Assessment of drought conditions under climate change scenarios in Central Europe (Poland) using the standardized precipitation index (SPI)

Studies using Drought Hazard Indices (DHIs) have been performed at various scales, but few studies associated DHIs of different drought types with climate change scenarios. To highlight the regional differences in droughts at meteorological, hydrological, and agricultural levels, we utilized historic and future DHIs derived from the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Soil Water Index (SSWI), respectively. To calculate SPI, SRI, and SSWI, we used a calibrated Soil and Water Assessment Tool (SWAT) for the Karkheh River Basin (KRB) in Iran. Five bias-corrected Global Circulation Models (GCMs) under two Intergovernmental Panel on Climate Change (IPCC) scenarios projected future climate. For each drought type, we aggregated drought severity and occurrence probability rate of each index into a unique DHI. Five historic droughts were identified with different characteristics in each type. Future projections indicated a higher probability of severe and extreme drought intensities for all three types. The duration and frequency of droughts were predicted to decrease in precipitation-based SPI. However, due to the impact of rising temperature, the duration and frequency of SRI and SSWI were predicted to intensify. The DHI maps of KRB illustrated the highest agricultural drought exposures. Our analyses provide a comprehensive way to monitor multilevel droughts complementing the existing approaches.

Droughts and floods are the most hazardous disasters to affect the landscape and human communities. Climate change is increasing their frequency and intensity all around the world. In this research, we study the impact that climate change in central Poland (Toruń) is having on the frequency of meteorological droughts and risk of floods occurrence in the region, based on the standardised precipitation index (SPI) as one of the most common and effective indices. Although the SPI was developed for monitoring droughts, previous studies have shown that it can also be applied to recognise wet and normal conditions. Therefore, in this study, we attempt to determine the relationship between SPI values and high flood risk. First, by an average mean ensemble of several general circulation models (GCMs), precipitation for a future period (2026–2100) in Toruń was projected under two climate change socio-economic pathway scenarios (SSPs), SSP1-2.6 and SSP5-8.5. Then, based on the projected precipitation for the future period, the SPI values were calculated. The results indicated that, in general, the precipitation in the study area will increase for scenarios SSP1-2.6 and SSP5-8.5. Estimation of future meteorological drought based on SPI calculation showed that the frequency of the “Extremely dry” (SPI ≤ ˗2.0) and “Severely dry” (˗1.50 ≤ SPI < ˗2.0) categories will decrease under scenarios SSP1-2.6 and SSP5-8.5, while the frequency of the “Moderately dry” category (˗1.0 ≤ SPI < ˗1.50) will increase for scenarios SSP1-2.6 and SSP5-8.5 relative to the historical reference period (1991–2014). Estimation of flood risk occurrences in the future period based on SPI values showed that the frequency of “Extremely wet” (SPI ≥ 2.0) will increase for SSP1-2.6 and SSP5-8.5, excluding the future periods 2076–2100 in SSP1-2.6 and 2026–2050 for SSP5-8.5. The frequency of “Very wet” (1.50 ≤ SPI < 2.00) will increase for both scenarios. It is also expected that frequencies of other categories including “Moderately wet” (1.00 ≤ SPI < 1.50), and “Near normal” (˗0.99 ≤ SPI <1) will slightly decrease for both scenarios. To conclude, a decrease in the frequency of “Extremely dry” and “Severely dry” categories and an increase in the frequency of “Extremely wet” and “Very wet” showed that the occurrence of floods is more probable than droughts in the study area in the future period. The work was supported by the National Science Centre, Poland project No. 2020/37/B/ST10/00710.

Standard Precipitation Index (SPI) and, its variant, Standard Precipitation and Evapotranspiration Index (SPEI) are among the most commonly used drought assessment indices worldwide. SPI uses precipitation as its only input to assess drought. Unlike SPI, SPEI uses both precipitation and temperature, thereby considering the influence of global warming to some extent. Assessments of performance between SPI and SPEI is well addressed. However, no adequate literature was found on the assessment of the degree of agreement between SPI and SPEI at different time scales. Hence, this research focused on examining the level of agreement between SPI and SPEI as drought assessment tools at 1-month, 3-month, 6-month, 12-month and 24-month time scales. The test of agreement between SPI and SPEI was conducted using Cohen’s Kappa statistics and the Bland–Altman method. Gridded monthly precipitation and temperature Climatic Research Unit time-series data version 4.01 were used to calculate SPI and SPEI for the period 1901 to 2016. The results of Cohen’s Kappa statistics indicate that there existed a fair degree of agreement between SPI and SPEI at all time scales. A positive linear correlation (r > 0.7, p < 0.001) was also observed between SPI and SPEI ratings at all time scales. Small mean difference (bias) in the Bland and Altman analyses result indicated for the presence of agreement between the assessment tools. This study has found that there is an acceptable level of agreement between SPI and SPEI ratings in the study area, at all timescales.

