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1
Behzadi, Jalal. "An Evaluation of Two Drought Indices, Standard Distribution and Deciles in Guilan, Iran." Greener Journal of Social Sciences 3, no. 9 (2013): 472–78. https://doi.org/10.15580/gjss.2013.9.100613885.
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Drought is a repetitive phenomenon in different continents and its effects are not limited only to dry and semidry areas, but they could also be seen in areas with high rates of precipitation and in any season of the year. One of the most important stages in monitoring the drought is to determine indices in order to analyze its intensity, continuity and frequency. The data related to the overall monthly precipitation collected from synoptic stations of the area during the statistical period of 1976-2005 have been used for monitoring drought in Guilan and analyzing its characteristics. In the present study, continuity, intensity and frequency of drought have been extracted using two indices, the standard distribution and deciles and by the help of time series from standardized precipitation index. The results of this study indicated that the two analyzed methods give the same results and drought is not an infrequent phenomenon in the rainy part of northern Iran, but it is a repetitive and reversible phenomenon. The surveys indicated that in 1991 and 1995, an intense drought has happened in the province.
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Patel, Ramesh Kumar. "Drought assessment in Tel River basin, Odisha using Standard Precipitation Index (SPI)." National Geographical Journal of India 69, no. 2 (2023): 135–45. http://dx.doi.org/10.48008/ngji.1831.
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The present study attempts to assess drought in the Tel River basin, the second largest tributary of Mahanadi flowing through one of the most drought-frequented regions, viz. Nabarangpur, Kalahandi, Bolangir, Boudh, and Kandhamal districts of Odisha using Standard Precipitation Index (SPI). The SPI is one of the most widely used indices for monitoring drought based on rainfall data. It expresses the actual rainfall as a standardized departure concerning the rainfall probability distribution function. The values of SPI are expressed in standard deviations, with positive SPI indicating greater than median precipitation and negative values indicating less than median precipitation. A monthly time series climatic dataset is obtained from the NASA power data for 15 meteorological observation sites around the study basin from 1981 to 2021. The long-term data is then fitted to a probability distribution to create a normal distribution to find the cumulative probability of an observed precipitation event for the given month and time scale at the location in question. The cumulative probability H(x) is then converted to the conventional normal random variable z with a mean of zero and a variance of one yielding the SPI value. A drought event occurs when the index reaches -1.0 or below continuously. For three-time scale analysis of three months SPI 3, six months SPI 6, and twelve months SPI, DrinC software and Arc GIS 10.5 are used. The result shows that in the 40 years (1981-2021), 67 per cent of the droughts have been mild droughts, 18 per cent moderate droughts, 10 per cent severe droughts, and 5 per cent extreme droughts in the study basin. The regional distribution at the sub-basin level did not show any significant pattern based on the selected years of drought in the study basin.
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Mishra, Deepak Kumar, Ram Kumar, B. R. Singh, and P. V. Singh. "Drought Forecasting Using Standard Precipitation Index Based on Rainfall of Western Region." International Journal of Environment and Climate Change 13, no. 11 (2023): 687–701. http://dx.doi.org/10.9734/ijecc/2023/v13i113214.
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Drought has always been one of the most dangerous natural disasters for manhood. Due to the continuous global climate change, drought occurrences have become more frequent and severe, affecting human existence and long-term social progress. PIStandard values are a measure of the probability of a given precipitation event occurring. They are calculated using a statistical distribution of precipitation data. The three statistical distributions that are most commonly used to model precipitation data are the gamma distribution, the normal distribution, and the log-normal distribution. Therefore, utilising all three of the above-mentioned theoretical probability distributions, the drought index PIStandard has been computed. PIStandard range more than 2 (extremely wet) to less than -2 (extremely dry), with 0.99 to - 0.99 considered the near-normal range. PIStandard is calculated at different time scales which can be 1, 3, 6, 12 and 24 months, time scales. The temporal trends of SPI at the stations were identified using the Mann-Kendall test. PIStandard were computation at 1, 3, 6, 9, 12 and 24-month time scales. PIStandard provides a better analysis of meteorological drought at multiple different timescales for short- and long-term planning because it uses the running sum of rainfall values at 1 to 24 months and more parameters for the statistical distribution used. For short-term drought monitoring and agricultural crop planning, a 1- to 3-month PIStandard can be utilized; however, long-term hydrological drought monitoring and water management planning require PIStandards of 6 to 9 months and 12 to 24 months, respectively. Drought analysis using PIStandard results can be used to design rainwater harvesting and storage structures in drought-affected areas for appropriate crop planning.
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Aye, Aye Thant. "Drought Assessment Using Standard Precipitation Index." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 1814–19. https://doi.org/10.31142/ijtsrd18181.
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Drought is one of the extreme climatic events among the most relevant natural disasters. This paper presents drought assessment using Standardized Precipitation Index SPI to overview the respective drought hot spots. Monthly rainfall data during the previous 36 years 1982 - 2017 are applied to generate Standardized Precipitation Index SPI with 3 month and 9 month scale on the basis of Gamma distribution for the areas in the central dry zone of Myanmar namely Mandalay, Nyaung U, Myingyan, Natogyi, Meikhtila, Kyaukpadaung, Wundwin, Sagaing, Monywa, Shwebo, Myinmu, Magway, Minbu, Chauk and Pakokku. The generated SPI values for different time scale are classified to assess drought hot spots for the central dry zone, Myanmar as an areal extent of annual drought. Aye Aye Thant "Drought Assessment Using Standard Precipitation Index" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18181.pdf
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Kunal, Kothari, A.L Bharath, Gadgihalli Vishal, and Hilal Aiman. "AN OVERVIEW OF DROUGHT ANALYSIS: ASSESSING DROUGHT SEVERITY BASED ON FLUCTUATION IN RAINFALL TREND BY STANDARD PRECIPITATION INDEX FOR SHIVAMOGGA DISTRICT." International Journal of Research - Granthaalayah 5, no. 4 RASM (2017): 17–26. https://doi.org/10.5281/zenodo.889379.
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The Standard precipitation index expresses the actual rainfall as a standardized departure with respect to rainfall probability distribution function and hence the index has gained importance in recent years as a potential job indicator permitting comparison across space and time. The computation of SPI requires long term data on precipitation. Droughts are hydro metrological events affecting vast regions and causing significant structural and non-structural damages. Drought predictions may prevent these type of adverse consequences to a significant extant. This work regarding the drought analysis by assessing the drought severity based on fluctuation in rainfall trend by standard precipitation index for Shivamogga district by 30years rainfall data from rain gauge reading of different station in different Taluk of Shivamogga district.
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Martins Careto, João António, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares. "Generalised drought index: a novel multi-scale daily approach for drought assessment." Geoscientific Model Development 17, no. 22 (2024): 8115–39. http://dx.doi.org/10.5194/gmd-17-8115-2024.
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Abstract. Drought is a complex climatic phenomenon characterised by water scarcity and is recognised as the most widespread and insidious natural hazard, posing significant challenges to ecosystems and human society. In this study, we propose a new daily based index for characterising droughts, which involves standardising precipitation and/or precipitation minus potential evapotranspiration (PET) data. The new index proposed here, the generalised drought index (GDI), is computed for the entire period available from the Iberian Gridded Dataset (1971 to 2015). Comparative assessments are conducted against the daily Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI), and a simple Z-Score standardisation of climatic variables. Seven different accumulation periods are considered (7, 15, 30, 90, 180, 360, and 720 d) with three drought levels: moderate, severe, and extreme. The evaluation focuses mainly on the direct comparison amongst indices in terms of their ability to conform to the standard normal distribution, added value assessment using the distribution added value (DAV), and a simple bias difference for drought characteristics. Results reveal that the GDI, together with the SPI and SPEI, follows the standard normal distribution. In contrast, the Z-Score index depends on the original distribution of the data. The daily time step of all indices allows the characterisation of flash droughts, with the GDI demonstrating added value when compared to the SPI and SPEI for the shorter and longer accumulations, with a positive DAV up to 35 %. Compared to the Z-Score, the GDI shows expected greater gains, particularly at lower accumulation periods, with the DAV reaching 100 %. Furthermore, the spatial extent of drought for the 2004–2005 event is assessed. All three indices generally provide similar representations, except for the Z-Score, which exhibits limitations in capturing extreme drought events at lower accumulation periods. Overall, the findings suggest that the new index offers improved performance and comparatively adds value to similar indices with a daily time step.
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Şen, Zekâi, and Mansour Almazroui. "Actual Precipitation Index (API) for Drought Classification." Earth Systems and Environment 5, no. 1 (2021): 59–70. http://dx.doi.org/10.1007/s41748-021-00201-0.
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AbstractThe Standard Precipitation Index (SPI) is a widely used statistical technique for the characterization of droughts. It is based on a probabilistic standardization procedure, which converts a Gamma-type probability distribution function (PDF) into a normal (Gaussian) standard series with zero mean and unit standard deviation. Drought classification based on SPI indicates dry and wet spell characteristics, provided that the hydro-meteorological records abide by normal (Gaussian) PDF only, otherwise the results will be biased. Therefore, in this paper, the actual precipitation index (API) method is presented, which provides drought classification and information regardless of the underlying PDFs. The main purpose of this paper is to explain the main differences between SPI and API and to prove that the use of API is the more reliable solution for classification of droughts into five categories described as “Normal dry”, “Slightly dry”, “Medium dry”, “Very dry” and “Extremely dry”. The application of the methodology is presented for two sets of precipitation data; one with exponential PDF monthly precipitation records from Istanbul City, Turkey and one for New Jersey, USA with almost normal (Gaussian) PDF based on annual precipitation records. The comparisons indicate that API is applicable regardless of the underlying PDF of the hydro-meteorology data. It produces real drought classification from the original data without recourse to standard normal PDF conversion.
