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Isia, Ismallianto, Tony Hadibarata, Muhammad Noor Hazwan Jusoh, Rajib Kumar Bhattacharjya, Noor Fifinatasha Shahedan, Aissa Bouaissi, Norma Latif Fitriyani, and Muhammad Syafrudin. "Drought Analysis Based on Standardized Precipitation Evapotranspiration Index and Standardized Precipitation Index in Sarawak, Malaysia." Sustainability 15, no. 1 (December 31, 2022): 734. http://dx.doi.org/10.3390/su15010734.
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Drought analysis via the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) is necessary for effective water resource management in Sarawak, Malaysia. Rainfall is the best indicator of a drought, but the temperature is also significant because it controls evaporation and condensation. This study examined drought periods in the state of Sarawak using the SPI and SPEI based on monthly precipitation and temperature data from thirty-three rainfall stations during a forty-year period (1981–2020). This analysis of drought conditions revealed that both the SPI and SPEI were able to detect drought temporal variations with distinct time scales (3, 6, 9, and 12 months). Taking precipitation and evapotranspiration data into account, the SPEI was able to identify more severe-to-extreme drought in the study area over longer time periods and moderate droughts over shorter time periods than the standard drought index. According to Pearson correlation coefficients, a substantial association existed between the SPI and SPEI during hydrological dryness. Based on the results, the temperature is a decisive factor in drought classification, and the SPI should only be used in the absence of temperature data.
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Pandya, Parthsarthi, Rohit Kumarkhaniya, Ravina Parmar, and Piyush Ajani. "Meteorological Drought Analysis Using Standardized Precipitation Index." Current World Environment 15, no. 3 (December 30, 2020): 477–86. http://dx.doi.org/10.12944/cwe.15.3.12.
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Drought is a natural hazard which is challenging to quantify in terms of severity, duration, areal extent and impact. The present study was aimed to assess the meteorological drought for Junagadh (Gujarat), India using Standardized Precipitation Index (SPI) and evaluate its correlation with the productivity of Groundnut and Cotton. The SPI was computed for eight durations including monthly (June to August each), 3 monthly (June to August and July to September) and 6 monthly (June to November) time scales for the year1988 to 2018. The results revealed that 54% to 67% of years suffered from drought for SPI-1. Drought years based on SPI-3 and SPI-6 were 48 % to 58%. Among all the eight durations, mild drought was the most dominant drought category. Years 1993, 1999, 2002 and 2012 experienced the most severe droughts for Junagadh. Severe droughts were observed only for SPI-1 (July), SPI-3 and SPI-6. No extreme drought was witnessed in Junagadh. Correlation of groundnut yield with SPI was higher as compared to cotton for all time scales. Kharif groundnut and cotton yield were better correlated with SPI-3 and SPI-6 for Junagadh with significant correlation coefficient ranging from 0.57 to 0.79 for groundnut and 0.46 to 0.56 for cotton. Among monthly SPI, the significantly highest correlation was found for June (0.59) for groundnut and September (0.48) for cotton. The SPI-3 and SPI-6 shown ability to quantify the drought and also shown the potential of yield prediction.
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Lucas, Matthew P., Clay Trauernicht, Abby G. Frazier, and Tomoaki Miura. "Long-Term, Gridded Standardized Precipitation Index for Hawai‘i." Data 5, no. 4 (November 26, 2020): 109. http://dx.doi.org/10.3390/data5040109.
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Spatially explicit, wall-to-wall rainfall data provide foundational climatic information but alone are inadequate for characterizing meteorological, hydrological, agricultural, or ecological drought. The Standardized Precipitation Index (SPI) is one of the most widely used indicators of drought and defines localized conditions of both drought and excess rainfall based on period-specific (e.g., 1-month, 6-month, 12-month) accumulated precipitation relative to multi-year averages. A 93-year (1920–2012), high-resolution (250 m) gridded dataset of monthly rainfall available for the State of Hawai‘i was used to derive gridded, monthly SPI values for 1-, 3-, 6-, 9-, 12-, 24-, 36-, 48-, and 60-month intervals. Gridded SPI data were validated against independent, station-based calculations of SPI provided by the National Weather Service. The gridded SPI product was also compared with the U.S. Drought Monitor during the overlapping period. This SPI product provides several advantages over currently available drought indices for Hawai‘i in that it has statewide coverage over a long historical period at high spatial resolution to capture fine-scale climatic gradients and monitor changes in local drought severity.
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Ariyanto, Dwi Priyo, Abdul Aziz, Komariah Komariah, Sumani Sumani, and Magarsa Abara. "Comparing the accuracy of estimating soil moisture using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI)." SAINS TANAH - Journal of Soil Science and Agroclimatology 17, no. 1 (June 29, 2020): 23. http://dx.doi.org/10.20961/stjssa.v17i1.41396.
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<span>The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are used to monitor and identify different types of drought, including meteorological, hydrological, and agricultural droughts. This study evaluates the accuracy of estimating soil moisture levels using the two indexes. The analysis correlated the SPI and the SPEI over three years (November 2016–October 2019) using <em>Rstudio</em>, with average monthly soil moisture taken using a Soil Moisture Sensor; 3-, 6- and 12-months SPI and SPEI showed a positive correlation for soil moisture (Sig <0.05), whereas 1-month SPI and SPEI results did not. A regression test was used to get an equation model for estimating soil moisture content. The correlation for soil moisture between the 1-month SPI and SPEI results was insignificant (p-value >0.05). In contrast, the 3-, 6-, and 12-months indexes were significant (p-value <0.05). Estimating soil moisture content using the SPEI (50–59.09%) had a higher accuracy value than the SPI (36.36%), which indicates the SPEI can more reliably predict soil moisture.</span>
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Salmin, A. S., I. F. Asaulyak, and A. I. Belolyubtsev. "ANALYSING TIME SERIES OF STANDARDIZED PRECIPITATION INDEX (SPI)." Успехи современного естествознания (Advances in Current Natural Sciences), no. 5 2021 (2021): 101–9. http://dx.doi.org/10.17513/use.37630.
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Kumar, Rohini, Jude L. Musuuza, Anne F. Van Loon, Adriaan J. Teuling, Roland Barthel, Jurriaan Ten Broek, Juliane Mai, Luis Samaniego, and Sabine Attinger. "Multiscale evaluation of the Standardized Precipitation Index as a groundwater drought indicator." Hydrology and Earth System Sciences 20, no. 3 (March 15, 2016): 1117–31. http://dx.doi.org/10.5194/hess-20-1117-2016.
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Abstract. The lack of comprehensive groundwater observations at regional and global scales has promoted the use of alternative proxies and indices to quantify and predict groundwater droughts. Among them, the Standardized Precipitation Index (SPI) is commonly used to characterize droughts in different compartments of the hydro-meteorological system. In this study, we explore the suitability of the SPI to characterize local- and regional-scale groundwater droughts using observations at more than 2000 groundwater wells in geologically different areas in Germany and the Netherlands. A multiscale evaluation of the SPI is performed using the station data and their corresponding 0.5° gridded estimates to analyze the local and regional behavior of groundwater droughts, respectively. The standardized anomalies in the groundwater heads (SGI) were correlated against SPIs obtained using different accumulation periods. The accumulation periods to achieve maximum correlation exhibited high spatial variability (ranges 3–36 months) at both scales, leading to the conclusion that an a priori selection of the accumulation period (for computing the SPI) would result in inadequate characterization of groundwater droughts. The application of the uniform accumulation periods over the entire domain significantly reduced the correlation between the SPI and SGI (≈ 21–66 %), indicating the limited applicability of the SPI as a proxy for groundwater droughts even at long accumulation times. Furthermore, the low scores of the hit rate (0.3–0.6) and a high false alarm ratio (0.4–0.7) at the majority of the wells and grid cells demonstrated the low reliability of groundwater drought predictions using the SPI. The findings of this study highlight the pitfalls of using the SPI as a groundwater drought indicator at both local and regional scales, and stress the need for more groundwater observations and accounting for regional hydrogeological characteristics in groundwater drought monitoring.
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Kumar, R., J. L. Musuuza, A. F. Van Loon, A. J. Teuling, R. Barthel, J. Ten Broek, J. Mai, L. Samaniego, and S. Attinger. "Multiscale evaluation of the standardized precipitation index as a groundwater drought indicator." Hydrology and Earth System Sciences Discussions 12, no. 8 (August 5, 2015): 7405–36. http://dx.doi.org/10.5194/hessd-12-7405-2015.
