Historical and Future Drought Intensification in the Pantanal Wetland: Evidence from Multi-Source Weather Data and CMIP6 Multi-Model Projections

] Dai [2011] (henceforth D11) reported that the PalmerDrought Severity Index (PDSI) is superior to other statisti-cally based drought indices including the StandardizedPrecipitation Index (SPI) and the Standardized PrecipitationEvapotranspiration Index (SPEI). D11 argued that given thephysical character of the PDSI water balance model, theindex provides robust estimates of drought severity becauseit takes the preceding conditions into account, in contrast toother drought indices that are based purely on past statisticsof particular climate variable(s). However, D11 has over-estimated the ability of the PDSI to realistically simulate thedistributed soil water balance at large spatial scales, andignored the inherent complexity and multiscalar character ofdrought phenomena, which are related to more than themoisture conditions of the soil. In this comment we discussthe complex characteristics of droughts and the limitationsof the PDSI to quantify drought conditions in a variety ofhydrological systems. We describe the advantages of statis-tically based drought indices including the SPI and the SPEI.ThefactthattheSPIandtheSPEIarenot(anddonotintendtobe) physically based indices is more liberating than con-straining, especially when the physical basis of PDSI can beseriously questioned.[

Climate change impacts on water security in global drylands

Drought has become a major threat to local sustainable development in dryland Asia, one of the largest grassland ecosystems in the world. However, empirical‐ and science‐based evidence regarding the extent of drought changes and the future trends of these changes in dryland Asia is variable and incomplete. Here, we first investigate the historical variations in drought conditions in dryland Asia, as measured by the drought intensity and arid area, using three widely used drought indices (the Palmer Drought Severity Index, the Standardized Precipitation Index, and the Standardized Precipitation Evapotranspiration Index). Then, we use Bayesian model averaging to reproduce the future drought conditions under two representative concentration pathways (RCP2.6 and RCP4.5) from the Coupled Model Intercomparison Project Phase 5 Earth system models. The Palmer Drought Severity Index, Standardized Precipitation Index, and Standardized Precipitation Evapotranspiration Index illustrate that dryland Asia has experienced an overall drying trend and an expansion of arid areas over the past 100 years (1901–2016). Both temperature and precipitation are projected to increase under both the 1.5 and 2.0 °C warming scenarios compared with the values from the reference period (1986–2005). The projected drought conditions in the 1.5 and 2.0 °C warming scenarios will worsen, especially across Kazakhstan and Northwest China. We found that the drought conditions under the 2.0 °C warming conditions will not be as severe as those under the 1.5 °C warming conditions due to the mitigating effect of the projected precipitation increase under RCP4.5. These results call for short‐term and long‐term mitigation and adaptation measurements for drought events in dryland Asia.

Drought evolution, severity and trends in mainland China over 1961–2013

Vegetation physiology responses to rising atmospheric CO2 can alter the global hydrological cycle, thereby influencing drought occurrence. It has long been controversial and poorly understood how vegetation physiological effects influence meteorological drought characteristics with increasing CO2. To investigate that, we employ multiple CO2 sensitivity experiments of the state‐of‐the‐art Earth System Models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We quantify drought characteristics in response to rising CO2 using two drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), with SPEI calculated using both the Penman‐Monteith method (SPEI_PM) and energy‐only method (SPEI_Rn). Our findings reveal that plant physiological effects can robustly induce more intense, frequent, and prolonged droughts under elevated CO2 levels. Spatially, drought intensity as measured by SPI, SPEI_PM, and SPEI_Rn, resulting from CO2 physiological forcing, is projected to increase over 61%, 69%, and 78% of global terrestrial areas, respectively. Notably, we found that the contribution of plant physiological effects () to drought characteristics, including intensity, frequency, and duration, exhibits a significant and spatially extensive declining trend with rising CO2 across most land areas. This declining trend is robustly depicted in both the multi‐model mean and individual models. Vegetation coverage plays an important role in the spatial pattern of . CO2 physiological forcing therefore exerts greater impacts in the tropics, particularly over tropical forests. Our results demonstrate that drought characteristics are expected to become less dependent on plant physiological effects with increasing CO2, a consideration essential for accurate drought projections.

