Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections
<p>The relief map of the study area. The black dots show the distribution of sample points across Canada.</p> "> Figure 2
<p>The Schematic of the CNN’s structure.</p> "> Figure 3
<p>Research flowchart.</p> "> Figure 4
<p>(<b>a</b>) Classification Accuracy for 1022 ELM models. (<b>b</b>) Area Under the Curve (AUC) for 1022 ELM models.</p> "> Figure 5
<p>Zoning of Projected Average Annual Precipitation Anomalies in Canada (2024–2100) Compared to the Observed Period (1983–2023) using CanESM5 from CMIP6.</p> "> Figure 5 Cont.
<p>Zoning of Projected Average Annual Precipitation Anomalies in Canada (2024–2100) Compared to the Observed Period (1983–2023) using CanESM5 from CMIP6.</p> "> Figure 6
<p>Zoning of Projected Average Annual Temperature Anomalies in Canada (2024–2100) Compared to the Observed Period (1983–2023) using CanESM5 from CMIP6.</p> "> Figure 6 Cont.
<p>Zoning of Projected Average Annual Temperature Anomalies in Canada (2024–2100) Compared to the Observed Period (1983–2023) using CanESM5 from CMIP6.</p> "> Figure 7
<p>Canada’s Percentage of Drought-Affected Area, Pr, and T under the SSP126 Scenario: A Comparative Analysis from 2003–2023 to 2024–2100, Examining Historical Trends and Future Projections Using CanESM5 within the CMIP6 Framework.</p> "> Figure 8
<p>Canada’s Percentage of Drought-Affected Area, Pr, and T under the SSP245 Scenario: A Comparative Analysis from 2003–2023 to 2024–2100, Examining Historical Trends and Future Projections Using CanESM5 within the CMIP6 Framework.</p> "> Figure 9
<p>Canada’s Percentage of Drought-Affected Area, Pr, and T under the SSP370 Scenario: A Comparative Analysis from 2003–2023 to 2024–2100, Examining Historical Trends and Future Projections Using CanESM5 within the CMIP6 Framework.</p> "> Figure 10
<p>Canada’s Percentage of Drought-Affected Area, Pr, and T under the SSP585 Scenario: A Comparative Analysis from 2003–2023 to 2024–2100, Examining Historical Trends and Future Projections Using CanESM5 within the CMIP6 Framework.</p> "> Figure 11
<p>Policy Implications for Canada in Addressing Climate Change and Drought Conditions.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Gathering
2.3. Climate Change
2.4. Canadian Drought Monitor
2.5. Convolutional Neural Network
2.6. Flowchart of the Proposed Framework
3. Results
3.1. Performance Evaluation of the CNN
3.2. Precipitation Analysis Under Climate Change Scenarios
3.3. Temperature Analysis under Climate Change Scenarios
3.4. Canadian Drought Monitor Projections
4. Discussion
4.1. Interpretation of Results
4.2. Policy Implication
4.3. Future Research Directions
4.4. Broader Context
5. Conclusions
- The SSP126 scenario assumes significant emission reductions, resulting in the least severe increases in temperature and drought frequency. The results highlight the benefits of aggressive emission reduction strategies in mitigating climate change impacts.
- The SSP245 scenario represents moderate emission reductions, projecting increased temperature and drought severity. The findings emphasize the need for enhanced emissions reduction efforts to prevent moderate climate impacts, suggesting that while some mitigation efforts are effective, they need to be strengthened to avoid more severe outcomes.
- The SSP370 scenario, characterized by higher emissions, forecasts more pronounced temperature increases and a significant rise in drought conditions. The implications of this scenario stress that without substantial mitigation efforts, Canada could face strained water resources, reduced agricultural productivity, and an increased frequency and intensity of extreme weather events, illustrating the critical need for robust climate policies to curb emissions.
- The SSP585 scenario assumes the highest emissions levels, predicting the most severe temperature increases and widespread droughts. This scenario underscores the catastrophic potential of unmitigated climate change, presenting a dire outlook with profound implications for ecosystems, the economy, and public health. Severe drought conditions under this scenario will likely cause significant disruptions in water access, agricultural failures, and heightened risks of heat-related illnesses and fatalities.
