The major contribution of this study is the development of a simple yet effective stochastic rainfall generator for the simulation of rainfall occurrences and rainfall amounts in a tropical area, specifically for the Kelantan River Basin, Malaysia. The stochastic rainfall generator is able to generate an infinite length of synthetic rainfall series for different research works and operations. The stochastic generation of rainfall series can be used to complement the inadequate length of rainfall records. This is especially advantageous for Malaysia as the rainfall stations with available complete rainfall series for a long duration are limited. The stochastic rainfall generator has been validated thoroughly, thus the research outcomes can be used as the foundation bases to improve the conceptualization and specifications of the stochastic rainfall studies. Also, through the simulation results, the developed stochastic rainfall generator presented its significant contribution in providing important information about the statistics and characteristics of monthly, seasonal and yearly rainfall series derived from the aggregation of synthetic daily time series. The major concern of Kelantan River Basin is monsoon flood, which is characterized by heavy and long duration rainfall. For the flood control, long and complete monthly, seasonal and rainfall series are required to calibrate the flood forecasting model and the flood control system. They are used as important inputs to simulate the hydrographs and predict runoff for the flood events. Besides, the time series of the monthly, seasonal and yearly rainfall series are necessary for the climate change assessment and optimum planning, designing and management of water resources engineering, such as the irrigation systems, reservoir operation and urban water supply.
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In addition, the significance of this study is the quantification of the uncertainty of rainfall DDF curves based on the replications of 104 synthetic rainfall series generated solely from the stochastic rainfall generator so as to become the novelty of this study. A comprehensive uncertainty assessment has been performed to obtain the uncertainty bounds of extreme rainfall series, which is often neglected or overlooked during practice. The results of this study can contribute to a better understanding of the impact of uncertainty in hydrological process. Also, the quantification of uncertainty can benefit the decision makers to react wisely to the uncertainty arise from the model output and make a reliable decision.
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REFERENCES
Abas, N., Daud, Z. M., & Yusof, F. (2014). A comparative study of mixed exponential and Weibull distributions in a stochastic model replicating a tropical rainfall process. Theoretical and Applied Climatology, 118(3), 597–607.
Abbot, J., & Marohasy, J. (2014). Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmospheric Research, 138, 166–178.
Abebe, N. A., Ogden, F. L., & Pradhan, N. R. (2010). Sensitivity and uncertainty analysis of the conceptual HBV rainfall–runoff model: Implications for parameter estimation. Journal of Hydrology, 389(3), 301–310.
Abon, C. C., Kneis, D., Crisologo, I., Bronstert, A., David, C. P. C., & Heistermann, M. (2016). Evaluating the potential of radar-based rainfall estimates for streamflow and flood simulations in the Philippines. Geomatics, Natural Hazards and Risk, 7(4), 1390–1405.
Adnan, N. A., & Atkinson, P. M. (2011). Exploring the impact of climate and land use changes on streamflow trends in a monsoon catchment. International Journal of Climatology, 31(6), 815–831.
Ajami, N. K., Duan, Q., & Sorooshian, S. (2007). An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resources Research, 43(1). Akaike, H. (1974). A new look at the statistical model identification. IEEE
Transactions on Automatic Control, 19(6), 716–723.
Al-Wagdany, A. S. (2015). Frequency distributions of areal rainfall intensity and duration in western Saudi Arabia. Arabian Journal of Geosciences, 8(10), 8853– 8859.
Amin, M. Z. M., Shaaban, A. J., Ercan, A., Ishida, K., Kavvas, M. L., Chen, Z. Q., & Jang, S. (2017). Future climate change impact assessment of watershed scale hydrologic processes in Peninsular Malaysia by a regional climate model coupled with a physically-based hydrology modelo. Science of The Total Environment, 575, 12–22.
Apipattanavis, S., Podestá, G., Rajagopalan, B., & Katz, R. W. (2007). A semiparametric multivariate and multisite weather generator. Water Resources Research, 43(11).
