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• Multi-year drought induces changes in evapotranspiration (ET) that involve complex mechanisms of climate-soil-vegetation interactions. • The ET reduction induced by drought strength increase showed asymmetric hydrological responses in the water and energy limited regions. • The ET responses underlying mechanism suggests catchments with higher wetness could effectively mitigate effects of multi-year droughts. Multi-year droughts constitute a natural experiment for testing the hydrological legacy across different climate regimes. Evapotranspiration (ET) responses to drought have not been fully elucidated because the underlying mechanisms involve complex interactions of climate, soil, and vegetation. This study collected monthly hydrometeorological data during 1982–2011 from 1069 catchments worldwide to investigate multi-year droughts and their impacts on ET and to analyse the underlying mechanisms of ET. As expected, multi-year droughts generally induced ET reductions across different climate regimes, while ET exhibited asymmetric hydrological responses with a greater portion of precipitation (P) being partitioned into ET in water-limited regions, and a smaller portion of P being partitioned in ET in energy-limited regions. Multi-year droughts induced soil moisture droughts and leaf area index decreases, which often led to increases in the vapour pressure deficit, while inconsistent responses occurred for air temperature. Interestingly, when long-term catchment wetness [i.e., (precipitation − streamflow) / precipitation] exceeded 0.50 we found that catchment wetness played a vital role in controlling the response of catchment properties to climate changes. During the multi-year droughts, attribution analysis revealed the enhanced role of available water (represented by P) in controlling ET , followed by available energy and vegetation. Although vegetation played vital role in controlling ET in energy-limited regions, it was P that controlled ET during drought periods. Taken together, these findings indicate that ET responses and their underlying mechanisms induced by multi-year droughts vary under different climate regimes. Specifically, a two-stage mechanism drove the ET responses in wet catchments, suggesting that catchments with higher catchment wetness (>0.50) could more effectively mitigate the impact of multi-year droughts. [ABSTRACT FROM AUTHOR]

• Ecohydrological fluxes and storage exhibited major declines in 2018 and 2022. • Groundwater recharge decreased by more than 40 % in 2022. • Evapotranspiration decreased up to 16 % due to reduced water availability. • Soil moisture decreased up to 6 % in 2018. • If past drought persists, 1/4 groundwater wells will not regain pre-drought levels. Recent extreme droughts in Europe have highlighted the urgent need to quantify their effects on ecohydrological fluxes (evapotranspiration, groundwater recharge) and water storage (mainly soil moisture) in the landscape. In response, we combined process-based (EcH 2 O-iso) and machine learning (NARX) models to estimate the enduring effects of long-term drought on water fluxes and storage and to project future short-term groundwater levels and recovery potential under various precipitation scenarios. The work was undertaken at the Demnitz Mill Creek (DMC), a 70 km2 mixed land use (arable crops and forestry) catchment in northern Germany. Our simulations indicated that the extreme drought years of 2018 and 2022 had the most marked impacts, leading to substantial declines in groundwater recharge (>40 %), evapotranspiration (up to 16 %) and soil moisture (up to 6 %). Simulations indicated that groundwater levels may not recover in the next 15 years if recent precipitation anomalies persist. These findings underscore the urgent requirement for enhancing resilience and promoting integrated strategies in managing land and water resources to optimise water retention in landscapes and to better respond to drought. [ABSTRACT FROM AUTHOR]

• Central Asia drought propagation time were longer early and late in the growing season. • Drought thresholds in Central and Western Central Asia were often below 30 mm during 1948–2022. • Soil moisture drought intensity was positively correlated with drought propagation characteristics. • VPD and AI are the main drivers of accelerated drought propagation. Drought propagation time (PT) and threshold (TR) play crucial roles in real-time monitoring of drought progression and severity under climate change, for which there is currently no unified method or standard for quantitative characterization. Central Asia (CA), a prototypical arid region, is frequently plagued by droughts that profoundly impact its ecology and socioeconomic framework, leaving a lack of comprehension about the transition from meteorological to soil moisture drought as for the complex impact of ground-atmosphere interaction. Therefore, this study revealed high-resolution PT and TR across CA based on precipitation and soil moisture datasets by employing a Bayesian causal framework for drought propagation modeling, in which the sliding window with the random forest method was integrated to investigate the dynamic changes and driving factors of drought propagation over recent decades. The conclusions drawn are as follows: (1) in the southeastern hilly and eastern plateau, a prolonged PT (i.e., longer than 100 days) was experienced during the extremities of the growing season, while most areas had a shorter mid-season PT (i.e., less than 40 days), in which the soil moisture drought intensity was positively correlated with longer PTs; (2) from 1948 to 2022, the central and western regions in CA generally displayed lower TR values than those in the northeast, seldom exceeding 30 mm, reflecting the regional precipitation gradient and the positive relationship between TR and severe soil moisture droughts; and (3) the increase in vapor pressure deficit (VPD), the decrease in aridity index (AI) for the reduced precipitation, and the considerable impact of vegetation transpiration on soil moisture consumption contributed to accelerated drought propagation. Overall, this study reveals the complex dynamics and mechanisms of drought propagation amid environmental changes, laying the foundation for innovative drought early warning systems and emergency management approaches in CA. [ABSTRACT FROM AUTHOR]

• Spatial distribution of drought onset time decreased with an increase in regional wetness in China. • Drought onset speed was higher in the southern humid region than that in the northern region. • Drought onset was accelerated after 1994, particularly in regions with precipitation range from 300 mm to 600 mm. • The greater the warming magnitude, the wetter the region, the faster the drought onset. • Increased water deficiency was the main reason for the accelerating drought onset. This study investigated the effects of warming on the drought onset in China using meteorological data and soil moisture data from 1980 to 2020. The variation characteristics of drought onset time and onset speed were analyzed systematically. The differences in drought onset time and onset speed during the period 1995–2020 and the period 1980–1994 were compared, and possible impact factors were analyzed. Results indicated that the spatial distribution of drought onset time decreased with an increase in regional wetness in China. In arid regions, onset time at 73 % of sites was over 35 days, and about 13 % was among 60–90 days. In semi-arid and sub-humid regions, the onset time was 25–40 days, and in humid regions, it was 20–30 days. After 1994, namely the significant temperature rise period, the onset time has remarkably shortened in parts of the southern Northeast, North China, the eastern Northwest, the western Southwest, the middle of Tibet, the lower reaches of the Yangtze River, and the coastal region of South China. The shortening was most obvious in sub-humid regions. The greater the warming magnitude, the wetter the region, the faster the drought onset speed. Drought onset speed (γ) was higher in the southern humid region than that in the northern region, and γ for southern Northeast China, northern North China, and eastern Northwest China was higher than that for the rest of North China. After 1994, drought onset speed was accelerated in most regions, especially in the semi-arid and sub-humid regions with precipitation in the range of 300–600 mm. Under the combined effect of increased evapotranspiration and reduced precipitation, the region has been the hot spot with increased drought speed after obvious warming in China. [ABSTRACT FROM AUTHOR]

• A comprehensive evaluation of the global soil moisture drought development and recovery characteristics is conducted. • Drought development speed statistically increased and drought duration statistically decreased from 1960 to 2020. • The timing of the occurrence of extreme meteorological conditions shows more significant effects on the propagation of drought than average meteorological conditions. Soil moisture drought is one of the driving forces behind agricultural drought and disastrousness for food production, but the understanding of internal drought propagation is limited, making it difficult to track and predict drought propagation. Based on the trend analysis, Spearman correlation analyses, and the random forest model simulations, we analyzed the spatial and temporal distribution characteristics of soil moisture drought propagation globally from 1960 to 2020 and evaluated the contributions of various meteorological factors on soil moisture drought propagation. The results show that the mean drought instantaneous recovery speed (IRS) is 1.52 times greater than the instantaneous development speed (IDS) globally from 1960 to 2020. IDS (IRS) in arid region is lower (higher) by 24.9 % (11.7 %) than in humid areas. The drought IDS statistically increased 0.012 % pentad−1 year−1 and drought duration statistically decreased 0.020 pentad year−1 from 1960 to 2020, especially in the mid- and high-latitudes of the Northern Hemisphere (45–67.5°N). The timing of the occurrence of extreme meteorological conditions shows more significant effects on the propagation of drought than average meteorological conditions within the drought period. Since evapotranspiration (precipitation) exerts greater influences on drought development (recovery) than other meteorological variables, intensified hydrological cycle under a warming climate could possibly accelerate the drought development and recovery. [ABSTRACT FROM AUTHOR]

• The drought indices SDCI, VHI and IDCI were constructed by four weighting methods. • Determination of weights was validated by comparing against scPDSI, SMCI and CY. • New classification schemes were executed by cumulative frequency curve of scPDSI. • Reliability of schemes was verified by drought affected area and frequencies. With the continuous development of remote sensing technology, an increasing number of drought indices are proposed to assess the drought process in different regions. However, most of the studies used the empirical coefficient method and standard drought classification scheme, without considering regional applicability, which may overestimate or underestimate drought intensity. Therefore, the GLDAS soil moisture dataset was firstly verified in the study area, Wei River basin, China, and then the response of precipitation accumulation to NDVI and the lagged effect were analyzed. Secondly, the Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI), and Integrated Drought Condition Index (IDCI) were constructed and the optimal weights of the drought indicators were determined against the scPDSI, SMCI and CY based on four weighting methods. Finally, the new classification schemes of the three indicators were executed based on the cumulative frequency curve of the self-calibrating Palmer Drought Severity Index (scPDSI) from June to September during the period 2000–2019, and the reliability of the new schemes was verified by comparing with drought frequency and drought affected area. Furthermore, the variabilities of drought frequency of the new classification scheme based on the three indicators were analyzed. The results showed that the correlation coefficients of 2-month accumulation of precipitation and NDVI produced the best value and no lagged effect was detected. The SDCI and VHI determined by the AHP weighting method and the IDCI determined by the entropy weighting method showed better performance. The new classified schemes of the three drought indicators demonstrated better performance than the original classified scheme with more similar frequency distributions to scPDSI and higher correlation coefficients against the drought affected area. Additionally, the three indicators based on the revised classification scheme all indicated a decreasing trend in the frequency of drought occurrence for all the different drought levels; the SDCI and VHI were better at capturing drought events than IDCI. Overall, the results of this study proved that the optimal weighting methods and new classification schemes could effectively improve the precision of drought assessment and may provide helpful information for agricultural drought disaster prevention. [ABSTRACT FROM AUTHOR]

