3-month Standardized Precipitation Index Research Articles

رصد ظاهرة الجفاف باستخدام الاستشعار عن بعد ونظم المعلومات الجغرافية في محافظة واسط / العراق

Drought considered being the most vulnerable natural hazard that affects a large number of people. Drought has nonstructural effects that spread across a vast geographic area, endangering human lives. The Normalized difference vegetation index (NDVI) based on Landsat 8 imaging and the meteorological-based Standardized Precipitation Index (SPI) were used to monitor the drought in Wasit governorate eastern Iraq for 2013 and 2018. To accurately assess the drought-affected area and provide appropriate drought-proofing, the Arc GIS Desktop 10.5 program was utilized to examine the spatial and temporal variance of drought across the study area. Using NDVI analysis in the study area, the vegetation area decreased by 7 %. Drought classification based on 12-month SPI indicated the drought of the near-normal type. The SPI data analysis data show that in 2013, SPI values were lower in Badrah station's northern part of the study area. While in 2018, the lower SPI value - was 0.54033 in Ali al Gharbi terrestrial station. This result is considered near normal according to the SPI usual range. These findings could aid drought management programs and reveal the genuine drought situation in the area.

Drought monitoring and its effects on vegetation and water extent changes using remote sensing data in Urmia Lake watershed, Iran

Abstract The assessment of drought hazards is important due to their socio-economic impacts on water resources, agriculture, and ecosystems. In this study, the effects of drought on changing water area and canopy of the Lake Urmia watershed in the northwest of Iran have been monitored and evaluated. For this purpose, the Standardized Precipitation Index (SPI) was calculated in short and medium periods (1-month and 3-month) to determine the dry-spell periods in the Lake Urmia basin. In reviewing this analysis, the annual average has been examined and evaluated. Furthermore, Moderate Resolution Imaging Spectroradiometer (MODIS) and remote sensing data were used to calculate the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Normalized Difference Water Index (NDWI), and the Temperature–Vegetation–Dryness Index (TVDI) to identify the area of water body, water level, and vegetation changes during 20 years (2000–2020). The Pearson correlation coefficient was also employed to explore the relationship between the drought and the remote sensing-derived indices. According to the results of drought analysis, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, and 2020 had experienced dry spells in the Lake Urmia basin. The NDWI changes also showed that the maximum area of the Lake Urmia happened in 2000, and its minimum was recorded in 2014. The variation of NDVI values showed that the highest values of vegetation cover were estimated to be 2,850 km2 in.2000, and its lowest value was 1,300 km2 in.2014. The maximum EVI and TDVI were calculated in 2000, while their minimum was observed in 2012 and 2014. Also, the correlation analysis showed that the SPI had the highest correlation with NDVI. Meanwhile, 1-month SPI had a higher correlation than the 3-month SPI with NDVI and EVI. As a concluding remark, NDVI and NDWI were more suitable indices to monitor the changes in vegetation and drought-related water area. The results can be used to make sound decisions regarding the rapid assessment of remote sensing-derived data and water-related indices.

Trends of Rainfall Variability and Drought Monitoring Using Standardized Precipitation Index in a Scarcely Gauged Basin of Northern Pakistan

