. This study focuses on the North China Plain (NCP), utilizing statistical theories, spatiotemporal kriging interpolation, and the Mann–Kendall trend test to examine the spatial and temporal distribution characteristics of groundwater from 2005 to 2021. Based on drought theory, the characteristics and propagation process of drought are further quantified. Key findings reveal the following: (1) Shallow groundwater depths in the NCP follow a zonal pattern from the western mountains to the eastern plains and coastal areas. Over two-thirds of this region showed an increase in groundwater depth at a rate of 0–0.05 m/a; (2) Groundwater drought frequency typically ranges from 3 to 6 times, with an average duration of 10 to 30 months and average severity between 10 and 35; (3) Delayed effects last between 0 to 60 months, with attenuation effects varying from 0 to 3 and prolonged effects extending from 0 to 16. Additionally, delayed effects intensify with increasing time scales, while prolonged effects weaken. Notably, both delayed and prolonged effects in the north of the NCP are more pronounced than in the south of the region. This study quantifies the process by which meteorological drought propagates to groundwater drought, offering a new perspective for understanding the interaction between groundwater and meteorological drought. It holds significant scientific importance for monitoring drought and managing water resources in the context of global climate change.

In the past several decades, drought events have occurred frequently around the world. However, research on the propagation of drought events has not been adequately explored. This study investigated the drought propagation process from meteorological drought to agricultural drought (PMAD) and from meteorological drought to hydrological drought (PMHD) using a 72-year reanalysis dataset in the tropical Lancang–Mekong River Basin. Firstly, we used a new method—Standardized Drought Analysis Toolbox—to construct drought indices. Then, a linear method (Pearson correlation analysis) and a nonlinear method (mutual information) were used to investigate the drought propagation process. Cross-wavelet analysis and wavelet coherence analysis were employed to explore the statistical relationship among the three drought types. Finally, the random forest method was applied to quantify the major factors in drought response time (DRT). The results revealed the following: (1) both linear and nonlinear methods exhibited strong temporal and spatial consistency for both PMAD and PMHD, with linear relationships being stronger than nonlinear ones. (2) The DRTs of PMAD and PMHD were around 1–2 months and 3–5 months, respectively. Significant differences existed in the DRT between the dry season and the rainy season. (3) A divergent spatial pattern of the proportion of DRT was observed between PMAD and PMHD. (4) Significant statistical correlations between meteorological drought and agricultural drought and between meteorological drought and hydrological drought were observed in specific periods for each sub-region; (5) Hydrometeorological factors contributed the most to DRT, followed by terrain factors and the land cover types. The findings of this study deepened our understanding of the spatial–temporal relationship of multiple drought propagation types in this transboundary river basin.

Recent studies on China’s arid and semi-arid regions, particularly the Tarim River Basin (TRB), have shown an increase in the intensity and frequency of extreme weather events. This research examines the link between meteorological droughts, as measured by the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts, as indicated by the Standardized Runoff Index (SRI) and the Standardized Terrestrial Water Storage Index (STI), over various time scales. Historical data indicate that SPEI drought frequency (DF) was 14.3–21.9%, with prevalent events in the northern oases. SRI DF ranged from 9.0% to 35.8%, concentrated around the Taklamakan and Kumtag Deserts, while STI DF varied between 4.4% and 32.7%, averaging 15% basin-wide. Future projections show an increased DF of SPEI in deserts and a decrease in oases; SRI DF decreased in deserts but increased in oases. STI changes were more moderate. The study also found a higher risk of drought progression from SPEI to SRI in the southwestern and northeastern oases, exceeding 50% probability, while central and eastern TRB had lower risks. The western TRB and inner Taklamakan Desert faced higher risks of SPEI to STI progression, with probabilities over 45%, in contrast to the lower risks in the eastern and central oases. The concurrence of SRI/STI with moderate to extreme SPEI droughts led to a higher probability and area of SRI/STI droughts, whereas consistent SPEI types showed a reduced induced probability and extent of SRI/STI droughts. This study enhances the understanding of drought propagation from meteorological to hydrological droughts in the TRB and contributes to the prevention of hydrological drought to a certain extent.