• The propagation time in most river basins in northern China was longer than that in southern China. • More attention should be paid to moderate and above levels of meteorological or agricultural droughts. • Precipitation shortage and soil moisture deficit were not the main factors contributing to extreme groundwater drought in east-central China. Clarifying drought propagation characteristics is crucial for grasping the mechanism of drought development and evolvement and also for identifying the early warning signs of droughts. Currently, the studies examining drought propagation characteristics are primarily focused on two of the three types of droughts (meteorological, agricultural, and hydrological droughts) and are concentrated at the river basin scale; however, few studies to date have examined all three types of droughts, particularly at a large spatial scale. In this study, the Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, and drought severity index of groundwater storage anomalies were used to characterize meteorological, agricultural, and groundwater droughts, respectively. Based on the copula probability model and Bayesian network analyses, time, probability, and threshold indicators were comprehensively utilized to reveal drought propagation characteristics in China. The results indicated that (1) the average propagation times from meteorological to agricultural, agricultural to groundwater, and meteorological to groundwater droughts in China were 4.1, 15.1, and 24.1 months, respectively. The propagation time in most river basins in northern China was longer than that in southern China. (2) Moderate and above meteorological droughts (agricultural drought) exhibited a much higher probability of triggering mild and moderate agricultural droughts (groundwater drought), and thus, more attention should be focused on drought early warning during this period. Statistics indicated that the average probability of extreme meteorological drought (agricultural drought) triggering mild agricultural drought (groundwater drought) was as high as 61.93 % (59.41 %), and the affected regions were primarily concentrated in the southern and northeastern areas of China. (3) When the meteorological drought level reached severe drought, it triggered mild agricultural drought in 64.3 % of the valid area. The Pearl River Basin exhibited the smallest absolute value of threshold from meteorological to agricultural drought in China, while the propagation threshold of extreme groundwater drought triggered by meteorological or agricultural drought was relatively high. Overall, the results have great implications for the framework construction of comprehensive drought monitoring. [ABSTRACT FROM AUTHOR]

• Seasonal water uptake differed among different tree species. • Deep-rooted tree species had lower growth resistance to drought. • Shallow-rooted tree species was more adaptative to seasonal precipitation areas. • The root structure and deep soil water availability were the key factors influencing the drought resistance of trees. Understanding the water use patterns of different tree species and their adaptability to drought is urgently needed due to global warming. In this study, we investigated the seasonal water uptake patterns of Pinus tabulaeformis and Picea wilsonii on the eastern Tibetan Plateau using stable isotope methods. In addition, tree ring width data was used to evaluate the radial growth resistance of the two species to drought in 2022. The focus was on identifying species differences in water uptake patterns during drought spells and radial growth resistance to drought. The results showed that although Pinus tabulaeformis could increase water uptake from deeper soil layers during drought spells, the radial growth resistance to drought was significantly lower (p < 0.001) than that of Picea wilsonii due to the limited water content in the deep soil layers. In contrast, Picea wilsonii had higher resistance and adaptations to drought based on rapid access to water resources from seasonal precipitation with denser fine roots. Overall, shifting water sources to deeper soils during drought spells was not always effective in alleviating drought stress, but the root structure and deep soil water availability were the key factors influencing the drought resistance of trees. The findings had significant implications for decision-making on the species suitability of plantation projects under future climatic change. [ABSTRACT FROM AUTHOR]

• A significant water storage loss of −158 ± 22 km3 occurred in August 2022 concerning July 2022 in the Yangtze River Basin. • The precipitation deficit and prolonged high temperature caused this extreme drought. • The hydrological drought identified by gravity satellite responds fast to meteorological drought. In 2022, the Yangtze River Basin (YRB) experienced its most extreme drought event since 1961. Conducting a comprehensive assessment of this severe drought is crucial for understanding the entire drought process. GRACE and GRACE-FO gravity satellites have the capability to monitor global mass transport, making them suitable for large-scale drought monitoring. In this study, we investigated the changes in water storage caused by the drought using GRACE/GRACE-FO data and multi-source observations. Our evaluation incorporated meteorological drought indices, including the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), to explore the drought propagation process. Additionally, we examined the effects of trends in terrestrial water storage in the YRB, resulting from reservoir impoundment and climatic factors, on the drought index calculation. The results indicate that the extreme drought in the YRB began in August 2022, resulting in a water storage loss of approximately −158 ± 22 km3 compared to July 2022. There was almost no delay in the response of soil moisture drought and hydrological drought to the meteorological drought at a monthly scale. This extreme drought had its most severe impact on the middle and lower reaches of the YRB, especially in the Dongting Lake Basin, where the GRACE drought index value remained below −2.5 until December 2022. This study demonstrates the significant water storage deficit during the extreme drought in the YRB in 2022, providing a comprehensive understanding of this historic drought. [ABSTRACT FROM AUTHOR]

• Most of the Euro-Mediterranean region affected by drought conditions during 2021/22. • Recursive high-pressure systems & a poleward jet shift explain rainfall deficits. • Most severe drought since at least 1891 when considering evaporative demand (AED) • Enhanced AED caused unprecedented low soil moisture & runoff since at least 1965. • Potential worst-case droughts if historic rainfall deficits occur with current AED. The Euro-Mediterranean region experienced a remarkable drought during the hydrological year 2021/22. Substantial and widespread impacts on water supply systems, agricultural crops, and the production of hydroelectric power were observed. This assessment characterises the drought from a long-term perspective using a multi-index approach and analyses the associated atmospheric circulation at the annual and monthly time scales. The main dynamical forcing of the drought was the unusual recurrence of high-pressure systems over western Europe, at least partly due to an anomalous southward shift in blocking activity and a remarkable occurrence of low-latitude blocks. This led to record-breaking positive geopotential height anomalies over western Europe and a poleward displacement of the North Atlantic eddy-driven jet. Although most of the region was affected by mild drought conditions, the 2021/22 event was not unprecedented in terms of precipitation deficits since other periods of the 20th century (e.g., in the 1920s, 1940s and 1970s) displayed moderate and severe drought conditions over larger areas. However, the 2021/22 drought has been the most intense since at least 1891 because of high atmospheric evaporative demand (AED) values associated with extreme temperatures, especially during the summer of 2022. This enhanced AED also contributed to depleting soil moisture and reducing runoff generation, leading to unprecedented deficits since at least 1965. Finally, we find important differences in the 2021/22 event as compared to other major historical droughts over the Euro-Mediterranean region. In particular, the contrasting effect of AED evidences its increasing role over the last decades and warns about the current risk of experiencing unprecedented droughts. [ABSTRACT FROM AUTHOR]

• A new composite index is proposed for seasonal drought detection. • The index is derived from precipitation, soil-moisture, and SIF datasets. • Model is developed under nonparametric joint probabilistic framework. • Seasonal drought events occurred from Indian during monsoon periods. • Spatial severity, duration or intensity helps to identify drought susceptible area. Effective drought monitoring is crucial for mitigation efforts and the implementation of early warning systems. Due to the intricate nature of drought phenomena, its impact cannot be accurately characterized solely through the use of univariate indicators. This study introduces a composite version of the modified multivariate standardized drought index (MMSDI) for the Indo-Gangetic Plains (IGP) in India. The MMSDI integrates precipitation, soil moisture, and solar-induced chlorophyll fluorescence datasets from 2001 to 2020, utilizing a nonparametric joint probabilistic framework. It combines three consolidated drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized solar-induced chlorophyll fluorescence Index (SSIFI). The drought patterns among SPI, SSI, SSIFI, and MMSDI indices are compared for short, and mid-term time scales (herein 1, 3 and 6 month) over IGP. The performance of MMSDI is evaluated through quantitative metrics e.g. probability of detection (POD), false alarm ration (FAR) and critical success index (CSI) at various time scales. Results reveal that MMSDI provides a reliable estimation of the severity and spatial coverage of major drought events over IGP at various time scales. The MMSDI demonstrates superior effectiveness in identifying and characterizing the spatial severity of drought, i.e. the visibility of the drought hotspots. The MMSDI has the capability to assess agrometeorological drought, indicating its potential as a valuable tool for identifying regions vulnerable to drought. Overall, this modified drought index derived from multi-sensor datasets produced comprehensive insights for policymakers in implementing effective agricultural drought management practices to understand intricate drought phenomena over IGP region in India. [ABSTRACT FROM AUTHOR]

• SWDI is good at detecting severe/extremely droughts in humid region. • Sand are more prone to agricultural drought than clay. • Impact of groundwater on agricultural drought differ with soil textures. • 95th percentile is the best estimator of field capacity for clay and loam. • Min(maxgs) is the best estimator of field capacity for sand, loamy sand. Soil moisture is an important indicator for monitoring agricultural drought, and is closely related to soil texture. However, the role that soil textures play in agricultural drought evolution in humid climates is not well understood. In the study, we systematically assessed the feasibility of the Soil Water Deficit Index (SWDI) for agricultural drought monitoring in humid regions, examined the impact of soil texture and groundwater depth on agricultural drought evolution, and estimated the field holding capacity for different soil textures in humid areas based on a large-scale regional soil moisture monitoring network. The results show that: (1) The SWDI has reasonably good performance in detecting severe and extreme agricultural drought, but it tends to overestimate the drought intensity during non-severe/extreme drought periods in humid regions, particularly for clay soils. (2) Sand soils are more prone to agricultural drought than clay soils, which are more resistant to severe and extreme agricultural drought with high air temperatures and low precipitation. (3) During drought aggravation, clay soil showed a delay of about 0–18 weeks from the lowest SWDI to the maximum groundwater depth, while the delay was only 0–5 weeks for non-clay soil textures. (4) The 95th percentile is proposed as the best estimator of field holding capacity for clay and loam, while Min(maxgs) is the best estimator for sand, loamy sand, and sandy loam. Overall, these findings provide insights into the role of soil texture and groundwater depth in agricultural drought evolution in humid regions, and offer practical guidance for field holding capacity estimation for different soil textures. [ABSTRACT FROM AUTHOR]

