Multivariate Standardized Drought Index Research Articles

Advancing Spatial Drought Forecasts by Integrating an Improved Outlier Robust Extreme Learning Machine with Gridded Data: A Case Study of the Lower Mainland Basin, British Columbia, Canada

Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance.

Multi-datasets to monitor and assess meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region.

This study analyzed the meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region from 1994 to 2016. In recent decades, this region has faced prolonged and severe droughts, leading to marked reductions in agricultural productivity and significant challenges to food security and water availability. The datasets employed included a digital elevation model, land use and cover data, soil characteristics, climatic data (temperature, wind speed, solar radiation, humidity, and precipitation), runoff data, images from the MODIS/TERRA and AQUA sensors (MOD09A1 and MODY09A1 products), and soil water content. A variety of methods and products were used to study these droughts: the meteorological drought was analyzed using the Standardized Precipitation Index (SPI) derived from observed precipitation data, while the hydrological drought was assessed using the Standardized Soil Index (SSI), the Nonparametric Multivariate Standardized Drought Index (NMSDI), and the Parametric Multivariate Standardized Drought Index (PMSDI). These indices were determined using water balance components, including streamflow and soil water content, from the Soil Water Assessment Tool (SWAT) model, and evapotranspiration data from the Surface Energy Balance Algorithm for Land (SEBAL). The findings indicate that the methodology effectively identified variations in water dynamics and drought periods in a headwater basin within Brazil's semiarid region, suggesting potential applicability in other semiarid areas. This study provides essential insights for water resource management and resilience building in the face of adverse climatic events, offering a valuable guide for decision-making processes.

Seasonal drought severity identification using a modified multivariate index: a case study of Indo-Gangetic Plains in India

Effective drought monitoring is crucial for mitigation efforts and the implementation of early warning systems. Due to the intricate nature of drought phenomena, its impact cannot be accurately characterized solely through the use of univariate indicators. This study introduces a composite version of the modified multivariate standardized drought index (MMSDI) for the Indo-Gangetic Plains (IGP) in India. The MMSDI integrates precipitation, soil moisture, and solar-induced chlorophyll fluorescence datasets from 2001 to 2020, utilizing a nonparametric joint probabilistic framework. It combines three consolidated drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized solar-induced chlorophyll fluorescence Index (SSIFI). The drought patterns among SPI, SSI, SSIFI, and MMSDI indices are compared for short, and mid-term time scales (herein 1, 3 and 6 month) over IGP. The performance of MMSDI is evaluated through quantitative metrics e.g. probability of detection (POD), false alarm ration (FAR) and critical success index (CSI) at various time scales. Results reveal that MMSDI provides a reliable estimation of the severity and spatial coverage of major drought events over IGP at various time scales. The MMSDI demonstrates superior effectiveness in identifying and characterizing the spatial severity of drought, i.e. the visibility of the drought hotspots. The MMSDI has the capability to assess agrometeorological drought, indicating its potential as a valuable tool for identifying regions vulnerable to drought. Overall, this modified drought index derived from multi-sensor datasets produced comprehensive insights for policymakers in implementing effective agricultural drought management practices to understand intricate drought phenomena over IGP region in India.

Development of multivariate standardized drought index for large lake basins based on the copula function: A case study of Hongze Lake Basin, China

Study regionThe Hongze Lake Basin, China. Study focusA drought index is an essential tool for monitoring and prediction, and the construction of a comprehensive drought index is at the cutting edge of current drought research. Considering the regulation effect of lakes, a new drought index aimed at large lake catchment areas, namely the Multivariate Standardized Drought Index for Lakes Basin (MSDILB), can comprehensively characterise meteorological and hydrological drought. Here, the Standardised Precipitation Evapotranspiration Index (SPEI), Standardised Runoff Index (SRI), and MSDILB drought indices were used to evaluate the evolution of drought in the study area. Moreover, the response and conversion relationships between the basin’s hydro-meteorological elements and the regulation of the lake were explored. New hydrological insights for the RegionThe results indicate that: 1) in a dry year, the spatial-temporal heterogeneous runoff in the basin is redistributed by the storage and regulation of large lakes, thereby reducing the frequency of drought events; 2) the MSDILB drought index merges the characteristics of hydrological drought and meteorological drought indices and can monitor the evolution of drought events in lake basins; 3) the MSDILB drought index can sensitively capture the effects of lake hydrological regime change on drought events in the basin. Thus, it has the advantage of comprehensively characterising the drought conditions in large lake basins.

