Palmer Drought Severity Index Values Research Articles

The evolution of “Hot” droughts in Southern California, USA from the 20th to the 21st century

Drought intensity and duration in southern California, USA during the 21st century has been exceptional, and the changing climate dynamics of drought in this arid and semiarid region have been linked to anthropogenic warming. We examine the frequency, intensity, and persistence of drought in southern California's two climatic divisions during 1900–2022 and use the monthly instrumental record of the Palmer Drought Severity Index (PDSI) to track how drought events have changed. We introduce an empirical definition of “hot drought” by combining mean summer PDSI values with standardized scores of mean summer minimum temperature. Droughts in southern California, USA have become increasingly more severe, frequent, and long-lived in the 21st century. Moderate drought conditions (PDSI < −2) were recorded during more than half of the months in the decade ending in 2020. Similarly, hot droughts, droughts associated with both water deficits and significantly above-normal temperatures, have become increasingly frequent in recent decades. By the 21st century, hot droughts were occurring in approximately half of the summers. Our findings support the idea that anthropogenic warming results in a changing drought climatology for arid and semiarid regions of southern California and that hot droughts will likely become the dominant drought type.

Drought Forecasting using Palmer Drought Severity Index with Wavelet Transform Technique and Machine Learning Methods

Drought is a climatic phenomenon characterised by persistent and anomalous water scarcity caused by weather anomalies. Drought is a slowly evolving phenomenon and affects a wide range of areas. The drought indices are one of the ways to monitor and measure drought. Therefore, the Palmer drought severity index (PDSI) was used as an operational and a valid model. This study calculated Palmer Drought Severity Index (PDSI) on a 12-month time scale with meteorological data for the Ceyhan Basin between January 1989 and July 2020. The calculated PDSI values were modelled with Discrete Wavelet Transform (DWT) technique using Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods at different training rates and drought forecasts were made. The forecasting success rate with the models created using machine learning methods was statistically evaluated. This study determined that machine learning methods could be applied in drought forecasting in drought monitoring and mitigation.

Determination of drought distribution using palmer drought severity ındex: Case study of Susurluk basin

The results such as decrease in agricultural production, product quality and change in diversity because of drought create important socio-economic problems. Due to these reasons, it is becoming an increasingly strategic study topic in academic circles. The fact is that it is not observed instantly like natural disasters makes it possible to take necessary measures on a basin basis in case of drought. Accordingly, obtained data, from meteorological stations in the Susurluk Basin, were used in this study. Within the scope of the study, the starting and ending dates, and intensities of dominant dry periods were determined by using the PDSI (Palmer Drought Severity Index). Using data such as precipitation, evaporation, transpiration, and the water holding capacity of soil as inputs, a tool was developed in the R environment for PDSI, and annual values were calculated for each meteorology station by running all inputs in this tool. For calculated PDSI values, spatial and temporal analyze were made using the digital elevation model of the Susurluk Basin using the ordinary cokriging interpolation method in ArcGIS 10.8 program.

Drought identification based on Palmer drought severity index and return period analysis of drought characteristics in Huaibei Plain China

The threshold level method for drought identification is challenging due to the problems of selection of drought index reflecting the drought process associated with water supply and demand as well as the underlying physical meaning of drought thresholds. The frequently used hydrological drought indices (e.g., runoff) are susceptible to being affected by human activities, and drought characteristics are incapable of revealing spatial and temporal comparability. Furthermore, the drought process with the same severity but a longer duration is more likely to be evaluated as a more severe event, which contradicts the actual drought situation. In this study, the Palmer drought severity index (PDSI) method, in which the meteorological factors less influenced by human activities were taken as the input, was adopted to determine the dry/wet states and the PDSI value at each period firstly. The dry/wet states were defined with dry period, wet period, transition period, transition period in dry spell, and transition period in a wet spell. Following that, drought identification criteria were established through the dry/wet states and PDSI value according to the consistency of the identified results and the actual drought situations. Particularly, drought severity and peak intensity were taken as drought characteristics in this paper, and the joint return periods of the characteristics were estimated based on the Gumbel-Hougaard copula function. And eventually, a case study was conducted in Huaibei Plain, China. The results showed that the most severe droughts identified by PDSI had a good consistency with the actual drought situations, drought severity and peak intensity were applicable to reflect the drought impacts. It is worth noting that the implications of the joint return period and the relationships among different types of them. The occurrence probability of a multi-characteristic drought event should be calculated by the integration of joint probability density function over the region corresponding to the event of interest, and the joint frequency of drought characteristics should not be used as the occurrence probability of the drought disaster losses greater (or less) than that caused by the drought with the same characteristics. In addition, drought processes identified by PDSI and standardized precipitation indices (SPI) from monthly and seasonal scales were compared, indicating the drought identified results through PDSI are almost consistent with the actual situations.

