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This study aims to analytically explore the effects of changing rainfall distributions in terms of variations in the mean and variance of gamma distributions on the drought analysis based on standardized precipitation index (SPI). Traditional SPI calculation involves the fitting of observed rainfall series to a time-invariant probability distribution; the gamma distribution is commonly used. Fitting a time-varying gamma distribution to a trending rainfall series leads to nonstationary SPI (NSPI) series. The effects of changing gamma distributions on the SPI and NSPI can be systematically summarized by the proposed nine-category distributional-change scheme in terms of variations in the mean and variance of the gamma distributions. The annual wet-season rainfall series at Taipei (1897–2017) and Dawu (1940–2017), which exhibit significantly increasing and insignificantly decreasing trends, respectively, were selected for demonstration. A clearly increasing rainfall trend at Taipei over the last four decades corresponds to less severe droughts in the SPI series and more frequent and more severe droughts in the NSPI series. These contradictory results are attributed to the time-invariant gamma distribution, which causes the trending SPI series to be identical to the rainfall series, and the time-varying gamma distribution, which results in the trend-free NSPI series. The modeling of nonstationarity in rainfall series in the proposed calculation framework depends on the purposes of the analysis since different information is revealed for drought assessments.

In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize the drought conditions in Southwest China from 1982–2012. The SPEI was calculated by precipitation and temperature data for various accumulation periods. Based on the SPEI, the multi-scale patterns, the trend, and the spatio-temporal extent of drought were evaluated, respectively. The results explicitly showed a drying trend of Southwest China. The mean SPEI values at five time scales all decreased significantly. Some moderate and severe droughts were captured after 2005 and the droughts were even getting aggravated. By examining the spatio-temporal extent, the aggravating condition of drought was further revealed. To investigate the performance of SPEI, correlation analysis was conducted between SPEI and two remotely sensed drought indices: Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI). The comparison was also conducted with the Standardized Precipitation Index (SPI). The results showed that for both SMCI and VCI, the SPI and SPEI had approximate correlations with them. The SPEI could better monitor the soil moisture than the SPI in months with significant increase of temperature. The correlations between the VCI and SPI/SPEI were lower; nevertheless, the SPEI was slightly superior to the SPI.

The Ningxia Hui Autonomous Region of China (Ningxia) is an important food production area in northwest China severely affected by drought. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were calculated based on monthly meteorological data to explore climate change and variation in drought intensity, duration, frequency, and spatial extent in Ningxia during 1972–2011. Results show that the SPEI is more applicable than the SPI for exploring climate change and drought variation in Ningxia. The Ningxia climate experienced a significant drying tendency. Annual SPEI decreased about 0.37 decade−1 during 1972–2011. Drought was exacerbated by this drying tendency. Regional average duration, maximum duration, intensity, and frequency of drought identified by the SPEI increased by one month, three months, 0.15%, and 36.1%, respectively, during 1992–2011 compared to the period of 1972–1991. The spatial extent of drought identified by the SPEI increased about 14.4% decade−1 in the spring during 1972–2011. Spatially, drought frequency increased from north to south. Average intensity (maximum duration) of drought calculated by the SPEI increased (decreased) northward and southward from the central arid area.

