A simplified model for estimating field‐scale surface runoff hydrographs

The problem of obtaining field‐scale surface response to rainfall events is complicated by the spatial variability of infiltration characteristics of the soil and rainfall. In this paper, we develop and test a simplified model for generating surface runoff over fields with spatial variation in both rainfall rate and saturated hydraulic conductivities. The model is able to represent the effects of local variation in infiltration, as well as the run‐on effect that controls infiltration of excess water from saturated upstream areas. The effective rainfall excess is routed to the slope outlet using a simplified solution of the kinematic wave approximation. Model results are compared to averaged hydrographs from numerically‐intensive Monte–Carlo simulations for observed and design rainfall events and soil patterns that are typical of Central Italy. The simplified model is found to yield satisfactory results at a relatively small computational expense. A proposal to include a simple channel routing scheme is also presented as a prelude to extend this conceptualization to watershed scales. Copyright © 2006 John Wiley & Sons, Ltd.     

Spatial reconstruction of rainfall fields from rain gauge and radar data

Rainfall is a phenomenon difficult to model and predict, for the strong spatial and temporal heterogeneity and the presence of many zero values. We deal with hourly rainfall data provided by rain gauges, sparsely distributed on the ground, and radar data available on a fine grid of pixels. Radar data overcome the problem of sparseness of the rain gauge network, but are not reliable for the assessment of rain amounts. In this work we investigate how to calibrate radar measurements via rain gauge data and make spatial predictions for hourly rainfall, by means of Monte Carlo Markov Chain algorithms in a Bayesian hierarchical framework. We use zero-inflated distributions for taking zero-measurements into account. Several models are compared both in terms of data fitting and predictive performances on a set of validation sites. Finally, rainfall fields are reconstructed and standard error estimates at each prediction site are shown via easy-to-read spatial maps.     

A semi-analytical model of expected areal-average infiltration under spatial heterogeneity of rainfall and soil saturated hydraulic conductivity

A semi-analytical model for the estimate of expected areal-average infiltration rate at hillslope scale is presented. It accounts for spatial heterogeneity of the saturated hydraulic conductivity, K_s, and rainfall rate, r. The K_s field is characterized by a lognormal probability density function while the rainfall rate r is represented by a uniform distribution between two extreme values. The model formulation relies upon the use of cumulative infiltration as the independent variable which is then expressed as a function of an expected time for use in practical applications. The solution is applicable for those ranges of r and K_s that allow for neglecting the infiltration of surface water running downslope into pervious soils (run-on process). The model was tested by comparisons with Monte Carlo simulations carried out for a variety of coefficients of variation of r and K_s over a clay loam soil and a sandy loam soil. The model was found to be very reliable both with coupled spatial variability of r and K_s and when only one variable is characterized by spatial heterogeneity while the other is uniform.     

Upscaling of saturated conductivity for Hortonian runoff modelling

Stochastic and deterministic upscaling techniques are developed that upscale saturated conductivity at the support of 0.04 m² to representative actual infiltration (I_b) for support units (blocks) of 10¹–10⁴ m², as a function of steady state rainfall and runon to the block, under Hortonian runoff (infiltration excess overland flow). Parameters in the upscaling techniques represent the surface runoff flow pattern and the spatial probability distribution of saturated conductivity within the 10¹–10⁴ m² block. The stochastic upscaling technique represents the spatial process of infiltration and runoff using a simple process-imitating model, estimating I_b using Monte Carlo simulation. The deterministic upscaling technique aggregates these processes by a deterministic function relating rainfall and runon to I_b. The stochastic upscaling technique is shown to be capable to upscale saturated conductivity derived from ring infiltrometers to I_b values of plots (1 m²) corresponding to measured I_b values using rainfall simulators. It is shown that both upscaling techniques can be used to estimate I_b for each time step and each block in transient rainfall–runoff models, giving better estimates of cumulative runoff from a hillslope and a small catchment than model runs that do not use upscaling techniques.     