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Ram, Kumar* Arvind Sagar Ankur Singh Bist. "ASSESSMENT OF NAGINA AREA OF DISTT. BIJNOR U.P. ON THE BASIS OF STANDARD PRECIPITATION INDEX (SPI) FOR DROUGHT INTENSITY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 10 (2016): 18–26. https://doi.org/10.5281/zenodo.159207.
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Drought is a natural and worldwide phenomenon, usually defined by periods of less than normal water availability, and is one of the major weather related hazards. Droughts have been dramatically increased in number and intensity in many parts of the world. Drought is a decrease of water availability in a particular period and over a particular area. Based on the drought analysis using the SPI criteria, appropriate crop planning and design of rainwater harvesting and storage structures in the drought affected areas can be proposed in drought affected areas. Standardized Precipitation Index (SPI) was calculated at different time scales (1, 3, 6, 12, and 24 months). The SPI is a drought index based on the probability of an observed precipitation deficit occurring over a given prior time period. The SPI, calculated for a desired period at any location, are based on the long term precipitation record (30 years or more). The positive SPI values show greater than medium precipitation, while negative SPI values indicate less than medium precipitation. The results shows that the SPI can be used for better assessment of drought as it considers larger range of moving sums of rainfall data. Since SPI uses for the running sum of rainfall values at multi-time scales (1 to 24 months) and more variables depending on the statistical distributions used, it gives better assessment of meteorological drought at multi-time scales. A proper rainwater harvesting and management program is an appropriate option for a severely-dry year, but, on the contrary, a situation of wet years with heavy rainfall during monsoon months followed by severely-dry period calls for the need of rainwater harvesting during monsoon and its proper utilization during subsequent dry periods.
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Kothari, Kunal, Bharath, Vishal Gadgihalli, and Aiman Hilal. "AN OVERVIEW OF DROUGHT ANALYSIS: ASSESSING DROUGHT SEVERITY BASED ON FLUCTUATION IN RAINFALL TREND BY STANDARD PRECIPITATION INDEX FOR SHIVAMOGGA DISTRICT." International Journal of Research -GRANTHAALAYAH 5, no. 4RASM (2017): 17–26. http://dx.doi.org/10.29121/granthaalayah.v5.i4rasm.2017.3364.
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The Standard precipitation index expresses the actual rainfall as a standardized departure with respect to rainfall probability distribution function and hence the index has gained importance in recent years as a potential job indicator permitting comparison across space and time. The computation of SPI requires long term data on precipitation.
 Droughts are hydro metrological events affecting vast regions and causing significant structural and non-structural damages. Drought predictions may prevent these type of adverse consequences to a significant extant.
 This work regarding the drought analysis by assessing the drought severity based on fluctuation in rainfall trend by standard precipitation index for Shivamogga district by 30years rainfall data from rain gauge reading of different station in different Taluk of Shivamogga district.
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Suwanlee, S. R., N. Homtong, and J. Som-ard. "DROUGHT MONITORING FROM 2001–2019 IN NORTHEAST THAILAND USING MODIS NDVI IMAGE TIME SERIES AND Savitzky-Golay APPROACH." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 367–73. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-367-2023.
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Abstract. Drought directly threatens food security and livelihoods, thereby increasing socioeconomic risks and remains a challenge for natural resource management, particularly in frequently affected regions. Earth observation (EO) satellites provide extensive spectral and temporal data for long-term drought monitoring. This study monitored droughts in Northeast Thailand from 2001 to 2019 using the MODIS normalised difference vegetation index (NDVI) image time series. The Savitzky-Golay (S-G) method was used to remove noise and fill gaps in the image datasets. Optimal indicators as the vegetation condition index (VCI) and the standard vegetation index (SVI) were used to monitor drought distribution patterns over the previous 19 years. S-G filtering effectively reduced the impact of undetected clouds and water vapour, while VCI had the highest accuracy coefficient of determination (R2) for rainfall data at 0.85. Long-term droughts occurred frequently in 2005, 2004, 2007, and 2001 with the northern and central regions most severely affected. Severe drought primarily impacted agricultural land, forest and miscellaneous areas. Inter-annual drought variability for one and three time steps was clearly demonstrated in May and April to June from 2001 to 2019. Overall, the VCI provided a high level of satisfaction for drought monitoring in this region and clearly displayed the spatial distribution of long-term drought regions. Our findings provide a valuable resource for drought mitigation planning and warning systems.
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Wu, Li, Youzhi Zhang, Limin Wang, et al. "Analysis of 22-year Drought Characteristics in Heilongjiang Province Based on Temperature Vegetation Drought Index." Computational Intelligence and Neuroscience 2022 (April 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/1003243.
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Heilongjiang Province is the main grain producing region in China and an important part of Northeast China Plain, which is one of the three black soil belts in the world. The cultivated region of black soil accounts for 50.6% of the black soil region in Northeast China. Due to the obvious rise of temperature and uneven distribution of precipitation in the 20th century, it has been considered to be one of the important reasons for agricultural drought and aridity. Under the background of climate change, understanding the multiyear changes and occurrence characteristics of cultivated land drought in different agricultural regions in Heilongjiang Province is of great significance for the establishment of agricultural drought prediction and early warning system in the future, guiding agricultural high-standard farmland irrigation in different regions, promoting black soil protection, and then improving grain yield. This paper calculates the temperature vegetation drought index (TVDI) based on the normalized difference vegetation index (NDVI) and surface temperature (TS) product data of MODIS from 2000 to 2021. Taking TVDI as the drought evaluation index, this paper studies the temporal and spatial variation distribution characteristics and occurrence frequency of drought in the whole region and four agricultural regions of Heilongjiang Province: Daxing an Mountain and Xiaoxing an Mountain (region I), Sanjiang Plain (region II), Zhangguangcai Mountains (region III), and Songnen Plain (region IV). The results show that medium drought generally occurred in Heilongjiang Province from 2000 to 2021, accounting for about 70% of the total cultivated land. The drought was severe from 2000 to 2009 and weakened from 2010 to 2021. In the 110 months of the crop growing season from 2000 to 2021, about 63.84% of the region suffered more than 60 droughts. It is found that the frequency of drought varies from region to region. More than 80 droughts occurred in the west of region IV and the middle of region II. The characteristics of region IV are large sandstorm, less precipitation, and lack of water conservancy facilities, resulting in frequent and strong drought. It is also found that the occurrence frequency, degree grade and regional distribution of drought are closely related to seasonal changes. In spring, the occurrence grade and frequency of drought in region IV are the strongest and the drought phenomenon is serious. In autumn, drought is frequent and distributed in all regions, but the grade is not strong (mainly medium drought), and the drought phenomenon is medium. It is humid in summer. Crops in Heilongjiang Province are one crop per annual. Spring drought seriously restricts the water content of crops. Long-term drought will lead to poor crop development and reduce yield. Therefore, only by clarifying the characteristics of regional time drought, monitoring accurate drought events and accurately predicting the occurrence of drought, can we guide high-standard farmland precision irrigation, improve crop yield and ensure national food security. At the same time, severe drought will affect the terrestrial ecosystem, resulting in the distribution of crops and microorganisms, and the transformation between carbon sink and carbon source.
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Aktürk, Gaye, Hatice Çıtakoğlu, Vahdettin Demir, and Neslihan Beden. "Meteorological Drought Analysis and Regional Frequency Analysis in the Kızılırmak Basin: Creating a Framework for Sustainable Water Resources Management." Water 16, no. 15 (2024): 2124. http://dx.doi.org/10.3390/w16152124.
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Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research.
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Wang, Quanwei, Yimin Wang, Chen Niu, and Mengdi Huang. "Constructing the Joint Probability Spatial Distribution of Different Levels of Drought Risk Based on Copula Functions: A Case Study in the Yellow River Basin." Water 16, no. 23 (2024): 3374. http://dx.doi.org/10.3390/w16233374.
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Joint multivariate distribution and calculation of return period are essential in enhancing drought risk assessment and promoting the sustainable development of water resources. Aiming to address the increasingly serious drought situation in the Yellow River Basin, this study first utilized the Soil and Water Assessment Tool (SWAT) distributed hydrological model combined with the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Standard Water Yield Index (SWYI); the duration, peak, and severity of meteorological, agricultural, and hydrological droughts were analyzed. Based on the selected copula function, a three-dimensional joint distribution of drought duration (D), drought severity (S), and maximum severity (M) was constructed. The corresponding copula joint probability was calculated, leading to the three-dimensional joint return period and concurrent return period of meteorological drought, agricultural drought, and hydrological drought. The findings reveal several key trends: (1) Meteorological drought intensifies over time. Although drought areas eased after the 1990s, the overall drought trend continues to rise. Agricultural drought has intensified in arid regions but eased in semi-humid areas after the 2000s. Hydrological drought was severe in the upstream regions during the 1990s but eased in the 2000s, while it was particularly severe in the midstream and downstream regions during the 2000s. (2) Meteorological droughts are more severe in arid and semi-arid temperate regions and milder in semi-humid cold temperate regions. Agricultural droughts are extreme in arid and semi-arid cold temperate regions. Hydrological drought events are fewer but more severe in semi-arid temperate regions and have the lowest probability of occurrence in semi-humid cold temperate regions. (3) The overall probability of the occurrence of meteorological drought is between 55.7% and 69%; that of agricultural drought is between 73.1% and 91.7%, and that of hydrological drought is between 66.9% and 84%. Drought risk assessment provides scientific references for the analysis of the uncertainty of water supply in the basin and the formulation of effective risk management strategies.