This study focused on the trends of rainfall variability and drought monitoring in the northern region of Pakistan (Gilgit-Baltistan). Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) model data were used for the period of 1981 to 2020. The Standardized Precipitation Index (SPI) was applied to assess the dry and wet conditions during the study period. The Mann–Kendall (MK) and Spearman’s rho (SR) trend tests were applied to calculate the trend of drought. A coupled model intercomparison project–global circulation model (CMIP5–GCMs) was used to project the future precipitation in Gilgit-Baltistan (GB) for the 21st century using a multimodel ensemble (MME) technique for representative concentration pathway (RCP) 4.5 and RCP 8.5. From the results, the extreme drought situations were observed in the 12-month SPI series in 1982 in the Diamir, Ghizer, and Gilgit districts, while severe drought in 1982–1983 was observed in Astore, Ghizer, Gilgit, Hunza-Nagar, and Skardu. Similarly, in 2000–2001 severe drought prevailed in Diamir, Ghanche, and Skardu. The results of MK and SR indicate a significant increasing trend of rainfall in the study area, which is showing the conversion of snowfall to rainfall due to climate warming. The future precipitation projections depicted an increase of 4% for the 21st century as compared with the baseline period in the GB region. The results of the midcentury projections depicted an increase in precipitation of about 13%, while future projections for the latter half of the century recorded a decrease in precipitation (about 9%) for both RCPs, which can cause flooding in midcentury and drought in the latter half of the century. The study area is the host of the major glaciers in Pakistan from where the Indus River receives its major tributaries. The area and volume of these glaciers are decreasing due to warming impacts of climate change. Therefore, this study is useful for proper water resource management to cope up with water scarcity in the future.

Short- and Long-Term Effects of Drought on Selected Causes of Mortality in Northern Bangladesh.

Drought has exacerbated morbidity and mortality worldwide. Here, a time series study was conducted in northern Bangladesh to evaluate the impact of drought on selected causes of mortality during 2007–2017. Rainfall and temperature data from six meteorological stations were used to analyze drought and non-drought periods and to categorize mild, moderate, severe, and extreme drought based on the 3-month and 12-month Standardized Precipitation Index (SPI) and Standardized Precipitation Evaporation Index (SPEI). A generalized linear model with Poisson regression with log link, a negative binomial with log link, and a zero-inflated Poisson model were used to determine associations between drought severity and mortality. The SPI and SPEI produced slightly different analysis results. Compared with the SPEI, the SPI showed a stronger and more sensitive correlation with mortality. The relative risk for respiratory disease mortality was high, and Saidpur was the most vulnerable area. Health care expenditure was negatively associated with mortality. High temperatures during the drought period were associated with suicide-related mortality in Rajshahi. The impact of drought on mortality differed with small changes in climate. The findings of this study improve our understanding of the differences between the two most used drought indicators and the impact of drought on mortality.

Assessment of agricultural drought based on CHIRPS data and SPI method over West Papua – Indonesia

This study aims to utilise Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data and Standardised Precipitation Index (SPI) method to assess agricultural drought in West Papua, Indonesia. The data used in this study is monthly CHIRPS data acquired from 1996 to 2019, daily precipitation data recorded from 1996 to 2019 from the five climatological stations in West Papua, Indonesia located at Sorong, Fakfak, Kaimana, Manokwari, and South Manokwari. 3-month SPI or quarterly SPI are used to assess agricultural drought, i.e., SPI January–March, SPI February–April, SPI March-May, SPI April–June, SPI May–July, SPI June–August, SPI July–September, SPI August–October, SPI September–November, and SPI October–December. The results showed that in 2019 agricultural drought in West Papua was moderately wet to severely dry. The most severely dry occurred in September– December periods. Generally, CHIRPS data and SPI methods have an acceptable accuracy in generating drought information in West Papua with an accuracy of 53% compared with climate data analysis. Besides, the SPI from CHIRPS data processing has a moderate correlation with climate data analysis with an average R2 = 0.51.

Drought Events in Western Part of Timor Island Indonesia

Drought is a below-averaged condition of water availability, which has detrimental impacts on many sectors. Many studies have been performed on drought analysis in Indonesia, yet knowledge about drought in western Timor is still limited. This research carried out a historical meteorological drought analysis based on a 3-month Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) using global climate data for 1989-2018. The index value was then categorized into three groups: moderate, severe, and extreme. We assessed: (i) the influence of El Niño phenomena to drought events, (ii) drought class frequency, and (iii) drought trend. Based on historical data, western Timor had a monsoonal pattern with dominant dry period, which occurred in April to November. The results showed that the drought events were mostly influenced by El Niño. Seasonally, El Niño not only increased the drought frequency in July-August (JJA) season, but also in other seasons. In El Niño year of 2015, drought covered most parts of study area during September-November (SON) season, especially in the western part. Dry conditions increased in June, reached maximum in September-November, and decreased in December. Other findings show that an extreme drought consistently had a downtrend, while the moderate drought had upward trends. Spatiotemporal drought analysis using SPI and SPEI showed similar patterns, SPEI detected a higher frequency of drought classes compared to SPI. This study suggests that knowledge on drought-related El Niño will benefit on drought mitigation action in the future.

