Drought Characteristics Research Articles

Critical Drought Characteristics: A New Concept Based on Dynamic Time Period Scenarios

In research on monitoring drought events, analysis is often carried out using a single period as a reference. On the other hand, changing this default period in drought calculations causes the drought index values obtained from research to differ. As a gap in the literature, this point highlights the necessity of investigating the effect of various time periods on drought characteristics. It underscores the need to propose a new concept and methodology to address this gap effectively. This research aims to analyze critical drought characteristics through dynamic time period scenarios. For the first time in the literature, drought indices and potential and critical characteristics were analyzed for various (dynamic) time periods. Drought analysis was carried out for 13 time period scenarios with 10-year intervals from a meteorological station in Durham (1872–2021) by changing the initial time condition using the Standardized Precipitation Index (SPI). The results showed that in addition to the similarities, there are significant differences between drought characteristics. For example, in some time period scenarios, a drought event was recorded during a specific period, while in other scenarios (S5–S7, S10–S13), no drought was detected during the same period, like in SPI 1. Additionally, for SPI 12, the drought duration varied significantly, lasting between 20 and 29 months, and for SPI 6, the drought duration varied between 3 and 13 months. Regarding the intensity, SPI 1 ranged between −0.89 and −1.33, indicating a 33% difference, and the SPI 3 intensity ranged between −1.08 and −1.91, indicating a 50% increase in intensity. This research significantly contributes to the field by providing a novel approach using dynamic time period scenarios to determine critical drought characteristics, offering valuable insights for water resource management, drought mitigation planning, and design purposes.

Using an integrated hydro-economic model to determine the drought and energy relationship in the Upper Euphrates Basin

ABSTRACT Decreasing precipitation in the Upper Euphrates Basin and the negative impact of climate change directly affect water resources and hydroelectricity generation in the basin. This basin, which contains the largest dams in terms of hydroelectricity generation potential, requires research studies to assess and characterize drought for risk prevention and mitigation applicable to water resources management. To better assess drought in the upper Euphrates Basin due to recent warming, FEHEM is developed, a hydro-economic optimization model of the integrated reservoir system of the Upper Euphrates Basin. Using a historical hydrological dataset, water management and hydroelectric operations are evaluated with a linear programming model at monthly time steps. This paper uses two different drought indices: (1) the standardized precipitation index, which is based on precipitation alone; and (2) the reconnaissance drought index, which takes into account both evaporation and precipitation. These indices were used to evaluate the impact of temporal drought characteristics in the Upper Euphrates Basin on the hydropower generation of 10 dams with a total installed capacity of over 3255 MW in the basin, based on 45 years of precipitation data from more than a hundred measuring stations in the basin.

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 < 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.

Comparative Analysis of Characteristics of Drought over Some West Africa Regions Based On Selected Drought Assessment Indices

Comparative Analysis of Characteristics of Drought over Some West Africa Regions Based On Selected Drought Assessment Indices

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.

Drought modelling: A comprehensive review

Droughts are a recurring natural hazard with significant social, economic, and environmental consequences. Accurate drought modelling is crucial for effective drought preparedness, mitigation, and response strategies. This review paper comprehensively overviews drought modelling approaches, including traditional statistical, physically-based and emerging machine learning techniques. We discuss the strengths and weaknesses of each approach, highlighting recent advancements and future research directions. Additionally, we examine the various drought indices employed for drought characterization and explore the integration of climate change scenarios into drought modelling frameworks. Finally, the review addresses the challenges and limitations of drought modelling and emphasizes the importance of data availability, model validation, and user-centric applications.

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.

Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment.

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

A novel GNSS and precipitation-based integrated drought characterization framework incorporating both meteorological and hydrological indicators