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Abstract. The lack of comprehensive groundwater observations at regional and global scales has promoted the use of alternative proxies and indices to quantify and predict groundwater droughts. Among them, the Standardized Precipitation Index (SPI) is commonly used to characterize droughts in different compartments of the hydro-meteorological system. In this study, we explore the suitability of the SPI to characterize local and regional scale groundwater droughts using observations at more than 2000 groundwater wells in geologically different areas in Germany and the Netherlands. A multiscale evaluation of the SPI is performed using the station data and their corresponding 0.5° gridded estimates to analyze the local and regional behavior of groundwater droughts, respectively. The standardized anomalies in the groundwater heads (SGI) were correlated against SPIs obtained using different accumulation periods. The accumulation periods to achieve maximum correlation exhibited high spatial variability (ranges 3 to 36 months) at both scales, leading to the conclusion that an a priori selection of the accumulation period (for computing the SPI) would result in inadequate characterization of groundwater droughts. The application of the uniform accumulation periods over the entire domain significantly reduced the correlation between SPI and SGI (&approx; 21–66 %) indicating the limited applicability of SPI as a proxy for groundwater droughts even at long accumulation times. Furthermore, the low scores of the hit rate (0.3–0.6) and high false alarm ratio (0.4–0.7) at the majority of the wells and grid cells demonstrated the low reliability of groundwater drought predictions using the SPI. The findings of this study highlight the pitfalls of using the SPI as a groundwater drought indicator at both local and regional scales, and stress the need for more groundwater observations and accounting for regional hydrogeological characteristics in groundwater drought monitoring.
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Zuo, Dongdong, Wei Hou, Hao Wu, Pengcheng Yan, and Qiang Zhang. "Feasibility of Calculating Standardized Precipitation Index with Short-Term Precipitation Data in China." Atmosphere 12, no. 5 (May 6, 2021): 603. http://dx.doi.org/10.3390/atmos12050603.
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At present, high-resolution drought indices are scarce, and this problem has restricted the development of refined drought analysis to some extent. This study explored the possibility of calculating the standardized precipitation index (SPI) with short-term precipitation sequences in China, based on data from 2416 precipitation observation stations covering the time period from 1961 to 2019. The result shows that it is feasible for short-sequence stations to calculate SPI index, based on the spatial interpolation of the precipitation distribution parameters of the long-sequence station. Error analysis denoted that the SPI error was small in east China and large in west China, and the SPI was more accurate when the observation stations were denser. The SPI error of short-sequence sites was mostly less than 0.2 in most areas of eastern China and the consistency rate for the drought categories was larger than 80%, which was lower than the error using the 30-year precipitation samples. Further analysis showed that the estimation error of the distribution parameters β and q was the most important cause of SPI error. Two drought monitoring examples show that the SPI of more than 50,000 short-sequence sites can correctly express the spatial distribution of dry and wet and have refined spatial structure characteristics.
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Aripbilah, Safrudin Nor, and Heri Suprapto. "ANALISIS KEKERINGAN DI KABUPATEN SRAGEN DENGAN METODE PALMER, THORNTHWAITE, DAN STANDARDIZED PRECIPITATION INDEX." JURNAL SUMBER DAYA AIR 17, no. 2 (November 30, 2021): 111–24. http://dx.doi.org/10.32679/jsda.v17i2.742.
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El Nino and La Nina in Indonesia are one of the reasons that caused climate changes, which has possibility of drought and flood disasters. Sragen Regency wherethe dry season occurs, drought happened meanwhile other areas experience floods and landslides. A study on drought needs to be carried out so as to reduce the risk of losses due to the drought hazard. This study is to determine the drought index in Sragen Regency based on several methods and the correlation of each methods and its suitability to the Southern Oscillation Index (SOI) and rainfall. Drought was analyzed using several methods such as Palmer Drought Severity Index (PDSI), Thornthwaite-Matter, and Standardized Precipitation Index (SPI) then correlated with SOI to determine the most suitable method for SOI. The variables are applied in this method are rainfall, temperature, and evapotranspiration. The results showed that the drought potential of the Palmer method is only in Near Normal conditions, which is 1%, Severe drought conditions are 29% for the Thornthwaite-Matter method, and Extreme Dry conditions only reach 1,11% for the SPI method. The PDSI and SPI methods are inversely proportional to the Thornthwaite-Matter method and the most suitable method for SOI values or rainfall is the SPI method. These three methods can be identified the potential for drought with only a few variables so that they could be applied if they only have those data.Keywords: Drought, PDSI, Thornthwaite-Matter, SPI, SOI
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Blain, Gabriel Constantino. "Standardized precipitation index based on pearson type III distribution." Revista Brasileira de Meteorologia 26, no. 2 (June 2011): 167–80. http://dx.doi.org/10.1590/s0102-77862011000200001.
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The initial step in calculating the Standardized Precipitation Index (SPI) is to determine a probability density function (pdf) that describes the precipitation series under analysis. Once this pdf is determined, the cumulative probability of an observed precipitation amount is computed. The inverse normal function is then applied to the cumulative probability. The result is the SPI. This article assessed the changes in SPI final values, when computed based on Gamma 2-parameters (Gam) and Pearson Type III (PE3) distributions (SPIGam and SPIPE3, respectively). Monthly rainfall series, available from five weather stations of the State of São Paulo, were chosen for this study. Considering quantitative and qualitative assessments of goodness-of-fit (evaluated at 1-, 3-, and 6-months precipitation totals), the PE3 distribution seems to be a better choice than the Gam distribution, in describing the long-term rainfall series of the State of São Paulo. In addition, it was observed that the number of SPI time series that could be seen as normally distributed was higher when this drought index was computed from the PE3 distribution. Thus, the use of the Pearson type III distribution within the calculation algorithm of the SPI is recommended in the State of São Paulo.
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Brleković, Tamara, and Lidija Tadić. "Hydrological Drought Assessment in a Small Lowland Catchment in Croatia." Hydrology 9, no. 5 (May 10, 2022): 79. http://dx.doi.org/10.3390/hydrology9050079.
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Hydrological drought is critical from both water management and ecological perspectives. Depending on its hydrological and physical features, the resilience level of a catchment to groundwater drought can differ from that of meteorological drought. This study presents a comparison of hydrological and meteorological drought indices based on groundwater levels from 1987 to 2018. A small catchment area in Croatia, consisting of two sub-catchments with a continental climate and minimum land-use changes during the observed period, was studied. The first analysis was made on a comparison of standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). The results showed their very high correlation. The correlation between the standardized precipitation index (SPI) and standardized groundwater index (SGI) of different time scales (1, 3, 6, 12, 24 and 48 months) showed different values, but had the highest value in the longest time scale, 48 months, for all observation wells. Nevertheless, the behavior of the SPI and groundwater levels (GW) correlation showed results more related to physical catchment characteristics. The results showed that groundwater drought indices, such as SGI, should be applied judiciously because of their sensitivity to geographical, geomorphological, and topographical catchment characteristics, even in small catchment areas.
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Almedeij, Jaber. "Drought Analysis for Kuwait Using Standardized Precipitation Index." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/451841.
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Implementation of adequate measures to assess and monitor droughts is recognized as a major matter challenging researchers involved in water resources management. The objective of this study is to assess the hydrologic drought characteristics from the historical rainfall records of Kuwait with arid environment by employing the criterion of Standardized Precipitation Index (SPI). A wide range of monthly total precipitation data from January 1967 to December 2009 is used for the assessment. The computation of the SPI series is performed for intermediate- and long-time scales of 3, 6, 12, and 24 months. The drought severity and duration are also estimated. The bivariate probability distribution for these two drought characteristics is constructed by using Clayton copula. It has been shown that the drought SPI series for the time scales examined have no systematic trend component but a seasonal pattern related to rainfall data. The results are used to perform univariate and bivariate frequency analyses for the drought events. The study will help evaluating the risk of future droughts in the region, assessing their consequences on economy, environment, and society, and adopting measures for mitigating the effect of droughts.
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Behifar, M., A. A. Kakroodi, M. Kiavarz, and F. Amiraslani. "COMBINATION OF METEOROLOGICAL INDICES AND SATELLITE DATA FOR DROUGHT MONITORING IN TWO DIFFERENT ENVIRONMENTS IN IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 197–200. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-197-2019.