Diagnosing anomalous characteristics of atmospheric water cycle structure during seasonal-scale drought events: A case study in middle and lower reaches of Yangtze River

Anomalous characteristics of the atmospheric water cycle structure are highly significant to the mechanisms of seasonal-scale meteorological droughts. They also play an important role in the identification of indicative predictors of droughts. To better understand the causes of seasonal meteorological droughts in the middle and lower reaches of the Yangtze River (MLRYR), characteristics of the atmospheric water cycle structure at different drought stages were determined using standardized anomalies. The results showed that the total column water vapor (TCWV) was anomalously low during drought occurrence periods. In contrast, there were no anomalous signals at the drought persistence and recovery stages in the MLRYR. Moreover, there was no significant temporal correlation between the TCWV anomaly and seasonal-scale drought index (the 3-month standardized precipitation index (SPI3)). During drought events, water vapor that mainly originated from the Bay of Bengal was transported southwest of the MLRYR. Meanwhile, the anomalous signal of water vapor transport was negative at the drought appearance stage. At the drought persistence stage, the negative anomalous signal was the most significant. Water vapor flux divergence in the MLRYR showed significant positive anomalous signals during drought events, and the signal intensity shifted from an increasing to a decreasing trend at different drought stages. In addition, a significant positive correlation existed between the anomaly of water vapor flux divergence and regional SPI3. Overall, water vapor flux divergence is more predictive of droughts in the MLRYR.

Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-year drought monitoring in Iran using satellite imagery within Google Earth Engine

• The Temperature-Vegetation-soil Moisture-Precipitation Drought Index was proposed. • Validation was performed using meteorological data. • More than 15,206 MODIS, TRMM and GPM datasets were utilized. • The overall drought trend across Iran gradually decreased over the past 21 years. • Significant drought was observed in south-west, north, and central parts of Iran. Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS-based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15,206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.

Development and application of SPI generator using open source for analyzing drought at a varying time scale

The Standardized Precipitation Index (SPI) is a probability index that gives a belier representation of abnormal wetness and dryness than any other drought indices. The primary objective of the current study is to develop a comprehensive tool to compute SPI on a spatial basis and analyze spa!iotemporal variability of drought in North West Indian region during 1951-2007 using APHRODITE waler resource data at 0.25-degree resolution. This tool was developed using the python programming language, and the site-packages such as Numpy, Scipy, Ma!plo!lib, Ne!CDF, PyQt were used. The result showed Iha! the SPI time series showed significant inter-annual and multi-decadal variations. In the whole data period, three consecutive droughts have occurred only once, 1999-2002. This prolonged drought hurt the agricultural and water resources sectors over the study area. The computed SPI for the year2002 showed an extreme dry spell over the study area signifying the major drought over India in the same year with a 56% deficit of rainfall in July. The computed 12-month SPI for the year 1996 shows a wet period over the northwestern part of India, especially over Haryana signifying medium to heavy rainfall, conforming 1996 flood. The developed SPI tool, portray a realistic picture of drought scenario over the Northwest region and improve the timely identification of emerging drought conditions that can trigger appropriate responses by the decision makers.

