Historical Drought Research Articles

Ecological stress memory in wood architecture of two Neotropical hickory species from central-eastern Mexico

BackgroundDrought periods are major evolutionary triggers of wood anatomical adaptive variation in Lower Tropical Montane Cloud Forests tree species. We tested the influence of historical drought events on the effects of ecological stress memory on latewood width and xylem vessel traits in two relict hickory species (Carya palmeri and Carya myristiciformis) from central-eastern Mexico. We hypothesized that latewood width would decrease during historical drought years, establishing correlations between growth and water stress conditions, and that moisture deficit during past tree growth between successive drought events, would impact on wood anatomical features. We analyzed latewood anatomical traits that developed during historical drought and pre- and post-drought years in both species.ResultsWe found that repeated periods of hydric stress left climatic signatures for annual latewood growth and xylem vessel traits that are essential for hydric adaptation in tropical montane hickory species.ConclusionsOur results demonstrate the existence of cause‒effect relationships in wood anatomical architecture and highlight the ecological stress memory linked with historical drought events. Thus, combined time-series analysis of latewood width and xylem vessel traits is a powerful tool for understanding the ecological behavior of hickory species.

Exploring the utility of the Enhanced Vegetation Index as rainfall and agricultural proxy in a Caribbean case study event

Highly fragile small island states experience disproportionate climate impacts given their limited capacity to implement cost‐effective tools for detecting emerging signals of drying conditions and monitoring systems for sensitive sectors such as agriculture, especially for uncertain, ‘creeping’ events such as droughts. Despite the existence of open‐source Google Earth Engine datasets, untapped potential remains for their full deployment in disaster management infrastructure. Given this gap, this paper explores the utility of the Enhanced Vegetation Index (EVI) for detecting spatio‐temporal variations of Mid‐Summer Drought (MSD) impacts on vegetation in the small island of Jamaica, with emphasis on major historical drought events. Geospatial analyses of EVI datasets from the Terra Moderate‐Resolution Imaging Spectroradiometer (MODIS) between 2000−2015 archived by the United States Geological Survey (USGS), were computed, and validated by station‐based precipitation and production data for selected parishes for historical case study MSD events. Results revealed highly asymmetrical drought impacts, with Jamaica's agriculturally intense Southern coastline displaying the most stressed vegetation (EVI < 0.5). North‐Western and North‐Eastern regions had the healthiest vegetation during the MSD (EVI > 0.6). A ‘fair’ to ‘moderate’ concurrent correlation was found between EVI and precipitation (R > 0.6), with lower correlations vis‐a‐vis agricultural production (R = 0.2–0.4). The results provide evidence of EVI's utility as a drought monitoring tool in a small island context.

Protective effects of diversified diets for dietary folate adequacy and serum homocysteine in breastfeeding mothers amid historic drought.

The United Nations recommends that women consume ≥5 food groups, also known as the minimum dietary diversity score for women (MDD-W), for nutritional health. This is increasingly unattainable for populations in climate hot zones coping with food insecurity by prioritizing calories over dietary breadth. Breastfeeding mothers may be particularly vulnerable to adverse health impacts of low dietary diversity due to elevated nutritional requirements for lactation. We investigated how the protective effects of MDD-W for folate adequacy varies by MDD-W score and mother-infant life history characteristics. We conducted a secondary analysis of cross-sectional data from breastfeeding mothers (n = 228) in northern Kenya, surveyed during the 2006 Horn-of-Africa drought. Logistic regression models for adequate dietary folate (and vitamins B12 and B6) and normal homocysteine (folate-replete status) evaluated the effect of MDD-W alone and in interaction with infant/maternal characteristics. MDD-W (as ordinal or dichotomous variable) was positively associated with adequate folate (and vitamin B12). Having male infant was inversely associated with adequate dietary folate. MDD-W was generally unassociated with homocysteine. However, there was an interaction between MDD-W and sex of the infant. Namely, MDD-W ≥ 3 predicted increased probability of normal homocysteine among mothers with female infants but not male infants. Diets consisting of three or more food groups may protect adequate folate intake for many breastfeeding mothers. More research is needed to establish what level of dietary diversity would protect against hyperhomocysteinemia during breastfeeding and what factors promote or hinder the benefit of diversified diets on maternal folate nutrition.

Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture

Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

The longest multiannual drought in Northeastern Brazil

The Northeastern region of Brazil has long history of drought with impacts on water supply for cities and agriculture. However, the last drought that occurred in the region had unprecedented magnitude and periodicity, making it the longest multiannual drought in Northeastern Brazil. Thus, understanding and characterizing the behavior of this phenomenon is necessary, considering that another episodes may occur. Thus, the objective of this study was to characterize the drought trend in the Northeastern region of Brazil, with the application of the Palmer Drought Severity Index (PDSI). The application of PDSI was carried out with the monthly calculation of the water balance coefficients referring to the soil water balance between the years 2000 and 2019, depending on the relative soil moisture values. The results obtained were submitted to the Mann-Kendall test to identify monthly trends in the index. The annual drought variability in Northeastern Brazil showed moderate to extreme intensity, with the worst drought being observed between the years 2012 and 2019. The monthly drought average shows the Mid-Northern and “Sertão” regions as the most critical, with monthly trend of significant drought severity. The positive trend was observed in the Eastern, Southern and Western coast regions of NEB.

Mini-Open Dumps in the Paraná River Floodplain: Local Problems with Global Effects.

The growing production of urban solid waste is a structural problem faced by most cities around the world. The proliferation of mini-open dumps (MOD; small spontaneous open-air waste dumps formed in urban and peri-urban areas) on the banks of the Paraná River is particularly evident. During the historical drought (June-December 2021), we carried out sampling campaigns identifying MODs of the Santa Fe River, a secondary channel of the Paraná River. MOD were geolocated, measured, described and classified by origin. The distance to the river and other sensitive places was considered (houses-schools-health facilities). Our results suggested a serious environmental issue associated with poor waste management. MOD were extremely abundant in the study area, being mostly composed of domestic litter. Plastics clearly dominated the MOD composition. Burning was frequently observed as a method to reduce the volume of MOD. We concluded that the proliferation of MOD is a multi-causal problem associated with a failure of public policies and a lack of environmental education.

Hydrometeorological Drivers of the 2023 Louisiana Water Crisis

AbstractDuring summer and fall 2023, Louisiana experienced a historic local drought while dry conditions elsewhere in the central US withheld vital runoff from the Mississippi River, leading to below‐normal discharge into the Gulf of Mexico. Thus, by late October 2023, Louisiana was gripped by two super‐imposed water crises: a severe local drought and saltwater contamination in the Mississippi River channel. This study frames the development of the water emergency through the lens of flash drought using the Evaporative Demand Drought Index (EDDI). The EDDI shows south Louisiana experience a flash drought during June 2023, while the Mississippi River basin was subsequently characterized by large expanses of high‐percentile EDDI in August‐September 2023 shortly before the saltwater intrusion episode along the lower Mississippi River. Over the last 15 years, MRB‐wide EDDI percentile has oscillated between years‐long elevated and depressed states, accounting for 23.7% of the monthly discharge anomaly near New Orleans.

Predictive simulation of the water-energy-food nexus for the City of Cape Town

The City of Cape Town (CoCT), South Africa faced a critical situation between 2015 and 2018 in which the municipal water supply was almost completely exhausted. This situation, commonly referred to as Day Zero in South Africa emanated from a decline in rainfall, resulting in one of the most severe droughts in history. The crisis was also aggravated by rapid population growth and urbanization. CoCT was on the verge of becoming the first city in the past decade to experience a complete cessation of water supply for urban and agricultural purposes. In addition to the effects of low rainfall and population surge, urban energy consumption and increased food demand impacted directly the available water resources. To evaluate the interlinkages between water utilization, water production, energy supply and demand, and food production and demand, this study employed a system dynamics modeling (SDM) approach. The model was developed as a stock and flow diagram utilizing Stella Architect and encompassed five interconnected nodes: water, energy, food, land, and population. The findings revealed that by the end of the 20-year modeling period, the volume of accessible and stored water in all the major dams will be approximately 459 million cubic meters, with residential use accounting for about 85 % of urban water use and agriculture accounting for approximately30.37 % of total water demand. The model illustrates the impacts of precipitation rate, runoff, and evaporation on variables such as land-use change and population dynamics. It is anticipated that the outcomes of this study will serve as valuable inputs for decision-making processes, not only within the CoCT as it aims to mitigate or prevent the recurrence of Day Zero, but also for other cities facing similar challenges.