- Across all scenarios, stable precipitation levels are offset by higher evaporation rates and decreased soil moisture due to temperature increases, exacerbating drought conditions. This trend indicates that maintaining current precipitation levels alone will not counteract the adverse effects of rising temperatures.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ehteram, M.; Achite, M.; Sheikh Khozani, Z.; Farrokhi, A. Drought prediction using ensemble models. Acta Geophys. 2024, 72, 945–982. [Google Scholar] [CrossRef]
- Ha, T.V.; Huth, J.; Bachofer, F.; Kuenzer, C. A review of earth observation-based drought studies in Southeast Asia. Remote Sens. 2022, 14, 3763. [Google Scholar] [CrossRef]
- Wang, T.; Tu, X.; Singh, V.P.; Chen, X.; Lin, K.; Zhou, Z. Drought prediction: Insights from the fusion of LSTM and multi-source factors. Sci. Total Environ. 2023, 902, 166361. [Google Scholar] [CrossRef] [PubMed]
- Livneh, B.; Hoerling, M.P. The physics of drought in the US central Great Plains. J. Clim. 2016, 29, 6783–6804. [Google Scholar] [CrossRef]
- Luo, L.; Apps, D.; Arcand, S.; Xu, H.; Pan, M.; Hoerling, M. Contribution of temperature and precipitation anomalies to the California drought during 2012–2015. Geophys. Res. Lett. 2017, 44, 3184–3192. [Google Scholar] [CrossRef]
- Veettil, A.V.; Mishra, A.K. Quantifying thresholds for advancing impact-based drought assessment using classification and regression tree (CART) models. J. Hydrol. 2023, 625, 129966. [Google Scholar] [CrossRef]
- Bonsal, B.R.; Wheaton, E.E.; Chipanshi, A.C.; Lin, C.; Sauchyn, D.J.; Wen, L. Drought research in Canada: A review. Atmos.-Ocean 2011, 49, 303–319. [Google Scholar] [CrossRef]
- Carrillo, J.; Hernández-Barrera, S.; Expósito, F.J.; Díaz, J.P.; González, A.; Pérez, J.C. The uneven impact of climate change on drought with elevation in the Canary Islands. NPJ Clim. Atmos. Sci. 2023, 6, 31. [Google Scholar] [CrossRef]
- Park, S.; Seo, E.; Kang, D.; Im, J.; Lee, M.I. Prediction of drought on pentad scale using remote sensing data and MJO index through random forest over East Asia. Remote Sens. 2018, 10, 1811. [Google Scholar] [CrossRef]
- IPCC. Climate Change 2007: The Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007. [Google Scholar]
- Ebtehaj, I.; Bonakdari, H. A comprehensive comparison of the fifth and sixth phases of the coupled model intercomparison project based on the Canadian earth system models in spatio-temporal variability of long-term flood susceptibility using remote sensing and flood frequency analysis. J. Hydrol. 2023, 617, 128851. [Google Scholar] [CrossRef]
- Zhou, Z.; Tang, W.; Li, M.; Cao, W.; Yuan, Z. A novel hybrid intelligent SOPDEL model with comprehensive data preprocessing for long-time-series climate prediction. Remote Sens. 2023, 15, 1951. [Google Scholar] [CrossRef]
- Luo, N.; Guo, Y.; Chou, J.; Gao, Z. Added value of CMIP6 models over CMIP5 models in simulating the climatological precipitation extremes in China. Int. J. Climatol. 2022, 42, 1148–1164. [Google Scholar] [CrossRef]
- Salimi, A.; Ghobrial, T.; Bonakdari, H. Comparison of the Performance of CMIP5 and CMIP6 in the Predic-tion of Rainfall Trends, Case Study Quebec City. Environ. Sci. Proc. 2023, 25, 42. [Google Scholar] [CrossRef]
- Sobie, S.R.; Zwiers, F.W.; Curry, C.L. Climate Model Projections for Canada: A Comparison of CMIP5 and CMIP6. Atmos.-Ocean 2021, 59, 269–284. [Google Scholar] [CrossRef]
- Lawrimore, J.; Heim, R.R.; Svoboda, M.; Swail, V.; Englehart, P.J. Beginning a new era of drought monitoring across North America. Bull. Am. Meterol. Soc. 2002, 83, 1191–1192. Available online: https://journals.ametsoc.org/downloadpdf/view/journals/bams/83/8/1520-0477-83_8_1191.pdf (accessed on 22 March 2024). [CrossRef]
- Mardian, J.; Champagne, C.; Bonsal, B.; Berg, A. A machine learning framework for predicting and understanding the Canadian drought monitor. Water Resour. Res. 2023, 59, e2022WR033847. [Google Scholar] [CrossRef]
- Tadesse, T.; Champagne, C.; Wardlow, B.D.; Hadwen, T.A.; Brown, J.F.; Demisse, G.B.; Bayissa, Y.A.; Davidson, A.M. Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: First results. GIScience Remote Sens. 2017, 54, 230–257. [Google Scholar] [CrossRef]
