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 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.

scholarly journals Varying soil moisture-atmosphere feedbacks explain divergent temperature extremes and precipitation projections in Central Europe

2018 ◽  
Author(s):  
Martha M. Vogel ◽  
Jakob Zscheischler ◽  
Sonia I. Seneviratne
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Central Europe ◽  
Climate Models ◽  
Global Climate ◽  
Summer Precipitation ◽  
Global Climate Models ◽  
Temperature Extremes ◽  
Extreme Temperatures

Abstract. The frequency and intensity of climate extremes is expected to increase in many regions due to anthropogenic climate change. In Central Europe extreme temperatures are projected to change more strongly than global mean temperatures and soil moisture-temperature feedbacks significantly contribute to this regional amplification. Because of their strong societal, ecological and economic impacts, robust projections of temperature extremes are needed. Unfortunately, in current model projections, temperature extremes in Central Europe are prone to large uncertainties. In order to understand and potentially reduce uncertainties of extreme temperatures projections in Europe, we analyze global climate models from the CMIP5 ensemble for the business-as-usual high-emission scenario (RCP8.5). We find a divergent behavior in long-term projections of summer precipitation until the end of the 21st century, resulting in a trimodal distribution of precipitation (wet, dry and very dry). All model groups show distinct characteristics for summer latent heat flux, top soil moisture, and temperatures on the hottest day of the year (TXx), whereas for net radiation and large-scale circulation no clear trimodal behavior is detectable. This suggests that different land-atmosphere coupling strengths may be able to explain the uncertainties in temperature extremes. Constraining the full model ensemble with observed present-day correlations between summer precipitation and TXx excludes most of the very dry and dry models. In particular, the very dry models tend to overestimate the negative coupling between precipitation and TXx, resulting in a too strong warming. This is particularly relevant for global warming levels above 2 °C. The analysis allows for the first time to substantially reduce uncertainties in the projected changes of TXx in global climate models. Our results suggest that long-term temperature changes in TXx in Central Europe are about 20 % lower than projected by the multi-model median of the full ensemble. In addition, mean summer precipitation is found to be more likely to stay close to present-day levels. These results are highly relevant for improving estimates of regional climate-change impacts including heat stress, water supply and crop failure for Central Europe.

scholarly journals An analysis of the risk of cocoa moniliasis occurrence in Brazil as the result of climate change

2012 ◽  
Vol 38 (1) ◽  
pp. 30-35 ◽  
Author(s):  
Wanderson Bucker Moraes ◽  
Waldir Cintra de Jesus Júnior ◽  
Leonardo de Azevedo Peixoto ◽  
Willian Bucker Moraes ◽  
Edson Luiz Furtado ◽  
...  
Keyword(s):  
Climate Change ◽  
Potential Risk ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Scenarios ◽  
Intergovernmental Panel ◽  
Climate Conditions ◽  
Third Assessment Report ◽  
Impacts Of Climate Change

The aim of this study was to evaluate the potential risk of moniliasis occurrence and the impacts of climate change on this disease in the coming decades, should this pathogen be introduced in Brazil. To this end, climate favorability maps were devised for the occurrence of moniliasis, both for the present and future time. The future scenarios (A2 and B2) focused on the decades of 2020, 2050 and 2080. These scenarios were obtained from six global climate models (GCMs) made available by the third assessment report of Intergovernmental Panel on Climate Change (IPCC). Currently, there are large areas with favorable climate conditions for moniliasis in Brazil, especially in regions at high risk of introduction of that pathogen. Considering the global warming scenarios provided by the IPCC, the potential risk of moniliasis occurrence in Brazil will be reduced. This decrease is predicted for both future scenarios, but will occur more sharply in scenario A2. However, there will still be areas with favorable climate conditions for the development of the disease, particularly in Brazil's main producing regions. Moreover, pathogen and host alike may undergo alterations due to climate change, which will affect the extent of their impacts on this pathosystem.

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 Varying soil moisture–atmosphere feedbacks explain divergent temperature extremes and precipitation projections in central Europe

2018 ◽  
Vol 9 (3) ◽  
pp. 1107-1125 ◽  
Author(s):  
Martha M. Vogel ◽  
Jakob Zscheischler ◽  
Sonia I. Seneviratne
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Central Europe ◽  
Climate Models ◽  
Global Climate ◽  
Substantial Reduction ◽  
Summer Precipitation ◽  
Global Climate Models ◽  
Temperature Extremes

Abstract. The frequency and intensity of climate extremes is expected to increase in many regions due to anthropogenic climate change. In central Europe extreme temperatures are projected to change more strongly than global mean temperatures, and soil moisture–temperature feedbacks significantly contribute to this regional amplification. Because of their strong societal, ecological and economic impacts, robust projections of temperature extremes are needed. Unfortunately, in current model projections, temperature extremes in central Europe are prone to large uncertainties. In order to understand and potentially reduce the uncertainties of extreme temperature projections in Europe, we analyze global climate models from the CMIP5 (Coupled Model Intercomparison Project Phase 5) ensemble for the business-as-usual high-emission scenario (RCP8.5). We find a divergent behavior in long-term projections of summer precipitation until the end of the 21st century, resulting in a trimodal distribution of precipitation (wet, dry and very dry). All model groups show distinct characteristics for the summer latent heat flux, top soil moisture and temperatures on the hottest day of the year (TXx), whereas for net radiation and large-scale circulation no clear trimodal behavior is detectable. This suggests that different land–atmosphere coupling strengths may be able to explain the uncertainties in temperature extremes. Constraining the full model ensemble with observed present-day correlations between summer precipitation and TXx excludes most of the very dry and dry models. In particular, the very dry models tend to overestimate the negative coupling between precipitation and TXx, resulting in a warming that is too strong. This is particularly relevant for global warming levels above 2 ∘C. For the first time, this analysis allows for the substantial reduction of uncertainties in the projected changes of TXx in global climate models. Our results suggest that long-term temperature changes in TXx in central Europe are about 20 % lower than those projected by the multi-model median of the full ensemble. In addition, mean summer precipitation is found to be more likely to stay close to present-day levels. These results are highly relevant for improving estimates of regional climate-change impacts including heat stress, water supply and crop failure for central Europe.