Monitoring and mapping of drought in a semi-arid region: case of the Merguellil watershed, central Tunisia.

Drought is defined as a period of time characterized by below-normal water availability, which may affect crops, animals and the environment. Recently, drought was shown to be more frequent and more intense, implying thereby the need for monitoring and analysis of this natural hazard. The present study aims to examine the spatial extent and temporal variation of droughts in the Merguellil watershed, located in central Tunisia. This contribution was mainly based on the analysis of annual and monthly rainfall time series recorded over the period (1983-2018) in 19 stations spread throughout the study watershed. Rainfall trend was first examined using the Mann-Kendall statistical test. Then, statistical (standard precipitation index (SPI) and Palmer drought severity index (PDSI)), spectral (continuous wavelet transform (CWT)) and mapping (geographical information system (GIS)) techniques were used to identify extreme dry events and to characterize their severity and their spatial and temporal extents. The results obtained revealed the recurrence and frequency of drought conditions in the Merguellil watershed over the study period. Seven drought sequences (1983-1984, 1986-1989, 1992-1995, 1999-2002, 2007-2009, 2013-2015 and 2017-2018), with different levels of severity, were distinguished based on the computed SPI and PDSI values. Spectral analysis of rainfall data also showed the occurrence of significant droughts in recent years. The period starting from 2010 was shown to be marked by recurrent episodes of drought in the Merguellil watershed. Extreme drought events mapping over this period confirmed drought severity at both time and space scales. All of these findings may be helpful for developing programs of water resource management in the study watershed.

Comparative analysis of climate and topography in Chaco and Oriental, Paraguay

The article reports the issues corresponding to the climate variability and possibility of droughts in spatially distinct regions of Paraguay, the Chaco and the Oriental (Paraná Basin). Based on the GMT scripting approach and high-resolution datasets from the TerraClimate and GEBCO, the study examined the extent of the annual climate and environmental variables over Paraguay in 2020: extreme temperatures, wind speed, precipitation, soil moisture, evapotranspiration, Palmer's Drought Severity Index (PDSI), vapour pressure deficit (VPD) and the topography of the country. The environmental and climate parameters demonstrated spatial variability in the Dry Chaco with notable differences in Gran Chaco and Chaco Boreal, wetlands of Pantanal, and tropical rainforests in east of the Paraguay. Besides climate analysis, technical snippets from the GMT scripts regarding the mapping techniques and data processing were explained and commented. Research results show that actual evapotranspiration reaches maximal values in the Paraná Basin (180 mm/yr), and has peaks along the Pilcomayo river (140-150 mm/yr) which well coincides with the soil moisture; the PDSI values are maximal in Chaco Boreal and Gran Chaco; the temperatures are the highest in the N and NW regions of Paraguay; the highest VPD (over 2.0) corresponds to the Chaco Boreal.

Long-Time Memory in Drought via Detrended Fluctuation Analysis

The persistence of drought events largely determines the severity of socioeconomic and ecological impacts, unfortunately the performance of current weather forecasting models (WFM) to simulate such events is subject to great uncertainties. This study is investigating time-domain characteristics of drought persistence over Turkey by applying the detrended fluctuation analysis (DFA) method to the Palmer drought severity index (PDSI). The existence of long-range power-law correlation in PDSI fluctuations is demonstrated for time scales ranging from monthly to decadal. Understanding of such statistical patterns in PDSI values can definitely be a step forward in drought predictability. From a climatological point of view, it is found that the areas with high level DFA scaling exponent (generalized Hurst) indicate the areas of higher sensitivity to droughts and associated risks. Furthermore, the characteristics of the persistence of the PDSI in climate zones have also been examined by applying the Holdridge Life Zones (HLZ) classification. HLZ classification over Turkey leads to two climate-zones: cool-temperate and warm-temperate. In addition, when topography is taken in account, montane (cool-temperate) and lower-montane (warm-temperate) climate zones can be treated as two different zones. It has been observed that the predictable index (PI) of the PDSI derived from the DFA Hurst exponent is relatively high in the cool-temperate and montane climate zones compared to others. In fact, very different PI values were also obtained in a few HLZ climate classes within the same climate zone and with same vegetation index (i.e. steppe, dry-forest, warm-forest etc.).