- Mardian, J.; Champagne, C.; Bonsal, B.; Daneshfar, B.; Berg, A. Agricultural Risk Assessments Based on the Canadian Drought Monitor: A Bayesian Neural Network Approach. Agric. For. Meteorol. 2023, 353, 110056, Available at SSRN 4583872. [Google Scholar] [CrossRef]
- Prasad, R.; Deo, R.C.; Li, Y.; Maraseni, T. Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors. Soil Tillage Res. 2018, 181, 63–81. [Google Scholar] [CrossRef]
- Salimi, A.; Noori, A.; Ebtehaj, I.; Ghobrial, T.; Bonakdari, H. Advancing Spatial Drought Forecasts by Integrating an Improved Outlier Robust Extreme Learning Machine with Gridded Data: A Case Study of the Lower Mainland Basin, British Columbia, Canada. Sustainability 2024, 16, 3461. [Google Scholar] [CrossRef]
- Haidar, A.; Verma, B. Monthly rainfall forecasting using one-dimensional deep convolutional neural network. IEEE Access 2018, 6, 69053–69063. [Google Scholar] [CrossRef]
- Wang, J.H.; Lin, G.F.; Chang, M.J.; Huang, I.H.; Chen, Y.R. Real-time water-level forecasting using dilated causal convolutional neural networks. Water Resour. Manag. 2019, 33, 3759–3780. [Google Scholar] [CrossRef]
- Kim, D.Y.; Song, C.M. Developing a discharge estimation model for ungauged watershed using CNN and hydrological image. Water 2020, 12, 3534. [Google Scholar] [CrossRef]
- Khosravi, K.; Panahi, M.; Golkarian, A.; Keesstra, S.D.; Saco, P.M.; Bui, D.T.; Lee, S. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. J. Hydrol. 2020, 591, 125552. [Google Scholar] [CrossRef]
- Song, C.M. Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability. J. Hydrol. 2022, 605, 127324. [Google Scholar] [CrossRef]
- Tripathy, K.P.; Mishra, A.K. Deep learning in hydrology and water resources disciplines: Concepts, methods, applications, and research directions. J. Hydrol. 2023, 628, 130458. [Google Scholar] [CrossRef]
- Ehteram, M.; Ahmed, A.N.; Sheikh Khozani, Z.; El-Shafie, A. Convolutional neural network-support vector machine model-gaussian process regression: A new machine model for predicting monthly and daily rainfall. Water Resour. Manag. 2023, 37, 3631–3655. [Google Scholar] [CrossRef]
- Maity, R.; Khan, M.I.; Sarkar, S.; Dutta, R.; Maity, S.S.; Pal, M.; Chanda, K. Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors. J. Water Clim. Chang. 2021, 12, 2774–2796. [Google Scholar] [CrossRef]
- Hao, R.; Yan, H.; Chiang, Y.M. Forecasting the Propagation from Meteorological to Hydrological and Agricultural Drought in the Huaihe River Basin with Machine Learning Methods. Remote Sens. 2023, 15, 5524. [Google Scholar] [CrossRef]
- Danandeh Mehr, A.; Rikhtehgar Ghiasi, A.; Yaseen, Z.M.; Sorman, A.U.; Abualigah, L. A novel intelligent deep learning predictive model for meteorological drought forecasting. J. Ambient Intell. Humaniz. Comput. 2023, 14, 10441–10455. [Google Scholar] [CrossRef]
- Chen, Z.; Wang, G.; Wei, X.; Liu, Y.; Duan, Z.; Hu, Y.; Jiang, H. Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China. Atmosphere 2024, 15, 155. [Google Scholar] [CrossRef]
- Liu, W.; Huang, Y.; Wang, H. Effective deep learning seasonal prediction model for summer drought over China. Earth’s Future 2024, 12, e2023EF004409. [Google Scholar] [CrossRef]
- Khan, M.I.; Maity, R. Development of a Long-Range Hydrological Drought Prediction Framework Using Deep Learning. Water Resour. Manag. 2024, 38, 1497–1509. [Google Scholar] [CrossRef]
- Muñoz-Sabater, J.; Dutra, E.; Agustí-Panareda, A.; Albergel, C.; Arduini, G.; Balsamo, G.; Boussetta, S.; Choulga, M.; Harrigan, S.; Hersbach, H.; et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 2021, 13, 4349–4383. [Google Scholar] [CrossRef]
- Soltani, K.; Ebtehaj, I.; Amiri, A.; Azari, A.; Gharabaghi, B.; Bonakdari, H. Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Sci. Total Environ. 2021, 770, 145288. [Google Scholar] [CrossRef] [PubMed]
- Jensen, J.R. Introductory Digital Image Processing: A Remote Sensing Perspective, 4th ed.; Prentice Hall Press: Upper Saddle River, NJ, USA, 2015; Available online: https://www.pearson.com/en-us/subject-catalog/p/introductory-digital-image-processing-a-remote-sensing-perspective/P200000006907/9780137551118 (accessed on 22 March 2024).
