Chun-Chao Kuo

Future streamflow patterns of three subbasins, i.e., East Holland, Beaver, and Pefferlaw River basins, located in the upstream of Lake Simcoe of Canada are assessed for 2021–2099. The individual set of parameters of a conceptual hydrological model, HBV (Hydrologiska Byråns Vattenbalansavdelning)-light, are first calibrated for these three subbasins. The calibrated model was validated and further used to estimate the future streamflow driven by statistically downscaled projected precipitation and air temperature from the Pacific Climate Impacts Consortium under Representative Concentration Pathways 8.5 and 4.5 scenarios. The uncertainty of annual streamflow, hydrograph, flow duration curve (FDC), and flood frequency were evaluated. The results reveal that the annual streamflows of the Beaver and Pefferlaw River Basins (PRB) are projected to slightly increase in 2020–2099 while the annual streamflows of the East Holland River Basin (EHRB) are expected to be similar in 2020–2099. The monthly streamflow in winter is projected to increase but to decrease in spring across three subbasins. Based on the projected FDCs, daily streamflow of EHRB and PRB will likely increase by 2070–2099.

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta’s smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen’s slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta’s sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982–2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.