The Machine Learning Attribution of Quasi-Decadal Precipitation and Temperature Extremes in Southeastern Australia during the 1971–2022 Period

Much of eastern and southeastern Australia (SEAUS) suffered from historic flooding, heat waves, and drought during the quasi-decadal 2010–2022 period, similar to that experienced globally. During the double La Niña of the 2010–2012 period, SEAUS experienced record rainfall totals. Then, severe drought, heat waves, and associated bushfires from 2013 to 2019 affected most of SEAUS, briefly punctuated by record rainfall over parts of inland SEAUS in the late winter/spring of 2016, which was linked to a strong negative Indian Ocean Dipole. Finally, from 2020 to 2022 a rare triple La Niña generated widespread extreme rainfall and flooding in SEAUS, resulting in massive property and environmental damage. To identify the key drivers of the 2010–2022 period’s precipitation and temperature extremes due to accelerated global warming (GW), since the early 1990s, machine learning attribution has been applied to data at eight sites that are representative of SEAUS. Machine learning attribution detection was applied to the 52-year period of 1971–2022 and to the successive 26-year sub-periods of 1971–1996 and 1997–2022. The attributes for the 1997–2022 period, which includes the quasi-decadal period of 2010–2022, revealed key contributors to the extremes of the 2010–2022 period. Finally, some drivers of extreme precipitation and temperature events are linked to significant changes in both global and local tropospheric circulation.

The phenomenon of drought in Ethiopia: Historical evolution and climatic forcing

ABSTRACT This study examines drought patterns in Ethiopia's 12 major river basins from 1981 to 2018 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Both indices reveal historical drought episodes with slight variations, with significant differences in 1984, 2009, and 2015. Except for the Wabi-Shebelle catchment in southern Ethiopia, all river basins show an increasing trend in SPI12 and SPEI12 indices. The eastern and central regions experience more drought according to SPEI3. Seasonal correlations show that during the March–May rainy season, precipitation is negatively correlated with the Indian Ocean Dipole (IOD) index, while in the June–September season, it negatively correlates with Nino 3.4 and positively with IOD. The study also found that El Niño leads to less rainfall in the Ethiopian highlands, while La Niña results in more rainfall in the central and northern highlands but less in the south.

Spatiotemporal analysis of historic and future drought characteristics over a monsoon dominated humid region (Kerala) in India

Spatiotemporal analysis of historic and future drought characteristics over a monsoon dominated humid region (Kerala) in India

Machine learning and remote sensing based time series analysis for drought risk prediction in Borena Zone, Southwest Ethiopia

Drought, a complex natural hazard, poses significant challenges for scholars in drought risk management due to the perceived lack of suitable assessment, prediction, and monitoring tools. Addressing these challenges requires sophisticated tools capable of precise and timely assessments. This study leverages the rapid advancements in machine learning (ML) and remote sensing (RS) to develop models for anticipating and estimating drought risk, particularly focusing on identifying affected and sensitive locations in Ethiopia's Borena Zone. The research involves a two-phase investigation, examining historical drought patterns and forecasting scenarios for 2028. The GridSearch algorithm is employed for optimal hyperparameter tuning in ML, highlighting the CatBoost algorithm as the most accurate predictor for the Standardized Precipitation Index (SPI). With impressive performance metrics, including Mean Squared Error (MSE) of 0.017, Mean Absolute Error (MAE) of 0.102, Root Mean Square Error (RMSE) of 0.129, and an R-squared (R2) value of 0.84, this study excels in providing precise spatiotemporal accuracy for drought prediction. The findings underscore the importance of time-series drought prediction, offering crucial insights for decision-makers and planners to address and mitigate drought impacts at various scales. This study contributes valuable information by emphasizing the significance of understanding drought occurrence's temporal and spatial dimensions.

The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features

In 2023 Amazonia experienced both historical drought and warm conditions. On October 26th 2023 the water levels at the port of Manaus reached its lowest record since 1902 (12.70 m). In this region, October monthly maximum and minimum temperature anomalies also surpassed previous record values registered in 2015 (+ 3 °C above the normal considering the 1981–2020 average). Here we show that this historical dry and warm situation in Amazonia is associated with two main atmospheric mechanisms: (i) the November 2022–February 2023 southern anomaly of vertical integrated moisture flux (VIMF), related to VIMF divergence and extreme rainfall deficit over southwestern Amazonia, and (ii) the June–August 2023 downward motion over northern Amazonia related to extreme rainfall deficit and warm conditions over this region. Anomalies of both atmospheric mechanisms reached record values during this event. The first mechanism is significantly correlated to negative sea surface temperature (SST) anomalies in the equatorial Pacific (November–February La Niña events). The second mechanism is significantly correlated to positive SST anomalies in the equatorial Pacific, related to the impacts of June–September El Niño on the Walker Circulation. While previous extreme droughts were linked to El Niño (warmer North Tropical Atlantic SST) during the austral summer (winter and spring), the transition from La Niña 2022–23 to El Niño 2023 appears to be a key climatic driver in this record-breaking dry and warm situation, combined to a widespread anomalous warming over the worldwide ocean.

