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

2021 ◽  
Author(s):  
Obaidullah Salehie ◽  
Tarmizi Ismail ◽  
Mohammed Magdy Hamed ◽  
Shamsuddin Shahid ◽  
Mohd Khairul Idlan Muhammad
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
River Basin ◽  
Climate Models ◽  
Global Climate ◽  
Extreme Temperature ◽  
Global Climate Models ◽  
Historical Period ◽  
Daily Maximum ◽  
Cold Extremes

Abstract 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.

scholarly journals Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

2020 ◽  
Vol 24 (6) ◽  
pp. 3251-3269 ◽  
Author(s):  
Chao Gao ◽  
Martijn J. Booij ◽  
Yue-Ping Xu
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
River Basin ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Internal Climate Variability ◽  
Low Flows ◽  
Uncertainty Sources ◽  
High Flows

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.

scholarly journals Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2130 ◽  
Author(s):  
Zhu ◽  
Zhang ◽  
Wu ◽  
Qi ◽  
Fu ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Water Resource Management ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Liaoning Province ◽  
Lower Envelope ◽  
The Impact

This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management.

Going with the trend: forecasting seasonal climate conditions under climate change

2021 ◽  
Author(s):  
Yawen Shao ◽  
Quan J. Wang ◽  
Andrew Schepen ◽  
Dongryeol Ryu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Daily Maximum ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Conditions ◽  
Seasonal Climate

AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast post-processing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical post-processing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast post-processing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting post-processed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast post-processing is expected to boost user confidence in seasonal climate forecasts.

scholarly journals Long-Term Rainfall Trends and Future Projections over Xijiang River Basin, China

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Muhammad Touseef ◽  
Lihua Chen ◽  
Kaipeng Yang ◽  
Yunyao Chen
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Scenarios ◽  
Emission Scenarios ◽  
Xijiang River ◽  
The Xijiang River

Precipitation trend detection is vital for water resources development and decision support systems. This study predicts the climate change impacts on long-term precipitation trends. It deals with the analysis of observed historical (1960–2010) and arithmetic mean method in assembling precipitation from CMIP5 Global Climate Models (GCMs) datasets for a future period (2020–2099) under four emission scenarios. Daily precipitation data of 32 weather stations in the Xijiang River Basin were provided by National Meteorological Information Centre (NMIC) of the China Meteorological Administration (CMA) and Global Climate Models (GCMs) with all four emission scenarios statistically downscaled using Bias Correction Special Disaggregation (BCSD) and applied for bias correction via Climate Change Toolkit (CCT). Nonparametric Mann–Kendall test was applied for statistical significance trend analysis while the magnitude of the trends was determined by nonparametric Sen’s estimator method on a monthly scale to detect monotonic trends in annual and seasonal precipitation time series. The results showed a declined trend was observed for the past 50 years over the basin with negative values of MK test (Z) and Sen’s slope Q. Historical GCMs precipitation detected decreasing trends except for NoerESM1-M which observed slightly increasing trends. The results are further validated by historical precipitation recorded by the Climate Research Unit (CRU-TS-3.1). The future scenarios will likely be positive trends for annual rainfall. Significant positive trends were observed in monsoon and winter seasons while premonsoon and postmonsoon seasons will likely be slightly downward trends. The 2040s will likely observe the lowest increase of 6.6% while the 2050s will observe the highest increase of 11.5% over the 21st century under future scenarios. However, due to the uncertainties in CMIP5, the future precipitation projections should be interpreted with caution. Thus, it could be concluded that the trend of change in precipitation around the Xijiang River Basin is on the increase under future scenarios. The results can be valuable to water resources and agriculture management policies, as well as the approach for managing floods and droughts under the perspective of global climate change.

scholarly journals Regional Temperature Response in Central Asia to National Committed Emission Reductions

2019 ◽  
Vol 16 (15) ◽  
pp. 2661
Author(s):  
Jintao Zhang ◽  
Fang Wang
Keyword(s):  
Global Warming ◽  
Central Asia ◽  
Emission Reduction ◽  
Climate Models ◽  
Greenhouse Gas Emission ◽  
Global Climate ◽  
Extreme Temperature ◽  
Global Climate Models ◽  
Mitigation And Adaptation ◽  
Extreme High Temperature

National committed greenhouse gas emission reduction actions are the center of the Paris Agreement, and are known as ‘Intended Nationally Determined Contributions’ (INDC) that aim to slow down global warming. The climate response to INDC emission reduction is a focus in climate change science. In this study, data from 32 global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were applied to investigate the changes in the mean and extreme high temperatures in Central Asia (CA) under the INDC scenario above the present-day level. The results show that the magnitude of warming in CA is remarkably higher than the global mean. Almost all the regions in CA will experience more intense, more frequent, and longer-lasting extreme high-temperature events. In comparison with the INDC scenario, the reduced warming of the 2.0 °C/1.5 °C target scenarios will help avoid approximately 44–61%/65–80% of the increase in extreme temperature events in terms of the intensity, frequency, and duration in CA. These results contribute to an improved understanding of the benefits of limiting global warming to the 2.0 °C/1.5 °C targets, which is paramount for mitigation and adaptation planning.

