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A drought is a complex event characterized by low rainfall and has negative implications for agricultural and hydrological systems, as well as for community life. A common meteorological drought index used for drought monitoring and water resource management is the Standardized Precipitation Evapotranspiration Index (SPEI). Using SPEI can assist in predicting drought onset and estimating drought severity. The objective of this research is to assess the accuracy of machine learning models in estimating the SPEI-1 (one-month) index in semi-arid climates. To achieve this goal, the data will be analyzed using remote sensing parameters, a worldwide database, and meteorological station information. SPEI-1 was predicted in Tabriz, Iran, between 1990 and 2022 using multilayer perceptron (MLP) and random forest (RF) techniques combined with genetic algorithm (GA) methods. The parameters used are average air temperature, average relative humidity, monthly precipitation, wind speed, sunny hours, as well as the one-month standard precipitation index (SPI-1) (from ground data), daily precipitation products from satellites named PERSIANN (PRC-PR) (from remote sensing), and SPEIbase data (from global databases). The results suggest that the use of satellite remote sensing characteristics and global databases has significantly enhanced the precision and efficiency of prediction models. Based on the GA-RF model with an R2 of 0.992 and an RMSE of 0.124, it exhibits the best performance among all models in Scenario 1. By combining remote sensing parameters, this study presents an innovative approach to predicting the SPEI index and demonstrates their capabilities in drought management and mitigation. [ABSTRACT FROM AUTHOR]

The study of regional drought characteristics is vital for identifying drought patterns and offering scientifically informed guidance for drought warnings. This research focuses on the Sichuan Basin, where the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated across various time scales using meteorological data from 1963 to 2022. Wavelet analysis was applied to examine the periodic fluctuations of the SPEI across different time scales. Drought events were identified using run-length theory and spatially visualized with ArcGIS 10.7 interpolation techniques to elucidate the temporal and spatial dynamics of drought occurrences. The findings are as follows: (1) Over nearly 60 years, the SPEI in the Sichuan Basin fluctuated between −1.5 and 1, with an insignificant annual downward trend but a significant downward trend in autumn (p < 0.05). (2) The SPEI displayed a primary cycle of 6 years in spring, autumn, and winter, while the summer cycle matched the annual SPEI cycle of 8 years. (3) Drought events were more frequent in the eastern part of the basin compared to the west. The area with high drought frequency shifted counterclockwise from east to north, northwest, west, and south with the changing seasons. (4) Drought duration was longer in the western and northern regions of the basin than in the east. Severe drought events were mainly concentrated in the Chengdu Plain and the Central Sichuan Hilly Region, although the drought intensity index was lowest in the Chengdu Plain and Chongqing in eastern Sichuan. The peak values of drought showed an insignificant decreasing trend, indicating a potential expansion in the extreme impacts of drought disasters in the study area. [ABSTRACT FROM AUTHOR]

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland. [ABSTRACT FROM AUTHOR]

Understanding how plants adapt to shifting water availability is imperative for predicting ecosystem vulnerability to drought. However, the spatial–temporal dynamics of the plant–water relationship remain uncertain. In this study, we employed the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI4g), an updated version succeeding GIMMS NDVI3g spanning from 1982 to 2022. We integrated this dataset with the multiple scale Standardized Precipitation Evapotranspiration Index (SPEI 1 to 24) to investigate the spatial–temporal variability of sensitivity and lag in vegetation growth in response to water variability across China. Our findings indicate that over 83% of China's vegetation demonstrates positive sensitivity to water availability, with approximately 66% exhibiting a shorter response lag (lag < 1 month). This relationship varies across aridity gradients and diverges among plant functional types. Over 66% of China's vegetation displays increased sensitivity to water variability and 63% manifests a short response lag to water changes over the past 41 years. These outcomes significantly contribute to understanding vegetation dynamics in response to changing water conditions, implying a heightened susceptibility of vegetation to drought in a future warming world. [ABSTRACT FROM AUTHOR]

In recent decades, abnormal rainfall and temperature patterns have significantly impacted the environment and human life, particularly in East Nusa Tenggara. The region is known for its low rainfall and high temperatures, making it vulnerable to drought events, which have their own complexities due to being random and changing over time. This study aimed to analyze the trend of short-term meteorological drought intensity in Timor Island, East Nusa Tenggara. The analysis was carried out by utilizing the standardized precipitation evapotranspiration index (SPEI) for a 1-month period to characterize drought in intensity, duration, and severity. A power law process approach was used to model the intensity of the event, which is inversely proportional to the magnitude of the drought event. Intensity parameters of the power law process were estimated using the maximum likelihood estimation (MLE) method to predict an increase in the intensity of drought events in the future. The probability of drought was calculated using the non-homogeneous Poisson process. The analysis showed that "extremely dry" events in Timor Island are less frequent than "very dry" and "dry" events. The power law process model's estimated intensity parameter showed a beta value greater than 1, indicating an increase in future drought events. In the next 12 months, two months of drought are expected in each region of Timor Island, East Nusa Tenggara, with the following probabilities for each region: 0.264 for Kupang City, 0.25 for Kupang, 0.265 for South Central Timor, 0.269 for North Central Timor, 0.265 for Malaka, and 0.266 for Belu. This research provides important insights into drought dynamics in vulnerable regions such as East Nusa Tenggara and its potential impact on future mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators' future projections demonstrated a tendency towards an increasing fire weather risk for the region's non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region's growing vulnerability to future fire incidents in the context of climate change. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Droughts have a substantial impact on water supplies, agriculture, and ecosystems worldwide. Agricultural sustainability and production in the Mekong Delta of Vietnam are being jeopardized by droughts caused by climate change. Conventional forecasting methods frequently struggle to comprehend the intricate dynamics of meteorological occurrences connected to drought, necessitating the use of sophisticated prediction techniques. This study assesses the effectiveness of various statistical models (ARIMA), machine learning, and deep learning models (Gradient Boosting, XGBoost, RNN, and LSTM) in forecasting the SPEI over different time periods (1, 3, 6, and 12 months) across six prediction intervals. The models were developed and evaluated using data from 11 meteorological stations spanning from 1985 to 2022. These models incorporated various climatic variables, including precipitation, temperature, humidity, potential evapotranspiration (PET), Southern Oscillation Index (SOI) Anomaly, and sea surface temperature in the NINO4 region (SST_NINO4). The results demonstrate that XGBoost and LSTM models exhibit outstanding performance, showcasing lower error metrics and higher R² values compared to Gradient Boosting and RNN. The performance of the model fluctuated depending on the forecast step, with error metrics often increasing with longer prediction horizons. The use of climatic indices improved the accuracy of the model. These findings are consistent with earlier research on drought episodes in the Mekong Delta and support studies from other areas that show the effectiveness of advanced modeling tools for predicting droughts. The work emphasizes the capacity of machine learning and deep learning models to enhance the precision of drought forecasting, which is vital for efficient water resource management and agricultural planning in places prone to drought. [ABSTRACT FROM AUTHOR]