Characterize and analysis of meteorological and hydrological drought trends under future climate change conditions in South Wollo, North Wollo, and Oromia Zones, in Ethiopia

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

The Impact of Climate Change on Hydro-Meteorological Droughts in the Chao Phraya River Basin, Thailand

This study evaluated the impacts of climate change on hydro-meteorological droughts in the Chao Phraya River Basin (CPRB), Thailand under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). We used three Reginal Climate Models (RCMs) of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA), which are bias corrected. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow for future periods. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were estimated and used for drought characterization at three time scales (3, 6, and 12 months). The lag time between meteorological and hydrological droughts is approximately 1–3 months. The results suggest that the CPRB is likely to experience less frequent hydro-meteorological drought events in the future. The meteorological drought is projected to be longer, more severe, and intense. The severity of hydrological drought tends to decrease, but the intensity could increase. Climate change has been discovered to alter drought behaviors in the CPRB, posing a threat to drought monitoring and warning because droughts will be less predictable in future climate scenarios. The characterization of historical and future droughts over the CPRB is therefore valuable in developing an improved understanding of the risks of drought.

Characteristics of groundwater drought and its correlation with meteorological and agricultural drought over the North China Plain based on GRACE

Groundwater drought is distinctive comparing with other droughts, featuring high concealment, long duration, and obvious hysteresis. It may have significant negative effects on agriculture, eco-environment, and social economy sectors. Therefore, investigating the temporal and spatial characteristics of groundwater drought and its driving factors is meaningful for monitoring and assessing the risks of groundwater shortage. In this study, a groundwater drought index based on groundwater storage anomalies (GWSAs) derived from GRACE satellites was developed to detect and analyze drought events. The spatiotemporal variations and trends of historical groundwater droughts from 2002 to 2021 in the North China Plain were evaluated. In addition, the correlations between groundwater drought and meteorological and agricultural drought were analyzed by the detrended cross-correlation analysis. The results indicated that (1) the standardized groundwater storage anomaly index (SGSAI) could better identify and characterize groundwater drought by eliminating the spatial heterogeneity of GWSAs; (2) from 2002 to 2021, the intensity, frequency, duration, and area of groundwater drought showed an increasing trend; (3) groundwater drought had an obvious hysteresis (>9 months) to meteorological drought and the correlation between groundwater drought and meteorological drought increased, while the relationship with agricultural drought decreased; (4) long-term overexploitation of groundwater resources might be the main driving factor for the exacerbating groundwater drought in this area.

The standardized precipitation evapotranspiration index based on cumulative effect attenuation

The drought index is an important indicator that reflects the degree of regional drought and plays an important role in drought disaster monitoring, prediction and evaluation. According to the process definition of the standard precipitation evapotranspiration index (SPEI), the influence of cumulative effect attenuation was considered and aggregated at different time scales k, the linear attenuation coefficient was defined, and attenuation parameters were used to adjust the degree of attenuation. A value closer to the current month was assigned a larger weight, and a value further from the current month was assigned a smaller weight. Thus, the cumulative water surplus or deficit was recalculated, and the standard precipitation evapotranspiration index based on the cumulative effect attenuation (CASPEI) was defined and applied to the drought analysis of Heilongjiang Province, which has the highest grain output in China. The comparative analysis showed that the CASPEI dataset was more consistent with the log-logistic distribution, and its sensitivity in identifying regional drought events was greater than that of the SPEI, which effectively corrected the original index's errors in identifying the drought levels of some stations and was more consistent with regional historical drought events. The temporal and spatial variation analysis revealed that drought occurrence is seasonal: summer is the season with the highest frequency of severe drought and extreme drought, and the area experiencing extreme drought is greater than that during other seasons. The areas with moderate drought in spring and autumn were relatively large. With the increase in time scale, the area of extreme drought decreased, and the area of severe drought increased. The area of moderate drought increased, while the area of light drought decreased. The CASPEI can accurately judge the extent to which drought occurs to respond to drought and effectively reduce or prevent large losses in people and the economy caused by drought.

