Comparative study of three stochastic future weather forecast approaches: a case study

Abu-Zreig, 2021, Assessment of the SWAT model in simulating watersheds in arid regions: case study of the Yarmouk River Basin (Jordan), Open Geosci., 13, 377, 10.1515/geo-2020-0238 Alodah, 2020, Influence of output size of stochastic weather generators on common climate and hydrological statistical indices, Stoch. Environ. Res. Risk Assess., 34, 993, 10.1007/s00477-020-01825-w Annisa, 2021, Analysis and projections of rainfall using representative concentration pathways (RCPs) scenarios in Sleman Yogyakarta, IOP Conf. Ser. Earth Environ. Sci., 653, 10.1088/1755-1315/653/1/012099 Arnold, 1998, Large area hydrologic modeling and assessment part I: model development, J. Am. Water Resour. Assoc., 34, 73, 10.1111/j.1752-1688.1998.tb05961.x Arnold, 2012, SWAT: model use, calibration, and validation, Trans. ASABE, 55, 1491, 10.13031/2013.42256 Blackmon, 2001, The community climate system model, Bull. Am. Meteorol. Soc., 82, 2357, 10.1175/1520-0477(2001)082<2357:TCCSM>2.3.CO;2 Brocca, 2014, Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data, J. Geophys. Res., 119, 5128, 10.1002/2014JD021489 Bui, 2021, Evaluation of the climate forecast system reanalysis data for hydrological model in the Arctic watershed Målselv, J. Water Clim. Chang., 1, 1 Buishand, 2001, Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest-neighbour resampling, Water Resour. Res., 37, 2761, 10.1029/2001WR000291 Buishand, 1977 Chen, 2017, k-nearest neighbor neural network models for very short-term global solar irradiance forecasting based on meteorological data, Energies, 10, 186, 10.3390/en10020186 Chung, 2010, Assessing distributed groundwater recharge rate using integrated surface water-groundwater modelling: application to Mihocheon watershed, South Korea, Hydrogeol. J., 18, 1253, 10.1007/s10040-010-0593-1 Collins, 2006, The community climate system model version 3 (CCSM3), J. Clim., 19, 2122, 10.1175/JCLI3761.1 Dimri, 2020, Time series analysis of climate variables using seasonal ARIMA approach, J. Earth Syst. Sci., 129, 149, 10.1007/s12040-020-01408-x Feng, 2011, Projection of future precipitation change over China with a high-resolution global atmospheric model, Adv. Atmos. Sci., 28, 464, 10.1007/s00376-010-0016-1 Gao, 2012, Climate change in China in the 21st century as simulated by a high resolution regional climate model, Chin. Sci. Bull., 57, 1188, 10.1007/s11434-011-4935-8 Gent, 2011, The community climate system model version 4, J. Clim., 24, 4973, 10.1175/2011JCLI4083.1 Gong, 2012, A comparison of single- and multi-gauge based calibrations for hydrological modeling of the Upper Daning River Watershed in China's Three Gorges Reservoir Region, Nord. Hydrol, 43, 822, 10.2166/nh.2012.021 Hamid Reza Golkar, 2019, Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment, Clim. Res., 77, 99, 10.3354/cr01545 Hanson, 1998, GEM (Generation of weather elements for multiple applications): its application in areas of complex terrain, Int. Assoc. Sci. Hydrol. J., 248, 27 Hu, 2013, EMD-KNN model for annual average rainfall forecasting, J. Hydrol. Eng., 18, 1450, 10.1061/(ASCE)HE.1943-5584.0000481 Jan, 2008, Seasonal to Inter-annual climate prediction using data mining KNN technique, Vol. 20, 40 Kareem, 2021, Predicting weather forecasting state based on data mining classification algorithms, Asian J. Res. Comput. Sci. AJRCOS, 9, 13 Katz, 1977, Precipitation as a chain-dependent process, J. Appl. Meteorol., 16, 671, 10.1175/1520-0450(1977)016<0671:PAACDP>2.0.CO;2 Khosravi, 2011, Comprehensive review of neural network-based prediction intervals and new advances, IEEE Trans. Neural Network., 22, 1341, 10.1109/TNN.2011.2162110 Lu, 2019, Assessing the climate forecast system reanalysis weather data driven hydrological model for the Yangtze River Basin in China, Appl. Ecol. Environ. Res., 17, 3615, 10.15666/aeer/1702_36153632 Lu, 2014, A coupled K-nearest neighbour and Bayesian neural network model for daily rainfall downscaling, Int. J. Climatol., 34, 3221, 10.1002/joc.3906 LV, 2020, Predicting of runoff using an Optimized SWAT-ANN: a case study, J. Hydrol.: Reg. Stud., 29, 100688 Mehrotra, 2009, Evaluating spatio-temporal representations in daily rainfall sequences from three stochastic multi-site weather generation approaches, Adv. Water Resour., 32, 948, 10.1016/j.advwatres.2009.03.005 Mehrotra, 2018, A software toolkit for correcting systematic biases in climate model simulations, Environ. Model. Software, 104, 130, 10.1016/j.envsoft.2018.02.010 Mette, 2002, Climate Analogues for temperate European forests to raise Silvicultural evidence using twin regions, Sustainability, 13, 6522, 10.3390/su13126522 Neitsch, 2004 Neitsch, 2005 Nguyen, 2019, Correcting systematic biases across multiple atmospheric variables in the frequency domain, Clim. Dynam., 52, 1283, 10.1007/s00382-018-4191-6 Nicks, 1980, Stochastic generation of temperature and solar radiation data, J. Hydrol., 48, 1, 10.1016/0022-1694(80)90062-1 Parlange, 2000, An extended version of the Richardson model for simulating daily weather variables, J. Appl. Meteorol., 39, 610, 10.1175/1520-0450-39.5.610 Rajagopalan, 1999, A k-nearest-neighbour simulator for daily precipitation and other weather variables, Water Resour. Res., 35, 3089, 10.1029/1999WR900028 Rezaur, 2003, Hydrologic behavior of residual soil slopes in Singapore, J. Hydrol. Eng., 8, 133, 10.1061/(ASCE)1084-0699(2003)8:3(133) Richardson, 1984 Richardson, 1981, Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resour. Res., 17, 182, 10.1029/WR017i001p00182 San José, 2016, Impacts of the 4.5 and 8.5 RCP global climate scenarios on urban meteorology and air quality: application to Madrid, Antwerp, Milan, Helsinki and London, J. Comput. Appl. Math., 293, 192, 10.1016/j.cam.2015.04.024 Sharif, 2007, Improved K-nearest neighbour weather generating model, J. Hydrol. Eng., 12, 42, 10.1061/(ASCE)1084-0699(2007)12:1(42) Singh, 2015, Post-validation of SWAT model in a coastal watershed for predicting land use/cover change impacts, Nord. Hydrol, 46, 837, 10.2166/nh.2015.222 Smith, 1974, Point processes of seasonal thunderstorm rainfall: 2. Rainfall depth probabilities, Water Resour. Res., 10, 418, 10.1029/WR010i003p00418 Srinivasan, 1998, Large area hydrologic modeling and assessment part II: model application, J. Am. Water Resour. Assoc., 34, 91, 10.1111/j.1752-1688.1998.tb05962.x Stein, 2011, Another look at the contingency tables: scores based on Manhattan distances in the phase space, Meteorol. Appl., 18, 28, 10.1002/met.199 Stern, 1984, A model fitting analysis of daily rainfall data, J. Roy. Stat. Soc., 147, 1 Todorovic, 1975, A Stochastic model of n-day precipitation, J. Appl. Meterol. Climatol., 14, 17, 10.1175/1520-0450(1975)014<0017:ASMODP>2.0.CO;2 Tseng, 2012, Application of multi-site weather generators for investigating wet and dry spell lengths under climate change: a case study in Southern Taiwan, Water Resour. Manag., 26, 4311, 10.1007/s11269-012-0146-6 Walega, 2020, Assessment of storm direct runoff and peak flow rates using improved SCS-CN models for selected forested watersheds in the Southeastern United States, J. Hydrol.: Reg. Stud., 27, 100645 Wilks, 1999, The weather generation game: a review of stochastic weather models, Prog. Phys. Geogr.: Earth Environ., 23, 329, 10.1177/030913339902300302 Wilks, 1999, Interannual variability and extreme-value characteristics of several stochastic daily precipitation models, Agric. For. Meteorol., 93, 153, 10.1016/S0168-1923(98)00125-7 Williams, 1996, Using soil erosion models for global change studies, J. Soil Water Conserv., 51, 381 Woolhiser, 1982, Stochastic daily precipitation models: 2. A comparison of distributions of amounts, Water Resour. Res., 18, 1461, 10.1029/WR018i005p01461 Xie, 2011, Development and test of SWAT for modeling hydrological processes in irrigation districts with paddy rice, J. Hydrol., 396, 61, 10.1016/j.jhydrol.2010.10.032 Yates, 2003, A technique for generating regional climate scenarios using a nearest-neighbour algorithm, Water Resour. Res., 39, 10.1029/2002WR001769 Yin, 2020, Projected spatial patterns in precipitation and air temperature for China's northwest region derived from high-resolution regional climate models, Int. J. Climatol., 40, 3922, 10.1002/joc.6435