Standardized Precipitation Evapotranspiration Index Research Articles

Variations in water use efficiency and carbon and nitrogen concentrations in red heart Chinese fir.

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.

Concurrent drought threatens wheat and maize production and will widen crop yield gaps in the future

CONTEXTDrought poses a significant threat to global crop production. As the global community grapples with the escalating challenges of climate change, understanding the multifaceted impacts of concurrent drought on food security becomes imperative. OBJECTIVEThis study delved into the response of wheat and maize, key staples in the global food system, to different types of drought, with a particular focus on the yield gaps resulting from concurrent meteorological and agricultural drought. METHODSThe DSSAT-CERES model was adopted to simulate phenophase, rain-fed, and potential yields of maize and wheat in China from 1962 to 2100. Meteorological (Non-stationary Standard Precipitation Evapotranspiration Index, NSPEI) and agricultural (Standard Soil Moisture Index, SSMI) drought indices were calculated from crop seeding to maturity stages. We employed bivariate and multiple cross-wavelet as well as vine Copula to qualitatively and quantitatively analyze the response of yield gaps to different drought types. Finally, we determined the relative dependence weights of maize and wheat on NSPEI and SSMI by least squares regression. RESULTS AND CONCLUSIONSSpanning from 2022 to 2100, a trend of shortened growth periods for these crops were detected, accompanied by increasingly drier conditions. These situations exacerbated the crops' vulnerability to concurrent drought, leading to considerable yield reductions. Our projections indicated that future yield gaps due to concurrent drought could be, on average, 2–30% higher than those caused by single-type drought. Concurrent drought affected wheat (5–50%) more severely than maize (0–35%). Western regions would be more affected than the Eastern regions. Under the SSP (Shared socioeconomic pathway) 5-8.5 scenario in 2022–2100, all four crops would have higher dependence weights on SSMI (51–99%) than NSPEI (26–59%), emphasizing the critical role of soil moisture in agricultural drought monitoring and yield loss alleviation. SIGNIFICANCEOur findings highlight the urgent need for integrated drought management strategies that address the compounded risks of concurrent drought, thereby contributing to the resilience of agricultural systems and global food security in a changing climate. Our research proposes to consider the relative weights of meteorological and agricultural drought in the future development of composite drought monitoring indicators for addressing food drought risk under climate change.

Evaluation of the performance of meteorological drought indices in Morocco: A case study of different climatic zones

AbstractUnderstanding drought occurrence and evolution is important in minimizing the impacts associated with it. This work assesses the performance of 10 commonly used meteorological indices to measure drought in Morocco. The studied indices are Deciles Index (DI), Percent of Normal Index (PNI), Z‐Score Index (ZSI), China‐Z Index (CZI), Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), its self‐calibrated variant (scPDSI) and Palmer Z Index (PZI). Rainfall and temperature gridded data is sourced from PERSIANN‐CDR and ERA5, respectively, for the period 1983–2021. The study area exhibits three main climatic regimes; subhumid, semi‐arid and arid, with a drying and warming climate, as depicted by the rainfall and temperature trends analysis. Results show that most rainfall‐based indices perform relatively poorly in drought monitoring in the study area. DI and PNI appear to be inconsistent and abnormally responsive to rainfall. RAI reports droughts 56.5% more frequently and slightly underestimate drought intensity compared to other indices. Similarly, ZSI and CZI largely underestimate drought intensity. PDSI and scPDSI are computationally demanding, often underestimate drought intensity and overestimate drought duration by at least 115% compared to SPI and SPEI. Conversely, PZI can be used for drought onset detection as it reported droughts early compared to the other indices. SPI and SPEI perform overall better regarding their consistent drought identification and severity assessment. However, SPEI is found to be more suitable than SPI in the arid and semi‐arid regions and performed better considering the warming climate of the country.