In recent decades, abnormal rainfall and temperature patterns have significantly impacted the environment and human life, particularly in East Nusa Tenggara. The region is known for its low rainfall and high temperatures, making it vulnerable to drought events, which have their own complexities due to being random and changing over time. This study aimed to analyze the trend of short-term meteorological drought intensity in Timor Island, East Nusa Tenggara. The analysis was carried out by utilizing the standardized precipitation evapotranspiration index (SPEI) for a 1-month period to characterize drought in intensity, duration, and severity. A power law process approach was used to model the intensity of the event, which is inversely proportional to the magnitude of the drought event. Intensity parameters of the power law process were estimated using the maximum likelihood estimation (MLE) method to predict an increase in the intensity of drought events in the future. The probability of drought was calculated using the non-homogeneous Poisson process. The analysis showed that "extremely dry" events in Timor Island are less frequent than "very dry" and "dry" events. The power law process model's estimated intensity parameter showed a beta value greater than 1, indicating an increase in future drought events. In the next 12 months, two months of drought are expected in each region of Timor Island, East Nusa Tenggara, with the following probabilities for each region: 0.264 for Kupang City, 0.25 for Kupang, 0.265 for South Central Timor, 0.269 for North Central Timor, 0.265 for Malaka, and 0.266 for Belu. This research provides important insights into drought dynamics in vulnerable regions such as East Nusa Tenggara and its potential impact on future mitigation and adaptation planning.

Temperature can significantly (P < 0.05) affect plant growth by modifying water use strategies, which are determined by intrinsic water use efficiency (WUEi). Red Heart Chinese Fir (Cunninghamia lanceolata) is one of the most important ecological and economic plantation species in China. However, the C. lanceolata water use strategy in response to increased temperatures and uneven temporal distribution of precipitation during the growing season is rarely reported. In a 7‐year‐old C. lanceolata plantation, differences in WUEi and C and N concentrations in different organs were analysed by anova, and the δ13C stable isotope, C, and N concentrations in stems determined at different tree heights. Stepwise regression and variance inflation factor were used to remove autocorrelated factors, and structural equation modelling was then used to explore relationships between WUEi and climate and biological factors. WUEi differed significantly between leaf and branch at different standardized precipitation evapotranspiration indices (SPEI). WUEi and N concentration decreased with age. The highest WUEi in branches and leaves were 92.7 and 88.4 μmol·mol−1 in 2020 (SPEI = 0.00), respectively. δ13C increased with relative tree height but N concentration and C/N ratio were not affected. Air temperatures has increased in between 2014 and 2020. WUEi and N concentration decreased with increasing branch and leaf age, but C concentration increased. SPEI significantly positively affected WUEi (P < 0.05), and WUEi was significantly negatively related to C concentration, which is consistent with the trade‐off between C and water. [ABSTRACT FROM AUTHOR]

Gansu Province is located in an ecologically fragile area, with complex climate conditions, high probability and wide range of drought. In order to better study the temporal and spatial variation characteristics of drought in Gansu Province, it was divided into four cli- mate zones according to the climate type and geographical characteristics and the standardized precipitation evapotranspiration indices (SPEI- 3 and SPEI-12) at quarterly and annual scales for the next 76 years (2024-2099) were calculated. Under the four scenarios of SSP126, SSP245, SSP370 and SSP585, Mann Kendall mutation test, spatial interpolation and other methods were used to estimate the temperature, precipitation, and drought evolution status of Gansu Province in the future 80 in the CMIP6 climate model. The main conclusion is as follows: In the CMIP6 model, the temperature and precipitation in different regions of Gansu Province in the future from 2024 to 2099 show an upward trend over time under different scenarios and the climate tendency rate of temperature and precipitation under different scenarios is from small to large as SSP126, SSP245, SSP370 and SSP585. Under the SSP126, SSP245, SSP370 and SSP585 scenarios in the CMIP6 model, Gansu Province will show the development trend of drought in spring, summer and autumn in the coming 2024 to 2099. The mutation years from humid to arid are concentrated between 2040 and 2060. In winter, there is a trend of wetting, with a sudden change from drought to humidification in the SSP121 scenario occurring in 2033, and other scenarios occurring between 2040 and 2060. The frequency of different types of drought is different between different scenarios. Light drought and extreme drought have the highest frequency under the SSP370 scenario, moderate drought has the highest frequency under the SSP245 scenario, and severe drought has the highest frequency under the SSP585 scenario. The frequency of different levels of drought in different climatic regions of Gansu Province under different climatic scenarios is from large to small of light drought, moderate drought, severe drought and extreme drought. [ABSTRACT FROM AUTHOR]

In the context of global warming, escalating water cycles have led to a surge in drought frequency and severity. Yet, multidecadal fluctuations in drought and their multifaceted influencing factors remain inadequately understood. This study examined the multide-cadal changes in drought characteristics (frequency, duration, and severity) and their geographical focal points within China's north-south transitional zone, the Qinling-Daba Mountains (QDM), from 1960 to 2017 using the Standardized Precipitation Evapotranspiration Index (SPEI). In addition, a suite of eight scenarios, correlation analysis, and wavelet coherence were used to identify the meteorological and circulation factors that influenced drought characteristics. The results indicate the following: (1) From 1960 to 2017, the QDM experienced significant interdecadal variations in drought frequency, duration, and severity, the climate was relatively humid before the 1990s, but drought intensified thereafter. Specifically, the 1990s marked the period of the longest drought duration and greatest severity, while the years spanning 2010 to 2017 experienced the highest frequency of drought events. (2) Spatially, the Qinling Mountains, particularly the western Qinling Mountain, exhibited higher drought frequency, duration, and severity than the Daba Mountains. This disparity can be attributed to higher rates of temperature increase and precipitation decrease in the western Qinling Mountain. (3) Interdecadal variations in droughts in the QDM were directly influenced by the synergistic effects of interdecadal fluctuations in air temperature and precipitation. Circulation factors modulate temperature and precipitation through phase transitions, thereby affecting drought dynamics in the QDM. The Atlantic Multidecadal Oscillation emerges as the primary circulation factors influencing temperature changes, with a mid-1990s shift to a positive phase favoring warming. The East Asian Summer Monsoon and El Niño-Southern Oscillation are the main circulation factors affecting precipitation changes, with positive phase transitions associated with reduced precipitation, and vice versa for increased precipitation. [ABSTRACT FROM AUTHOR]

