Meteorological Drought Indices Research Articles

Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana

The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation.

Monitoring of Extreme Drought in the Yangtze River Basin in 2022 Based on Multi-Source Remote Sensing Data

The Yangtze River Basin experienced a once-in-a-century extreme drought in 2022 due to extreme weather, which had a serious impact on the local agricultural production and ecological environment. In order to investigate the spatial distribution and occurrence of the extreme drought events, this study used multi-source remote sensing data to monitor the extreme drought events in the Yangtze River Basin in 2022. In this study, the gravity satellite data product CSR_Mascon was used to calculate the GRACE Drought Intensity Index (GRACE-DSI), which was analyzed and compared with the commonly used meteorological drought indices, relative soil humidity, and soil water content data. The results show that (1) terrestrial water storage change data can well reflect the change in water storage in the Yangtze River Basin. Throughout the year, the average change in terrestrial water storage in the Yangtze River Basin from January to June is higher than the average value of 33.47 mm, and the average from July to December is lower than the average value of 48.17 mm; (2) the GRACE-DSI responded well to the intensity and spatial distribution of drought events in the Yangtze River Basin region in 2022. From the point of view of drought area, the Yangtze River Basin showed a trend of extreme drought increasing first, and then decreasing in the area of different levels of drought, and the range of drought reached a maximum in September with a drought area of 175.87 km2, which accounted for 97.71 per cent of the total area; at the same time, the area of extreme drought was the largest, with an area of 85.69 km2; (3) the spatial and temporal variations of the GRACE-DSI and commonly used meteorological drought indices were well correlated, with correlation coefficients above 0.750, among which the correlation coefficient of the SPEI-3 was higher at 0.937; (4) the soil moisture and soil relative humidity products from the CLDAS, combined with soil moisture products from the GLDAS, reflect the starting and ending times of extreme drought events in the Yangtze River Basin in 2022 well, using the information from the actual stations. In conclusion, gravity satellite data, analyzed in synergy with data from multiple sources, help decision makers to better understand and respond to drought.

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.

Establish an agricultural drought index that is independent of historical element probabilities

AbstractCurrently, there are three main shortcomings in meteorological drought indices: first, they rely on historical climate probability functions; second, the timescale used in calculations has a certain degree of subjectivity; third, the same index value may correspond to vastly different levels of actual drought in different climate types of regions. The purpose of this article is to establish a meteorological drought index that does not rely on historical meteorological element probability functions. Through theoretical derivation, four drought‐level maintenance lines are established on the cumulative precipitation‐cumulative water surface evaporation coordinate plane, and the coordinate quadrant is divided into five drought‐level areas. Through forward daily rolling accumulation, the maximum distance point is selected from the dynamically changing coordinate points to determine the corresponding cumulative precipitation and cumulative evaporation. The meteorological drought index is established by the distance from the selected coordinate point to each drought‐level maintenance line. Using daily precipitation and evaporation data from meteorological observation stations, the index is calculated based on the established meteorological drought index model, and compared with actual drought evolution and drought disaster records. The results show that the index can capture the development of drought well, and its changes are very consistent with drought disaster records. The index is of great significance for drought monitoring or assessment, and can provide guidance for water resource allocation, crop layout, and urban planning. Furthermore, it can also provide a way of thinking that does not rely on historical element probabilities for future drought research.

Exploring the relationship of the winter-spring crop’s rice yield with meteorological drought and water resources in Ben Tre province

Recent environmental changes have significantly impacted rice cultivation in Ben Tre province, the Vietnamese Mekong Delta (VMD) coastal province. This research employed the Mann-Kendall (MK) non-parametric method and Sen’s slope (SS) technique to detect trends in the rice yield of the winter-spring crop (WSC) in Ben Tre province and environmental parameters from 2009 to 2020. These environmental parameters include the meteorological drought index and key water resource metrics, potentially affecting the region’s rice yield. Furthermore, it established the relationship between these environmental parameters and rice yield using multiple linear regression. The results indicated an average decrease of 0.13 ton/ha/year in the WSC’s rice yield, with a severe reduction to 2.86 ton/ha in 2016. This decline in rice yield is likely attributed to environmental shifts, particularly during years with extreme hydrological events. The study found that during the months of dry season, from January to April, there was a decrease of 0.14 per year in the minimum Standardized Precipitation Evapotranspiration Index (SPEI-4min) at the Ben Tre meteorological station; an increase in the minimum daily discharge at the Tan Chau station (QTCmin) by 81 m3/s/year; a decrease in the mean daily water level at the My Thuan station (QMTmean) by 0.22 cm/year; and an increase in the maximum daily water level at the Ben Trai station (HBTmax) by 0.67 cm/year. However, the maximum daily salinity concentration at the Tra Vinh station (STVmax) showed no significant trend, oscillating between 6.5 and 15 g/l, potentially because the three-hourly monitoring intervals might not have fully captured peak salinity levels. The regression model identified SPEI-4min (with a Correlation Index (CI) of 0.450) and STVmax (CI = 0.372) as the primary factors influencing rice yield. Additionally, QTCmin and HBTmax were also significant, though to a lesser extent, with correlation indices of 0.310 and 0.303, respectively. Conversely, QMTmean had a relatively minor influence, with a correlation index of 0.136. These findings are crucial to developing adaptive strategies and policies for improving the resilience of rice production in the study province and throughout the VMD.

