پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,228 |
| تعداد مقالات | 67,750 |
| تعداد مشاهده مقاله | 115,081,190 |
| تعداد دریافت فایل اصل مقاله | 89,819,843 |
ارزیابی تاثیر روشهای تصحیح اریبی بر مهارت پیشبینی فصلی بارش مدل اقلیمی CFSv2 | ||
| تحقیقات آب و خاک ایران | ||
| دوره 51، شماره 12، اسفند 1399، صفحه 3017-3032 اصل مقاله (1.86 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2020.306717.668680 | ||
| نویسندگان | ||
| فاطمه شعبان پور1؛ جواد بذرافشان* 2؛ شهاب عراقی نژاد1 | ||
| 1گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2دانشیار، گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| چکیده | ||
| روشهای تصحیح اریبی از جمله روشهای آماری متداول برای پسپردازش خروجی مدل‌های اقلیمی هستند. در این تحقیق، تاثیر پنج روش تصحیح اریبی بر مهارت پیشبینی بارش (فصل پاییز) مدل اقلیمی CFSv2 بر مبنای 12 ایستگاه واقع در حوضه آبریز گرگانرود (شمال ایران) مورد ارزیابی قرارگرفته است. روشهای تصحیح اریبی مورد استفاده در این تحقیق شامل دو روش ناپارامتری (نسبت‌گیری خطی(LS) ، نگاشت چندکی تجربی (EQM))، یک روش پارامتری (تبدیل توانی (Ptr)) و دو روش پارامتری مبتنی بر توزیعهای آماری (نگاشت پارامتری چندک (PQM)، نگاشت چندکی پارامتری تعمیم یافته (GPQM)) میباشند. از سنجههای متنوعی برای ارزیابی تاثیر این روشها بر مهارت پیشبینی فصلی بارش استفاده شده است که شامل متوسط اریبی، متوسط ضریب همبستگی پیرسون و همچنین دو سنجه مهارت پیشبینی احتمالاتی شامل امتیازهای مهارتی، ویژگی عملیاتی نسبی (ROCSS) و رتبه احتمال (RPSS) میباشد. نتایج این تحقیق نشان میدهد بیشتر روشهای تصحیح اریبی و در موارد بالایی به‌خوبی توانستند اریبی موجود در پیشبینیها را کاهش دهند. تاثیر استفاده از روشهای مختلف تصحیح اریبی بر مهارت پیشبینی احتمالاتی با استفاده از سنجههای RPSS و ROCSS نیز وابسته به محل و زمان متفاوت است و هر یک از روشها میتوانند این سنجهها را برای محل یا زمانی بهبود دهند و یا تضعیف کنند. از اینرو نتیجه این تحقیق پیشنهاد میکند ارزیابی روشهای مختلف تصحیح اریبی و شناسایی مناسبترین روش با توجه به هدف هر مطالعه میتواند به ارتقاء مهارت پیشبینی فصلی بارش کمک کند. | ||
| کلیدواژه‌ها | ||
| پیش بینی بارش فصلی؛ تصحیح اریبی؛ مهارت؛ ریزمقیاس‌نمایی؛ اقلیم شناسی | ||
| عنوان مقاله [English] | ||
| Evaluation of the Effect of Bias Correction Methods on the Skill of Seasonal Precipitation Forecasts of CFSv2 Climate Model | ||
| نویسندگان [English] | ||
| Fatemeh Shabanpour1؛ Javad Bazrafshan2؛ Shahab Araghinejad1 | ||
| 1Irrigation and Reclamation Engineering Department, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| 2Associate Professor, Irrigation and Reclamation Engineering Department, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| چکیده [English] | ||
| Bias correction methods are one of the most common statistical post-processing methods which are utilized on the output of climate models. This study evaluates the effect of five bias correction methods on the skill of seasonal precipitation forecast (fall season) from the CFSv2 climate model based on 12 stations located in Gorganrud basin in Iran. Bias correction methods that have been used in this study consists of two non-parametric methods (Linear Scaling (LS), Empirical Quantile Mapping (EQM)), one parametric method (Power Transformation (Ptr)), and two parametric methods based on the statistical distribution (Parametric Quantile Mapping (PQM), Generalized Parametric Quantile Mapping (GPQM)). Various metrics have been used for evaluating the effects of these methods on the skill of seasonal precipitation forecast which consists of bias, Pearson correlation coefficient, ranked probability skill score (RPSS), and the relative operating curve skill score (ROCSS). The Results of this study revealed that most of bias correction methods decreased the biases of the raw forecasts. The effect of each bias correction method on the RPSS and ROCSS (below and above normal events) scores may vary based on location and time, and each method can improve or worsen these two scores based on location and time. The results of this study suggest that the evaluation of various bias correction methods and distinguishing the most suitable method based on the goal of each study would be helpful in the improvement of seasonal precipitation forecast skill. | ||
