Drought Index Research Articles

A Statistical Analysis of Drought and Fire Weather Indicators in the Context of Climate Change: The Case of the Attica Region, Greece

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change.

A modification of normalized difference drought index to enhance drought assessment using remotely sensed imagery.

Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies.

Beyond Traditional Metrics: Exploring the Potential of Hybrid Algorithms for Drought Characterization and Prediction in the Tromso Region, Norway

Meteorological drought, defined as a decrease in the average amount of precipitation, is among the most insidious natural disasters. Not knowing when a drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges in accurately predicting and monitoring global droughts, despite using various machine learning techniques and drought indices developed in recent years. Optimization methods and hybrid models are being developed to overcome these challenges and create effective drought policies. In this study, drought analysis was conducted using The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 to 2022 in the Tromsø region. Models with different input structures were created using the obtained SPI values. These models were then analyzed with The Adaptive Neuro-Fuzzy Inference System (ANFIS) by means of different optimization methods: The Particle Swarm Optimization (PSO), The Genetic Algorithm (GA), The Grey Wolf Optimization (GWO), and The Artificial Bee Colony (ABC), and PSO optimization of Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), and RMSE-Standard Deviation Ratio (RSR) served as performance evaluation criteria. The results of this study demonstrated that, while successful results were obtained in all commonly used algorithms except for ANFIS-GWO, the best performance values obtained using SPI12 input data were achieved with ANFIS-ABC-M04, exhibiting r: 0.9516, NSE: 0.9054, and RMSE: 0.3108.

Predicting agricultural and meteorological droughts using Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM).

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population.

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

Drought Assessment based on Different Metrological Drought indices in Sulaymaniyah Governorate, KRG, Iraq

Drought is a dangerous situation that lowers individual standards of living. It is considered a devastating natural event. This study intends to thoroughly examine the occurrence and characteristics of drought events in the Sulaymaniyah Governorate from 1985 to 2015. The study employed three standardized indices: the standardized precipitation evapotranspiration index (SPEI), the standardized precipitation index (SPI), and the Reconnaissance Drought Index (RDI). These indices were utilized at different periods, namely 3, 6, 9, and 12 months. According to findings, the maximum percent of extremely dry highlighted in Sulaymaniyah and Dukan stations for SPI index and time scale three months was 48% and 30%, respectively; the maximum percent of severely dry highlighted using RDI index and time scale three months with 41%, 32%, 44% in Sulaymaniyah, Dukan, and Darbandikhan stations respectively. Results revealed that the years 1998, 1999, 2000, 2007, 2008, 2009, and 2010 saw a drought for the used indices, Dukan station has the maximum severity of 112.25 for SPEI12 for the event happened from Nov. 2007 to Sep 2015. The maximum durations were observed at Dukan station using SPI and SPEI as well, and Darbandikhan station has the highest drought frequency. The correlations of drought indices, using the coefficient of determination ( ) showed the lowest and highest agreement between SPI-SPEI and SPI-RDI, respectively. The findings revealed that water resources in the study area are at risk of depletion due to drought. Therefore, water management techniques are required to mitigate drought in the study area.

Comparative study of meteorological drought indices for Adilabad district, Telangana, India

Comparative study of meteorological drought indices for Adilabad district, Telangana, India

The optimal applications of scPDSI and SPEI in characterizing meteorological drought, agricultural drought and terrestrial water availability on a global scale

The Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are two of the most commonly used drought indices. However, scPDSI and SPEI at a specific scale are often used interchangeably to characterize meteorological drought, agricultural drought, or terrestrial water availability, leading to potential inaccuracies in research outcomes. This study thus presents a global-scale assessment of the applications of scPDSI and SPEI at various timescales (SPEIs) in these contexts. Our findings indicate that scPDSI is more suitable for monitoring agricultural drought than meteorological drought, and highlight the effectiveness of SPEI at one month scale (SPEI01) for meteorological drought. Additionally, SPEI at nine months scale (SPEI09) is more appropriate for agricultural drought. Regarding their relationship with vegetation water stress, scPDSI and SPEI09 are more closely associated with root-zone soil moisture, while SPEI01 is most closely linked to vapor pressure deficit. Furthermore, we evaluate the capability of scPDSI and SPEI in representing terrestrial water availability by analyzing the responses of diverse vegetation indicators to them, including the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP). All four vegetation indicators show the highest sensitivity of negative response to SPEI01 in cold climate regions, suggesting SPEI01 is most applicable in these regions. In drylands, vegetation indicators exhibit higher sensitivity of positive responses to SPEI at six months scale (SPEI06) and SPEI09, indicating SPEI06 and SPEI09 effectively characterize water availability in such areas. These findings enhance the understanding of scPDSI and SPEI, providing clearer guidelines for their global-scale applications in meteorological drought, agricultural drought, and terrestrial water availability.