• OpenKarHydro with multilayer soil water interactions was proposed. • OpenKarHydro reveals how climate and land use affect water budget and cause dry soil. • OpenKarHydro performs well in simulating the soil water content and runoff dynamics. • OpenKarHydro is a useful tool for land use optimization and controlling dry soil. In water-limited areas, soil water content (SWC) is a critical factor for plant growth and ecosystem stability. To understand the long-term effects of three different land uses (Vigna radiata : VR, Stipa bungeana : SB, and Medicago sativa : MS) and two climate scenarios (representative concentration pathway 4.5 (RCP45) and nonclimate change (Non_CC)) on water budgets and the mechanisms of dried soil layer (DSL) formation, a new complete multilayered model, OpenKarHydro, was developed. The results showed that the model adequately captured the water budget for different land uses, with root mean square errors (RMSEs) of 0.058, 0.046, and 0.046 and coefficients of determination (R2) of 0.615, 0.605, and 0.582, respectively, for SWC dynamics. This model performance was also demonstrated by the verification of runoff. Sensitivity analysis indicated that the soil porosity (n) was the most sensitive parameter in all land uses. The DSL was primarily caused by higher evapotranspiration (VR: 83.16% vs. 82.48%, SB: 80.62% vs. 77.69%, and MS: 79.93% vs. 76.26%) and canopy interception (VR: 11.92% vs. 11.99%, SB: 17.56% vs. 23.15% and MS: 22.60% vs. 26.19%) in the Non_CC climate pattern than in the RCP45 climate pattern. Climate change has caused an increase in evapotranspiration and interception for all land uses, reducing bottom leakage. Rainfall is sufficient to meet the water demand in the VR plot regardless of climate change; however, the additional water consumption caused by climate warming cannot be offset by the increased rainfall for the SB and MS plots. Compared to Non_CC, RCP45 resulted in increases of 0%, 54.19%, and 57.62% in proportion with extreme drought and decreases of 4.70%, 38.74%, and 5.24% in proportion with no drought for the soil profiles of VR, SB, and MS, respectively. For MS, RCP45 (2026) caused the DSL to penetrate the soil profile (>400 cm) 4 years earlier than in the Non_CC (2030), which should take no more than 5 and 9 years, respectively. OpenKarHydro is a useful tool for optimal water resource management and ecological restoration in water-limited areas. [ABSTRACT FROM AUTHOR]

• Proactive drought responses are crucial in a drought vulnerable future. • Antecedent conditions need to be considered in context of anticipated conditions. • We combine rainfall, soil moisture and evapotranspiration data with forecasts for early warning. • Validation efforts found promising accuracy in the drought early warning maps. • Proof-of-concept drought early warning system can aid proactive drought management. Droughts are a cyclical feature of Australia's climate that have compounding impacts on agricultural productivity and the wellbeing of drought-affected communities. Understanding anticipated drought conditions in context of antecedent observations is critical to providing early warning of drought. In this study we paired probabilistic seasonal rainfall forecasts with precipitation, soil moisture and evapotranspiration data that were objectively combined using Principal Component Analysis (PCA). The final Drought Early Warning System (DEWS) maps overlay forecasting information with the multi-variate PCA-weighted maps at 1-, 3- and 6-month timescales over the common period of overlap between all datasets (1982–2018). In this study period, the 1982–1983 Ash Wednesday tinder drought and the 1997–2001 Millennium drought were investigated. We validated PCA-weighted maps with satellite vegetation data and found performance was strongest over the Murray Darling Basin region (R = 0.63, p = 0.009) and poorest over Central interior Australia (insignificant correlations). We also validated PCA-weighted maps using agricultural commodity data from ABARES. Significant negative correlations at 95 %, 99 % and 99.9 % confidence intervals were found between %-Area in drought category and crop cultivation area; export volume/value; crop yield; and rural debt. Our findings indicate that early warning of drought can be categorised by concern – wherein dry antecedent conditions and dry forecasted conditions are of the highest concern. The developed proof-of-concept DEWS contributes to the growing body of proactive drought management research. In a drought vulnerable future, operationalising and communicating drought early warnings will be critical to reducing the harmful impacts of drought on communities, economies, and environments. [ABSTRACT FROM AUTHOR]

[Display omitted] • From spatiotemporal dimension to analyze drought propagation of SPEI to SSMI on the TP. • There is a lag time of 2–3 months from SPEI to SSMI and increases from summer to winter. • Climate, vegetation and permafrost type have an impact on drought propagation. • In winter, drought duration decreases in 45% of the TP after drought propagation, and drought severity decreases in 52%, particularly in the Inner TP. • The direction of propagation from SPEI to SSMI is predominantly northwest. Meteorological drought signals trigger different types of droughts by propagating in the water and energy cycle processes. The understanding of the propagation process from meteorological to soil moisture drought is not clear on the Tibetan Plateau (TP). We used the standardized precipitation index (SPEI) and the standardized soil moisture index (SSMI) to represent meteorological and soil moisture drought. By using Mann-Kendall (MK) trend test, wavelet analysis, run theory, Moran's I and drought migration model, we evaluated the trend of SPEI and SSMI on the TP from 1980 to 2018, analyzed the response relationship, explored the temporal (lag time (LT), propagation rate, duration (DD) and severity (DS)) and spatial (spatial autocorrelation, trajectory, direction and distance) properties of drought propagation, and discussed the potential factors. The result indicated that SPEI intensified at winter and annual scales, SSMI mitigated at all time scales. Both of them showed a consistent dry trend in the southeastern TP. In the time dimension, SSMI usually lagged behind SPEI by about 2–3 months, and the LT increased from summer to winter. The LT tended to be shorter in summer in regions characterized by humid and semi-humid climates, meadows and shrublands, and seasonally frozen ground. Propagation rates were greater in the southeast than northwest. After propagation, DD and DS became weaker in winter (45 % and 52 % of regions decreased, respectively) and stronger in summer (80 % and 76 % of regions increased, respectively). In the spatial dimension, the LT of adjacent regions is closely related, dominated by High - High values and Low-Low values. SSMI had shorter migration trajectories and slower migration rates than SPEI. In addition, the direction of drought propagation was predominantly northwest. For the first time, this study has provided insights into the spatial and temporal propagation of meteorological drought to soil moisture drought on the TP and provides a theoretical basis for understanding drought propagation and predicting soil moisture drought. [ABSTRACT FROM AUTHOR]

[Display omitted] • The top-down QPEs perform better over the humid and low-altitude regions. • IMERG-F (SM2RAIN-ASCAT) performs best (worst) in detecting drought occurrence. • The drought dynamic evolutions highly depend on the drought indices used. • All QPEs capture drought centroid trajectory better on smaller spatial scales. Satellite-based quantitative precipitation estimates (QPEs) have shown great potential in the precipitation-related extremes estimation. However, little is known about the capability of different QPEs to monitor the dynamic evolutions of drought on various spatio-temporal scales. Here, the drought utility of two top-down QPEs (i.e., Climate Hazards Group InfraRed Precipitation with Stations, CHIRPS; Integrated Multi-SatellitE Retrievals for Global precipitation measurement-Final run, IMERG-F) and one bottom-up QPE (i.e., Soil Moisture to Rain-Advanced SCATterometer, SM2RAIN-ASCAT), is evaluated comprehensively in mainland China, based on three commonly used drought indices (Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Composite index of meteorological drought). The results indicate that the top-down QPEs generally show better performance in detecting drought occurrence over the humid and low-altitude regions in eastern China than the arid and high-altitude regions in western China. IMERG-F performs best in detecting drought occurrence in the entire mainland China, followed by CHIRPS, and the least satisfactory by SM2RAIN-ASCAT. The trajectory of drought centroids and the temporal variability of drought characteristics are highly dependent on both the drought indices selected and QPEs used. IMERG-F and CHIRPS can better reproduce the trajectories of large-scale drought events (e.g., the locations of drought origin and termination) than SM2RAIN-ASCAT. The ability in tracking the drought centroid trajectory of all three QPEs is higher at the smaller spatial scales, also it improves from the semi-arid to humid regions. Our results highlight the superiority of the top-down QPEs in drought monitoring compared with the bottom-up QPE in mainland China, particularly over the humid regions. The application of SM2RAIN-ASCAT in drought monitoring to the large-scale arid areas should be cautious. [ABSTRACT FROM AUTHOR]

• Soil moisture, GWSC, and TWSC in the YRB increased during 2003 to 2019. • A new weighted GRACE drought standardisation index (WGDSI) was reconstructed. • The interannual variability was beneficial for detecting drought in the YRB. • The recovery times were 30 and 39 months in 2006 and 2011 droughts in the YRB. • Different signal spectrums components of TWSC are useful for drought monitoring. With the intensification of global climate change, droughts have frequently been occurring in the Yangtze River Basin (YRB), significantly affecting the production patterns, lives, and socioeconomic development of humans. Drought characteristics must be comprehensively identified and quantified to reduce the damage caused by droughts in the YRB. The spatiotemporal variability of precipitation, runoff, soil moisture, groundwater change (GWSC) and terrestrial water storage change (TWSC) in the YRB was comprehensively estimated using the Gravity Recovery and Climate Experiment (GRACE), hydrological and in situ observations by decomposing the variabilities into seasonal, sub-seasonal, trend, and interannual. The new weighted GRACE drought standardized index (WGDSI) and weighted groundwater standardized index (WGWSI) were reconstructed by weighing the seasonal, trend, interannual and sub-seasonal of TWSC and GWSC. They were compared with the standardized soil moisture index (SSI06), standardized precipitation index (SPI06), and standardized runoff index (SRI06). Additionally, the drought characteristics identified based on the observations of the water storage deficit, severity, peak, duration, and recovery time in the YRB were also quantified using the WGDSI/WGWSI over the YRB. The results indicated that soil moisture, TWSC, and GWSC in the YRB increased from 2003 to 2019, mainly based on seasonal and interannual signals. The correlation coefficients between the WGDSI and the SSI06, SPI06, and SRI06 at the monthly were 0.84, 0.56, and 0.75, respectively, representing increases of 9 %, 14 %, and 21 % compared to that with the unweighted GRACE drought standardized index, respectively. The interannual variability of the hydrologic variables was more consistent with the drought events in the YRB, which was beneficial for detecting droughts. Two severe serious droughts occurred in the YRB from 2003 to 2019. In 2006, a continuous seven-month-long drought occurred, with a peak at − 28.974 km3, a severity of − 174.767 km3∙month, an average drought recovery rate of 0.83 km3/month, and a recovery time of 30 months, while in 2011, a continuous five-month-long drought occurred, with a peak at − 18.384 km3, a severity of − 78.106 km3∙month, an average drought recovery rate of 0.40 km3/month, and a recovery time of 39 months. The results indicated that the WGDSI could effectively identify drought events and measure their severity accurately, offering a more holistic understanding of drought conditions compared to other available indices. The index proposed in this study can be applied to generate new datasets and methods for quantifying and detecting droughts on a global scale. [ABSTRACT FROM AUTHOR]

