Precipitation

D. Koutsoyiannis, and A. Langousis, Precipitation, Treatise on Water Science, edited by P. Wilderer and S. Uhlenbrook, 2, 27–78, Academic Press, Oxford, 2011.

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[English]

The study of precipitation has been closely linked to the birth of science, by the turn of the 7th century BC. Yet, it continues to be a fascinating research area, since several aspects of precipitation generation and evolution have not been understood, explained and described satisfactorily. Several problems, contradictions and even fallacies related to the perception and modelling of precipitation still exist. The huge diversity and complexity of precipitation, including its forms, extent, intermittency, intensity, and temporal and spatial distribution, do not allow easy descriptions. For example, while atmospheric thermodynamics may suffice to explain the formation of clouds, it fails to provide a solid framework for accurate deterministic predictions of the intensity and spatial extent of storms. Hence, uncertainty is prominent and its understanding and modelling unavoidably relies on probabilistic, statistical and stochastic descriptions. However, the classical statistical models and methods may not be appropriate for precipitation, which exhibits peculiar behaviours including Hurst-Kolmogorov dynamics and multifractality. This triggered the development of some of the finest stochastic methodologies to describe these behaviours. Inevitably, because deduction based on deterministic laws becomes problematic, as far as precipitation is concerned, the need for observation of precipitation becomes evident. Modern remote sensing technologies (radars and satellites) have greatly assisted the observation of precipitation over the globe, whereas modern stochastic techniques have made the utilization of traditional raingauge measurements easier and more accurate. This chapter reviews existing knowledge in the area of precipitation. Interest is in the small- and large-scale physical mechanisms that govern the process of precipitation, technologies and methods to estimate precipitation in both space and time, and stochastic approaches to model the variable character of precipitation and assess the distribution of its extremes.

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See also: http://dx.doi.org/10.1016/B978-0-444-53199-5.00027-0

Our works referenced by this work:

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Our works that reference this work:

1. D. Koutsoyiannis, A. Paschalis, and N. Theodoratos, Two-dimensional Hurst-Kolmogorov process and its application to rainfall fields, Journal of Hydrology, 398 (1-2), 91–100, 2011.
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3. D. Koutsoyiannis, Hydrology and Change, Hydrological Sciences Journal, 58 (6), 1177–1197, doi:10.1080/02626667.2013.804626, 2013.
4. F. Lombardo, E. Volpi, D. Koutsoyiannis, and S.M. Papalexiou, Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology, Hydrology and Earth System Sciences, 18, 243–255, doi:10.5194/hess-18-243-2014, 2014.
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6. C. Pappas, S.M. Papalexiou, and D. Koutsoyiannis, A quick gap-filling of missing hydrometeorological data, Journal of Geophysical Research-Atmospheres, 119 (15), 9290–9300, doi:10.1002/2014JD021633, 2014.
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10. F. Lombardo, E. Volpi, D. Koutsoyiannis, and F. Serinaldi , A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall, Water Resources Research, doi:10.1002/2017WR020529, 2017.
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Other works that reference this work (this list might be obsolete):

1. Khalil, B., and J. Adamowski, Record extension for short-gauged water quality parameters using a newly proposed robust version of the line of organic correlation technique, Hydrol. Earth Syst. Sci. , 16, 2253-2266, doi: 10.5194/hess-16-2253-2012, 2012.
2. #Langousis, A. and V. Kaleris, A statistical approach to estimate spatial rainfall averages using point rainfall measurements from a single location and runoff data, Proceedings of the 2nd Joint Conference of EYE-EEDYP "Integrated Water Resources Management for Sustainable Development" (Ed.: P. Giannopoulos and A. Dimas), 75-80, Patras, Greece, 2012.
3. Langousis, A., and V. Kaleris, Theoretical framework to estimate spatial rainfall averages conditional on river discharges and point rainfall measurements from a single location: an application to western Greece, Hydrol. Earth Syst. Sci., 17, 1241-1263, 10.5194/hess-17-1241-2013, 2013.
4. #Khalil, B., J. Adamowski and A. Belayneh, Evaluation of the performance of eight record extension techniques under different levels of data contamination: A Monte Carlo study, Proceedings, Annual Conference - Canadian Society for Civil Engineering, 3, 2249-2258, 2013.
5. Langousis, A., and V. Kaleris, Statistical framework to simulate daily rainfall series conditional on upper-air predictor variables, Water Resources Research, 10.1002/2013WR014936, 2014.
6. Khalil, B., and J. Adamowski, Comparison of OLS, ANN, KTRL, KTRL2, RLOC, and MOVE as Record-extension techniques for water quality variables, Water, Air, & Soil Pollution, 10.1007/s11270-014-1966-1, 2014.
7. Khalil, B., and J. Adamowski, Evaluation of the performance of eight record-extension techniques under different levels of association, presence of outliers and different sizes of concurrent records: a Monte Carlo study, Water Resources Management, 10.1007/s11269-014-0799-4, 2014.
8. Kienzler, P., N. Andres, D. Naef-Huber and M. Zappa, Derivation of extreme precipitation and flooding in the catchment of Lake Sihl to improve flood protection in the city of Zurich, Hydrologie Und Wasserbewirtschaftung, 59 (2), 48-58, 10.5675/HyWa_2015,2_1, 2015.
9. Müller, H. and U. Haberlandt, Temporal Rainfall Disaggregation with a Cascade Model: From Single-Station Disaggregation to Spatial Rainfall, J. Hydrol. Eng., 10.1061/(ASCE)HE.1943-5584.0001195, 04015026, 2015.

Tagged under: Course bibliography: Hydrometeorology, Course bibliography: Stochastic methods, Determinism vs. stochasticity, Extremes, Hurst-Kolmogorov dynamics, Rainfall models, Scaling