scholarly journals MODELING SPECIES DISTRIBUTION OF <i>SHOREA GUISO</i> (BLANCO) BLUME AND <i>PARASHOREA MALAANONAN</i> (BLANCO) MERR IN MOUNT MAKILING FOREST RESERVE USING MAXENT

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 441-448
Author(s):  
M. R. Tumaneng ◽  
R. Tumaneng ◽  
C. Tiburan Jr.
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Climate Models ◽  
Learning Algorithm ◽  
Global Climate ◽  
Principal Component ◽  
Global Climate Models ◽  
Future Climate ◽  
Emission Scenarios ◽  
Forest Reserve

Abstract. Climate change is regarded as one of the most significant drivers of biodiversity loss and altered forest ecosystems. This study aimed to model the current species distribution of two dipterocarp species in Mount Makiling Forest Reserve as well as the future distribution under different climate emission scenarios and global climate models. A machine-learning algorithm based on the principle of maximum entropy (Maxent) was used to generate the potential distributions of two dipterocarp species – Shorea guiso and Parashorea malaanonan. The species occurrence records of these species and sets of bioclimatic and physical variables were used in Maxent to predict the current and future distribution of these dipterocarp species. The variables were initially reduced and selected using Principal Component Analysis (PCA). Moreover, two global climate models (GCMs) and climate emission scenarios (RCP4.5 and RCP8.5) projected to 2050 and 2070 were utilized in the study. The Maxent models predict that suitable areas for P. malaanonan will decline by 2050 and 2070 under RCP4.5 and RCP 8.5. On the other hand, S. guiso was found to benefit from future climate with increasing suitable areas. The findings of this study will provide initial understanding on how climate change affects the distribution of threatened species such as dipterocarps. It can also be used to aid decision-making process to better conserve the potential habitat of these species in current and future climate scenarios.

Cores for concern: Peatland carbon dynamics in a changing climate; a multidisciplinary approach.

2020 ◽  
Author(s):  
Luke Andrews ◽  
James Rowson ◽  
Richard Payne ◽  
Simon Caporn ◽  
Nancy Dise ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Sequestration ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Carbon Accumulation ◽  
Gas Fluxes ◽  
Greenhouse Gas Fluxes ◽  
Climate Manipulation

&lt;p&gt;The effects of 21&lt;sup&gt;st&lt;/sup&gt; century climate change are projected to be most severe in the northern hemisphere, where the majority of peatlands are located. Peatlands represent important long-term terrestrial stores of carbon (C), containing an estimated c.600-1055GT C, despite covering only 3% of total land area globally. In addition, pristine peatlands act as net sinks of atmospheric CO&lt;sub&gt;2&lt;/sub&gt;, imparting a negative feedback mechanism cooling global climate, whilst simultaneously acting as sources of CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt;. Peatlands remain net sinks of C as long as the rate of carbon sequestration exceeds that of decomposition. Projected changes in temperature, precipitation and other environmental variables threaten to disrupt this precarious balance, however, and the future direction of carbon feedback mechanisms are poorly understood, due to the complex nature of the peatland carbon cycle.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Two methods are used in order to help understand future the carbon dynamics of peat bogs under climate change. These are experimental studies, which measure greenhouse gas fluxes under manipulated climatic and environmental conditions (warmer, drier), and palaeoecological studies, which examine the effects of past climate change upon carbon sequestration throughout the peat profile. However, both methods fundamentally contradict each other. Palaeoecological studies suggest that carbon accumulation increases during warming periods, whereas warming experiments observe greater carbon loss with increased temperature.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The aim of this project is to link contemporary experimental and palaeoecological approaches to explain this discrepancy. This will be achieved by comparing greenhouse gas fluxes between plots which have been subjected to 10 years of passive warming and drought simulation at an experimental climate manipulation site on Cors Fochno, Ceredigion, Wales. Long term rates of carbon accumulation will be compared with net ecosystem contemporary carbon budgets from each plot. Surface samples from each plot will be analysed by a range of palaeoenvironmental proxies to test how well the climate manipulations are represented by each proxy. Finally, a high-resolution multi-proxy palaeoenvironmental reconstruction spanning the past 1000 years will be compared with reconstructions derived from short-cores from each plot covering the duration of the experiment from each treatment, to see how faithfully climate manipulation mirrors real periods of climate change.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Understanding the future role of peatlands in future carbon sequestration and storage is of vital importance for modelling future climate change, in terms of both quantifying the potential ecosystem services peatlands may offer in mitigating the effects of climate change, as well as enhancing the predictive capabilities of global climate models. Currently, the uncertainty associated with peatland carbon cycling is such that peatlands are rarely included in global climate models.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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.

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