The global climate is noticeably warming, and drought occurs frequently. Therefore, choosing a suitable index for drought monitoring is particularly important. The standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) are commonly used indicators in drought monitoring. The SPEI takes temperature into account, but the SPI does not. In the context of global warming, what are their differences and applicability in regional drought monitoring? In this study, after calculating the SPI and SPEI at 1-, 3-, 6-, and 12-month timescales at 102 meteorological stations in Inner Mongolia from 1981 to 2018, we compared and analyzed the performances of the SPI and SPEI in drought monitoring from temporal and spatial variations, and the consistency and applicability of the SPI and SPEI were also discussed. The results showed that (1) with increasing timescale, the temporal variations in the SPI and SPEI were increasingly consistent, but there were still slight differences in the fluctuation value and continuity; (2) due to the difference in time series, the drought characteristics identified by the SPI and SPEI were quite different in space at various timescales, and with the increase in timescale, the spatial distributions of the drought trends in Inner Mongolia were basically consistent, except in Alxa; (3) at the shortest timescale, the difference between the SPI and SPEI was the largest, and the drought reflected by the SPI and SPEI may be consistent at long timescales; and (4) compared with typical drought events and vegetation indexes, the SPEI may be more suitable than the SPI for drought monitoring in Inner Mongolia. It should be noted that the adaptability of the SPI and SPEI may be different in different periods and regions, which remains to be analyzed in the future.

ABSTRACTUnderstanding the influence of human‐induced climate change on drought is critical for addressing global challenges. This study uses the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI) to identify detection and attribution of global drought patterns during the March–April–May (MAM) season. SPI focuses on rainfall, while SPEI accounts for both Precipitation and potential evapotranspiration. We analysed spatial trends using ensemble mean data from 15 global climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Simulations incorporating both natural and anthropogenic forcings (ALL) were compared with those limited to natural forcings (NAT) to evaluate human impacts on drought patterns. Results based on SPI from both observational data (OBS) and ALL simulations reveal wetting trends in Southwest Asia, South America, Eastern Europe, and Central Africa, and drying trends in Northwestern Europe, Southeastern China, India, North America, East Africa, and Australia. SPEI shows wetting in Northeast Asia, Europe, Southeastern North America, and Northwest Africa, with drying in North America, Southeastern Africa, Europe, and Southeastern China. Significantly, the drying trends in ALL simulations are stronger than in NAT, highlighting anthropogenic climate change as a key driver of worsening drought patterns. Detection analysis confirms a strong ALL signal in SPI and SPEI observations, while NAT remains undetected. Lastly, Severe droughts (indices < −2) were examined, in terms of likelihood ratio in All and NAT, show human activities significantly influence precipitation in Southeast Asia, tropical rainforests, and East Africa, with temperature variations driving droughts in Central Asia and other tropical regions.