Remote Sensing-Based Hydro-Extremes Assessment Techniques for Small Area Case Study (The Case Study of Poland)

Meteorological hazards, such as floods, can develop rapidly and are usually a local phenomenon. On the other hand, drought is a phenomenon arising over longer periods. Moreover, it occurs in areas that are remarkably diverse in terms of surface area. Drought has a massive impact on agriculture, socio-economic activities, and the natural environment. However, determining the losses associated with the phenomenon of drought and its identification is complicated. The aim of this paper is to identify and quantify droughts using climatic indices, which include the combined climatologic deviation index, groundwater drought index, water storage deficit index and multivariate standardized drought index. Based on the research, it was concluded that the CCDI, GGDI, WSDI, and MSDI indicators can be a useful tool, on the basis of which it was possible to analyze drought periods. These periods were not related to changes and loss of groundwater, but resulted from low rainfall and snowfall.

A novel comprehensive agricultural drought index accounting for precipitation, evapotranspiration, and soil moisture

Univariate drought indices do not reflect the integrated process through which droughts develop and occur. Therefore, developing a multivariate drought index that accounts for precipitation, evapotranspiration, and soil moisture is crucial for comprehensive drought prediction and evaluation. In this study, a comprehensive agricultural drought index (CADI) was developed using a combination of five commonly used binary copulas by considering evapotranspiration factors based on the Multivariate Standardized Drought Index (MSDI), which is based on both precipitation and soil moisture, and the drought monitoring capability of CADI was evaluated. The relationship between the CADI and the yield anomalies of winter wheat and summer maize in the rainfed and irrigated croplands of the North China Plain (NCP) was analyzed, and the drought evolution characteristics and trends in rainfed and irrigated croplands were examined. The results indicated that compared with the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSI), and MSDI, the proposed CADI accurately reflected the integrated process of meteorological and agricultural drought occurrence and evolution. In addition, the strength of the correlation between the CADI and the yield anomalies of winter wheat and summer maize in the rainfed and irrigated croplands of the NCP surpassed that of the SPEI, SSI, and MSDI, thereby confirming the validity and reliability of the proposed CADI for drought monitoring. Over the past four decades, droughts in the rainfed and irrigated croplands of the NCP have demonstrated a decreasing trend, and severe droughts have been concentrated in winter. Overall, the results of this study can be used in agrometeorological drought early warning and agricultural impact assessment and can provide theoretical guidance for drought monitoring in other regions.

Copula-based comprehensive drought identification and evaluation over the Xijiang River Basin in South China

The reasonable and appropriate drought indicator is a crucial instrument for drought investigation and assessment, which can objectively compare the severity, duration, and frequency of drought events, so as to achieve the purpose of accurately monitoring and evaluating drought. However, a univariate drought index is often difficult to precisely describe the complex drought situation, and it is urgent to establish a multivariate comprehensive drought index to identify and reveal the drought characteristics. Based on Copula models, combined with meteorological factors precipitation and hydrological factors runoff, this paper constructed a Multivariate Standardized Drought Index (MSDI), which can characterize meteorological drought and hydrological drought simultaneously and evaluate the spatial–temporal evolution characteristics of drought over the Xijiang River Basin (XJB) in South China. The results indicated that: (1) the proposed MSDI can capture the occurrence of drought as sensitively as the Standardized Precipitation Index (SPI), and can represent the termination of drought as effectively as the Standardized Runoff Index (SRI), which can comprehensively depict the drought situation in the XJB, China; (2) since the 1980 s, there was a tendency of aridity with a mutation point of 1983 in the downstream of Xijiang River (DXJB), and the worst comprehensive drought occurred in 1989; (3) among the tributaries, the comprehensive drought severity was the heaviest in the DXJB, with an average MSDI value of –0.75; (4) based on the run theory and Copula model, the most serious drought event occurred from September 1988 to October 1990, with a drought severity of 33.36, a drought duration of 27 months, and a drought return period exceeding 50 years; and (5) the drought evolution was significantly influenced by abnormal atmospheric circulation factors, while ENSO had the largest influence on the drought variation in the XJB, followed by sunspot.