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Muhammad, Zahraa, and Imad Obead. "Quantifying Meteorological Drought Severity for the Watersheds in Euphrates River Basin, Central Euphrates District, Iraq." Iraqi Geological Journal 57, no. 1D (2024): 240–56. http://dx.doi.org/10.46717/igj.57.1d.18ms-2024-4-28.
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Drought is a major natural extreme event that can cause significant damage to water resources. This study investigated the implications of drought for Watersheds in Euphrates River basin in Iraq which includes Hilla, Karbala, Najaf, Diwaniyah, and Najaf.. It was found that had an adverse impact on the study region. Among the most often used drought assessment indices globally are the Standard Precipitation Index (SPI) and its version, the Standard Precipitation and Evapotranspiration Index (SPEI).The study evaluated several probability distribution functions to model rainfall and water balance in Iraq. The Kolmogorov–Smirnov (K–S) test and the Anderson–Darling (A–D) test were used to test the goodness-of-fit for rainfall and water balance. The log-normal distribution was found to be the best fit for rainfall at approximately 40% of the stations considered, while the generalized logistic distribution (genlog) was the best fit for water balance at approximately 80% of the stations considered. The study also found that the extreme droughts in Samwah in 2012 and Diwiniyah in 1999 had the highest severity values of -2.8957 in SPI-12 and SPEI-12, respectively.
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Becherini, Francesca, Claudio Stefanini, Antonio della Valle, Francesco Rech, Fabio Zecchini, and Dario Camuffo. "Multi-Secular Trend of Drought Indices in Padua, Italy." Climate 12, no. 12 (2024): 218. https://doi.org/10.3390/cli12120218.
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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.
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Yuan, Shujie, Nan Jiang, Jinsong Wang, Liang Xue, and Lin Han. "Reclassifying the Spring Maize Drought Index on the Loess Plateau under a Changing Climate." Atmosphere 14, no. 10 (2023): 1481. http://dx.doi.org/10.3390/atmos14101481.
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Drought is the main meteorological disaster that affects the yield and quality of spring maize on the Loess Plateau. This study used data of the spring maize growth period, relative soil humidity, and yield at 18 agricultural meteorological observation stations on the Loess Plateau from 1997 to 2013 to determine the drought category based on the yield reduction rate. Through the drought index according to the conformity rate of category standard and individual case verification, a refined suitability drought index of spring maize on the Loess Plateau was constructed, and the spatial distribution characteristics of drought in different growth stages of spring maize were analyzed. The results showed the following: (1) The number of days in the whole growth period of spring maize in all regions of the Loess Plateau has been extended. The average sowing date of spring maize in the northwest region of the Loess Plateau was 9 April, and that in the east and central regions was 26 April. In terms of spatial distribution, each growth period was gradually delayed from west to east. (2) The correlation between relative soil humidity and yield of spring maize at the jointing stage and heading stage was the best, followed by the milky stage and mature stage, and the relative soil humidity at the sowing stage and emergence stage had little effect on the yield. (3) According to the national drought category standard “Drought Category of Spring Maize in the North”, based on the data of yield reduction rate, the drought index of spring maize on the Loess Plateau was refined by region and growth stage. The drought category index values of spring maize in different growth stages and regions changed according to the revised drought category standard, with 71.4% of the sites in the sowing seedling stage and 85.7% of the sites in the seedling jointing stage, and the revised drought category was more severe than the national drought category standard, while at 57.1% of the sites in the jointing and tasseling stages and 71.4% in the tasseling and milking stages, the revised drought category was less severe than the national drought category standard. (4) Based on the revised refined drought index for spring maize on the Loess Plateau, the spatial distribution of drought occurrence frequency across different growth stages of spring maize on the Loess Plateau was analyzed. The frequency of drought occurrence during the seeding and emergence stages was 25–75%. With the change in growth stages, the high-value area of drought occurrence frequency gradually moved northward, and the overall frequency of drought occurrence decreased. For the milky mature stage, the frequency of drought occurrence in a few regions was around 42%, and the drought frequency in most regions was between 8% and 33%.
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Araujo Bonjean, Catherine, Abdoulaye Sy, and Marie-Eliette Dury. "Spatially Consistent Drought Hazard Modeling Approach Applied to West Africa." Water 15, no. 16 (2023): 2935. http://dx.doi.org/10.3390/w15162935.
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A critical stage in drought risk assessment is the measurement of drought hazard, the probability of occurrence of a potentially damaging event. The standard approach to assess drought hazard is based on the standardized precipitation index (SPI) and a drought intensity classification established according to a fixed set of SPI values. We show that this method does not allow for the assessment of region-specific hazards, and we propose an alternative method based on the extreme value theory. We model precipitation using an extreme value mixture model, with a normal distribution for the bulk, and a generalized Pareto distribution for the upper and lower tails. The model estimation allows us to identify the threshold value below which precipitation can be qualified as extreme. The quantile function is used to measure the intensity of each category of droughts and calculate the drought hazard index (DHI). By construction, the DHI value varies according to the specific characteristics of the left tail of the precipitation distribution. To test the relevance of our approach, we estimate the DHI over a gridded set of rainfall data covering West Africa, a large and climatically heterogeneous region. The results show that our mixture model fits the data better than the model used for SPI calculation. In particular, our model performs better to identify extreme precipitation in the left tail of the distribution. The DHI map highlights clusters of high drought hazard located in the central part of the region under study.
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Weng, B. S., D. H. Yan, H. Wang, et al. "Drought assessment in the Dongliao River basin: traditional approaches vs. generalized drought assessment index based on water resources systems." Natural Hazards and Earth System Sciences 15, no. 8 (2015): 1889–906. http://dx.doi.org/10.5194/nhess-15-1889-2015.
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Abstract. Drought is firstly a resource issue, and with its development it evolves into a disaster issue. Drought events usually occur in a determinate but a random manner. Drought has become one of the major factors to affect sustainable socioeconomic development. In this paper, we propose the generalized drought assessment index (GDAI) based on water resources systems for assessing drought events. The GDAI considers water supply and water demand using a distributed hydrological model. We demonstrate the use of the proposed index in the Dongliao River basin in northeastern China. The results simulated by the GDAI are compared to observed drought disaster records in the Dongliao River basin. In addition, the temporal distribution of drought events and the spatial distribution of drought frequency from the GDAI are compared with the traditional approaches in general (i.e., standard precipitation index, Palmer drought severity index and rate of water deficit index). Then, generalized drought times, generalized drought duration, and generalized drought severity were calculated by theory of runs. Application of said runs at various drought levels (i.e., mild drought, moderate drought, severe drought, and extreme drought) during the period 1960–2010 shows that the centers of gravity of them all distribute in the middle reaches of Dongliao River basin, and change with time. The proposed methodology may help water managers in water-stressed regions to quantify the impact of drought, and consequently, to make decisions for coping with drought.
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Lee, Joo-Heon, Hyun-Han Kwon, Ho-Won Jang, and Tae-Woong Kim. "Future Changes in Drought Characteristics under Extreme Climate Change over South Korea." Advances in Meteorology 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/9164265.
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This study attempts to analyze several drought features in South Korea from various perspectives using a three-month standard precipitation index. In particular, this study aims to evaluate changes in spatial distribution in terms of frequency and severity of droughts in the future due to climate change, using IPCC (intergovernmental panel on climate change) GCM (general circulation model) simulations. First, the Mann-Kendall method was adopted to identify drought trends at the five major watersheds. The simulated temporal evolution of SPI (standardized precipitation index) during the winter showed significant drying trends in most parts of the watersheds, while the simulated SPI during the spring showed a somewhat different feature in the GCMs. Second, this study explored the low-frequency patterns associated with drought by comparing global wavelet power, with significance test. Future spectra decreased in the fractional variance attributed to a reduction in the interannual band from 2 to 8 years. Finally, the changes in the frequency and the severity under climate change were evaluated through the drought spell analyses. Overall features of drought conditions in the future showed a tendency to increase (about 6%) in frequency and severity of droughts during the dry season (i.e., from October to May) under climate change.
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Huang, Tao, Ligang Xu, and Hongxiang Fan. "Drought Characteristics and Its Response to the Global Climate Variability in the Yangtze River Basin, China." Water 11, no. 1 (2018): 13. http://dx.doi.org/10.3390/w11010013.
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The frequent occurrence of drought events in humid and semi-humid regions is closely related to the global climate variability (GCV). In this study, the Standard Precipitation Evapotranspiration Index (SPEI) was taken as an index to investigate the drought in the Yangtze River Basin (YRB), a typical humid and semi-humid region in China. Furthermore, nine GCV indices, such as North Atlantic Oscillation (NAO) were taken to characterize the GCV. Correlation analysis and a joint probability distribution model were used to explore the relationship between the drought events and the GCV. The results demonstrated that there were six significant spatiotemporal modes revealed by SPEI3 (i.e., seasonal drought), which were consistent with the distribution of the main sub basins in the YRB, indicating a heterogeneity of drought regime. However, the SPEI12 (i.e., annual drought) can only reveal five modes. Precipitation Indices and El Niño/Southern Oscillation (ENSO) Indices were more closely related to the drought events. A causal relationship existed between ENSO precipitation index (ESPI), NAO, East Central Tropical Pacific Sea Surface Temperature (Nino3.4) and Northern Oscillation Index (NOI) and drought in the YRB, respectively. Drought events were most sensitive to the low NAO and high NOI events. This study shows a great significance for the understanding of spatiotemporal characteristics of meteorological drought and will provide a reference for the further formulation of water resources policy and the prevention of drought disasters.
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Ma, Jianqin, Bifeng Cui, Xiuping Hao, Pengfei He, Lei Liu, and Zhirui Song. "Analysis of Hydrologic Drought Frequency Using Multivariate Copulas in Shaying River Basin." Water 14, no. 8 (2022): 1306. http://dx.doi.org/10.3390/w14081306.