Highlights • The dependence between flow and hydroclimatic factors in Baden-Württemberg is modeled. • The probabilities of droughts and floods under various hydroclimatic scenarios is examined. • The links between flow variability and atmospheric circulation are explored. Abstract This study presents an investigation of probabilistic relationships between streamflow and hydroclimatic variables (including precipitation, temperature and soil moisture) and the potential links to large-scale atmospheric circulation over Baden-Württemberg, Southwest Germany. First, the joint dependence between seasonal streamflow and hydroclimatic variables was established by using copulas. On the basis of the joint dependence structure, we estimated the probabilities of hydrological droughts and prolonged high-streamflow events conditioned upon two different scenarios (high and low) of hydroclimatic variables for various seasons over the study area. The results indicate that both precipitation and soil moisture are positively related to the streamflow variation in Baden-Württemberg for each season and strongly impact the likelihoods of hydrological droughts and high-streamflow events. Temperature tends to have less impact on the streamflow variation, and an inverse connection between streamflow and temperature is found in spring and summer. Then, the connections between streamflow variability and large-scale atmospheric circulation in summer and winter were explored by using composite analysis. Although the atmospheric circulation patterns vary in each season, it can be found that in summer high streamflow anomalies over Baden-Württemberg are related to a cyclonic activity over central Europe while low streamflow anomalies are linked to anticyclonic patterns over western Ireland. The high streamflow anomalies in winter are strongly impacted by the westerlies that play an important role in favoring warm and moist airstreams from the Atlantic Ocean towards the study area, while an opposite atmospheric pattern is found for the years with low streamflow anomalies. The presented probabilistic methodology could also be applied in other regions worldwide. [ABSTRACT FROM AUTHOR]

• TVDI applied for the first time over Ganga Basin to examine the SM variation. • High TVDI values are observed in central plateau and hill agro-climatic zone. • TVDI developed using EVI shows better correlation with SM compared with the NDVI generated TVDI. • Spatial and temporal valuation of TVDI shows that its applicability can be used mainly in the summer season. • TVDI have high negative correlation with SM for the 10–40-cm soil depth range. Soil Moisture (SM) is a critical parameter for land–atmosphere interaction, measuring drought conditions in agricultural areas, and it can significantly affect surface water and agricultural production. Based on remote sensing observations, the temperature vegetation dryness index (TVDI) can be generated to evaluate SM at a large spatial scale. Land Surface Temperature (LST) and Vegetation Index (VI) plots are generated to obtain the triangular/trapezoidal space to calculate TVDI. For the present study, TVDI was calculated by using LST and normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI) for a period of three years (2017, 2018, and 2019) over the Ganga Basin. The applicability of the TVDI at soil depths of 0–10, 10–40, 40–100, and 100–200 cm was also examined. The result from the study indicates that, a better correlation is obtained between TVDI generated using EVI and SM compared to TVDI generated using NDVI. Temporal variation of TVDI with SM shows that TVDI (EVI) almost captures the maximum and minimum SM variation in most locations. Also, SM at 10–40 cm shows a better negative correlation (r close to −0.5) with TVDI (EVI) than 0–10 cm depth for the summer season. The result thus reveals the potential of TVDI in assessing SM especially in summer season, while using EVI as the vegetation index. [ABSTRACT FROM AUTHOR]

• The path analysis method is proposed to explain the interplay of drivers in soil moisture drought. • Precipitation deficits dominates the interannual variation of soil moisture drought while potential evapotranspiration impacts are magnified by drought deterioration. • Atmospheric movement and temperate indirectly affects soil moisture drought through precipitation and potential evapotranspiration. • Proportion of drought explanation decreased with increasing soil depth. Soil moisture drought, as one of the most important drought categories, is determined by both water supply (e.g., precipitation) and demand (e.g., potential evapotranspiration). To shed light on the underlying mechanisms driving soil moisture drought, the statistical multiple linear regression, machine learning, and modeling experiments methods have been pervasively used in early studies. However, these methods neglect the collinearity and interactions of climate variables, and thus cannot reflect the direct and indirect interaction of factors leading to soil moisture drought. To reveal the synergistic effects of water supply and demand on soil moisture drought, this study quantified the contributions of key drivers to the change of soil moisture drought by a path analysis method to exhibit the relationships between atmospheric movement state and soil moisture drought. Prior to applying the systematic path analysis model, we identified the spatial patterns of soil moisture droughts at different depths by using a state-of-art three-dimensional drought recognition method in China. Our results showed that precipitation deficits dominated the interannual variation of soil moisture drought while increasing potential evapotranspiration only had marginal intensification in drought. The response of soil moisture drought to potential evapotranspiration was magnified by drought deterioration, especially in basically severe drought conditions. The total column water vapor and the horizontal divergence of the vapor flux, as well as temperature, directly affected precipitation and potential evapotranspiration and led to soil moisture drought through various direct and indirect processes. This study highlighted that the interactions among precipitation, potential evapotranspiration, and atmospheric vapor movement state in space and time were important for understanding the drought development mechanisms. [ABSTRACT FROM AUTHOR]

• Characterization of flash droughts from a three-dimensional perspective. • The Normalized Area-Time Accumulation (NATA) curve was proposed to identify flash droughts. • Rapid area expansion and high severity are two crucial features of flash drought in higher dimensions. Though flash drought is an intrinsically three-dimensional phenomenon that evolves in both temporal and spatial dimensions, most studies have overlooked its spatiotemporal features by analyzing its characteristics at individual grid points. The rapid expansion of drought over time and space also brings great challenges for drought monitoring and management strategies. Focusing on the mapping of rapid development trajectories of drought in the three-dimensional space, this study proposes the Normalized Area-Time Accumulation (NATA) curve to describe the evolution characteristics of flash drought. Using the NATA curve, information about the expansion rate of drought area over time and the dominant severity of drought area can be extracted. On this basis, such drought events were classified and investigated, considering drought monitoring difficulty and potential drought damages. Results showed that the shape of proposed NATA curve and the slope of curves of varying severity levels had explicit indicative function for distinguishing drought events characterized by "flash" and "drought" events from traditional slowly-evolving drought events. Drought events which have a rapid rate of area expansion and high drought severity mostly meets the characteristics of flash droughts in the spatiotemporal dimensions. The findings highlight the significance of incorporating information on drought area expansion with time for an efficient recognition and improved accuracy of rapid drought development from a three-dimensional perspective. This is a step forward for flash drought identification in a higher dimension. The proposed NATA curve provides intuitive and comprehensive information on how drought evolves over time and space and is a promising alternative method to analyze drought characteristics from a three-dimensional perspective. [ABSTRACT FROM AUTHOR]

• We propose a threshold-based framework for vegetation drought impact forecasting. • The lead time between threshold soil moisture and subsequent vegetation impact events was typically two to four weeks. • The optimal soil moisture integration depth for forecasting vegetation conditions varied between ecosystems, probably in response to rooting depth. Timely and skilful forecasts of vegetation drought impacts should enable more proactive drought preparedness, management, and mitigation. Numerous previous studies found a temporal correlation between soil moisture and vegetation condition. However, a correlation across the full range of soil moisture and vegetation condition does not automatically translate into skill in forecasting infrequent events such as agricultural droughts. Here, we develop a threshold- or impact-based forecasting framework to assess early warning capability (EWC). We analysed the skill and lead time achieved using soil moisture observations at multiple depths as predictors of subsequent vegetation drought impacts inferred from MODIS satellite observations at 93 sites across the United States. Forecast thresholds were expressed in terms of seasonally-adjusted standardised anomalies (z-scores) to distinguish climate-related impacts from any seasonal vegetation cycle. Different combinations of soil moisture integration depth, satellite vegetation indices and threshold levels were tested. Near-Infrared Reflectance of vegetation (NIRv) yielded a marginally better EWC than other indicators of vegetation drought impact. The optimal soil moisture integration depth varied between land cover types, from 0 to 10 cm for cropping systems to 0–100 cm for grassland and savanna. The greatest skill improvements were achieved using similar z-score threshold values for the soil moisture trigger and vegetation impact, producing typical lead times of two to four weeks. Further research is recommended to combine the framework developed here with spatially continuous soil moisture analyses or forecasts available from remote sensing and land surface models. [ABSTRACT FROM AUTHOR]

• A new drought assessment model was established based on fuzzy logic and neuro-fuzzy. • Conventional and new drought indices were correlated to various drought indicators. • Multiple model structures were evaluated by different statistical parameters. • Newly developed drought indices outperformed the nine tested conventional methods. • Best-performing fuzzy logic models were validated for other climates and seasons. Drought is a detrimental global warming effect that severely impacts the environment, society, and economy. Drought indices are used worldwide for drought monitoring and assessments. This study aims to develop a new meteorological drought index based on fuzzy logic (FL) and neuro-fuzzy models to describe and predict droughts. The developed models were compared to nine conventional drought indices and correlated with multiple drought indicators. Different combinations of inputs (such as maximum temperature, mean temperature, precipitation, and potential evapotranspiration) were tested to develop the models. Observed weather data from Alice Springs, Australia, were used to examine the developed models and train the adaptive neuro-fuzzy inference system (ANFIS) model. Additionally, historical records of various drought indicators were used to evaluate the predictions of all models, including deep soil moisture, lower soil moisture, root zone soil moisture, upper soil moisture, and runoff. This study showed that the rainfall anomaly drought index (RAI) was the best conventional drought index, with the highest correlation (0.718) between the drought index and upper soil moisture (drought indicator). The average of the best-performing FL models outperformed all conventional indices, with a correlation of 0.784 with the upper soil moisture. Moreover, when the average output of the best-performing FL models was used for training, the best ANFIS model had a correlation of 0.809 with upper soil moisture. The best ANFIS model in terms of correlation with conventional drought indices had a correlation of 0.941 with the RAI when the normalised average output of the best-performing conventional drought indices was used for training. To validate the developed models, drought assessment was conducted for five stations in different climate zones and seasons. The validation results showed that the developed models had similar performance to the best-correlated conventional drought index (RAI) in most cases. The developed models yielded better predictions compared to the conventional index in the subtropical and tropical regions. Overall, the developed soft computing drought indices based on fuzzy logic and ANFIS outperformed conventional methods, thus effectively contributing to more precise drought prediction and mitigation. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) has been in operation since 2002. Water storage estimates are calculated from gravity anomalies detected by the operating satellites and although not the true resolution, can be presented as 100 km × 100 km data cells if appropriate scaling functions are applied. Estimating total water storage has shown to be highly useful in detecting hydrological variations and trends. However, a limitation is that GRACE does not provide information as to where the water is stored in the vertical profile. We aim to partition the total water storage from GRACE into water storage components. We use a wavelet filter to decompose the GRACE data and partition it into various water storage components including soil water and groundwater. Storage components from the Australian Water Resources Assessment (AWRA) model are used as a reference for the decompositions of total storage data across Australia. Results show a clear improvement in using decomposed GRACE data instead of raw GRACE data when compared against total water storage outputs from the AWRA model. The method has potential to improve GRACE applications including a means to test various large scale hydrological models as well as helping to analyse floods, droughts and other hydrological conditions. [ABSTRACT FROM AUTHOR]

