scholarly journals Climate change projections and extremes for Costa Rica using tailored predictors from CORDEX model output through statistical downscaling with artificial neural networks

2020 ◽  
Vol 41 (1) ◽  
pp. 211-232
Author(s):  
Dánnell Quesada‐Chacón ◽  
Klemens Barfus ◽  
Christian Bernhofer
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Costa Rica ◽  
Statistical Downscaling ◽  
Model Output ◽  
Climate Change Projections ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks to Project Reference Evapotranspiration under Climate Change Scenarios

2021 ◽  
Author(s):  
Junaid Maqsood ◽  
Aitazaz A. Farooque ◽  
Farhat Abbas ◽  
Travis Esau ◽  
Xander Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
General Circulation ◽  
Reference Evapotranspiration ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Change Scenarios ◽  
Artificial Neural

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.

Simulation of seasonal precipitation and raindays over Greece: a statistical downscaling technique based on artificial neural networks (ANNs)

2007 ◽  
Vol 27 (7) ◽  
pp. 861-881 ◽  
Author(s):  
K. Tolika ◽  
P. Maheras ◽  
M. Vafiadis ◽  
H. A. Flocas ◽  
A. Arseni-Papadimitriou
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Downscaling ◽  
Seasonal Precipitation ◽  
Artificial Neural

scholarly journals Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind–wave model for wave forecasting

2006 ◽  
Vol 8 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Zhixu Zhang ◽  
Chi-Wai Li ◽  
Yok-Sheung Li ◽  
Yiquan Qi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Assimilation ◽  
Wave Model ◽  
Altimeter Data ◽  
Northwestern Pacific ◽  
Third Generation ◽  
Model Output ◽  
Artificial Neural ◽  
Wave Forecasting

Although the third-generation formulation of the ocean wave model describes the wave generation, dissipation and nonlinear interaction processes explicitly, many empirical parameters exist in the model which have to be determined experimentally. With the advance in oceanographic remote-sensing techniques, information on oceanic parameters including significant wave height (SWH) can be obtained daily by satellite altimeters. The assimilation of these data into the wave model provides a way of improving the hindcasting results. However, for wave forecasting, no altimeter data exist during the forecasting period, by definition. To improve the forecasting accuracy of the wave model, Artificial Neural Networks (ANN) are introduced to mimic the errors introduced by the wave model. This is achieved by training the ANN using the wave model output as input, and the results after data assimilation as the targeted output. The trained ANN is then used as a post-processor of the output from the wave model. The proposed method has been applied in wave simulation in the northwestern Pacific Ocean. The statistical interpolation method is used to assimilate the altimeter data into the wave model output and a back-propagation ANN is used to mimic the relation between the wave model outputs with or without data assimilation. The results show that an apparent improvement in the accuracy of forecasting can be obtained.

scholarly journals The climate change implication on Jordan: A case study using GIS and Artificial Neural Networks for weather forecasting

2013 ◽  
Vol 7 (2) ◽  
pp. 44-55 ◽  
Author(s):  
Mohammed Matouq ◽  
Tayel El-Hasan ◽  
Hussam Al-Bilbisi ◽  
Monther Abdelhadi ◽  
Muna Hindiyeh ◽  
...  
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weather Forecasting ◽  
Artificial Neural

Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel

2019 ◽  
Vol 166 ◽  
pp. 105031 ◽  
Author(s):  
Lei Zhang ◽  
Seydou Traore ◽  
Jiankun Ge ◽  
Yanbin Li ◽  
Shunsheng Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rice Yield ◽  
Upland Rice ◽  
Boosted Tree ◽  
Artificial Neural

scholarly journals Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

2015 ◽  
Vol 2 (4) ◽  
pp. 1317-1337 ◽  
Author(s):  
T. S. dos Santos ◽  
D. Mendes ◽  
R. R. Torres
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
South America ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Northeastern Brazil ◽  
Monthly Precipitation ◽  
Artificial Neural

Abstract. Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970–1999) and two scenarios (RCP 2.6 and 8.5; 2070–2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

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