The Global Navigation Satellite System (GNSS) has become instrumental in developing drought indices, particularly meteorological drought indicators derived from atmospheric precipitable water vapor and hydrological drought indicators based on inverted terrestrial water storage changes. However, these indices traditionally focus on individual aspects of droughts, either meteorological or hydrological droughts, and do not fully capture the integrated nature of drought phenomena. Addressing this gap, this study proposes a novel integrated drought characterization framework using the Gringorten plotting position to derive joint probabilities for GNSS-derived meteorological and hydrological drought indicators. This leads to the creation of a comprehensive multivariate drought severity index (GNSS-MDSI). The analysis across the western United States indicates significant spatial variability in multiyear average precipitation efficiency, ranging from 7.51% to 28.1%. This variability corresponds with marked differences in seasonal terrestrial water storage changes, which oscillate between 25 and 123 mm. Applying this framework in eight states, 9–13 comprehensive drought events from January 2006 to December 2021, with durations spanning from 3 to 54 months, were identified. The GNSS-MDSI not only captured these comprehensive drought periods across various temporal and spatial scales but also aligned closely with drought classifications provided by the US Drought Monitor. These results underscore the utility of this framework in providing a more nuanced and multifaceted perspective on drought conditions, surpassing the capabilities of single-indicator systems. Overall, this study presents an innovative framework for drought monitoring by integrating two GNSS-derived drought indicators, enabling precise and comprehensive delineation of drought characteristics, and offering a geodesy-based solution for integrated global and regional drought monitoring.

Autoencoder-based composite drought indices

Depending on the type, drought events are described using different indices, such as meteorological, agricultural, and hydrological. The use of different indices often causes confusion for making water-related management decisions. One simple summarized index which can describe the different aspects of drought is desired. Several methods have therefore been proposed, especially with the linear combination method which does not adequately describe drought characteristics. Meanwhile, autoencoders, nonlinear transformation in dimensional reduction, have been applied in the deep learning literature. The objective of this study, therefore, was to derive autoencoder-based composite drought indices (ACDIs). First, a basic autoencoder was directly applied as ACDI, illustrating a negative relation with the observed drought indices which was further multiplied by a negative. Also, the hyperbolic tangent function was adopted instead of the sigmoid transfer function due to its higher sensitivity to drought conditions. For better expression of drought indices, positive and unity constraints were applied for weights, denoted as ACDI-C. Further simplification was made as sACDI by excluding the decoding module since it was not necessary. All applied weights of different sites over a country can be unified into one weight, and the same weights were made for all the sites, called as sACDI1. In the context of model evaluation, a comprehensive analysis was undertaken employing metrics as root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficients. The collective findings underscore the superior performance of both the sACDI and sACDI1 models over their counterparts. Notably, these simplified models manifestly diminished RMSE and MAE values, indicating their enhanced predictive capabilities. Of particular note, sACDI1 exhibited a discernibly lower MAE in comparison to alternative models. Further alarm performance metrics was conducted including the false alarm ratio, probability of detection, and accuracy (ACC). The investigations revealed superiority of the simplified models in terms of alarm ACC, especially in the case of SRSI(A). The developed ACDI can comprehensively summarize multiple drought aspects and provide summarized information about drought conditions.

Characteristics of super drought in Southwest China and the associated compounding effect of multiscalar anomalies

Characteristics of super drought in Southwest China and the associated compounding effect of multiscalar anomalies

Regional and farm level droughts characteristics for southwestern Uganda during 1981–2017

An increase in drought events associated with rising temperatures indicates that Uganda is facing a growing challenge from climate variability. Despite these challenges, there is a lack of comprehensive information on how climate variability affects soils, particularly drought characteristics. In an effort to address this limitation, this study utilised high-quality, in-situ climate data and soil Available Water Capacity (AWC) specific to south-western Uganda. The study employed the Palmer Drought Severity Index (PDSI) and the Standardised Precipitation-Evapotranspiration Index (SPEI) to assess drought occurrences over the period 1981–2017. Notably, the results from the 3-month SPEI indicated higher frequencies across all drought categories. Meanwhile, the 12-month SPEI exhibited more stabilised negative and positive truncations for drought and wet events, respectively. Analysing the temporal evolution of monthly PDSI values for five major soil mapping units revealed distinct patterns. Moderate droughts were observed over Lixic Ferralsols between 1981 and 1992 and between 1998 and 2008. Extreme drought events occurred during the first 5–7 months of 2017. Luvisols exhibited prolonged negative truncation levels, indicating a water deficit during several periods: 1982–1984, 1990–1994, 1998–2000, 2010, and 2012–2014. Histosols experienced drought conditions from 1982 to 1986, interspersed with wet events in 1988, 1997, 1999–2002, and 2005. The study emphasises the differential response of soil units to drought and wet conditions. As a result, the research recommends implementing water management practices such as irrigation, mulching, conservation tillage, zero tillage, and drainage for all soils to enhance resilience and sustainable agricultural practices in the region.

