- Anandhi, A., Frei, A., Pierson, D. C., Schneiderman, E. M., Zion, M. S., Lounsbury, D., & Matonse, A. H. (2011). Examination of change factor methodologies for climate change impact assessment. Water Resources Research, 47(3).
- Bahrami, M., Amiri, M., Maharloiee, , Rezaie, & Ghafari, K. (2017). Determining the effect of data preprocessing on the performance of artificial neural network in order to predict monthly rainfall in Abadeh city. Ecohydrology, 1, 29-37. (In Persian).
- Bitew, M. M., Gebremichael, M., Ghebremichael, L. T., & Bayissa, Y. A. (2012). Evaluation of high-resolution satellite rainfall products through streamflow simulation in a hydrological modeling of a small mountainous watershed in Ethiopia. Journal of Hydrometeorology, 13(1), 338-350.
- Breinl, K., & Di Baldassarre, G. (2019). Space-time disaggregation of precipitation and temperature across different climates and spatial scales. Journal of Hydrology: Regional Studies, 21, 126-146. Available at: https://doi.org/10.1016/j.ejrh.2018.12.002
- Chivers, B. D., Wallbank, J., Cole, S. J., Sebek, O., Stanley, S., Fry, M., & Leontidis, G. (2020). Imputation of missing sub-hourly precipitation data in a large sensor network: A machine learning approach. Journal of Hydrology, Elsevier 588, 125126. Available at: https://doi.org/10.1016/j.jhydrol.2020.125126
- Duan, Z., Liu, J., Tuo, Y., Chiogna, G., & Disse, M. (2016). Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Science of the Total Environment, 573, 1536-1553.
- Villazón, M. F., & Willems, P. (2010, May). Filling gaps and daily disaccumulation of precipitation data for rainfall-runoff model. International Scientific Conference on Water Observation and Information Systems for Decision Support. (pp. 25-29).
- Faghih, H., Bahmanesh, J., & Khalili, K. (2018). Spatio-temporal simulation of annual rainfall using stochastic models. Journal of water and soil sciences (Agricultural sciences and natural resources). (In Persian).
- Farzandi, M., Sanaeinejad, H., Ghahraman, B., & Sarmad, M. (2019). Imputation of missing meteorological data with evolutionary and machine learning methods, case study: long-term monthly precipitation and temperature of Mashhad. Journal of Water and Soil, 33(2), 361-377.
- Gao, P., Mu, X. M., Wang, F., & Li, R. (2011). Changes in streamflow and sediment discharge and the response to human activities in the middle reaches of the Yellow River. Hydrology and Earth System Sciences, 15(1), 1-10.
- Gyau-Boakye, P., & Schultz, G. A. (1994). Filling gaps in runoff time series in West Africa. Hydrological Sciences Journal, 39(6), 621-636.
- Eslami Jamal Abad, S., Sharafati, A., Mohammadi Golafshani, E., & Farsadania, F. (2019). Estimation of missing daily rainfall and runoff data using self-consistent mapping, Case study: Mazandaran province. Journal of Water and Soil Sciences, JWSS, 23(4), 1-17 (In Persian).
- John, A., Fowler, K., Nathan, R., Horne, A., & Stewardson, M. (2021). Disaggregated monthly hydrological models can outperform daily models in providing daily flow statistics and extrapolate well to a drying climate. Journal of Hydrology. Elsevier B.V. 598(February): 126471. Available at: https://doi.org/10.1016/j.jhydrol.2021.126471
- Kassomenos, P. A., Paschalidou, A. K., & Vlachogianni, A. (2013). One-day-ahead prediction of maximum carbon monoxide concentration in urban environments. Stochastic Environmental Research and Risk Assessment, 27, 561-572.
- Khalili, A., & Rahimi, J. (2014). High-resolution spatiotemporal distribution of precipitation in Iran: a comparative study with three global-precipitation datasets. Theoretical and applied climatology, 118, 211-221.
- Kosari, M. R., Hosieni, M., Esmaielzade, S., & Miri, M. (2021). Investigating the efficiency of reconstruction methods of statistical defects in relation to precipitation parameters in dry areas of Iran. Earth and space physics. (In Persian).
- Lookzadeh, S. (2005). Evaluation of several methods in reconstruction of missing precipitation data in different periods at central Alborz region, MSc Thesis. Tehran University.
- Mengistu, S., Gessesse, B., Bedada, T. B., & Tibebe, D. (2019a). Evaluation of long-term satellite-based retrieved precipitation estimates and spatiotemporal rainfall variability: The case study of Awash basin, Ethiopia. Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation. Elsevier Inc. Available at: http://dx.doi.org/10.1016/B978-0-12-815998-9.00003-8
- Mengistu, S., Gessesse, B., Bedada, T. B., & Tibebe, D. (2019b). Evaluation of long-term satellite-based retrieved precipitation estimates and spatiotemporal rainfall variability: The case study of Awash basin, Ethiopia. Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation, 23-35.