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Abstract. The main problem using meteorological drought indices include inappropriate distribution of meteorological stations. Satellite data have reliable spatial and temporal resolution and provide valuable information used in many different applications. The Standardized precipitation index has several advantages. The SPI is based on rainfall data alone and has a variable time scale and is thus conducive to describing drought conditions for different application.This study aims to calculate SPI using satellite precipitation data and compare the results with traditional methods. To do this, satellite-based precipitation data were assessed against station data and then the standardized precipitation index was calculated. The results have indicated that satellite-based SPI could illustrate drought spatial characteristic more accurate than station-based index. Also, the standardized property of the SPI index allows comparisons between different locations, which is one of the remote sensing drought indices limitations.
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Aryal, Anil, Manisha Maharjan, Rocky Talchabhadel, and Bhesh Raj Thapa. "Characterizing Meteorological Droughts in Nepal: A Comparative Analysis of Standardized Precipitation Index and Rainfall Anomaly Index." Earth 3, no. 1 (March 4, 2022): 409–32. http://dx.doi.org/10.3390/earth3010025.
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Drought is an environmental disaster related to the extremes (on a drier side) in hydrometeorology. The precipitation amount modulates drought in Nepalese river basins. It is vital for efficient water resources management to quantify and understand drought. This paper aims to characterize the droughts in Nepal based on standard precipitation index (SPI) and rainfall anomaly index (RAI) using daily precipitation data and assess their impacts on annual crop yields. We used 41 years (1975–2015) of daily precipitation data to compute monthly means and then the drought indices, namely, SPI and RAI, at 123 stations across Nepal. Results showed that the northwest and eastern regions experienced drought compared to other regions, although the severity and duration were shorter. For stations 101 and 308, we found extreme drought events after 2005 for SPI-1, SPI-3, and SPI-6. However, for SPI-6, extreme drought was also observed in 1989 and 1994 at both stations. The year 1992 was one of the severest drought years for the western and northwest regions, where the severity crossed more than −2.0 for all SPI months. Similar to SPI, RAI also revealed a similar degree of drought in the country. RAI showed that the eastern region depicted a higher degree of severity of drought compared to other areas beyond 2004. The lesser severity is also seen in the far west part beyond 2005. The results showed that SPI and RAI could equally be used to analyze drought severity. More frequent drought incidents have been observed after 2010 at all the considered precipitation stations. With the increase in the drought severity, the crop yield (such as paddy, maize, barley, millet, and wheat) has been directly impacted. These results might be significant for planning water resource and irrigation water management systems.
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Nasher, NMR, and MN Uddin. "Analyzing Wetness and Dryness Severity in Bangladesh: Using Standardized Precipitation Index (SPI) in GIS Environment." Journal of Environmental Science and Natural Resources 7, no. 2 (February 14, 2015): 127–31. http://dx.doi.org/10.3329/jesnr.v7i2.22220.
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Daily precipitation data of 33 stations were analyzed to know the Standardized Precipitation Index (SPI). Both spatial and temporal SPI were analyzed. Inverse Distance Weighting (IDW) technique was used to map the spatial SPI with a decadal change. From 1983 to 2013, four maps showed the decadal changes of SPI over Bangladesh. 1993 was a dry year in the regarding time period. Station based trends were analyzed for Dhaka, Srimongol, Khulna, Coxs bazaar and Rangpur as preventative for five regions in Bangladesh. Dryness is increasing over Northern and Central regions whereas wetness is increasing over Southwestern, Northern and eastern region in Bangladesh.DOI: http://dx.doi.org/10.3329/jesnr.v7i2.22220 Environ. Sci. & Natural Resources, 7(2): 127-131 2014
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Ahmadebrahimpour, Edris, Babak Aminnejad, and Keivan Khalili. "Assessing future drought conditions under a changing climate: a case study of the Lake Urmia basin in Iran." Water Supply 19, no. 6 (April 12, 2019): 1851–61. http://dx.doi.org/10.2166/ws.2019.062.
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Abstract This study was conducted to assess the impacts of climate change on drought over the Lake Urmia basin, Iran. Drought events for 2011–2040, 2041–2070, and 2071–2100 were analyzed based on the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) and were compared with the adopted baseline period (1976–2005). The SPI and SPEI were calculated using the precipitation and temperatures obtained from the second-generation Canadian Earth System Model (CanESM2) under Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 as optimistic and pessimistic scenarios respectively. The results of SPI analyses revealed that under RCP 2.6 the frequency of droughts is almost constant while under RCP 8.5 drought frequency increased especially in the period 2071–2100. The calculated SEPI under both scenarios and during all future periods predict that the frequency and duration of droughts will increase. Generally, the difference between the SPI and SPEI is related to the input to each index. SPI is solely based on precipitation while the SPEI accounts for both precipitation and potential evapotranspiration (PET). Under global warming and changing climate, the significant role of PET was highlighted. It was concluded that the SPEI outperformed the SPI for drought studies under a changing climate.
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Łabędzki, Leszek. "Categorical Forecast of Precipitation Anomaly Using the Standardized Precipitation Index SPI." Water 9, no. 1 (January 1, 2017): 8. http://dx.doi.org/10.3390/w9010008.
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Putra, Erianto Indra, and Rahmadika Fairuz Husni. "Hubungan Curah Hujan dan Titik Panas (Hotspot) Kebakaran di Hutan Lindung Gambut (HLG) Londerang, Provinsi Jambi." Journal of Tropical Silviculture 12, no. 3 (December 31, 2021): 129–34. http://dx.doi.org/10.29244/j-siltrop.12.3.129-134.
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Jambi is one of the provinces that has high risk of forest fires in Indonesia.HLG Londerang is one of the protected peatland forests in Jambi that burned greatly in 2015. Precipitation become one of the climate factor that is affecting fires risk. This research is aimed to analyse the correlation between precipitation pattern peatland fires in HLG Londerang on 2013-2016. This research used hotspot data, precipitation, SPI (Standardized Precipitation Index), SOI (Southern Oscillation Index), and SSTA (Sea Surface Temperature Anomalies). Simple correlation test is used to acsess the relation between each parameters. This research showed that SPI-1 values could represent precipitation. In 2015, a great number of hotspot may relate to the El Nino event indicated by high positive value of SSTA and low SOI. There is a negative and weak correlation between precipitation and hotspot. Rainfall and SPI-1 has a positive and strong correlation. Combination of precipitation, SOI and SSTA is showing highest correlation with hotspot than other parameters.
Keywords: fire, hotspot, precipitation, SOI, SPI, SSTA
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Pieper, Patrick, André Düsterhus, and Johanna Baehr. "A universal Standardized Precipitation Index candidate distribution function for observations and simulations." Hydrology and Earth System Sciences 24, no. 9 (September 21, 2020): 4541–65. http://dx.doi.org/10.5194/hess-24-4541-2020.
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Abstract. The Standardized Precipitation Index (SPI) is a widely accepted drought index. Its calculation algorithm normalizes the index via a distribution function. Which distribution function to use is still disputed within the literature. This study illuminates that long-standing dispute and proposes a solution that ensures the normality of the index for all common accumulation periods in observations and simulations. We compare the normality of SPI time series derived with the gamma, Weibull, generalized gamma, and the exponentiated Weibull distribution. Our normality comparison is based on a complementary evaluation. Actual compared to theoretical occurrence probabilities of SPI categories evaluate the absolute performance of candidate distribution functions. Complementary, the Akaike information criterion evaluates candidate distribution functions relative to each other while analytically punishing complexity. SPI time series, spanning 1983–2013, are calculated from the Global Precipitation Climatology Project's monthly precipitation dataset, and seasonal precipitation hindcasts are from the Max Planck Institute Earth System Model. We evaluate these SPI time series over the global land area and for each continent individually during winter and summer. While focusing on regional performance disparities between observations and simulations that manifest in an accumulation period of 3 months, we additionally test the drawn conclusions for other common accumulation periods (1, 6, 9, and 12 months). Our results suggest that calculating SPI with the commonly used gamma distribution leads to deficiencies in the evaluation of ensemble simulations. Replacing it with the exponentiated Weibull distribution reduces the area of those regions where the index does not have any skill for precipitation obtained from ensemble simulations by more than one magnitude. The exponentiated Weibull distribution maximizes also the normality of SPI obtained from observational data and a single ensemble simulation. We demonstrate that calculating SPI with the exponentiated Weibull distribution delivers better results for each continent and every investigated accumulation period, irrespective of the heritage of the precipitation data. Therefore, we advocate the employment of the exponentiated Weibull distribution as the basis for SPI.
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Karavitis, Christos A., Stavros Alexandris, Demetrios E. Tsesmelis, and George Athanasopoulos. "Application of the Standardized Precipitation Index (SPI) in Greece." Water 3, no. 3 (August 16, 2011): 787–805. http://dx.doi.org/10.3390/w3030787.
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Gocic, Milan, Danilo Misic, Slavisa Trajkovic, and Mladen Milanovic. "Using GIS tool for presenting spatial distribution of drought." Facta universitatis - series: Architecture and Civil Engineering 18, no. 1 (2020): 77–84. http://dx.doi.org/10.2298/fuace200409006g.
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By using GIS tools, it is possible to improve the preview of hydrological processes such as evapotranspiration, precipitation, flood and drought. In order to quantify drought, different type of drought indicators have been developed such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Standardized Precipitation Evapotranspiration Index (SPEI) or Water Surplus Variability Index (WSVI). In this paper the precipitation-based SPI indicator was applied to the monthly precipitation data from Serbia during the period 1948-2012. The data were processed in the QuantumGIS software package. For the purpose of application in the monitoring of drought at the national level, a spatial presentation of meteorological drought was obtained.
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Mohammed, Safwan A., and Endre Harsányi. "Drought cycle tracking in Hungary using Standardized Precipitation Index (SPI)." Acta Agraria Debreceniensis, no. 2 (December 15, 2019): 97–101. http://dx.doi.org/10.34101/actaagrar/2/3685.
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Drought is one of the natural hazard risks which badly affects both agricultural and socio-economic sectors. Hungary, which is located in Eastern Europe has been suffering from different drought cycles; therefore, the aim of this study is to analyse the rainfall data obtained from ten metrological stations (Békéscsaba, Budapest, Debrecen, Győr, Kékestető, Miskolc, Pápa, Pécs, Szeged, Siófok, Szolnok) between 1985 and 2016, by using the Standardized Precipitation Index (SPI).
The results showed that 2011 was recorded as the worst drought cycle of the studied period, where the SPI ranged between -0.22 (extreme drought) in Siófok, and 0.15 (no drought) in Miskolc. In a similar vein, the study highlighted the year 2010 to be the best hydrological year, when the SPI reached 0.73 (mildly wet) on average. Interestingly, the Mann-Kendall trend test for the drought cycle showed no positive trends in the study area. Finally, more investigation should be conducted into the climate change spatial drought cycle in Europe.
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DEEPA B. KAMBLE, SHWETA GAUTAM, HIMANI BISHT, SHRADDHA RAWAT, and ARNAB KUNDU. "Drought assessment for kharif rice using standardized precipitation index (SPI) and vegetation condition index (VCI)." Journal of Agrometeorology 21, no. 2 (November 10, 2021): 182–87. http://dx.doi.org/10.54386/jam.v21i2.230.
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The monthly weather data for 31 years from 1985-2015 was used to analyze the extent of meteorological drought using standardized precipitation index (SPI) over Allahabad, Kanpur and Lucknow. MODIS NDVI data from 2000-2015 was used for monitoring of agricultural drought through NDVI based vegetation condition index (VCI) for all the three districts. The monthly SPI and VCI values from July to October were correlated with productivity index (PI) of kharif rice.Both the indices (SPI and VCI) were positively correlated with PI for all the districts. In Allahabad SPI and VCI during September month showed a significant correlation (0.70**& 0.61*) while in Kanpur VCI during October and SPI of July and August were significantly correlated with PI of kharif Rice. The multiple regression equation developed for predicting kharif rice PI in Allahabad, Kanpur and Lucknow districts explained 69 to 76 per cent variabilityin PI.
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Qonita, Ismi Rizqi, Yoga Satria Putra, and Arie Antasari Kushadiwijayanto. "Pola Distribusi Kekeringan Menggunakan Metode Standardized Precipitation Index (SPI) di Kalimantan Barat." PRISMA FISIKA 7, no. 2 (September 19, 2019): 139. http://dx.doi.org/10.26418/pf.v7i2.35574.
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Penelitian tentang analisis tingkat kekeringan di Provinsi Kalimantan Barat telah dilakukan menggunakan metode Standardized Precipitation Index (SPI). Penelitian ini menggunakan data curah hujan selama 32 tahun (1985-2016) dari European Centre for Medium-range Weather Forecast (ECMWF). Curah hujan menjadi satu-satunya parameter masukan dalam metode Standardized Precipitation Index (SPI), sehingga tingkat kekeringan yang dikaji hanya dipengaruhi oleh peningkatan dan penurunan jumlah curah hujan. Dari analisa yang dilakukan selama periode 32 tahun, kekeringan terparah terjadi pada tahun 2014 di daerah Kabupaten Ketapang, 2015 di daerah Kabupaten Ketapang dan 2016 di daerah Kabupaten Sambas.Kata Kunci : Kekeringan, Curah Hujan, SPI
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OLASORE, Adeyemi Joshua, Adebayo Ebenezer OLAGBAIYE, Taiwo Adedayo AJAYI, and Peter Oluwatobi ALABI. "Drought Monitoring in Northern Nigeria Using Four (4) Indices." International Journal for Research in Applied Sciences and Biotechnology 8, no. 1 (January 7, 2021): 13–31. http://dx.doi.org/10.31033/ijrasb.8.1.3.
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Drought can generally be defined as the extreme persistence of precipitation deficit over a region for a specific period. Eight study locations were picked from the Sudano-Sahelian agro-ecological zones of Nigeria (Bauchi, Bida. Kaduna, Kano, Maiduguri. Sokoto, Nguru, and Katsina) from 1981 to 2015. The Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index- Thornthwaite (SPEI.T), Standardized Precipitation Evaporation Index-Hargreaves (SPEI-H) and Standardized Precipitation Evaporation Index-penman (SPEI-P) were used as the primary indicators of meteorological and agricultural droughts. The correlation coefficient shows an increasing correlation among the indices with increasing time scale, with SPI and SPEI-H having the highest correlation. The regression analysis shows a monotonic increasing relationship between indices while SPI vs SPEI-H has the highest correlation coefficient. The number of drought occurrences captured by the indices also increases with increasing time scale with SPEI-P detecting the highest number of drought events. All the drought indices reflect the historical drought periods between 1982-1989, 1992-2002, and 2008-2011. SPI, SPEI-P, and SPEI-H detected similar duration and intensity for the historical drought between 1982 and 1989 while SPEI-P showed the highest intensity and duration for the historical droughts between 1992 and 2002 and between 2008 and 2011.Analytic Hierarchy Process (AHP) evaluated that SPEI-P was more robust and sophisticated, SPI and SPEI-P had the same score for tractability while SPEI-H being the least tractable, and SPI had the highest for transparency and extendibility.
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Khan, Muhammad Imran, Xingye Zhu, Muhammad Arshad, Muhammad Zaman, Yasir Niaz, Ikram Ullah, Muhammad Naveed Anjum, and Muhammad Uzair. "Assessment of spatiotemporal characteristics of agro-meteorological drought events based on comparing Standardized Soil Moisture Index, Standardized Precipitation Index and Multivariate Standardized Drought Index." Journal of Water and Climate Change 11, S1 (October 15, 2020): 1–17. http://dx.doi.org/10.2166/wcc.2020.280.
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Abstract Drought indices that compute drought events by their statistical properties are essential stratagems for the estimation of the impact of drought events on a region. This research presents a quantitative investigation of drought events by analyzing drought characteristics, considering agro-meteorological aspects in the Heilongjiang Province of China during 1980 to 2015. To examine these aspects, the Standardized Soil Moisture Index (SSI), Standardized Precipitation Index (SPI), and Multivariate Standardized Drought Index (MSDI) were used to evaluate the drought characteristics. The results showed that almost half of the extreme and exceptional drought events occurred during 1990–92 and 2004–05. The spatiotemporal analysis of drought characteristics assisted in the estimation of the annual drought frequency (ADF, 1.20–2.70), long-term mean drought duration (MDD, 5–11 months), mean drought severity (MDS, −0.9 to −2.9), and mild conditions of mean drought intensity (MDI, −0.2 to −0.80) over the study area. The results obtained by MSDI reveal the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. The results of this study provide valuable information and can prove to be a reference framework to guide agricultural production in the region.