The spatiotemporal characteristics of temperature and precipitation are complex in northern China. It is an area of frequent droughts and floods. Climate warming has exacerbated the complexity of drought and flood incidences. Based on the actual observation data from meteorological stations in northern China, this study analyzed the hazard characteristics of drought and flood disasters and revealed the distribution pattern and variation characteristics of high-hazard areas of drought and flood disasters before and after an abrupt temperature jump in 1995. The results showed that, from 1970 to 2021, the annual average temperature in the study area showed a significant increasing trend (0.34 °C/10 years), and the summer monsoon-transition areas showed a drying trend in spring and summer and a wetting trend in autumn and winter, with the summer monsoon-affected areas mainly getting wetter. The high-drought-hazard area is primarily located in the west and northeast of the summer monsoon-transition areas and the northeast of the summer monsoon-affected areas. The high-flood-hazard area is mainly located in the northwest and south of the summer monsoon-affected areas. The high-hazard area with overlapping drought and floods is primarily located in the northeast of the study area. After a major abrupt temperature jump in 1995, the normal probability distribution curve of the intensity of drought and floods at different levels generally moved to the dry side, the probability of occurrence of extreme drought and extreme flood events increased, the scale of high-hazard areas caused by drought and floods was significantly expanded, the high-hazard areas caused by drought and flood were transferred to the monsoon edge area, and the high-flood-hazard areas were transferred from the middle to the north to the south. The scale of the transition from low-hazard areas caused by drought and flood to high-hazard areas and from high-flood-hazard areas to high-drought-hazard areas was more extensive. The research results provided important scientific guidance for the formulation of drought and flood disaster prevention planning and agricultural production development strategies in China. [ABSTRACT FROM AUTHOR]

【Objective】 Drought is an abiotic stress detrimentally affecting crop production in many countries. Understanding its occurrence in a region is crucial to improving its water and agricultural management. In this paper, we analyzed the spatiotemporal variation in drought from 1961—2022 in Huaibei located in central China. 【Method】 The analysis is based on meteorological data measured from 1961—2022 from 21 weather stations across the region. Monthly, seasonal, and annual standardized precipitation evapotranspiration index (SPEI) in each station were calculated, from which we calculated the spatiotemporal variation in drought across the region. 【Result】 ① Monthly, seasonal and annual SPEI in Huaibei had been in decrease from 1961—2022. ② The frequency and duration of persistent droughts had both been in increase from 1961—2022, with Huaibei station showing the highest drought frequency at 0.73 times/year, and Sixian station showing the longest drought duration at 4.61 months. ③ Annual change in droughts in spring and autumn did not show any identifiable trend. Frequency and intensity of droughts in winter were the highest, reaching 34.43% and 21.86%, respectively. Spatially, spring drought was more frequent in the center, east, and southwest of the region; summer drought was less frequent in the southeast of the region; autumn drought was more frequent in the east and north of the region; winter drought was more frequent in the west, northeast and southeast of the region. ④ On average, annual change in drought was increasing at a rate of 0.01/10 a, but not at significant levels. Drought areas had been in increase from 1961 to 2022 at a rate of 1.4%/10 a. The north has a high tendency for occurrence of droughts, while drought intensity tends to be high in the west and southeast of the region. Drought is more frequent in the northeast and southwest. 【Conclusion】 Overall, there are noticeable trends in spatiotemporal variation in drought in the Huaibei region, with drought frequency and intensity varying between different regions and showing an increase trend from 1961 to 2022.

Livestock presence impacts plant biodiversity (species richness) in grassland ecosystems, yet extent and direction of grazing impacts on biodiversity vary greatly across inter-annual periods. In this study, an 8-year (2014-2021) grazing gradient experiment with sheep was conducted in a semi-arid grassland to investigate the impact of grazing under different precipitation variability on biodiversity. The results suggest no direct impact of grazing on species richness in semi-arid Stipa grassland. However, increased grazing indirectly enhanced species richness by elevating community dominance (increasing the sheltering effect of Stipa grass). Importantly, intensified grazing also regulates excessive community biomass resulting from increased inter-annual wetness (SPEI), amplifying the positive influence of annual humidity index on species richness. Lastly, we emphasize that, in water-constrained grassland ecosystems, intra-annual precipitation variability (PCI) was the most crucial factor driving species richness. Therefore, the water-heat synchrony during the growing season may alleviate physiological constraints on plants, significantly enhancing species richness as a result of multifactorial interactions. Our study provides strong evidence for how to regulate grazing intensity to increase biodiversity under future variable climate patterns. We suggest adapting grazing intensity according to local climate variability to achieve grassland biodiversity conservation. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km2 to 49.5 km2 in Umhlabuyalingana; 54 km2 to 1.6 km2 in Joe Morolong; and 254.6 km2 to 7.4 km2 in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas. [ABSTRACT FROM AUTHOR]

Drought is mainly triggered by the lack of precipitation, which can lead to insufficient water supply for crops thus affecting their growth and development. Reliable drought monitoring is crucial to understanding drought risk and avoiding drought-induced crop yield losses. Based on the Stacking regression method and multiple remotely-sensed drought factors from 2001 to 2017, this study developed an ensemble learning framework for monitoring agricultural drought in major winter wheat-producing areas in China. Stacking used five machine learning algorithms, namely, extreme gradient boosting, support vector regression, extra trees, and multi-layer perceptron, as the base learners to model the relationship between remote sensing drought factors and 1-, 3-, and 6-month standardized precipitation evapotranspiration index (SPEI). In this study, county-level winter wheat yield records and drought maps provided by the Global SPEI database (SPEIbase) were adopted to assess the suitability of Stacking-predicted SPEI drought maps in agricultural drought monitoring. The results show that Stacking outperformed other machine learning algorithms in terms of estimation accuracy, with the highest R2 value of 0.77 and the lowest root mean square error (RMSE) of 0.47. The longer the time scale of model-predicted SPEI, the higher its correlation with detrended winter wheat yields. The comparison with the drought maps of SPEIbase shows that the Stacking-predicted drought maps successfully captured the spatial pattern and intensity change of drought events. The approach presented in the study has good applicability for agricultural drought monitoring and could be extended to the rest of the areas. [ABSTRACT FROM AUTHOR]