In the face of climate change, extreme weather events (EWEs) pose significant challenges on a global scale. This study assesses the long‐term climate trends and extreme precipitation indices across Ethiopia using the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets under medium emission (SSP2‐4.5) and high emission (SSP5‐8.5) scenarios. We applied indices from the World Meteorological Organization, Expert Team on Sector‐Specific Climate Indices (ET‐SCI) to analyze both national averages and regional variations. Projected climate trends across Ethiopia show substantial warming and increased precipitation under both SSP2‐4.5 and SSP5‐8.5 scenarios. Compared to the 1991–2010 baseline, mean annual precipitation is projected to increase by about 18% (SSP2‐4.5) and 41% (SSP5‐8.5) by the late 21st century, with the greatest intensification during the main rainy season (June–September). However, dry‐season rainfall remains low, sustaining strong seasonality and heightening the risk of both floods and droughts. Spatially, rainfall increases are concentrated in the already wetter southwestern highlands, while arid lowlands see limited gains, potentially widening hydrological disparities. Mean maximum and minimum temperatures are also projected to rise by up to 3.6 and 4.6°C under SSP5‐8.5, respectively, with high‐temperature zones expanding into formerly cooler regions. Drought projections, based on Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI), suggest a notable decrease in drought occurrences, especially under SSP5‐8.5 scenario. Regional disparities highlight varying rainfall and temperature patterns, with increased risks of severe flooding in central, western, and southwestern Ethiopia under SSP5‐8.5 scenario. The study indicates no significant historical trends in extreme precipitation indices but notes substantial increases under both emission scenarios, particularly SSP5‐8.5. These findings highlight the need for targeted adaptation strategies, including improved water management, early warning systems, and sustainable land use planning, to enhance climate resilience and support vulnerable communities in Ethiopia.

Effective monitoring of drought plays an important role in water resources planning and management, especially under global warming effect. The aim of this paper is to study the effect of air temperature on historical long-term droughts in regions with diverse climates in Iran. To this end, monthly air temperature (T) and precipitation (P) data were gathered from 15 longest record meteorological stations in Iran covering the period 1951–2014. Long-term meteorological droughts behavior was quantified using two different drought indices, i.e. the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Linear and non-linear trends in T, P, SPI and SPEI were evaluated using non-parametric and parametric statistical approaches such as non-modified and modified Mann-Kendall Test, Theil-Sen approach, and simple regression. The results indicated that the significant trends for temperature are approximately all increasing (0.2 °C to 0.5 °C per decade), and for precipitation are mostly decreasing (−7.2 mm to −14.8 mm per decade). It was also indicated that long-term drought intensities monitored by the SPI and SPEI have had significant downward trend (drought intensification with time) at most stations of interest. The observed trends in the SPI series can be worsen if air temperature (in addition to precipitation) participates in drought monitoring as SPEI. In arid and extra arid climates, it was observed that temperature has strong effects on historical drought characteristics when comparing the SPI and SPEI series. Due to the determinative role of temperature in mostly dry regions like Iran, the study suggests using the SPEI rather than SPI for more effective monitoring of droughts.

Abstract. Frequent severe droughts in recent years in the humid southeast U.S. have called for pragmatic approaches to better prepare for the consequences of droughts. This article examines how climate change will influence future droughts in Alabama and Georgia. Historic and future droughts were quantified by means of the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), and changes in the frequency, severity, and spatial extent of droughts were examined using severity-area-frequency (SAF) curves. Precipitation and temperature data, regionally downscaled using a regional spectral model (RSM) for the southeast U.S. for the high emission scenario (A2) from three general circulation models (GCM), i.e., Hadley Centre Coupled Model Version 3 (HadCM3), Geophysical Fluid Dynamics Laboratory (GFDL), and Community Climate System Model (CCSM), from the Third Coupled Model Inter-comparison Project (CMIP3) archive were used for this study. Data from 1969 to 1999 were used for historical simulation, and 2039 to 2069 were used for future projections. The results showed that droughts similar to those in the past would be observed frequently in the future as well. The SPI and SPEI from the GFDL and HadCM3 models indicated higher frequency, severity, and spatial extent of droughts in the future. The SPI from the CCSM model did not show drastic changes in drought characteristics in either of the two states. The results of this research can be used by policymakers as a guide to determine how drought characteristics are expected to change in the future, and to develop drought mitigation policies. Keywords: Climate change, Drought, Drought indices, Severity-area-frequency curves, Standardized precipitation index, Standardized precipitation evapotranspiration index.

