Integrated Drought Index Research Articles

GTDI: a game-theory-based integrated drought index implying hazard-causing and hazard-bearing impact change

Abstract. Developing an effective and reliable integrated drought index is crucial for tracking and identifying droughts. The study employs game theory to create a spatially variable weight drought index (game-theory-based drought index, GTDI) by combining two single-type indices: an agricultural drought index (standardized soil moisture index, SSMI), which implies drought hazard-bearing conditions, and a meteorological drought index (standardized precipitation evapotranspiration index, SPEI), which implies drought hazard-causing conditions. In addition, the entropy-theory-based drought index (ETDI) is introduced to incorporate a spatial comparison with GTDI to illustrate the rationality of gaming weight integration, as both entropy theory and game theory belong to linear combination methods in the development of the integrated drought index and entropy theory has been applied in related research. Leaf area index (LAI) data are employed to confirm the reliability of GTDI in identifying drought by comparing it with SPEI, SSMI, and ETDI. Furthermore, a comparative analysis is conducted on the temporal trajectories and spatial evolution of the GTDI-identified drought to discuss GTDI's level of advancement in monitoring changes in hazard-causing and hazard-bearing impacts. The results show that GTDI has a very high correlation with single-type drought indices (SPEI and SSMI), and its gaming weight integration is more logical and trustworthy than that of ETDI. As a result, it outperforms ETDI, SPEI, and SSMI in recognizing drought spatiotemporally and is projected to replace single-type drought indices to provide a more accurate picture of actual drought. Additionally, GTDI exhibits the gaming feature, indicating a distinct benefit in monitoring changes in hazard-causing and hazard-bearing impacts. The case studies show that drought events in the Wei River basin are dominated by a lack of precipitation. The hazard-causing index, SPEI, dominates the early stages of a drought event, whereas the hazard-bearing index, SSMI, dominates the later stages. This study surely serves as a helpful reference for the development of integrated drought indices as well as regional drought prevention and monitoring.

A framework for drought monitoring and assessment from a drought propagation perspective under non-stationary environments

According to the coupled influence of climate variation and anthropogenic activities, hydro-meteorological variables are hard to keep stationary in a changing environment. Consequently, the efficacy of traditional standardized drought indices, predicated upon the assumption of stationarity, has been called into question. In China, the challenge of drought monitoring and declaration is exacerbated by the need for multiple drought indices covering meteorological, agricultural, hydrological, and groundwater aspects, often lacking real-time availability. To address these challenges, we developed a framework for drought monitoring and assessment from a drought propagation perspective. Central to this is the Nonstationary Integrated Drought Index (NIDI), which integrates responses from meteorological, agricultural, hydrological, and groundwater droughts, accounting for climate change and anthropogenic influences. First, we analyse the process of drought propagation to select the suitable time scale standardized drought index. Subsequently, significant large-scale climatic indices are selected through linear and nonlinear correlation analyses to identify climate anomalies. Anthropogenic influences are assessed using indicators such as the Normalized Difference Vegetation Index (NDVI), Impervious Surface Ratio (ISR), and population density (POP). Nonstationary probability models are then developed for precipitation, soil moisture, runoff, and groundwater series, incorporating climatic and human-induced factors. Finally, the NIDI is calculated using a D-vine copula model, with parameter estimation and updating facilitated by a genetic algorithm, representing the temporal dependence structure among the variables. A case study in the Hulu River Basin of western China validated the NIDI. Results showed that the NIDI effectively accounts for nonstationary hydro-meteorological variables due to climate change and human activities, accurately reproducing their time-dependent structure. Compared to conventional indices like SPI, SSI, SRI, and SGI, the NIDI identifies more extreme drought events. In conclusion, the presented NIDI offers a more comprehensive approach to drought identification, providing valuable insights for accurate drought detection and effective drought-related policy-making.