Ariff, N. M., Jemain, A. A., Ibrahim, K., & Wan Zin, W. Z. (2012). IDF relationships using bivariate copula for storm events in Peninsular Malaysia. Journal of Hydrology, 470–471, 158–171.
© COPYRIGHT
UPM
145
Asklany, S. A., Elhelow, K., Youssef, I. K., & Abd El-wahab, M. (2011). Rainfall events prediction using rule-based fuzzy inference system. Atmospheric Research, 101(1–2), 228–236.
Asong, Z. E., Khaliq, M. N., & Wheater, H. S. (2016). Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models. Climate Dynamics, 47(9–10), 2901–2921.
Babel, M. S., Badgujar, G. B., & Shinde, V. R. (2015). Using the mutual information technique to select explanatory variables in artificial neural networks for rainfall forecasting. Meteorological Applications, 22(3), 610–616.
Bárdossy, A., & Pegram, G. (2014). Infilling missing precipitation records – A comparison of a new copula-based method with other techniques. Journal of Hydrology, 519, 1162–1170.
Batou, A., Soize, C., & Audebert, S. (2015). Model identification in computational stochastic dynamics using experimental modal data. Mechanical Systems and Signal Processing, 50, 307–322.
Bernardara, P., De Michele, C., & Rosso, R. (2007). A simple model of rain in time: An alternating renewal process of wet and dry states with a fractional (non- Gaussian) rain intensity. Atmospheric Research, 84(4), 291–301.
Beven, K., & Binley, A. (1992). The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes, 6(3), 279–298.
Blanchet, J., Ceresetti, D., Molinié, G., & Creutin, J.-D. (2016). A regional GEV scale- invariant framework for intensity–duration–frequency analysis. Journal of Hydrology, 540, 82–95.
Blasone, R.-S., Vrugt, J. A., Madsen, H., Rosbjerg, D., Robinson, B. A., & Zyvoloski, G. A. (2008). Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Advances in Water Resources, 31(4), 630–648.
Borga, M., Vezzani, C., & Fontana, G. D. (2005). Regional rainfall depth–duration– frequency equations for an Alpine Region. Natural Hazards, 36(1–2), 221–235. Borujeni, S. C., & Sulaiman, W. N. A. (2009). Development of L-moment based models for extreme flood events. Malaysian Journal of Mathematical Sciences, 3(2), 281–296.
Boulard, D., Castel, T., Camberlin, P., Sergent, A.-S., Asse, D., Bréda, N., Badeau, V., Rossie, A., & Pohl, B. (2017). Bias correction of dynamically downscaled precipitation to compute soil water deficit for explaining year-to-year variation of tree growth over northeastern France. Agricultural and Forest Meteorology, 232, 247–264.
© COPYRIGHT
UPM
146
Bowles, D. S., & O’Connell, P. E. (1991). Recent advances in the modeling of hydrologic systems (1st ed.). Dordrecht: Kluwer Academic Publishers.
Breinholt, A., Møller, J. K., Madsen, H., & Mikkelsen, P. S. (2012). A formal statistical approach to representing uncertainty in rainfall–runoff modelling with focus on residual analysis and probabilistic output evaluation – Distinguishing simulation and prediction. Journal of Hydrology, 472, 36–52.
Breinl, K., Turkington, T., & Stowasser, M. (2013). Stochastic generation of multi-site daily precipitation for applications in risk management. Journal of Hydrology, 498, 23–35.
Breinl, K., Turkington, T., & Stowasser, M. (2015). Simulating daily precipitation and temperature: a weather generation framework for assessing hydrometeorological hazards. Meteorological Applications, 22(3), 334–347.
Burton, A., Kilsby, C. G., Fowler, H. J., Cowpertwait, P. S. P., & O’Connell, P. E. (2008). RainSim: A spatial–temporal stochastic rainfall modelling system. Environmental Modelling & Software, 23(12), 1356–1369.
