Assessment of parametric approaches to calculate the Evaporative Demand Drought Index

Noguera, Iván; Vicente‐Serrano, Sergio M.; Domínguez‐Castro, Fernando; Reig, F.

doi:10.1002/joc.7275

Cited by 13 publications

(3 citation statements)

References 42 publications

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“…The first group comprises indices that use only precipitation data in calculating drought indices such as SPI, Rainfall Anomaly Index (RAI), and ZSI (Aksan and Bacanlı 2021;McKee et al 1993;Misra and Singh 2010). The other groups are respectively those considering only potential evapotranspiration (PET) such as Evaporative Demand Drought Index (EDDI), considering both temperature and precipitation (i.e., Palmer Drought Severity Index (PDSI)), along with those considering both precipitation and PET such as Standardized Precipitation Evapotranspiration Index (SPEI) (Beguería et al 2013;Noguera et al 2021;Palmer, 1965). The following sections will focus on SPI and ZSI.…”

Section: Drought Indicesmentioning

confidence: 99%

Impacts of climate change on spatial drought distribution in the Mediterranean Basin (Turkey): different climate models and downscaling methods

Erkol,

Yesilyurt,

Dalkilic

2024

Theor Appl Climatol

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The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin. In doing so, this study utilizes precipitation data of three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are CNRM-CM5, GFDL-ESM2M, and MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four Statistical Downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. First, it compares the performances of SD methods in improving climate models' representation of observed climate variables. Second, the study shows how different methods will affect spatial drought distribution in the area under SSP2 RCP4.5 and RCP8.5 scenarios.Consequently, the study uses the Standardized Precipitation Index (SPI) and Z-score Index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to -0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions.

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Section: Drought Indicesmentioning

confidence: 99%

Impacts of climate change on spatial drought distribution in the Mediterranean Basin (Turkey): different climate models and downscaling methods

Erkol,

Yesilyurt,

Dalkilic

2024

Theor Appl Climatol

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“…O acompanhamento, a análise e previsão das secas meteorológicas de extrema importância (Noguera et al, 2022;Cao et al, 2022;Schwartz et al, 2022). Além disso, a seca meteorológica tornase a principal fonte de outros tipos de seca (hidrológica, seca agrícola, socioeconômica etc.)…”

Section: Introductionunclassified

Análise da Variabilidade dos Períodos Secos e Chuvosos da Precipitação Pluvial no Estado da Paraíba Utilizando o Índice Padronizado de Precipitação (IPP)

Rodrigues

Sousa

Lopes

2022

Rev. Bras. Geog. Fis.

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O estado da Paraíba situada na região Nordeste do Brasil, apresenta uma variabilidade climática com períodos que variam desde altos índices de precipitação até temporadas de secas, devido a influência direta de elementos e mecanismos climáticos que ditam o regime de chuvas na Região. Este artigo objetivou elaborar as análises das precipitações pluviais médias mensais e anuais e dos períodos chuvoso e seco. Foram utilizadas séries temporais de precipitação pluviométrica de 123 postos pluviométricos da AESA com 27 anos de observações, para estimar a variabilidade e tendência das chuvas no Estado. Foi elaborada uma planilha eletrônica com os dados históricos e estimadas estatísticas básicas como: média, mediana, desvio-padrão, coeficientes de assimetria, curtose e variação. O passo seguinte foi as estimativas dos SPIs no software RStudio. Utilizando o software Surfer versão 13, foram elaborados os mapas mensais e o mapa anual das precipitações pluviais. Os resultados demonstraram que a distribuição das chuvas ocorre de forma irregular e com grande variação durante todo o ano. Os valores do mostrou que todos os meses da série histórica dos dados de chuva a assimetria foi positiva. Os picos do estão concentrados na parte central do Estado. Os picos do estão nas regiões do Agreste e da Borborema que vai de janeiro a abril. Nas análises dos períodos secos e chuvoso foram utilizados os Índices de Precipitação Padronizado (IPP) nas escalas temporais de 1, 3, 6 e 12 meses. Entre os anos de 1997 a 2000 foram verificadas atuações de secas por todos os SPIs. Esses eventos possivelmente sofreram influências do fenômeno El Niño.Palavras-chave: Precipitação, IPP, Variabilidade espaço-temporal, Períodos seco e chuvosos. Analysis of the Variability of Dry and Rainy Periods of Rainfall in the State of Paraíba using the Standardized Precipitation Index (SPI)A B S T R A C TThe state of Paraíba, located in the Northeast region of Brazil, presents a climatic variability with periods ranging from high precipitation rates to dry seasons, due to the direct influence of climatic elements and mechanisms that dictate the rainfall regime in the region. This paper aimed to elaborate the analyses of the average monthly and annual rainfall and the rainy and dry periods. Time series of rainfall from 123 rainfall stations of AESA with 27 years of observations were used to estimate the variability and trend of rainfall in the state. A spreadsheet was prepared with the historical data and basic statistics were estimated as: mean, median, standard deviation, coefficients of asymmetry, kurtosis and variation. The next step was to estimate the s using the software RStudio. Using the Surfer software version 13, the monthly maps and the annual rainfall map were prepared. The results showed that the rainfall distribution occurs irregularly and with great variation throughout the year. The values of showed that all months of the historical series of rainfall data the asymmetry was positive. The peaks of are concentrated in the central part of the state. The peaks of are in the Agreste and Borborema regions from January to April. In the analyses of the dry and rainy periods, the Standardized Precipitation Indices (SPI) on the time scales of 1, 3, 6 and 12 months were used. Between the years 1997 and 2000 drought events were verified by all s. These events were possibly influenced by the El Niño phenomenon.Keywords: Precipitation, SPI, Spatio-temporal variability, Dry and rainy periods.