Spatial variations of throughfall through secondary succession of evergreen broad‐leaved forests in eastern China

Linking spatial variations of throughfall with shifting patterns during forest succession is important for understanding developmental patterns of ecosystem function. However, no such approach has been previously used for the chronosequence of evergreen broad‐leaved forests in subtropical regions. This study was conducted in a chronosequence of secondary forest succession in Tiantong National Forest Park, to determine the optimum number of collectors within certain limits of error. Throughfall was 66, 55 and 77% of gross precipitation in an early‐succession (SS), sub‐climax (SE) and climax (CE) forest, respectively. The coefficient of variations (CV) of throughfall reduced with increasing rainfall amounts. Monte Carlo resampling approach was used to find mean values and 90 and 95% confidence intervals of a variable number of collectors (n) ranging from 2 to 24. During the study period, with nine collectors at SS, five at SE and five at CE, the error in the mean individual throughfall did not exceed 10%, respectively. This error was reduced to 5% when using 16, 10 and 10 collectors at SS, SE and CE, respectively. The CVs decreased greatly with increasing sample size when the sample size was less than 16 for the three successional stages, regardless of rainfall amounts. Based on the Student's t‐value analysis of the mean individual throughfall volumes, a sample size of 16 at SS, five at SE and four at CE would be enough for throughfall estimates at an accepted error of 10% of 95% confidence level, respectively. Therefore, we concluded that the 25 of collectors used in the present study were sufficient to estimate the throughfall value at an accepted error of 10% at 90 and 95% confidence levels, even for those small rainfalls in eastern China. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of radar-rainfall error spatial correlation

The study presents a theoretical framework for estimating the radar-rainfall error spatial correlation (ESC) using data from relatively dense rain gauge networks. The error is defined as the difference between the radar estimate and the corresponding true areal rainfall. The method is analogous to the error variance separation that corrects the error variance of a radar-rainfall product for gauge representativeness errors. The study demonstrates the necessity to consider the area–point uncertainties while estimating the spatial correlation structure in the radar-rainfall errors. To validate the method, the authors conduct a Monte Carlo simulation experiment with synthetic fields with known error spatial correlation structure. These tests reveal that the proposed method, which accounts for the area–point distortions in the estimation of radar-rainfall ESC, performs very effectively. The authors then apply the method to estimate the ESC of the National Weather Service’s standard hourly radar-rainfall products, known as digital precipitation arrays (DPA). Data from the Oklahoma Micronet rain gauge network (with the grid step of about 5 km) are used as the ground reference for the DPAs. This application shows that the radar-rainfall errors are spatially correlated with a correlation distance of about 20 km. The results also demonstrate that the spatial correlations of radar–gauge differences are considerably underestimated, especially at small distances, as the area–point uncertainties are ignored.     

Estimation and spatial interpolation of rainfall intensity distribution from the effective rate of precipitation

Great emphasis is being placed on the use of rainfall intensity data at short time intervals to accurately model the dynamics of modern cropping systems, runoff, erosion and pollutant transport. However, rainfall data are often readily available at more aggregated level of time scale and measurements of rainfall intensity at higher resolution are available only at limited stations. A distribution approach is a good compromise between fine-scale (e.g. sub-daily) models and coarse-scale (e.g. daily) rainfall data, because the use of rainfall intensity distribution could substantially improve hydrological models. In the distribution approach, the cumulative distribution function of rainfall intensity is employed to represent the effect of the within-day temporal variability of rainfall and a disaggregation model (i.e. a model disaggregates time series into sets of higher solution) is used to estimate distribution parameters from the daily average effective precipitation. Scaling problems in hydrologic applications often occur at both space and time dimensions and temporal scaling effects on hydrologic responses may exhibit great spatial variability. Transferring disaggregation model parameter values from one station to an arbitrary position is prone to error, thus a satisfactory alternative is to employ spatial interpolation between stations. This study investigates the spatial interpolation of the probability-based disaggregation model. Rainfall intensity observations are represented as a two-parameter lognormal distribution and methods are developed to estimate distribution parameters from either high-resolution rainfall data or coarse-scale precipitation information such as effective intensity rates. Model parameters are spatially interpolated by kriging to obtain the rainfall intensity distribution when only daily totals are available. The method was applied to 56 pluviometer stations in Western Australia. Two goodness-of-fit statistics were used to evaluate the skill—daily and quantile coefficient of efficiency between simulations and observations. Simulations based on cross-validation show that kriging performed better than other two spatial interpolation approaches (B-splines and thin-plate splines).     

Spatial variations in throughfall in a Moso bamboo forest: sampling design for the estimates of stand‐scale throughfall

We investigated the spatial and seasonal variations in throughfall (Tf) in relation to spatial and seasonal variations in canopy structure and gross rainfall (Rf) and assessed the impacts of the variations in Tf on stand‐scale Tf estimates. We observed the canopy structure expressed as the leaf area index (LAI) once a month and Tf once a week in 25 grids placed in a Moso bamboo (Phyllostachys pubescens) forest for 1 year. The mean LAI and spatial variation in LAI did have some seasonal variations. The spatial variations in Tf reduced with increasing Rf, and the relationship between the spatial variation and the Rf held throughout the year. These results indicate that the seasonal change in LAI had little impact on spatial variations in Tf, and that Rf is a critical factor determining the spatial variations in Tf at the study site. We evaluated potential errors in stand‐scale Tf estimates on the basis of measured Tf data using Monte Carlo sampling. The results showed that the error decreases greatly with increasing sample size when the sample size was less than ～8, whereas it was near stable when the sample size was 8 or more, regardless of Rf. A sample size of eight results in less than 10% error for Tf estimates based on Student's t‐value analysis and would be satisfactory for interception loss estimates when considering errors included in Rf data. Copyright © 2009 John Wiley & Sons, Ltd.     

