Standardized Precipitation Evapotranspiration Index Research Articles

Development of multivariate standardized drought index for large lake basins based on the copula function: A case study of Hongze Lake Basin, China

Study regionThe Hongze Lake Basin, China. Study focusA drought index is an essential tool for monitoring and prediction, and the construction of a comprehensive drought index is at the cutting edge of current drought research. Considering the regulation effect of lakes, a new drought index aimed at large lake catchment areas, namely the Multivariate Standardized Drought Index for Lakes Basin (MSDILB), can comprehensively characterise meteorological and hydrological drought. Here, the Standardised Precipitation Evapotranspiration Index (SPEI), Standardised Runoff Index (SRI), and MSDILB drought indices were used to evaluate the evolution of drought in the study area. Moreover, the response and conversion relationships between the basin’s hydro-meteorological elements and the regulation of the lake were explored. New hydrological insights for the RegionThe results indicate that: 1) in a dry year, the spatial-temporal heterogeneous runoff in the basin is redistributed by the storage and regulation of large lakes, thereby reducing the frequency of drought events; 2) the MSDILB drought index merges the characteristics of hydrological drought and meteorological drought indices and can monitor the evolution of drought events in lake basins; 3) the MSDILB drought index can sensitively capture the effects of lake hydrological regime change on drought events in the basin. Thus, it has the advantage of comprehensively characterising the drought conditions in large lake basins.

Potential impacts of 1.5 °C and 2 °C global warming levels on drought modes over Eastern Africa

This study examines the impacts of 1.5 °C and 2.0 °C global warming levels (GWLs) on the characteristics of four major drought modes over Eastern Africa in the future under two climate forcing scenarios (RCP4.5 and RCP8.5). The droughts were quantified using two drought indices: the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI) at 12-month scale. Four major drought modes were identified with the principal component analysis (PCA). Multi-model simulation datasets from the Coordinated Regional Climate Downscaling Experiment (CORDEX) were analysed for the study. The skill of the models to reproduce the spatial distribution and frequency of past drought modes over Eastern Africa was examined by comparing the simulated results with the Climate Research Unit (CRU) observation. The models give realistic simulations of the historical drought modes over the region. The correlation between the simulated and observed spatial pattern of the drought modes is high (r ≥ 0.7). Over the hotspot of the drought modes, the observed drought frequency is within the simulated values, and the simulations agree with the observation that the frequency of SPI-12 droughts is less than that of SPEI-12 droughts. For both RCP4.5 and RCP8.5 scenarios, the simulation ensemble projects no changes in the spatial structure of the drought modes but suggests an increase in SPEI-12 drought intensity and frequency over the hotspots of the drought modes. The magnitude of the increase, which varies over the drought mode hotspots, is generally higher at 2 °C than at 1.5 °C global warming levels. More than 75% of the simulations agree on these projections. The projections also show that the increase in drought intensity and frequency is more from increased potential evapotranspiration than from reduced precipitation. Hence, the study suggests that to reduce impacts of global warming on future drought, the adaptation activities should focus on reducing evaporative loss surface water.

Quantifying the drought sensitivity of grassland under different climate zones in Northwest China

Grassland is essential for maintaining the stability and functionality of terrestrial ecosystems. Although previous research has explored how grassland responds to drought, the drought sensitivity of grassland (DSG) across climate zones and aridity gradients remains uncertain. In this study, we conducted a comprehensive investigation spanning 1982 to 2015 in Northwest China. To assess the time-cumulative effect (TCE) and the time-lag effect (TLE) of drought on grassland, we employed Spearman rank correlation analysis, utilizing long-term datasets of the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). This analysis allowed us to quantify the DSG in the region and further examine its variations across climate zones and aridity gradient. Our results revealed that 81.2 % and 99.7 % of the grassland in Northwest China was influenced by the TCE and TLE of drought, respectively, with 38.2 % and 60.9 % of these effects being statistically significant (p < 0.05). The mean accumulated and lagged timescales of drought on grassland were 7.89 and 9.41 months, respectively. Remarkably, the highest DSG was observed in the semi-arid zone (0.58), followed by the arid (0.54), sub-humid (0.51), and humid (0.44) zones. Furthermore, we identified significant nonlinear variation patterns of DSG along the aridity gradient, characterized by several discernible trend breaks. These findings contribute to our understanding of the impacts of drought on vegetation, particularly in ecologically fragile regions.

