Forecasting of Drought: A Case Study of Water-Stressed Region of Pakistan

Kumar, Pardeep; Shah, Syed Feroz; Uqaili, Muhammad Aslam; Kumar, Laveet; Zafar, Raja Fawad

doi:10.3390/atmos12101248

Cited by 16 publications

(15 citation statements)

References 49 publications

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“…Although the precipitation event is dependent on many dynamic and coupled Frontiers in Environmental Science frontiersin.org processes, which necessitates very complicated coupled simulation models for prediction, the ARIMA model was used to produce a simple and reliable time series analysis. The advantages of ARIMA over traditional statistical and numerical weather prediction approaches have been discussed by (Taneja et al, 2016;Kumar et al, 2021). Much of the tropics and subtropics of the Eastern Hemisphere, as well as a significant amount of the world's population, are influenced by the Asian monsoon circulation (Chattopadhyay, 2007).…”

Section: Figurementioning

confidence: 99%

Statistical analysis of precipitation variations and its forecasting in Southeast Asia using remote sensing images

Syed

Zhang

Rousta

et al. 2022

Front. Environ. Sci.

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The Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) dataset was examined for its variability and performance in explaining precipitation variations, forecasting, and drought monitoring in Southeast Asia (SEA) for the period of 1981–2020. By using time-series analysis, the Standardized Precipitation Index (SPI), and the Autoregressive Integrated Moving Average (ARIMA) model this study established a data-driven approach for estimating the future trends of precipitation. The ARIMA model is based on the Box Jenkins approach, which removes seasonality and keeps the data stationary while forecasting future patterns. Depending on the series, ARIMA model annual estimates can be read as a blend of recent observations and long-term historical trend. Methods for determining 95 percent confidence intervals for several SEA countries and simulating future annual and seasonal precipitation were developed. The results illustrates that Bangladesh and Sri Lanka were chosen as the countries with the greatest inaccuracies. On an annual basis, Afghanistan has the lowest Mean Absolute Error (MAE) values at 33.285 mm, while Pakistan has the highest at 35.149 mm. It was predicted that these two countries would receive more precipitation in the future as compared to previous years.

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Section: Figurementioning

confidence: 99%

Statistical analysis of precipitation variations and its forecasting in Southeast Asia using remote sensing images

Syed

Zhang

Rousta

et al. 2022

Front. Environ. Sci.

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“…The SPEI measures drought conditions based on the water balance, i.e., the difference between the atmospheric water supply (precipitation) and water demand (i.e., PET) [30]. The calculation procedure of the SPEI is similar to that of the SPI, and the difference is that the SPEI uses climatic water balance (the difference between precipitation and PET) rather than precipitation as an input [46,47]. The size of PET thus affects the calculation of the SPEI.…”

Section: Climate Change Characterized By Global Warming Has Become An...mentioning

confidence: 99%

“…The wide application of the ARIMA model is due to its flexibility and systematic search (identification, estimation, and diagnostic check) for an appropriate model [16]. It has been documented that the ARIMA model can outperform most statistical models, such as exponential smoothing and neural networks, in hydrologic and meteorological time series [46,47].…”

Section: Climate Change Characterized By Global Warming Has Become An...mentioning

confidence: 99%

“…The most famous model among stochastic sequential models is the autoregressive moving average (ARMA) model. ARMA models are formed by autoregressive (AR) models coupled with moving average (MA) models effectively [5,47], which can be used when the data are stationary. However, time series that reflect hydrologic aspects may be nonstationary over any time interval considered [109].…”

Section: Autoregressive Integrated Moving Average Modelmentioning

confidence: 99%

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Evidence for Intensification in Meteorological Drought since the 1950s and Recent Dryness–Wetness Forecasting in China

