Quantifying Extreme Drought Risk using Palaeoclimate and Model Observations from the Last Millennium

Iran faces climate disparities due to extreme topographic anomalies, the Caspian Sea and the Persian Gulf water bodies, influences from diverse air masses and moisture sources, and its considerable area. FLEXPART model has been utilized to determine the main marine and continental moisture sources for south-central (Shiraz box) and northeastern (Mashhad box) parts of Iran. The marine moisture sources directly influenced extreme drought and wet conditions in Shiraz and Mashhad boxes during the wet period, while no correlation was observed during the dry period. In addition to local components, extreme drought and wet conditions have also been influenced by the climatic teleconnections. Extreme drought conditions mainly occurred during the La Niña phase, while wet conditions mainly occurred during the El Niño phase. Scrutinizing the effect of marine moisture sources on the hydrology of water resources demonstrated that the moisture contribution from the Arabian Sea directly influenced the discharges of Chenar-rahdar (in the Shiraz box) and Kardeh (in the Mashhad box) rivers during the wet period. However, the Red Sea inversely correlated with the discharges of both rivers during the dry period. Hydrogeologists, hydrologists, and meteorologists can utilize the outputs of this survey to develop climatology and hydrology models in the future.

So‐called hot droughts are seen as one of the very foreseeable extremes amid the faster‐than‐expected pace of global warming. In particular, the western part of North America has been pummeled by severe droughts due to a lack of precipitation as well as record‐breaking hot temperatures. This study assesses the joint return period of drought severity and duration using the COordinated Regional Climate Downscaling EXperiment‐COmmon Regional Experiment (CORDEX‐CORE) simulations over the California domain. Six dynamically downscaled simulations with 25 km resolution are analyzed for the historical (1956–2005) and future (2050–2099) periods, whose warming sensitivities differ based on three global climate models (GCMs) driving two regional climate models (RCMs). Our focus is on estimating the joint probabilities of the drought duration and severity constructed from multiple drought indices such as the Palmer Drought Severity Index (PDSI) (with original temperature (PDSI) and its detrended counterpart (PDSI_detrended)) and Standardized Precipitation Index (SPI). Under the RCP8.5 scenario, an unprecedented level of droughts in terms of both duration and severity is likely to emerge only when the drought is characterized by PDSI. A comparison of PDSI and PDSI_detrended explicitly reveals that the occurrence of severe and prolonged droughts is mainly attributed to the warming trend of temperature. Both PDSI_detrended and SPI, which do not incorporate the warming effect explicitly, barely differentiate the joint distributions from the historical and future simulations. Unlike the PDSI based on water budget, the SPI, based on solely accumulated precipitation, shows a loosely coupled joint structure between the severity and duration of droughts, with marked differences in their marginal distributions.

Association between precipitation events, drought, and animal operations with Salmonella infections in the Southwest US, 2009–2021

Abstract. Distinction between drought and aridity is crucial to understand water scarcity processes. Drought indices are used for drought identification and drought severity characterisation. The Standardised Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI) are the most known drought indices. In this study, they are compared with the modified PDSI for Mediterranean conditions (MedPDSI) and the Standardised Precipitation Evapotranspiration Index (SPEI). MedPDSI results from the soil water balance of an olive crop, thus real evapotranspiration is considered, while SPEI uses potential (climatic) evapotranspiration. Similarly to the SPI, SPEI can be computed at various time scales. Aiming at understanding possible impacts of climate change, prior to compare the drought indices, a trend analysis relative to precipitation and temperature in 27 weather stations of Portugal was performed for the period 1941 to 2006. A trend for temperature increase was observed for some weather stations and trends for decreasing precipitation in March and increasing in October were also observed for some locations. Comparisons of the SPI and SPEI at 9- and 12-month time scales, the PDSI and MedPDSI were performed for the same stations and period. SPI and SPEI produce similar results for the same time scales concerning drought occurrence and severity. PDSI and MedPDSI correlate well between them and the same happened for SPI and SPEI. PDSI and MedPDSI identify more severe droughts than SPI or SPEI and identify drought occurrence earlier than these indices. This behaviour is likely to be related with the fact that a water balance is performed with PDSI and MedPDSI, which better approaches the supply-demand balance.