Transplanted seedling age and watering effects on the field performance of Senegalia macrostachya (Rchb. ex DC.) Kyal. & Boatwr., a high-valued indigenous fruit tree species in Burkina Faso

Senegalia macrostachya, ex-Acacia macrostachya is a multiple-use species providing many benefits to populations in arid and semi-arid regions. Despite its importance, limited research has been undertaken on its silviculture, growth, and productivity in plantations or in the wild. In this paper, we widely deployed the Generalized Additive Models for Location, Scale and Shape to identify factors controlling the temporal variability in S macrostachya annual pod and seed production. Data (survival rate, basal diameter, total height of each individual tree, and weight of pods and seeds produced) were collected from a factorial experiment testing different nursery production periods and the field performance of S. macrostachya under well-watered and water-stressed conditions. Long-term climate data were analyzed and the Standardized Precipitation–Evapotranspiration Index (SPEI) was calculated. Results showed that seedling survival and both height and radial growth were significantly influenced by seedling age at outplanting, watering, and climate aridity. The watered seedlings had the highest survival rate and grew taller than the non-watered seedlings. The watered seedlings also had the largest basal diameters, along with the highest pod and seed weights. There was temporal variation in S. macrostachya pod and seed production over time, indicating a cyclic production, depending mainly on climate factors. Seedling age at outplanting, early watering of outplanted seedlings, and climate aridity significantly influence not only seedling survival and growth parameters but also determine the levels of pod and seed production of S. macrostachya. Our results provide field-based evidence of the performance of this important species in the production of pods and seeds. The results also highlight the potential for domestication of S. macrostachya including use in plantations to help restore degraded rural landscapes in arid and semi-arid regions.

Multivariate spatio-temporal modeling of drought prediction using graph neural network

Abstract Drought is a serious natural disaster that causes huge losses to various regions of the world. To effectively cope with this disaster, we need to use drought indices to classify and compare the drought conditions of different regions. We can take appropriate measures according to the category of drought to mitigate the impact of drought. Recently, deep learning models have shown promising results in this domain. However, few of these models consider the relationships between different areas, which limits their ability to capture the complex spatio-temporal dynamics of droughts. In this study, we propose a novel multivariate spatio-temporal sensitive network (MSTSN) for drought prediction, which incorporates both geographical and temporal knowledge in the network and improves its predictive power. We obtained the standardized precipitation evapotranspiration index and meteorological data from the climatic research unit dataset, covering the period from 1961 to 2018. This is the first deep learning method that embeds geographical knowledge in drought prediction. We also provide a solid foundation for comparing our method with other deep learning baselines and evaluating their performance. Experiments show that our method consistently outperforms the existing state-of-the-art methods on various metrics, validating the effectiveness of geospatial and temporal information.

Different types of meteorological drought and their impact on agriculture in Central China

Challenges in drought monitoring hinder social development. In this study, a robust drought monitoring framework composed of ten indicators was established based on the standard precipitation actual evapotranspiration (SPAEI) index calculated using the maximum entropy production (MEP) model. Additionally, the relationship between meteorological drought and agricultural development was analyzed. The results indicated that the seasonal SPAEI performed better than the standardized precipitation evapotranspiration index (SPEI) in terms of drought area, vegetation drought, and drought identification in Central China. The annual drought duration was between 3.4 and 4.5 months, and the annual drought frequency ranged from 1.1 to 1.9 times per year. The main drought pattern was divided into persistent light drought and short-term severe drought, with the latter dominating crop yields in Central China. Although persistent light drought contributes more to drought, short-term severe drought has a greater impact on crop yields. Thoroughly investigating regional drought patterns and assessing their impacts on agriculture will contribute to a better understanding of drought characteristics and provide valuable guidance for regional agricultural water resource allocation and management.

