Regional climate change trends and uncertainty analysis using extreme indices: A case study of Hamilton, Canada

Razavi, Tara; Switzman, Harris; Arain, M. Altaf; Coulibaly, Paulin

doi:10.1016/j.crm.2016.06.002

Cited by 56 publications

(28 citation statements)

References 32 publications

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“…In the meantime, other potential options to deal with uncertainties could be adaptive water management, institutional capacity development, the development of robust and 'no regret' strategies, reliance on multiple models, downscaling methods and scenarios, etc. [40,41].…”

Section: Resultsmentioning

confidence: 99%

Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model

Mondal

Nowreen

Sakib

2020

Climate

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The regional climate model, Providing REgional Climates for Impact Studies (PRECIS), has been widely used throughout the world to generate climate change projections for impact studies and adaptations. Its recent application in South Asia also includes the projection of rainfall extremes. In spite of its wide application, a stringent validation of the model is yet to be reported. In this study, we assessed the performance of the model in simulating annual, monthly and extreme rainfalls over Bangladesh by using a number of statistical techniques, e.g., pattern (both spatial and temporal) correlation, root mean square difference (RMSD), mean absolute difference (MAD), Student’s t-test for significance, probability density functions, etc. The results indicated that the PRECIS model could capture the overall spatial pattern of mean annual and monthly rainfalls very well. However, the inter-annual variability was poorly simulated by the model. In addition, the model could not capture the rainfall extremes. A spatial aggregation of rainfall data did not improve the reliability of the model as far as variability and extremes are concerned. Therefore, further improvements of the model and/or its driving global climate model are warranted for its practical use in the generation of rainfall scenarios.

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Section: Resultsmentioning

confidence: 99%

Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model

Mondal

Nowreen

Sakib

2020

Climate

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“…Caruso et al () found a similar overall result with variability across basins and climate models (12 assessed) but an overall upward trend. The CanRCM4 model output has been found to lie within the upper range of the CMIP5 ensemble (Razavi, Switzman, Arain, & Coulibaly, ). So, although variable responses have been found by other researches and it is still recommended that studies assessing climate change impacts include more than one model scenario, one may suppose that future downscaled CanRCM4 output would generate increased discharge and therefore increased erosion potential, as was observed in this study.…”

Section: Discussionmentioning

confidence: 97%

Continuous prediction of clay‐bed stream erosion in response to climate model output for a small urban watershed

Brennan

Parsapour-Moghaddam

Rennie

et al. 2018

Hydrological Processes

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The response of the semi-alluvial clay-bed Watts Creek is assessed subject to climate change.Climate impacts are expected to have regional variability, and few studies have assessed the impacts of future climate in a small urban watershed. The 21-km 2 watershed located in Ottawa, Ontario, Canada, is highly urbanized (68%) and agricultural (20%) with limited forest cover (12%).Continuous simulations were performed using the SWMHYMO lumped hydrologic modelling platform for the open water year, excluding spring freshet (April 1 to October 31). A shear stress exceedance and stream power erosion routine was added to the platform to calculate erosion potential. To account for uncertainty in the collected data, 9 different field datasets were used to calibrate the model, each leading to a distinct set of calibrated parameter values. The difference between the datasets lies in the choice of the rating curves and calibration period.The 2041-2080 precipitation outputs of the 4th version of the Canadian Regional Climate Model (CanRCM4) ran under representative concentration pathways (RCPs) 4.5 and 8.5 at the MacDonald Cartier International Airport were downscaled using quantile matching and then used as input to the continuous hydrologic model. For each set of calibrated parameters, a cumulative effective work index based on the reach-averaged shear stress was calculated for Watts Creek using both the historic and projected future (2041-2080) flows, using a bed material critical shear stress for entrainment of 3.7 Pa. These results suggest an increase of 75% (respectively 139%) under RCP4.5 (respectively RCP8.5) in cumulative effective work index compared with historic conditions for the average measured bed strength. The work index increase is driven by an increased occurrence of above-threshold events and, more importantly, by the increased frequency of large events. The predicted flow regime under climate change would significantly alter the erosion potential and stability of Watts Creek.

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“…This could also be evaluated using bias expressed as relative difference; however, in case of rare events, such a measure would misleadingly magnify small differences. Besides the bias maps, RMSE is another performance statistical parameter used for climate indices, e.g., Sillmann et al (2013), Razavi et al (2016). Since simple bias can hide differences between two spatial patterns because opposite differences can eliminate each other, RMSE adds more detail to the comparison due to its definition containing squared differences.…”

Section: Validation Of Climate Indicesmentioning

confidence: 99%

Validation of RegCM regional and HadGEM global climate models using mean and extreme climatic variables

Pieczka¹,

Bartholy²,

Pongrácz³

et al. 2019

Időjárás

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⎯ The horizontal resolution of global climate models (GCMs) is still too coarse to evaluate regional climatic differences, therefore, to analyze regional environmental changes, it is essential to downscale the GCM simulation results. One of the methods widely and most often used for this purpose is dynamical downscaling. In the present paper we examine the ability of a specific global (HadGEM2-ES) and a specific regional climate model (RegCM) to describe present climatic conditions in different geographical areas within the Med-CORDEX domain. Our main goal with this validation is to inform researchers, who are planning to complete climate change impact studies about the major characteristics of the simulation outputs, serving as important input in such studies. So we analyzed annual and seasonal mean fields, mean error fields relative to the reference measurements, and selected climate indices. On the basis of the results, dynamical downscaling generally cools the HadGEM results, which depends on the distance from the ocean, and orography. A clear improvement can be recognized in the root-meansquare error (RMSE) of temperature indices when using finer resolution. Moreover, dynamical downscaling with higher resolution often increases the precipitation in mountains. Furthermore, in order to quantify the potential added value of RegCM simulations, a complex measure was introduced to take into account both the magnitude and spatial extent of bias. The analysis shows a general improvement in the cold-related indices in Central Europe and all temperature-related climate indices in Western Europe. The influence of model resolution is usually so strong, that it results in the underestimation of precipitation indices changing into overestimation and vice versa.

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Regional climate change trends and uncertainty analysis using extreme indices: A case study of Hamilton, Canada

Cited by 56 publications

References 32 publications

Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model

Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model

Continuous prediction of clay‐bed stream erosion in response to climate model output for a small urban watershed

Validation of RegCM regional and HadGEM global climate models using mean and extreme climatic variables

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