The Timing of Detectable Increases in Seasonal Soil Moisture Droughts Under Future Climate Change

AbstractGlobal warming exacerbates the increase of soil moisture drought by accelerating the water cycle, posing potential threats to food security and ecological sustainability. The design of drought prevention and mitigation policies should be based on the reliable detection of the future change signal in droughts, so it is critical to know when the signal can be detected (Time of Emergence, ToE) in the background noise of the climate system. While the ToE framework has been successfully applied for temperature‐related signal detection, the ToE for changes in drought has not been well studied. Based on 66 Coupled Model Intercomparison Project Phase 6 model ensemble members under four Shared Socio‐economic Pathways, we conduct a global ToE analysis of seasonal soil moisture drought characteristics and discuss the impact of different warming levels. Six subregions with robust increase in soil moisture droughts are identified. For drought frequency, most of the subregion's ToE is centered around 2080, however for drought intensity it is much earlier and can even reach around 2040 in AMZ. For drought frequency and drought intensity, approximately 14%–22% and 47%–49% of global land areas would reach ToE in 21st century. The global land areas with ToE of increasing droughts would increase by at least 1/5 when global warming level is kept to 2°C rather than 1.5°C above pre‐industrial conditions. This suggests that limiting global warming can significantly delay the emergence time of increases in seasonal soil moisture droughts, allowing additional adaptation time for the drought‐related sectors.

Spatial-temporal characteristics of meteorological drought and its relationship with circulation indices over Songhua River Basin, China

ABSTRACT Global warming has increased the risk of meteorological drought and associated hazards. Based on daily data from 35 meteorological stations in the Songhua River Basin (SRB) collected from 1960 to 2019, this study applied the standardised precipitation evapotranspiration index (SPEI) and used the ensemble empirical mode decomposition (EEMD) method and Pettitt's mutation test to perform a partitioned segmentation analysis of the multi-temporal and spatial variations in the meteorological droughts affecting the basin in terms of frequency and intensity, and discussed the relationship between the atmospheric circulation index and meteorological drought. The results showed that the overall meteorological drought trends shifted from a drought to a humid trend from 1960 to 2019. The drought in the basin had periods of 3, 6.7, 15, and 30 years, with mutation points in 1968, 1980, 1997, and 2012. The drought trend in Regions I and IV of the entire basin was enhanced, while that in Regions II, III, and V was weakened. Drought in the basin was strongly influenced by the Arctic Oscillation (AO) and Southern Oscillation Index (SOI).

The composite drought index incorporated solar-induced chlorophyll fluorescence enhances the monitoring capability of short-term drought

Understanding the response of vegetation to drought is significant for socio-economic development and biodiversity conservation. However, due to the complexity of drought, relying only on one approach, such as the surrounding physical environmental conditions or specific vegetation characteristics, does not yield precise results. To address this challenge, composite drought indices incorporating Normalized Difference Vegetation Index (NDVI) and other multiple variables, have been developed and shown promising effectiveness in drought detection. Compared to NDVI, solar-induced chlorophyll fluorescence (SIF) decreases due to the increased non-photochemical quenching and reduced photosynthesis during short-term drought stress. This indicates a direct association between SIF and vegetation photosynthesis, offering advantages over NDVI in capturing drought effects. Nevertheless, SIF has not yet been fully integrated into composite drought indices. Therefore, this study focused on assessing the advantages of SIF for short-term drought monitoring by constructing a new composite drought index (CPDI) using the Principal Component Analysis (PCA) method. Subsequently, CPDI was employed to forecast future drought conditions. Overall, CPDI performs exceptionally well as a composite drought index in drought trend analysis and drought event identification, indeed advancing the monitoring capability for short-term drought. Furthermore, the incorporation of SIF in CPDI, obtained from various data sources provides more timely monitoring of drought events, while the CPDI with NDVI reflects the cumulative effect of drought conditions over a longer period. In the future, it is potential to utilize the benefits of SIF in constructing drought indices or combine it with NDVI for comprehensive drought characterization.