Abnormal climate phenomena that exceed conventional weather observations occur worldwide, such as severe floods, droughts, and environmental issues, and have attracted increasing attention. To avoid indiscriminate industrialization and achieve sustainable development, environmental experts have presented various opinions based on climate change scenario data. In this study, shared socioeconomic pathway (SSP) climate change scenarios that consider socioeconomic conditions were used to assess the environmental impact of extreme climate conditions on watersheds. Using the UK Earth System Modelling (UKESM1) SSP scenarios, we analyzed the following: 1) Heat index for each SSP climate change scenario using the H-Index; 2) extreme climate for each SSP climate change scenario using the standardized precipitation index (SPI); 3) the impact of extreme climate on non-point pollution using event mean concentration (EMC); and 4) the environmental flow caused by extreme climate by combining the K-water Distributed RUnoff Model (K-DRUM) and global environmental flow calculator (GEFC) models. According to the heat index analysis regarding SSPs climate change scenarios, the heatwave index will continue to rise if high carbon emissions persist. Temperature serves as an important indicator that has the most meaningful impact on ecosystems. The SPI analysis showed an increase in “extremely dry” conditions, and overall, more severe droughts are likely to occur due to high carbon emission-induced climate change. The nonpoint source pollution is also higher in climate change scenarios with high carbon emissions. The extreme drought event assessment using SPI and environmental flow revealed a shift from the “moist conditions” category to grade C. Our assessments in this study conclusively indicate that drought frequency will increase along with nonpoint source pollution. Furthermore, environmental flows will shift to grade C, resulting in the disappearance of some sensitive ecological species and an increase in invasive species. These analyses determined that high carbon emission-induced climate change caused leads to severe droughts and significant alterations in the overall water circulation, thereby complicating water resource management. Furthermore, we identified watersheds that are highly vulnerable to climate change and designated these as “mid-watersheds” that first require non-point pollution management. Here, the aquatic ecosystem environment can be affected by climate change without any artificial environmental influence. Various research-based analysis methods based on modeling have been presented using SSP climate change scenarios, and are highly beneficial for establishing policies and coping strategies for environmental preservation and sustainable development.

Spatial and temporal variability of meteorological droughts including atmospheric circulation in Central Europe

Agriculture in the Global South is innately susceptible to climatic variability and change. In many arid and semi-mountainous regions of the developing world, drought is regularly cited as a significant threat to agricultural systems. The objective of this study is to assess the impacts of climate change on drought and land use and land cover (LULC) change in a semi-mountainous region of the Vietnamese Mekong Delta. We assessed previous drought trends (1980–2020) and future drought in the context of climate change, in accordance with three selected scenarios from the Coupled Model Intercomparison Project Phase 6 global climate models which have recently been released by the Intergovernmental Panel on Climate Change (IPCC) (2021–2060) using the Standardized Precipitation Index (SPI). The change of land use for the period 2010–2020 was then assessed and the associated climatic variability explored. The results show that for the period 1980–2019, SPI 3 responds quickly to changes in precipitation, whereas SPI 9 showed a clear trend of precipitation over time. The first longest duration occurrence of drought for SPI 3, SPI 6, and SPI 9 patterns were respectively 15–16, 21, and 25 months at Chau Doc station, and respectively 11, 14–15, and 16–17 months at Tri Ton station. Future precipitation and both maximum/minimum temperatures are projected to increase in both the wet and dry seasons. In addition, for all-time series scales and climate change scenarios, the levels of drought were slight, followed by moderate. In the future, the humidity at Chau Doc station is expected to decrease, while the occurrence of drought events is expected to increase at Tri Ton station, particularly in SPI 6 patterns (110 drought events in 1980–2020, and up to 198 drought events in the future). Moreover, between 2010–2020, the agricultural land area was seen to decrease, replaced by non-agricultural land uses that were found to increase by 22.4%. Among the agricultural land area, forestry, rice crops, and upland rice were found to reduce by 7.5, 16.0, and 21.2%, respectively, while cash crops and perennial crops increased by 26.4% and 170.6%, respectively. Amongst other factors, it is concluded that the variability of climate has led to drought and thus impacted on the conversion of LULC in the study area. Due to low economic efficiency, changing climate conditions, and a lack of irrigated water, the area of rice crops, forestry, aquaculture, and upland rice decreased, replaced by land for orchards for fruit production and other cash crops.