Evaluating the cumulative and time-lag effects of vegetation response to drought in Central Asia under changing environments

Central Asia (CA) is an arid region sensitive to climate change, and with the increase in drought events due to global warming, droughts are posing more severe threats to the vegetation in CA. However, the response of vegetation in CA to the cumulative and time-lag effects of prolonged drought remains unclear. This has limited our understanding of the mechanisms by which vegetation responds to meteorological drought in arid regions. To clarify the response of vegetation to drought in CA, this study analyzed the effects of drought and meteorological factors on vegetation growth dynamics. The extent of vegetation response to the cumulative and time-lag effects from 2001 to 2020 was evaluated using three satellite-derived vegetation indices (NDVI, EVI, NIRv) and the solar-induced chlorophyll fluorescence (SIF) and standardized precipitation evapotranspiration index (SPEI). We used the Copula-Bayes conditional probability method to assess the drought thresholds for vegetation under two scenarios: mild and severe losses. The effects of soil moisture (SM) and vapor pressure deficit (VPD) on vegetation dynamics were analyzed based on an improved partial wavelet coherence (PWC). The results show that the average response time of vegetation to the cumulative and lagged effects of drought was mostly concentrated within 4–6 months, and the lagged effect was stronger than the cumulative effect. The vegetation response to drought is nonlinear along the precipitation gradient. The SPEI values for triggering mild vegetation loss (below the 40th percentile) drought threshold range from −0.7 to −1.0, while the SPEI values for triggering severe vegetation loss (below the 10th percentile) drought threshold range from −2.0 to −2.5. The SM was the main driver affecting vegetation dynamics. This study provides deeper insight into the response of vegetation to drought in the arid zone of CA, which will further provide theoretical support for addressing global climate change and extreme drought events.

Drought trends projection under future climate change scenarios for Iran region.

The study highlights the potential characteristics of droughts under future climate change scenarios. For this purpose, the changes in Standardized Precipitation Evapotranspiration Index (SPEI) under the A1B, A2, and B1 climate change scenarios in Iran were assessed. The daily weather data of 30 synoptic stations from 1992 to 2010 were analyzed. The HadCM3 statistical model in the LARS-WG was used to predict the future weather conditions between 2011 and 2112, for three 34-year periods; 2011-2045, 2046-2079, and 2080-2112. In regard to the findings, the upward trend of the potential evapotranspiration in parallel with the downward trend of the precipitation in the next 102 years in three scenarios to the base timescale was transparent. The frequency of the SPEI in the base month indicated that 17.02% of the studied months faced the drought. Considering the scenarios of climate change for three 34-year periods (i.e., 2011-2045, 2046-2079, and 2080-2112) the average percentages of potential drought occurrences for all the stations in the next three periods will be 8.89, 16.58, and 27.27 respectively under the B1 scenario. While the predicted values under the A1B scenario are 7.63, 12.66, and 35.08%respectively. The relevant findings under the A2 scenario are 6.73, 10.16, 40.8%. As a consequence, water shortage would be more serious in the third period of study under all three scenarios. The percentage of drought occurrence in the future years under the A2, B1, and A1B will be 19.23%, 17.74%, and 18.84%, respectively which confirms the worst condition under the A2 scenario. For all stations, the number of months with moderate drought was substantially more than severe and extreme droughts. Considering the A2 scenario as a high emission scenario, the analysis of SPEI frequency illustrated that the proportion of dry periods in regions with humid and cool climate is more than hot and warm climates; however, the duration of dry periods in warmer climates is longer than colder climates. Moreover, the temporal distribution of precipitation and potential evapotranspiration indicated that in a large number of stations, there is a significant difference between them in the middle months of the year, which justifies the importance of prudent water management in warm months.

Temporal and Spatial Propagation Characteristics of the Meteorological, Agricultural and Hydrological Drought System in Different Climatic Conditions within the Framework of the Watershed Water Cycle

Investigating systematic meteorological–agricultural–hydrological drought propagation within an integrated watershed framework is crucial yet challenging for advancing robust early warning systems and targeted resilience strategies. To address this gap, this study employs the standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), and nonlinear joint hydrological drought index (NJHDI) to characterize meteorological, agricultural, and hydrological drought, respectively. Utilizing the strongest correlation method, variations in the propagation time along moisture movement pathways within the system are quantified using the Soil and Water Assessment Tool (SWAT) model. The Yellow River basin (YRB) is selected as the case study area. Key results reveal distinct seasonal patterns in meteorological–agricultural propagation across the basin, with escalated spring and summer timescales. However, weaker correlations and limited seasonality emerge for agricultural–hydrological linkages due to sustaining baseflow buffers. Specifically, the arid and semiarid region exhibited shorter propagation time with 1–5 months, while the semiarid and semihumid region displayed longer timescales with 7–12 months. These findings provide valuable scientific references for enhancing real-time early warning systems tailored to coupled watershed systems. The integrated methodology underscores the importance of unraveling fine-scale spatiotemporal propagation variability for localized drought resilience.