A field‐scale infiltration model accounting for spatial heterogeneity of rainfall and soil saturated hydraulic conductivity

This study first explores the role of spatial heterogeneity, in both the saturated hydraulic conductivity K_s and rainfall intensity r, on the integrated hydrological response of a natural slope. On this basis, a mathematical model for estimating the expected areal‐average infiltration is then formulated. Both K_s and r are considered as random variables with assessed probability density functions. The model relies upon a semi‐analytical component, which describes the directly infiltrated rainfall, and an empirical component, which accounts further for the infiltration of surface water running downslope into pervious soils (the run‐on effect). Monte Carlo simulations over a clay loam soil and a sandy loam soil were performed for constructing the ensemble averages of field‐scale infiltration used for model validation. The model produced very accurate estimates of the expected field‐scale infiltration rate, as well as of the outflow generated by significant rainfall events. Furthermore, the two model components were found to interact appropriately for different weights of the two infiltration mechanisms involved. Copyright © 2005 John Wiley & Sons, Ltd.     

Correlation model for spatially distributed ground‐motion intensities

Risk assessment of spatially distributed building portfolios or infrastructure systems requires quantification of the joint occurrence of ground‐motion intensities at several sites, during the same earthquake. The ground‐motion models that are used for site‐specific hazard analysis do not provide information on the spatial correlation between ground‐motion intensities, which is required for the joint prediction of intensities at multiple sites. Moreover, researchers who have previously computed these correlations using observed ground‐motion recordings differ in their estimates of spatial correlation. In this paper, ground motions observed during seven past earthquakes are used to estimate correlations between spatially distributed spectral accelerations at various spectral periods. Geostatistical tools are used to quantify and express the observed correlations in a standard format. The estimated correlation model is also compared with previously published results, and apparent discrepancies among the previous results are explained. The analysis shows that the spatial correlation reduces with increasing separation between the sites of interest. The rate of decay of correlation typically decreases with increasing spectral acceleration period. At periods longer than 2 s, the correlations were similar for all the earthquake ground motions considered. At shorter periods, however, the correlations were found to be related to the local‐site conditions (as indicated by site V_s30 values) at the ground‐motion recording stations. The research work also investigates the assumption of isotropy used in developing the spatial correlation models. It is seen using the Northridge and Chi‐Chi earthquake time histories that the isotropy assumption is reasonable at both long and short periods. Based on the factors identified as influencing the spatial correlation, a model is developed that can be used to select appropriate correlation estimates for use in practical risk assessment problems. Copyright © 2009 John Wiley & Sons, Ltd.     

Assessing spatially dependent errors in radar rainfall estimates for rainfall-runoff simulation

Weather radar been widely employed to measure precipitation and to predict flood risks. However, it is still not considered accurate enough because of radar errors. Most previous studies have focused primarily on removing errors from the radar data. Therefore, in the current study, we examined the effects of radar rainfall errors on rainfall-runoff simulation using the spatial error model (SEM). SEM was used to synthetically generate random or cross-correlated errors. A number of events were generated to investigate the effect of spatially dependent errors in radar rainfall estimates on runoff simulation. For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo?. The results indicated that spatially dependent errors caused much higher variations in peak discharge than independent random errors. To further investigate the effect of the magnitude of cross-correlation among radar errors, different magnitudes of spatial cross-correlations were employed during the rainfall-runoff simulation. The results demonstrated that a stronger correlation led to a higher variation in peak discharge up to the observed correlation structure while a correlation stronger than the observed case resulted in lower variability in peak discharge. We concluded that the error structure in radar rainfall estimates significantly affects predictions of the runoff peak. Therefore, efforts to not only remove the radar rainfall errors, but to also weaken the cross-correlation structure of the errors need to be taken to forecast flood events accurately.     