Drought monitoring using multi-time-scale Standardized Precipitation Index

Drought is a natural hazard that affects almost all regions. In recent years, it has become more intense and frequentcausing adverse impacts on the socio-economic conditions of the country. The primary cause of drought developmentis the deficiency in precipitation impacting crop production during kharif with a follow-up effect also in crops grownduring rabi. The present study was carried out over one of the most drought-prone regions of India, i.e. Marathwadaregion, Maharashtra to characterize meteorological drought through the Standardized Precipitation Index (SPI) duringboth kharif and rabi crop growing seasons. SPI was computed at different time scales (1, 3, 6, 9 and 12-month) usingin-situ precipitation data for 35 years (1980-2014). Drought area observed by multi-time-scale SPI was correlated withdrought declared by the Government and foodgrain production for 15 years (2000–14). It was observed that none ofthe time scales of SPI had a significant correlation with declared drought. However, correlation analysis of multiscaleSPI with foodgrain production showed that 3-month SPI had a significant correlation (r= -0.72) during kharif, while alow correlation was observed between multiscale SPI and foodgrain production for rabi season. Therefore, informationobserved by SPI would be useful if it could be combined with other biophysical conditions and drought indices toincrease its accuracy and reliability for effective drought characterization and monitoring.

Analyzing the duration frequency and severity of drought using copula function in the Yazd city

Abstract Duration and severity are the two main variables used in drought analysis. The copula functions are appropriate for multivariate drought analysis, as it lacks the limitations of the classical multivariate distribution function. This study investigated the bivariate frequency analysis of drought duration and severity of Yazd city in Iran synoptic station during 1953–2013. To this end, first, the drought duration and severity variables were derived from the 6-month Standardized Precipitation Index. Then, considering the distribution functions, the gamma distribution function was selected for analyzing the severity and the exponential distribution function was selected for analyzing the duration and then the Clayton copula function was selected out of the three copula functions as the most appropriate one. After conducting bivariate frequency analysis, the joint seasonal and conjunctive return period and conditional return period curves were plotted. The current study well signified that multivariate analyses could present better interpretations of the reality; for example, as it was identified in conditional return period curves of the drought, for every constant duration, the amount of the return period changed as the severity changed. On the contrary, in analyzing the univariate of duration, no effects of severity were observed.

Improving seasonal predictions of meteorological drought by conditioning on ENSO states

Useful hindcast skill of meteorological drought, assessed with the 3-month standardized precipitation index (SPI), has been so far limited to one lead month (time horizon of the prediction). Here, we quadruple that lead time by demonstrating useful skill up to lead month 4. To obtain useful hindcast skill of meteorological drought at these long lead times, we exploit well-known El Niño-Southern Oscillation (ENSO)–precipitation teleconnections through ENSO-state conditioning. We condition initialized seasonal SPI hindcasts, derived from the Max-Planck-Institute Earth System Model (MPI-ESM) over the period 1982–2013, on ENSO states by exploring significant agreements between two complementary analyses: hindcast skill ENSO–composites, and observed ENSO–precipitation correlations. Such conditioned hindcast skill of meteorological drought is in MPI-ESM significant and reliable for lead months 2 to 4 in equatorial South America and southern North America during these regions’ dry ENSO phases. When a region’s dry ENSO phase is present at the initialization in autumn (ASO), predictions of meteorological drought show useful hindcast skill for the upcoming winter (DJF) in the respective region. The area of this useful hindcast skill is further enlarged in both regions when the respective region’s dry ENSO phase is already present in the antecedent summer (conditioning on ENSO states in JJA). Active ENSO events constitute windows of opportunity for drought predictions that are insufficiently covered by typical predictability analyses. For these windows, we demonstrate predictive skill at unprecedented lead times with a single model whose output is not bias corrected. This contribution exemplifies the value of ENSO-state conditioning in identifying these windows of opportunity for regions that are arguably most affected by ENSO–precipitation teleconnections. During these regions’ dry ENSO phases, reliable predictive skill of meteorological drought is at long lead times particularly valuable and moves the frontier of meteorological drought predictions.