Caraway, N. M., McCreight, J. L., & Rajagopalan, B. (2014). Multisite stochastic weather generation using cluster analysis and k-nearest neighbor time series resampling. Journal of Hydrology, 508, 197–213.
Carreau, J., & Vrac, M. (2011). Stochastic downscaling of precipitation with neural network conditional mixture models. Water Resources Research, 47(10). Castellvi, F., Stockle, C. O., Mormeneo, I., & Villar, J. M. (2002). Testing the
performance of different processes to generate temperature and solar radiation: A case study at Lleida (northeast Spain). Transactions of the Asae, 45(3), 571–580. Cea, L., Legout, C., Grangeon, T., & Nord, G. (2016). Impact of model simplifications
on soil erosion predictions: application of the GLUE methodology to a distributed event-based model at the hillslope scale. Hydrological Processes, 30(7), 1096– 1113.
Chandra, R., Saha, U., & Mujumdar, P. P. (2015). Model and parameter uncertainty in IDF relationships under climate change. Advances in Water Resources, 79, 127– 139.
Chen, D., Achberger, C., Ou, T., Postgård, U., Walther, A., & Liao, Y. (2015). Projecting future local precipitation and its extremes for Sweden. Geografiska Annaler: Series A, Physical Geography, 97(1), 25–39.
Chen, H., Sun, J., & Chen, X. (2014). Projection and uncertainty analysis of global precipitation-related extremes using CMIP5 models. International Journal of Climatology, 34(8), 2730–2748.
© COPYRIGHT
UPM
147
Chen, J., & Brissette, F. P. (2014a). Comparison of five stochastic weather generators in simulating daily precipitation and temperature for the Loess Plateau of China. International Journal of Climatology, 34(10), 3089–3105.
Chen, J., & Brissette, F. P. (2014b). Stochastic generation of daily precipitation amounts: review and evaluation of different models. Climate Research, 59(3), 189-206.
Chen, J., Brissette, F. P., & Leconte, R. (2010). A daily stochastic weather generator for preserving low-frequency of climate variability. Journal of Hydrology, 388(3–4), 480–490.
Chen, J., Brissette, F. P., & Leconte, R. (2011). Assessment and improvement of stochastic weather generators in simulating maximum and minimum temperatures. Transactions of the ASABE, 54(5), 1627–1637.
Chen, J., Brissette, F. P., Leconte, R., & Caron, A. (2012). A versatile weather generator for daily precipitation and temperature. Transactions of the ASABE, 55(3), 895–906.
Chen, J., Brissette, F. P., & Zhang, X. J. (2016). Hydrological modeling using a multisite stochastic weather generator. Journal of Hydrologic Engineering, 21(2), 4015060.
Chen, W., & Chau, K. W. (2006). Intelligent manipulation and calibration of parameters for hydrological models. International Journal of Environment and Pollution, 28(3–4), 432-447.
Chun, K. P., Wheater, H. S., Nazemi, A., & Khaliq, M. N. (2013). Precipitation downscaling in Canadian Prairie Provinces using the LARS-WG and GLM approaches. Canadian Water Resources Journal, 38(4), 311–332.
Cibin, R., Athira, P., Sudheer, K. P., & Chaubey, I. (2014). Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty. Hydrological Processes, 28(4), 2033–2045.
Coles, S. (2001). An introduction to statistical modeling of extreme values. London: Springer.
Conover, W.J. (1980). Practical Nonparametric Statistics. New York: Wiley.
Cowden, J. R., Watkins, D. W., & Mihelcic, J. R. (2008). Stochastic rainfall modeling in West Africa: parsimonious approaches for domestic rainwater harvesting assessment. Journal of Hydrology, 361(1–2), 64–77.
