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Drought monitoring and early warning (M&EW) systems are an important component of agriculture/silviculture drought risk assessment. Many operational information systems rely mostly on meteorological indicators, and a few incorporate vegetation state information. However, the relationships between meteorological drought indicators and agricultural/silvicultural drought impacts vary across Europe. The details of this variability have not been elucidated sufficiently on a continental scale in Europe to inform drought risk management at administrative scales. The objective of this study is to fill this gap and evaluate how useful the variety of meteorological indicators are to assess agricultural/silvicultural drought across Europe. The first part of the analysis systematically linked meteorological drought indicators to remote sensing based vegetation indices (VIs) for Europe at NUTs3 administrative regions scale using correlation analysis for crops and forests. In a second step, a stepwise multiple linear regression model was deployed to identify variables explaining the spatial differences observed. Finally, corn crop yield in Germany was chosen as a case study to verify VIs' representativeness of agricultural drought impacts. Results show that short accumulation periods of SPI and SPEI are best linked to crop vegetation stress in most cases, which further validates the use of SPI3 in existing operational drought monitors. However, large regional differences in correlations are also revealed. Climate (temperature and precipitation) explained the largest proportion of variance, suggesting that meteorological indices are less informative of agricultural/silvicultural drought in colder/wetter parts of Europe. These findings provide important context for interpreting meteorological indices on widely used national to continental M&EW systems, leading to a better understanding of where/when such M&EW tools can be indicative of likely agricultural stress and impacts.

Yunnan is one of the provinces which had been frequently and heavily affected by drought disasters in China. Recently, large severe droughts struck Yunnan, caused considerable social, economic and ecological losses. A risk assessment of meteorological drought for Yunnan province is provided in this study. Based on the daily meteorological data of 29 stations during 1960–2010, duration and severity as two major drought characteristics, defined by the runs and the composite meteorological drought index, are abstracted from the observed drought events. Three bivariate Archimedean copulas are employed to construct the joint distributions of the drought characteristics. Based on the error analysis and tail dependence coefficient, the Gumbel–Hougaard copula is selected to analyze spatial distributions of the joint return periods of drought. The results indicate that a high risk is observed in the middle parts and the northeast parts of Yunnan province, while a relative lower drought risk is observed in the northwest of Yunnan province. The probabilistic properties can provide useful information for water resources planning and management.

Drought events exacerbated by global climate change occur frequently in China. Currently, high-spatiotemporal-resolution gridded meteorological drought index datasets are generally available for single time scales (e.g., 30, 60, 90, and 150 days) and do not fully account for seasonal differences in the impact of drought on vegetation, thus limiting their accuracy when monitoring drought in different regions of China. To compensate for the limitations of existing drought index datasets, a Chinese regional daily meteorological drought comprehensive index (MCI) dataset covering 1951–2022 with a spatial resolution of 0.1 degrees was developed, and standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) datasets at 30- and 90-day scales were constructed based on ERA5-Land datasets. Compared with the existing SPI and SPEI datasets, the generated dataset exhibits a high degree of consistency with those in eastern part of China (R2 > 0.5; the average biases were close to 0 and significantly smaller than RMSEs of the fitting). Additionally, the MCI dataset can more accurately reflect the changes in shallow soil moisture in the eastern part of China in a timely manner (R2 > 0.7 for the 0–7 cm depth), thus providing notable empirical support for research on drought development in different ecosystems.

Extreme cold and meteorological drought in the Mongolian Plateau (MP) were investigated during 1969–2017. Several drought indices were evaluated by analyzing recorded historical drought data in the Chinese region of the MP. The evaluated drought indices were then applied to detect drought characteristics in the entire MP. The trends of extreme cold indices showed that the climate of the MP has warmed during the past 49 years; however, the frequency of cold day/night has increased in the Mongolian region. The climate of Mongolia has also become colder in the spring season. The comprehensive meteorological drought index (CMDI) and the standardized precipitation index with a six-month scale (SPI6) exhibited better performances, showing high consistency between the spatial patterns of the two indices. However, drought represented by the SPI6 was enhanced greater than that expressed by the CMDI. Drought in the MP has been enhanced during the past 49 years, particularly in the Ordos and Alashan plateaus and the Xiliao River basin in China. Moreover, drought has been enhanced from August to October, particularly in the Mongolian region. However, spring drought has shown a weakening trend, which has been beneficial for agriculture and husbandry sectors in some regions of the MP.