Dendroclimatic Assessment of Ponderosa Pine Radial Growth along Elevational Transects in Western Montana, U.S.A.

Ponderosa pine (PP) is the most common and widely distributed pine species in the western United States, spanning from southern Canada to the United States–Mexico border. PP can be found growing between sea level and 3000 meters elevation making them an ideal species to assess the effects of changing climatic conditions at a variety of elevations. Here we compare PP standardized and raw growth responses to climate conditions along an elevational transect spanning 1000 meters in western Montana, U.S.A., a region that experienced a 20th century warming trend and is expected to incur much warmer (3.1–4.5 °C) and slightly drier summers (~0.3 cm decrease per month) by the end on the 21st century. Specifically, we assess if there are climate/growth differences based on relative (i.e., site-specific) and absolute (i.e., combined sites) elevation between groups of trees growing in different elevational classes. We find that values of the Palmer drought severity index (PDSI) in July are most strongly related to radial growth and that within-site elevation differences are a poor predictor of the response of PP to either wet or dry climatic conditions (i.e., years with above or below average July PDSI values). These results suggest that any generalization that stands of PP occurring at their elevational margins are most vulnerable to changing climatic may not be operative at these sites in western Montana. Our results show that when using standardized ring widths, PP growing at the lowest and highest elevations within western Montana exhibit differential growth during extreme climatological conditions with lower-elevation trees outperforming higher-elevation trees during dry years and vice versa during wet years.

Projected Spatiotemporal Dynamics of Drought under Global Warming in Central Asia

Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index.

CLIMATE-GROWTH RESPONSES FROM PINUS PONDEROSA TREES USING MULTIPLE MEASURES OF ANNUAL RADIAL GROWTH

When using old-growth trees from semiarid, open-canopy environments, basal area increment (BAI), an absolute measure of radial growth, is sometimes used instead of the more commonly used ‘conservative techniques’ (negative exponential or linear regression with a negative slope; NegX) because narrow rings have been shown to potentially bias results. In this study we explore the relationship between radial growth of ponderosa pine from four study sites in Montana and climate (temperature, precipitation, drought severity) using unstandardized raw ring width and BAI values, and standardized values generated via Friedman Super Smoother and NegX. All sites are minimally disturbed, and our selection criteria are limited to older (interior dates pre-A.D. 1850 at breast height) trees growing in open-canopy environments free of visible disturbance such as lightning strikes. We found the strongest relationships (r > 0.60) for radial growth with July and prior-year October Palmer Drought Severity Index values. Our results show that radial growth-climate responses generally fall within a narrow range regardless of the representation of annual growth (e.g. for July temperature r-values are largely –0.3 to – 0.4) and that site conditions determine which radial-growth values (i.e. unstandardized or standardized) optimize climate-growth responses.

Normalized difference vegetation index as a tool for the evaluation of agricultural drought in Slovakia

The paper deals with the evaluation of agricultural drought in Slovakia by remote sensing data. For this purpose, monthly normalized difference vegetation index (NDVI) values were derived from MODIS spectroradiometer at district level during the time period of 2000 to 2014. Verification was based on correlations with the Palmer drought severity index (PDSI) for twelve sites selected according to limited climatic parameters. Correlations showed a strong relationship (? ? 0.1) between NDVI and PDSI values, mainly in the middle of the growing season (June to September). The aim of the paper is testing NDVI as a suitable tool for drought assessment in an agricultural land in Slovakia.