Modeling and Analysis of Rice Root Water Uptake under the Dual Stresses of Drought and Waterlogging

The development of an accurate root water-uptake model is pivotal for evaluating crop evapotranspiration; understanding the combined effect of drought and waterlogging stresses; and optimizing water use efficiency, namely, crop yield [kg/ha] per unit of ET [mm]. Existing models often lack quantitative approaches to depicting crop root water uptake in scenarios of concurrent drought and waterlogging moisture stresses. Addressing this as our objective; we modified the Feddes root water-uptake model by revising the soil water potential response threshold and by introducing a novel method to calculate root water-uptake rates under simultaneous drought and waterlogging stresses. Then, we incorporated a water stress lag effect coefficient, φWs, that investigated the combined effect of historical drought and waterlogging stress events based on the assumption that the normalized influence weight of each past stress event decreases with an increase in the time interval before simulation as an exponential function of the decay rate. Further, we tested the model parameters and validated the results obtained with the modified model using data from three years (2016–2018) of rice (Oryza sativa, L) trails with pots in Bengbu, China. The modified Feddes model significantly improved precision by 9.6% on average when calculating relative transpiration rates, particularly post-stress recovery, and by 5.8% on average when simulating soil moisture fluctuations during drought periods. The root mean square error of relative transpiration was reduced by 60.8%, and soil water was reduced by 55.1%. By accounting for both the accumulated impact of past moisture stress and current moisture conditions in rice fields, the modified model will be useful in quantifying rice transpiration and rice water use efficiency in drought–waterlogging-prone areas in southern China.

Unveiling Water Allocation Dynamics: A Text Analysis of 25 Years of Stakeholder Meetings

Abstract Managing water resources in regions with high climate variability and frequent extreme weather events poses challenges for policymakers. To facilitate water allocation in these cases, participatory and collaborative decision-making approaches have become common. However, the evaluation of these approaches is hindered by the lack of structured methods and data to understand them. To address this knowledge gap, we propose a novel methodology that leverages text data to identify key topics, conflicts, and influential actors that shape water allocation dynamics. Our methodology is tested using records of 1020 water basin committee meetings held between 1997 and 2021 across twelve basin committees in Ceará, Brazil – a region known for its extensive history of droughts that have impacted water governance. To uncover key water management issues discussed during these meetings, we employed a three-step topic modeling framework: (1) sentence embedding, (2) dimensionality reduction, and (3) sentence clustering. Furthermore, we used entity recognition, dependency parsing, and network graphs to identify powerful actors influencing these meetings and, ultimately, the decisions taken. Our findings revealed stakeholders’ heightened concern for urban water supply over agricultural demand during droughts. We found that “reservoir operation” was the most recurring topic, especially in basins where the strategic reservoirs are located. Discussions related to “climate information” became significantly more important over time, which indicates that water allocation decisions are increasingly based on the seasonal forecast and data on oceanic indices provided by the meteorology agency. Despite the presence of local users in the committees, governmental representatives dominated the discussions and were central in all river basins. In conclusion, our proposed approach harnesses existing text data to uncover spatiotemporal patterns related to participatory water allocation. This study opens new avenues for investigating water governance using text-based analysis.

Agro-climatic zone-wise drought hazards in Karnataka under historical and future climate scenarios

This study performed the spatio-temporal analysis of drought hazards across the agro-climatic zones (ACZs) of Karnataka under historical and future climate scenarios. The India Meteorological Department’s high-resolution gridded data for1989-2019 was used for historical drought occurrence analysis. Coordinated Regional Climate Downscaling Experiment ensemble data of the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were used for analysing future drought hazards in the near (2031-2060) and end term (2061-2099) periods. The standardised precipitation index (SPI) was used to calculate the frequency of droughts at different accumulation periods of 1, 3, 6, 9, and 12 months. Subsequently, the ACZ-wise drought hazard index (DHI) was calculated and mapped geospatially using ArcGIS. The results indicated that moderate drought events have the highest frequencies of occurrence, followed by severe and extreme drought events for all accumulation periods. During 1989-2019, 54.8%, 28.3% and 16.7% of droughts were moderate, severe, and extreme, respectively. An increase of 2.6% and 2.4% in the frequency of moderate droughts is projected under the RCP4.5 and 8.5 scenarios, respectively, in the end term. Under both historical and future climate scenarios, a high frequency of extreme droughts was observed in the long accumulation periods (SPI-9 and SPI-12), whereas the frequency of moderate droughts was observed to be high in the short accumulation periods (SPI-1 and SPI-3). Under the historical scenario, the frequency of droughts in the extreme category was high in the southern transition, central dry, and north eastern dry zones, severe category in the northern dry, southern transition, and coastal zones, and moderate category in the north transition, hill, and southern dry zones. Among the 30 districts of Karnataka, Chitradurga, Udupi, Tumakuru, Ballari, Koppala, Raichuruand Gadaga districts have very high DHI. This study sheds light on the potential consequences of climate change on drought scenarios in the Karnataka state’s agro-climate zones and urges for zone specific drought adaptation and mitigation strategies to strengthen the State resilience.