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Droughts, considered one of the most dangerous and costly water cycle expressions, always occurs over a certain region, lasting several weeks or months, and involving multiple variables. In this work, a multivariate approach was used for the statistical characterization of hydrological droughts in Shaying River Basin with data from 1959–2008. The standard runoff index (SRI) and the run theory were employed to defined hydrological drought character variables (duration, severity, and intensity peak). Then, a multivariate joint probability analysis with four symmetric and corresponding asymmetric Archimedean Copulas was presented; and the multivariate frequency analysis with the joint return periods (Tand and Tor) were estimated. The results showed that the hydrological droughts have a severity of 4.79 and 5.09, and the drought intensity peak is of 1.35 and 1.50 in Zhoukou station and Luohe station, respectively; the rank correlation coefficients τ are more than 0.5, which means multivariate copulas can effectively describe the joint frequency distributions among multivariate variables. Drought risk shows a spatial variation: the downstream observed at Zhoukou station is characterized by a higher multivariate drought risk. In general, multivariate copulas provide a reliable method when constructing a comprehensive drought index and evaluating multivariate drought characteristics. Thus, this paper can provide useful indications for the multi-dimensional droughts’ risks assessment in Shaying River Basin.
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Kumwenda, Lenard, Patsani Gregory Kumambala, Lameck Fiwa, et al. "Projected Drought Prevalence in Malawi’s Lufilya Catchment: A Study Using Regional Climate Models and the SPI Method." Water 16, no. 24 (2024): 3548. https://doi.org/10.3390/w16243548.
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Droughts are caused either by a deficiency in precipitation compared to normal levels or by excessive evapotranspiration exceeding long-term averages. Therefore, assessing future drought prevalence based on projected climatic variables is essential for effective drought preparedness. In this study, an ensemble of three Regional Climate Models (REMO2009, RCA4, and CCLM4-8-17) was used for Representative Concentration Pathways (RCP 4.5 and RCP 8.5), covering two future time periods (2025–2069 and 2070–2100). The quantile distribution mapping technique was employed to bias-correct the RCMs. The ensemble of RCMs projected an increase in rainfall, ranging from 40% to 85% under both RCP 8.5 and RCP 4.5. Both RCPs indicated an increase in daily average temperatures. RCP 4.5 projects an increase in average daily temperature by 1% between 2025 and 2069 and 6.5% between 2070 and 2100, while under RCP 8.5, temperatures are expected to rise by 3.7% between 2025 and 2069 and 12.7% between 2070 and 2100. The Standard Precipitation Index (SPI) was used to translate these projected climatic anomalies into future drought prevalence. The results suggest that RCP 4.5 forecasts an 8% increase in drought prevalence, while RCP 8.5 projects an 11% increase in drought frequency, with a greater rise in moderate and severe droughts and a decrease in extreme drought occurrences.
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Zhang, Xuejun, Zhicheng Su, Juan Lv, et al. "A Set of Satellite-Based Near Real-Time Meteorological Drought Monitoring Data over China." Remote Sensing 11, no. 4 (2019): 453. http://dx.doi.org/10.3390/rs11040453.
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A high-resolution and near real-time drought monitoring dataset has not been made readily available in drought-prone China, except for the low-resolution global product. Here we developed a set of near real-time meteorological drought data at a 0.25° spatial resolution over China, by seamlessly merging the satellite-based near real-time (RT) precipitation (3B42RTv7) into the high-quality gauge-based retrospective product (CN05.1) using the quantile-mapping (QM) bias-adjustment method. Comparing the standard precipitation index (SPI) from the satellite-gauge merged product (SGMP) with that from the retrospective ground product CN05.1 (OBS) shows that the SGMP reproduces well the observed spatial distribution of SPI and the pattern of meteorological drought across China, at both the 6-month and 12-month time scales. In contrast, the UN-SGMP generated by merging the unadjusted raw satellite precipitation into the gauging data shows systematical overestimation of the SPI, leaving less meteorological droughts to be identified. Furthermore, the SGMP is found to be able to capture the inter-annual variation of percentage area in meteorological droughts. These validation results suggest that the newly developed drought dataset is reliable for monitoring meteorological drought dynamics in near real-time. This dataset will be routinely updated as the satellite RT precipitation is made available, thus facilitating near real-time drought diagnosis in China.
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Osman, Mahmoud, Benjamin F. Zaitchik, Hamada S. Badr, et al. "Flash drought onset over the contiguous United States: sensitivity of inventories and trends to quantitative definitions." Hydrology and Earth System Sciences 25, no. 2 (2021): 565–81. http://dx.doi.org/10.5194/hess-25-565-2021.
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Abstract. The term “flash drought” is frequently invoked to describe droughts that develop rapidly over a relatively short timescale. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. Instead, diverse definitions have been proposed, supporting wide-ranging studies of flash drought but creating the potential for confusion as to what the term means and how to characterize it. Use of different definitions might also lead to different conclusions regarding flash drought frequency, predictability, and trends under climate change. In this study, we compared five previously published definitions, a newly proposed definition, and an operational satellite-based drought monitoring product to clarify conceptual differences and to investigate the sensitivity of flash drought inventories and trends to the choice of definition. Our analyses indicate that the newly introduced Soil Moisture Volatility Index definition effectively captures flash drought onset in both humid and semi-arid regions. Analyses also showed that estimates of flash drought frequency, spatial distribution, and seasonality vary across the contiguous United States depending upon which definition is used. Definitions differ in their representation of some of the largest and most widely studied flash droughts of recent years. Trend analysis indicates that definitions that include air temperature show significant increases in flash droughts over the past 40 years, but few trends are evident for definitions based on other surface conditions or fluxes. These results indicate that “flash drought” is a composite term that includes several types of events and that clarity in definition is critical when monitoring, forecasting, or projecting the drought phenomenon.
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Salman, Abdul Basit Hamza Jalal, Ahmed Emad Milli Hamidan, Worood Abdul Redha Sharif Nayef Al-Mousawi, Zainab haider Mussa Saadoun, and Esraa Ali Hussein Ali. "PREDICTING DROUGHT USING ARTIFICIAL INTELLIGENCE TECHNIQUES." European Journal of Medical Genetics and Clinical Biology 1, no. 7 (2024): 226–32. http://dx.doi.org/10.61796/jmgcb.v1i7.778.
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The research dealt with climate drought, its types, the causes that lead to drought, and how to reduce drought. Drought in the city of Mosul, northern Iraq, was also analyzed and studied by obtaining monthly rainfall data for the Mosul climate station located within the study area for the period from 1981-2018. Previous studies and research conducted to study drought in different regions of the world were taken into consideration. Most of these studies and researches use drought indices, which are among the most widely used indices in estimating the amounts of deficit, their use, severity, and their impact on the water balance. The standard rainfall index (SPI) is one of the most widely used indices in estimating climate drought. (SPI) is characterized by many characteristics that distinguish it from other indicators. The standard rainfall index (SPI) technique was used in analyzing rain records. The analysis principle using the standard rainfall index is statistically based on the principle of converting the gamma distribution of the data series to the normal distribution. Positive SPI values mean that there is an increase in rainfall above the average rainfall, i.e. wet years, while negative values mean that there is a decrease in rainfall below the average rainfall, i.e. dry years. SPI values for a period of 12 months were adopted in the analysis because they cover the annual rainfall amount falling on the station during a year. Using the MATLAB program, several networks were created and tested, and the network with the best performance was selected from among the networks. 30 annual rainfall values were used against the SIP values calculated using equations and the Excel program to train the neural network on the data. While the rainfall data for the remaining 8 years were used to verify the results of the neural network by comparing the results of the neural network with the actual values recorded at the measuring station. This network was able to obtain the index value by simply entering the annual rainfall value. By comparing the index value with the drought classification table, the drought class can be determined without resorting to the calculation method. The network with the 1-7-1 structure (input layer, hidden layer containing seven neurons, and output layer) with the TRAINLM training function and the LEARNGDM learning function gave the best performance, as the correlation coefficient between its results and the actual results (which were not included in the training) was equal to 0.99 and the square error rate was 0.014, meaning that the results of this network can be adopted for the purpose of calculating the standard rain index with high confidence in the outputs
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BLAIN, GABRIEL CONSTANTINO, and ANA CAROLINA FREITAS XAVIER. "TRANSFORMING THE PALMER DROUGHT SEVERITY INDEX INTO A STANDARDIZED MULTI-SCALAR INDEX: ASSESSING THE NORMALITY ASSUMPTION UNDER SOUTH AMERICA TROPICAL-SUBTROPICAL CONDITIONS." Experimental Agriculture 55, no. 5 (2018): 752–64. http://dx.doi.org/10.1017/s0014479718000340.
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SUMMARYTwo of the most common criticisms over the widely used Palmer Drought Severity Index (PDSI) is that it cannot be calculated at different time scales and it is not as spatially comparable as other Standardized Drought Indices (SDI), such as the Standardized Precipitation Index (SPI). Therefore, the hypothesis that the PDSI may be transformed into a multi-scalar index sharing the same normalized nature of others SDI has been proposed in the scientific literature. This hypothesis was extensively evaluated in this study by statistical methods largely used to assess and improve the performance of others standardized drought indices (e.g. SPI). In general terms, these methods evaluated the ability of the transformed/probability-based Palmer's Index to approach the standard normal distribution. The strategy of basing the selection of a distribution for calculating such an index on its performance within the range of typical drought and flood events was adopted. The testing region was the State of São Paulo, a tropical-subtropical region of Brazil. Time scales ranging from 1- to 12-month and Available Water Capacity equal to 50, 100 and 150 mm were also considered. A computational algorithm for calculating the new version of the Palmer's index is also provided. The Generalized Logistic distribution with parameters estimated by the maximum likelihood method is recommended to calculate the new index. The results of the normality tests are consistent with the above-mentioned strategy. From a scientific standpoint, the results support the hypothesis of this study. Therefore, we conclude that the new Standardized Palmer Drought Index (SPDI) is capable of meeting the normally assumption under tropical-subtropical climatic conditions of Brazil. In other words, the new SPDI has shown to be capable of representing floods and drought events in a similar probabilistic/normalized way. This conclusion holds true for time scales ranging from 1- to 12-month and Available Water Capacity equal to 50, 100 and 150 mm.