scholarly journals UNCERTAINTY OF CLIMATE PROJECTIONS AND AN APPROACH UTILIZING CLIMATE MODEL OUTPUTS FOR HYDROLOGIC COMPUTATION IN THE BA RIVER BASIN

2018 ◽  
Vol 56 (6) ◽  
pp. 732
Author(s):  
Anh Thi Van Vu ◽  
Thuc Tran ◽  
Minh Truong Ha ◽  
Lanh Thi Minh Pham
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Climate Change Impacts ◽  
Global Climate Models ◽  
Climate Projections ◽  
Top Down ◽  
Basin Scale

A top-down approach begins with Global Climate Models (GCMs) is a common method for assessing climate change impacts on water resources in river basins. To overcome the coarse resolution of GCMs, dynamic downscaling by regional climate models (RCMs) with bias-correction procedures is utilized with the aim to reflect the meteorological features at the river basin scale. However, the results still entail large uncertainties. This paper examines the ability to capture the observed baseline temperature and precipitation (1986-2005) in the Ba River Basin from GCM outputs, RCM outputs, bias-corrected GCM outputs and bias-corrected RCM outputs by analyzing statistical indicators between historical simulations and observed data in 4 temperature and 6 rainfall stations. Bias-corrected results of both GCM and RCM have significantly smaller errors compared to the unbias-corrected ones. The uncertainty of future climate projection for the mid and late 21th century of the bias-corrected GCMs and RCMs are evaluated. It is found that there is still uncertainty in projected results. A concept of “Decision-Scaling” which combines top-down and bottom-up approaches is proposed to assess the climate change impacts on hydrological system to take into account uncertainties of climate projections by models.

scholarly journals Regions of intensification of extreme snowfall under future warming

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lennart Quante ◽  
Sven N. Willner ◽  
Robin Middelanis ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Observational Data ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Average Intensity ◽  
High Latitudes ◽  
Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

scholarly journals Reassessing Existing Reservoir Supply Capacity and Management Resilience under Climate Change and Sediment Deposition

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1819
Author(s):  
Eleni S. Bekri ◽  
Polychronis Economou ◽  
Panayotis C. Yannopoulos ◽  
Alexander C. Demetracopoulos
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Vital Role ◽  
Sediment Deposition ◽  
Global Climate Models ◽  
Exceedance Probability ◽  
Climate Change Effects ◽  
Management Scenario ◽  
Supply Capacity

Freshwater resources are limited and seasonally and spatially unevenly distributed. Thus, in water resources management plans, storage reservoirs play a vital role in safeguarding drinking, irrigation, hydropower and livestock water supply. In the last decades, the dams’ negative effects, such as fragmentation of water flow and sediment transport, are considered in decision-making, for achieving an optimal balance between human needs and healthy riverine and coastal ecosystems. Currently, operation of existing reservoirs is challenged by increasing water demand, climate change effects and active storage reduction due to sediment deposition, jeopardizing their supply capacity. This paper proposes a methodological framework to reassess supply capacity and management resilience for an existing reservoir under these challenges. Future projections are derived by plausible climate scenarios and global climate models and by stochastic simulation of historic data. An alternative basic reservoir management scenario with a very low exceedance probability is derived. Excess water volumes are investigated under a probabilistic prism for enabling multiple-purpose water demands. Finally, this method is showcased to the Ladhon Reservoir (Greece). The probable total benefit from water allocated to the various water uses is estimated to assist decision makers in examining the tradeoffs between the probable additional benefit and risk of exceedance.

scholarly journals Projecting heat-related excess mortality under climate change scenarios in China

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jun Yang ◽  
Maigeng Zhou ◽  
Zhoupeng Ren ◽  
Mengmeng Li ◽  
Boguang Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Excess Mortality ◽  
Climate Models ◽  
Global Climate ◽  
Specific Effect ◽  
Northern China ◽  
The Elderly ◽  
Population Ageing ◽  
Global Climate Models ◽  
Climate Change Scenarios

AbstractRecent studies have reported a variety of health consequences of climate change. However, the vulnerability of individuals and cities to climate change remains to be evaluated. We project the excess cause-, age-, region-, and education-specific mortality attributable to future high temperatures in 161 Chinese districts/counties using 28 global climate models (GCMs) under two representative concentration pathways (RCPs). To assess the influence of population ageing on the projection of future heat-related mortality, we further project the age-specific effect estimates under five shared socioeconomic pathways (SSPs). Heat-related excess mortality is projected to increase from 1.9% (95% eCI: 0.2–3.3%) in the 2010s to 2.4% (0.4–4.1%) in the 2030 s and 5.5% (0.5–9.9%) in the 2090 s under RCP8.5, with corresponding relative changes of 0.5% (0.0–1.2%) and 3.6% (−0.5–7.5%). The projected slopes are steeper in southern, eastern, central and northern China. People with cardiorespiratory diseases, females, the elderly and those with low educational attainment could be more affected. Population ageing amplifies future heat-related excess deaths 2.3- to 5.8-fold under different SSPs, particularly for the northeast region. Our findings can help guide public health responses to ameliorate the risk of climate change.

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

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