The drought is a prominent disaster the world is facing currently. The drought identification, its characterization, and monitoring are, thus, very important to mitigate this calamity. Many drought indices have been developed for this purpose in the past; however, their evaluation is necessary to select the best suited drought index for the specific area. This study aims to analyze the meteorological drought risk across the Raigarh district of Chhattisgarh state by evaluating five popular drought indices, namely, Reconnaissance drought index (RDI) rainfall anomaly index, standardized precipitation evapotranspiration index (SPEI), standardized precipitation index (SPI), and standardized anomaly index (SAI) considering all the tehsils of the district and proposes the methodology to select the best suited index for the region. The methodology has been selected in such a manner that it deals with all aspects of meteorological drought and the most suitable drought index for the region can be identified by comparing the drought indices based on the actual drought occurrence in the region which are identified by different agencies. These drought indices were computed for different assessment periods of 1, 3, 6, 9, and 12 months using the historical precipitation and temperature data derived from (NASA-POWER) for the period 1981–2021. The variation in severity in terms of extreme, severe, and moderate drought events has been observed with these drought indices in the district. Maximum correlation is observed between RAI and SAI across all assessment periods. Further, the SPI is seen to have fairly good agreement with all other drought indices and it rises with increasing assessment periods. Modified Mann–Kendall test shows a significant positive trend for all drought indices (except SPEI across all assessment periods, RDI-1 and RDI-3) across all assessment periods. The SPI performs exceptionally well in capturing the actual drought conditions reported by different agencies in the Raigarh region among all other indices considered in this study. It is also observed that although SPEI which comes out to be the second most suitable index for drought identification in the study region, it was not able to capture the drought events before 1990. However, SPEI identified most of the drought years post 1990 which shows the prevalent effect of temperature on the formulation of index driven by heavy industrialization in the region post 1990. Overall results suggest that the SPI is the best-suited meteorological drought index for the Raigarh region.

Increasing drought patterns with profound effects on livelihoods and food security have been documented in Ethiopia. From previous studies', assessments at various timescales, Ethiopia is regarded as a drought-prone country in East Africa. However, there is no documentation available. This paper investigates the spatiotemporal patterns of drought characteristics in 16 woredas (districts) as well as in the 14 homogeneous rainfall zones of homogeneous using monthly rainfall and temperature data over the period 1983 to 2020. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were computed using time-series meteorological data to depict the spatial extent of drought characteristics and patterns at 4- and 12-month timescales. Within the period investigated, 12-month, 2015, and 2019 recorded the most extreme countrywide drought. The most prolonged drought duration lasted for 12 months in 2015. Although Ethiopia is a drought-prone country, the frequency, magnitude, and severity vary spatially by region. In planning for future actions, particular emphasis must be paid to the northeastern, eastern, northwestern, and southeastern parts of the country, which are more vulnerable. The findings could potentially influence and redirect national drought management and disaster preparedness programs for the affected areas. 1984 and 2015 were years with the most severe and countrywide extreme drought episodes. The average SPI and SPEI from 1983 to 2020 showed a weak positive trend. Drought magnitude ranges from −1.7 to −2.1 for SPEI 4 and −1.9 to −2.1 for SPEI-12-months. 2015 experienced the most prolonged drought with a length of about 12 months. Stakeholders should give particular attention to Ethiopia's northeastern, central, and southeastern parts.

We explore future changes of temperature, precipitation, and drought characteristics in the Indochina Region (ICR) based on the optimal ensemble subset of global climate models (GCMs) of the Couple Model Intercomparison Project Phase 5 (CMIP5). The optimal ensemble subset is selected from 34 GCMs using an ensemble selection method by focusing on precipitation over ICR. Bias correction procedures for the optimal ensemble subset are examined for drought analysis in ICR. Based on the bias-corrected optimal ensemble subset, mean temperature in ICR is projected to increase around 1.1 °C (0.99 °C) in near future (2011–2040), 2.5 °C (1.8 °C) in mid future (2041–2070), and 4.3 °C (2.2 °C) in far future (2071–2100) time frames under representative concentration pathway 8.5 (RCP8.5) (RCP4.5) scenario. Mean precipitation decreases in the dry season and increases in the wet season. The 3-month Standardized Precipitation Evapotranspiration Index (SPEI-3) projects larger changes of drought characteristics than those of the 3-month Standardized Precipitation Index (SPI-3), especially quite large increases of drought duration, severity, and peak. Based on SPEI-3, the potential increase of severe drought hazard is expected in ICR in the far future period under both scenarios. The most drought-prone areas are detected over Thailand and Cambodia in which the drought characteristics are projected to expand to cover most parts of ICR in the mid and far future. The potentially dry condition over ICR is clearly depicted based on SPEI-3 with more reliable estimation after selecting the optimal ensemble subset and bias correction procedure.

