scholarly journals Stochastic Model for Drought Forecasting in the Southern Taiwan Basin

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2041 ◽  
Author(s):  
Hsin-Fu Yeh ◽  
Hsin-Li Hsu
Keyword(s):  
Water Resource Management ◽  
Moving Average ◽  
Arima Model ◽  
Standardized Precipitation Index ◽  
Warning System ◽  
Absolute Error ◽  
Drought Forecasting ◽  
Drought Conditions ◽  
Southern Taiwan ◽  
The Standardized Precipitation Index

The global rainfall pattern has changed because of climate change, leading to numerous natural hazards, such as drought. Because drought events have led to many disasters globally, it is necessary to create an early warning system. Drought forecasting is an important step toward developing such a system. In this study, we utilized the stochastic, autoregressive integrated moving average (ARIMA) model to predict drought conditions based on the standardized precipitation index (SPI) in southern Taiwan. We employed data from 1967 to 2006 to train the model and data from 2007 to 2017 for model validation. The results showed that the coefficients of determination (R2) were over 0.80 at each station, and the root-mean-square error and mean absolute error were sufficiently low, indicating that the ARIMA model is effective and adequate for our stations. Finally, we employed the ARIMA model to forecast future drought conditions from 2019 to 2022. The results yielded relatively low SPI values in southern Taiwan in future summers. In summary, we successfully constructed an ARIMA model to forecast drought. The information in this study can act as a reference for water resource management.

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 Hybrid Wavelet-Arima Model for Standardized Precipitation Index Drought Forecasting

MATEMATIKA ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 141-156
Author(s):  
Alfa Mohammed Salisu ◽  
Ani Shabri
Keyword(s):  
Wavelet Transform ◽  
Arima Model ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Drought Forecasting ◽  
Error Measure ◽  
Auto Regressive ◽  
The Standardized Precipitation Index

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.

scholarly journals A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ji Yae Shin ◽  
Muhammad Ajmal ◽  
Jiyoung Yoo ◽  
Tae-Woong Kim
Keyword(s):  
Bayesian Network ◽  
Standardized Precipitation Index ◽  
Severe Drought ◽  
Drought Condition ◽  
Probabilistic Framework ◽  
Drought Forecasting ◽  
Probabilistic Forecasting ◽  
The Past ◽  
Drought Conditions ◽  
The Standardized Precipitation Index

Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF) model was designed using the past, current, and forecasted drought condition. In this study, the drought conditions were represented by the standardized precipitation index (SPI). The accuracy of forecasted SPIs was assessed by comparing the observed SPIs and confidence intervals (CIs), exhibiting the associated uncertainty. Then, this study suggested the drought outlook framework based on probabilistic drought forecasting results. The overall results provided sufficient agreement between the observed and forecasted drought conditions in the outlook framework.

DROUGHT OCCURRENCE UNDER LITHUANIAN CLIMATIC CONDITIONS

2015 ◽  
Author(s):  
Laima TAPARAUSKIENĖ ◽  
Veronika LUKŠEVIČIŪTĖ
Keyword(s):  
Meteorological Data ◽  
Standardized Precipitation Index ◽  
Vegetation Period ◽  
Climatic Conditions ◽  
Precipitation Record ◽  
Calculation Step ◽  
Drought Conditions ◽  
The Standardized Precipitation Index ◽  
June July

This study provides the analysis of drought conditions of vegetation period in 1982-2014 year in two Lithuanian regions: Kaunas and Telšiai. To identify drought conditions the Standardized Precipitation Index (SPI) was applied. SPI was calculated using the long-term precipitation record of 1982–2014 with in-situ meteorological data. Calculation step of SPI was taken 1 month considering only vegetation period (May, June, July, August, September). The purpose of investigation was to evaluate the humidity/aridity of vegetation period and find out the probability of droughts occurrence under Lithuanian climatic conditions. It was found out that according SPI results droughts occurred in 14.5 % of all months in Kaunas region and in 15.8 % in Telšiai region. Wet periods in Kaunas region occurred in 15.8 %, and in Telšiai region occurrence of wet periods was – 18.8 % from all evaluated months. According SPI evaluation near normal were 69.7 % of total months during period of investigation in Kaunas and respectively – 65.5 % in Telšiai. The probability for extremely dry period under Lithuania climatic conditions are pretty low – 3.0 % in middle Lithuania and 2.4 % in western part of Lithuania.

scholarly journals Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Junfei Chen ◽  
Ming Li ◽  
Weiguang Wang
Keyword(s):  
Random Forest ◽  
Arima Model ◽  
Standardized Precipitation Index ◽  
Forecast Model ◽  
Haihe River Basin ◽  
Drought Forecasting ◽  
Haihe River ◽  
Proposed Model ◽  
The Haihe River Basin ◽  
Natural Climate

Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI). We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF-) based forecast model has consistently shown better predictive skills than the ARIMA model for both long and short drought forecasting. The confidence intervals derived from the proposed model generally have good coverage, but still tend to be conservative to predict some extreme drought events.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

scholarly journals Using integrated meteorological and hydrological indices to assess drought characteristics in southern Taiwan