The response of global terrestrial water storage to drought based on multiple climate scenarios

Drought is a hydrological hazard phenomenon that spreads across the world and will become more intense and frequent with climate change in the future. How to scientifically and effectively monitor and evaluate drought events has become a significant issue. At present, various types of drought monitoring indices have been proposed. However, the criteria for drought that occurs in each month is different and the interaction between drought and the environmental factors of earth is also difficult to understand. To solve this problem, evapotranspiration (ET), leaf area index (LAI), soil moisture (SM), precipitation (P) and surface temperature(ST) data from five different scenarios (Hist-1950, SSP126, SSP245, SSP370 and SSP585) of coupled Model Intercomparison Project Phase6 (CMIP6) were obtained to construct a standardized integrated drought index (SIDI) which was used to monitor drought at global level. Then the cumulative effects of drought on terrestrial water storage in different scenarios were explored. The results showed that the global land surface was a trend of wetting from 1950 to 2014, while the trend of dryness-wetness was different under four future scenarios. Besides, the global drought frequency will first increase and then decrease in the future, and drought frequency in three periods was ranked as follows: Historical period (6.03 months) < Pre-Century (6.15 months) > Post-Century (6.11 months). Moreover, it was found that the cumulative effects of drought on terrestrial water storage diminished gradually from 6.7 months during the historical period (1950–2014) to 5.36 months in the Pre-Century (2015–2057), and further reduced to 4.87 months in the Post-Century (2058–2100), indicating a continuous decline in the stability of global terrestrial water storage in response to drought. This study has a certain reference value for the comprehensive assessment and rational allocation of global terrestrial water storage

Spatial–temporal characterization of droughts in Angola

AbstractThis study presents a scientific study conducted in Angola, focusing on the impact and characteristics of drought events in the country. The research endeavours to assess the primary climatic conditions linked to drought events within Angola, including an investigation into their spatiotemporal patterns. To achieve this, an evaluation of drought events’ frequency, duration and intensity has been carried out employing the Standardized Precipitation Index (SPI). Furthermore, the extent of affected regions has been determined using the Integrated Drought Index (IDI). For this, the frequency, duration and intensity of drought events are evaluated using the Standardized Precipitation Index (SPI). The assessment of the affected areas was carried out using the Integrated Drought Index (IDI). The results showed that Angola was repeatedly affected by drought events. The longest drought events were identified in the South and Center regions of the country. In the South region, the longest drought occurred from 1992 to 1997, totaling 83 consecutive months of drought in the region, in the Center the longest event occurred during the years 1994 to 1997, and in the North region between 2003 and 2005, totaling 46 and 23 months, respectively. The climatic conditions evaluated for the drought events studied (1989/1990, 1994/1995, 2014/2015 and 2018/2019) showed that most drought events in the country are associated with the variation of sea surface temperatures in the South Atlantic Ocean. The analysis of the drought category showed that during the years analysed, about 29.7% of the territorial extension of Angola were affected by drought events in the severe to exceptional category.

A copula-based parametric composite drought index for drought monitoring and applicability in arid Central Asia

Due to the complexity of meteorological and hydrological conditions in a changing environment, previous drought indices for monitoring a specific drought type do not reflect the overall regional situation of water scarcity. Therefore, in order to obtain accurate and reliable drought monitoring, a more integrated drought index should be developed to identify drought events comprehensively. In this paper, a non-linear trivariate drought index (NTDI) was constructed based on the joint probability distribution of parametric copulas, combining precipitation (P), potential evapotranspiration (PET), and root zone soil moisture (SM) variables. Subsequently, it was respectively compared with four drought indices, SPEI, SSMI, SC-PDSI and TVDI, and cross-validated with actual recorded drought events and annual crop yield to evaluate its applicability in arid Central Asia (ACA). The results indicated that: (1) Frank copula (1-,3-month scale) and Gumbel copula (6-,12-month scale) were considered to be the best-fitted copula functions for constructing joint probability distributions in the ACA. (2) The NTDI integrated the P-PET and SM drought signals to sensitively capture drought onset and duration, reflecting the combined characteristics of meteorological and agricultural drought. (3) The drought information expressed by NTDI was generally consistent with recorded drought events, and the monitoring results are accurate. (4)The NTDI performed better in agricultural drought monitoring than other drought indices. This study provides a reliable multivariate composite indicator which is significant for drought monitoring, prevention and risk assessment in ACA.