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MOHAMMED SALISU, ALFA. "FORECASTING DROUGHT WITH ARIMA MODEL AND STANDARDIZED PRECIPITATION INDEX (SPI)." Science Proceedings Series 1, no. 2 (April 24, 2019): 32–34. http://dx.doi.org/10.31580/sps.v1i2.616.
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Drought forecasting is an important forecasting procedure for preparing and managing water resources for all creatures. Natural disasters across the regions such as flooding, earthquakes, droughts etc. have caused damages to life as a result of which numerous researches have been conducted to assist in reducing the phenomenon. Consequently, therefore, this study considered using Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting drought using Standardized Precipitation Index (SPI) as a forecasting tool which was used to measure and classify drought. The models are developed to forecast the SPI series. Results indicated the forecasting ability of the ARIMA models which increases as the timescales. The study is aimed at using ARIMA method for modeling SPI data series. The studies used data set made up of 624 months, obtained from 1954 to 2008. In the analysis only SPI3 series was non-seasonal while others have seasonality and Seasonal ARIMA was carried out, SPI12 was significant compared with the forecasting accuracy alongside the diagnostic checking having a minimum error of RMSE and MAE in both testing and training phases. The research contributes to the discovering of feasible forecasting of drought and demonstrates that the established model is good and appropriate for forecasting drought.
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Novita, Firda, Donny Harisuseno, and Ery Suhartanto. "Studi Kekeringan Meteorologi dengan Menggunakan Metode Standardized Precipitation Index (SPI) dan China Z Index (CZI) di DAS Lekso Kabupaten Blitar." Jurnal Teknologi dan Rekayasa Sumber Daya Air 1, no. 2 (July 31, 2021): 648–60. http://dx.doi.org/10.21776/ub.jtresda.2021.001.02.26.
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Kabupaten Blitar merupakan salah satu daerah yang rawan terjadi kekeringan, salah satunya yaitu DAS Lekso. Kekeringan yang terjadi di Kabupaten Blitar disebabkan oleh minimnya intensitas curah hujan yang turun, maka dari itu dibutuhkan upaya awal untuk memitigasi kekeringan meteorologi dengan cara memantau dan menganalisis kekeringan yang terjadi pada lokasi studi. Metode yang digunakan dalam menganalisis kekeringan yaitu metode Standardized Precipitation Index (SPI)) dan metode China Z Index (CZI) yang kemudian dibandingkan dengan data Southern Oscillation Index (SOI). Hasil indeks kekeringan kedua metode yang telah dikomparasi dengan data SOI akan digunakan sebagai penggambaran peta sebaran kekeringan menggunakan Sistem Informasi Geografis (SIG) dengan interpolasi Kriging. Pada hasil analisa perbandingan indeks kekeringan dengan data SOI bulanan didapatkan hasil persentase pendekatan metode CZI sebesar 57.45% dan metode SPI sebesar 42.55%. Pada perbandingan indeks kekeringan dengan SOI rerata tahunan didapatkan persentase metode CZI sebesar 63% dan metode SPI sebesar 60%. Pada hasil analisa korelasi indeks kekeringan yang dikomparasi dengan data hujan didapatkan nilai korelasi metode CZI memiliki tingkat hubungan korelasi mendekati positif sempurna dan metode SPI memiliki korelasi yang cukup. Sehingga metode CZI dipilih sebagai penggambaran peta sebaran kekeringan menggunakan interpolasi kriging yang kemudian didapatkan desa-desa yang terdampak kekeringan di Kabupaten Blitar khusunya di DAS Lekso.
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Vu, T. M., and A. Mishra. "Spatial and temporal variability of Standardized Precipitation Index over Indochina Peninsula." Cuadernos de Investigación Geográfica 42, no. 1 (June 27, 2016): 221. http://dx.doi.org/10.18172/cig.2928.
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Indochina Peninsula has abundant water resources; however, most of the rain falls during the wet season. An arid condition is quite pronounced throughout the dry season. The majority of population depends on the agriculture as the main source of livelihood income. It is, therefore, important to study the drought and wetness over the region because crops are vulnerable to extreme climatic conditions. We used gridded precipitation APHRODITE and Standardized Precipitation Index (SPI) to evaluate the spatial and temporal variability of drought and wetness over Indochina peninsula. Nonparametric Modified Mann-Kendall (MMK) trend test was applied to determine the SPI trends over this region. There is a decrease in precipitation over a large part of Indochina during winter (dry season) and an increasing pattern during summer (rainy season). The increasing trend of SPI indicates an increase in wet condition over most parts of Indochina peninsula except for Red River Delta in Vietnam, central parts of Vietnam/Laos and western parts of Cambodia.
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Afrin, R., F. Hossain, and SA Mamun. "Analysis of Drought in the Northern Region of Bangladesh Using Standardized Precipitation Index (SPI)." Journal of Environmental Science and Natural Resources 11, no. 1-2 (October 1, 2019): 199–216. http://dx.doi.org/10.3329/jesnr.v11i1-2.43387.
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Drought is an extended period when a region notes a deficiency in its water supply. The Standardized Precipitation Index (SPI) method was used in this study to analyze drought. Northern region of Bangladesh was the area of study. Monthly rainfall data of northern region of Bangladesh was obtained from the Meteorological Department of Bangladesh. Obtained rainfall data was from 1991 to 2011 and values from 2012 to 2026 were generated using Markov model. Then SPI values from 1991 to 2026 were calculated by using SPI formula for analyzing drought. Analysis with SPI method showed that droughts in northern region of Bangladesh varied from moderately dry to severely dry conditions and it may vary from moderately dry to severely dry conditions normally in future but in some cases extreme drought may also take place. From the study, it is observed that the northern region of Bangladesh has already experienced severe drought in 1991, 1992, 1994, 1995, 1997, 1998, 2000, 2003, 2005, 2007, 2009 and 2010. The region may experience severe drought in 2012, 2015, 2016, 2018, 2019, 2021, 2022, 2023, 2024, 2025 and 2026 and extreme drought in 2012, 2014, 2016, 2023 and 2024.
J. Environ. Sci. & Natural Resources, 11(1-2): 199-216 2018
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FAHIM, AHMAD M., SHAH M. ALI, SHEN RUNPING, and J. ZHANG. "Characteristics of drought variation in winter using drought Indices during the period 1971-2010 : A case study of Khyber Pakhtunkhwa (Pakistan)." MAUSAM 67, no. 3 (December 8, 2021): 697–708. http://dx.doi.org/10.54302/mausam.v67i3.1390.
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Generally drought is the outcome of reduction in precipitation for a long period of time. It can happen anywhere in the world and cause harmful effect to human life and eco system. There are different drought indices, derived for analysis and quantification of drought. In this study monthly precipitation and temperature data was used to analyze drought situation using Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitations Index (SPI) and Z-index (also known as China Z-index) for the period 1971-2010 over Khyber Pakhtunkhwa (KPK) province of Pakistan for winter season (December, January & February). Analyses were performed on 3, 6 and 12 month timescale for SPEI and SPI. Z-index is used to calculate drought/wet (flood) situation in winter season. Based on all these indices, dryness and wetness intensity varies with timescale and location. On basis of three time scale, during the years 1971, 1988, 2001 and 2002, majority of the stations of study area were under the drought conditions (of different intensities). SPEI and SPI sometime portray contrasting results, because the later does not take into account the effect of temperature. Based on SPEI, drought frequency increases from north to south. Dera Ismail Khan (D.I. Khan) & Kohat suffered drought conditions for highest number of year, while Balakot the least. Contrary to this D. I. Khan has the least number of drought years based on SPI.
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Magallanes-Quintanar, Rafael, Fidel Blanco-Macías, Erick Carlos Galván-Tejada, Jorge Isaac Galván-Tejada, Miguel Márquez-Madrid, and Ricardo David Valdez-Cepeda. "Negative regional Standardized Precipitation Index trends prevail in the Mexico’s state of Zacatecas." REVISTA TERRA LATINOAMERICANA 37, no. 4 (October 28, 2019): 487. http://dx.doi.org/10.28940/terra.v37i4.563.