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.

Sensitivity of Standardized Precipitation and Evapotranspiration Index (SPEI) to the choice of SPEI probability distribution and evapotranspiration method

Study regionThe state of Oklahoma located in the Southern Plains region of the United States. Study focusThe standardized precipitation evapotranspiration index (SPEI) is a widely used meteorological drought index that incorporates potential evapotranspiration (PET) into a precipitation-based index. However, the understanding of the appropriate PET method for SPEI across different temporal scales in non-arid climate conditions remains limited. We compared Thornthwaite (TW), Hargreaves (HG), and Penman-Monteith (PM) equations for SPEI at various accumulations, considering three temporal scales: 1) long-term (25 years), 2) event-based, and 3) monthly. Also, we examined the log-logistic and generalized extreme value distributions to test the normality of SPEI computed from the three PET methods. To do this, we utilized high-quality climate datasets measured at 107 stations across the state of Oklahoma, United States, which has a diverse climate ranging from semi-arid to humid subtropical. New hydrological insights for the regionThe log-logistic distribution was found to be suitable for SPEI. The SPEI-HG showed better agreement with SPEI-PM than SPEI-TW in this region for the analyses of three temporal scales. However, for accumulations of SPEI longer than one year, both TW and HG equations showed no significant differences with SPEI-PM. The findings provide practical guidance for selecting an appropriate PET equation in the Southern Plains region depending on the purpose of study without resorting to data-intensive methods for PET estimation.

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.

Multi‐Index Assessment of Hydrometeorological Droughts in the Tekeze River Basin, Northwestern Ethiopia

Drought is a recurring natural hazard impacting agriculture, water resources, and various socioeconomic sectors. This study evaluated the performances of multiple meteorological and hydrological drought index estimation methods in the Tekeze River basin, northwestern Ethiopia. Monthly rainfall and temperature data from 15 meteorological stations with varying record lengths (28–59 years) and streamflow data from nine functional stations (1991–2018) were used to compute hydrometeorological drought indices. Standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), reconnaissance drought index (RDI), rainfall anomaly index (RAI), decile index (DI), and streamflow drought index (SDI) were calculated using DrinC software, R programming, and empirical formulas at 1, 3, 6, and 12‐month timescales. Results from the analysis revealed a basin‐wide prevalence of mild drought, with SPI indicating severity and negative values over longer timescales. The RDI analyses showed lower drought severity but also decreased its frequency over longer timescales. The DI calculation using basin station averages indicated 48.4%, 10.7%, and 8.0% incidence rates for severe, moderate, and mild droughts, respectively. RAI had generally lower positive and negative values compared to SPI. SPEI‐1 analysis classified almost all stations as experiencing mild drought. Conversely, SDI6 analysis at a 6‐month timescale revealed diverse impacts, with the Tekeze River basin experiencing sustained severe drought (SDI6 < −2.0) for three consecutive years (2004–2006). Spearman’s rank correlation coefficients of the indices were positive, with RAI showing particularly strong and statistically significant positive correlations with other indices. This study provides valuable insights for policymakers, researchers, and water resource managers in the Tekeze River basin, aiding in the development of effective drought mitigation and preparedness strategies.

Agroecology-based analysis of meteorological and agricultural drought using time series remote sensing data in the upper Gelana watershed, Ethiopia

Drought, a recurrent environmental hazard, poses significant challenges to Ethiopia’s predominantly rainfed, agriculture-based economy, affecting millions of people. This study investigates the characteristics and interrelationships between meteorological and agricultural droughts across lower weina dega (LWD), upper weina dega (UWD), and dega agroecological zones (AEZs) within the upper Gelana watershed, Ethiopia. The results reveal distinct seasonality in meteorological droughts, with the kiremt season exhibiting higher frequency compared to the belg season across all AEZs from 1991 to 2021. The belg-season agricultural droughts were more frequent in LWD, followed by UWD AEZ. Agricultural droughts were also observed in all AEZs during the kiremt seasons in 2002, 2008 and 2009. A statistically significant positive relationship between meteorological and agricultural drought indices was found in both seasons, indicating the potential transmission of meteorological drought into agricultural drought. The findings highlight the need for tailored adaptation strategies suited to the specific conditions of each AEZ.