Dynamic Characteristics of Meteorological Drought and Its Impact on Vegetation in an Arid and Semi-Arid Region

Under the background of global climate warming, meteorological drought disasters have become increasingly frequent. Different vegetation types exhibit varying responses to drought, thus, exploring the heterogeneity of the impact of meteorological drought on vegetation is particularly important. In this study, we focused on Inner Mongolia (IM) as the research area and employed Standardized Precipitation Evapotranspiration Index (SPEI) and Vegetation Health Index (VHI) as meteorological drought and vegetation indices, respectively. The Breaks for Additive Seasons and Trend algorithm (BFAST) was utilized to reveal the dynamic characteristics of both meteorological drought and vegetation changes. Additionally, the Pixel-Based Trend Identification Method (PTIM) was employed to identify the trends of meteorological drought and vegetation during spring, summer, autumn, and the growing season. Subsequently, we analyzed the correlation between meteorological drought and vegetation growth. Finally, the response of vegetation growth to various climate factors was explored using the standardized multivariate linear regression method. The results indicated that: (1) During the study period, both SPEI and VHI exhibited a type of interrupted decrease. The meteorological drought was aggravated and the vegetation growth was decreased. (2) Deserts and grasslands exhibited higher sensitivity to meteorological drought compared to forests. The strongest correlation between SPEI-3 and VHI was observed in desert and grassland regions. In forest areas, the strongest correlation was found between SPEI-6 and VHI. (3) The r between severity of meteorological drought and status of vegetation growth was 0.898 (p &lt; 0.01). Vegetation exhibits a more pronounced response to short-term meteorological drought events. (4) Evapotranspiration is the primary climatic driving factor in the IM. The findings of this study provide valuable insights for the rational utilization of water resources, the formulation of effective irrigation and replenishment policies, and the mitigation of the adverse impacts of meteorological drought disasters on vegetation growth in the IM.

Assessment of the spatiotemporal evolution and driving forces of meteorological drought in the North China Plain

AbstractWith the changing trend of global temperature increase, the occurrence of drought events can no longer be ignored. An investigation of the spatiotemporal evolution characteristics of meteorological droughts and their responses to climatic variables can elucidate the mechanisms of the water cycle evolution in changing environments. In the present study, the standardized precipitation evapotranspiration index was used to characterize meteorological drought in the North China Plain (NCP) from 1970 to 2020. The spatiotemporal patterns of meteorological drought were analysed, and the contributions of climatic factors on annual and seasonal time scale drought evolution were determined. The results revealed that meteorological drought showed a decreasing trend (−0.07/10a) in the NCP from 1970 to 2020 and the most severe meteorological drought occurred in winter, with 56.56% of the region experiencing an increased drought trend in this season. Significant trends were identified for mean temperature (Tmean), temperature difference, and wind speed in the drought trend evolution. Precipitation (P) and Tmean were the primary controlling factors of the drought evolution. The strongest effect on drought change were exerted by P in spring (contribution [Con]: 1.57%), summer (Con: 1.79%) and autumn (Con: 2.43%), and the negative contribution of Tmean in winter (Con: −3.64%) was greater than the positive contribution of P (Con: 2.69%). The contribution of climatic variables to drought were increased by regional altitude difference. The results reveal a novel explanatory mechanism of climate change's effects on drought.

The Comparative Analysis of Carbon Pricing Policies on Canadian Northwest Territories’ Economy under Different Climate Change Scenarios

Policymakers in the Northwest Territories have introduced carbon pricing as a strategy to reduce fossil fuel consumption and CO2 emissions across various population segments and industries. This indirect approach, chosen for its acceptability, aims to influence behavior rather than directly limit carbon-intensive products. The main purpose of this study was to evaluate the economic and ecological impacts of this policy and its alignment with intended objectives. Using a CGE macroeconomic model incorporating economic structural and behavioral equations, we assessed the policy’s effects on NWT’s economy in general and on a subset of its key sectors. We also incorporated a few observed and simulated climate data for diverse climate change scenarios. The estimated results revealed that climate variables, especially precipitation, significantly influenced sectors like agriculture, construction, and manufacturing. The standardized precipitation evapotranspiration index (SPEI), which encompasses both temperature and precipitation, notably impacted the agriculture, oil, and gas sectors. However, temperature alone showed limited significance, except in the oil and gas sector. The simulation results indicated that, while carbon pricing reduced economic contributions of fossil fuel sector, household rebates could counteract these effects of the economic growth of NWT. Our findings offer valuable insights for shaping NWT’s environmental policies, aligning them with Canada’s goal of net-zero CO2 emissions by 2050.