The Construction and Migration of a Multi-source Integrated Drought Index Based on Different Machine Learning

The Construction and Migration of a Multi-source Integrated Drought Index Based on Different Machine Learning

Evaluating established deep learning methods in constructing integrated remote sensing drought index: A case study in China

Agricultural drought seriously threatens the food and ecological security of most of the world’s developing countries. Data-driven integrated agricultural drought index with remote sensing provides an effective tool to monitor, evaluate, and predict the agricultural drought. However, there is still a lack of comprehensive analytical work on taking the most effective machine learning (ML) and deep learning (DL) methods to construct such integrated drought index. In other words, it is still unclear whether the recent DL methods can improve integrated drought monitoring as compared with the currently widely used ML methods. Therefore, we critically evaluated the performances of four representative DL methods (represents the four currently popular DL network types) i.e., Entity Embedding Deep Neural Network (EEDNN), One-dimensional Convolutional Neural Network (1D-CNN), Gated Recurrent Unit (GRU), and Self-Attention Mechanism (SAM) and three widely used tree-based ML methods i.e., Cubist, Random Forest (RF), and Light Gradient Boosting Machine (LGBM), through constructing a QuickDRI like integrated drought index (abbreviated as QuickDRI-China). About 30 years of meteorological data, 14 years of remote sensing data, and various biophysical variables in China such as land use/land cover, available water capacity, irrigated agriculture, elevation, and ecoregion were employed in this study. Results showed that the EEDNN performed best, followed by the RF and LGBM, and then the other methods including the currently wide used Cubist, according to the station accuracy evaluations, spatial description evaluations, and responses to specific drought event. The tree-based ML methods such as RF and LGBM are still competitive in constructing the integrated agricultural drought index at the current stage. However, the higher accuracy, the smoother spatial description, and the more responsive ability of the EEDNN demonstrate great potential of DL methods. The future integrated agricultural drought monitoring with remote sensing should develop a specialized DL network for heterogeneous agricultural drought features.

Construction of a Time-Variant Integrated Drought Index Based on the GAMLSS Approach and Copula Function

Construction of an integrated drought index is a fundamental task to conducting drought disaster risk management and developing drought resistance planning strategies. Given the evident non-consistent features during the drought evolution process, firstly, the GAMLSS approach was utilized to construct multiple combination scenarios of time-variant parameters and corresponding probability distribution functions. Then, the time-variant comprehensive drought index integrating the variable characteristics of precipitation and soil moisture was established by means of the copula function. Finally, the reliability of the time-variant comprehensive drought index was verified through its application in frequency analysis and return period determination of drought hazard system in Huaibei Plain, China. The application results demonstrated that: (1) The variation of the time-variant integrated drought indicator presented strong consistency with both soil moisture and precipitation during historical years in Huaibei Plain. (2) The overall variation of the drought hazard system characterized by drought duration and severity presented a gradual mitigation trend from west to east and north to south in Huaibei Plain, which agrees with the geographic differences and water resources availability distribution features. (3) Drought recognition results, including the frequency of drought events and typical drought processes with extreme grades, are in agreement with the practical statistics and observed data series. On the whole, the proposed time-variant integrated drought indicator is capable of extracting complex variation characteristics within the drought hazard evolution process, and can be further applied in drought monitoring, recognition and assessment research fields.