Teleconnections between ocean-atmosphere circulations and historical integrated drought in the Middle East and North Africa.

In the Middle East and North Africa (MENA) with extreme water scarcity worldwide, drought significantly impacts various aspects of the local people. This study uses the empirical multivariate standardized drought index (MSDI), wavelet power spectrum, and a blind source septation technique of independent component analysis (ICA) to unravel linkages between Atlantic- and Pacific-based climate indices and MENA's drought between Jan 1979 and Dec 2016. It is found that drought in the eastern MENA is more than its western linked to climate indices. PDO, ENSO, and AMO, on the whole, have statistically stronger correlations with the MENA's drought than NAO (absolute R2 is less than 0.17, 0.22, and 0.21 for interannual, decadal, and interdecadal modes, respectively). The blind sources extracted by ICA confirm the higher impacts of PDO (R2 = 0.39), ENSO (R2 = 0.30), and AMO (R2 = 0.38) on the Middle East's drought. PDO's modulation linkage to MENA's drought occurs from interannual to decadal, and interdecadal variability cycles. ENSO largely impacts the 2-7-year interannual (R2 = 0.40-0.48 for the Middle East) and 8-12-year decadal (R2 = 0.18-0.30) drought cycles, while the PDO more affects 8-12-year decadal (R2 = 0.29-0.58) modes. The interdecadal (30-50-year) variability of MENA's drought mostly correlates with AMO and PDO. Overall, the interannual droughts are mostly controlled by ENSO while the decadal by PDO. For the interdecadal droughts, PDO and AMO are the most important in the Middle East and the western MENA, respectively. This study highlights that an integrated drought signal is not solely derived from a single climate index since each index may modulate a specific drought variability mode that may differ from other indices. The physical mechanisms behind these observations are further described.

Multi-Scale Analysis of Agricultural Drought Propagation on the Iberian Peninsula Using Non-Parametric Indices

Understanding how drought propagates from meteorological to agricultural drought requires further research into the combined effects of soil moisture, evapotranspiration, and precipitation, especially through the analysis of long-term data. To this end, the present study examined a multi-year reanalysis dataset (ERA5-Land) that included numerous drought events across the Iberian Peninsula, with a specific emphasis on the 2005 episode. Through this analysis, the mechanisms underlying the transition from meteorological to agricultural drought and its features for the selected region were investigated. To identify drought episodes, various non-parametric standardized drought indices were utilized. For meteorological droughts, the Standardized Precipitation-Evapotranspiration Index (SPEI) was employed, while the Standardized Soil Moisture Index (SSI), Multivariate Standardized Drought Index (MSDI), and Standard Precipitation, Evapotranspiration and Soil Moisture Index (SPESMI) were utilized for agricultural droughts, while their ability to identify relative vegetation stress in areas affected by severe droughts was investigated using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Anomaly provided by the Copernicus European Drought Observatory (EDO). A statistical approach based on run theory was employed to analyze several characteristics of drought propagation, such as response time scale, propagation probability, and lag time at monthly, seasonal, and six-month time scales. The retrieved response time scale was fast, about 1–2 months, and the probability of occurrence increased with the severity of the originating meteorological drought. The duration of agricultural drought was shorter than that of meteorological drought, with a delayed onset but the same term. The results obtained by multi-variate indices showed a more rapid propagation process and a tendency to identify more severe events than uni-variate indices. In general terms, agricultural indices were found to be effective in assessing vegetation stress in the Iberian Peninsula. A newly developed combined agricultural drought index was found to balance the characteristics of the other adopted indices and may be useful for future studies.