Dynamic Stochastic Model of Atmospheric Drought Forecast in Uzbekistan

The article considers a predictive model of atmospheric dryness with a one-month lead time, developed at the Scientific Research Hydrometeorological Institute. The model is based on a dynamic stochastic approach to constructing a regression predictive equation. From the set of existing methods for constructing regression equations, a method based on the characteristic roots (eigenvalues) of a correlation matrix, including a column of the predicted variable and predictor columns (extended matrix) is applied. The predicted variable is the standardized drought index SPI, and the predictors are the average monthly precipitation for the 3 months preceding the forecast month, the average monthly value of variations in solar activity (Wolf numbers) and the average monthly value of the Southern Oscillation index for the month preceding the forecast. The predictors were selected on the basis of mutual correlation and applied time series analyses between the aridity index SPI and the indicated heliogeophysical values. The performed estimates of the studied dependence of the aridity index SPI on the state of solar activity, the influence of El Niño (La Niña) and precipitation preceding the forecast date showed their high correlation. Estimates of the accuracy of the SPI forecast with a monthly advance lead time for the territory of Uzbekistan, performed on an independent sample, were quite high, which was the basis for the introduction of this model into the operational work of the hydrometeorological service of Uzbekistan (Uzhydromet).

Application of Machine Learning Models for Short-term Drought Analysis Based on Streamflow Drought Index

Application of Machine Learning Models for Short-term Drought Analysis Based on Streamflow Drought Index

Artificial Neural Networks for Drought Forecasting in the Central Region of the State of Zacatecas, Mexico

Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state of Zacatecas, a semi-arid region of Mexico, is explored by means of artificial neural networks (ANNs), forecasting numerical values of three drought indices—the standardized precipitation index (SPI), the standardized precipitation and evapotranspiration index (SPEI) and the reconnaissance drought index (RDI)—in an effort to establish the most suitable index for drought forecasting with ANNs in semi-arid regions. Records of 52 years of monthly precipitation and temperature were used. The indices were calculated in three different time scales: 3, 6 and 12 months. The analyzed models showed great capacity to forecast the values of the three drought indices, and it was found that for the trial set, the RDI was the drought index that was best fitted by the models, with the evaluation metrics R2 (determination coefficient), RMSE (root mean square error), MAE (mean absolute error) and MBE (Mean Bias Error) showing ranges of 0.834–0.988, 0.099–0.402, 0.072–0.343 and 0.017–0.095, respectively. For the validation set, the evaluation metrics were slightly better.

Assessments of various precipitation product performances and disaster monitoring utilities over the Tibetan Plateau

The Tibetan Plateau, often referred to as Asia’s water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study’s findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau.

Drought sensitivity analysis of meteorological and vegetation indices in Dak Nong, Vietnam

ABSTRACT Drought poses an increasing threat to agricultural sustainability in Dak Nong, Vietnam. This study investigates the sensitivity of meteorological and vegetation indices (VIs) to drought conditions in the region. We analyzed 23 years (2000–2022) of data from 11 meteorological stations and remote sensing imagery covering various crop types. Our methodology involved correlating multiple VIs (VHI, VCI, TCI, and TVDI) with meteorological drought indices (SPI and SPEI) at different time scales. Results reveal a strong correlation (r > 0.7, p < 0.001) between the vegetation health index (VHI) and the standardized precipitation evapotranspiration index (SPEI), particularly at a 4-month time scale (SPEI4). This combination proved most effective for drought monitoring across diverse vegetation types. Spatial analysis identified drought-sensitive zones covering 10.8% of the province, with steep terrain and limited river density. These areas, predominantly occupied by perennial agriculture, annual crops, and production forests, show heightened vulnerability to water scarcity. Our findings provide a scientific basis for targeted drought management strategies, including establishing an early warning system using SPEI4 and VHI, implementing water-efficient agricultural practices, and prioritizing farmer support in high-risk areas. This study enhances drought resilience and sustainable water resource management in Dak Nong and similar tropical regions.