Dynamic Evolution and Copula-Based Multivariable Frequency Analysis of Meteorological Drought Considering the Spatiotemporal Variability in Northwestern China

Meteorological drought is a continuous spatiotemporal phenomenon that poses a serious threat to water resource security. Dynamic evolution and multivariable frequency analysis of meteorological drought are important for effective drought mitigation and risk management. Therefore, this study aims to analyze meteorological drought events in northwestern China between 1960 and 2018 based on the standardized precipitation evapotranspiration index (SPEI) through a three-dimensional identification method. This study investigates the meteorological drought dynamic evolution on different time and space scales and evaluates the frequency analysis considering the spatiotemporal variability based on Copula. The results show that SPEI presents an upward trend in Northwestern China. A trend towards increased humidity is observed in arid regions, contrasted by a trend towards aridification in semi-arid and semi-humid areas, indicating that the spatial distribution of drought in the study area tends towards homogenization. The possibility of high-intensity drought events occurring in the same area was relatively low, whereas low-intensity drought events were frequent. Additionally, this study analyzes the dynamic migration process of individual drought events from a three-dimensional perspective. Neglecting any one drought variable could significantly underestimate the occurring probability of severe drought events. Therefore, a multivariable frequency analysis considering the spatiotemporal variability plays a crucial role in the formulation of drought prevention and mitigation strategies, as well as drought forecasting.

Shifting climatic responses of tree rings and NDVI along environmental gradients

Variations in the growth of aboveground biomass compartments such as tree stem and foliage significantly influence the carbon cycle of forest ecosystems. Yet the patterns of climate-driven responses of stem and foliage and their modulating factors remain poorly understood. In this study, we investigate the climatic response of Norway spruce (Picea abies) at 138 sites covering wide spatial and site fertility gradients in temperate forests in Central Europe. To characterize the annual growth rate of stem biomass and seasonal canopy vigor, we used tree-ring chronologies and time-series of NDVI derived from Landsat imagery. We calculated correlations of tree-ring width and NDVI with mean growing season temperature and standardized precipitation evapotranspiration index (SPEI). We evaluated how these climate responses varied with aridity index, soil category, stand age, and topographical factors. The results show that the climate-growth responses of tree rings shift from positive to negative for SPEI and from negative to positive for temperature from dry (warm) to wet (cold) areas. By contrast, NDVI revealed a negative response to temperature across the entire climatic gradient. The negative response of NDVI to temperature likely results from drought effects in warm areas and supporting effects of cloudy conditions on foliage greenness in wet areas. Contrary to NDVI, climate responses of tree rings differed according to stand age and were unaffected by local topographical features and soil conditions. Our findings demonstrate that the decoupling of stem and foliage climatic responses may result from their different climatic limitation along environmental gradients. These results imply that in temperate forest ecosystems, the canopy vigor may show different trends compared to stem growth under ongoing climate change.

Long‐Term Variability in the Arabian Peninsula Droughts Driven by the Atlantic Multidecadal Oscillation

AbstractDrought is a recurring hydroclimatic extreme over the Arabian Peninsula (AP). So far, no study has examined the changes in drought characteristics in recent decades, not to mention the background mechanisms for such changes. To this end, analyzing the Standardized Precipitation Evapotranspiration Index (SPEI) mainly from the European Reanalysis (ERA5) data sets, in addition to other observational/reanalysis data sets over the period of 1951–2020, we show that droughts over the AP have increased in frequency and severity over the last two decades. We show that this drought acceleration, which was not observed in the previous 40–50 years, is a combination of decadal variability and long‐term trends. Importantly, we demonstrate that the decadal SPEI variability is due to the Atlantic Multidecadal Oscillation (AMO). The unprecedented multiyear drought over the AP in recent decades is evidently associated with the current positive phase of the AMO. We also show that the recent warming of the AP is a more significant factor in the drought intensification than the concurrent weakening of local precipitation. Furthermore, we developed a machine learning model largely based on the observed AMO–SPEI relationship. This model predicts a reduced drought severity over the AP in the near future.

