scholarly journals Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index

2018 ◽  
Vol 10 (1) ◽  
pp. 181-196 ◽  
Author(s):  
Mehdi Bahrami ◽  
Samira Bazrkar ◽  
Abdol Rassoul Zarei
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Occurrence Frequency ◽  
Natural Phenomenon ◽  
Drought Severity ◽  
Data Series ◽  
Climate Data ◽  
Seasonal Drought

Abstract Drought as an exigent natural phenomenon, with high frequency in arid and semi-arid regions, leads to enormous damage to agriculture, economy, and environment. In this study, the seasonal Standardized Precipitation Index (SPI) drought index and time series models were employed to model and predict seasonal drought using climate data of 38 Iranian synoptic stations during 1967–2014. In order to model and predict seasonal drought ITSM (Interactive Time Series Modeling) statistical software was used. According to the calculated seasonal SPI, within the study area, drought severity classes 4 and 3 had the greatest occurrence frequency, while classes 6 and 7 had the least occurrence frequency. Results indicated that the best fitted models were Moving-Average or MA (5) Innovations and MA (5) Hannan-Rissenen, with 60.53 and 15.79 percentage, respectively. On the other hand, results of the prediction as well, indicated that drought class 4 with the highest percentages, was the most abundant class over the study area and drought class 7 was the least frequent class. According to results of trend analysis, without attention to significance of them, observed seasonal SPI data series (1967–2014), in 84.21% of synoptic stations had a negative trend, but this percentage changes to 86.84% when studying the combination of observed and predicted simultaneously (1967–2019).

scholarly journals FORECASTING DROUGHT WITH ARIMA MODEL AND STANDARDIZED PRECIPITATION INDEX (SPI)

2019 ◽  
Vol 1 (2) ◽  
pp. 32-34
Author(s):  
ALFA MOHAMMED SALISU
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Data Series ◽  
Drought Forecasting ◽  
Data Set ◽  
Forecasting Accuracy ◽  
Diagnostic Checking ◽  
Auto Regressive

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.

scholarly journals A copula-based assessment of Bartlett–Lewis type of rainfall models for preserving drought statistics

2013 ◽  
Vol 10 (6) ◽  
pp. 7469-7516 ◽  
Author(s):  
M. T. Pham ◽  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Important Variable ◽  
Drought Severity ◽  
Test Case ◽  
Rainfall Time Series ◽  
Climate Data ◽  
Drought Duration ◽  
Environmental Consequences ◽  
Stochastic Point Process ◽  
The Impact

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis type of models studied fail in preserving extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

scholarly journals Long-Term, Gridded Standardized Precipitation Index for Hawai‘i

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 109
Author(s):  
Matthew P. Lucas ◽  
Clay Trauernicht ◽  
Abby G. Frazier ◽  
Tomoaki Miura
Keyword(s):  
High Spatial Resolution ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Drought Severity ◽  
Spatially Explicit ◽  
Historical Period ◽  
The Standardized Precipitation Index ◽  
Climatic Information

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.

scholarly journals An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics

2013 ◽  
Vol 17 (12) ◽  
pp. 5167-5183 ◽  
Author(s):  
M. T. Pham ◽  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Important Variable ◽  
Drought Severity ◽  
Test Case ◽  
Rainfall Time Series ◽  
Climate Data ◽  
Drought Duration ◽  
Environmental Consequences ◽  
Stochastic Point Process ◽  
The Impact

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

scholarly journals Spatial Variation and Trends in PDSI and SPI Indices and Their Relation to Streamflow in 10 Large Regions of China

2010 ◽  
Vol 23 (3) ◽  
pp. 649-663 ◽  
Author(s):  
Jianqing Zhai ◽  
Buda Su ◽  
Valentina Krysanova ◽  
Tobias Vetter ◽  
Chao Gao ◽  
...  
Keyword(s):  
Time Series ◽  
Yellow River ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Yellow River Basin ◽  
Climate Impact ◽  
Middle Part ◽  
Trend Detection ◽  
Drought Severity ◽  
Northwest Region

Abstract Time series of the average annual Palmer drought severity index (PDSI) and standardized precipitation index (SPI) were calculated for 483 meteorological stations in China using monthly data from 1961 to 2005. The time series were analyzed for 10 large regions covering the territory of China and represented by seven river basins and three areas in the southeast, southwest, and northwest. Results show that the frequencies of both dry and wet years for the whole period are lower for southern basins than for the northern ones when estimated by PDSI but very similar for all basins when calculated by SPI. The frequencies of dry and wet years calculated for 5- and 15-yr subperiods by both indices show the upward dry trends for three northeastern basins, Songhuajiang, Liaohe, and Haihe; a downward dry trend for the northwest region; a downward wet trend for the Yellow River basin; and an upward wet trend for the northwest region. Trend detection using PDSI indicates statistically significant negative trends for many stations in the northeastern basins (Songhuajiang, Liaohe, Haihe, and Yellow) and in the middle part of the Yangtze, whereas statistically significant positive trends were found in the mountainous part of the northwest region and for some stations in the upper and lower Yangtze. A moderately high and statistically significant correlation between the percentage of runoff anomaly (PRA) and the annual average PDSI and SPI was found for six large rivers. The results confirm that PDSI and SPI indices can be used to describe the tendency of dryness and wetness severity and for comparison in climate impact assessment.