The frequency distribution and stochastic analysis of the hydrological drought in northern Algeria

The objective of this study was to examine drought using the Streamflow Drought Index (SDI) at various time scales and its temporal evolution using monthly streamflow data from 1973 to 2009. Streamflow records were collected from a network of 14 hydrometric stations distributed throughout the study area. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to assess the quality of the adjustment. According to these criteria, the gamma law better suited the time scales of 3, 6, and 12 months, whereas the log-normal law was better suited to the scale of 9 months. The analysis of the Streamflow Drought Index in the three study basins (Middle and Upper Cheliff, Lower Cheliff, and the Mina) revealed that different classes of drought among 3, 6, 9, and 12-month time scales in the period of 1973 to 2009 had occurred, notably beginning in 1980. The frequency of 19 to 54% was found at all stations and in years marked by a mild drought. The moderate years had a frequency of 6 to 19%, while the severe and extreme years had a lower percentage (about 3 to 6%) in the study area. Two consecutive years of drought (D-D) were more likely in the Middle and Upper Cheliff basins (> 60%) for the 6, 9, and 12-month time scales, according to the transition of probability of first-order non-stationary Markov chain. On a three-month time scale, the transition probabilities (D-D) were greater than 50% in the Coastal basin and Lower Cheliff basin, as well as the Mina basin, and less than 50% in the Middle and Upper Cheliff basins.

Drought prediction using artificial intelligence models based on climate data and soil moisture

Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models’ performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation.

Variability and Diversity Studies Using Morpho-physiological Traits in Wheat (Triticum aestivum L.) under Irrigated and Rainfed Conditions

A study was conducted at Rice and Wheat Research Centre, Malan and Department of Genetics and Plant Breeding, CSK HPKV, Palampur, Himachal Pradesh, India during October, 2013 to May, 2014 to identify traits for developing reliable selection criteria and conduct diversity analysis. In the present investigation, fifty-six diverse wheat genotypes were evaluated in an alpha-design using various yield attributes and drought indices under irrigated and moisture-stress environments over three environments in two locations. Results from the study indicated significant differences for almost all the traits under all three environments and pooled environments. Variability studies showed high phenotypic and genotypic coefficient of variability with high heritability coupled with high genetic advance for seedling vigour under irrigated conditions as well as under moisture-stress conditions along with membrane stability index, dry matter accumulation and grain yield. On the basis of low DSI and high DRI values and relative ranking of morpho-physiological traits, seven genotypes HPW 368, NI 5439, CM 33, CM 19, HPW 236, CM 11 and HPW 349 have been found to be moisture-stress tolerant. Association studies showed that grains spike-1, harvest index, thousand grain weight, excised leaf water loss and days to 75% maturity are most important traits for selection of tolerant genotypes. On the basis of D2 statistics, 56 genotypes were grouped into twelve main clusters and genotypes HPW 277, HPW 297 and NIAW 1415 were found superior as well as diverse for most of the traits. Hence, these genotypes can be used in breeding programmes aimed at enhancing drought tolerance.

Evaluating machine learning models in predicting GRI drought indicators (case study: Ajabshir area)

Examining the condition of groundwater resources and the impact of droughts is valuable for effective water resources management. Today, machine learning (ML) models are recognized as one of the useful tools in time series predictions. In this study, the groundwater condition of one of the most important aquifers in northwest Iran was investigated using MODFLOW, followed by estimating the groundwater resource index (GRI) utilizing the multivariate adaptive regression spline (MARS) and least squares support vector regression (LSSVR) for a period between 2001 and 2019. Meteorological and hydrological drought indicators along with precipitation and flow rate were used as input variables for prediction. The simulation results revealed a groundwater level decrease since the aquifer withdrawal amount is more than the recharge amount. Besides, results showed that there is a limited interaction between surface water and groundwater resources, mainly caused by the decrease in the river flow and aquifer groundwater level drop. Both ML models performed well in GRI estimation, using groundwater flow, streamflow drought index, standardized precipitation index, and runoff as input variables. The performance of the MARS model with RMSE, MAE, and NSE error evaluation criteria of 0.37, − 0.19, and 0.83, respectively, exerted slightly better results than LSSVR with RMSE, MAE, and NSE of 0.48, − 0.06, and 0.80, respectively. The findings reveal the appropriate performance of both models in forecasting drought indicators, highlighting the necessity of using ML models in hydrology and drought prediction problems.