We quantified the lag time of vegetation response to drought in the Pearl River basin (PRB) based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (ND-VI), and constructed a vegetation loss probability model under drought stress based on the Bayesian theory and two-dimensional joint distribution. We further quantitatively evaluated the spatial variations of loss probability of four vegetation types (evergreen broadleaf forest, mixed forest, grassland, and cropland) under different drought intensities. The results showed that the drought risk in eastern West River, the upper reaches of North River and East River, and southern Pearl River Delta was obviously higher than that in other regions during 1982-2020. The response time of vegetation to drought in high-altitude areas in the upper reaches of PRB (mostly<3 month) was generally shorter than that in low altitude areas (>8 month). Drought exacerbated the probability of vegetation loss, with higher vulnerability of mixed forest than the other three vegetation types. The loss probability of vegetation was lower in northwestern PRB than that in central PRB. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For farmers in the South Wollo Zone, drought has been one of the most devastating natural disasters, making better monitoring of agricultural drought with the aid of earth observation data essential. The main objective of this research is to characterize the spatiotemporal variation, frequency, and trends of agricultural drought from 2001 to 2021 using the earth observation-derived vegetation health index (VHI) and standardized precipitation evapotranspiration index (SPEI). The VHI and SPEI were developed using the following variables: potential evapotranspiration (MOD16A2GF), climatic hazards group infrared precipitation with stations (CHIRPS), surface temperature of the land and emissivity (MOD11A2), and normalized difference vegetation index (MOD13Q1 NDVI). The Mann–Kendall (MK) trends analysis and Pearson correlation were used to identify the trend and the relationship between VHI and SPEI. SPEI and VHI were validated using crop yield data. According to the findings, there were agricultural droughts of varying severity in 2002, 2004, 2009, 2010, 2014, and 2015. The results demonstrated a decreasing SPEI (87.5%) and VHI (57.4%) slope during July. The comparison between the SPEI and VHI was positive and significant on the seasonal scale (r = 0.56, p = 0.01). The regression analysis results showed that detrended crop yields agreed well with VHI (R2/r = 0.49/0.70, P < 0.01) and SPEI (R2/r = 0.34/0.58, P < 0.05). Hence, the findings of the present study illustrated the effectiveness and utility of the SPEI and VHI for agricultural drought evaluation, monitoring, and early warnings in the South Wollo administrative zone. [ABSTRACT FROM AUTHOR]

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland.

Understanding how plants adapt to shifting water availability is imperative for predicting ecosystem vulnerability to drought. However, the spatial–temporal dynamics of the plant–water relationship remain uncertain. In this study, we employed the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI4g), an updated version succeeding GIMMS NDVI3g spanning from 1982 to 2022. We integrated this dataset with the multiple scale Standardized Precipitation Evapotranspiration Index (SPEI 1 to 24) to investigate the spatial–temporal variability of sensitivity and lag in vegetation growth in response to water variability across China. Our findings indicate that over 83% of China’s vegetation demonstrates positive sensitivity to water availability, with approximately 66% exhibiting a shorter response lag (lag < 1 month). This relationship varies across aridity gradients and diverges among plant functional types. Over 66% of China’s vegetation displays increased sensitivity to water variability and 63% manifests a short response lag to water changes over the past 41 years. These outcomes significantly contribute to understanding vegetation dynamics in response to changing water conditions, implying a heightened susceptibility of vegetation to drought in a future warming world.

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change.

Droughts have a substantial impact on water supplies, agriculture, and ecosystems worldwide. Agricultural sustainability and production in the Mekong Delta of Vietnam are being jeopardized by droughts caused by climate change. Conventional forecasting methods frequently struggle to comprehend the intricate dynamics of meteorological occurrences connected to drought, necessitating the use of sophisticated prediction techniques. This study assesses the effectiveness of various statistical models (ARIMA), machine learning, and deep learning models (Gradient Boosting, XGBoost, RNN, and LSTM) in forecasting the SPEI over different time periods (1, 3, 6, and 12 months) across six prediction intervals. The models were developed and evaluated using data from 11 meteorological stations spanning from 1985 to 2022. These models incorporated various climatic variables, including precipitation, temperature, humidity, potential evapotranspiration (PET), Southern Oscillation Index (SOI) Anomaly, and sea surface temperature in the NINO4 region (SST_NINO4). The results demonstrate that XGBoost and LSTM models exhibit outstanding performance, showcasing lower error metrics and higher R² values compared to Gradient Boosting and RNN. The performance of the model fluctuated depending on the forecast step, with error metrics often increasing with longer prediction horizons. The use of climatic indices improved the accuracy of the model. These findings are consistent with earlier research on drought episodes in the Mekong Delta and support studies from other areas that show the effectiveness of advanced modeling tools for predicting droughts. The work emphasizes the capacity of machine learning and deep learning models to enhance the precision of drought forecasting, which is vital for efficient water resource management and agricultural planning in places prone to drought.