The association between drought conditions and occupational psychosocial stress among Midwestern U.S. farmers: an occupational cohort study

BACKGROUND AND AIM: Drought represents a globally relevant natural disaster linked to adverse health. Evidence has shown agricultural communities to be particularly susceptible to drought conditions, but there is a limited understanding of how drought may impact occupational psychosocial stress in farmers. METHODS: We used repeated measures data collected in the Musculoskeletal Symptoms among Agricultural Workers Cohort study, including 498 Midwestern U.S. farmers surveyed with a Job Content Questionnaire (JCQ) at six-month intervals in 312 counties (2012-2015). A longitudinal mixed effects model was used to estimate the change in job strain ratio, a metric of occupational psychosocial stress, during drought conditions measured with a 12-month standardized precipitation index. We further evaluated associations between drought and psychological job demand and job decision latitude, the job strain components, and applied a stratified analysis to evaluate differences by participant sex, age, and geography. RESULTS:During the growing season, the job strain ratio increased by 0.031 (95% CI: 0.012, 0.05) during drought conditions, an amount equivalent to a one-half standard deviation change, compared to non-drought conditions. The association between drought and the job strain ratio was driven mostly by increases in the psychological job demand (2.09; 95% CI: 0.94, 3.24). No risk differences were observed by sex, age group, or geographic region. CONCLUSIONS:Our results suggest a previously unidentified association between drought and increased occupational psychosocial stress among farmers. With North American climate anticipated to become hotter and drier, these findings could provide important health effects data for federal drought early warning systems and mitigation plans. KEYWORDS: natural disasters, mental health outcomes, occupational epidemiology, climate

Differential Susceptibility to Aridity for Mortality Outcomes in South Africa: Application of Mixed-Effect Regression Tree Analysis to a Case-Only Design

BACKGROUND AND AIM: We investigated whether the relationship between recent aridity and mortality might be different according to demographic subgroups in South Africa over 1997-2013. METHODS: DESIGN: This study drew on a case-only sample comprised of recorded deaths (n=8,509,130) in South Africa over 1997-2013, using data from the country’s civil registration system. The dataset included race, sex, and age of the deceased, as well as the district municipality (52 clusters) of the deceased. We linked to each person the district-level 6-month Standardized Precipitation Index (6-month SPI) representing the relative aridity over the 6 months preceding their death. ANALYSIS: For this analysis we excluded participants with missing data (included n=6,139,287). We applied an algorithm for recursive partitioning based on linear mixed-effect models to distinguish demographic subgroups with high homogeneity in their 6-month SPI at time of death (conditional on partitioning variables), while accounting for the clustering of participants in districts. Our predictors were: recorded race (5 categories recorded in dataset: Black African, White, Indian or Asian, Coloured, or Other/Unknown/Unspecified), recorded sex (male or female), and age category (4 categories: 0-4 years, 5-14 years, 14-64 years, and 65+ years). RESULTS:The recursive partitioning algorithm indicated six demographic subgroups that appeared to have potentially differential vulnerability to 6-month SPI for mortality outcomes: [1] “Coloured” males ages 14-64; [2] “Black African or White or Indian or Asian or Other” males ages 14-64; [3] females of any race ages 14-64; [4] “White or Indian or Asian or Coloured” of any sex ages 0-4; [5] “Black or Other/Unknown/Unspecified race” of any sex not ages 14-64; and [6] “White or Indian or Asian or Coloured” of any sex ages 5-14 or 65+. CONCLUSIONS:This approach suggested several composite demographic groups that may be the focus of future research on aridity-demographic interactions for mortality rates, potentially simplifying from 40 strata to 6 priority groups. KEYWORDS: Climate, Methodological study design, Modeling, Environmental disparities, Environmental epidemiology, Socio-economic factors

The association between drought conditions and increased occupational psychosocial stress among U.S. farmers: An occupational cohort study