2019 ◽  
Vol 50 (3) ◽  
pp. 901-914 ◽  
Author(s):  
Hsin-Fu Yeh
Keyword(s):  
River Basin ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Assessment Method ◽  
Drought Indices ◽  
Trend Test ◽  
Integrated Analysis ◽  
Drought Assessment ◽  
Southern Taiwan ◽  
The Standardized Precipitation Index

Abstract Numerous drought index assessment methods have been developed to investigate droughts. This study proposes a more comprehensive assessment method integrating two drought indices. The Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI) are employed to establish an integrated drought assessment method to study the trends and characteristics of droughts in southern Taiwan. The overall SPI and SDI values and the spatial and temporal distributions of droughts within a given year (November to October) revealed consistent general trends. Major droughts occurred in the periods of 1979–1980, 1992–1993, 1994–1995, and 2001–2003. According to the results of the Mann–Kendall trend test and the Theil–Sen estimator analysis, the streamflow data from the Sandimen gauging station in the Ailiao River Basin showed a 30% decrease, suggesting increasing aridity between 1964 and 2003. Hence, in terms of water resources management, special attention should be given to the Ailiao River Basin. The integrated analysis showed different types of droughts occurring in different seasons, and the results are in good agreement with the climatic characteristics of southern Taiwan. This study suggests that droughts cannot be explained fully by the application of a single drought index. Integrated analysis using multiple indices is required.

scholarly journals Evaluation of temporal drought variation and projection in a tropical river basin of Kerala

2020 ◽  
Vol 11 (S1) ◽  
pp. 115-132 ◽  
Author(s):  
M. A. Jincy Rose ◽  
N. R. Chithra
Keyword(s):  
River Basin ◽  
Standardized Precipitation Index ◽  
Drought Severity ◽  
Climate Projections ◽  
Severe Drought ◽  
Tropical River ◽  
The Past ◽  
Drought Conditions ◽  
The Future ◽  
The Standardized Precipitation Index

Abstract Temperature is an indispensable parameter of climate that triggers evapotranspiration and has vital importance in aggravating drought severity. This paper analyses the existence and persistence of drought conditions which are said to prevail in a tropical river basin which was once perennial. Past observed data and future climate projections of precipitation and temperature were used for this purpose. The assessment and projection of this study employ the Standardized Precipitation Evapotranspiration Index (SPEI) compared with that of the Standardized Precipitation Index (SPI). The results indicate the existence of drought in the past and the drought conditions that may persist in the future according to RCP 4.5 and 8.5 scenarios. The past drought years identified in the study were compared with the drought declared years in the state and were found to be matching. The evaluation of the future scenarios unveils the occurrence of drought in the basin ranging from mild to extreme conditions. It has been noted that the number of moderate and severe drought months has increased based on SPEI compared to SPI, indicating the importance of temperature in drought studies. The study can be considered as a plausible scientific remark helpful in risk management and application decisions.

Monitoring and predicting the outstanding 2017 drought in Spain

2020 ◽  
Author(s):  
Miguel Ángel Torres Vázquez ◽  
Amar Halifa Marín ◽  
Juan Pedro Montávez ◽  
Marco Turco
Keyword(s):  
Standardized Precipitation Index ◽  
Warning System ◽  
Empirical Method ◽  
Environmental Research ◽  
Drought Risk ◽  
Summer Drought ◽  
Drought Event ◽  
Streamflow Prediction ◽  
Drought Prediction ◽  
Drought Conditions

<p>The increase in societal exposure and vulnerability to drought, call to move from post-crisis to pre-impact drought risk management. Accurate and timely information of evolving drought conditions is crucial to take early actions to avoid devastating long-term impacts. A previous study indicated that a statistical empirical method, the ensemble streamflow prediction system (ESP; an ensemble based on reordering historical data), represents a computationally fast alternative to dynamical prediction applications for drought prediction (Turco et al. 2017). Extending this work, here we present an assessment of the ability of the ESP method in predicting the drought of 2017 in Spain considering also the uncertainties coming from the observations. For this, four different datasets are used: that cover a period of 36 years (1981-2017) and with a spatial resolution of 0.25 x 0.25º based on observations of interpolated stations (E-OBS, AEMET), on reanalysis data (ERA5), and on combining stations and satellite data (CHIRPS). Meteorological droughts are defined using the Standardized Precipitation Index aggregated over the months April–September. All the datasets show a similar spatial pattern, with most of the domain suffering extreme drought conditions. In addition, the ESP system achieves reasonable skill in predicting this drought event 2 months in advance with, again, similar pattern among the different datasets. These results suggest the feasibility of the development of an operational early warning system, also considering that the data of CHIRPS and ERA5 are updated every month, i.e., that are available for near-real time applications.</p><p> </p><p>References</p><p>Turco, M., et al. (2017). Summer drought predictability over Europe: empirical versus dynamical forecasts. Environmental Research Letters, 12(8), 084006.</p><p> </p><p>Acknowledgments</p><p>The authors acknowledge the ACEX project (CGL2017-87921-R) of the Ministerio de Economía y Competitividad of Spain. AHM thanks his predoctoral contract FPU18/00824 to the Ministerio de Ciencia, Innovación y Universidades of Spain. M.T. has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00).</p>

scholarly journals Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

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