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As the earth atmosphere warms, it is unclear how the precipitation will change or how these changes will impact regional rainfall. For the study of spatial and temporal variability of rainfall, several indexes have been developed. The Standardized Precipitation Index (SPI) that only involves recorded rainfall data has been used as a tool for climatic zone classif ication and a drought indicator. Then, the aims of the present study were: 1) to cluster monthly precipitation time series into groups that represent regions under the basis of similar precipitation regimes, 2) to compute regional SPI’s using all the members (time series) of each cluster, and 3) to estimate trends of the regional SPI’s. The cluster analysis approach was used to identify four groups of monthly precipitation time series that represent regions of similar precipitation regimes. Afterwards, regional SPI’s were estimated using all the members of each cluster. Finally, four regional SPI trends were estimated by means of the Mann-Kendall trend test and Sen’s slope estimator. Estimated decreasing SPI trends imply prevail of negative values at the end of the study period (1964-2014), which indicate less than median precipitation in the entire Zacatecas state territory. For instance, SPI at 12-month time scale Sen’s slope values were -0.17 and -0.18 for the wet and dry seasons, respectively in the Semi-desert region. Thus, the evidenced trends may be having influence on the availability of surface water, groundwater levels and aquifers recharge in the near future. So, it is imperative to adjust inhabitants’ activities according to design planned climate change adaptation strategies.
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AL-Timimi, Yaseen K. "Drought assessment in Iraq using analysis of Standardized precipitation index (SPI)." Iraqi Journal of Physics (IJP) 12, no. 23 (February 18, 2019): 36–43. http://dx.doi.org/10.30723/ijp.v12i23.336.
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The Present study investigated the drought in Iraq, by using the rainfall data which obtained from 39 meteorological stations for the past 30 years (1980-2010). The drought coefficient calculated on basis of the standard precipitation index (SPI) and then characteristics of drought magnitude, duration and intensity were analyzed. The correlation and regression between magnitude and duration of drought were obtained according the (SPI) index. The result shows that drought magnitude values were greater in the northeast region of Iraq.
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Bloomfield, J. P., and B. P. Marchant. "Analysis of groundwater drought building on the standardised precipitation index approach." Hydrology and Earth System Sciences 17, no. 12 (December 4, 2013): 4769–87. http://dx.doi.org/10.5194/hess-17-4769-2013.
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Abstract. A new index for standardising groundwater level time series and characterising groundwater droughts, the Standardised Groundwater level Index (SGI), is described. The SGI builds on the Standardised Precipitation Index (SPI) to account for differences in the form and characteristics of groundwater level and precipitation time series. The SGI is estimated using a non-parametric normal scores transform of groundwater level data for each calendar month. These monthly estimates are then merged to form a continuous index. The SGI has been calculated for 14 relatively long, up to 103 yr, groundwater level hydrographs from a variety of aquifers and compared with SPI for the same sites. The relationship between SGI and SPI is site specific and the SPI accumulation period which leads to the strongest correlation between SGI and SPI, qmax, varies between sites. However, there is a consistent positive linear correlation between a measure of the range of significant autocorrelation in the SGI series, mmax, and qmax across all sites. Given this correlation between SGI mmax and SPI qmax, and given that periods of low values of SGI can be shown to coincide with previously independently documented droughts, SGI is taken to be a robust and meaningful index of groundwater drought. The maximum length of groundwater droughts defined by SGI is an increasing function of mmax, meaning that relatively long groundwater droughts are generally more prevalent at sites where SGI has a relatively long autocorrelation range. Based on correlations between mmax, average unsaturated zone thickness and aquifer hydraulic diffusivity, the source of autocorrelation in SGI is inferred to be dependent on dominant aquifer flow and storage characteristics. For fractured aquifers, such as the Cretaceous Chalk, autocorrelation in SGI is inferred to be primarily related to autocorrelation in the recharge time series, while in granular aquifers, such as the Permo–Triassic sandstones, autocorrelation in SGI is inferred to be primarily a function of intrinsic saturated flow and storage properties of aquifer. These results highlight the need to take into account the hydrogeological context of groundwater monitoring sites when designing and interpreting data from groundwater drought monitoring networks.
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Chelton, Dudley B., and Craig M. Risien. "A Hybrid Precipitation Index Inspired by the SPI, PDSI, and MCDI. Part I: Development of the Index." Journal of Hydrometeorology 21, no. 9 (September 1, 2020): 1945–76. http://dx.doi.org/10.1175/jhm-d-19-0230.1.
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AbstractThe filtering properties of the standardized precipitation index (SPI), the Palmer drought severity index (PDSI), and the model calibrated drought index (MCDI) are investigated to determine their relations to past, present, and future precipitation anomalies in regions with a wide diversity of precipitation characteristics. All three indices can be closely approximated by weighted averages of precipitation, but with different weighting. The SPI is well represented by one-sided, uniformly weighted averages; the MCDI is well represented by one-sided, exponentially weighted averages; and the PDSI is well represented by two-sided, exponentially weighted averages with much higher weighting of past and present precipitation than future precipitation. Detailed analyses identify interpretational complications and other undesirable features in the SPI and PDSI. In addition, the PDSI and MCDI are each restricted to single regionally specific “intrinsic” time scales that can significantly differ between the two indices. Inspired by the strengths of the SPI, PDSI, and MCDI, a hybrid index is developed that consists of exponentially weighted averages of past and present precipitation that are implicit in the PDSI and MCDI. The explicit specification of the exponential weighting allows users to control the time scale of the hybrid index to investigate precipitation variability on any time scale of interest. This advantage over the PDSI and MCDI is analogous to the controllability of the time scale of the SPI, but the exponentially fading memory is more physical than the uniform weighting of past and present precipitation in the SPI.
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Firdaus, Alfian, Donny Harisuseno, and Ery Suhartanto. "Studi Analisa Kekeringan Metode Standardized Precipitation Index (SPI) dan Palmer Drought Severity Index (PDSI) di DAS Kemuning Kabupaten Sampang." Jurnal Teknologi dan Rekayasa Sumber Daya Air 1, no. 2 (July 31, 2021): 535–48. http://dx.doi.org/10.21776/ub.jtresda.2021.001.02.17.
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Kekeringan ialah bencana alam yang terjadi secara perlahan dan berdampak buruk untuk kelangsungan hidup penduduk Kabupaten Sampang. Mengingat hal tersebut, perlu dilakukan analisa indeks kekeringan serta pemetaan sebarannya sebagai upaya mitigasi bencana kekeringan. Studi ini bertujuan untuk mengetahui tingkat keparahan kekeringan dengan metode Standardized Precipitation Index (SPI) dan Palmer Drought Severity Index (PDSI), serta kesesuaiannya dengan data Southern Oscillation Index (SOI) yang mampu mempresentasikan kejadian El Nino Southern Oscillation (ENSO). Setelah itu, Indeks kekeringan yang lebih sesuai dengan pola SOI dipetakan dengan metode Inverse Distance Weighting (IDW) untuk mengetahui sebaran kekeringan. Metode SPI menghasilkan indeks kekeringan terparah di bulan April 2004 sebesar -3,651 pada periode defisit 1 bulanan. Metode PDSI menghasilkan indeks kekeringan terparah di bulan September 2001 sebesar - 20,628. Berdasarkan hasil analisa rerata PDSI periode 1998-2017, diketahui bahwa bencana kekeringan umumnya bermula sejak bulan Juli dan berakhir di bulan Oktober, sedangkan puncak kekeringan terjadi pada bulan September. Metode PDSI juga memiliki kesesuaian sebesar 60% terhadap nilai SOI berdasarkan penggambaran grafik surplus dan defisit indeks rerata tahunan, lebih baik daripada metode SPI yang hanya bernilai 53%. Penggambaran peta sebaran kekeringan berdasarkan indeks kekeringan PDSI menunjukkan bahwa Kecamatan Sampang, Torjun, dan Camplong perlu diprioritaskan dalam upaya mitigasi bencana kekeringan di masa mendatang karena memiliki potensi bencana kekeringan lebih besar jika dibandingan kecamatan lainnya.
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Nugroho, Arzaky Ardi Surya, Cahyoadi Bowo, and Joko Sudibya. "INDEKS KEKERINGAN SPI (STANDARDIZED PRECIPITATION INDEX) DAN PENGARUHNYA TERHADAP PRODUKTIVITAS HORTIKULTURA TAHUNAN DI KABUPATEN JEMBER." Berkala Ilmiah Pertanian 2, no. 4 (November 7, 2019): 149. http://dx.doi.org/10.19184/bip.v2i4.16312.