Assessment of habitat features modulated carbon sequestration strategies for drought management in tropical dry forest fragments

Habitat features, such as species diversity, functional diversity, tree size, disturbances and fragment sizes have differential impacts on carbon (C) storage and C-sequestration in forest ecosystems. Present study attempted to understand the tree strategies for modulating C-sequestration capacity across tropical dry forest fragments with variable edge distances. We evaluated the differences between drought strategies (i.e., drought avoiding and drought tolerant) for variations in stem density, relative growth rate (RGR), C-storage and C-sequestration, species diversity, functional diversity, tree size and disturbance indicators along edge distance gradient, besides analyzed the differences between drought strategies for responses of C-storage and C-sequestration to variations in species diversity, functional diversity, tree size and disturbance indicators. Various traits and functional indices were analyzed using standard statistical techniques. For total trees and for the two drought strategies, generalized linear modeling results showed a significant decline in stem density, RGR, C-stock, C-sequestration, species diversity, functional diversity and tree size indicators, while a considerable increase in disturbance indicators, along decreasing edge distance across the fragments. The drought strategies exhibited a high degree of variation in the slope of associations for above variables with edge distance across fragments. For predicting C-sequestration, structural equation modeling results showed highly significant influence of functional diversity indicators for drought avoiding strategy, while species diversity indicators were strongly significant for drought tolerant strategy. Moreover, fire index and drought index were critical predictors for C-sequestration for drought avoiding and drought tolerant strategies, respectively. This study provide inputs to understand the largely ignored processes of C-storage and C-sequestration in fragmented forests, which are currently prevalent due to heavy anthropogenic pressures. Our findings are useful for forest managers to understand vegetation responses to interactions of species diversity, functional diversity, tree size and disturbance indicators, for predicting the stability of larger fragments and for planning restoration of smaller fragments.

Improved identification and monitoring of meteorological, agricultural, and hydrological droughts using the modified nonstationary drought indices in the Yellow River Basin of China

Non-stationarity in hydro-meteorological series complicates drought monitoring and assessment. Our study aimed to better identify and monitor meteorological, agricultural and hydrological droughts in the Yellow River Basin (YRB) of China. We constructed nonstationary standardized precipitation index (NSPI), nonstationary standardized precipitation evapotranspiration index (NSPEI), nonstationary standardized soil moisture index (NSSMI) and nonstationary standardized runoff index (NSRI) based on the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We also investigated the performance of NSPI/NSPEI/NSSMI/NSRI by comparing them with historical drought events. Our results revealed that: (1) The probability distribution function (PDF), smoothing function (SF), and degrees of freedom (DF) significantly influenced the fitting effect of the GAMLSS model. The PDF emerged as the most critical factor in determining fitting accuracy, while SF and DF played crucial roles in preventing overfitting and accurately depicting data variations. (2) Stationary GAMLSS models were primarily suited for time series with lower-scales (1- and 3-month scales), whereas nonstationary models exhibited better suitability for time series with higher-scale (6- and 12-month scales). (3) NSPI and NSPEI demonstrated heightened sensitivity in identifying meteorological droughts, with NSPEI showing greater precision in determining drought intensity and duration. However, compared to SSMI, while NSSMI aligned more closely with historical agricultural drought events, it exhibited limited responsiveness to mild and moderate drought. (4) NSRI indicated that hydrological droughts in the tributaries of the YRB were significantly influenced by meteorological droughts, and the intensity of hydrological drought was stronger than that in the mainstream of the YRB. In conclusion, the nonstationary drought indices could better identify drought events in the period of climate change, and provide valuable information for drought management in the climate change environment.

Identification of propagation characteristics from meteorological drought to hydrological drought using daily drought indices and lagged correlations analysis

Study regionThe Juam Dam area in Suncheon, Jeollanam-do, South Korea Study focusThis study aims to analyze drought propagation characteristics using time-lagged correlation analysis based on daily drought index, determining the lag time from meteorological drought to propagation into hydrological drought. The target period for correlation analysis is the onset date of the dry spell preceding the occurrence of hydrological drought and the termination date of the dry spell following the termination of the drought for each drought event. The Standardized Precipitation Index (SPI) at various time-scales for meteorological drought and the Standardized Reservoir Supply Index (SRSI) for hydrological drought were applied. New hydrological insights for the regionThis study presents the objectivity and accuracy of drought onset and termination date for drought propagation analysis through daily lagged correlation analysis. Through ROC analysis, SPI90, SPI180, and SPI365 are shown to increase by an average of 16.0 %, 8.8 %, and 6.0 %, respectively. From 1993–2023, long-term hydrological droughts lasting 2 years occurred 5 times, with a maximum duration of 408 days, magnitude −629, and severity −1.76. The daily lag between the multi-scale SPIs and SRSI of individual drought events presents the possibility of predicting hydrological drought through multiple regression analysis. This research provides insights for improving hydrological drought monitoring, prediction, and response strategies through results of individual propagation characteristics of drought events.