As droughts propagate both in time and space, their impacts increase because of changes in drought properties. Because temporal and spatial drought propagation are mostly studied separately, it is yet unknown how drought spatial extent and connectedness change as droughts propagate though the hydrological cycle from precipitation to streamflow and groundwater. Here, we use a large‐sample dataset of 70 catchments in Central Europe to study the propagation of local and spatial drought characteristics. We show that drought propagation leads to longer, later, and fewer droughts with larger spatial extents. 75% of the precipitation droughts propagate to P‐ET, among these 20% propagate further to streamflow and 10% to groundwater. Of the streamflow droughts, 40% propagate to groundwater. Drought extent and dependence increase during drought propagation along the drought propagation pathway from precipitation to streamflow thanks to synchronizing effects of the land‐surface but decreases again for groundwater because of sub‐surface heterogeneity. Plain Language Summary As rainfall deficits develop into discharge and groundwater deficits, the impacts of droughts increase. While we know that drought impacts and properties change during drought development, it is yet unknown how the spatial characteristics of droughts change over the duration of an event. Here, we use a large dataset of 70 watersheds in Central Europe to study the development of drought characteristics over the duration of a drought event. We show that drought development leads to longer, later, fewer, and larger droughts. 20% of the rainfall droughts develop into discharge droughts, and 10% into groundwater droughts. Of the discharge droughts, 40% develop into groundwater droughts. Drought extent increases during drought development from rainfall to discharge thanks to effects at the land‐surface but decreases again for groundwater because of sub‐surface variations. Key Points Drought propagation affects local and regional drought characteristics and leads to longer, later, fewer, and larger droughts Only 20% of the precipitation deficits propagate to streamflow, while 40% of the streamflow deficits propagate to groundwater Spatial drought connectedness increases from precipitation to streamflow but decreases again for groundwater

Extreme climate events are becoming increasingly severe owing to global warming. In recent years, the Yalong River has experienced frequent droughts, which has seriously hindered the development of hydropower. Based on standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) values, this study employed transfer entropy, multivariate Copula functions, and a cloud model to study the response relationship and risk situation between meteorological and hydrological droughts. The results showed that: (1) The smaller the scale of SPEI and SRI, the more sensitive the identification of drought and the faster the detection of drought trend changes. (2) Combined with small-scale drought index analyses, the trend and severity of meteorological and hydrological droughts propagating from the upper and middle reaches to the lower reaches is increasing from year to year. (3) The Gumbel-Hougaard Copula function reveals the relationship between meteorological and hydrological drought, with the longest joint return period of 13-15 months. (4) In cloud model risk evaluation, the degree, uncertainty and stochasticity of drought risk in the basin are increasing, and the risk degree of interannual meteorological and seasonal hydrological droughts in the basin was high. This study provides theoretical support for optimizing water resource allocation and drought risk situation analysis in the basin.

Meteorological droughts are a recurring natural phenomenon that causes lack of precipitation. The severity of meteorological droughts is estimated by established algorithms known as drought indices. One such procedure, perhaps the simplest, is the Reconnaissance Drought Index, or RDI, which is based on the ratio between precipitation and potential evapotranspiration (PET) for a determined continuous period of months. In this study, the RDI is applied to three durations of meteorological drought at a weather station selected from each of the three geographic or climate zones in the state of San Luis Potosi, Mexico, which are: Villa de Arriaga (Potosino Plateau), Río Verde (Mean Zone), and Xilitla (Huasteca Region). The monthly rainfall records and average and minimum temperatures of each station cover more than 50 years. PET was estimated by four methods: (1) the Penman-Monteith formula, which is the reference method, (2) the Thornthwaite, (3) the Turc, and (4) the HargreavesSamani. The operating procedures for these criteria are detailed in appendices. The analysis of the results indicates that the RDIs estimated with the Hargreaves-Samani method are best for reproducing the results of the PenmanMonteith formula, in the three climatic locations processed. The Turc method also led to results similar to those of the reference. Therefore, it can be said that the RDI is a robust drought index, which practically does not depend on the method of estimating the PET. There is a noticeable difference in the operational procedures of the Penman-Monteith formula and the Hargreaves-Samani method. The latter is a practical solution that is worth mentioning.