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ABSTRACT Perennial tropical horticulture is a superior product whose productivity is affected by water availability. The impact of climate fluctuations due to global warming affects the availability of ground water. This study aims to determine the effect of the Standardized Precipitation Index (SPI) drought index derived from rainfall data on the productivity of perennial horticulture (durian, avocado and rambutan). The study was conducted from July 2016 - November 2018 in 9 sub-districts in Jember Regency where has the highest production. Rainfall data is proceed into SPI data according to the guidelines of the WMO (World Meteorological Organization). Productivity data derived from production data divided by the number of plants. The results of 12 monthly SPI calculations compared with the perennial horticultural productivity data. To find out the relationship between productivity and SPI, the correlation method is used. The results showed that the appropriate SPI value for observing annual horticultural productivity was SPI 9 and 12 monthly. The value of SPI greatly influences the correlation of productivity of durian, avocado and rambutan. Keywords: SPI, productivity, annual horticulture. ABSTRAK Tanaman hortikultura tropis tahunan adalah produk unggulan yang produktivitasnya dipengaruhi oleh ketersediaan air. Dampak fluktuasi iklim akibat pemanasan global mempengaruhi ketersediaan air tanah. Penelitian ini bertujuan untuk mengetahui pengaruh index kekeringan SPI yang berasal dari data curah hujan terhadap produktivitas hotikultura tahunan (durian, alpukat dan rambutan). Penelitian dilakukan mulai bulan Juli 2016 – November 2018 pada 9 Kecamatan dengan produksi tertinggi di Kabupaten Jember. Data curah hujan diolah menjadi data SPI sesuai pedoman WMO (World Meteorological Organization) dan data produktivitas berasal dari data produksi dibagi jumlah tanaman kemudian hasil perhitungan SPI 12 bulanan dibandingkan dengan data produktifitas hortikultura t ahunan. Untuk mengetahui hubungan produktivitas dan SPI digunkan metode korelasi. Hasil penelitian menunjukkan nilai SPI yang sesuai untuk mengamati produktivitas hortikultura tahunan adalah SPI 9 dan 12 bulanan. Nilai SPI sangat berpengaruh terhadap korelasi produktivitas durian, alpukat dan rambutan. Nilai SPI yang semakin tinggi menaikkan produktivitas durian, tetapi menurunkan produktivitas alpukat dan rambutan. Kata Kunci : SPI, produktifitas, hortikuktura tahunan
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Mohammed Salisu, Alfa, and Ani Shabri. "A Hybrid Wavelet-Arima Model for Standardized Precipitation Index Drought Forecasting." MATEMATIKA 36, no. 2 (August 1, 2020): 141–56. http://dx.doi.org/10.11113/matematika.v36.n2.1152.
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ABSTRACTThis paper proposes A Hybrid Wavelet-Auto-Regressive Integrated MovingAverage (W-ARIMA) model to explore the ability of the hybrid model over an ARIMAmodel. It combines two methods, a Discrete Wavelet Transform (DWT) and ARIMAmodel using the Standardized Precipitation Index (SPI) drought data for forecastingdrought modeling development. SPI data from January 1954 to December 2008 used wasdivided into two - (80%/20% for training/testing respectively). The results were comparedwith the conventional ARIMA model with Mean Square Error (MSE) and Mean AverageError (MAE) as an error measure. The results of the proposed method achieved the bestforecasting performance.
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Kubicz, Justyna. "The application of Standardized Precipitation Index (SPI) to monitor drought in surface and groundwaters." E3S Web of Conferences 44 (2018): 00082. http://dx.doi.org/10.1051/e3sconf/20184400082.
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The paper presents the initial studies with the aim to assess the possibility to apply of Standardized Precipitation Index SPI to monitor drought in surface and groundwaters. The fact that data about precipitation are highly available allows for precise monitoring of the periods of occurrence and intensification of meteorological drought by determining the standardized SPI index. The evaluation of current water deficits in surface water courses and groundwaters is very difficult due to the fact that the measurement network is relatively scarce. In order to apply SPI to monitor hydrological and hydrogeological drought, it is required to assess the significance and level of the correlation between drought indices in the test area and then to calculate the probability of correct determination of drought in surface and groundwaters with use of SPI.
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Malik, Anurag, Anil Kumar, Priya Rai, and Alban Kuriqi. "Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models." Climate 9, no. 2 (February 1, 2021): 28. http://dx.doi.org/10.3390/cli9020028.
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Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.
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Nagy, Patrik, Martina Zeleňáková, Slávka Galas, Helena Hlavatá, and Dorota Simonová. "Identification of dry and wet 6 months’ period in eastern Slovakia using indices." MATEC Web of Conferences 310 (2020): 00047. http://dx.doi.org/10.1051/matecconf/202031000047.
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In the paper we evaluated dry and wet 6 months’ periods, which reflect changes in water resources of the country. We assessed Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SPEI), Streamflow Drought Index (SDI), Reconnaissance Drought Index (RDI). The time period was 1960 - 2015 and the study area includes eastern Slovakia – selected water and climatic stations. The results indicate dry periods and wet periods. The results of work are presented in the table for separate evaluated indices.
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Bowden, Jared H., Kevin D. Talgo, Tanya L. Spero, and Christopher G. Nolte. "Assessing the Added Value of Dynamical Downscaling Using the Standardized Precipitation Index." Advances in Meteorology 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/8432064.
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In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical downscaling over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically downscale reanalysis fields to compare values of SPI over drought timescales that have implications for agriculture and water resources planning. The regional climate generated by WRF has the largest improvement over reanalysis for SPI correlation with observations as the drought timescale increases. This suggests that dynamically downscaled fields may be more reliable than larger-scale fields for water resource applications (e.g., water storage within reservoirs). WRF improves the timing and intensity of moderate to extreme wet and dry periods, even in regions with homogenous terrain. This study also examines changes in SPI from the extreme drought of 1988 and three “drought busting” tropical storms. Each of those events illustrates the importance of using downscaling to resolve the spatial extent of droughts. The analysis of the “drought busting” tropical storms demonstrates that while the impact of these storms on ending prolonged droughts is improved by the RCM relative to the reanalysis, it remains underestimated. These results illustrate the importance and some limitations of using RCMs to project drought.
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Koutroulis, Aristeidis G., Aggeliki-Eleni K. Vrohidou, and Ioannis K. Tsanis. "Spatiotemporal Characteristics of Meteorological Drought for the Island of Crete." Journal of Hydrometeorology 12, no. 2 (April 1, 2011): 206–26. http://dx.doi.org/10.1175/2010jhm1252.1.
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Abstract A modified drought index, named the spatially normalized–standardized precipitation index (SN-SPI), has been developed for assessing meteorological droughts. The SN–SPI is a variant index to the standardized precipitation index and is based on the probability of precipitation at different time scales, but it is spatially normalized for improved assessment of drought severity. Results of this index incorporate the spatial distribution of precipitation and produce improved drought warnings. This index is applied in the island of Crete, Greece, and the drought results are compared to the ones of SPI. A 30-year-long average monthly precipitation dataset from 130 watersheds of the island is used by the above indices for drought classification in terms of its duration and intensity. Bias-adjusted monthly precipitation estimates from an ensemble of 10 regional climate models were used to quantify the influence of global warming to drought conditions over the period 2010–2100. Results based on both indices (calculated for three time scales of 12, 24, and 48 months) from 3 basins in west, central, and east parts of the island show that 1) the extreme drought periods are the same (reaching 7% of time) but the intensities based on SN–SPI are lower; 2) the area covered by extreme droughts is 3% (first time scale), 16% (second time scale), and 25% (third time scale), and 96% (first time scale), 95% (second time scale), and 80% (third time scale) based on the SN–SPI and SPI, respectively; 3) concerning the longest time scale (48 months), more than half of the area of Crete is about to experience drought conditions during 28%, 69%, and 97% for 2010–40, 2040–70, and 2070–2100, respectively; and 4) extremely dry conditions will cover 52%, 33%, and 25% of the island for the future 90-year period using 12-, 24-, and 48-month SN–SPI, respectively.
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Dewita, Monika, Donny Harisuseno, and Ery Suhartanto. "Analisis Kekeringan Meteorologi dengan Metode Standardized Precipitation Index (SPI) dan China Z Index (CZI) Di Sub DAS Kadalpang, Kabupaten Pasuruan." Jurnal Teknologi dan Rekayasa Sumber Daya Air 2, no. 1 (January 31, 2022): 1–13. http://dx.doi.org/10.21776/ub.jtresda.2022.002.01.01.