Comparative analysis of SPI, SPEI, and RDI indices for assessing spatio-temporal variation of drought in Türkiye

AbstractThis research presents a comprehensive drought analysis using climate data obtained from 219 homogeneously distributed meteorological stations in Türkiye between 1991 and 2022. In this context, Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) drought indices were used and comparative analysis was made. Türkiye. The study demonstrates that below-normal precipitation over extended periods and increasing temperatures have contributed to the increased frequency of meteorological drought events. Türkiye's topographic conditions, particularly its location in the Mediterranean basin, significantly influence drought occurrences. It is noted that over the past 20 years, Türkiye has been trending towards drier conditions, with rising temperatures reinforcing this trend. The study observes that the moderate drought class range is the most frequently recurring in the SPI, SPEI, and RDI methods utilized. Regarding atmospheric conditions affecting the climate in Türkiye, it is observed that increased drought severity stands out prominently in years when the North Atlantic Oscillation is positive. During these years, increased drought severity is evident in the SPI, SPEI, and RDI indices, particularly in winter and autumn, while a wide area experiences drought effects in the summer months. Long-term analyses emphasize that drought periods occur less frequently but have more prolonged impacts, attributed to variations in precipitation patterns from year to year and the influence of rising temperatures due to global climate change. The potential future increase in drought in the Mediterranean basin due to global climate change and Türkiye's vulnerability to this situation could have adverse effects on water resources, food security, energy sources, and ecosystems.

Drought evaluation using various evapotranspiration models over semi-arid river basins

ABSTRACT Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman–Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000–2012. The performance of statistical indicators (Nash–Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman–Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.

Comprehensive evaluation of the response relationship between meteorological drought and hydrological drought in the Yalong River Basin, China

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.

Investigating the spatial propagation patterns of meteorological drought events and underlying mechanisms using complex network theory: A case study of the Yangtze River Basin, China

The spatial propagation patterns of meteorological drought events (MDEs) and underlying mechanisms contribute to elucidating and forecasting drought evolution. In this study, gridded MDEs in the Yangtze River Basin (YRB) throughout the entire year, wet season and dry season were extracted from 3-month Standardized Precipitation Evapotranspiration Index (SPEI-3) series. Event synchronization (ES) and complex networks (CN) were employed to construct the MDE synchronization networks and MDE spatial propagation networks for various periods. The former were utilized to identify MDE synchronized subregions where MDEs co-occur and co-evolve in the YRB, while the latter were used to quantify the MDE spatial propagation patterns over both the basin and its subregions. The driving mechanisms behind MDE spatial propagation were further investigated by diagnosing the concomitant drought-inducing climate systems. The findings reveal the presence of four MDE synchronized subregions during the wet season and five subregions during the entire year and dry season. These subregions exhibited distinct spatial propagation patterns of MDEs, aligning with overall findings across the YRB. Notable differences were observed between wet and dry seasons, with various subregions exhibiting distinctive spatial propagation patterns during each season. These patterns are driven by variations in the controlling atmospheric circulation systems, leading to anomalies of wind patterns and moisture distribution, ultimately resulting in deficient moisture supply. The variations of tropical sea surface thermal conditions, influences of the Tibetan Plateau and MDE self-propagation triggered by land–atmosphere feedback are considered as three primary influencing factors for MDE spatial propagation in the YRB.

Prediction of hydrological drought by the Standardized Precipitation Evapotranspiration Index in Chihuahua, Mexico, using machine learning algorithms

Despite being very common in the territory of Chihuahua, Chihuahua, Mexico, to experience drought, its consequences continue to severely impact the population without prior warning. Machine learning has proven to have a significant capacity for predicting time series, and the Standardized Precipitation Evapotranspiration Index (SPEI) is emerging as the most accurate drought indicator. In this study, predictive models were developed using Artificial Neural Networks (ANN), Long-Short Term Memory (LSTM), and Support Vector Regression (SVR) for estimating SPEI. Temporal scales of 12 months (SPEI 12) and 24 months (SPEI 24) for the period 1901-2020 in the mentioned territory were considered. This was done in order to simulate the behavior of drought cycles and enhance the ability to anticipate consequences. The accuracy indices used to evaluate the models were the mean squared error (MSE), mean absolute error (MAE), mean bias error (MBE), coefficient of determination (R2), and Kendall coefficient. In total, 956 experiments were conducted using the three methods, varying parameters such as the number of neurons, kernel, and polynomial degree. The two best models for each method were selected, and the average results revealed MSE = 0.0051, MAE = 0.0537, MBE = 0.0218, R2 = 0.8495, and Kendall coefficient = 0.7592 for SPEI 12; and MSE = 0.0024, MAE = 0.0375, MBE = 0.0162, R2 = 0.9218, and Kendall coefficient = 0.8558 for SPEI 24.

