It is important to comprehend the evolution of drought characteristics and the relationships between different kinds of droughts for effective drought mitigation and early warnings. The study area was the Pearl River Basin, where spatiotemporal changes in the multiscale water balance and soil moisture at various depths were analyzed. The meteorological data used in this study were derived from the China Meteorological Forcing Dataset, while the soil moisture data were obtained from the ECMWF ERA5-Land reanalysis dataset. The Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI) were applied to represent meteorological and agricultural droughts, respectively. By using the run theory for drought event identification, the characteristic values of drought events were analyzed. The correlation between the multiscale SPEI and SSI was examined to represent the propagation time from meteorological drought to agricultural drought. This study indicated that while the western part of the Pearl River Basin experienced a worsening atmospheric moisture deficit and the southern part had intensifying dry conditions for soil moisture, the rest of the basin remained relatively moist and stable. Soil conditions were moister in the deeper soil layers. The durations of agricultural droughts have generally been shorter than those of meteorological droughts over the past 40 years. Within the top three soil layers, the severity, duration, and frequency of drought events progressively increased, increased, and decreased, respectively, as soil depth increased. The propagation time scale from a meteorological drought to a four-layer agricultural drought was typically within 1-5 months. This study advanced existing research by systematically analyzing drought propagation times across soil depths and seasons in the Pearl River Basin. The methodology in this study is applicable to other basins to analyze drought complexities under climate change, contributing to global drought resilience strategies. Understanding the spatiotemporal characteristics of meteorological and agricultural droughts and the propagation time between them can help farmers and agricultural departments predict droughts and take appropriate drought-resistant measures to alleviate the damage of droughts on agricultural production.

Drought is a reoccurring natural phenomenon that presents significant challenges to agricultural production, ecosystem stability, and water resource management. The Central Highlands of Vietnam, a major region of industrial crops and vegetation ecosystems, has become increasingly vulnerable to drought impacts. Despite this vulnerability, limited research has explored the specific characteristics of drought and its seasonal effects on vegetation ecosystems in the region. This study addressed these gaps by providing a detailed analysis of recent soil moisture drought characteristics and their seasonal impacts on vegetation from 2015 to 2023 using weekly soil moisture active passive (SMAP) and moderate resolution imaging spectroradiometer (MODIS) satellite time series observations. This analysis derived the soil moisture anomaly index as a proxy to assess drought characteristics and used correlation analysis to quantify their impacts on seasonal vegetation dynamics. Our spatial analysis identified the most significant drought years in 2015 and 2019 in the study region, while the wettest conditions were detected in 2017 and 2022 over the study period. Notably, significant soil moisture deficits were observed in August and October throughout the study period, even though these months typically fall within the rainy season. On average, nearly 25 drought events were detected in the region from 2015 to 2023 due to soil moisture deficits, each lasting approximately 6 weeks. The impact of drought events on the vegetation ecosystem was seasonally pronounced in spring and winter, where droughts were notably higher. Our results provide valuable insights into informed decision-making and sustainable agricultural practices in the region. Understanding the spatial and temporal patterns of drought and its seasonal effects on vegetation can help policymakers and farmers develop targeted strategies to mitigate the adverse impacts, enhance water management practices, and promote drought-resistant crop varieties, thereby maintaining agricultural productivity and ecosystem health amidst increasing climate variability.

Agricultural drought is a complex natural hazard involving multiple variables and has garnered increasing attention for its severe threat to food security worldwide. In the context of climate change and the increased occurrence of drought events, it is crucial to monitor drought drivers and progression to plan the subsequent efforts in drought prevention, adaptation, and migration. However, previous studies on agricultural drought often focused on precipitation or evapotranspiration, overlooking other potential drivers related to crop drought stress. Additionally, macro-level analyses of drought-driving mechanisms struggle to reveal the underlying contexts of varying drought intensities. Northern Italy is one of the most important agricultural regions in Europe and is also a hotspot affected by extreme climate events in the world. In the summer of 2022, an extreme drought struck Europe once again, causing significant damage to the agricultural regions of Northern Italy. However, no studies to date have revealed the potential impacts and extent of extreme drought on this crucial agricultural area at a regional scale. Therefore, a comprehensive understanding of agricultural drought still requires further clarification and differentiated driver analysis. This study proposed a novel framework to comprehensively monitor agricultural drought with ensemble machine learning by constructing an integrated agriculture drought index (IADI) with remote sensing-related data including meteorology, soil, geomorphology, and vegetation conditions. Additionally, the Shapley Additive Explanation (SHAP) explainable model was applied to reveal the driving mechanism behind the drought event that occurred in northern Italy in the summer of 2022. Results indicated that the proposed explainable ensemble machine learning model with multi-source remote sensing products could effectively depict the evolution of agricultural drought with spatially continuous maps on an 8day scales. The SHAP analysis demonstrated that the extreme and severe agricultural drought in the summer of 2022 was closely related to meteorological indicators especially precipitation and land surface temperature, which contributed 68.88% to the drought. Moreover, the new findings also highlighted that soil properties affected the agricultural drought with a contribution of 28.3%. Specifically, in the case of moderate and slight drought conditions, higher clay and soil organic carbon (SOC) content contribute to mitigating drought effects, while sandy and silty soils have the opposite effect, and the contributions from soil texture and SOC are more significant than precipitation and land surface temperature. The proposed research framework could effectively contribute to improving the methodology in agricultural drought research, potentially bringing more instructive insights for drought prevention and mitigation.