As opposed to traditional drought events, flash droughts evolve rapidly and are characterized by soil moisture deficits. The general lack of high resolution soil moisture and evapotranspiration data makes identifying flash droughts at short-term scales (pentads or weeks) nearly impossible, particularly at the basin scale. In this study, we investigated the spatial patterns, temporal characteristics, and related mechanisms of flash droughts in a humid and subtropical basin (Gan River Basin) in China. The variable infiltration capacity (VIC) model can accurately reflect hydrological processes in the Gan River Basin at daily and monthly time scales; here, flash droughts were determined based on VIC outputs (soil moisture and evapotranspiration) and meteorological observations (maximum temperature and precipitation) during the growing season (March-October) from 1961 to 2013. We classified flash droughts into two categories (heat wave and precipitation deficit flash droughts) based on the formation mechanisms. Heat wave flash droughts are high temperature driven events, high temperatures (heat waves) cause evapotranspiration to increase and soil moisture to decrease rapidly. The main driver of precipitation deficit flash droughts is precipitation deficits, which cause soil moisture to drop and in turn cause evapotranspiration anomalies to decrease and temperature to increase. The northern part of the basin is apparently vulnerable to heat wave flash droughts, whereas precipitation deficit flash droughts tend to occur across the central and southern parts of the basin. Precipitation deficit flash droughts are more common than heat wave flash droughts in general. Both types of flash droughts became significantly more frequent from 1997 to 2013. These increases in both types of flash droughts are likely attributable to climate-related variables such as temperature, precipitation, evapotranspiration, and soil moisture during 1997–2013. As evidenced by our investigation of the evolution of the two types of flash droughts and the example of 2003 summer flash drought across the Gan River Basin, flash droughts can evolve into prolonged droughts. [ABSTRACT FROM AUTHOR]

Droughts are phenomena that affect people and ecosystems in a variety of ways. One way to help with resilience to future droughts is to understand the characteristics of historic droughts and how these have changed over the recent past. Although, on average, Great Britain experiences a relatively wet climate it is also prone to periods of low rainfall which can lead to droughts. Until recently research into droughts of Great Britain has been neglected compared to other natural hazards such as storms and floods. This study is the first to use a national-scale gridded hydrological model to characterise droughts across Great Britain over the last century. Firstly, the model performance at low flows is assessed and it is found that the model can simulate low flows well in many catchments across Great Britain. Next, the threshold level method is applied to time series of monthly mean river flow and soil moisture to identify historic droughts (1891–2015). It is shown that the national-scale gridded output can be used to identify historic drought periods. A quantitative assessment of drought characteristics shows that groundwater-dependent areas typically experience more severe droughts, which have longer durations rather than higher intensities. There is substantial spatial and temporal variability in the drought characteristics, but there are no consistent changes through time. [ABSTRACT FROM AUTHOR]

The use of non-conventional water (e.g., treated wastewater, desalinated water) for different purposes is increasing in many water scarce regions of the world. Its use for irrigation may have potential drawbacks, because of mineral dissolution/precipitation processes, such as changes in soil physical and hydraulic properties (e.g., porosity, permeability), modifying infiltration and aquifer recharge processes or blocking root growth. Prediction of soil and groundwater impacts is essential for achieving sustainable agricultural practices. A numerical model to solve unsaturated water flow and non-isothermal multicomponent reactive transport has been modified implementing the spatio-temporal evolution of soil physical and hydraulic properties. A long-term process simulation (30 years) of agricultural irrigation with desalinated water, based on a calibrated/validated 1D numerical model in a semi-arid region, is presented. Different scenarios conditioning reactive transport (i.e., rainwater irrigation, lack of gypsum in the soil profile, and lower partial pressure of CO 2 ( p CO 2 )) have also been considered. Results show that although boundary conditions and mineral soil composition highly influence the reactive processes, dissolution/precipitation of carbonate species is triggered mainly by p CO 2 , closely related to plant roots. Calcite dissolution occurs in the root zone, precipitation takes place under it and at the soil surface, which will lead a root growth blockage and a direct soil evaporation decrease, respectively. For the studied soil, a gypsum dissolution up to 40 cm depth is expected at long-term, with a general increase of porosity and hydraulic conductivity. [ABSTRACT FROM AUTHOR]

This study proposes a multi-wavelet Bayesian ensemble of two Land Surface Models (LSMs) using in-situ observations for accurate estimation of soil moisture for Contiguous United States (CONUS). In the absence of a continuous, accurate in-situ soil moisture dataset at high spatial resolution, an ensemble of Noah and Mosaic LSMs is derived by performing a Bayesian Model Averaging (BMA) of several wavelet-based multi-resolution regression models (WR) of the simulated soil moisture from the LSMs and in-situ volumetric soil moisture dataset obtained from the U.S. Climate Reference Network (USCRN) field stations. This provides a proxy to the in-situ soil moisture dataset at 1/8th degree spatial resolution called Hybrid Soil Moisture (HSM) for three soil layers (1–10 cm, 10–40 cm and 40–100 cm) for the CONUS. The derived HSM is used further to study the layer-wise response of soil moisture to drought, highlighting the necessity of the ensemble approach and soil profile perspective for drought analysis. A correlation analysis between HSM, the long-term (PDSI, PHDI, SPI-9, SPI-12 and SPI-24) and the short-term (Palmer Z index, SPI-1 and SPI-6) drought indices is carried out for the nine climate regions of the U.S. indicating a higher sensitivity of soil moisture to drought conditions for the Southern U.S. Furthermore, a layer-wise soil moisture percentile approach is proposed and applied for drought reconstruction in CONUS with a focus on the Southern U.S. for the year 2011. [ABSTRACT FROM AUTHOR]

• Drought response factors were decomposed from different types of vegetation. • Vegetation response factors are helpful in improving existing drought indices. • A new integrated drought index (TRADI) is developed for drought stress monitoring. • The TRADI is favorable compared with other frequently used drought indices. 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. [ABSTRACT FROM AUTHOR]

• Future change in global extreme meteorological droughts. • Two climate model large ensembles were used to assess the change of frequency. • Changes in meteorological droughts are mostly correlated to mean precipitation. • Projected increases in frequency are much more severe for short-term droughts. • Total area affected by a worsening of droughts gets larger with the return periods. Several studies have looked at the evolution of droughts in a changing climate in various locations and have typically found that droughts are expected to become more severe in the future. However, relatively little work has been done on extreme droughts. This study focuses on the evolution of extreme meteorological droughts over near (2036–2065) and far (2071–2100) future time periods. Monthly precipitation from two large multi-member climate model ensembles: the Canadian Earth System Model (CanESM2) 50-member ensemble and the Community Earth System Model (CESM1) 40-member ensemble, was used to calculate the Standard Precipitation Index (SPI) to quantify and explore the evolution of future meteorological droughts. The use of these large ensembles allows for the calculation of the 20- and 100-year droughts return period and offers a unique perspective into those rare events. Both climate models project increases in extreme meteorological drought frequency over many of the world's regions. The spatial patterns of regions with worsening droughts generally match those of projected change in mean annual precipitation, although the former is more extensive, indicating that changes in internal variability will increase drought frequency even in some regions projected to see increased mean annual precipitation. The projected increase in meteorological drought frequency is more significant for short-term JJA droughts, and for the larger return periods. Large increases in frequency are observed in many regions, all the way up to 20 times for the historical100-year JJA drought indicating a return period shift from 100 to 5 year. Considering the significant predicted increases in temperature, it follows that future changes for both agricultural (soil water deficit) and hydrological droughts (low stream levels), which result from the interaction between precipitation deficit and temperature (as well as other variables), will be even more severe. [ABSTRACT FROM AUTHOR]

• A hydrologic forecasting framework is built to predict drought and flood potentials for food insecurity early warning. • Hydrologic forecasts are improved by using entire suite of NMME total-precipitation forecasts in land surface modeling. • The higher skill of the entire suite of NMME precipitation forecast is also reflected in predicting hydrologic drought. In situ hydrologic monitoring over regions most susceptible to food insecurity can be a challenge in current times due to various socio-economic and political issues in combination with environmental factors such as ongoing famine or drought. Hydrologic monitoring and initializing forecasts based on remotely sensed and analyzed data can contribute significantly to early warning in such regions. Routine hydrologic forecasts, as provided by NASA's Hydrologic Forecasting and Analysis System (NHyFAS), are a recent addition to early warning systems. A custom instance of NHyFAS, termed FLDAS-Forecast, is used by FEWS NET's Land Data Assimilation System (FLDAS). The FLDAS-Forecast's dynamic forecasting component was originally set up with Goddard Earth Observing System (GEOS) forecast inputs and has been recently expanded with precipitation forecast forcing from the North American Multi-Model Ensemble (NMME). This paper describes the improvements in seasonal hydrologic forecasts produced with this updated system. Evaluations in this study focus on soil moisture across southern Africa's growing season. Soil moisture forecasts are benchmarked and evaluated relative to climatology-based forecasts and historic runs, which are driven by observation-based meteorological forcing fields, and they are verified with remotely sensed observations of soil moisture and vegetation. Through multiple deterministic and probabilistic skill assessments, we show that using the larger ensemble of NMME precipitation inputs in the forecast system results in higher quality hydrologic forecasts than are allowed by climatology- or GEOS-only-based forecasts. Further, the near-real-time NMME-based rootzone soil moisture forecasts were able to correctly predict developing drought conditions over southern Africa through late 2019 and into early 2020. [ABSTRACT FROM AUTHOR]

