Dae Il Jeong

Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada

This study compares three linear models and one non-linear model, specifically multiple linear regression (MLR) with ordinary least squares (OLS) estimates, robust regression, ridge regression, and artificial neural networks (ANNs), to identify an appropriate transfer function in statistical downscaling (SD) models for the daily maximum and minimum temperatures (Tmax and Tmin) and daily precipitation occurrence and amounts (Pocc and Pamount). This comparison was made over twenty-five observation sites located in five different Canadian provinces (British Columbia, Saskatchewan, Manitoba, Ontario, and Québec). Reanalysis data were employed as atmospheric predictor variables of SD models. Predictors of linear transfer functions and ANN were selected by linear correlations coefficient and mutual information, respectively. For each downscaled case, annual and monthly models were developed and analysed. The monthly MLR, annual ANN, annual ANN, and annual MLR yielded the best performance for Tmax, Tmin, Pocc and Pamont according to the modified Akaike information criterion (AICu). A monthly MLR is recommended for the transfer functions of the four predictands because it can provide a better performance for the Tmax and as good performance as the annual MLR for the Tmin, Pocc, and Pamount. Furthermore, a monthly MLR can provide a slightly better performance than an annual MLR for extreme events. An annual MLR approach is also equivalently recommended for the transfer functions of the four predictands because it showed as good a performance as monthly MLR in spite of its mathematical simplicity. Robust and ridge regressions are not recommended because the data used in this study are not greatly affected by outlier data and multicollinearity problems. An annual ANN is recommended only for the Tmin, based on the best performance among the models in terms of both the RMSE and AICu.

Land‐atmosphere coupling over North America in CRCM5

Land-atmosphere coupling and its impact on extreme precipitation and temperature events over North America are studied using the fifth generation of the Canadian Regional Climate Model (CRCM5). To this effect, two 30 year long simulations, spanning the 1981–2010 period, with and without land-atmosphere coupling, have been performed with CRCM5, driven by the European Centre for Medium-Range Weather Forecasts reanalysis at the boundaries. In the coupled simulation, the soil moisture interacts freely with the atmosphere at each time step, while in the uncoupled simulation, soil moisture is replaced with its climatological value computed from the coupled simulation, thus suppressing the soil moisture-atmosphere interactions. Analyses of the coupled and uncoupled simulations, for the summer period, show strong soil moisture-temperature coupling over the Great Plains, consistent with previous studies. The maxima of soil moisture-precipitation coupling is more spread out and covers the semiarid regions of the western U.S. and parts of the Great Plains. However, the strength of soil moisture-precipitation coupling is found to be generally weaker than that of soil moisture-temperature coupling. The study clearly indicates that land-atmosphere coupling increases the interannual variability of the seasonal mean daily maximum temperature in the Great Plains. Land-atmosphere coupling is found to significantly modulate selected temperature extremes such as the number of hot days, frequency, and maximum duration of hot spells over the Great Plains. Results also suggest additional hot spots, where soil moisture modulates extreme events. These hot spots are located in the southeast U.S. for the hot days/hot spells and in the semiarid regions of the western U.S. for extreme wet spells. This study thus demonstrates that climatologically wet/dry regions can become hot spots of land-atmosphere coupling when the soil moisture decreases/increases to an intermediate transitional level where evapotranspiration becomes moisture sensitive and large enough to affect the climate.

A copula-based multivariate analysis of Canadian RCM projected changes to flood characteristics for northeastern Canada