The Interannual Variability of Global Burned Area Is Mostly Explained by Climatic Drivers

AbstractBetter understanding how fires respond to climate variability is an issue of current interest in light of ongoing climate change. However, evaluating the global‐scale temporal variability of fires in response to climate presents a challenge due to the intricate processes at play and the limitation of fire data. Here, we investigate the links between year‐to‐year variability of burned area (BA) and climate using BA data, the Fire Weather Index (FWI), and the Standardized Precipitation Evapotranspiration Index (SPEI) from 2001 to 2021 at ecoregion scales. Our results reveal complex spatial patterns in the dependence of BA variability on antecedent and concurrent weather conditions, highlighting where BA is mostly influenced by either FWI or SPEI and where the combined effect of both indicators must be considered. Our findings indicate that same‐season weather conditions have a more pronounced relationship with BA across various ecoregions, particularly in climatologically wetter areas. Additionally, we note that BA is also significantly associated with periods of antecedent wetness and coolness, with this association being especially evident in more arid ecoregions. About 60% of the interannual variations in BA can be explained by climatic variability in a large fraction (∼77%) of the world's burnable regions.

Catchment-scale assessment of drought impact on environmental flow in the Indus Basin, Pakistan

Abstract. The impact of drought on environmental flow (EF) in 27 catchments of the Indus Basin is studied from 1980–2018 using indicators of hydrologic alterations (IHAs). The standardized precipitation evapotranspiration index (SPEI) was systematically propagated from one catchment to another using principal component analysis (PCA). Threshold regression is used to determine the severity of drought (scenario 1, drought severity that causes low flows) and the month (scenario 2, months where drought has resulted in low flows) that trigger low flows in the Indus Basin. The impact of drought on low EFs is quantified using range of variability analysis (RVA), which is an integrated component of the IHA used to study the hydrological alterations in environmental flow components (EFCs) by comparing the pre- and post-impact periods of human and/or climate interventions in EFCs. The hydrological alteration factor (HAF) is calculated for each catchment in the Indus Basin. The results show that most of the catchments were vulnerable to drought during the periods of 1984 to 1986, 1991/1992, 1997 to 2003, 2007 to 2008, 2012 to 2013, and 2017 to 2018. On a longer timescale (SPEI-12), drought is more severe in the lower Indus Basin (LIB) than in the upper Indus Basin (UIB). The IHA pointed out that drought significantly impacts the distribution of EFCs, particularly extremely low flow (ELF) and low flow (LF). The magnitude and frequency of the ELF and LF events increase as drought severity increases. The threshold regression provided useful insights, indicating that moderate drought can trigger ELF and LF at shorter timescales (SPEI-1 and SPEI-6) in the UIB and middle Indus Basin (MIB). Conversely, severe and extreme droughts trigger ELF and LF at longer timescales (SPEI-12) in the LIB. The threshold regression also divided the entire study period (1980–2018) into different time periods (scenario 2), which is useful for quantifying the impact of drought on low EFs using the SPEI coefficient. Higher SPEI coefficients are observed in the LIB, indicating high alterations in EF due to drought. HAF showed high alterations in EF in most of the catchments throughout the year except in August and September. Overall, this study provided useful insights for analysing the effects of drought on EF, especially during low flows.