Accurate prediction of droughts is vital for effectively managing droughts, assessing drought risks and impacts, drought early warning systems, drought preparedness, and mitigation policies. This study integrated the extreme learning machines algorithm into the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) techniques to predict 1-month lead time meteorological and hydrological droughts. Standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) values were calculated as meteorological and hydrological drought indicators, respectively. The effects of the previous SRI and SPEI values were evaluated to estimate 3- and 12-month SRI-based hydrological droughts. The previous SPEI values were used to estimate SPEI values. The cross-correlation matrix and partial correlation function were used to determine the model input combinations. It is recommended to input delayed drought indices up to 3 months as input to the model for predicting droughts with a lead time of 1 month. The performance of the models was compared with statistical indicators such as coefficient of determination, mean square error, and mean absolute error, and scatter diagram and violin box plot. As a result of the analyses, it was determined that decomposition techniques improved the drought prediction accuracy of the extreme learning machines (ELM) model. The highest prediction performance (R2: 0.926, MSE: 0.084, and MAE: 0.229) was achieved in the prediction of SRI12 (t + 1) values using the VMD-ELM hybrid approach with the input combination of SRI12 (t) and SRI12 (t − 1) values at the Hinis station. In addition, it has been revealed that the VMD decomposition method provides more successful separation than CEEMDAN in both SRI and SPEI estimations. The study outputs are essential in managing water resources, hydroelectric energy production, sizing of water structures, and transboundary waters. [ABSTRACT FROM AUTHOR]

With the changing trend of global temperature increase, the occurrence of drought events can no longer be ignored. An investigation of the spatiotemporal evolution characteristics of meteorological droughts and their responses to climatic variables can elucidate the mechanisms of the water cycle evolution in changing environments. In the present study, the standardized precipitation evapotranspiration index was used to characterize meteorological drought in the North China Plain (NCP) from 1970 to 2020. The spatiotemporal patterns of meteorological drought were analysed, and the contributions of climatic factors on annual and seasonal time scale drought evolution were determined. The results revealed that meteorological drought showed a decreasing trend (−0.07/10a) in the NCP from 1970 to 2020 and the most severe meteorological drought occurred in winter, with 56.56% of the region experiencing an increased drought trend in this season. Significant trends were identified for mean temperature (Tmean), temperature difference, and wind speed in the drought trend evolution. Precipitation (P) and Tmean were the primary controlling factors of the drought evolution. The strongest effect on drought change were exerted by P in spring (contribution [Con]: 1.57%), summer (Con: 1.79%) and autumn (Con: 2.43%), and the negative contribution of Tmean in winter (Con: −3.64%) was greater than the positive contribution of P (Con: 2.69%). The contribution of climatic variables to drought were increased by regional altitude difference. The results reveal a novel explanatory mechanism of climate change's effects on drought. [ABSTRACT FROM AUTHOR]

Quantifying variation in precipitation and drought in the context of a changing climate is important to assess climate-induced changes and propose feasible mitigation strategies, particularly in agrarian economies. This study investigates the main characteristics and historical drought trend for the period 1980–2016 using the Standard Precipitation Index (SPI), Standard Precipitation Evaporation Index (SPEI), Run Theory and Mann–Kendall Trend Test at seven stations across the Upper Draa Basin. The results indicate that rainfall has the largest magnitude over the M'semrir and Agouim (>218 mm/pa) and the lowest in the Agouilal, Mansour Eddahbi Dam, and Assaka subregions (104 mm–134 mm/pa). The annual rainfall exhibited high variability with a coefficient of variation between 35−57% and was positively related to altitude with a correlation coefficient of 0.86. However, no significant annual rainfall trend was detected for all stations. The drought analysis results showed severe drought in 1981–1984, 2000–2001, and 2013–2014, with 2001 being the driest year during the study period and over 75% of both SPEI and SPI values returned drought. Conversely, wet years were experienced in 1988–1990 and 2007–2010, with 1989 being the wettest year. The drought frequency was low (<19%) across all the timescales considered for both SPI and SPEI, with Mansour Eddahbi Dam and Assaka recording the highest frequencies for SPI-3 and SPEI-3, respectively. [ABSTRACT FROM AUTHOR]

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Drought assessment is necessary for creating adaptation and resilience measures for the livelihoods of the affected communities. This study assessed drought trends in Kenya's Upper Ewaso Ng'iro River Basin (UENB) from 1981 to 2020. A Standardized Precipitation Index (SPI), a precipitation-based index, and a Standardized Precipitation Evapotranspiration Index (SPEI), a multivariate index that considers the difference between precipitation and potential evapotranspiration (PET), were used to evaluate drought severity and frequency over varying timescales. Monthly rainfall and temperature data for 10 stations within the basin were analyzed to calculate the SPI and SPEI time series values for 3, 6, and 12 months. The results demonstrate an alarming increase in the severity and frequency of drought events in the UENB since 1999. Additionally, the study reveals that the SPI and SPEI indices differ in identifying temporal and spatial drought characteristics, with longer timescales showing improved accuracy. Notably, the SPEI identifies more extensive and severe drought periods in the region compared to the SPI. The research findings are crucial in guiding policy decisions related to SDGs as they provide valuable information on drought trends necessary for implementing effective drought adaptation and resilience measures and promoting sustainable development in the UENB. HIGHLIGHTS The study describes the temporal and spatial evolution characteristics of meteorological drought in the region.; There has been an increase in drought frequency in the basin over the 40 years, and it has significantly increased since 2000.; The study shows the relationship between the SPI and SPEI in identifying drought events and that the SPEI performed better than the SPI in identifying drought.;

The increasing frequency of global drought events poses a significant threat to the stability of grassland ecosystems' functionality. The Inner Mongolian grasslands stand out as one of the world's most drought-prone regions, facing elevated drought risks compared to other biomes. An in-depth comprehension of the impact of drought on grassland ecosystems is paramount for their long-term sustainability. Using the Standardized Precipitation Evapotranspiration Index (SPEI) from 1982 to 2018, this study identified various drought events within the Inner Mongolian grasslands, encompassing moderate drought, severe drought, and extreme drought. The resistance of the vegetation to the different drought conditions, assessed through net primary productivity (NPP) as a metric (reflecting its capacity to maintain its original level during drought periods), was examined. The research findings indicated that the period from 2001 to 2018 witnessed a substantial increase in both the frequency and the extent of drought events compared to the period from 1982 to 2000, particularly concerning severe and extreme droughts. The areas most severely impacted by extreme drought were the Xilingol League and the Alxa League. From 1982–2000 to 2001–2018, under moderate drought conditions, vegetation resistance exhibited a minor decrease in the central and eastern regions but experienced a slight increase in the western region. In contrast, under severe drought conditions, the western region saw a significant decrease in vegetation resistance. Remarkably, under extreme drought conditions, the western region showed a substantial increase in vegetation resistance, while the central and eastern regions experienced a slight decrease. Across all three drought conditions, as precipitation levels declined, the resistance of the meadow–steppe–desert ecosystems demonstrated a high–low–high distribution pattern. The temperate desert steppe exhibited a minimal vulnerability to drought, boasting resistance levels exceeding 0.9. Notably, extreme drought had the most pronounced impact on the temperate meadow steppe, temperate steppe, and temperate desert steppe, particularly within the temperate meadow steppe category. Given these findings, the authorities responsible for grassland management should prioritize regions characterized by frequent drought occurrences and low drought resistance, such as Ulanqab City, the Xilingol League, and the western part of Hulun Buir City. Safeguarding steppe ecosystems is of paramount importance for stabilizing vegetation productivity and land carbon sinks, especially under the anticipated exacerbation of climate conditions in the future. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