• The probability of meteorological to hydrological drought propagation increases with aggregation time. • The average lag time between the occurrence of meteorological and hydrological droughts is 4–6 months, increasing to 7–8 months at higher aggregation times. • The role of catchment and climate controls on the magnitude of hydrometeorological drought varies by catchment. Hydrological and meteorological droughts are complex hazards that cover a wide spatiotemporal spectrum and can affect the economic, environmental, and social sectors. However, it is challenging to characterize hydrometeorological drought and its propagation from precipitation deficits to soil moisture, discharge, and groundwater deficits and to ascertain to what extent catchment characteristics may affect drought features. This research explores the link between hydrometeorological drought characteristics and catchment characteristics. It investigates their propagation from meteorological to hydrological drought conditions in nine selected catchments from the Awash basin in Ethiopia using standardized indices of precipitation (SPI), soil moisture deficits (SPEI), streamflow (SRI), and base flow (SBI). The study results indicate that the propagation of meteorological to agricultural and hydrological droughts increases with increasing aggregation time scale and has a proportionally higher probability of propagation for moderate, severe, and extreme droughts. Across all catchments, the average lag time between hydrological and meteorological drought magnitude occurrence is 4–6 months, with lag times likely to increase by 7–8 months at a higher aggregation time scale. Meteorological drought has a 0.5 higher probability of occurrence than hydrological drought in a given period. In contrast, it has a lower (0.3) probability of the surface deficit becoming a groundwater deficit. At a 3-month cumulative time scale, the probability of propagating meteorological drought using the SPI to hydrological drought using the SRI is higher than that of propagating hydrological drought using the SBI. Therefore, the results suggest that while the propagation of meteorological droughts (SPI and SPEI) to hydrological droughts (SRI and SBI) slightly increases with aggregation time, the time taken for precipitation to become apparent in hydrological variables increases. The results confirmed that the probability of meteorological drought leading to hydrological drought progression is critical and needs to be considered in water resource management and planning. [ABSTRACT FROM AUTHOR]

• The mechanism of triggering and driving drought propagation differs. • Triggering drought propagation highly relies on meteorological drought patterns. • Driving drought propagation is primarily depends on underlying surface. • Meteorological and hydrological drought condition will intensify in future scenarios. Exploring the propagation from meteorological drought to hydrological drought and its potential influence factors is crucial for early warning of hydrological drought. As a case study in the Xijiang River Basin (XRB), South China, the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) series from 1961 to 2013 were used to represent the meteorological drought and hydrological drought with daily runoff generated by VIC model. An identification process for the threshold and the mechanism of drought propagation was conducted based on rank order analysis and Copula theory. The drought propagation regulations of drought variables, geographical location, and lag-time were established by linear or non-linear functions. Eventually, the propagation regulations were applied to the hydrological drought prediction from 2021 to 2050. Results indicated that: (1) The spatial-temporal patterns of meteorological drought are the critical factors in triggering drought propagation, and the effects of meteorological drought area, drought severity, drought duration, drought spatial concentration, and drought persistent duration increase successively. The critical threshold for triggering drought propagation is the combined frequency of the above five drought variables greater than 0.14. (2) The geographic location, center number, duration, severity, and area of two drought types follow the linear, two-piecewise, power, linear and logarithmic functions, respectively. Drought mainly propagation from west to east in three-dimensional space, and the lag-time was less than three months, with the maximum being 78 days. Unlike triggering drought propagation, driving drought propagation is synthetically associated with meteorological, soil water, groundwater, and topography conditions. (3) Under the MRI-ESM2-0 model, the severity and area of hydrological drought under different scenarios in the 2021–2050 will increase compared with the reference period 1961–2013, and the extreme hydrological drought exceeding history may occur under scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. The findings can help policy-makers manage the early stages of hydrological drought based on meteorological drought conditions and thus reduce the negative impact of drought hazards. [ABSTRACT FROM AUTHOR]

• Performance of ERA5, ECV, FLDAS, and GLDAS SM products was assessed in Central Asia. • ERA5 SM product has the highest correlation and lowest RMSE with in-situ stations. • GLDAS has the lowest uncertainty followed by FLDAS and ERA5 SM products. • GLDAS performs better than the other three SM products in capturing drought events. Reanalysis and remotely sensed soil moisture (SM) products are important for monitoring hydrology and the terrestrial carbon cycle, especially in water-limited arid regions. However, it is essential to assess the reliability of SM products before they are used. In this study, surface SM from the European Reanalysis-Interim v5 (ERA5), Essential Climate Variables (ECV), the Famine Early Warning Systems Network (FEWS) Land Data Assimilation System (FLDAS), and the Global Land Data Assimilation System (GLDAS) were assessed based on in-situ data and the Three-Cornered Hat (TCH) approach in a typical arid region, Central Asia (CA). The validation based on in-situ data indicated that all three products underestimated the observed surface SM, except for ERA5. The ERA5 SM was closest to the in-situ observations, averaging a higher correlation coefficient of 0.59, followed by the GLDAS, ECV, and FLDAS. The TCH method revealed low uncertainties (<0.05 m3/m3) for all four SM products, and high uncertainties were found at high altitudes. For ERA5 and ECV SM products, the uncertainty is high in the shrublands and forests, respectively. FLDAS and GLDAS SM products performed better in terms of uncertainty compared to ERA5 and ECV, with an average uncertainty below 0.03 m3/m3, except for forests. The above assessment results indicate that, compared with the other three SM products, ERA5 has a better performance and more potential for application in CA. There was an upward trend in surface SM from 1982 to 2018 in CA. The relationship between the four SM products and aridity index and drought events showed that GLDAS could capture the hydrothermal conditions and drought events, followed by FLDAS, ERA5, and ECV. Our research is intended to provide some reference for the application of gridded SM products to ecosystem and water resources monitoring in arid regions. [ABSTRACT FROM AUTHOR]

• A novel framework was developed to evaluate agricultural drought (AD) recovery. • Precipitation during drought recovery is the main factor affecting AD recovery. • Water required to recover from AD event for a given recovery duration was determined. • Recovery probabilities of AD under given precipitation scenarios were estimated. Agriculture is one of the sectors most directly and severely affected by droughts. Therefore, information regarding the water required (WR) to recover from agricultural drought and the probability of agricultural drought recovery under different precipitation scenarios is of great importance for drought risk management. In this study, we developed a quantitative evaluation method to estimate the WR of agricultural drought event recovery and a probabilistic drought recovery framework based on the copula function to calculate the probabilities of recovery from different grades of agricultural drought under given precipitation scenarios. The developed method and framework were demonstrated in the Yangtze River Basin (YRB) using the improved soil moisture anomaly percentage index (ISMAPI). The average duration of agricultural drought recovery in the YRB was 28 d. The difference in soil moisture persistence was the main factor affecting the spatial pattern of the grid mean drought recovery characteristics. For individual drought events, precipitation was the most important factor affecting drought recovery, followed by drought development intensity. There is a clear nonlinear relationship between the P CP value (percentage of cumulative precipitation during drought recovery to its corresponding climatological mean) and the drought recovery duration. In most grids, the three-parameter power function can be used to characterize this relationship and further estimate the WR to recover from an agricultural drought event under different expected recovery durations. We estimated the probabilities of extreme and severe agricultural drought recovery under different precipitation scenarios when the expected recovery durations were 10, 20, 30, 40, 50, and 60 d based on the developed probabilistic drought recovery framework. For example, when the cumulative precipitation is no less than its climatological mean within 30 d, the probability of recovering from extreme agricultural drought is 0.59. These findings are important for decision-making in water and drought management. [ABSTRACT FROM AUTHOR]

• Projections show a clear reduction of soil moisture through the entire annual cycle. • Soil moisture deficits up to 6x (7x) are projected to occur for mid- (end-) of-century. • Semi-arid climate is expected to cover almost 2/3 of the mainland in the RCP8.5. Climate change constitutes a major threat for all the Mediterranean countries due to the combination of large precipitation reductions and temperature increases and the higher frequency of climate extremes, especially driving water scarcity and all the derived multi-sectoral impacts. Portugal, as most of the Mediterranean countries, already endures larger frequencies of droughts and deficits in soil moisture and water storage. In the current study, the future projections of soil moisture are examined using a multi-model EURO-CORDEX regional climate ensemble, in agreement with three future emission scenarios (RCP2.6, 4.5 and 8.5). The drivers of future soil moisture dynamics are also analysed and its effect on relative humidity and evaporation rates. As expected, the projections show a clear reduction of soil moisture through the entire annual cycle, in response to the large decrease in precipitation and temperature increase, via a massive growth of potential evapotranspiration. The overall total soil moisture decreases ranges from −5% for the RCP2.6 to −20% (-10%) for the RCP8.5 (RCP4.5), w.r.t. the present climate. In the historical period, soil moisture deficits rarely reach values 3x over the standard deviation, but projections reveal that for the RCP4.5 (RCP8.5) for the mid-century deficits up to 5x (6x) are projected to occur, and for the end-of-century even 7x for the RCP8.5. The annual cycle of soil moisture is in present and future climate determined by precipitation and potential evapotranspiration, and deficit is both enhanced and covers a wider monthly window in the future, especially for the RCP8.5. The surface humidity also decreases importantly, up to −4% and −8% in spring and summer in the end-of-the-century, in agreement with RCP4.5 and RCP8.5, respectively. Resulting from the projected changes in precipitation and potential evapotranspiration, the typical semi-arid climate, which in present climate is confined to a small south-eastern region of Portugal, is expected to cover almost 2/3 of the mainland in the case of RCP8.5. Finally, this study was developed in the framework of the National Roadmap for Adaptation XXI - Portuguese Territorial Climate Change Vulnerability Assessment for XXI Century (RNA2100) project and aims at delivering a deeper and different featuring of terrestrial water for adaptation purposes in a Mediterranean country. [ABSTRACT FROM AUTHOR]