Drought Influence on Forest Plantations in Zululand, South Africa, Using MODIS Time Series and Climate Data

South Africa has a long history of recurrent droughts that have adversely affected its economic performance. The recent 2015 drought has been declared the most serious in 26 years and impaired key agricultural sectors including the forestry sector. Research on the forests’ responses to drought is therefore essential for management planning and monitoring. The effects of the latest drought on the forests in South Africa have not been studied and are uncertain. The study reported here addresses this gap by using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) and precipitation data retrieved and processed using the JavaScript code editor in the Google Earth Engine (GEE) and the corresponding normalized difference infrared index (NDII), Palmer drought severity index (PDSI), and El Niño time series data for KwaMbonambi, northern Zululand, between 2002 and 2016. The NDVI and NDII time series were decomposed using the Breaks for Additive Seasonal and Trend (BFAST) method to establish the trend and seasonal variation. Multiple linear regression and Mann–Kendall tests were applied to determine the association of the NDVI and NDII with the climate variables. Plantation trees displayed high NDVI values (0.74–0.78) from 2002 to 2013; then, they decreased sharply to 0.64 in 2015. The Mann–Kendall trend test confirmed a negative significant (p = 0.000353) trend between 2014 and 2015. This pattern was associated with a precipitation deficit and low NDII values during a strong El Niño phase. The PDSI (−2.6) values indicated severe drought conditions. The greening decreased in 2015, with some forest remnants showing resistance, implying that the tree species had varying sensitivity to drought. We found that the plantation trees suffered drought stress during 2015, although it seems that the trees began to recover, as the NDVI signals rose in 2016. Overall, these results demonstrated the effective use of the NDVI- and NDII-derived MODIS data coupled with climatic variables to provide insights into the influence of drought on plantation trees in the study area.

Effects of dry or wet conditions during the preweaning phase on subsequent feedlot performance and carcass composition of beef cattle

Our objective was to determine the effects of dry and wet conditions during the preweaning on subsequent feedlot performance and carcass characteristics of beef cattle. Steers (n = 7,432) and heifers (n = 2,361) finished in 16 feedlots in southwestern Iowa through the Tri-County Steer Carcass Futurity Cooperative were used for a retrospective analysis. Cattle originated in the Midwest (Iowa, Missouri, Indiana, Illinois, and Minnesota) and were born in February, March, or April of 2002 through 2013. Feedlot performance and carcass composition data were obtained for each animal. Palmer Drought Severity Index (PDSI) values were obtained for each animal’s preweaning environment on a monthly basis. Mean PDSI values were used to classify conditions as dry (≤−2.0), normal (>−2.0 and <2.0), or wet (≥2.0) for the cool (April and May), warm (June through August), and combined (April through August) forage growing seasons preweaning. Mixed models were used to evaluate the effects of PDSI class on subsequent performance. Calf sex, date of birth (as day of year), year, and feedlot were also included as fixed effects. When considering PDSI class during the cool season, cattle from normal and wet classes had a greater feedlot delivery BW (P < 0.0001) than dry. Dry and normal classes had greater (P ≤ 0.02) delivery BW than wet during the warm and combined seasons, however. For the cool season, average daily gain was greater (P < 0.0001) for the dry class than normal and wet. Cattle from the normal class for the cool season had greater (P = 0.001) final BW than wet, but the wet class had the greatest (P < 0.04) and dry class had the lowest (P < 0.01) final BW during the warm season. During the cool season, HCW was greater (P < 0.007) for the normal than wet class, although HCW was greater (P ≤ 0.02) for wet compared with dry and normal during the warm season. Calculated yield grade was lower (P ≤ 0.006) for the normal class during the cool season compared with dry and wet. For both the warm and combined seasons, the dry class had lower (P ≤ 0.004) calculated yield grade compared with normal and wet. Carcasses from cattle that experienced normal or wet warm seasons had greater (P ≤ 0.0005) marbling scores than dry, and normal had greater (P = 0.0009) marbling score than dry for the combined seasons. In conclusion, these data indicate that both dry and wet conditions during the preweaning phase may impact ultimate feedlot performance and carcass composition.