Revealing the mechanism of hydrological and agricultural drought has been challenging and vital in the environment under extreme weather and water resource shortages. To explore the evolution process from meteorological to hydrological and agricultural drought further, multi-source remote sensing data, including the Gravity Recovery and Climate Experiment (GRACE) product, were collected in the Huaihe River basin of China during 2002–2020. Three machine learning methods, including long short-term memory neural network (LSTM), convolutional neural network (CNN), and categorical boosting (CatBoost), were constructed for hydrological and agricultural drought forecasting. The propagation time from meteorological drought to surface water storage and terrestrial water storage drought, evaluated by the standardized precipitation evapotranspiration index, was 8 and 11 months with Pearson correlation coefficients (R) of 0.68 and 0.48, respectively. Groundwater storage drought was correlated with evapotranspiration and vegetation growth with a 12-month lag time, respectively. In addition, vegetation growth was affected by the drought of soil moisture at depths ranging from 100 to 200 cm with an 8-month lag time with an R of −0.39. Although the forecasting performances of terrestrial water storage drought were better than those of groundwater storage drought and agricultural drought, CNN always performed better than LSTM and CatBoost models, with Nash–Sutclife efficiency values during testing ranging from 0.28 to 0.70, 0.26 to 0.33, and −0.10 to −0.40 for terrestrial water storage drought, groundwater storage drought, and agricultural drought at lead times of 0–3 months, respectively. Furthermore, splitting training and testing data at random significantly improved the performances of CNN and CatBoost methods for drought forecasting rather than in chronological order splitting for non-stationary data.

Investigating systematic meteorological–agricultural–hydrological drought propagation within an integrated watershed framework is crucial yet challenging for advancing robust early warning systems and targeted resilience strategies. To address this gap, this study employs the standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), and nonlinear joint hydrological drought index (NJHDI) to characterize meteorological, agricultural, and hydrological drought, respectively. Utilizing the strongest correlation method, variations in the propagation time along moisture movement pathways within the system are quantified using the Soil and Water Assessment Tool (SWAT) model. The Yellow River basin (YRB) is selected as the case study area. Key results reveal distinct seasonal patterns in meteorological–agricultural propagation across the basin, with escalated spring and summer timescales. However, weaker correlations and limited seasonality emerge for agricultural–hydrological linkages due to sustaining baseflow buffers. Specifically, the arid and semiarid region exhibited shorter propagation time with 1–5 months, while the semiarid and semihumid region displayed longer timescales with 7–12 months. These findings provide valuable scientific references for enhancing real-time early warning systems tailored to coupled watershed systems. The integrated methodology underscores the importance of unraveling fine-scale spatiotemporal propagation variability for localized drought resilience.

Investigating the propagation and influencing mechanism that transitions a meteorological drought to a hydrological drought in a changing environment is crucial for understanding the formation process and mechanism of hydrological drought. Furthermore, it is essential to establish an effective hydrological drought warning system based on meteorological drought. To assess the dynamic changes in the spread of meteorological drought to hydrological drought during various seasons, this study employs the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Normalized Vegetation Index (NDVI) to represent meteorological, hydrological, and vegetation droughts, respectively, in the Ganjiang River Basin (GRB) from 2002 to 2020. Considering that meteorological drought can be caused not only by insufficient precipitation but also by excessive evaporation, an additional index, namely the Evaporative Demand Drought Index (EDDI), is constructed to quantify meteorological drought resulting from evaporation factors. The article analyzes the characteristics of the spatiotemporal evolution of meteorological, hydrological, and vegetation drought. The Spearman rank correlation coefficient is employed to calculate the propagation time of different seasons from meteorological drought to hydrological/vegetation drought and from hydrological drought to vegetation drought. Furthermore, we examine the propagation relationship among meteorological, hydrological, and vegetation drought in the time-frequency domain through cross-wavelet analysis and explore the key factors and physical mechanisms that influence the propagation of drought in various seasons. The result shows: The propagation time from meteorological to hydrological drought (SPI-SRI) is shortest in spring, extended during summer and autumn, and longest in winter. The meteorological drought arising from excessive evapotranspiration in autumn has the most substantial impact on hydrological drought. Vegetation drought and meteorological/hydrological drought exhibit significant intermittent resonance periods in 0~6 months and significant stable resonance periods in 7~15 months.