Space-Time Variability of Drought Characteristics in Pernambuco, Brazil

Drought is the most complex natural hazard that can occur over large spatial scales and during long time periods. It affects more people than any other natural hazard, particularly in areas with a dry climate, such as the semiarid region of the Brazilian Northeast (NEB), which is the world’s most populated dry area. In this work, we analyzed trends and the spatial distribution of drought characteristics (frequency, affected area, and intensity) based on the Standardized Precipitation Index (SPI) on annual (SPI-12) and seasonal (SPI-3) scales. The study used monthly precipitation data recorded between 1962 and 2012 at 133 meteorological stations in Pernambuco State, Brazil, which is located in the eastern part of the NEB and has more than 80% of its territory characterized by a semiarid climate. The regions of Sertão, Agreste, and Zona da Mata of Pernambuco were considered for comparison. The Mann–Kendall and Sen’s slope tests were used to detect the trend and determine its magnitude, respectively. The results indicated that annual droughts in the state of Pernambuco became more frequent from the 1990s onwards, with summer having the greatest spatial coverage, followed by winter, autumn, and spring. Sertão presented a greater number of stations with a significant positive trend in drought frequency. Regarding the drought-affected area, global events occurred in a greater number of years on an annual scale and during the summer. Trend analysis pointed to an increase in areas with drought events on both scales. As for the drought intensity, the entire state of Pernambuco experienced drought events with high intensity during the autumn. The relationship between drought characteristics indicated an increase in the affected area as the result of an increase in drought intensity.

Spatio-temporal characterization of drought variability in data-scarce regions using global precipitation data: a case study in Cauto river basin, Cuba.

Drought is considered the most severe water-related disaster in the Cauto river basin, which is the longest river and the main agricultural producer in Cuba. Better understanding of drought characteristics is crucial to drought management. Given the sparsity of ground-based precipitation observations in the Cauto, this study aims at using gridded global precipitation to analyze the spatio-temporal variations of drought in this river basin. Firstly, the monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was calibrated with the gauged precipitation using the Thiessen polygon-based method and linear least squares regression equations. Then, the gridded standardized precipitation index (SPI) with time scales of 3, 6, 9months and drought characteristics, namely, drought frequency, duration and intensity were calculated using the calibrated CHIRPS. Finally, the spatio-temporal analysis was performed to investigate the variations of drought in the Cauto river basin in time and space. The obtained results show that the calibrated CHIRPS is highly consistent with the gauged observations and is capable of determining the magnitude, time, and spatial extent of drought events in the Cauto river basin. The trend analysis by the Mann-Kendall test reveals that although the trend is not statistically significant, the SPI tends to decrease with time in the dry season, which indicates the more severe drought. The spatial analysis indicates that the lower altitude area of the Cauto river basin is suffered from longer drought duration and higher drought intensity than the upper one. This study expresses the importance of open global precipitation data sources in monitoring and quantifying drought characteristics in data-scarce regions.

Assessment of Hydrological and Meteorological Composite Drought Characteristics Based on Baseflow and Precipitation

Traditional univariate drought indices may not be sufficient to reflect comprehensive information on drought. Therefore, this paper proposes a new composite drought index that can comprehensively characterize meteorological and hydrological drought. In this study, the new drought index was established by combining the standardized precipitation index (SPI) and the standardized baseflow index (SBI) for the Jiaojiang River Basin (JRB) using the copula function. The prediction model was established by training random forests on past data, and the driving force behind the combined drought index was explored through the LIME algorithm. The results show that the established composite drought index combines the advantages of SPI and SBI in drought forecasting. The monthly and annual droughts in the JRB showed an increasing trend from 1991 to 2020, but the temporal characteristics of the changes in each subregion were different. The accuracies of the trained random forest model for heavy drought in Baizhiao (BZA) and Shaduan (SD) stations were 83% and 88%, respectively. Furthermore, the Local Interpretable Model-Agnostic Explanations (LIME) interpretation identified the essential precipitation, baseflow, and evapotranspiration features that affect drought. This study provides reliable and valid multivariate indicators for drought monitoring and can be applied to drought prediction in other regions.

Correction: Abu Arra, A.; Şişman, E. Characteristics of Hydrological and Meteorological Drought Based on Intensity-Duration-Frequency (IDF) Curves. Water 2023, 15, 3142

In the original publication [...]