Meteorological Drought Indices Research Articles

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Characteristics of propagation from meteorological drought to ecological drought in China: Lag and cumulative effects

The increasing frequency and severity of droughts will seriously impact agricultural production and the ecological environment. Studying the drought lag (DL) and drought cumulative (DC) effects of propagation between meteorological drought to ecological drought can help to enhance the capacity of ecological drought monitoring. Therefore, in this study, the meteorological drought index (non-stationary standardized precipitation evapotranspiration index, NSPEI) and ecological drought index (vegetation condition index, VCI) were obtained using ERA5 reanalysis data and MODIS vegetation index data, respectively. The purpose of this study was to reveal the DL and DC effects of the propagation of meteorological drought to ecological drought and investigate the spatial heterogeneity of drought propagation in different underlying surfaces and climate zones. The results indicated that: 1) Northern China is more vulnerable to severe and extreme droughts, and 68% of the region has significantly slower ecological droughts. 2) The western temperate zone, highland climate zone, and subtropical zone have shorter DL times for subtropical drought and longer DL times for eastern temperate zones. 3) DC and DL effects have similar spatial distributions. However, the DC duration of grassland and bare ground is shorter in the northwest and south, and longer in grassland and farmland in northern China. 4) China overall is more affected by the DC effect, while the DL effect is more significant in southern temperate zone.

On the timescale of drought indices for monitoring streamflow drought considering catchment hydrological regimes

Abstract. There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12–24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70–0.75 and 0.58–0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events.

Identification of the most suitable meteorological drought index for a region: a case study of Raigarh district in Chhattisgarh

Identification of the most suitable meteorological drought index for a region: a case study of Raigarh district in Chhattisgarh

Fusion-based approach for hydrometeorological drought modeling: a regional investigation for Iran.

The objective of this study was to model a new drought index called the Fusion-based Hydrological Meteorological Drought Index (FHMDI) to simultaneously monitor hydrological and meteorological drought. Aiming to estimate drought more accurately, local measurements were classified into various clusters using the AGNES clustering algorithm. Four single artificial intelligence (SAI) models-namely, Gaussian Process Regression (GPR), Ensemble, Feedforward Neural Networks (FNN), and Support Vector Regression (SVR)-were developed for each cluster. To promote the results of single of products and models, four fusion-based approaches, namely, Wavelet-Based (WB), Weighted Majority Voting (WMV), Extended Kalman Filter (EKF), and Entropy Weight (EW) methods, were used to estimate FHMDI in different time scales, precipitation, and runoff. The performance of single and combined products and models was assessed through statistical error metrics, such as Kling-Gupta efficiency (KGE), Mean Bias Error (MBE), and Normalized Root Mean Square Error (NRMSE). The performance of the proposed methodology was tested over 24 main river basins in Iran. The validation results of the FHMDI (the compliance of the index with the pre-existing drought index) revealed that it accurately identified drought conditions. The results indicated that individual products performed well in some river basins, while fusion-based models improved dataset accuracy more compared to local measurements. The WMV with the highest accuracy (lowest NRMSE) had a good performance in 60% of the cases compared to all other products and fusion-based models. WMV also showed higher efficiency in 100% of the cases than all other fusion-based and SAI models for simultaneous hydrological and meteorological drought estimation. In light of these findings, we recommend the use of fusion-based approach to improve drought modeling.

Multiscale Spatiotemporal Dynamics of Drought within the Yellow River Basin (YRB): An Examination of Regional Variability and Trends