Understanding how drought propagates from meteorological to agricultural drought requires further research into the combined effects of soil moisture, evapotranspiration, and precipitation, especially through the analysis of long-term data. To this end, the present study examined a multi-year reanalysis dataset (ERA5-Land) that included numerous drought events across the Iberian Peninsula, with a specific emphasis on the 2005 episode. Through this analysis, the mechanisms underlying the transition from meteorological to agricultural drought and its features for the selected region were investigated. To identify drought episodes, various non-parametric standardized drought indices were utilized. For meteorological droughts, the Standardized Precipitation-Evapotranspiration Index (SPEI) was employed, while the Standardized Soil Moisture Index (SSI), Multivariate Standardized Drought Index (MSDI), and Standard Precipitation, Evapotranspiration and Soil Moisture Index (SPESMI) were utilized for agricultural droughts, while their ability to identify relative vegetation stress in areas affected by severe droughts was investigated using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Anomaly provided by the Copernicus European Drought Observatory (EDO). A statistical approach based on run theory was employed to analyze several characteristics of drought propagation, such as response time scale, propagation probability, and lag time at monthly, seasonal, and six-month time scales. The retrieved response time scale was fast, about 1–2 months, and the probability of occurrence increased with the severity of the originating meteorological drought. The duration of agricultural drought was shorter than that of meteorological drought, with a delayed onset but the same term. The results obtained by multi-variate indices showed a more rapid propagation process and a tendency to identify more severe events than uni-variate indices. In general terms, agricultural indices were found to be effective in assessing vegetation stress in the Iberian Peninsula. A newly developed combined agricultural drought index was found to balance the characteristics of the other adopted indices and may be useful for future studies.

Drought is a natural phenomenon that is controlled by different factors such as natural climate and catchment controls, and in many worldwide regions, it is now driven by human activities (i.e., reservoirs, irrigation, groundwater abstractions). Reservoirs initially ensure water availability and help cope with drought, especially in semi-arid regions; however, this human modification of the environment may lead to both positive and negative effects on the hydrological cycle, which need to be understood. This involves a better understanding of hydrological processes and incorporating human interactions within coupled human–natural systems to improve drought management. We focused on a strongly irrigated area located in the northeast of the Iberian Peninsula, the northern part of the Canal of Aragon and Catalonia district supplied by the Barasona reservoir. We implemented a simple water management model to simulate the reservoir operation (human-influenced scenario) and examined the contribution of human activities, associated with irrigation, on the water budget and drought propagation. For this purpose, we used simulations performed by the hydrometeorological model SASER (SAFRAN-SURFEX-EauDyssée-RAPID), which provided a natural scenario (without human influence) to contrast with the human-influenced scenario. The model performance was evaluated through the Kling Gupta Efficiency (KGE) metric. The first results demonstrated satisfactory performance to simulate reservoir storage and outflows against observed data, with KGE values of 0.4 and 0.82, respectively. Then we explored the linkages between agricultural drought, associated with evapotranspiration, and hydrological drought. We applied standardized indices to identify different kinds of drought, compared them, and assessed changes induced by human activities. Human modifications modulate the hydrological response of the catchment, and alter the intensity of hydrological drought, while human activities reduce the intensity of agricultural droughts.

