Standardized Precipitation Index Indices Research Articles

Meteorological drought assessment using SPI and RDI indices in the Ourika Basin, Morocco

The Ourika basin is located on the northern edge of the Marrakech High Atlas, within a semi-arid to sub-humid climate. Its fragile natural environment is exposed to extreme and harsh climatic conditions, such as droughts and floods. The aim of our study is to evaluate and monitor meteorological and hydrological droughts in the Ourika basin during the experimental period from 1980 to 2019. The severity and duration of droughts are estimated using two drought indices: The Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI). These indices were computed and analyzed using Drinc software. 8 meteorological drought sequences have been identified: 3 episodes were classified as severe droughts (1998-1999, 2004-2005, and 2016-2017), and 5 episodes were classified as moderate droughts, recorded during the years 1980-1981, 1982-1983, 1990-1991, 1994-1995, and 2006-2007. Analysis of hydro-rainfall data over the experimental study period (40 years) reveals disruptions accompanied by a decrease in rainfall and runoff. This indicates that the Ourika basin has generally experienced periods of moderate and severe meteorological and hydrological droughts, with a noticeable trend towards increasing drought conditions.

Assessing drought risk conditions through SPI and NDVI indices in Oued Kert watershed

The Oued Kert watershed in Morocco is essential for local biodiversity and agriculture, yet it faces significant challenges due to meteorological drought. This research addresses an urgent issue by aiming to understand the impacts of drought on vegetation, which is crucial for food security and water resource management. Despite previous studies on drought, there are significant gaps, including a lack of specific analyses on the seasonal effects of drought on vegetation in this under-researched region, as well as insufficient use of appropriate analytical tools to evaluate these relationships. We utilized the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) to analyze the relationship between precipitation and vegetation health. Our results reveal a very strong correlation between SPI and NDVI in spring (98%) and summer (97%), while correlations in winter and autumn are weaker (66% and 55%). These findings can guide policymakers in developing appropriate strategies and contribute to crop planning and land management. Furthermore, this study could serve as a foundation for awareness and education initiatives on the sustainable management of water and land resources, thereby enhancing the resilience of local ecosystems in the face of environmental challenges.

Evaluation of meteorological drought indices using remote sensing

Drought indices are a suitable tool for management measures and dealing with drought and are widely used worldwide. One of the most important stages of assessing the drought situation in each region is determining the drought indices to analyze the intensity and duration of drought in that region. The drought index is actually a function of various environmental factors that affect the drought phenomenon. In this study, the drought indices of the standardized precipitation index (SPI), China-Z index (CZI), Z Score Index (ZSI), and modified CZI (MCZI) in different month time scales (1, 3, 6, 9, 12, 18, 24 and 48) and the present of normal index (PNI) in different monthly, seasonally and yearly time scales in Kurdistan Province (stations Saqqez, Qorveh, Bijar, Sanandaj) were evaluated. Global land data assimilation system (GLDAS), Climatic Research Unit (CRU) dataset, and Tropical Rainfall Measuring Mission (TRMM) precipitation data (2000-2020) and TRMM precipitation (2000-2019) were received, and the drought indices were calculated. Root mean square error (RMSE), maximum error (ME), Pearson, and Spearman correlation coefficients were used for evaluation. The results of the SPI index showed that there is a significant relationship between TRMM, CRU, and GLDAS at the Saqqez, Qorveh, and Sanandaj stations (at the 5% level), and there was no significant relationship for TRMM at Bijar station for the 24-month time scale. The correlation coefficient results for the Saqqez and Sanandaj stations in time scales of 1 to 9 months for the SPI, CZI, ZSI, and MCZI indices were better than those of the Bijar and Qorveh stations. In assessing the SPI and CZI drought indices, the highest RMSE was for GLDAS at Bijar station and for the 48-month time scale. In general, the results showed that the drought indices of the SPI, CZI, ZSI, MCZI, and PNI obtained from the TRMM satellite, CRU dataset, and GLDAS model have a good correlation with the drought indices of synoptic stations.

Application of the Standardized Precipitation Index for Forecasting Seasonal Drought in Vietnam

Drought is a natural disaster that causes significant damages to the economy, society and environment. Forecasting droughts is crucial for disaster risk management and water security. This study introduces a new method for forecasting seasonal droughts in Vietnam using the Standardized Precipitation Index (SPI) which is based on a combination of dynamic modeling and statistical methods. The precipitation forecast products from regional climate models (RegCM, clWRF) with inputs from Climate Forecast System (CFSv2) were used to calculate the SPI index after statistical calibration. Results show that calibrating model precipitation with statistical methods such as Artificial Neural Networks (ANN) and Multivariate Linear Regression (MLR) significantly improves the precipitation forecast accuracy. Evaluation indices indicate that the SPI calculated with the post-calibration model precipitation has a good reproducibility for drought events, particularly mild droughts. However, there remains some limitations on accurate forecast of the intensity and spatial distribution of moderate drought events in certain regions such as Central Vietnam.