Comprehensive Evaluation of Global Precipitation Products and Their Accuracy in Drought Detection in Mainland China

Abstract Thorough evaluations of satellite precipitation products are necessary for accurately detecting meteorological drought. A comprehensive assessment of 15 state-of-the-art precipitation products (i.e., IMERG_cal, IMERG_uncal, GSMaP-G, CPC-Global, TRMM3B42, CMORPH-CRT, PERSIANN-CDR, PERSIANN, PERSIANN-CCS, SM2RAIN, CHIRPS, ERA5, ERA-Interim, MERRA-2, and GLDAS) is herein conducted for the period 2010–19 giving special attention to their performance in detecting meteorological drought over mainland China at 0.25° spatial resolution. The cited precipitation products are compared against China’s gridded gauge-based Daily Precipitation Analysis (CGDPA) product, derived from 2400 meteorological stations, and their quality is assessed at daily, seasonal, and annual precipitation time scales. Meteorological droughts in the datasets are determined by calculating the standardized precipitation evapotranspiration index (SPEI). The performance of the precipitation products for drought detection with respect to the SPEI is assessed at three time scales (1, 3, and 12 months). The results show that the GSMaP-G outperforms other satellite-based datasets in drought detection and precipitation estimation. The MERRA-2 and the ERA5 are on average closer to the CGDPA reference data than other reanalysis products for precipitation estimation and drought detection. These products capture well the spatial and temporal pattern of the SPEI in southern and eastern China having a probability of detection (POD) above 0.6 and a correlation coefficient (CC) above 0.65. CPC-Global, IMERG, and the ERA5 reanalysis product are ideal candidates for application in western China, especially in the Qinghai–Tibetan Plateau and the Xinjiang Province. Generally, the accuracy of precipitation products for drought detection is improved with longer time scales of the SPEI (i.e., SPEI-12). This study contributes to drought-hazard detection and hydrometeorological applications of satellite precipitation products. Significance Statement The purpose of this study is to comprehensively evaluate the quality of 15 global satellite-based, gauge-based, and reanalysis precipitation products for meteorological drought detection at 1-, 3-, and 12-month time scales. This work systematically evaluates these products’ capacity to capture precipitation occurrence and intensity in different seasons. This is followed by a comparison of the precipitation products’ performance in drought detection. This work’s findings and evaluation results will improve the ability of those who develop precipitation products in identifying error sources and further improving retrieval algorithms. This paper’s results will serve as a valuable reference for end users seeking to better understand the application of precipitation products to drought detection.

Drought Characterization in Croatia Using E-OBS Gridded Data

Droughts are among the major natural hazards that are spreading to many parts of the world, with huge multi-dimensional impacts. An extensive analysis of drought phenomenon is presented for continental Croatia based on a meteorological E-OBS gridded dataset (0.25° × 0.25°), within the period of 1950–2022. The drought events were characterized by the Standardized Precipitation Evapotranspiration Index (SPEI), applied to different time-scales (6 and 12 months), in order to describe the subannual and annual variability of drought. The spatiotemporal patterns of drought are obtained through principal component analysis (PCA) and K-means clustering (KMC) applied to the SPEI field. An areal drought evolution analysis and the changes in the frequency of occurrence of the periods under drought conditions were achieved using a kernel occurrence rate estimator (KORE). The modified Mann–Kendall (MMK) test, coupled with the Sen’s slope estimator test, are applied to the SPEI series in order to quantify the drought trends throughout the country. According to the history drought events and considering the different morphoclimatic characteristics of the study area, the results showed that Croatia could be divided into three different and spatially well-defined regions with specific temporal and spatial characteristics of droughts (central northern, eastern and southern regions). A manifest increase is shown in the percentage of area affected by drought, as well as in the yearly drought occurrences rates, in both central northern and eastern regions, and an evident decrease is shown in the southern region for both 6- and 12-month SPEI time-scales. In the observation of the drought’s temporal characteristics, it was found that downward trends expressing increasing drought severities were strongly significant in northern and eastern regions, while a few significant upward trends were seen in the southern region. From this study, it is possible to obtain a broader view of the historical behaviour of droughts in Croatia, with the results providing useful support for drought risk assessment and decision-making processes.