BackgroundDrought represents a globally relevant natural disaster linked to adverse health. Evidence has shown agricultural communities to be particularly susceptible to drought, but there is a limited understanding of how drought may impact occupational stress in farmers. MethodsWe used repeated measures data collected in the Musculoskeletal Symptoms among Agricultural Workers Cohort study, including 498 Midwestern U.S. farmers surveyed with a Job Content Questionnaire (JCQ) at six-month intervals in 312 counties from 2012 through 2015. A longitudinal linear mixed effects model was used to estimate the change in job strain ratio, a continuous metric of occupational psychosocial stress, during drought conditions measured with a 12-month standardized precipitation index. We further evaluated associations between drought and psychological job demand and job decision latitude, the job strain components, and applied a stratified analysis to evaluate differences by participant sex, age, and geography. ResultsDuring the growing season, the job strain ratio increased by 0.031 (95% CI: 0.012, 0.05) during drought conditions, an amount equivalent to a one-half standard deviation change (Cohen's D = 0.5), compared to non-drought conditions. The association between drought and the job strain ratio was driven mostly by increases in the psychological job demand (2.09; 95% CI: 0.94, 3.24). No risk differences were observed by sex, age group, or geographic region. ConclusionsOur results suggest a previously unidentified association between drought and increased occupational psychosocial stress among farmers. With North American climate anticipated to become hotter and drier, these findings could provide important health effects data for federal drought early warning systems and mitigation plans.

Assessment of meteorological drought change in the 21st century based on CMIP6 multi-model ensemble projections over mainland China

Understanding drought dynamics spatially and temporally is essential for environmental and socio-economic systems, especially under climate change with expected increasing drought. This study aims to update the knowledge about drought spatiotemporal changes over mainland China in the 21st century by utilizing the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. Precipitation from the nine CMIP6 models is first bias-corrected using a quantile mapping approach and then used to calculate the 3-month Standardized Precipitation Index (SPI) for the historical period and future three scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). We conduct a clustering algorithm to identify three-dimensional (i.e., latitude, longitude, and time) drought events to access the drought dynamics in both space and time. Results show that the drought events occur more frequently during June to September than the other months for both historical and future periods. The drought frequency is significantly increasing during Sep–Dec in the 21st century (increase by about 8.5%, p-value<0.05). Compared with the historical period, drought centers shift toward south under the future projections, indicating a relatively high drought frequency in south China. Additionally, drought events are projected to have shorter duration, smaller affected area, but higher intensity under all three scenarios. The study contributes to our comprehensive understanding of how future drought characteristics will change due to climate change from the perspective of spatiotemporal dynamics. These results are helpful to drought risk management and provide information for developing drought adaptation strategies.

Spatial and Temporal Analysis of Dry and Wet Spells in the Wadi Cheliff Basin, Algeria

The Mediterranean Basin, located in a transition zone between the temperate and rainy climate of central Europe and the arid climate of North Africa, is considered a major hotspot of climate change, subject to water scarcity and drought. In this work, dry and wet spells have been analyzed in the Wadi Cheliff basin (Algeria) by means of annual precipitation observed at 150 rain gauges in the period 1970–2018. In particular, the characteristics of dry and wet spells (frequency, duration, severity, and intensity) have been evaluated by means of the run theory applied to the 12-month standardized precipitation index (SPI) values. Moreover, in order to detect possible tendencies in the SPI values, a trend analysis has been performed by means of two non-parametric tests, the Theil–Sen and Mann–Kendall test. The results indicated similar values of frequency, severity, duration, and intensity between the dry and the wet spells, although wet events showed higher values in the extreme. Moreover, the results of the trend analysis evidenced a different behavior between the northern side of the basin, characterized by a negative trend in the 12-month SPI values, and the southern side, in which positive trends were detected.

