scholarly journals Testing of Drought Exceedance Probability Index (DEPI) for Turkey using PERSIANN data for 2000-2021 period

2021 ◽  
pp. 15-28
Author(s):  
Emre Topçu
Keyword(s):  
Trend Analysis ◽  
Human Life ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Exceedance Probability ◽  
Monthly Precipitation ◽  
Analysis Method ◽  
Drought Analysis ◽  
Monthly Total Precipitation ◽  
Probability Index

Drought is a climatic event that threatens the environment and human life with an ambiguity of location and time. Recently, droughts can be analyzed for different periods with the help of different mathematical methods and developing technology. This study aims to perform a drought analysis in 126 designated study points of Turkey. The analyzed data includes monthly total precipitation values between March 2000 and February 2021, obtained from PERSIANN system (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). Monthly precipitation totals of these designated points were used as input parameters in the Drought Exceedance Probability Index (DEPI) which is a new drought analysis method. The analysis was conducted separately for the whole of Turkey from January to December. Moreover, the findings were compared with the Standardized Precipitation Index (SPI), a globally accepted and commonly used drought index, to measure the drought detection performance of DEPI. SPI was calculated for periods of 6, 12 and 24 months. Pearson correlation coefficients between drought values of SPI-6, SPI-12 and SPI-24 and DEPI results were calculated. The second part of the study includes possible trend of drought determined by the Mann-Kendall trend analysis method. Both DEPI and SPI results and trend analysis results were mapped and visualized with the help of ArcGIS package program. The highest correlation is between DEPI and SPI-12 with 0.75, while the lowest correlation is between DEPI and SPI-24 with a value of 0.62. SPI monthly drought maps indicated the wettest months were January and February, while the driest months were March and July. Besides the DEPI monthly drought maps, the wettest months were October and November, while the driest months were May and June. The Mann-Kendall trend maps showed a significant increase in drought for summer.

scholarly journals Analysis of total monthly precipitation of Susurluk Basin in Turkey using innovative polygon trend analysis method

2020 ◽  
Author(s):  
Gokmen Ceribasi ◽  
Ahmet Iyad Ceyhunlu
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Meteorological Parameters ◽  
Global Climate ◽  
Data Series ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Analysis Method ◽  
Distinctive Features ◽  
Monthly Precipitation Data

Abstract The effects of climate change caused by global warming can be seen in changes of climate variables such as precipitation, humidity, and temperatures. These effects of global climate change can be interpreted as a result of the examination of meteorological parameters. One of the most effective methods to investigate these effects is trend analysis. The Innovative Polygon Trend Analysis (IPTA) method is a trend analysis method that has emerged in recent years. The distinctive features of this method compared with other trend methods are that it depends on time series and can compare data series among themselves. Therefore, in this study, the IPTA method was applied to total monthly precipitation data of Susurluk Basin, one of Turkey's important basins. Data from ten precipitation observation stations in Susurluk Basin were used. Data were provided by the General Directorate of State Meteorology Affairs. The length of this data series was 12 years (2006–2017). As a result of the study, since there is no regular polygon in IPTA graphics of each station, it is seen that precipitation data varies by years. While this change is seen increasingly at some stations, it is seen decreasingly at other stations.

scholarly journals Assessing the correlation between climate signals and monthly mean and extreme precipitation and discharge of Golestan Dam Watershed

2015 ◽  
Vol 19 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Saeed Golian ◽  
Hossein Ruigar
Keyword(s):  
Strong Correlation ◽  
Caspian Sea ◽  
Extreme Precipitation ◽  
Pearson Correlation ◽  
Daily Rainfall ◽  
Monthly Precipitation ◽  
Maximum Correlation ◽  
Discharge Data ◽  
Monthly Total Precipitation ◽  
Climate Signals

