Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area Description
2.2. Methodology
2.3. Data Used
2.4. REA_QM Method
2.5. Support Vector Regression (ν-SVR)
2.6. Linking SWAT with ν-SVR
3. Results
3.1. Climate Variables from REA_QM
3.2. Historic and Future NEX-GDDP Climate Data Analysis
3.3. Performance of the ν-SVR Model
3.4. Effect of Climate Change on Water Availability
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Country and Institution |
---|---|
ACCESS | Commonwealth Scientific and Industrial Research Organization and Bureau of Meteorology, Australia |
BCC-CSM1 | Beijing Climate Center, China Institute of global change and Earth System Sciences, Beijing Normal University, China |
BNU-ESM | Institute of global change and Earth System Sciences, Beijing Normal University, China |
CCSM4 | National Center for Atmospheric Research, America |
MIROC5 | Atmosphere and Ocean Research Institute, Japan Atmosphere |
MIROCESM | Atmosphere and Ocean Research Institute, Japan Atmosphere |
MIROCHEM | Atmosphere and Ocean Research Institute, Japan Atmosphere |
CanEsm | Canadian Centre for Climate Modelling and Analysis, Canada |
CESM1-BGC | National Center for Atmospheric Research, America Centre National de Recherches Meteorologiques, Centre. |
CNRM-CM5 | Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique, France Commonwealth Scientific and Industrial Research |
CSIRO-MK3 | Organization/Queensland Climate Change Centre of Excellence, Australia |
GFDL-CM3 | Geophysical Fluid Dynamics Laboratory, America |
GFDL-ESM2G | Geophysical Fluid Dynamics Laboratory, America |
GFDL-ESM2M | Geophysical Fluid Dynamics Laboratory, America |
INMCM4 | Institute of Numerical Calculation, Russia |
IPSL-CM5A-LR | Institut Pierre-Simon Laplace, France |
IPSL-CM5A-MR | Institut Pierre-Simon Laplace, France |
MPI-ESM-LR | Max Planck Institute for Meteorology, Germany |
MPI-ESM-MR | Max Planck Institute for Meteorology, Germany |
MPRI-CGCM3 | Max Planck Institute for Meteorology, Germany |
NORESM1-M | Norway Consumer Council, Norway |
Precipitation | Maximum Temperature | Minimum Temperature | |||||||
---|---|---|---|---|---|---|---|---|---|
Model | Historic | RCP 4.5 | RCP 8.5 | Historic | RCP 4.5 | RCP 8.5 | Historic | RCP 4.5 | RCP 8.5 |
ACCESS1-0 | 0.0848 | 0.0379 | 0.0091 | 0.0531 | 0.0470 | 0.0454 | 0.0356 | 0.0480 | 0.0480 |
BCC-CSM1-1 | 0.0229 | 0.0527 | 0.0575 | 0.0437 | 0.0482 | 0.0475 | 0.0438 | 0.0469 | 0.0469 |
BNU-ESM | 0.0272 | 0.0560 | 0.0612 | 0.0492 | 0.0481 | 0.0483 | 0.0398 | 0.0484 | 0.0484 |
CanESM2 | 0.0166 | 0.0640 | 0.0424 | 0.0392 | 0.0479 | 0.0472 | 0.0332 | 0.0472 | 0.0472 |
CCSM4 | 0.0309 | 0.0355 | 0.0480 | 0.0542 | 0.0472 | 0.0475 | 0.0576 | 0.0483 | 0.0483 |