In the present work, climate change impacts on three spring (March–June) flood characteristics, i.e. peak, volume and duration, for 21 northeast Canadian basins are evaluated, based on Canadian regional climate model (CRCM) simulations. Conventional univariate frequency analysis for each flood characteristic and copula based bivariate frequency analysis for mutually correlated pairs of flood characteristics (i.e. peak–volume, peak–duration and volume–duration) are carried out. While univariate analysis is focused on return levels of selected return periods (5-, 20- and 50-year), the bivariate analysis is focused on the joint occurrence probabilities P1 and P2 of the three pairs of flood characteristics, where P1 is the probability of any one characteristic in a pair exceeding its threshold and P2 is the probability of both characteristics in a pair exceeding their respective thresholds at the same time. The performance of CRCM is assessed by comparing ERA40 (the European Centre for Medium-Range Weather Forecasts 40-year reanalysis) driven CRCM simulated flood statistics and univariate and bivariate frequency analysis results for the current 1970–1999 period with those observed at selected 16 gauging stations for the same time period. The Generalized Extreme Value distribution is selected as the marginal distribution for flood characteristics and the Clayton copula for developing bivariate distribution functions. The CRCM performs well in simulating mean, standard deviation, and 5-, 20- and 50-year return levels of flood characteristics. The joint occurrence probabilities are also simulated well by the CRCM. A five-member ensemble of the CRCM simulated streamflow for the current (1970–1999) and future (2041–2070) periods, driven by five different members of a Canadian Global Climate Model ensemble, are used in the assessment of projected changes, where future simulations correspond to A2 scenario. The results of projected changes, in general, indicate increases in the marginal values, i.e. return levels of flood characteristics, and the joint occurrence probabilities P1 and P2. It is found that the future marginal values of flood characteristics and P1 and P2 values corresponding to longer return periods will be affected more by anthropogenic climate change than those corresponding to shorter return periods but the former ones are subjected to higher uncertainties.

Climate change and resilience of tributary thermal refugia for salmonids in eastern Canadian rivers

Abstract River water temperature regimes are expected to change along with climate over the next decades. This work focuses on three important salmon rivers of eastern Canada, two of which warm up most summers to temperatures higher than the Atlantic salmon lethal limit (>28°C). Water temperature was monitored at 53 sites on the three basins during 2–18 summers, with about half of these sites either known or potential thermal refugia for salmon. Site-specific statistical models predicting water temperature, based on 10 different climate scenarios, were developed in order to assess how many of these sites will remain cool enough to serve as refugia in the future (2046–2065). The results indicate that, while 19 of the 23 identified refugia will persist, important increases in the occurrence and duration of temperature events in excess of 24°C and 28°C, respectively, in the mainstems of the rivers, will lead to higher demands for thermal refugia in the salmonid populations. Editor Z.W. Kundzewicz; Associate editor T. Okruszko

Projected changes to winter temperature characteristics over Canada based on an RCM ensemble

Cold temperature and associated extremes often impact adversely human health and environment and bring disruptions in economic activities during winter over Canada. This study investigates projected changes in winter (December to March) period cold extreme days (i.e., cold nights, cold days, frost days, and ice days) and cold spells over Canada based on 11 regional climate model (RCM) simulations for the future 2040–2069 period with respect to the current 1970–1999 period. These simulations, available from the North American Regional Climate Change Assessment Program, were obtained with six different RCMs, when driven by four different Atmosphere–Ocean General Circulation Models, under the Special Report on Emissions Scenarios A2 scenario. Based on the reanalysis boundary conditions, the RCM simulations reproduce spatial patterns of observed mean values of the daily minimum and maximum temperatures and inter-annual variability of the number of cold nights over different Canadian climatic regions considered in the study. A comparison of current and future period simulations suggests decreases in the frequency of cold extreme events (i.e., cold nights, cold days and cold spells) and in selected return levels of maximum duration of cold spells over the entire study domain. Important regional differences are noticed as the simulations generally indicate smaller decreases in the characteristics of extreme cold events over western Canada compared to the other regions. The analysis also suggests an increase in the frequency of midwinter freeze–thaw events, due mainly to a decrease in the number of frost days and ice days for all Canadian regions. Especially, densely populated southern and coastal Canadian regions will require in depth studies to facilitate appropriate adaptation strategies as these regions are clearly expected to experience large increases in the frequency of freeze–thaw events.

Rain-on-snow events over North America based on two Canadian regional climate models

This study evaluates projected changes to rain-on-snow (ROS) characteristics (i.e., frequency, rainfall amount, and runoff) for the future 2041–2070 period with respect to the current 1976–2005 period over North America using six simulations, based on two Canadian RCMs, driven by two driving GCMs for RCP4.5 and 8.5 emission pathways. Prior to assessing projected changes, the two RCMs are evaluated by comparing ERA-Interim driven RCM simulations with available observations, and results indicate that both models reproduce reasonably well the observed spatial patterns of ROS event frequency and other related features. Analysis of current and future simulations suggest general increases in ROS characteristics during the November–March period for most regions of Canada and for northwestern US for the future period, due to an increase in the rainfall frequency with warmer air temperatures in future. Future ROS runoff is often projected to increase more than future ROS rainfall amounts, particularly for northeastern North America, during snowmelt months, as ROS events usually accelerate snowmelt. The simulations show that ROS event is a primary flood generating mechanism over most of Canada and north-western and -central US for the January–May period for the current period and this is projected to continue in the future period. More focused analysis over selected basins shows decreases in future spring runoff due to decreases in both snow cover and ROS runoff. The above results highlight the need to take into consideration ROS events in water resources management adaptation strategies for future climate.

