Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin

Salehie, Obaidullah; Ismail, Tarmizi; Shahid, Shamsuddin; Ahmed, Kamal; Adarsh, S.; Asaduzzaman, Md; Dewan, Ashraf

doi:10.1007/s00704-021-03582-4

Cited by 21 publications

(11 citation statements)

References 72 publications

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“…The CHIRPS dataset has been utilized for drought monitoring in recent years, with positive results in a variety of countries and areas (Habitou et al, 2020;Pandey et al, 2021). Globally there are some other studies (Salehie et al, 2021) has contributed in analyzing the precipitation extremes and its variations in other countries. Some of these studies has used CHIRPS datasets.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…This dataset is available with a spatial resolution of 0.5 ×0.5 from 1979 to present at https://psl.noaa.gov/data/gridded/. Salehie et al (2021) assessed the ability of seven gridded precipitation datasets against 55 stations data over the Amu Darya basin. They found that CPC is the best dataset that capable of replicating the observed precipitation.…”

Section: Gridded Datamentioning

confidence: 99%

Selection of CMIP6 GCM with projection of climate over the Amu Darya River Basin

Salehie

Hamed

Tam

et al. 2022

Theor Appl Climatol

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Global Climate Models (GCMs) are considered the most feasible tools to estimate future climate change.The objective of this study was to assess the interpretation of 19 GCMs of Coupled Model Intercomparison Project 6 (CMIP6) in replicating the historical precipitation and temperature of climate prediction center data for the Amu Darya river basin (ADRB) and the projection of climate of the basin using the selected GCMs. The Kling Gupta efficiency (KGE) metric was used to assess the effectiveness of GCMs to simulate the annual geographic variability of precipitation, maximum and minimum temperature (Pr, Tmx and Tmn). A multi-criteria decision-making approach (MCDMA) was used to integrate the KGE values to rank GCMs. The results revealed that MPI-ESM1-2-LR, CMCC-ESM2, INM-CM4-8 and AWI-CM-1-1-MR are the best in replicating observed Pr, Tmx and Tmn in ADRB. Projection of climate employing the selected GCMs indicated an increase in precipitation (9.9-12.4%) and temperature (1.3-5.5 C) in the basin for all the shared socioeconomic pathways (SSPs), particularly for the far future . A signi cant variation can be seen in temperature over the different climatic zone. However, the intercomparison of selected GCM projected revealed high uncertainty in the projected climate. The uncertainty is higher in the far future and higher SSPs compared to the near future and lower SSPs.

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“…This dataset can be obtained with a resolution of 0.5⁰×0.5⁰ from https://psl.noaa.gov/data/gridded/. Salehie et al (2021a) assessed the ability of seven gridded Pr datasets' ability to estimate precipitation at 55 stations across the Amu Darya basin. They showed CPC as the best in replicating the observed Pr.…”

Section: Datasetsmentioning

confidence: 99%

Projection of Droughts in Amu Darya River Basin for Shared Socioeconomic Pathways

Salehie¹,

Hamed²,

Shahid

2021

Preprint

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Droughts significantly affect socioeconomic and the environment primarily by decreasing the water availability of a region. This study aims to assess the changes in drought characteristics in Central Asia's transboundary Amu Darya river basin for four shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The precipitation, maximum and minimum temperature (Pr, Tmx and Tmn) simulations of 19 global climate models (GCMs) of the coupled model intercomparison project phase 6 (CMIP6) were used to select the best models to prepare the multimodel ensemble (MME). The standard precipitation evapotranspiration index (SPEI) was used to estimate droughts for multiple timescales from Pr and potential evapotranspiration (PET) derived from Tmx and Tmn. The changes in the frequency and spatial distribution of droughts for different severities and timescales were evaluated for the two future periods, 2020–2059 and 2060-2099, compared to the base period of 1975-2014. The study revealed four GCMs, AWI-CM-1-1-MR, CMCC-ESM2, INM-CM4-8 and MPI-ESM1-2-LR, as most suitable for projections of droughts in the study area. The multimodel ensemble (MME) mean of the selected GCMs showed a decrease in Pr by -3 to 12% in the near future and a change in the range of 3 to -9% in the far future in most parts of the basin for different SSPs. The PET showed almost no change in most parts of the basin in the near future and an increase in the range of 10 to 70% in the far future. The change (%) in projected drought occurrence showed to noticeably decrease in the near future, particularly for moderate droughts by up to ≤-50% for SSP5-8.5 and an increase in the far future by up to ≥30% for SSP3-7.0. The increase in all severities of droughts was projected mostly in the center and northwest of the basin. Overall, the results showed a drought shift from the east to the northwest of the basin in the future.

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Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin

Cited by 21 publications

References 72 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

Selection of CMIP6 GCM with projection of climate over the Amu Darya River Basin

Projection of Droughts in Amu Darya River Basin for Shared Socioeconomic Pathways

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