13e SESSION DU COMITÉ CONSULTATIF KARACHI 4–15 NOVEMBRE 1957

Abstract

Event-based methods are used in flood estimation to obtain the entire flood hydrograph. Previously, such methods adopted in the UK have relied on pre-determined values of the input variables (e.g. rainfall and antecedent conditions) to a rainfall–runoff model, which is expected to result in an output flood of a particular return period. In contrast, this paper presents a method that allows all the input variables to take on values across the full range of their individual distributions. These values are then brought together in all possible combinations as input to an event-based rainfall–runoff model in a Monte Carlo simulation approach. Further, this simulation strategy produces a long string of events (on average 10 per year), where dependencies from one event to the next, as well as between different variables within a single event, are accounted for. Frequency analysis is then applied to the annual maximum peak flows and flow volumes.

Citation Svensson, C., Kjeldsen, T.R., and Jones, D.A., 2013. Flood frequency estimation using a joint probability approach within a Monte Carlo framework. Hydrological Sciences Journal, 58 (1), 1–20.     

Detachment and infiltration variations as consequence of regolith development in a Pyrenean badland system

The Araguás experimental catchment has been monitored to study badland dynamics in the Central Pyrenees. Previous studies of weathering processes within the catchment reported strong regolith dynamics associated with seasonal variations in the temperature and moisture regimes. A preliminary analysis of hydrological response and suspended sediment transport data recorded at a gauging station also demonstrated seasonal trends. The main objective of the present study is to understand the effect of regolith dynamics on sediment detachment and infiltration processes, based on field studies using simulated rainfall. The experiment design was based on seasonal differences in the physical conditions of surface regolith and the general trends of hydro‐sedimentological responses. Rainfall simulations were conducted on small plots using a pressure nozzle. Similar experimental rainfall conditions were set for all plots (rainfall intensity around 45 mm h^–1). The results showed strong variations in the infiltration and detachment responses closely associated with the regolith conditions and crusting development. Infiltration showed seasonal differences in time lag and intensity: average infiltration rates ranged from very low (2·05 mm h^–1) to moderated high values (44·04 mm h^–1) associated to regolith development conditions. Maximum sediment concentration, as an indicator of particles produced by detachment, also ranged from moderate (3 g l^–1) to extreme values (145 g l^–1). Mean and minimum infiltration rates showed negative correlations with initial moisture content. Sediment concentration showed a positive correlation with time lag, ponding, and sealing time, and a negative correlation with initial moisture. In terms of seasonal trends, infiltration and erosion responses were relatively stable during spring and autumn, whereas wide variations were recorded in infiltration rates and sediment detachment during summer and winter. As a general conclusion, the obtained results indicate that seasonal differences in detachment and infiltration depend on the nature of regolith development. Copyright © 2009 John Wiley & Sons, Ltd.     

Simplified modelling of areal average infiltration at the hillslope scale

Two models for estimating expected areal‐average infiltration rate, ī, at the hillslope scale are presented. The first relies upon the condition of a negligible infiltration of surface water running downslope (run‐on process) into a previous heterogeneous soil. It is an adapted version of an earlier semi‐analytical model. The second incorporates the run‐on process and is based on a lumped approach that uses an effective saturated hydraulic conductivity. This latter was parameterized in terms of the main characteristics of rainfall and soil. Both the models were tested by comparison with the results carried out by Monte‐Carlo simulations over different soil types. It was found that the first model simulated ī with maximum errors in magnitude typically less than 10%. The second model provided similar errors in the total volume of overland flow, and the rising limb of the hydrograph experienced a distortion. Lastly, satisfactory results were obtained by comparing the model without run‐on with an empirical approach particularly accurate for fine‐textured soils. Copyright © 2002 John Wiley & Sons, Ltd.     

An analysis of the impact of spatial variability in rainfall on runoff and sediment predictions from a distributed model

This paper investigates the effect of introducing spatially varying rainfall fields to a hydrological model simulating runoff and erosion. Pairs of model simulations were run using either spatially uniform (i.e. spatially averaged) or spatially varying rainfall fields on a 500‐m grid. The hydrological model used was a simplified version of Thales which enabled runoff generation processes to be isolated from hillslope averaging processes. Both saturation excess and infiltration excess generation mechanisms were considered, as simplifications of actual hillslope processes. A 5‐year average recurrence interval synthetic rainfall event typical of temperate climates (Melbourne, Australia) was used. The erosion model was based on the WEPP interrill equation, modified to allow nonlinear terms relating the erosion rate to rainfall or runoff‐squared. The model results were extracted at different scales to investigate whether the effects of spatially varying rainfall were scale dependent. A series of statistical metrics were developed to assess the variability due to introducing the spatially varying rainfall field. At the catchment (approximately 150 km²) scale, it was found that particularly for saturation excess runoff, model predictions of runoff were insensitive to the spatial resolution of the rainfall data. Generally, erosion processes at smaller sub‐catchment scales, particularly when the sediment generation equation had non linearity, were more sensitive to spatial rainfall variability. Introducing runon infiltration reduced the total runoff and sediment yield at all scales, and this process was also most sensitive to the rainfall resolution. Copyright © 2011 John Wiley & Sons, Ltd.     