This dataset consists of monthly Palmer Drought Severity Index (PDSI) over global land areas on a 2.5 degree grid computed using observed monthly surface air temperature and precipitation, plus other surface meteorological forcing data for the self calibrating PDSI Penman Monteith (sc_PDSI_pm) case. The PDSI can be used as a measure of surface aridity anomalies and changes on seasonal to longer time scales. It is correlated with observed soil moisture and water storage variations over land.

Controversial results in the drying and wetting trends were found with different indices and potential evapotranspiration calculations in previous studies. Here we make an attempt to find robust conclusions of drying and wetting trends over regions by coherent results of various independent indices by using China (1961–2012) as a study area. Precipitation, statistical, and physical drought indices, including the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) and self‐calibrating PDSI (sc_PDSI), with both the Penman‐Monteith (PM) and Thornthwaite (TH) approaches in PDSI calculation are considered. In consequence, four PDSI variants of PDSI_pm, sc_PDSI_pm, PDSI_th, and sc_PDSI_th are involved. To illustrate regional characteristics, six climatic regions based on the Köppen climate classification are defined. At the national scale, precipitation and SPEI indicate wetting trends but all PDSI variants have drying trends. On the other hand, these six indices exhibit coherent results in five of these six regions. Increases in wetness occur in arid region and the Qinghai‐Tibet Plateau. Drying trends were found in semiarid and cold and temperate semihumid regions. Only the humid region in southeastern China is seen to have increasing precipitation and SPEI and decreasing PDSI variants. From the perspective of climatic regions, the drying trends mainly occur in the transition regions between the humid and arid regions in China. The spatial pattern of changes in droughts could be categorized by climatic zones, and the changes at regional scale are robust based on these six indices.

Infectious diarrhea imposes substantial disease burden in China, with local climate identified as a key risk factor. In this study, we aimed to explore the association between hydrometeorological conditions and the incidence of infectious diarrhea, and investigate the modification effect of urbanization, which remained unclear previously. Monthly data were collected from 1 January 2021 to 31 December 2022 from each city of mainland China on cases of infectious diarrhea other than cholera, dysentery, typhoid and paratyphoid (i.e. other infectious diarrhea, OID), temperature and precipitation. We used Palmer drought severity index (PDSI) to represent local hydrometeorological conditions (dry and wet). A spatiotemporal Bayesian hierarchical model combined with distributed lag nonlinear model was specified to explore the association between PDSI and the relative risk of OID. The effect size of hydrometeorological conditions at different urbanization levels were quantified through a linear interaction term. A total of 1999 947 OID cases were included. There was a N-shaped cumulative association between PDSI and OID incidence over a maximum lag of 4 months. The effect of extreme dry conditions persisted over lag 1–3 months and peaked at the 2-month lag (RR = 2.67, 95%CI: 2.44–2.91). By contrast, the relative risk of OID increased immediately under extreme wet conditions (RR = 1.97, 95%CI: 1.68–2.32 at lag 0 month). Children and the older population were particularly susceptible to extreme dry and wet conditions, respectively. Areas with lower urbanization rate, healthcare resources and economic levels, and higher population density showed a higher risk of OID related to extreme dry conditions. While the modification effects of these urbanization characteristics were limited for extreme wet conditions. There was a nonlinear and lagged association between hydrometeorological conditions and the incidence of OID in mainland China, which may be modified in diverse patterns by urbanization indicators. Our findings will provide valuable reference for better prevention and control of OID.