Multiscale investigations on RDI-SPI teleconnections of Çoruh and Aras Basins, Türkiye using time dependent intrinsic correlation

This paper proposes a multiscale dynamic correlation framework based on time-dependent intrinsic correlation (TDIC) to investigate the teleconnections between reconnaissance drought index (RDI) and standardized precipitation index (SPI). The SPI and RDI indices were calculated at 3-, 6-, and 12-month time scales using data from six stations in the Çoruh and Aras (CA) basins in Turkey for the 1969–2020 period. The spatial variability of the evaluation of the drought class demonstrated that, with the exception of the northwestern region (Bayburt station), the correlation between RDI and SPI exceeded 97% and the difference in occurrence between drought classes is found to be marginal. In the multiscale analysis of the two indices, firstly, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN) was employed to decompose the series. The modes of RDI and SPI at different process scales exhibited a strong positive linear correlation, however, the association in their long-term trends may not be of the same nature. The TDIC analysis captured the long-range correlations between the modes of RDI and SPI at diverse process scales, and the newly proposed average significant correlation (ASC) measure could quantify their strength. Within the basin, there exists strong interconnections between SPI and RDI, with the exception of the observed alterations in their nature and strength at short time spells in some inter-annual scales. The proposed TDIC based framework is a generic and robust alternative for multiscale investigation studies while ASC can quantify the strength of non-linear associations at distinct process scales.

Comparative analysis of SPI and SPEI drought indices for Montenegro and the impact of teleconnections

ABSTRACT After 2000, Montenegro has been hit by several severe droughts. The results presented in this study were based on the analysis of the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) for the period 1961–2020. They showed the prevalence of a negative trend in both indices and a significantly higher frequency of drought in the second half of the observed period (1991–2020). The SPEI index is a more representative indicator of drought, because in addition to precipitation, it also takes into account potential evapotranspiration. Using the rescaled adjusted partial sums (RAPS) method, it was determined that the tipping point when the SPEI suddenly fell was the mid-1980s. Out of the 15 analyzed teleconnection variation indicators (atmospheric and oceanic oscillations), 11 oscillations have a significant impact on one or more of the 24 analyzed SPEI time series. The consequences of the drought in Montenegro, as part of the Mediterranean, are most noticeable in the summer, primarily in the form of water shortages, dried vegetation, and frequent fires. Decision-makers in Montenegro should pay attention to this extreme, as drought could pose a serious problem under the projected warmer climate conditions.

Drought Assessment based on Different Metrological Drought indices in Sulaymaniyah Governorate, KRG, Iraq

Drought is a dangerous situation that lowers individual standards of living. It is considered a devastating natural event. This study intends to thoroughly examine the occurrence and characteristics of drought events in the Sulaymaniyah Governorate from 1985 to 2015. The study employed three standardized indices: the standardized precipitation evapotranspiration index (SPEI), the standardized precipitation index (SPI), and the Reconnaissance Drought Index (RDI). These indices were utilized at different periods, namely 3, 6, 9, and 12 months. According to findings, the maximum percent of extremely dry highlighted in Sulaymaniyah and Dukan stations for SPI index and time scale three months was 48% and 30%, respectively; the maximum percent of severely dry highlighted using RDI index and time scale three months with 41%, 32%, 44% in Sulaymaniyah, Dukan, and Darbandikhan stations respectively. Results revealed that the years 1998, 1999, 2000, 2007, 2008, 2009, and 2010 saw a drought for the used indices, Dukan station has the maximum severity of 112.25 for SPEI12 for the event happened from Nov. 2007 to Sep 2015. The maximum durations were observed at Dukan station using SPI and SPEI as well, and Darbandikhan station has the highest drought frequency. The correlations of drought indices, using the coefficient of determination ( ) showed the lowest and highest agreement between SPI-SPEI and SPI-RDI, respectively. The findings revealed that water resources in the study area are at risk of depletion due to drought. Therefore, water management techniques are required to mitigate drought in the study area.

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.

Standardized precipitation index analysis and drought frequency tendencies in lower eastern counties of Kenya

The standardized precipitation index (SPI) is a fundamental indicator of meteorological, hydrological, and agricultural droughts in the world. This study aims to evaluate different timescales, 3 months (SPI-3), 6 months (SPI-6), 9 months (SPI-9), and 12 months (SPI-12) indices from meteorological data in quantifying drought characterization in lower eastern counties of Kenya from 1990 to 2018 for observed data and from 1986 to 2018 for Climatic Research Unit Time Series (CRU) data. Precipitation in-situ data (annual) and high-resolution (0.5 × 0.5 degree grid) monthly-scale precipitation data were sought from Kenya Meteorological Department and CRU TS, respectively. Z-Score (SPI) was computed for each year (in-situ data) and month (CRU TS data) using the SPI algorithm, expressed as the departure from the mean in standard deviation units. Quality control of CRU TS data was done by checking outlier values and comparing the data with precipitation data obtained from the meteorological department as well as ERA5 reanalysis data. Results showed that extreme to mild drought was experienced across the Kenyan counties for both annual in-situ and monthly gridded data. Machakos county experienced a year of extreme drought, while Makueni and Taita-Taveta have had 2 and 4 years of severe droughts, respectively. The monthly SPI indices of 3, 6, 9, and 12 months showed a remarkably consistent behavioral pattern detecting extreme droughts across the counties. Considering the uncertainties, unpredictability, and shifting of the long and short rainy seasons in Kenya, results were obtained related to dry and wet episodes and to their relationship with agricultural production as well as water availability and environmental management.

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.