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Fan, Linlin, Hongrui Wang, Zhiping Liu, and Na Li. "Quantifying the Relationship between Drought and Water Scarcity Using Copulas: Case Study of Beijing–Tianjin–Hebei Metropolitan Areas in China." Water 10, no. 11 (2018): 1622. http://dx.doi.org/10.3390/w10111622.
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Making the distinction between drought and water scarcity is not trivial, because they often occur simultaneously. In this study, we used Copulas to quantify the relationship between drought and water scarcity. Beijing–Tianjin–Hebei Metropolitan Areas (BTHMA) was chosen as the study area. Standard Precipitation and Evapotranspiration Index (SPEI) and water exploitation index plus (WEI+) was chosen to represent metrological drought and water scarcity. Inverse Distance Weighted method was used for spatial analysis of SPEI and WEI+, and Archimedean Copula was used to establish two-dimensional joint probability distribution of SPEI and WEI+. The results are as follows: (1) The southern part of the study area was wetter. The middle part was drier, with moderate drought happened for most times. (2) WEI+ of Beijing and Tianjin showed significant decreasing trends from 2000 to 2015, while WEI+ of Hebei Province did not, which indicated that Hebei Province is facing much severer water scarcity situation than Beijing and Tianjin. (3) Gumbel copula was the best-fitting model to establish the joint probability distribution of SPEI and WEI+. The condition probability provided a probability distribution of water scarcity under different drought conditions, which can provide technical support for government managers during policy making.
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Moccia, Benedetta, Claudio Mineo, Elena Ridolfi, Fabio Russo, and Francesco Napolitano. "SPI-Based Drought Classification in Italy: Influence of Different Probability Distribution Functions." Water 14, no. 22 (2022): 3668. http://dx.doi.org/10.3390/w14223668.
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Drought is ranked second in type of natural phenomena associated with billion dollars weather disaster during the past years. It is estimated that in EU countries the number of people affected by drought was increased by 20% over the last decades. It is widely recognized that the Standardized Precipitation Index (SPI) can effectively provide drought characteristics in time and space. The paper questions the standard approach to estimate the SPI based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data. We estimate SPI time series, for different scale of temporal aggregation, on an unprecedented dataset consisting of 332 rain gauge stations deployed across Italy with observations recorded between 1951 and 2000. Results show that the Lognormal distribution performs better than the Gamma in fitting the monthly precipitation data at all time scales, affecting drought characteristics estimated from SPI signals. However, drought events detected using the original and the best fitting approaches does not diverge consistently in terms of return period. This suggests that the SPI in its original formulation can be applied for a reliable detection of drought events and for promoting mitigation strategies over the Italian peninsula.
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Gebeyhu, Birara. "Power Data Access Viewer-Based Meteorological Drought Analysis and Rainfall Variability in the Nile River Basin." Advances in Meteorology 2024 (March 5, 2024): 1–12. http://dx.doi.org/10.1155/2024/9985773.
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Meteorological drought poses a frequent challenge in the Nile River basin, yet its comprehensive evaluation across the basin has been hindered by insufficient recorded rainfall data. Common indices like the standard precipitation index, coefficients of variation, and precipitation concentration index serve as pivotal tools in gauging drought severity. This research aimed to assess the meteorological drought status in the Nile River basin by using the Power Data Access Viewer product rainfall data. Bias correction procedures were implemented to refine the monthly rainfall data for Bahirdar, Markos, Nekemt, and Muger stations, resulting in notable improvements in the coefficient of determination (R2) that were increased from 0.74 to 0.93, 0.72 to 0.89, 0.71 to 0.96, and 0.69 to 0.84, respectively. The average spatial distribution of drought in the Nile basin was classified as extremely wet (3.81%), severely wet (9.01%), moderately wet (7.36%), near normal (9.97%), moderately drought (21.20%), severely drought (17.11%), and extremely drought (31.54%). Approximately 10.33% of the Nile River basin was situated in regions characterized by high rainfall variability, while around 21.17% was located in areas with a notably irregular precipitation concentration index. Overall, this study sheds light on the prevailing meteorological drought patterns in the Nile River basin, emphasizing the significance of understanding and managing these phenomena for the sustainable development of the region.
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Zhao, Yangyang, Jiahua Zhang, Yun Bai, et al. "Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors." Remote Sensing 14, no. 24 (2022): 6398. http://dx.doi.org/10.3390/rs14246398.
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Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring.
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W. Nyokabi, Elizabeth, Raphael M. Wambua, and Romulus O. Okwany. "EVALUATION OF SPATIAL AND TEMPORAL HYDROLOGICAL DROUGHT USING SURFACE WATER SUPPLY INDEX IN MALEWA RIVER CATCHMENT, NAIVASHA." Journal of Engineering in Agriculture and the Environment 8, no. 2 (2022): 19. http://dx.doi.org/10.37017/jeae.v8i2.91.
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There has been a critical problem of the devastating natural hazard (hydrological drought) which greatly affects a significant proportion of the large population particularly those living in arid and semi-arid areas. The flow regime of the Malewa River is reducing due to the river fluctuation, increasing scarcity of water during dry periods. The purpose of this research was to assess spatial and temporal hydrological drought using Surface Water Supply Index (SWSI) at Malewa River catchment. The data were based on hydro-meteorological data which included rainfall, level of water at Lake Naivasha, and streamflow of Malewa River for the years 1980-2018. They were obtained from Water Resources Authority (WRA) in Naivasha and Kenya Meteorological Department (KMD) in Nairobi, Kenya. The field data were first normalized to have all input attributes temporary variables with their distribution having zero means and a standard deviation of 1. Later the normalized data were calculated using basin-calibrated algorithm of SWSI to determine the hydrological condition. In SWSI, the highest percentage of classification for the stations were near the average of -0.9 to 1.0, with 34% for the Malewa area and 30% for the Turasha area. In spatial distribution analyses, hydrological drought severity was highest along the southern part of the catchment and lowest along with Eastern and North-Eastern areas. Therefore, hydrological drought severity was experienced in the catchment in terms of temporal and spatial analyses and increased along the flow path of the river. Hydrological drought assessment shows a technical manner for a comprehensive understanding of drought offering proper mitigation strategies and plan to control this natural disaster.
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Jang, Suk Hwan, Jae-Kyoung Lee, Ji Hwan Oh, Jun Won Jo, and Younghyun Cho. "The probabilistic drought prediction using the improved surface water supply index in the Korean peninsula." Hydrology Research 50, no. 1 (2018): 393–415. http://dx.doi.org/10.2166/nh.2018.045.
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Abstract This research proposes the Korean surface water supply index (KSWSI) which overcomes some limitations of the modified SWSI (MSWSI) applied in Korea and conducts probabilistic drought prediction using KSWSI. In this research, all hydrometeorological variables were investigated and four to six appropriate variables were selected for each sub-basin and probability distributions applicable for each variable were estimated. As a result of verifying KSWSI results, the accuracy of KSWSI showed better drought phenomenon in drought events than MSWSI. Moreover, the uncertainty quantification of KSWSI calculation procedure was also carried out using the maximum entropy (ME) theory. Estimating appropriate probability distributions for each drought component in the flood season is crucial because ME values and standard deviations of KSWSI are huge, implying that large uncertainty occurs in the flood season. It is confirmed that the accuracy of KSWSI may be affected by the hydrometerological variables selection, station data obtained, used data length, and probability distributions. Furthermore, monthly probabilistic drought predictions were calculated based on the ensemble technique using KSWSI. In 2006 and 2014 drought events, the accuracy of drought predictions using KSWSI was higher than those using MSWSI, demonstrating that KSWSI is able to enhance the accuracy of drought prediction.
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Zhao, Ying, Zhaohui Weng, Hua Chen, and Jiawei Yang. "Analysis of the Evolution of Drought, Flood, and Drought-Flood Abrupt Alternation Events under Climate Change Using the Daily SWAP Index." Water 12, no. 7 (2020): 1969. http://dx.doi.org/10.3390/w12071969.
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With the increase of drought and flood frequency, the drought-flood abrupt alternation events occur frequently. Due to the coexistence and rapid transformation of drought and flood, the drought-flood abrupt alternation events is often more harmful and threatening than the single drought or flood event to the security of the society. This study is to synthetically evaluate the evolving characteristics of drought, flood, and drought-flood abrupt alternation events under climate change, which are identified by using the Standard Weighted Average Precipitation (SWAP) index. The variability of drought, flood, and drought-flood abrupt alternation events in the future is predicted by using GCM projections, whose outputs are corrected by using a daily bias correction method. The results show that: (1) The SWAP index has the capability to judge reliably the onset, duration, and intensity over the study areas, and can be used to monitor drought-flood abrupt alternation events efficiently; (2) In the reference period (1961–2005), for the drought-flood abrupt alternation events, the frequency has a downward trend in the upper reaches and an upward trend in the lower reaches, and the spatial distribution of intensity shows a contrary law to that of frequency; (3) The frequency and intensity of drought-flood abrupt alternation events show an upward trend in the whole basin in the future period (2021–2095), under the RCP4.5 and RCP8.5 scenarios. These results indicate that drought-flood abrupt alternation events can be more frequent, and the intensity will significantly increase in the 21st century, which may likely pose a serious impact on this basin.