Cox, D. R., & Isham, V. (1994) Stochastic models of precipitation. New York: Wiley. Craigmile, P. F., & Guttorp, P. (2011). Space-time modelling of trends in temperature
© COPYRIGHT
UPM
148
Daud, Z. M., Mat Rasid, S. M., & Abas, N. (2016). A regionalized stochastic rainfall model for the generation of high resolution data in Peninsular Malaysia. Modern Applied Science, 10(5), 77–86.
Dayon, G., Boé, J., & Martin, E. (2015). Transferability in the future climate of a statistical downscaling method for precipitation in France. Journal of Geophysical Research: Atmospheres, 120(3), 1023–1043.
Dembélé, M., & Zwart, S. J. (2016). Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa. International Journal of Remote Sensing, 37(17), 3995–4014.
Demirel, M. C., & Moradkhani, H. (2016). Assessing the impact of CMIP5 climate multi-modeling on estimating the precipitation seasonality and timing. Climatic Change, 135(2), 357–372.
Deni, S. M., Jemain, A. A., & Ibrahim, K. (2009). Fitting optimum order of Markov chain models for daily rainfall occurrences in Peninsular Malaysia. Theoretical and Applied Climatology, 97(1–2), 109–121.
Dibike, Y. B., & Coulibaly, P. (2005). Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. Journal of Hydrology, 307(1), 145–163.
Dieckmann, N. F., Peters, E., Gregory, R., & Tusler, M. (2012). Making sense of uncertainty: advantages and disadvantages of providing an evaluative structure. Journal of Risk Research, 15(7), 717–735.
Dixit, P. N., & Telleria, R. (2015). Advancing the climate data driven crop-modeling studies in the dry areas of Northern Syria and Lebanon: An important first step for assessing impact of future climate. Science of The Total Environment, 511, 562–575.
Dlamini, N. S., Rowshon, M. K., Sahab, U., Fikri, A., Lai, S. H., & Mohd, M. S. F. (2015). Developing and calibrating a stochastic rainfall generator model for simulating daily rainfall by Markov chain approach. Jurnal Teknologi, 76(15), 13–19.
Du, H., Xia, J., & Zeng, S. (2014). Regional frequency analysis of extreme precipitation and its spatio-temporal characteristics in the Huai River Basin, China. Natural Hazards, 70(1), 195–215.
Dubrovský, M., Buchtele, J., & Žalud, Z. (2004). High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling. Climatic Change, 63(1), 145–179.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., & Liebert, J. (2012). HESS Opinions “Should we apply bias correction to global and regional climate model data?” Hydrology and Earth System Sciences, 16(9), 3391–3404.
© COPYRIGHT
UPM
149
El-Shafie, A., Jaafer, O., & Akrami, S. A. (2011). Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. International Journal of Physical Sciences, 6(12), 2875–2888.
Eum, H.-I., & Simonovic, S. P. (2012). Assessment on variability of extreme climate events for the Upper Thames River basin in Canada. Hydrological Processes, 26(4), 485–499.
Faghih, M., Mirzaei, M., Adamowski, J., Lee, J., & El-Shafie, A. (2017). Uncertainty Estimation in Flood Inundation Mapping: An Application of Non-parametric Bootstrapping. River Research and Applications.
Flecher, C., Naveau, P., Allard, D., & Brisson, N. (2010). A stochastic daily weather generator for skewed data. Water Resources Research, 46(7).
Fodor, N., Dobi, I., Mika, J., & Szeidl, L. (2010). MV-WG: A new multi-variable weather generator. Meteorology and Atmospheric Physics, 107(3), 91–101. Fodor, N., Dobi, I., Mika, J., & Szeidl, L. (2013). Applications of the MVWG
multivariable stochastic weather generator. The Scientific World Journal, 2013, 1–6.
Førland, E. J., Benestad, R., Hanssen-Bauer, I., Haugen, J. E., & Skaugen, T. E. (2011). Temperature and Precipitation Development at Svalbard 1900–2100. Advances in Meteorology, 2011, 1–14.