CESM1-BGC | 0.1612 | 0.0543 | 0.0255 | 0.0409 | 0.0466 | 0.0454 | 0.0427 | 0.0449 | 0.0449 |
CNRM-CM5 | 0.0403 | 0.0390 | 0.0427 | 0.0508 | 0.0480 | 0.0486 | 0.0439 | 0.0483 | 0.0482 |
CSIRO-Mk3-6-0 | 0.0322 | 0.0360 | 0.0353 | 0.0541 | 0.0484 | 0.0487 | 0.0799 | 0.0486 | 0.0485 |
GFDL-CM3 | 0.0311 | 0.0424 | 0.0420 | 0.0445 | 0.0473 | 0.0483 | 0.0592 | 0.0468 | 0.0468 |
GFDL-ESM2G | 0.0362 | 0.0439 | 0.0555 | 0.0525 | 0.0494 | 0.0486 | 0.0420 | 0.0498 | 0.0498 |
GFDL-ESM2M | 0.1678 | 0.0361 | 0.0450 | 0.0413 | 0.0477 | 0.0483 | 0.0408 | 0.0479 | 0.0479 |
INMCM4 | 0.0613 | 0.0467 | 0.0330 | 0.0518 | 0.0485 | 0.0479 | 0.0703 | 0.0490 | 0.0491 |
IPSL-CM5A-LR | 0.0281 | 0.0535 | 0.0613 | 0.0426 | 0.0478 | 0.0476 | 0.0498 | 0.0493 | 0.0493 |
IPSL-CM5A-MR | 0.0400 | 0.0392 | 0.0493 | 0.0462 | 0.0474 | 0.0484 | 0.0365 | 0.0482 | 0.0481 |
MIROC5 | 0.0285 | 0.0441 | 0.0657 | 0.0473 | 0.0478 | 0.0485 | 0.0719 | 0.0475 | 0.0475 |
MIROCESM | 0.0222 | 0.0740 | 0.0786 | 0.0452 | 0.0473 | 0.0474 | 0.0363 | 0.0470 | 0.0471 |
MIROCHEM | 0.0285 | 0.0572 | 0.0720 | 0.0459 | 0.0473 | 0.0473 | 0.0297 | 0.0467 | 0.0467 |
MPI-ESM-LR | 0.0328 | 0.0454 | 0.0312 | 0.0502 | 0.0469 | 0.0473 | 0.0415 | 0.0464 | 0.0464 |
MPI-ESM-MR | 0.0220 | 0.0499 | 0.0412 | 0.0535 | 0.0472 | 0.0464 | 0.0348 | 0.0462 | 0.0462 |
MRI-CGCM3 | 0.0637 | 0.0411 | 0.0456 | 0.0478 | 0.0471 | 0.0477 | 0.0612 | 0.0469 | 0.0469 |
NorESM1-M | 0.0219 | 0.0511 | 0.0578 | 0.0460 | 0.0468 | 0.0477 | 0.0497 | 0.0478 | 0.0478 |
Period | P (mm) | ΔP (%) | SF | ΔSF (%) | Tmax (°C) | ΔTmax | Tmin (°C) | ΔTmin | ET (mm) | ΔET (%) |
---|---|---|---|---|---|---|---|---|---|---|
IMD | 1143.04 | 56.76 | 33.42 | 22.07 | 595.66 | |||||
1988–2018 | ||||||||||
Historic | 1120.81 | −1.95 | 63.63 | 12.10 | 33.35 | −0.07 | 22.10 | 0.03 | 510.55 | −14.29 |
1988–2018 | ||||||||||
Future RCP4.5 | 1020.25 | −10.74 | 54.99 | −3.12 | 34.63 | 1.21 | 23.29 | 1.22 | 511.62 | −14.11 |
2021–2050 | ||||||||||
Future RCP8.5 | 1178.94 | 3.14 | 68.53 | 20.73 | 34.84 | 1.42 | 23.55 | 1.48 | 517.63 | −13.10 |
2021–2051 |
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Jayanthi, S.L.S.V.; Keesara, V.R.; Sridhar, V. Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR). Sustainability 2022, 14, 6974. https://doi.org/10.3390/su14126974
Jayanthi SLSV, Keesara VR, Sridhar V. Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR). Sustainability. 2022; 14(12):6974. https://doi.org/10.3390/su14126974
Chicago/Turabian StyleJayanthi, Sri Lakshmi Sesha Vani, Venkata Reddy Keesara, and Venkataramana Sridhar. 2022. "Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)" Sustainability 14, no. 12: 6974. https://doi.org/10.3390/su14126974