Vegetation dynamics over 18 years in response to seasonal climatic conditions and land use in the Kamiesberg mountains of the succulent Karoo, South Africa

Projections of vegetation change in response to future global change conditions are challenged by the lack of long-term, empirical data on how vegetation responds to the variability in climatic conditions. This is particularly true for biodiverse systems that are threatened by climate change such as the winter-rainfall Succulent Karoo biome of southern Africa. In this study we analyse long-term data (2002–2019) of two biodiversity observatories from the Kamiesberg Mountains in the Succulent Karoo. We employed the R package GLMMtmb to analyse counts of abundance and estimated cover values of vegetation as response variables and seasonal Standardised Precipitation-Evapotranspiration Index (SPEI), annual livestock density, topography and soil surface structure as predictors for the interannual change of life forms under low and high land use intensity. We compared the life form composition at the two contrasting land use types and found the cover and abundance of leaf succulents to be higher under low grazing intensity and that of stem succulents to be higher under high grazing intensity. The cover and abundance of herbaceous life forms varied over the years depending on the seasonal weather conditions. Our GLMM results reveal counts of abundance to be a sensitive predictor for population dynamics in various herbaceous and shrub life form types. Estimated cover values showed less of a response to interannual differences in the environmental drivers. All life forms responded strongly to high humid and cool conditions in the second half of the winter rainfall period, when the temperatures increase again. The abundance of leaf-succulent and non-succulent shrubs also responded positively to moderate conditions towards the end of the dry season. The extensive and hot dry summer season in turn had a significant negative effect on the survival of particularly young plants with low cover. Cover values showed no response to the conditions of the late dry season and were generally less sensitive to the variance in seasonal climatic conditions. Our results suggest that future changes in interannual patterns in climatic conditions will affect life form composition of the vegetation.

Characterization of Meteorological Drought Using Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index Methods in North Sumatera

Climate variability in Indonesia is influenced by several global factors including El Niño Southern Oscillation and Indian Ocean Dipole, one consequence of climate variability is drought. The drought index is used to identify and describe the level of drought in an area, the methods used in this study are the Standardized Precipitation Index and the Standardized Precipitation Evapotranspiration Index which are calculated based on climate data with a span of 24 years as many as 67 rain observation posts in North Sumatra using R Studio software. The purpose of this study is to determine the factors of global climate phenomena that affect drought in North Sumatra and its impact on rice crop productivity and analyze drought characteristics spatially and temporarily. The resulting index values are then analyzed using correlation methods to see their relationship with ENSO and IOD. The results showed that the incidence of the ENSO phenomenon had a very strong relationship with meteorological drought in North Sumatra with a correlation value range of -0.85 to -0.97, and as many as 62.5% of 32 districts experienced a decrease in rice crop productivity during El Niño with strong intensity in 2015.

Evaluating the effect of the SPI and SPEI methods on drought monitoring over Turkey

Drought indices are one of the most widely used methods for drought monitoring because they are easy to apply and interpret. The two most widely used drought index methods are the Standardized Precipitation Index (SPI) method, using the precipitation parameter as input, and the Standardized Precipitation Evapotranspiration Index (SPEI) method, which uses the Potential Evapotranspiration (PET) values addition to precipitation. The primary goal of this study is to compare the two methods for determining drought characteristics in different climatic and geographical features in Turkey. The drought characteristics are calculated using meteorological data between 1970 and 2021 for 199 synoptic observation stations in seven geographical regions of Turkey with SPI and SPEI methods for the 3-, 6-, and 12-month time scales. The study's findings indicate that while the correlation coefficient (CC) between the two indices is generally high (ranging from 0.81 to 0.93, depending on the region and time scale), it is lower in regions with a low Aridity Index (AI) value, particularly for the 3-month time scale. For the 6- and 12-month time scales, no relationship is observed between AI and CC. Additionally, SPEI detected a higher occurrence percentage of moderate and severe droughts across all geographical regions (with an increase of up to 15 % compared to SPI), while SPI observed higher percentages of extreme droughts. The study results conclude that SPI and SPEI methods demonstrate significant differences in detecting certain drought characteristics after the 1990s, underscoring the necessity to consider temperature variations in drought monitoring, particularly in the context of ongoing climate change.