While drought impacts are widespread across the globe, climate change projections indicate more frequent and severe droughts. This underscores the pressing need to increase resistance and resilience to drought. The strategic application of Preventive Drought Management Measures (PDMMs) is a suitable avenue to reduce the likelihood of drought and ameliorate associated damages. In this study, we use an optimisation approach with a multicriteria decision-making method to allocate PDMMs for reducing the severity of agricultural and hydrological droughts. The results indicate that implementing PDMMs can reduce the severity of agricultural and hydrological droughts, and the obtained management scenarios (solutions) highlight the utility of multi-objective optimisation for PDMMs planning. However, examined management scenarios also illustrate the trade-off between managing agricultural and hydrological droughts. PDMMs can alleviate the severity of agricultural droughts while producing opposite effects for hydrological droughts (or vice versa). Furthermore, the impact of PDMMs displays temporal and spatial variabilities. For instance, PDMMs implementation within a specific subbasin may mitigate the severity of one type of drought in a given month yet exacerbate drought conditions in preceding or subsequent months. In the case of hydrological droughts, the PDMMs may intensify streamflow deficits in the intervened subbasins while alleviating the hydrological drought severity downstream (or vice versa). These complexities emphasise a customised implementation of PDMMs, considering the basin characteristics (e.g., rainfall distribution over the year, soil properties, land use, and topography) and the quantification of PDMMs' effect on the severity of each type of drought.

Considering the global aggravated agricultural drought condition, the availability of reliable historical agricultural drought information with high spatiotemporal resolution is crucial for accurate drought monitoring, effective water resources management, and sustainable agricultural development. However, the coarse spatiotemporal resolution of available historical agricultural drought datasets imposes great limitations on drought prevention and mitigation. Recognizing the research gap, this study developed a novel approach of leaf area index (LAI) relative thresholds to generate a 500 m-resolution agricultural drought areas dataset in the North China Plain (NCP) spanning the timeframe from 2006 to 2019. A range of key LAI relative thresholds were established to capture various levels of agricultural drought severity. Subsequently, a 500 m-resolution agricultural drought area dataset was generated, encompassing critical parameters of drought-covered area, drought- damaged area, and crop failure area for both summer-harvest and autumn-harvest seasons in each year. The relative thresholds for drought-covered area, drought-damaged area, and crop failure area yielded percentages of 56 %, 51 %, 34 % for summer-harvest crops and 45 %, 41 %, 28 % for autumn-harvest crops, respectively. To validate the credibility of the generated approach, historical agricultural drought areas data from the Bulletin of Flood and Drought Disasters in China were juxtaposed across various harvest seasons and multiple years. The spatial verification in the Hebei Province (one main province of the NCP) revealed a remarkable consistency between the newly generated dataset and the authentic dataset, demonstrating correlation efficiency estimates ranging from 0.70 to 0.83. The developed approach gives insights into the spatial distribution and coherence of agricultural drought impacted areas and sheds light on revealing the inter-annual dynamics of crop growing seasons. It can support for impact-based agricultural drought monitoring and prediction, and subsequently assist in optimizing agricultural water management and ensuring global food security.

Drought is a major natural disaster worldwide. Understanding the correlation between meteorological drought (MD) and agricultural drought (AD) is essential for relevant policymaking. In this paper, standardized precipi-tation evapotranspiration index and standardized soil moisture index were used to estimate the MD and AD in the North China Plain (NCP) to identify the correlation between MD and AD during the growth period of winter wheat. In addition, we investigated the contributions of climate change (CC) and human activity (HA) to AD and the factors influencing the loss of winter wheat net primary production (NPP). Drought propagation time (PT) increased spatially from the southern to northern NCP (from 3 to 11 months). PT first increased and then decreased during the phenological period of winter wheat, and the decreasing trend was delayed with an increasing latitude. In general, the relative contribution of CC to AD was higher than that of HA; the correlation between MD and AD exhibited a weakening trend, particularly during the middle and late phenological stages of winter wheat. Precipitation was the main driver of the effects of HA on AD; the effects were stronger in areas with less precipitation. However, because of the improved irrigation conditions and scarce rainfall during the growth period of winter wheat in the study area, the effects of precipitation on AD were nonsignificant. Instead, tem-perature, wind, and total solar radiation, which are highly correlated with evapotranspiration, were identified as the primary drivers of AD; spatiotemporal variations were noted in these correlations. Prolonged drought PT reduced NPP; the sensitivity of winter wheat NPP to AD was higher in humid areas than in semiarid or semi-humid areas. NPP loss occurred primarily due to HA. Our findings revealed a correlation between MD and AD in agroecosystems and may facilitate policymaking related to drought mitigation and food security.