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Lemenkova, Polina. "Mapping environmental and climate variations by GMT: A case of Zambia, Central Africa." Zemljiste i biljka 70, no. 1 (2021): 117–36. http://dx.doi.org/10.5937/zembilj2101117l.
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Zambia recently experienced several environmental threats from climate change such as droughts, temperature rise and occasional flooding and they all affect agricultural sustainability and people wellbeing through negative effects on plants and growing crops. This paper is aimed at showing variations in several climate and environmental parameters in Zambia showing spatial variability and trends in different regions of Zambia's key environmental areas (Zambezi River and tributaries), Livingstone near the Victoria Falls and central region with Muchinga Mountains. A series of 10 maps was plotted using data from TerraClimate dataset: precipitation, soil moisture, Palmer Drought Severity Index (PDSI), downward surface shortwave radiation, vapor pressure deficit and anomalies, potential and actual evapotranspiration and wind speed with relation to the topographic distribution of elevations in Zambia plotted using GEBCO/SRTM data. The data range of the PDSI according to the index values ranged from minimum at -5.7 to the maximum at 16.6 and mean at 7.169, with standard deviation at 4.278. The PDSI is effective in quantifying drought in long-term period. Because PDSI index applies temperature data and water balance model, it indicates the effect of climate warming on drought by correlation with potential evapotranspiration. The maximum values for soil moisture of Zambia show minimum at 1 mm/m, maximum at 413 mm/m, mean at 173 mm/m. This study is technically based on using the Generic Mapping Tools (GMT) as cartographic scripting toolset. The paper contributes to the environmental monitoring of Zambia by presenting a series of climate and environmental maps that are beneficial for agricultural mapping of Zambia.
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Lemenkova, Polina. "Mapping environmental and climate variations by GMT: a case of Zambia, Central Africa." Zemljište i biljka 70, no. 1 (2021): 117–36. https://doi.org/10.5281/zenodo.4787154.
Full textAbstract:
Zambia recently experienced several environmental threats from climate change such as droughts, temperature rise and occasional flooding and they all affect agricultural sustainability and people well- being through negative effects on plants and growing crops. This paper is aimed at showing variations in several climate and environmental parameters in Zambia showing spatial variability and trends in different regions of Zambia's key environmental areas (Zambezi River and tributaries), Livingstone near the Victoria Falls and central region with Muchinga Mountains. A series of 10 maps was plotted using data from TerraClimate dataset: precipitation, soil moisture, Palmer Drought Severity Index (PDSI), downward surface shortwave radiation, vapor pressure deficit and anomalies, potential and actual evapotranspiration and wind speed with relation to the topographic distribution of elevations in Zambia plotted using GEBCO/SRTM data. The data range of the PDSI according to the index values ranged from minimum at -5.7 to the maximum at 16.6 and mean at 7.169, with standard deviation at 4.278. The PDSI is effective in quantifying drought in long-term period. Because PDSI index applies temperature data and water balance model, it indicates the effect of climate warming on drought by correlation with potential evapotranspiration. The maximum values for soil moisture of Zambia show minimum at 1 mm/m, maximum at 413 mm/m, mean at 173 mm/m. This study is technically based on using the Generic Mapping Tools (GMT) as cartographic scripting toolset. The paper contributes to the environmental monitoring of Zambia by presenting a series of climate and environmental maps that are beneficial for agricultural mapping of Zambia.
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Zaidman, M. D., H. G. Rees, and A. R. Young. "Spatio-temporal development of streamflow droughts in north-west Europe." Hydrology and Earth System Sciences 6, no. 4 (2002): 733–51. http://dx.doi.org/10.5194/hess-6-733-2002.
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Abstract. This paper examines the spatial and temporal development of streamflow droughts in Europe over the last 40 years, differentiating the climatic factors that drive drought formation from catchment controls on drought manifestation. A novel approach for quantifying and comparing streamflow and precipitation depletion is presented. This approach considers atypical flow or rainfall events, as well as more severe droughts, regardless of the season in which they occur (although unlikely to constitute drought in an operational sense, sustained atypical flows are important with regard to understanding how droughts arise and develop). The amount of flow depletion is quantified at daily resolution based on the standardised departure from the mean day d flow, or flow anomaly. The index was derived for 2780 gauging points within north-west Europe using data from the FRIEND European Water Archive for the 1960-1995 period. Using a simple interpolation procedure these data were used to produce a time-series of grids, with a cell size of 18 km2, showing the spatial distribution of flow anomaly over the study area. A similar approach was used to characterise monthly precipitation anomalies, based on existing grid data (see New et al., 2000). The grids were analysed chronologically to examine the spatial and temporal coherency of areas showing large flow and/or precipitation anomalies, focussing on drought development during the 1975-1976 and 1989-1990 periods. Using a threshold approach, in which an anomaly of 2 standard deviations represents the onset of drought conditions, indices were developed to describe the time-varying extent and areal-severity (flow deficit) of streamflow and precipitation drought. Similar indices were used to describe how the magnitude and temporal variation of flow depletion varied spatially. In terms of streamflow depletion, the 1976 drought was found to be a highly coherent event, having a well defined start (in January 1976) and end (in September 1976). The worst and most persistent streamflow droughts occurred in southern England and northern France. Central parts of Europe experienced only severe streamflow depletion during the ‘height’ of the drought in June, July and August when there was negligible precipitation across large areas of Europe. In contrast, the 1989/90 period was characterised by a series of shorter and less severe droughts, with much greater variability over time. The relationship between precipitation drought and streamflow drought was less clear, which might have resulted from periods of precipitation depletion occurring randomly in time. Particularly high levels of streamflow drought were again observed in southern England and northern France. Several possible explanations for the increased drought occurrence over southern England and northern France were investigated using data from the 1976 event. However, immediately antecedent precipitation deficits could not explain the level of streamflow depletion which appears to have been enhanced by decreased discharge of groundwater into the river networks in this region. This can probably be attributed to large precipitation deficits during autumn 1975 and spring 1976: the consequent reduction in groundwater recharge ultimately led to depressed groundwater levels. Keywords: drought, streamflow depletion, streamflow drought, low-flow regimes, Drought Index
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Anderson, Martha C., Christopher Hain, Brian Wardlow, Agustin Pimstein, John R. Mecikalski, and William P. Kustas. "Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States." Journal of Climate 24, no. 8 (2011): 2025–44. http://dx.doi.org/10.1175/2010jcli3812.1.
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Abstract The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.
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Abdulkadir, T.S., M.E. Abioye, O.O. Olofintoye, et al. "GIS-Based Impact Assessment of Landscape Characteristics on Soil Moisture Distribution in Mambilla Plateau, Taraba State, Nigeria." Nigerian Research Journal of Engineering and Environmental Sciences 9, no. 2 (2024): 813–24. https://doi.org/10.5281/zenodo.14566246.
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<em>Soil moisture is a crucial parameter linked to environmental issues like drought, erosion, flood and landslide. Its significance necessitates the need to understand the variables that could influence its distribution especially in a rugged watershed. Thus, this study assessed the impact of landscape characteristics such as slope, aspect, elevation and soil texture on the spatial distribution of soil moisture index in Mambilla Plateau, Nigeria. Landsat 8 data was applied to evaluate soil moisture, Digital Elevation Model for the landscape characteristics and digital soil map for soil texture in ArcMap environment. The results indicated that elevation, slope and normalized difference vegetation index (NDVI) ranged from1384 to 2399 m, 0 to 155.7% and -0.23 to 0.70. respectively. The soil moisture index (SMI) values ranged from 0.0 to 0.36 with average and standard deviation of 0.17 and 0.09 respectively. Analysis of developed SMI map indicated that the average SMI was higher in the western than eastern regions. Further analysis of impact of landscape characteristics on SMI on pixel-based correlation showed that higher values of average SMI occurred predominantly in areas having higher elevation, with gentler slopes, facing the western direction and having a relatively low proportion of sand. The sandy-clay-loam (26:8:66) texture which has the highest proportion of sand had the lowest average SMI value. This study would be beneficial to watershed managers and farmers in sustainable irrigation planning, geo-hazards and drought monitoring and prediction</em><em>.</em>
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Rodriguez-Vallejo, Carlos, and Rafael M. Navarro-Cerrillo. "Contrasting Response to Drought and Climate of Planted and Natural Pinus pinaster Aiton Forests in Southern Spain." Forests 10, no. 7 (2019): 603. http://dx.doi.org/10.3390/f10070603.
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Extreme drought events and increasing aridity are leading to forest decline and tree mortality, particularly in populations near the limits of the species distribution. Therefore, a better understanding of the growth response to drought and climate change could show the vulnerability of forests and enable predictions of future dieback. In this study, we used a dendrochronological approach to assess the response to drought in natural and planted forests of the maritime pine (Pinus pinaster Aiton) located in its southernmost distribution (south of Spain). In addition, we investigated how environmental variables (climatic and site conditions) and structural factors drive radial growth along the biogeographic and ecological gradients. Our results showed contrasting growth responses to drought of natural and planted stands, but these differences were not significant after repeated drought periods. Additionally, we found differences in the climate–growth relationships when comparing more inland sites (wet previous winter and late spring precipitation) and sites located closer to the coast (early spring precipitation). Response functions emphasized the negative effect of defoliation and drought, expressed as the June standard precipitation-evapotranspiration index calculated for the 12-month temporal scale and the mean temperature in the current February, on growth. The strong relationship between climatic variables and growth enabled acceptable results to be obtained in a modeling approach. The study and characterization of this tree species’ response to drought will help to improve the adaptive management of forests under climate change.