According to the Standardized Precipitation Evapotranspiration Index (SPEI), most extensive summer droughts in European Russia south of 55° N in 1950–2021 were observed during the extremely negative phases of the Eastern Atlantic/Western Russia (EAWR) and West Pacific (WP) atmospheric circulation patterns characterized by abnormal high atmospheric pressure and an increased frequency of the number of days with atmospheric blocking over European Russia. It is shown that the frequency of droughts in the study area in the years of the negative phases of both circulation indices and their extremes in the summer months as compared to other years was higher by five droughts per decade in the Volga and Central Chernozem regions and by three droughts per decade in the northwestern Caspian region. A statistically significant correlation was found between the EAWR in summer and the multidecadal variability of sea surface temperature in the North Atlantic. It was revealed that the increase in the drought frequency in the study area in recent decades has been caused by the restructuring of atmospheric circulation in the Euro-Atlantic sector accompanying the transition of the Atlantic Multidecadal Oscillation (AMO) to a positive phase. An increase in the stability of the EAWR and WP atmospheric circulation patterns was observed, as well as a related significant increase in the frequency of extensive droughts in the study area in 2010–2021. They were accompanied by the weakening of zonal atmospheric circulation in the Northern Hemisphere, the combination of the positive AMO phase and the effects of anthropogenic warming on the atmospheric circulation in the Northern Hemisphere, including those associated with blocking events. [ABSTRACT FROM AUTHOR]

In the conditions of rising air temperature and changing precipitation regimes in Central Europe and Slovakia over the last two decades, it is necessary to analyse drought, develop high-quality tools for drought detection, and understand its reactions to the emerging drought situation. One of the frequently used meteorological drought indices is the Standardized Precipitation and Evapotranspiration Index (SPEI). Several parameters can be modified in different steps of the calculation process of SPEI. In the article, we analyse the influence of selected adjustable parameters on the index results. Our research has shown that the choice of a statistical distribution (Log-logistic, Pearson III, or Generalized Extreme Value) for fitting water balance can affect the feasibility of calculating distribution parameters (and thus the index) from the provided input data, as well as lead to either underestimation or overestimation of the index. The normality test of SPEI can be used as a tool for the detection and elimination of highly skewed indices and cases when the indices were not well determined by the distribution function. This study demonstrated improved results when using the GEV distribution, despite the common use of the Log-logistic distribution. With the Pearson III distribution, unusually high or low SPEI values (|SPEI| > 6) were detected. [ABSTRACT FROM AUTHOR]

Drought is quantified with one or a set of drought indices for monitoring and risk management. These indices have a limited ability to capture drought impacts. Drought impact prediction models have been developed to explore the interactions between the drought impact data and the physical drought indices. This study demonstrates the use of extreme gradient boosting (XGB), a well-known machine learning technique, to predict the likelihood of impact occurrence (LIO) of drought on public water supply as a function of drought indices, with high accuracy and low uncertainty. Using text-based drought impact data from multiple sources, the prediction accuracy of drought LIO on the public water supply of South Korea was evaluated using XGB and reference models (log-logistic, support vector machine, and random forest). We also analyzed receiver operating characteristics and quantified the uncertainty of each model with bootstrapping. This study shows that XGB and random forest have a high level of suitability. However, random forest presents a higher level of uncertainty than XGB for predicting drought LIO on the public water supply in South Korea. Although some limitations exist, the results suggest that text-based drought impact data collected from multiple sources can provide insightful information for drought risk management. HIGHLIGHTS SPEI was used to model the likelihood of drought impact on public water supply.; The South Korean drought impact inventory was constructed using text-based data.; XGB showed the best predictive performance with high accuracy and low uncertainty.;

Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km2 to 49.5 km2 in Umhlabuyalingana; 54 km2 to 1.6 km2 in Joe Morolong; and 254.6 km2 to 7.4 km2 in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas.

Against the backdrop of global climate change, the frequency of drought events is increasing, leading to significant impacts on human society and development. Therefore, it is crucial to study the propagation patterns and trends of drought characteristics over a long timescale. The main objective of this study is to delineate the dynamics of drought characteristics by examining their propagation patterns in China from 1951 to 2020. In this study, precipitation data from meteorological stations across mainland China were used. A comprehensive dataset consisting of 700 stations over the past 70 years was collected and analyzed. To ensure data accuracy, the GPCC (the Global Precipitation Climatology Center) database was employed for data correction and gap-filling. Long-term drought evolution was assessed using both the SPI-12 (standardized precipitation index) and SPEI-12 (standardized precipitation evapotranspiration index) to detect drought characteristics. Two Moran indices were applied to identify propagation patterns, and the MK (the Mann–Kendall) analysis method, along with the Theil–Sen slope estimator, was utilized to track historical trends of these indices. The findings of this study reveal the following key results: (i) Based on the SPI-12, the main areas of China that are prone to drought are mostly concentrated around the Hu Huanyong Line, indicating a tendency towards drying based on the decadal change analysis. (ii) The distribution of drought-prone areas in China, as indicated by the SPEI-12, is extensive and widely distributed, with a correlation to urbanization and population density. These drought-prone areas are gradually expanding. (iii) Between 2010 and 2011, China experienced the most severe drought event in nearly 70 years, affecting nearly 50% of the country's area with a high degree of severity. This event may be attributed to atmospheric circulation variability, exacerbated by the impact of urbanization on precipitation and drought. (iv) The frequency of drought occurrence in China gradually decreases from south to north, with the northeast and northern regions being less affected. However, areas with less frequent droughts experience longer and more severe drought durations. In conclusion, this study provides valuable insights into the characteristics and propagation patterns of drought in China, offering essential information for the development of effective strategies to mitigate the impacts of drought events. [ABSTRACT FROM AUTHOR]