Drought is an environmental disaster related to the extremes (on a drier side) in hydrometeorology. The precipitation amount modulates drought in Nepalese river basins. It is vital for efficient water resources management to quantify and understand drought. This paper aims to characterize the droughts in Nepal based on standard precipitation index (SPI) and rainfall anomaly index (RAI) using daily precipitation data and assess their impacts on annual crop yields. We used 41 years (1975–2015) of daily precipitation data to compute monthly means and then the drought indices, namely, SPI and RAI, at 123 stations across Nepal. Results showed that the northwest and eastern regions experienced drought compared to other regions, although the severity and duration were shorter. For stations 101 and 308, we found extreme drought events after 2005 for SPI-1, SPI-3, and SPI-6. However, for SPI-6, extreme drought was also observed in 1989 and 1994 at both stations. The year 1992 was one of the severest drought years for the western and northwest regions, where the severity crossed more than −2.0 for all SPI months. Similar to SPI, RAI also revealed a similar degree of drought in the country. RAI showed that the eastern region depicted a higher degree of severity of drought compared to other areas beyond 2004. The lesser severity is also seen in the far west part beyond 2005. The results showed that SPI and RAI could equally be used to analyze drought severity. More frequent drought incidents have been observed after 2010 at all the considered precipitation stations. With the increase in the drought severity, the crop yield (such as paddy, maize, barley, millet, and wheat) has been directly impacted. These results might be significant for planning water resource and irrigation water management systems.

In North America, the occurrence of extreme drought events has increased significantly in number and severity over the last decades. Past droughts have contributed to lower agricultural productivity in major farming and ranching areas across the US.We evaluated the relationship between drought indices and crop yields across the US for the period 1961−2014. In order to assess the correlations with yields from the major cash crops in the country, we calculated several drought indices commonly used to monitor drought conditions, including 4 Palmer-based and 3 multiscalar indices (Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Precipitation Drought Index). The 3 multiscalar drought indices were aggregated at 1 to 12mo timescales. Besides the quantification of the similarities or differences be - tween these drought indices using Pearson correlation coefficients, we identified spatial patterns illustrating this relationship. The results demonstrate that the flexiblemultiscalar indices can identify drought impacts on different types of crops for a wide range of time periods. The differences in spatial and temporal distribution of the correlations depend on the crop and timescale analyzed, but can also be found within the same type of crop. The moisture conditions during summer and shorter timescales (1 to 3mo) turn out to be a determining factor for barley, corn, cotton and soybean yields. Therefore, the use of multiscalar drought indices based on both precipitation and the atmospheric evaporative demand (SPEI and SPDI) seems to be a prudent recommendation.

The aim of this work is to investigate drought variability in Padua, northern Italy, over a nearly 300-year period, from 1725 to 2023. Two well-established and widely used indices are calculated, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). They are compatible with a data series starting in the early instrumental period, as both can be estimated using only temperature and precipitation data. The Padua daily precipitation and temperature series from the early 18th century, which were recovered and homogenized with current observations, are used as datasets. The standard approach to estimate SPI and SPEI based on gamma and log-logistic probability distribution functions, respectively, is questioned, assessing the fitting performance of different distributions applied to monthly precipitation data. The best-performing distributions are identified for each index and accumulation period at annual and monthly scales, and their normality is evaluated. In general, they detect more extreme drought events than the standard functions. Moreover, the main statistical values of SPI are very similar, regardless of the approach type, as opposed to SPEI. The difference between SPI and SPEI time series calculated with the best-fit approach has increased since the mid-20th century, in particular in spring and summer, and can be related to ongoing global warming, which SPEI takes into account. The innovative trend analysis applied to SPEI12 indicates a general increasing trend in droughts, while for SPI12, it is significant only for severe events. Summer and fall are the most affected seasons. The critical drought intensity–duration–frequency curves provide an easily understandable relationship between the intensity, duration and frequency of the most severe droughts and allow for the calculation of return periods for the critical events of a certain duration. Moreover, the longest and most severe droughts over the 1725–2023 period are identified.

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover.