Standardized Precipitation Index Indices Research Articles

КӨПЖЫЛДЫҚ ГИДРОМЕТЕОРОЛОГИЯЛЫҚ МӘЛІМЕТТЕРІ НЕГІЗІНДЕ ЖАЙЫҚ-КАСПИЙ СУШАРУАШЫЛЫҚ АЛАБЫНДАҒЫ ГИДРОЛОГИЯЛЫҚ ҚҰРҒАҚШЫЛЫҚТЫ БАҒАЛАУ

The article considers the problem of effective detection of the beginning and end of hydrological drought in the territory of Zhayk-Caspian water basin. Standardized Precipitation index (SPI) recommended by the World Meteorological Organization selected as drought predictor indicator. Based on the SPI Generator Application determined the characteristics of severe and extreme droughts from the beginning of instrumental observations to 2020 year. The results of the studies showed the effectiveness of the SPI index in detecting dry periods of the area under consideration, which allowed a differentiated diagnosis of hydrological droughts and their timing. The findings have practical relevance for agriculture and other water-dependent industries, information on the types and timing of droughts can be used to develop effective strategies to manage the water resources of the region and minimize the negative impacts of hydrological droughts.

Investigating the Relationship between Precipitation and Vegetation Dynamics with Emphasis on Agricultural Land Cover in the Atrak Basin Area

The present study aimed to analyze the dynamics of vegetation within the Atrak catchment area, as well as its interplay with precipitation patterns. Moreover, additional emphasis was placed on exploring the impact of these dynamics on agricultural land cover type. To achieve this objective, the Enhanced Vegetation Index (EVI) derived from MODIS data and the Comprehensive Historical and Real-Time Satellite-based Precipitation (CHRIPS) data were utilized for the period from 2003 to 2021. Additionally, the Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) were employed to discern various degrees of drought and pluvial years within the Atrak basin. The study revealed that the years 2008, 2014, 2017, and 2021 exhibited the lowest vegetation coverage, while the years 2010, 2016, and 2019 showcased the most extensive vegetation extent. Notably, it was revealed from the VCI index that the year 2008 was the driest, and the year 2016 was the wettest. Furthermore, based on the SPI index findings, the years 2007, 2019, and 2020 were identified as pluvial years, while in the years 2008, 2014, and 2021 drought conditions occurred. All other years were classified as exhibiting normal conditions. Regarding seasonality, the observations ascertain that the spring season substantiates the most extensive vegetation cover, and a high correlation between spring precipitation and vegetation coverage was observed. Additionally, the anomaly detection outcomes indicate that the eastern regions of the basin have experienced an upward trend compared to the average of the first decade of the studied period.

Evaluation of Drought Using Meteorological Drought Indices, a Case Study: Alanya (Türkiye)

Drought is one of the most important challenges that many countries, especially countries in the Middle East region, are struggling with. Based on this, the study and monitoring of hydrological and drought factors is an important issue that can have a significant impact on management decisions in the field of water resources, especially in crisis management. Therefore, investigating the drought parameters is very important to understand the drought situation of a region. In this study, Alanya region, which is located on the southern coast of Turkey, was selected as a case study for drought analysis. Four drought indices for the selected region including: China Z-Index (CZI), Standardized Precipitation Index (SPI), Modified China Z-Index (MCZI) and Z-Score Index (ZSI) have been investigated. All these indicators have been investigated and evaluated using time scales of 1, 6, 12 and 24 months, the coefficient of determination (R2) has been calculated for each drought index with a different time scale and their results have been compared. The findings of the research showed that SPI and CZI drought indices performed better than other selected drought indices in identifying and effectively tracking drought severity. In addition to the study of dry events, wet events were also investigated, which indicates the presence of consecutive floods in the last years of the studied period in the region. The results indicated similar very dry events for the selected indicators in the 6-month period. Also, the rainfall trend for the period of 2015-2022 was taken into consideration to examine the rainfall of the last eight years. The results show that precipitation has decreased in recent years and has a downward trend in most months of the period in question, and the possibility of flood events due to sudden showers in the region has increased due to the continuation of droughts experienced in the years before 2015. Investigating soil moisture and vegetation for the selected period in the study area is also important for the evaluation of the drought level. Evaluation of the available land (vegetation) cover maps of the years 1975, 1985, 2000, 2010, 2020 and 2022 show that the vegetation cover has weakened over the years, and it has been evaluated as an indicator that the danger of drought in the region has increased.

Seasonal drought severity identification using a modified multivariate index: a case study of Indo-Gangetic Plains in India

Effective drought monitoring is crucial for mitigation efforts and the implementation of early warning systems. Due to the intricate nature of drought phenomena, its impact cannot be accurately characterized solely through the use of univariate indicators. This study introduces a composite version of the modified multivariate standardized drought index (MMSDI) for the Indo-Gangetic Plains (IGP) in India. The MMSDI integrates precipitation, soil moisture, and solar-induced chlorophyll fluorescence datasets from 2001 to 2020, utilizing a nonparametric joint probabilistic framework. It combines three consolidated drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized solar-induced chlorophyll fluorescence Index (SSIFI). The drought patterns among SPI, SSI, SSIFI, and MMSDI indices are compared for short, and mid-term time scales (herein 1, 3 and 6 month) over IGP. The performance of MMSDI is evaluated through quantitative metrics e.g. probability of detection (POD), false alarm ration (FAR) and critical success index (CSI) at various time scales. Results reveal that MMSDI provides a reliable estimation of the severity and spatial coverage of major drought events over IGP at various time scales. The MMSDI demonstrates superior effectiveness in identifying and characterizing the spatial severity of drought, i.e. the visibility of the drought hotspots. The MMSDI has the capability to assess agrometeorological drought, indicating its potential as a valuable tool for identifying regions vulnerable to drought. Overall, this modified drought index derived from multi-sensor datasets produced comprehensive insights for policymakers in implementing effective agricultural drought management practices to understand intricate drought phenomena over IGP region in India.

Response of vegetation to SPI and driving factors in Chinese mainland

Considering the sensitivity and vulnerability of vegetation to precipitation changes, exploring the response of vegetation to standardized precipitation index (SPI) is essential for ecosystem stability. Based on observed data from 624 stations and Moderate Resolution Imaging Spectroradiometer (MODIS) dataset from 2000 to 2018, Spearman's rank correlation coefficient (rs) of SPI and vegetation condition index (VCI) in Chinese mainland was analysed, response time of vegetation to SPI was investigated, and random forest models were constructed to explore the drivers of vegetation response. The results revealed that: (1) SPI and VCI exhibit a loop distribution. The correlation between VCI and 1-month SPI is mostly positive, accounting for 77.58% of the total; rs shows an increasing-decreasing distribution pattern, and the response time of VCI to 1-month SPI indicates an overall decreasing-increasing-decreasing trend. (2) Precipitation, temperature, altitude, vegetation, land use, landform and soil types all have influence on the correlation between 1-month SPI and VCI. Under various factors, rs is concentrated in the range of -0.1 ∼ 0.2, and the response time of VCI to SPI under various factors primarily falls into the ranges of 0 ∼ 1 month and 6 ∼ 9 months. Meanwhile, rs generally decreases with the increasing latitude, ranging between -0.1 and 0.2, whereas the mean response time increases with latitude, within a 0 ∼ 6 months range. (3) Precipitation, temperature and altitude are the top three important factors that affect both maximum rs of VCI and 1-month SPI and response time of VCI to 1-month SPI. The rs is positive in areas with low precipitation, high temperature and low altitude, whereas response time is generally long in areas with low precipitation, low temperature and high altitude. This study is significant for better understanding the interactions between vegetation and SPI changes, and provides reference for vegetation irrigation and vegetation management.

Comparative analysis of the influence of temperature and precipitation on drought assessment in the Marmara region of Turkey: an examination of SPI and SPEI indices

Abstract Drought is one of the most serious problems for human societies and ecosystems. Climate change has begun to affect the whole world, but regional climate change can have different effects. Precipitation is frequently used to determine drought; besides, temperature has started to be used in recent years. The effect of temperature on the occurrence of drought was investigated for the Marmara region, located in the northeast of Turkey. The spatiotemporal variability of drought in the Marmara region was analyzed by using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) for 3-, 6-, and 12-month series. Results show that SPEI droughts occur with longer duration and increased magnitude, which can be attributed to the rise in potential evapotranspiration rates with an increase in temperature. The correlation between SPI and SPEI is strong for the same time series, and the number of extreme drought values for SPI is higher than SPEI, while the number of severe and moderate drought values is lower compared to SPEI. The years 1989, 1990, 2001, 2007, and 2014 were characterized as the highest drought years throughout the region. The study concludes that temperature should be considered in future studies.

Assessment of drought trends in the Upper Ewaso Ng'iro River Basin using the SPI and SPEI

Abstract Drought assessment is necessary for creating adaptation and resilience measures for the livelihoods of the affected communities. This study assessed drought trends in Kenya's Upper Ewaso Ng'iro River Basin (UENB) from 1981 to 2020. A Standardized Precipitation Index (SPI), a precipitation-based index, and a Standardized Precipitation Evapotranspiration Index (SPEI), a multivariate index that considers the difference between precipitation and potential evapotranspiration (PET), were used to evaluate drought severity and frequency over varying timescales. Monthly rainfall and temperature data for 10 stations within the basin were analyzed to calculate the SPI and SPEI time series values for 3, 6, and 12 months. The results demonstrate an alarming increase in the severity and frequency of drought events in the UENB since 1999. Additionally, the study reveals that the SPI and SPEI indices differ in identifying temporal and spatial drought characteristics, with longer timescales showing improved accuracy. Notably, the SPEI identifies more extensive and severe drought periods in the region compared to the SPI. The research findings are crucial in guiding policy decisions related to SDGs as they provide valuable information on drought trends necessary for implementing effective drought adaptation and resilience measures and promoting sustainable development in the UENB.

Fusing morphometric characteristics with extreme precipitation indices for identifying the most vulnerable sub-basin at risk of flooding

Fluvial floods are commonly studied as an occurrence at the level of a specific basin and are speculated to be closely related to the basin's morphometry. It is possible to identify and rank sub-basins based on how susceptible they are to fluvial flooding events using morphometric criteria. However, one of the key causes that triggers fluvial flooding is the increase in precipitation extremes and changes to their patterns. In this study, influence of morphometric factors and extreme precipitation events on the hydrological responses of the Brahmani River, India as well as their sensitivity to fluvial flooding, are investigated to identify the most vulnerable sub-basin in a catchment. The morphometric parameters were calculated from a digital elevation model (DEM), and the change in trend of extreme precipitation indices was detected using precipitation data of period 1991 to 2021. Furthermore, the Standardized Precipitation Index (SPI) was used to determine the frequency of wet cycles on time scale of 1, 3, 12, and 24 months, as well as their link to fluvial flooding. The two sub-basins of the catchment that are most vulnerable to river flooding are recognised as Noamundi and Gomlai based on morphometric criteria. However, analysis of SPI and extreme precipitation indices showed that the Jenapur sub-basin is the most vulnerable to flooding. It is also corroborated with analytic hierarchy process (AHP) based weighted overlay analysis and historical flood records. The outcomes will assist researchers in better understanding the mechanisms causing flooding in the Brahamni River Basin and in developing flood mitigation practices for the most vulnerable Jenapur sub-basin.

Calculation of the SPI, SPEI, and GRDI Indices for Historical Climatic Data from Doñana National Park: Forecasting Climatic Series (2030–2059) Using Two Climatic Scenarios RCP 4.5 and RCP 8.5 by IPCC

In this study, we utilized three different indices to assess drought conditions in the Doñana National Park (DNP) located in southern Spain. These indices included the Standardized Precipitation Index (SPI), which is based on precipitation statistics, the Standardized Precipitation Evapotranspiration Index (SPEI), which incorporates both precipitation and air temperature data, and the Groundwater Recharge Drought Index (GRDI), a newly developed index specifically designed to evaluate groundwater drought. The analysis covered the time period from 1985 to 2015, and future projections were made for the years 2030–2060 under different climate scenarios (RCP 4.5 and RCP 8.5). Our findings revealed a significant decrease in total precipitation of approximately 13–14% compared to historical records (1985–2015). Moreover, severely to extremely wet periods exhibited a reduction ranging from 25% to 38%. A key contribution of this study is the application of the GRDI index, which allowed us to assess groundwater recharge rates. We observed a decline in the simulated mean recharge rates during the 21st century when compared to the historical period spanning from 1950 to 2009. This decline can be attributed to increased evapotranspiration. The results of this research provide valuable insights for the Spanish water resources administration. The observed reductions in precipitation and groundwater recharge rates emphasize the need for appropriate mitigation measures. The findings will aid the administration in formulating an integrated water resources management strategy in the Doñana National Park and its surrounding basin. By understanding the projected changes in drought conditions, the administration can make informed decisions to ensure sustainable water resource management in the region.

Identifikasi Kekeringan Meteorologi menggunakan SPI 3 Bulanan dan 6 Bulanan di DAS Brantas Hulu

Drought is a state of water availability that is far below the need for water for survival, agriculture, economic activity, and the environment. Drought in the Brantas Hulu watershed has been recorded as hampering the function of the Sutami Reservoir. This study used the Standardized Precipitation Index (SPI) method of 3 monthly and 6 monthly to determine the distribution of drought in the form of duration, severity, and maximum intensity of the Brantas Hulu watershed. This study also analyzed the relationship between the 3-monthly and 6-monthly SPI index to the sutami reservoir inflow discharge in the Brantas Hulu watershed using the Pearson Correlation method including correlations without lag, lag 1, and lag 2. The data used are monthly rainfall data for 30 years for the period 1991 - 2020 from 10 Rain Posts in Malang City, Batu City, and Malang Regency as well as monthly inflow debit data for 10 years for the period 2011 - 2020 from Perum Jasa Tirta 1. In general, the distribution of the maximum drought duration occurs for 15 – 17 months, while the SPI of 6 months occurs for 25 – 29 months. The maximum drought severity occurs for 5 - 6 months, while the 6-month SPI occurs for 9 – 11 months. The maximum drought intensity of SPI is 3 months and 6 months is categorized as Dry. Otherwise, the correlation of 3 monthly SPI and sutami reservoir inflow discharge without lag and lag 1 is worth 0,2 to 0,3, while for lag 2 it is worth 0,1 to 0,2. The correlation value of the 6 -month SPI index and the inflow discharge of Sutami Reservoir without lag is worth 0,3 to 0,4, while the correlation of lag 1 and lag 2 is worth 0,2 to 0,3.

Drought disaster modeling using drought index: a systematic literature review

A prolonged absence of rainfall that results in a temporary reduction or deficit in the amount of natural water available is known as drought. The goal of this article is to examine how drought indices from various nations might be used to simulate the features of drought. Understanding the various drought indexes, as well as their benefits and drawbacks, is crucial. The literature review methodology is employed in this investigation. The Standardized Precipitation Index, or SPI, is the output of the most used modeling technique. Because this technique solely uses the rainfall series, it was discovered that about 57% of the articles utilizing the SPI drought index used the SPI index. The Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index were used to find additional articles, nevertheless (SPEI). According to several studies, the study methodology often compares drought index approaches using hydrological and meteorological data. It is envisaged that this approach might be applied in other nations to see a place from multiple angles with regard to its hydrology and meteorology.

Trend Analysis of Different Climate Parameters and Watering Requirements for Hazelnut in Central Italy Related to Climate Change

In this study, the effects of climate change on the irrigation water requirement of hazelnut trees were investigated in Central Italy. The meteorological variables considered were precipitation, temperature, chilling units, and the Standardized Precipitation Index (SPI) in Central Italy. The hydrological variables were the reference evapotranspiration (ET0) and the water requirement based on soil water balance. Climate data were collected from eight meteorological stations for the period 1974–2021, and ET0 was estimated by the Hargreaves and Samani equation. The SPI index was calculated for a four-month time scale corresponding to the hazelnut growing season (April–August). A statistical analysis of the trends of the variables considered was conducted. The results showed an increasing trend for temperature, ET0, and water requirements, while a decreasing trend was shown for the chilling units. No significant trends were detected for precipitation and SPI.

Association of farmers’ wellbeing in a drought-prone area, Thailand: applications of SPI and VCI indices

Drought has major consequences for agricultural activity, economy, and the environment. For improved drought management, it is necessary to assess drought severity, frequency, and the potential of drought occurrence. The purpose of this study is to use drought indices, standardized precipitation index (SPI), and vegetation condition index (VCI) to characterize the severity of drought and investigate the association between drought severity and subjective well-being among local farmers. The SPI was used to quantify precipitation deficits at various time scales, while the VCI was utilized to monitor crop and vegetation drought conditions. Throughout the period 2000-2017, satellite data were incorporated, as well as a household survey of rice farmers in the dry zone research region in northeastern Thailand. The findings suggest that extreme droughts occur more frequently in the central part of the northeastern region of Thailand than in the rest of the region. The influence of drought on farmers' wellbeing was evaluated at various drought severity levels. Drought and overall wellbeing are strongly linked at the household level. Thai farmers in drought-prone areas are dissatisfied with their livelihoods more than farmers in less-affected area. It is intriguing that farmers who live in drought-prone areas are more content with their lives, their communities, and their occupations than farmers who live in less drought-prone areas. In this context, using proper drought indices could potentially improve the utility of governmental interventions and community-based programs targeted at assisting drought-affected people.

Projection of Future Meteorological Droughts in Lake Urmia Basin, Iran

Future changes (2015–2100) in precipitation and meteorological droughts in Lake Urmia Basin were investigated using an average mean ensemble of eight general circulation models (GCMs) with high-resolution datasets in socioeconomic pathway scenarios (SSPs) from the Coupled Model Intercomparison Project (CMIP6). In order to project the drought, the standardized precipitation index (SPI) was calculated. Overall, the results revealed that precipitation in Lake Urmia Basin will decrease by 3.21% and 7.18% in the SSP1-2.6 and SSP5-8.5 scenarios, respectively. The results based on 6-month-timescale SPI indices projected more “Extremely dry” events in SSP5-8.5 scenarios. The frequency of “Extremely dry” months in SSP5-8.5 compared to SSP1-2.6 is expected to increase by 14, 7, 14, 10, 5, 14, and 7 months for the Mahabad, Maragheh, Saqez, Sarab, Tabriz, Takab, and Urmia stations, respectively. In contrast, the frequency of “Extremely wet” months will decline for all stations in Lake Urmia Basin. The results of this study provide useful insight for considering drought prevention measures to be implemented in advance for Lake Urmia Basin, which is currently experiencing various environmental issues.

Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index

This paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes in the area. In this study, Standardized Precipitation Index (SPI) was used to predicted the future drought in the upper Godavari River basin, India. We have selected the ten input combinations of ML model were used to prediction of drought for three SPI timescales (i.e., SPI -3, SPI-6, and SPI-12). The historical data of SPI from 2000 to 2019 was used for creation of ML models SPI prediction, these datasets was divided into training (75% of the data) and testing (25% of the data) models. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine model using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to forecasting of the SPI-3,6 and 12 months. The ML models accuracy were compared with various statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The results of study area have been showed that the SMO-SVM poly kernel model precisely predicted the SPI-3 (R2 = 0.819) and SPI-12 (R2 = 0.968) values at Paithan station; the SPI-3 (R2 = 0.736) and SPI-6 (R2 = 0.841) values at Silload station, respectively. The SMO-SVM PUK kernel is found that the best ML model for the prediction of SPI-6 (R2 = 0.846) at Paithan station and SPI-12 (R2 = 0.975) at the Silload station. The compared with SVM-SMO poly kernel and SVM-SMO PUK kernel was observed, these models are best forecasting of drought (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is good for SPI-3 prediction at both stations. The results have been showed the ability of the SVM-SMO algorithm with various kernel functions successfully applied for the forecasting of multiscale SPI under the climate changes. It can be helpful for decision making in water resource management and tackle droughts in the semi-arid region of central India.

Comparative study on appropriate drought and flood index selection in a tropical farming island in China

The traditional drought and flood analysis method had not fully considered the proportion analysis of different drought and flood grades in the historical years of each rainfall station. This made results unconvincing and made it difficult to deeply understand the characteristics and applicability of various methods. Based on the daily rainfall data of 88 stations in Hainan Island from 1970 to 2019, the China-Z index and the Standardized Precipitation Index (SPI) were used to compare and analyze the spatial and temporal distribution characteristics of droughts and floods from three different time scales (flood season, non-flood season and the whole year). The results showed that both SPI and China-Z index can well reflect the actual drought and flood situations in Hainan Island. The analysis of the proportions of different drought and flood grades in the historical years of each rainfall station and regional historical drought and flood statistics suggested that the China-Z index had a better indication effect than SPI on the extreme drought and flood grades. The alternation of drought and flood between different eras were obvious. Hainan Island generally presented an east-west reverse drought-flood variation trend, as well as a north-south reverse drought-flood variation trend. The drought and flood in the central mountainous area of Hainan Island had been relatively stable. The distribution pattern of drought and flood had a good spatial consistency in the three periods. On the whole, Hainan Island had shown a trend of flood in the east and drought in the west in the past 50 years.

Assessment of Meteorological and Agricultural Drought Analysis in Kırklareli province

The Global warming in the world cause to the climate change in this century. Agriculture is one of the sectors that can be most affected by climate change. Agricultural production is affected by precipitation and temperature variables. Due to the changes in these two parameters, the increase in the temperatures and the changes in the amount of precipitation in parallel with this disrupt the ecological balance and bring along the problems of desertification and drought. Agricultural production is highly dependent on meteorological factors in Thrace region. For this reason, it is expected that changes in climatic factors will affect the agriculture of the region positively or negatively. The aim of this study is to determine the frequency and severity of meteorological and agricultural drought with two indexes between 1963-2019 years in Kırklareli province. Standardized Precipitation Evapotranspiration Index (SPEI) was used to determine monthly, seasonal, six monthly and annual agricultural drought. According to annual agricultural drought results, it was calculated ten years mild arid (1969, 1985, 1986, 1990, 1991, 1992, 1993, 2011, 2016, and 2019), five years moderate arid (1983, 1989, 1996, 2008, and 2015), one year severe arid (1994), and two years extreme arid (2000 and 2001). Another drought index which is Standardized Precipitation Index (SPI) was used to determine meteorological drought. According to annual meteorological drought results, Eight years mild arid (1964, 1969, 1982, 1985, 1986, 1990, 1991, and 2011), seven years moderate arid (1983, 1989, 1992, 1993, 1994, 1996, and 2008), and two years extreme arid (2000, 2001) was figured out. According to linear correlation analysis, a good correlation was obtained for SPI and SPEI values, between annual, six monthly (Spring-Summer), and three monthly only winter and spring periods (R2=0.871, R2=0.901, R2=0.974, and R2=0.919), respectively. In the 57-year observation period; 18 years were determined as the arid year with SPEI index and 67% of these total arid years (12 arid years) occurred and 17 years were determined as the arid year with SPI index and 76% of these total arid years (13 arid years) occurred, between 1982 and 2002 years. The SPEI drought index which use precipitation, temperature and evapotranspiration data could give much good results to policy makers in drought mitigation policies in terms of giving results that are more accurate in agricultural production and drought assessments.

Analyzing Drought Conditions, Interventions and Mapping of Vulnerable Areas using NDVI and SPI Indices in Eastern Ethiopia, Somali Region

Analysis of a long-term temporal and spatial drought conditions in Somali region was done using Standardized Precipitation Index (SPI) and Coefficient of Variation (CV) for 6 meteorological stations and Normalized Difference Vegetation Index (NDVI) analysis were performed by enhancing images from Landsat 5 TM of two different years. CV was computed for Belg season, Deyr season and yearly basis. Rainfall variability for Belg and Deyr was higher than annual variability. Characterization of the temporal variation of drought using 3-months and 6-months SPI for each meteorological station were analyzed and plotted against long time series in yearly basis. The NDVI values and SPI indices were able to show extent and variability of drought. The 3-months SPI results revealed that maximum frequency of severe drought events were experienced in Gode (7 times), followed by Hurso (6 times) within the study period. The 6-months SPI results show that less frequency of dry and wet events were observed in the entire study period as compared to 3-months SPI results. The frequency of any drought events (moderate to extreme) occurrence in the study area is once in every two years at all stations. Whereas, severe and extreme drought event in majority of study area is commenced nearly once in three years with the exception of Degahabur having once in five years. Overall, a preliminary comparison between results obtained using the SPI and NDVI shows a general good agreements.

Interactions between precipitation, evapotranspiration and soil-moisture-based indices to characterize drought with high-resolution remote sensing and land-surface model data

Abstract. The Iberian Peninsula is prone to drought due to the high variability in the Mediterranean climate with severe consequences for drinking water supply, agriculture, hydropower and ecosystem functioning. Because of the complexity and relevance of droughts in this region, it is necessary to increase our understanding of the temporal interactions of precipitation, evapotranspiration and soil moisture that originate from drought within the Ebro basin, in northeastern Spain, as the study region. Remote sensing and land-surface models provide high-spatial-resolution and high-temporal-resolution data to characterize evapotranspiration and soil moisture anomalies in detail. The increasing availability of these datasets has the potential to overcome the lack of in situ observations of evapotranspiration and soil moisture. In this study, remote sensing data of evapotranspiration from MOD16A2 and soil moisture data from SMOS1km as well as SURFEX-ISBA land-surface model data are used to calculate the evapotranspiration deficit index (ETDI) and the soil moisture deficit index (SMDI) for the period 2010–2017. The study compares the remote sensing time series of the ETDI and SMDI with the ones estimated using the land-surface model SURFEX-ISBA, including the standardized precipitation index (SPI) computed at a weekly scale. The study focuses on the analysis of the time lags between the indices to identify the synchronicity and memory of the anomalies between precipitation, evapotranspiration and soil moisture. Lag analysis results demonstrate the capabilities of the SPI, ETDI and SMDI drought indices computed at a weekly scale to give information about the mechanisms of drought propagation at distinct levels of the land–atmosphere system. Relevant feedback for both antecedent and subsequent conditions is identified, with a preeminent role of evapotranspiration in the link between rainfall and soil moisture. Both remote sensing and the land-surface model show capability to characterize drought events, with specific advantages and drawbacks of the remote sensing and land-surface model datasets. Results underline the value of analyzing drought with dedicated indices, preferably at a weekly scale, to better identify the quick self-intensifying and mitigating mechanisms governing drought, which are relevant for drought monitoring in semi-arid areas.

High Resolution Future Projections of Drought Characteristics in Greece Based on SPI and SPEI Indices

Future changes in drought characteristics in Greece were investigated using dynamically downscaled high-resolution simulations of 5 km. The Weather Research and Forecasting model simulations were driven by EC-EARTH output for historical and future periods, under Representative Concentration Pathways 4.5 and 8.5. For the drought analysis, the standardized precipitation index (SPI) and the standardized precipitation-evapotranspiration index (SPEI) were calculated. This work contributed to achieve an improved characterization of the expected high-resolution changes of drought in Greece. Overall, the results indicate that Greece will face severe drought conditions in the upcoming years, particularly under RCP8.5, up to 8/5 y of severity change signal. The results of 6-month timescale indices suggest that more severe and prolonged drought events are expected with an increase of 4 months/5 y, particularly in areas of central and eastern part of the country in near future, and areas of the western parts in far future. The indices obtained in a 12-month timescale for the period 2075–2099 and under RCP8.5 have shown an increase in the mean duration of drought events along the entire country. Drought conditions will be more severe in lowland areas of agricultural interest (e.g., Thessaly and Crete).