Extreme weather characteristics and influences on urban ecosystem services in Wuhan Urban Agglomeration

In recent years, there has been a pronounced increase in the frequency of extreme weather events. To comprehensively examine the impact of extreme weather on ecosystem services within the Wuhan Urban Agglomeration (WUA), this study utilized meteorological station data, the Mann-Kendall (MK) test, and the Standardized Precipitation-Evapotranspiration Index (SPEI) to quantify the variation trends in heatwaves (HW) and droughts from 1961 to 2020. Then the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was employed to evaluate and compare the differences in water yield and climate regulation ecosystem services under various HW, droughts, and HW-drought combination scenarios. The results show that over the past 60 years, the temperature, duration, and frequency of HW have significantly increased in the WUA. Specifically, the highest HW temperature, total HW days, HW frequency, and average HW temperature showed changing trend of +0.17 ℃/decade, +1.4 day/decade, +0.19 event/decade, and +0.07 ℃/decade, respectively. The year 2000 was identified as a mutation year for HW, characterized by increased frequency and heightened severity thereafter. The SPEI value exhibited an insignificant upward trend, with 1980 marked as a mutation year, indicating a decreasing trend in drought occurrences after 1980. Heatwaves have a weakening effect on both water yield and climate regulation services, while drought significantly weakened water yield and had a relatively modest effect on climate regulation. During HW-drought composite period, the average monthly water yield showed a notable discrepancy of 60 mm compared to humid years. Besides, as heatwaves intensify, the area of low aggregation for ecosystem services expands, whereas the area of high aggregation decreases. This study provides a preliminary understanding of the impact of urban extreme weather on urban ecosystem services under changing climatic conditions.

Projection of meteorological drought in Türkiye’s Mediterranean region based on multi-model ensemble from CMIP6

ABSTRACT In this study, drought projections in Türkiye’s Mediterranean region under future climate scenarios (Shared Socioeconomic Pathways SSP2-4.5 and SSP5-8.5) were investigated using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). A multi-model ensemble (MME) was created with the General Circulation Models (GCMs) that best predicted observed precipitation and temperature values using Artificial Neural Network (ANN)-based statistical downscaling. The MME outputs assessed changes in drought indices for 2021–2060 and 2061–2100, compared to the baseline period of 1979–2020. Results indicate that future drought indices calculated by SPEI are more severe than those using SPI, highlighting the significance of potential evapotranspiration (PET) in drought monitoring. There is a notable increase in PET, especially in coastal regions, projected to rise to 120 mm/year by the century’s end under SSP5-8.5. Due to significant temperature increases, the expected rise in drought severity and frequency is more pronounced in mountainous areas.

SPATIOTEMPORAL VARIABILITY OF DROUGHT IN ROMANIA DURING 1901-2021 USING THE STANDARDIZED PRECIPITATION EVAPOTRANSPIRATION INDEX (SPEI)

Drought represents one of the most frequent climatic events with impact on the environment, economy and society. In this study, we analyse the drought characteristics in Romania during the period 1901-2021, using a meteorological drought index: the Standardized Precipitation Evapotranspiration Index (SPEI). To provide a clear image over time, we have divided the data into four time periods: 1901 – 1930, 1931 – 1960, 1960 – 1990, and 1991-2021. The results indicate that the period between 1901 to 1930 is characterized by moderate droughts, in contrast to the period between 1991-2021 when extreme droughts prevailed. Also, the SPEI3's spatial analysis indicates that Romania's eastern and southeastern parts are becoming drier. This study provides a clear image of climate change and indicates that the extreme drought events affected Romania in the previous decade.

Investigate the Spatiotemporal Evolution of Drought and Its Interaction with Atmospheric Circulation in the Yellow River Middle Basin

Global warming contributes to an increased frequency and severity of droughts. Drought emerges as a highly prevalent natural calamity, distinguished by its formidable disruptive impact and the capacity to trigger considerable economic setbacks. Understanding the spatiotemporal characteristics of droughts and clarifying the driving role of atmospheric circulation on droughts is vital for agricultural, hydrological, ecological, and socio-economic systems. Leveraging meteorological data from 36 stations in the middle reaches of the Yellow River Basin from 1961 to 2020, we employed the Standardized Precipitation Evapotranspiration Index (SPEI) to calculate drought occurrence. Concurrently, we explored the influence of atmospheric circulation on the SPEI. The findings of our study underscore a concerning trend of worsening drought conditions within the study area. We discovered a significant correlation between the duration and severity of drought (R = 0.83, p &lt; 0.001); longer durations often corresponded to higher levels of severity. Turning our attention to atmospheric dynamics, the Nino Eastern Pacific index (NE) emerged as a critical driver of SPEI dynamics (the contribution of NE to SPEI was 0.22), significantly impacting drought patterns. In conclusion, the study significantly contributes to our comprehension of the evolving drought patterns under the influence of global warming. The findings can provide valuable information for water resource management and drought disaster control.

