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Rahimi ST, Safari Z, Shahid S, Hayet Khan MM, Ali Z, Ziarh GF, Houmsi MR, Muhammad MKIB, Chung IM, Kim S, Yaseen ZM. Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways. Heliyon 2024; 10:e28433. [PMID: 38571592 PMCID: PMC10988002 DOI: 10.1016/j.heliyon.2024.e28433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/19/2024] [Accepted: 03/19/2024] [Indexed: 04/05/2024] Open
Abstract
Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020-2059) and the distant future (2060-2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.
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Affiliation(s)
- Sayed Tamim Rahimi
- Department of Civil Engineering, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Ziauddin Safari
- Department of Water and Environmental Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia
| | - Shamsuddin Shahid
- Department of Water and Environmental Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia
- New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Baghdad, Iraq
| | - Md Munir Hayet Khan
- Faculty of Engineering & Quantity Surveying, INTI International University (INTI-IU), Persiaran Perdana BBN, Putra Nilai, Nilai 71800, Negeri Sembilan, Malaysia
| | - Zulfiqar Ali
- Department of Water and Environmental Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia
| | | | - Mohamad Rajab Houmsi
- Department of Water and Environmental Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia
| | - Mohd Khairul Idlan bin Muhammad
- Department of Water and Environmental Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia
| | - Il-Moon Chung
- Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si, 10223, Republic of Korea
| | - Sungwon Kim
- Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, Republic of Korea
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
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Chung PC, Lin IF. Sensitivity analysis of selection bias: a graphical display by bias-correction index. PeerJ 2023; 11:e16411. [PMID: 38025739 PMCID: PMC10657564 DOI: 10.7717/peerj.16411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 10/15/2023] [Indexed: 12/01/2023] Open
Abstract
Background In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence and direction of selection bias. Methods We used a BCI, a function of selection probabilities conditional on outcome and covariates, with different selection bias scenarios in a logistic regression setting. A bias-correction sensitivity plot was illustrated to analyze the associations between proctoscopy examination and sociodemographic variables obtained using the data from the Taiwan National Health Interview Survey (NHIS) and of a subset of individuals who consented to having their health insurance data further linked. Results We included 15,247 people aged ≥20 years, and 87.74% of whom signed the informed consent. When the entire sample was considered, smokers were less likely to undergo proctoscopic examination (odds ratio (OR): 0.69, 95% CI [0.57-0.84]), than nonsmokers were. When the data of only the people who provided consent were considered, the OR was 0.76 (95% CI [0.62-0.94]). The bias-correction sensitivity plot indicated varying ORs under different degrees of selection bias. Conclusions When data are only available in a subsample of a population, a bias-correction sensitivity plot can be used to easily visualize varying ORs under different selection bias scenarios. The similar strategy can be applied to models other than logistic regression if an appropriate BCI is derived.
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Affiliation(s)
- Ping-Chen Chung
- Department of Dentistry, Puzi Hospital, Ministry of Health and Welfare, Chiayi, Taiwan
- Institute of Public Health, School of Medicine, National Yang Ming Chao Tung University, Taipei, Taiwan
| | - I-Feng Lin
- Institute of Public Health, School of Medicine, National Yang Ming Chao Tung University, Taipei, Taiwan
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Atiah WA, Johnson R, Muthoni FK, Mengistu GT, Amekudzi LK, Kwabena O, Kizito F. Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices. Heliyon 2023; 9:e17604. [PMID: 37449185 PMCID: PMC10336502 DOI: 10.1016/j.heliyon.2023.e17604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 06/22/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
Like many other African countries, Ghana's rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, significant progress has been made in the development and availability of real-time satellite precipitation products (SPPs). SPPs may complement or substitute gauge data, enabling better real-time forecasting of stream flows, among other things. However, SPPs still have significant biases that must be corrected before the rainfall estimates can be used for any hydrologic application, such as real-time or seasonal forecasting. The daily satellite-based rainfall estimate (CHIRPS-v2) data were bias-corrected using the Bias Correction and Spatial Disaggregation (BSCD) approach. The study further investigated how bias correction of daily satellite-based rainfall estimates affects the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country's southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The novelty of this study is that, in addition to improving rainfall using CHIRPS data, it also enhances the identification of seasonality indices. The paper suggests the BCSD approach for correcting rainfall estimates from other algorithms using long-term historical records indicative of the rainfall variability area under consideration.