<title>Abstract</title> Context Globally, climate change is increasing the frequency and severity of droughts, significantly impacting forest ecosystems. While previous studies suggest taller forests are more vulnerable during drought, their capacity to recover following drought remains poorly understood, creating uncertainty for forest management. Objectives This study aimed to quantify the role of canopy height in post-drought forest recovery and determine how its importance changes with drought severity at a regional scale. Methods We focused on evergreen broad-leaved forests in southwest China following a severe drought in 2010. We used MODIS EVI to define a forest recovery index, SPEI to classify drought severity, and a LiDAR-derived canopy height map. We then used linear regression and Random Forest models to analyze the relationships and the relative importance of contributing factors. Results Forest recovery was positively correlated with the intensity of the preceding drought. Under severe and extreme drought conditions, forest recovery increased significantly with canopy height (p &lt; 0.001). The relative importance of canopy height as a predictor of recovery also grew with drought intensity, increasing from 18.03% under normal conditions to 20.08% under extreme drought. Conclusions Our findings reveal that while tall forests may suffer more during severe droughts, they exhibit heightened recovery and resilience afterward. This size-dependent recovery dynamic is a critical and previously overlooked factor that should be incorporated into forest dynamic models and climate-adaptive resource management strategies.

The objective of this study is to standardize the calculation method of Palmer Drought Severity Index (PDSI) for the three Drought Management Agencies (DMA) in south Korea, and to evaluate the PDSI applicability. For comparison and review of the method, the code and input data of PDSI are collected from each DMA. The calculation method is the same, but the used input data (number of meteorological stations, normal year period, Available Water Capacity (AWC) of the soil) are different. Through discussions with drought experts and literature review, the standardized method is determined. 61 stations which have the data period more than 30 years are selected. Also the normal year is fixed for 30 years and updated every 10 years. The observed AWC is utilized using GIS data. Empirical equation of PDSI is re-estimated according to domestic climate characteristics. For evaluating the standardized PDSI, past drought events are investigated and drought indices including the existing SPI and PDSI are used for comparative analysis. As results, although the accuracy of standardized PDSI through ROC analysis is lower than SPI, the newly standardized PDSI is better than existing PDSI from DMA, Also it reasonably explain the spatial drought situation through the spatial analysis.

Majapahit was the largest Hindu-Buddhist empire that ruled the Indonesian archipelago from the late 13th to mid-16th centuries CE. Only now there is still a lot of history surrounding the Majapahit era that has yet to be revealed. One is about how environmental factors influenced the political dynamics at that time. This study tries to discuss the influence of hydroclimate regimes using the Paleo Hydrodynamics Data Assimilation (PHYDA) product on the political history of Majapahit during the dry season, which occurs during the boreal summer. We conducted a spatial analysis of the area of drought by taking data from the Palmer Drought Severity Index (PDSI) in the Maritime Continent (MC) for six crucial episodes in the history of Majapahit, namely during the reign of Jayanegara (1309 - 1328 CE), which was marked by various political instability, the golden age of Majapahit (1309 - 1328 CE). 1350 - 1389 CE), the time of the Paregreg civil war (1405 - 1406 CE), the great famine event (ca. 1426 CE), the candrasengkala event (1478 CE), and in 1527 CE, which was marked by the complete conquest of Majapahit by the Demak sultanate. The results show statistically significant dif ferences in most of these six episodes (except during the heyday of Majapahit) against the reference period, which is the average PDSI over the entire Majapahit era (1293 - 1527 CE). In addition, we also conducted a temporal analysis linking PDSI with shif ts in the West Pacific Inter Tropical Convergence Zone (WP ITCZ) and El Niño Southern Oscillation (ENSO) represented by Niño 3.4 Sea Surface Temperature (SST). This temporal analysis results show a positive correlation between WP ITCZ - PDSI, a negative correlation between Niño 3.4 SST - PDSI and a negative correlation between ITCZ - Niño 3.4 SST. All of these correlations are statistically significant. So the probable cause of dry/wet conditions in MC during the Majapahit era was triggered by a meridional ITCZ shif t which triggered dif ferent ENSO phases through Bjerknes feedback. This preliminary study has implications as opening the way to understand the influence of environmental factors on political conditions in the Majapahit era in more detail.

