scholarly journals Uncertainty assessment of water resources and long-term hydropower generation using a large ensemble of future climate projections for the Nam Ngum River in the Mekong Basin

2021 ◽  
Vol 36 ◽  
pp. 100856
Author(s):  
Thatkiat Meema ◽  
Yasuto Tachikawa ◽  
Yutaka Ichikawa ◽  
Kazuaki Yorozu
2018 ◽  
Vol 11 (1) ◽  
pp. 93-112 ◽  
Author(s):  
Stanislav Myslenkov ◽  
Alisa Medvedeva ◽  
Victor Arkhipkin ◽  
Margarita Markina ◽  
Galina Surkova ◽  
...  

2018 ◽  
Vol 11 (1) ◽  
pp. 86-105 ◽  
Author(s):  
K. Ishida ◽  
A. Ercan ◽  
T. Trinh ◽  
S. Jang ◽  
M. L. Kavvas ◽  
...  

Abstract Impact of future climate change on watershed-scale precipitation was investigated over Northern California based on future climate projections by means of the dynamical downscaling approach. Thirteen different future climate projection realizations from two general circulation models (GCMs: ECHAM5 and CCSM3) based on four emission scenarios (SRES A1B, A1FI, A2, and B1) were dynamically downscaled to 9-km resolution grids over eight watersheds in Northern California for a period of 90 water years (2010–2100). Analysis of daily precipitation over the eight watersheds showed that precipitation values obtained from dynamical downscaling of the 1981 to 1999 control runs of ECHAM5 and CCSM3 GCMs compared well with the PRISM data. Long-term future trends of annual and seasonal basin-average precipitation were investigated. Although a large variability exists for the projected annual basin-average precipitation within each of the 13 individual realizations, there was no significant long-term trend over the eight study watersheds except for the downward trend in the A1FI scenario. On the other hand, significant upward and downward trends were detected in the seasonal basin-average precipitation except in the winter months (January, February, and March). The trend analysis results in this study indicated the importance of considering seasonal variability, scenario, and model uncertainty.


2021 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Jason P. Evans ◽  
Alejandro Di Luca ◽  
Michael R. Grose ◽  
Vanessa Round ◽  
...  

<p>Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.</p>


Author(s):  
Silvio Gualdi ◽  
Samuel Somot ◽  
Wilhelm May ◽  
Sergio Castellari ◽  
Michel Déqué ◽  
...  

2019 ◽  
Vol 11 (17) ◽  
pp. 4764 ◽  
Author(s):  
Anna Sperotto ◽  
Josè Luis Molina ◽  
Silvia Torresan ◽  
Andrea Critto ◽  
Manuel Pulido-Velazquez ◽  
...  

With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 different combinations of a global climate model (GCM)–regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041–2070) and long-term (2071–2100) periods with respect to the baseline (1983–2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between different GCM–RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.


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