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“…Many indicators have been introduced for evaluating and monitoring drought situations worldwide. The standardized precipitation index (SPI) is one of the most widely used indices in the world due to its user-friendly structure, low need for climatic variables (rainfall only), and ability to determine the severity of drought at different time scales such as 1-, 3-, 6-, 9-, 12-, and 24-month (Sahana et al 2021;Khanmohammadi et al 2022;Noguera et al 2022;Salvacion 2022). Abro et al (2022) evaluated the trend of the SPI in the Qinhuai River Basin using the Mann-Kendall (MK) and Sen's slope (Ss) tests.…”

Section: Introductionmentioning

confidence: 99%

Assessing the performance of SN-SPI and SPI and the trend assessment of drought using the XI correlation technique over Iran

Ghasemi

Mokarram

Zarei

2022

Journal of Water and Climate Change

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In recent decades, human activities such as the excessive production of carbon dioxide gas, deforestation, overexploitation of natural resources, and so on have caused changes in the climate regimes and occurrence frequency of droughts. Thus, the investigation of the trend of changes in drought can be effective in better understanding the drought condition and also more properly managing the adverse effects of drought. Therefore, in this study, the trend of changes in drought was assessed based on the standardized precipitation index (SPI) and the spatially normalized SPI (SN-SPI) in 34 stations over Iran on 1-, 3-, 6-, and 12-month time scales from 1967 to 2019 using the XI correlation technique (XICOR). Then, using the mean residual error (MRE) index and also a comparison of the correlation coefficient (CC) between the SPI and SN-SPI values with a perfect reliable line (line with the equation Y=X- or T-Statistics index), the performances of the SPI and SN-SPI were investigated. The results of the trend assessment indicated that the SPI and SN-SPI had almost the same performance in all chosen time scales (including 1-, 3-, 6-, and 12-month). However, the results of the T- Statistics and MRE revealed that the calculated values of the SN-SPI in more than 80% of the chosen stations (in the selected time scales) were different from the SPI, and in more than 70% of stations, the calculated values of the SN-SPI were less than the SPI. Therefore, it can be said that the SN-SPI has a different performance from the SPI, and under the influence of spatial normalization of the precipitation values in the SN-SPI, the overall level of the calculated SN-SPI values will be decreased.

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Assessment of parametric approaches to calculate the Evaporative Demand Drought Index

Cited by 13 publications

References 42 publications

Impacts of climate change on spatial drought distribution in the Mediterranean Basin (Turkey): different climate models and downscaling methods

Impacts of climate change on spatial drought distribution in the Mediterranean Basin (Turkey): different climate models and downscaling methods

Análise da Variabilidade dos Períodos Secos e Chuvosos da Precipitação Pluvial no Estado da Paraíba Utilizando o Índice Padronizado de Precipitação (IPP)

Assessing the performance of SN-SPI and SPI and the trend assessment of drought using the XI correlation technique over Iran

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