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Adeola, Omolola M., Muthoni Masinde, Joel O. Botai, Abiodun M. Adeola, and Christina M. Botai. "An Analysis of Precipitation Extreme Events Based on the SPI and EDI Values in the Free State Province, South Africa." Water 13, no. 21 (2021): 3058. http://dx.doi.org/10.3390/w13213058.
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Recognizing that, over the last several years, extreme rainfall has led to hazardous stress in humans, animals, plants, and even infrastructure, in the present study, we aimed to investigate the characteristics of droughts over the Free State (FS) Province of South Africa in order to determine the future likelihood of reoccurrences of precipitation extremes using the generalized extreme value distribution (GEV) and extreme frequency analysis (EFA). In this regard, daily rainfall datasets from nine South African weather service homogenous climatic districts, spanning from 1980 to 2019, were used to compute: (a) the total annual rainfall, (b) the Effective Drought Index (EDI), and (c) the Standard Precipitation Index (SPI). The SPI was calculated for 3, 6, and 12 month accumulation periods (hereafter SPI-3, SPI-6, and SPI-12, respectively). The trend analysis results of the EDI and SPI-3, -6, and -12 showed that the Free State Province is generally negative, illustrating persistent drought. An analysis of the GEV parameters across the EDI and SPI-3, -6, and -12 values illustrated that the location, scale, and shape parameters exhibited a noticeable spatial variability across the Free State Province with the location parameter largely negative, the scale parameter largely positive, while the shape parameter pointed to an inherent Type III (Weibull) GEV distribution. In addition, the return levels for the drought/wet duration and severity of the EDI and SPI-3, -6, and -12 values generally showed increasing patterns across the corresponding return periods; the spatial contrasts were only noticeable in the return levels derived from the wet/drought duration and severity derived from SPI-3, -6, and -12 values (and not in the EDI). Further, the EFA results pointed to a noticeable spatial contrast in the return periods derived from the EDI and SPI-3, -6, and -12 values for each of the extreme precipitation categories: moderately wet, severely wet, extremely wet to moderately dry, and severely dry. Over four decades, the FS Province has generally experienced a suite of extreme precipitation categories ranging from moderately wet, severely wet, extremely wet to moderately dry, severely dry, and extremely dry conditions. Overall, the present study contributes towards implementation of effective drought early warning systems and can be used to enhance drought related policy and decision making in support of water resource management and planning in the FS Province.
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Cui, Wei, Qian Xiong, Yinqi Zheng, et al. "A Study on the Vulnerability of the Gross Primary Production of Rubber Plantations to Regional Short-Term Flash Drought over Hainan Island." Forests 13, no. 6 (2022): 893. http://dx.doi.org/10.3390/f13060893.
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Rapidly developing droughts, including flash droughts, have occurred frequently in recent years, causing significant damage to agroforestry ecosystems, and they are expected to increase in the future due to global warming. The artificial forest area in China is the largest in the world, and its carbon budget is crucial to the global carbon sink. As the most prominent plantation plant in the tropics, the rubber (Hevea brasiliensis (Willd. ex A. Juss.) Muell. Arg.) ecosystem not only has important economic significance, but also has the potential to be a major natural carbon sink in hot areas. Frequent drought events have a significant impact on rubber ecosystem productivity, yet there have been few reports on the vulnerability of rubber productivity to drought. The objective of this study is to evaluate the vulnerability of rubber ecosystem gross primary production (GPP) to short-term flash drought (STFD) in Hainan Island, utilizing the localized EC-LUE model (eddy covariance–light use efficiency) validated by flux tower observations as the research tool to conduct the scenario simulations which defined by standard relative humidity index (SRHI), in a total of 96 scenarios (timing × intensity). The results show that, in terms of time, the rubber ecosystem in Hainan Island has the highest vulnerability to STFD during the early rainy season and the lowest at the end of the rainy season. From the dry season to the rainy season, the impact of STFD gradually extends to the northeast. Spatially, the vulnerability of the northern island is higher than that of the southern island and that of the western part is higher than that of eastern Hainan Island. With the increase in STFD intensity, the spatial distribution center of the vulnerability of rubber ecosystem GPP in Hainan Island gradually moves southward. The spatiotemporal pattern of the vulnerability of the rubber ecosystem GPP to STFD over Hainan Island plotted by this study is expected to provide decision makers with more accurate information on the prevention and control of drought disaster risk in rubber ecosystems.
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Sadri, Sara, Eric F. Wood, and Ming Pan. "Developing a drought-monitoring index for the contiguous US using SMAP." Hydrology and Earth System Sciences 22, no. 12 (2018): 6611–26. http://dx.doi.org/10.5194/hess-22-6611-2018.
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Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044∘ N/S) using an L-band (1.4 GHz) microwave radiometer in 2–3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May–October) and cold (November–April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov–Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters – which also gives the best coverage of the grids throughout CONUS – was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0–D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3-month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.
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Spiliotis, Konstantinos, Konstantinos Voudouris, Harris Vangelis, and Mike Spiliotis. "Analysis of Annual Drought Episodes Using Complex Networks." Sustainability 17, no. 4 (2025): 1441. https://doi.org/10.3390/su17041441.
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In this work, a new method to analyze the drought episodes based on the annual precipitation time series and utilizing complex networks theory is proposed. The precipitation time series is transformed into a complex network using the visibility algorithm.Then, several network measures are computed to characterize the underlying connectivity. The proposed analysis identifies important nodes which correspond to the low annual precipitation volume, providing a way to assess drought intensity without the use of the mean value and standard deviation, which are sensitive to climate change. Additionally, using community detection algorithms and network centrality measures, the method identifies ∼10-year and ∼4-year cycles within a period of 57 years. Using macroscopic measures like network distributions, we can identify rare high-intensity drought events. Finally, network analysis shows that the closeness centrality measure is in very good agreement with the well-known Standardised Precipitation Index (SPI) and thus can be used to characterize drought intensity.
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Sharma, Tribeni C., and Umed S. Panu. "Modelling Hydrological Droughts in Canadian Rivers Based on Markov Chains Using the Standardized Hydrological Index as a Platform." Hydrology 12, no. 2 (2025): 23. https://doi.org/10.3390/hydrology12020023.
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The standardized hydrological index (SHI) is the standardized but not normalized (normal probability variate) value of the streamflow used to characterize a hydrological drought, akin to the standardized precipitation index (SPI, which is both standardized and normalized) in the realm of the meteorological drought. The time series of the SHI can be used as a platform for deriving the longest duration, LT, and the largest magnitude, MT (in standardized form), of a hydrological drought over a desired return period of T time units (year, month, or week). These parameters are predicted based on the SHI series derived from the annual, monthly, and weekly flow sequences of Canadian rivers. An important point to be reckoned with is that the monthly and weekly sequences are non-stationary compared to the annual sequences, which fulfil the conditions of stochastic stationarity. The parameters, such as the mean, standard deviation (or coefficient of variation), lag 1 autocorrelation, and conditional probabilities from SHI sequences, when used in Markov chain-based relationships, are able to predict the longest duration, LT, and the largest magnitude, MT. The product moment and L-moment ratio analyses indicate that the monthly and weekly flows in the Canadian rivers fit the gamma probability distribution function (pdf) reasonably well, whereas annual flows can be regarded to follow the normal pdf. The threshold level chosen in the analysis is the long-term median of SHI sequences for the annual flows. For the monthly and weekly flows, the threshold level represents the median of the respective month or week and hence is time varying. The runs of deficit in the SHI sequences are treated as drought episodes and thus the theory of runs formed an essential tool for analysis. This paper indicates that the Markov chain-based methodology works well for predicting LT on annual, monthly, and weekly SHI sequences. Markov chains of zero order (MC0), first order (MC1), and second order (MC2) turned out to be satisfactory on annual, monthly, and weekly scales, respectively. The drought magnitude, MT, was predicted satisfactorily via the model MT= Id× Lc, where Id stands for drought intensity and Lcis a characteristic drought length related to LT through a scaling parameter, ɸ (= 0.5). The Id can be deemed to follow a truncated normal pdf, whose mean and variance when combined implicitly with Lcproved prudent for predicting MT at all time scales in the aforesaid relationship.
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Yuan, Xing, Eric F. Wood, Nathaniel W. Chaney, et al. "Probabilistic Seasonal Forecasting of African Drought by Dynamical Models." Journal of Hydrometeorology 14, no. 6 (2013): 1706–20. http://dx.doi.org/10.1175/jhm-d-13-054.1.
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Abstract As a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982–2007) seasonal hydrologic hindcasts run at 0.25°, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soil moisture percentile as indices. In terms of Brier skill score (BSS), the system is more skillful than climatology out to 3–5 months, except for the forecast of soil moisture drought over central Africa. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soil moisture than the SPI6. Drought forecasts based on SPI6 are generally more skillful than for soil moisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soil moisture drought forecast can be more skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode from observations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land–atmosphere coupling, is necessary.
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WANG, Y., Z. W. SHILENJE, P. O. SAGERO, A. M. NYONGESA, and N. BANDA. "Rainfall variability and meteorological drought in the Horn of Africa." MAUSAM 68, no. 3 (2021): 463–74. http://dx.doi.org/10.54302/mausam.v68i3.678.
