Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios

Abstract Evapotranspiration, one of the major elements of the water cycle, is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various climate change scenarios using artificial neural networks and a general circulation model (GCM) - the Canadian Earth System Model Second Generation (CanESM2). The Hargreaves method was used to calculate ET0 for western, central, and eastern parts of Prince Edward Island. The two input parameters of the Hargreaves method; daily maximum temperature (Tmax), and daily minimum temperature (Tmin) were projected using CanESM2. The Tmax and Tmin were downscaled with the help of statistical downscaling and simulation model (SDSM) for three future periods 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) under three representative concentration pathways (RCP’s) including RCP 2.6, RCP P4.5, and RCP 8.5, and the. Temporally, there were major changes in Tmax, Tmin, and ET0 for the 2080s under RCP8.5. The temporal variations in ET0 for all RCPs matched the reports in the literature for other similar locations and for RCP8.5 it ranged from 1.63 (2020s) to 2.29 mm/day (2080s). As a next step, a one-dimensional convolutional neural network (1D-CNN), long-short term memory (LSTM), and multilayer perceptron (MLP) were used for estimating ET0 due to the non-linear behavior of ET0 and the limited meteorological input data. High coefficient of correlation (r > 0.95) values for both calibration and validation periods showed the potential of the artificial neural networks in ET0 estimation. The results of this study will help decision makers and water resource managers to quantify the availability of water in future for the island and to optimize the use of island water resources on a sustainable basis.

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Predicting smallmouth bass (Micropterus dolomieu) occurrence across North America under climate change: a comparison of statistical approaches

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f07-178 ◽

2008 ◽

Vol 65 (3) ◽

pp. 471-481 ◽

Cited By ~ 32

Author(s):

Sapna Sharma ◽

Donald A Jackson

Keyword(s):

Climate Change ◽

Neural Networks ◽

Artificial Neural Networks ◽

North America ◽

Classification Tree ◽

Smallmouth Bass ◽

Climate Change Scenarios ◽

Micropterus Dolomieu ◽

Air Temperatures ◽

Artificial Neural

Smallmouth bass (Micropterus dolomieu) is a warm-water fish species that is native to central and eastern North America. Climate change scenarios predict further extension northward of suitable habitat for smallmouth bass, which may negatively affect native fish species. We developed and compared predictive models of the distribution of bass in North America using four statistical approaches: logistic regression, classification tree, discriminant analysis, and artificial neural networks. We collected 4181 geo-referenced records of smallmouth bass occurrence and matched them with climate data. Artificial neural networks performed the best with the highest sensitivity (correctly predicting species presence) and specificity (correctly predicting absence), followed by discriminant analysis. Artificial neural networks indicated that winter air temperatures were the most important predictors of smallmouth bass occurrence, whereas the other approaches indicated that summer air temperatures were the best predictors of bass occurrence. Logistic regression and classification tree exhibited very low sensitivity, but very high specificity as a result of the large proportion of absences within the data set. Business-as-usual climate change scenarios suggest that smallmouth bass are expected to have suitable thermal habitat throughout most of Canada and the continental United States by 2100.

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