Based on the meteorological and hydrological data of the Yalong River Basin from 1960 to 2019, meteorological and hydrological droughts were analyzed using the standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI); then, the spatio-temporal evolution and propagation characteristics of the droughts were studied on multiple time scales. The results showed that, firstly, on the annual scale, the frequencies of meteorological and hydrological droughts in the basin were 28.3% and 34.0%, respectively, in the past 60 years. From upstream to downstream, the longer the alternating period of dry and wet periods, the more significant the frequency of droughts. Secondly, on a seasonal scale, the frequency of meteorological droughts is high in autumn, and the frequency of hydrological drought is high in autumn and winter. The frequency of drought in different seasons decreases from the upper reaches to the lower reaches of the basin. Thirdly, on a monthly scale, the severe and exceptional meteorological drought frequencies are high from March to May, and the severe and exceptional hydrological drought frequencies are high in January, March, October, and December. The frequency of hydrological droughts is much higher than that of meteorological droughts, especially with respect to severe and exceptional drought. Meteorological and hydrological droughts spread in the same period without lag, but they tend to expand. The propagation time of drought is short in summer and autumn, but long in spring and winter. The conclusions can serve as a decision-making basis for the water diversion planning of the west route of China’s South-to-North Water Diversion Project and the cascade hydropower operation of the basin.

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.

The analysis of drought propagation has garnered mounting attention in the changing global environment. The current studies tend to focus on the propagation characteristics from meteorological to hydrological drought in rivers. Lakes, despite being a key component of watershed ecosystems, have received little attention to their response to meteorological and hydrological droughts. To this end, here, we investigated the characteristics of propagation from meteorological to hydrological drought for a lake in a changing environment. To determine the drought propagation time from meteorological to hydrological drought, we analyzed correlations between the standardized precipitation index (SPI), standardized runoff index (SRI), and standardized water level index (SWI). Lake Baiyangdian in China served as the case study. The results showed that meteorological droughts occur at high frequency but are short in duration, indicating that not every meteorological drought will necessarily lead to a hydrological drought. By contrast, lake hydrological droughts have low frequency and long duration and feature more severe consequences. Comparing drought characteristics before and after a changing environment, we found a reduced frequency of the SPI, SRI, and SWI, yet their duration was prolonged. For the SWI especially, these results were even more pronounced, which suggests the changing environment enabled further intensification of the lake hydrological drought. In addition, more time was needed for a meteorological drought to transition into a lake hydrological drought after a changing environment.

Drought is a multifaceted natural disaster that can impact the ecological environment, crop yield, and social economy through the hydrological cycle process. Meteorological drought occurs first, which then propagates to other forms. This study presents the propagation characteristics of meteorological to hydrological drought in different river basins of China. The main drivers of drought propagation are also quantitatively analyzed in this study. The standardized precipitation index (SPI) and standardized runoff index (SRI) were used to describe meteorological and hydrological drought, respectively. The Songhua and Liaohe River Basin (SLRB), Haihe River Basin (HARB), Huaihe River Basin (HURB), Yellow River Basin (YRB), Yangtze River Basin (YARB), Pearl River Basin (PRB), Southeast Basin (SEB), Southwest Basin (SWB), and Continental Basin (CB) were analyzed in this study. The precipitation and runoff datasets were used to compute the SPI and SRI, respectively. The results showed that the drought propagation time was mainly 1–3 months in China. In general, drought propagation had a stronger relationship in the central and eastern river basins of China than in the western river basins (SWB and CB). Spring and winter had a weaker drought propagation relationship than autumn and winter. Drought propagation was driven by precipitation in the HURB, YARB, SEB, and PRB; soil moisture and precipitation were drivers in the HARB and YRB; moreover, soil moisture and potential evapotranspiration were drivers in the SLRB and CB. This study improves the understanding of the characteristics and drivers of drought propagation in droughts in river basins. Therefore, this study might provide a reference to reveal the mechanism of drought.

