UNCERTAINTY OF REGIONAL CLIMATE MODEL AND IMPACT ASSESSMENT MODEL TOWARD CLIMATE CHANGE IMPACT ASSESSMENT

2018 ◽  
Vol 74 (5) ◽  
pp. I_109-I_114
Author(s):  
Yasuyuki MARUYA ◽  
Morihiro HARADA ◽  
Rui ITO ◽  
Hiroaki KAWASE ◽  
Koji DAIRAKU ◽  
...  
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Regional Climate Model ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Assessment Model ◽  
Climate Change Impact Assessment ◽  
Change Impact

scholarly journals Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast

2013 ◽  
Vol 100 ◽  
pp. 320-332 ◽  
Author(s):  
Kazi Farzan Ahmed ◽  
Guiling Wang ◽  
John Silander ◽  
Adam M. Wilson ◽  
Jenica M. Allen ◽  
...  
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
Climate Change Impact Assessment ◽  
Change Impact ◽  
The U.S

scholarly journals Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0227679
Author(s):  
Dragutin T. Mihailović ◽  
Dušan Petrić ◽  
Tamaš Petrović ◽  
Ivana Hrnjaković-Cvjetković ◽  
Vladimir Djurdjevic ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Malaria Vector ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
West Nile ◽  
Change Impact ◽  
Using Data ◽  
Vojvodina Province

scholarly journals The role of hydrological model complexity and uncertainty in climate change impact assessment

2009 ◽  
Vol 21 ◽  
pp. 63-71 ◽  
Author(s):  
R. Ludwig ◽  
I. May ◽  
R. Turcotte ◽  
L. Vescovi ◽  
M. Braun ◽  
...  
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Climate Change Impact ◽  
Hydrological Model ◽  
Climate Model ◽  
Model Complexity ◽  
Climate Change Impact Assessment ◽  
Model Response ◽  
Change Impact

Abstract. Little quantitative knowledge is as yet available about the role of hydrological model complexity for climate change impact assessment. This study investigates and compares the varieties of different model response of three hydrological models (PROMET, Hydrotel, HSAMI), each representing a different model complexity in terms of process description, parameter space and spatial and temporal scale. The study is performed in the Ammer watershed, a 709 km2 catchment in the Bavarian alpine forelands, Germany. All models are driven and validated by a 30-year time-series (1971–2000) of observation data. It is expressed by objective functions, that all models, HSAMI and Hydrotel due to calibration, perform almost equally well for runoff simulation over the validation period. Some systematic deviances in the hydrographs and the spatial patterns of hydrologic variables are however quite distinct and thus further discussed. Virtual future climate (2071–2100) is generated by the Canadian Regional Climate Model (vers 3.7.1), driven by the Coupled Global Climate Model (vers. 2) based on an A2 emission scenario (IPCC 2007). The hydrological model performance is evaluated by flow indicators, such as flood frequency, annual 7-day and 30-day low flow and maximum seasonal flows. The modified climatic boundary conditions cause dramatic deviances in hydrologic model response. HSAMI shows tremendous overestimation of evapotranspiration, while Hydrotel and PROMET behave in comparable range. Still, their significant differences, like spatially explicit patterns of summerly water shortage or spring flood intensity, highlight the necessity to extend and quantify the uncertainty discussion in climate change impact analysis towards the remarkable effect of hydrological model complexity. It is obvious that for specific application purposes, water resources managers need to be made aware of this effect and have to take its implications into account for decision making. The paper concludes with an outlook and a proposal for future research necessities.

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