Identification of Extreme Droughts Based on a Coupled Hydrometeorology Index from GRACE-Derived TWSA and Precipitation in the Yellow River and Yangtze River Basins

Global climate change and human activities have exacerbated droughts’ environmental and socioeconomic threats. However, there is still a lack of effective techniques to consider their combined impacts on drought identification. Therefore, a new copula-based multivariate standardized drought index (CMSDI) was proposed, which integrates precipitation data and terrestrial water storage anomaly (TWSA) data observed by Gravity Recovery and Climate Experiment (GRACE) satellites. The applicability of the CMSDI was assessed compared with the water storage deficits index (WSDI), the self-calibration Palmer drought severity index (sc-PDSI), the standardized precipitation evapotranspiration index (SPEI), and the standardized precipitation index (SPI) in the Yellow River Basin (YRB) and the Yangtze River Basin (YZRB) for 2002–2020. The assessments were conducted regarding both temporal evolution and spatial distribution. The results showed that the CMSDI was more synchronized with the WSDI and SPI than with the other two indices and presented different trends and correlations in the YRB and YZRB. The CMSDI outperformed the other drought indices due to the limitations of the sc-PDSI, SPEI, and SPI in detecting certain drought events, and the greater inaccuracy of the WSDI in identifying extreme droughts. Furthermore, the CMSDI revealed a clear upward trend in parts of the middle and lower YRB and a clear downward trend in the upper YZRB, emphasizing the need for more attention to droughts in the YRB. This study presents a new perspective on the integrated use of satellite and measured data in drought monitoring across different regions.

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

AbstractUnderstanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias‐corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological‐hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought‐related consequences.

Bivariate Hazard Assessment of Combinations of Dry and Wet Conditions between Adjacent Seasons in a Climatic Transition Zone

Accumulated evidence reminds one that abrupt transitions between dry and wet spells, though attracting less attention, have harmful influences upon global continents as extensively investigated droughts and floods. This study attempts to incorporate dryness–wetness transitions into the current hazard assessment framework through bivariate frequency analysis and causal attribution from a teleconnection perspective. In the study, regional dry and wet conditions are monitored using the multivariate standardized drought index (MSDI) which facilitates the integrated evaluation of water deficits/surplus from a combined viewpoint of precipitation (largely denoting the received atmospheric water) and runoff (representing an important source of surface water). On such a basis, a copula-based method is subsequently utilized to calculate joint return periods of dryness–wetness combinations in three (i.e., moderate, severe and extreme) severity scenarios. The changing frequency of diverse dryness–wetness combinations is also estimated under a changing climate using a 25-year time window. Furthermore, the cross-wavelet transform is applied to attribute variations in dry and wet conditions to large-scale climate indices, which benefits the early warning of dryness–wetness combinations by providing predictive information. A case study conducted during 1952–2010 in the Huai River basin (HRB)—a typical climatic transition zone in China—shows that the HRB is subject to prolonged dryness with the highest frequency, followed by the abrupt transition from dryness to wetness. Spatially, abrupt dryness–wetness transitions are more likely to occur in the southern and central parts of the HRB than in the rest of the proportion. The past half-century has witnessed the dominantly higher frequency of occurrence of dryness–wetness combinations under three severity scenarios. In particular, the occurrence of the continued dry/wetness escalates more rapidly than transition events under climate change. Moreover, a preliminary attribution analysis discloses the link of the dry and wet conditions in the HRB with climate indices, such as the El Niño southern oscillation, the Pacific decadal oscillation and the Arctic oscillation, as well as sunspot activities. The results of the study enrich the current atlas of water-related hazards, which may benefit more effective hazard mitigation and adaptation.