Drought is an extreme phenomenon that will likely increase in frequency and severity in the current context of climate change. As such, it must be studied to improve the decision-making process in affected areas. As a semi-arid zone, the Guadalquivir River basin, located in the southern Iberian Peninsula, is an interesting area to perform this study. The relationship between meteorological and hydrological droughts is studied using drought indices with data from 1980 to 2012. The chosen indices are the Standardized Streamflow Index (SSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Their correlations are calculated, based on SPEI accumulation periods, and these values are analyzed with a principal component analysis to find spatial patterns in drought behavior inside the basin. This analysis was performed for the continuous series and also for monthly series, to account for seasonal changes. It has been found that the relationship of drought types occurs at different time scales depending mainly on orography and catchment area. Two main patterns were found. Generally, for low altitudes and small catchment areas, accumulation periods are shorter indicating that hydrological system in this area respond rapidly to meteorological conditions. In mountainous parts of the basin, longer accumulation periods have a stronger influence due to effects such as snowmelt.

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.

Droughts grow concurrently in space and time; however, their spatiotemporal propagation is still not fully studied. In this study, drought propagation and spatiotemporal characteristics were studied in northern, northeastern, and central Thailand (NNCT). The NNCT is an important agricultural exporter worldwide, and droughts here can lead to considerable pressure on the food supply. This study investigated meteorological drought and soil drought in northern Thailand and identified 70 meteorological drought events and 44 soil drought events over 1948–2014. Severe droughts (droughts with long trivariate return periods) mainly occurred after 1975 and were centered in northern and northeastern Thailand. Meteorological drought and soil drought that occurred during 1979–1980 had the longest trivariate return periods of 157 years and 179 years, respectively. The drought centers were mainly located in the Chao Phraya River basin and the Mun River basin. The mean propagation ratios of all drought parameters (duration, area, severity) were lower than 1, indicating that the underlying surface can serve as a buffer to alleviate water deficits. Most of the probability distribution coefficients and all drought propagation ratios of the three drought parameters were found to change significantly based on a moving-window method, indicating that the drought parameters and propagation from meteorological drought to soil drought were non-stationary. Significant increasing trends were detected in mean values of most drought parameters, ranging from 2.4%/decade to 16.6%/decade. Significant decreasing trends were detected in coefficients of skewness (Cs) of all drought parameters and coefficients of variation (Cv) of most drought parameters, ranging from −3.3 to −12.4%/decade, and from −5.5 to −19.4%/decade, respectively. The propagation ratios of all drought parameters showed significant increasing trends, indicating that the function of the underlying surface as a buffer has become weaker. The drought propagation ratios were found to be positively related to two climate indices, the phase index (PI) and the climate seasonality index (CSI). These findings will help to develop a better understanding and management of water resources in Thailand.

An essential factor in the propagation of drought, from meteorological drought to groundwater drought, is the delay between a precipitation event and the groundwater recharge reaching the groundwater table. This delay, which mainly occurs in the vadose zone of the hydrological cycle, is often poorly studied. Therefore, this paper proposes a method for estimating the spatially distributed delay in the vadose zone using the kinematic wave approximation of Richards’ equation combined with the van Genuchten–Burdine and Brooks–Corey parametric model. The modeling was approached (1) using a detailed parametrization of soil and geological layers and (2) using lumped hydraulic and physical properties of geological layers. The results of both approaches were compared against the physically based flow model Hydrus-1D. This analysis shows that using a detailed parametrization of soil and geological layers results in good comparison, with a Nash–Sutcliffe efficiency of 0.89 for Brooks–Corey and 0.80 for van Genuchten–Burdine. The delay result of the Brooks–Corey model was incorporated into the groundwater recharge time series from 1980 to 2013 to analyze the effect of this delay on groundwater drought. The results show that the delay in the vadose zone influences groundwater drought characterization features such as the number, duration, and intensity of drought events.

Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), and Standardized Soil Moisture Index (SSMI) in three drought-prone regions: Sonora (Mexico), Maipo (Chile), and Mendoza-Tunuyán (Argentina) to study their temporal interdependence. For this evaluation we use precipitation, temperature, and streamflow data from gauges that are managed by governmental institutions, and satellite-based soil moisture from the ESA CCI SM v03.3 combined data set. Results confirm that effective drought monitoring should be carried out (1) at river-basin scale, (2) including several variables, and (3) considering hydro-meteorological processes from outside its boundaries.