We perform a global assessment of dryness and wetness conditions using an ensemble of observational, satellite-informed, and reanalysis-based soil moisture datasets [soil moisture (SM) Global Land Evaporation Amsterdam Model (SM GLEAM ), SM ERA5-LAND , and SM MERRA-2 ] and drought indices [Palmer drought severity index (PDSI CRU ) and standardized precipitation evapotranspiration index (SPEI CRU )]. Over the short term (1982–2023), four or more products consistently identify drying trends in western North America, South America, the Mediterranean, Europe, central Asia, and southern Australia. Wetting trends appear in southern and southeastern Asia, the Sahel, and parts of southern sub-Saharan Africa, while disagreements across products are most evident in central Africa, parts of South America, the Middle East, and Southeast Asia. The year 2023 ranks among the driest on record since 1982: driest in PDSI CRU and SM ERA5-LAND , second driest in SM GLEAM and SPEI CRU , and eighth in SM MERRA-2 . A longer-term analysis using PDSI CRU from 1903 to 2023 estimates that 2023 was the most severe and spatially extensive drought year in over a century, affecting more than 27% of the global reporting land area. Moreover, the 2021–23 period marks the longest consecutive stretch of extreme drought conditions globally since the early twentieth century. The global patterns of droughts over 2021–23 align with long-term drying trends, consistent with the broader influence of anthropogenic climate change on global hydroclimate. Despite the presence of long-term wetting trends, the wetness of the past decades has not produced events of comparable magnitude to those in the mid-twentieth century, a time that remains unmatched in both the severity and extent of wet extremes. Significance Statement On a global scale, the years 2021–23 have seen some of the most widespread and severe drought conditions in over a century. By analyzing five major datasets that characterize changes in soil moisture conditions, we find that 2023 was the driest year on record in over 120 years and that 2021–23 was the driest 3-yr period over the same time interval. We show that these recent twenty-first-century extremes are consistent with long-term drying trends and highlight a broad intensification of hydroclimate extremes across much of the globe. While previous work has emphasized regional drought events over shorter intervals, this study uses multiple datasets in a global framework to provide strong evidence that anthropogenic climate change is a key driver of an intensifying global pattern of more widespread and severe drought.

[1] Potential evapotranspiration (PET) is one of the inputs to the Palmer Drought Severity Index (PDSI). A common approach to calculating PDSI is to use the Thornthwaite method for estimating PET because of its readily available input data: monthly mean temperatures. PET estimates based on Penman-type approaches are considered to be more physically realistic, but require more diverse input data. This study assesses the differences in global PDSI maps using the two estimates for PET. Annually accumulated PET estimates based on alternative Thornthwaite and Penman-Monteith, parameterizations have very different amplitudes. However, we show that PDSI values based on the two PET estimates are very similar, in terms of correlation, regional averages, trends, and in terms of identifying extremely dry or wet months. The reason for this insensitivity to the method of calculating PET relates to the calculations in the simple water balance model which is at the heart of the PDSI algorithm. It is shown that in many areas, actual evapotranspiration is limited by the availability of soil moisture and is at markedly lower levels compared to its potential value. In other areas, the water balance does change, but the quantity central to the calculation of the PDSI is, by construction, a reflection of the actual precipitation, which makes it largely insensitive to the use of the Thornthwaite PET rather than the Penman-Monteith PET. A secondary reason is that the impact of PET as input to a scaling parameter in the PDSI algorithm is very modest compared to the more dominant influence of the precipitation.

Recent studies have linked the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) with drought occurrence in the interior United States. This study evaluates the influence of AMO and PDO phases on interannual relationships between climate and wildfire-burned area during the 20th century. Palmer's Drought Severity Index (PDSI) is strongly related to burned area at both regional and subregional scales. In the southern Interior West, PDSI is most strongly related to yearly burned area during warm-phase AMO, while for the same period no significant relationships exist between PDSI and burned area in the central Interior West. During cool-phase PDO, interannual climate has little influence on burned area in either the northern or the central Interior West. The opposite is true for the southern Interior West and the eastern slope of the Colorado Rockies using the Southern Oscillation Index and PDSI, respectively. The western slope of the Colorado Rockies is the only climate division or region in which burned area is not related to preceding PDSI. During warm-phase PDO, current PDSI explains 67% of the interannual variance in burned area on the western slope. These regional and temporal differences are most likely governed by variations in fuel dynamics associated with dominant regional and subregional vegetation types.

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.

Increased precipitation differentially changed soil CO2 efflux in arid and humid areas