- Intergovernmental Panel on Climate Change. Climate Change 2021–2023: The Sixth Assessment Report; Pörtner, H.O., Roberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A., Craig, M.H., Langsdorf, S., Löschke, S., Möller, V., et al., Eds.; Cambridge University Press: Cambridge, UK, 2021–2023. [Google Scholar]
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2022. [Google Scholar]
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC); Cambridge University Press: Cambridge, UK, 2022. [Google Scholar]
- Intergovernmental Panel on Climate Change (IPCC). AR6 Synthesis Report: Climate Change 2023. Contribution of Working Groups I, II, and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2023. [Google Scholar]
- Soltani, K.; Azari, A. Forecasting Groundwater Anomaly in the Future Using Satellite Information and Machine Learning. J. Hydrol. 2022, 612, 128052. [Google Scholar] [CrossRef]
- Amiri, A.; Soltani, K.; Ebtehaj, I.; Bonakdari, H. A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems. J. Hydrol. 2024, 632, 130936. [Google Scholar] [CrossRef]
- Canada, Agriculture and Agri-Food Canada. Canadian Drought Monitor. agriculture.canada.ca. Available online: https://agriculture.canada.ca/en/agricultural-production/weather/canadian-drought-monitor (accessed on 9 May 2024).
- Imanian, H.; Mohammadian, A.; Farhangmehr, V.; Payeur, P.; Goodarzi, D.; Hiedra Cobo, J.; Shirkhani, H. A comparative analysis of deep learning models for soil temperature prediction in cold climates. Theor. Appl. Climatol. 2024, 155, 2571–2587. [Google Scholar] [CrossRef]
- Hoa, P.V.; Binh, N.A.; Hong, P.V.; An, N.N.; Thao, G.T.P.; Hanh, N.C.; Ngo, P.T.T.; Bui, D.T. One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: A case study in North Central Vietnam. Earth Sci. Inform. 2024, 1–22. [Google Scholar] [CrossRef]
- Gyaneshwar, A.; Mishra, A.; Chadha, U.; Raj Vincent, P.D.; Rajinikanth, V.; Pattukandan Ganapathy, G.; Srinivasan, K. A contemporary review on deep learning models for drought prediction. Sustainability 2023, 15, 6160. [Google Scholar] [CrossRef]
- Grégoire, G.; Fortin, J.; Ebtehaj, I.; Bonakdari, H. Forecasting pesticide use on golf courses by integration of deep learning and decision tree techniques. Agriculture 2023, 13, 1163. [Google Scholar] [CrossRef]
- Zhao, Y.; Xu, Y.; Ye, J.; Zhang, X.; Long, Z. Urban Water Supply Forecasting Based on CNN-LSTM-AM Spatiotemporal Deep Learning Model. IEEE Access 2023, 11, 144204–144212. [Google Scholar] [CrossRef]
- Pang, Y.; Yu, J.; Xi, L.; Ge, D.; Zhou, P.; Hou, C.; He, P.; Zhao, L. Remote Sensing Extraction of Lakes on the Tibetan Plateau Based on the Google Earth Engine and Deep Learning. Remote Sens. 2024, 16, 583. [Google Scholar] [CrossRef]
- Dixit, C.; Satapathy, S.M. Deep CNN with late fusion for real time multimodal emotion recognition. Expert Syst. Appl. 2024, 240, 122579. [Google Scholar] [CrossRef]
- Zamani, M.G.; Nikoo, M.R.; Jahanshahi, S.; Barzegar, R.; Meydani, A. Forecasting water quality variable using deep learning and weighted averaging ensemble models. Environ. Sci. Pollut. Res. 2023, 30, 124316–124340. [Google Scholar] [CrossRef] [PubMed]
- Zeng, G.; Ma, Y.; Du, M.; Chen, T.; Lin, L.; Dai, M.; Luo, H.; Hu, L.; Zhou, Q.; Pan, X. Deep convolutional neural networks for aged microplastics identification by Fourier transform infrared spectra classification. Sci. Total Environ. 2024, 913, 169623. [Google Scholar] [CrossRef] [PubMed]
- Feng, F.; Ghorbani, H.; Radwan, A.E. Predicting groundwater level using traditional and deep machine learning algorithms. Front. Environ. Sci. 2024, 12, 1291327. [Google Scholar] [CrossRef]
- Ioffe, S.; Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 7–9 July 2015; pp. 448–456. Available online: https://proceedings.mlr.press/v37/ioffe15.pdf (accessed on 22 March 2024).