scholarly journals Performance of Drought Indices in Trichy Region, Tamil Nadu

2019 ◽  
pp. 1-10
Author(s):  
L. Sathya ◽  
R. Lalitha
Keyword(s):  
Tamil Nadu ◽  
Standardized Precipitation Index ◽  
Synthetic Data ◽  
Meteorological Drought ◽  
Severity Index ◽  
Rainfall Anomaly ◽  
Drought Indices ◽  
Drought Severity ◽  
Data Series

Droughts are regional phenomena, which are considered as one of the major natural environmental hazards and severely affect the water resources. Climate variability may result in harmful drought periods in semiarid regions. Meteorological drought indices are considered as important tools for drought monitoring, they are embedded with different theoretical and experimental structures. This study compares the performance of three indices of Standardized Precipitation Index (SPI), Rainfall Anomaly Index (RAI) End Palmer Drought Severity Index (PNPI) to predict long-term drought events using the Thomas-Feiring Model and historical data. For studies of areal drought extent, the 61 years (1951-2011) historical rainfall data of Trichy District were utilized to generate 58 years (2012-2070) synthetic data series so that the characteristics of long-term drought might be determined and the performance of those three indices might be analyzed and compared. The results show that SPI and PNPI perform similarly with regard to drought identification and detailed analysis to determine the characteristics of long-term drought. Finally, the RAI indicated significant deviations from normalized natural processes.

scholarly journals ANALISIS KEKERINGAN DI KABUPATEN SRAGEN DENGAN METODE PALMER, THORNTHWAITE, DAN STANDARDIZED PRECIPITATION INDEX

2021 ◽  
Vol 17 (2) ◽  
pp. 111-124
Author(s):  
Safrudin Nor Aripbilah ◽  
Heri Suprapto
Keyword(s):  
Drought Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Precipitation Index ◽  
Suitable Method ◽  
Drought Severity ◽  
Severe Drought ◽  
Drought Hazard ◽  
Dry Conditions

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

scholarly journals Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran)

2018 ◽  
Vol 20 (4) ◽  
pp. 975-988 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi ◽  
Hamid R. Safavi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Standardized Precipitation Index ◽  
Cuckoo Search ◽  
Precipitation Index ◽  
Urmia Lake ◽  
Support Vector ◽  
Drought Forecasting ◽  
Svm Algorithm ◽  
Svm Model

Abstract In this study, wavelet-support vector machine (WSVM) is proposed for drought forecasting using the Standardized Precipitation Index (SPI). In this way, the SPI time series of Urmia Lake watershed is decomposed to multiple frequency time series by wavelet transform. Then, these time sub-series are applied as input data to the support vector machine (SVM) model to forecast drought. Also, a cuckoo search (CS)-based approach is proposed for parameter optimization of SVM, finding the best initial constant parameters of the SVM algorithm. The obtained results indicate that the radial basis function (RBF)-kernel function of the SVM algorithm has high efficiency in the SPI modeling, resulting in a determination coefficient (DC) of 0.865 in verification step. In the WSVM model, the Coif1, which is considered as a mother wavelet function with decomposition level of five, shows a better performance with DC of 0.954 in verification step, revealing that the proposed hybrid WSVM model outperforms the single SVM model in forecasting SPI time series. Also, DC of cuckoo search-support vector machine (CS-SVM) is calculated to be 0.912 in verification step, indicating the fact that the proposed CS-SVM model shows better efficiency than single SVM model.

scholarly journals Are drought occurrence and severity aggravating? A study on SPI drought class transitions using log-linear models and ANOVA-like inference

2012 ◽  
Vol 16 (8) ◽  
pp. 3011-3028 ◽  
Author(s):  
E. E. Moreira ◽  
J. T. Mexia ◽  
L. S. Pereira
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Standardized Precipitation Index ◽  
Drought Severity ◽  
Drought Frequency ◽  
Long Duration ◽  
Long Time ◽  
Log Linear ◽  
Drought Occurrence ◽  
Drought Classes

Abstract. Long time series (95 to 135 yr) of the 12-month time scale Standardized Precipitation Index (SPI) relative to 10 locations across Portugal were studied with the aim of investigating if drought frequency and severity are changing through time. Considering four drought severity classes, time series of drought class transitions were computed and later divided into several sub-periods according to the length of SPI time series. Drought class transitions were calculated to form a 2-dimensional contingency table for each sub-period, which refer to the number of transitions among drought severity classes. Two-dimensional log-linear models were fitted to these contingency tables and an ANOVA-like inference was then performed in order to investigate differences relative to drought class transitions among those sub-periods, which were considered as treatments of only one factor. The application of ANOVA-like inference to these data allowed to compare the sub-periods in terms of probabilities of transition between drought classes, which were used to detect a possible trend in droughts frequency and severity. Results for a number of locations show some similarity between alternate sub-periods and differences between consecutive ones regarding the persistency of severe/extreme and sometimes moderate droughts. In global terms, results do not support the assumption of a trend for progressive aggravation of drought occurrence during the last century, but rather suggest the existence of long duration cycles.

Sign in / Sign up

Export Citation Format

Share Document