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Sub DAS Kadalpang, Kabupaten Pasuruan merupakan salah satu daerah rawan bencana kekeringan di Indonesia. Analisis kekeringan serta pemetaan sebarannya diperlukan sebagai upaya meminimalisir dampak kekeringan. Studi ini bertujuan untuk mengetahui hasil kesesuaian metode Standardized Precipitation Index (SPI) dan China Z Index (CZI) dengan Southern Oscillation Index (SOI) sehingga didapatkan metode yang lebih sesuai diterapkan pada Sub DAS Kadalpang. Pemetaan sebaran kekeringan menggunakan metode Inversed Distance Weight (IDW) dengan bantuan Sistem Informasi Geografi bertujuan untuk mengetahui daerah terdampak secara lebih akurat agar penanganan dapat dilakukan dengan optimal. Hasil indeks kekeringan terparah metode SPI sebesar (-3,711) pada periode 1 bulan, pada bulan Mei 2018. Hasil indeks kekeringan terparah metode CZI sebesar (-6,701) pada periode 1 bulan, pada bulan Mei 2018. Analisis korelasi CZI dan SPI dengan SOI menunjukkan hubungan linier yang lemah. Dipilih opsi perbandingan dengan pola curah hujan untuk menunjukkan kesesuaian dengan lokasi studi dan metode CZI lebih sesuai. Penggambaran peta sebaran kekeringan menggunakan metode yang lebih sesuai yaitu CZI. Hasil peta sebaran kekeringan dengan jumlah kejadian kekeringan terparah tahun 2007 dengan bulan kering terparah bulan Mei, dan terdapat 17 desa di Sub DAS Kadalpang berpotensi terdampak kekeringan sehingga perlu diprioritaskan dalam upaya mitigasi bencana kekeringan di masa mendatang.
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Cerpa Reyes, Luis José, Humberto Ávila Rangel, and Luis Carlos Sandoval Herazo. "Adjustment of the Standardized Precipitation Index (SPI) for the Evaluation of Drought in the Arroyo Pechelín Basin, Colombia, under Zero Monthly Precipitation Conditions." Atmosphere 13, no. 2 (January 30, 2022): 236. http://dx.doi.org/10.3390/atmos13020236.
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The evaluation of the meteorological drought is fundamental for the management of the water resource. One of the most used indices to evaluate the drought is the standardized precipitation index (SPI) due to its practicality and evaluation in a variety of time scales, however, this uses precipitation as the only variable, depending on the deviations in the precipitation values. This is important when evaluating the SPI, because in some ecosystems close to the equatorial zone, there are very warm periods with low rainfall, in which a large proportion of the data collected by the meteorological stations corresponds to zero. In this research, the SPI was calculated in the Pechelín basin located in Colombia, in which there is zero precipitation in a large proportion of the data, registering zero precipitation in the month of January and February in 67% and 70% respectively. As a result, the SPI values increased to “wet” ranges, only when the amount of data with zero precipitation exceeded half of the total data; this means that the SPI determines wrong values when it is calculated with zero-precipitation data in large proportions. Based on this finding, this study aims to modify the index by typing the distribution (using a correction factor K), finally correcting the SPI values, this correction was called SPI-C. The results indicate that the SPI-C improved the identification of drought, obtaining corresponding values that better represent the high frequency of zero precipitation existing in the study area.
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Oikonomou, Panagiotis D., Christos A. Karavitis, and Elpida Kolokytha. "Multi-Index Drought Assessment in Europe." Proceedings 7, no. 1 (November 15, 2018): 20. http://dx.doi.org/10.3390/ecws-3-05822.
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Any attempt for the application of integrated drought management requires identifying and characterizing the event, per se. The questions of scale, boundary, and of geographic areal extent are of central concern for any efforts of drought assessment, impact identification, and thus, of drought mitigation implementation mechanisms. The use of drought indices, such as Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), has often led to pragmatic realization of drought duration, magnitude, and spatial extension. The current effort presents the implementation of SPI and SPEI on a Pan-European scale and it is evaluated using existing precipitation and temperature data. The ENSEMBLES Observations gridded dataset (E-OBS) for precipitation, minimum temperature, and maximum temperature used covered the period 1969–2018. The two indices estimated for time steps of 6 and 12 months. The results for the application period of recurrent droughts indicate the potential that both indices offer for an improvement on drought critical areas of identification, threshold definitions and comparability, and towards contingency planning, leading to better mitigation efforts.
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Bloomfield, J. P., and B. P. Marchant. "Analysis of groundwater drought using a variant of the Standardised Precipitation Index." Hydrology and Earth System Sciences Discussions 10, no. 6 (June 14, 2013): 7537–74. http://dx.doi.org/10.5194/hessd-10-7537-2013.
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Abstract. A new index for standardising groundwater level time series and characterising groundwater droughts, the Standardised Groundwater level Index (SGI), is described. The SGI is a modification of the Standardised Precipitation Index (SPI) that accounts for differences in the form and characteristics of precipitation and groundwater level time series. The SGI is estimated using a non-parametric normal scores transform of groundwater level data for each calendar month. These monthly estimates are then merged to form a continuous index. The SGI has been calculated for 14 relatively long, up to 103 yr, groundwater level hydrographs from a variety of aquifers and compared with SPI for the same sites. The SPI accumulation period which leads to the strongest correlation between SPI and SGI, qmax, varies between sites. There is a positive linear correlation between qmax and a measure of the range of significant autocorrelation in the SGI series, mmax. For each site the strongest correlation between SPI and SGI is in the range 0.7 to 0.87, and periods of low values of SGI coincide with previously independently documented droughts. Hence SGI is taken to be a robust and meaningful index of groundwater drought. The maximum length of groundwater droughts defined by SGI is an increasing function of mmax, meaning that relatively long groundwater droughts are generally more prevalent at sites where SGI has a relatively long autocorrelation range. Based on correlations between mmax, average unsaturated zone thickness and aquifer hydraulic diffusivity, the source of autocorrelation in SGI is inferred to be dependent on aquifer flow and storage characteristics. For fractured aquifers, such as the Cretaceous Chalk, autocorrelation in SGI is inferred to be primarily related to autocorrelation in the recharge time series, while in granular aquifers, such as the Permo-Triassic Sandstones, autocorrelation in SGI is inferred to be primarily a function of intrinsic aquifer characteristics. These results highlight the need to take into account the hydrogeological context of groundwater monitoring sites when designing and interpreting data from groundwater drought monitoring networks.
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Nikravesh, Gholamreza, Mohammad Aghababaei, Mohammad Nazari-Sharabian, and Moses Karakouzian. "Drought Frequency Analysis Based on the Development of a Two-Variate Standardized Index (Rainfall-Runoff)." Water 12, no. 9 (September 17, 2020): 2599. http://dx.doi.org/10.3390/w12092599.
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Drought is one of the most drastic events, which has imposed irreparable damages on human societies and may occur in any climate regime. To define drought, given its properties of multidimensionality and randomity, one cannot rely on a single variable/index (e.g., precipitation, soil moisture, and runoff). Accordingly, implementing a novel approach, this study investigated drought events in two basins with different climatic regimes, using multivariate frequency analyses of drought duration, severity, and severity peak, based on developing a Two-variate Standardized Index (TSI). The index was developed based on the concept of copula, by applying rainfall-runoff data (1974–2019) and comparing them with two popular drought indices, the Standardized Precipitation Index (SPI) and Standardized Stream Flow Index (SSFI), in terms of derived drought characteristics. The results show that TSI determined more severe drought conditions with fewer return periods than SPI and SSFI in a specific drought event. This implies that the disadvantages of SPI and SSFI might not be found in TSI. The developed index can be employed by policymakers and planners to protect water resources from drought.
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Pandey, Yogesh, Ram Nath Jha, Sabah Parvaze, Latief Ahmad, and Shahzad Faisal. "Standardized Precipitation Index (SPI) for Drought Intensity Assessment in South Kashmir." International Journal of Current Microbiology and Applied Sciences 8, no. 12 (December 10, 2019): 2846–56. http://dx.doi.org/10.20546/ijcmas.2019.812.332.
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Shah, Ravi, Nitin Bharadiya, and Vivek Manekar. "Drought Index Computation Using Standardized Precipitation Index (SPI) Method For Surat District, Gujarat." Aquatic Procedia 4 (2015): 1243–49. http://dx.doi.org/10.1016/j.aqpro.2015.02.162.
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