<p>Climate teleconnection signals are one of the main factors influencing the earth's climate oscillations in global and regional scales. In the present research, the effect of these signals on precipitation and discharge of Madarsoo Watershed at the upstream of Golestan Dam was investigated. For this purpose, three raingauges and hydrometric stations with respectively 40 and 38 years of daily rainfall and discharge data were selected. Pearson-correlation coefficient was used to consider the correlation between climate signals and monthly mean and extreme precipitation and discharge. The results showed strong correlation between monthly total precipitation of Tangrah and Tamer with Sea Level Pressure (SLP) of Caspian Sea with 7 and 9 months of lag, respectively, and Galikesh with Sea Surface Temperature (SST) of Caspian Sea with 9 months of lag. For monthly mean discharge of Tangrah and Galikesh, the maximum correlation was calculated for SLP of Caspian Sea with a lag of 9 months and Tamer with SST of Greenland with a lag of 7 months. For the extreme monthly data, very strong correlation was detected between the precipitations of all raingauges in June with Greenland SST with 8 months of lag. In contrast, for maximum monthly discharge of Tangrah and Galikesh hydrometric stations, the maximum correlation coefficients were calculated for SST of Black Sea with 4 months of lag in August and for Tamer with SLP of Caspian Sea with 4 months of lag in August. With regard to the results, the utilization of these signals, especially SLP and SST, is strongly suggested to predict the maximum and mean monthly precipitation and discharge over this region.</p><p> </p><p><strong>Resumen</strong></p>Las señales de teleconexión atmosférica son uno de los factores principales que influencian las oscilaciones climáticas de la tierra a escala global y regional. En el presente estudio se investigan los efectos de estas señales en la precipitación y caudal de la cuenca hidrográfica Madarsoo, río arriba de la presa Golestan. Con este propósito se seleccionaron tres estaciones pluviométricas e hidrométricas con registros diarios de lluvia y caudal durante unas cuatro décadas. Se utilizó el coeficiente Pearson para considerar la correlación entre las señales climáticas y los índices medio y extremo mensual de precipitación y caudal. Los resultados muestran una fuerte correlación entre la precipitación total mensual de las estaciones Tangrah y Tamer con la Presión Barométrica a Nivel del Mar (SLP, inglés) en el mar Caspio, en un período de siete y nueve meses, respectivamente, y de la estación Galikesh con la Temperatura a Nivel del Mar (SST, inglés) en el Caspio durante nueve meses. Para la media mensual del caudal en Tangrah y Galikesh, la correlación máxima se calculó a través de la SLP del mar Caspio con un período de nueve meses, y la de Tamer con la SST del mar de Groenlandia en un lapso de siete meses. Para el índice extreme mensual se detectó una fuerte correlación entre las precipitaciones medidas en todos los pluviómetros en junio con la SST de Groenlandia en un período de ocho meses. En contraste, para el índice máximo mensual de caudal en las estaciones hidrométricas Tangrah y Galikesh se calcularon los coeficientes de correlación máxima para la SST del mar Negro en un lapso de cuatro meses en agosto y para la estación Tamer con la SLP del mar Caspio durante cuatro meses, también en Agosto. Con respecto a los resultados, se recomienda la utilización de estas señales, especialmente la SLP y la SST, para predecir los índices de precipitación y caudal medio y máximo en esta región.

scholarly journals The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area

2021 ◽  
Vol 13 (22) ◽  
pp. 12674
Author(s):  
Mohammed Achite ◽  
Gokmen Ceribasi ◽  
Ahmet Iyad Ceyhunlu ◽  
Andrzej Wałęga ◽  
Tommaso Caloiero
Keyword(s):  
Climate Change ◽  
Risk Management ◽  
Standard Deviation ◽  
Trend Analysis ◽  
Arithmetic Mean ◽  
Drought Risk ◽  
Monthly Precipitation ◽  
Precipitation Trend ◽  
Precipitation Data ◽  
Monthly Total Precipitation

Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.

scholarly journals Forecasting the Future Drought Indices Due to the Effects of Climate Change in Al Najaf City, Iraq.

2022 ◽  
Vol 961 (1) ◽  
pp. 012040
Author(s):  
H H Mahdi ◽  
T A Musa ◽  
Z A A Al-Rammahi ◽  
E J Mahmood
Keyword(s):  
Drought Index ◽  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Essential Element ◽  
Environmental Data ◽  
Exceedance Probability ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Time Period ◽  
The Future

Abstract Drought is a natural disaster associated with a shortage of water availability for specified region within a specific time period. The impacts of drought are significant and extend to damage many important life aspects such as environmental, economic, and social activities. The forecasting of the drought events is an essential element for planning this disaster, reducing its effectiveness and response. The three characteristic frequency, intensity, and time period are the key parts for forecasting and assessment of droughts. Here, two drought indices (The Reconnaissance Drought Index (RDI), standardized precipitation index (SPI)) were used for forecasting of the future drought within Al Najaf city, Iraq. Thirty years meteorological data (average monthly precipitation and temperature) were used for the period (2021–2050) downloaded from the site of the Centre for Environmental Data Analysis (CEDA) for five grid points to cover overall study area. The computation of these indices conducted at a 12-month time scale and included the calculation of potential evapotranspiration by Thorthwaite method. The temporal drought intensity as well as drought frequency configurations were calculated and analyzed for each drought index. The results showed that the general average drought level expected will mildly dry while the maximum drought level expected will extremely dry. The more severe seasons of drought were forecasted in the years 2038, 2034 and 2021, respectively. Also, the prevailing event will be a one year drought and the maximum drought interval occurred within the study period will four consecutive years, with a 3.33% exceedance probability.