| کلیدواژه‌ها [English] | ||
| Seasonal Precipitation Forecast, Bias Correction, Skill, Downscaling, Climatology | ||
| مراجع | ||
|
An-Vo, D. A., Mushtaq, S., Reardon-Smith, K., Kouadio, L., Attard, S., Cobon, D., & Stone, R. (2019). Value of seasonal forecasting for sugarcane farm irrigation planning. European journal of agronomy, 104, 37-48. Bhend J. 2015. easyVerification: Forecast verification metrics for large datasets. R package version 0.1.5.3 Block, P. (2010). Tailoring seasonal climate forecasts for hydropower operations in Ethiopia's upper Blue Nile basin. Hydrol. Earth Syst. Sci. Discuss, 7(3), 3765-3802. Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., ... & Gutiérrez, J. M. (2019). Statistical downscaling with the downscaleR package: Contribution to the VALUE intercomparison experiment. Brent, R. P. (1971). An algorithm with guaranteed convergence for finding a zero of a function. The Computer Journal, 14(4), 422-425. Chen, J., Brissette, F. P., Chaumont, D., & Braun, M. (2013). Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resources Research, 49(7), 4187-4205. Crochemore, L., Ramos, M. H., & Pappenberger, F. (2016). Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts. Cofiño, A. S., Bedia, J., Iturbide, M., Vega, M., Herrera, S., Fernández, J., ... & Gutiérrez, J. M. (2018). The ECOMS User Data Gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services. Climate Services, 9, 33-43. Doblas‐Reyes, F. J., García‐Serrano, J., Lienert, F., Biescas, A. P., & Rodrigues, L. R. (2013). Seasonal climate predictability and forecasting: status and prospects. Wiley Interdisciplinary Reviews: Climate Change, 4(4), 245-268. Fang, G., Yang, J., Chen, Y. N., & Zammit, C. (2015). Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology and Earth System Sciences, 19(6), 2547-2559. Frías, M. D., Iturbide, M., Manzanas, R., Bedia, J., Fernández, J., Herrera, S., ... & Gutiérrez, J. M. (2018). An R package to visualize and communicate uncertainty in seasonal climate prediction. Environmental modelling & software, 99, 101-110. Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Skaugen, T. E. (2012). Technical Note: Downscaling RCM precipitation to the station scale using quantile mapping–a comparison of methods. Hydrol. Earth Syst. Sci. Discuss, 9(5), 6185-6201. Gutiérrez, J. M., Bedia, J., Benestad, R., & Pagé, C. (2013). Review of the different statistical downscaling methods for s2d prediction. Technical Report. SPECS Deliverable, 52, 1. Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R., ... & San Martin, D. (2019). An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment. International journal of climatology, 39(9), 3750-3785. Gutjahr, O., & Heinemann, G. (2013). Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM. Theoretical and applied climatology, 114(3-4), 511-529. Hamlet, A. F., Huppert, D., & Lettenmaier, D. P. (2002). Economic value of long-lead streamflow forecasts for Columbia River hydropower. Journal of Water Resources Planning and Management, 128(2), 91-101. Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández, J., Frías, M. D., ... & Gutiérrez, J. M. (2019). The R-based climate4R open framework for reproducible climate data access and post-processing. Environmental Modelling & Software, 111, 42-54. Johnson, F., & Sharma, A. (2011). Accounting for interannual variability: A comparison of options for water resources climate change impact assessments. Water Resources Research, 47(4). Leander, R., & Buishand, T. A. (2007). Resampling of regional climate model output for the simulation of extreme river flows. Journal of Hydrology, 332(3-4), 487-496. Leander, R., Buishand, T. A., van den Hurk, B. J., & de Wit, M. J. (2008). Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output. Journal of Hydrology, 351(3-4), 331-343. Lenderink, G., Buishand, A., & Van Deursen, W. (2007). Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Marcos, R., Llasat, M. C., Quintana-Seguí, P., & Turco, M. (2018). Use of bias correction techniques to improve seasonal forecasts for reservoirs—A case-study in northwestern Mediterranean. Science of the total environment, 610, 64-74. Manzanas, R., Lucero, A., Weisheimer, A., & Gutiérrez, J. M. (2018). Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?. Climate dynamics, 50(3-4), 1161-1176. Manzanas, R., Gutiérrez, J. M., Bhend, J., Hemri, S., Doblas-Reyes, F. J., Torralba, V., ... & Brookshaw, A. (2019). Bias adjustment and ensemble recalibration methods for seasonal forecasting: A comprehensive intercomparison using the C3S dataset. Climate Dynamics, 53(3-4), 1287-1305. Manzanas, R., Gutiérrez, J. M., Bhend, J., Hemri, S., Doblas-Reyes, F. J., Penabad, E., & Brookshaw, A. (2020). Statistical adjustment, calibration and downscaling of seasonal forecasts: a case-study for Southeast Asia. Climate Dynamics, 1-14. Mendez, M., Maathuis, B., Hein-Griggs, D., & Alvarado-Gamboa, L. F. (2020). Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica. Water, 12(2), 482. Ogutu, G. E., Franssen, W. H., Supit, I., Omondi, P., & Hutjes, R. W. (2017). Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa. International Journal of Climatology, 37(5), 2734-2756. Parton, K. A., Crean, J., & Hayman, P. (2019). The value of seasonal climate forecasts for Australian agriculture. Agricultural systems, 174, 1-10. Piani, C., Haerter, J. O., & Coppola, E. (2010). Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology, 99(1-2), 187-192. Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., ... & Ek, M. (2014). The NCEP climate forecast system version 2. Journal of climate, 27(6), 2185-2208. Schmidli, J., Frei, C., & Vidale, P. L. (2006). Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods. International Journal of Climatology: A Journal of the Royal Meteorological Society, 26(5), 679-689. Siegert, S., Bhend, J., Kroener, I., & De Felice, M. (2017). Package ‘SpecsVerification’. Smith, D. M., Eade, R., & Pohlmann, H. (2013). A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Climate Dynamics, 41(11-12), 3325-3338. Steinemann, A. C. (2006). Using climate forecasts for drought management. Journal of Applied Meteorology and Climatology, 45(10), 1353-1361. Tall, A., Mason, S. J., Van Aalst, M., Suarez, P., Ait-Chellouche, Y., Diallo, A. A., & Braman, L. (2012). Using seasonal climate forecasts to guide disaster management: the Red Cross experience during the 2008 West Africa floods. International Journal of Geophysics, 2012. Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of hydrology, 456, 12-29. Thom, H. C. (1958). A note on the gamma distribution. Monthly Weather Review, 86(4), 117-122. Troccoli, A. (2010). Seasonal climate forecasting. Meteorological Applications, 17(3), 251-268. Von Storch, H., Zorita, E., & Cubasch, U. (1993). Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. Journal of Climate, 6(6), 1161-1171. Wetterhall, F., Winsemius, H. C., Dutra, E., Werner, M., & Pappenberger, E. (2015). Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin. Hydrology and Earth System Sciences, 19(6), 2577-2586. | ||
|
آمار تعداد مشاهده مقاله: 764 تعداد دریافت فایل اصل مقاله: 529 |
||