An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

Su, Y.Y.; Li, Yongping; Liu, Yuanrui; Huang, Guohe; Jia, Qimeng; Li, Yanfeng

doi:10.1002/joc.7026

Cited by 20 publications

(8 citation statements)

References 52 publications

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“…The increase in annual precipitation depends on radiative forcing at all latitudes (Figure 16). These results were similar to the regional and global climate signals estimated in the previous studies (Asia : Su et al, 2021;Kim et al, 2020;Hamed et al, 2022, Global: Wu et al, 2021Cook et al, 2020, Africa: Majdi et al, 2022, North America: Almazroui et al, 2021, South America: Lovino et al, 2021. The future trends in global monthly average temperatures showed an increase depending on the scenarios (Figure 14).…”

Section: Discussionsupporting

confidence: 91%

Global Future Climate Signal by Latitudes Using CMIP6 GCMs

Song,

Chung,

Shahid

2024

Earth's Future

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This study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes.

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Section: Discussionsupporting

confidence: 91%

Global Future Climate Signal by Latitudes Using CMIP6 GCMs

Song,

Chung,

Shahid

2024

Earth's Future

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“…Uncertainty can be existed in observation data, data processing, parameter estimation and model structure, due to the inherent unpredictability of system and simplification in model formulation. Climate change is likely to be predictable over the next century due to consistently increasing greenhouse gas emissions, which can bring more complexities and uncertainties for water resources predication and allocation (Su et al, 2021). Understanding how water resources system would be affected by various uncertainties is important for developing desired management strategies (Li et al, 2008;Ewertowska et al, 2017;Moeini and Soltani-nezhad, 2020;Yu et al, 2020).…”

Section: Importance and Motivationmentioning

confidence: 99%

A Non-Deterministic Integrated Optimization Model with Risk Measure for Identifying Water Resources Management Strategy

Nie¹,

Huang²,

Huang³

et al. 2021

J ENVIRON INFORM LET

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Water resources system planning often exhibits high modeling error and uncertainty. Uncertainty in system parameters as well as their interrelationships can strengthen the conflict-laden issue of water allocation among competing interests. In this study, a nondeterministic integrated optimization model with risk measure is developed for planning water resources management. It can (i) deal with complex uncertainties described as probability distributions, fuzzy sets, and their combinations, (ii) provide an effective linkage between the predefined policies and the associated economic implications, and (iii) reflect policymakers' preferences to the tradeoff between system benefit and economic loss. The developed model is then applied to planning water resources allocation of the Heshui River Basin (China), where 960 scenarios are analyzed under various uncertainty and risk measures. Results disclose that (i) not only uncertainties of fuzziness and randomness but also risk attitudes of decision makers have significant impacts on water-allocation scheme and system benefit; (ii) the selection of a suitable alternative among solutions under different α, μ and λ values is complicated; (iii) water shortage would occur when water availability is less than the promised target; (iv) agriculture would encounter most serious scarcity compared to municipal and industry; (v) the conflict between economic development and agricultural sustainability would be a challenged issue that would enforce the local authority to adjust water-allocation policy. The findings can provide superior fundamental understanding of the study basin to improve water-allocation decisions under complex uncertain condition.

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“…The basin's hydrology has undergone tremendous change in the recent past due to human intervention in river ow (Khaydar et al, 2021). The increased temperature in the basin has worsened this situation and imposed further stresses on water resources and public health (Hoell et al, 2020;Hu et al, 2021;Su et al, 2021b). Xu et al (2021) projected that increased temperature in the Amu river basin (ARB) would decrease river ow by 78.8-98.7% during 2021-2050.…”

Section: Introductionmentioning

confidence: 99%

“…The faster glacier melting under a warmer climate would shift the peak ow from summer to spring. Su et al (2021b) projected to decline in the glacier extent in ARB by 71.9% for RCP8.5 over 2021-2100. This decline of the glacier would reduce future runoff and water availability especially for irrigation in summer in crop season the downstream of the river.…”

Section: Introductionmentioning

confidence: 99%

“…The basin would experience a large increase in temperature extremes even the temperature rise could be limited to below 2\text{℃} by 2100. Increased temperature extreme might cause severe implications in the basin, including reduction of vegetation cover, agricultural irrigation demand, declination of glacier extent, increase in surface runoff and a shift in water available period(Luo et al, 2020;Su et al, 2021b;Wegerich, 2002). Water stress, land degradation and insu cient environmental management capability have made CA highly vulnerable to many natural hazards(Luo et al, 2020;Reyer et al, 2017;Yu et al, 2020;Zhang et al, 2019a).…”

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confidence: 99%

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Projection of Hot and Cold Extremes in the Amu River Basin of Central Asia using GCMs CMIP6

Salehie

Ismail

Hamed

et al. 2021

Preprint

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The extreme temperature has become more frequent and intense due to global warming, particularly in dry regions, causing devastating impacts on humans and ecosystems. The transboundary Amu river basin (ARB) is the most vulnerable region in Central Asia (CA) to extreme weather linked to climate change. This study aimed to project warm and cold extremes in ARB for three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and two time-horizons, 2020–2059 and 2060-2099, using daily maximum (Tmax) and minimum temperature (Tmin) simulations of global climate models (GCMs) of Coupled Model Inter-comparison Project phase six (CMIP6). Results revealed that the basin's west experiences more hot extremes and the east more cold extremes. Climate change would cause a significant increase in the annual mean of Tmax and Tmin. However, the increase in mean Tmin would be much higher (5.0ºC ) than the mean Tmax (4.6ºC ). It would cause an increase in the hot extremes and a decrease in the cold extremes in the basin. The higher increase in the hot extremes would be in the west, while the higher decrease in the cold extreme in the basin's east. The number of days above 40℃ would increase from 45 to 60 days in the basin's west and northwest compared to the historical period. The number of days below -20℃ would decrease up to 45 days in the basin's east. Overall, the decrease in cold extremes would be much faster than the increase in hot extremes.

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An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

Cited by 20 publications

References 52 publications

Global Future Climate Signal by Latitudes Using CMIP6 GCMs

Global Future Climate Signal by Latitudes Using CMIP6 GCMs

A Non-Deterministic Integrated Optimization Model with Risk Measure for Identifying Water Resources Management Strategy

Projection of Hot and Cold Extremes in the Amu River Basin of Central Asia using GCMs CMIP6

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