Construction of prediction intervals for Palmer Drought Severity Index using bootstrap

In this study, we propose an approach based on the residual-based bootstrap method to obtain valid prediction intervals using monthly, short-term (three-months) and mid-term (six-months) drought observations. The effects of North Atlantic and Arctic Oscillation indexes on the constructed prediction intervals are also examined. Performance of the proposed approach is evaluated for the Palmer Drought Severity Index (PDSI) obtained from Konya closed basin located in Central Anatolia, Turkey. The finite sample properties of the proposed method are further illustrated by an extensive simulation study. Our results revealed that the proposed approach is capable of producing valid prediction intervals for future PDSI values.

Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin

Abstract Examining the spatiotemporal dynamics of meteorological variables in the context of changing climate, particularly in countries where rainfed agriculture is predominant, is vital to assess climate-induced changes and suggest feasible adaptation strategies. To that end, trend analysis has been employed to inspect the change of rainfall and temperature in northcentral Ethiopia using gridded monthly precipitation data obtained from Global Precipitation and Climate Centre (GPCC V7) and temperature data from Climate Research Unit (CRU TS 3.23) with 0.5° by 0.5° resolution from 1901 to 2014. Data have been analyzed using coefficient of variation, anomaly index, precipitation concentration index and Palmer drought severity index. Furthermore, Mann-Kendall test was used to detect the time series trend. The result revealed intra- and inter-annual variability of rainfall while Palmer drought severity index value proved the increasing trend of the number of drought years. Annual, belg and kiremt rainfall have decreased with a rate of 15.03, 1.93 and 13.12 mm per decade respectively. The declining trend for annual and kiremt rainfall was found to be statistically significant while that of belg was not significant. The rate of change of temperature was found to be 0.046, 0.067 and 0.026 °C per decade for mean, minimum and maximum respectively. The Mann-Kendall trend analysis test result revealed increasing trend for mean and minimum average temperatures through time significantly while the trend for maximum temperature exhibited a non-significant increasing trend. We recommend strategies designed in the agricultural sector have to take the declining and erratic nature of rainfall and increasing trend of temperature into consideration.

English

Analysis of spatial and temporal drought variability in the upper Tana River basin using Palmer Drought Severity Index (PDSI) was conducted. The drought is critical for formulation of mitigation measures in the river basin. A monthly temporal and 90-m spatial resolution was applied. This was achieved within ArcGIS environment. Climatic data for 1970 to 2010 was used for computation of the PDSI while the missing data sets were filled using Artificial Neural Networks (ANNs). The results of PDSI for dry and wet seasons at meteorological stations indicate that the time series plots for the PDSI values for dry season are generally lower than those for the wet seasons. The PDSI values for meteorological stations located at the lower elevation of the basin are lower than those located at higher elevation. On the other hand, spatially distributed drought severity based on PDSI show that the ranges of maximum and minimum drought severity values in 1970 are -0.868 to -0.804 and -0.675 to -0.610 respectively. These values of drought severity occur respectively in the north-western and south-eastern areas of the basin. PDSI values increased from the range -0.675 to -0.610 in 1970 and from -1.087 to 0.957 in 2010 for the north-eastern areas of the upper basin. The south eastern areas of the basin are more prone to drought risks than north-western parts. Use of the PDSI reflects the spatial heterogeneity and temporal variability of drought across the basin. The drought assessment offer technical approach for comprehensive understanding of drought for effective drought-induced disaster mitigation and its management, with a view to reducing adverse effects on livelihoods. Key words: Palmer Drought Severity Index (PDSI), drought severity, upper Tana River basin, monthly resolution, drought-induced disaster.

Spatial, temporal and latitudinal components of historical fire regimes in mixed conifer forests, California