Forsythe, N., Fowler, H. J. J., Blenkinsop, S., Burton, A., Kilsby, C. G. G., Archer, D. R. R., Harpham, C., & Hashmi, M. Z. Z. (2014). Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus Basin. Journal of Hydrology, 517, 1019–1034.
Fowler, H. J., Blenkinsop, S., & Tebaldi, C. (2007). Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 27(12), 1547–1578.
Furrer, E. M., & Katz, R. W. (2007). Generalized linear modeling approach to stochastic weather generators. Climate Research, 34(2), 129–144.
Furrer, E. M., & Katz, R. W. (2008). Improving the simulation of extreme precipitation events by stochastic weather generators. Water Resources Research, 44(12). Gabriel, K. R., & Neumann, J. (1962). A Markov chain model for daily rainfall
occurrence at Tel Aviv. Quarterly Journal of the Royal Meteorological Society, 88(375), 90–95.
Giudice, D. D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., & Rieckermann, J. (2013). Improving uncertainty estimation in urban hydrological modeling by statistically describing bias. Hydrology and Earth System Sciences, 17(10), 4209– 4225.
© COPYRIGHT
UPM
150
Green, J. (1964). A model for rainfall occurrence. Journal of the Royal Statistical Society. Series B (Methodological), 26(2), 345–353.
Gronewold, A. D., Stow, C. A., Crooks, J. L., & Hunter, T. S. (2013). Quantifying parameter uncertainty and assessing the skill of exponential dispersion rainfall simulation models. International Journal of Climatology, 33(3), 746–757. Haberlandt, U., Belli, A., & Bárdossy, A. (2015). Statistical downscaling of
precipitation using a stochastic rainfall model conditioned on circulation patterns - an evaluation of assumptions. International Journal of Climatology, 35(3), 417– 432.
Haberlandt, U., Hundecha, Y., Pahlow, M., & Schumann, A. H. (2011). Rainfall generators for application in flood studies. In Flood Risk Assessment and Management (pp. 117–147). Dordrecht: Springer Netherlands.
Haddad, K., Rahman, A., & Green, J. (2010). Design rainfall estimation in Australia: a case study using L moments and generalized least squares regression. Stochastic Environmental Research and Risk Assessment, 25(6), 815–825.
Hai, O. S., Samah, A. A., Chenoli, S. N., Subramaniam, K., & Ahmad Mazuki, M. Y. (2017). Extreme rainstorms that caused devastating flood over the east coast of Peninsular Malaysia during November and December 2014. Weather and Forecasting.
Hailegeorgis, T. T., Thorolfsson, S. T., & Alfredsen, K. (2013). Regional frequency analysis of extreme precipitation with consideration of uncertainties to update IDF curves for the city of Trondheim. Journal of Hydrology, 498, 305–318. Han, J.-C., Huang, G.-H., Zhang, H., Li, Z., & Li, Y.-P. (2013). Bayesian uncertainty
analysis in hydrological modeling associated with watershed subdivision level: a case study of SLURP model applied to the Xiangxi River watershed, China. Stochastic Environmental Research and Risk Assessment, 28(4), 1–17.
Hankin, B., Bielby, S., Pope, L., & Douglass, J. (2016). Catchment-scale sensitivity and uncertainty in water quality modelling. Hydrological Processes, 30(2), 4004– 4018
Hao, Z., Hao, F., Singh, V. P., Sun, A. Y., & Xia, Y. (2016). Probabilistic prediction of hydrologic drought using a conditional probability approach based on the meta-Gaussian model. Journal of Hydrology, 542, 772–780.
Harisaweni, Zulkifli, Y., & Fadhilah, Y. (2016). The Use of BLRP Model for disaggregating daily rainfall affected by monsoon in peninsular. Sains Malaysiana, 45(1), 87–97.
Harrison, M., & Waylen, P. (2000). A note concerning the proper choice for Markov model order for daily precipitation in the humid tropics: A case study in Costa Rica. International Journal of Climatology, 20(14), 1861–1872.