The economic and human losses caused by drought are increasing, driven by climate change, human activities, and increased exposure of livelihood activities in water‐dependent sectors. Mitigation of these impacts for socio‐ecological securit is necessary to gain a better understanding of how human activities contribute to the propagation of drought as water management further develops. The previous studies investigated the impact of human activities on a macro level, but they overlooked the specific effects caused by human water management measures. In addition, most studies focus on the propagation time (PT, the number of months from meteorological drought propagation to hydrological drought), while other drought propagation characteristics, such as duration, magnitude, and recovery time, are not yet sufficiently understood. To tackle these issues, the PCR‐GLOBWB v2.0 hydrological model simulated hydrological processes in China under natural and human‐influenced scenarios. The study assessed how human activities impact hydrological drought and its propagation. Result shows that human activities have exacerbated hydrological drought in northern China, while it is mitigated in the south. The propagation rate (PR, proportion of meteorological drought propagation to hydrological drought) ranges from 45% to 75%, and the PT is 6–23 months. The PR does not differ substantially between the north and south, while the PT is longer in the north. The PR decreases by 1%–60% due to human activities, and the PT decreases (1–13 months) in the north and increases (1–10 months) in the south. Human activities display significant variations in how they influence the propagation process of drought across different basins. The primary factors driving the spatial pattern of drought disparities are regional variations in irrigation methods and the storage capacity of reservoirs. Plain Language Summary Under the combined impact of climate change and human activities, economic and human losses caused by drought in China have been increasing year by year. To mitigate the impact of disasters, we conducted research using PCR‐GLOBWB v2.0 model to investigate how human activities have altered hydrological drought in China. And the role of human activities in the propagation process of drought was explored. The results indicate that human activities have intensified hydrological drought in northern China, while providing some alleviation in the southern regions. Human activities disrupt the natural processes of drought propagation, resulting in a decrease in propagation rates. Furthermore, human activities have shortened the propagation lag time of drought in the north, while increasing it in the south. Additionally, smaller basins are more sensitive to human activities compared to larger basins. Our study reveals the impact of human activities on hydrological drought and drought propagation, providing valuable insights for the development of more effective drought adaptation strategies. Key Points We used the PCR‐GLOBWB v2.0 model to study the impact of human activities on the process of drought propagation Human activities play a varying role in the propagation process of drought in different river basins Human activities has led to a decrease in drought propagation rates and shortened/prolonging the drought lag time in northern/southern China

Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models’ performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation.

With climate warming, frequent drought events have occurred in recent decades, causing huge losses to industrial and agricultural production, and affecting people’s daily lives. The monitoring and forecasting of drought events has drawn increasing attention. However, compared with the various monthly drought indices and their wide application in drought research, daily drought indices, which would be much more suitable for drought monitoring and forecasting, are still scarce. The development of a daily drought index would improve the accuracy of drought monitoring and forecasting, and facilitate the evaluation of existing indices. In this study, we constructed a new daily drought index, the daily evapotranspiration deficit index (DEDI), based on actual and potential evapotranspiration data from the high-resolution ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts. This new index was then applied to analyze the spatial and temporal evolution characteristics of four drought events that occurred in southwest, north, northeast, and eastern northwest China in the spring and summer of 2019. Comparisons with the operationally used Meteorological Drought Composite Index and another commonly used index, the Standardized Precipitation Evapotranspiration Index, indicated that DEDI characterized the spatiotemporal evolution of the four drought events reasonably well and was superior in depicting the onset and cessation of the drought events, as well as multiple drought intensity peaks. Additionally, DEDI considers land surface conditions, such as vegetation coverage, which enables its potential application not only for meteorological purposes but also for agricultural drought warning and monitoring.