Yang

Bing

2022

Atmosphere

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Drought is one of the major environmental stressors; drought is increasingly threatening the living environment of mankind. The standardized precipitation evapotranspiration index (SPEI) with a 12-month timescale was adopted to monitor dry–wet status over China from 1951 to 2021. The modified Mann–Kendall (MMK) and Pettitt tests were used to assess the temporal trend and nonlinear behavior of annual drought variability. The analysis focuses on the spatio-temporal structure of the dry–wet transition and its general connections with climate change processes. In addition, the seasonal autoregressive integrated moving average (SARIMA) model was applied to forecast the dry–wet behavior in the next year (2022) at 160 stations, and the hotspot areas for extreme dryness–wetness in China were identified in the near term. The results indicate that the dry–wet climate in China overall exhibits interannual variability characterized by intensified drought. The climate in the Northeast China (NEC), North China (NC), Northwest China (NWC), and Southwest China (SWC) has experienced a significant (p < 0.05) drying trend; however, the dry–wet changes in the East China (EC) and South Central China (SCC) are highly spatially heterogeneous. The significant uptrend in precipitation is mainly concentrated to the west of 100° E; the rising magnitude of precipitation is higher in Eastern China near 30° N, with a changing rate of 20–40 mm/decade. Each of the sub-regions has experienced significant (p < 0.01) warming over the past 71 years. Geographically, the increase in temperature north of 30° N is noticeably higher than that south of 30° N, with trend magnitudes of 0.30–0.50 °C/decade and 0.15–0.30 °C/decade, respectively. The response of the northern part of Eastern China to the warming trend had already emerged as early as the 1980s; these responses were earlier and more intense than those south of 40° N latitude (1990s). The drying trends are statistically significant in the northern and southern regions, bounded by 30° N, with trend magnitudes of −0.30–−0.20/decade and −0.20–−0.10/decade, respectively. The northern and southwestern parts of China have experienced a significant (p < 0.05) increase in the drought level since the 1950s, which is closely related to significant warming in recent decades. This study reveals the consistency of the spatial distribution of variations in precipitation and the SPEI along 30° N latitude. A weak uptrend in the SPEI, i.e., an increase in wetness, is shown in Eastern China surrounding 30° N, with a changing rate of 0.003–0.10/decade; this is closely associated with increasing precipitation in the area. Drought forecasting indicates that recent drying areas are located in NWC, the western part of NC, the western part of SWC, and the southern part of SCC. The climate is expected to show wetting characteristics in NEC, the southeastern part of NC, and the eastern part of EC. The dry–wet conditions spanning the area between 30–40° N and 100–110° E exhibit a greater spatial variability. The region between 20–50° N and 80–105° E will continue to face intense challenges from drought in the near future. This study provides compelling evidence for the temporal variability of meteorological drought in different sub-regions of China. The findings may contribute to understanding the spatio-temporal effect of historical climate change on dry–wet variation in the region since the 1950s, particularly in the context of global warming.

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“…Literature reviews showed that despite numerous studies focused on using specific ML methods on predicting air temperature, few studies are comparing and evaluating the application of multiple ML methods in the same study [35]. Since, temperature has a direct effect on precipitation, which will create drought event, so different ML approaches would help in forecasting drought event [36]. This work could be used as reference for other hydrological and hydro-metrological parameters like rainfall, streamflow measurement etc.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Methods Application in Temperature Prediction

Azari¹,

Hassan²,

Pierce³

et al. 2022

CRPASE

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Machine Learning (ML) techniques for time series prediction are becoming increasingly accurate and helpful, particularly in considering climate change. As more methods are developed, it follows that differentiating between them is becoming increasingly more important as well. This work took a local temperature time series as a dependent variable and a collection of relevant climatology time series as independent variables and applied leading Machine Learning methods to them. The six methods tested included four simple models: Linear Regression (L.R.), k-Nearest Neighbor (kNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), In addition of two ensemble model methods: Random Forest (R.F.) and Adaptive Boosting (AdB). Results compared all the method's training and predictive performances to evaluate the method's overall effectiveness in forecasting the average daily temperature value. Actual data was used to train each of the mentioned ML methods, and then they were used to predict the future temperature in the study area. The analysis revealed that out of the six methods tested, the Artificial Neural Network outperformed the others in both training and prediction of temperature values in the Memphis, TN climate.

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Forecasting of Drought: A Case Study of Water-Stressed Region of Pakistan

Cited by 16 publications

References 49 publications

Statistical analysis of precipitation variations and its forecasting in Southeast Asia using remote sensing images

Statistical analysis of precipitation variations and its forecasting in Southeast Asia using remote sensing images

Evidence for Intensification in Meteorological Drought since the 1950s and Recent Dryness–Wetness Forecasting in China

Evaluation of Machine Learning Methods Application in Temperature Prediction

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