© COPYRIGHT
UPM
151
Hasan, M. M., Sharma, A., Mariethoz, G., Johnson, F., & Seed, A. (2016). Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework. Advances in Water Resources, 97, 205–218.
Hashmi, M. Z., Shamseldin, A. Y., & Melville, B. W. (2011). Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stochastic Environmental Research and Risk Assessment, 25(4), 475–484.
Hassan, Z., & Harun, S. (2013). Impact of climate change on rainfall over Kerian, Malaysia with Long Ashton Research Station Weather Generator (LARS-WG). Malaysian Journal of Civil Engineering, 25(1), 33–44.
Hassan, Z., Shamsudin, S., & Harun, S. (2014). Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theoretical and Applied Climatology, 116(1–2), 243–257.
Hosking, J. R. M. (1990). L-Moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society. Series B (Methodological), 52(1), 105–124.
Hosking, J. R. M., & Wallis, J. R. (1995). A comparison of unbiased and plotting- position estimators of L moments. Water Resources Research, 31(8), 2019–2025. Hosking, J. R. M., & Wallis, J. R. (2005). Regional Frequency Analysis: An Approach
Based on L-Moments. Cambridge: Cambridge University Press.
Hu, Y., Liang, Z., Li, B., & Yu, Z. (2013). Uncertainty assessment of hydrological frequency analysis using bootstrap method, Mathematical Problems in Engineering, 2013, 1–7.
Hu, Z., & Du, X. (2015). First order reliability method for time-variant problems using series expansions. Structural and Multidisciplinary Optimization, 51(1), 1–21. Huang, C.-L., Hsu, N.-S., Wei, C.-C., & Lo, C.-W. (2015). Using artificial intelligence
to retrieve the optimal parameters and structures of adaptive network-based fuzzy inference system for typhoon precipitation forecast modeling. Advances in Meteorology, 2015, 1–22.
Hundecha, Y., Pahlow, M., & Schumann, A. (2009). Modeling of daily precipitation at multiple locations using a mixture of distributions to characterize the extremes. Water Resources Research, 45(12).
Jaafar, J., Baki, A., Abu Bakar, I. A., Tahir, W., Awang, H., & Ismail, F. (2016). Evaluation of stochastic daily rainfall data generation models. In ISFRAM 2015 (pp. 203–220). Singapore: Springer Singapore.
Jenkinson, A. F. (1955). The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quarterly Journal of the Royal
© COPYRIGHT
UPM
152 Meteorological Society, 81(348), 158–171.
Jennings, S. A., Lambert, M. F., & Kuczera, G. (2010). Generating synthetic high resolution rainfall time series at sites with only daily rainfall using a master-target scaling approach. Journal of Hydrology, 393(3–4), 163–173.
Jeong, D. I., St-Hilaire, A., Ouarda, T. B. M. J., & Gachon, P. (2012). Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator. Climatic Change, 114(3), 567–591.
Jiang, P., & Tung, Y.-K. (2015). Incorporating daily rainfalls to derive rainfall DDF relationships at ungauged sites in Hong Kong and quantifying their uncertainty. Stochastic Environmental Research and Risk Assessment, 29(1), 45–62.
Jones, P. D., Harpham, C., Burton, A., & Goodess, C. M. (2016). Downscaling regional climate model outputs for the Caribbean using a weather generator. International Journal of Climatology, 36(12), 4141–4163.
Jones, P. G., & Thornton, P. K. (1993). A rainfall generator for agricultural applications in the tropics. Agricultural and Forest Meteorology, 63(1–2), 1–19. Jones, P. G., & Thornton, P. K. (2000). MarkSim: software to generate daily weather
data for Latin America and Africa. Agronomy Journal, 92(3), 445–453.
Jones, P. G., & Thornton, P. K. (2013). Generating downscaled weather data from a suite of climate models for agricultural modelling applications. Agricultural Systems, 114, 1–5.