Spatiotemporal analysis of multiscalar drought characteristics across the Loess Plateau of China

Wildfires represent a significant threat to natural ecosystems, biodiversity, and communities worldwide. Disruption in precipitation regimes and temperature rise caused by climate change are key factors that worsen and increase wildfire incidents. In Brazil, recent studies have shown the majority of fire incidents are initiated by anthropogenic action, as a consequence of agricultural expansion, deforestation and land disputes. Although the human use of fire as an illegal tool is difficult to predict, the occurrence of dry meteorological conditions, prone to uncontrolled spreading of fires, can be studied employing climate modeling, providing a useful instrument to aid authorities in preventive measures and improved responses to mitigate these impacts, contributing to more efficient and sustainable management of fire-related risks. The Standardized Precipitation Index (SPI) is a useful tool for assessing precipitation variability, allowing the analysis of drought period duration, distribution, and severity. The SPI uses precipitation data to standardize the deviation of accumulated precipitation from the historical average in each location. This process yields negative or positive values, which correspond to water deficits or surpluses, respectively. Aiming to identify areas in Brazil where predicted disruption in rainfall patterns, in face of climate change, may create drier conditions and increase vulnerability to fire incidents, we evaluated precipitation trends, comparing historical simulations from the 6th phase of the Model for Interdisciplinary Research on Climate (MIROC6) and future scenarios data from the Intergovernmental Panel on Climate Change (IPCC). We focused our analysis on 3 climate change scenarios, referred to as Shared Socioeconomic Pathways: SSP2-4.5, SSP3-7.0, and SSP5-8.5. These scenarios encompass anticipated global socioeconomic transformations up to the year 2100, based on different projections of greenhouse gas emissions, and offer an assessment of the climate outlook for current society. Thus, we calculated SPI indexes for the time spans 1960-1990 and 2020-2050, examining the variations in rainfall patterns across the country during both periods. Using SPI derived from MIROC6 climatological data, it is possible to identify past patterns that are the basis for understanding future changes' impact. The results from SPI climatological data are consistent with the climate and seasonal rainfall patterns historically observed in Brazil, where Northeast and Central Brazil exhibit greater water deficits. The scenarios employed suggested that the historical patterns of droughts would be worsened in severity in central Brazil and the areas of influence would be extrapolated, creating drier meteorological conditions to the Southern and East portions of Amazonia and the Southeast of Brazil. The SPI indexes calculated to the projected scenarios reinforce the understanding of the impacts of climate change, suggesting the pathway SSP55-8.5, with higher emission of CO2, implicates in increased occurrences of extreme events, particularly prolonged and severe droughts in regions that suffer from wildfires. Identifying regions with an increased likelihood of prolonged drought events in the projected future is a valuable instrument for examining fire hazard and mitigation plans within a country such as Brazil, which encompasses diverse climates and biomes across its territory with resources of significant conservation value.

Climate change, whose negative impacts are becoming increasingly apparent as a result of human actions, intensifies the drought problems to dangerous levels. The development of local-scale drought projections is crucial to take necessary precautions for potential risks and possible effects of drought. In this study, drought analysis was conducted in the Upper Kızılırmak Basin using the standard precipitation index (SPI) method for the near future (2020–2049), mid-century (2050–2074), and late century (2075–2099). The precipitation data required for the SPI were gathered from the data sets developed for the SSP climate change scenarios of the four chosen global climate models. Precipitation data has been made more convenient for local analysis studies with the statistical downscaling method. Forecasts have been created for the temporal variation and spatial distribution of drought events. The study findings indicate that, under the SSP 2-4.5 scenario, drought-related effects of climate change will decrease until 2100. On the other hand, the number and severity of drought events, as well as the duration of dry periods, will increase until 2100 under the SSP 5-8.5 scenario. According to the SSP 5-8.5 scenario, consisting of the most pessimistic forecasts, moderate drought will last 0–60 months, severe drought will last 0–30 months, and extreme drought will last 0–20 months in different regions of the area in the late century. The spatial distribution of droughts will differ based on the SPI index and climate change scenarios. Comparison of SPI and CZI data showed that both indices are effective in meteorological drought analyses.

Recently, in Korea the occurrences of floods and droughts have been increased due to climate change. Korean government intends to develop assessment techniques for the evaluation of climate change effects on flood and drought disasters and also for the reduction and prevention of flood and drought disasters for public security. In this study, we estimated quantitative effect of climate change on drought. For this, we estimated Standardized Precipitation Index (SPI) and drought Severity-Area-Duration (SAD) curve on the Korean Peninsula using climate change scenario and climate model. For the climate model, CNCM3 was selected as the most proper model for Korean peninsula because it is describing meteorological conditions of Korea relatively well. The simulation results showed that the number of drought occurrences will be increase in the future. Standardized Precipitation Index (SPI) was computed by downscaled monthly rainfall data and was used for the assessment of Korean drought characteristics due to climate change. Also SAD was estimated based on SPI. We compared SAD curves from the recorded rainfall data and the simulated rainfall data considering climate change. As the compared results based on the constructed SAD curves, the area of drought is expected to decrease during the 2010~2039 period while the drought severity will be increase during the 2070~2099 period.