• Temperature has a negative effect on SSM under drought conditions. • Higher temperature accelerates SSM drying even across different land covers. • Temperature has a greater effect on scaling than land cover across different land covers. Understanding the dynamic response of surface soil moisture (SSM; 0–7 cm) drying to rising temperature is vital to predict future changes in agricultural and hydrological drought. Here we use quantile regression to explore the scaling effects of 2-m air temperature on SSM (%/°C) of the driest month in 8 different land cover types in the Yellow River Basin by using temperature intervals and a sliding window approach. SSM decreases significantly with air temperature and decreases more rapidly in warmer conditions, except for plain field, suggesting that temperature has a greater effect on SSM-temperature scaling than land cover. For warmer conditions, scaling exhibits larger spatial heterogeneity, indicating that it is mainly affected by local factors. When SSM is moderate, the scaling is constrained by various factors, but mainly by temperature. Comparatively, the scaling is close to 0 when SSM is very high or very low. The study highlights that global warming effects on drought may be underestimated. The findings provide an important theoretical basis for effects of temperature on soil moisture and future drought prediction. [ABSTRACT FROM AUTHOR]

• We proposed a new drought monitoring index; • We corroborated the applicability of this newly-proposed drought index; • We characterized spatial and temporal patterns of droughts across China. In this study, we proposed a new integrated remote sensing drought monitoring indices, i.e. Multiple Remote Sensing Drought Index integrated by Principal Component Analysis (PSDI), Multiple Remote Sensing Drought Index integrated by multiple linear regression (MRSDI) and Multiple Remote Sensing drought index integrated by gradient boosting method (GBMDI), based on the Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI). The monitoring performance of PSDI, MRSDI and GBMDI was compared and verified based on the real-world observed droughts during 2002 to 2016. We also evidenced drought monitoring performance of the PSDI MRSDI and GBMDI by comparison between PSDI, MRSDI, GBMDI and SPEI, SPI and PDSI based on the in situ observed meteorological data. We found that the spatiotemporal characteristics of droughts monitored by the PSDI, MRSDI and GBMDI were generally in good agreement with those by the SPI and SPEI. The GBMDI performs better than PSDI and MRSDI in describing drought processes and spatial patterns of droughts of different drought intensities. Comparison between the real-world observed drought-affected croplands and those monitored by PSDI, MRSDI and GBMDI indicated better drought monitoring performance of GBMDI than PSDI and MRSDI in monitoring droughts across widespread drought-affected regions. Besides, the trend of GBMDI is in good agreement with standardized crop yield. Therefore GBMDI should be the first choice in drought monitoring practice. The GBMDI developed in this study can help to provide an alternative drought monitoring index for large-scale drought monitoring across China and also in other regions of the globe. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three types of r c models were embedded to single-source and dual-source ET models. • The r c coupled models optimized by GA performed better than original ET models. • The r c and ET were most sensitive to soil moisture and net radiation. • The SW-JA model performed best in modeling maize ET over the whole growing season. The reliability and accuracy of canopy resistance (r c) are crucial for the evapotranspiration (ET) estimation in sparsely vegetated ecosystems, but most studies are still limited to the single-source Penman-Monteith (PM) model. In this study, we coupled three types of r c models (three-way model: Jarvis, JA; two-way model: Kelliher-Leuning, KL; Farias, FA; one-way model: Katerji-Perrier, KP; Massman, MA) to the PM and Shuttleworth-Wallace (SW) models to assess their applicability and stability under single-source and dual-source hypotheses in a maize field in northwest China. The models were calibrated and validated against the observed data from a three-year (2011–2013) field experiment based on genetic algorithms (GA). The results indicated that the r c coupled models optimized by GA greatly alleviated the overestimation of the original PM and SW models with mean absolute error (MAE) decreased by 13.85%-40.77% and determination coefficient (R2) increased by 0.40%-7.37%. The SW-type models achieved overall high performance to simulate ET at a diurnal timescale over the entire crop season compared to the equivalent PM-type models. The three-way JA model including crop, soil and meteorological factors tended to perform more accurately than the other r c models, and the coupled SW-JA model was optimal for predicting maize ET over the entire growing season. The two-way KL model produced lower errors than the FA model, whereas the FA model had the potential to be improved due to the highest R2 with the field-based ET for both PM- and SW- hypotheses. In addition, the sensitivity analysis to environmental variables showed the effect of leaf area index (LAI) and vapor pressure deficit (VPD) was less on ET compared with net radiation (R n) and soil moisture (θ). This study contributes to the knowledge in the area of actual ET modeling over sparse vegetation, which provides a reference for agricultural and environmental applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three types of r c models were embedded to single-source and dual-source ET models. • The r c coupled models optimized by GA performed better than original ET models. • The r c and ET were most sensitive to soil moisture and net radiation. • The SW-JA model performed best in modeling maize ET over the whole growing season. The reliability and accuracy of canopy resistance (r c) are crucial for the evapotranspiration (ET) estimation in sparsely vegetated ecosystems, but most studies are still limited to the single-source Penman-Monteith (PM) model. In this study, we coupled three types of r c models (three-way model: Jarvis, JA; two-way model: Kelliher-Leuning, KL; Farias, FA; one-way model: Katerji-Perrier, KP; Massman, MA) to the PM and Shuttleworth-Wallace (SW) models to assess their applicability and stability under single-source and dual-source hypotheses in a maize field in northwest China. The models were calibrated and validated against the observed data from a three-year (2011–2013) field experiment based on genetic algorithms (GA). The results indicated that the r c coupled models optimized by GA greatly alleviated the overestimation of the original PM and SW models with mean absolute error (MAE) decreased by 13.85%-40.77% and determination coefficient (R2) increased by 0.40%-7.37%. The SW-type models achieved overall high performance to simulate ET at a diurnal timescale over the entire crop season compared to the equivalent PM-type models. The three-way JA model including crop, soil and meteorological factors tended to perform more accurately than the other r c models, and the coupled SW-JA model was optimal for predicting maize ET over the entire growing season. The two-way KL model produced lower errors than the FA model, whereas the FA model had the potential to be improved due to the highest R2 with the field-based ET for both PM- and SW- hypotheses. In addition, the sensitivity analysis to environmental variables showed the effect of leaf area index (LAI) and vapor pressure deficit (VPD) was less on ET compared with net radiation (R n) and soil moisture (θ). This study contributes to the knowledge in the area of actual ET modeling over sparse vegetation, which provides a reference for agricultural and environmental applications. [ABSTRACT FROM AUTHOR]

Summary Lake Chad has recently been perceived to be completely desiccated and almost extinct due to insufficient published ground observations. Given the high spatial variability of rainfall in the region, and the fact that extreme climatic conditions (for example, droughts) could be intensifying in the Lake Chad basin (LCB) due to human activities, a spatio-temporal approach to drought analysis becomes essential. This study employed independent component analysis (ICA), a fourth-order cumulant statistics, to decompose standardised precipitation index (SPI), standardised soil moisture index (SSI), and terrestrial water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) into spatial and temporal patterns over the LCB. In addition, this study uses satellite altimetry data to estimate variations in the Lake Chad water levels, and further employs relevant climate teleconnection indices (El-Niño Southern Oscillation-ENSO, Atlantic Multi-decadal Oscillation-AMO, and Atlantic Meridional Mode-AMM) to examine their links to the observed drought temporal patterns over the basin. From the spatio-temporal drought analysis, temporal evolutions of SPI at 12 month aggregation show relatively wet conditions in the last two decades (although with marked alterations) with the 2012–2014 period being the wettest. In addition to the improved rainfall conditions during this period, there was a statistically significant increase of 0.04 m/yr in altimetry water levels observed over Lake Chad between 2008 and 2014, which confirms a shift in the hydrological conditions of the basin. Observed trend in TWS changes during the 2002–2014 period shows a statistically insignificant increase of 3.0 mm/yr at the centre of the basin, coinciding with soil moisture deficit indicated by the temporal evolutions of SSI at all monthly accumulations during the 2002–2003 and 2009–2012 periods. Further, SPI at 3 and 6 month scales indicated fluctuating drought conditions at the extreme south of the basin, coinciding with a statistically insignificant decline in TWS of about 4.5 mm/yr at the southern catchment of the basin. Finally, correlation analyses indicate that ENSO, AMO, and AMM are associated with extreme rainfall conditions in the basin, with AMO showing the strongest association (statistically significant correlation of 0.55) with SPI 12 month aggregation. Therefore, this study provides a framework that will support drought monitoring in the LCB. [ABSTRACT FROM AUTHOR]

• We developed a modified drought severity index (MDSI) with a constrained optimization technique. • MDSI improves drought monitoring for forests and farmland. • Ecological restoration and cultivated land reclamation can help mitigate droughts, while urbanization can potentially intensify droughts. With advancement of remote sensing techniques, remote-sensing drought indices have been widely used for drought monitoring. However, the monitoring accuracy of a specific drought index regionally varies. Considering the deficiency of existing drought indices in reflecting vegetation growth, here we propose a Modified Drought Severity Index (MDSI) with local optimization method constrained by the inclusion of vegetation greenness, crop water shortage, canopy temperature, vegetation structure, and physiological status. We evaluated drought monitoring performance of MDSI across China, and detected high correlations between MDSI and soil moisture (SM), Standardized Precipitation Index at a 3-month scale (SPI-3), actual drought-affected areas (ADA), evidencing higher drought performance of MDSI when compared to 8 widely-used drought indices. Besides, MDSI performed better in monitoring agricultural drought. We found amplifying short-term drought intensity in the future. Ecological restoration and cultivated land reclamation can alleviate drought effects. However, urbanization can potentially intensify droughts. How to adapt human behavior to droughts is a challenging task. [ABSTRACT FROM AUTHOR]

• We evaluated soil moisture droughts with duration, magnitude and extremum in monsoon and non-monsoon regions. • We identified more evident impacts of anthropogenic forcing on soil moisture drought in monsoon region than in non-monsoon region. • We found larger impacts of anthropogenic forcing on drought magnitude, relative to drought duration and drought extremum. The spatio-temporal patterns of drought changes and relevant forcings are still open for debate, especially under global warming, even though agricultural drought has long been receiving increasing concern for food security and sustainable development. In this study, we depicted global spatiotemporal patterns of agricultural drought using the Soil Water Deficit Index (SWDI) and reflected on the underlying forcings using the optimal fingerprint method. Three aspects of droughts were analyzed, i.e. drought duration (DD), drought magnitude (DM) and drought extremum (DE) over three regions, i.e. global, monsoon and non-monsoon regions. We found distinct spatial heterogeneity of DD, DM and DE. However, DM (DE) had mainly a decreasing (increasing) tendency. Anthropogenic forcing ([ANT] including greenhouse gas, anthropogenic aerosol, and ozone) and greenhouse gas forcing (GHG) played a prominent role in driving drought changes and were followed by the combination of anthropogenic and natural forcing (ALL). Soil moisture drought (DD, DM and DE) responses to external forcing of ANT and GHG were detected more easily in the monsoon region than in the non-monsoon region. Specifically, DM changes due to ANT (2.58 per century) contributed 39.88% of the DM changes by ALL (6.47 per century) in the monsoon regions, comparatively, the GHG and ANT induced changes of DM in the non-monsoon regions were quite slight. This study further clarified the impacts of anthropogenic warming on agricultural drought over the globe. [ABSTRACT FROM AUTHOR]