A new integrated index for drought stress monitoring based on decomposed vegetation response factors

Drought is a complex process that usually stems from aberrant climatic conditions such as precipitation deficits and temperature anomalies. Drought can pose a serious threat to vegetation growth, which in turn regulates the process of drought evolution. An in-depth understanding of the interaction between drought and vegetation is crucial not only for drought monitoring but also for drought mitigation. To this end, this study divided the drought process into two sub-processes: (1) from physical dry conditions to drought stress and (2) from drought stress to vegetation destruction, and introduced vegetation response factors to quantitatively depict the vegetation contributions in each sub-process. Response factors of different vegetation types were decomposed respectively from Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Precipitation Condition Index (PCI) in the first sub-process, and from Vegetation Condition Index (VCI) in the second sub-process. Further, this study proposed a novel integrated drought index named Type Response-Aided Drought Index (TRADI) based on the response factors of different vegetation types. Vegetation response factors were applied to fine-tune several commonly used single drought indices to verify their effectiveness and the proposed TRADI was evaluated by comparing it with several combined indices, including Vegetation Health Index (VHI), Scaled Drought Condition Index (SDCI), Microwave Integrated Drought Index (MIDI), and Integrated Drought Monitoring Index (IDMI). Moreover, the TRADI was qualitatively evaluated with the maps from U.S. Drought Monitor (USDM) in the 2011 and 2012 drought events across the CONtinental United States (CONUS). Our results showed that adjusted by vegetation response factors, single drought indices as well as combined drought indices showed a closer correlation with the Palmer Drought Severity Index (PDSI) than their original forms, indicating the strength of response factors in modifying existing indices. Furthermore, the proposed TRADI performed better than VHI, SDCI, MIDI, and IDMI, and was consistent with USDM in typical drought events. This study provides a new perspective on understanding the process of drought affecting vegetation and highlights the importance of considering vegetation response in drought index establishment.

A New Drought Monitoring Index on the Tibetan Plateau Based on Multisource Data and Machine Learning Methods

Drought is a major disaster over the Tibetan Plateau (TP) that exerts great impacts on natural ecosystems and agricultural production. Furthermore, most drought indices are only useful for assessing drought conditions on a coarse temporal scale. Drought indices that describe drought evolution at a fine temporal scale are still scarce. In this study, four machine learning methods, including random forest regression (RFR), k-nearest neighbor regression (KNNR), support vector regression (SVR), and extreme gradient boosting regression (XGBR), were used to construct daily drought indices based on multisource remote sensing and reanalysis data. Through comparison with in situ soil moisture (SM) over the TP, our results indicate that the drought index based on the XGBR model outperforms other models (R2 = 0.76, RMSE = 0.11, MAE = 0.08), followed by RFR (R2 = 0.74, RMSE = 0.11, MAE = 0.08), KNNR (R2 = 0.73, RMSE = 0.11, MAE = 0.08) and SVR (R2 = 0.66, RMSE = 0.12, MAE = 0.1). A new daily drought index, the standardized integrated drought index (SIDI), was developed by the XGBR model for monitoring agricultural drought. A comparison with ERA5-Land SM and widely used indices such as SPI-6 and SPEI-6 indicated that the SIDI depicted the dry and wet change characteristics of the plateau well. Furthermore, the SIDI was applied to analyze a typical drought event and reasonably characterize the spatiotemporal patterns of drought evolution, demonstrating its capability and superiority for drought monitoring over the TP. In addition, soil properties accounted for 59.5% of the model output, followed by meteorological conditions (35.8%) and topographic environment (4.7%).

A robust ensemble drought index: construction and assessment

Drought indices have been widely used in drought monitoring and assessments, but it is difficult to select a uniform index suitable for different geographical and climatic conditions. In addition, the statistical parameters of drought index also vary with fitting reference period, which brings more challenges to the practicality of drought index under climate change. In this study, a new multitimescale and more robust integrated drought index, the EDI was constructed by integrating the common components of seven single drought indices (including SPI, PDSI_th, PDSI_pm, scPDSI_th, scPDSI_pm, SPEI_th and SPEI_pm). The EDI was compared with the single drought index over China in terms of the occurrence ability of historical drought events, the dependence of statistical parameters on reference periods, the goodness-of-fit of standard normal distribution and the correlation with soil moisture and runoff. We find that the EDI could more accurately reproduce the location and intensity of historical drought events, reduce the uncertainty caused by both potential evapotranspiration estimation and the reference period, has higher goodness-of-fit of standard normal distribution, higher correlation with soil moisture, more robust performance in hydrologic drought monitoring and less affected by the difference of geographical and climatic conditions. Moreover, the EDI improved the accuracy of drought monitoring in arid and semiarid regions of China, where assessment is always complex. Furthermore, the available evidence supports that a long-term stationary climate variables series could provide more accurate drought assessment.