Drought, as a recurring extreme climatic event, inflicts diverse impacts on ecological systems, agricultural productivity, water resources, and socio-economic progress globally. Discerning the drought patterns within the evolving environmental landscape of the Yellow River Basin (YRB) is imperative for enhancing regional drought management and fostering ecological conservation alongside high-quality development. This study utilizes meteorological drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI), for a detailed spatiotemporal analysis of drought conditions. It examines the effectiveness of these indices in the basin’s drought monitoring, offering a comprehensive insight into the area’s drought spatiotemporal dynamics. The findings demonstrate the following: (1) SPEI values exhibit distinct fluctuation patterns at varying temporal scales, with more pronounced fluctuations at shorter scales. Drought years identified via the 12-month SPEI time scale include 1965, 1966, 1969, 1972, 1986, 1997, 1999, 2001, and 2006. (2) A modified Mann–Kendall (MMK) trend test analysis of the scPDSI time series reveals a worrying trend of intensifying drought conditions within the basin. (3) Correlation analysis between SPEI and scPDSI across different time scales yields correlation coefficients of 0.35, 0.54, 0.69, 0.76, and 0.62, highlighting the most substantial correlation at an annual scale. Spatial correlation analysis conducted between SPEI and scPDSI across various scales reveals that, within diverse temporal ranges, the correlation peaks at a 12-month time scale, with subsequent prominence observed at 6 and 24 months. This observed pattern accentuates the applicability of scPDSI in the monitoring of medium- to long-term drought phenomena.

Characterization of groundwater drought and understanding of climatic impact on groundwater resources in Korea

Although South Korea is frequently subjected to monsoons, the shortage of summer monsoons and relatively dry spring and winter seasons cause droughts in the Korean Peninsula. Since the late 1960s, severe droughts in South Korea have encouraged extensive groundwater development for agricultural and economic growth. Therefore, identifying and estimating the impact of droughts on the country’s groundwater resources is crucial for establishing policies and regulations on groundwater exploitation and recharge. Current drought management of the country relies on a meteorological drought index. However, these estimates lack a comprehensive understanding of absolute drought severity and propagation. This study aims to develop a quantitative method for calculating the frequency and severity of groundwater droughts based on the Groundwater Deficit (GWD) derived from NASA's Gravity Recovery and Climate Experiment (GRACE) mission data. The estimated annual GWD in mass over the country ranges from -0.07 km3 to 0.04 km3, with a trend in the mass of -1.60×10−3 km3/year from 2002 to 2021. Thirteen groundwater drought events were identified based on GWD. Among those, the highest drought severities recorded in 2015 and 2019 were -34.27 km3 months and -55.38 km3 months, respectively. Monthly deficits define the quantity of water required to restore normal groundwater storage conditions. Hence, the minimum and average days to recover from groundwater droughts were estimated based on the strength of the storage-based approaches. The GWD approach aligns with meteorological drought databases with a time lag. It illustrates the transition from meteorological drought to groundwater drought, along with their onset, cessation, propagation, instantaneous severity, and peak magnitude.

Spatiotemporal analysis of meteorological drought in El Niño years over Oromia region, Ethiopia

Drought is one of the most common natural disasters globally, having major impacts on environmental, economic, and social conditions and Ethiopia is no exception particularly the Oromia region. In order to evaluate and characterize the meteorological droughts associated with El Niño years over the Oromia region, the satellite data CHIRPS was used. The monthly time series data for the period from 1991 to 2020 was used for temporal and spatial analysis of meteorological drought using standardized precipitation index (SPI) across SPI3, SPI6, SPI9 and SPI12 indices using GeoCLIM, GIS, and Python tools. The results of this study show that meteorological droughts during the El Niño years indicated an increment across weak, moderate, and strong El Niño events over the Oromia region. The dryness was visualized with frequency and duration in time-scale across short-term drought indices in time steps. The time-scale temporal meteorological drought indices in three to twelve months show that the drought indices varied in magnitude, duration, and frequency in meteorological droughts. In general, the meteorological drought severity of correlation for the remaining zones analysis between SPI3 and SPI6, SPI6 and SPI12, and SPI9 and SPI12 indices was dominated by an increment of the correlation values over short to long timescales over the study area.

A Novel Fusion-Based Methodology for Drought Forecasting

Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R2) value of 0.845 in each month in 2018. The spatiotemporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena.

Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province).

In most arid and semi-arid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes influencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these fluctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the fluctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indices in a 6-month time window for a long-term statistical period (1989-2018), the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immediately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coefficient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indices, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the significance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future.