Kaczmarska, J., Isham, V., & Onof, C. (2014). Point process models for fine-resolution rainfall. Hydrological Sciences Journal, 59(11), 1972–1991.
Kang, B.-J., Kim, J.-H., & Kim, Y. (2016). Engineering criticality analysis on an offshore structure using the first- and second-order reliability method. International Journal of Naval Architecture and Ocean Engineering, 8(6), 577– 588
Karamouz, M., Taheriyoun, M., Seyedabadi, M., & Nazif, S. (2015). Uncertainty based analysis of the impact of watershed phosphorus load on reservoir phosphorus concentration. Journal of Hydrology, 521, 533–542.
Katz, R. W., & Parlange, M. B. (1998). Overdispersion phenomenon in stochastic modeling of precipitation. Journal of Climate, 11(4), 591–601.
Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42(3).
© COPYRIGHT
UPM
153
Keller, D. E., Fischer, A. M., Frei, C., Liniger, M. A., Appenzeller, C., & Knutti, R. (2015). Implementation and validation of a Wilks-type multi-site daily precipitation generator over a typical Alpine river catchment. Hydrology and Earth System Sciences, 19(5), 2163–2177.
Kenabatho, P. K., McIntyre, N. R., Chandler, R. E., & Wheater, H. S. (2012). Stochastic simulation of rainfall in the semi-arid Limpopo basin, Botswana. International Journal of Climatology, 32(7), 1113–1127.
Khazaei, M. R., Ahmadi, S., Saghafian, B., & Zahabiyoun, B. (2013). A new daily weather generator to preserve extremes and low-frequency variability. Climatic Change, 119(3), 631–645.
King, L. M. M., Irwin, S., Sarwar, R., McLeod, A. I. A., & Simonovic, S. P. P. (2012). The effects of climate change on extreme precipitation events in the Upper Thames River Basin: a comparison of downscaling approaches. Canadian Water Resources Journal, 37(3), 253–274.
King, L. M., McLeod, A. I., & Simonovic, S. P. (2015). Improved weather generator algorithm for multisite simulation of precipitation and temperature. JAWRA Journal of the American Water Resources Association, 51(5), 1305–1320. Kossieris, P., Makropoulos, C., Onof, C., & Koutsoyiannis, D. (2016). A rainfall
disaggregation scheme for sub-hourly time scales: coupling a Bartlett-Lewis based model with adjusting procedures. Journal of Hydrology.
Kothari, M., & Gharde, K. D. (2015). Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment. Journal of Earth System Science, 124(5), 933–943.
Kottegoda, N. T., Rosso, R., & Kottegoda, N. T. (2008). Applied statistics for civil and environmental engineers. Chichester, UK: Blackwell Pub.
Kou, X., Ge, J., Wang, Y., & Zhang, C. (2007). Validation of the weather generator CLIGEN with daily precipitation data from the Loess Plateau, China. Journal of Hydrology, 347(3–4), 347–357.
Krzysztofowicz, R. (1999). Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resources Research, 35(9), 2739–2750. Kusangaya, S., Toucher, M. L. W., van Garderen, E. A., & Jewitt, G. P. W. (2016).
An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances. Global and Planetary Change, 144, 129–141.
Kwaku, X. S. X., & Duke, O. (2007). Characterization and frequency analysis of one day annual maximum and two to five consecutive days’ maximum rainfall of ACCRA, Ghana. ARPN J. Eng. Appl. Sci, 2(5), 27–31.
© COPYRIGHT
UPM
154
Kyselý, J., & Dubrovský, M. (2005). Simulation of extreme temperature events by a stochastic weather generator: effects of interdiurnal and interannual variability reproduction. International Journal of Climatology, 25(2), 251–269.
Landwehr, J. M., Matalas, N. C., & Wallis, J. R. (1979). Probability weighted moments