The Konya province in the Central Anatolia Region of Turkey features a semi-arid climate with cold winters and hot, dry summers. Although the annual precipitation of the Konya Closed Basin is about 350 mm, the basin is considered one of the main agricultural regions of Turkey. Given the effects of drought on crop yields and food security, evaluation of drought risks is crucial. This study aims to describe historical as well as future drought characteristics of the Konya basin by means of two widely used meteorological drought indices: the standardized precipitation index (SPI) and the standardized precipitation-evapotranspiration index (SPEI). The indices were calculated for different timescales (6–24-month timescale) to better assess agricultural drought conditions. For the SPEI index, the potential evapotranspiration (PET) was calculated using the Hargreaves and Samani method, commonly used in arid and semi-arid weather conditions. The analysis was performed over the period 1980-2020 using precipitation and temperature data from 18 weather stations located within Konya Closed Basin. Based on drought classification by SPI and SPEI, values equal to or lower than -2 are considered extreme droughts. The results show that the number of extreme climatic drought periods at the considered stations within the Konya basin based on SPI is higher than that based on SPEI. The findings also reveal that both SPEI and SPI characterize a general increase in drought severity, areal extent, and frequency over 2000-2010 compared to those during 1980-1990, mostly because of the decreasing precipitation and to a lesser extent rising potential evapotranspiration. To assess future drought frequencies, the drought indices were calculated using precipitation and temperature data provided by 17 regional climate models from the EUROCORDEX project. The results for both RCP 4.5 and RCP 8.5 scenarios show significantly more frequent extreme and severe droughts, particularly for the second half of the 21st century. Overall, this study implies that SPEI may be more appropriate than SPI to monitor drought periods under climate change since potential evapotranspiration increases in a warmer climate.This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA program supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No 1923.

In coastal Winneba-Ghana, drought occurrence negatively affects the ecosystems and agriculture and threatens food security and socio-economic livelihoods. Nevertheless, there exist dearth of information on a detailed statistical evaluation of drought indices over this area. This study made a comparative assessment of Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) over coastal-Winneba. A daily temperature and rainfall data from 1980-2019 acquired from the Ghana Meteorological Agency was used to perform SPI and SPEI. Pearson correlation coefficient and cross-correlation, and Bland and Altman plot were used to test for the strength and direction and the degree of agreement, respectively between SPI and SPEI. Results showed a strong and positive association between SPI and SPEI (0.90, 0.91, 0.84, and 0.93) at 1-, 3-, 6-, and 12-month timescales, respectively. Results again, showed a good degree of agreement between SPI and SPEI (-0.06138, -0.00736, -0.05211, and -0.01810) at 1-, 3-, 6-, and 12-month timescales, respectively. Results further, showed that while both SPI and SPEI correlated strongly with each other, SPEI performed better in the detection of severe and extreme droughts at all timescales than SPI. Additionally, results showed that in the absence of temperature data to perform SPEI, the SPI can be used since the study found an acceptable degree of agreement scores between SPI and SPEI at all timescales in the study area. The study, therefore, recommends the utilization of numerous drought indices in drought performance assessment at a particular region or locality to arrive at a strong decision.

Climate change is increasing the uncertainty of global hydrological cycles, leading to an increase in extreme weather events such as heatwaves, droughts, and floods. While global and continental-scale hydrological analyses based on CMIP6 (Coupled Model Intercomparison Project Phase 6) climate change scenarios have been actively conducted, detailed analyses for specific regions are still lacking. Furthermore, comparing model predictions with observation data for the initial 10-year period (2015-2024) of climate change models is important for validating short-term forecast accuracy and enhancing the reliability of long-term climate prediction. This study evaluates the performance of the CMIP6 prediction models for air temperature, evapotranspiration, precipitation, and soil moisture during the 2015-2024 period under the SSP 2-4.5 and SSP 5-8.5 scenarios. To validate the scenarios, a comparison with GLDAS (Global Land Data Assimilation System) and ERA5-Land reanalysis data is executed. Subsequently, SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) were calculated for the early (2015-2040), mid (2041-2070), and late (2071-2100) periods of climate change, to analyse the intensity and frequency of future droughts. The results show that air temperature and evapotranspiration exhibited a strong correlation, while precipitation and soil moisture showed relatively weak correlations. Based on this study, quantifying bias in climate models can contribute to improving the performance of regional climate predictions. Furthermore, it is expected to provide important information for future climate change forecasting. Keywords: Climate Change, CMIP6, Drought Indices, Monsoon RegionsAcknowledgementThis research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Environment (MOE)(RS-2024-00332300). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266).