Quantifying the 2022 extreme drought in the Yangtze River Basin using GRACE-FO

In 2022, the Yangtze River Basin (YRB) experienced its most extreme drought event since 1961. Conducting a comprehensive assessment of this severe drought is crucial for understanding the entire drought process. GRACE and GRACE-FO gravity satellites have the capability to monitor global mass transport, making them suitable for large-scale drought monitoring. In this study, we investigated the changes in water storage caused by the drought using GRACE/GRACE-FO data and multi-source observations. Our evaluation incorporated meteorological drought indices, including the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), to explore the drought propagation process. Additionally, we examined the effects of trends in terrestrial water storage in the YRB, resulting from reservoir impoundment and climatic factors, on the drought index calculation. The results indicate that the extreme drought in the YRB began in August 2022, resulting in a water storage loss of approximately −158 ± 22 km3 compared to July 2022. There was almost no delay in the response of soil moisture drought and hydrological drought to the meteorological drought at a monthly scale. This extreme drought had its most severe impact on the middle and lower reaches of the YRB, especially in the Dongting Lake Basin, where the GRACE drought index value remained below −2.5 until December 2022. This study demonstrates the significant water storage deficit during the extreme drought in the YRB in 2022, providing a comprehensive understanding of this historic drought.

Probabilistic monitoring of meteorological drought impacts on water quality of major rivers in South Korea using copula models

The impacts of drought range from water supply for humans to ecosystems. Drought affects river water quality by disturbing the hydrological regime in a variety of ways, and can degrade water quality by reducing surface and groundwater availability. In particular, drought-induced low flows, reduced nutrient dilution, and extreme increases in water temperature affect various water quality parameters in streams. Furthermore, the effects of drought on stream water quality may vary from season to season and from stream segment to stream segment, which requires careful investigation. In this study, Environmental Drought Condition Index - water quality (EDCI-wq) is proposed using a bivariate copula joint probability model between meteorological drought index and river water quality. Using this, environmental drought with respect to water quality is defined, and it is confirmed that environmental drought with respect to water quality can be routinely monitored through time series analysis and mapping of the proposed EDCI-wq. In addition, in order to express the environmental drought condition more explicitly to the general public, the environmental drought condition is graded into four classes based on the EDCI-wq. Furthermore, the sensitivity of river water quality to meteorological drought was estimated using the copula joint probability model, which allowed us to identify river segments that are relatively more sensitive to meteorological drought events.

Development of a nonstationary Standardized Precipitation Evapotranspiration Index (NSPEI) and its application across China

Drought is a natural disaster and its occurrence and development can be attributed to a range of driving factors, such as deficient precipitation, wind, temperature, land use/land cover changes, and so on. Therefore, attribution and monitoring of droughts are complicated and challenging. Standardized Precipitation Evapotranspiration Index (SPEI) has been widely used in meteorological drought monitoring. Nonstationarity has not been considered in SPEI-based drought monitoring. Here we propose a modified SPEI considering the nonstationarity of hydrometeorological processes, i.e. NSPEI hereafter. We observe nonstationarity SPEI in the Northeast, the Huang-Huai-Hai Plain, the Yangtze River Delta, and the Qinghai-Tibet Plateau. The goodness-of-fit can be determined for 76% of the total stations. Under the assumption of nonstationarity, the drought monitoring performance of the NSPEI is more robust than the traditional SPEI. Besides, we evaluate drought conditions across China under four shared socioeconomic pathways (SSPs), i.e. SSP126, SSP245, SSP370 and SSP585. We identify intensifying droughts in southwest China but a wetting tendency in northern China in winter under the SSP126 scenario; but a significant wetting tendency in southwest China and a drying tendency in northeast China under SSP245, SSP370 and SSP585 scenarios. This study provides a novel meteorological drought index and highlights a fresh picture of drought conditions across China under different SSPs.