scholarly journals Assessment of the Dissimilarities of EDI and SPI Measures for Drought Determination in South Africa

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 82
Author(s):  
Omolola M. Adisa ◽  
Muthoni Masinde ◽  
Joel O. Botai
Keyword(s):  
South Africa ◽  
South African ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Severity ◽  
Pearson Correlation Coefficient ◽  
Drought Duration

This study examines the (dis)similarity of two commonly used indices Standardized Precipitation Index (SPI) computed over accumulation periods 1-month, 3-month, 6-month, and 12-month (hereafter SPI-1, SPI-3, SPI-6, and SPI-12, respectively) and Effective Drought Index (EDI). The analysis is based on two drought monitoring indicators (derived from SPI and EDI), namely, the Drought Duration (DD) and Drought Severity (DS) across the 93 South African Weather Service’s delineated rainfall districts over South Africa from 1980 to 2019. In the study, the Pearson correlation coefficient dissimilarity and periodogram dissimilarity estimates were used. The results indicate a positive correlation for the Pearson correlation coefficient dissimilarity and a positive value for periodogram of dissimilarity in both the DD and DS. With the Pearson correlation coefficient dissimilarity, the study demonstrates that the values of the SPI-1/EDI pair and the SPI-3/EDI pair exhibit the highest similar values for DD, while the SPI-6/EDI pair shows the highest similar values for DS. Moreover, dissimilarities are more obvious in SPI-12/EDI pair for DD and DS. When a periodogram of dissimilarity is used, the values of the SPI-1/EDI pair and SPI-6/EDI pair exhibit the highest similar values for DD, while SPI-1/EDI displayed the highest similar values for DS. Overall, the two measures show that the highest similarity is obtained in the SPI-1/EDI pair for DS. The results obtainable in this study contribute towards an in-depth knowledge of deviation between the EDI and SPI values for South Africa, depicting that these two drought indices values are replaceable in some rainfall districts of South Africa for drought monitoring and prediction, and this is a step towards the selection of the appropriate drought indices.

scholarly journals Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

2013 ◽  
Vol 17 (6) ◽  
pp. 2359-2373 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Seasonal Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Blue Nile ◽  
Forecasting System ◽  
The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

scholarly journals Impact of Large-Scale Climate Indices on Meteorological Drought of Coastal Ghana

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Martin Addi ◽  
Kofi Asare ◽  
Samuel Kofi Fosuhene ◽  
Theophilus Ansah-Narh ◽  
Kenneth Aidoo ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Stepwise Regression ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Climate Indices ◽  
Severe Drought ◽  
Time Lags ◽  
Drought Prediction ◽  
Catchment Areas ◽  
Dry Seasons

The devastating effects of drought on agriculture, water resources, and other socioeconomic activities have severe consequences on food security and water resource management. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. In this study, meteorological droughts over six coastal synoptic stations were investigated using three-month Standardized Precipitation Index (SPI). The dry seasons of November-December-January (NDJ), December-January-February (DJF), and January-February-March (JFM) were the focal seasons for the study. Trends of dry seasons SPIs were evaluated using seasonal Mann–Kendall test. The relationship between drought SPI and ocean-atmosphere climate indices and their predictive ability were assessed using Pearson correlation and Akaike Information Criterion (AIC) stepwise regression method to select best climate indices at lagged timestep that fit the SPI. The SPI exhibited moderate to severe drought during the dry seasons. Accra exhibited a significant increasing SPI trend in JFM, NDJ, and DJF seasons. Besides, Saltpond during DJF, Tema, and Axim in NDJ season showed significant increasing trend of SPI. In recent years, SPIs in dry seasons are increasing, an indication of weak drought intensity, and the catchment areas are becoming wetter in the traditional dry seasons. Direct (inverse) relationship was established between dry seasons SPIs and Atlantic (equatorial Pacific) ocean's climate indices. The significant climate indices modulating drought SPIs at different time lags are a combination of either Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, or AMO for a given station. The AIC stepwise regression model explained up to 48% of the variance in the drought SPI and indicates Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, and AMO have great potential for seasonal drought prediction over Coastal Ghana.

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