Integrated Drought Index based on Vine Copula Modelling

AbstractAs a natural hazard, drought is a complex multivariate phenomenon that does not depend on only one hydrometeorological variable, as well as, considering a particular kind of drought may not be effective for drought management. Considering this, many multivariate drought indices have been developed based on linearity assumptions or conventional copulas assuming symmetric relationships among univariate drought indices. In this study, D‐vine copula was applied to construct a four‐dimensional index, named as Integrated Drought Index (IDI), by combining four univariate drought indices (Standardized Precipitation Index [SPI], Reconnaissance Drought Index [RDI], Standardized Soil moisture Index [SSI] and Standardized stream flow Drought Index [SDI]) to better reflect many hydrometeorological variables (precipitation, evapotranspiration, soil moisture and stream flow) and different kinds of drought (meteorological, agricultural and hydrological) simultaneously. Vine copula was used to solve nonlinear and asymmetric relationships among drought indices due to its flexibility over the free selection of copula(s) in each step of hierarchical structure in high dimensional modelling. The IDI was constructed for 1‐ and 4‐month timescales for the upper Tapti basin of the central region in India. The performance of IDI was tested with dependence measures (Pearson's correlation coefficient, Mutual Information) and evaluated against the Terrestrial Water Storage Anomaly data derived from the Gravity Recovery and Climate Experiment (GRACE) mission. Spatial analysis of drought was carried out by fuzzy c‐means (FCM) clustering algorithm with IDI. IDI based on vine copula solved the nonlinear and asymmetric relationships among different variables associated with the occurrence of droughts effectively with a reduction of uncertainty as compared to the single drought indices for different kinds of droughts. Analysis revealed spatially different drought risks in the upper and lower river basins. In general, the vine copula addresses nonlinear and asymmetric relationships that exist between the variables associated with natural hazards like drought.

Evaluating the impact of different normalization strategies on the construction of drought condition indices

Drought condition indices (CIs), including single condition (SCIs) and combined condition indices (CCIs), can reveal the characteristics of drought evolution and have been widely used in drought monitoring tasks. However, in practical applications, CIs are constructed in different normalization methods rather than based on a uniform standard. The diversely constructed CIs not only mislead the scientific community but also confuse farmers and stockholders to prevent them from taking timely measures when drought events occur. Thus, this study aims to comprehensively evaluate the differently constructed CIs and quantify the impact of different normalization strategies. To achieve this, four SCIs, including the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Soil Moisture Condition Index (SMCI), and the Precipitation Condition Index (PCI), together with three CCIs, including the Vegetation Health Index (VHI), the Scaled Drought Condition Index (SDCI), and the Microwave Integrated Drought Index (MIDI) were chosen as typical CI representatives. Each original CI was compared in the Continental United States (CONUS) with its variants by calculating the correlation coefficients with six in-situ indices (PDSI, Zindex, SPI-1, SPI-3, SPI-6, and SPI-9). Furthermore, the different CIs were qualitatively evaluated with the United States Drought Monitor in the extreme drought event in 2011. The results indicated that CI variants built through temporal normalization reduce the accuracy of drought severity estimation, and the CI variants built through spatial normalization lost the ability to track the spatiotemporal evolution of drought. The impacts of different normalization methods on single indices are transmissible and cumulative when integrating them into combined indices. These findings highlight the risks of diverse normalization methods in CIs establishment and underscore the necessity of considering seasonality and spatiality in drought applications.