Assessment of Drought and Desertification Outlook in China Using Meteorological Drought Indices

Changing land use patterns in China may result in adverse impacts on the climate in Northeast Asia. In particular, desertification of the Chinese mainland can also have a significant influence on the climate in South Korea, such as the occurrence of yellow dust storms. This study aimed to forecast the desertification in China through a comprehensive drought analysis. Three drought indices namely, SPI, GEVI, and SPEI, were determined for eight cities in China namely, Beijing, Guangzhou, Shanghai, Xianyang, Urumqi, Chongqing, Wuhan, and Weihai. There was a strong correlation between SPI and SPEI, while GEVI showed a slightly lower correlation. The long-term droughts that can trigger desertification exhibited high correlations among the considered indices. To assess the impact of climate change, periods of severe drought occurrence were compared in ten-year intervals since 1970. It was observed that severe droughts occurred throughout China in the 1970s and 1990s. However, there was no apparent trend in increasing drought days or worsening drought conditions. Desertification in China can be influenced not only by the rainfall but also by the changes in land use pattern and exploitation of water resources. Therefore, additional research is deemed necessary to evaluate desertification in China.

Drought characterization: A systematic literature review

<p>This study examined the worsening severity of global droughts caused by climate change. However, the multiple definitions and varied range of drought indices pose challenges in effectively monitoring and assessing the prevalence and severity of droughts. This study aims to give a comprehensive overview of the various drought definitions found in the literature and how they have evolved based on their applications. Specifically, the focus was to shed light on the dynamic nature of drought characterization and offer insights into the factors that shaped its conceptualization over time. Within this context, this study explored three primary categories of drought indices: climatic, remote sensing, and composite. Each category was discussed in relation to its utility in specific fields, such as meteorological, agricultural, and hydrological drought assessments, along with an analysis of their strengths and limitations. Furthermore, this study presents modified meteorological drought indices that have been adapted to better monitor agricultural droughts. Additionally, the authors used geographic information systems to create a map showing the distribution of drought-related publications globally over the past decade. The findings showed that countries with arid and semi-arid climates are more actively involved in drought research, highlighting their particular interest and concern regarding the subject matter. The implications of this study emphasize the urgent need for immediate and coordinated efforts to address the escalating issue of droughts caused by climate change. By improving monitoring and assessment methods and focusing on tailored strategies in vulnerable regions, it is possible to mitigate the far-reaching consequences of drought and to build more resilient communities and ecosystems.</p>

Recent European drying and its link to prevailing large-scale atmospheric patterns

The extreme 2018 and 2022 droughts pose as recent examples of a series of drought events that have hit Europe over the last decades with wide ranging social, environmental and economic impacts. Although the link between atmospheric circulation and meteorological drought is clear and often highlighted during major drought events, there is a lack of in-depth studies linking historical changes in meteorological drought indices and prevailing large-scale atmospheric patterns in Europe. To meet this shortfall, we investigated the relation between changes in large-scale atmospheric patterns and meteorological drought, as indicated by the geopotential height at 500mb (Z500) and the Standardised Precipitation-Evapotranspiration Index (SPEI), respectively. Calculations were done separately for four climate regions (North, West, Central-East and Mediterranean) over the growing season (March–September). Coherent patterns of significant changes towards higher pressure (increasing Z500) and drier conditions (decreasing SPEI) over 1979–2021 are found over West in spring and Central-East in summer. Z500 and SPEI are strongly linked, reflected by both significant (1979–2021) correlations and high co-occurrences (69-96%) between meteorological drought and high-pressure anomaly occurrences since 1900. North shows the most heterogeneous trend patterns and weakest links, but constitutes a hotspot of significantly increasing Z500 in September. Finally, we performed an ensemble-based, European wide analysis of future Z500, based on CMIP6 low-end (SSP126) and high-end (SSP585) 21st century emission scenarios. According to the projected changes, anomalously high-pressure systems will be the new normal regardless of scenario, and well exceeding the 2018 and 2022 levels in the case of the high-end emission scenario. However, due to the limitations of the model ensemble to represent the spatial heterogeneity in historical Z500 variability and trends (1979–2014), projected changes in large-scale circulation, and associated meteorological droughts, are highly uncertain. This paper provides new insight into significant trends in atmospheric circulation over Europe, their strong links to the observed drying trends, and the inability of a CMIP6 ensemble to reproduce the spatial heterogeneity of the circulation changes.