Towards flash-flood prediction in the dry Dead Sea region utilizing radar rainfall information

Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash-flood models. This paper presents a flash-flood warning model which utilizes radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipitation estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on the 5 years of data for one of the catchments. Validation was performed for a subsequent 5-year period for the same catchment and then for an entire 10-year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood warning model is feasible for catchments in the area studied.     

Dissecting the effect of rainfall variability on the statistical structure of peak flows

This study examines the role of rainfall variability on the spatial scaling structure of peak flows using the Whitewater River basin in Kansas as an illustration. Specifically, we investigate the effect of rainfall on the scatter, the scale break and the power law (peak flows vs. upstream areas) regression exponent. We illustrate why considering individual hydrographs at the outlet of a basin can lead to misleading interpretations of the effects of rainfall variability. We begin with the simple scenario of a basin receiving spatially uniform rainfall of varying intensities and durations and subsequently investigate the role of storm advection velocity, storm variability characterized by variance, spatial correlation and intermittency. Finally, we use a realistic space–time rainfall field obtained from a popular rainfall model that combines the aforementioned features. For each of these scenarios, we employ a recent formulation of flow velocity for a network of channels, assume idealized conditions of runoff generation and flow dynamics and calculate peak flow scaling exponents, which are then compared to the scaling exponent of the width function maxima. Our results show that the peak flow scaling exponent is always larger than the width function scaling exponent. The simulation scenarios are used to identify the smaller scale basins, whose response is dominated by the rainfall variability and the larger scale basins, which are driven by rainfall volume, river network aggregation and flow dynamics. The rainfall variability has a greater impact on peak flows at smaller scales. The effect of rainfall variability is reduced for larger scale basins as the river network aggregates and smoothes out the storm variability. The results obtained from simple scenarios are used to make rigorous interpretations of the peak flow scaling structure that is obtained from rainfall generated with the space–time rainfall model and realistic rainfall fields derived from NEXRAD radar data.     

The role of rainfall temporal and spatial averaging in seasonal simulations of the terrestrial water balance

The partitioning of rainfall into surface runoff and infiltration influences many other aspects of the hydrologic cycle including evapotranspiration, deep drainage and soil moisture. This partitioning is an instantaneous non-linear process that is strongly dependent on rainfall rate, soil moisture and soil hydraulic properties. Though all rainfall datasets involve some degree of spatial or temporal averaging, it is not understood how this averaging affects simulated partitioning and the land surface water balance across a wide range of soil and climate types. We used a one-dimensional physics-based model of the near-surface unsaturated zone to compare the effects of different rainfall discretization (5-min point-scale; hourly point-scale; hourly 0.125° gridded) on the simulated partitioning of rainfall for many locations across the United States. Coarser temporal resolution rainfall data underpredicted seasonal surface runoff for all soil types except those with very high infiltration capacities (i.e., sand, loamy sand). Soils with intermediate infiltration capacities (i.e., loam, sandy loam) were the most affected, with less than half of the expected surface runoff produced in most soil types when the gridded rainfall dataset was used as input. The impact of averaging on the water balance was less extreme but non-negligible, with the hourly point-scale predictions exhibiting median evapotranspiration, drainage and soil moisture values within 10% of those predicted using the higher resolution 5-min rainfall. Water balance impacts were greater using the gridded hourly dataset, with average underpredictions of ET up to 27% in fine-grained soils. The results suggest that “hyperresolution” modelling at continental to global scales may produce inaccurate predictions if there is not parallel effort to produce higher resolution precipitation inputs or sub-grid precipitation parameterizations.     

Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula

Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data.     

A stochastic model for the analysis of the temporal change of dry spells

In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall series registered at the Cosenza rain gauge (Calabria, southern Italy), as test series. The aim was to evaluate the different behaviour of the dry spells observed in two different 30-year periods, i.e. 1951–1980 and 1981–2010. The analyses performed through Monte Carlo simulations assessed the statistical significance of the variation of the mean expected values of dry spells observed at annual scale in the second period with respect to those observed in the first. The model has then been verified by comparing the results of the test series with the ones obtained from other three rain gauges of the same region. Moreover, greater occurrence probabilities for long dry spells in 1981–2010 than in 1951–1980 were detected for the test series. Analogously, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 resulted less than half of the corresponding ones evaluated with the data generated for the previous 30-year period.