English

Namibia is a semi-arid country with erratic and unpredictable rainfall. Extreme weather patterns, such as floods and extensive droughts, have become more common in recent years, with strong impact on surface and ground water availability, rangeland and agricultural productivity, food security, and further land degradation, such as bush encroachment or soil erosion. The previous 10 years were characterized by a perennial drought that lasted from 2013 to 2016 and an extreme drought that occurred during the rainy season of 2018/2019, which was the driest in 90 years. In January 2021, however, rainfall totals doubled to tripled the norm. The paper compares five drought indices in order to identify, visualize, monitor, and better understand the nature, characteristics, and spatial-temporal patterns of drought in northern and central Namibia. Based on their sensitivity to vegetation greenness, land surface temperature, evapotranspiration, and precipitation, the indices allow for calculation, time series analysis, and cross-comparison. Droughts occur every year, but the intensity of the drought varies depending on the index type.&nbsp; It is concluded that a comparative analysis of multiple indices provides a better interpretation of drought than single parameter systems. Future research should include biophysical properties such as soil characteristics, soil moisture, and hydrology, as well as socioeconomic studies, in order to develop an integrated drought index for northern Namibia. &nbsp; Key words: Remote sensing, moderate resolution imaging spectroradiometer (MODIS), drought indices, time series analysis, climate reanalysis, Namibia.

Assessing irrigation mitigating drought impacts on crop yields with an integrated modeling framework

Irrigation is one of the major adaptation strategies to combat the negative impacts of drought on crop yields. However, during droughts, the water resources are limited and determining the exact irrigation amount and its response to crop yields are crucial. Existing crop yield simulation models are data-intensive and a majority of these are point-based. Therefore, this study presents a novel integrated modeling framework by using two parsimonious models (CROPWAT, an irrigation water requirement simulation model; and AquaCrop-GIS, a spatial crop yield simulation model) to simulate corn and soybean yields under different irrigation application rates during drought years at a spatial scale in the Mobile River Basin (MRB), Southeast U.S. To simulate crop yields for drought years, first agricultural drought years are identified using an integrated drought index called the Standardized Precipitation Evapotranspiration Soil Moisture Index (SPESMI). The results indicate that a majority of the basin was affected by mild to extreme droughts during the years 2008, 2011, 2012, 2016. The results of the integrated modeling framework illustrate the potential corn and soybean yields can be increased from 10% to 259% and 20% to 229% respectively under different irrigation water application rates (50%, 75%, and 100% of irrigation water requirement) relative to rainfed crop yields at the counties in the MRB during drought years. These findings demonstrate the importance of overall irrigation and the integrated modeling framework in devising robust irrigation water management plans in drought-affected areas at a spatial scale. Failure in integrating irrigation water requirement models in crop yield simulations will result in erroneous yield simulations, especially during droughts – this is salient given projections for frequent and intense droughts globally. Although the study is focused on MRB, the framework developed is applicable for irrigation planners and water managers in any region that experiences mild to extreme droughts, as long as the farmers have the ability to adapt the irrigation in their practice given socio-economic constraints.

Assessment of Drought Using Earth Observation Data and Cloud Computing in Morocco for 2010-2020

Drought is an extreme event that has hit several countries in the world including Morocco. The aim of this research was to assess drought in Morocco with a view to providing information for planning and management of droughts. For this, three drought indices were chosen: Combined Drought Indicator (CDI), Soil Moisture Agricultural Drought Index (SMADI), and Microwave Integrated Drought Index (MIDI). Drought monitor was done during the growing seasons of 2010-2020 using Earth Observation data and cloud computing with the mapping of the drought indices and their inter-comparing via Pearson correlation. The main drought events were tracked and drought characteristics analyzed. Seven drought years were tracked for regions of cereal production. CDI and MIDI were very well correlated, whereas SMADI showed poor correlation with CDI and MIDI. Validation of results was done by comparing our results with another study for the 2015-2016 drought event and comparing yearly precipitation with the long-term average. An Earth Engine App of the three indices was published to make public drought maps.