Defining the Multiscalar Index Timescale—Soil Water Depth Continuum for the Southwestern United States

AbstractThe lack of long‐term reliable in‐situ soil moisture data sets creates unique drought monitoring challenges for the semi‐arid Southwestern United States (“Southwest”). Land managers use multiscalar meteorological drought indices, like the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI), as proxies for soil water availability; however, objectively identifying the index and timescale that best represents soil water availability in semi‐arid environments remains a significant gap for applying available climate information to land management action. Here, we couple site‐specific soil modeling with high resolution, spatially continuous meteorological data sets to define the relationship between index timescale length and soil water availability of different depths. By creating new matric potential indices (MPI) from 0 to 200 cm, we correlate MPI time series with the SPI and SPEI at different timescale lengths to identify the best index‐timescale combination for each depth. Results indicate the general relationship between highest correlating index‐timescale and MPI‐depth operates roughly on a 1‐month:5 cm step progression at shallow depths (<80 cm). Analysis by soil texture class shows that soils with higher clay content produce shallower sloped relationships (>1‐month:5 cm) than sandy soils (<1‐month:5 cm). Overall, the SPI produced higher correlations and less error with the MPI compared to the SPEI across all texture classes and depths. Thus, the SPI is a good indicator of soil water availability at shallow depths (<80 cm) and should be considered for soil drought monitoring on Southwestern drylands. However, land managers should consult local soils information, if available, given the impacts of texture class on timescale‐depth relationships.

Early crop yield prediction for agricultural drought monitoring using drought indices, remote sensing, and machine learning techniques

Abstract The unpredictability of crop yield due to severe weather events such as drought and extreme heat continues to be a key worry. The present study evaluated six meteorological and three Landsat satellite-based vegetation drought indices from 1986 to 2019 in the drought-prone-semi-arid Saurashtra region of Gujarat (India). Cotton and groundnut crop yield prediction models were developed using multiple linear regression (MLR), artificial neural network with MLP, and random forest (RF). The models performed crop yield estimation at two timescales, i.e., 75 days after sowing and 105 days after sowing. The standardized precipitation evapotranspiration index/reconnaissance drought index among meteorological drought indices, normalized difference vegetation anomaly index/vegetation condition index, and normalized difference water index anomaly were chosen as best highest correlations with crop yields. The RF-based models were found most efficient in predicting the cotton and groundnut yield of Saurashtra with R2 ranging from 0.77 to 0.92, Nash–Sutcliffe efficiency ranging from 71 to 90%, and root-mean-square error ranging from 80 to 133 kg/ha for cotton and 299 to 453 kg/ha for groundnut. This study demonstrated the method for making several decisions based on early crop yield prediction including timely drought mitigation measures.

Spatiotemporal variability of vegetation response to meteorological drought on the Korean Peninsula

Abstract To assess vegetation drought, it is important to understand the relationship between climate and vegetation and to accurately measure the response of vegetation activity to meteorological drought. In this study, we used the vegetation health index (VHI) to investigate the propagation time and time-lag of vegetation response to different meteorological drought indices, including the standardized precipitation index (SPI), evaporative demand drought index (EDDI), standardized precipitation–evapotranspiration index (SPEI), and copula-based joint drought index (CJDI). Using correlation analyses of meteorological drought indices with different time-scales and time-lags and VHIs with different weights, we determined which meteorological drought indices and their corresponding time-scales and time-lags best represent the effects of meteorological drought on vegetation activity on the Korean Peninsula. We also evaluated the relative roles of normalized difference vegetation index (NDVI) and land surface temperature (LST) in quantifying vegetation response to meteorological drought. The meteorological drought index for monitoring vegetation response to meteorological drought on the Korean Peninsula was best applied using EDDI in January–May and SPEI in June–December. Vegetation health was dominated by LST in January–September, with a higher impact of NDVI in November–December. We expect these results to provide useful information for vegetation drought monitoring.

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.