The goal of this study is to calculate meteorological drought using the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) for the Al-Lith and Khafji basins of the Kingdom of Saudi Arabia (KSA) from 2001 to 2020. The in situ (rain gauges, RGs) and Integrated Multi-satellite Retrievals for GPM (IMERG) data are used in the current study. The meteorological drought is monitored across the AL-Lith and Khafji watersheds. The climate of the Khafji watershed is like the climate of Al-Lith to some extent. Still, due to complex terrain, Al-Lith receives relatively high precipitation and has a higher average temperature than the Khafji watershed. Results show that the total drought periods observed are 166 and 139 months based on SPEI and SPI on a multiple time scale (1, 3, 6, and 12 months) in the Al-Lith watershed, respectively. While, based on SPEI and SPI, the Khafji watershed experienced a drought of 129 and 72 months, respectively. This finding indicates that the SPEI-calculated drought is more severe and persistent in both watersheds than the SPI-calculated drought. Additionally, the correlation coefficient (CC) between SPI and SPEI is investigated; a very low correlation is observed at a smaller scale. CC values of 0.86 and 0.93 for Al-Lith and 0.61 and 0.79 for the Khafji watershed are observed between SPEI-1/SPI-1 and SPEI-3/SPI-3. However, the correlation is significant at high temporal scales, i.e., 6 and 12 months, with CC values of 0.95 and 0.98 for Al-Lith and 0.86 to 0.94 for the Khafji watershed. Overall, the study compared the performance of IMERG with RGs to monitor meteorological drought, and IMERG performed well across both watersheds during the study period. Therefore, the current study recommends the application of IMERG for drought monitoring across data-scarce regions of KSA. Furthermore, SPEI estimates a more severe and long-lasting drought than SPI because of the temperature factor it considers.

Drought poses significant threats to humans and ecosystems, underscoring the need for robust and efficient monitoring systems for informed decision making and adaptive management. This study assessed the effectiveness of five indices in detecting historical droughts and low-flow periods in the upper and lower sub-basins of the Great Ruaha River Basin, Tanzania using hydrometeorological data (1981–2022). Historical droughts were obtained from government reports and scientific literature. The indices included the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Actual Evapotranspiration Index (SPAEI), Standardized Streamflow Index (SSI) and Water Scarcity Index (WSI). The standardized indices were calculated using basin monthly precipitation, evapotranspiration and discharge data at 3-, 6-, and 12-month timescales while the WSI was used to define low-flows based on a threshold of 0.001 m3/s, computed from the 10th percentile of daily discharge data. Drought characteristics were investigated using the run theory approach. The SPI captured 70–90% of historical droughts, SPEI identified 80–90%, SPAEI detected 50–60%, while SSI, and WSI each identified 80%. The SPI, SPEI and SSI classified majority of these events as severe while SPAEI identified them as moderate. The WSI was the most effective in detecting low flows, whereas all the standardized indices performed poorly. Between 1981 and 1989, discharge never fell below 0.001 m3/s. However, from 1990, lower discharges occurred almost every year, indicating a major shift in the hydrological system. Based on our research findings, we recommend using the SPI, SPEI, SSI and WSI for drought monitoring and more specific, the WSI for low flow analysis. Therefore, by using appropriate drought indices, water managers can achieve sustainable management of droughts and water resources in the basin.