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

• A new integrated satellite-based dryness index (TVSDI) is proposed by the combination of three variables (LST, EVI, and SWIR). • The TVSDI is compared with several popular dryness indices (TVMDI, TVDI, PDI, MPDI, SPI, and SPEI). • The TVSDI is assessed over different climate and land cover types. • The TVSDI is applied for dryness-wetness monitoring in the CONUS. Accurate dryness monitoring is important for formulating reasonable response measures to reduce social and economic losses caused by drought. The land surface temperature (LST), shortwave infrared (SWIR) reflectance, and vegetation index (VI) are popular remote sensing (RS) indices that can be individually used to characterize surface dryness. Given the interactions of these factors, limitations are inevitably associated with using a single factor. Integrated dryness indices that combine LST or SWIR reflectance with the VI have thus been successively proposed and applied for dryness monitoring and soil moisture (SM) retrieval work. However, the advantages of these three indicators have not yet been combined to construct a more comprehensive dryness index. In this study, we integrated the LST, enhanced vegetation index (EVI), and SWIR reflectance and developed an integrated satellite-based dryness index with simple calculations, called the temperature vegetation shortwave infrared reflectance dryness index (TVSDI). The proposed TVSDI was thoroughly assessed in the continental United States (CONUS) using the following data: the soil moisture active passive (SMAP) SM; six commonly used dryness indices (i.e., temperature vegetation soil moisture dryness index (TVMDI), temperature vegetation dryness index (TVDI), modified perpendicular dryness index (MPDI), perpendicular dryness index (PDI), standardized precipitation evapotranspiration index (SPEI), and standardized precipitation index (SPI)); in situ SM data collected from 24 Cosmic-ray neutron probe (CRNP) sites covering different climates, soil types, and land cover types; and the United States Drought Monitor (USDM) maps. The results demonstrated that the TVSDI was significantly correlated with SMAP SM (R = − 0.75, p < 0.01) and exhibited better performance than the use of LST, EVI, and SWIR reflectance individually. Moreover, the TVSDI and the other six commonly used dryness indices exhibited good spatiotemporal consistency, all with consistency areas > 60%. The evaluation based on in situ SM from 24 CRNP sites indicated that the TVSDI exhibited more stability and accuracy than other satellite-based agricultural dryness indices (TVMDI, MPDI, PDI, and TVDI). Moreover, the spatial patterns of TVSDI maps were not only well-matched with SMAP SM maps but also provided more detailed spatial information. TVSDI maps could capture more dryness and drought variations in natural ecosystems and areas with less intensive human activities than USDM maps. Furthermore, the application of the TVSDI for dryness monitoring in the CONUS revealed that the dryness distributions differed greatly across different geographic regions at monthly and annual scales. In conclusion, the TVSDI was found to be a reliable and accurate satellite-based dryness index. [ABSTRACT FROM AUTHOR]

Summary It is of great importance to construct an integrated drought indicator, which is of great importance to drought risk assessment and decision-making. Given the fuzzy nature of drought, the variable fuzzy set theory was applied to develop an Integrated Drought Index (IDI) combining meteorological, hydrological, and agricultural factors across the Yellow River basin in North China. The runoff and soil moisture were derived by driving the calibrated Variable Infiltration Capacity (VIC) model with observed atmospheric forcing. Furthermore, the law of mutual change of quality and quantity was adopted to identify qualitative change points of annual IDI series in the Yellow River basin. The results indicate that: (1) the Integrated Drought Index (IDI) has a better performance compared with Standardized Precipitation Index (SPI) and Standardized streamflow Index (SSFI), and it is more sensitive and effective to capture drought onset and persistence, largely owing to its combination with the information of different drought-related variables; (2) spatially, the middle reaches has a higher drought risk than the rest portions of the Yellow River basin; seasonally, drought risk in spring and winter is larger than other seasons; overall, the IDI of the basin is dominated by an insignificantly downward trend; (3) some qualitative change points of drought were identified in the Yellow River basin, and those are primarily induced by ENSO events and the construction of dams and reservoirs. This study proposed an alternative drought indicator coupled with multivariate drought-related variables by objectively determining their weights based on the entropy weight method, which has a great value in characterizing drought. [ABSTRACT FROM AUTHOR]

Summary Streamflow stations are typical hydrometric stations that serve as the basic components of hydrological monitoring networks. To effectively obtain information for predicting the magnitude and frequency of future floods and droughts, an optimal configuration of streamflow stations should be established to maximize the sensing capability with limited resources. In this paper, we propose a method to site streamflow stations in space to maximize the total area for streamflow monitoring. Considering the special regulations for deploying streamflow stations, a modified maximal covering location problem (MCLP) model is introduced. The effective coverage range of a streamflow station is determined based on the minimum density required and the site-specific terrain slope. The candidate sites are assumed to be continuously distributed along a river, and the river network is abstracted as a series of line-based river segments. The covering priority for each segment can be determined by the river length, the drainage area, or the level of flooding risk. The hydrometric network intersection point set (HNIPS) is proposed to identify finite candidate sites along a river. By narrowing the continuous search space to a discrete point set, this siting problem is solved using the MCLP-based model and HNIPS. The Jinsha River Basin is selected as a study area to test the proposed streamflow station siting method. Results show that the proposed method is effective in prescribing the optimal configuration of streamflow stations and the model solution achieves better coverage than that of the real-world deployment. The applicability of the proposed optimal siting method using HNIPS is analyzed. The criteria for candidate site selection and impacts of different weighting schemes applied to river segments are also discussed. [ABSTRACT FROM AUTHOR]

Summary Drought severity not only depends on weather anomaly, but is also related to terrestrial hydrological condition to a large extent. In this study, we analyzed droughts using indices based on precipitation and soil moisture during the period of 1960–2010 in Haihe basin, which is a typical drought-prone region in North China. The Soil Moisture Drought Severity (SMDS) and Standardized Precipitation Index (SPI) are used to evaluate drought severity. SMDS is calculated based on the monthly soil moisture of upper 50 cm from the simulation by Community Land Model (CLM 4.0) and SPI is calculated based on gridded precipitation at 0.05° resolution (5 km × 5 km approximately), which is spatially interpolated from observations. During the last 51 years, 36 severe drought events (affecting areas greater than 20,000 km 2 and durations longer than 3 months) have been identified based on SMDS, and 41 drought events identified based on SPI. Results derived from SMDS indicate that there is a significant increasing trend in the drought affected area, and that the drought event occurred in 1999 has the largest affected area. Compared with the drought events derived from SMDS, the events derived from SPI have shorter durations but larger affected areas on average. Although the mean NDVI of the whole basin has been increasing since the 1980s, the two declining periods of 1992–1994 and 1999–2003 show fairly good agreement with the drought events identified in the same periods. The Anomaly of Normalized Difference Vegetation Index (A-NDVI) is introduced as NDVI anomaly from its trend line, thus the negative value of A-NDVI can reflect the drought impact on vegetation reasonably. Result indicates that both the SMDS and SPI are significantly correlated with A-NDVI, and correlation between annual SMDS and A-NDVI is higher than that of SPI. [ABSTRACT FROM AUTHOR]

Summary In this study we analyzed the sensitivity of four drought indices to precipitation (P) and reference evapotranspiration (ETo) inputs. The four drought indices are the Palmer Drought Severity Index (PDSI), the Reconnaissance Drought Index (RDI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Palmer Drought Index (SPDI). The analysis uses long-term simulated series with varying averages and variances, as well as global observational data to assess the sensitivity to real climatic conditions in different regions of the World. The results show differences in the sensitivity to ETo and P among the four drought indices. The PDSI shows the lowest sensitivity to variation in their climate inputs, probably as a consequence of the standardization procedure of soil water budget anomalies. The RDI is only sensitive to the variance but not to the average of P and ETo. The SPEI shows the largest sensitivity to ETo variation, with clear geographic patterns mainly controlled by aridity. The low sensitivity of the PDSI to ETo makes the PDSI perhaps less apt as the suitable drought index in applications in which the changes in ETo are most relevant. On the contrary, the SPEI shows equal sensitivity to P and ETo. It works as a perfect supply and demand system modulated by the average and standard deviation of each series and combines the sensitivity of the series to changes in magnitude and variance. Our results are a robust assessment of the sensitivity of drought indices to P and ETo variation, and provide advice on the use of drought indices to detect climate change impacts on drought severity under a wide variety of climatic conditions. [ABSTRACT FROM AUTHOR]

Summary Agricultural droughts are often characterized by soil moisture in the root zone of the soil, but crop needs are rarely factored into the analysis. Since water needs vary with crops, agricultural drought incidences in a region can be characterized better if crop responses to soil water deficits are also accounted for in the drought index. This study investigates agricultural droughts driven by plant stress due to soil moisture deficits using crop stress functions available in the literature. Crop water stress is assumed to begin at the soil moisture level corresponding to incipient stomatal closure, and reaches its maximum at the crop’s wilting point. Using available location-specific crop acreage data, a weighted crop water stress function is computed. A new probabilistic agricultural drought index is then developed within a hidden Markov model (HMM) framework that provides model uncertainty in drought classification and accounts for time dependence between drought states. The proposed index allows probabilistic classification of the drought states and takes due cognizance of the stress experienced by the crop due to soil moisture deficit. The capabilities of HMM model formulations for assessing agricultural droughts are compared to those of current drought indices such as standardized precipitation evapotranspiration index (SPEI) and self-calibrating Palmer drought severity index (SC-PDSI). The HMM model identified critical drought events and several drought occurrences that are not detected by either SPEI or SC-PDSI, and shows promise as a tool for agricultural drought studies. [ABSTRACT FROM AUTHOR]