AbstractAimThis study seeks to document and compare historical temporal and spatial components of fire regimes in two watersheds in mixed conifer forests of the western slope of the Sierra Nevada, California, USA.LocationWatersheds in the southern Sierra Nevada (Sugar Pine, 2358 ha) and north‐central Sierra Nevada (Last Chance, 3021 ha), California, USA are compared.MethodsTemporal (frequency, return interval, season) and spatial (extent, fire rotation, spatial mean fire interval) fire regime metrics were reconstructed from fire scar samples. Superposed epoch analysis (SEA) was used to examine relationships between fire occurrence and the Palmer drought severity index (PDSI) at each site. Thin plate splines were introduced as a tool for interpolating historical fire extent from dendroecological data. Point fire return intervals were compared between sites to better understand possible influences of historical Native American burning practices.ResultsDifferences emerged between sites in temporal and spatial fire regime metrics. The northern site had longer fire return intervals, more synchronized fire years, fewer point intervals &lt; 4 years, longer fire rotation period and longer spatial mean fire interval. The northern site showed a significant reduction in PDSI values during fire years, whereas this climate–fire relationship in the southern site was likely decoupled by frequent Native American burning. Thin plate spline interpolation effectively reduced discontinuities at sample points compared to inverse distance weighting methods.Main conclusionsDifferences in both temporal and spatial fire regime metrics between sites were likely due to interplay in latitudinal influence on climate as well as differential Native American burning practices. Reconstruction of historical fire areas via geographical interpolation of fire scar data holds great promise for spatially explicit fire frequency reconstruction. The use of thin plate spline interpolation methods has the potential to reduce the impact of ‘false negatives’ in dendroecological data from frequent‐fire forests.

Increased tree-ring network density reveals more precise estimations of sub-regional hydroclimate variability and climate dynamics in the Midwest, USA

Understanding the historic variability in the hydroclimate provides important information on possible extreme dry or wet periods that in turn inform water management plans. Tree rings have long provided historical context of hydroclimate variability of the U.S. However, the tree-ring network used to create these countrywide gridded reconstructions is sparse in certain locations, such as the Midwest. Here, we increase (n = 20) the spatial resolution of the tree-ring network in southern Indiana and compare a summer (June–August) Palmer Drought Severity Index (PDSI) reconstruction to existing gridded reconstructions of PDSI for this region. We find both droughts and pluvials that were previously unknown that rival the most intense PDSI values during the instrumental period. Additionally, historical drought occurred in Indiana that eclipsed instrumental conditions with regard to severity and duration. During the period 1962–2004 CE, we find that teleconnections of drought conditions through the Atlantic Meridional Overturning Circulation have a strong influence (r = −0.60, p < 0.01) on secondary tree growth in this region for the late spring-early summer season. These findings highlight the importance of continuing to increase the spatial resolution of the tree-ring network used to infer past climate dynamics to capture the sub-regional spatial variability. Increasing the spatial resolution of the tree-ring network for a given region can better identify sub-regional variability, improve the accuracy of regional tree-ring PDSI reconstructions, and provide better information for climatic teleconnections.

Detecting persistence of meteorological drought via the Hurst exponent

ABSTRACTThis study proposes the Hurst exponent (H) to detect persistence in the Palmer Drought Severity Index (PDSI) over Turkey. Since a fractal structure admits the behaviour of global determinism and local randomness, the H exponent values could be used to detect self‐similar statistical structure of the time series. Additionally, the H value &gt;0.5 and near 1 shows the intensity level of persistence. The term persistence may be assessed as a criterion to be used as a measure of predictability. Moreover, the predictability index, fractal dimension and autocorrelation function of the PDSI values can also be obtained from the H values. Nevertheless, it is not easy to find a spatial meaning of information content of the results obtained by the methods used in the present study. For that reason, the Mann‐Kendall (MK) trend test was also used, and the results showed that significant negative trends are widespread throughout the country. As expected, the H values were close to 1 in places where statistically significant trends exist. The high values of the H exponent in all the regions can be explained by the droughts observed there that might be caused due to the possible associations with the large‐scale atmospheric circulations. In this context, it is suggested that the droughts can be predictable by constructing the appropriate general climate circulation models.

Revisiting the recent California drought as an extreme value

AbstractSpatially weighted averages of Palmer Drought Severity Index (PDSI) over central and southern California show that the 1 year 2014 drought was not as severe as previously reported, but it still is the most severe in the 1895–2014 instrumental record. Using the typical adjustment procedure that matches the mean and standard deviation of tree ring PDSI values to those of instrumental data shows over 10 droughts from 800 to 2006 that were more severe than the 1 year 2014 drought, with the 2014 drought having a return period of 140–180 years. Quantile mapping allows for a closer correspondence between instrumental and tree ring PDSI probability distributions and produces return periods of 700–900 years for the 1 year 2014 drought. Associated cumulative 3 and 4 year droughts, however, are estimated to be much more severe. The 2012–2014 drought is nearly a 10,000 year event, while the 2012–2015 drought has an almost incalculable return period and is completely without precedent.