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Affiliation(s)
- Winifred Ayinpogbilla Atiah
- Kwame Nkrumah University of Science and Technology (KNUST), Department of Physics, Meteorology and Climate Science Unit, Kumasi, Ghana
| | - Robert Johnson
- Kwame Nkrumah University of Science and Technology (KNUST), Department of Physics, Meteorology and Climate Science Unit, Kumasi, Ghana
| | - Francis Kamau Muthoni
- International Institute of Tropical Agriculture (IITA), Duluti, Arusha, P.O. Box 10, Tanzania
| | - Gizaw Tsidu Mengistu
- Botswana International University of Science and Technology (BIUST), Department of Earth and Environmental Science, Palapye, Botswana
| | - Leonard Kofitse Amekudzi
- Kwame Nkrumah University of Science and Technology (KNUST), Department of Physics, Meteorology and Climate Science Unit, Kumasi, Ghana
| | - Osei Kwabena
- Kwame Nkrumah University of Science and Technology (KNUST), Department of Physics, Meteorology and Climate Science Unit, Kumasi, Ghana
| | - Fred Kizito
- International Institute of Tropical Agriculture, Accra GA-184, Ghana
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Yao N, Li Y, Li N, Yang D, Ayantobo OO. Bias correction of precipitation data and its effects on aridity and drought assessment in China over 1961-2015. Sci Total Environ 2018; 639:1015-1027. [PMID: 29929271 DOI: 10.1016/j.scitotenv.2018.05.243] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/04/2018] [Accepted: 05/20/2018] [Indexed: 06/08/2023]
Abstract
The accuracy of gauge-measured precipitation (Pm) affects drought assessment since drought severity changes due to precipitation bias correction. This research investigates how drought severity changes as the result of bias-corrected precipitation (Pc) using the Erinc's index Im and standardized precipitation evapotranspiration index (SPEI). Daily and monthly Pc values at 552 sites in China were determined using daily Pm and wind speed and air temperature data over 1961-2015. Pc-based Im values were generally larger than Pm-based Im for most sub-regions in China. The increased Pc and Pc-based Im values indicated wetter climate conditions than previously reported for China. After precipitation bias-correction, Climate types changed, e.g., 20 sites from severe-arid to arid, and 11 sites from arid to semi-arid. However, the changes in SPEI were not that obvious due to precipitation bias correction because the standardized index SPEI removed the effects of mean precipitation values. In conclusion, precipitation bias in different sub-regions of China changed the spatial and temporal characteristics of drought assessment.
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Affiliation(s)
- Ning Yao
- College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China; Institute of Water Saving Agriculture in Arid Areas of China, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China
| | - Yi Li
- College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China; Institute of Water Saving Agriculture in Arid Areas of China, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China.
| | - Na Li
- College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China; Institute of Water Saving Agriculture in Arid Areas of China, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China
| | - Daqing Yang
- Environment and Climate Change Canada, Saskatoon, SK S7N 3H5, Canada
| | - Olusola Olaitan Ayantobo
- College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China
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Abstract
Correlated data are commonly analyzed using models constructed using population-averaged generalized estimating equations (GEEs). The specification of a population-averaged GEE model includes selection of a structure describing the correlation of repeated measures. Accurate specification of this structure can improve efficiency, whereas the finite-sample estimation of nuisance correlation parameters can inflate the variances of regression parameter estimates. Therefore, correlation structure selection criteria should penalize, or account for, correlation parameter estimation. In this manuscript, we compare recently proposed penalties in terms of their impacts on correlation structure selection and regression parameter estimation, and give practical considerations for data analysts.
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Affiliation(s)
- Philip M Westgate
- Department of Biostatistics, College of Public Health, University of Kentucky
| | - Woodrow W Burchett
- Department of Statistics, College of Arts and Sciences, University of Kentucky
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Handley IM, Albarracín D, Brown RD, Li H, Kumkale EC, Kumkale GT. When the expectations from a message will not be realized: Naïve theories can eliminate expectation-congruent judgments via correction. J Exp Soc Psychol 2009; 45:933-939. [PMID: 24619304 DOI: 10.1016/j.jesp.2009.05.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Research typically reveals that individuals like an object more when a persuasive message convinces them that this object is pleasant. In this paper, two experiments were conducted to understand the influence of such message-induced affective-expectations on judgments of experienced affect following direct encounter with an alcohol type of drink. As predicted, before trying the drink, recipients of the positive-expectation message had more positive expectations than recipients of the negative-expectation message. After drinking, participants judged the beverage to elicit affect congruent with message-induced expectations to the extent they did not endorse a naïve theory that their affective expectations congruently influence their experienced affect. In contrast, after drinking, the effect of the message disappeared when participants did endorse this naïve theory. Moderation of these effects, as well as theoretical and practical implications, are addressed.
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Affiliation(s)
- Ian M Handley
- Department of Psychology, Montana State University, P.O. Box 173440, Bozeman, MT 59717, United States
| | - Dolores Albarracín
- Department of Psychology, University of Illinois, 603 E. Daniel Street, Champaign, IL 61820, United States
| | - Rick D Brown
- Department of Psychology, University of Florida, Gainesville, FL 32611, United States
| | - Hong Li
- Department of Psychology, University of Florida, Gainesville, FL 32611, United States
| | - Ece C Kumkale
- Department of Psychology, University of Florida, Gainesville, FL 32611, United States
| | - G Tarcan Kumkale
- Department of Psychology, Koc University, 34450 Istanbul, Turkey
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