- Mianabadi, A., Alizadeh, A., Sanaeinejad, H., Awal, M. B., & Faridhosseini, A. (2013). The Statistic Assessment of CMORPH Model Output For Precipitation Estimation Over The Northeast of Iran (Case Study: North Khurasan Province). Journal of Water and Soil, 27(5), 919-927. (In Persian).
- Mirzaiee, N., & Saraf, A. (2021). Application of data integration models in simulating river flow using large-scale climate signals, case study: Jiroft Dam watershed. Journal of Watershed Engineering and Management, 13(4), 672-689. (In Persian).
- Matinzahe, M. M., Fatahi, R., Shayannejad, M., & Abdulahi, K. (2013). Estimation and reconstruction of 24-hour annual maximum rainfall data using the integrated model of genetic algorithm and neural networks (Case study: Chahar Mahal Bakhtiari province). Iranian Journal of Watershed Management Science, jwmseir 2013, 7(22), 53-62 (In Persian).
- Mwale, F. D., Adeloye, A. J., & Rustum, R. (2012). Infilling of missing rainfall and streamflow data in the Shire River basin, Malawi–A self organizing map approach. Physics and Chemistry of the Earth, Parts A/B/C, 50, 34-43.
- Mwale, F. D., Adeloye, A. J., & Rustum, R. (2012b). Infilling of missing rainfall and streamflow data in the Shire River basin, Malawi-A self organizing map approach. Physics and Chemistry of the Earth. Elsevier Ltd 50-52, 34-43. Available at: http://dx.doi.org/10.1016/j.pce.2012.09.006
- Tayefeh Neskini,, Zahraie B and Saghafian B (2016) Evaluation of different simulations of artificial neural network and geostatistical methods in supplementing missing data of daily precipitation. Journal of water resources engineering, 8(26), 69-88. (In Persian).
- Hamed, K., & Rao, A. R. (Eds.). (2019). Flood frequency analysis. CRC press.
- Ryberg, K. R., & Vecchia, A. V. (2017). Vignette for waterData-An R Package for Retrieval, Analysis, and Anomaly Calculation of Daily Hydrologic Time Series Data.
- Sachindra, D. A., & Perera, B. J. C. (2016). Annual statistical downscaling of precipitation and evaporation and monthly disaggregation. Theoretical and Applied Climatology. Theoretical and Applied Climatology, 131(1-2), 181-200. Available at: http://dx.doi.org/10.1007/s00704-016-1968-6
- Sadatinejad, S. J., Shayannejad, M., & Honarbakhsh, A. (2010). Investigation of the Efficiency of the Fuzzy Regression Method in Reconstructing Monthly Discharge Data of Hydrometric Stations in Great Karoon River Basin. Journal of Agricultural Science and Technology, JAST; 12 (1), 111-119.
- Searcy, J. K., & Hardison, C. H. (1960). Double-Mass Curves. WaterSupply Paper 1541B. Available at: http://dspace.udel.edu:8080/dspace/handle/19716/1592
- Serrano-Notivoli, R., de Luis, M., & Beguería, S. (2017). An R package for daily precipitation climate series reconstruction. Environmental Modelling and Software. Elsevier Ltd 89. Available at: http://dx.doi.org/10.1016/j.envsoft.2016.11.005
- Shirvani, A., & Shirazi, E. F. Z. (2014). Comparison of ground based observation of precipitation with TRMM satellite estimations in Fars Province. Journal of Agricultural Meteorology, 2, 1-15. (In Persian).
- Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., & Hong, Y. (2020). Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote sensing of environment, 240, 111697.
- Tardivo, G., & Berti, A. (2012). A dynamic method for gap filling in daily temperature datasets. Journal of Applied Meteorology and Climatology, 51(6), 1079-1086.
- Teegavarapu, R. S. (2014). Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbour classification and cluster-based interpolation methods. Hydrological Sciences Journal, 59(11), 2009-2026.
- Teetor, P. (2011). Recipes for State Space Models in R. (July):20
- Vakili, S. (2017). Monthly precipitation prediction with M5 tree model and its comparison with classical statistical methods (Case study: Urmia synoptic station). Iran-Water resources research, 13(4), 179-183, (In Persian).
- Zahmatkesh, Z., Karamouz, M., Goharian, E., & Burian, S. J. (2015). Analysis of the effects of climate change on urban storm water runoff using statistically downscaled precipitation data and a change factor approach. Journal of Hydrologic Engineering, 20(7), 05014022. (In Persian).
- Zhang, T., Yang, Y., Dong, Z., & Gui, S. (2021). A multiscale assessment of three satellite precipitation products (TRMM, CMORPH, and PERSIANN) in the three Gorges Reservoir Area in China. Advances in Meteorology, 2021, 1-27.
|