Improvement of the Ensemble Streamflow Prediction System Using Optimal Linear Correction

A monthly Ensemble Streamflow Prediction (ESP) system was developed by applying a daily rainfall-runoff model known as the Streamflow Synthesis and Reservoir Regulation (SSARR) model to the Han, Nakdong, and Seomjin River basins in Korea. This study first assesses the accuracy of the averaged monthly runoffs simulated by SSARR for the 3 basins and proposes some improvements. The study found that the SSARR modeling of the Han and Nakdong River basins tended to significantly underestimate the actual runoff levels and the modeling of the Seomjin River basinshowed a large error variance. However, by implementing optimal linear correction (OLC), the accuracy of the SSARR model was considerably improved in predicting averaged monthly runoffs of the Han and Nakdong River basins. This improvement was not seen in the modeling of the Seomjin River basin. In addition, the ESP system was applied to forecast probabilistic runoff forecasts one month in advance for the 3 river basins from 1998 to 2003. Considerably improvement was also achieved with OLC in probabilistic forecasting accuracy for the Han and Nakdong River basins, but not in that of the Seomjin River basin.

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec, Canada.

Abstract Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54 MJ m−2 d−1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46 MJ m−2 d−1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.

Application of Monthly Water Balance Models for the Climate Change Impact Assessment

This study attempted to determine a suitable hydrologic model for assessing the impact of climate change on water resources, and to assess the accuracy of streamflow scenarios simulated by the selected hydrologic model using the meteorological scenarios of the Seoul National University Regional Climate Model(SNURCM). Comparison of four water balance models and two daily conceptual rainfall-runoff models for the simulation capability of the Daecheong Dam inflow indicated that the abcd model performs the best among the tested water balance models and performs as well as SSARR that is popular as a daily rainfall-runoff model in Korea. Parameters of the abcd model were then estimated for 12 ungauged subbasins of the Geum River by the regionalization method. The model parameters were first calibrated at nine multi-purpose dams and were then regionalized using catchment characteristics for another four multi-purpose dams, which were assumed to be ungauged sites. The model efficiency(ME) coefficients of the simulated inflows for these four dams were at least 87%. The MEs of the hindcasted meteorological rainfall scenarios of the 12 subbasins of the Geum River were more than 60%. Moreover, the ME of the Daecheong Dam inflow simulated by the abcd model using the SNURCM rainfall scenarios was more than 80%. Therefore, this research concluded that the abcd model coupled with the SNU-RCM meteorological scenarios can be used for impact assessment studies of climate change on water resources.

Attribution of spring snow water equivalent (SWE) changes over the northern hemisphere to anthropogenic effects

Snow is an important component of the cryosphere and it has a direct and important influence on water storage and supply in snowmelt-dominated regions. This study evaluates the temporal evolution of snow water equivalent (SWE) for the February–April spring period using the GlobSnow observation dataset for the 1980–2012 period. The analysis is performed for different regions of hemispherical to sub-continental scales for the Northern Hemisphere. The detection–attribution analysis is then performed to demonstrate anthropogenic and natural effects on spring SWE changes for different regions, by comparing observations with six CMIP5 model simulations for three different external forcings: all major anthropogenic and natural (ALL) forcings, greenhouse gas (GHG) forcing only, and natural forcing only. The observed spring SWE generally displays a decreasing trend, due to increasing spring temperatures. However, it exhibits a remarkable increasing trend for the southern parts of East Eurasia. The six CMIP5 models with ALL forcings reproduce well the observed spring SWE decreases at the hemispherical scale and continental scales, whereas important differences are noted for smaller regions such as southern and northern parts of East Eurasia and northern part of North America. The effects of ALL and GHG forcings are clearly detected for the spring SWE decline at the hemispherical scale, based on multi-model ensemble signals. The effects of ALL and GHG forcings, however, are less clear for the smaller regions or with single-model signals, indicating the large uncertainty in regional SWE changes, possibly due to stronger influence of natural climate variability.