Evaluation of statistical downscaling model's performance in projecting future climate change scenarios
Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD m...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-10-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/14/10/3559 |
| _version_ | 1797202748181053440 |
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| author | Rituraj Shukla Deepak Khare Anuj Kumar Dwivedi Ramesh Pal Rudra Santosh S. Palmate C. S. P. Ojha Vijay P. Singh |
| author_facet | Rituraj Shukla Deepak Khare Anuj Kumar Dwivedi Ramesh Pal Rudra Santosh S. Palmate C. S. P. Ojha Vijay P. Singh |
| author_sort | Rituraj Shukla |
| collection | DOAJ |
| description | Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or multi-linear regression and the Least Square Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from general circulation model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for 1961–2001 and then for 2001–2099. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models.
HIGHLIGHTS
Precipitation values are under-predicted, while temperature values are over-predicted by statistical downscaling models.;
Large errors in downscaling precipitation are observed, since downscaling of precipitation is more problematic than temperature.;
Statistical measures (R2, RMSE, SSE, NSE, and MAE) showed good agreement between observed and downscaled climate variables for SDSM and LS-SVM.; |
| first_indexed | 2024-03-11T11:14:28Z |
| format | Article |
| id | doaj.art-7218329e6cf94800b7b37d8333178fa9 |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-04-24T08:08:22Z |
| publishDate | 2023-10-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-7218329e6cf94800b7b37d8333178fa92024-04-17T08:35:24ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542023-10-0114103559359510.2166/wcc.2023.207207Evaluation of statistical downscaling model's performance in projecting future climate change scenariosRituraj Shukla0Deepak Khare1Anuj Kumar Dwivedi2Ramesh Pal Rudra3Santosh S. Palmate4C. S. P. Ojha5Vijay P. Singh6 School of Engineering, University of Guelph, Guelph, Ontario, Canada Indian Institute of Technology Roorkee, Roorkee Uttarakhand, India National Institute of Hydrology Roorkee, Uttarakhand, India School of Engineering, University of Guelph, Guelph, Ontario, Canada Texas A&M AgriLife Research, El Paso Centre, Texas A&M University, El Paso, Texas, USA Indian Institute of Technology Roorkee, Roorkee Uttarakhand, India Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, Texas, USA Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or multi-linear regression and the Least Square Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from general circulation model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for 1961–2001 and then for 2001–2099. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models. HIGHLIGHTS Precipitation values are under-predicted, while temperature values are over-predicted by statistical downscaling models.; Large errors in downscaling precipitation are observed, since downscaling of precipitation is more problematic than temperature.; Statistical measures (R2, RMSE, SSE, NSE, and MAE) showed good agreement between observed and downscaled climate variables for SDSM and LS-SVM.;http://jwcc.iwaponline.com/content/14/10/3559hadcm3indira sagar canal command areals-svmsdsmstatistical downscaling |
| spellingShingle | Rituraj Shukla Deepak Khare Anuj Kumar Dwivedi Ramesh Pal Rudra Santosh S. Palmate C. S. P. Ojha Vijay P. Singh Evaluation of statistical downscaling model's performance in projecting future climate change scenarios Journal of Water and Climate Change hadcm3 indira sagar canal command area ls-svm sdsm statistical downscaling |
| title | Evaluation of statistical downscaling model's performance in projecting future climate change scenarios |
| title_full | Evaluation of statistical downscaling model's performance in projecting future climate change scenarios |
| title_fullStr | Evaluation of statistical downscaling model's performance in projecting future climate change scenarios |
| title_full_unstemmed | Evaluation of statistical downscaling model's performance in projecting future climate change scenarios |
| title_short | Evaluation of statistical downscaling model's performance in projecting future climate change scenarios |
| title_sort | evaluation of statistical downscaling model s performance in projecting future climate change scenarios |
| topic | hadcm3 indira sagar canal command area ls-svm sdsm statistical downscaling |
| url | http://jwcc.iwaponline.com/content/14/10/3559 |
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