Karst basins have a relatively low capacity for water retention, rendering them very vulnerable to drought hazards. However, karst geo-climatic features are highly spatially heterogeneous, making reliable drought assessment challenging. To account for geo-climatic heterogeneous features and to enhance the reliability of drought assessment, a framework methodology is proposed. Firstly, based on the history of climate (1963–2019) from the Global Climate Model (GCM) and station observations within the Chengbi River karst basin, a multi-station calibration-based automated statistical downscaling (ASD) model is developed, and the Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) are selected as performance metrics. After that, future climate (2023–2100) under three GCM scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are obtained by using the ASD model. Finally, the Standardized Precipitation Evapotranspiration Index (SPEI), calculated by future climate is applied to assess drought conditions. The results indicate that the multi-station calibration-based ASD model has good performance and thus can be used for climate data downscaling in karst areas. Precipitation mainly shows a significant upward trend under all scenarios with the maximum variation (128.22%), while the temperature shows a slow upward trend with the maximum variation (3.44%). The drought condition in the 2040s is still relatively severe. In the 2060s and 2080s, the basin is wetter compared with the historical period. The percentage of drought duration decreases in most areas from the 2040s to the 2080s, demonstrating that the future drought condition is alleviated. From the SSP1-2.6 scenario to the SSP5-8.5 scenario, the trend of drought may also increase. [ABSTRACT FROM AUTHOR]

Drought is one of the most destructive natural disasters in China and can cause serious environmental and socio-economic impacts. Based on monthly precipitation and temperature data from 571 meteorological stations, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated on a 12-month scale (SPEI-12) from 1985 to 2018 in mainland China, and it was compared with the records of the Bulletin of Flood and Drought Disasters in China to verify its drought monitoring accuracy. Then, run theory was used to explore in depth the spatiotemporal distribution of drought characteristics and trends in various climatic sub-regions. The results showed that: (1) the comparison with the Bulletin of Flood and Drought Disasters in China indicated that the results of SPEI-12 monitoring drought had high accuracy and could identify drought events in mainland China. (2) About 70% of the regions in mainland China experienced droughts more than 50 times, and about 43% of the regions had drought durations of between 100 and 110 months. Nearly 11% of the regions had drought severities of more than 130, which were mainly located in the Northwest Desert (NWC), the southern part of Northeast China (NEC), and the western part of North China (NC). (3) In the past 34 years, the droughts in the Northwest Desert (NWC), the western part of Inner Mongolia (IM), and the Qinghai-Tibet Plateau (TP) showed an aridity trend, while the droughts in South China (SC) and eastern Central and South China (CSC) presented a mitigation tendency. (4) Specifically, the droughts in South China (SC) were more influenced by precipitation, while those in the Northwest Desert (NWC) and Northeast China (NEC) were influenced by temperature and potential evapotranspiration, and the evolution of drought in North China (NC) and the Qinghai-Tibet Plateau (TP) was mainly influenced by soil moisture. The study could provide scientific guidance and a reference for drought response and sustainable development in China. [ABSTRACT FROM AUTHOR]

Mastering the pattern of food loss caused by droughts and floods aids in planning the layout of agricultural production, determining the scale of drought and flood control projects, and reducing food loss. The Standardized Precipitation Evapotranspiration Index is calculated using monthly meteorological data from 1984 to 2020 in Shandong Province of China and is used to identify the province's drought and flood characteristics. Then, food losses due to droughts and floods are estimated separately from disaster loss data. Finally, the relationship between drought/flood-related factors and food losses is quantified using methods such as the Pearson correlation coefficient and linear regression. The results show that: 1) there is a trend of aridity in Shandong Province, and the drought characteristic variables are increasing yearly while flood duration and severity are decreasing. 2) The food losses caused by droughts in Shandong Province are more than those caused by floods, and the area where droughts and floods occur frequently is located in Linyi City. 3) The impact of precipitation on food loss due to drought/flood is significant, followed by potential evapotranspiration and temperature. 4) The relationship between drought and flood conditions and food losses can be precisely quantified. The accumulated drought duration of one month led to 1.939 × 104 t of grain loss, and an increase in cumulative flood duration of one month resulted in 1.134 × 104 t of grain loss. If the cumulative drought severity and average drought peak increased by one unit, food loss due to drought will increase by 1.562 × 104 t and 1.511 × 106 t, respectively. If the cumulative flood severity and average flood peak increase by one unit, food loss will increase by 8.470 × 103 t and 1.034 × 106 t, respectively. [ABSTRACT FROM AUTHOR]

Drought, one of the main factors threatening social life today, is examined and analyzed by its types such as meteorological, agricultural, and hydrological droughts. Thus, decision-makers need advanced methods in monitoring and assessing the drought, which is important for future plans. Multivariate drought indices were developed to allow determining the actual and real level of drought by reducing the deficiencies of current methods. In a region having three different characteristics (BSk: semiarid cold, Csa: dry summer–hot summer, and H: unclassified highland), MSDI was modeled by utilizing the data from Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Surface Runoff Index (SRI). For the period between 2003 and 2021, the precipitation, evapotranspiration, and surface runoff data were obtained from SPEI Global Drought Monitor and ERA5 databases. Gaussian function was used in establishing the copula-based joint distribution functions according to AIC, BIC, and max-likelihood assessment criteria. The R packages offering a wide range of use for drought modeling and assessment based on the multivariate drought indices were used. The calculations performed for 4 different time scales as MSDI 1-3-6-12 (soil moisture, surface hydrology, and agricultural and hydrological perspectives) showed acceptable performance in multivariate estimation of the drought. It was determined that, for all four time scales (1, 3, 6, and 12 months), MSDI values obtained from modeling were more similar to SPEI values in comparison with SRI values. Considering all the data, it was determined that the years 2007 and 2016 were found to be the driest years for the basin on the 6-month scale, whereas the years 2016 and 2021 were the driest years on the 12-month scale. [ABSTRACT FROM AUTHOR]