Dynamic Variations of Agricultural Drought and Its Response to Meteorological Drought: A Drought Event‐Based Perspective

AbstractThe dynamic variations of agricultural drought can reflect the water shortage situation of the agricultural system, and there is a progressive relationship in the response of agricultural drought to meteorological drought on a spatiotemporal scale. In this study, the vegetation health index and the standardized precipitation evapotranspiration index were adopted as agricultural and meteorological drought indicators, respectively. Additionally, using the three‐dimensional spatiotemporal clustering technology, the dynamic evolutions of typical drought events were clarified, and the spatiotemporal response characteristics of agricultural drought to meteorological drought were revealed. The results indicated that: (a) there were 81 agricultural drought events in the North China Plain (NCP) during 1982–2020, with a largest drought severity (12.82 × 104 month km2), a 6‐month duration, and a 23.24 × 104 km2 affected area occurring in the No. 4 event; (b) from the 1980s to the 2010s, the agricultural drought gradually decreased and large‐scale droughts mainly concentrated in the border areas of Hebei, Shandong, and Henan; and (c) a total of 13 drought event pairs were successfully matched in the NCP, including 7 pairs of “one‐to‐one,” 4 pairs of “one‐to‐many,” 1 pair of “many‐to‐one,” and 1 pair of “many‐to‐many.” The spatiotemporal responses of agricultural drought were elucidated in a three‐dimensional perspective, which can propose a new approach for establishing drought propagation model, predicting future agricultural drought conditions, improving ecological environment quality, and can also be applied for the investigation of other drought types.

Radial Growth of Dahurian Larch (Larix gmelinii) Responses to Climate and Competition

The phenomenon of divergent responses in tree ring radial growth to climate change has been observed in the mid- and high-latitude regions of the Northern Hemisphere amidst global warming. However, the stability of the responses of the primary conifer species to climate factors in the mountainous regions of northeastern China remains unclear. Additionally, it is uncertain whether the radial growth in this area consistently responds to different competition indices. In our study, we developed tree ring width chronologies for Dahurian larch (Larix gmelinii) and analysed the radial growth responses to the regional climate from 1980 to 2012. This analysis was conducted by correlating meteorological data from different physiological stages of trees. We also evaluated the reliability of competition indices in predicting tree growth by constructing multiple linear regression models. Furthermore, we explored the relationship between the Basal Area Increment (BAI) of each tree and the competition indices in the sample plot over the previous five years. Our results showed that the temperature during the Non-Structural Carbohydrate (NSC) accumulation period and the NSC consumption period was significantly negatively correlated with the Ring Width Index (RWI) of Dahurian larch. Conversely, the RWI was significantly positively correlated with precipitation during these periods. The Standardised Precipitation Evapotranspiration Index (SPEI) at any period had a significantly positive correlation with the RWI. These findings suggest that future radial growth is likely to continue to be constrained by rising temperatures and water scarcity. Our findings also demonstrated that radial growth was less negatively impacted by competition from nearby trees and more influenced by the initial size of the tree. Compared to models that included other indices, models that included the BAL index as a single explanatory variable demonstrated superior statistical effectiveness. Our research suggests that before evaluating the competitive effects of the neighbourhood on radial growth, it is necessary to consider the stand diversity of tree species and the parameters related to spatial structure when selecting an appropriate competition index.