A composite drought index developed for detecting large-scale drought characteristics

Numerous studies have projected that drought will become more severe under changing climatic conditions which will have a profound impact on terrestrial ecosystems around the world. While a wide range of drought indices has been extensively applied to assess droughts, various issues have been raised, such as over- or underestimation of drought conditions, feature extraction techniques, or transformations used to develop the indices. In this study, a new composite drought index (CDI) was developed using the integration of potential and actual evapotranspiration, climatic water balance, and precipitation. The proposed CDI was evaluated by comparing it with soil moisture, Palmer Drought Severity Index (PDSI), scaled crop yield index (sCYI), evaluation integrated drought index (EIDI), and standardized runoff index (SRI) in China. Our results demonstrate that together with PDSI, CDI performs well in monitoring drought over 2417 grid cells obtained from in-situ latitude and longitude. It was found that CDI performs better than the PDSI and EIDI for reliable soil moisture detection and stable drought monitoring in the study area. Compared to PDSI, CDI shows a more positive correlation with sCYI for seasonal maize and wheat. CDI has less false alarm ratio in the detection of mild to extreme drought events compared to PDSI.

Assessment and attribution of China’s droughts using an integrated drought index derived from GRACE and GRACE-FO data

Different definitions of drought from multiple perspectives complicate drought studies. Studying droughts from an integrated perspective helps to better understand their evolutions and driving factors, but few studies have taken such a comprehensive approach in China. This study conducted an assessment and attribution analysis of droughts across China from 2002 to 2020 based on an integrated drought index derived from observed terrestrial water storage (TWS) data. First, the latest solution of satellite gravimetry data was employed to analyze spatiotemporal changes in TWS deficits. Second, droughts associated with TWS deficits were detected using the Water Storage Deficit Index (WSDI). Third, an attribution analysis was conducted using the stepwise regression method to determine the driving factors of droughts. The results show that China’s TWS decreases at a significant rate (−1.79 ± 0.43 mm/yr, P < 0.05) from 2002 to 2020. The droughts detected by WSDI can generally combine the durations and intensities of the meteorological, hydrological, agricultural, and socioeconomic drought indices, which provides a comprehensive perspective. The severity and intensity of countrywide drought have been at least doubled, especially in the northern regions (including the Northern Arid and Semiarid region, the Huang-Huai-Hai Plain, and the Loess Plateau). The attribution analysis revealed that both climate change and anthropogenic impacts played a dominant role in driving intensified droughts. Decreasing precipitation explains 20% to nearly 100% of TWS deficits across the Huang-Huai-Hai Plain. Human water use, especially for agriculture, has also exacerbated droughts in the water-scarce northern regions of China. The WSDI was highly correlated with the water supply for human activities in the Northern Arid and Semiarid region and the Loess Plateau. This study shows the need to adopt measures to limit human water use beyond the regional capacity.

Characteristics and Predictability of Midwestern United States Drought

AbstractCharacteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

A seasonally blended and regionally integrated drought index using Bayesian network theory

AbstractAmong the list of all‐natural hazards, the unique characteristic of drought is its multiplex nature. Besides, the inherited regional characteristics and seasonal variation of drought make it more complicated. Conventional drought indices are inadequate to integrate seasonal and local elements. Therefore, to integrate seasonal components and regional factors, the present study emphasizes the following features: the existence of multiple drought monitoring indicators, the regional broadcasting of drought‐related statistics, and seasonal fluctuations. The seasonally blended regionally integrated drought index (SBRIDI) based on two‐stage Bayesian network theory comprehensively captures seasonal and regional variability embedded in individual drought indicators and regionally scattered homogeneous meteorological stations. It extracts information from a widely accepted standardized precipitation index and other multiscalar variants (standardized precipitation evapotranspiration index, standardized precipitation temperature index). It offers a better estimate of drought severity and pattern in the whole study region. The application of the SBRIDI is based on six meteorological stations located in the Gilgit Baltistan region of Pakistan. Among 216 time series datasets, the stage I Bayesian network selects 72 seasonally relevant standardized drought indices datasets, and the stage II Bayesian network selects 12 of the seasonally most prominent meteorological station datasets. Temporal graphical evidence shows that the SBRIDI can assess the drought risk and depicts the spatial extent of drought conditions at the regional level. The SBRIDI can highlight hotspots of drought‐prone regions, which could help freshwater management agencies and stakeholders.