Evaluating spatial patterns of Asian meteorological drought variations and associated SST anomalies in CMIP6 models

This study evaluates the spatial patterns of the Asian summer drought variations and the associated sea surface temperature (SST) anomalies in 42 Coupled Model Intercomparison Project (CMIP6) models during 1950–2014. The analysis is focused on the meteorological drought measured using the standardized precipitation index (SPI). The evaluation is conducted for short-term, medium-term, and long-term droughts represented by 3-month, 9-month, and 24-month SPI, respectively. Most of the 42 models are able to capture the observed leading spatial pattern of short-term and medium-term drought variations, characterized by a north-south dipole structure. In contrast, most models fail to simulate the observed leading spatial pattern of long-term drought variations, featuring a southwest-northeast oriented tripole distribution. Further analysis shows that most models can represent the spatial pattern of interannual variation of long-term drought with a north-south dipole structure, but cannot produce the spatial pattern of interdecadal variation and trend of long-term drought. In most of the models, the dipole pattern of short-term and medium-term drought variations is associated with an El Nino–type SST anomaly pattern in the tropical Indo-Pacific region, which is similar to the observations, so is the dipole pattern of interannual variation of long-term droughts. This is attributed to the ability of most models to capture the tropica Indo-Pacific SST-related large-scale atmospheric circulation anomaly pattern.

Assessing the frequency of drought/flood severity in the Luvuvhu River catchment, Limpopo Province, South Africa

The Luvuvhu River catchment experiences rainfall variability with a high frequency of extremely dry and wet conditions. Understanding the frequency of drought and floods in this catchment area is important to the agriculture sector for managing the negative impacts of these natural hazards. This study was undertaken to investigate the frequency and severity of drought/floods and linkages with the El Niño Southern Oscillation (ENSO) phenomenon. Poor and resource-limited small-scale farmers in the Luvuvhu River catchment area struggle to adjust due to decreasing crop yields and livestock mortality caused by drought and floods. Monthly rainfall data from 15 grid points (0.5° × 0.5°) was used to compute the Standardised Precipitation Index (SPI) for the period between 1979 and 2016. The 3-month SPI was calculated for the December–January–February (DJF) period. The second half of the agricultural season was selected because the influence of ENSO is high during the late summer season (DJF) in the catchment. The SPI results indicate that the agricultural seasons 1982/83, 1991/92 and 2015/16 were characterised by extreme drought. Conversely, the SPI values also show that the wettest seasons were recorded in 1998/99 and 1999/00. The catchment experiences a high frequency of moderate to severe drought in the north and north-eastern parts. Spatially, the occurrence of moderate to severe dry conditions covers large areas in the north and south-western parts. Severe to extreme wet conditions cover large areas in the north and south-eastern parts of the catchment. The SST index (Niño 3.4) shows a strong influence on rainfall variability in the catchment, resulting in either dry or wet conditions. Therefore, this study recommends further research focusing on more climatic modes that influence rainfall variability, as well as further development of drought and flood forecasting to improve farmers’ adaptations options and reliability of weather forecasts used as a tool to manage crop production.

Production and turnover of mycorrhizal soil mycelium relate to variation in drought conditions in Mediterranean Pinus pinaster, Pinus sylvestris and Quercus ilex forests.

In forests, ectomycorrhizal mycelium is pivotal for driving soil carbon and nutrient cycles, but how ectomycorrhizal mycelial dynamics vary in ecosystems with drought periods is unknown. We quantified the production and turnover of mycorrhizal mycelium in Mediterranean Pinus pinaster, Pinus sylvestris and Quercus ilex forests and related the estimates to standardised precipitation index (SPI), to study how mycelial dynamics relates to tree species and drought-moisture conditions. Production and turnover of mycelium was estimated between July and February, by quantifying the fungal biomass (ergosterol) in ingrowth mesh bags and using statistical modelling. SPI for time scales of 1-3months was calculated from precipitation records and precipitation data over the study period. Forests dominated by Pinus trees displayed higher biomass but were seasonally more variable, as opposed to Q.ilex forests where the mycelial biomass remained lower and stable over the season. Production and turnover, respectively, varied between 1.4-5.9kgha-1 d-1 and 7.2-9.9 times yr-1 over the different forest types and were positively correlated with 2-month and 3-month SPI over the study period. Our results demonstrated that mycorrhizal mycelial biomass varied with season and tree species and we speculate that production and turnover are related to physiology and plant host performance during drought.