Evolution of multivariate drought hazard, vulnerability and risk in India under climate change

Abstract. Changes in climate and socio-economic conditions pose a major threat to water security, particularly in the densely populated, agriculture-dependent and rapidly developing country of India. Therefore, for cogent mitigation and adaptation planning, it is important to assess the future evolution of drought hazard, vulnerability and risk. Earlier studies have demonstrated projected drought risk over India on the basis of frequency analysis and/or hazard assessment alone. This study investigates and evaluates the change in projected drought risk under future climatic and socio-economic conditions by combining drought hazard and vulnerability projections at a country-wide scale. A multivariate standardized drought index (MSDI) accounting for concurrent deficits in precipitation and soil moisture is chosen to quantify droughts. Drought vulnerability assessment is carried out combining exposure, adaptive capacity and sensitivity indicators, using a robust multi-criteria decision-making method called the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In the worst-case scenario for drought hazard (RCP2.6-Far future), there is a projected decrease in the area under high or very high drought hazard classes in the country by approximately 7 %. Further, the worst-case scenario for drought vulnerability (RCP6.0-SSP2-Near future) shows a 33 % rise in the areal extent of high or very high drought vulnerability classes. The western Uttar Pradesh, Haryana and western Rajasthan regions are found to be high risk under all scenarios. Bivariate choropleth analysis shows that the projected drought risk is majorly driven by changes in drought vulnerability attributable to societal developments rather than changes in drought hazard resulting from climatic conditions. The present study can aid policy makers, administrators and drought managers in developing decision support systems for efficient drought management.

Copula-based multivariate standardized drought index (MSDI) and length, severity, and frequency of hydrological drought in the Upper Sakarya Basin, Turkey

Copula-based multivariate standardized drought index (MSDI) and length, severity, and frequency of hydrological drought in the Upper Sakarya Basin, Turkey

Future global concurrent droughts and their effects on maize yield

Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950–2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950–2014), near future (2021–2060), and far future (2061–2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions.

Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change

This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980–2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020–2056 period. Based on the normalized root mean square error (NRMSE), Cramer-von mises statistic (Sn), and Nash Sutcliffe (NS) evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe and longer droughts will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, the joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it will decrease for extreme droughts in the future.

Multivariate analysis of concurrent droughts and their effects on Kharif crops—A copula‐based approach

AbstractApart from creating an ecological imbalance, drought events could affect an agrarian country's economy and food security by reducing crop yields. The antecedent meteorological droughts could prolong into hydrological and (or) agricultural droughts and may co‐exist as concurrent droughts. The current study aims to comprehensively study Indian concurrent droughts, their effects on crop yield, and possible teleconnection with ENSO (El Niño–Southern Oscillation), adopting a copula‐based multivariate approach. The copula functions can replicate the correlation among the variables and keep the dependence structure intact. The concurrent drought characteristics are computed using a multivariate standardized drought index that incorporates the three primary drought indices using the Gaussian copula. Some of the severe concurrent drought years such as 2002, 1987, 1972, and 1965 caused considerable yield losses in Kharif season crops of groundnut, millet, and rice. This prompts to construct quad‐variate models involving the crop yield and the three drought indices using the vine copulas that perform better than the elliptical and symmetric Archimedean copula. Though the isolated forms of droughts could cause mild yield losses, the probability of concurrent droughts causing high to exceptional losses is more. Further, the ENSO teleconnection with the concurrent monsoon droughts is analysed and mapped. The above‐normal warming of the Nino 3.4 region over the tropical Pacific during the months leading up to the monsoon could signal concurrent monsoon droughts in the areas under the Ganga‐Brahmaputra basin at a probability of around 45%. These results could be helpful in drought mitigation measures and policymaking.

Ocean-atmosphere circulation controls on integrated meteorological and agricultural drought over Iran