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Basic rainfall characteristics and drought over the Horn of Africa (HoA) is investigated, from 1901 to 2010. Standard Precipitation Index (SPI) is used to study drought variability, mainly focusing on 3-month SPI. The dominant mode of variability of seasonal rainfall was analyzed by performing Empirical orthogonal functions (EOF) analysis. Gridded data is sourced from Climate Research Unit (CRU), spanning from 1901 to 2010. The HoA experiences predominantly bimodal rainfall distribution in time; March to May (MAM) and October to December (OND). The spatial component of the first eigenvector (EOF1) shows that the MAM and OND seasonal rainfalls are dominated by negative and positive loadings, respectively. The EOF1 explain 34.5% and 58.9% variance of MAM and OND seasonal rainfall, respectively. The EOF2, 3 and 4 are predominantly positive, explaining less than 25% in total of the seasonal rainfall variance in the two seasons. The last two decades experienced the highest negative anomaly, with OND seasonal rainfall showing higher anomalies as compared to MAM season. The OND season recorded 9% more drought events as compared to MAM season. The frequency of occurrence of moderate, severe and extreme dryness was almost the same in the two seasons. These results give a good basis for regional model validation, as well as mapping out drought hotspots and projections studies in the HoA.
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Şen, Zekâi. "Assessing Wet and Dry Periods Using Standardized Precipitation Index Fractal (SPIF) and Polygons: A Novel Approach." Water 16, no. 4 (2024): 592. http://dx.doi.org/10.3390/w16040592.
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In the open literature, there are numerous studies on the normal and extreme (flood and drought) behavior of wet and dry periods based on the understanding of the standard precipitation index (SPI), which provides a series of categorizations by considering the standard normal (Gaussian) probability distribution function (PDF). The numerical meaning of each categorization assessment is quite lacking in terms of future predictions of wet and dry period duration based on historical records. This paper presents a new approach for calculating possible formations of future wet and dry period durations based on historical records through an effective fractal geometric forecasting approach. The essence of the proposed methodology is based on the number of dry periods (steps) of non-overlapping monthly duration along consecutive broken line paths in the SPI classification for wet and dry period durations. It has been observed that the plot of periods on double logarithmic paper falls along a straight line against the number of such periods, implying a power function, which is the essence of fractal geometry. Extending the empirically derived straight line provides the number of periods that may occur in the future over a range of SPI levels. This methodology is referred to as SPI fractal (SPIF), and the classic SPI classification is converted into SPIF wet and dry polygons, which provide additional information about the drought period number within a valid polygonal area, compared to the classic SPI results. The wet and dry period features of any hydro-meteorology time series are constrained in SPIF polygons. The application of the methodology was carried out on monthly rainfall records on the European side of the Istanbul Florya meteorological station in Turkey.
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Osuch, M., R. J. Romanowicz, D. Lawrence, and W. K. Wong. "Assessment of the influence of bias correction on meteorological drought projections for Poland." Hydrology and Earth System Sciences Discussions 12, no. 10 (2015): 10331–77. http://dx.doi.org/10.5194/hessd-12-10331-2015.
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Abstract. Possible future climate change effects on drought severity in Poland are estimated for six ENSEMBLE climate projections using the Standard Precipitation Index (SPI). The time series of precipitation represent six different RCM/GCM run under the A1B SRES scenario for the period 1971–2099. Monthly precipitation values were used to estimate the Standard Precipitation Index (SPI) for multiple time scales (1, 3, 6, 12 and 24 months) for a spatial resolution of 25 km × 25 km for the whole country. Trends in SPI were analysed using a Mann–Kendall test with Sen's slope estimator for each 25 km × 25 km grid cell for each RCM/GCM projection and timescale, and results obtained for uncorrected precipitation and bias corrected precipitation were compared. Bias correction was achieved using a distribution-based quantile mapping (QM) method in which the climate model precipitation series were adjusted relative to gridded E-OBS precipitation data for Poland. The results show that the spatial pattern of the trend depends on the climate model, the time scale considered and on the bias correction. The effect of change on the projected trend due to bias correction is small compared to the variability among climate models. We also summarise the mechanisms underlying the influence of bias correction on trends using a simple example of a linear bias correction procedure. In the case of precipitation the bias correction by QM does not change the direction of changes but can change the slope of trend. We also have noticed that the results for the same GCM, with differing RCMs, are characterized by similar pattern of changes, although this behaviour is not seen at all time scales and seasons.
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Auliyani, Diah, and Muhammad Rekapermana. "Analisis Spasial Potensi Kekeringan di Daerah Aliran Sungai Kapuas, Kalimantan Barat." JURNAL PEMBANGUNAN WILAYAH & KOTA 16, no. 1 (2020): 61–70. http://dx.doi.org/10.14710/pwk.v16i1.21979.
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Kekeringan merupakan efek samping dari variabilitas iklim, yang dapat terjadi di daerah dengan curah hujan tinggi maupun rendah. Kekeringan dapat menjadi suatu bencana apabila terjadi secara terus menerus. Standardized Precipitation Index (SPI) memudahkan pemantauan kejadian kekeringan dengan memanfaatkan standar deviasi dari curah hujan. Penelitian ini bertujuan untuk menganalisis potensi kekeringan di Daerah Aliran Sungai (DAS) Kapuas. Lokasi penelitian merupakan DAS terbesar di Provinsi Kalimantan Barat. Dalam tulisan ini akan digunakan SPI periode kumulatif 1 bulan, 3 bulan, 6 bulan, dan 12 bulan untuk menentukan tingkat kekeringannya. Dengan menggunakan perangkat lunak Arc GIS, nilai rata-rata SPI setiap periode kumulatif kemudian diinterpolasikan untuk mendapatkan sebaran spasial potensi kekeringan di seluruh wilayah DAS Kapuas. Seri data curah hujan harian tahun 1995-2017 dari 5 stasiun hujan yang kelola oleh Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) Provinsi Kalimantan Barat digunakan dalam analisisnya. Kelima stasiun pengamatan hujan tersebut terletak di (1) Bandara Supadio Pontianak, (2) Pelabuhan Maritim Pontianak, (3) Bandara Susilo Sintang Kabupaten Sintang, (4) Bandara Nanga Pinoh Kabupaten Melawi, dan (5) Bandara Pangsuma Kabupaten Kapuas Hulu. Hasilpenelitian menunjukkan bahwa setiap lokasi pengamatan hujan mengalami kekeringan untuk setiap periode kumulatif dengan frekuensi 1 hingga 4 kali.Kekeringan tersebut memiliki durasi paling lama 2 bulan secara berturut-turut. Distribusi spasial SPI di DAS Kapuas memiliki nilai antara -0,1 hingga -0,07 yang termasuk dalam kategori normal. Secara keseluruhan, DAS Kapuas merupakan wilayah yang tidak berpotensi mengalami bencana kekeringan. Drought is a side effect of climate variability, which can occur in areas with high or low rainfall. Drought will become a disaster if it happens continuously. Standardized Precipitation Index (SPI) facilitates the drought monitoring by utilizing standard deviation of its rainfall. This study aims to analyze the potential for drought in the Kapuas Watershed. Kapuas Watershed is the widest watershed located in West Kalimantan Province. In this paper, 1 month, 3 months, 6 months, and 12 months cumulative periods of SPI will be used to determine the level of drought. Using Arc GIS software, the average SPI value for each cumulative period is then interpolated to obtain the spatial distribution of potential drought in the entire Kapuas Watershed area.The 1995-2017 daily rainfall data series from 5 rainfall stations managed by The West Kalimantan Province Meteorology, Climatology and Geophysics Agency (BMKG) were used in this analysis. The five rainfall stations are located at (1) Supadio Airport, Pontianak, (2) Pontianak Maritime Port, (3) Susilo Airport, Sintang Regency, (4) Nanga Pinoh Airport, Melawi Regency, and (5) Pangsuma Airport, Kapuas Hulu Regency. The results showed that each rainfall station experienced drought for each cumulative period with a frequency of 1 to 4 times. Its duration was 2 months or less. The spatial distribution of SPI in Kapuas Watershed has a value between -0.1 to -0.07 which categorized as normal. Overall, Kapuas Watershed is an area that has no potential for drought.
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Yao, Shuxia, Chuancheng Zhao, Jiaxin Zhou, and Qingfeng Li. "The Association of Drought with Different Precipitation Grades in the Inner Mongolia Region of Northern China." Water 16, no. 22 (2024): 3292. http://dx.doi.org/10.3390/w16223292.
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Drought has become an important factor affecting the environment and socio-economic sustainable development in northern China due to climate change. This study utilized the Standardized Precipitation Index (SPI) as a drought metric to investigate the correlation between drought characteristics and different grades of precipitation and rain days. The analysis was based on a long-term time series of precipitation data obtained from 116 meteorological stations located in Inner Mongolia, spanning 1960 to 2019. To achieve the objectives of the current research, the daily precipitation was categorized into four grades based on the “24-h Precipitation Classification Standard”, and the frequency of rain days for each grade was determined. Subsequently, the SPI was calculated for 1 and 12 months, enabling the identification of drought events. The results revealed pronounced spatiotemporal regional variations and complexities in the dry–wet climatic patterns of Inner Mongolia, with significant decreases in precipitation emerging as the primary driver of drought occurrences. Approximately 6% of the entire study period experienced short-term drought, while long-term drought periods ranged from 23% to 38%. Regarding multi-year trends, precipitation exhibited a weak increasing trend, while rain days exhibited a weak decreasing trend. Drought exhibited an alleviating trend, with 92% of stations displaying coefficients > 0 for SPI_Month and over 62% of stations displaying coefficients > 0 for SPI_Year. At the monthly scale, drought was most correlated with light rainfall trends and least correlated with moderate rainfall trends. At the annual scale, drought was relatively highly correlated with moderate and heavy rainfall distributions but poorly correlated with light rainfall. The results suggested that achieving the precise monitoring and mitigation of drought disasters in Inner Mongolia in the future will require a combined analysis of indicators, including agricultural drought, hydrological drought, and socio-economic drought. Such an approach will enable a comprehensive analysis of drought characteristics under different underlying surface conditions in Inner Mongolia.