- Ebtehaj, I.; Sattar, A.M.; Bonakdari, H.; Zaji, A.H. Prediction of scour depth around bridge piers using self-adaptive extreme learning machine. J. Hydroinform. 2017, 19, 207–224. [Google Scholar] [CrossRef]
- Ebtehaj, I.; Soltani, K.; Amiri, A.; Faramarzi, M.; Madramootoo, C.A.; Bonakdari, H. Prognostication of shortwave radiation using an improved No-Tuned fast machine learning. Sustainability 2021, 13, 8009. [Google Scholar] [CrossRef]
- Bonakdari, H.; Ebtehaj, I.; Ladouceur, J.D. Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications; Elsevier: Amsterdam, The Netherlands, 2023; ISBN 9780443152856. Available online: https://books.google.com/books?hl=en&lr=&id=dmilEAAAQBAJ&oi=fnd&pg=PP1&dq=60.%09Bonakdari,+H.,+Ebtehaj,+I.,+%26+Ladouceur,+J.+D.+(2023).+Machine+Learning+in+Earth,+Environmental+and+Planetary+Sciences:+Theoretical+and+Practical+Applications.+Elsevier.+ISBN:+9780443152856&ots=XUXJLaY_h0&sig=II-r41AtdFHMchVyVgyYUAJh3Q0#v=onepage&q&f=false (accessed on 22 March 2024).
- Ebtehaj, I.; Bonakdari, H.; Safari, M.J.S.; Gharabaghi, B.; Zaji, A.H.; Madavar, H.R.; Khozani, Z.S.; Es-haghi, M.S.; Shishegaran, A.; Mehr, A.D. Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes. Int. J. Sediment Res. 2020, 35, 157–170. [Google Scholar] [CrossRef]
- Bonakdari, H.; Zaji, A.H.; Soltani, K.; Gharabaghi, B. Improving the accuracy of a remotely-sensed flood warning system using a multi-objective pre-processing method for signal defects detection and elimination. C. R. Geosci. 2020, 352, 73–86. [Google Scholar] [CrossRef]
- Soltani, K.; Amiri, A.; Zeynoddin, M.; Ebtehaj, I.; Gharabaghi, B.; Bonakdari, H. Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods. Theor. Appl. Climatol. 2021, 143, 713–735. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Soltani, K.; Amiri, A.; Ebtehaj, I.; Cheshmehghasabani, H.; Fazeli, S.; Gumiere, S.J.; Bonakdari, H. Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections. Climate 2024, 12, 119. https://doi.org/10.3390/cli12080119
Soltani K, Amiri A, Ebtehaj I, Cheshmehghasabani H, Fazeli S, Gumiere SJ, Bonakdari H. Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections. Climate. 2024; 12(8):119. https://doi.org/10.3390/cli12080119
Chicago/Turabian StyleSoltani, Keyvan, Afshin Amiri, Isa Ebtehaj, Hanieh Cheshmehghasabani, Sina Fazeli, Silvio José Gumiere, and Hossein Bonakdari. 2024. "Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections" Climate 12, no. 8: 119. https://doi.org/10.3390/cli12080119