Drought belongs among the main impact factors considering crop yields. Therefore, this paper is focused on the assessment of drought occurrence and intensity as well as on its impact on crop yields on the Danubian and the East Slovakian lowlands with the spatial resolution at district level. Yield data were the main limitation of the study, which resulted in the limited length of the assessed period (1996–2013). The standardized yields of ten crops (winter wheat, spring wheat, winter barley, spring barley, rye, maize, potatoes, oilseed rape, sunflower, and sugar beet) were correlated with monthly, 2-, and 3-monthly standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI). For this purpose, the common significance level of alpha = 0.05 was used. The temporal evolution of both indices and drought occurrence during the period 1961–2013 were assessed for each district. Most crops show a higher correlation with the SPEI than with the SPI in contrast to potatoes, which reached a higher significant correlation using the SPI. The correlation also increases with increasing number of months within a time step. The highest correlation can be seen between maize and the 3-monthly SPEI in August representing summer precipitation and potential evapotranspiration conditions. Furthermore, a very high correlation was recorded considering sugar beet, which is influenced mainly by summer precipitation, because the correlation coefficient between the sugar beet and the 3-monthly SPI is as high as using the 3-monthly SPEI. Crop yields in the East Slovakian Lowland do not seem to be influenced by wet/dry periods identified using the SPI and the SPEI as their correlation with both indices is quite low and insignificant.

The main goal of this study is to examine future changes in meteorological, hydrological drought under the impact of climate change in Dong Nai River Basin, using Standardized Precipitation Index (SPI) and Stream flow Drought Index (SDI). The Soil and Water Assessment Tool (SWAT) is used as a simulated tool to estimate the streamflow in baseline (1980–2005) and climate change (RCP 4.5, 2016–2035) scenarios for meteorological, hydrological calculation. The results show that both types of drought tend to occur in the dry season. The area affected by meteorological and hydrological drought expand in both baseline and RCP 4.5 scenarios. In addition, meteorological drought duration is also significantly increased, especially severely drought months. Although it was detected slightly decreasing in the duration of hydrological drought, the number of months which is occurred moderately drought in sub-basins still goes up in the climate change scenario. These findings could be useful for water shortage assessment and allocation planning in this area in the climate change context in the Dong Nai River Basin.

Drought disasters that occur in the Sampean watershed from time to time have increased, both the intensity of events and the area affected by drought. The general objective of this research is to develop an assessment method for the impact of climate chan ge on vulnerability to drought disasters based on atmospheric circulation data. The specific objectives of this study are to model rainfall predictions based on atmospheric circulation data, predict rainfall in various climate change scenarios (Intergovernm ental Panel on Climate Change, IPCC – AR5), and assess vulnerability to drought disasters using a meteorological approach. The Standardized Precipitation Index (SPI) is one way to analyze the drought index in an area which was developed previous researcher. The Standardized Precipitation Index (SPI) is designed to quantitatively determine the rainfall deficit with various time scales. The advantage of the Standardized Precipitation Index (SPI) is that it is enough to use monthly rainfall data to compare drou ght levels between regions even with different climate types. To facilitate the presentation of the data base on the identification of d rought susceptibility, we need a system that can assist in building, storing, managing and displaying geographically ref erenced information in the form of spatial mapping. This research facilitates monitoring of the area of drought-prone areas, predicts drought levels, prevents future drought disasters, and prepares plans for rebuilding drought-prone areas in the Sampean watershed.

The study was carried out to assess meteorological drought on the basis of the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) evaluated in future climate scenarios. Yazd province, located in an arid region in the centre of Iran, was chosen for analysis. The study area has just one synoptic station with a long‐term record (56 years). The impact of climate change on future drought was examined by using the CanESM2 of the CMIP5 model under three scenarios, that is, representative concentration pathways RCP2.6, RCP4.5 and RCP8.5. Given that a drought is defined by several dependent variables, the evaluation of this phenomenon should be based on a multivariate analysis. For this purpose, two main characteristics of drought (severity and duration) were extracted by run theory in a past (1961–2016) and future (2017–2100) period based on the SPI and SPEI, and studied using copula theory. Three functions, that is, Frank, Gaussian and Gumbel copula, were selected to fit with drought severity and duration. The results of the bivariate analysis using copula showed that, according to both indicators, the study area will experience droughts with greater severity and duration in future as compared with the historical period, and the drought represented by the SPEI is more severe than that associated with the SPI. Also, drought simulated using the RCP8.5 scenario was more severe than when using the other two scenarios. Finally, droughts with a longer return period will become more frequent in future.

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