Precipitation plays a dominant role in regulating terrestrial carbon fluxes. In concert with global warming, aridity has been increasing during recent decades in most parts of the world. How global terrestrial carbon fluxes respond to this change, however, is still unclear. Using a remote-sensing-driven, process-based model, the Boreal Ecosystem Productivity Simulator (BEPS), this study investigated the responses of global terrestrial carbon fluxes to meteorological drought, which were characterized by the standardized precipitation evapotranspiration index (SPEI). The results showed that the response of terrestrial carbon fluxes to drought exhibited distinguishable spatial heterogeneity. In most regions, terrestrial carbon fluxes responded strongly to drought. With an increase in annual water balance (annual precipitation minus annual potential evapotranspiration), the response of carbon fluxes to drought became weaker. The lagged time of terrestrial carbon fluxes responding to drought decreased with the increasing strength of carbon fluxes in response to drought. The sensitivity of terrestrial carbon fluxes to drought also showed noticeable spatial heterogeneity. With an increase in annual water balance, the sensitivity first increased and then decreased. Terrestrial carbon fluxes exhibited the highest sensitivity to drought in semi-arid areas. [ABSTRACT FROM AUTHOR]

The current study proposes a new framework for quantifying and monitoring the severity of meteorological drought. The proposed framework consists of three phases. The first phase of the framework uses K-component Gaussian Mixture Distribution (GMD) in the computation. The second phase is mainly based on the dissimilarity matrix-based clustering using C-index and Monte Carlo Feature-based Selection (MCFS) method. The third phase uses the Markov chain, transition probabilities and a non-homogeneous Poisson process under the Bayesian estimation. The Relative Importance (RI) values are used to choose appropriate stations. The Deviance Information Criteria (DIC) is used to check model suitability, and Root Mean Square Error (RMSE) is utilized for determining model performance. The proposed framework is validated to the 52 meteorological stations in Pakistan for 49 years from 1968 to 2016. Moreover, the outcomes of the current analysis provide insight to quantify and monitor meteorological drought comprehensively and accurately.

The occurrence of compound drought and hot events has been shown to cause stronger socio-economic, environmental and health impacts than the isolated events. Moreover, the frequency of these compound events has increased unevenly throughout the world and is expected to keep increasing in several regions. In this work, an assessment of compound drought and hot events in the summer months in Australia was made, using copula functions. Drought and hot conditions were identified by the Standardized Precipitation Evapotranspiration Index (SPEI) and the indices Number of Hot Days (NHD) and Number of Hot Nights (NHN) for the summer months, respectively. We analysed drought conditions in the current and the previous 1 to 3 months and the periods 1950–1978 and 1979–2020. The results show that the conditional probability of the occurrence of hot events given drought conditions is very high for the concurrent month in most of the study area, reaching 0.9 in some cases. Considering previous drought conditions, the higher probabilities are obtained in the southeastern region in December and in the north in February but, in most of the study area, these values are higher than for the case of non-drought conditions, pointing to an effect of previous drought conditions on hot events of up to 3 months. Moreover, an increased frequency of compound drought and hot events from the first to the second period was identified in more than half of the study area for lags of 1 and 2 months. We show that, although the conditional probabilities are mostly higher when computed with NHD, NHN is also affected by drought conditions, and should also be considered in this analysis, since nights can have a relieving contribution when impacts in health and wildfires are being analysed.

Quantifying variation in precipitation and drought in the context of a changing climate is important to assess climate-induced changes and propose feasible mitigation strategies, particularly in agrarian economies. This study investigates the main characteristics and historical drought trend for the period 1980–2016 using the Standard Precipitation Index (SPI), Standard Precipitation Evaporation Index (SPEI), Run Theory and Mann–Kendall Trend Test at seven stations across the Upper Draa Basin. The results indicate that rainfall has the largest magnitude over the M’semrir and Agouim (>218 mm/pa) and the lowest in the Agouilal, Mansour Eddahbi Dam, and Assaka subregions (104 mm–134 mm/pa). The annual rainfall exhibited high variability with a coefficient of variation between 35−57% and was positively related to altitude with a correlation coefficient of 0.86. However, no significant annual rainfall trend was detected for all stations. The drought analysis results showed severe drought in 1981–1984, 2000–2001, and 2013–2014, with 2001 being the driest year during the study period and over 75% of both SPEI and SPI values returned drought. Conversely, wet years were experienced in 1988–1990 and 2007–2010, with 1989 being the wettest year. The drought frequency was low (

【Objective】 Global warming is expected to increase the frequency of extreme weathers such as flooding and drought. Understanding their spatiotemporal variation is important to help mitigate its detrimental impact. The purpose of this paper is to analyze the spatiotemporal variation of drought in Inner Mongolia of China. 【Method】 The analysis was based on meteorological data measured from 1951 to 2018 from 50 weather stations across the region. The occurrence and severity of the drought was quantified using the standardized precipitation evapotranspiration index (SPEI). 【Result】 An abrupt climate change occurred around 1979. Since then, most areas in the region have seen an ease of drought stress. Since 1996, however, western Inner Mongolian have seen a return of the drought. Spatially, severe droughts occurred mainly in central-western Inner Mongolia, and southern Chifeng and Tongliao. 【Conclusion】 SPEI can be used as an index to quantify drought at provincial scale. Its application to Inner Mongolia reveals that the province has undergone alternate drought and humidified climate since 1951. Drought occurred more frequently and severely in the central-western province than in other areas.

Rainfed agriculture ensures global food security. However, periodic droughts and floods endanger rainfed crop productivity. This work aims to identify drought and flood risks in rainfed agriculture using conditional probabilities and to reduce food losses caused by drought and floods. The standardized precipitation evapotranspiration index is produced from 1996 to 2020 using meteorological data from the three northeastern provinces and is used to establish four time-scale drought indicators. The Pearson correlation coefficient is then used to identify drought indicators related to rainfed crop production. The Copula-Bayesian conditional probabilities were used to calculate the probability of yield reduction of rainfed crops when drought indicators indicate varied drought and flood conditions. The results show that: (1) Droughts from May to July and floods from January to March had the most significant impact on rainfed crops in the three northeastern provinces. (2) The cities of Huludao, Changchun, Baishan, Baicheng, Yanbian, Hegang, and Jiamusi face a more significant danger of drought in rainfed crops. In contrast, Yichun and Suihua face a higher risk of flooding in rainfed crops. (3) When drought monitoring indicators decreased by one unit, rainfed crop output declined the most in Fuxin city; when the drought indicator increased by one unit, rainfed crop yield decreased the most in Liaoyuan city. The study’s findings can provide quantitative direction for food security work in China’s three northeastern regions.