•A new stochastic daily rainfall model SDRM-MCREM is developed coupling Markov chain and rainfall event model.•SDRM-MCREM can simultaneously preserve rainfall time-series statistics and rainfall ...event characteristics.•Combination of SDRM-MCREM and hydrological model can be effectively used for flood and drought risk assessment.
Stochastic rainfall models have been widely used for hydrological modelling and climate change impact studies, the accuracy of which can substantially affect the reliability of water resources planning, hydraulic structure design and flood and drought risk assessment. The primary objective of this study is to develop a stochastic daily rainfall model through coupling a Markov chain model with a rainfall event model (SDRM-MCREM) to simultaneously preserve the statistical properties of rainfall time series and rainfall events. The newly developed model is applied to the Qu River basin, East China and its performance is evaluated at catchment scale. Results demonstrate that SDRM-MCREM shows a good performance in reproducing most of the rainfall time-series statistics (i.e. rainfall percentiles, average monthly and annual rainfall, inter-monthly rainfall variability and extreme rainfall) and rainfall event characteristics (i.e. distributions of wet and dry spells, occurrence frequency of different rainfall event classes, temporal rainfall patterns and their occurrence frequency in different rainfall event classes). The statistics of average runoff and extreme runoff are also well preserved by using the SDRM-MCREM simulations as input of hydrological modelling except that the interannual variability of rainfall and runoff is slightly underestimated. Moreover, SDRM-MCREM shows a great potential to be used for flood and drought risk assessment in reproducing the exceedance probabilities of high flows (e.g. annual maximum 1-day, 3-day and 5-day mean runoff) and low flows (e.g. annual minimum 7-day, 30-day and 90-day mean runoff).
•A new multi-site stochastic daily rainfall model is developed by coupling a univariate Markov chain with a multi-site rainfall event model.•The univariate Markov chain can well preserve the spatial ...correlation of multi-site rainfall occurrence time series.•The multi-site rainfall event model constructed using Vine copulas can well maintain cross-correlations of pairs of multi-site rainfall event characteristics.•Stochastic simulation of correlated multi-site rainfall temporal patterns of different rainfall types is also considered in MSDRM-MCREM.
Multi-site rainfall models are useful tools to provide synthetic realizations of spatially-correlated rainfall at multiple stations, which are of great importance for flood and drought risk assessment and climate change impact analysis. Therefore, a good preservation of various observed rainfall characteristics including rainfall time-series statistics and rainfall event characteristics at individual stations and the inter-site correlations of these rainfall characteristics is very crucial. To achieve this purpose, this study aims to develop a multi-site stochastic daily rainfall model by coupling a univariate Markov chain with a multi-site rainfall event model (MSDRM-MCREM), based on our previously-developed single-site SDRM-MCREM. The univariate Markov chain model in MSDRM-MCREM is used to generate spatially-correlated multi-site rainfall occurrence time series and extract simulated rainfall events for individual stations based on continuous wet days. The multi-site rainfall event model is then constructed using Vine copulas to simulate spatially-correlated rainfall event characteristics of those simulated rainfall events that occur simultaneously at multiple stations, including rainfall durations, rainfall depths and temporal patterns. Subsequently, this model was applied to the Changshangang River basin in Zhejiang Province, East China and its performance in reproducing rainfall characteristics and spatial correlations was evaluated for three cases, i.e. simulations for two, three and four stations. Results show that except for overestimation of light rainfall, MSDRM-MCREM can simultaneously well preserve rainfall time-series statistics (i.e. different rainfall percentiles, mean monthly rainfall, standard deviations and probabilities and mean values of wet days), extreme rainfall (i.e. exceedance probabilities of annual maximum 1-day, 3-day and 5-day rainfall) and rainfall event characteristics (i.e. cumulative probabilities of wet spell, dry spell and rainfall depth, temporal patterns and occurrence probabilities of rainfall types for different depth-based event classes) at individual stations. In addition, the spatial correlations of rainfall characteristics have also been well maintained, including rainfall occurrence time series and rainfall event characteristics in different groups, with the inter-site correlations of rainfall time series being slightly underestimated.
Urban trees play an important role in the built environment, reducing the rainfall reaching the ground by rainfall interception. The amount of intercepted rainfall depends on the meteorological and ...vegetation characteristics. By applying the multiple correspondence analysis (MCA), we analysed the influence of rainfall amount, intensity and duration, the number of raindrops, the mean volume diameter (MVD), wind speed and direction on rainfall interception. The analysis was based on data from 176 events collected over more than three years of observations. Measurements were taken under birch (Betula pendula Roth.) and pine (Pinus nigra Arnold) trees located in an urban park in the city of Ljubljana, Slovenia. The results indicate that rainfall interception is influenced the most by rainfall amount and the number of raindrops. In general, the ratio of rainfall interception to gross rainfall decreases with longer and more intense rainfall events. The influence of the raindrop number depends also on their size (MVD), which is evident especially for the pine tree. For example, pine tree interception increases with smaller raindrops regardless of their number. In addition, MCA gives a new insight into the influence of wind characteristics, which was not visible using previous methods of data analysis (regression analysis, correlation matrices, regression trees, boosted regression trees). According to the nearby buildings, a wind corridor is sometimes created, decreasing rainfall interception by both tree species.
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