Drought may affect more people than any other climate extremes worldwide, especially in (semi)arid climates such as Iran. In this paper, the empirical Multivariate Standardized Drought Index (MSDI) that incorporates the meteorological and agricultural drought conditions is first computed. The wavelet analysis is then used to identify teleconnections between ocean–atmosphere oscillations from Atlantic (AMO, AO, and NAO) and Pacific (ENSO, PDO, and PNA) oceans and Iran’s drought at a highly dense network of regularly gridded points (2485) during 1979–2016. It is found that among the droughts of 1980, 1985–86, 1990–91, 1998–99, 2000, 2007–08, and 2010–11, both the 2007–08 and 2000 are the broadest and strongest across the country. Overall, those droughts that occurred in the 2000–2016 period (average MSDI = -1.14) were, on the whole, stronger than those happened over the latest 20 years of the 20th century (average MSDI = -0.77), reflecting the strengthening of the influence that ENSO has on the Middle East climate in the most recent decades. According to the wavelet coherence results, unlike the Atlantic-based oscillations that almost have an anti-correlation with droughts, there is a positive correlation between Pacific-based oscillations and Iran’s drought. It is also identified that the severe historical droughts across the country were usually associated with a strong La Niña coupled with cold PDO and positive NAO phases in the latest 30 years. Overall, droughts in Iran tend to be substantially controlled by multiple interacting of climate indices since three-coupled indices have stronger coherence with drought than either two-coupled indices or single ones. A combination of the three indices of ENSO, PDO, and NAO has the strongest coherence with Iran’s drought compared to all the other three- and two-coupled, and all single indices, indicating that integrated impact of climate indices can better capture hydroclimate teleconnections. These multiple interacting impacts from the different large-scale climate circulation patterns mark a significant step towards an improved prediction of severe droughts across Iran and can also be used for other parts of the world.

Bayesian-based time-varying multivariate drought risk and its dynamics in a changing environment

Drought is one of the most damaging but least understood environmental disasters. The time-varying multivariate drought risk and its dynamics have remained unresolved in a changing environment. To this end, a Bayesian framework with time in its location parameter as a covariate was introduced in this study to conduct time-varying distributions of duration and severity. Besides, the joint distribution of precipitation and runoff was developed by bivariate non-parameter density kernel estimation for multivariate drought index NKMSDI (Nonparametric Kernel Multivariate Standardized Drought Index) and Expected Waiting Time (EWT)-based return period was used to estimate drought risk. Finally, the time-varying risk trends were explored and verified via correlations between drought risk and Normalized Difference Vegetation Index series. Results indicate that: (1) bivariate return period is more accurate than univariate return period for drought risk assessment and return periods under non-stationary assumption are more reasonable than those under stationary assumption; (2) the multivariate drought risks present obviously increasing trends and the western basin shows the highest increasing rate; and (3) the increasing drought risks exhibit strong association with sunspot activities and local vegetation dynamics. In general, this study provides new insights into drought risk and its dynamics under the time-varying drought properties condition, which is highly important for robust and effective management practices.

The development of a novel nonstationary meteorological and hydrological drought index using the climatic and anthropogenic indices as covariates

Hydrometeorological variables such as precipitation and runoff are not necessarily stationary under change environment due to anthropogenic influences and climate change. Therefore, the assumption of stationary of standardized drought indices cannot necessarily be applied in the future. The novelty of this study is to develop a nonstationary multivariate standardized drought index, entitled the Nonstationary Meteorological and Hydrological Drought Index (NMHDI), as an improvement of the Meteorological and Hydrological Drought Index (MHDI). The method used consisted of three parts: (1) simulation of the nonstationary marginal distribution of precipitation data using climatic indices as covariates; (2) the nonstationary probability model fitted to runoff data using the climatic and anthropogenic indices as covariates and; (3) using the time-varying copula model to describe the temporal dependence structure of precipitation and runoff, following which the NMHDI was calculated. The performances of the NMHDI and MHDI was compared at the Huaxian station in Weihe River Basin, western China for the period 1961–2010 to illustrate the capabilities of the NMHDI. Moreover, bivariate frequency analysis was conducted using the two indices. The results indicated that the nonstationary model showed better performance in reproducing the variations in precipitation and runoff data and their dependence structure over time as compared with stationary one. The NMHDI identified more frequent extreme droughts. The improved performance of the NMHDI was attributed to its ability to respond to the continuously changing environment. In addition, the MHDI and NMHDI estimated significantly different recurrence periods. The proposed NMHDI provides a novel approach to comprehensive drought monitoring, and can provide valuable information for drought management policymaking under a changing environment.