Decoding uranium variation over the Indian peninsula through leveraging standardized precipitation evapotranspiration indices and groundwater level fluctuations

The present study aims to predict the concentration of uranium (U) in groundwater in India by utilizing water quantity indicators, such as Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), and Gravity Recovery and Climate Experiment (GRACE) based groundwater levels. The study adopts a multi-scale approach, ranging from state-level to agroclimatic zones. The findings indicate that the highest levels of U (101–500 μg.L−1), which surpass the World Health Organization (WHO) prescribed limit of 30 μg.L−1 for drinking water, are primarily concentrated in India's plateau and hills regions. The GRACE data map portrays a downward trend in groundwater levels throughout India, with the mid-Gangetic plains experiencing the most significant decline. The meteorological aspect of the study, as indicated by SPI and SPEI, reveals that the plateau and hills region is experiencing a decline in rainfall. The SPEI further underscores the grim picture of decreasing precipitation in northern India. Additionally, the study employs cluster analysis to cluster states according to the division of agro-climatic zones. Lastly, the study employs a random forest algorithm to assess the relative importance of each predictor and predict U concentration under the trinity of precipitation, extraction, and evaporation. The most significant contribution of this work is to identify the hotspots in India that require the most attention in terms of U toxicity owing to groundwater decline. Overall, this study highlights the need for immediate attention to mitigate the adverse impacts of U contamination and aims at sensitizing the stakeholders towards the compelling need to fulfil SDG-3 (health aspects due to U hazard) and SDG-6 (groundwater over-exploitation and deteriorating water quality).

A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin

This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984–2009) and testing (2010–2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.

Unveiling the spatiotemporal impacts of the 2021 Central Asian drought on vegetation: A comprehensive quantitative analysis

In the growing season of 2021, a severe drought occurred in Central Asia impacting various local ecosystems and socio-economic conditions. However, there is limited research on the spatiotemporal characteristics of this drought and its effects on local vegetation. To address this gap, this study utilizes the standardized precipitation evapotranspiration index (SPEI) to quantitatively evaluate the spatiotemporal drought characteristics, and standardized anomalies to assess drought impacts on vegetation greenness and productivity. The drought-induced reduction in vegetation greenness and production was also decomposed into environmental drivers based on a random forest model. Our findings can be summarized as follows: During the 2021 growing season, Central Asia experienced one of the most severe drought events in the past 40 years, with approximately 42.57 % of the region facing record-breaking drought conditions. Abnormally low precipitation (PRE), prolonged high temperatures (TMP) and evapotranspiration also occurred during the drought event. The drought had a significant detrimental effect on vegetation, leading to an approximately 10 % reduction in vegetation greenness and around 13 % in productivity. Soil moisture (SM) was found to be the most critical factor influencing vegetation greenness loss during drought conditions. Leaf Area Index (LAI) and SM emerged as the primary drivers for the reduction of Gross Primary Productivity (GPP) and Solar-Induced Fluorescence (SIF). The antecedent environmental conditions had a significant impact on the decline in vegetation greenness and productivity during the 2021 drought event, accounting for approximately 30 %–35 % and 52–69 % of the respective losses. The findings of this study highlight the importance of taking into account antecedent climate factors when studying the impacts of drought on vegetation.

General dry trends according to the standardized precipitation evapotranspiration index in mainland Chile

Droughts are one of the main challenges affecting humanity in a global change context. Due to its spatial configuration, Chile experiences droughts of different severities, from arid to humid climates, ranging from sea level to elevations above 6,000 m above sea level (a.s.l.), but it is still unknown how this phenomenon behaves in distribution, duration and intensity. The goal of this study is to identify how droughts have affected the different climate regions of the country between 1979 and 2019. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated for March and September, at the end of the humid season in the north and center-south of the country, respectively, and calculated at 3-, 6-, 9-, 12- and 24-month, allowed to determine the trends of the drought severity in a 5 × 5 km grid between 1979 and 2019. We found that negative and significant trends, indicating dry conditions, appear mainly in the Andes above 2,000 m a.s.l., where the main water reservoirs are located, affecting all climate types, except Mediterranean ones between 33°S and 38°S. The SPEI indicates general trends towards drier conditions across various elevations and climate types, with more pronounced negative trends in the north and central regions and some positive trends in the south. These trends suggest a significant impact on water availability, and highlight the need for focused policy initiatives to combat drought effects and manage water resources effectively. These findings are of main interest to Chile, one of the world’s leading producers of lithium and copper, with both industries requiring substantial amounts of water for extraction and processing, demanding high water availability in a drier territory.